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diff --git a/A_Method_to_Evaluate_the_Performance_of_Predictors_in_Cyber_physical_Systems_(_ICPE_2023).ipynb b/A_Method_to_Evaluate_the_Performance_of_Predictors_in_Cyber_physical_Systems_(_ICPE_2023).ipynb
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@@ -0,0 +1,5567 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Steps for Reproduction\n",
+        "\n",
+        "1. Download the dataset from: https://archive.ics.uci.edu/ml/datasets/Condition+monitoring+of+hydraulic+systems"
+      ],
+      "metadata": {
+        "id": "3WId_Iz0Potl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 231,
+      "metadata": {
+        "id": "gm38-aBDgTjN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9f77db42-25b5-4ede-fe63-70bc75486747"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import keras\n",
+        "from sklearn.feature_selection import SelectKBest\n",
+        "from sklearn.feature_selection import mutual_info_regression\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from scipy.stats import pearsonr\n",
+        "import tensorflow as tf\n",
+        "from math import isnan\n",
+        "import operator\n",
+        "from google.colab import drive\n",
+        "from tensorflow.keras import layers\n",
+        "import os\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OjF6jf54rJZo"
+      },
+      "source": [
+        "# Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JRwzUkvorF1j"
+      },
+      "source": [
+        "## Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class HydraulicDatasetTrace:\n",
+        "  def __init__(self, file_name, signal_name, sampling_frequency):\n",
+        "    self.file_name = file_name\n",
+        "    self.signal_name = signal_name\n",
+        "    self.sampling_frequency = sampling_frequency\n",
+        "\n",
+        "class DataLoaderHydraulicDataset:\n",
+        "  def __init__(self):\n",
+        "    self.traces = []\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS1.txt\", \"PS1\",100))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS2.txt\", \"PS2\",100))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS3.txt\", \"PS3\",100))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS4.txt\", \"PS4\",100))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS5.txt\", \"PS5\",100))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"PS6.txt\", \"PS6\",100))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"EPS1.txt\", \"MPW\",100))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"FS1.txt\", \"FS1\",10))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"FS2.txt\", \"FS2\",10))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"TS1.txt\", \"TS1\",1))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"TS2.txt\", \"TS2\",1))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"TS3.txt\", \"TS3\",1))\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"TS4.txt\", \"TS4\",1))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"VS1.txt\", \"VS1\",1))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"CE.txt\", \"CE\",1))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"CP.txt\", \"CP\",1))\n",
+        "\n",
+        "    self.traces.append(HydraulicDatasetTrace(\"SE.txt\", \"SE\",1))\n",
+        "\n",
+        "  def load_data(self,directory_path, profile_file_name):\n",
+        "    df = pd.DataFrame()\n",
+        "    df_profile = pd.read_csv(directory_path + \"/\" + profile_file_name,sep='\\t', names = ['cooler condition', 'valve condition', 'internal pump leakage', 'hydraulic accumulator', 'stable flag'])\n",
+        "    for trace in self.traces:\n",
+        "      df_trace = pd.read_csv(directory_path + \"/\" + trace.file_name,sep='\\t', header=None)\n",
+        "\n",
+        "      trace_data_array = np.array([])\n",
+        "      for i in range(df_trace.shape[0]):\n",
+        "        if(df_profile.iloc[i]['cooler condition'] == 3):\n",
+        "          continue \n",
+        "\n",
+        "        if(df_profile.iloc[i]['valve condition'] == 80 or df_profile.iloc[i]['valve condition'] == 73):\n",
+        "          continue\n",
+        "\n",
+        "        if(df_profile.iloc[i]['internal pump leakage'] == 2):\n",
+        "          continue\n",
+        "\n",
+        "        if(df_profile.iloc[i]['hydraulic accumulator'] == 100 or df_profile.iloc[i]['hydraulic accumulator'] == 90):\n",
+        "          continue\n",
+        "        \n",
+        "        trace_data_array = np.append(trace_data_array, df_trace.iloc[i].to_numpy())\n",
+        "\n",
+        "      if(trace.sampling_frequency != 100):\n",
+        "        trace_data_array = pd.Series(trace_data_array).repeat(100//trace.sampling_frequency).to_numpy()\n",
+        "\n",
+        "      print(trace.signal_name)\n",
+        "      df[trace.signal_name] = trace_data_array\n",
+        "    \n",
+        "    print(df)\n",
+        "    \n",
+        "    return df"
+      ],
+      "metadata": {
+        "id": "x3z3szes57j4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# preprocessing dataset\n",
+        "\n",
+        "#loader = DataLoaderHydraulicDataset()\n",
+        "#directory = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data\"\n",
+        "#df = loader.load_data(directory,\"profile.txt\")\n",
+        "#df.to_csv(directory + \"/hydraulic.csv\", index=False)\n",
+        "#df = pd.read_csv(directory + \"/hydraulic.csv\")"
+      ],
+      "metadata": {
+        "id": "P0mAqnwZ65Nr"
+      },
+      "execution_count": 287,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 232,
+      "metadata": {
+        "id": "0pRF_h2ZhWsq"
+      },
+      "outputs": [],
+      "source": [
+        "class Dataset:\n",
+        "  def __init__(self, path):\n",
+        "    self.path = path\n",
+        "    self.df = pd.read_csv(path,sep=',')\n",
+        "    self.selected_features = {}\n",
+        "    self.number_of_features = 0\n",
+        "\n",
+        "  def print_columns(self):\n",
+        "    for column in self.df.columns:\n",
+        "      print(column)\n",
+        "\n",
+        "  def plot_data(self):\n",
+        "    rows = self.df.columns.size//4 + 1\n",
+        "    fig, ax = plt.subplots(ncols=4, nrows=rows, figsize=(50, 60))\n",
+        "    i = 0\n",
+        "    j = 0\n",
+        "\n",
+        "    for column in self.df.columns:\n",
+        "      ax[j][i].set_title(column)\n",
+        "      ax[j][i].plot(self.df[column])\n",
+        "      i = i + 1\n",
+        "      if(i == 4):\n",
+        "        i = 0\n",
+        "        j = j + 1\n",
+        "    \n",
+        "    plt.show()\n",
+        "\n",
+        "  def scatter_selected_features(self, target_feature):\n",
+        "    selected_features = list(self.selected_features[target_feature].keys())\n",
+        "    fig, ax = plt.subplots(ncols=1, nrows=self.number_of_features, figsize=(20, 20))\n",
+        "\n",
+        "    for i in range(self.number_of_features):\n",
+        "      ax[i].set_xlabel(selected_features[i])\n",
+        "      ax[i].set_ylabel(target_feature)\n",
+        "      ax[i].scatter(self.df[target_feature],self.df[selected_features[i]])\n",
+        "    \n",
+        "    plt.show()\n",
+        "\n",
+        "  def select_features(self, number_of_features=5, autoregressive = True):\n",
+        "    selected_features = {}\n",
+        "    pearson_correlations = {}\n",
+        "\n",
+        "    for target_column in self.df.columns:\n",
+        "      pearson_correlations[target_column] = {}\n",
+        "      \n",
+        "      for feature_column in self.df.columns:\n",
+        "        if feature_column != target_column or autoregressive:\n",
+        "          if feature_column == 'THREAD_ID':\n",
+        "            pearson_correlations[target_column][feature_column] = np.abs(pearsonr(self.df[feature_column][0:dataset.df[feature_column].size-1],self.df[target_column][0:dataset.df[target_column].size-1])[0])\n",
+        "          else:\n",
+        "            pearson_correlations[target_column][feature_column] = np.abs(pearsonr(self.df[feature_column][0:dataset.df[feature_column].size-1],self.df[target_column][1:dataset.df[target_column].size])[0])\n",
+        "\n",
+        "      pearson_correlations[target_column] = {k: pearson_correlations[target_column][k] for k in pearson_correlations[target_column] if not isnan(pearson_correlations[target_column][k])}\n",
+        "\n",
+        "    for target_column in self.df.columns:\n",
+        "      selected_features[target_column] = dict(sorted(pearson_correlations[target_column].items(), key=operator.itemgetter(1), reverse=True)[:number_of_features])\n",
+        "\n",
+        "    self.selected_features = selected_features\n",
+        "    self.number_of_features = number_of_features\n",
+        "\n",
+        "    return selected_features"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wa7u6vFIrMaU"
+      },
+      "source": [
+        "## Data Generator"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 233,
+      "metadata": {
+        "id": "Oojk63mKmdWr"
+      },
+      "outputs": [],
+      "source": [
+        "class PredictorDataGenerator:\n",
+        "  def __init__(self,dataframe, target, features):\n",
+        "      scaler = MinMaxScaler()\n",
+        "      self.df_features = scaler.fit_transform(dataframe[features][8500:9500])\n",
+        "      self.features = features\n",
+        "      self.df_target = scaler.fit_transform(dataframe[target].to_numpy()[8500:9500].reshape([-1,1]))\n",
+        "      self.number_of_datapoints = self.df_features.shape[0]\n",
+        "      self.input_size = self.df_features.shape[1]\n",
+        "      self.output_size = self.df_target.shape[1]\n",
+        "  \n",
+        "  def generate_data(self):\n",
+        "      i = 1\n",
+        "      while True:\n",
+        "        \n",
+        "        if(i == self.df_features.shape[0]):\n",
+        "          i = 1\n",
+        "\n",
+        "        x = []\n",
+        "        for feature in self.features:\n",
+        "          x.append(self.df_features[i-1][self.features.index(feature)])\n",
+        "\n",
+        "        x = tf.convert_to_tensor(np.array([x]))\n",
+        "        y = tf.convert_to_tensor(np.array([self.df_target[i]]).reshape([1,1]))\n",
+        "            \n",
+        "        yield x, y\n",
+        "        \n",
+        "        i = i + 1  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aGG03-Y7rPCt"
+      },
+      "source": [
+        "## Predictor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 234,
+      "metadata": {
+        "id": "TTM_zMwfkTuI"
+      },
+      "outputs": [],
+      "source": [
+        "class Predictor:\n",
+        "    def __init__(self):\n",
+        "      self.build_model()\n",
+        "\n",
+        "    def build_model(self):\n",
+        "      model = keras.Sequential();\n",
+        "      model.add(layers.Dense(64, activation='tanh', name=\"hidden1\"))\n",
+        "      model.add(layers.Dense(1, name=\"output\"))\n",
+        "\n",
+        "      model.compile(optimizer = keras.optimizers.Adagrad(learning_rate=0.001), loss=\"mean_absolute_error\")\n",
+        "\n",
+        "      self.model = model\n",
+        "\n",
+        "    def load_model(self,target, autoregressive = True):\n",
+        "      if autoregressive:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_4.ckpt\"\n",
+        "      else:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_na3.ckpt\"\n",
+        "      \n",
+        "      checkpoint_dir = os.path.dirname(checkpoint_path)\n",
+        "      self.model.load_weights(checkpoint_path)\n",
+        "\n",
+        "    def compute_mae(self,data_generator,target, autoregressive):\n",
+        "      if autoregressive:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_4.ckpt\"\n",
+        "      else:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_na3.ckpt\"      \n",
+        "      \n",
+        "      checkpoint_dir = os.path.dirname(checkpoint_path)\n",
+        "      # Create a callback that saves the model's weights\n",
+        "      cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
+        "                                                 save_weights_only=True,\n",
+        "                                                 verbose=1)\n",
+        "      \n",
+        "      self.model.load_weights(checkpoint_path)\n",
+        "      print(\"training for: \" + target)\n",
+        "      print(\"checkpoint_path: \" + checkpoint_path)\n",
+        "      self.dataset = tf.data.Dataset.from_generator(data_generator.generate_data,output_signature=(tf.TensorSpec(shape=(1,data_generator.input_size), dtype=tf.float32),tf.TensorSpec(shape=(1,data_generator.output_size), dtype=tf.float32)))\n",
+        "      self.model_history = self.model.fit(self.dataset, verbose=True, epochs = 1, steps_per_epoch=1,callbacks=[cp_callback])\n",
+        "      self.MAE = self.model_history.history['loss'][-1]\n",
+        "      self.estimate_maximum_gradient(data_generator)\n",
+        "\n",
+        "    def fit(self,data_generator, target, autoregressive):\n",
+        "      if autoregressive:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_4.ckpt\"\n",
+        "      else:\n",
+        "        checkpoint_path = \"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/\" + target + \"_model_na3.ckpt\"\n",
+        "      \n",
+        "      checkpoint_dir = os.path.dirname(checkpoint_path)\n",
+        "      # Create a callback that saves the model's weights\n",
+        "      cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
+        "                                                 save_weights_only=True,\n",
+        "                                                 verbose=1)\n",
+        "      \n",
+        "      print(\"checkpoint_path: \" + checkpoint_path)\n",
+        "      self.model.load_weights(checkpoint_path)\n",
+        "      print(\"training for: \" + target)\n",
+        "      self.dataset = tf.data.Dataset.from_generator(data_generator.generate_data,output_signature=(tf.TensorSpec(shape=(1,data_generator.input_size), dtype=tf.float32),tf.TensorSpec(shape=(1,data_generator.output_size), dtype=tf.float32)))\n",
+        "      self.model_history = self.model.fit(self.dataset, verbose=True, epochs = 1, steps_per_epoch=1,callbacks=[cp_callback])\n",
+        "      self.MAE = self.model_history.history['loss'][-1]\n",
+        "      self.estimate_maximum_gradient(data_generator)\n",
+        "\n",
+        "    def estimate_maximum_gradient(self,data_generator):\n",
+        "      gradients = []\n",
+        "      generator = data_generator.generate_data()\n",
+        "\n",
+        "      for i in range(data_generator.input_size):\n",
+        "        x_tensor = next(generator)[0]\n",
+        "        with tf.GradientTape() as t:\n",
+        "            t.watch(x_tensor)\n",
+        "            output = self.model(x_tensor)[0][0]\n",
+        "\n",
+        "        gradients.append(t.gradient(output, x_tensor).numpy()[0])\n",
+        "\n",
+        "      gradients = np.array(gradients)\n",
+        "      gradient_max = np.max(gradients, axis=0)\n",
+        "      gradient_min = np.min(gradients, axis=0)\n",
+        "      gradient_worst_case_estimate = []\n",
+        "      for i in range(gradient_max.size):\n",
+        "        if gradient_max[i] > np.abs(gradient_min[i]):\n",
+        "          gradient_worst_case_estimate.append(gradient_max[i])\n",
+        "        else:\n",
+        "          gradient_worst_case_estimate.append(gradient_min[i])\n",
+        "\n",
+        "      self.gradients = {}\n",
+        "      for i in range(data_generator.input_size):\n",
+        "        self.gradients[data_generator.features[i]] = gradient_worst_case_estimate[i]\n",
+        "\n",
+        "      return self.gradients \n",
+        "\n",
+        "    def compute_gradient(self,input):\n",
+        "      with tf.GradientTape() as t:\n",
+        "          t.watch(input)\n",
+        "          output = self.model(input)[0][0]\n",
+        "\n",
+        "      return t.gradient(output, input).numpy()[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xJpLoIkprS2S"
+      },
+      "source": [
+        "## Predictor Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 235,
+      "metadata": {
+        "id": "tpWH31eEkP4w"
+      },
+      "outputs": [],
+      "source": [
+        "class PredictorSet:\n",
+        "  def __init__(self, generator_dictionary, predictor_files = None, train = False, autoregressive = True):\n",
+        "    self.targets = list(generator_dictionary.keys())\n",
+        "    self.generators = generator_dictionary\n",
+        "    self.predictors = {}\n",
+        "\n",
+        "    if predictor_files == None:\n",
+        "      for t in self.targets:\n",
+        "        self.predictors[t] = Predictor()\n",
+        "        if train:\n",
+        "          self.predictors[t].fit(self.generators[t],t,autoregressive)\n",
+        "        else:\n",
+        "          self.predictors[t].compute_mae(self.generators[t],t,autoregressive)\n",
+        "\n",
+        "  def test_stability(self, target, missing_features):\n",
+        "    target_gradient = self.predictors[target].estimate_maximum_gradient(self.generators[target])\n",
+        "    \n",
+        "    features_gradient = {}\n",
+        "    loop_stability_factor = 0\n",
+        "    feedback_stability_factor = 0\n",
+        "    for feature in missing_features:\n",
+        "      features_gradient[feature] = self.predictors[feature].estimate_maximum_gradient(self.generators[feature])\n",
+        "      if target in features_gradient[feature]:\n",
+        "        if feature != target:\n",
+        "          loop_stability_factor = loop_stability_factor + np.abs(features_gradient[feature][target]*target_gradient[feature])\n",
+        "        else:\n",
+        "          feedback_stability_factor = np.abs(target_gradient[feature])\n",
+        "\n",
+        "    stability_factor = feedback_stability_factor**2 + 4*loop_stability_factor\n",
+        "    stability_factor = np.sqrt(stability_factor)\n",
+        "    stability_factor = max(np.abs(feedback_stability_factor + stability_factor),np.abs(feedback_stability_factor - stability_factor))/2\n",
+        "  \n",
+        "    return stability_factor, stability_factor > 1\n",
+        "\n",
+        "  def evaluate_normal_operation(self, target, samples, predict_errors=False):\n",
+        "    measurements = []\n",
+        "    prediction = []\n",
+        "    error = []\n",
+        "    predicted_error = []\n",
+        "\n",
+        "    generator = self.generators[target].generate_data()\n",
+        "\n",
+        "    if predict_errors:\n",
+        "      normal_operation_features = {}\n",
+        "      for feature in self.generators[target].features:\n",
+        "        normal_operation_features[feature] = self.evaluate_normal_operation(feature, samples)\n",
+        "\n",
+        "    for i in range(min(self.generators[target].number_of_datapoints,samples)):\n",
+        "      next_data_point = next(generator) \n",
+        "      next_input = next_data_point[0]\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      prediction.append(self.predictors[target].model.predict(next_data_point[0])[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "      \n",
+        "      if predict_errors and i > 0:\n",
+        "        error_prediction = 0\n",
+        "        gradient_estimate = self.predictors[target].gradients\n",
+        "        for feature in self.generators[target].features:\n",
+        "          feature_index = self.generators[target].features.index(feature)\n",
+        "          if feature == target:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*error[i-1])\n",
+        "          else:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][2][i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "\n",
+        "        error_prediction = error_prediction + self.predictors[target].MAE\n",
+        "        predicted_error.append(error_prediction) \n",
+        "\n",
+        "    return measurements, prediction, error, predicted_error\n",
+        "\n",
+        "\n",
+        "  def evaluate_cascade_fault_operation(self, target, missing_feature, fault_point, samples):\n",
+        "    measurements = []\n",
+        "    inputs = []\n",
+        "    prediction = []\n",
+        "    error = []\n",
+        "    predicted_error = []\n",
+        "\n",
+        "    normal_operation_features = {}\n",
+        "    for feature in self.generators[target].features:\n",
+        "      normal_operation_features[feature] = self.evaluate_normal_operation(feature, samples, predict_errors=True)\n",
+        "\n",
+        "    generator = self.generators[target].generate_data()\n",
+        "\n",
+        "    for i in range(fault_point):\n",
+        "      next_data_point = next(generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      prediction.append(self.predictors[target].model.predict(next_data_point[0])[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "    for i in range(fault_point, samples):\n",
+        "      next_data_point = next(generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      next_input = next_data_point[0].numpy()\n",
+        "      index_missing_feature = self.generators[target].features.index(missing_feature)\n",
+        "      next_input[0][index_missing_feature] = normal_operation_features[missing_feature][1][i-1]\n",
+        "      prediction.append(self.predictors[target].model.predict(tf.convert_to_tensor(next_input))[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "      error_prediction = 0\n",
+        "      gradient_estimate = self.predictors[target].gradients\n",
+        "      for feature in self.generators[target].features:\n",
+        "        feature_index = self.generators[target].features.index(feature)\n",
+        "        if feature == missing_feature:\n",
+        "          error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][3][i-1]) \n",
+        "        else:\n",
+        "          error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][2][i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "\n",
+        "      error_prediction = error_prediction + self.predictors[target].MAE\n",
+        "      predicted_error.append(error_prediction) \n",
+        "    \n",
+        "    return measurements, prediction, error, predicted_error\n",
+        "\n",
+        "  def evaluate_feedback_fault_operation(self, target, missing_feature, fault_point, samples):\n",
+        "    measurements = []\n",
+        "    inputs = []\n",
+        "    prediction = []\n",
+        "    error = []\n",
+        "    predicted_error = []\n",
+        "\n",
+        "    normal_operation_features = {}\n",
+        "    for feature in self.generators[target].features:\n",
+        "      normal_operation_features[feature] = self.evaluate_normal_operation(feature, samples)\n",
+        "\n",
+        "    generator = self.generators[target].generate_data()\n",
+        "\n",
+        "    for i in range(fault_point):\n",
+        "      next_data_point = next(generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      prediction.append(self.predictors[target].model.predict(next_data_point[0])[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "    for i in range(fault_point, samples):\n",
+        "      next_data_point = next(generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      next_input = next_data_point[0].numpy()\n",
+        "      index_missing_feature = self.generators[target].features.index(missing_feature)\n",
+        "      next_input[0][index_missing_feature] = prediction[i-1]\n",
+        "      prediction.append(self.predictors[target].model.predict(tf.convert_to_tensor(next_input))[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "      if len(predicted_error) == 0:\n",
+        "        error_prediction = 0\n",
+        "        gradient_estimate = self.predictors[target].gradients\n",
+        "        for feature in self.generators[target].features:\n",
+        "          if feature == target:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*error[i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE)\n",
+        "          else:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][2][i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "      else:\n",
+        "        error_prediction = 0\n",
+        "        gradient_estimate = self.predictors[target].gradients\n",
+        "        for feature in self.generators[target].features:\n",
+        "          if feature == target:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*predicted_error[i-fault_point-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE)\n",
+        "          else:\n",
+        "            error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][2][i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "\n",
+        "      error_prediction = error_prediction + self.predictors[target].MAE\n",
+        "      predicted_error.append(error_prediction)     \n",
+        "\n",
+        "    return measurements, prediction, error, predicted_error\n",
+        "\n",
+        "  def evaluate_loop_fault_operation(self, target, missing_feature, fault_point, samples):\n",
+        "    measurements = []\n",
+        "    inputs = []\n",
+        "    prediction = []\n",
+        "    error = []\n",
+        "    predicted_error = []\n",
+        "\n",
+        "    normal_operation_features = {}\n",
+        "    for feature in self.generators[target].features:\n",
+        "      normal_operation_features[feature] = self.evaluate_normal_operation(feature, samples)\n",
+        "\n",
+        "    generator = self.generators[target].generate_data()\n",
+        "    missing_feature_generator = self.generators[missing_feature].generate_data()\n",
+        "\n",
+        "    for i in range(fault_point):\n",
+        "      next_data_point = next(generator)\n",
+        "      if i != 0:\n",
+        "        next_missing_feature_data_point = next(missing_feature_generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "      prediction.append(self.predictors[target].model.predict(next_data_point[0])[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "    for i in range(fault_point, samples):\n",
+        "      next_data_point = next(generator)\n",
+        "      measurements.append(next_data_point[1].numpy()[0][0])\n",
+        "\n",
+        "      missing_feature_predictor = self.predictors[missing_feature].model\n",
+        "      next_missing_feature_data_point = next(missing_feature_generator)\n",
+        "      \n",
+        "      next_missing_feature_input = next_missing_feature_data_point[0].numpy()\n",
+        "      index_target = self.generators[missing_feature].features.index(target)\n",
+        "      next_missing_feature_input[0][index_target] = prediction[i-2]\n",
+        "\n",
+        "      next_input = next_data_point[0].numpy()\n",
+        "      index_missing_feature = self.generators[target].features.index(missing_feature)\n",
+        "      missing_feature_prediction = missing_feature_predictor.predict(tf.convert_to_tensor(next_missing_feature_input))[0][0]\n",
+        "      missing_feature_prediction_error = next_missing_feature_data_point[1].numpy()[0][0] - missing_feature_prediction\n",
+        "\n",
+        "      next_input[0][index_missing_feature] = missing_feature_prediction\n",
+        "     \n",
+        "      prediction.append(self.predictors[target].model.predict(tf.convert_to_tensor(next_input))[0][0])\n",
+        "      error.append(measurements[i]-prediction[i])\n",
+        "\n",
+        "      error_prediction = 0\n",
+        "      gradient_estimate = self.predictors[target].gradients\n",
+        "      for feature in self.generators[target].features:\n",
+        "        if feature == missing_feature:\n",
+        "          error_prediction = error_prediction + np.abs(gradient_estimate[feature]*missing_feature_prediction_error) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "        else:\n",
+        "          error_prediction = error_prediction + np.abs(gradient_estimate[feature]*normal_operation_features[feature][2][i-1]) + np.abs(gradient_estimate[feature]*self.predictors[feature].MAE) \n",
+        "\n",
+        "      error_prediction = error_prediction + self.predictors[target].MAE\n",
+        "      predicted_error.append(error_prediction)     \n",
+        "\n",
+        "    return measurements, prediction, error, predicted_error\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scenario 1 - Cascade Configuration"
+      ],
+      "metadata": {
+        "id": "4AZ0uj_IpSrG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 236,
+      "metadata": {
+        "id": "OwRMOJ6fwZnm"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Dataset(\"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data/hydraulic.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 237,
+      "metadata": {
+        "id": "NO_zy5MGwZno"
+      },
+      "outputs": [],
+      "source": [
+        "selected_features = dataset.select_features(number_of_features=3, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 238,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7991c481-df12-4eaf-9a0f-b312f98fb67b",
+        "id": "VOZPV4j8wZno"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'PS1': 0.9768350574890512,\n",
+              " 'SE': 0.8221303782937127,\n",
+              " 'FS1': 0.7408146512995961}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 238
+        }
+      ],
+      "source": [
+        "selected_features['MPW']['FS1'] = 0.7408146512995961\n",
+        "del selected_features['MPW']['PS2']\n",
+        "selected_features['MPW']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "selected_features['SE'].keys()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "82edfefe-5c2e-4f5f-db34-61039ded34b5",
+        "id": "vEsFv2oHwZnp"
+      },
+      "execution_count": 239,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['PS2', 'FS1', 'PS1'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 239
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 240,
+      "metadata": {
+        "id": "nT9smx5WwZnq"
+      },
+      "outputs": [],
+      "source": [
+        "targets = ['PS1','PS2', 'PS3', 'PS4', 'PS5', 'PS6', 'MPW', 'SE', 'TS3', 'TS4', 'FS1', 'CE','TS1', 'TS2']\n",
+        "generator_dictionary = {} \n",
+        "for t in targets:\n",
+        "  generator_dictionary[t] = PredictorDataGenerator(dataset.df,t,list(selected_features[t].keys()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 241,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c48f11ed-c34f-4d2f-d0df-df33ae0b4f3f",
+        "id": "z3JMJUn2wZnq"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "training for: PS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 1.4858e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 316ms/step - loss: 1.4858e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "training for: PS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.9521\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 412ms/step - loss: 0.9521\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "training for: PS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0253\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 334ms/step - loss: 0.0253\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "training for: PS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 2.4744e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 388ms/step - loss: 2.4744e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "training for: PS5\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0443\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 322ms/step - loss: 0.0443\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "training for: PS6\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1140\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 352ms/step - loss: 0.1140\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "training for: MPW\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0015\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 342ms/step - loss: 0.0015\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "training for: SE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 2.0266e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 317ms/step - loss: 2.0266e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "training for: TS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0226\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 397ms/step - loss: 0.0226\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "training for: TS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0016\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 303ms/step - loss: 0.0016\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "training for: FS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0406\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 310ms/step - loss: 0.0406\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "training for: CE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0531\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 344ms/step - loss: 0.0531\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "training for: TS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1198\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 344ms/step - loss: 0.1198\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "training for: TS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.2768\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 367ms/step - loss: 0.2768\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictorSet = PredictorSet(generator_dictionary, train=True, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "measurements_ps1, prediction_ps1, error_ps1, predicted_error_ps1 = predictorSet.evaluate_cascade_fault_operation('PS1','MPW',50,100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fc6bc2fd-cd5d-4258-a552-7dc024a5e689",
+        "id": "O4QY2HecwZnr"
+      },
+      "execution_count": 242,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 50ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 24ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
