20220503
10370600
1.0
FALSE
Project "FEMA Flood Zones 2016" C:\Users\byelton\Documents\ArcGIS\Projects\ConsolidationMap\gis-sql.ad.talbotdes.org.sde\TalbotData.BYELTON.FEMA_Flood_Zones_2016 PROJCS["NAD_1983_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1312333.333333333],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-77.0],PARAMETER["Standard_Parallel_1",38.3],PARAMETER["Standard_Parallel_2",39.45],PARAMETER["Latitude_Of_Origin",37.66666666666666],UNIT["Foot_US",0.3048006096012192]] # GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]] NO_PRESERVE_SHAPE # NO_VERTICAL
CopyFeatures TalbotData.BYELTON.FEMA_Flood_Zones_2016 "C:\Users\byelton\Documents\ArcGIS\Projects\Map fixes\Map fixes.gdb\FEMA_Regulated_Floodzones" # # # #
FEMA_Regulated_Floodzones
002
file://\\PZ-BYELTON22\C$\Users\byelton\Documents\ArcGIS\Projects\Map fixes\Map fixes.gdb
Local Area Network
Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
NAD_1983_StatePlane_Maryland_FIPS_1900_Feet
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.0.0'><WKT>PROJCS["NAD_1983_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",1312333.333333333],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-77.0],PARAMETER["Standard_Parallel_1",38.3],PARAMETER["Standard_Parallel_2",39.45],PARAMETER["Latitude_Of_Origin",37.66666666666666],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2248]]</WKT><XOrigin>-120561100</XOrigin><YOrigin>-95444400</YOrigin><XYScale>3048.0060960121928</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102685</WKID><LatestWKID>2248</LatestWKID></ProjectedCoordinateSystem>
20230203
09562400
20230203
09562400
Microsoft Windows 10 Version 10.0 (Build 22621) ; Esri ArcGIS 13.0.2.36056
FEMA_Flood_Zones_2016
File Geodatabase Feature Class
dataset
EPSG
5.3(9.0.0)
0
Simple
FALSE
0
TRUE
FALSE
FEMA_Regulated_Floodzones
Feature Class
0
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
DFIRM_ID
DFIRM_ID
String
6
0
0
VERSION_ID
VERSION_ID
String
11
0
0
FLD_AR_ID
FLD_AR_ID
String
32
0
0
STUDY_TYP
STUDY_TYP
String
28
0
0
FLD_ZONE
FLD_ZONE
