An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model

Ionospheric scintillation is a well-known effect that occurs when electromagnetic waves pass through the ionosphere, leading to rapid fluctuations in the phase and intensity of the received signal. In 1979, Charles Rino introduced a theory to compute the expected ionospheric scintillation. However,...

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Detalles Bibliográficos
Autores: Molina, Carlos, Camps, Adriano
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/418385
Acceso en línea:http://hdl.handle.net/10261/418385
https://api.elsevier.com/content/abstract/scopus_id/105020067503
Access Level:acceso abierto
Palabra clave:Electromagnetic propagation
Ionospheric scintillation
Rino’s model
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spelling An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation ModelMolina, CarlosCamps, AdrianoElectromagnetic propagationIonospheric scintillationRino’s modelIonospheric scintillation is a well-known effect that occurs when electromagnetic waves pass through the ionosphere, leading to rapid fluctuations in the phase and intensity of the received signal. In 1979, Charles Rino introduced a theory to compute the expected ionospheric scintillation. However, Rino’s model requires knowing some input variables related to the physical properties of the ionosphere’s plasma density irregularities. The wideband ionospheric scintillation model (WBMOD) model was especially developed to provide these parameters from climatological data as a function of several environmental conditions; however, the use of this model requires a license. In this study, using large datasets from past studies, a neural network has been trained to estimate the main output parameters from WBMOD: the probability density function of C<inf>k</inf>L and the value of the p-slope (slope of power spectra of phase scintillation). This allows retrieving Rino’s input variable to compute the scintillation indices S<inf>4</inf> and σ<inf>φ</inf>. The resulting software, called IonoSciNN, has been published as an open web application.This work was supported in part by project GENESIS: GNSS Environmental and Societal Missions–Subproject UPC under Grant PID2021-126436OBC21 sponsored by MCIN/AEI/10.13039/501100011033/ and in part by the IEEC INTREPID project, in which this study is contextualized.Peer reviewedInstitute of Electrical and Electronics EngineersAgencia Estatal de Investigación (España)Ministerio de Ciencia e Innovación (España)Molina, Carlos [0000-0003-0300-4106]Camps, Adriano [0000-0002-9514-4992]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/418385https://api.elsevier.com/content/abstract/scopus_id/105020067503reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126436OB-C21https://doi.org/10.1109/JSTARS.2025.3625408Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4183852026-05-22T06:33:51Z
dc.title.none.fl_str_mv An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
title An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
spellingShingle An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
Molina, Carlos
Electromagnetic propagation
Ionospheric scintillation
Rino’s model
title_short An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
title_full An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
title_fullStr An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
title_full_unstemmed An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
title_sort An Improved Neural-Network to Estimate the Inputs of Rino’s Ionospheric Scintillation Model
dc.creator.none.fl_str_mv Molina, Carlos
Camps, Adriano
author Molina, Carlos
author_facet Molina, Carlos
Camps, Adriano
author_role author
author2 Camps, Adriano
author2_role author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia e Innovación (España)
Molina, Carlos [0000-0003-0300-4106]
Camps, Adriano [0000-0002-9514-4992]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Electromagnetic propagation
Ionospheric scintillation
Rino’s model
topic Electromagnetic propagation
Ionospheric scintillation
Rino’s model
description Ionospheric scintillation is a well-known effect that occurs when electromagnetic waves pass through the ionosphere, leading to rapid fluctuations in the phase and intensity of the received signal. In 1979, Charles Rino introduced a theory to compute the expected ionospheric scintillation. However, Rino’s model requires knowing some input variables related to the physical properties of the ionosphere’s plasma density irregularities. The wideband ionospheric scintillation model (WBMOD) model was especially developed to provide these parameters from climatological data as a function of several environmental conditions; however, the use of this model requires a license. In this study, using large datasets from past studies, a neural network has been trained to estimate the main output parameters from WBMOD: the probability density function of C<inf>k</inf>L and the value of the p-slope (slope of power spectra of phase scintillation). This allows retrieving Rino’s input variable to compute the scintillation indices S<inf>4</inf> and σ<inf>φ</inf>. The resulting software, called IonoSciNN, has been published as an open web application.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/418385
https://api.elsevier.com/content/abstract/scopus_id/105020067503
url http://hdl.handle.net/10261/418385
https://api.elsevier.com/content/abstract/scopus_id/105020067503
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126436OB-C21
https://doi.org/10.1109/JSTARS.2025.3625408
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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