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+            "1/1 [==============================] - 0s 23ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 27ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 22ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 22ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 27ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 25ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 22ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 243,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "outputId": "9b306fed-9482-4dba-b6b4-0af1e3b4c1c0",
+        "id": "0mDpIc58wZnr"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,measurements_ps1[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,measurements_ps1[49:100], linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50)/100,prediction_ps1[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,prediction_ps1[49:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Measurement', 'Measurement', 'Prediction - No Interdependence', 'Prediction - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
+        "plt.savefig(\"loop_stable.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,np.abs(error_ps1[:50]),linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,np.abs(error_ps1[49:100]), linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50,100)/100,predicted_error_ps1[:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Prediction Error', 'Prediction Error','Estimated Prediction Error - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=1)\n",
+        "plt.savefig(\"loop_stable_error.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "outputId": "2c22873e-69a1-4d3a-da84-09ae39687541",
+        "id": "GI06jzwRxQyL"
+      },
+      "execution_count": 244,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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qpAwcfQiMCEE8FS9qZbx7dHfR9eZNvmIfAokQhAIRBIIgCFUkmU4yPDEMQJNqcjzZzwS3qTBvHHSnC6ByhKCSIJBOheFABIEgCMI0WXdgHe/73fv4ycaflDzncKLQg2BB2wKaIk2BfHZLUwvt0XYAMjrDaMoag2waCvNU8hB49iEwPQTSqTAUiCAQBEGYJleuu5I/7/8z//nwf3Jg/IDnOdVIF+TpaTV8BEnLR+A3QlCpMZFUGYQPEQSCIAjTJJ+bz+osG49s9DynGj0I8njNM/CKEByKHyq6qTsEQbRCp0IRBKFABIEgCMI0SGVTjE6O2vvPDj3reZ4pCBa2B1NymMfhIygTIdBo9o3tcxybUoRAWheHAhEEgiAI0yBvFMyzZXCL53nVjBA42heXiRBAcdrA9BB4lR1KhCB8iCAQBEGYBu5mQH4iBEF7CMyUgVeEoCPWYW+7jYWmUdCrMZFMOwwfIggEQRCmgbsZ0I7hHaQyqaLzqpky6G3ttbfzEQKzKdGpC061t6caIXD0IZAqg1AggkAQBGEauLsBpnWaHSM7is6rxqTDPF4DjsyUwekLT7e33YLA7EPgGSGQPgShQwSBIAjCNMiH6E2eHXSmDbTWHI4X+hAEXnboMQLZTBk4BMHYDCIE0qkwFIggEARBmAb5EL3JliGnsXB4YthuEdwR63Dk9IPA4SGYGGIiM8FYagyAqIqytm+t/bp7DHKlKgPzmHgIwoEIAkEQhGng9hBAcYTATBcENeXQxGxMNJgcdEQHelt76W3ttUVIPB23RUxWZyvOMjA7FYogCAciCARBEKaB20MAxZUGZtvioNMFUBwhMAVBX1sfSimWz1tuH8v7CBzpgkgzSqmi95ZOheFDBIEgCMI08PIQ7B3bSzwVt/er2YMAoKuly94enhh2CJDeNqsCYXlnsSColC4A6UMQRkQQCIIgTAMzZdCkCgOLTB+BI2UQcMkhQDQSZX7zfMDqRrhteJv9Wl9rHwArOlfYx/LGwkptiwFikRgKK3KQzqbJZDPBLl6oOUQQCIIgTAMzZXBy38n2tikIqh0hAKePYOvQVnu7r80SBF4pAz8RAqWUGAtDhggCQRCEKaK1dkQIzlhyhr1tGgurWXKYx/QROARBLkLgSBnkIwTpyoIApFth2BBBIAiCMEUS6YR9g2yONHPawtPs10xj4axECIxeBGbKIN/F0NNDkPUpCCIiCMKECAJBEIQpYvYg6Gnt4YTuE+x9M0JQzS6FecxuheZNO58yWNqx1PYCHBg/QCqTqtiUKI9ECMJFtNQLSqnLp/me/6O1PjDNawVBEGoes8Kgp7WHZZ3LaIu2kUgnGEgOcCRxhPnN822fgULZN+igMSMEJvmUQawpxpKOJewf32+NQR7fV7FtcR7pVhguSgoC4NPTeD8N3AyIIBAEoWExIwTdLd1EVIRju47lqSNPAVYuP6MLrvy+tj5ikVhV1mJGCExMAbK8czn7x/cDVtognU3br5WNEIipMFRUShmcrbWO+PkDNAPF3S0EQRAaDHP0cd7lf3zP8faxGzbdwEfu/oi9f1LvSVVbi1eEIKIijuNmpcGmwU2+qgzcr4kgaHzKCYKtwFRiRNlpXCMIglB3OARB7sZ7XPdx9rHf7/w9iXQCsMx9Hz/j41Vbi1llYB5rihR6Izx30XPt7fv23OerDwGIIAgbJVMGWuvjS71W4vwsMKVrBEEQ6hGz5DAfsjcjBHkWty/m6pdfzaquVVVbi9mHII/br3DB8gtQKDSaRw89ynnLzrNf8112mBZB0OhUrDJQSjUrpb6mlDpzNhYkCIJQ6ziqDFqKUwZghemvvfjaqooB8I4Q5A2F9n5bnz0KOauz3LHzDvu1coJA2heHi4qCQGs9CVwKtFV/OYIgCLWPl4egr7WPs5acBcCxXcdy7cXXsmzesqqvxU+EAODFK15sb284ssHeLicIZMBRuChXZWDyKHAqcF8V1yIIglAXeHkIlFL8z0v/h40DG1nbt7ZqVQVuOps7iagIWZ21j7kjBGAJgq+v/3rR8XJVBhIhCBd+GxN9HPgnpdRrlNecTEEQhBDh5SEA6+Z6+sLTZ00MgFVR4E4beEUIVnetdgw6ylOuD4FECMKFX0HwM6AP+BWQUErtVkrtMv7srN4SBUEQagszQpBvETyXFAkCjwiBUsqRNshTNkIQLYgFaUzU+PhNGdyJ1XRIEAQh1GSyGYYnh+39rpauOVyNhZ8IAcCFKy7kuqevcxyTPgRCHl+CQGv9riqvQxAEoS4YnRy18/Wdsc5ZTQ+Uwm0sLBW1eM6i5zC/eT4jkyP2MREEQh4ZbiQIgjAF3IONagE/KQOAWCTG+cvPdxwTQSDk8ZsyAEApdTpwIlDkQtFa/zCoRQmCINQqpn+g1ByB2aYoQtBW2tfw4uUv5rZtt9n7ZfsQRKXKIEz4EgRKqW7gNuDs/KHc36avQASBIAgNj1dTornGjBB0tXSVTWOcu+xcoipKWlsDjsq1LnZUGUinwobHb8rgS1hVBhdgiYHXARcBPwa2AS+oyuoEQRBqDHP0sVeXwLnA9AyUShfk6Wzu5Jyl59j7S+ctLXmu9CEIF35TBq8APg88mNvfo7V+BLhHKfW/wEeAd1RhfYIgCDWFGSGohZJDcIqAhe0LK57/mbM/w7ef+DZre9eyumt1yfOkD0G48CsIjgK2aa0zSqkk0Gm8dhNwQ+ArEwRBqEFq0UNw5pIzWdO7hu3D23nTiW+qeP5R847i8y/8fMXzJEIQLvwKggNA/jt/J3AOcE9u/zivCwRBEBoRs0thrXgIYk0xfvqan5JIJ2iPtQf2vo5phyIIGh6/guB+LEPhrcB1wGeVUquANPBO4JZqLE4QBKHWGEgO2Nu1UnYIVifCIMUAOCsQkhnpVNjo+DUVfh64Pbd9JXAV8GrgLVhi4MN+P1AptUIp9XOl1LBSakQpdZNSaqXPa1uVUlcqpfYrpRJKqT8ppS5wndOplPqpUmqLUmpcKTWklHpIKfV2v2sUBEEoRS2aCquFKQgmM5NzuBJhNvDbqXArsDW3ncIadvTxqX6YUqoduAuYwIosaOCLwN1KqdO01uMV3uL7WELkn7GqGz4I3K6UOkdr/VjunGasyMUVwA6gBXgTcJ1SaqHW+mtTXbcgCEKeWmxMVC0cEQKZZdDwTKkxUQC8H1gNnKi13gKglHoCeBa4FPhqqQtzTZHeCrxHa/2D3LF7gQ3A5cAlAFrrI7nzTP5PKXUC8B5ABIEgCNPGYSoMUYRAPASNT9mUQS78/orc2ON5uWMnKqWuV0ptUErdo5T6qyl83iXAg3kxAKC13g48ALzWx7Up4Ebj2jRWhcMrlFKlu2tYHMGKHAiCIEyLicwE8XQcgCbVxPzm+XO8ouoinQrDRUlBkHui3gD8H5ZPYLNS6vnAH4CXAmPAKcDPlFIv9fl5JwNPeRzfAKz1ce12rXXc49pmXNUOyiKqlOpTSn0Aq5eCRAcEQZg2bv+AUqrM2fWPuw+B1jL0tpEpFyH4ApAEXo5VYfA0cDPwKLBCa30WsBK4F/hXn5/XCwx6HB8AKiXjyl2bf93kg1gRhX7gW8BHys1bUEp9QCm1Tim17vDhwxWWIghCGHGUHDa4fwCsYUhNqgmArM7a7Y6FxqScIDgXuFxrfafW+iGsSoJlwLe01kmA3NP6N7EiBbXGjcCZwMXA1cA3lVKXljpZa/1drfUZWuszFi6s3OlLEITwUaslh9VE5hmEh3KmwiXkKgty5Lf3uc7bD/i9gw7iHQko9fTvvvboEtdCIVIAgNb6MJB/1P9trsLhy0qpa3KVEoIgCA601vzkmZ9wJHGEd53yriKPgBkhaHRDYZ7WplYS6QRg9SKYx7w5XpFQLcoJggiQMfbz2+4k0lSSShuwvABu1mKlJCpd+zqlVLvLR7AWmAS2eF9msw6r1HExsMffcgVBCBMPHXiI/3joPwDLYX/p6c6gollhUCtdCqtNS7TFKhRHehE0OpUaEy1TSq1WSq3GKhd0HMsdXz6Fz7sFODt3HQC5jofnUrnb4a+BGPBG49ooVo+B32mtK8WyXoRlhDw0hfUKghAitg9vt7e3DW8ret3sQVArcwyqjXQrDA+V+hD83OPYza59hf8owfeADwG/Ukp9OnfdF4DdwHfsN1TqaKwUxeVa68sBtNaPKqVuBL6ulIoB24G/A44B3mZceymWCfIOrEhAH/DXwBuAf9Vai8QVBMETs7QuX15oYkYIamXSYbWRboXhoZwgeHfQH6a1HldKXYRV/ncdlpi4E/io1nrMOFUBTRRHMN4N/DtWd8Nu4HHglVrr9cY5T2L1NPgylr+gH9gIvEZrfVvQ/yZBEBoHsxtfPFUsCMLqIcgj3Qobm5KCQGt9bTU+UGu9C3h9hXN2YIkC9/EE8I+5P6Wu/SPwqpmtUhCEMOKIEHgIgpGJEXu70ZsS5XH3IhAal3KNiTYppf5TKXXObC5IEARhrjBz5OPp4tEq5rF5zeFw28sI5PBQzlT4A+AC4P7cdMHvKqUuVko1l7lGEAShbjHr7L0iBOax9miwo4ZrlZaICIKwUFIQaK3/Q2t9DlYzos8DK4BfAodz44XfopQKR8xMEIRQYEYIvATBeKoQIeiIdczKmuYaM0IgHoLGplLZIVrrA1rrb2utL8ZqQHQpVk+C/8USB79TSv2dUmppldcqCIJQVdxVBu7e/WEUBKapUKoMGpuKgsBEaz2qtb5Ba/0WLHHwWqzywE9jlQ4KgiDULWbKIKMzDoGgtXZEDcIiCExTofQhaGwqCgKlVLNS6mtKqTPN41rrlNb6t1rrv9NaLwNeWLVVCoIgzALuG57Zi2AyO2kP94lGoo4bZSMjEYLw4CdlMImVJmircN6fg1qUIAjCXOA2zZkpgjCmC0AiBGHCb8rgUeDUai5EEARhrnGb5swUgUMQRMMjCFqjhQiBTDtsbPwKgo8D/6SUeo1SqqhhkCAIQiPgjhCYKQNHyWEsHCWH4GxdLGWHjU2lWQZ5fgZ0Ab8CUkqpwzjnF2ittddoYkEQhLqhSBCUihCEKGUggiA8+BUEdzK1MceCIAh1hztlIB4CEQRhwpcg0Fq/q8rrEARBmHPKpQzMtsUiCIRGZEp9CARBEBqZorLDVAkPQUjaFoNrloGYChsavykDAJRSpwMnAq3u17TWPwxqUYIgCLNNJpshnU07jjkiBCFNGZh9CCRC0Nj4EgRKqW7gNuDs/KHc36avQASBIAh1i9fNTkyFMv44TPhNGXwJ6MOafqiA1wEXAT8GtgEvqMrqBEEQZgmvpjumCAhr2eH85sIMu4Pxg3O4EqHa+BUEr8ASBQ/m9vdore/RWr8DuAP4SDUWJwiCMFt45cclZQCru1YTi8QA2Du2l4HkwByvSKgWfgXBUcA2rXUGSAKdxms3Aa8OemGCIAizSaUIQVirDGJNMdb0rrH3N/RvmMPVCNXEryA4AHTntncC5xivHRfoigRBEOYATw9BqQhBiFoXA5zcd7K9/dSRp+ZwJUI18VtlcD+WofBW4Drgs0qpVUAaeCdwSzUWJwiCMFu4mxJBmbLDEHkIAE5ZcApssrYlQtC4+BUEnweW5ravxDIYvgloxxIDHw5+aYIgCLOHVBmU5pQFp9jbG45sQGuNjLVpPPx2KtwKbM1tp7CGHX28iusSBEGYVbwEgbQutlg1fxXt0Xbi6Tj9iX4Oxg+ypGPJXC9LCBjpVCgIgkCJlEGJaYdhEwRNkSbW9q219yVt0JiUFQRKqU6l1CtyY4/n5Y6dqJS6Xim1QSl1j1Lqr2ZnqYIgCNWjYsrAqDIIm4cAnGkDMRY2JiVTBkqpE7B6DCzDakZ0QCn1F8BvcvvbgFOAnymlXqG1vmMW1isIglAVvMoOk5kkmWyGiIqEdpZBnpMXGJUG/SIIGpFyEYIvYPUceDlWhcHTwM3Ao8AKrfVZwErgXuBfq7xOQRCEqlJqcE8inSCRTqBzndpbm1qJRqY0BqYhOKWv2FgoNBblBMG5wOVa6zu11g9hVRIsA76ltU4CaK3jwDexIgWCIAh1i1eEACwzoWkoDGO6AGDZvGV0t1jtaEYnR9k1umuOVyQETTlBsIRcZUGO/PY+13n7gYVBLlBj6ZcAACAASURBVEoQBGG2mcxMeh6Pp+OhrjDIo5SStEGDU04QRICMsZ/fdseJJG4kCELdUypCEE/FQ9u22I2ZNhBB0HhUSoQtU0qtzm03GceGjHOWB78sQRCE2aWUh8AsPYRwGgrzuBsUCY1FJUHwc49jN7v2FRIlEAShzvEqOwRnQyIId4TAnGmw8chG0tl0KA2WjUq5/8l3z9oqBEEQ5phyKQNtPPOEWRAsbF/IovZFHIofIplJsnlwM02qic2Dm1nQtoBzlp5T+U2EmqWkINBaXzubCxEEQZhLzJRBZ6yT0dQoYDUkMkvswiwIwPIR3BW/C4A33/pmh1i66iVXccHyC+ZqacIMkdbFgiAIOCMEvW299nY8FQ/1pEM3po9Au7LFjx9+fLaXIwRISUGglLpMKbW01Ose56vcNTLxQhCEusP0EPS09NjbUmXg5JWrXklbtM3eN7cHk4NzsSQhIMp5CD6L1abY3XegFJHcNbcCB2a4LkEQhFnFTBn0thoRgnScjC5UYHdEwy0IVsxfwS1/eQu7R3ezums1Dx94mH++75+BOhMEWsOWOyAxBCf/JTTF5npFc045QaCAy5RSh32+lwzHFgShbjFTBj2thQjBeGqcrM7a+2FPGQAs6Vhijz82v1aDE3UkCPY8DD9+g7U9OQZniI++nCDYxdRbEu8CvGt3BEEQahgzZVAUIcgaEYKQpwzc5NsZQ51FCH7zicL2rR8VQUD5KoNVs7gOQRCEOSWZNkyFrU5TYTqbtvdFEDgxv1ZDE0NlzqwxlHjq3UhHCUEQBMpECFJxUtmUvS+CwEl3ayFCMDQxRFZnidTDzTbaOtcrqDnq4H9NEASh+jiqDIy8eDwdd7QvFg+Bk1gkRmesE4CszjIyMTLHK/JJtGWuV1BziCAQBCH0aK1Lpgzc44/DXmXghSmgBiYG5nAlU8AolxQsRBAIghB6UtmU3WQnGonS2dxpvybjjytjpg3qxlh49t8Wto8+d+7WUUOIh0AQhFnnwPgBrt1wLWcuOZOLVl4018txlBy2NrU6JhqOp8bFVFiB3hbDWJisE2PhMRfA54bnehU1hQgCQRBmlXQ2zd/f+fc8O/gsNzxzA79/4+9Z0LZgTtdkNiVqbmp2+ATMPgQK5ejMJ1jUZcpAKMJ3ykAp1aGU+gel1M+VUncrpY7PHX+zUmpN9ZYoCEIj8astv+LZwWcBSOs0O0d2zvGKiiMEzU3N9lhfd1MipaQHmxtHpUG9RAiEInwJAqXUCuAJ4ErgeOACIJ9kuxD4p6qsThBqBK012ayufKJQlngqzlWPXeU4NpCc+ydKM0LQknOfm2mDPGIo9MZMGdTC/6cwPfxGCL6C1YHwBOD5ONsU3wucH/C6BKFmGJ9Ic8m3HuDsK+7ksd3y9DMTrt1wLYcTzm7otWBCM0sOW5us+nQvr4CUHHrj7kVQF/z6o/C5LuvP/5wDozKCx68geBnwWa31TsD9mLQXWBboqgShhrjrmUM8uXeYQ6MTXPvHHXO9nLrlcPwwP9jwg6LjR5JH5mA1TsyUQUtTmQiBGAo9Mcs0a0Hg+SJufN8dehqG98zdWmoEv4KgGRgt8VoXkC7xmiDUPcOJQpe63QPxMmcK5bjqsatIpBNFx2vhBuKVMvC6+Ysg8MYcF103KYNMyrlfLw2VqohfQfAE8PoSr10MPBLMcgSh9kimCoNt9g8ny5wplGLL4BZ+ueWX9v4lx15ib9eCIHCbCgHaYsXVBJIy8KYuUwZZtyAo9cwbHvwKgiuB9yqlvodlKARYq5T6PPDe3OuC0JCYguDgSFLMhdPgRxt/ZLv1X7j0hbx69avt12rhidL0EORTBl4GQokQeFOXKYPMpHNfBIE/QaC1vgn4e+CNwB25wz8EPgp8SGv92+osTxDmnoQhCNJZTf+YTPieKhsHNtrb7zr5XfS19tn7tSAIzLbFrbmhN17RAKky8KY92k4sEgOsaItXaqjmKEoZiCDw1ZhIKdUF/AC4DjgHWAQcAf6otZavotDQJFNZx/7+4SSL5sukNL9kdZbtw9vt/TW9axzTA2tBEHhGCMRD4BulFD2tPRyKHwKsKEHbvBpv4CQRgiIqRgiUUlGsm//LtdbjWus7tNY/0VrfLmJACANmygBg/3AdPP3UEPvG9tlPjL2tvfS09jhMaMMTw47mP3OBlyDwqjIQD0FpHGmDiTpIG4ipsIiKgkBrnQYOAplK5wpCI5IoEgRiLJwK24a32duru1YDEGuK2QOEMjoz5yNzvVIGXqZCiRCUprulzgYcScqgCL+mwh8B76vmQgShVpnwSBkI/tk6tNXePrb7WHvbfKKc67SBmApnjjnPoD4EgTtlMDY366gh/A432gG8VSn1MPArYD+uBkVa62uCXZog1AYSIZgZ5QRBfo7BQHKA1aye9bXl8epU6JUekJRBacw0UH0IAokQuPErCPLNx5dhtS52owERBEJDUuQhGBIPwVQwUwbHdhUEQS01s3FECMo1JpIqg5I4IgT14CF4209hzzq49z9h1flklj2Xe3bdSWeskxcc9YK5Xt2c4FcQHFPVVQhCDVNsKpQIgV+01o4IweruQhSglkLMDg9BPkIgrYunRN1FCBadZP153t8A8OstN/OZuz8KwDWvuIYzl5w5l6ubE/z2IdhZ6Y/fD1RKrciNUB5WSo0opW5SSq30eW2rUupKpdR+pVRCKfUnpdQFrnNOUEr9t1LqCaXUWO7cW5RSp/tdoyCYJFwegoMjSTJVbE40HE9xpEF6HRwYP0A8bbV77m7pdvQfqHUPgWcfAhEEJaklgTcdHj/8uL19564753Alc4dfU2EgKKXagbuANcA7gb/BGqd8t1LKz0/a94H3A5cBr8HyMtyulHqOcc7LsUYyXwv8BVZDpYXAg0opr3SHIJRlwhUhSGd11W7YWw6N8YIv3cHZV9zJ+l3190vVzdZhIzrQtRqlCoNSa0kQOIYbRUsLAvEQlMYUBHXTvtggnirMKXny8JNzuJK5w29jou0UTzl0oLX24wh6P7AaOFFrvSX33k8AzwKXAl8ts4bTgbcC79Fa/yB37F5gA3A5kG+OfgNwldZaG9fehWWM/AjwDh/rFAQbt6kQYF+VmhP99qn9TKStiMRvntzP81b2VLiitillKITaqls3hxvZ44+lymBK1JInxBfZDESa7F1TEGwc2EgqkyLWFJuLlc0Zfj0E91IsCPqAFwJjWE/9frgEeDAvBgC01tuVUg8Ar6WMIMhdmwJuNK5NK6VuAP5VKdWitZ7QWve7L9RaDyulNiNjmoVp4PYQABwYTsCKbo+zZ8ZgvOB8Hk3W/xDRcoLAfKKc6xuIpAxmTl2ZCrNZuLwXVAR0Fp73TuLDhRl9qWyKTYObOGXBKXO4yNnHlyDQWr/L67hSqhv4LYX5BpU4Gats0c0GrDkJla7drrV2z5/dgDWe+bjcttc6e4FTsNovC8KU8IwQDFXHWGiOWm4IQTDsM0Iw16bCTHFjIvfNP6qiNEeaZ3Vd9URXS5e9PTIxQjqbJhrx+8w5y+RbZ+c7ZK6/lvGli6GlxT7licNPhE4QzMhDoLUewpp0eJnPS3oBr5/8AaBSbLTctfnXS/FNQAFfL3WCUuoDSql1Sql1hw8frrAUISxorYtmGQAcGKmOIBgyIwQT9S0ItNZsG/IuOYTa8hCYKYN8hCCfOsjTHmt3eCAEJ9FI1BYFGs3wxPAcr6gM7qZEQFw5b4dP9T81W6upGYIwFSaB5QG8T1VQSn0Sy3vwITNV4UZr/V2t9Rla6zMWLlw4ewsUapp8Pt/Nvir1IhhOFH5RjSVTZc6sfQ7FDzGWsrq/dTZ3sqBtgeP17tZCymVoYohMdu66ozsiBDkh0BRpoi1aaF8s6YLKmD6CmjYWupsSAeMRp9h7sj98xsJpCwKlVDTn7v8cJUL1HgziHQko9fTv91ooRArMNf4t8CXg09JJUZgOXv4BgANV6kVgRgjG6jxC4EgXdB1b9HQdi8SY3zwfsCYiDk/O3ROlV2MicPYiEEFQmVryhZTFI0KQcEUIdozsqO0oRxXwJQiUUlmlVMb8A0wAj2Dl7j/m8/M2YHkB3KwFnvZx7TG50kX3tZOA4+lfKfU3wP8AX9Fa/7vP9QmCA690AVSvOdFQA3kIyhkK8/j1EfQn+nmq/ymM4qFA8UoZgNNYKCWHlanXCIGmOEIA4Usb+I0QXO7x59+AtwGrtNZ/9Pk+twBnK6XsEkWl1Crg3Nxr5fg1EMMwH+ZGM78J+J3WesI4/josA+HVWut/8rk2QSjCNBQu6izcKKrRnEhr7TAVjoVAEPh5ouxP9HPJLy/hLbe9heuevi7YReZw9CEwBIEZFZC2xZWpm+ZErgjBhFJkPfwhYUsb+K0y+FxAn/c94EPAr5RSn8YSZl8AdgPfyZ+klDoa2ApcrrW+PLeGR5VSNwJfV0rFgO3A32G1VX6bce0FwPXA48D/U0qdbXz+hNb60YD+LUIIMFMG3e0xUpksg/GU3ZwoyF4EyVSWScOzMDaZJpvVRDyeXOqBUjMMTPwYC+/bcx+jKWvwzL177uUdJwfbSiSTzZDKFoSYI0IgKYMpUT8pA2eEYLyEWTRsgmAmHoK1SqnXK6WW+r1Gaz0OXARsBq4Dfox1Y79Ia23OnlRAk8f63o315P9F4DZgBfBKrfV645yLgBbgecADwJ+MP7/0/Q8UAHhizxCv/Pp9fPj6R6varrdWMQVBW6yJo7oKJrN9AacNhhLOpxatIV7Cw1DrlJthYOLnifLpI4VsYjVuMu4eBKbXoWFTBrsfgm+fD7d+zPpGC4j6SRk4f9bihuiOqsJz8pOHn6xamqoW8esh+JZS6tvG/l9hPYH/DHhaKeV7CoTWepfW+vVa6/la606t9V9qrXe4ztmhtVbuyITWOqG1/ket9RKtdavW+iyt9T2ucz6Xu9brzyq/6xQsvnvfNp45MMqvH9/HfZvDV45ppgxaYk0c1VWICBwYDrbSwDQU5hmt00qD/kQ/I5MjAMyLzWNx+2LP8/xECExBUI0wtFdTojwNGyG4+9/hwBOw7hp47CeBvW29RgjikcKtcFXXKjpjnYDVYGnv2N5ZXdpc4jdCcDFg+gQ+D9wKnA48BHw24HUJNYLppn/2UPjmhU8YpsK2WBNHdRcEQdDNibwEQb36CBwzDLpXl6zfryQIUtkUmwY22ftDE0NktbfRc7qYgsDde8DhIWgkQbDtnsL2Q98pedpUccwzSM48QjCYHOStt72VN9zyBvaP7Z/x+9lkXYLA+P7siHVw8oKC9z1MaQO/guAorFkAKKWWY1UKXKG1fhL4BhC+OZEhwXS97zzibhLZ+JgRgtZYxJEyCLo50XCiuBSqXpsTOQyFJfwDUHlk7rahbUxmC1+XjM4wOhmsMDVHH5slh+BMEzSUIDBJBzeoy/H/GUD74hs23cCT/U+yaXATP9v8sxm/n82y58PHN8NHn4I3/Yj4aX9tv9QR6+DUBafa+08cfiK4z61x/AqCODAvt/0iYARYl9sfAzoDXpcwRZKpTFVyXeZTaxgFQbGHwIwQBJsyMCsM8tRrhMDRobBEhQFAb1v5CIGZLih33kwolzI4f9n59va5S88N9HPnlO6jC9tv/H+BvW3QKQOz7G/f+L4Zv59NUww6F0P3CjjpLxg/+bX2S+3Rdk5beJrnGhodv42m1wMfVErtAj4I/F5rO253DNYYYmGOuPWJffzTzx7npKPm87NLzyHaFMxUa6sMrvB0tuPIeCDvW084IwRNLHF4CKqfMqjXXgQXrryQjlgHW4a2cHKfV+sRi0oRAi9BMJgc5JiuY4JZKN5dCvOcu+xcbn7tzTRHmlkxf0Vgn1lTuKIiM8GdMtBaT7vds9aaDf2FnneH4odmvL5SxNOFh532WLtjhsHGgY2ksilikcaffOhXEHwKa4jR48AQ8LfGa3+J5SMQ5ojr/rSTZCrLo7uGeGTnIGet7gvkfeOTGVKZQtRh31CCyXSW5mgwgqMeMBsTtcaaWGqkDIJuTjTkFSGYqE9T4XnLzuO8ZedVPK+vrfC96hkhGPAWBEHiaErkcXMsF+GoW8w0QTS40tm2aButTa0kM0kms5PE0/Fpp1oOxg9yJHnE3j8cr56peTxVeNhpj7azoG0BSzuWsm98HxOZCZ4dfJa1fWur9vm1gq/f7Frrh4GVwAuAY7TWZlLlu4ipcE7pHyv8cA/Gi/PQ08Udws5q2DMYrrRBskyEIOjmRI0UIfCLOSHPPc8gnU2zeWBz0TUDE8GmDMpFCBqWTHUEAQTXnGjDEWdH/IPxg8GlRSfGYHgvjB2GiTES6UL6L+8bOa7nOPvY/vFwBMF9P+pprce11o9orUfyx5RSfVrr27TWxT+1wqxh3khGEsHdQLxuUDsHwiwIIrTGmujtsEbgprPaIcZmipepsN7nGVTCnGeg0Y7a9e3D2x036zyBRwjKeAgaFjNCsOtB+N1nYN9jgbx1d0thaNWMBEG/UxAk0gl7WNaMeeZW+Npa+PJx8Iv3Mf749fZL+YiGo6dCABUT9YDfPgTvV0r9s7F/qlJqD3AoNzJ4SdVWKJRFa+0INY8EWLfubpQDsLM/XD4Ct6kQYInRnTDItIGXqbDRIwRQep6Bl3/AfU4QlKsyaEi0BuPfzA1vgT9+Azb+OpC3N/8/zZD/VPH6/w8sbWA2Joq2EB8oVMXke0+YwqammywFiN8IwYcB01L9VSwvwUeBLqzZBsIcMJJMO8LWIwHeQIYlQlBkKgRYavQi2B9gpUEj9SGYCg5BYJSqbRzYaG8vn1eYsF7NKoPQpAw+vhk+tgFeYmR7t94ZyFuv6CyYL80eElNBa12UMgArbRAIpiBo62bcaEzUkfsecI/nDgN+BcHRwDMASqkurNLDT2itv4nlH3hFdZYnVGLI5RkY8XjKnPZ7e7xX2EoPk67GRIDDRxBk+2JPQdDgKQMo/URpPiGeu6xQ8icpgxmiFMxbCF3L4Yz3gLK+r9n3GIxP/4k+z6kLCzX80y3Z2zu21/MmHFilQcb4uWpqIR5ttnfbclURU4kQDCWHOPf6c3nDLW/gE/d+Ipg1zgF+BUEEyP9mPA9rKNE9uf3dwKJglyX4ZdB1EwkyZeAVwg5b6aGzdbH149LbUbhpBCnAPFMGIRAEXia0TDbDMwPP2MfNioUgGt6YmCmD1oANdjVPWzcsz/eV07Dt7srXZDNw+6fg5++BkeLeAGbJ3pP905sF4BUdADicqELKoClGvKlQcNehLUEwFQ/B/vH9jEyOsGlwE5sH69dS51cQPAu8Orf9ZuCPWuv8o+JSoIabVjc27qqCapsK9wwkQjXkaMIjZZCPFAAk08EMH0plsp7RgHqdZTAVvJrZ7BjcYju/F7Yt5Pie44vOCYrQRQjcHPeSwvaWOyqf/8SN8KdvwVO/gNuKp8uvmr+KeTGrj92R5BEOjB+Y8pJMQWCWLR4cr0LKoClGvKnwM92etZ593RUw5TD/jUvm1a+lzq8g+DLwUaVUP/BW4JvGaxcC4entWGMMjjsFQZA3EC/X+2Qmy/6Ah/rUMl4pg9ZY4ccmORmMICgVaQidhyA5CJkUT9/8bvvYSX0nFTUwCrIrp6PsMAwRgvQkDO+B8X6r/O5YQxBsvavy9ENzGNKm24pejqjIjGcBPN1vpIuMDpGBRQiyZsqg2ekhyAkCR5OlCoLALEtc0t7ggkBr/RMs38AVwIVa65uMlw/iFAjCLFKcMqhuhABgV4h8BF6mQkeEIBXMoB3Tr2E2fgqbh2AgOQAbf83T44UJc2v71tIea7cNf6lsytFIZqY4GhOFIUJw5Fn42slw5bFw9Uth6XOgLXfzGzsI/RVC3se/vOJHmLMApioIsjrr8I9cuPJCezs4D4ErQmCOvM75C6YbITiq46hg1jgHTKUPwf1a669ore9zHf+s1vr/gl+a4IeqmgoNQbCws/CLckeIBIFX2WGrIQhMwTATzK/18u5CN8SwRQgGkgOw+XaebimYvNb2Wh3igmp44yZ0KQNHl8IWiDTBirMLx3ZXaDy70jh32Rmep7h9BFNh9+huRlPWAKuelh5OX3i6/VpVqgyamolTiIq050Yjm4JgZHLE0TTLjUMQzAuBIFBKdSil/kEp9XOl1N1KqeNzx9+slFpTvSUK5SjyEFTJVHj68oLjdmeIjIXuaYfW32aEIBhBYKZnlvUYgmAyTbbBPRvmjf7A+AFuPPBHNjYbgqCvWBAE2a3QUXZYSymD/i2w68+w7V6IB+ib8GpbvMIYWLungiBoMWbZTXhPnjQjBE8feZp01r+wNRsSrV2wlkXtBc/6kcSRsjdm32ScKYM4hUhfe9r6WYxFYnTGrH9rVmfLTtkMVcpAKbUCyydwJXA8cAGFCYcXAsXOEmFWcKcMxiaCu4E4BUFBLYep9HDCNcvA+rvwY1ONCEFvRzPtzdZnaQ3jk40dJTAjBHvH9vLFDkjkcrp9rb32DSF0EYLbPgbXvBx+eAnsfzy49zWbEuUbMS1/QeHY7ofLX+9DECxqX2T/vyXSCbYNb/M8zwvTUHhy38m0NLXYJYAZnQnGVKqNFtkqQjInCJTWtE0WPFJ+exGYgiAMKYOvABPACcDzAXN81b3A+V4XCdXHnTLQOrhSNfO9T1tR+MEIU+mhe5YBOD0EE0F5CAxB0N0Wo7O1UAbV6D6C7pZuFMUT8SLA+0/7gD0tr7fFu6NhPBXn6iev5hebfzEts6Gj7LCWGhNFC5Eix018prhTBgDLngenvhEu/i943f+Wv96HIABnlGAq/QjcggBgYftC+1ggPoJXXQmfHYLP9JM47a/tw+1Nrag1r7b3/fQiSGfTDrPj4o7FM1/fHOFXELwM+KzWeifg/onbCywLdFWCbwbHvdrdzjxtMJnOMp5z0EcUnLJ0vv3aroF4oC7vWqZSyiCoCIEZjelqb2ZeiyEIGtxHEI1EeeGyFwLWL6QzE0n+rX+A3x/7bt520tvs8xwRAqMXwY82/oj/Xv/ffO5Pn+PB/Q9O+fMdEYJaaV08MQbP3l7YD1QQeEQImjvg9VfDWZfC0ueWv/6OzxW2J0ch6y2Ky/kISv3+yGQzbDxS6FCZFwRm2iAwY6FS0BRjPFv4/+9o6YK+wnRLP8bCw/HDZLX1Nehr7aO5qdnzvHrAryBoBkpJwS6gsX9j1TDuCAEE04vAcYNqi9Hb0Ww/tcYnMxwOcKhPraK19o4QNFfDQ+D8es9rLcxeD7JypFb5xoXf4JqXfJs79/ZzzYFDvGV0jEVr/9JxTqmUwfqD6+3tUg1tymGWHdZMyiA57NxPVStCMI2IyEHXjIFJ74FDXhECrTVfe+RrnHP9Ofzv48WRiJ0jO4mnrZTkgrYFthBY3F546g5MEOTIfx4UJh3mcZe7etEo6QLwLwieAF5f4rWLgUeCWY4wVdweAgjGWGjeoLrbm1FKsaqv0CAkDD6CyUyWvB0jGlHEmnIRgmg1qgwKwq67LUZnS3hSBgDNTc2cmUywYNL4vvrVh+G7F8LQbsCjGiHHjpEd9vZ0muDUZNnhxIhzPx1g74+MR8pgKky6UoYlBMHJfSfbqaBnB58lkU5w67ZbueapaxhPjfOdx79DPOX8PWIKurV9a+100cK2QspgupUGWZ3lU/d/iktuvoR1B9bZx801uAWBGSEYnnCJtByNUmEA/gXBlcB7lVLfwzIUAqxVSn0eeG/udWGWSaYynjekIEoPTdd7V5v1tLqyr/DDEgZB4NWUCKC1OeJ5zkwYcgiwWKhSBjaRKBx9nvU3wM77Yd96++bo9bSWTCfZN1Zonzudp8eaHG401xGCkX0lUwGYbvsPr4f5Sz1Pm9c8j9VdqwHLDHjP7nu44s9X2K9ndKaoza/Zrvqk3pPsbTNlMN3mRA/uf5Bbtt7C9uHtXL3+G7B3PRx4ini83z4nP+kwT6k0lYkZITAjGfWI38ZENwF/D7wRyPe2/CHWtMMPaa1/W53lCeUo1TgoiBCz+d55QbDKIQga31iYdMwxMARBFcoOHabCdrepsPHbFwOw+sXw7tvgE9uga2XheMp6OvZKGewa3YU2bE3TeXp0pAxqxUPgFgRBRggcHgKXILj1H+Gra+GrJ1kNjLyYMCICzfPKfpTpI7jsgcvs/gJ5zImW4E8QTDdl8OThgo/hwOEN8L0L4dvnMr7/Mft4x5518KM32PumqdBXhCAMKYPchMMfYJkHXwG8HStVsFxr/d3qLU8oh7sHQZ4gIgTuGxTA0b21mzIYTqR4/w/X8abv/IndAY1odjQlMqICZsogKEEw4vAQNDPPEASjYYkQ5Gntgm5TEFj/n15PazuGdzgunVaEoBZTBklXyiDICEE2jV0o5hZAI/tgJNclslSDIjNF0FJeEJg+AlN45TEFgNbaIRDW9BXa2wQhCMx0xIAu/EzFje32iXGrrXMOP1UGoUoZKKWiwBHg5Vrrca31HVrrn2itb9dal645EapOKUEQxA3EEcLORQiOruEIwZdv38Tvnz7In7cP8OM/7wrkPc10gCkCYk2Kpoj1CzWd1aQyM08bDLlMhaaHIHSCACBmlNx5RAjsIUiGfwCsxjWp7NQEsWOWQc2kDFw3nyCrDM77GHx2ED7TDy/6V+drjgZFHv0IMmljLQpcOXc3pyw8pejY8xY9z942Kwr2j++3m/90NneytKOQighEEBgNj4bI2k74cVMQZLWjlNIUBH5MhfXclAh8CAKtdRprXkEwj0JCYJROGQRrKuxqt8poVi0oRAhqqX3xlkOj/OShggg4ENDwJa85BgBKKVqjpo9gZj8a2ax2mAqtKoNwmQqLiBk35pwg6Ix1Es35CxLpBMl0kp0jOx2XaTT9Rk64ElrrGi07dJsKAxQEYJfcEXWVyC2vIAgcBkJtneMxAjnPCd0n0BwpfMbxPcfzlRd/xd7fMrTFFnCO6EAmghorpH96PhqoJgAAIABJREFUW3uJKuv/fmRyxNE7wg+H4oc4lCgICa1gKGcSThhdFNt11ikIWn2kDOIhihDk+BHwvmouRJg6ZoQgGik0dgnEVOhyvQMs6myxa/GHEynPkse54D9+84xjJPNQQPMcvOYY2PvNwVUajE2m7WqGjuYmmqMROo2yw1CYCr/7Yrj9U1abXq2dT545QaCUKjIWulMGMDUfQTqbtmvIm1QTsUiswhWzhNtD0NbrfV7QLH0eqNxt4dDG4nW4Kwq+/zLYcHPJt4s1xTh/udW3LhaJccV5V7CgbYH99J/Kptg2ZHUxNNMHawZ2wy8Kt5yIirCgfYG9fzg+NWOhOSwpz5GI9TM8ni38HuvIakuM5fokOCIEHqbCeCpuC4VYJOaohKlH/AqCHcCZSqmHlVKfVkq9Vyn1HvNPFdcolMCMEJj974OIELhD2GD9Ql5mDN7ZPxzwU8s0+OPWfu7Y6AwhloqcTJWEw1To/FFpiQbXrXA47izxBBxVBqONbipMT8K+R+FP34LrXmcdc6QMCtEo9zyD7SPbi95uKoKgJnsQgPNG/Kovw4WfnJ3PbZkHi/OjizXsdVWUu0sOoWy3QoDLzrmMfzvr3/jpa37Kib0nArCmt+APyEcGnjliGAonJ2HHHxwzB8y0wVTNo179KQZyEYK4kWJq11lA2/9Ot6nQ3VDJ9A8sbl9MRPm9pdYm0cqnAHBV7u9lWK2L3WjgmkBWJPhmcLygbFf2tttGvyAaE3mZCgH65rWw9fB40efPBdms5t9v21h0fDigCMHELEUIvCo6QmUqdJvUlHK27U0VUkCmINg2tM1z4MzBcf83i5odbGSaCo2wdSD0P2s9BUdboWu5ZeI0Wf4COJBz5O9+GI69qPDahEfPAXd6w0Vvay9vWfMWx7E1fWu4a/ddQCEy8MygESHIi+C96+zpiovapu8jMP0DeQaaSkQIwBI5LfNobmqmPdpOPB0nozOMpkaZ31zo2tpIhkLwHyE4psKf1VVZnVAWsymRafgLOkJgCoLe9kI+cGCOUwa/fHQvG/ZZv4zMlElQqYykx2Cjwr5/D8Fdzxzkfdc+zB1Pe9+o3F0KgXA1JjKfMJtzffLNCIFRcmfOM1h/qNCh0GQqNwszF11TEYKXXAbv+j948/Vw9AuDfe+7vgjfuwj+94Ww5c7i11cYg47ckw/7VsNbboCjzy0cqxAh8MIsKdx4ZCNDySH75tqM4phU7mfCWN90exForUtECKyf6bghCtvyvReMf5MpQoddKZRG6lII/gXBOHBQa72z1J9qLlLwxrzxrewtCIIgnijdZXB5ejoMQTCHEYJkKsOXf7fJ3n/f+QVNOpxIBTLx0WuOQR4zYpCYLC0ItNZ84udPcMfGQ3ziF084vA55howmUHnxFSoPgVcZm4eHAJy/nB89+Ki9beb+pxJOrtlJh73HwKpzYc2roCvgUTGVGhOZxsJ9jzpfa+uBEy+GM4ws8TQEgZky2DS4iacHCjn+49uPKoSutxYEgTngaCr/xwfGD3hOSDySTxkY3wMd2ogQ5DC7Fbp9BKahsN6bEkEZQaCUalJKfU4pNYhVZTCilPqFUirg+JUwXQYdgqBQARBIhCBefJMC6KsRQfDY7iHbw7BgXjMfuug4O++e1ZZRb6aUMxU6mhOlS3sIRpJp+sesr9PA+KSn4dMrPROqlIFXo5sTL4a/+h789XVw2pvsl01BsHV4q739nEXPsbenFCEwSw5rKWUAkBiCjb+GJ34Gm34T3Ps6GhN5DOLpPrqwHR/w7ljoc+JhKRa3L7bz8+Opce7YeYf92polzy8YG/eut9aA84Y7FVNhqfkWXhGCdjtCUEiDlOtFsH/MiBA0eMrgb4HLgEeBLwO/Al4LfG0W1iX4YKhUyiCRKjuNcPdAnDuePshE2vvJNpvVnmFscEYI/HgIthwa5Z5NhzyfjGeCKVieu7KHeS1RxzqHAzAWlio7dO+XixC4RdMRj6/ZsEc0Zl6YUgamDyAfIVhyCpz217D2Elh4ov1yKRf3C5YUwtxTeXo0PQg104Mgz9BOuPHtcNP7rDB/UFSKEDRFC6mblvneswpmKAiUUo4owW+2FwTPSQtPh2V5q5qGbXcD0+9FYAqCo+cXxM5Acxu09TBupKQcHoIc5boVNlKXQigvCN4PfE9rfZHW+l+01m8EPgi8XSlVv/MdGwgzQrCws8V+is1q7NHFbobik7zqG3/gfT9cx3/+ZpPnOaMTzjK4/FAfcEYIvG5uJruOxHnVN+7nXT94mO/fv83Xv8kvYxOFf1/+5mkKgiAqDcp7CIwqgxLCCooFgVczKa9ojFsQBJECqVmm0ArXjBCYnHXUWfb2wfhBu5SwEpsGCj8Dx3Qd4+uaWSGTdhorqzb+uIQI+scNcNkAfHIXtM4vfn2GggCcPoKxVOF7YM3Oh+Gg8VSf8xFMN2WQn7QIcP6y8+3tgWXPgX/ZQdwYf9z+F9+E998Nx1xgHyvXnMhMGdR7UyIoLwhWAz9zHbsRaAKOLj5dmE3cT/HdbTHmtxVuIqV6ETy8Y9AOQd++wXsynFcZXB5HhKCCee+uZw4ymQun37fZf7MYP4wbT80dLdbN2UxtmHn56eI1+jhPm+EpKBchcEdRvNIsXtGYpoiiw6hkGA8gBVKzODwEnaXPwzngyOTEnhPpzD3VprPpkl3l3JjNcE7qO6nMmbNIegK+0AdXGbn8qg03KuGbaO2CSFPx8T98Bb6yxppCmWeagsCMEOSJACc8eLWj1JStd4HWRSmDfBR0ZHKkpAB0GwrzPRHA6moJVsoiT8fqF8Oy50FbQQSYzYnMlIHW2pEyWNLR2IJgHuCuJ8n/z5f/qRWqzkgyZT/Fd7ZGiTY5m9mUyjtvO1z45bt3KOEZWi+VLgBXlcF4+afwjfsLvyj2BdQ9MM+YQxBYQsgUBEGUHjo9BM4fFb8DjtwCwEsQODwExtc7ND4Cs7a9QoTAK2WwuH0x7bF2xw3D7xOk2TrXfGKdU9xzDKCK44+nmCYZ74fR/WC2h65QdlgKc1ZBnlWqlTYz3XnGe+HVXwGdpSPWYU8jnMxO8uihR/nwXR/m3OvP5c23vtlhEM2zZ3SPnRbqaunitAWn2a8NJAfQWpMwTKttZlQmRykPwUBygMlcyWJnrJN5Fb5364FKVQbLlFKr838olBc6judeE2YRs+SwJ3eTnm/cQEoZC7f3OxuLPL2/+IfZy/Wep3eeKQiKfwBLvfeB4WRZX8NUMSME85rzKYPC2oJJGZSLEJh9CEqHp92lmZ6CwNEm2hAEYfEROMoOc+bYPY/At86Er50CN7zNftkrZbCqaxXgNJ35yTHHU3G2D1uNjSIqwgk9J0xj8VXA6wY72xGCUrijAZ1HWeOPp/GzfXTn0UU34DUYEcm33Aiv+SqsebUdrTB9BO/87Tu5Z/c9gBXpuX/v/UWfYUYHTu47mY5Yh11NkswkiafjjKcLvxPbPWYzmFEpUxA40gXz6j86AJUbE/28xHGvXpUe8SWhWpjh+p7cTWS+8XRZKmWw7bBTEGzcP8I5x/Y5jnk1ysljRggGxy3zolIKN+lMlk0HC7884pMZRhJpxw1vJox7RAgcpsIAIgSJMh6CFp8RAnfKwMuI6UjRGKJmno+IT0Ow4gVw7ket1MGKnBdAZ6B/s7U9r3Cjn988n4iKOELEq+avAlyd7Hw0J9o8uNkenXzM/GM8bwZzgnuwEcy+h2D8iDX1MDEIPUdDzyrruJneef334dQ3eF7uh6ZIEyf0nMDjhx+3j63JGs+ozcX/H4vbFxcNtMrzx71/5CUrX+I45hYESil6W3rYn7uZ73/mFvt7qTnSbJWvZlJWlKbD+r1olh06BMFYYxkKobwgePesrUKYMk4jWj5CYAiCEhGCbb4iBN5NicDq0Ncai5BMZZnMZBmfzDieZM3PmXSV4+0fSQQnCCaLTYUOD0EAzYn8RgjKCQK38dI7QuAdkZkflgFHx1zgMHEBntMOwbqJdDV3OerB82bAxR1TSxmY/e1rxj8AxfMDwBJImZQ1kGim+IkQ3P9Vq5U0wEs/D+d91Np2pHc6iq+bImt61zgFQdqINHgItGWdy8CwPp3YcyKbBi1j6AP7Hih6QDENhSf3WS2Ze1u6bEGw+87PQK7te0e0Fb58Aowdgr7j4MPrAGdUyhQEjTTlME9JQaC1vnY2FyJMjcFxM2WQjxCYpsLiG8hIMkX/mDPMv9FDEAw7Ju8VF5T0dbSwd8j6JT0wNukpCLzed/9QkjVLPBzL08ArQtAdeJVB6cZEfjsVFpkKPasMvAWYY55BAL0l6gpHYyLnZM2e1h6HIMhHCKbqIXAYCv36Bw5vht99ChaugZddbrVZDhovDwFYwigIQTBvsfX1TSehVDMms12yGbGYQkWIH9xf95PMuR0OUZiEVJy3nfQ2nux/ks5YJ5eedilnLjmT8244j3g6zt6xvewc2WmnkLI66/g/PnmBJQj6mgv/tt2xwtezPdoO+QmLo4WbvcNDYHwtGq1tMfifZSDUGIOVIgQeIfPth4sHkzx7cIxUJusoLRwuEyEA6OmIFQRBfJKVfcVK/ul9HoIgwGFIYxWrDII2FZaLEPj3ELgFQjKVYSIXSYk1Kcf7OjwEjZwy8MIMZbvC5T2tPWA8ROdvAFOtU3cYCv1GCP7wZdh+H+x7zGqYtOQUf9dNBa8IAeS+DgEI6tyTb1naTEFgrMerZ8QMML/uR3UcRfeg8fgfa4MdD8D9X4Md98M5H+SEl3yGmy65yfEeZx11FnfvtnoVPLDvAfv74aEDD9kVBH2tfbZg7G0ueOJ3G0PK2ps7re+7dNJKjUyMQktnUcogH4VwRAgaoMIA/LcuFmqMIQ9ToaPKwCPE7DYUAkxmsmw97Gw8Usr17v48KG0s9EpF7A+w0sBhKmwpNhUG4yHw2ZhoClUG7hSCuymRGe6cN42UwTMHRrj6D9s4ODL3kyhnRIlph+CsNGhparHzt1OJEExkJtg6VOh06FUC58nwXuuGMX4I4kf8XTNVSrn2g/QRVMKMECRKRAgGtlsdFJ/4qfV1mQYn9Z7Ey45+Ga1NrVx62qWO9BCxDjj0NGz5vVVlkShuPwxw7tLCXIUH9j5gb/9i8y/s7Zevern9s9VrDCfaYzwItcfaodO4sY9YN/y2aJvdtCqVTRFPW9+PjdaDACRCULc4TIUdXimD4hvitsMeHcewnubNUL7X6GMTZ/ti7xuvZ8ogwAjBuNGYyNNUGHBjInO6IUy/7NAdISiVLgDngCM/psLEZIa3X/0Q/WMT3LnxENd/4OyK19QEv/03GNljdcd70T9bBrYSswzA6fpeOX+lPXJ2KlUGWwa3kNbW13Rl50q7h0FFHE/IVaq+dkcIVr7QyvWrWfRtt5VIGZgegj9+ozDr4E0/ntbMBaUUX33xV0ln00QjUUj9Q+HFWJsrdeEdOXnhssLwp3UH1zGRmSCeinPnrsIchNcf/3p7uzdWiGzsMb6k7dF2q2picId1YHQ/LLQqT7pbu+0UwdDEEB2xDqepsEFSBhIhqFOGPJoHVTIVmobCpV2FkKz75m3eTL1MgJXaFx8aTdr9+00O+BAEA+OTvrryjXlECKbamKh/bKJsKaTDQxB1CwKjMVEJQZDKZItu5OOTGcf7OsyhLvHlp6+EyeN7hmyPyGO7PZzqtcrWu+DpX8FjPyo8gUZbgFy0JDMJ2cLXzDR55f0DYLnB8yVl46lxxrxa7uYwh+ms7Vvrf60TsywIXnEFvOc38I6bgx9yVA5zJLIZITC/pp1LC9vTbE6UJxqJWqWLZjQo1l56HQYrOlfYLYkT6QTrD67nlq23kMr1Sjh1wamc2Ftof90XK5gh96jCz39HrMMSBHlGCzd8dy+CeCpuT1xUKEe6qp4RQVCnVC47LL6BmCWHrz6t8I1vNhACl+vdw1Rolh56tS82/QOmU75Sc6If/mkHz//i7/mLb91POlO+9azZuc+rMVElU+GXb9/EGV+8g3dc81BJUVDOVGjm+idKeAhKzXow/+8GSwyRAnfKoHLEwxQBiVSmbAfFmsJr2qFSJSsNTCPa8xY9z95WSvn2EUzLPwCBm+r+P3tnHudIVa7/70k66X2ZXqd79n1jZ1gGZN83RVFQwauIoCii1x9eXO9VROWqF69evVcR3FAEQURxA0H2RWUfBph9htmnl5nel3RSvz9OKnVOpapSSSfd6Z56Pp98ulKpSqoq6TrPed/nfV5HqKJCdUDMB4b7YM1vZah/69Pu2zmJChNxfcCuVrr7jZEQAGAk4KgPweGXwcHvkj0V1PN301YAx7VZUYKndz7Nb9Zb6QI1OgBQH7YIwaiiCZUpA5UQ7EwtajqCof08seOJVMnqwmkLtW6bkxlBymCSIltjIsMwNA3BeYe08aMnpCnLa7t6tHKdTKJC1ZzIadBTCcZJS5q5/2X5j2WaEzn5FgD89OktGAas2dnDy9u7OXKOs00tOFsXl0fCRMMhRuIJhkcTDMXiabl/kLbPP3lKnvsT6zvY1jXoKIzUehl4pAzcIgROFQUgoyCttXKwU6MmzTV6TXi2xkQvvanPoLoGRpgRTXdeKzpoxkTKrDtSbg1AscEUWTh51sl85ujPMDg6yLuWvEt7q5aKFrb1bgNkjnd+nbNnWs4Ohf0KyXjjD3D0lf739Yvzb4bTviiJQXWec9M9O+HuD8jlhoXw8eedtyt30hAIuPoZmTaI9cOmR61tcnQr1BAKw7nf1Nf5JARvmfEWfvXGrwC4Z909qd4IFSUVnDPvHG3bBgc3QnNbqpXfnxIhsJsTmSJGgDNmn+F6XJMNQYRgksKpIU61R5XB7p6h1MBVWx7hkBm1qQGnq3+Evb2WONArrw02+2KHQU8VFB4zr56K5GBqmhM5YXg0ztZOa/bR3uueXhgejROLS3YeCQtKk+F8IYSW4nATFr7ZNaD5GLhFLgY9UgaqpsBNQ+DWHlpdr+oq1DQOZG9dbE8T+OlGWRRQ89Kqcl3VEYzqXgSXLruUDx38oVSKwITqReAWIYglYqzbty713DchiNu+gy3pznh5QbQSamdCy3KocO7umDP8mBJBeu7eMCAUksc06yiYf3JeGhxlhFu1gw0rW1amZulqo6Rz5p2TZjhV79LVsiJSIV0XTSilh2qEYHf/bh7f/njq+RlzAkIQYIKhpwySEYJy9wFELTmc31RJKCRYOt36hzbD/F5lcKnP00SFThECixAsb6uhVRnodvU4D76bO/q1Fsntve62yE6CQhN+Oh7aNRNO1Q+xeCJ1POGQIBLWoxoqQXCNEGRJCKbX6jOXmiwIwe7uIXbbKgvcPr+oMDps+eKHIrpRzkW3wuV/gase09wKveDHrXDT/k0pD/q2yjateY0n7JqEQg2CKjY8DM98Hx7/Fux9PfP2meDXtrgkahEyI+58rqVKCWShroUWIdjvapFcEangiJYj0ta/c3G6k+I0F+8FqSFIrzIAXbfyx81/ZDBJUOfVzmNB3QLPU5hMCAjBJMTgSDwVzo6GQ6kZuF1UqObGNyrpgnmNMoe2vM36hzZn9bptcdQxvN/gISocisVT1QxCwNLp1anwOEhzIies36PfbNsdRIkmtHRBVCcEujmR83vYSyKdqh90QWEo7TqURVVjoiw1BBohsMhIWoSg1DqXTCmDl7ald/ebFIRAy8nbnO9mHwtzVkHbYb499/1UGmiGRNnczNMIQR7C5Jnw8p3wwOfgb1+BXS9n3j4T/EYIAGpnQd1smH6ITiRMjEeEoKQMwsn7TXzEs/RSLT8E6WJouhNqb1laS52Rfl9LVRmYUL5fVVS4ft/61PIZc85wTYFORgQagkkIuxDN/EGWRcJES0KMjCaIxQ2GYolUaFuNECxokmHZZa0OhMCjsZEJLUJgG3TX7u5NdWGc11BJRbREjxC4VBqs32sjBB4RAidTIqdjdjMnskcInKof1Fm/veQQ/JUdqiWZQliTG/cIgXvKIJMx0YsOVQWTghDkuYzPjxeBph9Y/xjsuACO/SgsOkuGxd0wbCMEHiHsMaFrk5x9l9ZARPlNxPLg45FNY6Nr/qE/j4/KaIG5X74JQcd6aUIUKYfGJXDMVfIfp6zO0m4MdetiUwXHzziem5+/OfX8osUXOQ/Ws4+lvm4e+7s3aasrI5WSAF3znIwUKOenEgIVZ845M8uTLG4EEYJJCKd0gQm30sNNHdbNLBUhUAiBOUh2ZzAlsq/fPxDTKgLUwXZZMgKhEwLnm9qGvfoNxTtlkF5hYEIzJ3JJGdhdFHc6RC3UyoHSknRC4KeXgWraNGualcc0SVQiYWgGQq22lEE2okK7oBD030nRIs+q/awjBD0d0nnw9x+XM1Av2Ae9QhCCRAK+ewR8cwHc2CTTKCacZunZYiytjzf8FW5shhsapTAx34Sgezu89Ev4561SsGnCp7BwUd2iVPlhVaSK8+af57qtUxvt8ki5tIZuXJRGTp0IwazqWcXTITNPCCIEkxBeor+a8pJULXrPYIyWpHJdrTCY3yQJwZLp1YQEJAz5+sDIqGdjIxMl4RB1FZHUcewfjNFYJWcNajjeJBytdUrKwCVCsC4tZeAvQmDvo1CXQVS4f2CEnbZj2O2gaxj0KDkEiIRDhEOCeMJgNGGk2T8DdCnf04KmSt7skqJJsw9FR/9wShxZVxFJi0TYCUE8YRAOpc944gmD1TvSb5ROJaFFB6eSQzsSCcBItcD1gqYhsEUI1nat5Vdv/IrV7atT65aPJK/RsrfC/jflYOAWAi6tggWnSt8EcO85MBYM90CynI1olZ5GUYSVdO+AR74qt5k2F1Z91N/7aymDbFsfJ7+rRAwQNkKQh2th9yAwcf63AUNGCurmuO4uhODbJ3+b3234HafPOZ2aqLvNsxMhqCxxb9bkpDOZaukCCAjBpIRXhKDaIUIwPBpnW3IwEgLmNsgfflkkzLzGSja292MYMtyvzqprXCIEICsNTEKwr38kRQhedyAE0zNECEZGE2yx2Sp3+BUVRj1EhQ7mRI6Wyg4RgqEMKQOQUQKTnAzF4mmEQNUKLGyu4pG10sikMxk5UD/XHh0AKWasjIZTFRH9I6NaBMjEuj29DDh4DkyKKgOvCMHvroHVd8tBzGer3cbyRsIiTNyI0zXUxWPbHmPtvrU8teMpXtj7grbtjNgojWZ067nb5ONzuxzb7gLQvAwuuxduaJCh89FBGB2RArx8YdjmQWBv8GOid7ecTQO0HpYFIRhDhMBO3sqnQctBkhg0LMzuvZyg2RYr5z3vBN9vsWjaIq476rqM2zWUN6St82p/7RQhmGrpAggIwaSE5kFQaYsQaF4EcrDa1jWQyuu31ZZr+e/lbbVsTOoL/uP3a7SWxU6mRNbnRiE5iHf2j7AIGQJXPQhMjUJbrXeEYGtnP6M2d8L2pIugEwP3ShlkMieymzCZx2/3LFBNfewlh6n1kRBmIGMwFtfImPm+JhY2W4OdGSFQr0VrrfPNuaqsJEUI+oacCYFabqhGbiaFhqBpiZwBDvfpJV8gRRfmjNbWz8AN4VCYxvLGVHTgmr9d47jdElHG59q3pr8w1O1OCCCZ0661fPWHe6Ck0dex+YIaEi+rtTV5UgbMXLUX2UQIOjfCnjVS3d9ykE4IotUyMnH1U667Zw23CEE2ePPvMuVw8Dth8VnO2+xeTX3H5rTVlaaD4eiwJFy9u6SWYNpczYcAZHVKVg6XkwSBhmASYn+/KvyzaQg0t0I5MGxsT08XmFjWat1MXtnezRu7rRuNW8oAoN6h0mD7vsHUjLm+MkpLjbzhaBGC/UNpzoB2QSHIqEGPi5BOTxnog7UeIUgnBE5dGIG0ZkBDo+59DEyUZXArVGfoppATLA3BbiVaYhcUmvCjI1D1AyctbrI+ZzIQgrpZsPKDcPy16REAt9lxBrh1nisRJZwz9xx+fvx/cvfmDRwx7BCFGnK2x9XgM6edE9T3K63RCYF6DdTIyo4XZKTCD7KJEKy+G379PqmvWPsn74qQfGBEIQRepMwLt78dVv8a7rjYXYS55Unq3/hT2uqKkuRnPnoTfOcQ+PFZ8PJdgGxwpLoRTsV0AQSEYFJCdym0RwjUlIEcQDT9QKP+j3zuQa1Ew+k/AyHgtGXu/txO9sWv7bJuZstaq1P/MDVlJVQmB9XBWLo50bo9zoIkN2Ghd4TAW1SopjTU87ZHLtSUgZOoELzdCg3D0Cow9AjBCIZheJoSmdD7GTiLJNUIwalLre9sUogKvaAp7P1FCAAuWXIJYRGmNFzKIU2HcMmSS7jhuBt48J0P8o2TvsHhG55EGEkCN/9kmHmUtbOfAb5MyU37IRDZwG5bHHGLECiDc6zff+fFyiaYebQsJayd6b2tvY+AH73HWKBFCBwqCRJxb2GlYchrYaJnp/N28REa4ukptlTKwMG+WAhBY7kVCTpj7tQxI1IRpAwmIdSBwd6N0KnjodrlcJ6NEMxtrOTxfzslzeXu4Jm1zKhzt711si9+TQnHqxUMQgim15alIhU7uwc1R0GnCAHI5kPqQGqiz6GPgQm1AsIuKhwZTbBB+axj5tfzxPoOIF3b4FdD4LQ9yCZGZvqlLBKiriKa0gOMJgx6hkY9TYlMVGcwJ+obHmVdskIjJPQIwb6BGImEQchBiDgp4NHx0AsXLLiAM+eeSViEZdMcFfEYvHi79XzVNfD3H1rPXRroAPDGn2DHc9C1RQ6q806UA2w+kZYyUH4X6mBoV/X7JUwHv9OXFkN+vq2fgRoVKEQfB01DoHz3/7wVHvqyTM8c93E480bn/Uds7d3dyJobITAjBDXODY7ev+L93PzczZw25zQOaTzE60wmLQJCMAmhCsjsA6JT2aFeYZD+jzy9toyza7PzTHeyL1bD8arHAUBbXXmKEOzuHtJe36BUGEyvKUs57vmJENirDLxEhRvb+xhJishm1JW4x9u5AAAgAElEQVSzdHq1QgjcIwRlJc6BNK3joU3Up6YLzGs1rTJK/8hg6nUvUyITmVIGr2zfn/I3WNxSTV1FlOrSEnqTVQk9Q7G0tNKkgTpLHM2uBt9uaZxCz05LuFfRCAtOk+Y/JrwiBBsfloMTyEH1mA9ndUy+oIkKPXwI7CZJHp0dc4a9n4Ga6jMJwZr7ZK59uBdWXgGV6WI933CLEIiwdV28vh97pcNAl/N28VHqbc3TBIJyk3xpEQLLrfDSZZdyyZJL0knmFMLUPbMpjD4Ppz5NVDiYnjKwRwhyhZN9sd2yWMV0pXGP2jtgNJ7QPBJWLWjgty/uALwIgTsh8hIVah4JrTXarNxeaaCJCh3sm+3rVc0B6Pl781o1VEbZvk+ee2f/iKcpkQk1QuBURqlGdg6bVZf6vN7kb6Srf6S4CcFj35D56WiVnK0vOdt6TZ0d58OUB2Cgw1quaZVGRHZ7XDeoOfTxaH1cVgu1s2H52+S1mKFY89pNkkb8p1R8w97PQC37NFMGj30D9q6Ry4vPziMhUCIEPlogy2O0EYL+Duft4iPU2yIEFZEKSxPgYl8MTGkyAAEhmJQY8AiZq6LC3qEYL2/bT0fSBri0JOSZBsgGDTZC0D0YY8d+edOOhkOaiA50LwLVGXBr10CqFr+1tkwjLG5eBP0eosLqskjKFbB3aJTReIKSpFZAjWAsb6vRZuVpEYIsRYX2CIFKCEwBpkqiOvuGPU2JTKj7OFVNqIJCkxDUV0Ytz4Ni1xF0boSdL8rlQ9+jv6aJCvM04IVLYeHpcrBoWirX+Wygo3dlLFTrY5uocOaRcPHP07dLixD0p28zVpTbUgZqVYJ5/vk0J3JLGfgVcaZFCNwJQaVhEE0YjCTTaZoHQVULIAAD+ttlmik8NdobZ8K4EwIhxCzg28AZyKv+EPBJwzDe9LFvGfAV4DKgDngJuN4wjMdt230KOAVYCUwHvmwYxpfyeBoTij5lhlxhG6zUlEH3YIyv/tFyZTt9WUve8snqQLVvYESbfS9qqUqryVfL6lRnwPWKoHBhcxVN1dZNx82LoN+DEIVDgpqySGo23TM0mhqQX9+teiRUp0ybIF1DoJcdOqcMVA3B8GhmQqCmWdbv7fM0JUrtq4o3Hfo7qIZEh822CIHXPkUFL6FajhoCT0w/CC77jb7O74CjlfoViBAkRiFUIv+qx2WHPUIQ80kI1j8EXRvl4D73BGjw6OVgn5mr30dpAQjB8rdB/XxJ/lqUHgT2SIUbps2DEz8trZ8bFsKRlztvF48hgIZEnF3JGb/mQRCOSG1I/17AgL69UDsj59OaTBhXQiCEqAD+BgwD70dact0IPCKEOMQwjEy/6tuA84BPA5uAjwEPCCFWGYbxkrLdlUAPcB/wkfyexcTDO0JgPf/H5q5U58KSkOC6s5bk7Rg0DUHfSFo43g41JK46A6pNjRY1V6cMjsA9QtDnkTIAOcCahKB7MEZ9ZRTDMPQIQWstpYoGwN7PYEgZ4MtcIwQeGgIH8yh1oFaPxS06YN/HPtuPJwytw+H8xirt85z2KTp4zbojLiV3+YbfkLQ6CP/5M9LVcN5J0nM/XzjnP+Hsm+SgKDycGUdsg6/fCMHLv4JX75HL7/hRBkJgixB84mX5fY30QU1ygMwnIVhyjnzYYY9UuKGqCU79QubPSXbXrI/H2VXiQAhApg3M/gm9uwJCUCBcCcwHlhiGsQFACPEKsB74MHCz245CiEOB9wIfNAzjJ8l1jwFrgBuAtyqbrzAMIyGEKGEKEgI9h+4eIRhWwt7vWzUnb/oB0KsMugZGbINtOiFoc8nXqxUGi1r0CEEuokKwt0AeASrZ0zOcKtesLi1h5rRyDCRRGk0YaeZEqq+AmzGRV5WBakrU4JAyWLPTmum4mRKBTgjsvgL7B0ZSOq+ashKiyUhGg/rd9DuXKhYNRjzy8oVIGTjBHPjCUTCcO1cC+rF2rJWPQqQOhMhc53/QO+G13ynH5pMQZGNMFCmX1yQ+YvV5qG4BlFbU+bYvdkK+fR+S56IKC1MVBiZq2mD3K3K5V9cRTGWMNyF4K/CsSQYADMPYLIR4CngbHoQguW8MuEvZd1QIcSfwGSFEqWEYw8n1Hv/Vkx9e7X+d7IZrykq49tRFeT2GymiYaDjESDzBUCzBC29a7XczRQh2dQ+lXAhVQrA4B0LgFCFwMidSPRKWtlanUictNWUp7cOeniHmJG2dsxUVDtqMifY5iArVwX1LpzXAeRECJ/Gm0/MGJbKiRgjUBktFCXUgsw+us1fBx1+QA1OhcvYAS86Fz+927aKXgtMMuFAdD9X3f+JmOZBHyuH0L8n1y98qS/Ce/h/53DchyMKYKK3T4P50w6DxaIFcqvo+dEuB0FhMgeJWhMBEyqXQhCosVEoPpzrGmxCsAH7nsH4N8C4f+242DMM+VVgDRIGFyeUpjXjCSJngCKHPUkFXpZv4+KmLtIElHxBCMK0ywp4eeYNR3RCdIgSmOVH/SJzBWJzuwRjVZRE2Kh4JC5uqKYtaYfjO/hHHOnrNqTDqlDJINyd63cUjYXqtRQh27rcIgZoyKI+6lR26RwgcNQQu34EXIbCLN1V09KVHIeTnRJR9ijxC4OV+F610DmnHY/Do16Wy/tQvZJfPf/5nUihW2ShV8dXT/fcisOftoXCzYhOjw/DUf8vlikaLEIBOkgoRIQCYdbQchMvrkJIvG9TBulCEoCQq9QuxARnBGe7VzaGcMDIgj1v1FDDRehiM9NMwsh0MWZqYFiGobpVkqLo1+54PkxjjTQjqgX0O67uAaQ7r/e5rvp4zhBBXAVcBzJ49eyxvVVCo+oGKSDhtsCyPhFNhcICZ08r5l+PcO4SNBfWVpSlCYGJGXblmOmTCbk60q3uIfQOxlHlPc3Vpar+ashJ6hmQd/b6BEW32axiGFiGoKE2fvdelpQz0EL0awWh10Tb462WQL0LgPjO1izczfYZcLnXdp+iQiyf/a7+DJ/5LLkfK9EEyE174uTQXArj8L/pM0AuG4Vzrn+8IwebHIVIpB7z6BbZeBjYdRXk9VE2Xs/ZMA6QJtcWzn4Hu3ckGSom4rAjpMSQRMT8vnxGCez4oSwejFXDezZK0mSirs9JGQ93O5/voTfD8T60Qf2ktfNZBq77qo7Dqo9Sv+Rk89y3AQUNw0vVw8mfGdj6TEIF1cRKGYdxiGMZKwzBWNjXl2X0sj1BNiSocwuVCCG1wuP7spa7Wu2NFfWX6wK/2RrCjTWuDPKhVGCxqsWY7WtrAJiwciiVSjZqiJaG0agawt0AeJZEweHaTZVJy8EwrJ+lW/aCWHbqJCssVUWEaIRhwKDt08QPwihBUl5YQCUvSNzAS1z6nU0kHqKRJ/V6KugWyYXh3O3TDk992XvYDtRRNHXAyYaSfVFtiFfkkBIYBt78Dbj0VvrdSDt5ehOCYq+C6tXDtizJ94Ae5tj/ub4fvHwU3L4P/UfwQ8kkINj8OG/4qCV/C5iToR0ew/0093z/c7dnj4ZRZp6R8BU6dfar+4hTsU+AH4x0h2IdzJMBt9m/f12mqa0YGXGypphYyCeoArjpxPt/4y1rOP6SV8w9xCJnlCU4DnFO6wIRqTnTd3a9oTY4WNVs3lqbq0lQkob13mKXKJK7Px/nb3QrX7OxJzaYbKqMsm65GCJz9EXSnwux7GWgaggrLmMgJbqZEkEzNVETZm9RTdPWPpIhVp0vKQKsyKGZCMDok2wiDFK/ZQ/eGIQeZ2CDEh6EuGbm78P/gh8mWuNlaB/crnv8qIWhfJ3PkQ93SvTDkMFdadY2MEgx1w5rfynV2M5yxIDaYUsATLpXRD8MAEZKh8sQoxEchXAI/v1AO6NEqePsP/NfJ59r+2E3rkU9RoVv7Y5CRinBERgrcIklORGGg0zltAMyumc1D73yIgdgAs2pm5XjQUwvjTQjWILUAdiwHXvOx79uFEBU2HcFyYATY4Lzb1EK/hweBiQ+dMJ8r3jKv4N24nAY4J0GhiRnTrH9yez5cjRBopYc2YaEuKHQ+f5UQdA/EeGzd3tTzExc3aWmW1lpnLwJfvQyiasrAiijEE4bWadFsQFVTHiEkwNbp2TNlADLC4EgItAiB9V00qCmDYiYEXoJCkIPvTckbdaQSPp9sVtO4iJRxzECnLElUSxS7NkPnBujZAXOOT26PHAzNFEWoRC+ru/U0a0C7fqte6gZSp3DWV+VyIm4RguEeSCScCUS2sNsWg5yplpRbPgOjg0AZbHok+XoYLrrV/2fkGiFwKw+tmw1LzpODtOqkmC0Mw7v9sVd5ZOoYHQjJQIcrIQBoKG+goXwM7opTDONNCH4PfEsIMd8wjE0AQoi5wPFApoTN/cCXkeLDnyX3LQEuAR40KwymOjRTHgdBnYnxaM3pJFS0WxareMfhM7n9ma1pYeyZ08o5/+C21HPNnMiWMvCybTahigr3D8Z4fJ0VJj5xsR4mtlc/mNAiBBHnm72ailEjBGo5YG15JOWUGA4J6iqiGhnyMiUy4eYr4KYhqC4rIRwSxBMGvcOjDI/GfaWN1u7u5St/eI1lrdV87txlhf8NlVbDZfdKYuBUGFRiKzs01eWRcpg2B/Ztkft1bdSNbB7/Frz0C7l8/rctQqBa2VY06GHhslrFL39/OiFQEQpDtDpJLgz518tEyC/stsUmImUWIYgNpVTygCQq2XxP2UYIdr4Im5+Q9tLqZ5qYcQS85w7/n++G+Ij1GwhHZRQkWzilLJzsi//xI+jbA6EIHH6Zu8fA1mckqezdBUd9KHMVyhTAeBOCHwHXAL8TQnwBmZT7CrANSLUcE0LMATYCNxiGcQOAYRgvCiHuAv5bCBEBNgNXA/OAS9UPEUKsBOZiaSSWCyHMFl9/cqhUmDTwM0MeL9hFcpXRMLOmufcxn91QwTOfPS3NBGjGtHLCyqzdq/TQT8pE1RBs3zfAJqUC4oRFeohZ1zVYxzXoI2WgRwis7fc56AfU5+pAnik6ADbPB2VftcpAjaqEQjLNYJKp/QMxWmoy/1a+8/A6ntzQwZMbOjjn4FaOmJ1J5ztGlJTCwtPcXw+XWHXwGMmcevI8G5dIQgDQ/oZOCGoscql50av6gQqbfqCsFrq3yWVfLZBrrWjDUHf+CYGq3td0BDbHxqFuWH2PnFEvPTfzZ2gRAh+EYMtT8Ncv6usKUQKqRotyHXid0jdObaFf+qVll73wdHdCcO9V0J0UJS4511+UYpJjXAmBYRj9QohTkdbFtyPjfg8jrYtVCa8AwqSLHi8Hvop0N6wDXgbONgzjBdt21yCdEE28C6uscR6wZcwnM0HozyAqHE/YB7tlrTUZrZGjJSFmN7iTBoAmr5SBh0ujCbXKYJ3ihLiirUYbOEEOpGZVRpdiTqSmAFx7GZQ4iwrV3H4aIbDpLrwEhU77qITALUIgn0dShKCrf0SzaXaD2hp6W9dA4QmBH5SUW8r42IAkBJsf1wfG9nXWcjymh4h7dlrL6mzR3oTHrz1uavsa6YXqd3s/UAc0lWCoA3dsSGoJVPzmCmhe7o8QLDlXHu/okD9C4BQpyWSalAvc+hiYSMTlcQ91S8LgVB3ilDJwihDElevnpb2onm4Rgt5dASEoBJI9Cy7KsM0WHIpeDcMYBD6VfHjt/wHgA7keYzFjIEMN/njCPrh56QeyQaNHlYFqW+xHVKjixMXpArRwSGjmRLu7h5jbWMmQj7JDNw2Bk21x6rmtMsMPIdBKDxUS0NnnrCGwf65dr+EG1UGyaHogRMqlWhzkYFiWVOInlLB5+xvW8l8+C//8kfW8VyEE6mzRKUJgwsm+ePMT8PxPZJpjzvFw7NXJevhaqG5L3z4XqLa8WspAbQM9lF5tAP7bH1/4v9kdU5kDIShEHwcvQSHAs/8HD35eLh9zNZxzU/o2jhECJ0Kg/LbDHh4UdbNg+z/k8v6MrXamBIJuh5MMfRlq8McTdg1BvgiBGiHo6NUHJj8pEye3RoATFzkr0lVzol0mIdB6GWQ2JlJTDKoZkL00U/UIAH+EQBVvmvqLdOGi/l00uKQZ3NA7FEu1TIYi8i+w2xcP9+pkAKBDiRDssXmTuUYIPAiB04y/Yy28qjRFuuA73sedC5xEhZBeeuiUKy9E+2NwToVEbSr/R74uj324By74rt4m2S/U5kxOEYJM38/osKxEscMpZaARAo8IwbS51nLXZvftphACQjDJoPoQeIkKxwP2KgMvQWE2aPaIEGSyLQY5UJdHwtogXRkNc+Qc5xC4vdJgNJ5IdSIUQrZzdoJbLwPVLthOAOwEwY+GwMmcaJ8iXKyriKT5MWTb4Miu6xgX/4KX7oAHPicHmEMuhtO+mL6NRggGnW/wHetlGDgUdiAEPjUEmRro5OKXAFKYtvUpOPIDmX0P3ESFB78T5qyS6ZOqFmdv/UK0PwbnlIE9QvDM9y09xVlfy01PkSllkIkQuHkgOKYMFELplxCYepUpjoAQTDL4GRDHC3UVUSJhQSxuUBISLGnx6TSXAfWVUYSQovKu/hFi8URqwPPjQyCPLcJgtzVIr1rQmGr+Y0errdJANSUqj4Rd1falLsZEXhEC+0w+Vw2Bl07Bvs5P+H+njRCMS7ni4H4Y3Ccfbjf0tHC5bRZ44qelwNBIQPcuK71gYrhbDualVTBjJay8QpKK6Qfr22UacLyaMLlhcB/cfqE87rV/gise8i5P1ESFyvEce7W+3ZYn0/cdHZR59lxm515wShnYNQSl1RYhGM6x4kIrOXQgydr340DY3HQcTk2x1AiTV8pg2jxreV8QIQhQhNBFdRObMoiWhPjYKQu57YnNXHXi/Izlc35REg5RXxFNzVI7+0ZS5YF+CVFteUSrGjhpsfvsTJ2l7+oetJUcup+THiHwpyGw5/pb63xUGTj0M9A8CDIQAj8Rgl37dfW6X93BmKANsi6zbnW2GBvQ2yAvOFVvd2uPDpjo3QWli6Tozk14l0lDMJwDIejYYOX7dzyfLKvzIARltdCwUA5uFR5O7G56gdiA97EN7pMlmSVlMlphJxpOcBQV2r6r0mow+ZwTsRsdydwvomkZvP0WeQ5Vzd7H4TT417RJK+rhXpk6mLFSXkMnrwW/GoIgQhCg2DEwXDwpA4BPnr6YT5y2KO81603VpSlC0NE3nCIEfVrrZ29CoMJJUGhC62fQPWTrY+B+A3fTEGitjz3EfqC7N7pBJwRydqO7FKbf9LzaJjthly1CMC6EwM3sRoWmsB+EAcWQtMJWKbDnVef36NlheRG4IVOVgf1YX78fnv2B3HbFhXDiden7DCrHuuC0zLX1b/lX+cgEt2jKSAZCMNAFz3xPLk+b648QRKuk+ZHpKPm5XelhdvUznYR9/zlX7l9WBx9/3pn81bTCoZe4H0emCE6kXKZV/EBNGYQ8vpOaNulVkIhJ6+bhXv9kcJIi6GUwydBXRD4EJgphYOPmRaD7ELifv+pFMKehItXF0AnqLP2FN/fz5fst00y3PgYAkXCIkmSZZTxhEEv2V3eyLTZRr1kMZzYlAr0yQWoHDL3ksCp9lpNtlYHq0mh+TsHhJwxvho9FWM7sBr0IgUuEQBUWuqGiQebnGxc7z1C1JkxVMu2w9UnYs9p99jiouLF7zfizxdLz4MIfwLnfsh1jhkqDXGyLhdAH45E+b0JgJyvxmBQMjg7JNspO+gA/8NPLwC/8RghCYcsuG2Df1rF97iTAxE8xA2QFrblREUQICgU3L4J+H06FAHXl1j/6SR7RAdAjBF39Izz0+p7Uc7eSw9TrkXCKpA3G4kTCIfb2WrNtu+/B/KYqqstK6B0a5cg5/gaJ0pIwVaUl9A3LDpA9g6NayWFjhpRBLhGCfQMxx9bTeUUm62KAd9wCoR9bg9DDN1ivqYTAMHRCsPR8OaurbpWPTFh8Jly3zv11TVRYjVYV7TZAqdGM8jF4Orx8F6z7ixxUD7sUlp0Pzcvka8/9GPYmCaxTvlxFrrbF5XUWERvcn06YvPoZqBEDIwHrH4Al5/j/7NRnqG2We3LXSxiG/yoDgPp50gkTpI5g+kHZf+YkwtQdUaYo+opIVFhIuHkRqBoKL1HhiYubuOu5bYRDgnceOdPzs5qrSzl4Ri2rd6Tf2L1SDaATgqFYnPJIONV3QAjSDIGqSkv4xRXH8OymTt5+uItDmgOmVUZSn9M1MKKlJTKJCv3M9nfaNATxhEHPUEyzgc47hv1oCGwaC7XKIFoFv/0ItK+Frk3KwCwkkVDFb/EY3P0BOVOvatG1B35gj2aog5EbIVCjGeVJ8rfzJdj+Tzj6Sv+fvWc1rLlXLs86Wn9NPcdMlQbZtj42sewCSW7K65y/J22wtkUI7ALAbX/PjRCEwlJoaYpG7ToL09baxECXtCfu74DWQ6wIg2HA0R+WaYB4LDOpmHWM3GfaXN0Bc4pi6o4oUxQDRSQqLCTcIwT+NATnHdLKzGnHU1kaZmGzd95PCMGdVx3L4+vaNS1AS00Zq+Z7Nz5R+xwMjSTYGx9OlQM2VpU6VjYcOquOQ2d5eOU7oL4iyrYuOWh39Y/oGoKqzBoCwzBcUzuGYaRFCMz9CkoI1DC8X/c7lRDUtMLj39QHXoD6+envN9AJb/xBLlc0ZE8Ihm0pAzXU7NblT00ZRCvg3g/DK3fK9Me8k6BpsfX66Aj881Y5+65pgznHWa+pPR3sVRYtK2Q3xEhFZsvfXCMEZ9wgLYyFkANsRaMuEvSMENgIgVMZIMjSxed/Js/h6CtljwE7yjwIwePfgie+JcnJ8Z+A138vyQfAB/4Ec4+Xy6EQnPuNzOds4qR/87/tFEBACCYZ+otMVFgoNLlFCLKIkGQz6FaWlnDOwdm3itYqDUbj9PRagiU/JYV+YR/g1TSAU5VBWSRMRTTMwEicWNygb3iU6jLn8GjP0KiWijJhjyys3d3LA2t2c/4hrcxvyoNbXVoY3gcqm+SAP9ApB/ampfDm0/o2LcvT9+v38CDI+liroESxv/WTMqhuhQ0PyWUjDn//P9l4yUTfHnjgs9a2/09xX4zYhJUqsjFIyrX1McBvPmS5Pn7yVeniZ0I1UbKLCu3XZsClS33PTmn+BM5eEyAJQU9Y/rVfh+Fuy8UxEdO/Yye3wgCOmLojyhSFHy//qQC3FsjFJqrUKg1G4tpMO5+EwG5f3KG1Pnae7U2riDIwMpjcJ+ZKCOyCQhNqFCKeMLjiZ/9k+75B/rR6F3/55IlZn0Ma/JQdDnTJwSI2KEvl1EEUpHugSQhO+w/ZrMYpEqIOCm4GQRsekp831C2NhNT88kn/JjUJI31yf3VAck0ZKBGC8npYdY3swwDSTElFn6VbScvR250K77xUtneOVsHbvg/NS50/345cIwTg/V2VKzN19ZzBgRC4DM5erY9NfOiv8lo4fb8qESmt0aMHbgQjQBqm7ogyRaGWHVbkqe6/GKG1QHYTFRYBISqzmROpg6sfF0K/0MyJBkY8Gxup601L5s7+YdemUk7pAtAjBO29w2zfJ9/rjd29qSZQY4If97+X7rA87I/9KJz9df31JmUw3LdZ5otNPP8z2cGwZxfMfYu13l6dYOLeq6zBY/mFUKXoR454n75tSCELQz3pOWywaQim6WmMnh36thohsDXusZdedm6w+jcY6ZEdV+QaITAMnRDYv6u2w2R/gfJpMPNI/bU0QuAyOGdyKgTvlMiwrTGUSvr6A0LgFxN/Rw0AwCNv7OVvb+zlX1bNYZGL49/IaIKRZGlbSUhQ6lEjP9nhlDJIJAyt22MxpEzsXgSFihCopYV7e4bZPyBTE0LI8kXHfXwKC9WmRipUx0V7FGFvjzvB8I3L/5T0wO+TQj8naOFyBxV9o5KHb7dVCTz6dcvmVzW2cYsQlNVaA9bQfp0QOB1XuFSa4CRickCL2q6HYSCrEQyomKaHsXt26iTCK0IQsWkIcrVRzjVCsPHhpKlSEnZl/uxj5cMJdpMnNw1BJqfCTNA0HtXuKYO+dnjwC/Icqlqc7bLteOHnsHu17GdwwXfc2yVPAUz8HTUAe3uG+PAvnmdkNMGzmzp58F9PdBSAqYLCiqi7pe5UQF15JNWWuHdolIGRURKG9Xp5JEy4kCVxPlFmcytUB9fp+SQESoRgY7s1INSVRyhx6bXgZGjkBHWwLy0JMZy0blZ7Mth7HeztHdIIwbauAX729BZa68pZ3FLFCS6NpPQDnJd5G82p0IG4qBGC9jf0Qba61SIEu1db27lpCLKtdS+rlbX15vZ2QvCRJyCRkOSirFYq2qNJm9/RIRleN0PbfXut/ezkSEsZDOpizK5NsiRxpB9mHAnzT3I/3lwjBDtf9L+tHfbrOLRf9p2wmzT5iRB4fo4tZaBFCBRCMNQthZ0A9Qt8EoLbra6HXRunNCGYulPMSYS/vr6HkeRNeP3ePl7c5mCdin8f/6mAUEhoA866PX1Fly6A9AZHu3qsQavNhy2xX6gagg17LULgph8AuzmRQye4JHYqJGap0rFSjxDog/GeHv391u7u5dYnN/O1P6zmF4+5OAbmAnXg6t4ulfhr7oPtz8l1ainY0H59YFVfUwmBa4QgQ4OjtO1VMZ0LgQiF5KBvlrepx6QaJmkRAhsh0Bo8DekRgu3PwZ//DR7+Mmz4q/fxtqyQaZeVH4TZPl39wNvNLxOcrotdZwB6t0Y7sTIx0AU7XoCNj8Ce1/TX7J0i3SIE2XgQmDiALIyL4656gOPBNXu05/e+sJ0jZqcbmWimREUyIBYSy1pr2NQua6tf29lDdZl1zl4uheOJNA2BUs/vx5bYL9RKgh3KZ7jpB8DeAtk9QrC7x3q/FW01vJwkpGqaYXdPeoRARXzLk/y99FM00s36/ccAp7h+XlZQZ4t7Vkt3QJCz4Sv/lp63//nb4GPPymV18PVyODTh1s+gfR3cd7UU0zWvgLO/Jtef+VWZwy+t0VX3Xqhps9T0PTstoxcOCzgAACAASURBVBstQuAhKhzab+kGwlE9FZLJh2DOcXo5o1+EfA6cTjjra1KQ+V9LrHUDHenpGD+iwld+DX+5Xi4fdSWcpzg1aimDGqhUBn5VQzBWQjDF2yAHEYIJRu9QjKc36nm1+1/exfBoulhId+krjgGxkFiuzFZf39VT9BGC3qHRlN7ByZRoLJjmMvA7lRym9qnQKxPcoKY5VigtrFXzo0wRgj0DIVrEfsLCoCHhQ8QVH5Vh3kTCezt1dqzONtVBfe4J1vKyC6xlNyMZLw2B02cNdMCO52DTo/KviSVnSxvheSf491HQIgSKsNArQqASAjX8Ha3KzpgoVxz0Dmt5xdvTXzcMuO+jcMe74bazpIugiUgZVE+HWYrGwElYqKUMXCJrXimdIZ8RgoRSLuplW6xCTW0FEYIAhcSja9uJxQ1tXfdgjEfe2MvZB+l18X5NeaYK7ISgGF0aVQ3B1q7+jKZEuaLexSDI3jxJ20fpgdDpQggMw2CnoiE4qM266aokYrddVGiLEGwcsr6rmtheMmLvGvhhsnRxxkq48mHn7dwGB5UQnPMNuPO90qPg+Gut9dUuhMBNQ+DWUc8uWPOD/g6ZpiifJgfE6mTlgEoITH0DQK+HqLB+HpzxFXktRoetqovScSIENW1w2W9g2z9kusEOIaTp05CLaRBIIjFzpSRjtQ7OoX5EhW4tkBNxm8lVlR7V6O+wtCV++xio0FIGUztCUBx31QMYD75m3QjqK6OpcrLfvLAjnRBoosKp/9Utb7MRgqHi01CohGBzh3VDbsujoBBk98aQQBNWAtQ7dDo00axEKNbt6XV0K+wejKVaN1dGw8xRdBsqIdhpq0RQvSEA1vWXEjPCRESc0li3nPF5qcXVwcsrR+2HELQsh2tfTE8f2CMEZ3xFegm4icLcBhw/XRnt2PYPuPM9cnnRmXDp3XJ52lyYNk8em9ljwTC8IwTV0y2is+sV5Viqx4cQgPR3WHi6++vl0yxCoIolTRz7Ee/31yIELtEWP4QtmrSVjlZITUikXBJAswpEJQR+tRGBhiDAeGBkNMGjb1izqRsvPIiP/vIFQJYhdvWPaDliv53+pgqaq0tTJKl/JM4bu61//GKMEGxut27I+awwACmynFYRTZvpN3pECA5qq025Fb7ZNcCWzgHmNeo32522qoiasgjhkCCeMOgdHmV4NE4kFGJPjz1loD/f1RNjL3XMIBkO7tkJDQvcT0grnfMIt7vlk+0DjlPFjUoIamfr0QMnuIWk/Rgo2WE3JTJx+GXptrzxGBz8LkkKhrq9P8N+LOrgmam50XM/hq3PyJLDwy713y7YD8qnWYOleu6dGyWJKqvVS0jtePcdUhgYG3RvBOX2/ZTWwKfekMRAvQbXb0n/XcRzSBlUTZdpG7MyZHC/Tk6mEAINwQTi2U2d9CYH+Vn15Zxz0HQOny1/aKMJgz+8ordt7T/ARIVCCC1t8M8tljCsWAhRuSIq3Kl5EOSvwsBEpiZGdkRLQhy3wAqPP7o2PZSvlhy21ZUniYcVbt0/EKOjf5hRW2hirxIhMHsh7DaUgU8NhzvB3k7YDW7lcW7CQBVql8PenZn1CmVuM1B1EFYqC57/KXz7IPj6LHjoy/p72U2JvFAShQu/D5fdI934vGD3IMgmQvDm32H1r+HF2/Mf+lbPUbUn/t9V8F+L4asteiWBHbOOgoWnyU6OJS6/aTdCEArJvhZNi6VJkgknkphLyiAUgro51vMpHCUICMEE4sHXdqeWz1w+HSEE7zjCyq/95gXdzWzgABMVAixrtXK2L2y1Zh7FYEoEuLr15dOUyISTsLDBI2UAcPISS8392Lr2tNd3OhgpqSSjs28kzYMAJFEYSjaC2jcQY3g0wW5DGRR6dqbto8FvH4NIeTIPbwv/+yEE0QproEqMZva0r5khu9stOhOmK46HbimDeEw6IQ73pJcpqrNkezRjLJh1FFz+Z3jv3XDS9dkRgrFYF2eCSgjMc48NSeMmkINvLoZDKlTCZjc88otcqgzAJiycujqC4rirHoBIJAz+qugHzlgu84YXHNLKDfevIRY3eHnbfjbs7WNhs7wJFaPKvtBQdQSaS2GRnH+5CzFrzaMHgQknYaGXqBDgJKV98zMbO9Msh3c7WC1r1QkDI5qYU0V77zCz6itSUYbdhjJIZyIEfsPwFfUy9Atw25lWBzs/hABkeN4wZPog04xwziq44kH/x+qleh/IIkKQCfFRuP1CGU5PjMKHH7Ne67UmFQVrf+wHToRAJUnxEcsaun6+XjLoF9FK2SnSiEuDptHh7IlNLlUGcMDoCIrjrnoA4pUd3anSrWkVEVbOkf9QdRVRTlvawl/WyH/03764nU+fJd3Yis22dzywTEkZqCgWUWFpyfhFCOodBn+vskOAWfUVLGiqZGN7P8OjCf6+uUsjCWrJoVOEwN5ZUcXe3iFJCJLvoUUIMqUMcrHfVcvV/BKCM2+EdQ/CwzfA+gdh4Rlw3DX+9jXhFiFQ0wf2Ln+ahsBGCDY+Ipsb9eyAYz7sXh5pIhSGLU8CybRNIm4ZHWlOjhk0BOMdIbCTpNW/ln9z7T4ohMzdp+yle6SfwXCvTEeU1ejNj/Zvg72vSSFp01JZ5dCyAk77d0my/DaFAhk1Kq+XxGDmytyOfxKgOO6qByAeXGMx+9OWtWj2s289rC1FCJ5XwuQHYoRgQVMV0XAo1cPBRLGcv2uEoBCEwBYhEEISyEw4aXEzG9tlmPPRtXs1QqCWHJpRDTshcGt+tDdJaE13Rk1DYG/eY4dfDYGKXAgBQPeb0tQIoG62//1MjLhoCLwiBF4agse/CVufkssLToGtT0vHwapmWHQGTD9Y314IGW43B/zRIStVEK2EJefJv5mu41jaH2eCU8dDzTei0SIC9mZDHRvgB8fLc2xaBh/8s/vnaP0muiUheOXX8MdPyXVHXg4X/LdcfvUeeOhLcnnVNXIgb14mH9li4WnyMcVRHHfVAxBqueGZy/UyowVKr3lVvKWHzA8MDUEkHGJRSxVrduozsGI5/zIHr4F8mxKZsGsI6iuivvo5nLykiR8/JQmBXUegDvZtLhECNa1QHgkzmNQOmJUGpjujRggGHOxpVWQbITAMObvvb5cDQlkWKm91AHLzIPDCsAt5UQnBsC1CMOChIVDFjj07YfMT8PId8nllYzohADmjNwlBTCEEoTC85w5/56FFCMYhZaDm+RsWWITAbkwU65fHNjqUfh3tMMV9ZbVWwyV1H/X70cyJgo6HfhAQggnA9n0DKT/60pJQWiOYlhornLdXcYRTRYUHgg+BieWtNWmEoFhSBk4RgqaqUiIuDYfGAnt6wKvCQMXR8+opi4QYiiXY1N7Ptq4BZtVXpKoDTJgRAruGQN3moBk1/HOLvOGbZNUUHa425vHnE+/jnOOO1H3+naDNujOY/Wx9Wg4yi8+SD7/mQCbUELWbS6GJ534ijWyG9sMpn5fCxJOuh0PfI4mBOlh79TLwShnY3Qq9PAhMlJQDyff85nxpwnTmV+HQS7zPR4UWIShkyiAZHVGvianhiI8kGzQNWD0Lsmls9C/3pa+zNzYy4dbgKIAriuOueoDhyfXWj/OY+Q1pg0pteYRoSYiR0QR9w6P0DY9SVVpic+orjhnyeMBJR1AsKQOnKoNCpAsgPUKQSVBooiwSZtX8Bh5ZK6MDj65r533HzqGrfyTVVKu6tCRFstT37ewf0foYHDqzLo0QmGmHIUqpnHlQZjIAugAuU4Tg/k9AR7K18cf+AU1LvLdX0bkR/nGL9TxThOCx/7T0D8d+VA5aM1c65421lIFdQ6CmDGwRghrFGKlnl3frYxP2Gv7+dlKaAr8oZISgZQWcfZMkBqYATxUVltXJa9+bFJsOdEA0mb7Ja+tj5bfnZl88VhiGjE6Ept49OCg7nAA8oRCCExel36CEELYogfxHHjgARYWgVxqYKJbzL3ckBPmvMIB0DUGmkkMVqm7gsaQfgR4dsAYIrUtinx4hOGSWFao3Uwa7HUoX09C5EX5yHnz/WEkGLr4drt8K/7pG5tG9oA5emYRzdoza9A+VGbQHbm6FTohWgUjeQmP9sgwRpGCtaYkMb5fXp0c0amwpA98RAofPzwajOdTg+0XdLDj2ajj03TA72bdAIwS1uu5DDeGPtfWxvdOhCTVVY6aNXr4Tfn4h/PJiqT3IBk/+N9xyMnxtBqx7IPvjnAQojrvqAYR4wuDJDRYhcOsb31JdxrYu+Y+yp2eY+U1VNlHh1GOnblg23SlCUBznXxpJ59T5dik0Ya8y8BshADh5STPcL1vGPr2xk+HRuE4IFBKjpiI2dfRZUYSyEuYrToftvcOuaYc0PPp12akwWi0H+FBYKsb9OL5pSnpngaMr7Ar+TBECN3MiJwghZ6TmwDfUIwlHuASuetTjmJQIwf439XB2pfP9wDHEb+bLn/uJrFqI9cNx17o7RBYyQuAE9fqV1+kDtEoI/LQ+9vwcl5SB2T8CoG+3NKbq3AibHpHrZhyR3ed0b4edL8rljrXAudkfa5EjIATjjNU7uukelDOJ5upSFrc4s3xVlGY2klF7GRRLyHw8UFsRYUZdudb2t2g0BA4Rgra6AhGCitw0BABzGyuZ01DB1s4BBkbifPyOF7X2xurMXn1ftatha20ZzdWlymtDKVMiSKYdSgxZ7tWzU84azQHZvJGO9MqmP6qjXCaoYeRHvw4nf8Z/G1+7+DBTOsOtBbIbyussQjC4L3MEAnSSsneNtVzR4G6W4xRKNw2d1twLmx+Xy8svdCcEJ18vB8/4SPY6jFxgGJblb1mtLaevRgiySBnER2Hbs7KnQ88OOOurNlGhcl7RSvn9D+2X5zzQAQmlDXg2xkSgp6ra12a37yRBcdxVDyA8rqi8T1jUlNZsxkRTdbqwcEDpdnggiQpB6ghUQlAshMhJQzC9QCmD8mg4JQ6EzB4Edpy0uImfP7MV0KtcQI8QTHMpZZxeW05DVWmqydK+gRhbOy0tQGtdGfzp0/D8T+SKc74h6+xHBuTMzEQ2GgDQB4nNj0Hrof4JgRBw6Hulir/tCNlYyAv2UsJ4DG5eJsPz5XVw5SO6Je4F35H7NCz0P8hWNstUg2GzUnZLF4DzjN6MEPjtZ3DUh/wdX75w1lflw4zq7H3dei3XlIERh5+/zTIYOul695QByGiMSdh6dlhpHdA7IvrBAUAIAg3BOOOJ9RYhOHGxe/hSjRDs6RnCMAw9QnCAWBebWK5YGAsBFUVy/pFwiBJb6V++Ox2qUHUDDVXZKcUvOmImblWKpyy1QtXl0bCzNqKmjHBI0Kh87ivbrbDw9Npym4I+KSBrf52UAK5xcfbCMfv22XgQALzt+3Ig/+BfnP3tVdg76g33SgHfvs3QuSl9//knQ9vh2c24wyWyYY4dboJCcIkQJAnBeHU8zIS7L5cakW8tgS7F3jdSJh+uGoIsIgQlpdJkyMSeNe4pA9A7W3bvyK2XgYlGhRB0rCfV63wKoTimWQcIeodivPCmFYY8fqEXIVBCs73DDMUSqda3pSUhzcjoQIAqLKyMlrhGViYCZZGwVgFSKA0BSN2AGSlpzJIQHDqrjj9eewKvbNdD4YfNmsaS6fqAVl8Z1SIyYJ1Xc01pqsLg5W3We7XVljkTgt2vWutaVmR1zEC6oC5bQhAK+c8X20WFuXQ63L0adjwvFfdNS50jIjWtluLehFeEYNXHZEfEu9+ffjxq3n0iCUHXxiT5I1llYYvGLDpDfncVDdBykLXeT+tjFS0HwZ7kb2r3alvKwCFCYMIeIcg2ZVDVLH8fQ90y9dWz072V9iRFQAjGEc9s7CSeHNVXtNV43tDtEYK+A9ClUMWKNutGnY2YbjygEoJCmRKZuHjlLFbv6GZxc3WqM2Y2WNZa42oHrcKJEJg6g5bqMl5F3oRfUsjF9NoyW4fBZPneHiVPngshGGuEIBvYG+gMZ+GXYGLDw/DQf8jlVdfIsLkdC0+XaYZX7rLWeUUI5hyX3q0xFSFQiMpEEgIncyIVM46UDzuyLTucfjC8cqdc3v2yPsjbUwbNy2HmUZKo1s2W2gMT2RICIWSUYPs/5POOtQEhCJA71HJDt+oCE/ayw4GRA7PCwMSs+gquOnE+9724g2tPXTTRh6OhTKk0KJQpkYnLjp3DWSumU1/pz6UwVzh1VlQjBCY2tVsDUFttefqMDGyEwMGFLxPGkxCoyvTX7oNFp1vP3cr8DEM2Gdq3RTZIUj0I3DodnvI5+XflFbDpUVl6OHuV97HFlME+UpFdP4P+TrjrUhkmr2qBi37k/Vm5QCME+yUxilbJWXXjYhmpccKJ18HKD8pjr/QgRSZUc6jdr8LndkjTpaGe9GqMY66SDxOv3mst51J62aQQgvZ1sODU7N+jiBEQgnGEph9w8B9Q0axFCIb1CMEBJig08blzl/G5c3PwIS8w1Hx7Iboc2qEKTgsFJ8GiKTxsrnaOgEyvLYMaZYbWs0sOlnvGmDIYT0Kw6Ew5KPXvldqB9X+1XnNKGSQS8M0FFgn4zLbsOh3OPkY+/MDN7lnTECjbqBjpgzefkcu1s/x9XrZQz3WgC+690hJOfqEdQi4DcPm07DpCqoRg7+syQlBSKvsaZMJYqgzAJix8I/v9ixwHViJ6AvFm5wBbOiV7L4+EOXKu9z9AdWlJauY5GItrFsYHYsqgmKFWGrQWMF0wnnCqNHCKEKhoqyuTOVwzDzw6KLvNmSrv0lqonZn9wdTYwrJus+58oKxGdkicfzJc/Yw+a3eKEIRCundA5wabbXEej7WqGT71Onzsn/C+3zofl5kyMEs/TeGb1vq4QIRSHdS7t1lkIFIBJXlM81XUQ03ydxQflgI/vxhLlQHYhIXrst+/yBEQgnHCExus6MCx8+td2+aakG6F1uCyqcMKFxaLwj6AhB4hmBqEoL5Sv1lWRMPUlEki2uIaISiXeVZVWLj+QWu5ZUVmlb8TDnuvtSxC2TU2ygWHXAzvuw+aFrt3OlTRqKSwOjd69zHIFc/9BL57GPzwJFjzW5iuiPI0UWEyZfDI12S55NdmwKu/GR9TIvVc922xlk2hZiIBt50J/3MkfGNBuiYiG2hpg9X+9xtLlQHI34SJ/W9mv3+RI5hqjhOeWOdfP2CipbqMrcmowqZ268Z0oKYMihWqW2Gh+hiMN+pttsjTa8tSlR1OEYLqMqsXAjWt0JmctVU2w9tvke2HzU512WLA1hfALRedL6ikxa3ToQrVCKhzvU4I3KIZw72yDXLPTpn/vuR272OKDVgDkF2w55Qy6NyQ3K9fplgK2djIhBoN2aeUHZoELhSCvW/AcLJUdWi/vD4DXRAqkefhtz/A9INhXbJN8nO3QeshMlLj1LzqlV/LKELPTtmkavmFkhjkkr6qnQ3vvVsSg9ocWmkXOYKRZZyweodVr71qgb8cqHrj3axECIKUQXGhptyaTc+oy8F6tQhhjxCoRMdJQ6ARITXEb8Th0EuBLLry2aHWrBdSP+AENW+PS3SjQY0QbPCnIQhF4KnvWM//9Gk495vux6HO6u39GRoXy0ZMkQprkDMJAchqBtUXYDwiBF1brGW1lLOywSIEA52SEPziItj5glz3ob/BTIdKBDvUCMG2v8P/HgtLz4d3/zJ926e+KwkpSPGin/d3QygEi8/Mff8iRzCyjAMGRkZTJVzhkGBBk796ZjVloBOCIGVQTHjPUbN5fF07M6dVcNoyHyrpSQC7hmB6jSXsa6yKIoTuy6I1dKpulXqBmtb8NNEpq4UTrpPWs5l6EeQbdbPlZw50uIvW1JRBx3p/GgJ798J/3uafEAx2yYtvRjJaVsDZX7deH+jSKx1+9zHY/ITyXuOgIRhRIisqIahogK5NyePsBBbZfAh8inLbDoNlb4VdL1mRE/VzVNTOsAhBzw5gDIRgiiMgBOMAtTRrTn0F0RJ/IU/VN15tIBNECIoLb1nUyHNfOJ1oOFRUhkljgd3rQY0AlIRDNFSW0tE37Pg6p34BTv+P/B1M7Qw47Yv5e79sUF4nB9dwFBae4bxNw0JrefdqUq6M4VL/A5wR935dJRCv/Q7+8Elpm+wENToAsqxRRXgcCIG2XtF8OLkVauWUPq9X3WyZZnnm+/BAsoTTzSfCqRQ2gCOCkWUcsFHJ/y9o9t+y1M3g5kCzLZ4MyCQSnWxIixDYtBEtNXZCoNzIzTxwfFQuT2aStPgs+PjzMsRf51KuV9FgNdFBCZtU1Ofv3O1ujV6tj+2EIO29CkQI6mbBu38licGr98A/b5XrtQiB2uAoqasaS/tjL9tiE6rItXt7du/vhtFhS0A69/j8vGcRICAE44CNexVC4DNdAO7lXQdaY6MA44+6Cj0tYBdLNleXotgNOYspV/8a/ny9DGkf+h448v3p20wG1M/3fl0IGSXY8Zx8XtEozzlfFQaQnmLwIgSZyvAKpSGIVsLSZEvgzY9Z6zVC4NACOZeUgQmvxkYm1FLXZ74HG/8mPQguvh2m5SB07dwI3ztKRnVqZ8G/vpp5n0mCYGQZB2xUUgYLmnx4dSfhFiEolta/AaYuwiFBXXmEfQOybjs9QqA/d+zfsPtVecN+8xmYf0rBjrUo0LjIIgSnfA6OuiLzPsddC09/Vy4f+1Hvbe2DuFrxEBuE+z8hPQiMhFTsmzjoIll2qL1X4Y2tGLJE1FqZqD1lYBi63XK2EQK31scq7D4We1+TfzOladygGjt1b5PCU799LoocwcgyDtiwN78pg4pAVBhgHLBybj1/fW0PTdWlaZGtZptbYpvdf2HbP+DZ71vPcynxmkxoWChz8w0L/A+4J34a9m+FRFwue8FOCNQIQajE6okgQno3wCPeD6/93nLoO/1LMOtYf8c3FlTUy+MY3K+XAqrLA53JckhTcxGVnSD9or8TXvyF9dxPykBFLsZEIE2W6udbpbWd62XHyymAgBAUGPGEoVUIZJMyqCotoTIapn9EZ7KBqDDAeOAbFx3Cn5fs5pj59ZobI+jW2pA0JVJxzwf151OdEKz6GLzlX/3X0YMMcV/8c3/b2kPp6mw4HJGDaXxERgjMGTBA66Ew62jY+pR8XjND9lsoNI67Fo7/ZLo9sD1CkG1jIxVRW7TVTa9hjxCYGEsFTNMSixC0r5syhCBwKiwwtu8bYCQuHbmaqkupLc+OldpvvBAYEwUYH0yrjPLeY2Y7klg1QqCZEpkQtltLrqZEkwWR8uzIQLbwihCAHmqvmi63r2ySCn81XbPxkcIdI8Ddl8NXW+HGZtjyRPrrdkLQudF6nm26wK6rcGuMFClzLlfNpZeBiSna0yAYWQoMrcIgC/2AiebqUi3CAIF1cYCJR5vSxGmGU0Mn1ZwHCu8uWCxYc5+sjS+vl53wVIvhsUBtKw3pOetoldUz4ooHZZ57IKnin38yPHKjXN70iO5hkG8YcWvW79QCuWkJXHqPTClUtej2v9kSApDCwPuvhdnHyRbRbqhps66HibEQgina0yAgBAXGxr25pQtMOOkIAlFhgInGirYaTl/WwtMbO/jwSQ4q/Jo22S8e8tvgp9ix/q/wUjKvXVabP0IQKZPufKZvvz1cHrW1QA6FZDMkkOHs0lrpENi7C9rXQvNSCgKtBbIDISirhUWKn0PtTJh5tGwpvOyC7D9v+VulQ2EmwnnQRTDvRFllYGJMKQOlp0H72tzfp8gQjCwFxoYcSw5NtDiUHgaiwgATDSEEt75/JbF4gkjY4WZ81tfglxfJ5YtuHd+Dmyh0bbbIAOS37BBs7Y9tinqtn4EeUSRcokcE3nx6fAjB32+RkYqFp3unU079vOxCuPD03D7TT/TpLZ+UkRGVEOQqKgRpFy1CUrPRuUF6Kjj1UZhkOEDieBMHNWWwMIsKAxNBhCBAMcORDAAsPA2ueEg+Fp42vgc1UXj2f/Xn+W7TrHbqs6cMIh6EAOCy38iQfM0MWHJufo9LhUoIOtbCHRdn3mf+yTJqUGgDq8SotSzCY0tjRStlZAMAAzY8NKZDKxYEI0uBkatLoYkmW3mXEFA2xVzxAkxBCAGzjprooxhfqBbGkP8Iwadek2V6w33p761GCF66Q4r3mpdZM/OZK+H6rYBRWB8C+3GV1hRWbJkNxtr62I7FZ8G2Z+Xyugfg0HeP/T0nGEGEoIDo6h9JGbuUR8K0uvgKeMEeIaiIhAmFJrEVbIAAUxVphKAA2omSUtkx0D67VTUEr9wJPzxBnxGDrJ8vtCmRnRC4NRyaCMRj1nK+CEG4FBacNmWiYEGEoIBQowPzmypzGsjthCDwIAgQoEhR6AiBF+wiw7rZ4+NIaEcaIahz3m68MdQtW0ybef93/njs79m8HK7fnH7tJzGCCEEBMVZBIaQ7wgWEIECAIkXtLMtvoWlpep18IbHiHTBHabLTsMh920LCHhUplghBSZnsrWEkJCmYf/LY31OIKUUGICAEBYXa1CgXQSFIAlCtkIDKoMIgQIDiRCgE77kTTv4cXPKLzNvnEwttYWt7tGK8UKwpg5JSy7jISEDf7ok9niJFMN0sIHRTotybXzTXlNLbLvOBQafDAAGKGC3L5WMi0KG0PW6cKEJgSxEUCyEA6Y3Rv1cud+/QuyDmA4Yh+1Jk04+hyBBECAoIrcthc+6hpeZqK/RYGbgUBggQwAmdCiGYqAhBpBwOu9R6bicIEwmVAPTsyN/77nge/ngdfOcQeP4n+XvfCcC4EwIhxCwhxD1CiG4hRI8Q4l4hxGyf+5YJIb4phNglhBgUQjwjhDjRYbuQEOKzQogtQoghIcTLQoiL8n827hiKxdm2T1p4hgTMbcidEKjmRIGGIECAAI7QCMEEaQhA7y5YTBEC1U77nsvz9747XoB//kjaMK97IH/vOwEYV0IghKgA/gYsBd4PvA9YBDwihPAzYt4GXAn8O3A+sAt4QAhxmG27rwBfAr4HnAM8C9wtADPQxAAAIABJREFUhCigI4eOzR39GMmunrPqK9K6xWUDtdIgaGwUIECANLz2exhUBjx774PxxFC3tVwsVQaQf6MoE4vOtJY3P+5sDDVJMN6jy5XAfGCJYRgbAIQQrwDrgQ8DN7vtKIQ4FHgv8EHDMH6SXPcYsAa4AXhrcl0zcB1wk2EY30ru/ogQYiFwE/CnApxXGvKlHwCYo0QXnKyMAwQIcIBDbRIEE9tMqmkpLD4bBvfL8sdiQVVLYd532hx5zu1vQHxYkoIl5xTmswqM8f7VvBV41iQDAIZhbAaeAt7mY98YcJey7yhwJ3CWEMIcKc8CooBd5vsL4GAhxLwxnYFP6E2NxlaacuHhbZyxvIXjFjTw7qOL6B8sQIAAxYFDLrbMdk64bmKP5agr4L13wRUPwNJxC8pmxuGXWcvLL8zve6tRgkmcNhjvCMEK4HcO69cA7/Kx72bDMAYc9o0CC5PLK4BhYIPDdgDLgc1ZHHNOyGeEoCJawo/+ZeVYDylAgABTFVXN8JEn5Sx18dkTfTTFiRlHwNu+L3P+b/lkft972QUySrP47NybNBUBxpsQ1AMOPTHpAjLZennta75u/t1vGGYG33U7DUKIq4CrAGbPHvssfPs+i7fk6kEQIECAAL7RtEQ+Arjj8Mv0SEG+MOto+ZjkCBRqSRiGcQtwC8DKlSvtZCJr3POR49jdM8SGvX0sa60Z8/EFCBAgQIAAhcR4E4J9OEcC3Gb/9n3nuOwLVgRgH1AnhBC2KIF9u4IiFBK01ZXTVlc+Hh8XIECAAAECjAnjLSo0c/x2LAde87HvvGTpon3fESzNwBqgFFjgsB0+PidAgAABAgQ44DDehOD3wLFCiPnmCiHEXOD45GteuB+IoIgPhRAlwCXAg4ZhDCdX/wVZjXCpbf/LgFeTVQ0BAgQIECBAAAXjnTL4EXAN8DshxBcAA2kitA34obmREGIOsBG4wTCMGwAMw3hRCHEX8N9CiAiyUuBqYB7K4G8Yxl4hxM3AZ4UQvcALSNJwKkmvggABAgQIECCAjnElBIZh9AshTgW+DdwOCOBh4JOGYfQpmwogTHoE43Lgq8CNQB3wMnC2YRgv2Lb7PNAHfAKYDqwFLjYM4w/5PaMAAQIECBBgakCkV+cFWLlypfHcc89N9GEECBAgQIADFEKI5w3DGFcDmqDbYYAAAQIECBAgIAQBAgQIECBAgIAQBAgQIECAAAEICEGAAAECBAgQgIAQBAgQIECAAAEICEGAAAECBAgQgIAQBAgQIECAAAEICEGAAAECBAgQgIAQBAgQIECAAAEInAodIYRoB7bm6e0agY48vdeBguCaZY/gmmWH4Hplj+CaZY+xXLM5hmE05fNgMiEgBAWGEOK58bafnOwIrln2CK5ZdgiuV/YIrln2mGzXLEgZBAgQIECAAAECQhAgQIAAAQIECAjBeOCWiT6ASYjgmmWP4Jplh+B6ZY/gmmWPSXXNAg1BgAABAgQIECCIEAQIECBAgAABAkIQIECAAAECBCAgBDlBCDFLCHGPEKJbCNEjhLhXCDHb575lQohvCiF2CSEGhRDPCCFOLPQxTzRyvWZCiJVCiFuEEG8IIQaEEG8KIX4phJg3Hsc9kRjL78z2Pp8RQhhCiCcLcZzFhLFeMyHEMiHE3UKIjuT/51ohxCcKecwTjTHez2YLIX6W/L8cFEKsE0LcKISoLPRxTxSEEDOFEP+TvHcPJP+35vrcNySE+KwQYosQYkgI8bIQ4qLCHrF/BIQgSwghKoC/AUuB9wPvAxYBj/j8J7gNuBL4d+B8YBfwgBDisMIc8cRjjNfs3cAK4LvAOcBngCOA54QQswp20BOMPPzOzPeZD3wB2FuI4ywmjPWaCSFWAn8HSoEPAecC/wWEC3XME42xXLPk6w8BJwJfRF6vW4H/B/y4gIc90VgIXAzsA57Ict+vAF8Cvoe8nz0L3C2EODefB5gzDMMIHlk8gE8AcWChsm4eMAp8KsO+hwIGcLmyrgRYC/x+os+tSK9Zk8O6OUACuGGiz60Yr5ntfR4Afgg8Cjw50edVrNcMOTl6DfjtRJ/HJLpmZybvZ2fa1t+U3L9ios+vQNcspCx/KHkN5vrYrxkYBr5sW/8w8MpEn5dhGEGEIAe8FXjWMIwN5grDMDYDTwFv87FvDLhL2XcUuBM4SwhRmv/DLQrkfM0Mw2h3WLcVaAdm5Pk4iwlj+Z0BIIR4LzKa8tmCHGHxYSzX7GRgGXBzwY6uODGWaxZN/u2xrd+PJFgiXwdZTDAMI5Hjrmchr9kvbOt/ARxcDGnQgBBkjxXAqw7r1wDLfey72TCMAYd9o8hQ1FTEWK5ZGoQQy5Bs+/UxHlcxY0zXTAgxDfg28G+GYXTl+diKFWO5Zm9J/i0TQjwrhIgJIfYKIb4rhCjP61EWF8ZyzR4C1gP/KYRYLoSoEkKciow6/MAwjP78HuqkxwpkhGCDbf2a5N+s74X5RkAIskc9MndkRxcwbQz7mq9PRYzlmmkQQpQAP0BGCG4b+6EVLcZ6zb4JrAN+msdjKnaM5Zq1Jf/eBTwInAF8AxkSviNfB1iEyPmaGYYxhCRSIeSg1osMf/8BuCa/hzklUA/sN5J5AgVFc/8vmegDCBAgS3wPOA44zzAMpxvZAQ8hxAnAvwBHONx8AjjDnBz9wjCMf08uPyqECAM3CSGWGYYxlSNSWUMIUYYkUM1IMeKbwNFIwfQocPXEHV2AXBAQguyxD2fm7Ma07fvOcdkXLKY41TCWa5aCEOIm4Crg/YZhPJinYytWjOWa/RAZPdkuhKhLrisBwsnng4ZhDOftSIsHY7lmncm/f7WtfxApkjucqZmiGss1uwKpvVhoGMbG5LrHhRDdwC1CiB8YhvFy3o508mMfUCeEEDaiXjT3/yBlkD3WIHNBdixHqpQz7TsvWepj33eE9NzSVMFYrhkAQojPA9cD1xqGcXsej61YMZZrtgz4CPIGZD6OB45NLk/VmdtY/ze9kKuQrNgxlmt2MLBPIQMm/pH8u2yMxzbVsAZZ0rrAtt7UDvi6FxYSASHIHr8Hjk3WdwOQNKU4PvmaF+4HIsC7lH1LgEuAB6forA3Gds0QQlwL3Ah83jCM7xXoGIsNY7lmpzg8XkaKx04B7sn/4RYFxnLN/owUfJ1lW3928u9z+TnEosNYrtluYJoQwi6GPib5d0eejnGq4C/IKrNLbesvA15NVndMLCa67nGyPYBK5Ex+NbIs563Im+0moErZbg4yj/bvtv3vRM7SPgSchrw5DyHzvRN+fsV2zZDGRAnkDftY22P5RJ9bMV4zl/d7lKnvQzDW/83/SK7/GnA60gRrEPjpRJ9bMV4zYC6y5HAd0tToFODTyXXPodTrT7UH8M7k4/+QPgRXJ5+fpGwzCtxm2++m5P3+U8h0y/8l72/nT/Q5GYYREIIcfwyzgd8kf/i9wH3YjCmS/ywG8CXb+nJkrfPu5A/j78DJE31OxXrNkCp5w+Xx6ESfVzFeM5f3mvKEYKzXDFk3/6nkADkCbAVuACITfV5FfM2WA78GtiHJ0zrgW8C0iT6vAl+zjPek5POf2vYLI51DtyIjUq8A75zo8zEfQfvjAAECBAgQIECgIQgQIECAAAECBIQgQIAAAQIECEBACAIECBAgQIAABIQgQIAAAQIECEBACAIECBAgQIAABIQgQIAAAQIECEBACAIEmHIQQhg+HluEEHOTyx8ogmOeazu+k7PY9wvKftsLeJgBAkxpBM2NAgSYelhle/5bpPvcl5R1w8Cu5LZ2L/qJxI3AH8nO1/0nwEPAF4FDC3FQAQIcCAgIQYAAUwyGYTyrPhdCDAMd9vVJOK2bSGx0OU5XGIaxA/j/7d15WBPX3jjw72QlgbAlYQkY9rIFQqCAUgVEe3GXCi3W4q3y60LVLlIREW/1uoC1ymZbl1Zft0ur0toFX8XiQq3aqtXy1nJdi7LvSxK2kGR+f+DQEKKA4trv53nyiGfOnDmZmcz5zjlnkkqCIOofUJ0Q+lvAIQOE/qYMDRkQBLGDIIgKgiCeJQjiNEEQHQRBXCEIYvLt5Ym3hxvkBEF8SxCEUK9MBkEQKQRBXCYIoosgiCqCIDYQBGF0H/WMvF2XVoIglLfr88E9v3GEkEHYQ4AQ0mcKALug5zvpqwAgFQC+IgjiEwB4BgDmA4A1AGQBwCcA8JLOunsAYCoAfAgAp6HnJ3BXQc934UcPtSK3f4XvO+j5EbCV0PMbA24A4Hy39RBCQ4cBAUJIHw8AEkiS/BEAgCCIKuiZgzAFen5hUnM7XQIAbxMEQSdJUkMQxBjo+SnvV0mS3HW7rEKCIJoAYA9BEH4kSf42xLr4AwALAN4iSVJ+O+3Yfb07hJBBOGSAENLXRgUDt12+/W8hFQzopDMAwPb2/ydAzx183u2hAwZBEAwAOHJ7eeg91OU36PkN+S8JgoghCMLqHspACA0CBgQIIX0tuv8hSVJ1+89mvXxUOjU/wAp67ubboKcRp151t5fzh1oRkiSvA0Ak9FyrdgNADUEQPxMEETbUshBCd4dDBgih4dIIAJ0AMOYOy6vupVCSJI8DwHGCINgA8Bz0zCU4SBCEI0mSDfdUU4RQPxgQIISGy2EASAYAM5Ikjw534SRJdgHAMYIgTADgWwBwAgAMCBAaJhgQIISGBUmSJwiC+AJ65hBkAMBZANBCzxMGkwAgmSTJq0MpkyCIBOiZe/C/AFAOAAIASIGe3oZLw1d7hBAGBAih4RQHAG8DQDz0PK7YBQA3AaAAAGrvobxiAJgIAOnQM0ehCQB+AoBXSJLsGIb6IoRuI0iSfNR1QAj9zREE4QgApQDw/6DnOxA05CAvTgRBEABAB4BtADCOJEn7B1RNhJ5q+JQBQuhxsg16nkwYylMEqbfX+ecDqRFCfxPYQ4AQeuQIgmABgK9O0hWSJBWDXNcWAOxu/1dFkuT/DXf9EPo7wIAAIYQQQjhkgBBCCCEMCBBCCCEEGBAghBBCCJ6C7yG4cOFCJIPBWE6SpA1ggIMQQgjp0xIE0UKSZLFarU4LCAi4ZijTEz2p8MKFC5FsNvtjR0dHFYfD6aTRaE/um0EIIYQeAJIkobu7myGXy41ramrIrq6udwICAg7r53ui76gZDMZyR0dHlbGxcQcGAwghhFB/BEEAi8VSCwSCVkdHRzWTyVxsKN8THRCQJGnD4XA6H3U9EEIIoSeBsbFxO0mSToaWPdEBAQDQsGcAIYQQGpyeb/oGwtCyJz0gQAghhNAwwIAAIYQQQhgQPE5ycnL4BEEEUC9jY2OZu7u7V1pamrC7u/uBbvvKlSssgiACcnJy+FRadHS0o52dnc9QysnPz+clJiaKNBrNgOU/DPr7VPfF4/H8HmZdEELocfbEfw/B02j79u1/isViVWtrK33fvn0Wqamp4rq6OmZWVlbVw6zHypUrq1taWob0G/bHjh3jZWZm2n744YdVdDq9N10sFncXFhZe9vT07Br2ig4CtU9105hMJs4/QQih2zAgeAwFBga2SySSLgCAGTNmyEtLS9nbtm2zulNA0NXVRTCZTJJGG94OH29v72FrvDkcDjlu3Li24SpvqHT36WB1dHQQHA6nX9Cg1WpBpVIRRkZG9xxQ3KlshBB6VHDI4Akgk8nalUolvbKykkF1va9du1aYkJBgb2Vl5cvhcPwbGxvpAAA7d+40l0qlHhwOR8bj8fwmTpzofO3aNZZueQqFghYXFyc2Nzf343K5soiICNebN2+y9LdraMhALpfT5s2bZzdixAgJi8XyFwgE0sjISJfy8nJGYmKiKDMz0xYAgMVi9XbNA9x5yODTTz+1dHd392Kz2f4WFhbSqKgop1u3bjF189jZ2flMnz7daevWrRbOzs7eHA5HJpFIPAsKCkyGZw//NbRw6NAhk4kTJzrzeDw/mUzmqbv9rKwsvpOTkzeLxfLft2+fGQBAXl6eqZ+fn4eRkZE/j8fzGz9+vEtxcTFbt+ygoCD3gIAA99zcXDNPT08vFovlv27dOuFw1R0hhIYD9hA8AW7dusWi0+lgamqqVSqVNACAjIwMW19f37bs7OxbGo2G4HA42nXr1gmTk5PFMTExjUuXLq2Wy+W09PR0UXh4uPulS5f+sLCw0AIAzJ492+HgwYMWiYmJVcHBwe0FBQWmc+bMMfhcqq7Ozk4iLCzsmcuXL3MWLFhQExIS0tbS0kIvKCgwbWhoYMyfP7++srKSuW/fPkFBQcFl3SEDQ9avXy9ISkpymDx5cvPKlSsrKysrmatXr7YLCwtzLy4uLjEzM9NSec+dO2dy48YNo3/9619VRkZG2pUrV9rFxMS4lpaW/i4QCDR32w4AgEajAf15GDQaDfTrOHfuXOeoqKimN99884Zare59NOfMmTO8kpIS7pIlS6ptbGy6XV1dVXl5eaaxsbFuwcHB8m3btt1QKBT0tLQ0UXh4uMeFCxdKnJycejdYWlpqlJSUJE5KSqpydXVVCYVC9UB1Rgihh+mpCwgclxwMeNR1oNxcO/nXe1mParxaWlroO3bssDhy5IjF2LFjW3g8Xm8DKRAIuo8cOXKDGiZobW2lrVq1yi4mJqZx//79N6l8Y8aMafPx8ZFs3LhR8MEHH9QVFxezv//+e8vk5OTKtLS0GoCeYQmlUknLzc29613r5s2bLX/77TfjPXv2XH/llVdaqfS5c+c2U3/b2dl1AwCMHTu2jclkGioGAADUajWkp6fbBQUFKfLz8/+k0r29vTsnTJjgvnHjRsGyZcvqqHSlUkkvLi4uEQqFGmo7YWFhnnl5eWYJCQlNA+1TPz8/iX5aeHh46/Hjx6/rpk2ePLl58+bNFfp5FQoF/fz58yVisbi3IY+NjXW2t7fvKioquka91/DwcKVEIpGsWbPG+vPPP+8tp6WlhZGfn18SEhLSMVBdEULoUXjqAoKngW7jRaPRYNq0aY2bN28u180zadKkFt05A8eOHTNRKpX0uLi4Rt07YRcXF5WTk1PnTz/9xAOAupMnT5potVqIi4vr04jOmjWraaCAoLCw0EwgEHTrBgP3qri42KipqYkRGxvbpx6RkZFKkUikOnnyJA8AegMCmUympIIBAIDAwMAOAICysrJ+Qx2G7Nq164aDg0OfSYWWlpb9ehZiYmKa9dMAAKRSaZtuMCCXy2klJSXcBQsWVOsGPh4eHip/f/+2M2fO8HTXF4lEKgwGEEKPMwwIHkNU42VmZqZxc3NTcbncfpPPbG1t+/R/19TUMAAAoqKinjFUppmZmQYAoLq6mgkAYG9v36fLWiQSDdiF3dTURLe2th6W5x8bGhoYt7fbrzyBQNDd0tLSpy/f3Ny8T+NNTcjr7Owc1DwYmUzWMZhJhfb29gbfn5WVVZ/0+vp6OkmS/Y4DlffixYvGd1sfIYQeN09dQHCv3fSPk8E0Xre/frIXNSadk5NzUyqV9rsTpQICqgGrqKhgeHl59d4xV1VVDXgu8Pl89bVr1ziDehMDEAgEaoC/AhRdDQ0NTIlE0j4c2xkq/f16p3ShUKghCAJqamr61b+uro5pbm7eJ8AiCAKfKEAIPdbwKYOnREREhNLY2Fh7/fp1dmhoaLv+SyqVdgEAjBkzRkmj0WDPnj2Wuuvn5uZaGi75L+PHj5c3NDQwc3Nzze6Uh81mawEAqMmPdyKVSjv5fL56//79FrrpP/zwg3FVVRUrNDRUMVB9HiVTU1Otl5dX+3fffWehVv/V9l+9epV18eJF45CQkMe6/gghpO+p6yH4u7K0tNQuX768PCUlxaG+vp4xadIkubm5uaa8vJz5448/8sLCwhQJCQlNUqm0a+rUqU0fffSRSKvVQnBwcPvhw4dNjx8/fsdGnpKQkNC0Y8cO4WuvveZcXFxcPWrUqDa5XE4/fPiwaVJSUp1MJuv09vbuBABYtWqVzZQpU1oZDAYZGhra726fwWDAkiVLKpOSkhymT5/uNHv27Mby8nLWmjVr7BwcHLrefvvthuHcP+fOnePW1tb2O99DQ0PvOvnxblasWFEZGxvrFhER4ZaQkFCnUCjo6enpIhMTE01qauqQvtAJIYQeNQwIniJJSUkNYrG4OyMjwzohIYGv0WjAysqqOygoSBkYGNjbKO/evftWQkKCZtOmTTY5OTnEyJEjFTt27PgzMjLS427ls9ls8sSJE1cXL14s2r17tzAjI0Nkbm6uDggIUFpZWakBAGbOnNlSUFBQv2PHDmFWVpYtSZJAkqTBYZxFixY1cLlcbXZ2ts2sWbNcuVyuNjw8vDU7O7vC1NRUa2idexUfH+9sKL2qqqrY1tb2nh4BjImJke/du/fa6tWrRfHx8S5MJlMbFBSkyMjIqHB0dMQ5AwihJwpBkk/u0GZxcfFNqVQ6rHeSCCGE0NOsuLhYIJVKHfXTcQ4BQgghhDAgQAghhBAGBAghhBACDAgQQgghBBgQIIQQQggwIEAIIYQQYECAEEIIIcCAACGEEEKAAQFCCCGEAAMChBBCCAEGBI+VnJwcPkEQAdTL2NhY5u7u7pWWlibs7n6wX41/5coVFkEQATk5OXwqLTo62tHOzs5nKOXk5+fzEhMTRRqNZsDyHwb9far74vF4fg+zLggh9DjDHzd6DG3fvv1PsVisam1tpe/bt88iNTVVXFdXx8zKyqp6mPVYuXJldUtLy5B+te/YsWO8zMxM2w8//LCKTqf3povF4u7CwsLLnp6eXcNe0UGg9qluGpPJfHJ/yAMhhIYZBgSPocDAwHaJRNIFADBjxgx5aWkpe9u2bVZ3Cgi6uroIJpNJ0mjD2+Hj7e09bI03h8Mhx40b1zZc5Q2V7j4drI6ODoLD4fQLGrRaLahUKsLIyOieA4o7lY0QQo8KDhk8AWQyWbtSqaRXVlYyqK73tWvXChMSEuytrKx8ORyOf2NjIx0AYOfOneZSqdSDw+HIeDye38SJE52vXbvG0i1PoVDQ4uLixObm5n5cLlcWERHhevPmTZb+dg0NGcjlctq8efPsRowYIWGxWP4CgUAaGRnpUl5ezkhMTBRlZmbaAgCwWKzernmAOw8ZfPrpp5bu7u5ebDbb38LCQhoVFeV069Ytpm4eOzs7n+nTpztt3brVwtnZ2ZvD4cgkEolnQUGByfDs4b+GFg4dOmQyceJEZx6P5yeTyTx1t5+VlcV3cnLyZrFY/vv27TMDAMjLyzP18/PzMDIy8ufxeH7jx493KS4uZuuWHRQU5B4QEOCem5tr5unp6cVisfzXrVsnHK66I4TQcMAegifArVu3WHQ6HUxNTbVKpZIGAJCRkWHr6+vblp2dfUuj0RAcDke7bt06YXJysjgmJqZx6dKl1XK5nJaeni4KDw93v3Tp0h8WFhZaAIDZs2c7HDx40CIxMbEqODi4vaCgwHTOnDlOA9Wjs7OTCAsLe+by5cucBQsW1ISEhLS1tLTQCwoKTBsaGhjz58+vr6ysZO7bt09QUFBwWXfIwJD169cLkpKSHCZPnty8cuXKysrKSubq1avtwsLC3IuLi0vMzMy0VN5z586Z3Lhxw+hf//pXlZGRkXblypV2MTExrqWlpb8LBALN3bYDAKDRaEB/HgaNRgP9Os6dO9c5Kiqq6c0337yhVqsJKv3MmTO8kpIS7pIlS6ptbGy6XV1dVXl5eaaxsbFuwcHB8m3btt1QKBT0tLQ0UXh4uMeFCxdKnJycejdYWlpqlJSUJE5KSqpydXVVCYVC9UB1Rgihh+npDAgOp4jg509tB5VXEt0AMdtv9UnLi3eAS18JBrX+yHnVMCG9b1f+rdNccAhpH1xl+6Mar5aWFvqOHTssjhw5YjF27NgWHo/X20AKBILuI0eO3KCGCVpbW2mrVq2yi4mJady/f/9NKt+YMWPafHx8JBs3bhR88MEHdcXFxezvv//eMjk5uTItLa0GoGdYQqlU0nJzc+9617p582bL3377zXjPnj3XX3nllVYqfe7cuc3U33Z2dt0AAGPHjm1jMpmGigEAALVaDenp6XZBQUGK/Pz8P6l0b2/vzgkTJrhv3LhRsGzZsjoqXalU0ouLi0uEQqGG2k5YWJhnXl6eWUJCQtNA+9TPz0+inxYeHt56/Pjx67ppkydPbt68eXOFfl6FQkE/f/58iVgs7m3IY2Njne3t7buKioquUe81PDxcKZFIJGvWrLH+/PPPe8tpaWlh5Ofnl4SEhHQMVFeEEHoUns6A4Amn23jRaDSYNm1a4+bNm8t180yaNKlFd87AsWPHTJRKJT0uLq5R907YxcVF5eTk1PnTTz/xAKDu5MmTJlqtFuLi4vo0orNmzWoaKCAoLCw0EwgE3brBwL0qLi42ampqYsTGxvapR2RkpFIkEqlOnjzJA4DegEAmkympYAAAIDAwsAMAoKysrN9QhyG7du264eDg0GdSoaWlZb+ehZiYmGb9NAAAqVTaphsMyOVyWklJCXfBggXVuoGPh4eHyt/fv+3MmTM83fVFIpEKgwGE0OMMA4LHENV4mZmZadzc3FRcLrff5DNbW9s+/d81NTUMAICoqKhnDJVpZmamAQCorq5mAgDY29v36bIWiUQDdmE3NTXRra2th+X5x4aGBsbt7fYrTyAQdLe0tPTpyzc3N+/TeFMT8jo7Owc1D0Ymk3UMZlKhvb29wfdnZWXVJ72+vp5OkmS/40DlvXjxovHd1kcIocfN0xkQTEiv6teNPxQx22/1G0YYivsYLgAYXONFEESf/1Nj0jk5OTelUmm/O1EqIKAasIqKCoaXl1fvHXNVVdWA5wKfz1dfu3aNM6g3MQCBQKAG+CtA0dXQ0MCUSCT3tQ/vlf5+vVO6UCjUEAQBNTU1/epfV1fHNDc37xNgEQSBTxQghB5r+JTBUyIiIkJpbGysvX79Ojs0NLRd/yWVSrsAAMaMGaOk0WiwZ88eS931c3NzLQ2X/Jfx48fLGxoamLm5uWZ3ysNms7UAANTkxztK26SiAAAdjklEQVSRSqWdfD5fvX//fgvd9B9++MG4qqqKFRoaqhioPo+Sqamp1svLq/27776zUKv/avuvXr3KunjxonFISMhjXX+EENL3dPYQ/A1ZWlpqly9fXp6SkuJQX1/PmDRpktzc3FxTXl7O/PHHH3lhYWGKhISEJqlU2jV16tSmjz76SKTVaiE4OLj98OHDpsePH79jI09JSEho2rFjh/C1115zLi4urh41alSbXC6nHz582DQpKalOJpN1ent7dwIArFq1ymbKlCmtDAaDDA0N7Xe3z2AwYMmSJZVJSUkO06dPd5o9e3ZjeXk5a82aNXYODg5db7/9dsNw7p9z585xa2tr+53voaGhd538eDcrVqyojI2NdYuIiHBLSEioUygU9PT0dJGJiYkmNTV1SF/ohBBCjxoGBE+RpKSkBrFY3J2RkWGdkJDA12g0YGVl1R0UFKQMDAzsbZR37959KyEhQbNp0yabnJwcYuTIkYodO3b8GRkZ6XG38tlsNnnixImrixcvFu3evVuYkZEhMjc3VwcEBCitrKzUAAAzZ85sKSgoqN+xY4cwKyvLliRJIEnyV0PlLVq0qIHL5Wqzs7NtZs2a5crlcrXh4eGt2dnZFaamplpD69yr+Ph4Z0PpVVVVxba2tvf0CGBMTIx8796911avXi2Kj493YTKZ2qCgIEVGRkaFo6MjzhlACD1RCJJ8coc2i4uLb0ql0mG9k0QIIYSeZsXFxQKpVOqon45zCBBCCCGEAQFCCCGEMCBACCGEEGBAgBBCCCHAgAAhhBBCgAEBQgghhAADAoQQQggBBgQIIYQQAgwIEEIIIQQYECCEEEIIMCB4rOTk5PAJgggw9OLxeH6DLefKlSusxMREUUlJCUt/mZ2dnU90dLTjsFZ8EBITE0Xfffcdb7jLJQgiIDExUXS3PPn5+TzdfWlkZOTv4uLivWjRIlulUmn4944fYB0TExNFBEEEDKWM06dPcxITE0W1tbX0gcp/GPT3qf6roaGhXz2fFKNHj3azt7f30U9PTU21IQgiICEhwV5/mUQi8ZRIJJ4Af32Or1y50vv5u9P5Hx0d7Whtbe17L/W8cuUKiyCIgIyMDMFQ173b+fSgPKprz3AzdHyfFvjjRo+h7du3/ykWi1W6aUwmc9A/OnHt2jV2ZmambWhoqMLLy6tPOfv27btubm4+rD8cNBiZmZm2arUapk2b9sh+Fnj16tXlI0eObGtra6MdOnTINDMzU3Tjxg32gQMHbj7MesyfP79+8uTJrUNZ5/z589zMzEzb+Pj4Rmtra43ussLCwsuOjo6qO637IFH7VD/d3NxcYyj/k+C5555TnDp1yvTGjRtMFxeX3h+pOnXqlImRkZH2zJkzJrr5W1tbaZcvX+bOmTOnFgAgJiam1dvb+7JYLO5d93E4/3Xd7XxCf18YEDyGAgMD2yUSSdeDKPu5557reBDlPgm8vb07xo0b1wYAMG3aNEV9fT3zq6++4tfW1pYbuiiq1WogSRLu9eeR78TFxaVbt6G5X9R7ehR09+lgdXR0EBwOx2CAe7dl91v2YEVERCjXrVsHP/zwA8/FxaUJAECj0cCvv/5q8vLLLzfs3r1bqFAoaDweTwsAUFhYaKLRaCAsLEwJACASidQikeiefkHzSdbV1UUwmUySRsOO5ycVHrknUFlZGWPGjBmOVlZWviwWy18oFPqOHTvWtbKykpGfn8+bOnXqMwAAL7zwwjNUF25+fj4PoH+3HdX99cMPPxhPmjTJ2djYWMbn86UpKSk2AAB5eXmmnp6eXhwORyaRSDxPnjzJ1a3L119/bRoWFuYqFAp9ORyOzM3NzXv58uXWavVf10Oqe3zjxo22VH10u7gPHjxoMmrUqGeMjY1lHA5HNnr0aLdz584Z6W5HrVbDO++8I6K2ExQU5H7+/Pk+eYbq2WefbQMAKCkpYVP1fPvtt+2WLl1qY2dn58NmswPOnj3LGe46Ghoy6O7uhtTUVBsXFxdvNpvtb2FhIR0zZozbxYsXjXJycvjvvvuuIwCAj4+PhNqHVJeloSGDvLw8Uz8/Pw8jIyN/Ho/nN378eJfi4mK2bp6goCD3gIAA92+++Ybn5eXlSR2/Xbt2md/PftVFDS3s3LnTfObMmQ4WFhZSKysrqe72c3NzzTw9Pb1YLJb/unXrhAAAx48f54aEhDzD5XJlHA5HNmrUqGeOHz/e59yjutsLCwuNZTKZh5GRkf+8efP6decPVVhYWBubzSZ//PHH3p6An3/+maNUKukpKSm1NBoNjh49akwtO3HiBI8gCHj++ecVAP27lAc6/wEATp06xQkICHDncDgyBwcHCbUfhoo6t37//Xd2eHi4K5fLlYlEIp9FixbZajQ9Me9A51N3dzekpKTYODk5ebNYLH8rKyvf119/3b69vb13eI0arli7dq0wISHB3srKypfD4fg3NjbSAQBWrVpldfsz5C+RSDwPHz5s0r+2AJcvX2ZNmzbNycLCQspisfw9PDy89M8/6j2dPXuWExwc/AyHw5EJhULf9957T0S9J0pVVRVj1qxZYura6OTk5L1+/fo+QyrU8Tl69KjxtGnTnExMTGRWVla+c+bMGaH7HgEASkpKWOHh4a4cDkdmYWEhnTt37oiuri6Dw4zr168XuLu7e1Gf35deeslBf0iGIIiAd955R7R69WorOzs7H2NjY1lgYKDB68SuXbvM/f39PbhcrszExETm4+Pj+Z///MeMWj6Y4zRU2EPwGNJoNNDd3fcGkkajAZ3ec27NnDnTqaqqiv3vf/+7wtHRUVVdXc0sLCzkKZVKWkhISFt6enpZSkqKWLc7VyaT3bVn4LXXXnN66aWXGt944436ffv2Wa5du9aupaWFfvToUbNFixZV83g87bJly+xffPFF15s3b/5uZGREAgBcv36dHR4erpg/f34dh8Mhz549y12/fr2ovr6e8emnn1YC9HRpjx8/3iM6OrrxrbfeqgcAoLq4v/zyS7O4uDjXsLCwli1btpQCAGzYsMFm3LhxHhcuXPjD1dW1GwDg/fffF3388ce2r732Wu2ECRPkZ8+e5b7wwguu97OfS0tL2QAAfD6/96qyd+9e/ogRI7rWrFlTbmJiohWLxd0Po45Tp051LiwstIiPj6/9xz/+Ie/o6KAVFRWZVFRUMGNiYlpv3LhRnZOTY6s7nKTbJa0rLy/PNDY21i04OFi+bdu2GwqFgp6WliYKDw/3uHDhQomTk1PvemVlZexFixaJExMTq62srNQZGRnW8fHxLv7+/pcG00ul1WoJ/XOVIAhgMPpeWpKSksRjx45t/eyzz0o7Ojp6b0RKS0uNkpKSxElJSVWurq4qoVCo/uWXXzgTJ070cHFx6fjkk09uEgRBrl+/3nbixIkex48f/++oUaN6z2WlUkn/5z//6Tx//vxaqVRayeVy73s4zMjIiPT19W375Zdfesf8jx07xnNwcOhyc3NTSSSSthMnTvCioqIUAACnT582cXV17bCxsTHY9X638x8AoK2tjT579mznt956q9bV1bV6+/bt/OTkZLGnp2fn1KlT72mIYcaMGa6zZs1qeO+992q//fZb8w0bNohGjBihevfddxsHOp9eeOEF56NHj5rNnz+/ZvTo0co//viDs3btWlFZWRm7oKDghu52MjIybH19fduys7NvaTQagsPhaDMzMwUffPDBiOjo6MaZM2c2Xb16lf3qq686t7e397kBvX79OjMkJMSTz+er16xZU25tba3+8ssvLefMmeNCp9Ovv/LKK32G1aKjo11mzZrVkJycXHPo0CHT7OxsWxqNBhkZGVUAAE1NTbSQkBCPrq4uIjk5ucrFxaXr0KFDZsnJyQ5dXV201NTUOt3y4uPjnV544YWm+Pj466dOnTLJyMgQWVhYaDIzM6sAADo7O4nIyMhnurq6aGvXri2ztrZWb926VXjo0CEL/f09b948u61bt1rPnTu3Lj09vaK8vJy5Zs0au+eff55z4cKFy7qfh6+++orv5OTUuW7dujKVSkVbtmyZ/YwZM1xv3LhxieqNXLNmjdWyZctGjB8/vuXTTz+t4fF42vPnz3Opa9ZQj9NgPXUBgc9OnyFN1nqQfn/191/vZT0/Pz+Jflp4eHjr8ePHrwMA/PbbbyZLly6tfOutt5qo5fHx8c3U3xKJpBNgaN25L774YuNHH31UDQAwefJkxeHDh80/++wz60uXLl3y8PBQAQBotVqIi4tzPXr0qPHkyZOVAACLFy+up8rQarUwYcIEhUqlIjZt2mSzcePGSjqd3tulLRKJVPr1Wbx48YjAwEDF0aNHe0/gSZMmyV1cXHzS0tJstm/fXl5fX0//7LPPrGfOnFm/devWCgCAGTNmyOl0OqSlpdkN5v3drh/R3d0NCoWC9u2335rt3r1b6OHh0eHr69un4SsqKrpqYmLS2+38oOv43Xff8QoKCixWrVpVvmzZst6L1uzZs1uov11cXLoABjectGLFCjt7e/uuoqKia9QFJjw8XCmRSCRr1qyx/vzzzyuovM3NzYxjx45d8fHx6QIAGDVqVLtYLJbu2bPHYu3atTV33aEAEB0d7aaf5urq2nnt2rU/dNOkUmnb3r17b+nnbWlpYeTn55eEhIT0NvITJkxwZjKZ2qKioqsCgUADADBt2jS5k5OT7/Lly0VHjhzpPQ7t7e20LVu2lMfFxbXol30/nnvuOUVWVpZtdXU1w9bWVv3TTz/xgoODFQAAo0aNUp4+fZp3e/vEpUuXjF966aWGO5V1t/MfAKCtrY2WnZ1dRjX+EyZMUNja2prl5uZa3mtAsGDBgpp33323EQAgKipKcerUKd6+ffss33333UaRSKS+0/l0+PBhk4MHD1ps3Ljx5oIFC3rXt7S0VM+bN8/p9OnTHN1jJRAIuo8cOXKDGibQaDTw4YcfikaPHi3Py8u7SeUTCoXqN954w1m3jkuXLhWRJAknT568TAVT0dHR8pCQEOaqVavs9AOC2bNnN6SlpdUA9Hy2FAoFfcuWLdZLly6tFQgEmvT0dOvq6mrW+fPn/6DO56ioKEVrayt9/fr1tosXL67THf6bMWNGE9X4R0VFKc6fP2984MABSyrtk08+4VdUVLALCwsvU8ftxRdfbHV3d/eura3tLejKlSusLVu22CxcuLBq/fr11VS6p6dnZ2RkpMcXX3xhrvtZZjAY5NGjR6+z2ezea0x8fLzziRMnjJ9//vm2pqYmWlpamt3zzz/fonuuR0dHy+/1OA0WDhk8hnbt2nWjqKjov7qvjRs3llPLfXx82j7++GObVatWWZ09e5aj1d7/HMGpU6f2fviYTCY4ODh0OTo6dlHBwO3tdgIA3Lp1q3d27a1bt5izZs1yEIlEPiwWy5/FYgWsW7fOTqFQ0CsrK+8acP7+++/s8vJydmxsbGN3dzdQLx6Pp5XJZG0///yzCQDA+fPnOR0dHbSZM2c2667/6quvNhku2bDo6Gg3FosVwOfzZfHx8c4jR45UfPvtt9d184SFhcl1g4GHUcfDhw+bEgQB7733Xv1AeQcil8tpJSUl3OnTpzfpXvw8PDxU/v7+bWfOnOkz093BwaGLungCANjZ2aktLS27y8rKBjWDOj09vUz/XP3iiy/63Z1Mnz7dYIMtEolU+heus2fP8iIiIlqpYAAAwNLSUjt+/PiWs2fP9qk/g8EgZ86cOWAwQPW6US/dIS1DwsPDFQAAR44cMQEAOHfunMno0aOVAABjxoxR/t///Z9xZ2cncfz4cWOVSkWEhYXd82RBIyMjrW7Dz+FwSEdHx86Kiop7nsUeExPTpzF1d3fvqKqqGrC8gwcPmjGZTPLVV19t1t1f06dPlwP09JTo5p80aVKL7pyBP//8k1VbW8uMjo7uc97PmTOnmU6n95nbceLECbOxY8e28vl8je62xo8fL79y5QqnqampT/sUFxfXp8yXX365qb29nfbrr79yAACOHj1q5uvr2+bh4dGlW15kZKS8paWFceHCBY7u+tOmTetz3nh5eXVUV1f37qOff/7ZxMbGpk8QR6fTYfr06X3qkZ+fb6rVamHu3LlNutsdO3Zsm7GxsbaoqKjPcMmYMWPkusGAv79/OwBAaWkp6/Y+Nmlvb6e98cYbd7weDPU4DdZT10PwNJDJZB13uwv8+uuv/1yyZIlo48aNNh988MEIoVDY/c9//rP+ww8/rKaGFYZKt9scoOepBjMzsz5XTeok7uzspAH0XGQnT57sWldXx0xOTq7y9vbu5HK52ry8PPONGzfa6nYNG1JdXc0AAFi4cKHjwoULHfWX29raqgAAKioqmAAAIpGoT9+0vb39kCbmpaenl4WEhLRxuVztM888ozI1Ne0XSdnY2PQp82HUsampiWFmZqbWDUTuVX19PZ0kSbC1te23XSsrq+6LFy8a66aZm5v3axlZLBbZ1dU1qJsFDw+PztDQ0PaB8tnZ2RncD1ZWVv3S5XI5Q/84AABYW1t3y+XyPie4hYWFWn94wpCkpCRRZmamLfX/wMBA5dmzZ6/cKX9EREQbg8Egi4qKeD4+Pp3Nzc2McePGKQAAxo8fr+zu7iaKioqMT5w4wQMA+Mc//nHPAYGpqWm/oYahHANDrKys+hxXFotFqlSqAcurr69ndHd3E6ampjJDyxsbG/vsbP3zrLy8nAkAYGNj02f7TCaz35MnTU1NjAMHDvBZLBbf0Lbq6uoYlpaWvTck9vb2fcqkPmtlZWVMqm5lZWVsFotlsJe4rq6uT92FQmGf+rDZbFKlUvWOv9fW1jL5fH6/z4e1tXWfNKpciUTSr2eXep+6/7e0tOyzPjX8Sl1X6+vrGQAADg4Od3xyaKjHabCeuoDgXrvpnyR2dnbq3bt3lwFAWXFxMfuzzz4TbNiwQSQUCtXJycn3fZc5WCUlJew//viD+8knn5TOmzevN2o+cODAoCalUR/IlJSUygkTJsj1l1MBCNWoVlVVMQGgk1pONcKDNZjGiyCIPo3yw6gjn89Xt7a2MpRKJXG/QYFQKNQQBAE1NTX9tltXV8c0FAA8DPr79W7ppqamat0uWUptbS1Tv/EkiMHNn3rnnXfqo6Kieu8IzczM7vqoHY/H00okkvaff/7ZxNPTs0MoFHZTj/Dy+XyNm5tbx/Hjx01OnTpl4uDg0CUWi5+KpwosLS3VbDabPHLkyGVDy/Xnrejv/xEjRnQDANTU1PRpW7q7u6GlpaVPMGdubq4JDAxUpKSkGByacnBw6LOtiooKhu5j1Lc/a711Mjc3V/P5fHVWVlaZofKoHs7Bsra27r527Vq/yX61tbV93ht1M/X1119fMxRA6AdnA6Hyl5WVsQIDAw3WeajHabCeuoDg70YqlXZ9/PHHlbt37xZeunSJA9DTBQnQM776ILetVCppAH2/I6Grq4v46quvLPXzMplMUr/HQCqVdopEIlVJSQmHGhs0JDAwsIPD4Wi//PJLC93nuHfu3NlvO8PtYdRxwoQJ8k8//dQmOztbqD/xiUIFHgMdU1NTU62Xl1f7d999Z7Fhw4Yq6u756tWrrIsXLxrPmTPHYPmPk+DgYMWJEyfMmpubaRYWFloAgObmZtrRo0fNqXH8oXJ0dOx2dHQc0kVy1KhRii1bttgcOnTIPCgoSKm7LCgoSPnjjz+a/vbbb8bTpk0bcFjI0Pn/KN3pfJo0aZJ806ZNNs3NzfTp06cPeV87OzurbGxsVF999ZXle++910il79ixw0Kj0fSJHsLCwlp//fVXE39//47BBMJ79uyx1P0MfvHFF5ZcLlf77LPPdgAAjBs3Tr59+3YrFxcXlZ2d3X0HaCNHjlTm5eXxjx49akwNG2g0Gvj222/7fKYnT54sT0lJgZs3b7JeeOGFfjcNQxUREaHkcrnarVu3CnXnDei63+N0JxgQPIbOnTvH1Y9CAQBCQ0Pb5HI5PSws7JmXXnqp0cvLq5PJZJIHDhwwl8vl9MjISDlAz6RCOp1O/s///I9AIBCojYyMSB8fn07q4jpcZDJZp0gkUq1atcqOwWAAk8kkc3JyrA3ldXFx6SwsLDQ7cOCAnM/nq8Vicbejo2N3RkZG2SuvvOIyefJk4sUXX2wSCoXq6upq5unTp03EYrFqxYoVtQKBQPP666/Xbty40ZbH42knTJgg/+WXX7j/+c9/hvwNbUN1exbzA63j1KlTFZGRkc3Lly+3Ly8vZ40fP16uUqmIoqIi3tSpU1unTJmi8PX17QAAyMrKEsbHxzeyWCwyKCiog+pu1LVixYrK2NhYt4iICLeEhIQ6hUJBT09PF5mYmGhSU1Nrh3P//PHHHxzqeXxdgYGBHYaGZAZjxYoV1WFhYeahoaHu77//fjVBELBhwwabzs5O2ooVK6ruv9aDExYWpty0aROcOHHCbM2aNX3uOseMGaPYs2ePkCRJGDNmzIAX5Dud/w+u9nd3p/NpypQpiilTpjTNnj3b5c0336wdOXJkG41Ggz///JN1+PBhsw0bNlToT8LVRafTYfHixdWJiYkOMTExji+//HLT1atX2dnZ2bYmJiZ9emXWrl1bNWrUKM+RI0d6vPnmm3XOzs5dTU1NjN9//51TWlrK3r9//03d/Lt37xZotVoIDg5uP3TokOnevXsFiYmJVdQdempqau0333xj8dxzz3nMmzev1tPTs1OpVNL++9//Gp06dcpEd1LwYMyfP78xKyvL5uWXX3ZZtmxZpbW1tXrLli1CpVLZp6fD29u766233qpJSUkRX7lyxSg8PFzB4XC0t27dYhUWFpq+/vrrDUOZHGphYaFNTU2tSE1NFUdGRrrMmjWr0dTUVHvhwgWOkZERmZqaWne/x+lOMCB4DMXHxzsbSq+qqio2NzfX+Pr6tu/atUtYVVXFotFo4Ojo2Llp06ZSaqa1jY2NJj09vSw7O9t20qRJHhqNBr7//vurU6ZMGdZvSTMyMiL3799//e233xbPmzfP0dTUVPPyyy83iMVi1fvvv++gmzc7O/vWwoULxTNnznRVqVTEwoULqzMyMqpiY2Nb+Xz+lTVr1ti+8847jl1dXTSBQNAtk8naZs2a1XvntWHDhiqSJCE3N1e4c+dOK19f37Zvvvnm+rPPPus9nO/JkIdRx++///7PZcuW2ezdu1ewbds2KxMTE42vr2/bvHnz6gEARo0a1ZGYmFi1e/du4d69e4VarRYuX778u7u7e79xxpiYGPnevXuvrV69WhQfH+/CZDK1QUFBioyMjIrhboSWLVs2wlB6UVHRfwczt8CQ4ODgjv/93/+9smzZMrt58+Y5kSQJfn5+bYcOHbqs+8jhg/b8888r6HQ6aDQaiIiIUOotU5Ik2fv3QGXd6fx/QFUf0N3Op2+++aY0LS3Nas+ePYKcnBxbFoulFYlEqrFjx8oHc+e9cOHCBqVSSdu0aZP1999/b+nm5taxc+fOP+Pj451087m5ual++eWXkqVLl4pWrVpl19zczDA3N1e7ubl1xMXFNeqX+/XXX19fsGCBOCsrS2RiYqJ55513qtetW9c7q5/P52t++eWXy0uWLBFlZ2fb1NXVMXk8nsbJyalz+vTpzfrlDcTIyIgsKCi4+tZbb4mTk5PFRkZG2qioqKaJEye2LF68uM/17eOPP6709PTs2Lp1q9XOnTuFBEGAjY2NavTo0QovL68hDVUAACxdurTe1tZWnZmZaf3mm286MxgM0tnZuSMlJaX3/d7vcTKEoE7qJ1FxcfFNqVR6x8d9EEIIPdkSExNFmZmZtiqV6tfh/tbQv6vi4mKBVCp11E9/bMa0EEIIIfToYECAEEIIIQwIEEIIPb4yMjKqSJLE4YKHAAMChBBCCGFAgBBCCKEnPyDQarXae/6pR4QQQujv5PaThQYfL3yiAwKCIGo6Ojr6fbUkQgghhPpra2vjEgRRamjZEx0QqNXqf9+8eZPV1tbGwZ4ChBBCqD+SJEGlUjEaGhrMb968yeju7l5nKN8T/cVEAAAXLlyIZDAYy0mStIEnPMBBCCGEHgAtQRDNJEkWq9Xq9ICAgGuGMj3xAQFCCCGE7h/eUSOEEEIIAwKEEEIIYUCAEEIIIcCAACGEEEKAAQFCCCGEAOD/A5uy8wb/lhGRAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scenario 2 - Stable Loop Configuration"