String
17
0
0
ZONE_SUBTY
ZONE_SUBTY
String
57
0
0
SFHA_TF
SFHA_TF
String
1
0
0
STATIC_BFE
STATIC_BFE
Double
8
0
0
V_DATUM
V_DATUM
String
17
0
0
DEPTH
DEPTH
Double
8
0
0
LEN_UNIT
LEN_UNIT
String
16
0
0
VELOCITY
VELOCITY
Double
8
0
0
VEL_UNIT
VEL_UNIT
String
20
0
0
AR_REVERT
AR_REVERT
String
17
0
0
AR_SUBTRV
AR_SUBTRV
String
57
0
0
BFE_REVERT
BFE_REVERT
Double
8
0
0
DEP_REVERT
DEP_REVERT
Double
8
0
0
DUAL_ZONE
DUAL_ZONE
String
1
0
0
SOURCE_CIT
SOURCE_CIT
String
21
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
20230203
/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a
HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy
MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA
AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA
AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3
ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm
p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA
AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx
BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK
U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3
uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD1uiii
vSPJJYIvNfnoOtX44wi1HaxsiA8YYZp/mYYo7Yz901w1JOcnbZHfSioRTe7ILm0jmiIcHbu5C8Zq
JdHtQA26fg5A85v8anj3s5jY/d5+tT5ywXGMVEo20LjK+oxokAxvYD3OR+tVgVkkK5BHbNWWidwV
YgJ7VRCgXAXtuxzWtKKs9TKrJprTQmaFMqWyFxjjnNPjMMcuVweO3NMlsoHkRZsFQdyjJBpBo9iF
GImxjH+sY/1rOc5d9DSEI721LQ3S9eF9M9acnDsOPWobaG3tFZIcKM8jdnH50skiLICHHzcEZqI6
6FydtSyRkYPNRYaM4Ubl9M8ipR92m5yM1JQK4JxyD6HrSnk0yVolIEjAemTijDLyrbh6GmBJRSKQ
ygilpARyIr43gEfypsQkVMSsGb1HcVKetIBx9OlMBrdBzjPeq06LGo2tkmrMkUc8WyRQyntWNd6e
glxAqj13Fv8AGokJmralRFgNn8anFZ9vp1vtSRox5g7hjjP51fPFNbAL14orMOp3CuFOnXGTxxir
8MjTQrIY2jJH3W6iqGSVXntrWdszQRSuBgblBOKTybhiN1xgdwq4p8cKo7N1boSepoEyH+yrEdbO
An/cHFWIoY4V2xIqL1wowKfTqQxM+tRzEfIfRx/hUtRT/wCrz6Mv8xTAkznpRiq2+aFjvBlTOQVH
I9qkSeOT7rjPoeDQBKQD1AoyKTr3psjBELGgBxPIo59KZGZCqmQAMT0HYVJj3oAx6VRlgPU0lPiG
6ZB7ivTeiPKSuzVxgUySJZBz17H0pZDhM5xioluFklMePUV58VLdHpScfhZHbRESsxJIHGaa91tk
faPamwyOk7Ip4JPFV2zuOeua6lC8m5HG58sUol2G4PksTlmHWqbMWct3PNOhmMTE4yD1pHYudxXA
