Channel_Estimation_Attacks_Github.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning  Models in Next-Generation Wireless Networks\n",
        "\n",
        "This research paper focuses on the security concerns of using artificial intelligence in future wireless networks (5G, 6G, 7G and beyond), also known as Next Generation or NextG. It is crucial to protect the next-generation cellular networks against cyber attacks, especially adversarial attacks. The paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while making models more robust against any attacks through mitigation methods. The paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method could defend the DL-based channel estimation models against adversarial attacks in NextG networks.\n",
        "\n",
        "## Typical adversarial ML-based adversarial sample generation\n",
        "![Adversarial Example](https://github.com/ocatak/6g-channel-estimation-dataset/raw/main/typical_adv.png)\n",
        "\n",
        "## Defensive Distillation\n",
        "\n",
        "In computer security, defensive distillation is a technique for transforming a machine learning model to make it more robust to adversarial examples. The goal is to distil the knowledge from a complex model into a simpler one that is less susceptible to being fooled by adversarial examples. There are a few different ways to do defensive distillation, but the most common is to train a new, smaller model using the predictions of the original model as labels. Carlini and Wagner first proposed this technique in their 2016 paper \"Towards Evaluating the Robustness of Neural Networks\".\n",
        "\n",
        "![Defensive Distillation](https://github.com/ocatak/6g-channel-estimation-dataset/raw/main/6g-defense-channel_estimation_distill.png)\n",
        "\n",
        "\n",
        "## Dataset Description\n",
        "\n",
        "MATLAB 5G Toolbox provides a wide range of reference examples for next generation networks communications systems, such as 5G . It also allows to customize and generate several types of waveforms, antennas and channel models to obtain datasets for DL-based models.  \n",
        "In this study, the dataset used to train the DL-based channel estimation models is generated through a reference example in MATLAB 5G Toolbox, i.e, \"Deep Learning Data Synthesis for 5G Channel Estimation\". In the example, a convolutional neural network (CNN) is used for channel estimation. Single-input single-output (SISO) antenna method is also used by utilizing the physical downlink shared channel (PDSCH) and demodulation reference signal (DM-RS) to create the channel estimation model.  The reference example in the toolbox generates 256 training datasets, i.e., transmit/receive the signal 256 times, for DL-based channel estimation model. Each dataset consists of 8568 data points, i.e., 612 subcarriers, 14 OFDM symbols,  1 antenna. However, each data point of training dataset is converted from a complex (real and imaginary) 612-14 matrix into a real-valued 612-14-2 matrix for providing inputs separately into the neural network during the training process. This is because the resource grids consisting of complex data points with real and imaginary parts in the channel estimation scenario, but CNN model manages the resource grids as 2-D images with real numbers. In this example, the training dataset is converted into 4-D arrays, i.e., 612-14-1-2N, where N presents the number of training examples, i.e., 256.  For each set of the training dataset, a new channel characteristic is generated based on various channel parameters, such as delay profiles (TDL-A, TDL-B, TDL-C, TDL-D, TDL-E), delay spreads (1-300 nanosecond), doppler shifts (5-400 Hz), and Signal-to-noise ratio (SNR or S/N) changes between 0 and 10 dB. Each transmitted waveform with the DM-RS symbols is stored in the train dataset, and the perfect channel values in train labels. The CNN-based channel estimation based is trained with the generated dataset. MATLAB 5G toolbox also allows tuning several communication channel parameters, such as the frequency, subcarrier spacing, number of subcarriers, cyclic prefix type, antennas, channel paths, bandwidth,  code rate, modulation, etc. The channel estimation scenario parameters with values are given for each in the following table.\n",
        "\n",
        "![Defensive Distillation](https://github.com/ocatak/6g-channel-estimation-dataset/raw/main/channel_est_param.png)\n",
        "\n",
        "\n",
        "## The vulnerable CNN model\n",
        "\n",
        "![Defensive Distillation](https://github.com/ocatak/6g-channel-estimation-dataset/raw/main/model_plot.png)\n",
        "\n",
        "### 1. Install Libraries\n",
        "You need to install the required libraries using \n",
        "\n",
        "`pip install <library_name>`.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_l-N2ktK0M9K"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "XgioBSd1LwA2"
      },
      "outputs": [],
      "source": [
        "!pip install -q cleverhans\n",
        "!pip install -q plot_keras_history\n",
        "!pip install -q keras-tuner"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Import libraries\n",
        "Import requiered libraries"
      ],
      "metadata": {
        "id": "a66ExgVGqzM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.io\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.linalg import norm\n",
        "\n",
        "from tqdm.keras import TqdmCallback\n",
        "from tqdm.notebook import tqdm\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from itertools import product\n",
        "from random import shuffle\n",
        "\n",
        "from plot_keras_history import plot_history\n",
        "import matplotlib.pyplot as plt\n",
        "import keras_tuner as kt\n",
        "from keras.models import load_model\n",
        "\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from cleverhans.tf2.attacks.projected_gradient_descent import projected_gradient_descent,fast_gradient_method\n",
        "from cleverhans.tf2.attacks.basic_iterative_method import basic_iterative_method\n",
        "from cleverhans.tf2.attacks.carlini_wagner_l2 import carlini_wagner_l2\n",
        "from cleverhans.tf2.attacks.momentum_iterative_method import momentum_iterative_method\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "HYPARAMETER_TUNING = False\n",
        "TRAIN_MODEL = False\n",
        "GOOGLE_COLAB = True\n",
        "\n",
        "PROJECT_FOLDER = ''\n",
        "\n",
        "np.random.seed(10)"
      ],
      "metadata": {
        "id": "-51pyaXqMRUm"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Download \n",
        "Download our dataset and Defensive Distillation Implementaion from our GitHub repository."
      ],
      "metadata": {
        "id": "jOEhruljq4bc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q -O data.mat https://github.com/ocatak/6g-channel-estimation-dataset/blob/main/data.mat?raw=true\n",
        "!wget -q -O util_defdistill.py https://github.com/ocatak/6g-channel-estimation-dataset/raw/main/util_defdistill.py\n",
        "from util_defdistill import Distiller"
      ],
      "metadata": {
        "id": "gFre2q1wMWD9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Import dataset\n",
        "#### 4.1 Load dataset\n",
        "Convert MATLAB mat file into NumPy matrix using scipy library"
      ],
      "metadata": {
        "id": "kefci2RPrTgP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = scipy.io.loadmat(PROJECT_FOLDER + 'data.mat')\n",
        "\n",
        "trainData = data['trainData'].transpose((3,0,1,2))\n",
        "trainLabels = data['trainLabels'].transpose((3,0,1,2))\n",
        "valData = data['valData'].transpose((3,0,1,2))\n",
        "valLabels = data['valLabels'].transpose((3,0,1,2))"
      ],
      "metadata": {
        "id": "Y67he41eNIGK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.2 Plot example inputs and outputs\n",
        "\n",
        "Import randomly selected 2 inputs and outputs from the dataset. We visualize the inputs and outputs."
      ],
      "metadata": {
        "id": "S4LmIKesrk-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cmap_name = 'Paired'\n",
        "font_size = 18\n",
        "\n",
        "rand_idx = np.random.randint(0,valData.shape[0],2)\n",
        "\n",
        "test_input1 = valData[rand_idx[0]:rand_idx[0]+1,:]\n",
        "real_output1 = valLabels[rand_idx[0]:rand_idx[0]+1,:]\n",
        "\n",
        "test_input2 = valData[rand_idx[1]:rand_idx[1]+1,:]\n",
        "real_output2 = valLabels[rand_idx[1]:rand_idx[1]+1,:]\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=4, figsize=(10,6), sharey=True)\n",
        "\n",
        "im1 = ax[0].imshow(test_input1.reshape(612,14), cmap=cmap_name)\n",
        "ax[0].set_aspect(0.05)\n",
        "ax[0].set_title('Pilot Signals',fontsize=font_size)\n",
        "\n",
        "ax[1].imshow(real_output1.reshape(612,14), cmap=cmap_name)\n",
        "ax[1].set_aspect(0.05)\n",
        "ax[1].set_title('Actual Channel',fontsize=font_size)\n",
        "\n",
        "im1 = ax[2].imshow(test_input2.reshape(612,14), cmap=cmap_name)\n",
        "ax[2].set_aspect(0.05)\n",
        "ax[2].set_title('Pilot Signals',fontsize=font_size)\n",
        "\n",
        "ax[3].imshow(real_output2.reshape(612,14), cmap=cmap_name)\n",
        "ax[3].set_aspect(0.05)\n",
        "ax[3].set_title('Actual Channel',fontsize=font_size)\n",
        "\n",
        "ax[0].set_ylabel('Subcarriers',fontsize=font_size, fontweight='bold')\n",
        "fig.text(0.5,0.05, \"OFDM Symbols\", ha=\"center\", va=\"center\", fontsize=font_size, fontweight='bold')\n",
        "fig.text(0.33,0.12, \"Example 1\", ha=\"center\", va=\"center\", fontsize=font_size, fontweight='normal')\n",
        "fig.text(0.68,0.12, \"Example 2\", ha=\"center\", va=\"center\", fontsize=font_size, fontweight='normal')\n",
        "\n",
        "cbar_ax = fig.add_axes([0.92, 0.152, 0.02, 0.7])\n",
        "fig.colorbar(im1,cax=cbar_ax)\n",
        "cbar_ax.set_aspect(50.5)\n",
        "cbar_ax.tick_params(labelsize=font_size) \n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "lo68nSbuOeAX",
        "outputId": "4b1a182f-a617-4422-a4fc-6c7de63a4ec5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Undefended Model\n",
        "#### 5.1 `get_model`\n",
        "This method is our model architecture. `mult_factor` parameter is the multiplication factor to set the number of neurons for each layer."
      ],
      "metadata": {
        "id": "x6sOI1gcrzLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model(mult_factor = 1.0, model_name=''):\n",
        "    model = models.Sequential(name=model_name)\n",
        "    # selu, softplus, selu\n",
        "    model.add(layers.Conv2D(int(48*mult_factor), (9, 9), padding='same', activation='selu', input_shape=(612, 14, 1)))\n",
        "    model.add(layers.Conv2D(int(16*mult_factor), (5, 5), padding='same', activation='softplus'))\n",
        "    model.add(layers.Conv2D(1, (5, 5),  padding='same', activation='selu'))\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "Hc2EuyCBQFgc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.1 Undefended `Student` model\n",
        "We will train the undefended student model that is same for the defended student model. In this jupyter notebook there are 3 CNN models\n",
        "- undefended student model (variable name: `model`)\n",
        "- defended student model (variable name: `student_model`)\n",
        "- teacher model (variable name: `teacher_model`).\n",
        "\n",
        "Train `model` and plot the training history"
      ],
      "metadata": {
        "id": "D0fLWIivsVBR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "STUDENT_MODEL_MULTIPLICATION = 0.5\n",
        "TEACHER_MODEL_MULTIPLICATION = 1.0\n",
        "\n",
        "model = get_model(STUDENT_MODEL_MULTIPLICATION, model_name = 'Student')\n",
        "\n",
        "es_teacher = EarlyStopping(monitor='val_loss', \n",
        "                          patience=50, \n",
        "                          verbose=1,\n",
        "                          restore_best_weights=True,\n",
        "                          mode='min')\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "                      loss='mse',\n",
        "                      metrics=['mse'])\n",
        "# Train and evaluate teacher on data.\n",
        "hist_model = model.fit(trainData, trainLabels, \n",
        "              epochs=100000,\n",
        "              verbose=0,batch_size=128,\n",
        "              callbacks=[es_teacher,TqdmCallback(verbose=1, desc='DL training')],\n",
        "              validation_data=(valData, valLabels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "82d3182e80704e51864b67278e95f924",
            "18e702baff764d62b1bc0c47b7fc978e",
            "5be07bfaf2f74911915bfc5de2b2e842",
            "21b1822daae24fffa33d2ad58834f384",
            "d00ffe254818402ab4daec8bec75f9ef",
            "78be65e864e445ae92da490708e11937",
            "0c204159ed704a08b2a75bdc0c742b82",
            "7689589baa53403da8492df2fdbaa941",
            "6a8e2126ef824c528c63b80bd68b5789",
            "cc566e48959242ee82737c18550b10a9",
            "fb1efc72849b46b1a2d2dcf734778671",
            "c47d074145334694853c142d9ca0544d",
            "c472ab24e404443d986b78e42f7125be",
            "500c92a4ffdf458686bf45bd6526b85a",
            "71aa36a35e8f456a90f1bdfb36c88e17",
            "d25472c2b534463ca48d5659e9b6e532",
            "46c4d77c21e74525a0bc135f83561d53",
            "247aedf17fb14fa5aa943894ebaeed6e",
            "4911b225f470492eb749de419020d50e",
            "6cf43ede9c30447687dc6ec01a0456bb",
            "719666225be1481eabd6bfc647d43710",
            "8bef1b78d02a4d31bc59bf4371a34dfe"
          ]
        },
        "id": "GOq96Z-LtnKN",
        "outputId": "6bdb629b-4e34-4f80-b61b-5f4f53d0c674"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0epoch [00:00, ?epoch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82d3182e80704e51864b67278e95f924"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0batch [00:00, ?batch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c47d074145334694853c142d9ca0544d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 823.\n",
            "Epoch 873: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(hist_model.history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "GT5iPWtYt0gn",
        "outputId": "b2042ce7-a44b-4ca9-f143-83c6356f8f9c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the input, actual outpu and predicted output."
      ],
      "metadata": {
        "id": "MaOzmFtivFEl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rand_idx = 4\n",
        "\n",
        "test_input1 = valData[rand_idx:rand_idx+1,:]\n",
        "real_output1 = valLabels[rand_idx:rand_idx+1,:]\n",
        "\n",
        "pred = model.predict([test_input1,])\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10,6), sharey=True)\n",
        "\n",
        "im1 = ax[0].imshow(test_input1.reshape(612,14), cmap=cmap_name)\n",
        "ax[0].set_aspect(0.05)\n",
        "ax[0].set_title('Pilot Signals',fontsize=font_size)\n",
        "\n",
        "ax[1].imshow(real_output1.reshape(612,14), cmap=cmap_name)\n",
        "ax[1].set_aspect(0.05)\n",
        "ax[1].set_title('Actual Channel',fontsize=font_size)\n",
        "\n",
        "ax[2].imshow(pred.reshape(612,14), cmap=cmap_name)\n",
        "ax[2].set_aspect(0.05)\n",
        "ax[2].set_title('Predicted Channel',fontsize=font_size)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "UVHfGMKYt4TJ",
        "outputId": "3a9766e6-45d5-453f-be18-2ba67c12db96"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Defensive Distillation\n",
        "Create teacher and student models"
      ],
      "metadata": {
        "id": "c0m6-3oyvORQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "student_model = get_model(STUDENT_MODEL_MULTIPLICATION, model_name = 'Student')\n",
        "teacher_model = get_model(TEACHER_MODEL_MULTIPLICATION, model_name = 'Teacher')\n",
        "\n",
        "#print('*'*70)\n",
        "#student_model.summary()\n",
        "#print('*'*70)\n",
        "#teacher_model.summary()\n",
        "#print('*'*70)"
      ],
      "metadata": {
        "id": "CycufmGwSraw"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "6.1 Teacher Model\n",
        "\n",
        "Train the teacher model and make example prediction."
      ],
      "metadata": {
        "id": "kyaQg7GQvYGy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "es_teacher = EarlyStopping(monitor='val_loss', \n",
        "                          patience=200, \n",
        "                          verbose=1,\n",
        "                          restore_best_weights=True,\n",
        "                          mode='min')\n",
        "\n",
        "teacher_model.compile(optimizer='adam',\n",
        "                      loss='mse',\n",
        "                      metrics=['mse'])\n",
        "# Train and evaluate teacher on data.\n",
        "hist_teacher = teacher_model.fit(trainData, trainLabels, \n",
        "              epochs=100000,\n",
        "              verbose=0,batch_size=128,\n",
        "              callbacks=[es_teacher,TqdmCallback(verbose=1, desc='DL training')],\n",
        "              validation_data=(valData, valLabels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "302ebded5fc44367a80e52eb245de34f",
            "1e90573cd294422d8458a0389f451a34",
            "121dbe52d56548f7b386aac398224100",
            "2a2d9166befc47048ca6df006708c5e5",
            "1faba7ce121d41d4b0f8f321f4ae04b7",
            "bda093c9a37647fea0acf196d2c51726",
            "74853925ab4945f59ad16edb44ab17e4",
            "e41903156cbc442a9b338fded29f2bfe",
            "d2fa1e0f9082416e8a57f10a0e9ac92e",
            "97f74594496d4e7893a97c0b60977345",
            "5c7770879d524c98b9f06ae01563c616",
            "de97324856bc4dd29cb897b0bdb5030a",
            "5a6d72ecd0084ea1b49b5388358d5486",
            "28f5d175f77d457dbdbec08b69c11923",
            "f5429d14fc3b4125b46b827e30244150",
            "6eeed3c35bba461fa3579720e19d5f52",
            "9d67c7f7c09241e883dca1aacc58425a",
            "3f288160ba6d4a91a54fba60688a986e",
            "42c06a6628c74063ac4acb135b17dbed",
            "04533dbb4d854b9da3104ade9c75cab9",
            "65c150274123472daa9687a4a574c0fb",
            "bc9442c1ca124b4c87c940b31cdb42cb"
          ]
        },
        "id": "Vo9thlLCTB3N",
        "outputId": "9a274da2-2e16-404d-8cb1-1f2dc3117c00"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0epoch [00:00, ?epoch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "302ebded5fc44367a80e52eb245de34f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0batch [00:00, ?batch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de97324856bc4dd29cb897b0bdb5030a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 2286.\n",
            "Epoch 2486: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(hist_teacher.history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "jMzU8UpXTfdd",
        "outputId": "1c433d3e-c566-4d74-ec22-4c2871cd0748"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rand_idx = 4\n",
        "\n",
        "test_input1 = valData[rand_idx:rand_idx+1,:]\n",
        "real_output1 = valLabels[rand_idx:rand_idx+1,:]\n",
        "\n",
        "pred = teacher_model.predict([test_input1,])\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10,6), sharey=True)\n",
        "\n",
        "im1 = ax[0].imshow(test_input1.reshape(612,14), cmap=cmap_name)\n",
        "ax[0].set_aspect(0.05)\n",
        "ax[0].set_title('Pilot Signals',fontsize=font_size)\n",
        "\n",
        "ax[1].imshow(real_output1.reshape(612,14), cmap=cmap_name)\n",
        "ax[1].set_aspect(0.05)\n",
        "ax[1].set_title('Actual Channel',fontsize=font_size)\n",
        "\n",
        "ax[2].imshow(pred.reshape(612,14), cmap=cmap_name)\n",
        "ax[2].set_aspect(0.05)\n",
        "ax[2].set_title('Predicted Channel',fontsize=font_size)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "ZHtt0AvsVvvh",
        "outputId": "1f545754-ceb6-4280-a9a3-3f39e756c96a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6.2 Student Model\n",
        "\n",
        "Train the student model using teacher model and training data. Make an example prediction."
