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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3LQB2C8
Repositorysid.inpe.br/mtc-m21b/2016/06.01.18.19   (restricted access)
Last Update2016:06.01.18.20.52 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2016/06.01.18.19.22
Metadata Last Update2022:03.18.22.11.52 (UTC) administrator
DOI10.1002/2015SW001349
ISSN1542-7390
Citation KeySouzaVMSAKSWKJRDSMMGB:2016:NeNeAp
TitleA neural network approach for identifying particle pitch angle distributions in Van Allen Probes data
Year2016
MonthApr.
Access Date2024, Apr. 29
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1286 KiB
2. Context
Author 1 Souza, Vitor Moura Cardoso e Silva
 2 Vieira, Luis Eduardo Antunes
 3 Medeiros, Cláudia
 4 Silva, Lígia Alves da
 5 Alves, Livia Ribeiro
 6 Koga, Daiki
 7 Sibeck, D. G.
 8 Walsh, B. M.
 9 Kanekal, S. G.
10 Jauer, P. R.
11 Rockenbach da Silva, Marlos
12 Dal Lago, Alisson
13 Silveira, Marcos Vinicius Dias
14 Marchezi, José Paulo
15 Mendes, Odim
16 Gonzalez Alarcon, Walter Demétrio
17 Baker, D. N.
Resume Identifier 1
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12 8JMKD3MGP5W/3C9JGH3
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16 8JMKD3MGP5W/3C9JJC4
Group 1 GES-CEA-SPG-INPE-MCTI-GOV-BR
 2 DGE-CEA-INPE-MCTI-GOV-BR
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Affiliation 1 Instituto Nacional de Pesquisas Espaciais (INPE)
 2 Instituto Nacional de Pesquisas Espaciais (INPE)
 3 Instituto Nacional de Pesquisas Espaciais (INPE)
 4 Instituto Nacional de Pesquisas Espaciais (INPE)
 5 Instituto Nacional de Pesquisas Espaciais (INPE)
 6 Instituto Nacional de Pesquisas Espaciais (INPE)
 7 NASA Goddard Space Flight Center
 8 Boston University
 9 NASA Goddard Space Flight Center
10 Instituto Nacional de Pesquisas Espaciais (INPE)
11 Instituto Nacional de Pesquisas Espaciais (INPE)
12 Instituto Nacional de Pesquisas Espaciais (INPE)
13 Instituto Nacional de Pesquisas Espaciais (INPE)
14 Instituto Nacional de Pesquisas Espaciais (INPE)
15 Instituto Nacional de Pesquisas Espaciais (INPE)
16 Instituto Nacional de Pesquisas Espaciais (INPE)
17 University of Colorado Boulder
Author e-Mail Address 1 vitor.souza@inpe.br
 2 luis.vieira@inpe.br
 3 claudia.medeiros@inpe.br
 4 ligia.silva@inpe.br
 5 livia.alves@inpe.br
 6 daiki.koga@inpe.br
 7
 8
 9
10
11 marlos.silva@inpe.br
12 alisson.dallago@inpe.br
13 marcos.silveira@inpe.br
14 jose.marchezi@inpe.br
15 odim.mendes@inpe.br
16 walter.alarcon@inpe.br
JournalSpace Weather
Volume14
Number4
Pages275-284
Secondary MarkB1_INTERDISCIPLINAR B1_GEOCIÊNCIAS B2_ASTRONOMIA_/_FÍSICA
History (UTC)2016-06-01 18:20:52 :: simone -> administrator :: 2016
2016-06-06 23:47:37 :: administrator -> simone :: 2016
2016-06-20 13:25:29 :: simone -> administrator :: 2016
2016-07-04 12:30:01 :: administrator -> simone :: 2016
2017-01-09 16:20:39 :: simone -> administrator :: 2016
2022-03-18 22:11:52 :: administrator -> simone :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordspitch angle distributions
self-organizing maps
Van Allen belt's monitoring
AbstractAnalysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namely, 90° peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt's monitoring.
AreaCEA
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDGE > A neural network...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > GES > A neural network...
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4. Conditions of access and use
Languageen
Target Filesouza_a neural.pdf
User Groupsimone
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Linking8JMKD3MGP3W34P/3J3GFGP
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3EU29DP
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Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.15.01.50 3
sid.inpe.br/bibdigital/2013/10.12.21.02 2
sid.inpe.br/mtc-m21/2012/07.13.14.39.46 2
DisseminationWEBSCI; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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