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VheQAA/EI/rxGiKKpWq41GY3Bw0O8Eh1cT7jpubAoAm69QKNTr9SAIwjBM07RPvussy6ampqIoOnv2rDaAbu0cHQkAW3pinpmZGR4eLpVKvf2dRlFUq9Xq9bow6NAzn+2BCcsDAHB0boM0TWdnZ3ft2tXzCeTwijwAADbH+hph7969R48e7aXva2lpaXl5uVqtup2rw6sJu/YAANiofD5fr9f37NkzNTUVx3EPfEdpmk5NTQVBUK/XtQH0TufoSABopyzLFhYWWq3W5ORk997009Kg+858tgcmvDG2BwDQVvl8/vjx44cOHTp27Fg3rhGSJAnDMLA0gF7tHB0JANuiG9cIzWZzdXV1dnbWw86678xne2DCG2N7AADb48oaYXx8PIqiDv/VJklSKpWKxWK9XtcG0MudoyMBYNt1+Lvyi4uLa2trlgbdfeazPTBheQAAXWT9M/3lcrlYLHbOryqO40ql0mm/KuSBCcsDAOgLzWZzZWVlbm5u29+nz7Jsfn4+l8tNTExYGji8Ig8AgO2RJMnJkydHR0cPHjy4Xb+GOI7L5XKtVhsaGvKKOLwiDwCAbba0tHThwoVardbmm4daGji8Ig/8RgGATpSmaaVSaecaIYqiv/qrv3ryySctDRxekQcAQCdqz/OJsyybmZkZHh4ulUpm7vCKPAAAOleaprOzs7t27dqis3sURbVarV6vDw4OmnYPH14NYavJAwCgfaIoqlarjUZjEw/xWZZNT0+PjIxYGgDyAAC6zPpHgPbu3Xv06NGb/6etf2zpqaeesjQArrjFCACgW+Tz+Xq9vmfPnlKplCTJ5/7npGk6NTUVBIEPFAFXsT0AgO6TZdmpU6d27NgxOTl5o7cfbc+1zkCXsj0AgO6Tz+efeeaZQ4cOHTt2LI7jDf5/pWkahmEQBPV6XRsAn8j2AAC6WJZlCwsLrVbrM9cIzWZzdXXV0gC4PtsDAOhi+Xz++PHjhw4dGh8f/7Q1QpIkR44cKRaLlgbAZ7I9AIBekGXZuXPnVldXZ2dnP75GWFxcXFtbu+qLAPIAAHpfkiRTU1OVSqVYLCZJEoZhuVwuFosmA8gDAOhTzWbz9OnTDz744PHjx3fu3GkggDwAgL72X//1X++9997LL7/8b//2bzt27HjrrbeGh4eLxeKdd97pQQeAPACAfpem6dtvvx1F0erq6u233x4Ewa5du/bt2zc4OOh6ZUAeAEC/S5Lkvffei6Lo/PnzX/7yl4MgeOCBB3bv3n333Xe7jhnkAQDQ7+I4/slPfvLOO++srq4GQXDfffd96Utf2r1799DQkOGAPAAA+lqWZe++++7rr7/+9ttvv//++5cvXx4eHr733nvvv/9+FzCAPAAA+l2apkmSuOIZ5AEAwCcHw5Urnu+77773339/165dpVLJZEAeAAAESZLYJIA8AAAAut4tRgAAAMgDAABAHgAAAPIAAOglcRxnWWYOsIlcmgwAdKulpaW33377rbfeCoJg/ZFtv/Zrv+YZzyAPAIB+d+UZz6+++moQBG+88cbY2NimPLJtbW1t7969Jow8AADoYh9/xnMQBJcvXx4eHt63b9/g4GChUNj4P+dXf/VXd+7cubKyks/nTRV5AADQI5Ikee+99656xvO+ffvuvvvuTzv6Z1k2ODj4wQcf3HnnnT/84Q93795tjMgDAIDeFMfxT37yk3feeWd1dfX222/fsWPHl770pd27d18JhjiOv/jFL67/zbfeeuvJkyfDMDQ35AEAQO+7cgHD22+//f7771++fDmXy9Xr9Y//PaOjo+fPn/dBI+QBAEDfqdVqlUrlqi/ee++9r7zyyl133WU+9B7PPQAA+FQ/+tGPrv3iHXfc8Qd/8AfNZtN86D32YgAAn+0LX/jC7t27H3roobfeeuvP//zPh4aGoigKw/DJJ5/0pAV6iQ8XAQB8ql/5lV/54he/ODExcfTo0aeffnrv3r2PPPLIlQsPsiybn5/P5XITExOuRkAeAAD0uDRNC4VCFEW1Wq1er3/iE9biOK5UKtVq1RoBeQAA0MuyLJuZmRkeHi6VStf/26wRkAcAAL1saWlpeXn5qaee+sSlwbXW1wjlcrlYLJoeXcqdiwAArpam6frjzz7tA0WfaGhoqNlsvvbaa2EYZllmjHQj2wMAgP9jaWnpwoULtVqtUCh8vn9CkiRTU1OVSsUaga5jewAA8ItjfRiGAwMDjUbjc7dBEASDg4Nnz55dv/NpmqYGSxexPQAACIIgaDabKysrc3Nzm3htcZqmlUpldHT04MGDJkxXsD0AAPpdkiRHjhwpFouNRmNz7ztUKBTq9XoQBNYIdAvbAwCgf2VZtrCwcPny5RMnTmzpDUnTNC2XyyMjI9e/QSrIAwCA7RHHcblcrtVqbXuc2fUfrwbyAABgG6wvDVqt1uTkZJufYpZl2fT0tDUC8gAAoCNEUfTd7353enq6bUuDT/w1VKvVRqNhjYA8AADYHlmWzczM7N279+jRo53wi5mfn8/lchMTE23eYIA8AAD6XWe+Yd/+6x9AHgAAfW394QPDw8Od+XF/awTkAQBAmywtLS0vL1er1Zt5CnIbxHE8Nzf32GOPFYtFrxryAABgk3XjE4sXFxfX1tZmZ2etEZAHAACbptlsrqys1Gq1Dl8aXCtJkqmpqUqlYo3AtrjFCACAXpIkSalUKhaLjUaj69ogCILBwcGzZ89GURSGYZqmXlDazPYAAOgdzWZzdXW1Nz6c040fjqIH2B4AAL0gjuMwDIvFYr1e740P7hcKhXq9HgSBNQLtZHsAAHS3LMsWFhZardbk5GRPXtGbpmm5XD5w4IA1Am1gewAAdLE4jo8dOzY2NvbEE0/06t1+CoVCo9EYGBgolUpJknjR2VK2BwBAV8qy7NSpUzt27OjVpcEnfsvT09MjIyOd+Xw35AEAwPaIoqhWq9Xr9cHBwT783qvVaqPR6MPvHXkAAPB/ZFk2MzOzd+/eo0eP9vMQTp06dfvtt09MTHiAGvIAAOhTFy9efO6557xxvi6O43K5XKvVhoaGTAN5AAD0EQ8B+ERZls3Pz+dyOWsE5AEA0C+WlpYuXLhQq9W68SnIbRDHcaVSqVar1gjcPDc2BQA6V5qmYRgGQdBoNLTBpxkaGmo2mxcvXgzDMMsyA+Fm2B4AAB2q2WyurKzMzc352MwGJUkShmG5XC4Wi6bB52N7AAB04jG3VCoVi8VGo6ENNm5wcLDZbEZRZI3A52Z7AAB0kCzLzp07t7q6Ojs7Kww+N1dy87nZHgAAnSKO4/Hx8X379tXrdW1wMwqFQr1eD4IgDMM0TQ2EjbM9AAC2X5ZlCwsLrVZrcnJSGGyiNE3L5fKBAwesEdgg2wMAYJvFcXzs2LGxsbEnnnhCG2yuQqHQaDSCIAjDMEkSA+Ez2R4AANsmy7KZmZm9e/c+8sgjwmCrRz09PT0yMlIqlUwDeQAAdJwoimq1Wr1eHxwcNI22zbxarTYaDTNHHgAAnWJ9aTA8POyd7G0c/uHDh21skAcAwDZbWlpaXl6uVquegryN4jgul8u1Wm1oaMg0kAcAwDZwM/6OkmXZ/Px8LpebmJiwRkAeAABttbS0dOHChVqtZmnQUeI4rlQq1WrVGoF1bmwKAGytJElKpdLAwECj0dAGnWZoaOiFF164ePFiGIZZlhkItgcAwBZqNpsrKytzc3M+vtL5FReGYblcLhaLptHPbA8AgK06bh45cqRYLDYaDW3Q+QYHB5vNZhRF1gh9zvYAANhkWZYtLCxcvnz5xIkTwqAbu+7kyZOHDx/ev3+/acgDAICb4o6ZvcH9Z+UBAMBNWV8atFqtyclJS4MekKZpuVw+cOCAG9HKAwCAG9NsNn/wgx/8+7//+/nz573f3DPiOP7d3/3dYrH4F3/xF9ZBfcKlyQDATbl48WKpVCoWi4uLi+fPn69UKktLS8bS7bIs+853vnP+/PkoiprN5ssvvxyGYRzHJtPzbA8AgM8fBmfOnPn6179+1TWsPrbe7aIoqlarV11AkmXZuXPnVldXn3zySZsEeQAA8NlhcEWappVKZXh4uFQqGVcXybJsZmbmOi+cSJAHAAC/EMfx6dOnN3juj6KoVqvV6/XBwUGj63wbf72yLKtWq0EQzMzM2BHJAwCgr8Pg8OHDG78x0fq70Xv37j169KgZdqwsy6anp0dGRm5o25Om6ezsrEiQBwCAMLjhf0KlUqlWqz6O0oFu8loRkSAPAIB+sX4JwX333TcxMXGTjzLwVITOfH0368kGaZo+9thjY2Njn7shkQcAQEcfHLfiLWHPVO4cW3GDqZtfNCEPAIC+CIMrrtz6ZnZ21glyWyRJcvLkycOHD3/ajadEgjwAAPjfz/+89dZbbXhkQZIkYRiWy+VisWjy7dRsNtvTZjd0kyvkAQDQWWGwLTezbzabKysrc3Nz3mNug21Jss98RAbyAAAQBr+wfvXz6OjozV8dy3Ve5e9///tra2vb9YEukSAPAIAusH5xaic8/nZpaenChQu1Ws2dMTdd59xVViTIAwCgQ3XgQc0aYdNlWTY/P5/L5W7+vrSbaP3ih06IUuQBANDp7+BevHjxueeeazQag4ODXqyb0cn3kN32j7QhDwCAX5y/O/yjHVmWzczMuOPNzQ+ww+8rKhLkAQDARkVRVKvV6vW6NcKNzq1arXbR+uVKJLThdrrIAwCgi2VZdurUqR07dkxOTrrz6UbGNT09PTIy0o1bl/WH8T399NNeR3kAAHA9nXPjnU62fhOqp556yrIFeQAA9Lj1xzm3Wi1rhGulaVoulw8cOOCOT8gDAKCPxHF8+vTpRx99tJ0P/e1w60sDn9pHHgAAfWr9rvnb9fTfzuExEcgDAIAgCIIkScIwLJfLfbtG0EhsnVuMAADoLoODg81mM4qiqampNE37LY1KpVKxWKzX69qArWB7AAB0q377gM3i4uLa2pqlAVvK9gAA6FaFQqFerwdBEIZhb68R4jgulUr79++3NGCr2R4AAF1vfY0wPDzcjU8Eu74sy+bn53O53MTEhDCgDWwPAICut75GKBaLR44cSZKkZ76vOI7Hx8cPHTp0/PhxbUB72B4AAL0jy7KZmZm9e/cePXq027+RU6dO3X777ZYGyAMAgJsSRdF3v/vd6enpoaGhLv31V6vVRqMxODjo1UQeAADcrCzLFhYWWq3W5ORkF737nmXZ9PT0yMhI711EgTwAANhmcRyXy+VardYVa4SLFy+eOXOmXq9bGmyiNE0LhYI5yAMAgCD4/2uEy5cvnzhxomPXCGmalsvlAwcO9MkDHNrpm9/8ZhAEMzMzIkEeAEB38O5mGyRJMjU1ValUisVip/3alpaWlpeXq9Wq3wZb9+qfPHlyeHj48OHDrvOWBwDQ0WEwOzsbBMHTTz9tGm3QbDZXVlbm5uY65IzYb0993l5xHJ8+fVokyAMA6ERZlp07d251dfWpp57yQfN2SpIkDMOvf/3r+/fv3/ZWWV1dtTQQCfIAAITBudXV1SeffLJLb7vZA5aWli5cuFCr1bblaN7Jn3Tqn0ioVCqdUInyAACEgTDoCNv1wZ7FxcW1tbXZ2VlvXW+79VtFiYSr3GIEANCeg8ixY8f27dtXr9e1QScoFAr1ej0IgjAM0zRtw78xjuOpqak9e/bU63Vt0An279/fbDaDICiVShcvXjSQdbYHALDlYeAdyk6WZdnMzMzw8PDWPYksy7L5+flcLjcxMSEMOvm/03K57BNftgcAsIUHjvUTZ7PZ1AYdK5/P1+v1YrFYKpWSJNn0f34cx+Pj44cOHTp+/Lg26Fj79+9/4YUXoigKwzCO434ehe0BAGw+Fz52o/U1wt69ex955JFNOcdbGnTpb4M+v0ZIHgDA5zlAfNppz20TeyPtqtXqTR4NoyiqVquNRsONa3syErIsC4KgJ/8blwcAcMMeeOCBb3zjGw8//LAw6NWj4cLCQqvVmpyc/BwvZZZl09PTIyMjW3cxA237nVCtVoMgmJmZueoeuM8+++ylS5e+973vyQMA6A8ZPlsAACAASURBVHf1ev1P/uRP7r///n/9139d/0qapo899tjY2Jgw6CVxHJfL5VqtdkNrhCiKarVavV63NOgZV55ufiUSsiy76667/uM//uP8+fO/93u/Jw8AoH/99Kc/vf/++z/66KMgCF566aWRkZGrzg30kiufMNnIYwrSNC2Xy5YG/RAJf/mXf/lnf/ZnQRDcc889URQNDAzIAwDo08Pirl273nnnnfX/eccddzz++OPCoOclSRKG4fVvebm0tLS8vFytVv1m6PlI+MY3vvE3f/M36+8RBEFQLBZfe+21Xvoe3dgUADbqa1/72pU2CILgZz/72cMPP+w42PMGBwebzWYURVNTU+sXpF51XgzDMAiCer3uN0PPKxQKd99995U2CIIgiqJKpdJL36PtAQBsyA9+8IOxsbGrvjgyMvLKK68YTp9I07RSqYyOjh48eHD9K81mc3V11dKgf2RZ9pWvfOXNN9/8+Be/8IUvvPLKK7t37+6N79H2AAA+209/+tM/+qM/uvbrb7zxxqVLl8ynTxQKhXq9HgTB1NRUFEVHjhwpFouWBn3lzJkzV7VBEAQ///nPH3rooWs3S13K9gCAbfoJlMsZwpbyI37rpGn64osvulGVPyJ68k8JeQDAtv3s9zPIeMHv4U6bsA8XAQAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAG5E3ggA2C65XM4QAH9EdNbYW62WKQDARk4qfmgCPf9HhA8XAQAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAgN6TNwIA+EQffvhhFEUf/8o//uM/BkHwy7/8y1/96lfNB/inf/qn//zP/7zqj4ggCEZGRvL5bj1m51qtlpcWAK516dKlPXv2fPxn/7rR0dELFy6YD7Bnz56r3kQIguC222579913C4VCl35TPlwEAJ9s586dv/3bv33tD/4zZ84YDhAEwenTp6/94te+9rXubYPA9gAAruPSpUsPPPDABx98cOUrVgfAx121QLj11lvffffdgYGB7v2ObA8A4FPt3Lnzy1/+8se/Mjc3ZyzAFVctEEZHR7u6DXK5nO0BAFzP66+//pWvfGX9r0dGRl555RUzAT7u13/91998882gJ1YHuVzO9gAArmf37t0jIyPrf/38888bCHCVU6dOrf/F/v37u7oNgiBotVq2BwC0Wy6XM4S2/aQ3BGiDoaGhDz/88NVXX925c2e3fy+2BwAAcFO+9a1v7d69uwfaIPBYNAAAOlA3rhm77tf8iQtGeQAAQNccXtnqmJEHAPjZ3y8/9QE+k2sPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAD0lEuXLl26dMkcAHkAAH3tww8/PHDgwFe/+tXz58+bBnAdeSMAgN4Ogz/+4z9eXl7+6KOPTAOQBwDQvx599NHz58//7Gc/MwpAHgBAv3v++ecNAbghuVarZQoAtPVnTy63/hd+Bm31hG+99VafKaJ7+SNiq/+g+MQJywMA5EHPTviDDz646sKDb3/722EYGhHde3hlqyfszkUA0LMGBgb+7u/+7s033xwdHb3tttsMBPhM8gAAetxdd9114cKFV199dXR01DSA67O1AaDtP3t8uGj7JpxlWT7vxiR0x29jf0Rsy4RtDwCgj2gD4PrkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAADQJ/JGAMB2yeVyhgAgDwAA4DN4B0EeAABAEARBq9UyhO2pMqMHAADWuTQZAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5IERAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAAAJAHAACAPAAAAOQBAAAgDwAAAHkAAADIAwAAQB4AAADyAAAAkAcAAIA8AAAA5AEAACAPAAAAeQAAAMgDAABAHgAAAPIAAACQBwAAgDwAAADkAQAAIA8AAADkAQAAIA8AAAB5AAAAyAMAAEAeAAAA8gAA4P+1d/+xUd73HcAfm0urCdo1PwzHCqpJUduk8SVtVLFgU0Y5JzEwMRQGsxNFQoWqZASW0KkJbf2DKsk6KWX87kqqaCjYigtN0cBOwZRBbGcsSgh3NI0ykriFqg6XdsvCVUs47P3xrFfXBgLGPp/t1+uP6PEHcr58n7v2eT/f7/fzAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAACAeAAAA4gEAADAKRQxBvikoKBjw1+zu7jawAACIB8PSwF7ND0beAABgRLK4CAAAEA8AAADxAAAAEA8AAADxAAAAEA8AAADxAAAAEA8AAADxAAAAEA8AAICBETEEXLpMJnPq1KnweNKkSZGIzw8AgHjAaHXq1KkpU6Zc6E8rKyuDIJg+ffo111xTUlLykY98pLi42KABAAwjBd3d3UYhv05JwQCflAF8wY6OjovEg/OKRqOzZs2aPn369ddff+ONNwoMAADiASMkHgRBkEql0ul0r+KvfvWrX/ziF0EQHDlyJJVK/exnP0skEhd6hXg8PmPGjAULFtxwww2WJwEAiAcM43hwWUGis7MzmUyeOHHi1VdfbWhoOG9UmD9//uLFi4uKipx6AADxgBEbD84bGF577bWjR49u27at1/RCLBa76667FixYUFJS4jMAACAeMPLjQU/pdPrll19ubGxsbGzs7OzM1qPR6PLly5cvX24+AQBAPGC0xIOeksnkoUOHek0pxOPx2traadOm2Z8AACAeiAejKB70zAnPPPPM1q1bs/MJJhMAAMQD8WCUxoOstra2zZs399zNXFlZ+eijj+qLCgAgHogHoy4ehNLp9JNPPvnII49kJxNisdiWLVtKS0t9TgAAxIMBlkwmk8nkokWL8mp1u3jQSyaTOXLkyH333ZfdmRCPx7dt22YmAQBgwBWO5v/4j3zkI3ffffdVV121du3aVCrl05CfIpFIaWnpsWPHWltbY7FYEAQtLS1Tpkypqqpy1gAAxIOBV1NTM378+Kqqqra2NqORt3qFhIaGhvHjx2/atCmTyRgcAADx4IpkMplTp071rDQ0NJSVld1888319fWuOPM8JOzduzcajQZBcP/990+ePDmZTBoZAIArN4r2HoTP4Tp69Gh7e/vBgwd7Poqrr8rKyvr6+qE5JfYeXPIJXbNmzYYNG8IfV65c+eijj44dO9a3GgBAPLhYJDhw4MCuXbt6PnLrImKx2FNPPVVSUjJkp0Q8uBwdHR3z588PT240Gv3BD34wZ84cX2wAAPHgD1Kp1P79+5988smWlpZefxSPx2fMmDF16tTp06cXFRWNGzcu+0fRaPTxxx+vqqoa4lMiHlymTCbT2Nh49913hz+uXLny8ccf96xlAIDRHg8ymcy+ffsefvjhXhMF8Xh8/vz5M2fOvOGGG3pdNRYUFIQHGzdu/OpXv5oP15TiQb8zYVVVVRgIo9FoIpHwoGUAgFEaDzo6OrZv315TU9OzuHLlykWLFt1yyy0XWY8+ceLEWbNmbdu2LX/WrIsHV2LTpk33339/eNza2uoBagAAoysetLW1bd68uaGhoWcqWLp06SVuHkin0/m2mVU8uELJZPL2228Pt55v3LhxxYoVvucAACM/HrS1tfV8km64c2D+/PnDvXeNeHDlUqlUPB4PPxuVlZXbt2+3FQEAYMTGg57NaoIgiMViW7ZsGTHLSMSDAZHJZO69995wWikejzc3N0sIAAAfaJg9Fi2dTldVVU2ZMiXMBvF4vLW19dixY5aY00skEqmvr9+4cWMQBC0tLbfeemsqlTIsAAAXN5zuK9fX12ebV46wGYM/OiVmDwZUW1tbWVlZoJ0RAMCIiQcdHR3Lli3LPsRgx44dixYtGqlrRcQDCQEAYKjk++KiTCazadOmKVOmhNmgsrLyzJkzVVVV1pFz6UpLS1tbW4Mg6OzsjMViVhkBAFxIXt9X7vWgq507d46GPQZmDwZJdg4hFou9+OKLEiYAQF/5O3vQ1tYWi8XCbLBy5cqTJ0+Onv3HBQPKpzyUnUNIJBIVFRWZTMaYAAAMj3iwdu3asrKyzs7OaDS6d+/e9evXj557vd2DwAc9mxB27NgRBEFLS8u9995rQAAAehlTW1ubV28ok8ncc889YT/KeDze0tJyyy23OE8MlJKSksLCwn/7t387fvx4YWHhzJkzjQkAQFZ+rUrPZDIVFRXZXcgedssgqaqqCp+Ytnfv3jlz5hgQAIC8iwc9s0FdXV11dbXTQw4+bKdPn9bqFAAgv+JBz8u1jRs3rlixwrlhUKXT6alTp4atTjUyAgAI5cXW5J7ZoLW1VTYgB8aOHbtv374gCBKJxOrVqw0IAECQD7MHvbLB6OleSj7YtGnT/fffH4aEkpISAwIAiAdDGQ9kA4Y8nd56662JRCIajZ44cWLs2LHGBAAYzYZycVHPbLB3717ZgNyLRCLhJ7Czs3PNmjUGBAAQD4bM6tWrs32KNJdkqBQVFYXP2diwYUNHR4cBAQBGsyFbXJRd862HKUMuk8lMnjw57GJ07NgxAwIAiAc5lUwmY7FYEATxeHz//v1OA0Oura2trKwssAcGABAPcvwrs/3mo9HoyZMn9ZsnT9x8882JRMIEAgAwmg3B3oO/+qu/6uzsDILg+eeflw3IH7t37w6CIJFItLW1GQ0AQDzIhfr6+nA78o4dO4qLi50A8kdxcXFlZWUQBPfdd5/RAABGp5wuLkqlUuPHjw+CoLKysr6+3uiTbzo6OqZMmRLYgQAAjFY5nT2oqqoKgiAajW7bts3Qk4dMIAAA4kGONDU1hcuKfvCDH3g2LXnr4YcfDoIgkUgkk0mjAQCIB4Mik8l8+ctfDoKgsrLSE9DIZyUlJWHX3SeeeMJoAACjTY72Hqxdu7ampiYIgtOnTxcVFRl38ll9ff3dd98dBMHZs2c11wIARpVczB50dHSE2WDjxo2yAflv/vz54cGRI0eMBgAgHgywNWvWBEEQjUa/+tWvGnHy39ixY+PxeBAEtbW1RgMAGFUGfXFRtlPkjh07ws5FkP+amprmzp0bBMGZM2fspAcARo9Bnz3ITh0sWrTIcDNczJw5Mzx4+eWXjQYAIB4MjI6OjoaGhiAIHn/8cVs8GUay64saGxuNBgAgHgwMUwcMX+EGZfEAABhVBnHvQTqdHjduXGDXAcNTdtuM7QcAwOgxiLMHTz75ZHhg6oDhaNKkSeGB7QcAgHgwAB555JEgCOrq6uw6YDiKRCLh45OPHj1qNAAA8eCKJJPJzs7OIAjuvfdeo8ww9Rd/8RdBELS3txsKAGCUGKz7+k888UQQBLFYrLi42CgzvGQymVOnTgVBcP311wdB0NzcXF9ff+LEiVdfffXhhx8uKSkxRADASDUoW5MzmcxVV10V2JTM8FRfX3/33Xf3rUej0ZMnT1osBwCMYIOyuOjnP/95eFBeXm6IGXaqqqrCXQe9LF++XDYAAMSDy/bMM88EQRCLxYqKigwxw9Hu3bvPGw+MDAAgHly2rVu3BkGwbNky48swVVxcvHLlyp6VyspKcRcAGPEGfu9BKpUaP358EASJRMImToavdDo9derUsAFXEAStra2lpaWGBQAY2QZ+9mD//v3hgWzAsDZ27NjHH388PI5Go7IBACAe9MeePXuCIOi1MAOGo+we5W984xtGAwAQD/rj4MGDQRBMmzbN4DIChHuUlyxZYigAgNFggPcepNPpcePGBUHw5ptveiAaI0MymbRSDgAYJQZ49uCNN94ID/R4YcSQDQAA8aCfkslkEATRaHTs2LEGFwAARnU8OHLkSBAEs2bNMrIAADDa48Err7wSBMG8efOMLAAAjPZ40NLSYkwBAEA8+IPp06cbWQAAGNXxIJ1OG1AAABAPgiAIUqlUeKBtEQAAjPZ4kOWhBwAAMNrjQdi2CAAAEA+C//7v/zagAAAgHvxBNBo1rAAAIB4EgUcmAwCAeBAEwSc+8QlTBwAAMHwVdHd3GwUAACAYpMamAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAACAeAAAAIgHAADA6FWQ/2+xu7vbeTrPmSsoMAj4mgAAAyuS5xcWLoJdEfb8MLgI9jUBAAaVxUUAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAAiAcAAIB4AAAADBeRIAgKCgoMBAAAEOnu7jYKAABAYHERAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAOIBAAAgHgAAAIMjYgiGr4KCAv/JAAAM5OVWd3e3UQAAAAKLiwAAAPEAAAAQDwAAAPEAAAAQDwAAAPEAAAAQDwAAAPEAAAAQDwAAgCsWMQRcuo6OjldeeeXGG28cO3ZsUVGRAQEAGGHMHnAZ9uzZM3fu3ClTpjz99NNGAwBAPPgAmzZtSqfThnWkam9vDw+uueYaozHYCoYDpwkARpgBXlz0yCOPtLe319fXG1m4ct3d3XkeYJwjABhhBnL2IJlMdnZ2NjQ0NDU1GdmR7WMf+5hBAAAQDy7mmWeeCQ++/OUvW2I0IjU0NIQHN954o9EAABAPLiiTydTU1ITHnZ2dy5YtM7gAADBK48GRI0d6/miJ0cjTc0Zo0qRJBgQAQDy4oM2bN/eqWGI0wrz88svhQTQajUQ8MQMAQDy4gHQ6nV2VnmWJ0Qhz9OjR8GDWrFlGAwBAPLigQ4cOnbduidFIsnv37vBg3rx5RgMAYEQqGJDG6uXl5S0tLef9o2g0euLEibFjxxrrYS2dTo8bNy48fvPNN4uLi43JoH85Cwry/7kHef4OAYDLNQAryFOpVN9sEI1GH3/88SAIpk+fbpRHgOwEUTQalQ0AAMSDC0qn062trR//+MeLioreeOONWCwWBEFnZ+f8+fNNGowYDz/8cHiwaNEiowEAMFIN/NqAgoKC8KC1tbW0tNQQjwAdHR1TpkwJj60syt2X0+IiACDnCgf8FVeuXBke9G11yjC1bt268CAWi8kGAAAj2MDf/GtraysrKwuPz5w5Y33RcNdzU/LevXvnzJljTHL05TR7AADk3MDPHpSWlkaj0fA43J3MsJY9idFo9PbbbzcgAADiweX5xje+ER5s3bo1k8kY5eErlUrV1NRkc4KHJQMAiAeXbcmSJeFBZ2dnY2OjUR6+Vq1aFR5Eo1E9iwAAxIP+GDt2bF1dXXi8evVqEwjDVFtbW0NDQ3hs6gAAYDQYrJ2FPfez1tXVVVdXG+vhJZPJTJ48ubOzMwiCWCx27NgxY5LrL6etyQBAzhUO