6cVUYcsnoTKfNFaiM7OQWJNaULIyYQkgetZdXrEfI596mvFcly8PJ89hsml2ckpdovmY5b5iMmoF
0uza5DLCQF/2j1rQzl/ZRUVuHIZhgBjnNc0VZNnVJ3aRZx8uKY7rGjM5woGSaduAIBPWqmot/o4j
4w7AZPQd+azLbsVJYLW5tk+1SN5kjbiVbkeg47UsWmWkgREubk7Af+Wh5zWlEsWwNGqgMM5Axmo7
m4FvtJHDcDHXPYUDGxILa2A85gq5GX570sVxJIxAjbGMgkYzVFJppZBvATa2fLdTk/iOKvLPOGxJ
bnk8FCDQBL5mPvKw/CgOvZh9KWQybf3YBOe9VxYo4JuP3rnPfgfSgCzx1FUNu64IGTUd1YXe8vZ3
CxqFwsbDIzVVItTS4iiaZEds5ZVGCKTjcTN1QEXA6Uuc9BUEEU6RgSz73/vbQKlxJ6qfwoGKB3pa
arc7WGG9KdTAD0NImMe/ehiFBJ4FIn3frzT6C6j+KSlopDEqO4/1D+wzUtMmGYZB6qaAHDpTHhjf
7yA++Kcpyin1FLQBEkCRvuXcOMYzTHVpLlVP3EG76mrFRhMTNJnqoGKAH8k/SkLqDywH1pw6VWuL
UTyBsIcDHzUAUKfC6xzKWIH/AOqmUyS3S5wjorH+HcMgH1r0p/Czy4fEjWLxtEFeReRz81ZzlRKw
RuAfWq/9jzvEExZFATxsY8enBqRLVLQlVVVYgbtowCa5sO/eaOrEL3UyRG2uG9DRIweQsOhptFdV
tbnJd2sFPaTMSoBgDnNMooauCdgq1aybFOemaq1LbnEobrtBNTUV4sqk7SRdZiscj7SM9jWXPcz2
yqRt8vuWbGDVmSWSXvhT2FUr04G2N5TIo+4hxn9DXHN8kdHqdN1J3ewv9oyzyhV8ps/dxJj+lXpw
1zZFBncuMj1xWRA8gmAnMsGORukUk+3ArR+2RRufmOUIyQCQKyjNte9sW7dGX7Obz7RHOMkYIAxi
q5Ky3ErzkeXCdqqR3Peq0QuJVZEVkilkIzsxjIye/AqyLByzyPJ++3Aq3bgdcVF+xom2tiaLbw5B
QnIANOSVpZR5YHlDqx7/AErPFwrK32ppWO8gRqmEY+x7/nV22uElIxIDxjy0GQn1I6GrLLNFIzKg
yzAD1JxVWW9IPlLEwlbhA2CM++D7UATmaLft81M5xjcM5qpdDOo23zbhnlM9PehrYS3HlSkMzLuk
YADIHAA9qqXVulreCWNfL2rkN8xyfT0qo2uJm2aSmxSCaJZF6EU+pGNZQwwab869PmH61JSUARM+
9SDE/P0/xoSUIgDKw7ZxUF/DLIqtFdPDjghQDu6etRw288LANdSSjBwhQD9RT6C6mhketFNVdqqP
QU+kMSqT3lwJJIxYyMAcBgwww9au0YpAULS7uH8qNrKREK4LlhgcelX8gkjNRW5xCB6ZH602Ni0z
Ecr60N2AnNIQarXFmLl9xnnjOMfu321C2lKRg3V3/wB/TxTA0KKFG1QuScDGT1ooAx6mtU3zrxwO
TUNXrQKkBkP416FWXLE82jHmmiyx2r7VQuwPNDDoRmnSXhYsoUbSMc1XdzI25qyo0pRd2bVqsZKy
G0UUE4GTXScoEgDJOBSBgwypBHtUUjZIUggNkKRToo/LXBOc1hCs5zaitF1G1ZaklTQHaJHzjC9c
VDTZ5vLtWUcFq0nsVTV5aFhpoY4FP8R71Bay27NOJm+/yMentUUOn3UqKHVVUkHcHqw9s8N0FhUN
tG8A151WSbukdUYzW5btlt5rbYoJA+UhvvCqkhFlclF2yAoW2EfdHr06VdVHaaOZP3e4fvFbqfSp
niRw+VGWG0kdSKmxvYbBOJ4RJtZAezDH40RTJMDsOfWq7bGKwA/uYf8AWknjAHA/lTheRptiigky
fuKE2g/0p2GSxJHseLgjJ4qKG32yOjs5x90hiPl9Miq8DXKXczGFRvbO3Iz/AIVoxszjLIUPoTTG
JLFHLHskXcvpmsuDS7C4mkf7JH5a/uxwfm9T/n3rUeRUwWOATgE+tNimMjupXbtxQA2GAx3EshOQ