+      ],
+      "metadata": {
+        "id": "rw6gk_oppVVR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 245,
+      "metadata": {
+        "id": "DUpLgAYIxYkH"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Dataset(\"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data/hydraulic.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 246,
+      "metadata": {
+        "id": "3tOFtsSsxYkI"
+      },
+      "outputs": [],
+      "source": [
+        "selected_features = dataset.select_features(number_of_features=3, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 247,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "99df4555-03a4-4993-e459-554309a30b50",
+        "id": "jn867m2exYkJ"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'PS1': 0.9768350574890512,\n",
+              " 'SE': 0.8221303782937127,\n",
+              " 'FS1': 0.7408146512995961}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 247
+        }
+      ],
+      "source": [
+        "selected_features['MPW']['FS1'] = 0.7408146512995961\n",
+        "del selected_features['MPW']['PS2']\n",
+        "selected_features['MPW']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "selected_features['PS5']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "271202a6-7dc0-4438-a341-8bb651380440",
+        "id": "irXAfaznxYkK"
+      },
+      "execution_count": 248,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'PS6': 0.9991653470544763,\n",
+              " 'TS3': 0.9978117198581092,\n",
+              " 'TS4': 0.9977387488762435}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 248
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 249,
+      "metadata": {
+        "id": "NTgoZcghxYkK"
+      },
+      "outputs": [],
+      "source": [
+        "targets = ['PS1','PS2', 'PS3', 'PS4', 'PS5', 'PS6', 'MPW', 'SE', 'TS3', 'TS4', 'FS1', 'CE','TS1', 'TS2']\n",
+        "generator_dictionary = {} \n",
+        "for t in targets:\n",
+        "  generator_dictionary[t] = PredictorDataGenerator(dataset.df,t,list(selected_features[t].keys()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 250,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f61f641e-967a-4e19-a952-191147de6ec3",
+        "id": "Hqt9DH8OxYkL"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "training for: PS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 1.8555e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 312ms/step - loss: 1.8555e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "training for: PS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.9518\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 357ms/step - loss: 0.9518\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "training for: PS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0250\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 333ms/step - loss: 0.0250\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "training for: PS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 9.1131e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 342ms/step - loss: 9.1131e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "training for: PS5\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0441\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 312ms/step - loss: 0.0441\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "training for: PS6\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1138\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 308ms/step - loss: 0.1138\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "training for: MPW\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0014\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "1/1 [==============================] - 1s 705ms/step - loss: 0.0014\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "training for: SE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 1.5104e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 313ms/step - loss: 1.5104e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "training for: TS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0225\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 327ms/step - loss: 0.0225\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "training for: TS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0014\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 346ms/step - loss: 0.0014\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "training for: FS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0404\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 315ms/step - loss: 0.0404\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "training for: CE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0529\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 350ms/step - loss: 0.0529\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "training for: TS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1196\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 328ms/step - loss: 0.1196\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "training for: TS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.2766\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 324ms/step - loss: 0.2766\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictorSet = PredictorSet(generator_dictionary, train=True, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "measurements_ps5, prediction_ps5, error_ps5, error_prediction_ps5 = predictorSet.evaluate_loop_fault_operation('PS5','PS6',50,100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QD9qEBBFxuMG",
+        "outputId": "f177780c-3977-432f-d3cb-7c369cafdbd8"
+      },
+      "execution_count": 251,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 55ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 252,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 544
+        },
+        "outputId": "ded70a5b-4c21-4266-e919-181b899a5396",
+        "id": "S5c--FYwx6mP"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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LbTKbVHbZAP5w++2y/QF42VW19zE+Fq5ihIXBYPAGvRMh1GNvb79J1V5M74Wn/Q0cd17719ZJ2i0ssgnoXaX+Hv6gbePtiFh4KCwcHSlHaeTKCAtXMcLCYDB4Q0aLWOjC4vHr4fdfVdtrntKFwmK5vT3fhlRIdAguf0xt6zUeem1HclqlLLyYHVJtTsjRhJlw6ipGWBgMBm9w1FhowkLvRvC6zfFo4/uXwCPX2bfbEbHQ0ceSB8IQiEIuCTKv/l5eRBSqzQkpcvsnVTtqcgpe8tnKBlZeYyacuooRFgaDwRuymrDQaywG2tjmeLSx+zfO2+0WFuUs26JqMKKD6rsXwqJaR0iRP37TdmF99gfdFxYH74MHf6CMsdacDltfsHCf4oTToq14chp6ly/cz1AXRlgYDAZvqBqx2Ghvd1PEIjm1cB7G/Hhn1lLkrXcsvk+r6O+DivNChm1hkZiC4YW7tMThh+CuL6ntJ7+2srAAlQ4pCYspIyxawAgLQ9vJW3n2xfexsX8jwoucruHooG+NGm6VmXOKCUcqpIvmhUzuXHhfOyIW03vh8MMqDTGwFoY2eX9MHb3VVBeYRbwunKzXddMUcLqGERaGtiKl5LIbL+Oe0Xt41Ymv4oNnf7DTSzJ4xWmvVF/l9CxXLY+5FKRnlIdCN7ghTu5y3t5wNgwf7/1xd/wafvZutf2US+DiL3h/TJ1c2q5fCFU4sXt9Qne4btZIs7Rr0moXYISFoa2Mzo9yz6gyB7pux3V84KwPmKhFtyGEqrOY2K5uT++DVV0gLCZ22NvnvAuee2V7jrvYyHCvOf218OTXOP0sdDwXFnVGLPS5JSZi0RJmCJnBc1LZPL9+9DATc2kOzB2w78+nmEl76PhnqMhsKsv1fz7ETDLbuUW0c07F0YKeChlpQ6SiSC1hceQRVdh491Ww7/ferUEICMUq11g4TugeRArqnWy67ERYdyZseR70rHB/HV2EiVgYPOdd372PGx4+zAkrevnblzh7xEcTowzqDoAGTxmfS/OyL/2WvZMJnrx+kGv/9tzOLGSgC+ssJrWIxfDm9h230pyQIg9dA7d9XG2f/w+w/sz2ravI0VJjcf7l6svQMkZYGDxFSsltj6sCtSeOzPHg6G7Hzw/NHeKk4ZM6sLLuI5XN86Zv3sveSWVc9cC+adK5POGA35sDPvB9mD+iWk23vhD6V9s/G9RHdneLsNAiFm0VFjVOrO1y36yF18JiMR8Ng+sYYWHwlHg6Rzpn51UfG9/j+PloYrTdS+pKLEvy9z94gPv3OU8e8VSOcK9HwuL3X7OHba042SksNp4L575bRS7WneHN8Y8mktP2wLFABGYOwKO/UF0hJz4f1j3Nu2PXcr5sx7yQI48oF9ZwLwysW9gZ4rmwMDNr2o0RFgZPGYunHbf1GgtQxZwG7/nkDY/x8z8dWnB/PJVjWW+FaY9uUM3HAmDj2eqrW9CjFUPHwZ9+aNuax4a9FRaOK/ayGoN2RCxu+jA8/ku1/eqrYduLy9agCYuEBzUW2RrvQ4MnGGFh8JQjs05hMZM9AtoFshEW3nPt/Qf40q07Kv5s1ssCzqPpA33uCPz0reDzw0u/4iwYbAcrTobLblN1FsIHY4/bP/Pay6JW8Wb5vBAvWGwI2eBGOOedSmB44bFRbWZNOYlJ+POP1esQjMI573B/LV2CERYGTxmb04WFRc43hd5caoSF93z9N7Z/wjO3LieZyXP3LnVlOJvyUFjUili0m/uvhh03q+2fvgVe98P2Hj8YUXbSa05Xt3//dftnbRUWNVIhXkUsHKmYCjUO/avhuR/x5tgAT70EZg8poRsbqb5fcgp+USjeHNxohEULtL3dVAixXgjxIyHEjBBiVgjxEyHEhsUfWXr8NiHED4UQ40KIpBDiMSHEu71cs6F59FSICMQRIu/4uREW3nN4NlXavvLiJzEUC5Vux1M57w58NOW2t9+obd/g7BToBI4Jpx7beteKWETbUGPR6bHp574bXvBxuOjzzpH15ZgJp67RVmEhhIgBvwZOAi4FLgG2ALcIIRa9pBFCnAHcDYSBNwEvBD6FI7huqIdb9t7CRddcxL/d/W+eHudI3D6p+YILC7OOJI6Qt/IL7l/q3H/kfl75s1fy4d99GCmlp8ea08TDQCxIf9QOVM4k0nzs7o9x0TUXceu+W907qGUtngq549Pw3y+Ez5yq3CG95K+vUxbjRe7/jrfHWwyHsPA4YrFiG6x8kurEKa+xKI9YePFedESujtKx6VB4LQrx1PQM5D0U3UucdqdC3gxsBrZKKZ8AEEI8CGwH3gJ8utoDhRA+4H+Am6WUL9V+dIt3y12abJ/azvtufx/pfJpdM7t4zUmv4biB4zw5liNiUUFY5GSOidQEK2LdZUjzxfu/yKOTj/Lo5KO86LgXccYqbzojLEsyn7GFW08oQF/EHgd9y+Hv87up75TW9Mz1z3TnwLmkvR2IqtqGcsa3w5471fbUbneOWw1/QHkU/Py96vY9V8HTL3OOEfeS+Cj0rlRGUdBeYVEr7ROMgj8E+Yz6yiaVkZWbOKabHsXFkz6fiuAUO1NS07UjHIaqtDsVchFwV1FUAEgpdwF3Ahcv8thnAtuoIT4Mi5PIJrj8tstJ5+0T/v74fs+OpwuLShEL6M50yGOTj9nbU4/V2LM15jP2VVdPyI/fJ+gvCAt/dDd3TdlX7ofnD7t3YEfBXJUTlcPLog3um6e9GsKFYsXJHXbNhdckp+FTW+Hf1sJXn62iAvoJa36iPeuohBDOAs6Uy064UjpTIdUiFjdfCd96GVz1LBj9s7traAQziMwV2i0sTgEqvWseAk5e5LHPKHyPCCHuEkJkhRBHhBCfE0JEXV3lEuZj93yMnTPOKYtHEkc8O54zYlE5b3lofmEb5FJmMjXJVNr+0No5XWHqpUvMpTVhEVYByv5oAPzzRNZ+F4ntMTKTmXEvLZWp4yq1b6W9nWjD+PBwryrkK3L3V7w/Jtitptl59boUT+a+QuQoE1eRgk5x3Pmw5blKeAmXTwmZeaCQXglEVeSoEvvvUULv4B+VqZpbJCbhe6+Dn1wGN/7L4vsbYeEK7RYWw0Clv9YkMFThfp1igvT7wA3AhcB/oGotOpwwPTb42Y6fcc0T1yy4v13CorfHLpgbCtknlW6LWOyYdrZ+7pip3ArqBvOasOiNqA/13nCA6Oof4gs6r04taRHXR1y3QlYv3KwiLPT79QiHF4w9roaAnawFRp+4EcafqP4Yt3A4bhZmhAhRFrVog7Cqxiv+S6VLXnaVU+y5Qb2Fm169F5JT8Oj/woPfh4cXfvYtIKq1IXvhqdElHEvtpkUR9G0p5YcK27cKIfzAx4UQ26SUj5Q/SAhxGXAZwIYNdTefLDl2zeziyrvsaYpD4aHSVfPhhIshcI1s3mIykQHU52g4OkMx3d/PiUyhjtttwqI8QuFlxELv+ugrRCwemP0Zgb5HS/eH/eFSamwqPeXO7JZQL5z+enVi6V9TZR8tReL1FfvVr4Dpguvrsq0wXkg//f6r8IJ/9/bYjuFjmpV3zzKIF6J182POwWxuER9V80DCfcr1cvNfun+MWqTrSIOAM6qVdVFY6IWj1QSujolYuEK7IxZTVI5MVItk6BQTkTeW3X9D4ftTKj1ISnmVlPIMKeUZy5cvr7RLV/Dth79NslBQt6l/E+87832ln3kVsZiYy5SKzId7AiQt+6osFbdFnlfC5milPEIxlZ5iIulNnr08FWJJixsPfbt033DuOWwd2lq6PZ12qc1uaCP8ny/Cq74Jz/9Y5X30FlS9g8QL9CtnfdDU/d/xXtRMVBk+Vkw/nP0OZ9unm4xvh+v/Ea59O9zmsYCqhMyr37lnhbNgtRxdZOp/q1bJ1lHro2OEhSu0O2LxEKrOopyTgYfreGwtrEV+3tXsi9vFce956ntY2WOHPL0SFnoaZKQ/yyGpzJhkLsaBsQEC69TPDs11V41FeSoEYOfMTkaiNcx7mkRvNe0NB5hMTZLIqXSHzIeJxF/C4KYfl/aZSrXxwzTUxlSIfuV80otg7Rmw9mmqMyTocYmWPmRNFxbP/tDCfd2mlodFO1i+Fd513+L76dEMN98LjoiFERbtot3C4jrgk0KIzVLKnQBCiE3AucD7F3nsL4E08DzgZ9r9zy98v9fVlS4x9CvRlT0rHe2dXkUMxuZsD4u+3lmK8sHKDZJK9lP8KOm2QWSVhMWO6R2cucr9kdVzZTUWuoi0soPMpWAwbF8tz6Rd7gqohSNi4aGwyGeh2AUlfOq4b7rJbv30Gn26aLTNVuL1TPbcf6/yEUlOw6ZzlfBqNw6R6WL0KlunnXeR1U+GU1+pBEY3DMfziHYLi68C7wCuFUJ8AFUufCWwDyiVaAshNgI7gCuklFcASCknhBAfAz4ohJhFGW2dAXwI+KbewmpYiC4sBsODjERG8As/eZlnOj1NOp8m7Hd3GJU+JyQcmYWCe7SVGULm+hD4kFhMJCfI5rME/cEqz7R0mE5NM5FamPaoJDbcwCEswk5hIXMDzCazDmGhd6t4jlcnk3LK2x3bJSgqHr/NPg66qKlW47D3Lrjlo2pbWp0RFl6lxRp1fz3pherL0BJtFRZSynkhxAXAZ4BvoWzObgbeI6XUE2sC5aZZXgNyBRAH3g5cDhwCPoESJ4Ya6MJiKDKE3+dnWXRZKVpxJHGE9X3uFo/pqRB/eKokLGR2CPATEgOk5RQSyeHEYdb1rXP1+EcTliX53c4JDqUrZ/zKW4DdojwV4ohY5PqZy+QYDNnCYtqteRHbb4LHr1d57eP+Ek549sJ92hWxqLeA0Cs66TxZTyrE4WPRIStrRyrEzYiF/tqbkentou1dIVLKvcDLF9lnN7DgskIq7+NPY0yyGiKZS5YKN4O+ILGA+gdbGVvprbDQBpDlfXbrlpVVJzIrMwAF06zR+dElLSyuuf8A7/3BAwQH7yayWt335OVP5oGxBwAPIxYZZypET3vJbD9SQsRv2zy7FrE4cK89FtwfqiwsYsPwym+oav1I/8Kfu0WnZ1VUG8Y2uVMNI5sfUx0bXtRcpOtIhXg5L+TwQ3DkEXXsZVucNSY6juJNF0WmXphbT1eIwRXaPoTM0H70vPlQeAhRCAXrdRZeFHDqqZC0tDtCVMQCsmn7Smmpm2Td8piybfaF7df5vLXnEfFHAJhITbgXLdCoFbGQOfX6h4R9wnFtDfVMNg2E4ZSXwonPhQ1nuXPcSlSKWOz4NXzzIrjqmXDzFd4d28qXeXpoJ9DkFPzuC8pjYXt5s5tL1BWx0OeFuFxj8/B18OM3wndeBQ98r/p+jrSYi10h9TjAGlznWPKxMDSJXumvexR4LSz0iMVsTi8aVMIil+kvvQE70XK6b3Yfl99+OZa0+OyzPsua3ip+Cy5wYEp9wPlC9u+5ZWgLxw0cxyOTyn5lx8wOnhZ5mqvHXVBjMeFMhQAEsK/iXYtYNOof4CWVIhbJKdh1m9oe9NDfRvjgnw6p1yMz55xNordfJjyy9a5HWETLBpG5Sb31JWueCi/5nNpncKN7x8822BWSmoFff0SJQX8YXmyC481ghEUXoJ8s9EI9rztD7BoLyWTa7vyQuaKwGCi9AdttkpXOp/m7W/+uNKfjuh3X8dYnv9Wz4x2YViFZPWJx/ODxbB7cbAuL6R08baW7wmK+VvFmVgkLn7SFhWs+Fo1W43tJpVkVYb2uwMMR6kKoK+VQDCjzcYjpzptjaq6G24Wl9RRvRjxMhejCplZ9ydBGeNql7h4b4MQXKA+N7DxsOm/x/a28GlAHqvbECIumMMKiC9DD29WEhdsRCyllaWS68M+RsZQDZ1+wj5yvl2Q+j8zZa/FKWHz8l49y62NH+KcXbuP8E+0P9k/+/pOO4V9jCe8mTKZzeY7E0+BL4guqD/qQL8Ta3rUcP3B8aT8vCjh1580FNRaFVIjM2VdyrvlYOE7mHQ5BS0u1D6bj9slNr+lIu2Rj3iihmFpPZk5NFk3POgsp3aCedtN2RSw64aOx4S/UV704HEA7OL/lGMcIiy6gvCOkyMqYdyZZc+kcqazyLItE7aumNb1rSAZ9JLN5rKy3NRZ7Jub58m2qKPLfr3+0JCxu2nMT33hZfqcAACAASURBVHvMme/1ss3y0HQKKcEXsV/jTQObCPgCbB60i9m8KODUUyHBQK40C0TgR+bVCT+XiyIQSCTxTJyclSPga/GjwZHbrnGl+j8XqwK/TAIuuxWWn9jacStx8sXqS0p1RQrOk1zaw4jFYsRG7JPv/Lj7wmLD2SoVlZ5VY9srEeoF4VcumdkE5DIQCLlz/E52xDSDP6TSV9IqjJLPVR+cZqiKecW6gHIPiyJeRiz0VtOBvjjFj5c1vWs4EPQDWWTW24jFoRnboGv7kTksS3IocZAP3bmw+t61FEAFimkQf8h+jY/rV4LihMETSvd5MTNET4WkpS2eomKI2ULt9nzaoj/cz0x6BolkNjPLcKRFI6d6HQ+TUyoNAO4W7VVCCPskEdYiFl6mQtJxmD2kroQj/Quv2nuW2zNM5sdg5PiFz9EKz1zMdxD1ukQH7TqP1DT0rqj9mHpJd7gjp1GEUO/X4nsxmwC/hx1LSxTTFdIF6OFtPWJRLixkcbCHCxzRhEU05oxYRIJ+AGS+h4BQplizmVkSLnsZTBcGoAFkchb7p+f4h9v/gXhWXbVHA7aVs5dW1gemivUVdhpiZVQVqK3tXUvIp64OjySPMJtx9ySnRywSlt3y2xOw7cNnkzmH4HSlMyRbR1cIOAs7vfSyKKddqZA9v4UvngmfORl++IaFP9cLOOe9S8ctild1Fvq03Frvg/QcfPk8+NxT4fPu1hk1TLv8VZYwRlh0AdUiFrFgjL6guoLKWllX0wF6xCIYto+/pmcN4UDxbedjOGJ/sLpt7T2dyDpu//KJW3lw7EEAAiLAR879SOlnXlpZ7y92hGiFmwP+tWodvgCbBjaV7nc7aqHXWMzl7M6DgdCIto8H7pv1tJuCd/4FixHsoWSVk51XIW8vWKwroqesgLNTnPoKNTfl/H9w11PE0epbo8bCH4LRB2Fyh/L3cOsi56dvhf96PnzrZTBepzmzPjvGCIumMKmQLkA/UQyFncNlV8RWEJ9RVxVHEkdaD4EX0IWF5Z+Ewuf22t61pYgFwFBoBUeSBwEYnRtl80AVA50mmE46hcV9R/5U2n75iS/n/HXnl25PpaeQUpY8Ptxk//TCiEVY2q2txw8cz+NTjwOqzuL0Fae7ctxs3iKdU3UuPgFTafvENRS2Bd1sKsvQkP2+cCViUa+wcHyIe2TrffA+lXIJ9cGyE1Qhp8+n0iFFQZmJOwdQucViNQaOiMX4wp+3i2f9kzfPW685WSCkxEU+o+obcmkIRlo//sH7YUx1XZFL1d63SLBDYncJYYRFF+DoCtFDnihhURzjfSRxhJOGT3LlmHoqJIN9pby6dzWRoB0dGAy1L2KxO769tH3a8tOIBCJEA1GSuSQ5K8d8dp5eDwrMDkwlwZfCV/i9pfSTTdsCzlHAOeNeAWd5q+lY0hYWyyN2Gmw2mWP5apcjFme9HZKT6sRaqyBRT4V49SF+x6fhkevU9iv+G570MrUd7rOFRWrWG2GxmJ24l6mQbFJ5MgRj6nc7++3uPn89NGKnHowpYQHqfeOGsGjG0lvfz3SGNIURFl1ANR8L8M7LQo9YJCxNWPSsJhK0P2z6AnYo2O0CTr3GAmA8s7u0vXVoK6AiOEW786n0lCfCYv9UEl/IPmlY6WWMxe2T/vGDWsupi6mQeA3XzZU99t89ns5yohbJcqWQ9RnvqW+/UBvy2XrUQC+ejPRDsaTFqzqLxSI36/9CpR96lsMadyJVJVKzytkT1PO3W1hIqRxV07NKYCzmZxLqtdtds/PASM3d68IxhKxOPxWvBqJ1EUZYLHGklI76gVrCws3OkJLrpsiSttQ/d0AEGAgPENVSIb2eCgstYuFLkUaFmgO+QCnlMhAe4OC8SsVMp6Zdn5eSy1uMzqbw9dmizcqsYFTrWNGFhZsRi/KR6bpwXNO7Cgq9OrPJnCOS5YW1eFUcYWePPsQrGWQBPPtfVHg80u+d++ZiNRbrnqa+OnFsrxECLvlJ/fuHPHgvZJuw9Hak50zEohmMsPAQKSX/+OMHuX/fNB996amcucmd+oVGSOaSpPPqJB/2hx2dEOCdl8WR2aI5lv0BMRQZwid8jhqLqM8WFm57WUwn7YiFP2w/9/EDx5dGtOtdMl54WRyOp8lbkrBWuGmlncJifd96Ar4AOSvH6Pwo89l5elywwa7lurm+fxWgREw8lXXU3nRsdLpXH+LpKifYrc/35ng6nfRxyDZwtb7rdjXLIzkNxz8Lnv5mb9dWiZDLaTHLqj6npRZPegWsfZoSGMs88FXpAoyw8JA/7p3iB/fuB+A/rn+UH771nLavobwjpLw40atUyHghYiEC9od6sTA0ErSbkSLYYsvLiIUvYguLrcNbS9t6BMeLzpBiq6kI2G2kVnaIw7O2sAj6gqzrXcfu2d3qMXMHOHGo9Q+0uCYsYmEfuxJ2ceCmodUUhcVsqqzd1ENPjwW0I+ystzy220uh3iLWTh97cifcf7Xa9qLWpB4c9TYueJrkNKEaiIDPX31fnSe/uvVjdzmm3dRDdozZ/9gP7J8hU6jQbyeOjpDIwg+MFT3up0JyeYuJeRUt8AXs16AoLMIB+x88pAmLw4nDrnppOIXFwdK2ftJ2RCw88LIotpoKv33lJPO9HJpJOX7XlT125MgtgaVPNo2EE+Skuj0QHmBZj32CnU1m3fWxmNoNV79S+Tb8+iO193X7KrUS9bY8ekEn0xGZBgoXHRNO2ygsdUIue5pkmohWGFzBRCw8pHi1Csqg6bHROKeuc9mydxGqzQkp4kUqZGI+U2pD740lKZgoMxJVxVh6KgQrWurMSOaSzGZmGQi78xo5UyH2ybpaxMKLK/VSxEIXFrkY6ZzFTDLLYEyZY62KrSr93C1hoadC/ME4xT/EitgKwgE/4YCPdM4iZ0mifvs1bzkVMj8O229Q22ueChd8oPq+2y5SYedQj3Mol5vU2/Lo+bEriBop4dsvU2mgbAIuu829QWSNpGEc80JcitxN7oKHfqqOPbIZTnhO7f3drrGo16TN4DpVhYUQ4oomn/NLUsr2jqo8SilaORe5f99U24VFLQ8LUFGEgAiQkzlm0jOkcikigdbavPSOkJ5oqlR4XykVks5ZrOpZxa6ZXYA6qbohLFLZfGlWCeTx6cJiyBYWXtcWFN8Deq2JzKsPuUMzKVtY9NjCwq2UlF68qadiiumvvkiQdCFl5ZP2h3rLEYtGrtL7Vqovr8hl7BZG4Vch8SJ/+Cb85tOqe+LMN8EF/+z+8X1BdbWcTVR+LYRQ7pxFj4Vs0r2hbY3UF3jhvHnkEbj5w2p7y/PqEBZ69MoFYWEiFh2jVsSixmVGVSRwDWCEBXYYvMh9+6a55Oz2rkE/SVQ6YfuEj2WxZaWr5COJI2zob61CXhcW4Yj9GtjCwo5YpLJ5VsVsYXE4cdgRUWiWGc0cyxeaQPjUSTbEkCP9MaB5LHjRDbF/qpKwUB9yo7Mptq1WLodepEL0dlPLZ/9uxShVfzRQqoWx8hF8woclLeLZOFkrS9AXbO7AjgFkR9HI9HCvMxqQS6m0DSgDLS943Q/UdytPyemznGDUG2HRiMDTvUbc+j9oNFJ01tvh1FeqCMfQca0fXy8GbuQ1ffQX8MdvKnGz7SXwF29pfS1dxmKpkLOklPfU80RCiACQWXTHLmJhxKL9uctqk011VsRWlE5mhxOHWxYWxXHpAIHgfCkEXxIWATtikcparBp0Pw0wpXlY+LXCTZFZ49hvyG3/hjIOTCdB5BB+tR6BDyx11XxY6wzRUyGH592JWOipkKywf7dixKI/EtT2lQyEBkpRm5n0DMuiTaYm6h1A1g50f4ry+gp9EJnXE05rFQ4GY7awySZwxb8BGhN4UQ8iFo7Xvg5hsfIUd45bZPg4+KvvqNehkZHtM/vh8evVtukKaYpawmIHUKcHKgBWE49ZsuQtyaFp50uxcyzOx+/+FKl8nL972t+5VktQi2pzQnTcrrPQIxb450rColhjEQ05IxbH9bgvLPTCzZHh8dJ01cTcCixL4vOpq0cvaywsS3JgOumorwj7+iheuerTV/VUiFsOpHoqJI19RW6nQux//9lUlsHIYElYTKemmxcWjea2pVRXl/kMMtyP8FWuKc+NjTH/u9/h6+khevrpBEbU+0nm86Qeeoj53/6WzL59BIaHCSxfQWDFckhOkDt0ItnpFNlUGPn42/DFoohoFF/qMPKxAayswPrjA1jXXApCIIJBRCAAAT/k8shsVn3lcuATCOFTluAAloW0LNXaKKWKiBS/pHR+FdGjJlLCqIBc4bX+0zud6ZpWmD0As4Xnvft2+K+/qr3/fu3v/cCrqRphqZf4KMwUnvOuP8DVixzfc75R326JcZgsrPvOO+Fbat2xpz+dFX//Xm+WtsSoKiyklFsaeSIppQU09JilzJF4ipzl7HAI9D3E1Y+qlq6hyBDvfuq7PV9HtcmmOm6bZOl23jnsq5ZKqZBkNu88qXogLAJRO2KRSaxidDbFmkHl5+FlV8j4XJpMzsIXtk+0vYEBih6cestp+WvgxtwSvd00kbcnm9qpEDtiMZtUXha7UCmplupNarQ5ylyO/OwsgeFCN9D4dvjCmYAkkd7M/jt6EUIwfOlfM/Ta1+KLxZC5HFPf+Q5jn/0c1rz93MGNGwht3EjygQexZuopOMzBY7eW3Vdc3yxQV3DWI1StDROPefS848Bis0hC9ubEgy4ffxKYrLnn0UVx3bOw7wEAAitcGiXfBSzabiqECAkhPiOEOLMdC1oq7J9aaPajFxD+afxPC37uBfpV+Cd+sZ+3X/0Hcnln26vbwkKPWKSlHWKu1G6aylqOiIlbV+szWkdISuwvbefTa9g9YZ+cyiMWbra77q9QuNkfsqNUo5qw6Av1lUyx0vm0K9ETPRUyn7dt1SulQuJuellUCcHn5+bY+eKXsP0Z5zH+n/+p7gxEAEku5ePAr1Lkx8fJjY1x5JOf4okLn8vYl77Erle+isP/9jGHqADI7tnL/O131CkqDAZDu1i03VRKmRFCvAX4aRvWs2TQW02DfkE2L/EF7Q9rt8djV0O/8tx9BHbuG+VlTxnjOSfbJ3O3TbKKBYEgmc/Zv3PlrhBvIhZThYiF8M+RlmoN0goiMyPsHk9wTsFFO+QPEQvESOQS5GWeeDZOf8idsdGVWk1Hopoh2IwzVbYqtqpk6T06P1o1wlQvuo/FTKaSsHCmQtyK3uTGx/AXsgJ6jcXsL35BZvduAMY++zn8IyMMvfg5SAmH7hkkV2ZdkJ+YYPxzn3fcFzruOPyDg6QeegiZscVjYPlyes45h8ipp2LFZ8keOULuyBjkcgRWryK4Zi3BNWvwxaJYiSRWYh45vh/u/AT+gMQ3tBzfq74CqKhK8UsEgio1EgwiAn6QEmlJNYFTSvD5ET4Bfj8qdSDttEguCd9+OQgJgShcep0qb1dHoZRq+NX/hQP3qu3nfQzWneH4cdu45u0wURjUd9EXYHmLRdS/+TQ89ku1fe674aQX195/z2/htk+o123jufDsD7Z2/GY5eD/88n1qe/Xp8MJPAOAfqJxKNiykXh+L+4BTgds9XMuSQi/cfMYJy7jlsTFEwL6yGkuOMZOe8bzOYlKrdrfbHJ3RFLdrLErdCL4U+YIpU0+wp9TGuqArpMdZuOhGGqCYCvFpVt5WehXgY8+E88p3KDJEYk6d1aZT0+4Ji+mFwmJFjyYsZsuERY9TWGwb2dbS8Us1FiJDMq8q9IO+YKlgVU+FxFM5Bvtaj1iMfuSjTH37Z/SsHmb9+ZMILWIRv/Em574fvoLgimVktvcwd9CuKxh+wxuYvf56cqO2yBSRCMve9jZG/uYNiFAIK5Mh/fDDZPbtJ7z1RMJbtjT+npkbgyOFdsjoHJzjsjPu7CFYXhA/vcPwlKdU3u/xEUgXUnfHr4CTq+znOe9QHSGRAdjybOhdvvhDavFEACYKv9cpJ8Fpi/xesVG4v/B5tdpf/fWql999CX77OSVuz3xT/UPYlufh91l7u9V1dCH1Cou/B74rhNgD/Fy6GS9eouitpudtWc69e6bIB50h210zuzh9hcsTDTWSmRwTyenSlU+xzXFy3jlO3O1USCKjqjUr2XlDubCw6An20BfsI56Nk7EyTKYmS4WezVJMhehW3vnUagB2jTuFxWB4kANzBwB1Qt2AOwOpSq6bmvvoqt4RAj5BzpJMJ7KksvnS6+F2AWdRWAjtfbcitqJ0AnZELJJZTmwxYiHzeaa//30A5g9FSI6HiBWERX52lvm77nI+IJ/nwHvfh0zZQm74ktez8v3/yPK/ew/TP/wR0z/5MeFNx7H8ve8ltG5taT9fKET09NOJnr7I/8+hB+DAH1VXwIptzs6DSFlXSLH40i3qtdR22Jp3cOjVU17n7vM12m7qto9FYgLihf//Rrp+zBCylqlXWPwQGACuBbJCiDG0oB4gpZQb3V7csYxeY7F+OMZp6wZ4wO8UFjtndnomLCxL8p4f3A1CnVykFQSprlCnysaJ68JiLDGGJS18onm392Ju3+evJiz0dlMlQlb2rCQ+rQo9RxOjLQuLYsRCbzW10kpY7Jlwxtwdkz1d7Aw5UMHDYigyyIq+MAcLaZDRmRSblqkPVLe9LIrCwlfBHAuUQVYRN+aFZA8dQmZt0TqdfgaxtWeotdx2GxR+Ftq8GSuZJHfoEFYiQVH5hgezLH/nZWrN4TDDr38dw69v8WS3/Ub49ZVq+9z3wIUftn8WCIM/pAy0rJzykghGKz9PM9TrI6Ef080Jrzd9WHWGhHpUKmJok3vPXQ8OK/U6hEXQZWGhi4JG2p7N2PSWqVdY3IxTSBgWQU+FrB2Msm1tgAePOE/oO6bdG5Fdzg0Pj3LDYzvpPUHdDgq7j3ty3rmOaCBaqjPIyRzz2Xn6WpipULpSrhax0Is3c0pYrOpZxRPTTwDqpHrKSGs97UXx5EiFFCIWuyfmHS2nDvdNFztDKqVChiJDrByI2MJi1hYWbtp6SylLNRaVXDdBGWQViaec80Ka6QrJ7NnjuD37x72s7NmIH4jfcGPp/oGLLqL3gmex57Wvw5pT7xHht1h7zhQ+crjKYlfN4X7VXgjKgdNVYVGnpfZTLoHjzlcntFVPcu/422+Ew4Ui8ae9wb3nrZcTLoCBtUpg9K1afH+3IxaOtudmhYWJWDRDXcJCSvkGj9expJBSOoo31w5FWb88C2VZhmI+3Qvu2zftuFJe3TtScjIoj1gA9IZ6SRSq5+KZeNPCIpe3SBeGrVUaQAblPhZqX7cLOKcTWRA5fNq48j7feqZRNuKH4ylWD6iTiBdeFlJKzXXTFhaD4UFWD0S4r3B7tJqXRYuvQXEGCEAwVEfEIpl1RG5mqsyLyMfjTP73NwisXMnQq1/l+Fm5sJCJBPHrf0n/i17E3B132Md97oWEN29m3ec+y753vBOZTLD6zBnC/Tl3hk/pLDaA7DXfBX9QCYyYS8ZURepNhaw/U325TabBiIHbnP++xvb3dAhZAw6wISMsWsUMIfOA8blM6eTaFwkwEA0y3L/wH8XLzpC5VG7BCa1IJWHRH+ov1VfE9THTDTKfzpe2w6GFdt5QFrEo1GO47Tw5k8ziC04ghPo7rO1dS2R4hPvnlXDYNT5fUVi4FbGYTmRLtSb+gBaxCA+xst9OBY1W8bJotTtHN8cKReIUG4z1Qt3+slRIPXNTJr/xTca/9CX1vBvW03O27VGf3bN3wf7TP/4JvoEBZEr9nqHjjye8eTMAPeecwwk33oD82osIJgvTZ91MBZQ/X6WIxfqnu3s8x7EbdJ50/fgNGpU98jP43RfV406+GM6/3Lu1VcIRsXBhbLouThqNWLzoU+p7p51jj1EaEhZCiCcDW4EF1nBSyv9xa1HHOuVpEICknFiw36H5QySyCWIevHnn0zlHxEJvc5wqK94EHBGKVoTFXMY+oQVCiVJgW6+ZCOs1FloqpIhbEQuhndCXR5ezbFlPyVZ9z4Tdcqq3WboVsdDfA4FgojThVUUsbGGnRyz0k/7hxOGWal30VlN/cLaysChPhdRRa5J+/PHSduL39zqERXnEAiB5331MfM0Wm30XOgdRBUZGYKQXilYjbkcsHCf3To4t74CwaGQIGaiptHt/p7ZXP9mbNdXCISzciFjo1vIN/O19ftVFYmiauoSFEGIQ+DlwVvGuwne97sIIiwJ6GmTdUGHgVJWT5a6ZXZyyzGWPfNQVq96NoLc5ltdYAPQG7Q++uWzzVwvOUd1zJWGhRyzCAV/J8Tibl+Qt6erVeiqbJ5nN4w/bJ+3eUC8bR+wP191aZ4gXqRC9eFf6tGNFBlnZb6cmdGERC8boD/Uzm5klZ+WYSE6wPNZcy99ik02hPBWSoy/Yh1/4ycs889l5MvkMIb/mxohKhRRJb9/u+Flmrx2xCA36yUwrQZF6wHZx7LvwwoWL1U96bkcsFkuFeEmjEQM3kbLx47udimgUf0hNoJV5sLJqMm0gtPjjqtFsxMLQMvVeDv0bajLO+ShR8VLgAuBqYCfgYTzx2OPAtP2GXjekIha6sJDSbmnzqs4iXpYKWRYdIuRXf+5kNk8yk3fs71rEQj+h6RGTiB2xEEIQDjg7Q9yMWBQnmwqf09ly04j9wblvyllQWcStVMho0StEZLGEMgwLiAC9wV5W9dsBv0peFqWftfA66H8H6bP/nsujtlDpCfkp1K+SzObJWXJRkWXN2iJFFxYynye7b599nJPHKCe4di2Rk09euNhX/Df8wy7458Nw/AWL/GYNUk/Lo2Wpwk03rpKrHbvWif3xG+BzT4VPbYPr3uXOsbNJStd9gUjtIWhFHAKvxdciM69+l1/9M/zmM/U9RghnZKfVjgwzNr1j1CssnocSF8VG9P1SylullH8N3ATUPfRCCLFeCPEjIcSMEGJWCPETIURdxgFCCFnlyzsziCbQr1aLqRDdl8BKrStte9UZMp/JLehGGOqxr1DL6yx0YTGbaX7SY0KrsZD+hXNCipSbZJWbdOUtp/BphOmS66Z90u4P9TMYcxpCFdFNytyKWMwXvTz0OpfIIEKIUm0HOOeFgHt1FnoqJC/sv4MuooQQC0yyFhNZeU1YZPbuxUor0ZQ9NFpqNfVH8vStSxGIlonXCy+sbGLVMwKxYQhG3PWRgMVbHm/4IFwxDB9fD3/4hrvHjgzC8m0wsAF6akSe8mmY3AHxgzC/UJA1RTPRkpCLbZbJaTV6/HdfgLu/Uv/jQi6Km0aH4Rlco94ai9XATillXgiRAvSY4k+A79XzJEKIGPBrIA1cipLUHwFuEUKcJqWs5938DaD8nfp4hf06RnlHCDgLEnNzW/BH1dWdVwWcc6kcIuIMwQ/FQhyeVSeCyflMaRAXOIXFXAuFU/qVck47oQ1Hy4RFwA+oE1EqZzESiDIUHmIqPUVO5phITTjC9o0wXRBNwmf/HXqDvQt8G4p4MTq9mBLSozbFaMCK/nDpviPxNHlL4i+EDtxqOdVdNy2hXo+gL1iaR1KkLxIoCbHZZHbRiIWeCsGyyOzcSWTbNjJ7dpfuDvXmED4Y2Jxi4iH7eH3PrZAG8ZrFijf9IUpX9unmI3UVefqb1ddiOAyZXIqaZJuoLwi6WONQb7SmnEt/Br6AEoGx4cX3r7mGFiIWP3mLMlfLzsNffdfdNuAuoF5hMQoUP3H2AGcDtxZun9DA8d4MbAa2SimfABBCPAhsB94CfLqO5zggpbxr8d06h164t24oipTScfWZnz8Rlv8aUCZZXjCXziF6NGOm8BDDPXZJTHnEQq+xaK0rpHjCzpEvDCv3CR8DIad1eSWTrFU9q0rdCKPzo00Li6kKEYu+UJ9jTPhcyi5g1U+mM+mZlg3CQHMfrdCZEwn6GYoFmUpkyVuS8bk0KwvpEbdTIU5zrqEFEQPVGaLer+XzQsqFhbQsrLjzvZHevp3Itm1k9fqKPvW7D54omHhYFdMEVqxY3CXTCxzFmxVqLMrdNzuBfkJ3q72x5YhFi8KiUXOsIstcHJD919cosZiZrx0xqsTUbhh7RG27LTi7gHqFxW9QhZv/C3wL+BchxCYgh4o8XFfn81wE3FUUFQBSyl1CiDuBi6lPWBzVLPCwGIwylZ4inVeRApkPk0+tBSlASPbP7SeVS5XmaLjFXDqHr+ykNqQJjfICTkfEopXizUzRlMl5bH9Zjrc8FQLKefKRSfXPPDo/ymnLT2tqDaXJpr7qwkJPhQT9QXqDvcxl59Qgsky85RkuieLr4K9cy7FqIFoSQIdmUpWFRQu23rZJWWUvkSLlr4kjYpFyCgtrfl7VI2gU6ywyu+2OkFCvOnZoWYzVV17B7PW/YuRNb0T4qoi19JyyX84mVPqgf3U9v2J9HP9sSE6pk0ulE2xYExsdExa686ZLEYtME4WLbhbR6oIu3Oai2SIrWpi144giGffNRqlXWHwYWFPY/gSqkPPVQAwlKt5Z5/OcgrIFL+ch4JV1PsfbhBDvA/Komo9/kVLeschj2sZsMke88KEeCfoY7gnxyKRdR2FlB0EGCchl5ISyz94zu4etwy1OEtTI5i01gyPgvFodjtlX6VNlwkIfvNVKjYV9pVzZdbNI+bwQcC8NUKnGQgmLyjUWoMRPUVBNp6ddEBbFeSmVu09Geuxq92KxKbgYsUhVT8Xo9JebZNVw39QLN4ukHy8IC0fEovDahmIMvuIVDL7iFbUXe/eXq9tut8orvl7752EtYpHqkLDwohtjYB08/+ONXa27uY5jvXDSuG+2RL3OmzuAHYXtLGoo2d83cbxhoFLZ/SRQz4zob6OiJgeBjcD7gF8LIS6UUt7axHpcZ7+jIySGEMLZEZJTJyx/bhW5kCrU2jG9w1VhodIRckEYfqjHvgKdTDi9LHq1cGUrNRbzFey89Y6QItVSIUVauVqfLnWFOGssYkF/qc01mc2Ty1sEDYPPMQAAIABJREFUCp0yg+FB9s8pM4Wp1BQb+1sbfWOnQiqf2HX30aTm/eF2jUV5KqScBRNOY9VrLPLxhSHhUsRC87AI9haKNusOwXewzTGiCUi3IxYPX2dHSo47D6JVPuK8GHrVvxrOeltjj3GzKySnFSUHG4jG5rOqPiOTUGmqTkU73Cwi7UKOKedNKeUl2s07hBDXAn9GFYA+o9JjhBCXAZcBbNjgztTKWpSnQcB5grCy6oNbZFdCSPn4u11nEU/lwJcuuU7GAjFC/hDDWldEecTCrXbTovOmfkJbPGLhrklWqXizLGLh8wl6w4FStGIunWMwpiIHbg8is4s3K7ufxjRhkdBaf/VBZGPJMXJWjoCv8X/TZlIhs6ksK4ftk99katKxb35m4Yk3e/Ag+Xjc0WpailjUXTToYm6/URypEJdz6bf9hz2r4y231xAWR8nQq3KB18q0V11YNJLmve5d8MB31PZFX4CnXlJ7/2pYlvLD8AcX37cSXhTUdhE1K9SEEH1CiOcJIV4shOgt3LdVCPFdIcRDQohbhRAva+B4U1SOTFSLZNREShlHGXdVNdqXUl4lpTxDSnnG8uXNmQ01gsN1c2hhq2kxYpFP24WJbgsL1Wq68Ep1SAu/T5a3mwY1YZFt3cfCMYAsWkFYBBamQhzOky3YehdTIeU1FgB94cp1Fm4PIktmFxZv6hGDasIi5A+VBIAlLcaT400dv1IqRP8di9RKhcyknfNCrHjlK/r5O+6wW00HevEHC0XCzUQs2n116GUqpN5ZHUdL2N3nh5d/Hf7qO3DJT5WwaBb992hEWLg1iGziCbhyGVyxDL7yl40/Pthhs7BjnKqXQkKIE1EeFWtRplijQoiXAL8s3N4JPAn4oRDieVLKm+o43kOoOotyTgYebnDtOkfN5NWKHhaOiIUSFtnEciWncN/LQs0JWRiCH9aEhVcRC7toscVUiCs1FvbfoiQsIkEouF3qwsIxgCtdeQBXI9RqNwWIBu1/vXKzslU9q0rRgtH5UcfrUvfxM42nQmbLfCwWpEJmK78v4jfdXNoOrdZEZDNFg25+iE/uhD98U7WZjpwAp7x04T5eFm/W25kRCIPwgbTUCPd8DvwdCiafukg9TL04UiENTIx1y0uj+FgrqyIXjWIiFi1R6917JZACngvEUQZZ1wD3ARdLKVMFX4r/Bd6PEiGLcR3wSSHEZinlToBCd8m5hedoCCFEP/Bi4J5GH+sVTjvvhR4WspAKSSZGKL51987uJWtlCfqaDNuVEU/nnF0ZhZPmUEwTFmU1Fu45b6p/Yl+VkelFHKmQwryQlbGVCAQSyVhyrOnXZLqK8yaUd0HYr0E9A7gaIVmh3bSeiAWo1+HhCaWzmxVY8dTCVEglYVGeChkI2yKwvCskP6sJrmKxCjB3222lu0PHb4PXv09FHnrrbBcOeWTpPbET7vx/anvzsyoLC0e7qcupkHqFhRBKXBUjHNkE+Pur718Pf/gG3P9dddzTX+ueYKiXZlMhemSnlfdCs5NNS+swNRatUEtYnAu8X0p5M4AQ4p2oiMPbpZQpACllQgjxeeA/6zzeV4F3ANcKIT6AijRcCexDM70SQmxEFYteIaW8onDf5agBaLdgF29eDqwCXlfn8T2n3MMCnA6KvvwgeSCXD7G6Zw2H5g+Skzn2ze5j8+BmV9ZQPoCseNIcqhGxCPvDBHwBclaOrJUlnU8T9odplErFm/V2hQT9QUaiI4wnx5W4SIyxpnfNgscuxnQiAyKH8Km1BESAiF99uPXqXhbpyhELV2osKnSF6J0mevFmIuvsUHEjclP83XwNpUJqTzi1tIhFeMuW0kAya94+RmjLSXCCc9DYongVdna0PFZJRejeFpk5sPL12V8vhmU1ZlL1uh+CL6iulN3oopjcCfsKdj+bKpafecu6p8Mz3qsExoazF9+/iFsFpK3OCTla0lPHKLWExSoKnSAFitsHy/Y7BNRVvCClnBdCXAB8BuWHIYCbgfdIKfVWBAH4cdaAPIaaUfJSYACYBe4E3iilPGoiFvu1GRRrB2NY0nIIi4gYpnidvLFvE4fm1cu5Y2aHa8Kiaiok5qyxkFKWDJOEEPSH+ksh+HgmTjjagrDQizcr1VhUKN4E1RVRrCsYnR9tUlhkF0Qrir9ntZZTt0enV4xYhCtHLCqlQoo02x1TqcaiksDrL4vg9AR7CIgAOZkjmUs6BKbeFRJ92lMdk06LhJopkPbq6tARMajSXeDzwXv+pFIioT53RAUsFBXVPDyKbDzHneMWcfhYdMDO+rjz1FejOApIW4lY6K9/q8LCRCwapZaw8AH6J15xu7yeoaH6BinlXuDli+yzG3uCavG+nwE/a+RY7SaRyZVSDEG/YEVfmInkODlLfcgPhAdIB6PEU8osa03PBuC3AOyL76v4nM0wl8456guKV8rRkJ9I0Ecqa5HJWSQyeXq0YsbeYK9DWCyLLmvq2FBPxML+oE3rwqJnFX+e+DPQ3NV6cbKpCGmtplp4tVcv3kxXFhatRiyyeYtM3gKRRfhUZCjgCzjstGulQtxoOZ2v0BWyWI3FTDKLEILByGBJ3E2npkudKpaWComecgoz4TCyMCukSHBjE226XnVFpOu0lR70oFusk5NNWzn+j98Mu39jW1lvOtf9tdXCreJNPcrQzOt/wnPgdT9S783+xi9uup3FKoTWCiGKl9F+7T79k3cdBkDrRgBGesL4fE4Pi1WxVRzRrtT7g/YJ162pmlAQFj471aGf0IZjIQ4Wihcn5zMOYeFGnYXtobFY8aZ2xV4mLIo0c7U+W6O+AhZeoRdxc15IJTvvobDTTjsaql28WaQZYWFZkrlMDsiXBKZAOEzQiuhCq7juwbAmLNK2sNCLN/2Dg4SPP57Uw86a61AzwsKrrpB6UiFe0WlhkW3yij05pYahgbOrpV249V5o9vcvMrhefRmaYjFh8aMK911TdltwFHVldBL9yrN4RaqfHFf1rCKunVBjAfsqudwzoBXmCj4W9nHsf6yhHltYTCUyrB+2f6Zf2TcvLPLgy5TqGyL+CNHAwqrwiGNsum0T3epJteKcEK2Vtpqtt6PGItWqsFiYhih38owF/Qv2L9Lqa5DI5pUFQZmHRrmtOkAsvHAd1aI3ea3d1NfXT3jLFoew8I+M4L/tX+DPP1FFeM+9onLBZDnBqOqKCPa4KwCanVfhBvW2mpaTzwKi9a4Qh7Bp4PheFdLWfXxdWLQgbDqdCupyar17/6Ztq1gi6LUCxStyR8SiZxX7tBRARNgnm4nUhGvrmEvnEMKOWMQ0xa63nJbPC9GvaJvxssjkVApABJ1pkEqjsqvVWOhpk2YiB5Umm+oRC/0Kfa7K6PSZzAx5K1/xRFwPlVw3y9MQtVIhegpqJj3jqIWph3rTIGodCyMWjtHpWgGnXrzpH+gnvMU5fzC0YQMkJiFZ+LLqbPML9cKHJt0fma6fmGo5OKZm1XrTcehf2/pUTVh8qmo5P7gUHv25ao98zfdh6/PdO34jJ1a3Cmlv/4SaDhqIwtlvhzVPae/x9ccei5bixzhVhYWU8pvtXMhSQA/pF6v+y4WFfkIN++wT+WTSxYhFWSrEEbFwtJy662VRulLX7byjC9MgUN5uakcsdHHTjLV4qdVUi1jokRhn8aadCgn6gvQF+4hn41jSIp6JO6IYjZBIL0yFlM/piIQqp4JAmWSF/WHS+XSpiDLWwIdjqdVUS0dVExbRoFPgWJasOogsr80K8ff1Ed7inEQZ2rgRsprZW71rdltQFKk3YvGzd8FDP1XbL/+6O62ZoV448QVK3Kw4ub7HWIX3oxt1Jg5h0cCJ1a1C2r13wxM3qu1GXk9dBFm56vstRrO/fyVacSDtUmoZZD2GSntcI6X8XfuWdOxSMRWiCYuVsZWONsOQ0ISFm6mQdFkqpGrEomxeSLC1eSF2i2Ptwk2obJAFradjZmq4bkL1dlNQ6ZBipGYqPdW8sKgw4bW81bNWxKK45nRS/Q3jmXhDwmKugp14pVZTAL9PlAp6QXmKVEuF6EPIfP39FYTFBsj8SbujwyFoR8SihrBwuG+2bo4GwOrT4LXfq39/RzeEC+2NzaZC3CqkbdbHYvlW+L/71Tpa6dDJtuhjMbkTvnahep7+tfDOe5tfSxdSqwfqv4Hzgd8IIQ4JIa4SQrxACBGq8ZiuRi/CK16R662mq3pWOa4Q/dI+4U2mJpGtWOhqzKXqjFi47L5ZmhOySEcIVE+FtDq+farKnJDStsMQyiks3CrgrDiArEykxGo4b4JzzY1Om20kFQLQo6VD5tOVhYXM5bAShQ9rnw9fTw+B1avx9dgf2qGNGztftKijG17VOrk6TLKWyOj0ZlMBrhVPauKoEedNn1+lrVpt+73wSvj7x+Fd98Fp9Q7O1vCHIDGuXkfTbtowtVIhHwc+LoRYBfwf4GLgp0BaCPGrwvbPpZQd+k88+khqRkeVIharYquIBu1ainw+SDQQJZlLkrWyzGXnHCeUZplL5yBmi4ao9o891GOnAhbMC2nhZFY6LouPTAensEhrxZt6oWUza6g02VR/zv5ao9NdKuBcbLIplBlkZRaGfFsReY2kQkprKSw1mck7aywK3Uq6h4Wvrw9R8GWInHYqid8pI6bwSSfBziaFxeGHIDGhTmYbzoJoc9EiBye9GEaOVymRgRoV/mEP3TfrxW3fhKZrLFxahyNi0bgfTsuEYq2lQIIdLmI9xlm09FhKOQp8GfiyEKIPeBFKZPwnEBVC3IYSGddKKcvNs7qKZMY+QUaDfnJWjrHkWOm+lT0riYTsE1Yym2c4MsyBuQOAilq4JSxEb5WukDojFs1ECypdKdcVschViVg0U2ORqN1u6ijeTDtTQW55WdhzOqrXWCyWCnEU0jYoLCqNTK/2d4CyiEUm5yxkLcxNscrqK4qsfP/7Gf/CF4idcQbhzZubv1K+9m/h4H1q+003w7oz6n9sNf7isvr283IQWb24LSxe/jUlkjJznekKcQiLBiIWRwtejLLvIhrqaSpME/0e8D0hRBB4NkpkfAD4ArbXRVeiX3lGQ37Gk+NYUomN4cgwIX/IkQpJZfKMREYcwmJjfxM+AGUsKN6ssyvEMeG0qVRIhYhFBddNqF5jEQvGSvNCErlEw2PDZ5ILUyHO4s3K7aZAxRNqMyQq1TiURQz090E6Z5G3JH6fXSDmTiqkurBxrKUsejIUW9gV4vCw6LdPxJGtW1n3+c/bT9Zsm2XQJWOkZvBiENn2G+HAH1S0YNN5sOb02vu7PfTqxOc19zjXujL0IWQN1FiAqm9Ix9X7YO0ZEOhA9j0QoeSkkE+7Z/XeJSziMwtCiJAQ4jNCCMdocillVkp5vZTybVLKtYDLnrTHHvoJMhr0L+gIKd5fpBixKOJGZ4iUkvl0FjRhoftI6BGL6bJBZPoJuJXiTUeNRbiKsAhU7orwCZ9jHfMNFpCVfifNeVS/+ncUb6ZyjrqWgZAtLFqqsSiOTNdrHMqKJ32FoskiqbLOEHciFvWlQnrCzuhJpa4QfQCZr7/GgCw3uhHandP2YhDZ9hvh1o/BDR+AvXXUvndydLxjHW5FLPSx6Q1GLP7r+fCV8+G/X6DqHJpheh/MHoTktJrb0ijFwXBFTJ1FQ9STCskIId6CSnfU2u9u11Z1jKKfIGMhv8NNs+g+GS1rMxzu14RFunVhkc5ZZK0sEaH+mUK+kGNCqCNikajuY9FMfUPxStkXsB+7IlZ5wmWlIWRF+oJ9pZPpbGZ2gblULaYqpEL0bpdwwE8o4COTs8hZklTWKv1N3EqF2O2m1Q2yQHlIpLLqb1Bur+5OjUV9qRB9hPt8Ol9xIJul1VjoqRAHuYwa+60O3lg3QCdz2l6kQhrtyjhaQu+bzoM33qTWE6vcKl4XrUQs3BBZ334ZjBdm2bz9LlixrfHnCMXszphMorYXisFBvTHm+4BTgds9XMsxT6KsK0Q/ORVPWg4r64z7EYtaaRCAwZgtMqbmnYPIHBGLZmosMnlAIoL27120gy6n1tV6X6ivVEzYaORkZpGuEFC23uNzar94KlsSFgMR++TfjLAqUtHSu0LEIFr2XtBpJRVSrB2pFTHR0SMWyWyO3mBvaRBZIpcgnU87PCx8A1UiFuXtnY30/odcCsGXniMF17xVndRjw3DhFdX39SIVoovBeoonvZqX0iixYXcMwlqpsQi64L6ZccEgy+30VBdRr7D4e+C7Qog9qE4QY+FdgVSZQZZ+QigNAitrs3QICxe8LObLPSwCzn+qSNBPT8jPfCZPzpLE07lSp0Tr7aY58CdKdt49wZ6qxajVukKgNS+L6UVmhYAq4CwJi3SOYkzFtVRIJgcig/AVBtL5ggv+DlBWwFk2Or2VVIhq+7UWFTaV1vH/2XvvOEuu8sz/e27uvh1nNEkzozAKoywhiWiSvAQLhIi2tLbJJtngvP7B2mDjsMZgYxavbcC7YAzL7pIMJq4RNhjBgkEiCCGhrNGMJmim48236p7fH+fWrVPVVXVPhdvdw9zn8+nP3LmpTt/urvPU+z7v89TbNkIIZsuzAzfYpdYSBY94M4RYVGbhDbeqdoLdDX5OGLKOqW6vuqZXE0OIhUYosyMWcSsWGX7/D38P/teNitDsvgpe8L507xcXdhdk/1wo8vHtybMgmd0Mxp7HrZDEMP2JfwwVVf5poCuEeARvPoiUUqZXHZ7k8BtkHdA2Jzdh1L1SX6OxyIBYrPo9LALY+ny1RL2jTl6L9c6AWFQL7h9gvVuPbWtdb1vkCm4vXk/p9KOsZYV0bK940UNwYliLO4mt0POQK70VAmbR6anEmx07MoDMQdRkSOpWSK6F6LfDqsUqpXy4AG4yIBBtvjLvEov2EvMe8WZYBHlejXcmQdYZFXECyMozKqukPJ1duTvuuOfZT4Jf/Z56blr/j/YKrB5Wt6fC/wZHBwHXvUtVLZK4Z2bxu5BJxSJjsnsKwZRYfJlx0NhQ6OXsiWKepdW1rRB/+TtrYlFrq6tlB0FXyluqJQ4uqj+UhXqHM7eqE1k+l2eqODVog9S6tVj6hlrbRhTdDTmsDQIghNfxsW3Zgw0u6XTKYCon10EI9etaLVbXkCOTvJC0FQuPvqIS/Bl69DYZEotGxzJug4CvYhESRDajB5BFiTeTIqtpBAceO+8hZKF6WvZZJZ7pGAOiUKrClrMzOnYGV+t2V22mlQQ/63wBrk4RNZU2Or1nq0kOAEQ8gy4dYy+LxDAiFlLKl414HT8R8GaFFDxXvc6m5Y8LH0UrRGhX6xMBf1TD8kIcYrHaWY1FLNZULKrRV0uVYt61ku72cJaV1Mui7mgbNHMsf7UC/COnbsl+lBWLIAQFgDlII6Std2xyhsJN/zocgqN/FovtRfYsa62QURCLrDIqBu8RIwRsFDkQSS21Mz92zKv15iK84zyVW1KZhTceyHZtJkg7eqy/pjiZ/Od7/bsVSSlOwFSwCH2MYKTM5h1Dh79iEUQsJnzaAj2kK6uKRZidt4N5TcC5Ji+kNOUKJ2MKOOsdyyPcjGqFgDNyqo6fRV6I6x8Rrq+A8FbIRGGCYq5It9elbbdpWS0qcSYb+qh3bCMPiSj3zTTizUbbikxW9SOwYqFNhiy3ltc4bwai21JTIaUpyA2dZPcia/FiUj+NrNA2zCkZBdJULIqTbhjaRo29ptVY6K9J476ZtK03RjxiIYS4HNgPrDnbSin/IatFnazwayx0YjFohfjGTT1Xhq3FVHHd4PTXh2ssBsfM0CSr5qtYRLVCwDsZold7ksa3B1UsgomFVrHQgsgc0eLxppqdX2ovsbMQv0fd7FgeD4kwYjEZMRWSRrzZ6NieVkiUORYEaz30Kstie9HrvBlWsfjhx5WDJsCVL4Hr/yr4eUGongbzZ6mr1Sx0Ae0YGotRIO7mLiW0llT7wWqna4ukCeDKl5TgUtqKYNhdyBeHvy5LeKpXCaZCxpHpGw4jYiGEmAM+BzzOuav/r667OOWJhX7V7R83DapYNLs2hVyBufIcS+0lJJKl9lJo1LgJ/K2QQI3FZLiXRZrefr1tIcpm4k0IDyLT2xdx1uD4aDC0YhHcCgG1CTvEYrm9PLSdE7wOG1EcHh0fJd70j/72ZI+cMKsC1NoWYtK8FaL7ZzgeHH69iV6xCCUW+lV6PmY+xEXPVV9ZQU8pNRFkHr0d6o+4WSVpRi57PW/VxWRz69Tgz87qP78Kv5siHSGuvkOHEOo1znRMpx4/t+XwDxTBLE7ArivgWW+P9/piSh8L/TUbHYR3isK0YvFfgK2otNOvAc8HloFXAI8HbhzJ6k4yNCPGTcN8LECd+B0SstBaSEUsVGT6kFZIVMUiFbGwyU2ZayzKISZZiTUWTiskxBxrcF+IeBO8lYKkOotm10ZMuJ/daROnBT5vQtc2+Lw8ijk3oK4nezS6DQ/ZiEKjY5GbNm+FeFoyXXcqxMFSe8nrYxE2bmoaU74eaLrmdITYynvw2d+Eh1SYGi//ApyZwkhY2vDY1yqiZbfNrKD9o41SJtcGdFK2AoqTLrHoNuITi+YiHPmBuh0xjRSKiTn1MytNJVt/VhWLXk85iHab6vtIImQ9RWFKLJ4JvBXo/+VxUEp5C/AVIcTfAr8GvGQE6zupoF915nMWzb6tbUEUBhu8fhJ3rtK3VLZw3/J9QHqdhRo31SoWAX9YkXkhKYhFrdNFxGmFaCOn7Sw0FgNjqmGtkIiE0wzcN5WI1V33toltgc+bNEg4dX6HVjurRsSiY/Xo2pKKYWQ6eEPIHJ2K39bb2woJqQCkuVLOGjqxMKk+ZCkezRfh2j+L95pcXlV57DYg1ahm0mmGtMLRtJ+Fbo6V5Ht43OvUV1LYHfezTKOx+Orb4Kv9n+NT3gjXvCn5e51iMCUWu4D7pJS2EKIF6GeWT6KCyU556FedHeluLLPl2YGPgX8qBLwn/rTEom4k3gyfCvG0IWLoGwAa1jKVvjnWVHGK6pD+rodkWVloLNZWLPT3cuDVWPhaIbposRO/YmH3JG2rx6Rma75t0oRYBCecHmscA5SAcxe7hh6/0Vlr5x1n3LQRMBWyWltAdvqOpsUiohIiaI0z4jlq6C62E9HfP7A5nC+LE+6YZLeZnFjEbcOsWUfKz0L3fNiIyPSznghvPtYfO+0Mf34Yxs6biWFKLI4AzpnmQVT74yv9/5+b8ZpOStg9ScdS5XwhoGm7G6J+ktav0ptdGyllpiOna1ohQyoWi74gsqSiwbZlY+e0iRADbYIeRKa3QpJqLNyMjuBk08F9UQmnmvtmklbIYGMvmLRCwsWbkGzkdCBgTTgV4qxfJyPdJfd3MjczE2j2BaRrhXTqcNcX1aaUK8DlKburl/4sbLtAVS52DUkWBZ9nwQZtIsVJJeCE/kaWUOeR1sci7WexWSLTc3nIpTj+2HkzMUyJxc0o4eZngQ8Bvy+EOAuwgJcC/zSKxZ1MaPqSTYPsvAEK+RylfI6O3UNKFRrmBJQBnGieSLWOmt/HIuAPe77qzQvRkTThtN62Y7VBIDwvJLmPhWOQFS3enBlhdLoiCLZnY9d/vjqGVSyStKUGI7cxWiFBfhq6qVdn2SWMoQFk4J3EiFuCby7Bx1+hbk/tTE8s9j1VfZmitAkqFlm1Y572Vnjcryiit/2idOtI8ll4WiHxx7U3DcbOm4lhSizeCpzev/0OlJDzBmASRSrekP3STi7oPXL/qKnfZKpSVMQCss8LMalY+FshvZ4kF2SnHXMiI1c0nwiB8IRT/xr0oLRhawCfj0Vx7UY4VXaJVa0dTiySaCzq/VFPx/lzvjxPMWRcT08VzYpYuJHpMQyyymsJznRxmrzIY0ubXN39PEMDyCBdxWIjY9Mh/SSCjsPfh2++R1ULdl0OV77YcA0Zld7nz1RfSZH2s+imrFi0VuDgv6tjFyfgvKfHf48soP88xs6bsWDqvHkvcG//dhcVSvZbI1zXSYdWx90Y1aipSxD8xGKilGelf6Xc7NpsmciQWLQsRCVaY1HM55iuFFhtWfQkrLS6zPXJRlJiUWtbCSoWweOmpXyJcr5M225jSSWCDSJIfiTysQgYN3WQiFi0LW8bZDK4DQJ+T5Ng8eZgnaYVi47tCUAr5UqBvwM6dD+NRscaELm58hwnWieYarlT5aEBZJBOY+F3W0wzFZEEWRKbhfvg+x9Rty+8Pgax2CSl97SfhZVSY7F0AD78QnV7+0XxicXC/bB6RH0fM7uVR0oSeIy6xhWLOIhpjzdGGPytkKDIdP3xwesyzgsxaYWAt2pxQmuHeAyy4ggn2xa5GK6bAOUQgyzw6ixMHUCDnDeDNBbThq2QlQRJl82u7SEWYRMhMJpWiCI2mjlXZW5otaeQz1Hqa396/fYcuL+3k+6vU/hECKSrWBRKSlsBalwzjeguCbLMhUg6lbFZiMXPvA1+6y5400G49Ofiv76bciokbQjZrf8AH/gZeO+T4TsfiP96B2PxZmJEEgshxLQQ4plCiOuEEFP9+/YLIf6XEOJ2IcRXhBAvWJ+lbm74WyH6prS2FTK6vJA1IWQhV/ozE8FeDolbIR2vxiKueLPtIxaJNtVBxSJaY1EtFQYXw42Ojd1zr8hTt0LaFjltIiRMuAlrCaYfScSb/pySYW0QB97odK+td1XbJyIDyNKOOabNiBi8tgF/+0T4++vgE79k9posoroHx08onsxKQBo3st6P6mkwvUMZi8W1ZgefeDOBxkL/3UmbFZJm7DnrYLxTCKGtECHE+cBNwG6U0+YRIcRzgC/0/38fcAnwMSHEM6WUN63Dejctmgaumw78XhZ7MiIWUkpqbYvJIc6b4BXs1TVSlFy86dVYxG6FWD3PY8naAGudN4PGTXM5wVSpMLDzrrUsZvv5KWmDyJodG5EfPhECZuOmDsynQqxYo6YOqqUCS/0JoUbHZqv2Wp1YRLZCfvV7qmrySVtIAAAgAElEQVTRqSWz5S5NgvOZp5mKaC7C0dvUbdN1ZDkVktTPw7lSL1TcvI4keMe5anMtTcKvfd9s3DZLpCUWaSs3WRm1eSoW41ZIHERpLP4IaAHPAFZR7pufAr4LPFdK2RJCTKImRd6IIiGnLJoGOSEOvFeqPWZK8xREAUta1Lt12nabclxLZNSGICVDxZvOGoPWnlQ4udrqeCsWBq2QiZCpkKB1mKDeXquxCHLeBDVy6hCL1XZ3QCz8UyGm3/9gDZ04rZBw501IOhVie1ohwyZCHEwEkBzns6i23YpOLqoVki/0XRNjOjU6yGpzj+thAeoqfcs5ajOe3Z382JA8gOy5fwPPf1+yKoGObkMRk9byxox7XvVyOPvJajPedXn81/uJRc82cy91kGY6ScdGC4pPYkQRi58C3iil/DKAEOINwO3AL0spWwBSyoYQ4q+Avx35Sjc5/HbenopFyVex8LVChBBsqWzhWFOZIS22FhNmVDgGUdHiTfC6LdY1YlHOlynlSnR6HSxp0bJboToNHY80TiBy6n2KomoktgwTb0IyjUW9bYHouuvIFUMJ2nSlwOE+D9J1FuV8eWCl7RA9Uytt6AeQJRFvZjgVEsfDwkE1yMuiElCxmPH+LmeKrEY+47puAuy/Vn1lgaQtoUIC+2s/rI6rTxH5ZOLJbkuRM2cqIy7R2n6B+kqKXE6RC2cz7zbM8l4ceCoWKYzaPASnFf68MdYgihrvpD8J0odz25+OcxgIvyw7RaCXsieKhVAfC4CKL+EU8EyGnGgl87JQV+A2ou9+mRO50I3VW7EIj+w23dAe6ZMigGreLOskbNw06RoaHXuNviKs2hBl6+2pWsR036x3bCM7b1hrTCWl9DyetB2UpBUSVLFwKm1eYjFCR81iRip8T07IOrcBwEss1jtds+sjNUkma277KLzzQvhvV8G//kl2a4uDUgq9TVqtj4Pqdvid++H3jsFv3Zn8fU5BRBGLHKBfRjm3pe95/v+fkmh5Kha5aI2FvqFqQWQOFprJdBY1f2R6YTJ0Y/WK9dK3IY63XGIxUzDjmWEGWUnXUO9YQ5NNHXiCyHy23nqFKa6AU0WWm4k3i/kcxbz6+ejTGA6SCljjmGM5qAaYZAVNhYQGkHVbcOQ2NerXSKgTysogSj9+0rZMGmxkGFvaADLIdkImKcra71kr5nRW0laUH7mcqngVyus7+vwTgGE+FruFEPv6t/PaffrZdk+cAwoh9gJ/CTwdJQK9Cfh1KeWBmO/zRuBPga9LKZ8Y57WjQNNTsciztGo4btoNIBYJBZw1g5yQwWPl8P5+kg1tse0Si7mSGbEoZy3ebNuI4nB9BUSPnKYRcNZbXY/GIapiAep3oWv3PU06tqeKk2gqpG2Ry7vHN50KmYhshWg+FmEGWSfugff0/wy3XQi/8s3g50Vh1xUge2pjS1Np2EwVizhXzI0FWH5IkYPJrbDt/JTHTjgRkeWETFJozq+0Yoqo9fPFRofhnaIYRiw+HnDfp3z/FxhWLfpiz38B2igrcAn8MfCvQojLpJRG9LhPdn4PODbsuesFvRVSKHSxeurkXMlXqPiU0RNBrZDMiEV0sqn72NrxQgdJNvXV7vHB7S0Vw4pFYW3lxkFcjUXH6tGxe+QrZhUL41ZITGKx3F0ZtKKKYmKo1mSyVBiYpTW6Nvo26P8M7J5NfoiIrd6xEUV3Y901NTy4DIIrFgPxpmcqxCDZNOlV4tN+P9nr/IgbmQ6q9XLn59TGLHLmplZBSLq5/+hT8NnfULevfAlc/1fxj502gMz/uiSVow+/EI7eriZCfu6DyQScekR53OmszRSGd4oiili8fATHexWwD9gvpbwHQAjxA+Bu4DXAOw3f52+B/wnsx9yWfKTQS/m6j8BMee0VXiXAvyATYuFrhUSJLvWKhX/UMYlwsma7xGL7xPBRU/C1QqzwqonJ1XrT0MNi8JihSVbcVsiSpo+ZLg7f1KK0Lvlcnqni1OBnUOvW1rTV/Kh3uuRK7u/PnimzgqJu6+0QzaBx01AfC8/JfJ3L/34kmQrp1OETr+y/Zks6YnH1K+Ccn1Zkay6GtXYWGpMs9AWeikWCVkjtGKweVrdlwk75aftVfkxlNv73kWUravmgasV0m7Dj4pM7+2QdEbopSyk/OILjXQ980yEV/ePcL4T4OvBcDIiFEOLngSuB/4iKbN8U8GzOOZdY+Nsg4NNYbFTFIqD07SBJxaLZWxgodkwnWqKmQuIGkdUGceHRdt6Dx8pmtt5xKxarXc3KvTR8UwsSTXrWWZoeEIvVzupQYrHcWUJU1fczWZga+nwHQePHc5U5kJKq7rwZWrHQfk/WW1fgR1Mjg6bEIkvXy8t+NtnrsvBNyFxjkTbdNOFG/Ky3J3udbcGWfYpgdZvJj+/gg9fDQn9u4fW3wGnjMG8TrPfV/sXApwPuvx0Y+tcohJhH6TN+R0q5EMdfYNTQdQo94bL8oBP7RICVtU4skk6FrAkgi9JYRGxoSaLT27gb6umG5feJDKdCHDtvT7JpQACZAz06PSqILC6xqFmLg7+q+fLwjAITW+/DdXX1Z/I51Kwjg9s7Jk6PeKZ/HWvHj6eL01S7eXKyXw2amEAUgwPVNlX5OYnGojjBoKtrteJ7J2SBLESTSc25dKT1b/BYeq/zFX6+AL/yrezer5jRCPQphijnzbcA/11K6R8vDXu+AN4MvE9KeSTkaVuAxYD7FwCTM8A7gLuAvzdZ03pCF2/aQstqCKpYBFwdZjIV4hdvRlYsIlohWunRZDPryR6WpufdO2NGLLL0sRjYeQ/JCXEwKo1Fw14Y/FVtrQwnFhO6SdYQ902Tn0Wz98jg9ulT5v4DQS0ZIQQ7e9OAIrpiOqISkUX5+ejtcM9NyuBo52Vw0fXJ3ucZfwwrhxTB2H6h2WuE6Hsn9DePuN4JWcCzoSesWOhEoJiQWKS1VveEkG2AQVeWyOJncgoiqmLx+yj7biNigSqE/z7KiTOMWCSGEOJJwEuAK6V/4D/6da8GXg1wxhlnZL2sAfSKhUV0xSLIyjqLhNNay9cKiahYBBkiOYhbLVhoLYBQ33/PmmTLpNkJLWrcNO5ExCCALAONRZqE05Z0nx81aurAmywabZJl8jm0hEss9k7vHfp8B2GGaTt7UzjEojcVUVrPQmNx6Fb40lvU7St+MTmx2H2l+oqLkkYsOhtALDytkIRXx5f+LJz3DEX0krYB0oZvWVrv7GTXJIyj0xMhilgI4C1CaGeqaJj0JRYJrkyEVTJ0vBf4H8BBIYRz5i8A+f7/m1LKtv9FUsr3Ae8DuPrqq0fmuaFfbXake5L1u25CcPiUbmS00FqIbSUNjvOkWcUiqrevVwtMEk6P1F0eKa1Zj0dEFKKyQuJmlrgVi/QaizQVi7ZGLHZUh0/HRGldID7Js8TxwVz4mbPmk+BhLqCn2e6J1apGuDhmUbHQX5cgWTY1sih7d+rw4RepNkT1NHj+e2IcPwPxZqEMU9uB7cleD97PIUlbSF97UnKzdADu/Rc1ajq7Fy7ZoKzL4rhikQRRO8ABVMhYHBxAjZKG4XaUzsKPi4AfDXnvC/tfrw14bBH4DeBdBmscCRraFXe7F90KCWoBTBYnB1bS3V6XWrcWuTEGYdUv3oyqWERMhcQtvx+uHR7clt1ZT5slCuWCW7HoWD16PUkup8hUtVhFIJBIGlYDq2dRyIW/r7MpC0ONxaicNy3hPn9XdfjJPWj0WEecyk3H6iELrj7nrJkYFQt9KkQjOFstl1h0J0P0FeDr7SclFtrPq22m7ckUWWSVtGtw4BvqtgGx9B5/k4ReOZbajqeI1TLXa/RsLUBNQD6hTfnR2+Ezv6Zun/cMc2Kx9BA8cLMiqbN74fQrkh3fwWaJsj/JEDUVctYIjvdPwJ8LIfZJKe8DEEKcRT+XZMhrrwm4710o4643APcEPL5u0H0Ymna4nTeEbyZbKls4VDsEqKpFXGKxxnkzqmKhl+B94sW41YJDNbdiIew5SgWzECUhBOVCbuA42bZ6g88mJ3JMFacGFZN6tx454TAQYBpqLCLFmymcN3u51UHp7vSZ4WO3JuJNB8NIXqNjeUZN98YgFhPFYKI533XJRGsigjBm0QrRR7NjJOtmhixCp9KIJ9PYWGeNNx1MJl7VCVFxIrljZVLnzYdvhU/1rz0vuA5u/J/Jju8gbVvoFEXKGL3Y+DvgAeDTQojnCiGuR02JPIRqdQAghDhTCGH1BaQASCm/4v8CloDl/v8Prut34kOj625OTdvdAIKnQoLLzlsrbsZGEp1FveMVb0b5WHgqFimdNw+uuBWLoozndFgJcCENWsewq/VGQLJpUGT64L0rZq2QlfYKPelt04Sh15OQd9e5Z3o4sZiIENFCvOrRYrPp2olLwa6qmYgWvBULvSUz09HWV4nYJAol5Tkg8slbITohSVqxOPJD+NO98K5L4WMvi/fazKcyYn4OWVQsasdg5WE1cttb+/tkjKQTMbq+Is2oZ1LnTY+ddwYaGQ/ZGxMLU6zruKmUsi6E+GnUyOiHULqML6MsvfVLFIGqRKw38UmMZsfdfOqWu7mYtkKg7xvQx1Ir3pUy9MWbRbNWiOdK2ZcVous9HmkOl9gcqR8d3C4LQ6fDPirFHMv9c+iayZDSFI4OdljlpD7wsTCdCvFWLHRNSyFXYLo4zWp3FYk08o8AWGzWB8eXMs8Wg5yKKIMsiEfyDiwfQgglI8r15ijFKEOHVU5m2i6ZqJUjJErX/5X6kjK5KVIWrZDmgtJntFdUKTwOsrCyTmOpXaio9klhQpGMXi9+hPoX3wQ/7Bsmv+Dv4LKfi/f6tLAy0FdAcmKRxbitjnHFIhHW3bWynwnywiHPeQADMaiU8qnZrCo99E2h1k3WCqlq4q26Ff+KabVtQdmsFVIu5MgJFX7VsXt07R7FvDqJnTZxGuV8mbbdZqWzwkpnJfLq/1jDdVafzMcjFkFmYQ7itQHiOW+WC3lK+Vz/e5e0rZ43p6M8M2jDLLeXjYjFwVWXYAl7mpwYvinEaYUMq9o8uPLQ4HZJDp9I8a5Dq0xoRHP64su54wn3M9XOseVig361ECnK3xlULNLkhOy6QkWOFychIu4+EmmcL4WA/5Syo5tFVkgaVLfDa29OHzPusfSO0QrRf2+ycIAtboLclJMQm8IO+2SHlNJDEKIi0yE4hAx83g0Jesz1GCFkQggmS4WBvqDRsZmdyA0e2z21m/uW7wPg0OohZraGE4sTbbeqMVUwi0x3EBmdrokvh02n1J3I+LxbsakWok+s05UCJ+rq81ppdT1rmSvPDfQuS+0lzmD4qPLhVZdg5Xvhn5eOsLbYYI0xyNWhVbcbWBHxhIOetFuNJD/2hjfADW+I9V6JUfJVLKSMT1I8xCJmsuk1b4r3/CBkfcWc5vhpIttP3KvaKt067Lwcpgx/nwol2Hlp8uM6KE0zMCzr1JSjZt5gu8o6WbY8pfQexYmT35NjHXHStBo2M9pWj16/+lvKw4o2SWA6bgo+0aRhRocDuyfVFa+hpTcE2zg72DPtjioerIXLV6SULLbdnJDZUrwrPW/CabqKhSi4z9la2To0sMsj4MwgL+Rw3SUWBcystIdZeseZCjnccC1nqrl444aTPqOuGFYx2SFf0DZDmUzncLImm2YF/ao6zfG/+Cb4wM+oQLGD306/rrjI5ZJVLbJ2gH3c6+BND8Fv3wVP+U/p3+8UwZhYZAC9hF8udwdiv2qxSjG/dkRPH7Ns98cswVuxqMeco68NDKLMKhbgFXDWff393Zpr46HVQ6HvsdpdpdtTZEb2SszEvEqrFMJNsuJMp9TaFjkt1fP0qeF21qZBZKZeFsfqbuWmLMyulidjjJsOI1dHmy6xmCmY5bU4KBVyFPqjvlZP0rHNBKsD/PiLcN9X4OAt6USDaQWcDT2ALF5bLhNsdCsiq+NnMSGTFuUEOgv9+9/ozJpTGMatECFEFXgl8GRgK/BqKeXdQogbge9JKe8c0Ro3PfQNoVJu4XQXg6oVALmcoFLMDUr/LctmslTwaCzitkLqQcRiSMXCO3Lqq1hMmVUsjmlX6bI7w9RUhNdBAPT2QztFXkijYyGKbmXBJAhtuuyu1T9ymiSI7JGmW7mp5MyIhT7mmbYVcqLlTudsKccjFqBIjhPh3uzYlAuGkwFSwv/+j8r3AODNx4GEUwWXvNC10y5EGHKFYaMrFp4ef4KN/aF/h9UjairknGv6Zlcx4CEWKVohSW29e7b6PQi4oIqNyiw4f3rGxCLl5z9GJjAiFkKIvcBXgD3AnSjjLOeMdw3wNOCXRrC+kwJ6CbusE4sIwd9EMT8gFs2OIhZZVCwwNMiC8BFD8LVCViOIRdMlFj1rxlMFMUGUrXc8jYXtrVhUh1cspgxHTk1NshZaLrGYzJttah7xZnftVMhkcZKcyNGTPRpWg26vSzEXfNJe7Lp+IlvL5qOm7loKA2JR79jMme5L3aZLKvLldJvKtW9L/lpIRywOfx/u/pIiNqc/Ci58Tvzjp22F3PRWePBmdfuln4lPLPRk10pMjYmOpBWLOz8HH32xGju++HnwovcnX4M+GbJRrZAxEsF0F/gLlKPm+cAhoKM99lVURsgpC/1Ks1h01dBBo6YOJop5FlGbmVPxiGulrcMp5ceqWER4KHhaIbXwVog+ESKtGQ9ZMYGpj0XcisUug4RVvRWyEpUXYjj6u9RxXS+nC2Zl+GFTIY5RmKOvqHVqzFfWbpjL7WU6PbWpyV6RLeX4bYDJcvToayg2U/k5SWS6g0O3wr/8kbr9qBcnIxaXvFAFn3VqipzERZrQK9vSrtiF12QqLpJ6ejiR6dLGLOUhAmc9UdmiV2bMp3SyFm+OkQimxOLpqNbHg0II/85xCDCPUfwJhL4hForuySCqYlEprR2z1DUFcSsWqhXS8xCLSj56jrwasanpFYtDtUPYPTtQDPlIw9UVyAQVi6jo9Dgai3rbJjftbiomFQs9L8Qv3tS1DaatkOWO29+fLZlt7GEZHZ51lqYHxGK1sxpILPR2Va+zhWolftXAE0TWjqGT6GQ84pcGejLwZExylYWPxc5L1FdSpAm90tsFlZn4Hhg6kn4WljZmmsbHApJN6ey6QlXNOqsbo7EZAzAnFiUg7JJxFohxefOTB31DyBXMiIV3MkRtqGnGTWttC4Rbzp8oTAydipgIGTEEJTydL8+z2F6k2+vySPORQN3C0Ybr3dDrzhgHkDmIik6PX7GIK94MzwvxaCwMWyE1y93U5kpmY7dR8fUOZkozHEJVjcI+B71dJbtbPKTRFMMmVEKRhZ13VkjTCskiKyQt0gSRtTJqg0Dyz0L3r9iIZNNn//n6H3OMNTDdBX6AMrX6YsBj1wK3ZLaikxD6SVjk3T9CY2LR31A9BllxNRYtcztvB9VStHBwz/QeFtvqRH1w9WAgsVhTsTAMIHNQ1jUW/nHTmD4WpZitEG8QW7rodKtn0bBdAjJfid8KCatYmIyc6sSi190Su3IE/gpWnFZIhuXnOz4L935ZiSAveRHs/5l4r/+lm9RkSHNRmTXFgacNsUFZHWmcHrsNpSvorMb38PAj6WeRlfPmGCc1TM8+7wA+3rc8/kj/vouEEM9FTYpcP4K1nTTwXGnn6tCv6EdqLALGDNP4WNTaVizhJoSbIjnYM7WH247fBqhS+9VcveY5uuV3klZIpRDeCjGtWHSsHl1WKedUxaZarEYmmzoIS/SE+OOmKttFjQ33rCrTZTMznXIhhxBqsKJj97DsHoW8t4Rt8jnoOpheZ944YVaHSfUkEFlWLA5+G77TF/xtuyA+sZjdo76SoLgJciHSjHnuvBT+80GltUg7Ipr0s/BULMaGUqcqjM4+UspPCiF+GXgb8Ir+3f+Aao+8XkoZVMk4ZaBrLGSuYUQsKgEmWWnGTWt+100D1z2/KZIfu6dd6UzYZIinFWKla4W0E/pYNDu2V7hZ3TXI/YiCZ9zW9/3HJRbHtVFTaU17SFsUhBBMFvPU+8dvdG1mIoiFecUifitkMnHFQiM7aSsWGxmdnoV3w4dfBEsPKoL1gr+D086N9/os2jH5AuRTCDch+WfhqVgkGBfWceSHKvektQw7LoFHvzLd+42xbjAdN50FPoAKDns8sB04AXxDSrnOf/2bD/qmZOOWDU1bIU7Fo5KvUBAFLGnR6XXo2B3jIKk1xCJuxSJArKd7WQRNhtg9mxNNdxIi2VRIxLip70pdDwrTUe/EN8cCXyukvfbYzphnrVuLHPMEf0vInFiAms5xiEWzYzPjE16aVCx08absbE1YschCY5FyxG8jo9OzSDdduBcW7kuxhk0SelWZUyFuxUmYjjG6rFcs0lpgL9wLN/+lun3BdcOJRbsG3/grRU6rp8HlN6Y7/hiJMfTsI4QooEjE86WUnwFuGvmqTjLoG6KFezKMq7EQQlAtVQdXyPVuPRaxiGPnDb7xwgAPhWFeFgutBWyp1t6zJkEWUoo3va2Qcr5MKVei0+tgSYuW3QrUjqwZNTWMC/eIFX2kJidyzJRmBvqKlfYKWyfCBZneys1srI192IY+zH3T7tkcrrnmWL3ufLKKRTlhKyRLjUWaikW3qb4qs8livzc63RQ2T+jV2U+C3/hh/NdZGYo34yacNo7DV/s+KLN7x8RiAzF0HklKaQFHgRQ+vT/Z0MvGHS39Pcx5E8LHDD2TITF0Fn7xZhYVC93LIsh9UzfHkpba/CbTGGRZa9dgcrWuzLG0UVPTioUn0XMtsYrjvnmk7ppTye6sh7QNw7AWxLBWyNHGUSypXtezpkCWYotoASaLCVshE/Ow8zLYsg+m4zt+euBJOI1Zsbj7S/D2s+EPt8LHE5TNs2hDpCYWesUi5lTIsTvggZvhyG1eP4/1hJVhxSIusdhM00mnOEzPPh9GOWt+foRrOWnhjIuCl1hEaSz0aYjQ6PQYauxEFYshYr2d1Z3kRR5b2hxvHqdpNT0VA4+dd59YTMXc0EzSPU+0VLul1qmxfXKt0r/e9o2aGnhYwPBKwbbJbTyw8gAAB1YPsG9uX+h76RULac16NulhGOZlMYxceUZNO2oaJclUiE4KY/lYXPHz6isLeCoWMeKyQRs1lckmEoo+XUHcdNVeLz2xmN4Fp1+p1rL1nHiv/ebfwK3/oG4/+50bo0noZqix0NtiJsRibI61aWB69nkA+HkhxLeBTwOHcSTwfUgpU3i3ntxw2wg27b77oUB4NgQ/gjQW4HPfHOLdoCPJuOmwK+VCrsCu6q5BteLh2sOcM+ee7PSJkF6fWMQtwXvTTdcGX5kIF+sdb8XCZNQUhn//58+fz7ePqGTHuxbv4ql7nxr6XnrFotedjbWxp22FeMyxulvWvGeSdYSNvo4caVohujlWknHLfAGu+AXIlxQp6NlmUd0OrCaD02JhIlk75rynqa8k0DfftOOmSfG8v4Fr364+i0kzL5dQ6F4cJiRzXLHYNDD9q/nr/r+7gasCHpfAqUss+idhkXfLgNOl6UiDqlBikdB9M+1USFhPfff07sHGdXD1oIdYeK7SuzOUC7k1o5LD4B03XbsGE6LV6FjkCvE1FsO+//Pnzx/cvmvxrsj38n4Wc54qxDDoQWRB69DJlVO90eFUVUARi3xOeBJ0TTFs/HhdoJPxuOJN3Rwrruumg+f9TbLXwcZbm2eVEwJqZPXHn1eVG7sDV77E7HWlanbhX3psemtleAUpy+mkMVLBlFicPdJVnOQYtDI0cyz9KjMIQT4W4Bs5jaOxaFuQT+5jEdZT3zO1h2/xLWCtzsI7CTHrscg2xbCrZH1TDfs8Fps1REF99jkKnDZhliswWY6uFJgSCyklR+te8WYcjYM3On3tz2HfrNuCuWvhrjUtqe8d+5577NYuJkt5o3HbteuIHj9eF6SqWJzkyaZpoTtvZlGx+OiL1b8ip7JTEvxOpUK+qFpC3YbKHunUownDOIBs08DUx+LBUS/kZIazKYmcW7GYGRIAVAmw9AbvFXo9xshbrW0hpuKJN6sGFYuoyRA9gKxnTSdze/T09aOFi2EVi6MNdyJiIreVnDC7WveLFf3jrOfMnTMYOX1w5UFaVotKQO9+ub1My1Y/e2mXoVeJVbEY1gqZq8xxzuw53Lt8L5a0uO2R23jMrscA0LJa/PC4q963G2dRrcb/OYDXeTNWxeL2T6lNrTQF+66BaooS+MlMLNImm6ZFlhWLfEG1hOyOSq612htj0V2ZdadjWsvRxGKssdg0SHYGGsMDp4Qv8q5waWjFwkRjESPhtNa2EDPJx01DiYXmZeGvWPinQqrT8X+d9PHUWgCx0AlaUBsA4JGmWy2YKWwzPnYhn6NUyNGxevQktK2eh/BNFCY4Y/oMHlh5gJ7sce/SvVx82sVr3sc/agrxNA4mQWRX7biKe5fvBeCWo7cMiMVtx2+j21OOo3Z7G9KejjWREraOWOOmX/+v8PCt6vYrb0pHLEpVeOJvKIJRnoknoGxsJmKRsGJhdeD7/0tpCqw2PPm3zV+bZVYIqGqB3b9Y6TbMiMXSARWZXpxIPvarozwDq/0Lh9YyzEbkXXbGGovNAlODrPvxiTX9kFKGS+Z/wjGoWCQkFs2UGou2ZdOxelRE8nHT0FbIdLhJlicyvTvLVIINzWOrHUAsdGLz0MpDge9xou0KJ2dLO2Idf7KUp9MXjTY6todYgGqHOBqGuxbvCiQW/lHTUj5HMYbWxMSY6qodV/HRuz4KKGLh4Najtw5u2w3VsYzrJeKguhl8LISAp/1Bstd6KhYJNRbfeh8cvU2Nmz7pN2HH2p93KLIgFkj4zK+qm7kCPOm3zIhVr6d0CA4q4aPuxihOumSlUzfTrfz9sxW5APjV76oR5DTQv49hAk5PK2QDWlFjDGB6Bvoqa4nFVuAJQA34lywXdbJhYHCV04jFkFZI2FVqEltvZ705raYAACAASURBVDQwrnizUsgPcipa3R52T5LPeU9inorF6sFBu6BttwfeDlLmkHY1dT5FvWPT60ly2hrOnDlzcPvB1eCO3FLHJThby/GIRbVUYKmhrvjrbYstVa8h2fnz5/PPD/4zEK6zWGOOFZNgebQNAQJWgCt3XDm4/f1Hvk/X7lLMFz0kw26c1X+/hBULvTUUQPJCsVnU+Fm0Qu7+v3BP3wPwsp+LRyz2XA2v+GdFtIb8/YeiUFajslYLev3MD5NNsr3C4BRdmo43zRKGJLbells1Te1jAXDpi+DMxyuCMTNkjNxDcMcai42EqcbiZUH3CyHmUImnp7QbZzOgYhE1ago+jUVIK8S0YjG40o8ZQpbLCSaK+cHVaaNjeaLEQbmHVotV6t06TavJQmuBrRNbvdUKaxrIJbpSzvfX4HwGja7teR+dWBxYORD4HquWKyI9rRKPWAwr/+sCzh8v/jjwPdaYY8XwsIDhXh6gPEV2T+3mUO0QLbvFjxZ+xMVbL+Z7j7jCTadikcQcC3wVixCCE4jN0tvOgliksfWemIMzHpvsuDoqs1Dr67VaK2bEImvhJiT7LDyW3il9LAAe+xrz5+55jPLRaK/CaeelP/YYiRF/Jk2DlHIJlXz6lmyWc3KiOSKNhelUyGpLEYu4FQsYPgkghAjMDPHHpUN8DwsHUQLO7ZPbKefVCWqpvRTogNnouWvZORnP+XFYVPj5W7yTIVKu7QiuMceKSbAmDFpSoNohDm45egt3LtxJsx/6NFvcjrTUZhr3+A48LRlTgywpfb3tDbpS7LZcPUCumLwUnoWtd1rENYaCbIWbDpJ8FnoI2Xqnm15+gxoXvuFDcM5Pr++xx/AgFbHoowUkzCn+ycDgSjcXg1iEjZuW4o+bDkSPMS29we9dYD4Zogs3e12HWCTb0HRthl/AmRM59k7vHfw/qGrRkq6oc5ehnbeDYRWL06unD8jecnvZU6lx4Bk17c7GbkWYhn/pxOLWo7d62iB7Ji4a3K4mbIWUCzmcLlTH7tG11xqWrUG3oaYGQJXwsyjBf/Xt8KHnw39/Ohz4ptlrihV4ywl440Oqt590NDILW++0iGtl7WDHpSojY1jLwBRxP4ue7ZI7SOZ+OsZPBBKfBfrhZJcAfwDcntWCTjbYPTkQ/+XyMTQWIeXv6aLm22CssXAqFvGmQsBMwBmUGRJo552BaDBIwHnmzJncs3QPoHQWl267dPBY1+7SRV2tSSnYEyeJkeEjt0IIzp8/n1uPKZHkXYt3saPqbbccaeitkDnPz9YEJq0QgCu3uzoLXbQJsKPkEoskWhdwQvAKrPZ/Bo2OzezEkGuPUegrjvwA7u3LtlYPRz9XhxDKVKmSUN8A6aLT41qAhyGOYNHB6VfA625Of2wdns/CoBXi0VdU1t/3YoxNA6OKhRCiJ4Sw9S+gDdwCnAv8xigXuZmhtzHyBc3HophsKiRJVshqe20rxMTSG8yuloMqFrqdt9sKyYJYrF3DGdNnDG77KxZHG0dByP46ppmdiFd+NWlDnDfv9mv9OotAc6yYn4OJAyoogrW1okY5V7ur3HzI3Ujmc/sHt5NM5zgwGX31YBT6Cp2Uxw0iS4s06aJffCP80Xb4s7PdzI4kqCRohYwCelvL5OdgZayvAHjg6/Cxl6kK1tfemc17jjFymJ4B/5C1UyEt4EHgC1LKDfzt31joG0Gu0Bx8SEMNskpaqmfIuKlxK6RlATJRK8RkxFAXUH714FdpWS3fJETaVkh0xeKMGY1YrHqJxeG6e0Uru3OxhYsmJmFRDpxec6wS9MqxzLEgOr5dhxCCq3ZcNZhScSLrt1S2UOztBO4HkmssoP8zXFVXnkYmWaPQV6QxyUqLUgrxZqcGdhuabVW9SApPK2SDUkoBtl8AZ/6U+nmYpNZ6Asgy0lesHobb/1HdHjZC+8U3gd1VBPeJv5mucjVGKphOhfzBiNdx0sKTb6E7bw7RWJTyqp/dk9C1JV27RzGfS1SxqLctEBZCqJZMMVekmC8OeZWCyYjho3c8mp3VnRypH2GhtcAn7v5EoHgz6ZWyp2IRsJlFTYY8XHt4cLvXnY+tb9A39SBSA7B/i1sNuHvxbs9jXoI1B4jYUyFeW/PozfzKHVcOiMXgvu1X0ljSdTopKhaGbZkBRuEdoLdUTIlF7RisPKw2k8mtyX0c/AmncZCJjwXJxJujwE/9mvoyhV6xyMqlM47e5Lv/Exxx9xN+NZvjj5EIicWbQoiLhBAvFEJkpBQ6OeG5ys2ZZ4UIIQInQyr5Cnmh7m/bbbp2d+gaVhMEkDkwqVgU80VedvHLBv//wA8/4NnQZV+8mbS3r2+EQe6beivkwRWvl8XDdZ1YzMWumuiTLGEb6Xlzbivk/uX76WgCNf+oKcT3kTAVbwJcvePqNfddteMqz+uS/hxguGHZGlRm4MLrlQp/z9q1JYJesTBN+L3jn+B9T4F3Pwq+9PvJj60TgrjizawsvT2bqaHG4r6vwHc/DHd8VhGsjYCnFZJRxcL0s5DS+7sydt7cUJhqLP6bEOI92v9fAHwf+BjwIyHEo0e0vk0PVx/RQwr3D2vK4Bc7aDJECBG7HVJrWbE9LILWEDXq+MLzXsiWinLeO9o46t3QRyze3D65faAZWemssKSVhw+taidRay52qqffoCvwOcXJwWSKLW3uXbp38FiQnfdEzI09jq7h3LlzPQJfUFUM/XNLOvar1hLTy2LnpWq878X/CM/8k8TH9SBJK0R/XhpzpGJMwaJnDRnpTbZfBBc+Bx71i3D6o8xec8sH4dO/Av/nF5QuYSNgd6A/Gp6ZxsK0etNtutNJ+RIUSuHPHWPkMD0LXwt8Q/v/W4HPApcD/w6kuEQ4uTHYjHOtgYiwWqxSyA3fXHSTrFZIEJkRsWh3vRWLGMSiani1XClUeMlFa6OThSxBT5U9sxBv1gLEm0IIz8ip7sB5UCMWZbk1dqqnaRsiTGeRTcXC3Eo7n8vzqB3uZlMtVtk/vz+7ikUSL4uskYRYeOysU/TWt18IT/4dZSt++c/He20no7bQBc+CGz4Mz/1ruPh5Zq/x5IRkYOedBKc/Ct58DN6yCK/85+HPN4FpK2SjA+DG8MCUWOwCHgAQQuwBLgb+VEp5G/BuwLhiIYTYK4T4uBBiWQixIoT4pBDiDIPXnSmE+LQQ4kEhRFMIcVwI8VUhxLNMjz0KJAkgc5DVZEi9bYNI1gqZMLhid3DD/hvWOIrmerOA2syTaiymDMrvQToLKSUP9nM8ACZy5gFkDkx8PAD2z7s6C51Y+M2x/O9pAv/vQa8XLfzT/Swetf1R5HN5TwspKcED8wrWSJGoYqERi6R22gDb9sNP/64KQrsg5qllIze35gicN5cPwf/7G/jK2+A77zd/XS6XXcXCdPRWb4OMk003HKbEogE4P62nACvAd/r/rwFGtUchxCQqV+QC4KXAi4HzgH8VQgyj+FPAceD3gGcBrwRWgc/1WzMbAifyXOTNhZsOTGy9TbwslMYiWSukGkM4OFWa4hcv/EXPfY5wE7IaNw1eQ5DO4v7l+zneUn4a0i5Tzcdz3YThzqMOwioWfnMsIPZUSD4nPC2clhVN8J6z7zlsqWyhIAr8/AXqqlonAWlaISZTMiNHOeaYI/haIRs0DZCVeDMJsk42BVg+CP/3TfCVP1XCyI1AcUKFsYHScOiW4Tram8D9dYwBTHeCW4FfEUIcAH4F+JKUTkOLswFTF5tXAfuA/VLKewCEED8A7gZeA4QOKkspb0eRiQGEEJ9Dzdi9HPik4RoyhXNCjxNA5iBMge8JIjPSWHhbIRMxrHRNr9gd/MKFv8AHb/8gDUsJ2+xuemIxLDodgisWXzv0tcF9Vv1cpkrxleje79+8FeKEsXnMsRJWLJzXtLWU1ah2xrbJbXzpRV+iaTWZLatj6v4fSbNCnHU4MCIWd3wWDt2irhLP+Q/KqCkt0rZCNiqAakOJhdYmyKpiobeUTI26soYQqmrROOGuI2jiZLPk1YwBmBOL30WFjX0fWAJeqz32PJTOwgTXA990SAWAlPJ+IcTXgecSQSyCIKW0hBDLwAbVbINbIX5xXRgq2kk8jZdFvW1D3lWw6xWPYTC9YncwW57lhv038IHbPwBAt+VGKScOvzK4Sta9LByNxdcOusTCrl3A5JYkG7rZFfru6d2DMLaF1gJ3LtzJBVsu8FUs1Al9ohj/c5iuFFnsp6yuNLucNhVdSi7lS5TyrkBNr1gkTTdVr9U/D4M/q3u+BLf8vbpdmc2GWMydAde9SxGEqe1mr9EJyEb4F6zJTEmxubVrcPM7XbLw7L8YfuzmCDQWOkEzmU5ZOQyLD6iNf2pHdtbi5RmXWLRWgn8nxpHpmwqmPhbf7usgLgDullLqv2XvQ1UcTHAx8OmA+28HftbkDYQQOVQL5zTg1cD5QIxh62zhbEYihp23g4miW/4OTTg1MOmptS1yRffEsrNq3hKIPV4IvPby13KseYzVdp3P3/U4QFVf/JHr5muIX7GodWrccszNyrDq+6nuir+h699/1EaaEzmeuvepfO6+zwHw2fs+y67qroE5Vk6WoafIQJKNfW6yyIEFdXupOXzE2A+92pRGYxG7YuG5Ss+oUjAxD1e/PN5r9HC6NBWLTh0+8So1ESJyatrFBN0GAw/BwgTkkpM7AL72F+57DSMWnRr0zdIoTIxmIsOkcnTnZ+Hzv61uX/0KuO4vs1mHiYBzPGq6qWB8BpJS1lEW3gMIIbZKKT8X43hbgMWA+xcA05zjtwO/1b9dA26UUn45xhoyxYAQxAggcxDWCok7FbLa6pKbd4nFrqp5XoYu3mwaRmVPFid525PexrGVFp+7SX30afr6w5w3AbZWtjJZmKRhNah1a3zhgS9g9dRz7dYupDWTuAXhYNhGet2+6wbE4vP3f55n73v24LG8nMcRsSYjFm71YbkRj1h07Z6bVyOIPXKrY9KQaA2Q1YhlWrQyEm/mCvDj/iktZ2YyB6gN/bd+rIiJ7kCZBKUqiLwiC1YTrE70+OQohJvQ36AFIBXRsq3okLlR+FgAPOENqgVSnoH5s4Kf4/k9HGssNhpGxEII8SpgTkr5jv7/LwW+AOwSQnwXuE5KeSTqPTLEu4D/DewEXgJ8RAjxIinlZ4OeLIR4NaqywRlnDB0+iY1mUMXClFiEJZzGmAqRUlLv2JSKLl+LQyyqBs6TYcjsKtmgaiKE4MyZM7lj4Q4APnLHRwaPWTU1sZHER8NT+h8yXvm4XY9ja2UrJ1onON48zmfu/czgsZztntArMZ03AeYm3E1sqdmJeOZa6OuulguxR2516G2poNyWNciq/J8WWYk38yV3U+91lUW0iYttLmdme20CJ0yt2f+bbq9A4bTw549CuAnqeypPu/qKzqqqJoWhOwLnTYBLXzT8OadfAdf8rvo9MPX+GGNkML20eQOg0/B3orQWvw7MorJETLBIcGUirJKxBlLKg1LK70gpPyul/Dngm8CfRzz/fVLKq6WUV2/bFn8ccRiaQeOmhie2SoDzJng1FqtDnAdb3R52T5IruiXC02NEhw+LDY+Cx5QphWBwaoiPhQNdZ+GknYLSV0Ay/waTdFcHhVyBa8++dvD/T9z9icFtabkn9KStEAeL9XgVC110mubnAAnGTT1jnhtILCa3qE1P5NNpLITwuW/GNMnKCnFsvUdVsVizjiE6C0vPClnnyPRdl8NTfgee8UdwyYYNCY7Rh+lZ6EzgTgAhxCxq5PR5UsrPCyFOAH9q+D63o3QWflwE/MjwPfz4DorgbAgGFQutFeL3eghDWBneo7EYUrFYbXeBHqKQrBWSZrxQ10Mkdd0Es3FT8I6cOiiJKqvNvf33GZ1408F151zHh+/4MABN7URqd9wTcBKC461YxCMWHuFmipYUJPh9aGrXA1FXs3HxkRvh+F2qIvLKL8H8mdHPf0O/S5tFdHlxwiVM3Ub2m7UJ4mRklKfhguvU83ZckvE6ZpS5AAyfDNErFutNLMbYVDA9A+YAZ7z0iSiV0lf6/38IMJRu80/Anwsh9kkp7wMQQpwF/BTwRsP3GKAv5HwicO+w544KjRQGWWGbWhyNRb1tIwo1RE69frY8G8sgK84V+9pjZ+OdMOnz87B7MlAIqgs4HewsXc4J1OuTbOilQo5CTmD1JFZP0rF6lCI0ChdtuYh9s/u4b/k+z/3dtvszj+tjAX6NRbxWSFajppCggtUYEbFYOgAL/T/rOKOOaUkFeG29TfNCOg21zlJVRa/nkutcgHjE4vQr4MYR+UzEGf31hJBlqLEY46SD6W//3YCjVLsR+IaU0vmLOx0lvjTB36EcPD8thHiuEOJ61JTIQ8B7nSf1XTYtIcRbtPv+QAjxbiHEDUKIpwghbkCNwD6GDbQUbw00FvENsrziTb2cba6xqLUshKavOL0ab8Rr0iCELPTYGbk95nLCq/UIIThBxGJr7jJtDcnITRxyJYTgun3Xrbm/03ZPwGlbIXErFvWMRk3BfEoGUPoDp1UnclDO0Ep6Q6PTtVaIaV7IPV+Cv9gPf7oHPvri9GuIQyxGiVitkBFVLO74DLzvGnj3lXDTW7N73zFGBtPd4M+BDwkhXorSSOijodcAPzB5EyllXQjx08BfAh9CSY6/DPy6lFK/NBdAHi/xuRXV8rgRpes4gvLVeJKUcoNSd7Rx0wQGWSatkGEai9V2l1zCNgj4YtM7yk46Zzg2ql8pp2mFgCImjhi03raYqawVzOkaCwczvUtQw0HJr9YnSwVWWmoTbXRs5oYUfJ6171m8+7vv9txn9103i3lBMR//atWjsYg5FeIXb6bBZDEG0Wz6RINpr9J16HqN9SYWniAywwkPXYuRxVSCqZX1qLHvqVDdplois7ujn6t/VllWLNqr8PCt6vbyweDnfPkP4chtSkD8U7+WjZ/KGIlh6mPxkb7r5mOBb0sp/017+CiqxWEEKeUB4IVDnvMAzuyee98/xTnOemEwzZFyKqQR5mNhULHQPSziCDdB2UlXijlaXdXpalnRro866hlVLEARk2Or7f77Bm9o8+V5povTrHbVRnPx1ovpLFQZEIukFYuYI5a7p3Zz5fYrufXYrYP7XHOsZGuYnUjRCulk93PwfhbDiIVWqMyyDQLxKhYrD8ND/642v5ndKu8jDUp6K8SwYpG1QdNmqVg84fXmz7XcWIFMKxYmJOvgt+H+/rZ0ZQYVozFSIY6Pxc3AzQH3n7LJpuCIN2VqjYXHxyKG82a9422FxDHH0tfR6qrNrN42JxZZtUL8r48aOd07s5cfnVA63yfteRL/70j6VM/YplDAc855zoBYTOQnWR2YYyVbw3yaVohHY5GuFRJLc1OegSf+phJwbiSxOPgd+NhL1e39z4b/+JHo5w9DUW+FmGosMiYWcVoQ3/sILNynqkb7r4Wt56Q/fhKMairEhGSNs0I2FYzPgv2QsFcCTwa2Aq+WUt4thLgR+J6U8s4RrXFTo9m1IddBCHXFX8lXPFbLUfC2IbRNOoaPRdqKBajNZKF/GBNb78HaPOOm2W1oUZMhj931WH504kcURIFnnvlMbvqGa6mdXGMR07sBePqZT+fdt76bxfYiF8xfxrEU5ljgFW8uxW2FeDQW6QhepZBHCDVc4YwyhzqqzuyCp43ouqIUg1joV7FZ2HmXkog3M042PeNxyhiqMgtnPCH6ubf/I9zdjynfes7GEYvqNpg9QxGMLEePTUZvx1khmwqmBll7UVMge1Bjp5fgJppeAzwN+KURrG/To9GxvfoKw2oF+AyytA29kq+QF3lsadO223TtLsUQk57VtoXQiUVM8SaYB3H5kZVBFpgFkQG87vLXcdbMWZw1cxbnzp9Lo/3w4LEsKhbNrtn3P1ue5f3PfD/fOfod9pYex7997XYg2UQIwEzFXftKqxu9ofvgqVikHDfN5QQTxfygctPs2qn1M4kQp2KRleumg0e9GM5+iiIYex5t9pqsA8jOuUZ9mcCvddkovOB9o3lfT8UipHrT3iRGbWMA5hWLvwDaqFyOQ4DeBP4qGziVsdFodqxE5lgQXoIXQlAtVlnpqD+iWrfGfD641FxvW+R0182peOJNtY5kkyH1jHwswNcKiSA3E4UJXnDeCwKfm7RaENttso9z58/l3Plz+eZ9J1KvoZDPMV0psNqykFIFkc1XzSpf9QwrFs57OL8Hjba18cSiM8TW3uO6mUEZfN9T4r8m61ZIHLRGaJB1+Afw3Q+rz3jnpfD4X872/U2gt9kax4O9SsYVi00FUxn304Hfl1I+yCBpZ4BDwBC58E8mej1lp+1JNo3R34vq7evvE6WzWGgsI/JKNFUQJebL8XvdSb0sshRvVg3dN/1oZFA1CascmUJ/zUSKjX1eb4fE0Fl4jMoqWRCL5G6smcFTsRiiMci6FZIEG2ltPsqKxdIB+Pf3wvc/4ooj1xuVGbcSZbWgftz7+Jpk2bHGYqNhSixKQFg9cpYNjC3fSAzsvLNohfgCwEx1FsfbrsZgtrg9UU5E0opFLSODLICpBCmrkE1cuImHRvQaNAFpwqkQ8HlZxJgMqbXcNU9nUF0wbo198z3w0ZfCZ35dTWVkiTjjpnrfPYtWSBJkrbEwhZSjrVjoRG0jx15n97q3lw94H+s2QPb9GwuV6KC0MdYFpj+BH6BGRL8Y8Ni1+FJPTxUMNsAEEyEQPhUCPvfNiFLwUufY4PbW8g7jY3vXkbBi0RlRK8SQWNg9ORiTFUIJD5NgIoWtuXpNNgZVs7qtdwwBZ5bTOeDTnER9Hg99C370KXX7zJ+CvY9JfewBznwivPhTiihMDTH2zboVkgRZaywaCyqCvLWiYtDDnDW7TbD7JDRfyt5KO07l6IefVHHxhQmlDzEJbzPF3F44pnRMLB2A3Vdp6xrrKzYbTM9C7wA+3r8adma5LhJCPBc1KXL9CNa26eGc0JNqLCZ8Vta6OZVpxWLFOkbf0Zptk8nSFatxvAs01DM0ZvJGp5utQa/yTBTzxsZeflQTEqvAdaQgFp7JkBgJp1lltjjw6l0ifhajygkBmN6hvkzgaYVk4P754y/Al/9IuW7ufzb8zH8Z/hrdwyErjcUP+yF3UY6merWmMpeNpbmOOGOvn/pld+T0Px/OmFho5nhLD3kfG+srNh1MDbI+KYT4ZeBtwCv6d/8Dqj3yeillUCXjJx7OBpi0FZLPCcqFHG1rrTmVx32zG14KrtuPDIjFzoTEIk50uI4sN7QkFYtGOxvR4oRn1DVJxUL30khOLDxeFnEqFnorJAONRZjV/BroBlmTGROLOMi6YtGpu1fHqw9HP9fBq77ctzivZ+9j0V6BXi/Y2XSUbRAwdwCVcrTppo96MZz7NNUS8QfSjfUVmw6m46azwAdQNtyPR4WOnUBlhqyz3+7mQVDFIo54E9RG5BCLRkcjFlpJrx7h/teS7kTC7gQeFs4aHCSdCkmbUaG/vmZYNchqQ9dJTRLxZiMj8ebcJmmFVE2rR6OsWMRB1uOmnth0Qx8LUFfoWW3u+YIq63dqgFSZLEHVmFGPmvrHfsPSY/WKTb6Urb07wK7L1FcQZvfC89+nPqON0tiM4cHQs5AQooAiEc+XUn4GuGnkqzpJUA9qhcSoWIC60nayITzum4YJp13hXjWeMczLP2INDkxbAb2e9E5kpBxznEpQscgqfCupj4eDZlYaCz3hNMZUSJZjvxBuNb8G+qY2KmIhpZoEiMqe2HmJ8pxor2azsXuyQmIQi6xRmXWvxlvLwcRi1BWLQhnyZbDb0LOUpqMUEKbjqVasc7Lp5Ba4/Ib1PeYYkRh6FpJSWkKIo8AGzZ1tXjibkMjFTzZ1EBZTbaqxsHMLg1CVs+f2xDq2gyQVC33DmSwl1zc4SNIKaWZUsYgS0Zogq8qJXrFYjDEVstrOthXi0ZyE/SzsrlsazzrZFMC24B3n9NscEt6yEK4feNH7sz22p2JhmBUyClRmYeWQuh2mb5jdC49/vSJ5Oy4ezTrK09DoVyTaqyHEQqtYFDNug4xx0sH0LPRhlLPm50e4lpMOacWbED6RYZIX0uy2EAXViZJScOZsfHMs/xpMr9iz9LAAv/Om2eaelfNn+oqFV0SaFPPV+K2QtmXT6bfSCn3NTloYTcmMMtkUVCvAaoPsH7/bWD/jqbgVC9uCQ7eo9ZWnYP6sbNZhYmW98xLY+SfZHC8MlRllTAWKTAaJanXiUxwSD5wGtgWrh2FqBxTMDOTGWH+Yno0fAH5eCPFt4NPAYXxGWVLKjC8bNj+Cxk3jaiy8QrmQVkjIuOkDS4fc/1izlBP+oSW5Yh/pJIJxxcJ9XpoN3Xi8MgTeikXyz0JPODU1yPJP5iTxMfHDaEpmPfQV5Sm3xN5eXT9iETcrpLkA73+Guj25FX7nvmzWsVkSTk0mQ3SR63SyC5yh+OD18MDNimy+9mblBApgdcYkY5PB9Cz41/1/dwNXBTwugVOQWPSnQlJpLILbECYai/uXDw5u53vJT+56VHbkeKGGeobmWP73MNZYZDTu6gkhS0Isuhm1QrSpENPo9Kz1FQCTJuOm60IspqH+iLrdrrnpRKOGJ93UoBUyKjtv04mMUePKF0P9WkUwZkIE4iuH3dszIyIWuYJbwVo64BKLv3+2ahltvxCuffvGhbCNMYDpmejska7iJMVAvJlw3BR8wsmuLoYcrrE4sOxeJRTl1ljH9ayhaNBT98EziZBBPoVpCJmORkb+EWkrFq1ONuvwaizMKharGY+agvf3IfTzWC9i4SBsY109At//36pcP7sXznt6+uPGrViMynWzYtAKWQ882iBfUq9YhJGPtJjT3DcdLwsp4dgdaiJk5dD657SMEQjTM1EdqEkpW0OfeQqh3rFAdBE5dXIv5ApMxFREe3MqtKtPg4rFoZr7xzwhkhML/WrfVLypVwsymUQo5skJ6EloWz0ses1xCwAAIABJREFUu0chH923b3jIzcZNhTS0RNSsnDdNE06zHjVV72NQPdpxETznvyqCodstZ4mSAbFYuB9u6mcg7nlMNsRC/xu2mtCzlaNkGLJ23XRgkur5lT+DE/eoiZBH/xJs25/d8eNgRW+FjIhYeGy9+8Ri+aAiFaC0PlPJ3IfHyBahZyIhRB54M/BrwAxgCyE+A7xSSrkU9rpTCbW2vaYNErfHHdYK0adCwjQWR+pu+bFa2BbruDomTHrqPng8LDLY0IQQVEuFwYRDvWMzOzGEWGTkH5GEWIWuI4XWw59wutrqetw4gzCKVoj+WfozbAaYOwOuelkmxwtfiDY+2Qw55YwigCyXU2V/573bK9FVmVEFkO1/lrr6r8y5ZX8/7vwMHLlN3b5sA0cut12gDKxWHoYtIypwz2nGWEv9vJBjd7j3bb8oe+fRMRIh6kz0WuAtwFeAbwP7gOcDK8DLR76ykwD1tpWqDQLh46Ym6aaPNI+4z88PyVOIQDVBVoZXvJleYwFK6zEgFm3LcwUfBJ0EpalYlAs5hOiHJBpWS3Q0M2qFgNJZOO2NpcZwYqGPmmaRbAq+ULYYgXCZY1KrwjVOBD/HY46VoQjjhf9DjU1OzA93cxxVxWLP1eorCrq99agqRyZ4zKvU1ygxF1CxeEQnFheO9vhjGCPqTPQq4O+klK9x7hBCvAb4b0KI10gpzQftf0JRa1uphJsAk8XgiQwTH4sFLdl0vpycWISRmyh4N/WsSvAFQM3Dm2xo9Yz8I5xqiUOWGl2bmRjEIqupEIC5iRIPoX6nFhsdziJ6o9LtvKcy+jkk+X0YCSa3uLd1+3Ad7YxdNx2c/wzz525UsmlrxTXIypehmrxqGYm7/i985/3qeBc8G57w+tEcZxh04hRYsRgTi82CqLPnPuBjvvv+DyqZ4sy1Tz/1UG9bkHdlJ9MJrpjCWiGVfIW8UI+17TZd2yvm68key93jg/9vqyTvLfq9NKSUEc9WqGUYQOYgroCzmeGG7vkMYuaFZGXUBb7odIOR0/pIKhbpWkOZwVOxMCAWWbVC4mJUFYthWNaqFXN7s/cScbDyMNz1RTjwDTj+49EcwwTTO9VkCKgKVqcOx37kPj4mFpsGUb+JU6i2hw4nF2Sc9EL2rZCmJgIUQkRWLY43j9NDPb9nTTJbSX6lVMznKPWNlXqSQRR5FEbR29c3NJMwsFFllcRJOLXsHh3bjW5Pa1Cltz6WDSZDVkcxbmpSsfjH18F7nwIfej4c/kEmx127EINWiCeAbIOIha7/WM/Y9vVqg+jf07CE01Eil4dZzV146QA8ohGdbWNisVkw7Ey0WwixT/t/Xrvfo6aSUmbkCnPyoN6xEQV3HC1RKyTiJD5TmmGlo/6QF9oLzGkhQw9rEyGyO5/6anW6XOCEpbpbq+3uUK1A1s6b/vcxqljo/hEp1zCZ8Crd42FRzKc2qPIGkQ3vNnpaISPwsQglWcd+BIe/p25f83uZHHcN9l8Lr79FtUTCArayDiDzo2erjIxCOfw5ozKHWj4EH32JGjWd3AKv/Gff476Kxajg8dMIyJw8fjd8+7+r733nJUrEOSrM7oXFB9Tt+7+mcmRATYNUk0/GjZEthp2JPh5y/6cC7stGwXcSod62EOWUGosIYnH61OkcrCkTrIdrD7Nv1uV4zv0Ave480yk3lelKgRP1PrFoWWwfcuFVy9ggC7wiUCONxcgqFubEopnRZIoDvRVi4mUxilaIxw22a9PrybVZMB4fixGEX4ESTg7zyBhVK+QbfwX/9g61qT/1P8NT/7/w55ZnlI33ysPZejjkCnDoO+p287S1jy896N6ePSO74/rhj3D34+jt8K33qNv7nz1aYjHX/z5L0/DQN937x22QTYWoM9F48iMCTrpnOXUrxP0RtHyjfbun3LTSg6sHPY/p/5fdLamrBvqmpF8Fh2EkrRCP4+PwNWQV/gV+t0nzVkiWawCvl4VJwmnW1uoA+ZygUszR6vaQElqWvVbDsh7JpibwtEKybEMI15RKJ1FBePpb1ZeUIIe3EY3ht/T2R5brrZC5URILX3S6H7qHxahcNx087Q/gGX+sfue+9V744ScBqUZNx9g0CD0TSSk/uJ4LOdkwSDbVxJtxA8jAe3Xov1LeM+32Ew/VDnke0//f62xJvalMl90NbdWIWGQnnHTgjU4fXjXIMrbdyG0ycA3ZVU0A5jWNhUkrZBQaC1A/01ZXHb/R8REL24K24wQpguO81wujaoXoZClsIsUPIUBkWLgtViBfArsDve7a+Ph1a4UMyQpZj5wQB1Pa9NvjXqvsxh/58cbpa8YIRHZnolMMg40v7bhpRAk+qmLhIRZZaCz0ikV7Y66UPZkdBq2QbCsWybwbsvSwgM0xFQLq81zo64UbbVtJuR209GrFXLQrZVp0m0q42TgB2y9Wqac69j0VprapDW96Z3bH9Yy6DqlYjBKVWTcvpbXsJRbrJt4c0grx5ITsXvv4KFGqwu4r1/eYYwzFmFgkxCAyPZc82RTCLb0humKhE41eN33FQt+UVkwqFp3sNRb6+5iINz3VgtTiTa+uwBRZt0LiaixGId4EH+Ht+n4W65ET4uBdl0H9mLr9m3es1TA85T+N5rieisVmIRY+8vS8v1U6i+WHRlspKE0BApDKZdRvcb6erZAxTgqMiUVCDALINqBi0bW7HG0ocywphZoKSbmpzFSSt0Ky2tC8rZDoNfR60kMA0lhpQ/xRVwdeO+/0n4MenW6ScDqKyhH4q0e+z2M9icXkVpdYNE6MLuDKD1Nisfgg3P3Pal1bz80+q6McEUR23ghFkjpyOaWzGFicr3oFu6vrkBOiY/WoGjVdehAuvH4cmb4JMSYWCRFILBL0+cKcNwG2VrYyUZigaTVZ7a6y3F5mtjzL4fphen2RmLRmQBZSl8E95lQxxZujGDcdJqBsWTaOj1elmBsa1jUMUZWjKDQzCiBzMB+zFTI6YhGhOVlvYuEgzMtiFNC/rzBzLlBTG5//bXX7wufADR/Odh1+AedGwUMsVlxiIeX6RKbr+LtrVJIpwMu/AGc8fpwRsskwIqu2n3yoE7pE5F3jqtlSfBGbxz65a3tcL4UQ3qpFf8TUP2oK6TcVXWOx2ore0GytWiBENhsq+J03o6sGWdpog69iEUu8OdqpkF4v3AVVSjmSdFPwVSz8REvfaCe2MFLoWoeNIhatJeiFTHvom+oortY9HhIbSCye9lZ4/nvhxo+sdUS1lQ0/5Zn1MQjTJ2A+cC3865+M/phjxMK4YpEQjY6NKKwOItNnSjNMJcgJKBVyFHICqyexe5KO3aNccDeo3VO7uWfpHgAOrR7i4q0Xe0dNO1vIifStgGmtFTJM31D35YSkNYUavFeMVkijne2GnjQfI2vxZiGfY7qsUl6lVPHpYUFkrW4Pu088ygXXPTULbM6Kha9y0FyEL/+h2symT1dTAlkhX1ReCZ1VNUKqX6XrGLW+oBLSCvGPno4al/1s8P0rmvZr1BMhDmb3Av/P/f/Wc9fnuGMYY92JhRBiL/CXwNNRiqCbgF+XUh4Y8rqrgVcDTwbOAI4DXwN+T0p5/0gXHYBa20IU3ROsXlmIi4lSfqBraHZsD7EIEnB6J0KUcDPt5j7lqVgMIRYjMMfyv9dQYpFxC0I/dhxL76wrFgCzk8XBGGlUwumo2iDg+1n4P4+Lnw87LlIb+9yIY4OiWiH1EyocC2D+7GyJBSjS1On7NjQXg4mFri8YxUSEpxWiTWR86nVw/7+pTfY/vBnOemL2xzbBqt4GWSf9i3+0dmyOtemwrsRCCDEJ/AsqwvKlgAT+GPhXIcRlUsrgGE+FG4GLgXcDtwO7gTcD3xFCXCGlfCjitZmj3rbIFd0rKJ0AxMWkRiwaHZu5SfexIAGndyIkvXATvK2QlSGtkFHoKyBeCFnWPhq68DKWpXfGLRlQXhYHF5V2J0pnURvRqCn4Pg9/W2pm1/qp/6MqFnprYBQBZBNzsNy/3mkuAGevfY6nFTKCz+SKX1QjtZU5L4lbfEBVC1YOKcvxjcL82cqZdOUQbLtgfY7pT3E97fz1Oe4YxljvisWrUKmp+6WU9wAIIX4A3A28BnhnxGv/TEr5iH6HEOLrwP39933LSFYcgnrbIlfSiMVUGmLhxoWvMcmaiq5YyM5WqtMZEIsYm7on2TSjzRTi5XVkmSgKySsWrQwnUxx4vCwiJkNGNWoK/s9jsySc+ioWow4g+9m/V7bak1vC49A9rZARXLFvv0B9+bGkFXhH6WExDNvOj7Y7HwX8uh7d22OMTYH1Fm9eD3zTIRUA/TbG14HnRr3QTyr69z0IPIKqXqwram3bU7FI1QqJcH3cPT1cvDmdwdXqdIxx01G1QmJVLDyOl+m//6RZIVk7b4JXwLkU4WUxKuEm+EneBl4RR4k3Rx1AtvUcmD9TaTiCWo29nrcVsF4aA7urHVd4Ez9HhW+9F/76sfDOi+Cb7xn98aJw4XUw1ffzeOqbNnYtYwRivSsWFwOfDrj/diBEHRQOIcSFwHbgjpTrio1620Jk2Apx4D+J6xWLh2sPs9xeZrlfApa9AtKaZkojBUkxHSMrZBQ5IaDGRnNCRbd3rB5du0cxH8x9s65YeDbSpD4WGREL44qF9nNIG0LnRyTRWk/hYBSx0CsWo2iFDEPjhLLaBtWqKE1GPz8rrBxyM0mmd0Ynr2aF1jI8cqe67fiKbBRKVXj9t2Hxfth52cauZYxArDex2AIEuc0sALHk5UKIAvAeVMXif6RfWjzUOt5WSFrxpoOGz/VxsjjJlsoWFloLdHtdvnfse4PH1KhpzpMKmhRTMcZNva6b2f0KCSGolguDikm9bYUKF7N2/ox0moxAc0QaCwfRGgv3saw1FvrnsUa8+f+zd+bxTZT5H/88OZs26d1CW+hBactVSilULuUWPKAIuCIrl7sKCx4rKyLLD3DlkOWSQ0RUECgi1yousCByuCKii6J4A62cpdCWlt5Xkvn9MZl0kqZt0maSlH7fr1dfTZ65nnky88x3vufaZD4EUxMATNov7RuzYApR+wJKqwe3OL20K8IcrRFHREidytpoAK5/wwt0+pr6RC4zgzRUiMzVePkCYUnu7gVRB8053PQNAH0APMRxXJ2p8RhjT4OPJkFkpPMqABZXVoAp+ImNgSFc23j7ar2hfeC1FvkVvBDzVXZNqWCumn+bc4bWQGuVx8Fg5OpMOmXhY+HkN2WtSLAoqUewKHdyxktnaCxcbgqpcI0ppNY1WZbPO06WF9R+2Dsbv0jg/3JtZ1eU2sfCaKw5T5mydjRCsYsSQ2UeB/ZM4YW59kOAzqNrlklZfEyMRQZQkUC3dQS/TBfGVx1VermmP4RH42ofiwLY1kzUpcmwCWNsKXhh4UmO447Uty7HcW9zHNeD47geISEh9a3qEEVVuWCMzyHgrwqGSt74tLINOS2KtSFf3/za/NlYJSTHaropRCZjdvs43LhTk23UX9P0Y4uxzGVR9wO+1EK4ca7GwqGy6dVSmELsq3AqFvCcbgqxCDcV/Q6urmwqk9Wdslmc10EKjcUPO4F/RvMamuMLay/3DgYSHwWi+gFh3Zx/fIGg9jWF3y5/AeRdqFnmKo2F2NQkaIqqyviQ198OAGe38pVYCQKu11j8DN7PwppOAH6xZweMsbkAZgN4luO4dCf2zSGK9DfNn1t5N00N2lA6abED58WCi+bPRrPGwjkPNJ2XwixQFFdUW7w5i/n+ak11yy4Rzn2w+Ngp3IjNFc54oIsdaIXEU/akCS+XwHlTLKzZbQpxtmChrOOadGVl04aQ2seioXohbXvyf1LjHwkEJwB553kzyLkPLJe5AlumEAvH1da8EEgQcL3G4t8AejHG2gkNjLFoAH1Ny+qFMfYc+LwXczmOe0OiPtpFqbEmSCXcp2lhZuJJ3JbGoq5QVrMpxEn2dcvS6bYf6gYjhx+u1zxcurW1kTSoCWjtTJJV5uSQV5mMWUbn2Fnh1LIImXMesgE+7jeF1Kk5cmXWzYaolDgqxFMqnAJA3NCazyW3aj67TLCwkQHUlT4mRLPC1YLFOwAuA/iYMZbGGBsJPkrkGoCNwkqMsSjGmJ4xNl/UNg7AagCHARxnjPUS/XVy6VkAqESNZ3Rb36Y5rzVUslussRBjrBI0Fs4xR4jfeusKOc3MLTGrxkN1aoT5OdemallltB7BQoJojMbksnB2Sm/AqsJpvRoLUYVZZyfIquuadIdgUXAFuHwK+OXfQKHoYdZxBND3r0CPP0mTJMmTBIv2dVQydZnzpg1TiNTJwYhmi0tNIRzHlTLGBoFP6Z0OPqX3MfApvUtEqzIAclgKPsNN7cNNf2L+C2CARN22STW7be5ctG/Tbm5NfY5yqFtjIZhCnJVLwqJeSB2ChdgMktTW32l1QgQsK5zWrTUos6pX4gwsonMqDYAdZnspMm+Kw00L6vWxqBE6nO1jUaeAZ1GAzEWCxdEFwM8f8Z/HbAISx/Kfu4zh/6TC3gqnriCqD6D0AapNyYkf2847lAZEu+b4Fj4WJlOIhcbCRem8iWaBy6NCTDVB6p0NOI67DF6IELdNBjBZqn45gt5gBKeomWhimuiZ3VByptY+rSFnchi4mmVyzhsw8toCZyTIAizfeutK6/3dNenMIID99UKkiMbwcSDzJwAYRVVeASdm3tQowRifLuJOWTX0BiMUNvJ5SJrSu65IJQuNhcSVTQU8oXS6UOFU7Efw8TN833zDgR5P8oXLpEKhBmLuAy4c4r8X3QDumSrd8ayxjgrhOPfUCSGaBeRt0whKqyyzbrZtssaifsFCIVOgtU9ryzZjsPmzs0whvnYUIvteJFgkSyJY2Om8KYHTpKaeRGW2qNDX/FZqhcwuZ097UMhlFg6c+XVoLaQM+7WOkuE4U/l2d5hC3CVYCBVOgZoKpwKVxcB36cCp1cCReXzqb6mJE5lDLn4q/fHEKNS8hgTgk4LpKy3TmZMphBDRnPNYuI2ckjtgijL+C6dAsCa4/g0awNLHwvYDrY22jUWNEGaomWwlMYXYeKiXVelx4RavBmUMSGzj/FBDbSN8LJxlgnBUYyGF1kQgSKtGgclx83ZJFUJ1tX1ZSiqkM4Uo5TKo5DJUGYwwckCl3ggvpbxlCRZA3RVOi6xyWLgiE2l7kQPn5ZNAdbnr6mQwBvxxN5+3RK3jha6rNTl1SGNBiCGNRSO4XFhTAEhuCIKMNW0Y7alTYZ0yXHDcBACdJM6btU0hP2UVwWDk31zbh2gtBBFnYRmNYKdg4STBylGNhRRZNwWCfGocOPNL69JYSGcKASzH1Tze5W7wsbAlWBiNQPpoYPck4N/P8ap5KRCXShefuzsiIgKiajQoMiVw4/v613c2sYOAyF5Aq858mHFZXs0yEiwIESRYNIIroklFhaZpKwD7SnZbpwyvrqyZ8CQJN7VhCvn+Ws3bqhT+FYCVcFOPYCEWOpznYyH273BMY+GsiBCBYG1N/Ye8kkqb65RKaAoBrMOgTeM95BXgue+Bp04AnUc5/Zi2O2KjXkjhNSDzGPDLPuCXj6XTGIiPLdbWuKP4GAB0e5z/ry+3DDt1B6kmHw8vv5qiYAQBMoU0iqzimuqiGtb0bJ51OsqJsNZYVJTVPNidZQppKNxU7F/RLVIawcJX07CfByCNtkAsoNmTfVMKPw+BIG2NxuJ2SW2NhdHIWVY3dbLGBAC81TYEXrXOlCwpxunHq7sjYo2FSWuQ9U1NW7iEWS81AQCT8UXGDCItnrsiIgbN4zNeKjVAwgOuO64tAqJ5h9J7ptWdHZVokZBg0QhulNZMKlp5aJP3V191U4HaGgteDa2Sy6BWON/HosiWYCEONW0jkWAh7kMdORw4jrMqm+4swcr+0vGAdb0SJwsWPjUai9ultTUWFkXYVHKnOY6KaWwpeaejsaGxuP5tTVtED+mOPWoDMGZz7aySRW6KiPDyBUatd93x6qP3dP6PIKwgwaIR5FbUTCq+ilZN3p9Fxkc7NBYMDJyef7A707bua5F50/KhnlNUgRuFfFVFL6UMHVpLU01SLNzU9XCv1PMOhQAvWNVVWt1RxFk/64tIEZDWebN+jYXUZhDASrCwYzwkw9rHguOALLFgkSLdsetyjqRQS4KoE/KxaAS3K2smlUB10+2rliW7bQsWAeoA9ArrBQDoGzYI4PiHiTNrRGjrCTcVm0ESI/xs5lVwBmJTSF25NKTybdDa6TgqIM5h4WznzWCRYJFnQ7CQsmS6QK3ieJXFwK2fLatbugKVN6AwPeANVbyvQ7bIcbGNhBqLuhCbQnQkWBCEGNJYOIiRM6Kwuiadd4hX0yeVhqqbAgBjDBuGbEDmnUxUloXi0PEvATj3bbW+cNPvJU6MJWCPKaTMygzgLLR2ZB4VI0U6b4Egbf2mELHg5+xQU4FaFV+vfg28b8ptFz8cGL9LkuPapFUnQF/FO1PeOMsX4wL4surappsjHcZdphCCaAaQYOEgeeV5MHD8GySn90aAE6oqeill5kyLVfq6K2sqZAokBCbgqzs1sfzOfKjU57wpFiySJBQstFaF0IxGDjKrsbAMNZXo/O0yhUjovOnjYaaQKgOgvypaGGRjCwl56njN5zPv1nxuI6EZBACqSvky5eUFgFwNRPflBZxSUxFCJgO0TTeHEsTdBAkWDiJOUmWsDnBKRAZjfGVN4YFZVqWvN0eE+G3amWpwnYUppBocx4ExBqORww/XC83LpNRYKOUyeKv4sTBy/Juy9VhI5dsgFizs0ViIzVZO11iInTdthJtKWTJdoJYmreJazUJXFb+yRdbZms9S+lcAQPYPwHum0kRtUoE/f8pn4XxwOW8OqSwB5DSNEoQYuiMc5Loo1NRYHei0SV14mAK8ir1ewUIcZujEh4qXUm7Otlht4MzZFjNzS8zHDNaqEeEvbbY/Xy+leSyKKmwIFhLksAAcDze1CHlVOvdW8tUooJAx6I0cSqsMqKg28JkvTRRLJFyKsaj2Wqnnc0cINLE+TpO4Lgo1lTIiBLBd4VTpBaQ+Je1xCaIZQ86bDnK9RCRYVAU67cHeUL0QMRYZF538tmrLgfPCrZrCs4kRvk6vaGqNtebEGinSeQON0FhIGBXCGLOMDLHKvil2LpXOx0Kksag2AHdEphD/SEmO2SCVJUB+Jv+ZyYGwJGmPZ5Egy80VTgmimUAaCwfJKq4xhXBOMoUAlm+8jggWzqpsKt6fkEK6pFKPEJ0aWXfKzMujgnycejxb+GrEDpy1H/BS5LAAGuNjIZ0pBODNIbeKeDPI7ZJKC02RVForMbXCTe+40RSSlwFc+5oPNx37Hq9JKLjMR4xIiZc4pXdB7QqnBEHUggQLB7HQWFQHOi3joUX2zToKkQmI36adnXHRVr2Q6wXl5japzSCAdZXV2hqLcql8LMSmEHvCTSV03gTqz2VRLHGdEMDynCorKkS5G5jr6mMI/H4C+M+L/OeUKcCI1UDMvdIfV6ECVFqgqoT3rSjP5x02vV1UMp4gmiEkejuIhfOmE00hFhVOq4z1ritl8Slb9UKyRIJFmwDpBQvLDKC1BYtSiUwh3kq5ueREWZXBXHCtLqQ0hQD11wspcUm4ac1+1WXZAEzj4Rvu+hTOtuqFuAqxn8XPHwHLY4F3hwDfbnFtPwiimUCChYO8PfRt+BRMRcXNNHDV/k513hRoqLJmiYT2dVtpvbPuiDQWLhAsLJJk2TCFSKUpkMmYhQaooeyb5RZRIc5/uFuEnNbjY+EKU4hP+Y2aBe6ICLFVL8RViAWLH/fwmovrZ4C8i67tB0E0E0iwcJAYvxhUFSeguqA3AIUTnTdr9lNeR/ZNAQtTiLMFCytTCMdxLjeFWKb1rl9j4ezzdyT7ptQaC4skWdYaCwkdeAXEGgtt5c2aBe6ICBELFgWXXHtssWBx7euaz+2HuLYfBNFMIMGiEUgxqVuWqK5fsBA7L0pqCqnUo6hcbz5fjVKOQB/pVeC+DRRDk7L4l3WCrvook7AfQAM+Fi4INxULSwa93uRXwdwTESIWLIqygI/+4rpjiwULAaUPENXHdX0giGYEOW86SLXBiCo97wMhY3zWTGfgSLipxUNF4nDT66KIkIgAjeShpoC1KcSGxsLCDODcB7pPPdlHrZHceVMkxOVZm0KqpNdYiMf2I9lgPDtzIZ910lA7E6jkaKycJXUuzHZpS7CIuQ9QqGu3EwRBGgtHKbNKpeysB62l86YDPhZO11hY1gvJcrEZxLoPth7uZRL6NugaaQqRJNxUZArJL63beVMqwcLCPCecq0IFqLWSHK9elF6W34MTXHdsvwggINqyrf1g1x2fIJoZJFg4SIlFASwnRiQ4kiBLSh8Lq1BPC/8KFzhuApbhpraiQsSZN51ZhAywSpLVkPOmhJk3gfrrhUgZGSQgHlt7wm8lJzy55nPsQNcd975ZwPSvAIVIuIkb6rrjE0Qzg0whDiKVGl5jR4VTW31wuilELX6o6y0iQlwRagpYJ8iqP/OmszUFPnZm3+Q4TtJaIUBtHwuhdgvgGudNy9wq9V+TLuGhlcDp9UCHhwFda9ce+/KpmoqqQXG1NRgEQZghwcJBpJrQLU0hdU/iRiNnoTVx9kPF16p0eJbB9aYQXxtpxcWIBQtnJwiz0NjU85ZeZTCa81woZAwqhfOVf94qhbmGTJXBiOJKPXy9lKg2GFFRXePnI4XjKACo5DIoZAw+xmI8hK9RfV4JZXA7IChWkuM1SEQKMHaze46d8WnNZ9JWEES9kCnEQaTKH2BhCqnn7bCs2gDOlKdIo5TbLK/eFLTWphCR86bLNBYNJMiSsly5veGm5RL7VwjYigyx1lhJ5VDLGINGJUccu44lyk1QfjAW+NefJTmWx3NRJFhQmClB1AsJFg4ilWAhfuusz3mzVGLbunW4qWXWTYnrMpiwMIU0oLHwljAqpj6cw6XZAAAgAElEQVQfC6lzWAjYKp/uCjOIgI9KgTYsr6bBXcXH3EllCRAYA+jC+e9Rfd3bH4LwcEiwcJASUVSIc00h9vlYFEucylkckXGrqBIFZbzGQCWXIUTrmvA6tUIGpZx/C6/SG1FhpcGxeKg72Qxgb7ip2JkywFu63B7BIo1FXklNcTgBqRw3BbxVckRYCBZuLJfuLpTeQEkOUHwD6P1M7QgVgiAsIB8LB5HOebNGxqtPsJC6qqVYWCoUOU6G+XtB5mSzS10wxuDrpTSnsS6qqIaXRQIxkSnEyXks7A03zS2pMH8O9ZXuQWOhsTCFnLoi1FTAWy1HBMutafBrgRoLmQx46jhfNj6wnbt7QxAeD2ksHEScmMi5phAbOQNsHV9iNXhd+3SV46aA2Bwi1hxU6Y2oNvBOJnIZg0ru3EvY3nDT3OKavBJSanJs+VhYaiyUtbZxJt5KMoUAAORK3mnVBQniCKK5Q4KFg1hoLKTKY1FP2XSpUznLZcxmbghXOW4KiH09xCGn1iXTne24aG+4aU6RSLDQSSdYiFOo55faECycrLGxhtdYtHBTCEEQDkGChYOUVkpTAMvecFNXOO7pbLwFR/i7xnFToK56IWKhSwqnSWvn1brIFRUFC5VQsLBVOv38zWJzm9hUIgU+SivBwh2VTQmCaFaQYOEgUr0t2lsrRGpTCGA7Tbirsm7a6oO4wqmFYCdBqfJGmUIkFCxsmUI+O1/j89A7NqjWNs4kVFYIL8aPf5XCF/DylfR4BEE0f0iwcBDp8lhYlk3nhGQVVrgiIsDWfl1tCrHQWJTXnLPU+SN87BQsclwlWFg5b+YWV+LHrEIAvNmqb/tgyY4NAGGoEWKKNWGSHosgiLsDlwsWjLG2jLG9jLFCxlgRY+xDxphdHmGMsSWMsSOMsduMMY4xNlni7tZCqqgMuSh7I8cBlaYKqtZIWdlUwLYpxNXOm3VoLCSq1SJgtynERYJFsJXG4vMLNQ/6lKgA+Gmkdd4MNdYcr1Dl4jTaBEE0S1wqWDDGvAEcB9ABwCQAEwDEATjBGPOxYxfPAtAAOCBZJxtASlOEPYXIXGIKsdqvjAGt/Vwbu6+rI/umONRUCo2FWsGnsQb4CJRKfe3fgeM4C8FCSh+LALHzZlkVjv+WY/4+ICFEsuMKGDTBOGDohe+M7ZGtiZP8eARBNH9cncfiKQDtACRwHJcBAIyxHwBcBDAVwKoGtvfjOM7IGGsPYKKkPa0DKW383ko57oB/iJZV6S0iAgRc47xpud/Wvl5QOjmssyEsKpyKTCEWdUIkiIhgjMFHrTDn8CitNECtsDxOSaXeXJTLSymTNJeEUi6Dv7cSd8qqwXHA0V9vmZcNiA+V7LgCBaGpWFStAwBMaRUNyjlJEERDuFqwGAngK0GoAACO4y4xxk4BSEMDggXHcbbtAy7krQkpKCirQmmlHlFBzo2U0NgRGSJ1uClQW7BwVSpvMZZ5LEQaC5Fgp5GgVDnAC2yCYFFSUVvAszaDSFWrQyDIR4U7pgyogomsla8aHcN0kh4XAB5IDEPncD/4qOVoJWEiMIIg7h5cLVh0BvCxjfafATzq4r40iphgH8TAHquN49iT1tsVphCt2tJu7+qIEMDaFCLWWEiT+dTy2PX7WViaQaR/2AZp1cjMLbVo6x8fIrlAA/C+Na72ryEIonnjaufNQAAFNtrzAQS4uC8ehz0hp+4whbjjweJbR4KsUhdUFW0oMiTHRVk3BcQOnAIDEqQ3gxAEQTSGuz7clDH2NGPsG8bYN7m5uQ1v4EZ0ddTpEOOOcFNXh5oCdaf0FpuIpIgKAaxzWdT+HVwVESJgbYpxRZgpQRBEY3G1YFEA25qJujQZTYbjuLc5juvBcVyPkBDpveibgvghlSfK7CjGFeGmvtYaC7eYQkQaizrCTaUqV25ZOr225shVWTcFrLNrpkRKH2ZKEATRWFwtWPwM3s/Cmk4AfnFxXzwOsWAhVreLcU3mTSsfC3eYQjTiBFl11QqRPtzWVr0QV9UJEbA2hfR3QZgpQRBEY3G1YPFvAL0YY+baw4yxaAB9TctaNOK331wbgoXeYDSHOTIm4Ru7lcAS7gbBQqtSmAtJllYZoDcYzZ8FpHLe9GnIFFLiWsEiyMqPwxX5KwiCIBqLqwWLdwBcBvAxYyyNMTYSfJTINQAbhZUYY1GMMT1jbL54Y8ZYf8bYWADDTU09GGNjTW3NnpAGBAtxDg3+wStNVIBYzR6iU8NLKW0FTVvIZMxm3Y4SkVlEI1G/LI9rwxTi4qgQcSGyUJ0ancKoXgdBEJ6LSwULjuNKAQwCcAFAOoD3AVwCMIjjuBLRqgyA3Eb//gFgD4B1pu8zTN/3SNhtl2EpWFTUWl5SJb3jJgBEBXmjRxTvCvN4T/dVsxTXCxF8S37Pqwm7lCq/hkW4qQ1TiPi3cYXGoltbf8SG8CHOT93bziVhpgRBEI3F1XkswHHcVQBjGljnMnjhwrp9gDS98gzEb7+2NBYlLnDcBPjsk7un9sat4gqE+bkvh4H4AV9YXo2gKj2u5pcB4CMj2oVIk0+kPlOI3mDE7VK+yihjltVHpUKlkOHwX+/D7ZIql6dWJwiCcJS7Pty0OSFWeeeWVNaqcCp+yDmzAJotZDLmVqECsHLgrKhGRk4JhCGJCvKWzEQjFtpKrUwh+aVV5j4EeqtclupcKZeRUEEQRLOABAsPQqOSmyMSqg2cOY2zgNjeb53E6m7E2hRy/max+XtCK+nSWYvNTMVWCbJcVS6dIAiiuUKChYcR4muptRDjKlOIp2CdffPCrRrBIl5KwcIi3NRSuHN1ciyCIIjmBgkWHoY4RbS1n4UrTSGegKUpRI/zt2r8exNau0awsDaF5Lo4nTdBEERzgwQLD8MySZZlZIjYFNLSNBbFFdW46A6NhZUpxCKHhS8JFgRBENaQYOFh1JfLQmwKaQk+FuIMoNcLypFdyAtaKrkM0U4uWS9GLFgUW5lCcopEoaaksSAIgqgFCRYeRn0hp2JTSIvQWGhqzvHbKzWlZNqF+EAhYTSGdXVTcXSOq7NuEgRBNDdIsPAw6qsXIjaFtAgfC5HG4pIoMZaU/hUAnzdCreBvDSMHVFQbzctcnXWTIAiiuUGChYdRrymksuWaQsRI6V8hYGEOEWmKKNyUIAiifkiw8DDqK0QmDn1saaYQMVLmsBDQ1pHWm8JNCYIg6ocECw+jPlNIaQs2hYiR2hQC2A45La3Uo8xUXVWtkFlErRAEQRA8JFh4GIHeKshlfJmUwvJqVOprhAlxFsiWoLGwZe7RKOWIcEEZd1umEGszCBUDIwiCqA0JFh6GTMYQLCpslVdSZf4sjgppqT4W8a20kMmkf6BbZt/kBToygxAEQTQMCRYeiIU5RJQ3QWzrbwmmEJVCBi+l5SXqCsdNwNLHorSqtmARSoIFQRCETUiw8EDqSutd2sIybwK1/Sxc4V8B2NZYiDOhksaCIAjCNiRYeCAWSbJMCZlKKvWoMvD5FJRyZs6zcLcjrhcCuFBjoa5d4dSyTgjlsCAIgrBFy3g6NTMsTSH8w+ynrEJzW7tgbYtxHLT2JXGHYFFqQ7AIpTohBEEQNiHBwgOxSJJl0licu3bH3JbU1s/lfXIXYlOIr5cCrVz0QLeVxyKHKpsSBEE0CAkWHoitJFnfiwSLbm0DXN4ndyE2hSS01rlMU+PTkCmEfCwIgiBsQoKFB2IrSZalYOHv8j65C7EpxFVmEADQWTlvchxHzpsEQRB2QIKFByJ+aOUVV+JWUYW5ZLhGKUd8K627uuZy4kNrzjU1JtBlx7UON/32SoE5p4iXUkaCBUEQRB20jJjFZoZ1IbLvrtZoKxLb+ElaMtzT+EPPtigoq4aXUo4RXcNddlwfK43Fe19eNn8f1S0Cyhb0GxAEQTgCCRYeiLdKAa1aYQ4x/fxirnlZSzKDAPxYvDA03uXHFZtCruaX4acbRebvk/pEu7w/BEEQzQV67fJQxFqLT3+5Zf7c0gQLdyE2hRSUVcNg5AAA98QEomOYr7u6RRAE4fGQYOGh1JV9kwQL11BXyvTJpK0gCIKoFxIsPJQQG/kaQnVqhPlRxkdX4KOqLViE+3lhaKdWbugNQRBE84EECw/FVgKmpLb+LSbjpruRyxh8VHKLtid6R7Uox1mCIIjGQLOkh2IrnJHMIK5F7GehVsgwrmekG3tDEATRPCDBwkOxVZY7mQQLlyL2s0jrFo5AH5Ube0MQBNE8IMHCQ7HWWDDG57AgXEdsCJ+cS8aAyX1i3NwbgiCI5gHlsfBQrAWL9iFa6LyUdaxNSMGCEZ3QNsAbqTGB6BROIaYEQRD2QIKFhxKqs4z+IP8K19MmwBvzR3RydzcIgiCaFS43hTDG2jLG9jLGChljRYyxDxljdnnFMca8GGPLGWPZjLFyxthpxth9UvfZHQT6qCATBYB0iyTBgiAIgvB8XCpYMMa8ARwH0AHAJAATAMQBOMEY87FjF5sAPAVgPoCHAWQD+IQx1k2aHrsPuYxZmENIY0EQBEE0B1ytsXgKQDsAoziO28dx3McARgKIAjC1vg0ZY0kAxgN4geO4dziOOwbgDwCuAnhV2m67h4m9o8EY0D8+BJ0ojTRBEATRDHC1j8VIAF9xHJchNHAcd4kxdgpAGoBVDWxbDWCXaFs9Y2wngJcZY2qO4yrr3LoZMmNgezzRKwq+XgpKjEUQBEE0C1ytsegM4Ccb7T8DaMhLrjOASxzHldnYVgWgfdO753n4aZQkVBAEQRDNBlcLFoEACmy05wMIaMK2wnKCIAiCINzIXZ8gizH2NGPsG8bYN7m5ue7uDkEQBEHc1bhasCiAbc1EXdoIe7cFajQXFnAc9zbHcT04jusREhJid0cJgiAIgnAcVwsWP4P3lbCmE4Bf7Ng2xhSyar1tFYCM2psQBEEQBOFKXC1Y/BtAL8ZYO6GBMRYNoK9pWX3sB6AE8KhoWwWAxwAcudsiQgiCIAiiOeJqweIdAJcBfMwYS2OMjQTwMYBrADYKKzHGohhjesbYfKGN47jvwIearmaM/ZkxNhjATgAxABa48BwIgiAIgqgDlwoWHMeVAhgE4AKAdADvA7gEYBDHcSWiVRkAuY3+TQHwHoBFAA4CaAtgOMdxZyXuOkEQBEEQduDyImQcx10FMKaBdS6DFy6s28sBzDT9EQRBEAThYdz14aYEQRAEQbgOEiwIgiAIgnAaJFgQBEEQBOE0SLAgCIIgCMJpkGBBEARBEITTIMGCIAiCIAinwTiOc3cfXAZjLBfAFSftLhhAnpP21VKgMXMMGi/HoTFzHBozx2nKmEVxHHdXF65qUYKFM2GMfcNxXA9396M5QWPmGDRejkNj5jg0Zo5DY1Y/ZAohCIIgCMJpkGBBEARBEITTIMGi8bzt7g40Q2jMHIPGy3FozByHxsxxaMzqgXwsCIIgCIJwGqSxIAiCIAjCaZBgIYIx1pYxtpcxVsgYK2KMfcgYi7RzWy/G2HLGWDZjrJwxdpoxdp/UfXY3jR0zxlgPxtjbjLHfGGNljLGrjLH3GWMxrui3O2nKdWa1n5cZYxxj7Asp+ulJNHXMGGMdGWN7GGN5pvvzPGPseSn77E6aOJdFMsa2mu7JcsbYBcbYIsaYj9T9dieMsTaMsXWmubvMdG9F27mtjDE2hzF2mTFWwRg7xxirt4r33QwJFiYYY94AjgPoAGASgAkA4gCcsPOG2gTgKQDzATwMIBvAJ4yxbtL02P00cczGAegMYC2ABwC8DKA7gG8YY20l67SbccJ1JuynHYD/A5AjRT89iaaOGWOsB4CvAagB/BnAgwBWApBL1Wd30pTxMi0/CuA+APPAj9W7AP4GYLOE3fYE2gP4A4ACACcd3HYhgFcAvAF+PvsKwB7G2IPO7GCzgeM4+uP9TJ4HYADQXtQWA0APYGYD2yYB4ABMEbUpAJwH8G93n5uHjlmIjbYoAEYAr7r73DxxzKz28wmAjQA+A/CFu8/LU8cM/MvTLwA+cvd5NJPxut80l91v1b7UtL23u89PwnGTiT7/2TQO0XZsFwqgEsA/rNqPAfjB3efljj/SWNQwEsBXHMdlCA0cx10CcApAmh3bVgPYJdpWD2AngGGMMbXzu+sRNHrMOI7LtdF2BUAugAgn99OTaMp1BgBgjI0Hr92ZI0kPPY+mjNkAAB0BrJKsd55HU8ZLZfpfZNV+B7yQxpzVSU+D4zhjIzcdBn7ctlu1bweQ2BLMu9aQYFFDZwA/2Wj/GUAnO7a9xHFcmY1tVeBVbHcjTRmzWjDGOoKX/n9tYr88mSaNGWMsAMDrAF7iOC7fyX3zVJoyZv1M/70YY18xxqoZYzmMsbWMMY1Te+k5NGW8jgK4COCfjLFOjDEtY2wQeC3IWxzHlTq3q3cFncFrLDKs2n82/Xd4LmzukGBRQyB425o1+QACmrCtsPxupCljZgFjTAHgLfAai01N75rH0tQxWw7gAoAtTuyTp9OUMQs3/d8F4AiAoQCWgVd173BWBz2MRo8Xx3EV4IUxGfgHYzF4lf4BAM84t5t3DYEA7nAm+4eIu33+rxOFuztAECbeANAHwEMcx9maFFs8jLF7AUwE0N3GJEbYRnh52s5x3HzT588YY3IASxljHTmOu5s1ZA7BGPMCL4SFgnf6vAogFbxTuh7AX9zXO6K5QIJFDQWwLc3XJf1bbxtVx7ZAjeR6t9GUMTPDGFsK4GkAkziOO+KkvnkqTRmzjeC1OdcZY/6mNgUAuel7OcdxlU7rqefQlDG7bfr/qVX7EfAOicm4+0xvTRmvP4H3S2nPcVymqe1zxlghgLcZY29xHHfOaT29OygA4M8YY1YC/90+/9cJmUJq+Bm8rcyaTuC9yhvaNsYU5mW9bRVq297uFpoyZgAAxthcALMBPMdxXLoT++apNGXMOgKYBn4iE/76Auhl+ny3vk029d6sj8Y67HkyTRmvRAAFIqFC4H+m/x2b2Le7kZ/BhzLHWrULvhV2zYV3EyRY1PBvAL1M+QEAAKbkKH1Ny+pjPwAlgEdF2yoAPAbgyF36Fgk0bczAGHsOwCIAczmOe0OiPnoaTRmzgTb+zoF31BsIYK/zu+sRNGXMDoF3rBtm1T7c9P8b53TRo2jKeN0EEMAYs3Y4v8f0P8tJfbybOAw+KvCPVu1PAPjJFJHTsnB3vKun/AHwAa9Z+BF8SNZI8JP27wC0ovWiwNsa51ttvxP8W+OfAQwGP8lXgLeHu/38PG3MwCfIMoKf+HtZ/XVy97l54pjVsb/PcPfnsWjqvbnA1L4EwBDwydjKAWxx97l52ngBiAYfanoBfHKtgQBmmdq+gSjXw934B2Cs6W8D+DwWfzF97y9aRw9gk9V2S03z/UzwpqQNpvntYXefk1vG0d0d8KQ/AJEA/mW6iYoB7INVghTTjccBeMWqXQM+Vv6m6QL7GsAAd5+Tp44Z+KgGro6/z9x9Xp44ZnXs664XLJo6ZuBzL8w0PWyrAFwB8CoApbvPy0PHqxOA3QCugRfALgBYASDA3eflgnFrcE4yfd9itZ0cfCbcK+A1ZD8AGOvu83HXH1U3JQiCIAjCaZCPBUEQBEEQToMEC4IgCIIgnAYJFgRBEARBOA0SLAiCIAiCcBokWBAEQRAE4TRIsCAIgiAIwmmQYEEQzQTGGGfH32XGWLTp82QP6HO0Vf8GOLDt/4m2uy5hNwmCcCJUhIwgmg+9rb5/BD6j4iuitkoA2aZ1res9uJNFAA7CsboJ7wE4CmAegCQpOkUQhPMhwYIgmgkcx30l/s4YqwSQZ91uwlabO8mso591wnFcFoAsxliuRH0iCEICyBRCEHcZtkwhjLEtjLHrjLEejLEvGWPljLHzjLGHTMtnmswoRYyxjxljIVb7VDDG5jDGfmOMVTLGbjDGVjLGvJrQz2GmvhQyxkpM/Znf6BMnCMIjII0FQbQcfAFsA1/34QaAuQD+xRhbDyAewAwArQCsBrAewB9E224HMALAPwF8Cb589kLw9SbGONoRU+XNf4Mv1vcq+BoecQDa1bcdQRCeDwkWBNFy0AGYxnHc5wDAGLsB3kfjYfAVZQ2m9i4AnmWMyTmOMzDG7gXwGIBJHMdtM+3rKGMsH8B2xlg3juO+d7Av3QGoAPyF47giU9vxJp0dQRAeAZlCCKLlUCoIFSZ+M/0/KggVonYFgDDT9+HgNQp7TSYRBWNMAeCIafl9jejL9wCqAexkjI1ljIU2Yh8EQXggJFgQRMvhjvgLx3FVpo8FVusJ7YL/RCh47UIpeGFA+MsxLQ9ytCMcx2UAGAZ+DkoHcJMx9hVjrL+j+yIIwrMgUwhBEA1xG0AFgHvrWH6jMTvlOO4EgBOMMTWAvuB9LQ4yxqI5jstrVE8JgnA7JFgQBNEQhwHMBuDHcdwxZ++c47hKAMcZY1oAHwOIAUCCBUE0U0iwIAiiXjiO+4wx9gF4H4tVAP4HwAg+IuRBALM5jrvgyD4ZY9PA+2b8B8A1AMEA5oDXfvzkvN4TBOFqSLAgCMIengDwLIAnwYepVgK4DOATALcasb9zAB4A8Bp4H458AF8A+CPHceVO6C9BEG6CcRzn7j4QBHGXwhiLBnAJwJ/A59AwcHZOOowxBkAOYBOAwRzHtZGomwRBOBGKCiEIwhVsAh9J4kjUx1zTNhMl6RFBEJJAGguCICSDMaYC0FXUdJ7juGI7tw0DEGH6WsVx3A/O7h9BEM6HBAuCIAiCIJwGmUIIgiAIgnAaJFgQBEEQBOE0SLAgCIIgCMJpkGBBEARBEITTsCtB1tmzZ4cpFIoFHMe1BgkjBEEQBNESMTLG7nAcd06v1y9JSUm5aGulBqNCzp49O0ytVr8RHR1dpdFoKmQyGYWREARBEEQLg+M4VFdXK4qKinxu3rzJVVZWPpeSknLYer0GtQ8KhWJBdHR0lY+PTzkJFQRBEATRMmGMQaVS6YODgwujo6P1SqXyJVvrNShYcBzXWqPRVDi/iwRBEARBNEd8fHzKOI6LsbXMHn8JGWkqCIIgCIIQ4Ev5gNlaRo6YBEEQBEE4jRYpWKxduzaIMZbCGEv54Ycf1NbLDx48qBWW79u3T+eOPrZUDhw4oJs5c2a4wWBwd1fuCsTXOmMsxcfHJzkhIaHTkiVLQqqrqyU99vnz51WMsZS1a9cGCW1jxoyJjoiISHRkP3VdE7b272nMnDkznDGW0rZt2y6VlZUWb3c//fST2pn9F47VmN917dq1QatXr3bZOB44cEDHGEs5cOBAs59fU1NTE1JTUxPc3Q9PokUKFgI+Pj7GTZs21bqZtmzZEuzj42N0R59aOsePH9e9/vrrYUYjDb8z2bx58+9Hjx79LT09PTM5Obl07ty5kbNmzQp3dT9effXV7N27d2c4sk1d10RkZGT10aNHfxs7dmyhUzspAdevX1evWbMm2N39qIvt27cHp6ene2z/iOZFixYshg0bVrB3794g8YRVUlLCDh06FDB8+PACN3atSZSXl9u0exEtl549e5YNHjy4dPTo0UU7d+68kpqaWrxp06bQutavrKxkUgh3nTt3ruzbt2+5M/al0Wi4wYMHl4aHh+udsT8p6du3b9HKlSvDysrKWsy9SfNQy6VFCxYTJ068nZ2drTpy5IhWaNu+fXuA0WjEmDFjagkWBw8e1Pbu3Tvex8cnWaPRJPfr1y/uzJkzXuJ1PvzwQ9/+/fu3DwkJ6arRaJLj4uI6L1iwoJVebzn3vfXWW4EdO3bs5O3tnazVapPj4+M7LV++3PzGUJd6LSIiInHMmDHRwndB1X3o0CHtAw880E6n03VLTk7uCADV1dWYM2dO65iYmM4qlap7aGho16eeeqqNeHIT1MnLli0LmTFjRkRwcHCSj49PclpaWkxxcbHsp59+Uvfr1y/O29s7OTIyssu6detqaXhOnz6tGTRoUHtfX99uXl5e3bt3797h8OHDWvE6Y8aMiW7VqlXXU6dOaVJSUhI0Gk1yVFRUl2XLloUI68ycOTP89ddfDwMAlUplVt/b/vWIppCcnFxWUlIiz8rKUgjXwNKlS0OmTZvWJjQ0tKtGo+l++/ZtOQBs3brVPykpqYNGo0nW6XTdHnjggXYXL15UifdXXFwse+KJJyL9/f27eXt7Jw8aNKj95cuXVdbHtWUKKSoqkk2fPj2ibdu2XVQqVffg4OCkYcOGxV67dk1R3zVRlynkzTffDExISOikVqu7BwQEJI0aNSrmypUrSvE6ERERiWlpaTFvv/12QLt27TprNJrkLl26dPzkk08srltnsWTJkqzc3FzlP//5zzqFOYG9e/f6duvWrYOXl1d3nU7XbciQIbHnzp2rZbK1B3vOMzU1NeHMmTPas2fPmk3A4rnnt99+U40cOTImICAgSaVSde/QoUOnbdu2+YuPI5hhzpw54yXMFw8//HA7ALhx44ZixIgRMVqtNlmn03V75JFHogsKCuS2+mvPtSac08qVK4MjIyO7qNXq7p06deq4f//+WmYVe+bs1NTUhJSUlIR9+/bpOnXq1FGYt63PEQDefvvtAGE+bd++vc11hHMeP358ZGhoaFeVStU9Jiam84oVKyw0QsLcfezYMZ+RI0fGaLXa5NDQ0K6TJ09uay2A1nePOPI7uYoWLVjExsZW9ejRo2Tr1q3mien9998Puv/+++/odDqL17WdO3f6paWlJXh7exs2btx46Z133rlUWloqHzx4cIeMjAzzpJWRkaEeMGBA8fr16y/v3r07Y9y4cXkrVqwIf+655yKEdT755BPt9OnTY3r37l28Y8eOjG3btmVOnDgx786dO3ZlQrXFlClT2kVFRVWlp6dnLlq06DoAPPLII+1Wr14dNmbMmPzdu3dffP7552/u3Lkz+JFHHmlnvf3q1atbZ2dnKzdu3HjppZdeyjp48GDAxIkTI0ePHh07fPjwwvfffz+zQ4cO5c8//3z0N998YwiFDlgAACAASURBVL4xv/jiC+9BgwZ1KCwslK9Zs+bK1q1bM/39/fUjR46MP3nypLf4GKWlpfIJEya0Gzdu3O0dO3Zkdu3atXT27NmRwoQwY8aM3D/84Q95pjH67ejRo78dPXr0t8aOCVE3V65cUcnlcvj6+pqv81WrVoVlZGSo16xZc2Xbtm2ZGo3GuGzZspDJkyfHxsfHV2zZsuX3VatWXTl//rxmwIABCQUFBeb5Y8KECVG7du0Knjp16s3t27dnxsXFVUyePNlmKJqYiooK1r9///j33nsvdNy4cbd37dqVsXz58qsBAQH6vLw8haPXxIoVK4JnzJgRExcXV7Ft27bMefPmZX3++ee+/fv3TygsLLSY786cOaNdu3Zt63nz5t3YvHnz7waDgY0dO7Z9Xl6ezYdeU+jdu3fZAw88ULB27drW4nGzZu/evb6PPfZYnLe3t2HTpk2Zy5cvv3rhwgXNgAEDOly6dElZ13b10dB5btiw4UrHjh3L4uPjy4Xx3bBhwxUAyMjIUPbp06fjr7/+6r148eJrH3zwQUZiYmLZ5MmTY99//30/62ONHj26fb9+/Yp37tyZ8cILL+QAwIgRI2JPnDjh//e///36li1bflcoFJg1a1ak9bb2XmsA8NVXX+nWr1/fat68eVnvvvvu7yqVihs7dmycWACzd84GgKtXr6pffPHFyGeeeeZWenp6ZkhISNWTTz4Z+9NPP5n3t2/fPt20adPaxcTEVG7bti3zueeeuzl79uy2ly5dshD68vPzZX369Olw4sQJv9mzZ9/YvXv3xfvvv79w9uzZUYsXL64lWD755JMx7dq1q9y+fXvGpEmTctPT00Pnzp0bJixv6B5pzO8kNY1+kN0tjB8/Pm/+/Plty8rKrubm5ipOnz7tu2fPnlppSl966aW2PXv2LD527Fim0Pbggw8WxcbGJi5ZsqT15s2br5nWyxWWG41GDB8+vLiqqopt2LCh9bp167LkcjlOnTrlo9PpDMI2ADB69OiippzHQw89VPDWW29dF74fPnxYe/DgwYB169ZdfuaZZ24DwKhRo4oDAwP106dPj/nyyy81ffr0MaukIyMjKz/88MPLADBmzJiiU6dO6fbt2xe0fv36S9OnT88HgH79+pWGhYV1++CDDwJ69OiRDQCzZs1qExYWVnXq1KkLXl5enGn7wvj4+M7/+Mc/wo4ePWoer9LSUtmaNWuujhgxohgAhg8fXhwWFua3Y8eOwBEjRhTHxsZWR0REVAPAwIEDS5XKRs2jhA0MBgOqq6tx584d+ZYtWwKOHDkSMHDgQAsBOjg4uPrIkSOZMhk/hxcWFsoWLlwYMXbs2Nt79uy5LKx37733liYmJnZZt25d8Pz583POnTun3r9/f+Ds2bOzlixZchPgr+eSkhLZjh07Qqz7Iuatt94K/P777322b9+e8cc//tHsKzFlyhSzxtDea0Kv1+O1116LSE1NLT5w4MDvQnvnzp0rhg8fnrBu3brg//u//8sR2ktKSuTnzp37JSQkxCAcp3///h337t3rN23atHw7htUhXnvttazu3bt3Wbx4casVK1Zk21rnlVdeiWjTpk3lf//734vCuQ4YMKCkS5cuXRYvXtzq3XffvW5ru/po6DxTUlIqtFqt0WAwYPDgwaXibf/+97+HcxyHkydP/ta6dWsDwM8Pffr0US5cuDBC/JsBwNNPP50zb9488xh/9NFHvmfPntVu3Ljx96effrpA2P6+++6Lu3XrlvnHtPdaE9rz8/MVp06d+rV9+/bVAPDwww8XRUdHd12wYEH4vn37LgH2z9kAUFBQoDh+/Pj5xMTESoAXBCMjI5O2b98esHTp0psA8Oqrr0bExMRUfPrppxlyOS97du7cuWLIkCEdYmJiKoV9vfbaa62ys7NV33zzzc/C/kaNGlVcWFgoX7FiRdhLL72UI76OR48enf/666/fENb75ptvfD766KNAoc2ee8TR30lqGi1YRL980GNU1JeXPvRtY7edNGlSwZw5cyJ37tzpf/nyZVVQUFD1yJEjiw4dOmRWq/3444/qa9euqWfOnJkt9rjW6XTG5OTk0q+++sqsVrxy5Ypyzpw54Z999plvTk6O0mAwmFVaWVlZisjISP0999xTWlRUJE9LS4sZN25c/tChQ0uCg4ObFAYxduxYC9PNwYMH/ZRKJTdp0qQCcZ/T0tKKpk+fjuPHj+vEgsXQoUMtLrz4+PiKTz/9FKNGjTILPCEhIYbAwMDq69evqwDeH+XMmTO6Z555Jlsul3Pi49x7771F+/bts1BRe3l5GQWhAuBt5NHR0RXC/jyVxK2JHnOt/zjpx0Zd6926desifJbJZBg5cuTtt95665p4nQcffPCOIFQAwPHjx7UlJSXyJ5544rb4t42Nja2KiYmp+OKLL3QAck6ePKk1Go144oknLB7G48ePz29IsDh69KhfcHBwtTMmvnPnznnl5+crHnvsMYt+DBs2rCQ8PLzq5MmTOgDmh1NycnKJ8LAFgJ49e5YDwNWrV+u8Ho1GI6yjU+wVgLt27Vo5evTovI0bN7aaNWtWjvXyoqIi2S+//OL9zDPPZIv32aFDh6ru3buXnj59ulERFI05T4HPPvvMb+DAgYVBQUEG8TUwZMiQooULF7bJz8+XBQYGmoXTcePG3RFvf+rUKR+5XI5JkyZZtD/66KP5J0+e9BW+23utCe1JSUmlglABAAEBAcaBAwcWnj171gdwbM4GgKioqEpBCACAiIgIfWBgYLUwRnq9Hj/++KP3jBkzbgpCBQCYfHyqxPs6duyYX9euXUs7dOhQKT72sGHDinbt2hV89uxZzT333GOee0eOHGkxNp06dSr/8ssvzWNjzz3i6O8kNS1eYxEQEGAcMmTIne3btwddv35dNXr06NviCwcAsrOzFQDwwgsvRL/wwgvR1vsICwurAvi3woceeqh9Tk6Ocvbs2Tc6d+5c4e3tbdy7d6//unXrwsrLy2UA8NBDD5Vs3rz59zfffDN0woQJsQCQmppa8vrrr18TX3CO0KZNG4sYs9zcXEV1dTXz9fVNtrX+7du3LX77gIAAi9lSpVJxABASEmLhHKJUKrmKigqZcAyDwYA1a9aErVmzJgw2MBgMEMbT19e3lvCkUqm4ysrKFm2ScwXbtm3LjIqKqvLz8zPExcVVeXt710p6FxYWZnEN3bx5UwEAo0aNire1Tz8/PwMAZGdnKwGgTZs2FteKPU6V+fn58latWjkl7lVQC4eHh9faX3BwcPWdO3csbmx/f3+L61Gj0XAAIFzftvjPf/6jGzFihMV4cBxnt7C3ePHi7I8//jjolVdeaT1jxow88bLc3Fw5x3G1fgcACA0Nrf7uu+987D2OmMacp0B+fr7io48+ClKpVDZDUXNychSBgYHmB2tkZKRF37Ozs5U6nU6vVqstrrfGXmsCISEhNscoJydHZTquXXO2gL+/f61rVTw3ZWdnK/R6PbN1rQYHB1u03b59W3H16lW1SqWy+UKSk5NjMfeKhT4AUKvVXFVVlfmF1J57xNHfSWpavGABAJMmTbr92GOPxRmNRuzYseN36+XCDz9nzpys4cOH1zJZCDfNL7/8ov7555+9xeYDAPjoo49qOdBMmTKlYMqUKQWFhYWy//znP7p58+a1GTFiRFx2dvYPcrkcarXaWFJSUsvWW1hYaPM3M2VBMxMYGKhXq9XckSNHbNqjrSeAxhAUFGSQyWSYMGFCzpNPPnnb1jrWQhrhHpKTk8u7dOlSWd861teQIFSuXbv2clJSUi2BV5jshYfE9evXFZ06dTJPXjdu3GhwfgkKCtJfvHhRY9dJNEBwcLAeqBF0xOTl5Sm7dOlS1tRj9O3bt/S///3vr43dPi4urmr8+PG5W7ZsCR01apTFG2hISIiBMYabN2/W6n9OTo7S1sNPavz9/Q09e/YsnjNnzk1by6OioizmEesszWFhYdXFxcWKyspKJhYurH8je681gdzcXJtjFBoaWmXan11ztr2EhYXpFQoFJzbfCOTl5SkjIiLM172/v78+KChIv3r16qu29pWYmOhQiQx77hFHfyepabRg0RTzg6cxatSoogcffDDf19fX0KNHj1o/elJSUkV4eHjVL7/8ohFsyLYoKSmRAfxbvdBWWVnJ/vWvfwXWtY2fn5/x8ccfL8zMzFTPmzev7a1btxTh4eH6tm3bVh06dMi/oqKCCb4Lhw4d0paWltr1dv/ggw8WbdiwoXVBQYE8LS2tuOEtHMfX19eYkpJS8vPPP3v37dv3mjOECLVabQT4sQwICPCIZBaNNT80dwYNGlTi4+NjzMjIUD/77LM2BUcAuPfee0tkMhm2b98eKL4/duzYUed1LzBkyJCiAwcOBO7YscNv/PjxNlW99l4TSUlJFUFBQfo9e/YEvPDCC2ZtwKeffupz48YN1fTp02811J+GCAgIMN53331NElAWLVqUvWvXruBFixZZaPl8fX2NnTp1Kvv3v/8dsHLlyhsKBT89X7hwQfXdd9/5TJ48uZb5xFmoVCpjQUFBredB//79C7/99ltt9+7dy7VarcOlHfr27Vu6cuVKbN261V/wsQCAPXv2WFwb9l5rAufOnfPJyMhQCuaQgoIC2YkTJ/wGDhxYCNg/Z9uLQqFAYmJi2f79+wNWrlx5Q5jrjh8/7nPjxg2VWLAYPHhw0ebNm0NjY2OrIiIimiwM2nOPNPV3cjaksQB/0ezfv/9SXctlMhlWrVp19Y9//GPsQw89xB599NH8kJAQfXZ2tvLLL7/URkZGVr3yyiu3kpOTK8LDw6sWLlwYoVAooFQqubVr17ay3t9f//rX8JycHOWAAQOK2rZtW33lyhXV22+/HdqhQ4dyQX38+OOP53/wwQfBjz32WPSUKVPyMjMz1evXr2+l1Wrt8sV4+OGHix9++OH8CRMmxE6dOvVWr169SmUyGX7//XfV4cOH/VauXHm9a9eu9b7B2sOqVauu3X///Qn33ntv3OTJk/MiIiKqc3NzFd9++62PwWDAm2++meXI/jp37lwBAAsXLmz98MMPFyoUCq6pEznROAIDA40LFiy4NmfOnKjc3FzFgw8+WOTv72+4du2a8vPPP9f179+/eNq0aflJSUmVI0aMyF++fHm40WjEPffcU3b48GHfEydONOiNPm3atPwtW7aE/PnPf2537ty57N69e5cWFRXJDx8+7Dtr1qyc5OTkCnuvCYVCgZdffjlr1qxZUWlpaTETJky4fe3aNdXixYsjoqKiKp999tm82j1wPREREfo//elPOW+88UZr62WvvPJK1mOPPRY3aNCguGnTpuUUFxfLX3vttXCtVmuYO3dukwWjukhISKhIT08PeeeddwISEhIq/fz8DElJSZVLly690bt37469evXqMHXq1Jx27dpV5ufnK3788UfNpUuX1GJHS1s88sgjRd27dy/529/+FpWXl6dISEio3LlzZ+CFCxcs3sDtvdaE9YOCgvT3339//Jw5c254eXlxK1eubF1eXi579dVXbwD2z9mOjNH8+fOzRo8eHT906ND2Tz/9dG5OTo5i6dKl4damkLlz597at29fQN++fTtMnz79VseOHStKSkpkv/76q9epU6e0YmdSe7DnHmnq7+RsSLCwk8cee6wwKCjo/OLFi8Oee+656MrKSllwcHB1cnJy6fjx4/MBwMvLi9uzZ0/Gs88+Gzl9+vRoX19fw+OPP54XGRlZ9be//S1K2FevXr1K33jjjdC5c+e2LSwsVAQGBurvu+++wmXLlt0Q1hkxYkTxsmXLrrzxxhutx40bF9CxY8eyrVu3Xho3blysvX3et2/fpSVLloRu3749eO3atWEqlcoYHh5eNXDgwCJnSNIA0K9fv7Ivvvji1/nz54e//PLLkSUlJfKAgAB9586dy6ZOnZrb8B4sGTdu3J1PPvkkd8uWLSGrV68O4zjOIRs24VxmzZqVFxkZWb1q1apW06ZNCzIYDAgNDa1OTU0t6dmzp/nhnp6efmXatGmGDRs2tF67di3r1atX8ZYtW34fNmxYh/r2r1aruc8+++zCSy+9FJ6enh6yatWqcH9/f31KSkpJaGioHnDsmnjxxRfzvL29jWvWrGk9fvz49t7e3sYBAwYUrlmz5ro4tNbdLFiw4Oa2bdtCioqKLNR8Y8eOLdq1a9fFRYsWhT/55JOxSqXSmJqaWrxq1arr0dHRkqmzFyxYkJ2RkaH+61//Gl1WVibr2bNnyf/+97/zcXFxVV9//fUvf//738MXLlwYUVBQoPD399fHxcWVP/HEEw1qFgBg//79mVOnTm27ePHiNjKZjBsyZMidFStWXBX8ywTsvdYA4J577inu379/8auvvhpx69YtVWxsbMXevXsvil+W7JmzHWHUqFHFGzZsuPTaa6+FT5w4MTYyMrJy6dKl19544w2Ll8egoCDD119//dvLL78cvmbNmtY5OTlKnU5niImJqUhLS3M48aI994gzfidnwjiufq3JuXPnLiclJXmEpE8QBEG0bCIiIhJ79OhR8vHHH9epZSZcw7lz54KTkpKirdvJG58gCIIgCKdBggVBEARBEE6DfCwIgiCIZkNWVtaP7u4DUT+ksSAIgiAIwmmQYEEQBEEQhNMgwYIgCIIgCKdBggVBEARBEE6DBAuCIAiCIJwGCRYEQRAEQTgNEiwIgiAIgnAaLVKwWLt2bRBjLIUxlvLDDz+orZcfPHhQKyzft2+fzh19bKkcOHBAN3PmzHCDwa5aa0QDiK91xliKj49PckJCQqclS5aEVFdLW0n5/PnzKsZYytq1a4OEtjFjxkRHREQkOrKfuq4JW/v3JObMmdOaMZZy8eJFlbj9+PHjPoyxlMTExI7W20ydOrWNXC5PuX37ttzW+a1duzZo9erVtc5X+J1/+umnWvOZPURERCSmpaXFOLpdXl6efObMmeFffPGFd2OO2xgacw15Ip5+/TaFFilYCPj4+Bg3bdpU60fdsmVLsI+Pj8cULGpJHD9+XPf666+HGY00/M5k8+bNvx89evS39PT0zOTk5NK5c+dGzpo1K9zV/Xj11Vezd+/eneHINnVdE5GRkdVHjx79bezYsTZLSbubgQMHFgPAp59+qhW3nzhxQuvl5WX89ddfNYWFhRZz8OnTp7UJCQllQUFBBlvnt3379uD09PRg15xBw9y+fVv++uuvh/3vf/9zmWBBeD4tWrAYNmxYwd69e4PEE1ZJSQk7dOhQwPDhwx2uQucplJeXM3f3gfAsevbsWTZ48ODS0aNHF+3cufNKampq8aZNm0LrWr+yspJJIdx17ty5sm/fvuXO2JdGo+EGDx5cGh4e7pRKvc7mvvvuK/Py8jKePHnSQut56tQp3SOPPJKvVCq5Y8eOmYWOoqIi2S+//OLdq1evEsDzz08qjEYjKioqaA5rxrRowWLixIm3s7OzVUeOHDHf3Nu3bw8wGo0YM2ZMLcHi4MGD2t69e8f7+PgkazSa5H79+sWdOXPGS7zOhx9+6Nu/f//2ISEhXTUaTXJcXFznBQsWtNLrLeeGt956K7Bjx46dvL29k7VabXJ8fHyn5cuXm99EUlNTE1JTUxOs+xAREZE4ZsyYaOG7oAI9dOiQ9oEHHmin0+m6JScndwSA6upqzJkzp3VMTExnlUrVPTQ0tOtTTz3VpqyszHzTCuq4ZcuWhcyYMSMiODg4ycfHJzktLS2muLhY9tNPP6n79esX5+3tnRwZGdll3bp1tTQ8p0+f1gwaNKi9r69vNy8vr+7du3fvcPjwYYu3tDFjxkS3atWq66lTpzQpKSkJGo0mOSoqqsuyZctChHVmzpwZ/vrrr4cBgEqlMqvvbf96RFNITk4uKykpkWdlZSmEa2Dp0qUh06ZNaxMaGtpVo9F0v337thwAtm7d6p+UlNRBo9Ek63S6bg888EA7a/V+cXGx7Iknnoj09/fv5u3tnTxo0KD2ly9fVlkf15Yau6ioSDZ9+vSItm3bdlGpVN2Dg4OThg0bFnvt2jVFfddEXarkN998MzAhIaGTWq3uHhAQkDRq1KiYK1euKMXrCKr/t99+O6Bdu3adNRpNcpcuXTp+8sknFtdtU/Dy8uKSkpJKv/rqK/M+DQYDvv32W+2gQYOKEhMTyz777DPzsmPHjmkNBgMbMGBAsa3zS01NTThz5oz27NmzZlOt9Rxx69YtxciRI2O0Wm1yaGho18mTJ7cV3+/2cuDAAR1jLOX999/3mzhxYmRAQEBSQEBAUlpaWkxeXp5c6F+HDh0SAeBvf/tblNAn8e9hz7Uj/BarV68OEuaq3bt3+wHAxx9/rOvUqVNHtVrdvW3btl3Ec6SY4uJi2V/+8peIiIiIRKVS2T0iIiJx9uzZrcXmM+GctmzZ4j9mzJhoX1/fblqtNnnkyJExN2/etChf78jcuXz58uC//vWv4SEhIV11Ol23QYMGtc/MzLS43uy9PwD7njOpqakJKSkpCfv27dN16tSpo/Cs2bZtm7/1/k6fPq0ZOnRorL+/fzcvL6/u0dHRXebMmdNavI49v5MjtGjBIjY2tqpHjx4lW7duNd8I77//ftD9999/R6fTWbyu7dy50y8tLS3B29vbsHHjxkvvvPPOpdLSUvngwYM7ZGRkmC+ijIwM9YABA4rXr19/effu3Rnjxo3LW7FiRfhzzz0XIazzySefaKdPnx7Tu3fv4h07dmRs27Ytc+LEiXl37txpdO2WKVOmtIuKiqpKT0/PXLRo0XUAeOSRR9qtXr06bMyYMfm7d++++Pzzz9/cuXNn8COPPNLOevvVq1e3zs7OVm7cuPHSSy+9lHXw4MGAiRMnRo4ePTp2+PDhhe+//35mhw4dyp9//vnob775xnyRf/HFF96DBg3qUFhYKF+zZs2VrVu3Zvr7++tHjhwZf/LkSQv1aGlpqXzChAntxo0bd3vHjh2ZXbt2LZ09e3bk/v37dQAwY8aM3D/84Q95pjH67ejRo78dPXr0t8aOCVE3V65cUcnlcvj6+pqv81WrVoVlZGSo16xZc2Xbtm2ZGo3GuGzZspDJkyfHxsfHV2zZsuX3VatWXTl//rxmwIABCQUFBeb5Y8KECVG7du0Knjp16s3t27dnxsXFVUyePLlBm31FRQXr379//HvvvRc6bty427t27cpYvnz51YCAAH1eXp7C0WtixYoVwTNmzIiJi4ur2LZtW+a8efOyPv/8c9/+/fsnWJsdzpw5o127dm3refPm3di8efPvBoOBjR07tr3w4HQGffr0Kbl8+bJXVlaWAgD+97//aYqKiuRDhw4t6d27d/GXX35p1mZ89tlnWsYYhg4dWmJrXxs2bLjSsWPHsvj4+HJhHDZs2HBFvM6TTz4Z065du8rt27dnTJo0KTc9PT107ty5YY3t/0svvRTJGOM2bdp06cUXX8z+5JNPAp5++um2AG+K2rp1ayYAzJgx46bQJ8F0Y++1AwCnT5/WrV+/vvXLL7+c/a9//etiSkpK+dmzZ70effTROC8vL+7dd9/9ff78+VkbNmxoderUKV/xttXV1RgwYEDcBx98EDJ16tRbe/fuvfjEE0/krl69OnzatGltrM/p5ZdfjmSMYfPmzb/PmTMn69ixY/5paWmx4nUcnDvDMjMz1evXr7+8ZMmSa999953P448/brGevfeHvc8ZALh69ar6xRdfjHzmmWdupaenZ4aEhFQ9+eSTsWI/mxMnTngPGjSow5UrV9SLFi26tnv37oszZsy4ef36dbPQ4MjvZC8tvgjZ+PHj8+bPn9+2rKzsam5uruL06dO+e/bsuWi93ksvvdS2Z8+exceOHcsU2h588MGi2NjYxCVLlrTevHnzNdN6ucJyo9GI4cOHF1dVVbENGza0XrduXZZcLsepU6d8dDqdQdgGAEaPHl3UlPN46KGHCt56663rwvfDhw9rDx48GLBu3brLzzzzzG0AGDVqVHFgYKB++vTpMV9++aWmT58+ZpV0ZGRk5YcffngZAMaMGVN06tQp3b59+4LWr19/afr06fkA0K9fv9KwsLBuH3zwQUCPHj2yAWDWrFltwsLCqk6dOnXBy8uLM21fGB8f3/kf//hH2NGjR83jVVpaKluzZs3VESNGFAPA8OHDi8PCwvx27NgROGLEiOLY2NjqiIiIagAYOHBgqVJpcR8RTcBgMKC6uhp37tyRb9myJeDIkSMBAwcOtBCgg4ODq48cOZIpk/FzSWFhoWzhwoURY8eOvb1nz57Lwnr33ntvaWJiYpd169YFz58/P+fcuXPq/fv3B86ePfv/27n3oCautgHgT+5cck9ASCDgBRDBxoASrEVAESgCYSwdLspo2s7AMGKlLa1MtfKNo1bbYkFHHTtjpTC0HWG0rRe8DGKrjK1K5fXTakULchUCSLJArpvvD12+BKIsmt5ez+8vXZbdZfc55zx7zpN0btu2rQfgUTxjGEatrq72GH8ttvbv3y+8du2ae1VVVcvKlSvHagnUavXYjCHZmDCbzbB9+3ZpRESE7tixY/eI7SEhIfrExMSg3bt3izdu3NhLbMcwjNbc3HzTw8PDQpwnOjo6uKamhpeXlzdA4rZOKjY2Vrdr1y7v06dPc9Rq9WB9fT3H19fX4OfnZ4qOjsb279/vNTo6SnF1dbU2NjZyZsyYoff29na49BEeHq5ns9m4xWKBpUuXDjvaZ8WKFQO7du3qAnjU3q9cueJ+5MgRIbFtqpRKpa6iooLop7S3b992+fbbb8U4jre6urpalUrlCADAjBkzDLbXRDZ2iO06nY525cqVmzKZbOxvT01Nne7u7m5paGj4nUiAY2NjseDg4Lmenp5jlccHDhwQNjU1sU+cOHH71VdfxQAAVCqVDgCgtLRUUlJS0iOVSseOGxAQMFpTU0Nck5boE7/77juOSqXSTbXvlEqlxh9++OEP4v99fX30LVu2+LS2GZSFWwAADf1JREFUtjL8/f1NU2kfZMcZAIDBwUF6fX397blz5xoAABYuXDgik8nkVVVVgo8//rgHAKCoqMiXx+NZrl69esumreue9TmR9XyJRV2xBC7tJZcNh76mgfSDdtk11LzhB/9bS64QKTK/GxK32zeOtkY38Ht5hNzFOrZ69erB4uJi2TfffMNvbW1likQiU2pqqvbkyZNjbxLXr19ntbe3s955551u20p6DoeDKxQKu6nOtrY2RnFxsaShoYHb29vLsFgsY1NnnZ2ddJlMZlYqlcNarZamUqmmZ2ZmDixbtgwTi8XP9TGI9PR0u6Wb48eP8xgMhnX16tWDttesUqm0+fn5UF9fz7FtHMuWLbMrgAsMDNSfOXMG0tLSxhIeDw8Pi1AoNBHZLoZhlMuXL3PWrl3bTaPRrLbniYqK0h49etRuitrFxQUnkgqAR2vI/v7+etvs+Z/ot9nB/5jlmOBbv119lt+bN29eKPFvKpUKqamp/fv372+33ScpKekhkVQAANTX17MxDKOtWrWq3/bZzpw50zh9+nT9hQsXOADQ+9NPP7FxHIdVq1bZDcbZ2dkDkyUWZ8+e5YnFYpNtUvGsmpubXQYGBugZGRl215GQkIBJJBLj41qHsU5SoVBgRFIBALBgwYJRAID79+8/MR5xHIfxn055WrITGxuLMRgM6/nz59lqtXrwwoUL7IiICIz4mcVioTQ0NLjHxMQMX79+3T09PV0zxT/bTmpq6kPb/8+ZM2e0sbGR+6T9J5OUlGT3XObOnTtSWVlJ6ejooNsmAeORjR1iu1wuHx5/vKamJnZMTMyQ7azarFmzTGFhYVhHR8fYW/mpU6d4EonEGBcXh9meKykpSbtz505pQ0ODu218vfbaa3bxoVarB9euXTv94sWLbJVKpZtq3xkfH293z+Vy+SgAwN27d5n+/v4msu1jKuMMAICfn5+BSCoAAKRSqVkoFJqI+NXpdNRff/2VnZub2zN+Bp4w1edE1gs/YyEQCPC4uLiHVVVVoo6ODuaKFSv6aTT7mdDu7m46AEBhYaF/YWGh//hjeHt7GwEevRUuX758Vm9vL+ODDz7oCgkJ0bu5ueE1NTX83bt3e4+OjlIBAJYvX44dPHjw3t69ez1zcnJmAgBERERgu3btalcqlc9U2Obj42P32cG+vj66yWSicLlchaP9+/v77Z69QCCw6y2ZTKYVAMDDw8OusTMYDKter6cS57BYLFBWVuZdVlbmMMG0WCxA3E8ulzsheWIymVaDwfBCL8n9Fb766qu7fn5+Rh6PZwkICDC6ublZx+/j7e1tF0M9PT10AIC0tLRAR8fk8XgWAIDu7m4GAICPj49drJApOhwYGKBNmzbNKZ971Wg09MfnnXA8sVhsevjwoV3D5vP5dvHo6upqBQAg4tuREydOcFJSUuzuh9VqfWKyx2azraGhoSOXLl3iAABcvnyZs3Hjxg6AR31PUFDQyLlz5zgUCgUMBgNl8eLFuicdiwzbRAkAgMViWY1G4zMXQorFYrtnyGKxrAAARF/2JGRjh2A7A0HQaDQMT0/PCTHk4eFhtk0sNBoNvauri8lkMh2+ABBxQfDy8rI7pouLi5XL5Zo7OzsZAFPvO4VC4YR7DvD/94hs+yA7zhD4fP6Ee2Pbn2o0GhqO4+Dj42Mcvx9hqs+JrBc+sQAAWL16dX9GRkYAjuNQXV19b/zPicZaXFzcmZiYOGHJggikmzdvsm7cuOFmu3wAAHDkyJEJBTVqtXpQrVYPDg0NUU+cOMHZtGmTT0pKSkB3d/d/aDQasFgsHMOwCWu9Q0NDDp8ZhWLfdwiFQjOLxbKePn3a4Xq0TCZ77s5cJBJZqFQq5OTk9L7xxhv9jvYZn6Qhfw+FQjEaGhpqeNo+42OISCrLy8tbibcwW0SnQyQkHR0d9Dlz5ox1Yl1dXZP2LyKRyHznzh1XUn/EJIhBkOjIbWk0GkZoaOhzzW4CACxatGj4/Pnzv03ld15++WXdvn37vOrr6937+/vpS5cuHauhUCqV2MWLF9kUCsUKABAfH++wvuLfhmzsEMbHHsCjZLC3t3dCDPX19U0Y2KVSqbG6uvru+H0BAAICAuwGVmIwJej1eopWq6UTS27O7jvJtg+y4wxZYrHYQqVSobOz84kzcFN9TmQ9X2KRuL1rwvLEVKQfbJuwPDIVz7kMQkhLS9MmJSUNcLlcy/z58/Xjfy6Xy/USicR48+ZNV2KNzBEMw6gAj97qiW0Gg4FSW1srfNLv8Hg8PCsra+ju3busTZs2+T548IAukUjMvr6+xpMnT/L1ej2FqF04efIke3h4mNTbfVJSknbfvn1eg4ODNGK90dm4XC4eHh6O3bhxw23RokXtzkgiWCwWDvDoXgoEgn/El1k86/LDv92SJUswd3d3vKWlhVVQUOAwcQQAiIqKwqhUKlRVVQlt20d1dfUT454QFxenPXbsmLC6upqXnZ3tcDmEbEzI5XK9SCQyHz58WFBYWDi2pHDmzBn3rq4uZn5+/oPJrmcyAoEAX7x48ZT6ncfF3F5bt271EolEZtvp66ioKOxxzQJFJpMZ/P39nzpoMZlMfHBw8B/zQkj0TeNnMMjGztOEhYVhDQ0NPK1WSyWWQ1paWhhNTU1s2xmO+Pj4obq6Ov7jJYMJ/fd4tbW1wvXr149d05dffinAcRwWLVqEATi/7yTbPsiOM2RxOBw8LCwMq62tFe3YsaOLzWZPSEyc8Zwc+ccE6N+JTqeDbfHNeFQqFUpLS++vXLly5vLlyymvv/76gIeHh7m7u5vR2NjIlslkxpKSkgcKhUIvkUiMW7ZskdLpdGAwGNby8vJp44+3fv16SW9vLyMmJkbr6+tramtrYx44cMBz9uzZo8T0WFZW1sDXX38tzsjI8Fer1ZrHVcfT2Gw2qQwyOTlZl5ycPJCTkzMzNzf3QWRk5DCVSoV79+4x6+rqeJ999lnHSy+99NQ3WDJKS0vb4+Pjg6KiogLWrFmjkUqlpr6+PvrVq1fdLRYL7N27t3MqxwsJCdEDAGzZssUrOTl5iE6nW6fakSPOIRQK8c2bN7cXFxf79fX10ZOSkrR8Pt/S3t7O+PHHHznR0dG6vLy8AblcbkhJSRn45JNPJDiOg1KpHKmrq+OeO3eON9k58vLyBg4dOuTx1ltvzWhubu5euHDhsFarpdXV1XGLiop6FQqFnmxM0Ol02LBhQ2dRUZGfSqWanpOT09/e3s7cunWr1M/Pz1BQUPBc9QvPaunSpRiNRoNz587xExIS7Gqh4uLidCMjI9Sff/6ZQ6a+IigoSF9ZWenxxRdfCIKCggw8Hs8il8ufux0/Kx8fHxOfzzfX1tYK582bN8LhcPDAwECDl5eXhUzsPO3YJSUl3UqlUhATExNYWFjYYzAYKNu3b5eIRCK7JYDc3NyByspKcUJCQmB+fv4DhUIxYjAYKC0tLazjx4/z6+rq7trWGNy5c8c1PT3dPysra+DWrVsuRMEvkUQ4u+8k2z7IjjNkzwsA8Omnn7YnJiYGzZ8/P7igoKBHJpOZWlpaWNeuXXOtqKhoJ9vGp3JOAJRYkJaRkTEkEolub9261XvdunX+BoOBKhaLTQqFYjg7O3sA4FH2fvjw4ZaCggJZfn6+P5fLtWRlZWlkMpnx3Xff9SOOFRkZObxnzx7PDz/80HdoaIguFArNixcvHtq5c+fY7E9KSopu586dbXv27PHKzMwUBAcHj1RUVPyRmZk509H1OXL06NE/tm3b5llVVSUuLy/3ZjKZuEQiMcbGxmptq6SfxyuvvDJy4cKF3z766CPJhg0bZBiG0QQCgTkkJGQkNze3b/Ij2MvMzHx46tSpvkOHDnl8/vnn3lar9alr2Mifq6ioSCOTyUylpaXT8vLyRBaLBTw9PU0RERHYggULxgb3ysrKtry8PMu+ffu8ysvLKZGRkbpDhw7dS0hImP2047NYLGtDQ8Pv77//vqSystKjtLRUwufzzeHh4Rixvj6VmHjvvfc0bm5ueFlZmVd2dvYsNzc3PCYmZqisrKzDtgjwryQQCPDZs2ePPJ7Zs1vq8PX1Nfv5+Rna2tpYUVFRky6DbN68ubulpYW1fv16/5GREeqCBQuwX3755fafd/VPR6PRoLy8vK2kpESqUqkCLRYLpaysrHXdunX9ZGPnScLCwvSHDx++U1xc7Pvmm2/O8PT0NL399tvdly5dYhM1KwCPYuj8+fO/b9y40buiokK8bds2lqurK+7r62uIj48fcnFxsXvuO3bsuP/999/z16xZMwPHccqSJUseHjhwwK6Q2dl9J9n2QWacmYro6OiRs2fP3tq0aZN0w4YNMpPJRPX29jZkZ2ePzU4873NyhGK1Pn3Zprm5uVUul/8tmT6CIAiCOMOxY8c4KSkpgUeOHPk9LS3tT1keftE0NzeL5XK5//jtqBofQRAEQRCnQYkFgiAIgiBOg2osEARBkP96ycnJOlSv9ddAMxYIgiAIgjgNSiwQBEEQBHEalFggCIIgCOI0ZBILHMfxZ/6ueQRBEARB/rs8/qoKh99XMWliQaFQekZHR12cfVEIgiAIgvw7DQ8Pu1EoFIffWD1pYmE2m/+ntbWVOTw87IpmLhAEQRDkxWS1WsFoNNI1Gg2/tbWVbjKZdjrab9Jv3gQAaGpqSqDT6ZutVqsXoLoMBEEQBHkR4RQKZdBqtTabzebt4eHhdxztRCqxQBAEQRAEIQPNPiAIgiAI4jQosUAQBEEQxGlQYoEgCIIgiNOgxAJBEARBEKdBiQWCIAiCIE7zf53dcO5W69a+AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,measurements_ps5[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,measurements_ps5[49:100], linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50)/100,prediction_ps5[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,prediction_ps5[49:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Measurement', 'Measurement', 'Prediction - No Interdependence', 'Prediction - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
+        "plt.savefig(\"loop_stable.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,np.abs(error_ps5[:50]),linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,np.abs(error_ps5[49:100]), linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50,100)/100,error_prediction_ps5[:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Prediction Error', 'Prediction Error','Estimated Prediction Error - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=1)\n",
+        "plt.savefig(\"loop_stable_error.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "outputId": "2e1bf57d-915a-4b86-c712-f664486a88b2",
+        "id": "gaKyXv1ax6mR"
+      },
+      "execution_count": 253,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['PS5'].estimate_maximum_gradient(generator_dictionary['PS5']).values()))\n",
+        "plt.bar(list(selected_features['PS5'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "id": "CaxGaQNpzIdh",
+        "outputId": "5c304a43-c72f-463c-947d-53e9b0148a4f"
+      },
+      "execution_count": 254,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['PS6'].estimate_maximum_gradient(generator_dictionary['PS6']).values()))\n",
+        "plt.bar(list(selected_features['PS6'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "id": "nipd5zAqzzLQ",
+        "outputId": "621480a6-823d-48ad-93da-ec6694e74797"
+      },
+      "execution_count": 255,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.sqrt(0.7*0.67) < 1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_3Ojd1Wgz5v0",
+        "outputId": "44c5abe8-72e7-4926-ebf9-78ad37d6407d"
+      },
+      "execution_count": 256,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 256
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scenario 3 - Loop Unstable"
+      ],
+      "metadata": {
+        "id": "cwhY05mjpbPM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 257,
+      "metadata": {
+        "id": "uCiQ5dDR0OuM"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Dataset(\"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data/hydraulic.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 258,
+      "metadata": {
+        "id": "9ROlg_JD0OuN"
+      },
+      "outputs": [],
+      "source": [
+        "selected_features = dataset.select_features(number_of_features=3, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 259,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0V5P1NJz0OuO",
+        "outputId": "561142d8-5cba-4f2d-f64c-df64341ccbed"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'PS1': 0.9768350574890512,\n",
+              " 'SE': 0.8221303782937127,\n",
+              " 'FS1': 0.7408146512995961}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 259
+        }
+      ],
+      "source": [
+        "selected_features['MPW']['FS1'] = 0.7408146512995961\n",
+        "del selected_features['MPW']['PS2']\n",
+        "selected_features['MPW']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 261,
+      "metadata": {
+        "id": "3BXk6MeP0OuP"
+      },
+      "outputs": [],
+      "source": [
+        "targets = ['PS1','PS2', 'PS3', 'PS4', 'PS5', 'PS6', 'MPW', 'SE', 'TS3', 'TS4', 'FS1', 'CE','TS1', 'TS2']\n",
+        "generator_dictionary = {} \n",
+        "for t in targets:\n",
+        "  generator_dictionary[t] = PredictorDataGenerator(dataset.df,t,list(selected_features[t].keys()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 262,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DeC8GHvD0OuP",
+        "outputId": "a6da89be-dc87-400b-9d10-52559eb136ec"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "training for: PS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 1.4855e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 345ms/step - loss: 1.4855e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "training for: PS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.9515\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 332ms/step - loss: 0.9515\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "training for: PS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0248\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 349ms/step - loss: 0.0248\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "training for: PS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 6.5204e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 326ms/step - loss: 6.5204e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "training for: PS5\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0439\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 311ms/step - loss: 0.0439\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "training for: PS6\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1136\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 344ms/step - loss: 0.1136\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "training for: MPW\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0012\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 325ms/step - loss: 0.0012\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "training for: SE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 2.0266e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 336ms/step - loss: 2.0266e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "training for: TS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0224\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 335ms/step - loss: 0.0224\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "training for: TS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0012\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 329ms/step - loss: 0.0012\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "training for: FS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0401\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 1s 830ms/step - loss: 0.0401\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "training for: CE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0527\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/CE_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 314ms/step - loss: 0.0527\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "training for: TS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.1194\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS1_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 343ms/step - loss: 0.1194\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "training for: TS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.2764\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS2_model_na3.ckpt\n",
+            "1/1 [==============================] - 0s 401ms/step - loss: 0.2764\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictorSet = PredictorSet(generator_dictionary, train=True, autoregressive=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "measurements_ps1, prediction_ps1, error_ps1, error_prediction_ps1 = predictorSet.evaluate_loop_fault_operation('PS1','MPW',50,100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4kG1Ju350OuQ",
