<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article
PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.4 20190208//EN"
       "JATS-journalpublishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.4" xml:lang="en">
 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">Solar-Terrestrial Physics</journal-id>
   <journal-title-group>
    <journal-title xml:lang="en">Solar-Terrestrial Physics</journal-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Солнечно-земная физика / Solnechno-Zemnaya Fizika / Solar-Terrestrial Physics</trans-title>
    </trans-title-group>
   </journal-title-group>
   <issn publication-format="online">2712-9640</issn>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="publisher-id">96013</article-id>
   <article-id pub-id-type="doi">10.12737/szf-113202509</article-id>
   <article-categories>
    <subj-group subj-group-type="toc-heading" xml:lang="ru">
     <subject>ДВАДЦАТАЯ ЕЖЕГОДНАЯ КОНФЕРЕНЦИЯ «ФИЗИКА ПЛАЗМЫ В СОЛНЕЧНОЙ СИСТЕМЕ», 10–14 ФЕВРАЛЯ 2025 Г., ИНСТИТУТ КОСМИЧЕСКИХ ИССЛЕДОВАНИЙ РАН, МОСКВА, РОССИЯ</subject>
    </subj-group>
    <subj-group subj-group-type="toc-heading" xml:lang="en">
     <subject>20TH ANNUAL CONFERENCE “PLASMA PHYSICS IN THE SOLAR SYSTEM”. FEBRUARY 10–14, 2025, SPACE RESEARCH INSTITUTE RAS, MOSCOW, RUSSIA</subject>
    </subj-group>
    <subj-group>
     <subject>ДВАДЦАТАЯ ЕЖЕГОДНАЯ КОНФЕРЕНЦИЯ «ФИЗИКА ПЛАЗМЫ В СОЛНЕЧНОЙ СИСТЕМЕ», 10–14 ФЕВРАЛЯ 2025 Г., ИНСТИТУТ КОСМИЧЕСКИХ ИССЛЕДОВАНИЙ РАН, МОСКВА, РОССИЯ</subject>
    </subj-group>
   </article-categories>
   <title-group>
    <article-title xml:lang="en">Prediction of electron fluxes in a circular polar orbit: Selection of predictors</article-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Прогнозирование потоков электронов на круговой полярной орбите: отбор предикторов</trans-title>
    </trans-title-group>
   </title-group>
   <contrib-group content-type="authors">
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Белова</surname>
       <given-names>Анастасия Олеговна</given-names>
      </name>
      <name xml:lang="en">
       <surname>Belova</surname>
       <given-names>Anastasiya Olegovna</given-names>
      </name>
     </name-alternatives>
     <email>belova.ao20@physics.msu.ru</email>
     <xref ref-type="aff" rid="aff-1"/>
    </contrib>
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Мягкова</surname>
       <given-names>Ирина Николаевна</given-names>
      </name>
      <name xml:lang="en">
       <surname>Myagkova</surname>
       <given-names>Irina Nikolaevna</given-names>
      </name>
     </name-alternatives>
     <bio xml:lang="ru">
      <p>кандидат физико-математических наук;</p>
     </bio>
     <bio xml:lang="en">
      <p>candidate of physical and mathematical sciences;</p>
     </bio>
     <xref ref-type="aff" rid="aff-2"/>
    </contrib>
   </contrib-group>
   <aff-alternatives id="aff-1">
    <aff>
     <institution xml:lang="ru">Московский государственный университет имени М.В.Ломоносова</institution>
    </aff>
    <aff>
     <institution xml:lang="en">Lomonosov Moscow State University</institution>
    </aff>
   </aff-alternatives>
   <aff-alternatives id="aff-2">
    <aff>
     <institution xml:lang="ru">Научно-исследовательский институт ядерной физики имени Д. В. Скобельцына</institution>
     <city>Москва</city>
     <country>Россия</country>
    </aff>
    <aff>
     <institution xml:lang="en">D.V. Skobeltsyn Scientific Research Institute of Nuclear Physics</institution>
     <city>Moscow</city>
     <country>Russian Federation</country>
    </aff>
   </aff-alternatives>
   <pub-date publication-format="print" date-type="pub" iso-8601-date="2025-09-22T08:08:22+03:00">
    <day>22</day>
    <month>09</month>
    <year>2025</year>
   </pub-date>