QFT2UdvzovYRPbMhIB6gk8Zqeq7TQvgSxsBnjenGaAI7O4bcYJdqsv3QO9XazrmOKG4RiuyPHVB1
Pp7VoKQygjoRmldt6kR00FpKKRjjAHJPSmWQyyoJVQjOOTT8BZQf7wpiwkyu7qOemDUjIWA55HSq
lbRIiF9Wx9LUavj5X+Vv50+pLFpKWmnNICg19DBJJDJIqsGOATjqf/r1ZgHJbkA9qasMbyyF0BIb
r+Aqc9BStrcQdTTZI/MXAZlIOQQaePalpjK4neL5Z1P++oyD/hSPf20ZG6TqMjAJqyTSADFMDIqZ
Z9sBjwcnvUNFem0nueVGTjsFFFFMQE4GTUUr/LlecckGpFUyToignnkVdjgtVuyqFvMUZKnOK4q9
Zu8Im9Ki5alC7hTIlbKpsGwDuaSF90YGckDmr989uVEcyFsMO3T3zWddL9nud0TAxygFWxx9B+VZ
Up+zlfobVqd1dE1RTEpslUZKNmkj8+NnWfHDYyBx0yKW4YiLC/ebgc12OpGVNtHNHmpzXclhlmvo
92/cAwJjXK/rWn5SmSN+jKMCq8Mi2dqElkzIF3YPX6cUyw/0hnuZPv5wAOgFecjvXYuvIkYy7Bfq
aoz3okgQIzIzYJI/hFXnjWRSrqGU9iKoWWUleNUzGDjd2FDb2B3vYsLYwAqRux1IB4b61PLEkqgM
OnQjgio550iiLBlLYO0Z6mmwXZnkCqhUBctuGDVFIYrNby7WbzELcFvvCpLyRkh2R581zhcU4KDc
FvSq+oODFsUnz8/IF65poZbKrJEY2wcjBqi5e1njBcsoOC56hcd/XmrEMS21szhcMV3MM55AqWOZ
WgWXIIIySvNAD1ZXUMpBB6GopRI25TGHjI6A4NQwhRdlA26MgyRj0Ofm/mPzqcylJdrrhTwrD+tA
FJ4rmQK2wYiOVD9WH0q3Bc+b8kgCSjqtOnmMKqQAcsB1qreSKZkyMmM5P0+tS3bVkvTUv1Su57mK
dPItvNXHLbsYqaO488DYCM9cjpUvloDkjJq7W3C/NsUor25ldVaydQTy24ce9XwMUAUVJQEA8EZq
J1KJujJB9OoqakPWmBXtppJAwlXawPA9qnxnvQ6hh6EdD6UiPk7W4YfrQBGu4TSADPQ08lsfd/Wk
HFw/uo/rTznmhgNBfH3R+dNln8lN8mAvrUgHAoZFkXDgEehpARxv5qB1YYNPAb+9+lKqKi7VUAel
HI6UwMiiinW00ZnwCrY4Ir027I8qMbsbRTnx5jbfu54qN2wv1pOSjHmYW1sPsbiBbhnM8YDAhcsM
HpS3Vy8GoCeILJEV2sQ3A59u9JPp6f2aAiQq5HWRPug9gPWq9ndC1WGPy8oVCuAgAJz1wPavLlK7
10bPQiuWKiWJZIri+DFnMSpudDkDNWJbgGaGOIjYSM4XOPSqIkwHeNQoIxhhyQcjr/npU1rNHbRF
/KknYNhnjQYX6e1RGTYRk2T6lc/ZygaLfGwJc9Mehz9cVlSoZbZA5wpbqOwqxc6wLjKQ213gckqn
3lOcEexIplvJ5ieW8UkZA5DrjNdFJKUnF9UZV200y5ZW3nubiQZVj0bqSO9W2vLWCTyWkWNh2PA/
OslJ5rdTAgc7zgsXPA9ua0LX7GNxUbmTlpHHP51ivdfL1Nack1oXMxzxZyGRh+dZK72jaCNljQtu
8xj0x0/pRPKbvccCSIPgDsnue9OsPs1xGyMFDoeQue3r2qW3J6BdyehZhNlaKp8yMvjlhz+NWnCu
gdSD6Ed6bDFFAhVB8uScEdKDcR+WG3qEPQ5rQ1QwyLDuLA7cgEgdKLgLJCsyAMyEMpHOadEcu5BB
Bwc1Xn+zAskUIac/wqCM/U+lNAXRIrKpBGGGR71TS4gt5JyCAm5cjptJ4I/TP40n2KbyUiW42oMH
JXLL9DT10y1QA7CzghtxOST70PyERxTxSaizRLuwm3Kg85IOc/hV9lV1KsAQexrNiukh86UqPMdu
Ezjge/SllupLgKkTBWJ/gfJP6Vn7RbdSPaRW42W3/wBICWzOSpyQW+VKnj09Ad0jszZyQDxUtnbG