      ],
      "metadata": {
        "id": "tXHyQUKKvj8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "distiller = Distiller(student=student_model, teacher=teacher_model)\n",
        "loss_fn = keras.losses.MeanSquaredError()\n",
        "\n",
        "distiller.compile(optimizer='adam',\n",
        "                            metrics=['mse'],\n",
        "                            student_loss_fn=loss_fn,\n",
        "                            distillation_loss_fn=keras.losses.KLDivergence(),\n",
        "                            alpha=0.1,\n",
        "                            temperature=20)\n",
        "\n",
        "es = EarlyStopping(monitor='val_student_loss', \n",
        "                   patience=150, \n",
        "                   #min_delta=0.000001,\n",
        "                   verbose=1,\n",
        "                   restore_best_weights=True,\n",
        "                   mode='min')\n",
        "\n",
        "hist_distill = distiller.fit(trainData, trainLabels, \n",
        "                              epochs=10000,\n",
        "                              verbose=0,\n",
        "                              callbacks=[es,TqdmCallback(verbose=1, desc='DL training')],\n",
        "                              validation_data=(valData, valLabels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
          "referenced_widgets": [
            "1ebcfca4ee15439eaece62bb6c12969a",
            "72d0f6ee29274830910f69199c9445a0",
            "eb33d471d3f84275835285a8454543df",
            "a0264dcc35a642ecaaf35a0f8b316bbd",
            "cb62ca2b3598406fbfb9d0521858f345",
            "9d075bd8b63c45c29650c3194c9e65cb",
            "01c9dbe7fcbe42319178e51c096f9a2b",
            "ab15de81b8544fa887a85a0f96006cee",
            "70e1bbc6b12842b1bcd145942dd39326",
            "82a09a1b188141b19668f09b0549da5b",
            "06602e40e99b4203a1d0ea3bd0e74513",
            "b62216a18d7440c5ac12013889d977fb",
            "719d4ef089c54e78b51e459e38c916a3",
            "43bf1f857a7f4ffe8c8b021d0293d6a6",
            "a9588aa8297e4d8c9800c3e53e539720",
            "d81e330ff3d94962ac1e3c2e487f7f94",
            "df32cca6f8074f5db1603680c89f795d",
            "a1ed19685a2042f3a1843b31a6898ccc",
            "1f60a98a76214e0aa15f9e43df2d6284",
            "c7a8807a5c4d4b78b5d437226bc9f3f9",
            "37587ffd62b74e1fbd60bd5edebd567f",
            "1f51fafb77554bd3a1ac356f88eca372"
          ]
        },
        "id": "Gvcdby0aoaQT",
        "outputId": "615f3c1b-2cb7-41da-ff1e-51abf0879de2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0epoch [00:00, ?epoch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ebcfca4ee15439eaece62bb6c12969a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "DL training: 0batch [00:00, ?batch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b62216a18d7440c5ac12013889d977fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0063s vs `on_train_batch_end` time: 0.0124s). Check your callbacks.\n",
            "Restoring model weights from the end of the best epoch: 1091.\n",
            "Epoch 1241: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(hist_distill.history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "sr-6dsXHo6ZG",
        "outputId": "18df6db0-334b-42da-f02a-8f050869c10e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ],
            "image/png": 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zHdq1axff3rlzZ7nXLlmyhHHjxlXqfh06dOCrr76qdJwzZszgs88+q/R1ZRk1ahSzZ8+ulrYSLV26lO7du9O5c2fGjRuHu+9zjrszbtw4OnfuTI8ePXjrrbcAyMvLo1+/fhx77LH06NGDv//97/tcO27cOA499ND49o4dOxg2bBidO3fmhBNOID8/v9o/k4hEg3J59anOXD5r1qy94u3YsWP8zyUvLy9+LBaLkZ2dzbHHHsupp1bLCuAikiESn/2qIj8/n0ceeaRaYsnPz6dbt+ofeby/nFra/vLz119/zYABA+jSpQsDBgzgm2++OWC7M2fOpEuXLnTp0oWZM2dWy+dIWgHEzOoAU4FBQFfgopICR4JH3L27u2cDk4DJCcc+cvfs8GtMsuIUiYKWLVuSl5dHXl4eY8aM4Zprrolv169fn927d+/32pycHO69996UxFmdD83JcsUVV/CXv/yFVatWsWrVKubNm7fPOc8991z8+PTp07niiisAaNSoEQ8++CDLli1j3rx5jB8/no0bN8avW7JkSTyZl/jrX/9K8+bNWb16Nddccw3XX399cj+giNRYyuXVpzpz+S9/+cu9cvnvf//7+J9LdnY2ABs3buTKK69kzpw5LFu2jCeeeCI1H1REaoXqLIAky/5yamn7y88TJ07kjDPOYNWqVZxxxhlMmTKl3Ha//vprbrnlFl5//XXeeOMNbrnlln2es6simT1A+gCr3X2Nu+8EHiNYkzzO3TcnbDYG9i3fi0iZRo0axZgxYzjhhBP49a9/zRtvvEG/fv3o2bMnZ555Jh9++CEQvLE699xzAbj55pu59NJLyc3NpVOnThV6mD7//PM5/vjjOfbYY5k+PZhDbc+ePYwaNYpu3brRvXt3pkyZwuzZs1myZAkXX3wx2dnZbNu2Ld7GihUr6NOnT3w7Pz+f7t27A3DrrbfSu3dvTjjhBEaPHl3mW7zEt5hLliyJrxm+ZcsWLr30Uvr06UPPnj35xz/KX111/fr1bN68mb59+2JmjBgxgqeeemqf8/7xj38wYsQIzIy+ffuyceNG1q9fz1FHHUWXLl0A+O53v0vr1q358ssv4z+T6667jkmTJu3T1siRIwG48MILWbhwYZmfUURqp/Jy+YknnsiqVauA6OTybt267bdHRk3N5YcffvgBe8o88sgjXHDBBXzve98D4PDDDy/3fBGRp59+mhNOOCH+bP7FF18A8NJLL8V7mfXs2ZPCwkLGjx/PokWLyM7OjhcGSgwfPpxnn302vl3Ssy4/P5+TTz6ZXr16cfLJJ/Pqq6/uE8OMGTMYO3ZsfPvcc88lFosBsGDBAvr160evXr0YOnQoRUVF5X6e/eXUROXl58Rn4pEjR/LMM8+U2+78+fMZMGAALVq0oHnz5gwYMKDMYndlJXMVmHbA2oTtAuCE0ieZ2f8DrgXqA6cnHOpoZm8Dm4HfuPuiMq4dDYwGaNOmTfwPs7KKioqqfG26KObUKIm5adOmFBYWpjscIBhSUa9ePXbt2sXnn3/O/PnzqVOnDps3b2bu3LnUrVuXhQsX8utf/5qHH36YrVu3snv3bgoLC9mxYwfLli3j2WefpaioiF69enHJJZfss3SUu1NUVESDBg245557aNGiBdu2bSM3N5ezzjqLTz/9lE8//ZTXXnsNCN6MNWvWjJ49e3LbbbfRq1ev+D0B2rVrx/bt23nvvffo0KEDDz74IOeffz6FhYWMHDmSa665hj179jBmzBieeOIJBg0axK5du9i2bRuFhYV7xbNlyxb27NlDYWEht9xyC/369eOee+5h48aNnHbaaZxwwgls3ryZsWPH7jNEZeXKlbRt2zYeV4sWLfjkk0/2+bP95JNPaNmyZXx/27ZtWbly5V7dG5csWcLOnTs5/PDDKSws5L777uOss86Kn1Ny7dq1a2nevHl8u0mTJvH2E23fvj1y/3+ISPUoKCjg1VdfjefyRYsWUbduXV544QVuueWWMgsCK1as4MUXX6SwsJCjjz6aK664otxlAO+///54Lu/duzc//vGPyc/PZ926dbz//vvAt7n8j3/8I3fddRc5OTl7tfGDH/yAnTt38vHHH9OxY0dmzZrFsGHDABg7diwTJgQLRg0fPpxnnnmGH/7whxX6/Lfffjunn346999/Pxs3bqRPnz6ceeaZbNq0icsuu4y5c/damIp169bRvn37+Hb79u1Zt27dPu2uW7eOI444Yp/z2rZtG9/3xhtvsHPnTjp16hTfd+ONN3LrrbdyxhlnMHHiRBo0aMDKlSvZtWsXubm5FBYWcvXVVzNixIgKfT4RqZ1OOukkFi9ejJnxv//7v0yaNIm7776bu+66i6lTp9K/f3+Kiopo2LAhEydO5K677ooXBRINGzaMxx9/nMGDB7Nz504WLlzIn/70J9yd559/noYNG/L2229z+eWXs2RJxVa8/uqrr7jtttt44YUXaNy4MXfeeSeTJ09mwoQJTJgwgZycHIYMGbLXNRXJqeXl5y+++CJ+7ne+8534S8T9tbu//Qcr7cvguvtUYKqZ/RT4DTASWA98z903mNnxwFNmdmypHiO4+3TCZb1ycnK85E1CZQyf/hpjjoKqXJtOsVhMMadAScwffPDBXss0dRj/bDlXVU3+xMEVOq9BgwY0aNCAevXqcdFFF9GsWTMgeHC99NJLWbVqFe7Onj17aNKkCY0aNaJu3bo0adKEBg0aMGTIEFq1akWrVq1o06YNW7du3StRQTCh0KGHHkqTJk24++67efLJJ4EgQX3++ed0796dTz75hP/6r/9i8ODBnHXWWWRlZVGnTh0aN25c5pJWJdXr8ePH89RTTzFr1iyaNGnCggULmDRpEkVFRWzcuJHs7GyaNGlCvXr1OOSQQ2jSpMle8TRu3Jg6derQpEkTYrEY8+bNY+rUqQDs3LmTb775hmOOOYYFCxbsE0PitcBeP5tEdevWpVGjRvH9pT/X+vXrGTNmDPfddx9Nmzbls88+4+mnnyYWi1G3bpBWS87NysqKx17WdomGDRvSs2fPCv0dkH1t2bGbKUu3E7EUIzVAOvN5iaFDh1KnTh0ANm3axMiRI1m1ahVmxo4dZS/eMXjw4Pi/B4cffjhffPHFPrk80b333hvP5WvXrmXVqlUcffTRrFmzhquuuiqeyw/kJz/5CbNmzWL8+PHMmjUrPn/Giy++yKRJk9i6dWt8rpOKFkAWLFjAnDlzuOuuu4CgIPzpp59yzDHH7FP8qE7r16/nZz/7GTNnziQrK+gUfccdd/Cd73yHnTt3Mnr0aO68804mTJjA7t27Wbp0KQsXLmTbtm3069ePvn37ctRRRyUtvpru5ZVf8vRHO5V3pcapCXkdguL2sGHDWL9+PTt37owv1dq/f3+uvfZaLr74Yi644IJyczfAoEGDuPrqq9mxYwfz5s3jlFNO4ZBDDmHTpk2MHTuWvLw8zIzVq1dXOLbFixezfPly+vfvDwTP0P369QOCHn3JZmZpm7g2mQWQdcARCdvtw3378xjwJ4Bwqa4d4fdLzewj4CigYiWtSli85mvGHNW4upuVDFeVJJgMjRt/+3f3pptu4rTTTuPJJ5/k/fffj3eVLq1Bgwbx7+vUqVPumPNYLMYLL7zAa6+9RqNGjcjNzWX79u00b96cd955h/nz5zNt2jQef/xx7r///nJjHTZsGEOHDuWCCy7AzOjSpQvbt2/nyiuvZMmSJTRr1oy77767zKWs6tatS3FxMcBex92dv//97xx99NHl3rtEu3btKCgoiG8XFBTQrl27Ms9bu3Ztmedt3ryZwYMHc/vtt8e7gr/99tusXr2azp07A7B161Y6d+7M6tWr4221b9+e3bt3s2nTpn16f8jB2+POym/2pDsMiaCakM/3l8vz8/P3O9lmTc3lRxxxBDfccEOkcnnfvn336vEHwc/35z//ebwo0759e1q2bEnjxo1p3Lgxp5xyCu+8806tLoB8VbSDz4qK0x2GyD4OJq8XFhaW+SKvKq666iquvfZahgwZQiwW4+abbwZg/PjxDB48mLlz59K/f3/mz59fbjsNGzYkNzeX+fPnM2vWLIYPHw7AlClTaNOmDe+88w6bNm2idevW+1ybmHfh29zr7gwYMIBHH320wp+nvJyaeM7+8nObNm1Yv349bdu2Zf369bRq1arcdtu1a7dX7+iCgoJqeZmezDlA3gS6mFlHM6sPDAfmJJ5gZl0SNgcDq8L9rcNJVDGzTkAXYE0SYxWJvE2bNsUTzN/+9rdqa7N58+Y0atSIFStWsHjxYiDoNldcXMyPf/xjbrvttvhszU2aNNnvUKEjjzySOnXq8Lvf/S7eZbokCbdq1YqioqL9rhTQoUMHli5dCrDXsJazzz6b//mf/4mPNX/77bfL/Txt27blsMMOY/Hixbg7Dz74IOedd94+5w0ZMoQHH3wQd2fx4sU0bdqUtm3bsnPnTn70ox8xYsQILrzwwvj5gwcP5vPPPyc/P5/8/HwaNWoUr8IPGTIkPmv17NmzOf3007VUW5JoahXJBIm5fMaMGdXWZipz+f7m8KjpuRyIj2d3d5566qn4SgrnnXce//rXv9i9ezdbt27l9ddf55hjjik3zkyXZabJ+0TKkZjPE1cw+eijj+jevTvXX389vXv3ZsWKFeXmXQiKzw888ACLFi1i4MCB8fbbtm1LVlYWjz32GHv27PsiqEOHDuTl5VFcXMzatWt54403AOjbty+vvPJK/Hl1y5YtrFy5stzPs7+cmqi8/Jz4TDxz5kwGDx5cbrtnn302CxYs4JtvvuGbb75hwYIFnH322eXGWBFJK4C4+25gLDAf+AB43N2XmdmtZlYyoGismS0zszyCeUBGhvtPAd4N988Gxrj710mMNVlNi6TMr3/9a2644QZ69uxZ7pvAyhg4cCC7d+/mmGOOYfz48fTt2xcIhsLk5uaSnZ3NJZdcwh133AF8O5lf6YnzSgwbNoyHH36Yn/zkJwA0a9aMyy+/nG7duvGjH/2I3r17lxnHb3/7W66++mpycnLi3cQheFO6a9cuevTowbHHHstNN90EwGeffcY555xTZlv33Xcfl112GZ07d+bII49k0KBBAEybNo1p04IVt8855xw6depE586dufzyy7nvvvsAePzxx3n55ZeZMWMG2dnZ9O/ff68lEsvyi1/8gg0bNtC5c2cmT57MxIkTyz1fqkYlJckUUc/lZ599Nr169Sozjpqay7Ozs3n33XcBuPjii+nevTvdu3fnq6++4je/+Q0AxxxzDAMHDqRHjx706dOHyy67LCnLTEaJmQrPIiVKhpSXfE2ePJmbb76ZoUOHcvzxx8d7OwD84Q9/oFu3bvTo0YN69eoxaNAgevToQZ06dTjuuOP2mQQV4KyzzuKll17izDPPpH79+gBceeWVzJw5k+OOO46VK1fu1ZuwRP/+/enYsSNdu3Zl3Lhx8fzcunVrZsyYwUUXXUSPHj3o168fK1asAGDChAnMmTNnn7b2l1OB+IpZsP/8PH78eJ5//nm6dOnCCy+8wDXXXFNuuy1atOCmm26id+/e9O7dmwkTJtCiRYvK/cGUwTLll/+cnByv6KQviTqMf5YHzm7EaaedloSokifK82lESeIcIFF601Od3fdSpbbHXNbfMTNb6u45+7kkY1Ulnxdu30Xv3y1gxe3pH85QWVHOjVGifJ46tT3mKObzquTdOe98xsMvvsvjvxyYpKiSI4r5C6IZd6pirs68HsX8BdGMu7pirmzOTeYQGBERERERyUBZhobAiEjkqACCkreISNRpXhURkdQyTENgRCRyan0BRM/MIiIiIiKVox4gIhJFtb4AIiIimUEP4iIiqaNJUKUmyZR5LaVyqvLnrgKIiIhEnjrziYiklmkZXKkhGjZsyIYNG1QEqWXcnQ0bNtCwYcNKXVc3SfGIiIiklB57RERSx1APEKkZ2rdvT0FBAV9++eVBt7V9+/ZK/0JdE0Qx7uqIuWHDhrRv375S19T6AojeGkoUbNiwgTPOOAOAzz//nDp16tC6dWsA3njjjfh64PsTi8WoX78+J5544j7HZsyYwZIlS/jjH/9YqZjy8/N59dVX+elPf1qp6/bX1jnnnMPy5ZelGHUAACAASURBVMsPuq1E7s7VV1/N3LlzadSoETNmzIivf55o6dKljBo1im3btnHOOedwzz33YGZcd911PP3009SvX58jjzySBx54gGbNmpGfn88xxxxDly5dyMrKom/fvkybNg2AnTt3MnbsWGKxGFlZWdx+++38+Mc/rtbPJfvSfE4SBbUhl5977rm89tprB91WomTn8qOPPpri4mJOPPFE5fJKyFIPEKkh6tWrR8eOHaulrVgsRs+ePaulrVSKYtzpillDYEQioGXLluTl5ZGXl8eYMWO45ppr4tsHemCGIMG8+uqr1RpTfn4+jzzySLW2Wd2ee+45Vq1axapVq5g+fTpXXHFFmeddccUV/OUvf4mfO2/ePAAGDBjA+++/z7vvvstRRx3FHXfcEb/myCOP5JVXXiEvLy/+wAxw++23c/jhh7Ny5UqWL1/OqaeemtwPKSKRoVxeNcnO5Xl5ebzyyivK5ZWUlaWedyISPSqAoO57Ek1Lly7l1FNP5fjjj+fss89m/fr1ANx777307t2bHj16MHz4cPLz85k2bRpTpkwhOzubRYsW7bfNp59+mhNOOIGePXty5pln8sUXXwDw0ksvkZ2dTXZ2Nj179qSwsJDx48ezaNEisrOzmTJlyl7tDB8+nGeffTa+PWrUKGbPnk1+fj4nn3wyvXr1olevXmU+yM+YMYOxY8fGt88991xisRgACxYsoF+/fvTq1YuhQ4dSVFRU7s/oH//4ByNGjMDM6Nu3Lxs3boz/nEqsX7+ezZs307dvX8yMESNG8NRTTwFw1llnUbdu0FGub9++FBQUlHs/gPvvv58bbrgBgKysLFq1anXAa6SaKJdLBJWXy7t27Uq/fv2Uy5XLayQtgysiUVTrCyCmftMSQe7OVVddxezZs1m6dCmXXnopN954IwATJ07kX//6F++++y7Tpk2jQ4cOe71pPPnkk/fb7kknncTixYt5++23GT58OJMmTQLgrrvuYurUqeTl5bFo0SIOOeQQJk6cyMknn0xeXh7XXHPNXu0MGzaMxx9/HAi6ES9cuJDBgwdz+OGH8/zzz/PWW28xa9Ysxo0bV+HP/NVXX3Hbbbfxwgsv8NZbb5GTk8PkyZMBmDBhAnPmzNnnmnXr1nHEEUfEt9u3b8+6dev2OSdx7GBZ50DwMDxo0KD49scff8xJJ53EqaeeGv9FZOPGjQDcdNNN8Qf7kl88JLlMAxolgg6Uy99++21ee+015fIk5/KePXsyaNAg5fJKMi2DKyIRVOvnABGpkpubJqHNTRU+dceOHbz//vsMGDAAgD179tC2bVsAevTowWWXXcaFF17I+eefX6kQCgoKGDZsGOvXr2fnzp3x8ZT9+/fn2muv5eKLL+aCCy444GRDgwYN4uqrr2bHjh3MmzePU045hUMOOYRNmzYxduxY8vLyqFOnDitXrqxwbIsXL2b58uX0798fCB7G+/XrB8Ctt95aqc9ZWbfffjt169bl4osvBqBt27Z8+umn1K9fn5UrV3L++eezbNkydu/eTUFBASeeeCKTJ09m8uTJ/OpXv+Khhx5KanwichDSmM8PlMsvvvhizj77bC666KJK3V65vGz7y+UtW7bk5Zdf5qc//alyeSWYmVbdEJHIUQEEVa+lCipRrEgGd+fYY48tc6K5Z599lnnz5rFw4UJuv/123nvvvQq3e9VVV3HttdcyZMgQYrEYN998MwDjx49n8ODBzJ07l/79+zN//vxy22nYsCG5ubnMnz+fWbNmMXz4cACmTJlCmzZteOeddyguLi5z5ue6detSXFwc396+fXv8Mw8YMIBHH320wp+nXbt2rF27Nr5dUFBAu3bt9jknsTt06XNmzJjBM888w8KFC+M9xho0aECDBg0oLCzk+OOP58gjj2TlypUcf/zxNGrUiAsuuACAoUOH8te//rXC8crBUS6XKkljPj9QLn/55Zf5+9//zuTJk5XLk5jLAXr27KlcXklZ6ngnIhGkITDpDkCkCho0aMCXX34Zf2jetWsXy5Yto7i4mLVr13LKKadw5513smnTJoqKimjSpAmFhYUHbHfTpk3xB8aZM2fG93/00Ud0796d66+/nt69e7NixYoDtjls2DAeeOABFi1axMCBA+Ptt23blqysLB566CH27Nmzz3UdOnQgLy8v/lneeOMNIBi3/corr7B69WoAtmzZcsC3jkOGDOHBBx/E3Vm8eDFNmzaNv10t0bZtWw477DAWL16Mu/Pggw9y3nnnATBv3jwmTZrEnDlzaNSoUfyaL7/8