0uv2nECoqalJpVLGeni59957w2wQBMHu3bsNCACAeHBF1qxZk21hlF3CzrBQX1+fXVa0ceNGzzoAABglBndtQFNT09y5c8NjLfOHi2QyGYvFwuN4PN7c3GzXwdB8OS0uAgBGWDwIgqC8vLylpSUIgmg0euLECU9Jy3OpVCoWi4XLiqLRaCKRKCoqeLLLOQAAHURJREFUMiwuvsUDABAPBuxyc/z48eFxPB7fv3+/Qc9bmUzm1ltvTSQS4Y+JRKKkpMSwuPgWDwBg9Cgc7F9QVFS0cePG8LilpWXt2rUGPW+zQUVFRTYb7N27VzYIh+W2227TnBcAEA8GzIoVK+LxeHhcU1PT1tZm3PMzG4TLwIIgqKurs1Ekmw06OjqOHDliNAAA8WDANDc3Z7sYlZWVdXR0GPp8zgaeU5HNBvPmzdu3b9/mzZsNCAAgHgyYSCSSSCSyCeG2227zJIQ8kUqlZIPzqqqqmjt3bk1NTUlJyfHjx9PptDEBAEa8nO4sbGtrKysrC491xcmTbJDtUyQb9PS1r33t7Nmz69ev7+7uLigo2Lhx4zvvvPPNb34zp19OW5MBgJEdD4I/fhKChDC0ksnk7bffLhv09fWvf/2ll17KPvChu7u7sLCwpKQku2/bxbd4AAAjVWGOf9+cOXPq6urC487OzlgsZpXRkKivrzdvcF4NDQ0vvfRSU1NTNhuMGTOmq6vrpptuSiaTxgcAGNmG5ubf2rVra2pqwuNoNPr8888XFxc7GbmRyWTuvffehoaGbKW1tbW0tNTIBEFw+PDhFStWvPTSSz2zwblz5woKCtra2hobG9evX5+7L6fZAwBglMSDXgnBFWrO9FpQFI1G9+3b5/kG2WywePHi7IK3rq6uSCQSZoMwVk2aNOnUqVNhcnDxLR4AwIhUOFS/uLq6eu/evdkfy8rKNm3a5HwMnkwms2rVqp4LiuLxuOciZ/3617/umQ26u7sjkUgmkwmzQRAEY8aMeeutt/bt22esAIARbIhv/vXsZRQEQWVl5fbt23N2d3b06DVpEATBxo0bv/rVrxrqUNjBKZFIXHfddeEd8WwqCI/Df6ZSqXg8fuzYsRx9Oc0eAAA5Vzi0v760tPT06dPZ5yE0NDRMnjzZQ9MGUDqd7jVpEIvF3nzzzRUrVsgGPbPBzp07i4qKwuvdMWPG9LoCDitFRUVFRUU+nwCAeDCIioqKTpw4EY/Hwx87OzunTJmyadOmTCbj9FyJTCZTX18/bty4DRs2ZIsbN2588cUXbQTvOUrxePyHP/xhuPUluxc5e1O8V+XBBx/cvn27cQMARqo8WhvQa7NyLBbbvXu3C9n+aWtrW7hwYc/VRMbzvNmgoqLigQcemDNnTvDHfYp6ZYNs5ezZs9dcc81//dd/5WDuxeIiACD3CvPnrVRXVycSiexCo0QiMWXKlFWrVplGuNxgcPPNN5eVlfVsT7Rjxw6TBufNBjNmzAizQVdXV68kcN7Khz/84a997WtHjhwxgADAiJR3N/8ymczq1at7roeJRqM7d+7U9vRSgsF9993X68m+dXV1q1evHjt2rPHpZdWqVddee234MLhwliCTyRQW/n9gvkjlZz/72YMPPrh///5B/3KaPQAAcq4w395QJBJZv359IpGIxWJhpbOzs6ysrKqqypbQCwWq+vr6iRMnlpWV9cwGlZWVb775ZnV1tWzQV/jo7urq6u7u7tra2oKCgq6ursLCwvBiN+xTdKHKTTfd9NZbb6XTacMIAIw8+XvzL5PJfO9737v//vt7FisrKx999FGLZEKpVGrr1q09N2yE4vH4tm3bjNKFrF279rnnnmtubh4zZkyvKYLg97MEXV1dF6ns2LHj5MmTDz300OB+Oc0eAADiQS8dHR3Lli1raWnpWYzFYlu2bBm1y40ymcyRI0dqa2t7DYv4dCmamprWrVuXzQbh1oLsgw6ye5HDa98LVQoLCydMmNBz57d4AACMDIV5/v6Ki4v379/f2tqaXWsUBEEikSgrK7v55pvr6+tH1cblZDK5du3ayZMnl5WV9cwG0Wi0rq7uzJkz9fX1ssFFtLW1PfTQQ72yQd8kUFBQcPFKV1fXrFmzksmkIQUARpjhdPPvvFtvo9Ho8uXLly9fXlRUNFJPUkdHx549e7Zt29brvz0Iglgs9thjj91+++2ecXYpn5+FCxcmEolrr702Eon0bEkUBEFXV1ev4sUrbW1tjY2N69evH8Qvp9kDAEA8uJRr5TVr1jQ0NJz3QnnmzJkjYyduuILowIEDW7duPe8ilpUrVy5durSkpMSH+FKEj0ZOJBLXXXfdhfYbXGLnomxl4sSJJ06cGLzPm3gAAORe4bB7x8XFxfX19adPnw6bz2QlEom5c+eOGzeuvLy8qakplUoNx0iQTCY3bdpUXl5+1VVXlZWV1dTU9MoG8Xh87969Z86cWb9+vWzQj2ywfv36Xi2JLrdzUbayfPnyQ4cOGV4AYCQZ3jf/MplMY2Pjd77znb6rboIgiMVid91114IFC2644Ya8XXuTTqffeOONQ4cO7d69u+9W456pYP78+UuWLNGltB/ZoKSkZNeuXdOnTz/vQ5H70bkorLz11lvxePzYsWOD9eU0ewAAiAf909HRsX379gutw8leXn/uc5+75ZZbhvYKu6Oj41e/+tXRo0fb29sPHjx4ke430Wh00aJFd9xxx4hZMTUkAfLWW2/dsmVLNhsE59uIfLmdi7KV8vLyJ554YpC2g4sHAIB4cKWSyeQzzzyza9eu884nZFVWVk6fPv3666+/8cYbi4qKBuPiO5PJnDp1KgiC9vb23/72t+3t7alU6iLzAz3f27x586ZPn64H0ZWfgjvvvPPBBx+sqKjolQR6ZoPs3+9b/MBKW1vbgQMHwkcviwcAgHiQv1Kp1AsvvPDUU0/13cR8XrFY7LOf/exnPvOZqVOnBkHwiU984uMf//gH/lvvvvtutrtlmAGCIPjZz3528XByobgyb948kWAAs0FFRcWMGTO++c1vfmBLouDyOxeFlffff//DH/7w2bNnB2P1mngAAIgHg6Kjo6O9vX3Pnj0XX8yTM2EUmTdvXklJyfXXX2/h0GBYtWrVtdde+61vfesDGxAF/e1cFFaqq6vLy8sH4yF94gEAkHujoll+cXFxcXFxVVVVEATpdDqVSrW3tx85ciSVSl3i3EK/RaPRWbNmFRUVTZs2LZyRMD+QA2FXq29961thn6Lg962Huru7+1bq6upqa2t7FcM+RZdSCR/svX//fsMOAIwAbv79f2DILhMKY0P2Ty+SHyorK3v+OG/evPBg+vTpQRAM0pYGPtDatWufe+65pqamD3/4w5eykaDfnYuylVgsduDAgQF/MJ/ZAwBAPIAr0tTUtG7dumw2CC6hAdGVdC4KKzt27Dh58uRDDz0kHgAA4gHki7a2tuXLl7/44ovZeYO+fYoGtnNRWCksLJwwYcKAb2sRDwCA3Cs0BIyYbLBw4cL9+/f3XFMUbg/oeUHftxLqW7z0SldX1+LFi9va2pwFAEA8gKGXSqUWLlx47NixiRMnZjKZnrf2I5HIxSuX+NcuXlm0aFFjY6MTAQCIBzD02SAWix07dqy+vr6rq6uwsDBc8ZLtU3TxSm1tbTil0LN4uZXS0tLGxsaem9oBAIYjS4cZ9tmgpKRk586dX/ziF/uxbWBAOhcFPXYgJJPJgWphZO8BAJB7Zg8YxjKZTDwez2aDXpfvl1jJTgj0LPaj0tXVlUgkYrGYTQgAgHgAQ5ANKioqHn300X7PG/Tcr9yr2L9KUVFRIpFYuHDh4cOHnSAAQDyA3GWDO++8s7S09C//8i8vq0/RYHQu6vni1157bWdnp0ZGAMAwZekww9KqVauuueaaurq6njsH+u4luJRKv//Fi1S6uroqKipmzJhRXV3d/y+nvQcAQM6ZPWD4qaurC4LgT//0T/vRp2iQOheFlePHj4eVMWPGNDc3P/fcc2vWrHG+AIBhxM0/hpm1a9c+99xzLS0tXV1dV7LfIBjozkXnfZry7Nmzv/jFL/ZvDsHsAQCQe2YPGE6ampqy2aDvpXn/KgPVuajvi3d1dTU3NwdBUFVVlclknD4AQDyAAdPW1vbQQw8N4LzBgHcu6vvikUikurr605/+9G233SYhAAD5z9oAhk02WLhwYWdnZ89s0NXVFYlEerUS6kdlAF/qQpWamprW1tbm5uZIJHKpX06LiwCAnIsYAvJfKpUKs0Gvm/SRSCSTyVxhZQBf6iKVwsLCpqamW2+9taWlZaAeqwwAMOAsLmIYZINYLPbII4+Em32vsE9RbjoXnbcyZ86czZs3f/rTn06lUk4rAJCfrA0g37NBSUnJW2+9NeD7DYLB71x03srhw4cXLlyYTCY/cA7B4iIAIPfMHpC/MplMPB4Ps0HfC/GBqgxe56LzVkpLS/fv3x+Px5PJpFMMAOQbN//I32xQUVGR7VMULubpeUt+oCp9r+D7PSlx6W/g9OnTkydP/ulPf1paWnrBL6fZAwAg58wekKfZ4M477+zZwzRc6N+rG+mAVEJ9i/2oXPobiEajJ0+eXLhwYVtbm9MNAIgHcDGrV68+cODAYPQpGqrORX0r48ePTyQSNTU19fX1zjgAIB7A+dXV1Z04caJXn6If/ehHA9KnaAg7F/WtFBUVNTc3r1u3rq6uznkHAPKBpcPkl7Vr1z7yyCNnzpy56qqrwsrA9ika8s5FfSvvvffenDlzZsyYUV1d/UdfTnsPAICcM3tAHmlqagqzQfbRwgPep6hvJcedi/pWIpFIc3Pzb37zm7Vr12YyGR8DAGAIuflHvmhra7vzzjt/+9vfXnXVVYPXpyhPOhedt1JYWBiPx5ubm8N0ZPYAAMg9swfkSzb40pe+9Prrr4drigavT1GedC46b6Wrq2vGjBkVFRXmEACAoeLmH0MvlUpNmjTpl7/85YQJE8JK310Bg1fJ8a/7wMqmTZt+/OMf7969e9y4cWYPAIAcM3vA0GeDT3/606dOnZowYcJF+hSNyM5F56387d/+7axZs6ZOneqzAQDkXsQQMLTZ4Prrr29ubi4qKgp+fze65xbeHFR6ruTJFrNdiS6rMoBv8ty5c9/5zndSqVQ4MgAAuWH2gCGTyWQ+97nPNTc3l5aWhpUc9CnqWxnyzkU9K4WFhTU1NRMnTvzNb37z3nvvXX311T4nAIB4wKjIBrfddts///M/l5WV9bpcHqTnG1ykEv6zZ7F/lX6/gXQ6vXHjxo9+9KOzZ8/+whe+8Prrr69fvz7seeqjAgDkkosPhiwbzJs3b+7cucHvW/dEIpFezXxyUwn1Lfajcrlv4Ny5c/v27Vu3bt3p06crKys7OzvHjh3r4wEADCGNRxgC99xzz9SpU2tra8Mfh7Zx0JC8gUOHDu3cufPpp5++77777r333uLi4vN8OT33AADIOYuLyLWvfe1r1157bW1t7WX1KRoZnYsSicSKFSsmTJiwc+fOpUuXdnZ2VldXnzcbAAAMCTf/yKmvf/3rL7744rPPPhuuqh/C/QbBRWcSsl2JLqtyoV+XSqWefvrp73//+2PGjNm0adO0adMuZVOB2QMAIPfMHpA7P/rRj1566aXm5uZe2aDvRXYuK4PXueh//ud/mpqa4vF4PB6/5pprnn/++aNHj5aWltpwDACIB4xYmzZtSqVSH/jXDh8+XFtb2zcbDOG8wSB1Ljp79mxhYeHixYunTp365ptvNjQ0HDt2rKqqyrZjACD/WRvAFX+GLmGFyeHDhxcvXpxIJMKHfA1Im6ABrAzUSyWTyV27dtXV1dXV1S1YsKCkpGSwBzb/Tz0AMLyYPeCKJJPJlStXXvzvpFKpntmgu7s7EolkMpme9/KHsHLlL/X2229v2rQpGo0++uijX/rSl86ePVtdXX2F2QAAYEhYA80VOXTo0B133HHxbBCLxRKJxHXXXRf06EEU/L75T78rYQ+iK6/U1dXV1tb2KoZdiS5e+d3vfvfUU0997nOfGz9+/AMPPPD6669bPgQAiAeMatu2bWtpabl4Nti5c2d23qDnFt7wInvIK5lMJvuGs8VsV6K+lXPnzv37v/97RUXFlClTvvKVr7S0tIT/dQAA4gGjWjqdDoLgQhfHmUwmHo//8Ic/LC0tDfKmT1HfSnZyIPjjrkR9K8ePH3/iiSeefvrpu+66q7293fIhAGDksfeA/nv55ZfvuuuuC2WDioqKxx57rFc2yIc+RcFldi56/fXXv/3tb//Zn/3ZE088sWjRolOnTm3evFk2AABGJLMH9F9jY+PSpUsvlA1mzJgxZ86coE97n3ARf/5UQn2L77777kc/+tGSkpJly5Y9+OCDJ0+e9LwCAGDE05eQ/ps4ceJ5L5pXrVp17bXXVldXB+d7MnG+VXoVM5nMvn37vvvd77766qsPPfTQkiVLhmrDscamAEDuWVxEP6VSqZtuuqlvNqirqwuC4Fvf+lZ42X38+PGurq7sk4mzPYiuvBL2ILrySm1tbUFBwfvvv//888+vWrVq8uTJL7zwwhNPPHHq1KkVK1bkPhuk0+m2trZVq1b5jAEAuefmH/1UX18fBEFVVVWvbNDa2ho+GjnP9xtkK0ePHt29e/eWLVsWL168dOnSodpUkEqlXnjhhXXr1h0/fnz58uULFiyIxWJmDwAA8YDhoby8vL6+vmfboqampu9+97vPPvtsz2wQ9Nn4myeV06dPT5gwoaSkZMKECbW1tdOmTRuSrQXJZPLQoUPbtm0bP378/PnzFy9enB1Si4sAgNyz1ZL+yGQyx48f75kN2traHnrooRdffLHXvEF2DU+eVM6cOXP48OHvfve7//mf/7l3796ZM2fmfvlQJpM5cuTIgQMHtm7dOmvWrHvuuae9vd0j1QCAfGDvAf3x85//fNGiRT2zwcKFCw8cOHDVVVd1dXX16hmaD5X33nuvvb29srJy6tSpb7zxRkNDwy9+8Ys5c+bk8qI8nU43NTVVVVVNnjz5wIEDs2fPPnnyZH19fY7fBgDARVgbQH+sXbt29uzZ4TMNwkcjJxKJoqKiPOxTVFhYWFNTU1dXV1dXt2DBgtxvLUilUk8//fTu3btPnz69bNmymTNnXuJ7sLgIAMg9i4voj127dq1evTqbDY4dOxZmg7BPUXhdHl479q2EPYiuvBL2ILpQ5e23396yZcs//dM/rVy5sry8/Jvf/GaOtxYkk8lnnnlm69atN9100wMPPNBrnwYAgHjACJFKpcaPHz927NhUKlVSUrJr167x48f33Psb/P6+cu4r6XR69+7d3/nOd4qKiu67775Tp07lePnQyy+/3NjYuGHDhpUrVy5atGj16tUWDgEA4gEj2QsvvDB//vxMJjN79uxdu3aVlpb2ul4P/rhTUA4qhYWFzz33XHl5eSqVWrZsWUtLSy5v1ffqSbp06dL169f7nAAA4gGjwk9+8pMlS5ZUVFQ89thjPbPBkHQlam1tveOOOyZMmPDTn/503bp1udxa0KsnqeVDAIB4wGi0YcOGV155ZcaMGXPnzu3q6opEIr06BeWg0tHRsX379i1btnzxi1/ct29fuEk6B/QkBQDEA/iDZDL5+c9/fsaMGdXV1d3d3ZFIJJPJ9HwO8aBW3nnnnc2bN3//+98fP378gw8+eOrUqdxsOE6n04cOHXrqqacOHjy4fPny2bNnr1mzZkgeowYAIB6QRzZs2FBaWhpmg0vpUzQgnYvOnDnz2GOPfelLXyotLf3KV77y/PPP5+aGfa+epA8//HB9fb3PAAAwgmlbzuWZMmXKf/zHf1x33XU9nz4W/PGugIGqhCt5nn766SeffPLv//7vly9fnpvF/b16kn7hC18Ykk0FnnsAAIgH5LV33nlnypQpv/nNb7K9g8LL+p7dhAakkkwmf/zjH2/ZsmXx4sVLly7NwYbjvj1Jb7nllqHdVCAeAADiAXnte9/73ltvvVVXV5ftHdTzmQNXXiksLNywYcO2bdsmTJhQW1s7bdq0wV7f36sn6YIFC2644YY82VQgHgAAuWfvAZdh7969e/bs6erqGtiuRL/73e8OHjz413/917FY7JOf/GQOthZke5IGQbBs2TI9SQEAxAP6Ew/ee++9gepKdO7cuSNHjmzatOngwYN/93d/98tf/nJQr9H1JAUAEA8YMK+++uqsWbM+9KEPXVafovN2Ljp27NgDDzzQ2Ni4fPnyNWvWNDQ0DN7b7tmTdNGiRYsWLdKTFADgQiwd5lI98MADn/zkJ1esWBH0tyvR6dOnJ0yYEI1Gw8v0Qd1a0Ksn6cyZM3P5QOWB+XLaewAA5Jx7qFyqF154YdWqVcEfdxzqlQTOW0mn07t37/6Hf/iHMWPG7N27d+bMmYO3pKdnT9IlS5bYVAAAcFnc/OOS/O///u+kSZPefvvtS+9K9N577+3fv3/dunXh/fvFixcP0pV6uKkgr3qSDsyX0+wBAJBzZg+4JE1NTTfffPNbb701ceLED+xKNGbMmPvvv3/y5Mn33Xfftm3biouLB+Mt9epJunTp0scff9ymAgAA8YBBN378+Pfee++mm276kz/5k8LCwng8XlRUNHfu3Hvuuef111//xS9+MWnSpFOnTv3Lv/zLP/7jP4a38Dds2DAY76Sjo2PPnj16kgIADAZrA+iPV1999e233z5+/PiJEydef/31VCr1zjvvpNPpq6+++lOf+lRpaek111xTUlLykY98pKio6MrX+fTtSTqoGxjy5ctpcREAIB4wrGUymVOnTr377rvJZPK3v/1te3t7EAQNDQ2xWOyzn/3sZz7zmalTp37sYx+78cYbx44d+4F3/cOepD/5yU8aGxtz0OzIxbd4AAD4f3dypKOjIwiCMDDs2bMnCIKDBw8GQTBr1qyioqJp06YFQbBo0aJIJDICepKKBwCAeAD9kUql0un0K6+8kkwm33333X/9138dP378kiVLysvLR/mmAvEAABAPGF1Gak9S8QAAEA/gUvXqSbpgwYIbbrhBT1LxAAAYci7IyB09SQEAxANGtb49Sdvb2y0fAgDIT9YGMChGeU/SgflyFhTk/5v0PyAAIB7ABelJCgAgHjDaJZPJZ555ZteuXXqSAgCIB4xGepICAIw8hYZgxGhraysvL89kMh94WV9eXt7W1ta/35JKpZqamsrLyydPnnzgwIGlS5eePXt2/fr1paWlsgEAwHBn9mDkZIOysrIgCOLx+I9//OMLXalnMpmKioqWlpYgCFpbW0tLSy/x9Xv1JF28eLHlQwAA4gH5KJlMxmKx7I/RaDSRSPS9fO+ZDUIXTwh9e5LOnDnTFAEAgHhAXut73d83IVzK3wnpSQoAIB4wkhPCpWQDPUkBABAPRn5CuPrqqy+SDfQkBQBAPBhFCeFTn/rU4cOHe1ZeeumlN954I9uT9I477rCpAAAA8WBUJISerr766pKSktdee2358uULFiy44YYbbCoAAEA8GI0JIRKJfPvb3/6bv/mb4uJiowQAgHgwWvz6178uLi5+//33s5UPfehDr7322ic+8QmDAwDAhXhq8giUSqU+//nP98wGQRC8//77f/7nf55KpYwPAADiwSjKBrFYrLOzs+8fdXZ2xmIxCQEAAPFglGaD2bNnz549W0IAAEA8GO3ZIB6PP/vss88++2w8HpcQAAAQD0Z1Nmhubo5EIpFIpLm5WUIAAEA8GO3ZIPxRQgAAQDwYpXplg4skhNdee81wAQAgHowoRUVFiUQiGo1eKBucNyG0traWlpYaPQAAsjwWbeRIpVKrVq3avn37ebNBVvhM5draWtkAAADxAAAAOD+LiwAAAPEAAAD4YxFDMEwVFBQM+GtaaQYAMMqZPRjGurq6CgoKzp07191D3+IlVownAADiwTC2fv36rq6uwsLC8K5/d3d3bW1tQUFBr+IlVownAAA6Fw3bM1dQEE4ChD92d3ePGTMmk8kUFv4h8oXFnjMDF6kUFPgwAACMdmYPRoLwEv/cuXPZCYGexcuqAAAwmtmaPHKyQTiTEP6zV/ESKwAAjHJmD4axcH1R30v8vsVLqQAAgHgwjHV3d0cikUwm0/MSv2/xEivGEwAA8WAY07kIAICBpVnNsD1zOhcBADDQzB6MBDoXAQAwIHQuGjnZQOciAACukNmDYUznIgAAxAP+n85FAAAMLLtRh+2ZG4S7/j4MAACj3P8B9dZmmejXmPoAAAAASUVORK5CYII=)