3jbc2Xc5arNXZXuHKpasbtGDjigdaqyapZRSPHJcKrp94HORTre/tbiTy4p0d8ZwPSmaFqkpaKAC
k7mioo2czyhs7BgLxQBLTWUNjPXsRTqKAICrrcDDZyp6j3plzepahRMCu7oRk/yFTN/r4z7EfypG
iWVwXUFR0yKAHxSCWNXXowyKfUYQxjEeNv8AdNKr78jGCOoNADicUnNLiigDClOcJnGeWPoB1quH
aeZZR8qAhY1HpUl2zRqsigEDIOewNVIpWVNhHzL09AfSvQa967KwVNSoStu7moetV/3jXEaDASQk
M3U8dqfLJi3Lqeo4P1o0y3md4L5pPkYPujPRBzjH4VzYy75YnnUaTbv0K88punaSKNgWGGRsc46H
61JbJ5kMZiWXeG52N0PufSoWuEjScx4cZ4ZeeDV6K+S2P7tcxNGpUKO/fNc1SmrRbZvytyvLRIdO
s8FhIXA3FlYRjqBTVuLmGJlhEbhmyC/AwfU1Ua7jeeNyzCcMQDk4C9eaZJILmWJVw4cnzT6kcdPp
Uwg5TSiVbXTovwHQzvbmFYGhEjyY9n7AE+nv61o39rM8kckaEvtwdvrVGwcLflXjUoucFh0Hp+eK
vvqo8uFwuFkBJOM4x2p8ri3GW41ZxtIozxNKTE4KyJyAaNlxIpjVmO4/MqjjJ9as3BW6hXUImbAX
GypLSyWSMTOz5bnaG4/StHJPVr3u5lGk1Oy2IbOHyY2ilDIQd0uCTu9O/FOvpQ8ERtsYWTb5bDAb
8K0QYrcBGcAnpuPJrFviizSExsUDbeWxyaznKyNpPlWhZlvblCYmeNskAvEp+QetRWungvI1tdOg
3HOYRnPXqRk1o2UEfkLJ5aq7Dsc8U6W3dHMsG3efvKxwGpq5auRQSCGUwzT7nOMFgAW/KpJrF5JT
It3NGT2XGB9MioraEzSSy3EK/MQFB5IAq8sispbIwOtMZnnTpSWX+07nnsCOP0old0ge1kdmYYCy
nuOOuOhps9jtY3AmaL1KAHHvnrS2qpPOxmUPJ2z0x2o1eiInK2i3Ytvb/aCpJYxg5Lf3jWisaISV
RQfYUo4GAMD2ozSiuVWQQgorzFpM56CkHNLTLGlRgkgZ9cVGJrcENuUNj8amPSmhEx91fyoAatxE
7BVcEnoKkpAqg5CjP0paYBUEPzXE747gZxU/QVBaZMRcjlmJoAnopGYKpZjgDrUULtNGJHXbnoPa
gVxLh8GMr13Y/Q0sDsSd2cUTfwE9Awppdi/y8L64qWwLNMZN3IOGHQ04cUVQxqvk7WGG9KdkUjKG
HP5+lUZtTgtZWhkkXevXINAFR0WRCjDKkYNQLYx7iWLMD2J4qzRXp2PMjUnFNRdrkF0mbYqp24Ix
+YqrYm5lDiOZlSJQWTpwT/gKuz/6h/cYH1rNimltZniaE/6Queem3BxXLiVex2YZc1O3n+hPYRLE
sqE7txLDP92rYKHTodzRx7WOWPXA/nUQhCWpwDkrjj0rL23W9GLgxo5GMAnGf1pVoPkS3LhDnc3D
UuuIFuXDBF29HGec+1SxM0dvv2/MTxmnKiSyjODtUcYpbyUQ25Y4A/yaMLTUYc3cxTg5KCW7VwJc
JGzsriQkDYOmKhY+WhiUZTjA6YA9Ks28Mk1tdQb1MyMrqFPB4/z+lXDYRyvAHcFViI2jueOawm+Z
u+ppOilNuGxBp/l3cIh4EK5Ow/ebnqasWM8C3Mlpbg7Y8kk+uegqg1xBps0ywEzzEAf7v1p3mNaa
XNPACZmcbiF6Vmn0LTtoPvoAt7uz5nIYq3Qc1A7ST7vMfnO5yPugYxjNV1ZZcS3EhBY72YDJHsK1
or2IwMGtiA2QVA6jHeonHXVmXxO7diO1nkz8ySymM7eDgA1ZF4wMnnI0f90bc/rRLcw2qqyAbpiG
xnr0H8quqyum5SCp6EGtEvM2SfcppObhTFCuVwB5nYetTSgxxbEQlcY+XrUJQSxloGZHX5SyjrS3
cbNZAA8gfex/SqKKpcyosFvKzE8sjjOAPerVpBIszSSIF+UKBmqcTOsUMkYYSDIOUwMHtWjaT+fG