   <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-09-22T08:08:22+03:00">
    <day>22</day>
    <month>09</month>
    <year>2025</year>
   </pub-date>
   <volume>11</volume>
   <issue>3</issue>
   <fpage>77</fpage>
   <lpage>87</lpage>
   <history>
    <date date-type="received" iso-8601-date="2025-03-10T00:00:00+03:00">
     <day>10</day>
     <month>03</month>
     <year>2025</year>
    </date>
    <date date-type="accepted" iso-8601-date="2025-07-21T00:00:00+03:00">
     <day>21</day>
     <month>07</month>
     <year>2025</year>
    </date>
   </history>
   <self-uri xlink:href="https://naukaru.ru/en/nauka/article/96013/view">https://naukaru.ru/en/nauka/article/96013/view</self-uri>
   <abstract xml:lang="ru">
    <p>Исследовалась связь вариаций потоков электронов с энергиями &gt;0.7 и &gt;2 МэВ внешнего радиационного пояса Земли на круговой полярной орбите с параметрами солнечного ветра и межпланетного магнитного поля, а также с геомагнитными индексами и логарифмом потока электронов внешнего радиационного пояса Земли на геостационарной орбите с целью выяснения возможности их прогнозирования. Был проведен отбор оптимальных входных признаков при прогнозировании потоков электронов на низких полярных орбитах, что актуально в рамках обеспечения радиационной безопасности будущих космических миссий.&#13;
Рассматривались интегральные и максимальные потоки электронов указанных энергий за сутки. На основе линейной регрессии получены прогнозы с горизонтом 1 и 2 дня на интервале 2 месяца 2020 г. для максимальных и интегральных потоков за сутки.</p>
   </abstract>
   <trans-abstract xml:lang="en">
    <p>We have investigated the relationship of variations in &gt;0.7 and &gt;2 MeV electron fluxes of Earth's outer radiation belt in a circular polar orbit with solar wind and interplanetary magnetic field parameters, as well as with geomagnetic indices and the logarithmic electron flux in the geostationary orbit in order to explore the possibility of predicting them. We have selected the optimal input features for predicting electron fluxes in low polar orbits, which is important for ensuring the radiation safety of future space missions.&#13;
We have examined integral and maximum electron fluxes of these energies over the span of a day. We have obtained forecasts with a horizon of 1 and 2 days for an interval of 2 months in 2020 for daily maximum and integral fluxes based on linear regression.</p>
   </trans-abstract>
   <kwd-group xml:lang="ru">
    <kwd>радиационные пояса Земли</kwd>
    <kwd>потоки релятивистских электронов</kwd>
    <kwd>прогнозирование</kwd>
    <kwd>машинное обучение</kwd>
    <kwd>круговая полярная орбита</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>Earth’s radiation belts</kwd>
    <kwd>relativistic electron fluxes</kwd>
    <kwd>forecasting</kwd>
    <kwd>machine learning</kwd>
    <kwd>circular polar orbit</kwd>
   </kwd-group>
   <funding-group>
    <funding-statement xml:lang="ru">Исследование выполнено за счет гранта Российского научного фонда № 22-62-00048, [https://rscf.ru/ project/22-62-00048/]</funding-statement>
    <funding-statement xml:lang="en">The work was financially supported by the Russian Science Foundation (Grant No. 22-62-00048) [https://rscf.ru/project/22-62-00048/]</funding-statement>
   </funding-group>
  </article-meta>
 </front>
 <body>
  <p></p>
 </body>
 <back>
  <ref-list>
   <ref id="B1">
    <label>1.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Белов А.В., Виллорези Дж., Дорман Л.И. и др. Влияние космической среды на функционирование искусственных спутников Земли. Геомагнетизм и аэрономия. 2004, т. 44, № 4, с. 502–510.</mixed-citation>
     <mixed-citation xml:lang="en">Alwosheel A., van Cranenburgh S., Chorus C.G. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J. Choice Modelling. 2018, vol. 28, pp. 167–182. DOI: 10.1016/j.jocm.2018.07.002.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B2">
