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,...
| Autores: | , |
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| 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 |
| Sumario: | 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. |
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