+        "outputId": "a97e18af-ca9f-4945-d615-845bc9a422a6"
+      },
+      "execution_count": 263,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 26ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 25ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 264,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "NGOYwmYs0OuQ",
+        "outputId": "facb86d4-9fbc-444a-cb6e-28daaeba0044"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,measurements_ps1[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,measurements_ps1[49:100], linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50)/100,prediction_ps1[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,prediction_ps1[49:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Measurement', 'Measurement', 'Prediction - No Interdependence', 'Prediction - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
+        "plt.savefig(\"loop_stable.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,np.abs(error_ps1[:50]),linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,np.abs(error_ps1[49:100]), linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50,100)/100,error_prediction_ps1[:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Prediction Error', 'Prediction Error','Estimated Prediction Error - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=1)\n",
+        "plt.savefig(\"loop_stable_error.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "id": "IPxyZEoz0OuR",
+        "outputId": "563996ac-ed39-475c-9cf4-a5ac9b882481"
+      },
+      "execution_count": 265,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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Y1caMipqdobL2qdiHEgcREelxPtsZWjGyVeLQsAcaarxyRjb07p/kyLo+JQ4iIpLW1lSs4cLnL+T6N66noq6CZtccu8VhZ2mo3Ff7VESjWRUiIpLW7l1yL4u3L2bx9sWsqljFT2b+hJpAq8KA3gMYkjskVHnRn0LlkUclN9BuQi0OIiKS1sI3siqtKOXKl68MHk8qnIS1tCrUVcLHj4RuPPKbyQqxW1HiICIiaWtv017KqspandvTGFqboVU3xccPh8Y3DD4QRh+XjBC7HSUOIiKSttZVrqPJNQGQl51HdkbrjaxaLfyUOwgKx3nloy7X+IYYlDiIiEjaWlWxKlg+cuiR/Pbk35KTmQNAhmUwdfDUUOUp58HVC+HCf8KU85MdarehwZEiIpK2Vu0KJQ4TBkzg6BFH8+DpD/LQpw8xY9gMivoVtb4hIwPGn5rkKLsXJQ4iIpK2SitC0ysnbPkM6u7m4OxcfjHgSMgZ4Q2IzMlPYYTdjxIHERFJW6W7QonD+I/+Dxoa9q2UU+B1TxRNS2Jk3ZfGOIiISFqqrq9mc81mALKcY1S0pAGgrgJe+BEs/gc0NSYxwu5JLQ4iIpKWwrfNHt3QQDbAoRdARqbXRbGjFHaugqZ62LQQFmbCIeelLN7uQomDiIikpVYDIzP7Qd9m+OL/Qq/cUKWmRihf4+2IOWyqNzhS2qTEQURE0lL4wMjx066AKVfsWykzCwZPBCYmL7BuTqmViIikpVYDIwvGpzCS9KLEQURE0lL44k8TBkxIYSTpRYmDiIiknZ17dlJeVw5An6w+jMgbkeKI0ocSBxER6fa2125nRfmK4HH4+IZxGX3I+PhhqNqcitDSjhIHERHp1rZUb+FLT36Jc585lwc+eQCIGBi5swye+g5s+yzWI6QDlDiIiEi39tK6l6gJbIf9m49+w7Kdy1pNxRxfH1j4acDoFESXfjQdU0REurUlO5YEy02uiTlvz2m1ffaElhUj84sib5VOUOIgIiLd2uLti1sdh3dTQKDFod8wyOqdzLDSVtK7KsxspJk9ZmaVZlZlZo+bWbHPe281s3+Z2U4zc2Z2SZQ6w8zsNjNbaGYVZrbdzOaZ2fFR6v4p8JzIz6/i8FVFRCTBttZsZVvttpjX85uaGNzUBAWjkhhVektqi4OZ5QKvAnuBiwEH3AK8ZmZTnHM17TziGuBj4Fngohh1jgDOBx4E5gO9gG8Dr5vZbOfcsxH1twOzI85t8feNREQklT7Z8UmwfMSeOrKA9/vkBM+Nr2/AAAp8/X0qPiS7q+JyYCxQ4pwrBTCzJcAq4Ergjnbuz3fONZvZeGInDm8DE51zwS3OzOwlYBlwPV7SEa7eOTe/w99ERERSbsn20PiGqXv3cv7uar4yZgI1jbUAjG8Z3zBALQ7xkuyuitnA/JakAcA5txZ4Bzi7vZudc80+6lSEJw2Bc414LRVaAUREJI2EJw5T9tYzvLGJ/xoT+nVyYu0er6AWh7hJduJwELA0yvllwOREvdTMegEzgWiTeIeY2Q4zazSzlWb2IzPLTFQsIiISHw3NDXy689Pg8ZS9ewE4uz6DR858hL83DuLYPXXeRSUOcZPsropCYFeU8+XAgAS+9yagCLgg4vzHwCK8xCUH+DJwGzAB+Ga0B5nZFcAVAMXF+ocoIpIqq3atoq7JSwyGZfdncFOgUXrjAg4+8QaoCBuupsGRcZP20zHN7OvADcDNzrm3wq855yJnTzxvZtXAtWb2C+fcqojrOOfuA+4DmDZtmktQ2CIi0o5W3RSDD4GVgQbtjQuguRmOvQ52rYWKMuivnup4iZk4mNncTj7zHufc1hjXdhG9ZSFWS8R+MbMvAX8CHnDO3ejztkeAa4FpeIM2RUSkC1qydWGwPCWrAHIHQu1OqKuEnaUw/YoURpe+2mpxmNOJ5zngSSBW4rAMb5xDpMnAp1HOd5qZnQI8CjyBN2Ojo9SaICLShX3y+YfB8pRVb0LRkbDyRe/ExgUweGKKIktv7Q2OnOGcy/DzwVsvwdp53tPADDMb23LCzEYDxwSuxYWZzQSeAuYBF/qZjRHmArykYUG84hERkfiqqKtgXd12ALKc48Di42HkdBhyEBx+MQwcl+II01dbLQ6rgboOPKvZxz33A1cDT5nZHLxf0DcDG4B7WyqZ2ajAs+Y65+aGnT8BGAwMDZyaFhiTgHPusUCdScBzwA7gduAIs1A+07JmQ+AdDwF/B0qB3niDIy8B7nXOre7AdxcRkSQKX/hpUn09vUcfDyVnwHHXpTCqniFm4uCcm9CRBwX+qm/zHudcjZmdDNyJ90vb8FoFrnXOVYdVNSCTfVtEfgqcEHb8ncCn5R6AGXjjKAYAr0UJo6XebrzZHD8CDsBLfJYD3wXuaet7iIhIan0SPr6hrh5GzYSwPxJ58b/h86Xewk9HXQlDD05BlOmp3VkVgTUQfgE87Jzb7+Z751wZ8NV26qwjSreHc+5EH8//E96AyPbqlQPntFdPRES6niWb3gmWp/QZAn0ixt1v/MAb57AWmHJ+coNLc+0uAOWcq8cbXNgn8eGIiIi0bceeHSypXBM8njJsxr6Vdq0PlbWGQ1z5XcfhI+AQ4M0ExiIiIhJVs2vmg60f8OiKR3l1w6s0BnYWGNDURNHY00IVqzbDgj9ATWDHzIws6D88BRGnL7+Jw38Cj5jZeuA555ymKoqISNLc8OYNvLDuhX3Of6GmFht9TOjE3t3w1i9Dx/1HQIZ2EYgnv4nDo0A+3hTHBjPbTut1DpxzTm1BIiISdxV1FfskDYfV1XHu7hrOzB0FuYWhCwM7NK5fOsFv4jAPLYgkIiIpsLZqbbA8Im8EvxkwnQlvBnYMKDmqdeWMiKF71dsSHF3P4ytxcM5dkuA4REREolpXuS5YPmTQIUw4/iaYehFsWgQDRu97w9HfhXfv8sond2YRZGlL2m9yJSIi3dvaylCLw5j8Md56DQPHxV4d8oQfQfXn4BwcflGSouw5OpQ4mNlUoARvC+pWnHN/iVdQIiIiLfZJHNrTOw++cl8CI+rZfCUOZlaAt4xzy2TZlsWZwsc9KHEQEZG4W1e1Llge3X90yuIQT7sLQAXcCgwEjsdLGr4MnAz8DVgDHBX7VhERkc5paGpgw+4NweNRNRWw7TNo7sjehRJPfhOHWXjJw/zA8Ubn3OvOuYuAV4DvJSI4ERHp2TZUb6DJNQEwrO8wcufdDPfMgJ8Xw7p32rlbEsFv4jAMWOOca8Lb/bJf2LXHgTPjHZiIiEj4+IbR/UfB5o+8g/rdUOhjvIPEnd/EYStQECivB2aGXRsf14hEREQCWg2MzM6HhlrvoN9wLSWdIn5nVbyNNzDyWbztsG80s9FAI3Ax8HQighMRkZ4tfA2HMQ2NoQtFRyQ/GAH8Jw4/BVpSu9vxBkqeD+TiJQ3XxD80ERHp6cJXjRy9e0fowgglDqnid+XI1cDqQLkBb9Or/0xgXCIi0sM551p3VWwrDV0cMS0FEQn4H+MgIiKSVOV15eyu3w1AblYuQ7at8C5YBgw/LIWR9WxtJg5m1s/MZpnZWWaWFzhXYmaPmNkyM3vdzL6SnFBFRKQnaTWjImcQ5gJrNwye5K0OKSkRs6vCzCbirdEwAm/Rp61m9iXghcDxGuBg4FEzm+WceyUJ8YqISA8RPr5hDNmhCxrfkFJttTjcjLdmwxfwZlR8CjwJfASMdM5NB4qBN4AbEhyniIj0MOEzKkbX1YQuFGl8Qyq1lTgcA8x1zs1zzn2AN3NiBPBb51wdgHOuFvgNXsuDiIhI3LQaGFk4CYpnQlYfDYxMsbZmVQwlMJMioKW8OaLeFmBwPIMSERFplTjMuAYGTISmBrDMFEYlbSUOGUBT2HFL2UXUizwWERHZL3ub9rK5xvs71TCK+xV7FzKz27hLkqG9dRxGmNnYQDkz7FxFWJ2i+IclIiI9WVlVGc2BWRTD84aTk5WT4oikRXuJw2NRzj0ZcWyo1UFEROJoXdW6YHlMvjaz6kraShwuTVoUIiLSo2154Tp+X1dGft9hfPGwK1lTsSZ4bfSWT+GlH8OIw+HAsyHT724Jkggx/9d3zv05mYGIiEgPtXc3v1rzJM/n9YWqFTy45XWyM0JjGcZsK4XVH0NOARykNQdTLeZ0TDNbYWa/MLOZseqIiIjst52rWdy7d6tTDc0NwfKYhkB5+GFglszIJIq21nF4EDgeeNvMtpjZfWZ2hpn1SlJsIiLSA+zetpRN2V4DeIZz9GluDl7rRQYT6wOJw4jDUxGeRIiZODjnfu6cm4m36NNPgZHAE8B2M/s/M/uamfVPUpwiIpKmVm1ZFCxPqG/g9bJN3JZ3COdOPJdfNheS35JIDFfi0BW0uzumc26rc+73zrkz8BZ6uhJvTYff4SUR/zKzq8xseIJjFRGRNLSiYmWwPLG+gVznOGvrGm6cPocTN68IVVSLQ5fQoW21nXO7nXN/d859DS+JOBtvRck5wIYExCciImluRe3WYLmkvt4rbP8M1r8LDYE9KvKGQn/9fdoVtJs4mFkvM7vTzI4MP++ca3DOveicu8o5NwI4OmFRiohIenKOVU2hDawm5ITtYPDeb0NltTZ0GX66Kurxuif6tFPv/XgFJSIiPUNT1UZWZYf2nigZf0bo4soXQ2WNb+gy/HZVfAQckshARESk59m4cT57MrxfRYNcBgPHnRq94ojDkhiVtMVv4vCfwA/M7CwzTaIVEZH4WLF1YbA8Mas/jJwOGVHWJlSLQ5fhN3F4FBgIPAXsMbMNZlYW9lnv94VmNtLMHjOzSjOrMrPHzazY5723BmZx7DQzZ2aXtFH3cjNbbmZ7A4tZfStGvXPM7CMzqzOz9WY2x0x7toqIJMPKXqGFn0oKJ0Gvvq2ThIlnwHE/gNzCFEQn0fhd8HsecdjIysxygVeBvcDFgWfeArxmZlOcczVt3Q9cA3wMPAtc1MZ7LgfuBW4DXgFOAe4xM3PO/S6s3izgn8ADwHXAYcCtQD/gR535jiIi4t+Khl3B8oSS2V5h9LGw8QOvPGAUnPI/KYhMYvGVODjnLonT+y4HxgIlzrlSADNbAqzCG4B5Rzv35zvnms1sPDESBzPLAn4GPOSc+3Hg9GuBdSZuNrM/OOda1jL9OfC2c+6KsHp5wBwzu9M5txUREUmYleWhNRxKCku8wuhj4N3fQNGRMGhiiiKTWDq0jkMczAbmtyQNAM65tcA7eGtCtMk519xeHWAm3hoTf404/xBed8ux4HWZAIfGqJcNnIGIiCTM7vrdbK7ZDEBWRhZj+ge2zx5zAtxQBpe9AEd+I4URSjQd2pvUzKYCJUBO5DXn3F98POIgvHESkZYB53UklnbeAbA0yjsAJgOvxarnnFtrZrWBeiIikiArd4VaG8bljyM7M7AjZma295EuyVfiYGYFwHPAjJZTgZ/h4x78JA6FwK4o58uBAX5i8fkOorynPOJ6rHot56KOxDGzK4ArAIqLfY3pFBGRKFYufzJYnogShe7Cb1fFrXjN/MfjJQ1fBk4G/gasAY5KSHRdkHPuPufcNOfctMGDB7d/g4iIRLVi56fBcklDYwojkY7wmzjMwkse5geONzrnXnfOXYQ3a+F7Pp+zi+gtC7FaIjqj5TmR72lpQShvp17LufIo50VEJE5W1W4JlicMmJTCSKQj/CYOw4A1zrkmoA5vumKLx4EzfT5nGaGxBeEmA59GOd8ZLWMZIt/TMmbh07bqmdloIDeO8YiISISm5iZWNVYHj0uG95iG627Pb+KwFSgIlNfjzVxoMb4D73samGFmY1tOBH5RHxO4Fg/vATuACyLOX4jXivAOgHOuDFgco14D8EKc4hERkQgbdm9gj3nD5AY1NjFwmJaU7i78zqp4G29g5LN40xVvDPzCb8RbyMnvL/37gauBp8xsDt7gypvxtuS+t6WSmY3C2657rnNubtj5E/CmWg4NnJpmZtUAzrnHAj8bzOx/8BZ82oTXlXIycBlwTWDTrhb/DTxrZvcCj+AtADUH+LXWcBARSZyVn38YLE9sbIJ+2jK7u/CbOPwUaPl/9Xa8gZLn4zXpP423omO7nHM1ZnYycCdeAmJ4q1Je65yrDqtqQCb7toj8FDgh7Pg7gU/LPT1HKCQAACAASURBVC3v+b2ZObw9Nn4IlAFXO+fuiYjneTM7F7gRuAT4HG8sx8/8fB8REem4ZtfMswvuCh6XZOZBRrKXFZLOMuf2eyXpHmvatGlu4cKF7VcUEZGgu96+iftX/zN4fF/vicz893/GvkFiMrNFzrlpyXynUjwREUmsJf8Hmz8C4KnSp1olDV+r3M3MIdr5sjtps6vCzPoBR+Mtwfy6c67azEqAm4ApwHbgLufc44kOVEREuqHacnj2+1BfzYJxR3NT2PCxY2v3cH15BUz6YgoDlI6KmTiY2US8gYUj8MYPbDWzL+HNNjC8hZ8OBh41s1nOuVeSEK+IiHQn798L9dXsyMzg+00baQyMRptQMIHbT76erD6DYFBHJudJqrXVVXEz3poNX8CbUfEp8CTwETDSOTcdKAbeAG5IcJwiItLd1FXB+78H4MW+fakMJA0DcwZy9yl3kzdyhpKGbqitxOEYvOmQ85xzH+DNnBgB/NY5VwfgnKsFfoPX8iAiIhKy8AGoqwBgVV5o+5/LDr6MYXnDUhWV7Ke2EoeheGsptGgpb46otwVvbQURERFPwx547+7gYWnB0GB5YuHEVEQkcdJW4pABNIUdt5Qj529qPqeIiLS26mWo2Q6A619EaUNl8NL4AnVPdGftLQA1Imx56MywcxVhdYriH5aIiHRrK0Kr9m85eDa1W54FoKB3AQNzBqYqKomD9hKHx6KcezLi2FCrg4iItGhugpUvBg9LD5jgdWrjtTaYWYwbpTtoK3G4NGlRiIhI+tjwAewp98p5Q1llzcFL6qbo/mImDs65PyczEBERSRNr3wyVS06ntDI0zn7CgAkpCEjiSUtOi4hI+5yDN2+Hzz9tv+4J18O33oaTfgxT/p3SitLgJbU4dH8xEwcz+4mZ+d7n1Dw/MbOh7dcWEZFuZeMCePUW+N1M+MvZXiIRixkMPQROuJ7GkUeypmJN8NK4gnFJCFYSqa0WhxvxFnzqyLNuJLT9toiIpIsPw3qv84u85MCHDbs3UN9cD8CQ3CHk985PRHSSRG0NjjTgJ2a23eezNExWRCQd1VXB0rC9DA+/xPet4d0UEwo0viEdtJU4lNHxpaTLgL2dD0dERLqcpY9BQ61XHjIZiqZFr1dbDsuegJIzoL/X+KzxDemnrVkVo5MYh4iIdFWLwroptn0Kdx8FeQfAJc+2rrfqZXjuOu9z6IVwzt2U7gpLHAYocUgH7S0AJSIiPdmWxbDl49bndqyEusp96654PlQe6C06rBaH9KPpmCIiElt4a8NBXwEL/Nqo/hwaw3qmmxqgdF7ouOSL1DfVs75qffDU2PyxSPenxEFERKKrr4FPHg0dT7sM8sJm3FeFbZa8czXU7/bK/YbD4EmsrVxLk/P2RyzKKyI3OzcJQUuiKXEQEZHolj0Je6u8cuE4GH2sNxWzRdWmUHlnqEuCIZPArHU3hcY3pA0lDiIiEt2a10Llwy701m7ID1vep3JjqByeOAz0kgRNxUxPGhwpIiLRnf5zOPKbUL4WRs30zvWPkTiUh/ajCCYOuzQwMh35ThzMrC/wDeB4YCBwhXNulZn9O/Cxc255gmIUEZFU6DvI+xTPCJ3LHxkqt2pxCEscCr1lpVdVrAqeUldF+vCVOJjZSOB1oAhYjrcwVL/A5ZOAU4FvJiA+ERHpSsK7KmKNcRg4jtqGWjZVe9ezLIsx/cckKUBJNL9jHH6JtyLkROAIWi8v/QZwXJzjEhGRrih8cGRlIHGoq/KmZwJkZENBMZ+VfxasNqr/KLIzs5MYpCSS366K0/C6JtabWWbEtU10bDMsERHp6vZUAA76DGh9vn944hDoqmisg0Mv8FodLAMyMnluzXPBaocOOTTx8UrS+E0cegG7Y1zLBxrjE46IiHQJCx+AeXMhpwCO/wEcfY13vu8gOPdBb6xDS7dF3hA4557grXWNdby49sXg8exxs5MZuSSY38RhCfBV4MUo184AFsUtIhERSb3ytd7PugrI7B06bwYHf6XNW1/b8Bq7G7y/NUf2G8lhQw5LVJSSAn4Th9uBx8zbf/3hwLnJZnY23kwLpZMiIulk17pQubBjAxufLH0yWD573NkEfndImvCVODjnHjezbwM/By4LnP4LXvfF1c65aC0RIiLSXbW0OAAM8J84bK3Zynub3wPAMHVTpCG/0zHzgQeBh4CZwBBgJ/Cucy7W2AcREemOGveGplpaBhQU71vHOa8bo6kRXvpvb7zDwPE8k1GDwwEwfdh0huUNS2LgkgztJg5mloWXJHzZOfcM8ErCoxIRkdTZtR4Cv/zpXwRZvVpfX/o4PHU1NNTA+NOg9GUAXK88npoUGs9w9vizkxSwJFO76zg45xqBz4GmxIcjIiIptyusm6Jw9L7Xc/p7SQPA2jeCpxcPGsX63d422nnZeZxSfEoCg5RU8bsA1F/RypAiIj1D+ZpQOdr4hvC1HJrqg8Un+4ZmX8waPYs+WX0SEZ2kmN9ZFeuAr5vZAuApYAvBdiyPc+6P8Q1NRERSInxgZLQZFfn7rvnXALzYVBE8Pmf8OQkITLoCvy0Od+OtDnkEMBe4H/hD2Od+vy80s5Fm9piZVZpZlZk9bmZRRt5EvTfHzG43sy1mtsfM3jOz4yPqXGJmro3P0LC6r8eoc63f7yMiknZadVWM3fd6736Qk9/qVFl2FjXOWwtwSO4Qpg6emsgIJYX8tjjEZXcSM8sFXsXb9+JivFaLW4DXzGyKc66mnUc8AJwJ/BBYA3wHeMnMZjrnPg7UeQ5v5kerVwPPAGucc1sjri0Brow4t873lxIRSTdN9d5sCtcceypm/yKoqwwebsoK/ToZ03+M1m5IY37XcVgfp/ddDowFSpxzpQBmtgRYhffL+45YN5rZVODrwGXOuQcD594AluG1gswOxLod2B5x73F4W4HfGOXRu51z8/fva4mIpJGLnoKmBqgoa72Ndrj8Iti2LHi4MSxxGNFP2xelM79dFfEyG5jfkjQAOOfWAu8A7c3bmY3XjfaPsHsbgb8Ds8ysd6wb8Vo36oFHOhm3iEjPkpkNA8ftOxWzRcQ4h019+gXLRXlFkbUljfhdAGotEYMhIznnonSE7eMgvMGVkZYB5/m4d61zrjbKvb2A8YFyK2bWJ/DsZ51z5VGee5iZVQK5wGfAr51zD7QTi4hIz9Y/WuJQB8CIPLU4pDO/YxzeYN/EYSBwNFCNN27Bj0JgV5Tz5cCAKOf93ttyPZpzgP7An6NcexP4G7ASKAAuAv5gZsOcc7dEe5iZXQFcAVBc7GtMp4hI+onowtiUlQnNXlldFenN7xiHS6KdN7MCvB0zu/JqkhcD24DnIy84534SceopM3sC+LGZ/co5Vx3lnvuA+wCmTZvWZiuMiEi3U/a+t5T0gDEwYBRkxegFjuyqsNAagWpxSG/7NcbBOVeBt3Nm5C/gWHYRvWUhVmuC33sh1PIQZGbDgFOBhwPjIfx4BMgBDvFZX0Qkfcy/Gx7+N7j7SG9p6VhGTIPvfwo/Kafyv8rY3eR1U/TJ6sPAnIFJClZSIR6DI+sAvyNhluGNVYg0GfjUx71jAlM6I++tB0r3vYULgUyid1O0R60JItLzlLezhkOL7Byv1SEjk43VG4Onh/cdrqmYaa7TiYOZZZnZocBNRBmUGMPTwAwzC/5rNLPRwDGBa215BsgmbBBlYAOu84F/Oef2RrnnImBJ2BoPflwA7AE+6cA9IiLdn3Owa13oONqqkVFs2r0pWNb4hvTnd1ZFM7H/Aq/CW5TJj/uBq/HGEswJPPNmYANwb9j7RgGrgbnOubkAzrmPzOwfwK/MLBtYC1yFtzjVBVFiPhw4GPjPGN/pOOAG4HG8BZ/y8cZDzAZu8LEYlYhIeqkth71VXjm7L/Qd7Ou2TdVhiYPGN6Q9v7Mq5rJv4lAHrAdecM5V7nvLvpxzNWZ2MnAn8BDeio7zgGsjBiIaXhdDZIvIpcDP8FabLAAWA6c75z6M8rqLgUa8WRPRbAk8fy4wCG+NiCXA151zWu9BRHqeXRF7VPjsclDi0LP4nVVxU7xe6JwrA77aTp11eMlD5Pk9wHWBT3vv+R7wvTaulwJntPccEZEeI3x8w4DRvm8LH+NQ1E+LP6W7/RnjMNnMvmpmw+MZkIiIpMiWsOFgbQ2MjBA+xkGrRqY/X4mDmf3WzH4fdvwVvG6CR4FPzezIBMUnIiLJsvKlUHn0cb5uaXbNbK7eHDxWV0X689vicAbwbtjxT4FnganAB0TfPEpERLqLnath5yqvnNUHxvhLHHbs2UF9cz0A+b3zyeuVl6gIpYvwmzgMI7DVtJkV4a3FcJtz7hPgLkAtDiIiqbBnF7z4X/DB/d50ys5a9a9QeeyJkN3H120aGNnz+J1VUQu0pJEn4E3BXBg4rgb6RbtJREQS7KUfw8eByWOZveCIizv3nFHHwNHXeN0VE7/g+7aNu0MDI5U49Ax+E4cPge+YWRnwHeBl51xgOxPG4E1tFBGRZGpqDCUNAL36dv5Zw6Z4ny/cAs3N7dcPaDWjQgMjewS/icOP8TazWgxUAN8Ku3YO3jgHERFJpg3vh8p5Q+HgNme6+5fhf8Jdq1Uj1eLQI/j61+GcWwAUA0cBY5xzS8Iu34cGR4qIJN+qsFkQJWf4XrCJbZ/Bo5dCw579DiF8jIPWcOgZ/LY4EFiCeVH4OTMb6Jx7Lu5RiYhI+1aGDWicOMvfPQse8AZTNu2FvCEw6zZvmek+BZ0KQYMjex6/6zhcbmY/DDs+xMw2AtvMbKGZDU1YhCIisq+KMtj+mVfO7A1jjvfKzc3w8SPw2TPR78vI8pIGgPd/D+/cCf9vLDx4Jix8sEMhNDQ38Hnt5wAYxvA8rQfYE/jtyLoGb8fIFnfgjXW4Fm9zqLlxjktERNoSvljTmOO8gZG71sODp8OT34LnfhC9K+Lwi2Bi2Gr78+aCa4L1b7ceM+HD1uqtNAfGyQ/OHUyvzF6d+SbSzfhNHEYBywHMLB9vSub1zrnf4I1v8NlGJiIicRG+7sKEwH+CcwtD22JXb4U1b+x7nxnMvgtyB+17bYL/aZigGRU9ld/EIQNomZ9zLN5Oma8HjjcAQ+IbloiIxFRfC2vfDB23rLvQux9MOT90fuWLofKGBfDsdbDkUWhqgNm/af3MjCwYd3Kbr91eu527PryLl9a9hHNO4xt6KL+DI1cBZwKvAv8OvOucqw1cGw6UJyA2ERGJZtMiaKzzyoNKWu9kOfF0ePcur7zqX95qkmZQ+gosfMD7TPsGnHWH123x4V+8usUz2x0geev7t/JK2SsAnD76dAp6h+qP6KfEoafwmzj8L/CQmV0MDADOC7t2ErAk6l0iIhJ/Y46D7y/zEoPM3q2vjZwOOflQVwlVm+DzpTD0ECh7L1Rn1NHez1m3efV2rIJZP2vzlfVN9by96e3g8YvrXmx1XS0OPYevxME593Bg1cjpwALnXFgbGZ8DTyciOBERiSG/CKZdtu/5zCwYfxosfcw7XvkiDJ4EGxeE6hTP8H72zoN/+4uv1y3evpi6prqY15U49By+lwdzzr3tnPtlRNKAc+5G59zz8Q9NREQ6ZeLpofLKl2DrEmgI9C7nj/SSjg76YGtogeDJAyeTm5Xb6roGR/YcvhMHM+trZt81s8fM7DUzmxA4/+9mNilxIYqISIeMPwUs8J/3jQvhs2dD14pnduqRH2wJJQ6XHnwpfz/r70wcMBGAw4YcxtC+Ws6np/DVVWFmI/FmURThTcs8mNCOmCcBpwLfTEB8IiISbtMi6NUPCsd63RLR5BZ6Yx3K3gMcvH1H6FpLN0UH1DbUsmR7aCjbUUOPojCnkP876/8orShlbMFYzO9y19Lt+W1x+CWwF5gIHAGE/wt5AzguznGJiEg0z3wP7j4Sbh0Gmz+KXS/WEtSdaHH4cNuHNLpG77EDJlKYUwhAZkYmJYUlZGdkd/iZ0n35TRxOA250zq3HW8Mh3CZAo2JERBKtucmbAQHQVN96GmakiWfAqGO9KZctcgq8gZIdFN5NcdTQozp8v6QXv4lDL2B3jGv5QGN8whERkZh2rQut35A3FPoMiF13yCS49DkoOjJ0rnhGh7bMbvH+1tBS1NOHTe/w/ZJe/P4LWgLE2uj9DCJ2zRQRkQTY9lmoPMRny0HZ/FC5E+MbKvdW8tlO770ZlsERBxzR4WdIevG7ANTtwGOBwS8PB85NNrOzgW8AsxMQm4iIhNseljj47XI46cfeUtJl77W7pHQ0C7cuxAV6qA8aeBD9evVr5w5Jd34XgHrczL4N/BxoWXHkL3jdF1c7516MebOIiMTHtuWhst/EIX8EHHKu9+mE8G4KjW8Q8D8dMx94EHgImIm3qdVOvD0rYo19EBGReNoeljgMOTAhr1iwdQG1DbUcPfxosjOzWw+MHKbEQXwkDmaWhZckfNk59wzwSsKjEhGR1poaYcfK0HEnZke0562Nb/Hted8GYFCfQcweN5vVlasByM7I5rAhh8X9ndL9tDs40jnXiLcfRVPiwxERkah2rfWmYAL0G9buTpad8UTpE8Hyjj07+OPSPwaPpw6eSp+sPnF/p3Q/fmdV/BWtDCkikjrbOjEwsgPqGuta7X4ZSd0U0sLvrIp1wNfNbAHwFLCFiIWgnHN/jHKfiIjEQ1ZvbxnpbcsTkji8u/ld9jTuAWB0/9FcNfUq/rb8byzZvoQ+WX04a+xZcX+ndE9+E4e7Az9H4C05HckBShxERBJl4izv4xw07o374+eVzQuWTx11Kl8c+0W+OPaLbNy9kb7ZfRmQ08ZiU9Kj+E0cxiQ0ChER8ccMsnPi+siG5gZe3/B68PiU4lOC5aJ+2i5bWvObONQA1c65ukQGIyIiybfo80VU1VcBcEDuARw08KAURyRdWczBkWaWaWY3mdkuvFkVVWb2TzOL/1BeERFJmVfWh2bZn1J8irbIlja11eLwLeAnwOvAAmAs8GWgCrg04ZGJiIin7H1Y8RwMPhCKpsGgCXF7dLNr5rWy14LHp446NW7PlvTUVuJwOXC/c+7KlhNmdiXwWzO70jlXn/DoREQE1rwO7/zaK8+8Gmb9LG6P/mTHJ2zbsw2Agt4FWuRJ2tXWOg5jgUcjzv0DyARGdfaFZjbSzB4zs0ozqzKzx82s2Oe9OWZ2u5ltMbM9ZvaemR0fpd46M3NRPudEqXu5mS03s71mtsLMvtXZ7yYiElfr3ob62s5tbuVT+GyKk0aeRFaG36Fv0lO19S8kD69bIlzLvhSd2h7NzHKBV4G9wMV40zhvAV4zsynOuZp2HvEAcCbwQ2AN8B3gJTOb6Zz7OKLuS8BNEedWRMRzOXAvcBveUtqnAPeYmTnnftfBryciEj+rXoZHvgZFR0L11tD5OO5R4Zxj3vpQ4hA+m0IklvZSyxFmNjbsODPsfEV4RefcGh/vuxyvJaPEOVcKYGZLgFXAlcAdsW40s6nA14HLnHMPBs69ASwD5rLv1t47nHPziSGwB8fPgIeccz8OnH7NzIYDN5vZH5xzDT6+k4hIfFWUwT8uhOYGKHu39bVBE+P2mjc2vkHZ7jIAcrNymTF8RtyeLemrvSWnH8P7pd7yadma7cmI86t8vm82ML8laQBwzq0F3gHO9nFvA153Scu9jcDfgVlm1ttnDC1mAoPxltMO9xAwEDi2g88TEYmPgmI4ec6+5/sXQU7/uLzi/S3v84M3fhA8Pqn4JHpndvQ/o9ITtdXikIiZEwfhLVkdaRlwno971zrnaqPc2wsYHyi3+JKZ1eK1knwE/Nw592TE8wCWRnkewGTgNUREkqVqC/QdBJnZcPQ10KsvPHsdwRX+h8RnfMOHn3/INa9ew94mbwXK4X2Hc+3h18bl2ZL+YiYOzrk/J+B9hcCuKOfLgfbWM23r3pbrLZ7Bm0K6FjgAuBp4wsz+wzn314j6kc+M9rwgM7sCuAKguNjXmE4REX+e/BZsWQwHfgmO+wFMuwyy+8JT34bmRjjoK/v9iqU7lvLted8O7ksxJHcIf5j1B4b2Hbrfz5aeIS2Hzzrnrgk/NrMngPl4gyAjuyY6+uz7gPsApk2b5tqpLiLiT81OWPsWuCb48C9wwg3e+annw+hjoWY7DJu6X6+obajl6nlXU9PgjUMfmDOQB77wACP7jdzf6KUH8butdrzsInrLQqzWBL/3QqilYB/OuSa8qaVFZjYs7HlEeWa7zxMRibvlz3hJA0DRUZA/InQtfwQMP9Tbp2I/vLP5HXbW7QS8NRvu/8L9jM4fvV/PlJ4n2YnDMkJjC8JNBj71ce+YwJTOyHvrgdJ9b4mqpZWgZSxDZDyTAz/bi0dEJH6WhQ3BOujLCXlF+EZW55ecz4QB8VuBUnqOZCcOTwMzwqd4mtlo4JjAtbY8A2QTNogyMKXyfOBfzrmY+8yG1StzzrVMiH4P2AFcEFH9QrzWhnfa/zoiInFQsxPWvhk6ntzeJLOOa2pu4s2NoXecNPKkuL9DeoZkj3G4H2+g4lNmNgfvr/+bgQ14CzEBYGajgNXAXOfcXADn3Edm9g/gV2aWjTfw8Sq8Lb8vCLv3a3hTO58PPPcAvIWiDge+1lLPOddgZv+Dt+DTJrwFoE4GLgOu0ZLaIpI0y5+N3U0RJ4u3L6Zir7f8zuA+gzlwYPwWkpKeJamJg3OuxsxOBu7EWy/BgHnAtc656rCqhjeNMrJF5FK8RZtuAQqAxcDpzrkPw+qsBYYAt+ONV6gBFgbqvRQRz+/NzAH/ibcaZRlwtXPunjh8XRERfz4N76bYZ2X8uHh94+vB8gkjTyDDkt3gLOki6bMqnHNlwFfbqbMOL3mIPL8HuC7wiXXvfLyWA7/x3EtYa4eISFJtWOBtYtUiAd0U0Hp8w4lFJybkHdIzKOUUEUmVhj3w5FXgmr3j8adBflHcX7O+aj1rK9cCkJOZw/Rh0+P+Duk5lDiIiKRK6SuwM7Bif688OCvmdj37Jby1YcbwGeRk5STkPdIzKHEQEUmVA78EFz/j7U0x62fezwQITxw0m0L2V1quHCki0m2MOR6+PR+yI5eoiY/KvZV8tO2j4PHxRccn5D3Sc6jFQUQk3nauht8fC386C+qq2q/fq+9+rwoZy1ub3qIpMNVzyqApDOozKCHvkZ5DLQ4iIvH27l2w9ROv/MYvvG6IJFpftZ41FWvYXLOZp1eH1tY7YeQJSY1D0pMSBxGReNu1LlR+77etE4c1b8Cbt8PEWVDyRRg4Lq6v/t3Hv+OexdGXojlx5IlxfZf0TOqqEBGJt3MfhOACSwbV20PXVjwP696Cf82BD+6P62vrm+r586d/jnptyuApTCjQ3hSy/9TiICISb7mFMHI6lL0HOCh9GQ79OjgHK14I1Zs4K66vfX/L+8Etswt6F3DaqNMYnjec4n7FHFd0HJagcRTSsyhxEBFJhImzAokDXrJw6Ndhx0qoWO+d65UHo46J6yvnlc0LlmePm80Pj/xhXJ8vAuqqEBFJjIlnhMqrX4XGelj5YujcuJMhq1fcXtfU3MRrG14LHp9SfErcni0STi0OIiLx9O5vYctiGDwxdK6+Gta/DSvD9tmbeHpcX7t4+2LK68oBKMwpZOrgqXF9vkgLJQ4iIvFU+nJo06r8Yqgs88qL/w5l8wOVDCacFtfXhndTnDTyJDIzMuP6fJEW6qoQEYmn7StD5WO+Gyov+QcEFmJixBGQNyRur3TOtUocTh11atyeLRJJiYOISLzUVcLuzV45sxccegH06rdvvTh3U6zctZJN1ZsAyMvOY/pQ7X4piaPEQUQkXnasCpUHjodeufDVP8DVCyGnIHRtP6Zh7q7fzfVvXM8Nb93Axt0bgdbdFMcVHUd2Znanny/SHo1xEBGJl+3LQ+VBgcGRJae3Tij6DYehh3T6FQ99+hAvrPPWgnhjwxvMmTGnVeKg2RSSaEocRETiZfuKUHnwpFB50AT44WrY+AHU7NivDa3e2/xesFzdUM0Nb90QPO6V0YvjRhzX6WeL+KHEQUQkXlolDhNbX8vMglFH79fj9zTuYenOpTGvzxw+k9wEbc8t0kJjHERE4iW8qyK8xSFOFm9fTGNzIwCj+o9i9rjZra6rm0KSQS0OIiLxUF8LFYE1GyzDGxwZZ4s+XxQszxg2gzkz5nD08KO5d8m9jOo/ijPHnhn3d4pEUuIgIhIPO1cBzisPGANZveP+ioVbFwbL0w6YBsCZY89UwiBJpcRBRCQeCsfCBf/0uisy47cHRYu9TXtZsn1J8PiIA46I+ztE/FDiICISQ31TPb0ye0FDHSz4A2x4H2ZeDcVRFljq3Q8mnOp9EuCT7Z9Q31wPeOMbBucOTsh7RNqjxEFEJIpff/hr/rj0j3xl4GHcuGpRaPzChg/g2iUJ6Ypoy8LP9+2mEEkFzaoQEYmwrXYbf/zkAZpdM4/tWMSuKm+FRrJy4LSfeoMfkyw8cVA3haSSEgcRkQjPr3me5paBjsDGrEDj7Gk3wyHnQeSSzs3NXndGgjQ0NbB42+Lg8ZFDj0zYu0Tao64KEZEwzjmeWvV4q3MbJ5/JIbN+BX0Kot+0YyX8biYUjIIxx8Psu+Ia07Kdy6hr8hKTEXkjGNp3aFyfL9IRanEQEQmzvHw5pVVrW53bOPaY2EkDwNo3wTXDrrVQtSnuMWl8g3QlanEQEQnz9Oqn9zm3sXpj6xPOwep50H8ElK+Fl/4rdG344fsdw/wt8/lo20fMHDaTqYOntl6/YagSB0ktJQ4iIgENzQ08v/b5fc63bF8NwPp34fkfwudLYdhU2PYZBJaBZtBEmHHVfsWwcfdGrnrlKhqbG7nn43soyitix54dwetqcZBUU+IgIhLw7qZ3Ka8rB6B3czN7M7ze3FaJQ688L2kA2BIasMiAMXDR05BbuF8xvLjuxeB+FNC6tWNo36GMyBuxX88X2V8a4yAiEvDU6qeC5fPyD6Rl8+uttVtpaGrwsm0rrwAAIABJREFUDoZNgbEntr4xvxgufgb6D9vvGP617l/BclZG67/tpg+dju3Hltwi8aDEQUQEqNxbyesbXg8ef/WknzMk9wAAml0zW2q2hCof871Qud9wuPgpKBi53zFs2L2Bz8o/AyA7I5uXz32ZO068gy+M+gInFp3IVYfuXzeISDyoq0JEeqQ9jXv466f/n737jo+qSv8H/nlmkkkjvUBCSIFAKoQQukJCQGkKrqC4CLuCumYVd/2hERQrIlipfpV1RVFYVhB1VVACCKgoKAgEIZQAAUIKSQjpdTLn98edcqckmZBJIXner9e8mDlz751zr3HuM+c855yNyC7PRp2mDrkVuajTSK0KUd5RCPMMQ6BrIK5WXgUgdVcEuQVJO/dJAqasAXKOAbf8A/AMadZnZ1zPwKbTm5DUKwmjAkfpy3dd2qV/PjJgJHycfHBb8G24Lfi2lp0sYzbEgQNjrEtafWQ1Np7aaPG9KX2mAAACuwXql7I2G1kx6C/So5mEEHjyhyeRWZKJLzK+wJY7tiDcKxyAcTcFBwuso2rzrgoi6kVEW4mohIhKiegLIgqycl9HInqTiHKJqIqIDhDRaJNt+hHRKiI6TkTl2m2/JqJYC8fbR0TCwuMJW50vY6zjqVJX4X/n/mfxPW9Hb0wOlZapDnQN1JcbJUi2wPni88gskeaJ0AgN3jz8JoQQyC7PxslrJwFIuQ1jgsbY5PMYs7U2bXEgImcAewDUAPgrpMXrlwDYS0QDhBAVTRxiHYDJAFIAXADwGIBUIhohhDim3eZ2AGMAfAzgCAAPAE8DOEhEtwohfjc55nEAj5iUXbyB02OM3SR2XdqF8rpyANJMjMmxybBT2MFB6YBBP74Dj/V3An6RCAwbod/HrMXhBv1w5Qej17/m/oofrvyAiyUX9WUj/EfATeVmk89jzNbauqviYQC9AYQLIc4BABEdB5AB6ea9vKEdtS0GMwHMFUJ8pC37AcBJAIsBTNFu+imA/xNCCNm+eyAFA/8EYNq2WCaEONjiM2OM3TQ+P/u5/vk9/e7BXWF3SS+EAHL+BlQVAXnHEThwmn67rLIsm3z2j1d+NCt7+/DbcLZ31r/mbgrWkbV1V8UUAAd1QQMACCEyAfwMYKoV+9YB2CzbVw0pUBhPRA7askJ50KAtKwFwFgAPgGasi8ssycSR/CMAACUpMTVM9tVTni8FDQCg6obAHoZVKK+UXYHJV0uzFVcX41iB1DhKILjYuwAALpZeRPq1dACAHdkhKSipRZ/DWGtq68AhGsAJC+UnAURZsW+mEKLSwr4qAGEN7UhEXgBiAJyy8HacNt+iTpsX8WAT9WCM3cS+zPhS/zwhMAE+Tj6GNwtkXxG+4fB28oaTnRMAoLyuHCU1JVZ/TrW6GhV1xr2v+3P2QyM0AIABvgOQPCDZbL9h/sPg7uBu9ecw1tbaOnDwAnDdQnkRAM8W7Kt7vyFrABCAlSblPwJ4AlJrxnRIXSYfENFzTdSFMXYTqtPUGU3yNK3fNOMN8k8bnvtGgoiMZmq0Ns/hWP4xjNg0And8eQfOXj+rL/8xy9BNkRCYgJmRM9HL1Xj+h9tDbrfqMxhrL51+AigiegZSbsQ8eRcJAAghXhBC/FsI8YMQ4ishxDQA/wOwiIi6NXC8vxHRYSI6XFBQ0PonwBizmR+zftRPKe3n7IeRASONN5C3OPhFAmh4ZMXX57/Ga7+9hqsVV80+56MTH0Et1CisKsTLv7wMjdCgTlOH/Tn79duMDhwNlVKF+fHz9WVKUmJMLx5NwTq2tg4crsNyy0JDrQnW7gsYWh70iCgZwFIAzwkhPrSyjv8F4Aigv6U3hRDvCyEGCyEG+/r6WnlIxlhH8HmGISnyrrC7zKZ0Nmpx8IsAAKMWAV2Lw7H8Y1i0fxH+c+o/eP3Q60aHUGvU+C3vN/3r44XH8dW5r3As/xjKassASGtO9PPsBwAYGzQW9/S7B/YKeyTHJsPTsanGV8baV1uPqjgJKVfBVBSAdCv2/RMROZvkOUQBqAVg1JpARLMBvAvgbSHEqzdQ15ZlQTHG2t2x/GP4NfdXFNcUo7imGD/n/Kx/709hfzLeWAiTHAdti0M38xaHL88Z8iR+vPIjKusq9aMiThSe0A/11Fnx+wqjeRkSAhP0a04QEV4Y8QKeH/48r0PBbgptHTh8DeAtIuothLgAAEQUAuAWAAub2PcbAC8DuAfSHA0gIjsAMwDsFELU6DYkoj8B+AjAB0KIp5pZx/sBVAH4o5n7MYY9p68iLasED4wMgaeLqr2r06UduXoEc1Ln6JMR5Yb7DzfqggAAlOUB1drkRwc3wC0AgHlXRbW6GqkXU/VlNfU1OJh7UD8S4kDOAbPPu15zHV9kfKF/PTpwtNk2HDSwm0Vbd1X8G9J8Cl8R0VQimgLgKwBZAP6l24iIgolITUQv6MqEEEchDcVcSUQPEdFYSEMxQwG8KNt3NKTuhjQA64louOwRJ9tuFBFtJ6IHiWgsEd1NRF9BSpR82YrJqBgzklVUiYc/+R2rvs9Ayta0pndgraZeU49lvy2zGDQQCA/1f8h8J6PWhghAeyM3ChzKr2Bv1l6z0RLySZ0O5BoCh/Eh480+xsnOCcP8h1l9Lox1NG3a4iCEqCCiJAArAGyANNLhewBPCCHkbXsEQAnzwGYOgFchzTbpASk4mCCEOCLbJgmAA4BBkOaHkLsEIET7PFd7/MUAfCDNEXEcwEwhxH9v/CxZV5V2pRj1GqmHa/epfFy6VoFgb5d2rlXX9OW5L3G6SMpXcFQ64tGBj8LL0QseDh7o69kXAd0CzHcqOGN4rs1vAGA0qiK3Iteo5UDnh6wfoBEaVNZV4njBcX35s8OeRVltGX7J+UVfNsx/GByUDi05PcbaVZsvciWEuAxgWhPbXIQUPJiWVwGYr300tO9LAF6yoh7nAExsajvGrHXpmvEUI//59TKenRTZTrXpInLTgBOfA/3vAXpI+cyltaVYfWS1fpMH+z+IOTFzzPe9ehLYOheI/hOQuBAYlgxETJYSJF0Mczs4KB3g5+yH/Mp8aIQGB3MNE812s++G8rpyXKu+hhOFJ3Ct6hrqRT0AINIrEl6OXnhm6DP409d/glqjBiDlNzB2M+v0wzEZayuXTQKHLYezUF1X3yqfVa8RWLD1OO57/wDOF5Q3vUNnVK9G3ab7cPLQe6j8dKaU3AhgbdpaXK+RBmn5u/jjgegHLO+f+ixQcBpQKKXXRIBHENDvdqDnIKNN5QmSOoO7D8a44HH61/uy9hl1U4wIkNa5CHEPwfz4+SAQArsF8jwN7KbHgQNjNnKpyLjfu7iyDtuP57bKZ23/IxebD2fh4IUirNqd0Sqf0dGJvD+Q7KLGfT17YIy7Bi/sexLfXvgW/z1l6Gl8cvCTcLRzNN/5+kXgwj7p+fHPgKrGR4ObJVJCWno7MTBR/3pv1l6jxEhd4AAAs6NmY+f0nfhy6pe8eBW76bV5VwXrvGrU9bBXKKBQdM3scNMWBwDYcPASpsWb33Raan+GYfKx3y81NQVK55Se8Q1+c5KCgkqFAl9e3oUvL+/Svz+4+2DcHtzAr/ujGw3PXbsDTo3PnWAaODgqHXFb8G1QkAIqhQq1mlqcKzaMCHdQOiDOL85onx4uPaw5LcY6PG5xYDaRllWMIUt2Y+RreyzeQDu7GnU9ckurAQAKAlRK6X+tY1nFOJFt/foG1vrl/DX98+ziKlwrr2lk687p6+wfGnxPQQosGLrA8hDHejVw9D+G10MebvKzTLsqkoKS0E3VDc72zhjqP9Rs+/ju8ZwAyTotDhy6kPzSajz2nyNY/E26PvvfVj78OROl1WrklVbjtR2W1hLr3LKKqnRd7PB3d8Kk/oZflxsPXrLxZ1XiyvUqo7LjrRCcdGR1mjrsqDVM9ZzSbybu6XcPXO1dAQDJA5IR4RVheefz3wNlOdJzF1+g34QmP890PYkpfabon1uaInqE/wizMsY6C+6q6ELe3nkW2/+Q+tzjgjxwZ6yFIWk3QAhh9Av42z/ycCK7BDE9u84Kf5dl+Q3B3s6YNTwY/zsm3Zz+dywbz0yKhLuTvU0+65fzhWZlx7NKMCbczybHvxn8cvYrFGm7xPzU9bh/8Hwo7R2wcOhClNWWwdvJu+Gdj3xieB77Z8Cu6Ym6Qt1DYa+wR52mDn7OfkbzMFiazEme38BYZ8MtDl2EEAJ7zuTrXx+4cK2RrZvnfEE5CsqMm8pX7DrbwNadk3woZrC3M+KDPRHRQ/r1W12nwZdHrFtV0RryIE3n+JVimx2/o8mryEN5rfHIkW/OfKZ/PtnOC0p7qVtApVQ1HjSUXQXOfGd4PegvVtXB3cEdL498GYm9EvFWwltGa1z0cOmBSC/DsFtvR2/9OhSMdUYcOHQRp/PKjG7uv1+0XUKdpRvZ96fzceRy10nakwcOQV4uICLcN8TQvH3YRgmMpq07OmlXSiBE51teZevZrbht622Y/OVk/fLUpbWl2FtsWIzqzrxLwKf3A6vjgGvnGz9g2iZAO88CgkYCPn2trsudfe7EmqQ1ZkmPgHF3xfCA4Tx9NOvUOHDoIn48a7wE+Nn8MpRU1dnk2L+cM9zIXB0Nv8SW7+w6rQ5ZRcYtDgAQFWDoqrlcZJuEUXnrjpujHVxU0hwEheU1yC2ptslndBQVdRVYeWQlAKCougjzvp+HwqpC7Lq4C7WQppKOqKlF36pS4PQ2oOgCkN9Ifo0Qxt0UVrY2WOO+iPvQz7Mferj0wIMxD9rsuIx1RBw4dBE/ZRj3iwsBHLVBi4BGI4y6Pd66JxZKbd/z/nOFOGDh13F7yyyswG+ZRTb9hX6pSN7iIAUOIdoAAgAuFtpm6RN5a8OIPt5GeSTHr3SuBMnNZzajpMZwTrkVufjHnn/gS9mUz3dWVAIDZxl2aixwuHoSKMqUnju4AVFTbVZXT0dPfD7lc+ycthN9Pa1vxWDsZsSBQxdQVVuP3y4WmZXbYvx/em6pvuXCp5sDbo/qjrvjDHP7L991psM0oR+/UoyHPzmMMW/tw73/OoB39zXRrG0ljUYYtSgEaQMGX1cHONlLLQKl1WoUV9a2+LPkrTsj+/ggtpeH/nVnynOoUlfh45Mfm5X/UfgH0gqltSAUpMCkye8DvWTDIQsaCRx6xABP/AEkPguMmAeonBve9gZxFwXrCjhw6AJ+zbyGWrX5KoG2CBzkLQoj+3iDiPCPsX1hr5S+QA9dvI4jl9v3hpaeU4q/fvgbprzzM3alG4bwfWGjhMWrZdX66+vpbA83R2n0BBHpWx8A87UsTJ3JK8OCrceRejLP4vumrTsj+3hjQGDnbHH4/OznKKqWgt3uzt3x1OCnzLYZETACPv0mAn5RhsLGWhwAwKMXkLhAejDGbggHDl3Aj2cN3RTy+QWOZRVDXW8eUOh8/vsVPPTxIfyWad5aoSMfGjiyj5TN3svLGVNiDa0Ou09dNdtPTgiBj37ORPKG35FxtazRbZurvEaNe/91AD+Y5HgAwPmCClwtbXlegFFipMlqmEGy7opLTeQ5PLH5GDYfzsK8TUeQZyFfwbR1J8yvG2IDjVscOkrrTkvU1tfioxMf6V/PjZmLv0b/FTMjZhptd2fvO6UnvuGGwsIMoN42uTuMMcs4cOgCfpJNT3zv4F4IcNdO01tbj1O5lm/Ux68U48nP0rD7VD7mrj9klPynU1evMQoqRvYxrCg4Prq7/vmeU/lozBdHsvHyN+nYcTIPj206YtOb3/GsYpTXSKsSKgi4MzYA0QGGtQJskYMhnykz2Mu4+Vv++vK1hvMcLl2rwKncUgBAXb3A96fNgy1LrTuBnk7wdJZaOEqr1U22atwM/nfuf8ivkv5mfJx8cHffuwEAKUNSMDZoLACgt3tvJAUlSTs4ugHu2hEsmrqmR1YwxlqEA4dOLqe4Chn50hh4lZ0Cw0K9ER/ipX//90vmrQlCCCzZbmjyLa9R46nP0qAxmW3y+JUSVNRKQ9t6ejihl5eT/r1bwnz00y6fuVqG7GLjmQ51sour8NLXJ/Wvz14tx6+NtHA0V7r2ZgwA0+MDsebPcZgQbWh1sTSZUnNdMpn8SS7Yx9ACcbGRm/qe08bB1d7T5sGWpdYdIkJ/WatDmpV5DtV19fj+1FVcr2h53oUt1WnqsO6PdfrXD0Q/oF+kyk5hhxUxj2KL/yT8JyoZTvWylUf9ZMuX56cbH/T0diB1kTTqgjHWYhw4dHL7ZaMphoZ4wUmlRHyQ4UZjaX6BXelXzbonfs0swoc/ZxqVHTC5kckTw1wc7DC8j2EiHtMbIyD12T+1JQ1l2hYBnQ1NTNGcX1aNp7em4d1955psnUjPMQQO0drhkSPDDPWyNCeCXGZhBZ749Cg2HLjY4DbGczg01uJgfeCw/1yh0ZLcjbXuxDYzz6G0ug5T3tmPBz8+jNkf/moWELanLWe2IKdCmnHTw8ED9/S7x+h9Or8Hkb+sRbdN9wHf/MPwhq9semnTPIdf1gAH3gFWDwKOb2mtqjPWZXDg0Mn9IOumGN1PutkMNmpxMA4c6uo1eO07w+Q6um4NAHgj9QzOynIQ5Ddd+c1YJyncV/98j4U8h/W/XNQn+8mT0VNP5CG/zHLugUYjkLzhd2w5fAVv7DiD1JON50/IWxyitF0UAwI94Kyd/+DK9SqL3TCA1NIye92v+N+xHDz/1UkcbGC2zctGczgY5zgEG+U4WO6qKK9R49cLxoFadZ3GKBFyf0Zhg607AwKtH1mh0QjM35yGs1elVqgT2aUdZp2L9GvpePvw2/rXf4n6C5ztTUY+ZB00PO9lmPbZKEFSPrIi9zhwWbvUtcIOCE2wYY0Z65o4cOjE6jUCP58ztAqM6ivdyCN6uOpvnLkl1ciRdSP85+AlXNDOOeDqaIf/PXaLPiegVq3B/9t8DCeyS3D8SrFRa8WI3oZfwDpJEYY8h1/OX0NVreEX9Ln8Mry+wxCg/D2hD4aESEsbqzUCm3/LsnhOWw5nGY3S+N/R7AbPv7quHufyDVMV66aAtlcqMDTUEDw11F2xZFu60WJSGw5YbgkxnW5aLsDDST+vxdXSGqNroLM/oxC1FpJU5bkh8laYyQP8jVp35C0OJ7JLG014XbPnnFmyqqWgrq2V1ZbhyX1Pok4jJTaGe4ZjdtRs442EAC7/anhtFDjIuiquyroqDv3b8DxqqrSENmOsRThw6MT+yC5BcaX0Rezr6qC/cdopFRjYy7y7oqSqDqu+z9CXP54UBj83R6yYMRAqO+lP5WROKe5Ysx9T3vlZPwSxt68LeshaJnSCvJ0R5tcNAFCj1uDABekGXVevwfwtaajR7h/p74YnxvXDrOHB+n3/+9tlsxtgUUUtXpMFGwCw50w+yqotZ9Gfyy+HWtsMH+ztDFdHwyJTI/s03l3x/amr+PSQcfCSejIP+SajMEoq6/QjHRztFfBzNV5K2V6pQE8PQ+uApRkk5fkMg2TdSHtO50MIgayiSuyVrTNy/7Ago/393BzRw026/lV19ThfYLll4/tTV7Fit/lsnvI1TNqDEAIv/PwCrpRLw2Nd7F3wduLb+twGveLLhlUtVd2A7jGG93zDgcnLgTk7gIe/l8rK8oDjhjUtMLTp5bMZY03jwKETk2fhj+rrY/QrdXCwp/757xeLkF9WjWe//APXtYFGoKcT/jIiBADQr7srUm6XDXkzMbqvb4PvjY0wrNj4vfYX9P/tPafvi1cpFVgxIxYqOwUmxPSAt4u0UmFOSbVZv//r353WB0I6tWqN0dwMcvL8hih/N6P35DkCv5y/ZpQrUVRRiwWf/6F/rbtsao0wCybk3Q9BXs4WJwAy6q4wGVmh0RgvPvb0hAi4OkjTdmcXV+Hs1XJs+u2yfsnu0f18zbpDABjN52ApQfJCQTme+PSY/vXQEC/YaVtCTmSX2mRY6o36z6n/YPfl3frXL498GcFuweYbZslaGwIHA0rZ4r72TsCQB4HgEYCTJ1BeAHw8BVBrW4x69DduoWCM3TAOHDqxU7L+/XhZoADAaGTFl0ezcevre7H9eK6+bMGECDhqZz0EgLm3hiI5oQ+iA9wQ5W94TOrfA/OSwhqswxhZ4LD3dD7SsoqxZs85fdn82/shood0U3ewU2KGbGEoefP84YtF2HzYcNO+RZZT8U1ajsXPNspvMAkcIv3d9MtcF5TV4HyB1KUhhMCiL/9AYbm0HoSvqwNenhKt32/Tr8YtIaaLW1kiDxxMWxxO5pTq157wclFhSIgXRvUzBDU7TuRhsyxYmT3cwg0VMJpB0tIQ0xe/PqlPQu3p4YS1s+ONumssjeJoC2kFaUZ5DX+O+DPGh4y3vPFlWX5DUCPLVlcUAp9MAQrPSK9JCYx7yTiRhjF2wzhw6MRO5xlunLqbs05ckIf+e7S0Wm00s+TEmB64Y4C/0fZKBWHhxAhs/8cofPtPw+Pd++Ph0824eV4uPtgTbtqFr3JKqvHQJ4dRr+0+GBLiiYdH9Tba/s9Dg/T1+imjEKt2Z+C9feex8AtDC8DtUd3x2t0D9K9/yii0OKzQqMUhwPj8lQrC8N7yPAfpZvvxLxfx3QnDzI1vTBuA+4YEwaeb1BKSV1qN72U32csWFrcyFSwLKEznWZDP15DQzxdKBRnlhrz3wzkUac8twN0RSbJATC6hn6HVZ9+ZfP01BoDrFbX6XBci4F+z4+HlojI6lqVRL21hzdE1UAspoIn2jrY4Q6ReVgP5DXKVRcAnUw1DMkkBTPs3EDbORjVmjHHg0EnVqI37usO1+Q06bo72ZsHEgEB3/Psvg/F/MwfZbM59e6UCo2U3Nd2va2eVEm/fM1CfOKjTy8sZSeGGG9qK3Wfx+o7T+iRHJ3slXpwSjV5ezojT5gOoNQI7TKZpFkIYtbiYBg6ASXfFuWv44KcLeOkbQ2Ldn4cGYUyEH1R2CqOWkI2ylhB514PpUEx9uXyxK5OuCvkvfd2NPDHcVx88VdcZArqZw4LMrpdOdIAburtJAdz1yjocyzIkrn5/Oh+6OCKul4d+YSx5a9D+c4WoUZsnblpy6VoFpr6zHw989Bsqa9VN79CIFYkrcFvwbXBVueKthLegUqosb1hVLC1SBUjBQOBgy9tdOQx9vw4pgD+9D8RMa1EdGWPGOHDopM7ll+t/dQZ5OaObg53ZNinj+yHA3REj+3jjk7lD8dVjt+C2qO5QNHBzulFjI81/JT9/R5TRDVVu7q2hDR7riXF99cmGdw4I0JebdldcuV6lb5r3dLbXJw/KyRMkd5+6ajTp1cBeHnhusiFT37QlJFM78sR4uukGWhwa6KooKKtBmjbXQ6kgfYDl083BaCppALBXEu6VBS+miAhjws3zSQBgV7ohqLotyjD5VW8fF/0KnpW19WZDQhuyZPsppF0pwb4zBdj062Wr9mmIq8oVbye8jc13bEaga2DDG145DEAbEPToDzi4mm9Tmgt8/TiQrw0wpr4LDLjHfDvGWItw4NBJnZZNJR3Rw8KXLKThkr88MxabHh6O0f18W21lv4R+fkbdy2PCfXFfIzfBW8J88MFfBuORhN5GjzenD8DfRhu6NqRhidLzAxeuGY14OGnSTWHp3ML8uum7WdSypv0hIZ7Y8OBQuMiCrUBPZ6NEz1e3p+PD/Zn6WTkB8+mmdeQtEdnXq/Q5EvKREoODPfU5FwDMuiTGR/eAn6t58CM3xkLXQ3VdvdFaJbfLpgInIov7NCa7uArfy4Zv2qKLg4jQy7XhvwcAJvM3DLe8jZs/MGg20L0/MP0jYOCfW1w3xpg5Dhw6KaP8Bn/zZvq25OWiwqT+Us6Er6sDXp82oMkgZVxUdzwzMdLocc/gXkb7dXdzxDBtgp8QwLd/GJI7G0uM1CEio1YHQGqF+HjuUKOhmzr3yxITd5/Kx+Jt6fr8AwVJwYUlzio7/TBNtUYgp1gKcHbKuldMAwXT1w0lRcrdKpvm+3SeNM33/oxCVGlnoOzj64I+vt2M9hkry6f4/vTVJmfi3PTrJcgnmvwtswilJsNhq+vqsT+j0CbLiOvFTAfGLwUi7wT6JDW8XdJzwN/3AzF32+6zGWNGOHC4yZzLL8O7+84ZBQaWnM4ztDhENtDi0JbemDYAHz4wGKlPjIafhW6DG3VnrKy7QjYqpLHESLmJMYam+4R+vvjwgSFwVpl36wBAQl9f/bwUpuKCPPVzXVgSbJLnkF1cZfRr/XbZ+hmAlLOgG2I5NMTLaAREQ1wc7DCst/FIiZ0NdFPoDA31got2MrCsoir96BJLatT1+NRkYi61RhhNaw4Aj//3KGat+xV3v/eLxeXcNxy8hP0Zhc1bzMwvAhjxGDBjIxA+wfr9GGM2Z/kbknVIO07k4p+fHkONWoPlO89iwYQIPHhrqMWcBPmql+3d4gBINzX5aAFbmRjjjxe+Ool6jcDvl67jyOXrGBTkaZwY6e/e4P4TYnpgxYxYVNVqMC2+JxzslA1uq1AQPvzrEGw+fBkVNYZEQjcne9w7uJH+eUhDNQ9dlBIWLxVV4rfMIv0v91vDfBDqYzyUk4iw6eHhOHr5OuKCPK3uRhob4YeftDfy3aeu4g/Z2hXybgodlZ0Ct/b10U/dved0PsL8LAeaO07k4ZqF0Svfn8rXtyidySvTz6txoaACe07nY4IsOCuprMOSbemoUWvQ3xtY//dx8G5kVA5jrOPhwOEmIITAuv2ZePXbU/qEcbVG4NVvT+Hn84V4+55Yoy/fgrIa/TwETvbKBrP9OwMvFxXGRvhhp/Zm9eSWNGx6eJh+NU6VnQK9fS3PrwBIN+g/xTV+05cL8nZGyviIpjc0IW9xOJ9fjm2y1pFZDXRDdHOw008Tbq2kiO76kSH7zhjWKfF1dcBAk4RLnbER3fWBw+ZDWbgnvhc8XcxHN3wim3J7XGR3/dTV+87kQ6MRUCgIm341npb7iyNXjAKHL37LwN/EVoxBXt9RAAAgAElEQVRXHUbPymJ4OPES2IzdbLirooNT12vwwlcnsWS7IWiwVxp+fe47U4CJq37CCdlCRWdk3RT9erg2OISvs3j+jij9qJHMwgo8suF3/Xvh3V1hr2z/P3N54LD19yv6wK6HmyPGWRh1cqOCvJ3Rx0KgNC6y4dEyuiGnAHC+oAL3vX/QbJGxkzkl+gXR7JWEpX+K0c9tca2iFmlXilFZq8YXR4zXDtl7Jl+fB6LRCGw4lIe/2O1CjOIiPEUx6Mqhpk+qpqzpbRhjbab9v1FZg2rVGjy26YjRDIqDgz3x84Iko9EF+WU1+H+bj+n7jOX5Dx0hv6G19fJyxgt3GlZHlC8t3VBiZFuTTxNdLltG/M9Dg2Bn48BmbKR5l8TtUQ13E/m6OmDJ1Bj9CJUzV8tw79oD+lYbwHjuigkx/vBzc0RiuPGIjG/ScsyWSK+rF/qhsr+cv4YL16qwqz7esMHpbY2fjBDAmsHAqljgi0ek+RwYY+2KA4cOqkZdj0f/87vRstF3xgZg40PD4OfmiGcnReKjOUPgpJ0WOiO/HEezpC/VU1YMxexs7okPxDgLN8zGEiPbkqWhmkoF4b6hTQxDvAHy+RwAwEWlxIg+5suey907pBdWzjBMyHXxWiXufvdnPPzJYTz8yWF8KVuF9C8jpK4V05kn5XM6yP/uPj8iLV614eBFAMBOjWzyptPbDRM2WXL9IlCeJ/175lvL8zcwxtoUBw4dUHVdPZI3/I7dskl8Hro1FKtmDDRaP2JMuB/ujDVMDf3579IXdEcaitlWiAjL7u4PL5O++Y4SOHg428PV0TilaHx0d3S34QgTncEhnkaflRDua/R305CpA3vi3fsH6Yd0Xi2twa70q9iVflU/g2VED1f9Ammj+vroF8o6mVOqn8xKZafAe7Pi9cc5fqUEP2UU6P+ef9FEQ2OvbYG5ngnkGybeMmO0sNUQQNH0eTDGWhcHDh1MdV09HtnwO/bKEtv+ntgHiyZHWuyjnh5v+MX6TVoOKmrUyLhqGFLXVVocAKnJfemf+utfKxXUYc6fiMzWspg1rOm5GW6EvVJh1BowPtp8GGZDxkf3wAd/HQxnleUb9ONJffUjPFwd7S0OE72jvz9CfVyMZgz956fH9DOZxvfuAUW/2w07nNluuTJ5fwD7VxheBzUw8RNjrE3xqIoO5pVt6fjhrCFomDcmDE/e3q/B4XhDQjwR5OWMy0WVKK1W4/0fL6BWOzOhv7sjPJwbmPu/k5oQ0wNP3d4PH/18EXNvDbU4kVN7CfZ2wYlsqTWot69Lk90HLfH0hAhU19Wjl6ez0dTc1hjdzxcHFo7FbxeLoJF1IwR5OSPSpAUrKcJPv0CYzsxhQQCAuwcF6hcMK5IN45w9IhjAHcDJL6WC09uB0SmGA9TXAT8tB358A9DociYI6CsLNhhj7YYDhw7kRHYJNv1m6Cf+59i+eGJc30bH8BMRpg0KxIrdZwEA7/94Qf9eR/m13dbmJfXFvKS+7V0NM7GB7vqlyx8YGdJqU3wD0tLZ/5rdwEJQVnB3tsdtjSRU6iRF+Bmt8RHe3VW/hHtCP194uaiMggY/VwfpuHW3AQp7QFMH5BwFSq4A7oFAcRaw+X4gN83wIXaO0qyRAQNv+HwYY7bT5l0VRNSLiLYSUQkRlRLRF0QUZOW+jkT0JhHlElEVER0gotEWtlMQ0TNEdJGIqokojYgsLpFHRA8T0WkiqiGiM0SU3NJzvBFCCLz09Ul9nlhiuC/+320NtzTI3T2op/65bnphoOvkN9wsZg0Pxj+SwrBwYkSrdVO0td6+3Ywmr7p/eJD+b1Zlp8CUWOPWjvuGBknDYx3dgdBRhjfOfCcFDx/fYRw0BA4FkvcDQx5s1fNgjFmvTQMHInIGsAdABIC/ApgNoC+AvUTU8Cw9BusAPAzgBQB3AMgFkEpEpj9FXgHwEoB3AEwEcBDAZ0Q0yaQ+DwP4F4DPAUwA8BmAd4no7zdyfi3xdVoODmvHydspCM/fEdXEHga9vJwxord5s3dXbXHoqJxVdph/eziSE/rYfAXS9jRvTBjsFIQofzdMG2Q8mZb8tVJB+LN8FEnEZMPz09uAX96RRk8AgFIF3L4EmLsD8Ol4rUeMdWVt3VXxMIDeAMKFEOcAgIiOA8gA8AiA5Q3tSESxAGYCmCuE+Ehb9gOAkwAWA5iiLfMD8BSA14QQb2l330tEYQBeA/Ctdjs7AK8C2CCEWCTbLgDAK0T0gRDCePWeVlJZq8ayb0/rX8+5JcRsMaKmTIsPxIELxn3Npv3RjLWGafGBmBDTA84qpVkLWUxPN9wd1xNfpeXg0cQ+8Hd3MrwZPgnY/iTgGgD4RQG3LQbKcoDT3wL3buA1KRjroNq6q2IKgIO6oAEAhBCZAH4GMNWKfesAbJbtqwbwKYDxRKSbc3k8ABWAjSb7bwTQn4hCta9HAPC1sN0GAN4AbrXynFrsvX3nkaddEtqnmwqPj23+L6yJ2i9uHZVSYbb+AWOtxcXBzmK3GhFh+YyBSF88Hk/eHm78plsA8OhBYH46MGEZoLQHpq2TWhk4aGCsw2rrwCEawAkL5ScBNNU2Hw0gUwhRaWFfFYAw2XY1AM5Z2A6yz4nW/mtaH9PtWtXla5X4lyyh8enxEXC7gZEALg52+oWGACDMr1uHmGqZMQANLx7mFwnIAw6lPRB440mdjLHW19Z3Fi8A1y2UFwHwbMG+uvd1/xYL8zV7LW0HC8c03a5Vvfptun7p4QGB7pgeb/2CS6ZmDgvSfwff2tfHFtVjjDHGjPBwzGYior8B+BsABAVZNRikQRqNQG/fblApC1Bbr8FLU6JblDQ3KMgTHz0wBJmFFbh3sO2nMmaMMcbaOnC4DsstCw21Jpjua2kMm65loEi2nQcRkUmrg6XtoK1PbiPbGRFCvA/gfQAYPHhwI5PsN02hICyYEIEZg3th75l8DApqqtGlaYnhfkgMb3o7xhhj7Ea0dVfFSRhyC+SiAKRbsW+odkin6b61MOQ0nATgAKCPhe0g+xxdLoNpfUy3a3UhPi6Yc0to0xsyxhhj7aytA4evAQwnIv2a0EQUAuAW7XuN+QaAPYB7ZPvaAZgBYKcQokZbvAPS6Iv7TfafBeCEdhQHABwAUNjAdkWQRnowxhhjTKatuyr+DWAegK+I6DkAAtJkTVmQJmICABBRMIDzABYLIRYDgBDiKBFtBrCSiOwBZAL4O4BQyG7+Qoh8IloO4BkiKgNwBFJwkQTtXA/a7eqI6HlIEz5lA9it3WYugMeFEIZ5chljjDEGoI0DByFEBRElAVgBab4EAvA9gCeEEOWyTQmAEuYtInMgTdq0BIAHgDQAE4QQR0y2WwSgHMA/AfQAcAbAvUKIbSb1WUtEAsCTAFIAXAYwTwjxbkvPlTHGGOuMyHzUIrPW4MGDxeHDh9u7GowxxrooIvpdCNGmk5/wDEGMMcYYsxoHDowxxhizGgcOjDHGGLMaBw6MMcYYsxoHDowxxhizGgcOjDHGGLMaBw6MMcYYsxoHDowxxhizGgcOjDHGGLMaBw6MMcYYsxpPOd0CRFQA4JKNDucDabVOZj2+Zs3H16z5+Jo1D1+v5mvJNQsWQvjasjJN4cChgyCiw2093/jNjq9Z8/E1az6+Zs3D16v5brZrxl0VjDHGGLMaBw6MMcYYsxoHDh3H++1dgZsQX7Pm42vWfHzNmoevV/PdVNeMcxwYY4wxZjVucWCMMcaY1ThwYIwxxpjVOHBoRUTUi4i2ElEJEZUS0RdEFGTlvo5E9CYR5RJRFREdIKLRrV3n9naj14yIBhPR+0R0mogqiegyEf2HiELbot7tqSV/ZybHWUhEgoj2t0Y9O5KWXjMiiiSiz4ioUPv/5xki+mdr1rm9tfD7LIiIPtb+f1lFRGeJaAkRubR2vdsLEQUS0Rrtd3el9v+tECv3VRDRM0R0kYiqiSiNiKa1bo2tx4FDKyEiZwB7AEQA+CuA2QD6Athr5f8s6wA8DOAFAHcAyAWQSkQDW6fG7a+F1+w+ANEAVgOYCGAhgEEADhNRr1ardDuzwd+Z7ji9ATwHIL816tmRtPSaEdFgAL8CcADwEIBJAN4GoGytOre3llwz7fu7AYwG8Dyk6/UBgCcBfNiK1W5vYQDuBXAdwE/N3PcVAC8BeAfS99lBAJ8R0SRbVvCGCSH40QoPAP8EUA8gTFYWCkANYH4T+8YCEADmyMrsAJwB8HV7n1sHvWa+FsqCAWgALG7vc+uI18zkOKkA/gVgH4D97X1eHfWaQfqxlQ7gy/Y+j5vomt2u/T673aT8Ne3+zu19fq10zRSy5w9pr0GIFfv5AagB8LJJ+fcAjrf3eQkhuMWhFU0BcFAIcU5XIITIBPAzgKlW7FsHYLNsXzWATwGMJyIH21e3Q7jhayaEKLBQdglAAYCeNq5nR9KSvzMAABHNhNQ680yr1LDjack1SwQQCWB5q9WuY2rJNVNp/y01KS+GFIiRrSrZkQghNDe463hI12yjSflGAP07QvcrBw6tJxrACQvlJwFEWbFvphCi0sK+KkhNYJ1RS66ZGSKKhBS9n2phvTqyFl0zIvIEsALA00KIIhvXraNqyTW7VfuvIxEdJKI6IsonotVE5GTTWnYsLblmuwFkAHidiKKIqBsRJUFqxVgrhKiwbVVvetGQWhzOmZSf1P7b7O9CW+PAofV4QerbMlUEwLMF++re74xacs2MEJEdgLWQWhzWtbxqHVZLr9mbAM4CWG/DOnV0LblmAdp/NwPYCeA2AG9AaoreZKsKdkA3fM2EENWQAi4FpJtfGaRm920A5tm2mp2CF4Bioe2fkOkw3/927V0BxlrJOwBGApgshLD0hdflEdEoAH8BMMjClxSzTPdja6MQ4gXt831EpATwGhFFCiE6cwtXsxGRI6RAyw9SUuVlAEMhJX6rAfy9/WrHbgQHDq3nOixH4g1F7qb7BjewL2CIPDubllwzPSJ6DcDfAPxVCLHTRnXrqFpyzf4FqTXmChF5aMvsACi1r6uEEDU2q2nH0ZJrdk377y6T8p2Qkv3i0Dm7xlpyzR6ElBsSJoQ4ry37kYhKALxPRGuFEGk2q+nN7zoADyIik4C+w3z/c1dF6zkJqa/KVBSkrOym9g3VDoEy3bcW5n1fnUVLrhkAgIgWAVgA4B9CiA02rFtH1ZJrFgkgGdIXle5xC4Dh2ued9ZdgS//fbMyNJsR1dC25Zv0BXJcFDTq/af+NbGHdOpuTkIb69jEp1+U2WPVd2Jo4cGg9XwMYrh0fDwDQTv5xi/a9xnwDwB7APbJ97QDMALCzk/4KBFp2zUBE/wCwBMAiIcQ7rVTHjqYl12yMhUcapCS4MQC22r66HUJLrtl3kBLXxpuUT9D+e9g2VexwWnLN8gB4EpFpUvcw7b/ZNqpjZ7ED0qi6+03KZwE4oR3N0r7aezxoZ30AcIHUMvAHpOFKUyB9KV8A0E22XTCkfr4XTPb/FNKvvocAjIX0JV4NqT+63c+vo10zSBNAaSB9sQ83eUS197l1xGvWwPH2ofPP49DS/zdf1JYvBTAO0mRjVQDWt/e5dcRrBiAE0lDMs5AmjxoDIEVbdhiy+Q462wPAdO3jPUjzOPxd+zpBto0awDqT/V7Tft/Ph9TN8572++2O9j4nIQQHDq38RxME4HPt/yBlAP4HkwlAtP9TCQAvmZQ7QRornqf9A/oVQGJ7n1NHvWaQRgWIBh772vu8OuI1a+BYnT5waOk1gzTvwHztjbQWwCUAiwHYt/d5deBrFgVgC4AsSEHWWQBvAfBs7/Nq5WvW5HeS9vV6k/2UkGZyvQSphes4gOntfT66By+rzRhjjDGrcY4DY4wxxqzGgQNjjDHGrMaBA2OMMcasxoEDY4wxxqzGgQNjjDHGrMaBA2OMMcasxoEDY10YEQkrHheJKET7/IEOUOcQk/olNmPf52T7XWnFajLWafEiV4x1bSNMXn8JaUbAl2RlNQBytduarjfQnpYA2I7mzd3/EYDdAJ4HENsalWKss+PAgbEuTAhxUP6aiGoAFJqWa1kqa0/nG6hng4QQ2QCyiaiglerEWKfHXRWMsSZZ6qogovVEdIWIBhPRL0RURURniGiy9v352m6OUiL6ioh8TY5pR0TPENFpIqohohwiepuIHFtQz/HaupQQUbm2Pi/c8IkzxsxwiwNjrCXcAHwCad2BHACLAHxORP8HoB+AxwB0B7ASwP8BuFe270YAdwJ4HcAvkJZXfgXSegfTmlsR7cqNX0NaEG4xpHUk+gLo3dh+jLHm4cCBMdYSrgCShRA/AgAR5UDKkbgD0qqk9dryGACPE5FSCFFPRKMgLRP/VyHEJ9pj7SaiIgAbiWigEOJYM+syCIAKwN+FEKXasj0tOjvGmBnuqmCMtUSFLmjQOq39d7cuaJCV2wHw176eAKlFYKu2y8KOiOwA7NS+P/oG6nIMQB2AT4loOhH53cAxGGNN4MCBMdYSxfIXQoha7dPrJtvpynX5C36QWgcqIN3sdY987fveza2IEOIcgPGQvtc2AMgjooNElNDcYzHGGsZdFYyx9nANQDWAUQ28n3MjBxVC7AWwl4gcANwCKddhOxGFCCEKb6imjDEjHDgwxtrDDgALALgLIb639cGFEDUA9hBRNwBfAQgFwIEDYzbAgQNjrM0JIfYR0X8h5TgsB/AbAA2kERWTACwQQpxtzjGJKBlSbsS3ALIA+AB4BlLrxQnb1Z6xro0DB8ZYe5kF4HEAcyEN46wBcBFAKoCrN3C8NAATASyDlENRBGA/gPuFEFU2qC9jDAAJIdq7DowxZjUiCgGQCeBBSHNI1Asrv8iIiAAoAawDMFYIEdhK1WSs0+JRFYyxm9U6SCMxmjNqYpF2n7+0So0Y6wK4xYExdlMhIhWAAbKiM0KIMiv39QfQU/uyVghx3Nb1Y6yz48CBMcYYY1bjrgrGGGOMWY0DB8YYY4xZjQMHxhhjjFmty8zjcOTIkfF2dnYvCiF6gAMmxhhjzJSGiIqFEGlqtXppfHx8hqWNukRy5JEjR8Y7ODi8ExISUuvk5FStUCg6/0kzxhhjzSCEQF1dnV1paalLXl6eqKmp+Ud8fPwO0+26xC9vOzu7F0NCQmpdXFyqOGhgjDHGzBERVCqV2sfHpyQkJERtb2//tKXtukTgIITo4eTkVN3e9WCMMcZuBi4uLpVCiFBL73WJwAGAglsaGGOMMetIs7ODLL3XVQIHxhhjjNkABw6MMcYYsxoHDjeh1atXexNRvO7h4uISFx4eHrV06VLfurq6Vv3sM2fOqIgofvXq1d66smnTpoX07Nmzf3OOs23bNtf58+cH1NfXN3n8tmB6TeUPV1fXgW1ZF8YY68i6zDwOndGHH354ISgoqLakpES5ZcsWz0WLFgXl5+fbr1y5Mqct67F48eLc4uLiq83ZZ8+ePa4rVqzwf/3113OUSqW+PCgoqG737t2nIyMja2xeUSvorqm8zN7envNjGGNMiwOHm9iQIUMqY2JiagDg7rvvLs3MzHRYt26dX0OBQ01NDdnb2wuFwrYNTdHR0Ta7yTs5OYmxY8dW2Op4zSW/ptaqqqoiJycns+BCo9GgtraWHB0dbzjwaOjYjDHWXrirohOJi4urLC8vV2ZnZ9vpmvxfe+013+Tk5EA/P78BTk5Og65du6YEgI8//tgjNjY2wsnJKc7V1XXgxIkTe2dkZKjkxysrK1PMmjUryMPDY6Czs3NcUlJS2MWLF1Wmn2upq6K0tFTx6KOP9uzVq1eMSqUa5OPjEzt+/Pg+WVlZdvPnzw9YsWKFPwCoVCp9lwDQcFfFu+++6xUeHh7l4OAwyNPTM/auu+4KvXTpkr18m549e/afOnVq6Pvvv+/Zu3fvaCcnp7iYmJjI1NTUbra5woYuje+++67bxIkTe7u6ug6Mi4uLlH/+ypUrvUNDQ6NVKtWgLVu2uAPA1q1b3QYOHBjh6Og4yNXVdeC4ceP6pKWlOciPPXTo0PD4+PjwTZs2uUdGRkapVKpBb7zxhq+t6s4YY7bALQ6dyKVLl1RKpRJubm6a8vJyBQAsX77cf8CAARWrVq26VF9fT05OTpo33njDd8GCBUHTp0+/9uyzz+aWlpYqli1bFpCYmBh+4sSJk56enhoAmD17dvD27ds958+fnzNs2LDK1NRUtwceeMDiuF656upqSkhI6Hf69GmnefPm5Y0cObKiuLhYmZqa6lZYWGj32GOPFWRnZ9tv2bLFJzU19bS8q8KSt956yyclJSV48uTJ1xcvXpydnZ1tv2TJkp4JCQnhaWlp6e7u7hrdtocOHep2/vx5x+effz7H0dFRs3jx4p7Tp08Py8zM/MPHx6e+sc8BgPr6epjmiSgUCpjWcc6cOb3vuuuuokceeeS8Wq3WD1k6cOCAa3p6uvPChQtze/ToURcWFla7detWtxkzZvQdNmxY6bp1686XlZUply5dGpCYmBhx5MiR9NDQUP0HZmZmOqakpASlpKTkhIWF1fr6+qqbqjNjjLWlLhs4hCzcHt/eddC5+Nrk329kP91Nrri4WLl+/XrPnTt3eo4ZM6bY1dVVfyP18fGp27lz53ld90RJSYnilVde6Tl9+vRrn3322UXddqNGjaro379/zJo1a3xeeOGF/LS0NIdvvvnGa8GCBdlLly7NA6TukPLycsWmTZsa/RW8du1ar2PHjrls3Ljx3P3331+iK58zZ8513fOePXvWAcCYMWMq7O3tLR0GAKBWq7Fs2bKeQ4cOLdu2bdsFXXl0dHT1hAkTwtesWePz3HPP5evKy8vLlWlpaem+vr71us9JSEiI3Lp1q3tycnJRU9d04MCBMaZliYmJJXv37j0nL5s8efL1tWvXXjHdtqysTHn48OH0oKAg/Q1/xowZvQMDA2t++OGHDN25JiYmlsfExMS8+uqr3T/44AP9cYqLi+22bduWPnLkyKqm6soYY+2hywYOnYH8JqdQKDBlypRra9euzZJvM2nSpGJ5TsOePXu6lZeXK2fNmnVN/su6T58+taGhodX79+93BZD/008/ddNoNJg1a5bRzXbmzJlFTQUOu3fvdvfx8amTBw03Ki0tzbGoqMhuxowZRvUYP358eUBAQO1PP/3kCkAfOMTFxZXrggYAGDJkSBUAXL582ayLxZJPPvnkfHBwsFFypJeXl1lLxfTp06+blgFAbGxshTxoKC0tVaSnpzvPmzcvVx4gRURE1A4aNKjiwIEDrvL9AwICajloYIx1ZBw43MR0Nzl3d/f6vn371jo7O5sl0fn7+xu1u+fl5dkBwF133dXP0jHd3d3rASA3N9ceAAIDA42aygMCAppsOi8qKlJ2797dJuNCCwsL7bSfa3Y8Hx+fuuLiYqM+BA8PD6ObvC6xsLq62qp8nri4uCprkiMDAwMtnp+fn59ReUFBgVIIYfbfQbft0aNHXRrbnzHGOpouGzjcaPdAR2LNTU47baiers989erVF2NjY81+2eoCB92N7sqVK3ZRUVH6X+A5OTlN/s14e3urMzIynKw6iSb4+PioAUMgI1dYWGgfExNTaYvPaS7T69pQua+vbz0RIS8vz6z++fn59h4eHkaBGBHxCArGWIfGoyq6mKSkpHIXFxfNuXPnHEaPHl1p+oiNja0BgFGjRpUrFAps3LjRS77/pk2bvCwf2WDcuHGlhYWF9ps2bXJvaBsHBwcNAOiSOBsSGxtb7e3trf7ss8885eW7du1yycnJUY0ePbqsqfq0Jzc3N01UVFTl119/7alWG2KEs2fPqo4ePeoycuTIDl1/xhgz1WVbHLoqLy8vzYsvvpj1zDPPBBcUFNhNmjSp1MPDoz4rK8v+xx9/dE1ISChLTk4uio2NrbnzzjuL3nzzzQCNRoNhw4ZV7tixw23v3r0NBgM6ycnJRevXr/d96KGHeqelpeWOGDGiorS0VLljxw63lJSU/Li4uOro6OhqAHjllVd63HHHHSV2dnZi9OjRZq0HdnZ2WLhwYXZKSkrw1KlTQ2fPnn0tKytL9eqrr/YMDg6uefzxxwtteX0OHTrkfPXqVbP/L0aPHt1oEmdjXnrppewZM2b0TUpK6pucnJxfVlamXLZsWUC3bt3qFy1a1KyJsxhjrL1x4NAFpaSkFAYFBdUtX768e3Jysnd9fT38/Pzqhg4dWj5kyBD9zXvDhg2XkpOT6997770eq1evpuHDh5etX7/+wvjx4yMaO76Dg4PYt2/f2aeffjpgw4YNvsuXLw/w8PBQx8fHl/v5+akB4L777itOTU0tWL9+ve/KlSv9hRAQQljsPnrqqacKnZ2dNatWreoxc+bMMGdnZ01iYmLJqlWrrri5uWks7XOj5s6d29tSeU5OTpq/v/8NDY2cPn166ebNmzOWLFkSMHfu3D729vaaoUOHli1fvvxKSEgI5zQwxm4qJETn71JNS0u7GBsba9NfpowxxlhnlpaW5hMbGxtiWs45DowxxhizGgcOjDHGGLMaBw6MMcYYsxoHDowxxhizGgcOjDHGGLMaBw6MMcYYsxoHDowxxhizGgcOjDHGGLMaBw6MMcYYsxoHDowxxhizGgcON6HVq1d7E1G87uHi4hIXHh4etXTpUt+6utZd+uDMmTMqIopfvXq1t65s2rRpIT179uzfnONs27bNdf78+QH19fVNHr8tmF5T+cPV1XVgW9aFMcY6Ml7k6ib24YcfXggKCqotKSlRbtmyxXPRokVB+fn59itXrsxpy3osXrw4t7i4uFmrPO7Zs8d1xYoV/q+//nqOUqnUlwcFBdXt3r37dGRkZI3NK2oF3TWVl9nb23f+BV0YY8xKHDjcxIYMGVIZExNTAwB33313aWZmpsO6dev8GgocampqyN7eXigUtm1oio6OttlN3snJSYwdO7bCVsdrLvk1tVZVVRU5OTmZBRcajQa1tbXk6Oh4w4FHQ8dmjDPdJMoAACAASURBVLH2wl0VnUhcXFxleXm5Mjs7207X5P/aa6/5JicnB/r5+Q1wcnIadO3aNSUAfPzxxx6xsbERTk5Oca6urgMnTpzYOyMjQyU/XllZmWLWrFlBHh4eA52dneOSkpLCLl68qDL9XEtdFaWlpYpHH320Z69evWJUKtUgHx+f2PHjx/fJysqymz9/fsCKFSv8AUClUum7BICGuyreffddr/Dw8CgHB4dBnp6esXfddVfopUuX7OXb9OzZs//UqVND33//fc/evXtHOzk5xcXExESmpqZ2s80VNnRpfPfdd90mTpzY29XVdWBcXFyk/PNXrlzpHRoaGq1SqQZt2bLFHQC2bt3qNnDgwAhHR8dBrq6uA8eNG9cnLS3NQX7soUOHhsfHx4dv2rTJPTIyMkqlUg164403fG1Vd8YYswVucehELl26pFIqlXBzc9OUl5crAGD58uX+AwYMqFi1atWl+vp6cnJy0rzxxhu+CxYsCJo+ffq1Z599Nre0tFSxbNmygMTExPATJ06c9PT01ADA7Nmzg7dv3+45f/78nGHDhlWmpqa6PfDAA6FN1aO6upoSEhL6nT592mnevHl5I0eOrCguLlampqa6FRYW2j322GMF2dnZ9lu2bPFJTU09Le+qsOStt97ySUlJCZ48efL1xYsXZ2dnZ9svWbKkZ0JCQnhaWlq6u7u7RrftoUOHup0/f97x+eefz3F0dNQsXry45/Tp08MyMzP/8PHxqW/scwCgvr4epnkiCoUCpnWcM2dO77vuuqvokUceOa9Wq0lXfuDAAdf09HTnhQsX5vbo0aMuLCysduvWrW4zZszoO2zYsNJ169adLysrUy5dujQgMTEx4siRI+mhoaH6D8zMzHRMSUkJSklJyQkLC6v19fVVN1VnxhhrS107cNjxTAAOvutv1bYx0wox/cNLRmVb5wbjxOc+Vu0//NFcTFhm3IVw6RdnBI+stK6y5nQ3ueLiYuX69es9d+7c6TlmzJhiV1dX/Y3Ux8enbufOned13RMlJSWKV155pef06dOvffbZZxd1240aNaqif//+MWvWrPF54YUX8tPS0hy++eYbrwULFmQvXbo0D5C6Q8rLyxWbNm1q9Ffw2rVrvY4dO+aycePGc/fff3+JrnzOnDnXdc979uxZBwBjxoypsLe3t3QYAIBarcayZct6Dh06tGzbtm0XdOXR0dHVEyZMCF+zZo3Pc889l68rLy8vV6alpaX7+vrW6z4nISEhcuvWre7JyclFTV3TgQMHxpiWJSYmluzdu/ecvGzy5MnX165de8V027KyMuXhw4fTg4KC9Df8GTNm9A4MDKz54YcfMnTnmpiYWB4TExPz6quvdv/ggw/0xykuLrbbtm1b+siRI6uaqitjjLWHrh043OTkNzmFQoEpU6ZcW7t2bZZ8m0mTJhXLcxr27NnTrby8XDlr1qxr8l/Wffr0qQ0NDa3ev3+/K4D8n376qZtGo8GsWbOMbrYzZ84saipw2L17t7uPj0+dPGi4UWlpaY5FRUV2M2bMMKrH+PHjywMCAmp/+uknVwD6wCEuLq5cFzQAwJAhQ6oA4PLly2ZdLJZ88skn54ODg42SI728vMxaKqZPn37dtAwAYmNjK+RBQ2lpqSI9Pd153rx5ufIAKSIionbQoEEVBw4ccJXvHxAQUMtBA2OsI+PA4Samu8m5u7vX9+3bt9bZ2dksic7f39+o3T0vL88OAO66665+lo7p7u5eDwC5ubn2ABAYGGjUVB4QENBk03lRUZGye/fuNhkXWlhYaKf9XLPj+fj41BUXFxv1IXh4eBjd5HWJhdXV1Vbl88TFxVVZkxwZGBho8fz8/PyMygsKCpRCCLP/Drptjx496tLY/owx1tF07cBhwrIcs+6D5pj+4SWz7ovmaEE3BWDdTY6IjF7r+sxXr159MTY21uyXrS5w0N3orly5YhcVFaX/BZ6Tk9Pk34y3t7c6IyPDyaqTaIKPj48aMAQycoWFhfYxMTEtuoY3yvS6NlTu6+tbT0TIy8szq39+fr69h4eHUSBGRDyCgjHWofGoii4mKSmp3MXFRXPu3DmH0aNHV5o+YmNjawBg1KhR5QqFAhs3bvSS779p0yYvy0c2GDduXGlhYaH9pk2b3BvaxsHBQQMAuiTOhsTGxlZ7e3urP/vsM095+a5du1xycnJUo0ePLmuqPu3Jzc1NExUVVfn11197qtWGGOHs2bOqo0ePuowcObJD158xxkx17RaHLsjLy0vz4osvZj3zzDPBBQUFdpMmTSr18PCoz8rKsv/xxx9dExISypKTk4tiY2Nr7rzzzqI333wzQKPRYNiwYZU7duxw27t3b4PBgE5ycnLR+vXrfR966KHeaWlpuSNGjKgoLS1V7tixwy0lJSU/Li6uOjo6uhoAXnnllR533HFHiZ2dnRg9erRZ64GdnR0WLlyYnZKSEjx16tTQ2bNnX8vKylK9+uqrPYODg2sef/zxQlten0OHDjlfvXrV7P+L0aNHN5rE2ZiXXnope8aMGX2TkpL6Jicn55eVlSmXLVsW0K1bt/pFixY1a+Isxhhrbxw4dEEpKSmFQUFBdcuXL++enJzsXV9fDz8/v7qhQ4eWDxkyRH/z3rBhw6Xk5OT69957r8fq1atp+PDhZevXr78wfvz4iMaO7+DgIPbt23f26aefDtiwYYPv8uXLAzw8PNTx8fHlfn5+agC47777ilNTUwvWr1/vu3LlSn8hBIQQv1s63lNPPVXo7OysWbVqVY+ZM2eGOTs7axITE0tWrVp1xc3NTWNpnxs1d+7c3pbKc3Jy0vz9/W9oaOT06dNLN2/enLFkyZKAuXPn9rG3t9cMHTq0bPny5VdCQkI4p4ExdlMhITp/l2paWtrF2NhYm/4yZYwxxjqztLQ0n9jY2BDTcs5xYIwxxpjVOHBgjDHGmNU4cGCMMcaY1ThwYIwxxpjVOHBgjDHGmNU4cGCMMcaY1ThwYIwxxpjVOHBgjDHGmNU4cGCMMcaY1ThwYIwxxpjVOHC4Ca1evdqbiOItPVxdXQdae5wzZ86o5s+fH5Ce/v/bO++oqK6tge/phRnaMDAMOJQBpSMoIFhAxIAiRbEg0ais2NCoYCGUFc1CSnwUEctCX3xPMSoGxfpQAwGSh1FEjE8lYgMBQUDaFIbp3x/m8s0Mo4LtPeP9rTVrwTnnnrPvOfueu8/e595bR9TMMzMzc46IiLB8p4IPg7i4OPbZs2fp77peDAYzLi4ujv2qMufPn6er9iWZTHbncrmOmzZtMhUIBNq/o/0eZYyLi2NjMJhxI6njypUrlLi4OHZ7ezvudfV/CDT7VPP3/PnzIXJ+LEyaNMnW3NzcWTM9KSmJhcFgxq1atcpcM8/JycneycnJHuD/r+P6+vrB6+9l+h8REWFpYmLi8iZy1tfXEzEYzLjs7GyjkR77Kn16X/y35p53jbbx/auAfuTqI+bgwYOPORyORDWNQCAM++MjDx48IOXk5JhOmTKF7+DgoFbPiRMnHurr67/TD0gNh5ycHFOZTAahoaH/tc9Nb9++vXnChAlCoVCILSkp0c3JyWE/evSIVFxc3Pgh5VizZk1ncHBw30iOqampoebk5JhGR0d3mZiYyFXzSktL71laWkpeduz7BOlTzXR9fX25tvIfAxMnTuRXVVXpPnr0iMDlcgc/VlZVVUUjk8mK3377jaZavq+vD3vv3j3q0qVL2wEA5s6d2+fo6HiPw+EMHvu/oP+qvEqfUD5dUMPhI8bDw6PfyclJ/D7qnjhxouh91Psx4OjoKJo2bZoQACA0NJTf2dlJOHnyJKO9vb1Z2+Qpk8lAqVTCm352+2VwuVyp6g3pbUHO6b+Bap8OF5FIhKFQKFoN4VflvW3dw8Xf31+wY8cO+Omnn+hcLrcbAEAul8ONGzdoCxcufF5QUMDk8/lYOp2uAAAoLS2lyeVy8PX1FQAAsNlsGZvNfqMvrn7MiMViDIFAUGKxqMP7YwUdub8wTU1N+Dlz5lgaGxu7EIlEdyaT6TJ16lSbp0+f4s+fP08PCQkZDQAwe/bs0Yjr+Pz583SAoe5CxO32008/6cycOdNaR0fHjcFguCYkJLAAAIqKinTt7e0dKBSKm5OTk/2vv/5KVZXl1KlTur6+vjZMJtOFQqG42draOm7dutVEJvv/eRNxy+fl5Zki8qi61i9cuEDz9vYeraOj40ahUNwmTZpke/36dbJqOzKZDNatW8dG2vH09BxTU1OjVmakjB8/XggAUFdXR0Lk/Oqrr8wSExNZZmZmziQSaVx1dTXlXcuoLVQhlUohKSmJxeVyHUkkkruBgYHr5MmTbW/evEnetWsXY/369ZYAAM7Ozk5IHyKuUm2hiqKiIt2xY8fakclkdzqdPjYgIIB769YtkmoZT0/PMePGjRtz+vRpuoODgz0yfocPH9Z/m35VBQlpHDp0SD8yMtLCwMDA1djY2FW1/aNHj+rZ29s7EIlE9x07djABAMrLy6k+Pj6jqVSqG4VCcfP29h5dXl6upnuIm7+0tFTHzc3Njkwmu8fExAwJI4wUX19fIYlEUv7yyy+DnoWrV69SBAIBLiEhoR2LxUJZWZkOkldRUUHHYDAwffp0PsBQV/br9B8AoKqqijJu3LgxFArFzcLCwgnph5GC6Nbt27dJfn5+NlQq1Y3NZjtv2rTJVC5/YRu/Tp+kUikkJCSwrKysHIlEoruxsbHL8uXLzfv7+wfDekiYJCMjg7lq1SpzY2NjFwqF4t7V1YUDAEhJSTH+8xpyd3Jysr948SJtqLQA9+7dI4aGhloZGBi4EolEdzs7OwdN/UPOqbq6muLl5TWaQqG4MZlMlw0bNrCRc0JobW3FR0VFcZC50crKyjEzM1MtlIOMT1lZmU5oaKgVjUZzMzY2dlm6dOko1XMEAKirqyP6+fnZUCgUNwMDA9dly5aNEovFWsObmZmZRmPGjHFArt/58+dbaIaCMBjMuHXr1rG3b99ubGZm5qyjo+Pm4eGhdZ44fPiwvru7ux2VSnWj0Whuzs7O9j/88IMekj+ccRopqMfhI0Yul4NUqr4gxWKxgMO90MHIyEir1tZW0rfffttiaWkpaWtrI5SWltIFAgHWx8dHmJ6e3pSQkMBRdSO7ubm90tPw5ZdfWs2fP79rxYoVnSdOnDDMyMgw6+3txZWVlelt2rSpjU6nK5KTk83nzZtn09jYeJtMJisBAB4+fEjy8/Pjr1mzpoNCoSirq6upmZmZ7M7OTvzevXufArxwpQcEBNhFRER0rV69uhMAAHGtHz9+XG/RokU2vr6+vfn5+Q0AAFlZWaxp06bZ1dbW3rWxsZECAGzcuJG9e/du0y+//LI9KCiIV11dTZ09e7bN2/RzQ0MDCQCAwWAMzj6FhYWMUaNGiVNTU5tpNJqCw+FIP4SMISEh1qWlpQbR0dHtn332GU8kEmErKytpLS0thLlz5/Y9evSobdeuXaaqYSxVV7gqRUVFugsWLLD18vLiff/994/4fD4uLS2N7efnZ1dbW1tnZWU1eFxTUxNp06ZNnLi4uDZjY2NZdna2SXR0NNfd3f3OcLxeCoUCo6mrGAwG8Hj1KWjz5s2cqVOn9h04cKBBJBINLmwaGhrImzdv5mzevLnVxsZGwmQyZdeuXaPMmDHDjsvlivbs2dOIwWCUmZmZpjNmzLArLy//w9vbe1CXBQIB7osvvrBes2ZNu6ur61MqlfrWYTgymax0cXERXrt2bXBPws8//0y3sLAQ29raSpycnIQVFRX08PBwPgDAlStXaDY2NiIWi6XV5f8q/QcAEAqFuMWLF1uvXr263cbGpu3gwYOM+Ph4jr29/UBISMgbhTbmzJljExUV9XzDhg3tZ86c0c/KymKPGjVKsn79+q7X6dPs2bOty8rK9NasWfNs0qRJgrt371IyMjLYTU1NpEuXLj1SbSc7O9vUxcVFmJub+0Qul2MoFIoiJyfH6JtvvhkVERHRFRkZ2X3//n3SkiVLrPv7+9UWtA8fPiT4+PjYMxgMWWpqarOJiYns+PHjhkuXLuXicLiHn3/+uVo4LyIighsVFfU8Pj7+WUlJiW5ubq4pFouF7OzsVgCA7u5urI+Pj51YLMbEx8e3crlccUlJiV58fLyFWCzGJiUldajWFx0dbTV79uzu6Ojoh1VVVbTs7Gy2gYGBPCcnpxUAYGBgABMYGDhaLBZjMzIymkxMTGT79+9nlpSUGGj2d0xMjNn+/ftNli1b1pGent7S3NxMSE1NNZs+fTqltrb2nur1cPLkSYaVldXAjh07miQSCTY5Odl8zpw5No8ePbqDeDdTU1ONk5OTRwUEBPTu3bv3GZ1OV9TU1FCROWuk4zRcPlnDwfmQ84g2nb1Pbi+5feNNjhs7dqyTZpqfn19feXn5QwCA33//nZaYmPh09erV3Uh+dHR0D/K3k5PTAMDI3Mjz5s3r+tvf/tYGABAcHMy/ePGi/oEDB0zu3Llzx87OTgIAoFAoYNGiRTZlZWU6wcHBAgCALVu2dCJ1KBQKCAoK4kskEsy+fftYeXl5T3E43KArnc1mSzTl2bJlyygPDw9+WVnZoKLPnDmTx+VyndPS0lgHDx5s7uzsxB04cMAkMjKyc//+/S0AAHPmzOHhcDhIS0szG875/SkfRiqVAp/Px545c0avoKCAaWdnJ3JxcVG7QVZWVt6n0WiD7u73LePZs2fply5dMkhJSWlOTk4enNwWL17ci/zN5XLFAMMLY23bts3M3NxcXFlZ+QCZiPz8/AROTk5OqampJn//+99bkLI9PT34n3/+ud7Z2VkMAODt7d3P4XBcjxw5YpCRkfHslR0KABEREbaaaTY2NgMPHjy4q5rm6uoqLCwsfKJZtre3F3/+/Pk6Hx+fQWMgKCjImkAgKCorK+8bGRnJAQBCQ0N5VlZWLlu3bmVfvnx5cBz6+/ux+fn5zYsWLerVrPttmDhxIn/nzp2mbW1teFNTU9m///1vupeXFx8AwNvbW3DlyhX6n+1j7ty5ozN//vznL6vrVfoPACAUCrG5ublNiJEQFBTENzU11Tt69KjhmxoOa9eufbZ+/fouAIDw8HB+VVUV/cSJE4br16/vYrPZspfp08WLF2kXLlwwyMvLa1y7du3g8YaGhrKYmBirK1euUFTHysjISHr58uVHSHhCLpfDd999x540aRKvqKioESnHZDJlK1assFaVMTExka1UKuHXX3+9hxhdERERPB8fH0JKSoqZpuGwePHi52lpac8AXlxbfD4fl5+fb5KYmNhuZGQkT09PN2lrayPW1NTcRfQ5PDyc39fXh8vMzDTdsmVLh2rYcc6cOd2IkRAeHs6vqanRKS4uNkTS9uzZw2hpaSGVlpbeQ8Zt3rx5fWPGjHFsb28frKi+vp6Yn5/Pio2Nbc3MzGxD0u3t7QcCAwPtjh07pq96LePxeGVZWdlDEok0OMdER0dbV1RU6EyfPl3Y3d2NTUtLM5s+fXqvqq5HRETw3nSchgsaqviIOXz48KPKyso/VH95eXnNSL6zs7Nw9+7drJSUFOPq6mqKQvH2ex1DQkIGL1ICgQAWFhZiS0tLMWI0/NnuAADAkydPBncTP3nyhBAVFWXBZrOdiUSiO5FIHLdjxw4zPp+Pe/r06SsN2Nu3b5Oam5tJCxYs6JJKpYD86HS6ws3NTXj16lUaAEBNTQ1FJBJhIyMje1SPX7JkSbf2mrUTERFhSyQSxzEYDLfo6GjrCRMm8M+cOfNQtYyvry9P1Wj4EDJevHhRF4PBwIYNGzpfV/Z18Hg8bF1dHTUsLKxbdZK0s7OTuLu7C3/77Te1nf0WFhZiZJIFADAzM5MZGhpKm5qahrVjPD09vUlTV48dOzZktRMWFqb1xs5msyWaE1x1dTXd39+/DzEaAAAMDQ0VAQEBvdXV1Wry4/F4ZWRk5GuNBsSLh/xUQ2na8PPz4wMAXL58mQYAcP36ddqkSZMEAACTJ08W/Oc//9EZGBjAlJeX60gkEoyvr+8bb3okk8kKVQOBQqEoLS0tB1paWt541/7cuXPVbrpjxowRtba2vra+Cxcu6BEIBOWSJUt6VPsrLCyMB/DC86JafubMmb2qexoeP35MbG9vJ0RERKjp/dKlS3twOJza3pOKigq9qVOn9jEYDLlqWwEBAbz6+npKd3e32n1s0aJFanUuXLiwu7+/H3vjxg0KAEBZWZmei4uL0M7OTqxaX2BgIK+3txdfW1tLUT0+NDRUTW8cHBxEbW1tg3109epVGovFUjP2cDgchIWFqclx/vx5XYVCAcuWLetWbXfq1KlCHR0dRWVlpVqYZvLkyTxVo8Hd3b0fAKChoYH4Zx/T+vv7sStWrHjpfDDScRoun6zH4a+Am5ub6FWrylOnTj3++uuv2Xl5eaxvvvlmFJPJlH7xxRed3333XRsSzhgpqu56gBdPcejp6anNroiyDwwMYAFeTMbBwcE2HR0dhPj4+FZHR8cBKpWqKCoq0s/LyzNVdUlro62tDQ8AEBsbaxkbG2upmW9qaioBAGhpaSEAALDZbDWfuLm5+Yg2GKanpzf5+PgIqVSqYvTo0RJdXd0hFheLxVKr80PI2N3djdfT05OpGixvSmdnJ06pVIKpqemQdo2NjaU3b97UUU3T19cfcgclEolKsVg8rMWHnZ3dwJQpU/pfV87MzExrPxgbGw9J5/F4eM1xAAAwMTGR8ng8NQU3MDCQaYZFtLF582Z2Tk6OKfK/h4eHoLq6uv5l5f39/YV4PF5ZWVlJd3Z2Hujp6cFPmzaNDwAQEBAgkEqlmMrKSp2Kigo6AMBnn332xoaDrq7ukBDHSMZAG8bGxmrjSiQSlRKJ5LX1dXZ24qVSKUZXV9dNW35XV5daZ2vqWXNzMwEAgMViqbVPIBCGPGnT3d2NLy4uZhCJRIa2tjo6OvCGhoaDCxdzc3O1OpFrrampiYDI1tTURCISiVq9zh0dHWqyM5lMNXlIJJJSIpEM7g9ob28nMBiMIdeHiYmJWhpSr5OT0xBPMXKeqv8bGhqqHY+EfZF5tbOzEw8AYGFh8dInpUY6TsPlkzUc3jQ88DFhZmYmKygoaAKAplu3bpEOHDhglJWVxWYymbL4+Pi3XrUOl7q6OtLdu3epe/bsaYiJiRm0wouLi4e1uQ65cBMSEp4GBQXxNPMRQwW5+ba2thIAYADJR27Ww2U4NzkMBqN28/4QMjIYDFlfXx9eIBBg3tZ4YDKZcgwGA8+ePRvSbkdHB0GbofAh0OzXV6Xr6urKVF3BCO3t7QTNmywGM7x9YOvWresMDw8fXGHq6em98hFEOp2ucHJy6r969SrN3t5exGQypcijzQwGQ25raysqLy+nVVVV0SwsLMQcDucv8RSFoaGhjEQiKS9fvnxPW77mvhrN/h81apQUAODZs2dq9yCpVAq9vb1qRp++vr7cw8ODn5CQoDUkZmFhodZWS0sLXvXx8j+vtUGZ9PX1ZQwGQ7Zz584mbfUhHtPhYmJiIn3w4MGQTYvt7e1q54Ysuk6dOvVAm6GhacS9DqR8U1MT0cPDQ6vMIx2n4fLJGg6fGq6uruLdu3c/LSgoYN65c4cC8ML1CfAi/vs+2xYIBFgA9XdMiMVizMmTJw01yxIIBKWmB8LV1XWAzWZL6urqKEjsUhseHh4iCoWiOH78uIHqc/CHDh0a0s675kPIGBQUxNu7dy8rNzeXqbmBCwExUF43prq6ugoHB4f+s2fPGmRlZbUiq/H79+8Tb968qbN06VKt9f8v4eXlxa+oqNDr6enBGhgYKAAAenp6sGVlZfrIPoORYmlpKbW0tBzRZOrt7c3Pz89nlZSU6Ht6egpU8zw9PQW//PKL7u+//64TGhr62nCUNv3/b/IyfZo5cyZv3759rJ6eHlxYWNiI+9ra2lrCYrEkJ0+eNNywYUMXkv7Pf/7TQC6Xq1kZvr6+fTdu3KC5u7uLhmMwHzlyxFD1Gjx27JghlUpVjB8/XgQAMG3aNN7BgweNuVyuxMzM7K0NuQkTJgiKiooYZWVlOki4Qi6Xw5kzZ9Su6eDgYF5CQgI0NjYSZ8+ePWRxMVL8/f0FVCpVsX//fqbqvgZV3nacXgZqOHzEXL9+napp1QIATJkyRcjj8XC+vr6j58+f3+Xg4DBAIBCUxcXF+jweDxcYGMgDeLE5EofDKf/xj38YGRkZychkstLZ2XkAmYTfFW5ubgNsNluSkpJihsfjgUAgKHft2mWirSyXyx0oLS3VKy4u5jEYDBmHw5FaWlpKs7Ozmz7//HNucHAwZt68ed1MJlPW1tZGuHLlCo3D4Ui2bdvWbmRkJF++fHl7Xl6eKZ1OVwQFBfGuXbtG/eGHH0b8xryR8ueu7fcqY0hICD8wMLBn69at5s3NzcSAgACeRCLBVFZW0kNCQvpmzZrFd3FxEQEA7Ny5kxkdHd1FJBKVnp6eIsTNqcq2bdueLliwwNbf39921apVHXw+H5eens6m0WjypKSk9nfZP3fv3qUg7zNQxcPDQ6QtFDQctm3b1ubr66s/ZcqUMRs3bmzDYDCQlZXFGhgYwG7btq317aUeHr6+voJ9+/ZBRUWFXmpqqtoqdvLkyfwjR44wlUolTJ48+bUT98v0//1J/2pepk+zZs3iz5o1q3vx4sXclStXtk+YMEGIxWLh8ePHxIsXL+plZWW1aG4mVgWHw8GWLVva4uLiLObOnWu5cOHC7vv375Nyc3NNaTSampcnIyOj1dvb237ChAl2K1eu7LC2thZ3d3fjb9++TWloaCD9+OOPjarlCwoKjBQKBXh5efWXlJToFhYWGsXFxbUiK/6kpKT206dPG0ycONEuJiam3d7efkAgEGD/+OMPclVVFU11c/NwWLNmTdfOnTtZCxcu5CYnJz81MTGR5efnMwUCgZrnxNHRUbx69epnCQkJnPr6erKfnx+fQqEo1SPniQAABBJJREFUnjx5QiwtLdVdvnz585FscjUwMFAkJSW1JCUlcQIDA7lRUVFdurq6itraWgqZTFYmJSV1vO04vQzUcPiIiY6OttaW3traektfX1/u4uLSf/jwYWZraysRi8WCpaXlwL59+xqQneUsFkuenp7elJubazpz5kw7uVwO586duz9r1qx3+tY6Mpms/PHHHx9+9dVXnJiYGEtdXV35woULn3M4HMnGjRstVMvm5uY+iY2N5URGRtpIJBJMbGxsW3Z2duuCBQv6GAxGfWpqqum6dessxWIx1sjISOrm5iaMiooaXMllZWW1KpVKOHr0KPPQoUPGLi4uwtOnTz8cP36847s8J218CBnPnTv3ODk5mVVYWGj0/fffG9NoNLmLi4swJiamEwDA29tbFBcX11pQUMAsLCxkKhQKuHfv3u0xY8YMiYPOnTuXV1hY+GD79u3s6OhoLoFAUHh6evKzs7Nb3vXNKjk5eZS29MrKyj+Gs/dBG15eXqJ//etf9cnJyWYxMTFWSqUSxo4dKywpKbmn+ijm+2b69Ol8HA4Hcrkc/P39BRp5AqVSOfj36+p6mf6/J9Ffy6v06fTp0w1paWnGR44cMdq1a5cpkUhUsNlsydSpU3nDWcnHxsY+FwgE2H379pmcO3fO0NbWVnTo0KHH0dHRVqrlbG1tJdeuXatLTExkp6SkmPX09OD19fVltra2okWLFnVp1nvq1KmHa9eu5ezcuZNNo9Hk69ata9uxY8fgUwwMBkN+7dq1e19//TU7NzeX1dHRQaDT6XIrK6uBsLCwHs36XgeZTFZeunTp/urVqznx8fEcMpmsCA8P754xY0bvli1b1Oa33bt3P7W3txft37/f+NChQ0wMBgMsFksyadIkvoODw4hCJAAAiYmJnaamprKcnByTlStXWuPxeKW1tbUoISFh8Hzfdpy0gUGU+q/MrVu3Gl1dXV/6GBQKCgoKysdNXFwcOycnx1Qikdx4129x/VS5deuWkaurq6Vm+v9MLA0FBQUFBQXlfx/UcEBBQUFBQUEZNqjhgIKCgoLy0ZOdnd2qVCrRMMUHADUcUFBQUFBQUIYNajigoKCgoKCgDJtPxXBQKBSKN/6EKAoKCgoKyqfEn09can3s8pMwHDAYzDORSDTklaAoKCgoKCgoQxEKhVQMBtOgLe+TMBxkMtm3jY2NRKFQSEE9DygoKCgoKENRKpUgkUjwz58/129sbMRLpdId2sp9Ei+AAgCora0NxOPxW5VKJQs+EYMJBQUFBQVlBCgwGEyPUqm8JZPJ0seNG/dAW6FPxnBAQUFBQUFBeXvQlTcKCgoKCgrKsEENBxQUFBQUFJRhgxoOKCgoKCgoKMMGNRxQUFBQUFBQhg1qOKCgoKCgoKAMm/8Dlu5asHdD+QwAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['PS1'].estimate_maximum_gradient(generator_dictionary['PS1']).values()))\n",
+        "plt.bar(list(selected_features['PS1'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "6-tNy6lg0OuS",
+        "outputId": "90d5c8a8-3ab0-4a10-8e68-31886812ad6b"
+      },
+      "execution_count": 266,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['MPW'].estimate_maximum_gradient(generator_dictionary['MPW']).values()))\n",
+        "plt.bar(list(selected_features['MPW'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "LqRkqiJ-0OuT",
+        "outputId": "b9d1af7d-1096-47d1-dbda-0481ed144c63"
+      },
+      "execution_count": 267,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.sqrt(1.03*1.01) < 1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uKyBdkbc0OuT",
+        "outputId": "93f8f5fa-b36a-414a-f59e-95f35a410c31"
+      },
+      "execution_count": 268,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "False"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 268
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scenario 4 - Feedback Stable"
+      ],
+      "metadata": {
+        "id": "9LcvjX2xphii"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 269,
+      "metadata": {
+        "id": "HMljet7q2FR0"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Dataset(\"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data/hydraulic.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 270,
+      "metadata": {
+        "id": "sIj-hLoY2FR2"
+      },
+      "outputs": [],
+      "source": [
+        "selected_features = dataset.select_features(number_of_features=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 271,
+      "metadata": {
+        "id": "MLIsyCeB2FR4"
+      },
+      "outputs": [],
+      "source": [
+        "targets = ['PS1','PS2', 'PS3', 'PS4', 'PS5', 'PS6', 'MPW', 'SE', 'TS3', 'TS4', 'FS1']\n",
+        "generator_dictionary = {} \n",
+        "for t in targets:\n",
+        "  generator_dictionary[t] = PredictorDataGenerator(dataset.df,t,list(selected_features[t].keys()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 272,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dc4e6840-e141-4322-d78d-e722eba0505a",
+        "id": "rpXMwR5j2FR5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_4.ckpt\n",
+            "training for: PS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 5.8911e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 312ms/step - loss: 5.8911e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_4.ckpt\n",
+            "training for: PS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 7.5706e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 363ms/step - loss: 7.5706e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_4.ckpt\n",
+            "training for: PS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 2.6572e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 313ms/step - loss: 2.6572e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_4.ckpt\n",
+            "training for: PS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 2.2342e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 339ms/step - loss: 2.2342e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_4.ckpt\n",
+            "training for: PS5\n",
+            "1/1 [==============================] - ETA: 0s - loss: 9.6691e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 304ms/step - loss: 9.6691e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_4.ckpt\n",
+            "training for: PS6\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0591\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 319ms/step - loss: 0.0591\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_4.ckpt\n",
+            "training for: MPW\n",
+            "1/1 [==============================] - ETA: 0s - loss: 5.5947e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 335ms/step - loss: 5.5947e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_4.ckpt\n",
+            "training for: SE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 7.6026e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 310ms/step - loss: 7.6026e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_4.ckpt\n",
+            "training for: TS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0010\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 298ms/step - loss: 0.0010\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_4.ckpt\n",
+            "training for: TS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 9.4295e-05\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 337ms/step - loss: 9.4295e-05\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_4.ckpt\n",
+            "training for: FS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0079\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 341ms/step - loss: 0.0079\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictorSet = PredictorSet(generator_dictionary, train=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "measurements_ps6, prediction_ps6, error_ps6, error_prediction_ps6 = predictorSet.evaluate_feedback_fault_operation('PS6','PS6',50,100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3e874ff7-149a-4dc5-d384-14471948752b",
+        "id": "7E5h2gSj2FR5"
+      },
+      "execution_count": 273,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 0s 46ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
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+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 21ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 20ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 26ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 274,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "outputId": "ae0a7384-2d1a-41ec-f173-91e42bb74c16",
+        "id": "4463o8Jd2FR5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,measurements_ps6[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,measurements_ps6[49:100], linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50)/100,prediction_ps6[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,prediction_ps6[49:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Measurement', 'Measurement', 'Prediction - No Interdependence', 'Prediction - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
+        "plt.savefig(\"loop_stable.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,np.abs(error_ps6[:50]),linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,np.abs(error_ps6[49:100]), linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50,100)/100,error_prediction_ps6[:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Prediction Error', 'Prediction Error','Estimated Prediction Error - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=1)\n",
+        "plt.savefig(\"loop_stable_error.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "outputId": "4050a06d-be68-4603-9fda-8af588f4dab5",
+        "id": "ZzH6GEdh2FR6"
+      },
+      "execution_count": 275,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['PS6'].estimate_maximum_gradient(generator_dictionary['PS6']).values()))\n",
+        "plt.bar(list(selected_features['PS6'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "outputId": "459bfa21-833a-44fd-bb6a-83716cf48b7f",
+        "id": "NbTQUGfJ2FR7"
+      },
+      "execution_count": 276,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "0.36 < 1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c3b57099-9c49-4e0a-ca00-0dbc52f70363",
+        "id": "i3d-eXUo2FR8"
+      },
+      "execution_count": 277,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 277
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Scenario 5 - Feedback Unstable"
+      ],
+      "metadata": {
+        "id": "X_PY6FQnplYI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 278,
+      "metadata": {
+        "id": "KD7V9bAQ3MPY"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = Dataset(\"/content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/data/hydraulic.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 279,
+      "metadata": {
+        "id": "egnqozZv3MPf"
+      },
+      "outputs": [],
+      "source": [
+        "selected_features = dataset.select_features(number_of_features=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 280,
+      "metadata": {
+        "id": "7WzdP47i3MPg"
+      },
+      "outputs": [],
+      "source": [
+        "targets = ['PS1','PS2', 'PS3', 'PS4', 'PS5', 'PS6', 'MPW', 'SE', 'TS3', 'TS4', 'FS1']\n",
+        "generator_dictionary = {} \n",
+        "for t in targets:\n",
+        "  generator_dictionary[t] = PredictorDataGenerator(dataset.df,t,list(selected_features[t].keys()))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 281,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c7075fbb-e7d7-471a-cb2e-670a62c242dc",
+        "id": "ujz5XnET3MPg"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_4.ckpt\n",
+            "training for: PS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 5.9790e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS1_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 358ms/step - loss: 5.9790e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_4.ckpt\n",
+            "training for: PS2\n",
+            "1/1 [==============================] - ETA: 0s - loss: 6.8811e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS2_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 345ms/step - loss: 6.8811e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_4.ckpt\n",
+            "training for: PS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0013\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS3_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 353ms/step - loss: 0.0013\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_4.ckpt\n",
+            "training for: PS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 5.8038e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS4_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 338ms/step - loss: 5.8038e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_4.ckpt\n",
+            "training for: PS5\n",
+            "1/1 [==============================] - ETA: 0s - loss: 1.1063e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS5_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 296ms/step - loss: 1.1063e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_4.ckpt\n",
+            "training for: PS6\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0580\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/PS6_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 362ms/step - loss: 0.0580\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_4.ckpt\n",
+            "training for: MPW\n",
+            "1/1 [==============================] - ETA: 0s - loss: 3.5787e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/MPW_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 353ms/step - loss: 3.5787e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_4.ckpt\n",
+            "training for: SE\n",
+            "1/1 [==============================] - ETA: 0s - loss: 5.4127e-04\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/SE_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 490ms/step - loss: 5.4127e-04\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_4.ckpt\n",
+            "training for: TS3\n",
+            "1/1 [==============================] - ETA: 0s - loss: 9.5367e-07\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS3_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 347ms/step - loss: 9.5367e-07\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_4.ckpt\n",
+            "training for: TS4\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0015\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/TS4_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 322ms/step - loss: 0.0015\n",
+            "checkpoint_path: /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_4.ckpt\n",
+            "training for: FS1\n",
+            "1/1 [==============================] - ETA: 0s - loss: 0.0039\n",
+            "Epoch 1: saving model to /content/drive/My Drive/experiments/A Method to Evaluate the Performance of Predictors in Cyber-physical Systems ( ICPE 2023)/FS1_model_4.ckpt\n",
+            "1/1 [==============================] - 0s 348ms/step - loss: 0.0039\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictorSet = PredictorSet(generator_dictionary, train=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "measurements_ps2, prediction_ps2, error_ps2, error_prediction_ps2 = predictorSet.evaluate_feedback_fault_operation('PS2','PS2',50,100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f2378382-2d98-4722-cf40-6c86b2d3adf8",
+        "id": "WjU3s8nd3MPi"
+      },
+      "execution_count": 282,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 14ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 17ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 19ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 16ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n",
+            "1/1 [==============================] - 0s 15ms/step\n",
+            "1/1 [==============================] - 0s 13ms/step\n",
+            "1/1 [==============================] - 0s 18ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 283,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "outputId": "f7db3ef1-db58-4027-8a0d-8b57a06989ff",
+        "id": "BpPEEFCN3MPi"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,measurements_ps2[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,measurements_ps2[49:100], linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50)/100,prediction_ps2[:50],linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,prediction_ps2[49:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Measurement', 'Measurement', 'Prediction - No Interdependence', 'Prediction - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=2)\n",
+        "plt.savefig(\"loop_stable.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(figsize=(7.5,7))\n",
+        "\n",
+        "plt.plot(np.arange(50)/100,np.abs(error_ps2[:50]),linewidth=3)\n",
+        "plt.plot(np.arange(49,100)/100,np.abs(error_ps2[49:100]), linestyle='--',linewidth=3)\n",
+        "plt.plot(np.arange(50,100)/100,error_prediction_ps2[:100],linewidth=3)\n",
+        "\n",
+        "plt.xlabel('Time [s]',fontsize=16)\n",
+        "plt.ylabel('Pressure [Bar/Bar]',fontsize=16)\n",
+        "ax.xaxis.set_tick_params(labelsize=16)\n",
+        "ax.yaxis.set_tick_params(labelsize=16)\n",
+        "\n",
+        "plt.legend(['Prediction Error', 'Prediction Error','Estimated Prediction Error - With Interdependence'],fontsize=16,loc='upper center', bbox_to_anchor=(0.5, -0.1), ncol=1)\n",
+        "plt.savefig(\"loop_stable_error.eps\", dpi=600, format='eps',bbox_inches='tight')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 565
+        },
+        "outputId": "561c2052-bf49-4e17-ec7f-4436f3162cb6",
+        "id": "H_Oll6D73MPj"
+      },
+      "execution_count": 284,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
+            "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 540x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "gradients = np.array(list(predictorSet.predictors['PS2'].estimate_maximum_gradient(generator_dictionary['PS2']).values()))\n",
+        "plt.bar(list(selected_features['PS2'].keys()),np.abs(gradients))\n",
+        "xlocs, xlabs = plt.xticks()\n",
+        "for i, v in enumerate(np.abs(gradients)):\n",
+        "    plt.text(xlocs[i] - 0.25, v + 0.01, f'{v:.2f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "outputId": "a956f1c7-a852-4b4c-8497-c2589b642342",
+        "id": "IB__XKlZ3MPk"
+      },
+      "execution_count": 285,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "1.01 < 1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2bcbeaea-4389-4015-ddf3-586536308d0c",
+        "id": "7RI7bUNN3MPl"
+      },
+      "execution_count": 286,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "False"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 286
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/models/checkpoint b/models/checkpoint
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index 0000000000000000000000000000000000000000..a64573a424888bae3d4f892a933f0ca217996cbc
--- /dev/null
+++ b/models/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "FS1_model_4.ckpt"
+all_model_checkpoint_paths: "FS1_model_4.ckpt"