    <label>2.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Вернов С.Н., Григоров Н.Л., Логачев Ю.И., Чудаков А.Е. Измерения космического излучения на искусственном спутнике Земли. Доклады Академии наук. 1958, т. 120, № 6, с. 1231–1233.</mixed-citation>
     <mixed-citation xml:lang="en">Baker D.N., McPherron R.L., Cayton T.E., Klebesadel R.W. Linear prediction filter analysis of relativistic electron properties at 6.6 RE. J. Geophys. Res. 1990, vol. 95, iss. A9, pp. 15133–15140. DOI: 10.1029/JA095iA09p15133.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B3">
    <label>3.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Демиденко Е.З. Линейная и нелинейная регрессия. М.: Финансы и статистика. 1981, 302 с.</mixed-citation>
     <mixed-citation xml:lang="en">Belov A.V., Villoresi J., Dorman L.I.,  et al. The influence of the space environment on the functioning of artificial Earth satellites. Geomagnetism and Aeronomy. 2004, vol. 44, iss. 4, pp. 502–510.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B4">
    <label>4.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Кузнецов С.Н., Мягкова И.Н., Юшков Б.Ю. и др. Динамика радиационных поясов Земли во время сильных магнитных бурь по данным ИСЗ «КОРОНАС-Ф». Астрономический вестник. Исследования солнечной системы. 2007, т. 41, № 4, с. 369–378.</mixed-citation>
     <mixed-citation xml:lang="en">Balikhin M.A., Boynton R.J., Walker S.N., et al. Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit. Geophys. Res. Lett. 2011, vol. 38, iss. 18. DOI: 10.1029/2011GL048980.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B5">
    <label>5.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Мягкова И.Н., Широкий В.Р., Шугай Ю.С. и др. Краткосрочное и среднесрочное прогнозирование потоков релятивистских электронов внешнего радиационного пояса Земли методами машинного обучения. Метеорология и гидрология. 2021, № 3, с. 47–57. DOI: 10.52002/0130-2906-2021-3-47-57.</mixed-citation>
     <mixed-citation xml:lang="en">Botek E., Pierrard V., Winant A. Prediction of radiation belts electron fluxes at a Low Earth Orbit using neural networks with PROBA-V/EPT data. Space Weather. 2023, vol. 21, iss. 7, e2023SW003466. DOI: 10.1029/2023SW003466.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B6">
    <label>6.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Новиков Л.С., Воронина Е.Н. Взаимодействие космических аппаратов с окружающей средой. М.: КДУ. 2021, 560 с.</mixed-citation>
     <mixed-citation xml:lang="en">Cole D.G. Space weather: Its effects and predictability. Space Sci. Rev. 2003, vol. 107, pp. 295–302. DOI: 10.1023/A:1025500513499.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B7">
    <label>7.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Оседло В.И., Калегаев В.В., Рубинштейн И.А. и др. Мониторинг радиационного состояния околоземного пространства на спутнике «Арктика-М» № 1. Космические исследования. 2022, т. 60, № 6, с. 439–453. DOI: 10.31857/S0023420622060085.</mixed-citation>
     <mixed-citation xml:lang="en">Demidenko E.Z. Linear and nonlinear regression. Moscow: Finance and Statistics, 1981, 302 p</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B8">
    <label>8.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Романова Н.В., Пилипенко В.А., Ягова Н.В., Белов А.В. Статистическая связь частоты сбоев на геостационарных спутниках с потоками энергичных электронов и протонов. Космические исследования. 2005, т. 43, № 3, с. 186–193.</mixed-citation>
     <mixed-citation xml:lang="en">Denton M.H., Henderson M.G., Jordanova V.K., et al. An improved empirical model of electron and ion fluxes at geosynchronous orbit based on upstream solar wind conditions. Space Weather. 2016, vol. 14, iss. 7, pp. 511–523. DOI: 10.1002/2016SW001409.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B9">