cjDKcEVKSbv1Ri7Oor7ljpRSUtUbCYzQKCQASeg5qn/alln/AI+U59aALh5ozzimRypLGHiYMp6E
U8DFABRS0lABSD0paTuaAK1wxllW3Q453P8ASrPbFQW8RQM7kNI5yTU9DJXcin+4PZh/OpQoA6VH
N/qXPoM1LSKCo5/NMeIiA56Me1Na8tlZlM8YK8EFhxUiSJKu6N1ZfUHNMBsKyqh85wzZPIGOKcY0
Y5KKT6kUpYKMtwPWjGeaAMiiiivUPJKOo+YIyd/7sgjaBznBOf8APpUEJklvJC0mSxUhjFtBGPQ/
T9auXjKiBpMmMZLY6/5xmoJ7oXNy7KkaxxEKhx82Mev4/nXNUfLUTPTw7/dJLrc0Ka0asMECkjYt
GCetPrpPO1i/QiigETswYnPr2pZoxLEVIB9M1JRSt0G6knLnb1MqKR7e7lf5Y2C5XaAM49R+lTW9
5IPs4TgFsI5XIJzgjOf0qzOimNn2jeBkHvUiTC20+3jm2TLI7biQF2DnOPpXBXoqFmjtg1NOa01I
dRUSX6COZd5Xa4jA49STUT+ZDE8EM22JvvE5JB+tQSpJia5t48h/u4OwhQKdpCSyzKBHmJsiRi3B
9vrRK0IpW1Yndy91li2s0keODfyBkuD/ACq1bweawjgnVoVOGwcMOfcU/wDsVVYESsy78kHgBfQe
/vUo0+KNFWSRVKyblYAAn2Oaxkud6oI0rL3kLa2AQSeao6lVGOg6Z/EU1bSK3XZcK0qBRtYgtjjn
6VPNPMLlYoEV8Ll9xxj05/OrSBtgDkFsckDAppJbGySWxBbiOCAhBiNeR9KeJo3j35+XOMmkeMO5
UkgEdiR/KorcNE4tm27VXK4/iFMYrwx3QD5PGQPY1TilNvfYk+Q7fmHrVp0ltEZrfYU5OxjjB9jV
OeVr7GIGCoRyME+/tUtqMuYyqRvZrc04biOcsEbJHUVITxxWPn7LeBnysWdqvjAOP/11qRzxy8q4
OelaWuroITe0tyWomgiPJjXPrinGRFzlgMdeelM3NICVICe461NjS46Mr0AC+g6U522jgZbsB3pq
bXT+Fl/OnKqr91QPpQMh3XTdEjQe5JpVeYMQ8YI7FT/SpjRQAzzAOoIHqRUJvLbkCeLJ/wBsVO4y
h4zVd9OsiSxtYdx6nYKAHrdWxHFxFwM/fFTKysoZSGB6Ed6pLp9mAHFpBt9QgzirkcaRRrHGMIow
AOwpAJKMxOPY05DlFPqKVuVI9qZCf3Cf7opgRPZWrks1vGWPJJXrUsUUcKbIkVF64UYp9GKAEYBl
IIyDUHkzx/LFINnYMMkVP3paAMeiiivUPJIrmLzYSvX29arQWatAnmKUZc8A++elXqKlxTdzSNWU
Y2XqIqhVCjoKWiiqM3qFFFFABVS5tmZcRYxnpgcfSrdFTKKkrMuFRwd0Q3ETSWzRoSpIxxVXR4pI
JAJJgI0OWVztA/xrQqrdwIwEmPmB61lWpc6v2NsPNXUGaN7f+XKgjLFVOXIHB9Oaryi7uAgbLKcM
u0Aj0/rU1taLNbGVWbDp8gbjB9auxAWtsiyMBtwCfevO5ZP4jp5ZP4in9huFXKylcncdvDH6mr8M
bRqQ0jOSc5NUtQuQE8pGYOQGRlPfNSQT3CqiPbtycbywP51StsilZaItPw6n8Kr3EcxuEeAKGxhm
bpj0qvLPqKyqotI35JyJMVoRFzGpkUK5HKg5xVFFC9lV7Z454MSY43DI+oNPtvOtnigYiSNh8r9M
cdKszxmZfLwNrdSe1SZWNBkgKB3pW1FbUiukkkgMceMtxk4wB+NZVta/6Y8UbnYnGc9a0ry4WKPb
1Lg9+3rVHSQSzNjgkkGqh8V10Matm0jUWFEAwM47mnnhD2GKWork4t5DnHFK9zZKxXSKWW0VYpDD
xncvJ/WrMEbxx4eUyNknJGKIsLEoHAAp9IYUUUUwA4xz0phCdlX8qTJd8fwjrTgADwBQ9BXuKMBc
dqrl5LdiX+aEnggcp/8AWqwOtLjIwaBgCCuQcg1HB/qQPTI/WmojQO2GzEei/wB2nQ/cPsx/nQBJ