Mh77mjVrWLVqFZ06dcLM+OEPfxgf575w4UK6du1aboxSPTSaUaLoQLn8tNNO49Zbb1UuT0Eu//jjj5XLKynLjGJVnkUkYmp9AUQkirKyspg9ezbXX389xx13HNnZ2bz66qvs2bOHSy65hL59+9KzZ0/GjRtHs2bN+OEPf8iTTz55wInzbr75ZoYOHcrxxx+/14Rvf/jDH+jWrRs9evSgXr16DBo0iB49elCnTh2OO+64fSbOg2DSuZdeeokzzzwzvrrBlVdeycyZMznuuONYsWIFjRs33ue6/v3707FjR7p27cq4cePiSx22bt2aGTNmcNFFF9GjRw/69evHihUrgP2PGz/nnHPo1KkTnTt35vLLL+e+++6LH8vOzo5/f99993HZZZfRuXNnjjzyyPj48LFjx1JYWMiAAQPIzs5mzJgxALz88sv06NGD/v37c+GFFzJt2jRatGgBwJ133snNN99Mjx49eOihh7j77rv3+/OW6qXncImaA+Xy7t27c9JJJymXJzmXZ2dnM2LECOXySjKUd0Ukgtw9I76OP/54r4ojb3jWn1/4zypdm04vvvhiukOotCjHvHz58vQGUkmbN29OdwiVVttjLuvvGLDEa0B+TfVXVfL5tp27/cgbnqn0dTVBlHNjlCifp05tjzmK+bwqeffV1V/5WRPnVvq6dIti/nKPZtyKOXWiGHcyYy4v59b6HiDqNi0iIiIiUjlaBUZEoqjWF0BERCRD6ElcRCRlsvQWUUQiSAUQERGJPD2Hi4h8y8yONrO8hK/NZvbL6r0HmgRVRCJHy+Cil4ZSMe4eXzpPpDoFQxXlYOmnKBWlfC7JUlPyubt/CGQDmFkdYB3wZHXeQ8vgikgU1foeIKaFcKUCGjZsyIYNG2rMg41kDndnw4YNNGzYMN2hRJpyuVSU8rkkSw3O52cAH7n7J9XZqGkZXBGJIPUAEamA9u3bU1BQwJdffpnuUCpk+/btNfEBrFy1OeaGDRvSvn37aojo4JjZQOAeoA7wv+4+sdTxU4A/AD2A4e4+O9x/GpC4fuYPwuNPmdkM4FRgU3hslLvnJfWDiJRD+Tz5anPMNSWflzIceLS6G9UyuCISRSqAAHoJJAdSr149OnbsmO4wKiwWi9GzZ890h1Epijm9wi7SU4EBQAHwppnNcfflCad9CowCfpV4rbu/yLddrVsAq4EFCadcV1IsEUk35fPkU8w1h5nVB4YAN5RxbDQwGqBNmzbEYrFKtb1m0x727NlT6evSraioKHIxQzTjVsypE8W40xWzCiDqNS0iAtAHWO3uawDM7DHgPCBeAHH3/PBYcTntXAg85+5bkxfqvjSdg4hImQYBb7n7F6UPuPt0YDpATk6O5+bmVqrhFgUbmbnsNSp7XbrFYrHIxQzRjFsxp04U405XzLV+DhAREQGgHbA2Ybsg3FdZZXW1vt3M3jWzKWbWoKoBiohIpV1EEoa/gJbBFZFoUg8QERGpFmbWFugOzE/YfQPwOVCf4E3j9cCtZVx7UF2xdxc77h657p+gbqupEsWYIZpxK+aawcwaEwxr/I9k3UOToIpI1NT6Aohq1yIiQLBE4hEJ2+3DfZXxE+BJd99VssPd14ff7jCzByg1f0jCeQfVFXv3nmJ4/rnIdf8EdVtNlSjGDNGMWzHXDO6+BWiZrPbVA0REokhDYEREBOBNoIuZdQwnzRsOzKlkG/t0tQ57hWBmBpwPvF8NsZZJE1qLiKROVhZaTlpEIkcFEBERwd13A2MJhq98ADzu7svM7FYzGwJgZr3NrAAYCvzZzJaVXG9mHQh6kLxUqum/mdl7wHtAK+C2ZMRvehMpIpJShlHejNgiIjVRrR8CA1rDXEQEwN3nAnNL7ZuQ8P2bBENjyro2nzImTXX306s3ShERqQmyDD1Ei0jk1PoeIHppKCIiIiJSOWaoB4iIRE6tL4CIiEj0qZYtIpJaZqYeICISOSqAgJK3iIiIiEglaASMiERRrS+AmN4biohkBD2Ii4ikTpYZxUq8IhIxtb4AIiIi0af5nEREUkt5V0SiKKkFEDMbaGYfmtlqMxtfxvExZvaemeWZ2b/MrGvCsRvC6z40s7OTGaeK1yIiIiIiFWeYnqFFJHKSVgAxszrAVGAQ0BW4KLHAEXrE3bu7ezYwCZgcXtsVGA4cCwwE7gvbS0KcyWhVRERSyZTMRURSygxcFRARiZhk9gDpA6x29zXuvhN4DDgv8QR335yw2ZhvO2OcBzzm7jvc/WNgddieiIiIiIiIiEil1U1i2+2AtQnbBcAJpU8ys/8HXAvUB05PuHZxqWvblXHtaGA0QJs2bYjFYpUOcs+ePWzZsqVK16ZTUVGRYk6BKMYM0YxbMUt1cHf1BhERSQGtgisiUZTMAkiFuPtUYKqZ/RT4DTCyEtdOB6YD5OTkeG5ubqXvX+ef82jUuAFVuTadYrGYYk6BKMYM0YxbMYuIiERHlorNIhJByRwCsw44ImG7fbhvfx4Dzq/itVWm1C0iIiIiUjlmaBlcEYmcZBZA3gS6mFlHM6tPMKnpnMQTzKxLwuZgYFX4/RxguJk1MLOOQBfgjSTGKiIiGUAT8omIpIbpNaKIRFDShsC4+24zGwvMB+oA97v7MjO7FVji7nOAsWZ2JrAL+IZw+Et43uPAcmA38P/cfU+yYhURkejTo7iISOpoDhARiaKkzgHi7nOBuaX2TUj4/upyrr0duD150QU0WZ6ISAbYUcjs+r/FOSfdkYiI1AqGet2JSPQkcwiMiIhIangxR1lBuqMQEak9DNQHRESiRgUQVL0WEYk+w/QgLiKSMlmmrCsi0VPrCyAaACMikgHC4YyuiraISEpoCIyIRFGtL4CIiEhm0LtIEZHUMfUAEZEIUgEEjV4UEYm+YEFG5XMRkdRQL2oRiSIVQJS9RUSizzQHiIhIKplpCIyIRI8KICIikgFUABERSSVlXRGJIhVAREQk+uKToKY5DhGR2kK9qEUkglQAERGRjKBncRGR1MnSEBgRiaBaXwDRA7OISCbQFKgiIqmkVWBEJIpqfQFEREQyQDgJqutxXEQkJVR2FpEoUgEEdd8TEYk+9ecTESlhZs3MbLaZrTCzD8ysX/XfA1VARCRy6qY7gHQz00OziEgmMKBYD+MiIgD3APPc/UIzqw80qu4baBUYEYmiWl8AERGRDGB6FBcRATCzpsApwCgAd98J7Kz++6gDiIhEj4bAiIhIBlABREQk1BH4EnjAzN42s/81s8bVfRMNgRGRKKr1PUA0AkZEJAMomYuIlKgL9AKucvfXzeweYDxwU+JJZjYaGA3Qpk0bYrFYpW6yu9gpxit9XboVFRVFLmaIZtyKOXWiGHe6Yq71BRAREckMKoGIiABQABS4++vh9myCAshe3H06MB0gJyfHc3NzK3WTXXuK4fnnqOx16RaLxSIXM0QzbsWcOlGMO10xawiMiIjEmdlAM/vQzFab2T4PzGZ2ipm9ZWa7zezCUsf2mFle+DUnYX9HM3s9bHNWOCFfdUdOlqkvtoiIu38OrDWzo8NdZwDLq/s+hlZSFJHoUQEEDV8UEQEwszrAVGAQ0BW4yMy6ljrtU4KJ9R4po4lt7p4dfg1J2H8nMMXdOwPfAL9IQvCAHsZFREJXAX8zs3eBbOC/q/sGWklRRKKo1g+BUeoWEYnrA6x29zUAZvYYcB4Jbw7dPT88VlyRBi14Qj4d+Gm4ayZwM/Cn6go6vFG1NiciEmXungfkJPMemgNVRKJIPUBERKREO2BtwnZBuK+iGprZEjNbbGbnh/taAhvdfXcV26wU9wrVZURE5CCp7iwiUVTre4CAqtciItXk++6+zsw6Af80s/eATRW58GBXIwDIBRa9/DIN6tWp9LXppJnbUyOKMUM041bMtYPFhx66hsOISGTU+gKIEraISNw64IiE7fbhvgpx93Xhf9eYWQzoCfwdaGZmdcNeIGW2ebCrEQAUv2icfPJJNGrYoNLXppNmbk+NKMYM0YxbMdceBhQ71NHjtIhEhIbAiIhIiTeBLuGqLfWB4cCcA1wDgJk1N7MG4fetgP7Acnd34EWgZMWYkcA/qj1ygt58rllQRURSSnlXRKJEBRDQGBgRESDsoTEWmA98ADzu7svM7FYzGwJgZr3NrAAYCvzZzJaFlx8DLDGzdwgKHhPdvWTy1OuBa81sNcGcIH9NSvya1lpEJKXM9BgtItGiITDpDkBEpAZx97nA3FL7JiR8/ybBMJbS170KdN9Pm2sIVphJPr2JFBFJGUNpV0SiRT1AREQkIziG612kiEhKKe+KSJSoAIK67omIZAIHvYoUEUkhM6VdEYmWWl8A0SIwIiKZwjQZn4hICmkIjIhETa0vgIiISCbRk7iISKoYGgIjItGiAoiIiGQER32xRURSSmlXRCJGBRBUuRYRyQSO8rmISCoFPUBERKJDBRAthCsikhFc+VxEJKWCOUBUAhGR6FABREREMoYX60FcRCSVlHVFJEqSWgAxs4Fm9qGZrTaz8WUcv9bMlpvZu2a20My+n3Bsj5nlhV9zkhmnMreISPS5OmOLiKSUlsEVkaipm6yGzawOMBUYABQAb5rZHHdfnnDa20COu281syuAScCw8Ng2d89OVnzfxpnsO4iISCo46ElcRCSFNARGRKImmT1A+gCr3X2Nu+8EHgPOSzzB3V90963h5mKgfRLjERGRjGbqACIikkLqASIiUZO0HiBAO2BtwnYBcEI55/8CeC5hu6GZLQF2AxPd/anSF5jZaGA0QJs2bYjFYpUOcufOnRRtKa7StelUVFSkmFMgijFDNONWzHKwNAmqiEjqqf4hIlGSzAJIhZnZJUAOcGrC7u+7+zoz6wT808zec/ePEq9z9+nAdICcnBzPzc2t9L0bvPICjRtnUZVr0ykWiynmFIhizBDNuBWzVA89iouIpIqGwIhI1CRzCMw64IiE7fbhvr2Y2ZnAjcAQd99Rst/d14X/XQPEgJ5JjFVERCIumAOkON1hiIjUGpp6WkSiJpkFkDeBLmbW0czqA8OBvVZzMbOewJ8Jih//Ttjf3MwahN+3AvoDiZOnioiI7EWrwIiIpJbmABGRqEnaEBh3321mY4H5QB3gfndfZma3AkvcfQ7we+BQ4AkLlmP51N2HAMcAfzazYoIizcRSq8eIiIjsxdGDuIhIapmGwIhIpCR1DhB3nwvMLbVvQsL3Z+7nuleB7smMrYSWwRURySB6EBcRSRnT4lsiEjHJHAIjIiKSQhoCIyKSSsEkqOmOQkSk4lQAQY/LIiKZwNUVW0Qk5VxP0iISIbW+AGJoDIyISCbQI7iISGqpB4iIRE2tL4CIiEgm0ZO4iEiqaA4QEYmapE6CGhWqXIuIRJ9jUFyc7jBERGoNA4qL9SAtItFR6wsgWgVGRCQzOIbpXaSICABmlg8UAnuA3e6ek96IRETSr9YXQEREJHOo/CEispfT3P2rZDVupp7UIhItmgNEREQyiJ7ERURSJVh8XHlXRKKj1hdANAJGRCQzOJqNT0QkgQMLzGypmY1O2k2Ud0UkQjQERkREMoJjmJ7ERURKnOTu68zscOB5M1vh7i+XHAyLIqMB2rRpQywWq/QN3ItZ/Prr5DeOzjvVoqKiKn3WdIti3Io5daIYd7piVgFEREQygoNeRYqIhNx9Xfjff5vZk0Af4OWE49OB6QA5OTmem5tb6XvUeXkuffr0oVPrQ6sl5lSIxWJU5bOmWxTjVsypE8W40xVzdMq1SaTHZRGR6HMNahQRAcDMGptZk5LvgbOA96v9PoBWwRWRKKn1PUBM6+CKiGQMpzjdIYiI1ARtgCfD59y6wCPuPq/a72KgV4kiEiW1vgAiIiKZQusxikhmMbP+QJ67bzGzS4BewD3u/kl517n7GuC4pMeH0q6IRIuGwKDELSICYGYDzexDM1ttZuPLOH6Kmb1lZrvN7MKE/dlm9pqZLTOzd81sWMKxGWb2sZnlhV/ZyYpfc4CISAb6E7DVzI4D/hP4CHgwvSF9K1gGV0QkOlQAERERzKwOMBUYBHQFLjKzrqVO+xQYBTxSav9WYIS7HwsMBP5gZs0Sjl/n7tnhV15SPgCaA0REMtJud3fgPOCP7j4VaJLmmL6ljnciEjEaAiMiIhCsDrA67DaNmT1G8MC9vOQEd88Pj+010Ya7r0z4/jMz+zfQGtiY/LBL05O4iGSUQjO7AbgEOMXMsoB6aY4pLgtw5V0RiRD1ABEREYB2wNqE7YJwX6WYWR+gPkE37RK3h0NjpphZg4MLc/8cw/UqUkQyyzBgB/ALd/8caA/8Pr0h7a1Yc0+LSITU+h4gWgRGRKR6mFlb4CFgpLuXPBLfAHxOUBSZDlwP3FrGtaOB0QBt2rQhFotV+v5dgLeWLqXBR59VJfy0KSoqqtLnTSfFnDpRjFsxV6tCgklP95jZUcAPgEfTHFOcmakHiIhESq0vgIiICADrgCMSttuH+yrEzA4DngVudPfFJfvdfX347Q4zewD4VVnXu/t0ggIJOTk5npubW6ngAda9aPTq1YvW3zu60temUywWoyqfN50Uc+pEMW7FXK1eBk42s+bAAuBNgl4hF6c1qgTqeCciUaIhMCIiGczMmptZjwqc+ibQxcw6mll9YDgwp4L3qA88CTzo7rNLHWsb/teA84H3KxN/pelJXEQyi7n7VuAC4D53Hwp0S3NMcepILSJRowIImjJPRDKLmcXM7DAzawG8BfzFzCaXd4277wbGAvOBD4DH3X2Zmd1qZkPCdnubWQEwFPizmS0LL/8JcAowqozlbv9mZu8B7wGtgNuq+eN++xn0KC4imcfMrB9Bj49nw3015vndtAqMiERMrR8CozlARCQDNXX3zWZ2GUGvjN+a2bsHusjd5wJzS+2bkPD9mwRDY0pf9zDw8H7aPL2ywVeVA45m4xORjPJLgrmUngyL0p2AF9Mc0140B4iIREmtL4CIiGSguuHQk58AN6Y7mFRx9CpSRDKLu78EvGRmh5rZoeFS5ePSHVeJLJR2RSRaakwXOhERqTa3EgxlWe3ub4ZvDFelOaYUUJc+EcksZtbdzN4GlgHLzWypmR2b7rjiDIpVARGRCKn1PUAMQ7OAiEgmcfcngCcSttcAP05fRKnjehAXkczyZ+Bad38RwMxygb8AJ6YzqBJ6ihaRqFEPEBGRDGNmk8JJUOuZ2UIz+9LMLkl3XMnmhvpii0imaVxS/ABw9xjQOH3h7EtpV0SiRAUQlLhFJOOc5e6bgXOBfKAzcF1aI0oB17tIEck8a8zsJjPrEH79BliT7qBKBAMPlXdFJDpqfQFEq8CISAYqGd44GHjC3TelM5hU0TK4IpKBLgVaA/8XfrUO99UIWgZXRKKm1s8BIiKSgZ4xsxXANuAKM2sNbE9zTCmiJ3ERyRzu/g01aNWX0tTvTkSiRgUQlLhFJLO4+3gzmwRscvc9ZrYFOC/dcaWCFxenOwQRkYNmZk9TziOquw9JYTj7ZQbFxXqSFpHoqPUFEHWYFpFMY2b1gEuAUywY5/cSMC2tQaWAhsCISAa5K90BVJTKHyISJbW+ACIikoH+BNQD7gu3fxbuuyxtEaWAHsJFJFO4+0vpjqEitPiWiESNCiAiIpmnt7sfl7D9TzN7J23RpJKexEVEUspVfhaRCEnqKjBmNtDMPjSz1WY2vozj15rZcjN718wWmtn3E46NNLNV4dfIZMYpIpJh9pjZkSUbZtYJ2JPGeFLC0XIEIiKpZJoFVUQiJmk9QMysDjAVGAAUAG+a2Rx3X55w2ttAjrtvNbMrgEnAMDNrAfwWyCFIq0vDa79JQpwoc4tIhrkOeNHM1hD0UP4+8PP0hpQKyuciIqmkrCsiUZPMITB9gNXuvgbAzB4jWIUgXgBx9xcTzl9MMGkfwNnA8+7+dXjt88BA4NEkxisikhHcfaGZdQGODnd96O470hlTKjioB4iIZBQzO4qgqP19Ep7b3f30tAWVwAyKlXdFJEKSWQBpB6xN2C4ATijn/F8Az5VzbbvSF5jZaGA0QJs2bYjFYpUOcuvWrWzZUlyla9OpqKhIMadAFGOGaMatmA+emV2wn0OdzQx3/7+UBpRiWgVGRDLQEwSreP2FGjiUUZOgikjU1IhJUM3sEoLhLqdW5jp3nw5MB8jJyfHc3NxK37vxkhiNGhVTlWvTKRaLKeYUiGLMEM24FXO1+GE5xxzI6AIIgOtJXEQyy253/1O6gyiPsq6IREkyCyDrgCMSttuH+/ZiZmcCNwKnJnTRXgfklro2lpQoRUQyhLvXgnk+yqPR6CKScZ42syuBJ4H4UMaSYeLpZpgKzyISKcksgLwJdDGzjgQFjeHATxNPMLOewJ+Bge7+74RD84H/NrPm4fZZwA3JClRpW0Qk+jQHiIhkoJKVEK9L2OdApzTEsi/Tc7SIREu5y+CGQ1NKvu9f6tjY8q51993AWIJixgfA4/+fvfuOk6q+/j/+OjOzhQ4CooIKiL0L9kbsfm1JNF81xpIYiV9jEpOYxORnCxpbYkxibNg1do0Kil0WVKRKk6IUpSMddll2d8r5/XFnZ2cLu4A7O8zs+/l4rMzcez93zghc7pw5n/Nx9+lmNtjMzk4e9legPfCSmU02s6HJsauBWwiSKOOBwRnLdGvKuIhIXnBVgIhInnH3Pg38bBvJyQqSyAAAIABJREFUD5K30brsikgOaaoC5DfAf5KP7wUOSdv3E+DfjQ129+HA8Drbbkx7fFIjYx8DHmsiPhERqcPMiuqu+tLQNhER2baZWQHwf8BxyU0lwEPuHt2MsWFgArDY3c/MTHzgyoCISA5ptAKE2vURdWslVDshIrJt+nQzt+UVx/CEbsRFJK88APQH7k/+9E9u2xy/IqjCzhgDEolMvoKISPNqqgLEN/G4oeciIpJFZrYDwZLhbZI9lqoT1R2BtlkLrEXpnyYRySuHuvuBac8/NLMpTQ0ys17AGcBfCCq6M0ZXXRHJJU0lQPYys6kEN9G7JR+TfL7NzD/8NlTGIiJ55FTgMoKVs/6etr0U+FM2AmpJriu6iOSfuJnt5u5zAcysLxDfjHH/AH4PdMhkcIaWHxeR3NJUAmTvFolCRES+NXd/EnjSzM5191eyHU82aC66iOSZ3wEjzGweQb5hV6DRJc/N7ExgubtPNLOBjRw3CBgE0KNHD0pKSrY4uEQ8xrTPP6dwxawtHpstZWVlW/Vesy0X41bMLScX485WzI0mQNx9fvpzM+tK0IRpgbtPzGRgLUm3yyKSZ94wsx8CvUm7zrv74KxF1AIcw/RNpIjkEXf/wMx2B/ZMbvpiMxpaHw2cbWb/AxQDHc3sP+7+o/SD3H0IMARgwIABPnDgwC2O79+T3mavvfdl4AE7bvHYbCkpKWFr3mu25WLcirnl5GLc2Yq5qWVw3zCz/ZKPdwQ+J1j95Wkzu6YF4ss4M5VMi0jeeR04B4gBG9J+8poD7urGJyK5z8xOSP76fYJeHv2SP2ckt22Su//R3Xu5e2/gAuDDusmP5hI2iKkLqojkkKamwPRx98+Tj38MvOful5hZB+ATgvmFIiKybenl7qdlO4iWp4S2iOSN44EPgbMa2OfAf1s2nIaFQ0Y0rso7EckdTSVA0tcYPxF4GMDdS80sf9K9um6LSH4ZbWb7u/u0bAfS8nRBF5Hc5+43JR8Odvev0veZWZ8tOE8JUNJ8kdUWNojF8+cjgYjkv6YSIAvN7BfAIuAQ4G0AM2sDFGQ4thah7wtFJA8dA1xmZl8BlaQa9fsB2Q0rsxzTagQikm9eIbgHT/cy0D8LsdQTDkE0oeuuiOSOphIglwODgZOA8919bXL7EcDjmQxMRES22unZDiAbHCW1RSQ/mNlewL5Apzo9PzoSNDbdJqgCRERyTVOrwCwHrmxg+whgRKaCamnKW4tIPnH3+WZ2DLC7uz9uZt2B9tmOK/MMVAEiIvlhT+BMoDO1+4CUAldkJaIGRAxi6gEiIjmk0QSImQ1tbL+7n9284bQ8LQIjIvnGzG4CBhDcQD9OMGXxPwRLIzY19jTgn0AYeMTd76iz/ziCBtgHABe4+8tp+y4Frk8+vdXdn0xu7w88AbQBhgO/8gzMVXFMCW0RyQvu/jrwupkd6e6fZjueTQmFjJimwIhIDmlqCsyRwELgOWAsqi4WEckF3wMOBj4DcPclydW7GmVmYeA+4GSC3k/jzWyou89IO2wBcBlwbZ2x2wHViRcHJibHrgEeIPjGcixBAuQ04K1v8wY3SRUgIpJfJpnZzwmmw6Smvrj7T7IXUg1NgRGRXBNqYv8OwJ+A/Qi+ETwZWOnuI919ZKaDExGRrVKVrLBwADNrt5njDgPmuPs8d68CngfOST/A3b9296lA3TveUwmWSl+dTHq8B5xmZjsCHd19TDKmp4DvbvU7a0SQ+tCNuIjklacJ7sdPBUYCvQimwWwTwqYmqCKSWxpNgLh73N3fdvdLCRqfzgFKzOzqFomuheiyLSJ55kUzewjobGZXAO+TXMa8CT0Jqv6qLUpu2xybGtsz+XhrzrlFXD1ARCT/9HP3G4ANyWmFZwCHZzmmlHBIFSAikluamgKDmRURXGwvBHoD/wJezWxYLcc0q0dE8oy7/83MTgbWE/QBudHd38tyWI0ys0HAIIAePXpQUlKyxefoCMyaNYs56wqbN7gMKysr26r3m02KueXkYtyKuVlFk7+uNbP9gGXA9lmMp5aIoR4gIpJTmmqC+hTB9JfhwJ/d/fMWiUpERL6VZMJjS5Mei4Gd0573Sm7b3LED64wtSW7v1dQ53X0IMARgwIABPnDgwLqHNGlSCey15570OmTLx2ZTSUkJW/N+s0kxt5xcjFsxN6shZtYFuAEYSrCi143ZDalGOGREVQEiIjmkqQqQHwEbgF8Bv7SaJVMMcHfvmMHYRERkC5hZKY3M6tuMa/Z4YHcz60OQpLgA+OFmvvw7wG3JG3WAU4A/uvtqM1tvZkcQNEG9BLh3M8+5RTz4pykTpxYRyQp3fyT5cCTQN5uxNCSsZXBFJMc0mgBx96aapOY8LYMrIvnC3TsAmNktwFKC5nkGXATsuBnjY8keT+8QLIP7mLtPN7PBwAR3H2pmhxJMg+wCnGVmf3b3fZOJjlsIkigAg919dfLxVdQsg/sWGVoBJugBkokzi4i0LDP7TWP73f3vLRVLY0JqgioiOabJHiAiIpJzznb3A9OeP2BmU9iMsml3H04w7TF9241pj8dTe0pL+nGPAY81sH0CwXTKzFL+Q0TyR5NLl28LwiHYqCkwIpJDlAABXKsGiEh+2WBmFxEsY+sETaw3ZDeklpFI6EZcRHKfu/852zFsDjVBFZFcowSIiEj++SHwz+SPA5+w+b08cpiRcCVARCT3mdm/Gtvv7r9sqVgaEzY1QRWR3KIEiIhInnH3r4Fzsh1HS3MM9E2kiOSHidkOYHOEQxCL6rorIrlDCRARkTxjZo/TQDsMd/9JFsJpQUZCUxpFJA+4+5PZjmFzhA1imnooIjlECRDUNE9E8s4baY+Lge8BS7IUS4tK6IouInnAzP7h7teY2TAaTmifnYWw6gmHIKplcEUkh7TuBIg7vy+/m4VsE9MoRUSahbu/kv7czJ4DPs5SOC3HwPVNpIjkh6eTv/4tq1E0IWwQ19RDEckhoWwHkG0nVJWASqZFJL/tDmyf7SAyz3Q5F5G84O7VPUAOcveR6T/AQdmMLZ2aoIpIrmndCRCz5APdMYtI/jCzUjNbX/0DDAP+kO24Ms+0DK6I5JtLG9h2WUsHsSnhkJbBFZHc0rqnwABxQpgSICKSR9y9Q7ZjyAYHXCUgIpIHzOxCguXL+5jZ0LRdHYHV2YmqvrBBTBUgIpJDWn0CJKAbZhHJH2b2gbuf2NS2fGOGVoERkXwxGlgKdAPuTtteCkzNSkQNCJuaoIpIbmn1CZAEIVDpnojkATMrBtoC3cysC1A9z68j0DNrgbUYUwWIiOQFd58PzDezk4CN7p4wsz2AvYBp2Y2uRjhkWgZXRHJKq0+AAJoCIyL54mfANcBOwERqEiDrgX9nK6iW4phWgRGRfDMKODaZ1H4XGA+cD1yU1aiSgikwuo8WkdzR6hMgjqEpMCKSD9z9n8A/zewX7n5vtuNpeYbSHyKSZ8zdy83scuB+d7/LzCZnO6hqYVMTVBHJLa17FRiSU2BERPKAmR1qZjtUJz/M7BIze93M/mVm22U7vpagChARyTNmZkcSVHy8mdwWzmI8tYRDaoIqIrklo5/+zew0M/vCzOaY2XUN7D/OzD4zs5iZnVdnX9zMJid/htYd26xxui7cIpIXHgKqILi+AncATwHrgCFZjKtlqAmqiOSfa4A/Aq+6+3Qz6wuMyHJMKWGDqCpARCSHZGwKjJmFgfuAk4FFwHgzG+ruM9IOW0Cwlvm1DZxio7sflKn4qqkCRETySNjdq5dHPB8Y4u6vAK9sSyXTmaMmqCKSX9x9JDAy7fk84JfZi6i2cMhUASIiOSWTPUAOA+YkL9SY2fPAOUAqAeLuXyf3Ze/KaZo1LiJ5I2xmEXePAScCg9L2tYKeT4arok9E8oCZ/cPdrzGzYTTQrM7dz25ifDFBA9Uiguv/y+5+U3PHqSaoIpJrMnlD3BNYmPZ8EXD4FowvNrMJQAy4w91fq3uAmQ0ieYPfo0cPSkpKtjjI/m6Ul2/cqrHZVFZWpphbQC7GDLkZt2JuFs8BI81sJbAR+AjAzPoRTIPJewmVYotIfng6+evftnJ8JXCCu5eZWQHwsZm95e5jmie8QMQgqt5LIpJDtuVvBHd198XJuY4fmtk0d5+bfoC7DyE5r33AgAE+cODALX6RspFG2zZFbM3YbCopKVHMLSAXY4bcjFsxf3vu/hcz+wDYEXjXa+aDhIBfZC+yFqJFvUQkT7j7xOSvI82se/Lxii0Y70BZ8mlB8qfZr5BBE1RdeEUkd2SyAcZiYOe0572S2zaLuy9O/joPKAEObs7gqiUIYZk4sYhIFrj7GHd/1d03pG370t0/y2ZcLcNIaAqMiOQJM7s5WdH3BfClma0wsxu3YHw42f9pOfCeu49t7hhDWgZXRHJMJitAxgO7m1kfgsTHBcAPN2egmXUByt290sy6AUcDd2UmTAPdMIuI5AE1QRWR/GBmvyG4/z3U3b9KbusLPGBmv3b3e5o6h7vHgYPMrDPwqpnt5+6fp73Gt55KXr5hA2B8OGIEIcuNrxS3wemrmyUX41bMLScX485WzBlLgLh7zMyuBt4hWK/8seTyXYOBCe4+1MwOBV4FugBnmdmf3X1fYG/goWRz1BBBD5AZm3ipbxdn2n9FRCSHGUqAiEi+uBg42d1XVm9w93lm9iPgXaDJBEjauLVmNgI4Dfg8bfu3nkpeUlJCOFTOsccdT0E4N1ZW3Namr26uXIxbMbecXIw7WzFntAeIuw8HhtfZdmPa4/EEU2PqjhsN7J/J2FKvpWVwRUTyhpqgikieKEhPflRz9xXJpqaNSvYNiSaTH22Ak4E7MxAnYTPiCacgnImzi4g0r225CWqLcMA0BUZEJA+oC6qI5I2qrdxXbUfgSTMLE1RTv+jubzRLZHWEQpBQ9Z2I5AglQEwVICIi+UE9QEQkbxxoZusb2G5AcVOD3X0qGVpAoK7qChARkVygBAimChARkTzgBqoAEZF84O45M6EkFDISupUWkRzR6ssfHNMyuCIieSDIfygBIiLSksIhI65rr4jkCCVAMEBpaxGRXOeaAiMi0uI0BUZEcokSIKn/iIhIblMCRESkpYVCpiaoIpIzlAAhhDIgIiL5QVdzEZGWpQoQEcklSoBo2UQRkfyhbyFFRFpUOKQEiIjkDiVAMEwJEBERAMzsNDP7wszmmNl1DewvMrMXkvvHmlnv5PaLzGxy2k/CzA5K7itJnrN63/YZCl5TYEREWpgSICKSS5QAMdM3hiIigJmFgfuA04F9gAvNbJ86h10OrHH3fsA9wJ0A7v6Mux/k7gcBFwNfufvktHEXVe939+UZegeoqbWISMvSKjAikkuUAMEwXbRFRAAOA+a4+zx3rwKeB86pc8w5wJPJxy8DJ5pZ3dXEL0yObVFaBUZEpOWFDBKqABGRHKEEiKbAiIhU6wksTHu+KLmtwWPcPQasA7rWOeZ84Lk62x5PTn+5oYGESfNQSycRkRanChARySWRbAeQfbpjFhFpLmZ2OFDu7p+nbb7I3RebWQfgFYIpMk/VGTcIGATQo0cPSkpKtvi1ixIJli9fvlVjs6msrEwxt4BcjBlyM27F3LqEtAqMiOSQVp8ASWDgmjMuIgIsBnZOe94rua2hYxaZWQToBKxK238Bdao/3H1x8tdSM3uWYKrNU3WOGQIMARgwYIAPHDhwi4MfO/ZuunfvxtaMzaaSkhLF3AJyMWbIzbgVc+sSDhkJ3UqLSI5o9VNgggoQEREBxgO7m1kfMyskSGYMrXPMUODS5OPzgA892XjDzELA/5LW/8PMImbWLfm4ADgT+JyMUA8QEZGWpikwIpJLWn0CxE1TYEREINXT42rgHWAm8KK7TzezwWZ2dvKwR4GuZjYH+A2QvlTuccBCd5+Xtq0IeMfMpgKTCSpIHs7IG1A+W0SkxZVWxBgzb1XTB4qIbANa/RQYR8vgiohUc/fhwPA6225Me1wB/GATY0uAI+ps2wD0b/ZAN8E1pVFEpEV9tXIDd7w1iyuP3y3boYiINEkVIIRwVYCIiOQBU0GfiIiIiGxSq0+ABIvA6I5ZRCT3qam1iIiIiGxaq0+AOCElQERE8oCjAhARERER2TQlQNQDREQkL5ihVWBERFqYen+ISC5p9QkQzACVTIuI5DonhGkKjIhIizqvfy92694u22GIiGyWVp8AcUwFICIieSCY0qgEiIhISyqKhKiM6dorIrmh1SdAgi6oumiLiOQ6txDm8WyHISLSqigBIiK5pNUnQNxUASIikg8SmgIjItLiwiEjkdDNtIjkhlafAAHTDbOISB5wC2Mey3YYIiKtSjhkxJQAEZEc0eoTIE4ILZwoIpL7EhaChBLaIiItSRUgIpJLlAAx07KJIiJ5IKgAUQ8QEZGWpAoQEcklrT4BAoapAkREJOcFPUCUABERaUnhkLExGmfC16uzHYqISJNafQJEy+CKiOSLECElQEREWlTYDID7S+ZmORIRkaa1+gQIpmVwRUTyQcJCoASIiLRyZrazmY0wsxlmNt3MfpXJ1wuHLJOnFxFpVpFsB5BtTkg9UEVE8oCbKkBERIAY8Ft3/8zMOgATzew9d5+RiRezZAVIQiXVIpIDVAGiChARkbzgFgItay4irZy7L3X3z5KPS4GZQM9Mv25cjVBFJAe0+gSIY6oAERHJAwnCGKoAERGpZma9gYOBsZl+rVhcN9Qisu3L6BQYMzsN+CcQBh5x9zvq7D8O+AdwAHCBu7+ctu9S4Prk01vd/ckMBalvDEVE8oBbiFBCCRAREQAzaw+8Alzj7usb2D8IGATQo0cPSkpKtvg1ysrKUuNWr1mzVedoaekx55JcjFsxt5xcjDtbMWcsAWJmYeA+4GRgETDezIbWmX+4ALgMuLbO2O2Am4ABBPUZE5Nj1zR3nE4IlYCIiOS+sliI5avWcXS2AxERyTIzKyBIfjzj7v9t6Bh3HwIMARgwYIAPHDhwi1+npKSEgQMHwttv0r5jJwYOPGrrg24hqZhzTC7GrZhbTi7Gna2YMzkF5jBgjrvPc/cq4HngnPQD3P1rd59K/SYcpwLvufvqZNLjPeC0jERphqlpk4hIzlu6AcLq6SQirZwFXUkfBWa6+99b6nVj6gEiIjkgk1NgegIL054vAg7/FmPrNW9qjtK9jpWVVBVXqWSoBSjmlpOLcStm+bYSFlICREQEjgYuBqaZ2eTktj+5+/BMvmhCCRARyQE5vQxuc5TufT7xLiKRiEqGWoBibjm5GLdilm/riJ2KWDBXCRARad3c/WPAWvp1VQEiIrkgk1NgFgM7pz3vldyW6bFbyDD1ABERyXmRggI6FKgJqohINoRb/dqSIpILMnmpGg/sbmZ9zKwQuAAYuplj3wFOMbMuZtYFOCW5rflZCNQDREQk94WLKPLKbEchItLqXHrkruzfs1O2wxARaVLGEiDuHgOuJkhczARedPfpZjbYzM4GMLNDzWwR8APgITObnhy7GriFIIkyHhic3JYBpgSIiEge8HAhhV6V7TBERFqdfXt2IhrX/bSIbPsy2gMk2WxpeJ1tN6Y9Hk8wvaWhsY8Bj2UyPgA3Q8vgiojkPg8VUoQSICIiLa0wHCIWVw8mEdn2abaeEiAiInnBI0WqABERyYJI2FQBIiI5QQkQTYEREckP4UKKUQ8QEZGWFgmFiKoCRERygBIg+l8gIpIfwsUUWxUJLcUoItKiCiOmBIiI5AR9+jcD1wVbRCTXxcOFFFNFNKFruohIS4qEQsSUfBaRHNDqEyBBE1QREcl1iVAhxUSJ6yZcRKRFFYRDVMWUfBaRbV+rT4BACFMFiIgIAGZ2mpl9YWZzzOy6BvYXmdkLyf1jzax3cntvM9toZpOTPw+mjelvZtOSY/5llpnMcyJURDGVlFbEMnF6ERHZhIKwMfar1cxZXpbtUEREGqUEiBmuVWBERDCzMHAfcDqwD3Chme1T57DLgTXu3g+4B7gzbd9cdz8o+XNl2vYHgCuA3ZM/p2Ui/kSogALijJm7PBOnFxGRTSgIBx8pTvr7yCxHIiLSOCVATKvAiIgkHQbMcfd57l4FPA+cU+eYc4Ank49fBk5srKLDzHYEOrr7GHd34Cngu80fOmBGLFRIG6IZOb2IiDSsXVEk2yGIiGyWVp8AMTNcCRAREYCewMK054uS2xo8xt1jwDqga3JfHzObZGYjzezYtOMXNXHOZlNlRXi0PFOnFxGRBvToWJTtEERENkurT9eahUBTYEREvq2lwC7uvsrM+gOvmdm+mzvYzAYBgwB69OhBSUnJFgdQVlZGOUXMnTGFkvJVWzw+W8rKyrbq/WaTYm45uRi3Ym59OhQXZDsEEZHN0uoTIFhIFSAiIoHFwM5pz3sltzV0zCIziwCdgFXJ6S2VAO4+0czmAnskj+/VxDlx9yHAEIABAwb4wIEDtzj4kpISYgUd6LdzD7ZmfLaUlJTkVLygmFtSLsatmFunsX86kbPu/TjbYYiINKrVT4HBQuoBIiISGA/sbmZ9zKwQuAAYWueYocClycfnAR+6u5tZ92QTVcysL0Gz03nuvhRYb2ZHJHuFXAK8nqk3UBluRyiqVQhERFpau6KIVuESkW1eq68AMTVBFREBgp4eZnY18A4QBh5z9+lmNhiY4O5DgUeBp81sDrCaIEkCcBww2MyiQAK40t1XJ/ddBTwBtAHeSv5kRDTcFotuyNTpRURkE9oVhkm4U14Vo21hq/+IISLbKF2dLKRlcEVEktx9ODC8zrYb0x5XAD9oYNwrwCubOOcEYL/mjbRhVeF2hKtUASIi0tLMjMpYgtuGz6T/rl3472eLefryw7MdlohILa0+ARJUgCSyHYaIiDSDaKQtYU2BERHJmv+MWcDiNRv5aPbKbIciIlJPq+8BYqGQZsCIiOSJaLg9IU2BERHJip23awPAxmg8y5GIiDRMCRAzTBUgIiJ5IRZpSySmChARkWw4pl93AMbMW93EkSIi2dHqEyBYCKU/RETyQyzSnkhMFSAiItlw3el71Xr+6xcmZykSEZGGtfoEiGkZXBGRvBEraEskWp7tMEREWqWiSO2PFq9OWpylSEREGqYEiJbBFRHJG4nCDsxfuizbYYiItEqF4fofLSbOX0OFeoKIyDZCCRALgSbBiIjkhapQW9pRQWVMN9siIi0tFLJ62859YDR/eGVqFqIREamv1SdACEcIuW6URUTyQUW4A52tjHUbo9kORUREkpatq8h2CCIigBIgeLiQkMeyHYaIiDSDyk592M2WsK5cCRARkWzoUBypt83qF4aIiGRFq0+AEC4krASIiEheOO+4g2lHBf9zz4fZDkVEpFUadGzfetsMZUBEZNvQ6hMgkYIiTAkQEZG8UBAJs562dEArwYhI62Vmj5nZcjP7vKVf+/T9d+DCw3bhrnMPSIunpaMQEWmYEiAFRVhCCRARkXyx3tvS0TZkOwwRkWx6AjgtGy/cb/sO3P79/TnjgB1rbb/s8XHMX6Vrs4hklxIghUWEXXPFRUTyxWo6sqOtznYYIiJZ4+6jgKxeCNsWhlOPK2MJSr5YwUezVzY57t3py7SSl4hkjBIghUWEVAEiIpI3JiT24BCbne0wRERaNUub97KxKkhoVMUSqV8/mdNwMmTQ0xMZMWtF5gMUkVapfpvmVqawsFirwIiI5JGFvj372Pxa2yqicYoLwpsYISLS+pjZIGAQQI8ePSgpKdnic5SVlW3WuDXrSwH4ZOqX9I3N55PFUR6eVsUTp7Vr8PhZMz6neOWs1PPKmDPhmxhH9yzY4hi3NuZtTS7GrZhbTi7Gna2YW30CpKCwSKvAiIjkkcXejZNCn6WeV0TjHHvXCMb/v5Oa7TXKq2K0LWz1/4SKSA5z9yHAEIABAwb4wIEDt/gcJSUlNDru7TcBWLrBAfhgQYzff/8odi5aBdOmM3DgQB75aB7H7dGdPXp0SI3pf/CBHLt799RpXpu0mIffn8z/u+jkLY5xi2PeRuVi3Iq55eRi3NmKudVPgSls044ir8x2GCIi0kyWeFd62krcgxvuteVRVpU173V+nxvf4fPF65r1nCIircGp/xjFja9PB4IE9a1vzuSyx8Yx7qvV7HPj2wAUhGt/RIknvMXjFJH81OoTIJG2XehoG4jGE9kORUREmkGnnnvQy1YQ3VgGwPqKKHvzFYlY8za8XlNe1aznExFpLmb2HPApsKeZLTKzy7MVy0E7d97kvr1uCBIeS9ZVcNPQ6ZQne4Ukkglsd6f3dW9Spft0EWkmrT4BYm260Nk2sDGqbtMiIvngxatPZCWdKbyrF/HKctZvjPJm0f8jMemZLT/Z+qVQ1nAzPsMa3C4ikm3ufqG77+juBe7ey90fzVYs5/bvtVnHFUZqPpbE4kECJJas/KhOjKxV4llEvqVWnwChTWc6sSHVnVpERHLffHYAIPbJvZSVBc33fO3CLT6P/+tg/NHknPNYJYx/JLXPmjn/8b37P2HWsvXNe1IRkSya/ZfTueDQnTfr2CkL16YexxJBxUdlctWYjVVBv7673/2ymSMUkdYmowkQMzvNzL4wszlmdl0D+4vM7IXk/rFm1ju5vbeZbTSzycmfBzMWZJtgCkxphRqhiojki5/HfkPcjaJRt9H+q6DEumzNMsoqY3z5TWm940fPXUmigTnmFttI5dplwZM1X8Obv4Wy5cG+rYxtQ2WswfnskxasZey81Vt51hrfrK/41ucQEWkOBeFQrX4eLww6gu8etFOT475eWQ7ULJu7IflF5YZK3a+LyLeTsQSImYWB+4DTgX2AC81snzqHXQ6scfd+wD3AnWn75rr7QcmfKzMVJwVtMGB9af0bYhERyU3r4oX8I3YuAAMm/h6AT6d9yRu3nMsp94ysd/wPHx7LhPlrWLi6PNU8tZp7cu55LEgsTJydrCRpIgOyqaZ9+970Dv/+cE6D+0Kh2if943/spbz9AAAgAElEQVSn8vgnX6Wefzp3Fec9MLrBsbF4goWryzn8tg+YvkQNWkVk27Fdu0JmDD6Vw/t2Zc8dOqa2d27b8LK2g9+YwagvV1AZCxIf1ZXa5Y1UbK8qq2TSgjUN7lteWkHv697c2vBFJI9ksgLkMGCOu89z9yrgeeCcOsecAzyZfPwycKJZcxcVN63M2lG+bnlLv6yIiGTQM/GT2Nh1v9TzPraMCyIlHBP6HG7uVO94d+fYu0YwavbKWttT/1BGNwLw/rQFyeNhzvKyBl+7aul0zvx/DwCwx/VvMerL2n1E2iwsYenylcxYUnvKS538B8+NW8jTn85PPS/5YjkT5jd8g//suAUce9cIANZv1Lek0rw2VsVZs2ET/RdWfwW3b940B2mdPrvh5NTS4Vce35cvbz2dr+84gz2277DJMZc8No7KaJCAfmL01wC8PX0Zf3h5KpMWrKlXtffnYTP43v0NJ4jXljdvE2wRyV2RDJ67J5A+4XoRcPimjnH3mJmtA7om9/Uxs0nAeuB6d/+o7guY2SBgEECPHj0oKSnZqkD7WAdmTR5LbH3DN5XborKysq1+v9mimFtOLsatmKW5raYjH53wCoXv/p6B615n71CQuOhvaXPIb+4EP3qFAmKYGQfYXC59DO774SHMmzyKXwBhksmEaFCS3TYUAwoZ+eUKhoyax9d3nFHvtQsePYG3iiqIxa+kKpZgysK1dGlbSN/u7QAYNP9aFj94Nz/b+HtuObVvalyoie8A0r8jmDh/De2LIuy5Q/ABIn0q5xYvGfn091gTjfDGPn/j4iN2rdk+9iHotjvsdsKWna+O5esr2L5j8bc6h2TXL5+fxHszvmnwzztr50Ol+tfI5jEzCiPBteyQXbsw7utNT/2r7gGS7oUJC1mybiMfzV7J6OtOYNSXK7juv9M2eY4ZS9YTTmaX61b4iUjrk8kEyLexFNjF3VeZWX/gNTPb191r/evq7kOAIQADBgzwgQMHbtWLfT56O3beri1bOz4bSkpKcipeUMwtKRfjVsySCYOenkhXTuPYUE9uKXicDraRXxe8Euwc3C349T/ncl74corKd2No0Q30rniGJ0Z/RdGCr/hFIURIllwnK0CKqQTaMmTUPAD+9s4X/OLEfhRFwgBUxuIUJafLVKTdvF/073e4+vDOnBSaCEDPxFLOCI0F0hMgDbyJtG3p+899YDQ9OhYx9k8nBYel7atuILgp0XiCqliCdotGwc6Hw9wPaUcBN3z5OXO+KeXGs/bl61Ub2O2t30P7HeDaLxo8z/WvTWPZukoeuXRAo6932G0fMOH6k+jWvqjevkVryunWvojignCj53h3+jIO3qUL3TvUPwcEvU96ZDrJsnYhdG680mHh6nJ6dm5TbzrTt/HZgjUcskuX1POSL5Zz7O7dUx/qWsKStRs3vbOwPQALV6xj9qqKrS8vXj4TOu8Che229gxN+t1LU7jqO/3o0y1zryGb7w+n7cl5/Xtx0t/rT00EuGDIpw1u/yhZqXfUHR/W27fXDW8x65bT+f79n3D1Cf34yRMTuOu8A4AgoTJuaYzDqmKpihQRaV0yOQVmMZB+l9Arua3BY8wsAnQCVrl7pbuvAnD3icBcYI9MBVoe7gxlyzJ1ehERaWFt0j5Mr6ITryWO4fSq23k+NrDmoERNSfTtBY/S4ZvxAHxdfBEvLTudayKv1D5p+SoAjlnxYmpTJ8p4eMQMFqwqZ/n6Cu58exYjZgVTKuNuVCSXWC9IVDC1+Ar2Wf4mPwx/kBofJRnnyjkUU1lTARKPQbSC6yLPUrpiUaoRYPXu/re8FzxPy46kV480VAGyYFU5S9cFH2IHD5vBvje9A09/r2Zlm+T4Z8ct4LVJiznx7uQHkviml518fdIS3p/5zSb3p3sqWcJe1zF3juCe95pe2WHQ0xO5vyTonTL7m1IWri5P7auMxTn8tg8y++3u6q/gH/s1edixd41g2NQlTR43d0XZZvck+P79o1m2rqa57WWPj2fcV9++Ye4mLZ9Vb5pYo8VJ8eDv0r/eGMtPnpiw9a97/xEw4ratH78ZXpq4iPdmNH7PVxGNM3OpKlpagpnRb/v2fPDb4xvcv6Y8Ss/ObbbonBXRIMH72YK1vDcjuD798/3ZyX1x7p9SyVvTNv1nYPHajfyxkYoSEcltmUyAjAd2N7M+ZlYIXAAMrXPMUODS5OPzgA/d3c2se7KJKmbWF9gdmJepQDcWdiG0QT1ARETyxbSbT6m3bZFvz3WxQfSueBYue5N5xfvW2t931DW1ng8IpX0on/ICzA++idx73Uj2t3lcHn6TKcWD+KL4MnZ/oBc8dgoXfXoG8eqeqUQo+WIFrxdezz7LXgOg14bp7GA10y2LiTJzVRz+3Z8/RJ4n7FE+eHcor998Fty9B1dG3uDo0PRUU9OQGW2ooGDDUgDuid4Ck58DIJpWbdLji//A+qW13s9xfx3BDx8cxWej3uTpMTV9RVgVJBU8mUzp0raQ9RU1yaHYpm4VSr8h7o1XmqT71yYav0LT8/O/WV9BiAQnLbwPgJPvGcWFD49J7a8uk69VLl+xDpqohNkiieQUI3d46HhYNRfmj4Zo/VV31lfEmLO88ebqjVZUNCAar/1eImEjGk+kEm4Njpn7EVWTXtii1wFgxcx6m6yxrr/xSgCK4xuaPPVnC9Y0mGBJqarpq7O2vH7y7fInxrOqrLLJ19lay0sr+MMrUzn9n/VmXksG7da9PQfu3BmgXqVYv+3bc/aBTa8ck+7oO4PKkOfGBbPxFyf/vlX3XZq+ZNMJrg9mfsNz4xZscv/waUtZXqrVtkRyVcYSIO4eA64G3gFmAi+6+3QzG2xmZycPexToamZzgN8A1UvlHgdMNbPJBM1Rr3T3jH3VES9SAkREJJ9Ewo3/87aw4yF8v/Imdqt4muiP3+Oaqqu4Nvoz1nnbhge8OgimPEupB99EDiu6nhsKnql1yPZrp9DLVnLGf/cCoMiinDdsXw4MzeO4uX8DoPf68ewTqkk+tLMK7hwf3Eh3t7X0WfQaJ46+mHPCo4MP8EA5RWw3/QmY+CRmxjWRVxhT/AsAjvRJjHnjMaYuWsvdySqK9pSz3+TB8NlT9d7GgetKOOTDHwLQkeSH1dRxwQfckBnpBSTrKhL1mg3+fUIF3L0Hx3nD3/bf8fpEnh1b/wOEu/PTJyfg7ny5cDnrbtsTCOb0D5+2tN7x1a78z0S6UMrRy58JEhCkfgFINUqsrrgJgtiF6Cf/2uQ5a1k5p/YJG5JMgKxbvx6WToYlk+Dx02HKs/UONeCkv4+iIhqnIhpPVXokEs77yW+kI6Haf0a//KaUBavK654KT8QoIFYvYVIQDvHapMX8+Inx9cbEE851r0xlyQu/pvD1QXw0ewVPpK0mFBzUWNKpfrKj0QqQWJCQmDp30x8aq33//tGsXxZ8p7WxkRU9lqzdyEGD30s9rk6GfDBrOTPqVGes2xhleRPLP//s6QmpCqLGXP3sJF6f3HQFjzS/J398KADFBbX/brQpCPOvCw/eonOtKG04SfbDR8YC8Fjdvw9pbnx9eq3ny0sr+MVzk/hhMul61TOf8UDJ3C2KR0S2HZmsAMHdh7v7Hu6+m7v/JbntRncfmnxc4e4/cPd+7n6Yu89Lbn/F3fdNLoF7iLsPy2Sc1qYLRRUrmj5QRERyxlG7dU09/tnxfWvtO/auEazdGCNOmD9PasNriWN4OX48B1Y+Qu+KZ+ld8SxHVNwLwBVVv+FXsV+R6LoHx1feQ6UH88YXe1e+rasiQ/m6OEhI9LVlHDzlz/WOaUMlu469GYb9knEjXmdPWwTApeF3ADgiNo7CKTXJjjPCwQ0+80qILZzI8pkfp6bEdLGgKmFgaDJTi6+o91oH2hzGVH2fIz75aWpbZQKeKvmcp4eX8PHslawsq2TqyuCD64/sbYw6VRZVG7hu0gk8/tFslpdWsGhNzYf6Rz/+ivdnfsPcFWX85P436VS1LDX+o9kN/Du8IZh2FI3F6WXJ/ckpOYvXbuRfH8wmnnA+nbeKCDEqSmuv4PP8Ox9x0HXPM3xyzQfz0oootw+fSe/r3uTjF++BRRPh3/2JzhweTKFZMAYSCXpf9yazlqV90E72gLn7xXeD56Fg+lJ5vP6tVHUqJeHOhsqa5rRTF6/jp08FSaOCcE1GYeSXKzjlnlGcee9HUFlWq3Jl99lDGFP0c84fEnz4cnd2sW/ouHISv3t5KgAXDqmphgEor4rx/PiFLI4FVRa3D5/FzcNmBJU9N3di3LRZcEu3oBKjIQ1kOxpdpC+ZAOlgjVe1VE9Rqv7/8+U3DVTJJI9Jb+p71B0fMuipiXUPSfnpk+M57LYPaEz5zPfoPPq2BscDrKlIUBGNN5qUkczq3LYQgMI6CexQRj+twGuTFjNr2fpN/t4f9pcPGDZlCaPnrkpti8UbT5gedfsHrNzKSqXSCq1YI5JJGb6k5IZwuy60q1rZ9IEiIpIznr3iiNTjy47qvcnj/jOm4W+tl9GV3hXP8l5iAK/HDqfv4ptZTUf2rHyKgZV3c2zlP/l/0Z+wb8Wj3Bm9gMMq7uOR2OlsKNqeN+KHc3f0PCYkgvZVpaGOACQ8+BA5PrEHcTdGxfdPvV56ZUi6fxben3r8fOGtHBGaAcCfC55Mbd9r/A18XvQTfhx+izsLHg42LhhN5NET2P6FM1g84mE6UkYPWwvAqaH6VQNFXsHrRTcCsO/GCVR/TC0iysGTruficefwo0fHcsKtr6XGHBP6nL6WrNxwh0UTWPD8rwHoHlrPiX8byTF3Bkvz9rIVlLwV9E/Z8eEDkg1goR3BN/fPjVvI8vUVNf1LvpkOf+3LTx8fS+dlo1OxMfYh+lrwDf3f3/uSYVOW8MvnPmNO8SXs8MDeAIx451UAQjiTi39GpOTWYOzIvzJz2kQeSjawPWbGzfDcBQA88uxz7PPH/8JjpxKdO5LOlNJmVFo/imRj28u+uR2AT5Ifhtq+9auggmTFF6lpHYmEEybO6M8m0+Hl85P/e5x1G+t/sIlNH8qy5UEV6vqKGNzekxmv3pHa36F0Dl2Tias/D5vOeQ9+yuMFd9H39e+mjvl03qraJ/36Y8BT05eqP0AefPNbAHR86bxgQ8Xa1JCl6zamKlU+mZN2T/TaVTD7fQzoxjri015N7Trh7hIeHjUvlZTqQP0KlvSpO9VTlKqrlZaPe5nhb9TutVP9obF6XHXSZNWGSiZsYrWQNmu/4J3C39feOPt9mPJ86umg8Bv8sCp4rdRH13kjufxPg1m9di2/LtnIn4dNb7B/zgMlcxlw6/vqC9ECTt23BzefvW+D+849pBcA15+xN7slV9Qace1Avn9wz9Qxe+/YcbNfa0VpJb2ve5NrXpjMaf/4iF89P6neMW9/XrtXyA8eDJbZrYw1nihbsq6CBavr/32AoPHz1EVr623/43+nEU84+9/8bpMVTSKy9dT+GLBOu9DP51NZWUFRkZbpExHJFy9deSSzvyljh2ZeHeRr3xGAZ+LBCiwPxIOZnbfGLubWdRenjrs3/n0OsS9Z7N3oYmXM8l3qnWvv2HxCJPi/yDDODI/hJ1XXsto78lr1B/46ZvnOHGS122K9tcctnP7lDdxU8HSDY3b56HdMTftfcGFkRNPvsfgiALpaKV1LSwDYyxbwdtF1DInVLIV6aOhLov86lMiep2Kf3kv1O9zNFzG6si8RYgwKv8kRoRkcF57GcZX30C66mj8WBL1LXi28iaW+HRUUctRtMR46fBUDt1vFqpIH2R54ZP4pUJgW2Hs3cG/Brny36haiRPjrC+8xtPAfqd3xMQ/y0sjFfKeQVHXJKWtf4KPYd+HjW9lpz8uAtB4xySmw/xcZRjh5fMEz3+XY0NXsOuMBPPEXLBSGpUG1xc6xIFH15tgZHF2QPMfjp8O+3wPgO6FJ3PFWmOGFf6TfW4sJm/Na4QLKSo/moylfMqbo51Dan2g0yEpEXrqYDjv9gp3YnZAFr7/d1CGsX/UJf9/hLq5J+65qzLzVzFy6nqKiIJHyQuFgrqy6hjUEH/oWri5nx46FdHj+u+zEv4h6MLbNknEU0ZfOBP019goFfRFYORt2OgQihSxaU1O98dSYBRxdCO/N+IaTJz9DYuUcPHE9P468RfiVoYxtdxyH9+3KvBUbGPHFcq7oHvx5ujD8IeeHS4DBsH4pFeF27HXLR3w1+DvYsF8ybc9rKaKK+avKoRBOnvZbFia6w5nnpsoy3p+xjNPLo1RE4xxgc6mKVtGdNewVW8R5D9b0GJm/agMdigvYbvw9HB+bx56hoDLq0zkriccqOGrolYTKV8CBQYLLaOAb+6fO5tFCWDmiHDiW0rWr2K9qBhusmPm+Q+qwN6ctYWVZJc+NW8Dt39+//nmk2Tx0cbCi1Oy/nM6qsiqOuL2msufu/z2QVz5bRJe2hVx8xK7c/tYs+nRrx1kH7cR/Jy3m8csO5cCdO3NIskl0Ux79uPY0mPdmflMvATZtce1Exfivgx5OL05YxF3nHVhr3+VPjOcv39ufHToFF9tN1Uxd8dREZi5dX29J6efGLeC604MplBujqkQSyRQlQIB4UWc2WDvWL5lPzz57ZjscEZGsMbPTgH8CYeARd7+jzv4i4CmgP7AKON/dvzazk4E7CD6qVgG/c/cPk2NKgB2B6k9Yp7h7izReOrT3dhzae7tGjzmsz3apFTUeuWRAaopCc/nMgyqQb7zhOGb6rgBcHf0lV0d/mdreu+JZIsRoQxWltGXHwnKsqpwoEY4Ofc7YxN50sHJ2spWUTN2Nk0K/5cTQZ3zhO/Nk/BTmtfspDyXOoWN0BQ/Fz2Rk0W94MHYmz8VP4MGCf7B3aAFTEn25L3YOQwrvSb3u76KD+GvBkNTzV+LHcm44aAj5dlHQqmtQpGb1kjsKHobVwKe1V3K5pfR6rirajh2t9rf2o4p+Xev57qHF7J5cJG5O+BKYEmzfvpH/p/uG5jO7+BJeiA3k/EhJavvKUDe6vf0H7k8mTA4P1UzxOPbjCwFIRCu4PDycTlZGXenv697CfwPw8QM/59CqsRSvC5JOBQRTM24reLRm4IblMO4hAB4v/Gu98x4Umgt/35WD4oezQ3gN3L0nRwLji4KKkf9Zci//k5ag2sHWwJKP6bbgTjpHgtUrIsToWhTnzNCn9GBN6v1dERlOZ0ohcRr/99dH+XffMfQGzgp/yokeTI15qWgwAA/H/geApZ78fRl6dfBz3UI8FlRxJKa/RjcLpv787en/cnIRhBaN5XXO4kHOBGDVqhXQPUjCzF9VzuQVMzkIOC4cVEgs/vIhKBlO8JaeZePskbSd9hKHTnuJL4rhyqqahsOVFDBmxjyOeDHo8XBBpIQvnruK8iOuZ2jRDZTP6MlNBU9z5sYxlIX+wMjEgTw2chYL582kb69ePLziNi5PnmvuvNk89djTPFD4T9bRgU7AtS9N4fqB3emafE83RJ4m6rewrjxKdRtWr9pAP1vEvxcEVSQTC3bn3KqbYdFE5hXuweeL1xMhxg6WwZV3pJaCcIgdOhVzw5n7cHifmmvnMz89nEN7b0dhJMRlR/cB4ICenTh9vx34zl41V43nrjiiVqPkhjw4snYfD3c4eRPL8W7K54vX0b4oQu9u7fhg1nLOXbAm1UC1etrYitJKurQtSPWmqq5qqojGU8t/x5IVT3X7LYlI81MCJGlNQQ9YMEUJEBFptZKrb90HnAwsAsab2VB3n5F22OXAGnfvZ2YXAHcC5wMrgbPcfYmZ7UfQALtn2riL3DfRMbMFtS+KUJbWkwHgxZ8dmSr9r16FYGt8+scTOPL2D7d43E7tjSVlDd/0xohQmvynemlVWyBo0vpa4phgm3flSw9WnH8/0Z/3E/1TY2/Y//1a03v6VvyHRLKa4PSqO+hrS5iXrGQ5vvLvLPPtqKQAMOYmdmKJd+XA0FzeSRzGb6NX0oM1PFb4V26N/YgIcTqxgbGJvTkyNJ2hiaM4cteOnL/kdva0RdwcvZQXim5JJT9mJnZm79BC7o+djQMXFX7MK1WH81z8O/y38GaihFPTPMq9iGnehw5s5MHYWXwv/BHfCU/hr9H/5XcFNUsQV3hBreRHmRdzQvltjCj6bepcu4XqN1bdZd7z3FBQ83xeYgeejZ/I9cmmtuVeRFurmbt/7IrnGvy9aUqlRyiy2n/Wzqzuz5LU3dY1eo6rI6+nHs8pvgSWUbsahqCPDACDt+ONIkjmklIVNunODgfl+3WTUtyxM4cBV4XPJ/TSC1wVCfrbvFN0Xa3Droy8AcCpbx8Pb1bwdTGMLd+Lg0K1e4n0XDI89Xhw5HHefn4j369ZmZoH0yp2OtkGbvjPyxyR9r72XPgiey4Mfq8jnz1BLwvypU8W3hkcsBgoAuq0jdntqQE8kDxPJ4I/A+snvUrn6ffQOVlIc3nkLeZ8PIdOJbNT4xKLJ/F+UU0fnf6h2VwXeR4eGca7sbP4uji9Fd2PkZZz+TF9aj0/ul+3esd0bV/EAz+qufZ9fccZqSTDR7//DkOnLGHErOVMmL+m3ti65q2sqTKaOH81kxbUn6oC0KE4wkezV3Dxo+PYrl1h6vUM+Gh2MIWsuhHroX95n2tP2YOrT9gdqGnWXFoRY/3GKNGE06VtcFHaUBVcM6J1eozUmh6YFI0nWFVWlao4kfw0ZeFa9t2pY5PN3WXzKQGS9FW3gez25dtw/P9mOxQRkWw5DJhT3ZDazJ4HzgHSEyDnADcnH78M/NvMzN3TJ09PB9qYWZG7Z269yq3gdbof3nluUM5+2VG9OX6P7rQvavqfxau/049/j6i/mkTBFtycnLJPD95NrgbSqdBYgrPPjh1Tq1v06tKm1pSExnRuW9DgMrJ1e5sk6rT9muc1y0qml/tDTdXKskR1o1fjG7bjjKrb673O0MTRAHw6v5RPuTq1vXfFswTdFoJvQbuyjlXJ79yHdbuCWcuCD6gHVD7S6PsbljgCjxpg3BcP+l70shUs8u4UUcUetoh5viPlFOGEOKvyLxwSms3inU6h39JhLPPtWO0d2cW+obzr/pSvXMD5kRIejv0PO9oqRiUOJEGIR+Jn8KPwe0xL9GGJd2U97fhN5CU+S+zBuXu3ZdUXoxmaOIrrIs/x5+gl7Bf6ipfjx3P9gASlk18lhDMxsQcPFv6Dt+OHcm30Zzxa+DfWeAe+8h1oSwWVFPJA7CyuigzlikiQJLim6irGJ/akjVXyp8iznBCeDAR9YnraSsoKezB6485cFnmXBbYTu3jQ/2RhojuvJ46qlSSpFo2058PKvbgpeilPFd5Be9vITraaHraWV+LHcG74YyojHSiK1W5C+vuCYMncnla7p8hi70pPW8U6b8uL8YFcwXA2Whva+EYOD81isXflwqrrGVX0a8Ym9qpVeXNJJJiOsPyc59j+9QtrnffO6AX8oeB5niv8S2rbCu+USgy9G+/PKQtGcdC3uO9Pr26q1i9Wk/y4Pvpjbl3/eL1jrowMq/Wr5Jb0pr0//04/fv6dfvzmxcn02749KxZ+xePT6y+xfECvTkxdVJOUPPeBTzd5/tKKGBc/Oi71fE3yGvx/z3yW2nbFUxNok6zwWLRmI8+PW8A5B/Xk6+RqT/GEc+xdH7L79h145qeHA7B0XdD7I713jrtz2G0f0LdTiB57rmO/nsF1dMioefz1nS/qTaWR/HLOfZ9w9w8O5Nz+vbIdSsac8LcShv7imM26B2sOSoAkhfscTYeJN2Q7DBGRbOoJLEx7vgg4fFPHuHvMzNYBXQkqQKqdC3xWJ/nxuJnFgVeAW71uJqKFVDdh3L9nJ6YtXscP+gfVE9VN9+qG1aEoQmmdipFrT93zWyVAXr3qKHbs1Ibdtm/PAyVzaV8Y3Kjf8t19UzfcVbEElx3VmydGf93k+YoiLf+tUGEY0hdMOKz3doxrsEFlzYeQVakJB6SSH5vDG+jXvsi7A1BJIdO89go/S+jGkkQ3WFTKJAamtk/33smKgb0YHw3m2X9RpyfLf+In13p+eyzog/LOdIDgz8g5VUFD1c/iQZLouaWdmJY8DqoTP4Hzqxru4/KX2I+4M3YBcUI178/hJ9HfQxSODE1ncmI3NlIMyb9Fg2OX1EtiAQyLH8lS74rhbKCYuy8YwK+en5zaf0pVMCXHSGAEibCbopcRJ0RnNlBkVameNuD0bB/m4opnGBY/kum+K/vafKb7rmxHKRsoppJC7o79gAqK6MFqHiz8B5dXXctqOtKv4iliRAgT5/jQFOb5jpwZGsNS78rxoYP4Q8VjHBuaxqjEAUSIU0Zb5vqOLPTtmec7EiVCghB72QJ2tWW8kziUH+zRkbO//CP/iZ/EmMQ+fDf8Ccu8C2u8A906FPG3i4/jjPvHs9S3o6etZK23p71tpNyL2Su0gONCU/kwcTA7sYpKCvgssTtnhT9lnu/Ie4kByd/3k+jOWr4X/pgIcR6Ln85Z4U9pz0Z2seVMSOzJp4l9ODT0BQ+5N7EmsGxL2qV9oPr7/x4EQEnJIh6fXkXntgXE4566xv/qxN25/MktL1RcvaF+MqVadS+PDVVxrvvvtFoViJWxONG4M2Ppeg5O9i35wYM1/wZU+zjZlHjeugRn3vsxEFS5rCrb9Otua9aWV1EVS/z/9s47PKoqbeC/MzPpPYGEEEpCCU06ovSgVLGXta599bNjx96Qta6uq2vBVddV7IoFBEU6ovTQa2ihJQRIrzPn++PeaZlJCChJJry/55ln7pxb5r1n7rz3nve8hcToUD5dsgul4NJTfXNiBSq/ZeVx3xeZLHzwjOM+xto9+VTaHfRuE+ezrtzjejgW3pq3jXmbcvnkptOPvnEDknWwmP35ZXRIjKyX7xMDiEmrzv2JWnwAZjwMI54EWzD8cA/0uw5aHGPCq8pS0A4IjjgRogqCIDRalFLdMMJiPLJMcqXWevbpA+4AACAASURBVI9SKgrDAPJXjDwinvvdBNwEkJSUxNy5c4/5u4uKimrdz2aBKtOFWFUUMb5PCPPn1x7vbcE3Ed3cuXM5v0MQU7d6e138/utCnhsSxoQFpfylUxD9W9i4b56vF0d+Vib5QJpZ6jSEKkBRuH21a5vmwZVkROfyQa3SGTgqa38IfmZQGI8tMuToHG9h46G6P0gp8Jc6kkd7a55arnB6aSdb627QcB7z0k7BfLYpcB7ga2LNntrDWGqiqpZHsMUO3yoY/owf4GvEmb10nd/tNBbXb1lkhlKVElrtB1bsKXLwHJfTO9EKOXbDcAQcwl1do4wQAA4QzwUVT/uckx0rsx19AHjdbiSHXT4tk1JC+clxqpdc1T8DbNRtXAmDv1hXyBc87Fr3S9gYss2QMVsRXP75PrabBpxt2oi6y9OGsS3XEcsCRw+f479tP6daiyKXON7xaP/CnuGzn2ozgLnzji1HhNBwbP/7WbWWbz5SUukKl9lfUEZyTBhLHxnBqc/O+tNl+T7T8NyaOG2Dq+3/PlpR0+ZU2B2uEJqC0iqf9W/N28Z7i7b7tDspqagi1GbFYnGff2mFnSCrwqKUV/uxkF9SSUx40NE3NKlyaM57YxF2h4O1ewq4uG8rvlyejaURGUCq7A46PPLjUa8Xf+SXVtLzqZ+4d2R6nb02a+Lit36lrNLh16PH4WfOqNLuICu3mE4tomo85sx1+2sM46oLBWWV7MorcXkdnVjqb15MDCAmHVsm8IM+lQt/ewPWfwtXfwvL/gMVxXDh28d2sP9dAAV7YPwaqCyDz/8Kl3wgBhFBEBo7e4DWHp9b4coo4LNNtlLKBsRgJENFKdUK+Aa4Wmvtyi6ntd5jvhcqpaZghNp4GUC01u8A7wD069dPZ2RkHLPwc+fOpbb9Ru1Zzs68EtbtLSA+Pp7xf+nvd7vxVZt5dZbhIh8dEUp+ufdDTUZGBhkZMHXCNK/2M4dnYLUoJiyYxtC+p3B2j5bcN897G+f+AIeLK2D+z8SFBwOVRvuMaV5J/pjhu391oiLDyS0trnH9xaOH8diiGQDMeGCsK99Jdc7ukcwPq905MzY+M4blOw9z5bveeSu+vnUgBVmZvHtNV677wCin26dbOt9ucw+8/eVacdI6Ppxdh0q4eHhfPttUs4t5bcSEBblKyt4wOM2nmsOAdgk+pWFDrVDmYc965dKe3P1Zptc2vVrHsmp33R8Wz+vVkm9X7T1G6f9cNjw9hi6Pz3B9/nabbzjU8XBal1RW5mw7+oZ1ZEfB8c1gAiRGhZBjDgY/v304A58zcu1UOSAz13+1jFFdkzhQWE6mx+/50NjO/P3HjX63rwutW7cmI6Prce8v1C+1DWZfvbQXhWWVru2SY8IAaB4VUusxh6U3p0NiJOv25vNb1iH6tIllxa4jpMSGsefIsQ2AN5ghj/5weoIAvHFFH5/1//rFHcaVOmGaz6C56+MzAVzty3Yc4mLzmJf0bcWLl7gr2Ow4WEx4iJWYsCBCbFbu+yKTyBAbj4zrwumTfmH5YyMprbDz1YpsHp26lhWPjSS3sJxOLaKYuymHXYdKuHpAqt/zKK0yclg0izT69cvlRrUmhzYSvm7JKSI5NpTo0KMbVaobX7blFpGWEHHcxhwnTu+KCruDEJu1xu2W7zxMelIkUR6yFpv3uT/DKazKXrMBwJ/T7JTfd/HEd+vImnRWjX1g+4N989LMTXy4eKdfo0zqhGn8+8o+nNU92c+ebnILy9m0v5DBHX3z94A7AbD9+G8Rx4xkUzEJtllY2Plx1rc4Dwqy4XUjmZLj4BZ4fxyUF8H2BXU7WM56OGLGXhfugy0/QZ6vu7QP9irDYFITlaWuMnGCEPBoDfvXNLQUgjdLgY5KqTSlVDBwGfBdtW2+A64xly8GZmuttVIqFpgGTNBaL3JurJSyKaWamctBwNnA2hN8Hn55/fI+fH+7kTy0ron2h6UboRYrHhvJOD83eatFsWniGNcywKPjurj2m3Ljacy9L4Mdz43j5qHtuOp094xXVKgxBxEe5P2AEmS1GMaPOpAQEcyILkmuz/4e3kODvI9lreGB6PVqD9mhQVYGtk9gwQPD2fHcOObdn8GFvVPoY7rnOqsXAJzfO4Uf7hjs+uwZx/vqpb28jpvWzJgMSE04/kmBcT3cv8W1A1N91o8fYSQbTDT7Y2TXJBLC3Od9zYC2XNDbN576WMsld682K/b8Rd1Z/uiIo+43ppuRc+WiPq1Iinb/ZgseGM7Q9Oa0b270zaiuSX739yQsuOYHdieXndr6qNtU544zOvht95T3RHJeL3eOmrYJhsfKQ2M7k1TH32hoenPevNL7mj67Z8satnbTKi6M/93Qv87/QSEwOb93Cn+tYdAOcKGp0+bel8HGZ8a42s/ukcxjZ3dltPkfjo8w/g89W8ewaeIYmkUG+xyrpv9SXTlY5JtKy1JtxP3PWVtcg3FP1mTnk5Vb5BV2WN04nPHSXPo/+wudHp1Bld3Bl8uzmfL7LorKqsgrrqC0ws67C7J4dKpx677/i0xGvzqfm/+3jGvfX8rj3xrG7/zSSkrN2MiySjvLdhyiuNK42ZZX+RoqP1m6i9GvzufGD5ZRUeXgoa9X+2zjZO2efHo+/ZNX25kvz2POpuMvKFdYVklhWaUr3Kis0nsEvvtQCXlm3+85UspFb/7K67O9x3NOz4yanikqaghd+Wp5NtsPek9cVNXyYOJvlTO8auqq6vNUbjzv9yUVVczbnFvjttWZtzmX3YdKat3G6QFpd2i/FYx6P/0TF/x7EVf9xz2R4nBocgrd490K0/Lh7xo5UYh29+COEZ24PvdyFqY/BDbjBmvZuxx2LoR5z8N/z4bC/fBCO1j6Hyg+aAziVn3iNkwU7IUyD3fYUjPjdFkdXGS/uRle7+fbrrVhGHm2BbzWG/au8t2mIZj3Anw/3rd9zZewvvqYSRCqcXgHvDUYHPVo8hVqRWtdBdyOUcFlA/C51nqdUupppdS55mb/ARKUUluBewBnmYjbgQ7A40qpVeYrEaNWw0yl1GpgFYYHyeT6Oys3Fovb7be2FCR/Pb0tV55mGCqcg+v4iGAfF9Qnz+nKE+d0JcRm9ZoduXFIO9cM0cAOzUg1B/wPndWFiee7QyptVgsdEyOJ9nheHt0tiU5JbnfWU1PdscBOA4znjM5Pdw/ldo+Ha+fA+fGz3bPUSil6t4kl8wkjKumpc33DK1Y+NtKnzblv63hj8Nk2IYJ/eBgznAPvwR2aER1q83KRjQy1kWxWJnA+pzuNEV1bGqEU/gYKACmxxkzsd7cPYuMzY7i8v/FbPDquC69dbpRKtXvMlDkHqiM9jAVOl+DSCju/TjiDyVf3w3O48NR5pwAw+Wrve67V6t5qcLVqExf7SUDXKs7oG6tF8e7V/bigdysSIo9uIHA+6EaF2pj/wHBXe2iQlQ+v78+9o4yKdI+fU7O3gWf/XT2gba3fl9Epkel3DjmqXI95XDfhfgwr47on8+qlvY96HDDKkP4RHhzT2bXsnJ0PD7bWaMCrjlLQ0ryWAObdn+G6Bj1xGiKdvHBxD4Z0bM5/rzM8xDxLsAonB/PvH84z55/CKSkxpDaLIDTIStaks1j+6AiXHrjOLMF7ejvj+mgTH0GIzcqZnb2Nls9d2J17R3Xiwt4pHC+Z2b5eadVzU70yazPdnpjJrrwSLy+/c15fyBkvz3MZL8CYkU+dMI3UCdP4y9veXngdHvkRMAalj39nGDbmbc7h5Z/dZc6d3z1z3QFX2+uzt9DzqZ/o8vgMtNZ0fmwGF7+1mE82GmGOpRW+g9tHvjFkWrLjEI98s4ZPluz22cZJgenxt3THIR780m0oqbQ7yD7sPucDBWVe9/e/vL2Y26d4hxp9szKbORtzOPf1RVzw719dlXec1XmcDHlhjssD8nfTaFQ9F4fTwOGZtPaWj5Zz6rOzeGnmJtIf/dFr+/zSShwOzb1fZPKahxePk5rUm92hyS+pdJ2b1prNplHLMwn6kZIKr5w0Not7qD915V6uec+duNfJlgOFLi+Mg0Xlrv695r0lzNnkazDZsK/Ax+ty5D/mcd8XmT7bHi6pZL+Z2Hfw87Mpq7TzwFer6f/sL65tqhug6iNFnBhAPEhrFsHk6wZyx9Y+XBb7MddX3Mc/Ki82Vv76mvH+cicoyYNp98CL7Q0jwNT/g3+fDpPPhPkvuQ/osMNh8wLJ/NQwZGgNh3cahpLSI8bLaRzZsRDyzT9/6WGoKoeXu8Dy940wGjCOt8oo1ce+TCjysHxWFMOhLFj2vmGoqYnyQuPYx8uTMbDif/Dbm4Zs1fnqRre8JztVFUZ/aW14EQWCB4/Wx2eUKD0CH5xd9+2rTOtvfs03PB+cnlWe/Ksv/Pp63Y8h1IrWerrWOl1r3V5r/azZ9rjW+jtzuUxrfYnWuoPWur+zYozWeqLWOkJr3cvjlaO1LtZa99Va99Bad9Na36W1rj8zvx8Gtk9gzCktalyfEBnC0+edwlseZRUBnxKE1w5Kq9Htt678fM8wQm3uJ563/9rPy8X3zav6MvW2Qbx7dT/+flF30pMi6dMmzjW4DLJZiA4NoptpVIgINgZz11crHfnNrYOICTOOe3HfVvx091Cv9XERxmB6zZOjqCtOQ8zkq/v5uJpHhthcBodh6c2544wO/HzPMAB6pMSw7NERKKVcs/ye3jXOmdUgq4XQICtPmEaA5lEhJJhyehqjnAPi5lEh3DPSSEzqdGMuqbS7BsE3dPcd/J7ROdHrc7DVwqx7hpEcE8pHN57GT3cPZeVjI/n94TN58WLfXBKpzQwDyJPndGVE1yQfr4H4CG8jz9ZnxwJgN3XsyK5JhNisrHtqtHnOypTfOI7z90xPiuTRcV28jpWeFOW6Ru8wy2s6aWMarZxEhtiIDqs96vmlS3p6ebT4Cx+wOzQD2iew47lxPm75Tu8qJwPaJ3BrRnuvtqHpzV2/p9WieNnDDd84D7cxz9Mw4SwRWpt7enWUeQXeNNRIkhtssxBktTDlxtO44jS3J9aqx41rvrnpIdSvrTGgdRr4XvKQMRBu4cIfp01CuFfyVDAM6AmRIV7/i6xJZ3HD4DQ2PD2G+0cbRssHx3Zm4YPD+fB6w4B2mWnAvbBPK9f/KyEimAfGdGLxQ/4TZo7ulkRSdAjtTIP21ytqnuGvzs0fLT/qNp6D+CXb/SWvNnDmLXnlZ++Bur+Z/pd+chtI0h5yl8F2hqjV5t0A8IUZGtP+4emkTpjG+E9XUlZpZ+N+I0yozPQOuOStxXy2bLfLoLItt5gDBcaY5uf1Bzht0i/c9L/l3PXpSl75eTNLth/ih9X7ePp7o5jdlN93cfdnmVz3wVK2Hyxma06RK7GspwFk+U6jXzbuL+TL5dmukM7V2Ue8vCJc4TMeffrj2v3kFpa7kqXf87kxcb1sxyF6PvUT/128w+hHPwrFs9Ttoq0HXYYdh9b0fPon0h6azjvzt7E4K4+vVxrXxdM/rGf2RsMY1evpn+ljJtQFbw8Q5zPAj2u8y8OPfGU+XyzPprzKzqKtB/lsWc3P5de8t4Qr3/2dZ35wFwcsq7STdbCYdXsLePmnTThMY43dw9APRiWkvOIKVxgUwOebKli/1/iNb/zvUlInTCPtoekcKTGMONWfu/4sxABSje6tYvj94RGM7t2evYnD+CbmSlLLpnBO3Le8l/5v9rapljhr7iTjPXcj7Flm5A1x8nQ8fHm9sbzqY5iUAk/Fwj97wD+6wPNtjddzbWDmI1BkGi2mPwDPp8LERCjcCz/cDTt/dR+3vAjlqIK3hxpeIzsWGoPsSS0ND5EfxsPqz2DzTFj7lbHPzsXw0UXG8kud4LNqBgrnQH2ZH4NG3jaY1Mq48x/KMtq+ux08xzD2KuMFoGq5rD48H7JryLB9pA4DYa1hzt/r/hTicBjnVVXHRHvf3g4bpx99O4AD68F+lFhrpwdQ6WH4e4rxe/yjK2z6sfb9/iDKUWl4KB0PC142rj9PKkw3vYJ9Nfd93lbYUccwMTAMQmDky6kLh7LgVT8JifO2Qtacun+vcNIz5W+nc+Vptc+YWy2KMae08ArzOFE34s7xVtcgrTrNIkPo1TqWEV2TiA4NYvqdQ5jyt9PolmIYPILNhyVnBZpJF3bnUzPb++8Pn8mZ1Qb4YHgZpJteJi1jQr1CaMKDbZzeLt7L5bsmmkeF0Do+zCcEo1VcmFdYSmx4MPeO6uQqBxkZanPFg19hDg60mfxswQPDXQN9pzEgNMjK2FNaMKB9gsswYDf1ULtmEUSHBvHd7YOYMLYzXZKjzX0snJoaxykt3Yk702KsXDswlfM9QiuqexPYLIoOiZEsfuhMwDAyxEUEkxQdilLeA/aYsCA6tzCOHx3mHb/uNA5c1Md71tdmtZAQEUyv1nHseG4cg0wvk4gQGy9d0tP1gOq87pyfJ57fnRuHeF8jzmsUfL1pvr9jMJmPj+KjG4wiTuEhVtc14hkSlRIb5grh6pIcxampcXx+8wB+vGuIV/84x3yeg5iUOLd3BcApKdE+8ea3ZLTnk7+dzuc3D+CWjPa8dEkPrhuUhtWiePLcbj5lHW8e5jaYOI0/AFGhQdw/uhNju9dsuKyJh8Z29jqXgR2aMekC973EalHMHD+Uc9sbfe00YjlnIVtXMyYJghOLRaGUIszDMyk+IphWceEMTW/u5Rk4uGMzl8fZjPFDuTWjA8kxYWx8Zgxf3TIQMAyy8+7P4M0r+zL/geHMvjfDVRUjJTaM90aHM6BdguuYgzq4lx8/uysX9kmpNa/I8bLpgHeS62U7D//p3+HEeZ+dumovX63IZsyrCxjz6nyu/8B77ODMe/TizE1c9KYxRnJ6VPy8/gDfrtrLPz08LN5btJ1DxRV+E8c6PSg2HyhyGUG2H3QbOe77IpO35xnjnxW7jjDkhTm8uyCLDfsKvPKHAH5zbH29Yo/LSADwlGmMMebFNQcK3OEgceFBaK05XFzhlX/L0zgzf/NBH0+UN+d652tat9eYXI8IMe4l936eSZVpePcs1ezMg/O/xTs591+LXBXE/HlhpE6YxrzNuT5Vj64y5dx0oJB/zd7K4ZIKej79E7d+bBjjPCvkLa927UzfXsmnS43xX0GZ26up19OGEaf9w9OZfwxhO3VFkqD6Idhm4bpBaVw3KI3SCjvbDxbz1YpsPtygeTrvcuByQoMspCdFsTo7nxiKiLeUoHQVLVUej9s+5Av7MEZ1aU7ikZVcs+c8Xoj9mv6li6iMbEVQUbbvly72mMFe4ifpakWRezlzCsMwy+xtm228qvOzR+m9Ra8ZVWn2rzaMAQBbZhrL/W+CjqNgyTtG+w/j3caWLudA6mDY8D1UFBrGnWn3uo/r9Fx5dyRkmy5Vj+a6DSMbpxnhMJe8b3gHgDFQzZoDd2VCXKrRlrfNMCB9egXcuwmiPB5wKkqMUImkrrD0XWjeBeY9B6fdDGFxvlmHKkuN41z2CZQXwD/Nh9Xt86BFD4iqFk89ZxLkZ8P5/zbOZ+X/DO+ZnHXG9oTA7iUQlwaRRkw/n15pJLRd/RkMuReGPQjvjYGzXoLknoYRoL3p0lxmnvcq8/cq3GcM+D+5DO7fBsGRRr8OuM1Yb68CewVsmg6nXFRzVqW9KyGhI4SY5aLmPg9pQ6DNAFCKdln/hfkXw5MeoVfLP4B13xgJfv1xYD0kdjHkK68WsjWppXGe856HK76A9FpmibWGXYshKhni04zwrX2Z0KZaNdUK84ZaUMcEgs78OAe3GEa2BI+ZRX0cHiuVZUZ/9Lrc3Za/xzBEppgz/8veMwySj3sYkw5lQclhSOyM0PRpFRfOrxOOv6xdXYgIUjw8ssvRN8Q9OxRqPps4HyzSkyJZtfsISdGhrhwJSdGh/Oda3wobngzvnMiz1QaDn940oE6yJEWHsuAB375JiAzh/N4pTF6Qxd5894Od06AR5mFU6p8Wz68TzuDzZbuZvma/12DT6uG6+6bp6dA8MoRf7h3Gh2Z54Nn3ZQDQo1UsAEXl7qSGH9/oG4LxpJ/wH4Dbhrfnu8y9NSZpczK2ewusFsX4z1Z5hSpFVpstHtcjmdumQLeWMbQxk746WV5DuJFniE3X5GgGd2jmCtmqHqZhyOz2llBKkfnEKHIKyhj5ynyX4aR3m1iXfM0iQ7h7RDoX9U3hQEE5NouiZ+tY1zFsFgtKKfp7hHxsm3QW+SWVVNgdnPrsLK8ZS+fv6Uwmq5Ri1ROjOOWJma5tokKDGNDeGKR5HnfLxLGuc5sxfghjXl3ArHuGuY4JxuByyo2n8Y+fNzOqW5LrN3Zy+/AOfLp0FweLKhg/oqMrcfHgDs1YuPWgKzTBOWNf3XtkZNckfl5vzJp2ahFF9btIr9axfHPrQJ9+F4TjxZmjydMzLDTISt+2cUy/cwgtYkJd60IsxvU6c/xQHFqjgIUL5vP+dadSaXe4POQ+W7qLB79awxmdE0lPivLyFkmKDuGSvq1dngjhwVbm3pfB5ZN/Y1tuMcFWC2O7t+CK/m249J3fXPu9dnlv7vxkJWA8htZlzrFLcvSfbnxxhsjUtWz60SpyeXpG+ONvHxpGlvv7hfL+wg1e66onuHVW83FOOBSV+eZf8aTzYzN82r5fvZfCskrmbMp1Gc0PFJQz7rWFrK/Wl54eNit2HSZ9s3fll6U7DpOf5r5/jnttIVueHcseszLNVyuyWbPHO5Rq0/5CRr86H8Dn+/Z7GGUALnvHf8LytXvyfQxiw16cC7hDpI6UuieLndcVuI1F/nLcAPx7rnHdjv9sFStquG8eL2IAOQphwVa6toyma8uuPHZ2VyqqHFTYHWzNKSKvqJzyKgflVXa+WbmXJdvziE3uygsRI5i14QCT1wKcxphuLbhvX3t2ld0GZRBOGVXWMGz2UkoIoW9sKaUlheyrCMOKJphKbmy+jo8PdmBwdC4bCkLII5rDOooEVUCBDueFjhvYXhHDGLWYFvvnsKfLDSwL7s95mTdTHtGSkOK9aGVFaTvsW4WjZV8fdx9H76vRq7/E6jR+OFnzufe7E0/jhyfZHvFkEw0jgT0oEuunVxhth7bBvkyGWDzcj98cZBh1wuKh1MP97j8jYeCdhvHil6chvBmUHIR2w71n+V/wcO+OaQP51UIjnq1m6PjYDGXqdz10Phuy5rrDmgB2/w49LjWWC/bA7IkA9IztDnPXGPtkTDAMOZtngsP8My942XgBvHuGW86rvzVCnZzfMfc5433hK+7vfLG9YdDJ3QAzH4bz3zLCqZx8dQOcfhukj4YPz4UBt0NEc0jsClMuMY0dVohOhjVfwFxzvxtmkbzPVPIOhyFrca7h3bNvlWH4+utUsFghbahh+MnbBpOHw6Ufu5P9TmoFva9yGwjmPW+8T7kE+l5rGHxQYLUZRiqnsSd/N7xvuHkzehIEhRuGtUdzoLLEMFxp7fZOmjEBvrqB2J4T4ffNhnHuqq8MQ9SrPaDVqdB2ALQ3ZmR5vR+EJxgGpPkvGm3bF8DWWcY20+6BU/9myDP1Frh2OsSkGCFplSVgCYJ3hkHzTkbFp67nGiFha76AdVNh16+G4cheic6aZ3jT7F0Jyb2MJ4HXzPj3PtdgDR8NMx4yjCLthsPp/2fI7WnEKy9yG6qEgMQZQvH3C7uz+w+WufuzCLIq3rqqj2vWceL53b3yN9SFb24d6EpI+mfiHMJ+eH1/yjxmqZwDUU8XX6UULWPDuPOMjtxSLVzCn/lXKUX75pE8OLazT5gPQLtmkS67cV2TWDpnae8ffXSjZniwjfN7p9CrdayXUaJ9c9//+OaJYwmyKoamN+fq935n7Z66Dw7iIoL56EbDaPze6HCXZ8vPdw9l5CvziQsP4nSPmWAwvEViwoKYfe8wV5vz+ogIsWG1KO4yk8M6c5d4EunHyAJ4hWR5eoA4vY8iQ9zrI0NsrHlyFMt/W0RteFYs6JhoPMg3iwwmxGZl2aMjuGKyMRgb2KEZAzv4GqWuOr0N43okc9/oTnR9fAaDOzRjyfZD/Lotj3tHpePQmnYev4m/CgYD2iW4DCAAfRNttDzLfQ0auXPifPYThOPFalF+r0Vw50byt4/VQxuGBlm9PBMvPbUNw9ITaRET6so3deeZHbl+UCqAK+ll5hOjCA2yEGKzMvH87uQWlXNutaTAP9wxmOSYUBIiQ1wD1WWPjKDvxJrLAnduEcXG/YWUVFSx47lxpE6YRrPIYA4WHX+J82sGtOW/i3ce9/5/lBeX1VKQohqXmYYjf7kyjobW7v1u88hTUt0YUZ2SCrtfT5a75ngnLO34iLe3+eYD7sn0T5fsYsLXNRcieGd+ltfn37L8h0ot2OLrbV69AlxNiWCd/Lotj0v6tnKFQTl5YcYmwDBG/9mo+kg0Uh/069dPL1tWQ2jFUTha6cQ/QkWVg2CbBbtDU+Vw4HAYWW5jw4Mpq7Szfl8Bq3YdYfvBYnq3iWVHnnHx5haWu7Lh7s8vo7TSzpYDRZzfuyXZh0uZewx/NIUDjSKCMsotoVgcVYRRTj6RhFPG4BZVLNvv4BDRdFK7OKKNh4YcYnkxfQObw/vw0bpykh05DGcpG3QbljvSuTPiZ8LLD9J/zFXMzE0gcvsMurSIJHLj59xXeTNTzongYO4BOmR9SGlSP7J27yGryMoCew+eTP6Nim4XcyAnh7aHfiU6Zwm/xF5MRtkvWMsOoy1BxsAT0HFpqPAE7HtWssHRmkNtRjE0+50az7dUhRGS3AXL3hXoxK6onPU1bmsPjWPP6Pdo8+0Fde5PJ7rNANSu4yvh2KRIG2Z42BwVRfUa31scKXS0+AmBCYuHsFh3yJU/Oo2DTbWUCA2KgMqaS4PWSnIvw1hUG7YwqKo2GHZ+Z0JHyPOImX2yDkmQ/aCUWq619pMZuWlzvPr8ROryE0kgyl2TzB//vpOOiVFes/2epE6Yxrz7HYWFYwAAFzBJREFUM2h7lAowqROmMf/+4bRJ+PPCDxqyn695bwnzNufWOPipjepyp06YRqu4MBY+eHTPJLtD0/7h6WQ+PsrLkFGdw8UVrjwwNfH09+sZ2D6BEWay2Z15xQx7cS6/3DuMM1+e53Vux9rXqROmsfrJUXUqhVkTReVVDHl+Nisfr1seG4dDU1Jpd3nv1Cazc6byxsFpPHqMhkYnjV2fn0x6NxBlhrrJ/eqszVzQO8VLx9od+qjJg6vrgN2HShj5yjw2PjOWORtziAkP4sJ//0rnFlFMvW0QwVYLd3++ilcv7UXfibNoGRvKD3cMYcWuwyREBHPF5N956txuPPLlclrER5OZnc+Sh8/kkalrCQuy8l3mXlY9PtIV6gAQHWqjoKyK3x46k8kLsnwSbTppFRfGE+d041+ztzDpgu7szy9jf0EZj05dS3JMKPvyj27AuHtEOq/M2sx9o9LZklPErPUHKPaTqNUf7ZtHsC33OJ8vq+FZfr5rcrTL+JEUHcJ7157KuNcW8sYVfcgrLndV23GSEhvGPy/r5SpvXBvjeiQzbfW+o24HhoH525V7fRLtHg+npsaxdIdvyNSFvVNcOUzASD79gEdyW0/WPz2a8OBj99moTeeKB8gJxjkDZbUorKY7mzNeOjTISp82ca6SgseCUwnuOVJKYlQIdoemoKySIIuFCrsDhTHjk324lL1HSokJDyIi2MbG/QXkFpYzLL05O/JKDK+V8GAe/WtLpizZRZU9jbV78xnYvhkVVQ62ODqQV1jB5acF8eu2aA4k9uKegW35cc1+XlgYQveUGN6cX0bLWMXYftcxeWseOQmDOFJYTv/vyoE4YBLBeRbSYxWlsaFsyy1m6p7BRB60UVSeChiJotgPcKHXeQZRhT4QRGqzCLaWmZbLrQAZfmuuJ0QEk1dcAVnmQHuXW+GHUUarxGa0TYhgyYYsCglDl1ngs1LaRn+Ko3Afu3USKeRSTjAV2AihgtiEJOwVZfy1YwVrypoxe/0+jhAFm6Fd8wlYDm4iX0eQRwwD2yewL2stp3ZOIz4iFFWSQ2ZRHFEHV7GopBXxqoCdOolwykkPPULH5HgOVIbSvXAhi4pS2KBb05+1lOgQ9pPA1d2C6NQiikmZEYQfXMP4tN0kqiNktr2OPVWRlJTb6Wjdy+GsFYy1z+Uj+yjO7NuF31ZvZHZOBHf1thLbrAXNC9YxeWkeh0Lb0rFDBzrmzcVSkE2H1DbE2IwMzW9sb0EFNlr0HMOWVfNpH2sho1kB7eMs3L80mhtt08kM7c8Ayzo6VG1lVdKFxMbE0D3sIKFpowidP4k55em0aBZPm4QI1kcOICc7i1l7Q8jSLXix5wFKQ5sTZ6sgbdlEMns8xkVLOvJQ0lK69BpAxeY5nFa1hIqELkRkz6M0LIWXO73C0MqFnJH1IhUpp7GwIImCXjcxZMmtWEorCe5/J19srOCz3LZMD30EpR28lf4uZ5d9T8tdP2AB9iRmkJIzF4Cq9LOwFxxAJXeH8kI2ljejJblEhgZRVniIAnsIM3Zq/pb7C0UxncgqDqF95Wb+ax/FJaHLiE9uy4qo4Xxa0IPbk9ZStWsZHXNn4khI50DHy0he9w5VWqHKCrFGtcQeHIUlOAJLZSkEecfLC0JT5Gi5VdY9NdonuWBNxIQd/2C4sXHnmR284vX/CLPvHeY1A1wbzkHP0UrlHs34Ab4VaZyx+mkJEcdl2PGkbUK4V2jU8RAZYquz8QMML5TqoUs1Me/+DJdbtyA0ZsaPSPdpq0vlpOo6oHV8OBufMbx5h5u5pL69bRAtYkJd+ueflxkesb/cM8zleecc0ywyQ0dtQ8MZOHggJRVVxIYHM/nqfjgcmmcvOIWo0CAmX92PxChjPGGxKHILy2keFcKj47pwa0Z77vx0JaO6tuDqAW1ZnJXHFZN/5/ObB9AyNsxV+euUlBjmmuVwFz14Bm/O28aLMzcx5W+nccVkIz+FU0dNW72PpOgQ+raN46rT23hV7fpmZTanpsbz7g+L+GBdBTcPbce1g1IZ8Hd3qoFnzj+FK/q3YdXuI67cI86wu80TxzJj3X6X98ySh89k3b4CkmNCiQi2MeSFOUy6oDsb9xdw94h08ksraR0fTsvYMN6Zn8W1g1KpsmuKyisZe0qyKyR0WKfmRARbvQwgM8YPIT0xCotF8dENpzF5QZbL26drcjR/6deK+VsOkrn7CF/dMpC2CeFMW72PTknGBMX/fjM8bBY8MJyvV+zhlVmbOSUlmu9vH8xXK/bw0W+7CA2yUFbpYESXJLIOFpFlGn3+eVkv7v9iNbPvG8b1Hyz18iyZetsgXp+9he4psbwyazPPX9TDqEI0rgsjuiSR8dJcAJ48rxu7DpVwZVoZZ4/MIMhqoUuLaM55fSHtmkW48qUAf/je4A/xACEwLcGNXWatNUoptNYUlVdRXG5n48rfXDI713smFbQo2H6w2Cj1VFpJRIiNIyWVtE+M4EC+oRCbRQZTVF7F4ZJK1u7JJ9hmIdhqQaMJtVnZdrCYC3qnsD+/lKU7DtMsMoTi8io0mpTYcH5YvZeEiBB6to5hTXY+KXFhZHRK5Kd1+zlQUE7HpEg27S8kNjyIFbuOkL3vABcO7EJFlYMjJRXszy8jPjKYsacksy2nCKtFsX5fAYu35XFaWjw78kro2jKabDPeOyzYSovoUPKKKygoq2TVriP0bhNHWaUdpYyY5PX7CmjfPIKtOUWkJkTQrWU0W3OLOFRcgd2hqbQ7CLFZWbj1ICO6JDJrQw4juiRxpKSCvOIK4iOCsSpFXEQQq3YfISo0CEd5CXtLjJJS/drGkVtUzs68EvqnxbNk+yEigq0uS3d4sJUWMaHYHZqdeW73uRCbhdjwIFd27X5t41i28zBndk7kl405xIYHeZXecuJpmHLu44+0ZhHcdWZHXp21mSOllV7HsllUjRnDQ2wWn+RPngTbLFiUbz13qHssq0X5r7nuub8/OWo6/tqnRtf5Qdv7eI17xvBEcTLNREJgyi0y1x9/VO6ySnudDSbHgsOh+WVjjlf5YSeB2NdHkzl1wjTuPLOjq9LQsdLY9fnJpHcDUWYITLnrS2atjbFDbHiw67O/SlZ1YfacOewNa8fl/dtgtSju+WwVF/ZpRa82sV7PcrsPlTBx2nqePLcb+/PLXCFzOQVlrNx9xFXRzEltnjh/eWsxL/+l51GTLt/5yUrsDs0bV/bxWTflh9nkhLbixiHt/D5z7soroXlUCGHBVro/MZPC8iqXYejuz1ZxRudEzunZkuLyKlZn53NaWrzLS05rzber9rI3v5RbM9z5p8oq7RSUVhIdFkRecYWrjH1NvDBjI51aRHFeLyNBuOf1UV5l5/1FO/g/Mxn2g1+uZkz3Fgzv5JvMvS6IB4hQ7ziVjlKKqNAgokKD2OhnfXVF0M5PHDVAYlSoazk2PJjY8GC/sevOWOEOiVF0SIzyWe/plp3h8YdylikDOMssx3jdIPOPebr/GU1nTNr5f6C2+4nij95wqt84CssqifLjmuw0YGltKHXnPuVVdp+Ec1V2BxalsFgUOYVlrt90XI9kLEoxf95c+g8cQkSIjUq7g4NF5STHhLn2tWtNkMWCxaKoMHPvBFkthNiMxH3lVXbKKh3EhAW55Kq0O9DanUt2f34ZseFBlFc5OFxSQVqzCILMRIullXbySytpER3K2r35dG4Rzb78UppFhlBQVklphZ2SCjuJUSEkRIZgtSi+mD6bUcOHEGRVaA1bc4romBRJXlEFBwrKcGijUsLxGD8EQRD+DE6E8QMMDwp/xo+myqx7htEqTjz5BKExopRyGT+cn48Xi1Jc5fHs/49Le/ndrnV8OG//1RhfO59XARKjQ32MH1C7J87n/1e35OOvXd67xnUtIy1ckVGzgdYzpHTpoyPIPuye9HzF4xwjQmyu5NXO51ellN/xjmdOmqMZPwAeGFNzrq0Qm9Vl/AB43k/p+T8LeSoXBMGH6jcOf8YP8FTm3ttXN36Ad+JDT4OWs4qFUsrlGh9ktXjdTGxWi5eyCrZZfBIchtisru91yuUMO3OS6mE0c1bKcBIRYnN9v7PagDOGtiaX/ebhFi83fWdFhfB4m5ROFARBaEI4y5EGCkqpMcA/ASvwrtb6uQYWSRCERkJokNXvRPHJQt1SpAuCIAiCIAiC0OhRSlmBN4CxQFfgcqXU8WVvFQRBaGKIAUQQBEEQBEEQmg79ga1a6yytdQXwKXBeA8skCILQKBADiCAIgiAIgiA0HVKA3R6fs802QRCEkx7JASIIgiAIgiAIJxlKqZuAmwCSkpKYO3fuMR+jqKjouPZrSAJRZghMuUXm+iMQ5W4omcUAIgiCIAiCIAhNhz1Aa4/Prcw2L7TW7wDvgFEG93iqx0lp1vojEOUWmeuPQJS7oWSWEBhBEARBEARBaDosBToqpdKUUsHAZcB3DSyTIAhCo0A8QARBEARBEAShiaC1rlJK3Q7MxCiD+57Wel0DiyUIgtAoEAOIIAiCIAiCIDQhtNbTgekNLYcgCEJjQ0JgBEEQBEEQBEEQBEFo8ogBRBAEQRAEQRAEQRCEJo8YQARBEARBEARBEARBaPKIAUQQBEEQBEEQBEEQhCaP0lo3tAx/CkqpXGDnce7eDDj4J4pTH4jM9UMgygyBKbfI7EtbrXXzE3j8Rskf0OeBeA1BYMotMtcfgSi3yOxLo9bnJ5neDUSZITDlFpnrj0CU+0TKXKPObTIGkD+CUmqZ1rpfQ8txLIjM9UMgygyBKbfILPxRAvX3CES5Reb6IxDlFplPHgKx3wJRZghMuUXm+iMQ5W4omSUERhAEQRAEQRAEQRCEJo8YQARBEARBEARBEARBaPKIAcTgnYYW4DgQmeuHQJQZAlNukVn4owTq7xGIcovM9Ucgyi0ynzwEYr8FoswQmHKLzPVHIMrdIDJLDhBBEARBEARBEARBEJo84gEiCIIgCIIgCIIgCEKT56Q2gCilxiilNimltiqlJjS0PE6UUq2VUnOUUuuVUuuUUneZ7fFKqZ+VUlvM9zizXSmlXjPPY7VSqk8Dym5VSq1USv1gfk5TSv1uyvaZUirYbA8xP28116c2oMyxSqkvlVIblVIblFIDGntfK6XuNq+NtUqpT5RSoY2xr5VS7ymlcpRSaz3ajrlvlVLXmNtvUUpd0wAyv2heH6uVUt8opWI91j1kyrxJKTXao71R6pemSmPtb9Hn9S6z6PMTJ6fo80amXxqaxtovonfrVd6A07mmLI1e7waizq1F7sald7XWJ+ULsALbgHZAMJAJdG1ouUzZkoE+5nIUsBnoCrwATDDbJwDPm8tnAT8CCjgd+L0BZb8HmAL8YH7+HLjMXH4LuMVcvhV4y1y+DPisAWX+L3CjuRwMxDbmvgZSgO1AmEcfX9sY+xoYCvQB1nq0HVPfAvFAlvkeZy7H1bPMowCbufy8h8xdTd0RAqSZOsXamPVLU3w15v4WfV7vMos+P3Gyij5vZPqlIV+NuV9E79arvAGlc005AkLvBqLOrUXuRqV36/2iaywvYAAw0+PzQ8BDDS1XDbJ+C4wENgHJZlsysMlcfhu43GN713b1LGcr4BfgDOAH80940OOCd/U5MBMYYC7bzO1UA8gcYypBVa290fa1qbh3m8rMZvb16Mba10BqNSV4TH0LXA687dHutV19yFxt3QXAx+ayl95w9nUg6Zem8Aqk/hZ9fkJlFn1+4uUVfd6I9Ut9vgKpX0TvnjB5A07nmt8bMHo3EHWuP7mrrWtwvXsyh8A4L34n2WZbo8J0s+oN/A4kaa33mav2A0nmcmM5l1eBBwCH+TkBOKK1rvIjl0tmc32+uX19kwbkAu+bLofvKqUiaMR9rbXeA7wE7AL2YfTdchp/Xzs51r5t8D6vxvUYVnYIHJmbOgHR36LPTziiz+sf0ecnLwHRL6J3TygBp3Mh4PVuoOtcaAR692Q2gDR6lFKRwFfAeK11gec6bZjDdIMI5gel1NlAjtZ6eUPLcozYMNy03tRa9waKMVzKXDTCvo4DzsO48bQEIoAxDSrUcdLY+vZoKKUeAaqAjxtaFiGwEH1eL4g+b0AaW98eDdHnTR/RuyecgNO50HT0bmPs26PRWPTuyWwA2QO09vjcymxrFCilgjCU9sda66/N5gNKqWRzfTKQY7Y3hnMZBJyrlNoBfIrhvvdPIFYpZfMjl0tmc30MkFefAptkA9la69/Nz19iKPPG3NcjgO1a61ytdSXwNUb/N/a+dnKsfdsY+hyl1LXA2cCV5k0HGrnMJxGNur9Fn9cbos/rH9HnJy+Nul9E79YLgahzIbD1bkDqXGhcevdkNoAsBTqaGX+DMZLafNfAMgFGJl/gP8AGrfU/PFZ9B1xjLl+DEdPobL/azAB8OpDv4R5VL2itH9Jat9Jap2L05Wyt9ZXAHODiGmR2nsvF5vb1bsXUWu8HdiulOplNZwLracR9jeGyd7pSKty8VpwyN+q+9uBY+3YmMEopFWda7UeZbfWGUmoMhlvquVrrEo9V3wGXKSNDeBrQEVhCI9YvTZRG29+iz+sP0ecNgujzk5dG2y+id+uHANW5ENh6N+B0LjRCvXu8yUOawgsjY+5mjCyzjzS0PB5yDcZwaVoNrDJfZ2HEm/0CbAFmAfHm9gp4wzyPNUC/BpY/A3f26nbmhbwV+AIIMdtDzc9bzfXtGlDeXsAys7+nYmRJbtR9DTwFbATWAv/DyJ7c6Poa+AQjvrISY6bghuPpW4x4wa3m67oGkHkrRiyi8//4lsf2j5gybwLGerQ3Sv3SVF+Ntb9Fn9e7vKLPT5ycos8bmX5p6Fdj7RfRu/Uqa8DpXFOWRq93A1Hn1iJ3o9K7yvwCQRAEQRAEQRAEQRCEJsvJHAIjCIIgCIIgCIIgCMJJghhABEEQBEEQBEEQBEFo8ogBRBAEQRAEQRAEQRCEJo8YQARBEARBEARBEARBaPKIAUQQBEEQBEEQBEEQhCaPGECEkwqllF0ptcrjNeFPPHaqUmrtn3U8QRAEoWZEnwuCINQvoneFpoCtoQUQhHqmVGvdq6GFEARBEP4wos8FQRDqF9G7QsAjHiCCACildiilXlBKrVFKLVFKdTDbU5VSs5VSq5VSvyil2pjtSUqpb5RSmeZroHkoq1JqslJqnVLqJ6VUmLn9nUqp9eZxPm2g0xQEQWjyiD4XBEGoX0TvCoGEGECEk42waq57l3qsy9dadwdeB1412/4F/Fdr3QP4GHjNbH8NmKe17gn0AdaZ7R2BN7TW3YAjwEVm+wSgt3mc/ztRJycIgnASIfpcEAShfhG9KwQ8Smvd0DIIQr2hlCrSWkf6ad8BnKG1zlJKBQH7tdYJSqmDQLLWutJs36e1bqaUygVaaa3LPY6RCvyste5ofn4QCNJaT1RKzQCKgKnAVK110Qk+VUEQhCaN6HNBEIT6RfSu0BQQDxBBcKNrWD4Wyj2W7bjz7IwD3sCwci9VSkn+HUEQhBOH6HNBEIT6RfSuEBCIAUQQ3Fzq8b7YXP4VuMxcvhJYYC7/AtwCoJSyKqViajqoUsoCtNZazwEeBGIAH+u5IAiC8Kch+lwQBKF+Eb0rBARiPRNONsKUUqs8Ps/QWjtLeMUppVZjWJ8vN9vuAN5XSt0P5ALXme13Ae8opW7AsFDfAuyr4TutwEemclfAa1rrI3/aGQmCIJyciD4XBEGoX0TvCgGP5AARBFyxi/201gcbWhZBEATh+BF9LgiCUL+I3hUCCQmBEQRBEARBEARBEAShySMeIIIgCIIgCIIgCIIgNHnEA0QQBEEQBEEQBEEQhCaPGEAEQRAEQRAEQRAEQWjyiAFEEARBEARBEARBEIQmjxhABEEQBEEQBEEQBEFo8ogBRBAEQRAEQRAEQRCEJo8YQARBEARBEARBEARBaPL8P4MzztU+q7euAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rand_idx = 4\n",
        "\n",
        "test_input1 = valData[rand_idx:rand_idx+1,:]\n",
        "real_output1 = valLabels[rand_idx:rand_idx+1,:]\n",
        "\n",
        "pred = student_model.predict([test_input1,])\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(10,6), sharey=True)\n",
        "\n",
        "im1 = ax[0].imshow(test_input1.reshape(612,14), cmap=cmap_name)\n",
        "ax[0].set_aspect(0.05)\n",
        "ax[0].set_title('Pilot Signals',fontsize=font_size)\n",
        "\n",
        "ax[1].imshow(real_output1.reshape(612,14), cmap=cmap_name)\n",
        "ax[1].set_aspect(0.05)\n",
        "ax[1].set_title('Actual Channel',fontsize=font_size)\n",
        "\n",
        "ax[2].imshow(pred.reshape(612,14), cmap=cmap_name)\n",
        "ax[2].set_aspect(0.05)\n",
        "ax[2].set_title('Predicted Channel',fontsize=font_size)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "nBvJXFpqo8Ma",
        "outputId": "1fff967c-0eb5-445d-e268-b5a7c6d90b4f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Adversarial Machine Learning Attacks.\n",
        "\n",
        "The previous models are not compatible with `Cleverhans` attack library. We add a dummy layer to make them `Cleverhans` compatible.\n",
        "\n",
        "#### 7.1 Generate malicious inputs for the undefended model"
      ],
      "metadata": {
        "id": "PHQnBAnCvv78"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, Flatten\n",
        "student_copy = tf.keras.models.clone_model(distiller.student)\n",
        "for layer in student_copy.layers:\n",
        "    \tlayer.trainable = False\n",
        "\n",
        "student_copy.add(Flatten())\n",
        "student_copy.add(Dense(2, activation='softmax'))\n",
        "student_copy.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "iYsphvukshiL"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logits_model_student = tf.keras.Model(student_copy.input, student_copy.output)\n",
        "\n",
        "#attacks = ['FGSM','BIM','MIM','PGD','CW']\n",
        "attacks = ['FGSM','BIM','MIM','PGD']\n",
        "eps_vals = [0.1, 0.5, 1.0, 2.0, 3.0]\n",
        "\n",
        "mal_diff_distance_list = []\n",
        "real_mse_list = []\n",
        "mal_mse_list = []\n",
        "mal_predicted_diff_list = []\n",
        "attack_name_list = []\n",
        "\n",
        "eps_val_list = []\n",
        "\n",
        "params_list = list(product(eps_vals,attacks))\n",
        "shuffle(params_list)\n",
        "\n",
        "for i in tqdm(range(valData.shape[0])):\n",
        "    test_input = valData[i:i+1,:].astype(np.float32)\n",
        "\n",
        "    for eps_val, attack in params_list:\n",
        "        if attack == 'FGSM':\n",
        "          mal_input = fast_gradient_method(logits_model_student, test_input, eps=eps_val/10.0, norm=np.inf,\n",
        "                                            clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                            targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'BIM':\n",
        "          mal_input = basic_iterative_method(logits_model_student, test_input, eps=eps_val, norm=np.inf,\n",
        "                                              #clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                              eps_iter=0.01,nb_iter=20,\n",
        "                                              targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'MIM':\n",
        "          mal_input = momentum_iterative_method(logits_model_student, test_input, eps=eps_val, norm=np.inf,\n",
        "                                                  clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                                  eps_iter=0.01,nb_iter=20,\n",
        "                                                  targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'PGD':\n",
        "          mal_input = projected_gradient_descent(logits_model_student, test_input, eps=eps_val, norm=np.inf,\n",
        "                                                  clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                                  eps_iter=0.01,nb_iter=20,\n",
        "                                                  targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'CW':\n",
        "          mal_input = carlini_wagner_l2(logits_model_student, test_input,\n",
        "                                targeted=False, #y=y_output,\n",
        "                                batch_size=512, confidence=10.0,\n",
        "                                abort_early=False, max_iterations=1000,\n",
        "                                clip_min=test_input.min(),clip_max=test_input.max())\n",
        "        \n",
        "        #malicious_input_list.append(mal_input)\n",
        "\n",
        "        mal_diff = mal_input.reshape(612,14) - test_input[0,:,:,:].reshape(612,14)\n",
        "        mal_diff_distance = norm(mal_diff, ord = np.inf)\n",
        "        mal_diff_distance_list.append(mal_diff_distance)\n",
        "\n",
        "        real_output = valLabels[i:i+1,:]\n",
        "        test_output = model.predict(test_input)\n",
        "        real_mse = mean_squared_error(real_output.reshape(612,14),test_output.reshape(612,14))\n",
        "        real_mse_list.append(real_mse)\n",
        "\n",
        "        malicious_test_output = model.predict(mal_input)\n",
        "        mal_mse = mean_squared_error(real_output.reshape(612,14),malicious_test_output.reshape(612,14))\n",
        "        mal_mse_list.append(mal_mse)\n",
        "\n",
        "        mal_predicted_diff = malicious_test_output.reshape(612,14) - test_output.reshape(612,14)\n",
        "        mal_predicted_diff = norm(mal_predicted_diff, ord = np.inf)\n",
        "        mal_predicted_diff_list.append(mal_predicted_diff)\n",
        "\n",
        "        attack_name_list.append(attack)\n",
        "        eps_val_list.append(eps_val)\n",
        "\n",
        "        df_tmp = pd.DataFrame({'Malicious_Distance':[mal_diff_distance],\n",
        "                          'Real_Predicted_MSE':[real_mse],\n",
        "                          'Malicious_Predicted_MSE':[mal_mse],\n",
        "                          'MalOut_RealOut_Diff':[mal_predicted_diff],\n",
        "                          'Attack':[attack],\n",
        "                          'eps':[eps_val]})\n",
        "        df_tmp.to_csv(PROJECT_FOLDER + 'channel_estimation_def_distill.csv', mode='a', header=False, index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "c5907ad498a34bb5a45eeae5f0c629f4",
            "5996764c12454887b2e77d35152d07b4",
            "4a31ecb69c254d3da30097aa266cd161",
            "a18ed0865f6241259993e041cfd3513b",
            "d0a7cf38a691452db4e41d8638fdfcfc",
            "392df48f2f8f4225afcb50c8694804a2",
            "9b29d45db0b146a99b451f6bdafe2cfb",
            "4b075aaa7cc544b08495594842b0721d",
            "7d943924f5344629b32f65db04beb90b",
            "f350d4f45d284293a0e39f817e88b5e4",
            "e7ccc70ee20a432884a397b7c4c6880b"
          ]
        },
        "id": "H370IBZlstdG",
        "outputId": "3366d4b3-a66f-4709-dda6-61492da298f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1.0, 'FGSM'), (0.5, 'PGD'), (0.5, 'MIM'), (0.1, 'BIM'), (0.1, 'PGD'), (0.5, 'BIM'), (0.5, 'FGSM'), (0.1, 'MIM'), (1.0, 'MIM'), (1.0, 'BIM'), (2.0, 'FGSM'), (2.0, 'BIM'), (3.0, 'FGSM'), (0.1, 'FGSM'), (3.0, 'PGD'), (3.0, 'BIM'), (2.0, 'MIM'), (3.0, 'MIM'), (1.0, 'PGD'), (2.0, 'PGD')]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5907ad498a34bb5a45eeae5f0c629f4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 7.2 Generate malicious inputs for the defended model (i.e. `student_model`)"
      ],
      "metadata": {
        "id": "iC7O5Aohwd6Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_copy = tf.keras.models.clone_model(model)\n",
        "for layer in model_copy.layers:\n",
        "    \tlayer.trainable = False\n",
        "\n",
        "model_copy.add(Flatten())\n",
        "model_copy.add(Dense(2, activation='softmax'))\n",
        "model_copy.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "dew-LISGwIog"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logits_model = tf.keras.Model(model_copy.input, model_copy.output)\n",
        "#attacks = ['FGSM','BIM','MIM','PGD','CW']\n",
        "attacks = ['FGSM','BIM','MIM','PGD']\n",
        "\n",
        "mal_diff_distance_list = []\n",
        "real_mse_list = []\n",
        "mal_mse_list = []\n",
        "mal_predicted_diff_list = []\n",
        "attack_name_list = []\n",
        "eps_vals = [0.1, 0.5, 1.0, 2.0, 3.0]\n",
        "eps_val_list = []\n",
        "\n",
        "params_list = list(product(eps_vals,attacks))\n",
        "shuffle(params_list)\n",
        "\n",
        "for i in tqdm(range(valData.shape[0])):\n",
        "    test_input = valData[i:i+1,:].astype(np.float32)\n",
        "    #for eps_val in eps_vals:\n",
        "    #  for attack in attacks:\n",
        "    for eps_val, attack in params_list:\n",
        "        if attack == 'FGSM':\n",
        "          mal_input = fast_gradient_method(logits_model, test_input, eps=eps_val/10.0, norm=np.inf,\n",
        "                                            clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                            targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'BIM':\n",
        "          mal_input = basic_iterative_method(logits_model, test_input, eps=eps_val, norm=np.inf,\n",
        "                                              #clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                              eps_iter=0.01,nb_iter=20,\n",
        "                                              targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'MIM':\n",