    <label>9.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Alwosheel A., van Cranenburgh S., Chorus C.G. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J. Choice Modelling. 2018, vol. 28, pp. 167–182.  DOI: 10.1016/j.jocm.2018.07.002.</mixed-citation>
     <mixed-citation xml:lang="en">Glauert S.A., Horne R.B., Meredith N.P. Three-dimensional electron radiation belt simulations using the BAS Radiation Belt Model with new diffusion models for chorus, plasmaspheric hiss, and lightning-generated whistlers. J. Geophys. Res.: Space Phys. 2014, vol. 119, iss. 1, pp. 268–289. DOI: 10.1002/2013JA019281.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B10">
    <label>10.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Baker D.N., McPherron R.L., Cayton T.E., Klebesadel R.W. Linear prediction filter analysis of relativistic electron properties at 6.6 RE. J. Geophys. Res. 1990, vol. 95, iss. A9, pp. 15133–15140. DOI: 10.1029/JA095iA09p15133.</mixed-citation>
     <mixed-citation xml:lang="en">Iucci N., Levitin A., Belov E., et al. Space weather conditions and spacecraft anomalies in different orbits. Space Weather. 2005, vol. 3, iss. 1.DOI: 10.1029/2003SW000056.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B11">
    <label>11.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Balikhin M.A., Boynton R.J., Walker S.N., et al. Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit. Geophys. Res. Lett. 2011, vol. 38, iss. 18. DOI: 10.1029/2011GL048980.</mixed-citation>
     <mixed-citation xml:lang="en">Kalegaev V., Panasyuk M., Myagkova I., et al. Monitoring, analysis and post-casting of the Earth’s particle radiation environment during February 14 – March 5, 2014. Space Weather Space Climate. 2019, vol. 9, iss. A29. DOI: 10.1051/swsc/2019029.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B12">
    <label>12.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Botek E., Pierrard V., Winant A. Prediction of radiation belts electron fluxes at a Low Earth Orbit using neural networks with PROBA-V/EPT data. Space Weather. 2023, vol. 21, iss. 7, e2023SW003466. DOI: 10.1029/2023SW003466.</mixed-citation>
     <mixed-citation xml:lang="en">Kalegaev V., Kaportseva K., Myagkova I., et al. Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations. Adv. Space Res. 2023, vol. 72, iss. 12, pp. 5376–5390. DOI: 10.1016/j.asr.2022.08.033.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B13">
    <label>13.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Cole D.G. Space weather: Its effects and predictability. Space Sci. Rev. 2003, vol. 107, pp. 295–302. DOI: 10.1023/A:1025500513499.</mixed-citation>
     <mixed-citation xml:lang="en">Kataoka R., Miyoshi Y. Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit. Ann. Geophys. 2008, vol. 26, iss. 6, pp. 1335‒1339. DOI: 10.5194/angeo-26-1335-2008.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B14">
    <label>14.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Denton M.H., Henderson M.G., Jordanova V.K., et al. An improved empirical model of electron and ion fluxes at geosynchronous orbit based on upstream solar wind conditions. Space Weather. 2016, vol. 14, iss. 7, pp. 511–523. DOI: 10.1002/2016SW001409.</mixed-citation>
     <mixed-citation xml:lang="en">Koons H.C., Gorney D.J. A neural network model of the relativistic electron flux at geosynchronous orbit. J. Geophys. Res. 1991, vol. 96, iss. A4, pp. 5549–5556.DOI: 10.1029/90JA02380.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B15">