103
distance with approach line to berthing point(
).
Two item of output are engine revolution per second
(n ) and rudder angle(
). Fig. 3 shows final diagram
of neural networks used in this research. Here
indicate links coefficients,
means input layer, hidden layer and output layrer
respectively.
The number of neuron in hidden layer was set to
15 that is bigger more than that of input layer. The
value of input, output and weight are normalized
between 0 and 1. The propagation method is adopted
as learning way. Sigmoid nonlinear function also
used in neural networks. Matlab 6.0 was used as the
programming language for numerical simulation.
3.2 Structure of ship automatic berthing system
When a ship approaches to pier and is under berthing
operation, the ship usually maintains certain heading
angle toward final berthing point. It is similar with
air plain that is about to take on the ground. The
phnomiem is maintained for the safety of ship and
air plain. The mass of ship is larger than other
vehicles in a land and it speed is greatly slow under
its berthing operation. It requires spare water space
to make it turns and rudder effect extremely down
when its speed is under certain limit line.
Therefore when we take a look at previous
researches on neural networks controller, most of
them show such as study that a ship approaches the
final berthing point from very limited direction. In
other words, when we take an example like Fig. 4,
the ship approaches the berthing point through
“Area-1” in many study case. This is the reason that
it is physically impossible to make a ship turn or to
maintain the ship with desired heading angle with
slow speed within certain distance to berthing point.
B
A
Berth Point
Area-1
Area-2Area-3
Fig. 4. Berthing concept
Be r t hi ng
condt i ons?
Yes
No
Suggest
re-tri al
Appr oachi ng
Saf e Ar ea?
Yes
Cont r ol l er f or
Ar ea 1, 2
No
Be r t hi ng
Oper at i on
Cont r ol l er f or
Ar ea 3
Tur ni ng Shi p
Fig. 5. General flow of berthing control
In this research, new approach is suggested to
overcome these problems. New system is suggested
to guide a ship to final berthing point with selective
controller even the ship come from multi-direction.
The Fig. 5 shows its brief concept.
When a ship approaches goal point, the system
reasons whether it is possible to make a berthing or
not. At this moment ship’s present conditions such
as present location, speed and heading angle are
used. If the system estimate berthing is impossible
under present conditions, it suggests the ship make
turn and approaches new areas.
If the present conditions are clear, assigned
controller at each area is used to make the ship berth.
For example, when a ship approaches to Area-3, the
possibility of berthing is almost impossible, because
the remained distance to final point is too short and
the ship have to make turning under slow speed. In
this case, the system guides the ship make a turn
toward area 3, area-B, Area-A and Area-2. If a ship
enters into Area-2 that is safe zone for berthing,
assigned controller will guide the ship to final
berthing point.
In other hands, a ship approached from Area-1 or
Area 2 originally will be guided toward berthing
point by assigned controller in each area because
these areas are safe zone for berthing.