RRSZA7igAJwaWkJBXgiqk1/bwSbJZ1RuuCaAKdFFFeoeSFFFFABRRTkAZwp796T0BK42inOhRyvU
+1WILdJI95Y8dRUymoq7LjBydkVaKVsbjjpSc9hk+lWQFNdQ6FT3pEN3uGbCQj/eFNnluY5MGxYD
thhislVi3ZGvsppcw63ubi1xGqF416AnmpbrUIZrdo5ImO5flH+16UxSSoJGCe3pUE0K+bHKezZP
PYc/0rnqUFGN0zWnWblab0LtkscNi8tyu0McfN/d7Cr0KwxKDGcK/TnIP0rHuLia8kWSLb5QHAPO
fz/CnWc9xPKGZQ5Q4C5AA/D+tY8jUebodUeVpuPQ2T/rv+A06mHh1Y8ZGD7VXkuGt533o7REZBAz
g1JRbNYl/M6Q3Nud7gkYdh0J7Vp2iybGkkfcZDux2FQapGZLfKsMx/OV9RTSu7AlfQoeXNdSb52J
H8EY4C1q2vlxwAAbT34qvY7XkD8YxkZrRGK6KrjH3Ejnp803zsTeKguf3hSFSPmOT9BVjHFRRxYl
eQ4yeB7Cuc6CXFVpr62glMckoVhyQatUxo0Y5ZFJ9SKQEUF5b3LlYZA5AycVNmmxxqg+VVGfQUSN
hPc8CmlcTdgU5BOOpqol3O115RgITP36uKNqgDtRSb1BCg9aXNIopaYxM57VFCCu9c9G/oKnqJP9
ZIPcGgAeESMCWbgYwDTRawgH5c/UmpenWjcCMg5oAjWERnC8Ke1RyRwM/wC8RGYdyuafJMEDZP3e
uaZFDKyl2kILHOMdKaS6iu3sUKKKK9M8oKKKKACjpRRQBbiMe4OTliPuimLqcGGUQ3HCk/6ojOKW
0I8wDYCfX0q25JbZgDI4NcVVPmsd1FrluUoZrW5cRKku45IYxlf1qxBbBJGYg4H3c1YOFGewoJxi
o9pK1jT2cLp22GyP5eDjIqlduWZcqRx3p81xfJIVjskkXPykyhf6VOiGaFGuIlWTuoOcfjTpzUXd
oVSDmrJlEQsYjJj5RULosiFWGQa12GE2qoxVOe2ZCWRfl/lW8Kym2pHNUoOKuioiCNAq9BUctukm
eWVuuVOKvR2jty3yrUcsTRk8fLng1opQvyohKpD31oVxdy28iNcM0iDqBV2e+SFDGwLZB4B+Ycd6
pTRCWMqe4xVWRXbyoyrOQQCCCd/pk/8A164K8JU27LQ2p1m1ruaelTwlPLUESBcnJySKrXkks8zF
UaI48tg4+8ppPndzyq4XZ8h4x6U+K18tS6qxBPJp0qc5JPZDlXaVluieyiQMEYk+maeNJCjC3d0B
ntJ0qWzUru3Iee/pVytK8ry0NKK9273Mm3uYraV0Ml5K2cYkUnH07VatdQhum2IsynGfnjK/qaSf
TLe4nMzNKHIwdkrAEfTNSWlpHaqwjaRgT/G5b+dYmxYoNGKTB+tAC01wWXj1zTs0UXsJq40NuGcY
9RRTWQ5LKSD+lOVsgGhrqJdmOooooKFqJeJ3z0wP61JUMmBKfdM4/GgBZCWGY2zjrUcbkkj+KnQH
gndx6VEsjCXPGSai/wBolsVowZI8j5wc81cphUEg45pefSmrjRkUUUV6x5QUUUUAKMZ56VcEUUkI
SNuRzRRWNa6V0bUbN2fUlX9y6RhRgjriiRsTRg429c0UVzpXa8zqbsmuzQsjbZAWUlcenANDnfKq
g9OTRRSS0uDfvWCdtqbu4PFLGJAOSDnnB7UUUtoIrebJPrSNyCPUUUVncsF+4AewqGVFdCDRRQm7
3BrSxSeFlJxyB3qJl3eoPqKKK7qT54e8cFaKjKyHwW3O0AgHvitNIlSMKBj1oorCs7WitjooRVrk
M1p5twsvmFdpzgDr9asZoornOgXFIvT8aKKYDqSiikAHFJ+JoooApy6esszyfablC2OFkwB9BUf9
lBACt5efL283r+lFFNCNBTwMcilzRRQxoKgmYJIpIzlSMUUUmBCzY+aL8fahIy4GDyaKKJJLQyjq
7kivL2AOOtVVs70D/kIzH/tmvH6UUUR1Vy0j/9k=