        "          mal_input = momentum_iterative_method(logits_model, test_input, eps=eps_val, norm=np.inf,\n",
        "                                                  clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                                  eps_iter=0.01,nb_iter=20,\n",
        "                                                  targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'PGD':\n",
        "          mal_input = projected_gradient_descent(logits_model, test_input, eps=eps_val, norm=np.inf,\n",
        "                                                  clip_min=test_input.min(),clip_max=test_input.max(),\n",
        "                                                  eps_iter=0.01,nb_iter=20,\n",
        "                                                  targeted=False)\n",
        "          mal_input = mal_input.numpy()\n",
        "        elif attack == 'CW':\n",
        "          mal_input = carlini_wagner_l2(logits_model, test_input,\n",
        "                                targeted=False, #y=y_output,\n",
        "                                batch_size=512, confidence=10.0,\n",
        "                                abort_early=False, max_iterations=1000,\n",
        "                                clip_min=test_input.min(),clip_max=test_input.max())\n",
        "        \n",
        "        #malicious_input_list.append(mal_input)\n",
        "\n",
        "        mal_diff = mal_input.reshape(612,14) - test_input[0,:,:,:].reshape(612,14)\n",
        "        mal_diff_distance = norm(mal_diff, ord = np.inf)\n",
        "        mal_diff_distance_list.append(mal_diff_distance)\n",
        "\n",
        "        real_output = valLabels[i:i+1,:]\n",
        "        test_output = model.predict(test_input)\n",
        "        real_mse = mean_squared_error(real_output.reshape(612,14),test_output.reshape(612,14))\n",
        "        real_mse_list.append(real_mse)\n",
        "\n",
        "        malicious_test_output = model.predict(mal_input)\n",
        "        mal_mse = mean_squared_error(real_output.reshape(612,14),malicious_test_output.reshape(612,14))\n",
        "        mal_mse_list.append(mal_mse)\n",
        "\n",
        "        mal_predicted_diff = malicious_test_output.reshape(612,14) - test_output.reshape(612,14)\n",
        "        mal_predicted_diff = norm(mal_predicted_diff, ord = np.inf)\n",
        "        mal_predicted_diff_list.append(mal_predicted_diff)\n",
        "\n",
        "        attack_name_list.append(attack)\n",
        "        eps_val_list.append(eps_val)\n",
        "\n",
        "        df_tmp = pd.DataFrame({'Malicious_Distance':[mal_diff_distance],\n",
        "                          'Real_Predicted_MSE':[real_mse],\n",
        "                          'Malicious_Predicted_MSE':[mal_mse],\n",
        "                          'MalOut_RealOut_Diff':[mal_predicted_diff],\n",
        "                          'Attack':[attack],\n",
        "                          'eps':[eps_val]})\n",
        "        df_tmp.to_csv(PROJECT_FOLDER + 'channel_estimation.csv', mode='a', header=False, index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "3541966df9104b089d1d60da4ab7389a",
            "0a597f620dce4bb3bd6c6775a364531e",
            "4ede6531818541ad8194bce622f8dd90",
            "7e30c6a6288f44d08d899cd6db4e020f",
            "8326479bb9b843758ea4aa593f2cd7f3",
            "793257d907b04edba40448539bad6c8e",
            "439f175a2d30470a96587dabff9e2012",
            "895222814a61461081531f15a19d5802",
            "866d8a18453b4720ba4609badc8eb93d",
            "a0d3e0840d2e414c94f52d81f27b1beb",
            "ead6410001e141ea9ab6bae05729461f"
          ]
        },
        "id": "JpX0mT4Hv90V",
        "outputId": "ed92718e-e85a-434e-a8fa-770a355f85f7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(2.0, 'MIM'), (0.5, 'PGD'), (3.0, 'BIM'), (3.0, 'PGD'), (1.0, 'FGSM'), (2.0, 'PGD'), (1.0, 'MIM'), (1.0, 'PGD'), (1.0, 'BIM'), (3.0, 'FGSM'), (0.1, 'BIM'), (0.1, 'FGSM'), (0.5, 'BIM'), (2.0, 'BIM'), (0.5, 'FGSM'), (0.1, 'PGD'), (3.0, 'MIM'), (2.0, 'FGSM'), (0.5, 'MIM'), (0.1, 'MIM')]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3541966df9104b089d1d60da4ab7389a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. Attack results"
      ],
      "metadata": {
        "id": "ZkdXj51dwnZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "COL_NAMES = ['Malicious_Distance','Real_Predicted_MSE','Malicious_Predicted_MSE','MalOut_RealOut_Diff'\n",
        "             ,'Attack','eps']\n",
        "#attacks = ['FGSM','BIM','MIM','PGD','CW']\n",
        "attacks = ['FGSM','BIM','MIM','PGD']\n",
        "\n",
        "df_undefended = pd.read_csv('channel_estimation.csv', names=COL_NAMES)\n",
        "df_undefended = df_undefended.query(\"Malicious_Predicted_MSE < 1.0\").sort_values(by=['Attack'])\n",
        "\n",
        "df_distilled = pd.read_csv('channel_estimation_def_distill.csv', names=COL_NAMES).sort_values(by=['Attack'])\n",
        "df_distilled = df_distilled.query(\"Malicious_Predicted_MSE < 1.0\")\n",
        "df_distilled.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "5BLFno5bu8fp",
        "outputId": "86766ebf-dc90-428a-d5d8-c05cd428d3d5"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Malicious_Distance  Real_Predicted_MSE  Malicious_Predicted_MSE  \\\n",
              "611            2.800021            0.018313                 0.019575   \n",
              "125            2.800014            0.039694                 0.040615   \n",
              "272            4.200001            0.012887                 0.016460   \n",
              "154            2.799999            0.028059                 0.029071   \n",
              "493            0.140001            0.034496                 0.034492   \n",
              "128            2.800005            0.039694                 0.040679   \n",
              "353            0.140000            0.031892                 0.031925   \n",
              "144            1.400000            0.028059                 0.028163   \n",
              "537            2.800003            0.033426                 0.035785   \n",
              "420            1.400000            0.052117                 0.052598   \n",
              "\n",
              "     MalOut_RealOut_Diff Attack  eps  \n",
              "611             0.819117    BIM  2.0  \n",
              "125             0.882066    BIM  0.5  \n",
              "272             1.442788   FGSM  3.0  \n",
              "154             0.834533    PGD  3.0  \n",
              "493             0.048550   FGSM  0.1  \n",
              "128             0.837483    MIM  1.0  \n",
              "353             0.041318   FGSM  0.1  \n",
              "144             0.463262    PGD  0.1  \n",
              "537             0.779131    MIM  3.0  \n",
              "420             0.475293   FGSM  1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa6229a0-e02f-4236-91c6-790b2a38b83d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Malicious_Distance</th>\n",
              "      <th>Real_Predicted_MSE</th>\n",
              "      <th>Malicious_Predicted_MSE</th>\n",
              "      <th>MalOut_RealOut_Diff</th>\n",
              "      <th>Attack</th>\n",
              "      <th>eps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>2.800021</td>\n",
              "      <td>0.018313</td>\n",
              "      <td>0.019575</td>\n",
              "      <td>0.819117</td>\n",
              "      <td>BIM</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>2.800014</td>\n",
              "      <td>0.039694</td>\n",
              "      <td>0.040615</td>\n",
              "      <td>0.882066</td>\n",
              "      <td>BIM</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272</th>\n",
              "      <td>4.200001</td>\n",
              "      <td>0.012887</td>\n",
              "      <td>0.016460</td>\n",
              "      <td>1.442788</td>\n",
              "      <td>FGSM</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>2.799999</td>\n",
              "      <td>0.028059</td>\n",
              "      <td>0.029071</td>\n",
              "      <td>0.834533</td>\n",
              "      <td>PGD</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>0.140001</td>\n",
              "      <td>0.034496</td>\n",
              "      <td>0.034492</td>\n",
              "      <td>0.048550</td>\n",
              "      <td>FGSM</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>2.800005</td>\n",
              "      <td>0.039694</td>\n",
              "      <td>0.040679</td>\n",
              "      <td>0.837483</td>\n",
              "      <td>MIM</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>353</th>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.031892</td>\n",
              "      <td>0.031925</td>\n",
              "      <td>0.041318</td>\n",
              "      <td>FGSM</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>1.400000</td>\n",
              "      <td>0.028059</td>\n",
              "      <td>0.028163</td>\n",
              "      <td>0.463262</td>\n",
              "      <td>PGD</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>2.800003</td>\n",
              "      <td>0.033426</td>\n",
              "      <td>0.035785</td>\n",
              "      <td>0.779131</td>\n",
              "      <td>MIM</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>1.400000</td>\n",
              "      <td>0.052117</td>\n",
              "      <td>0.052598</td>\n",
              "      <td>0.475293</td>\n",
              "      <td>FGSM</td>\n",
              "      <td>1.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1, 1, figsize=(10, 4))\n",
        "sns.histplot(data=df_undefended, x='Malicious_Predicted_MSE', element=\"poly\",\n",
        "             cumulative=False, lw=2,stat='percent', log_scale=(False,False),\n",
        "             color='red',kde=True,cbar=True,\n",
        "             bins=20)\n",
        "sns.histplot(data=df_distilled, x='Malicious_Predicted_MSE', element=\"poly\",\n",
        "             cumulative=False, lw=2,stat='percent', log_scale=(False,False),\n",
        "             color='blue',kde=True,cbar=True,\n",
        "             bins=20)\n",
        "plt.xlabel(r'MSE', fontsize=28)\n",
        "plt.ylabel(r'Count', fontsize=28)\n",
        "plt.setp(ax.get_xticklabels(), fontsize=23)\n",
        "plt.setp(ax.get_yticklabels(), fontsize=23)\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "Q9RRS5c1yS6t",
        "outputId": "83fed0ce-2d21-45d1-ac2d-a971deb9f875"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1, 2, figsize=(12, 4), sharey=True)\n",
        "for attack in attacks:\n",
        "    df_tmp = df_undefended.query(\"Attack=='\" + attack + \"'\" )\n",
        "    sns.lineplot(data=df_tmp, x='eps', ax=ax[0], y='Malicious_Predicted_MSE', ls='--', lw=2, label=attack)\n",
        "    \n",
        "    df_tmp = df_distilled.query(\"Attack=='\" + attack + \"'\" )\n",
        "    sns.lineplot(data=df_tmp, x='eps', ax=ax[1], y='Malicious_Predicted_MSE', ls='-.', lw=2, label=attack)\n",
        "\n",
        "for i in range(2):\n",
        "    ax[i].set_ylabel('MSE', fontsize=20)\n",
        "    ax[i].set_xlabel(r'$\\epsilon$ values', fontsize=20)\n",
        "    ax[i].legend()\n",
        "    ax[i].set(yscale=\"linear\")\n",
        "    ax[i].grid()\n",
        "    ax[i].tick_params(axis='both', which='major', labelsize=12)\n",
        "    ax[i].tick_params(axis='both', which='minor', labelsize=8)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "EVeRuTBzyWSS",
        "outputId": "0688c98e-6c87-44e6-8764-866ea6817984"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}