    <label>15.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Glauert S.A., Horne R.B., Meredith N.P. Three-dimensional electron radiation belt simulations using the BAS Radiation Belt Model with new diffusion models for chorus, plasma-spheric hiss, and lightning-generated whistlers. J. Geophys. Res.: Space Phys. 2014, vol. 119, iss. 1, pp. 268–289. DOI: 10.1002/2013JA019281.</mixed-citation>
     <mixed-citation xml:lang="en">Kudela K. Space weather near Earth and energetic particles: Selected results. J. Physics Conf. Ser. 2013, vol. 409, iss. 1. DOI: 10.1088/1742-6596/409/1/012017.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B16">
    <label>16.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Iucci N., Levitin A., Belov E., et al. Space weather conditions and spacecraft anomalies in different orbits. Space Weather. 2005, vol. 3, iss. 1. DOI: 10.1029/2003SW000056.</mixed-citation>
     <mixed-citation xml:lang="en">Kuznetsov S.N., Myagkova I.N., Yushkov B.Yu., et al. Dynamics of the Earth's radiation belts during strong magnetic storms according to data from the CORONAS-F satellite. Astronomical Bulletin. Studies of the Solar System. 2007, vol. 41, iss. 4, pp. 369–378.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B17">
    <label>17.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Kalegaev V., Panasyuk M., Myagkova I., et al. Monitoring, analysis and post-casting of the Earth’s particle radiation environment during February 14 – March 5, 2014. Space Weather Space Climate. 2019, vol. 9, iss. A29. DOI: 10.1051/swsc/2019029.</mixed-citation>
     <mixed-citation xml:lang="en">Landis D.A., Saikin A.A., Zhelavskaya I., et al. NARX neural network derivations of the outer boundary radiation belt electron flux. Space Weather. 2022, vol. 20, iss. 5, e2021SW002774. DOI: 10.1029/2021SW002774.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B18">
    <label>18.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Kalegaev V., Kaportseva K., Myagkova I., et al. Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations. Adv. Space Res. 2023, vol. 72, iss. 12, pp. 5376–5390. DOI: 10.1016/j.asr.2022.08.033.</mixed-citation>
     <mixed-citation xml:lang="en">Li W., Hudson M.K. Earth’s Van Allen radiation belts: From discovery to the Van Allen Probes era. J. Geophys. Res.: Space Phys. 2019, vol. 124, iss. 11, pp. 8319–8351. DOI: 10.1029/2018JA025940.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B19">
    <label>19.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Kataoka R., Miyoshi Y. Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit. Ann. Geophys. 2008, vol. 26, iss. 6, pp. 1335‒1339.  DOI: 10.5194/angeo-26-1335-2008.</mixed-citation>
     <mixed-citation xml:lang="en">Li X., Baker D.N., Kanekal S.G., et al. Long term measurements of radiation belts by SAMPEX and their variations. Geophys. Res. Lett. 2001, vol. 28, iss. 20, pp. 3827–3830. DOI: 10.1029/2001gl013586.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B20">
    <label>20.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Koons H.C., Gorney D.J. A neural network model of the relativistic electron flux at geosynchronous orbit. J. Geophys. Res. 1991, vol. 96, iss. A4, pp. 5549–5556. DOI: 10.1029/90JA02380.</mixed-citation>
     <mixed-citation xml:lang="en">Ling A.G., Ginet G.P., Hilmer R.V., Perry K.L. A neural network-based geosynchronous relativistic electron flux forecasting model. Space Weather. 2010, vol. 8, iss. 9. DOI: 10.1029/2010SW000576.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B21">
    <label>21.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Kudela K. Space weather near Earth and energetic particles: Selected results. J. Physics Conference Ser. 2013, vol. 409, iss. 1. DOI: 10.1088/1742-6596/409/1/012017.</mixed-citation>
     <mixed-citation xml:lang="en">Lyatsky W., Khazanov G.V. A predictive model for relativistic electrons at geostationary orbit. Geophys. Res. Lett. 2008, vol. 35, iss. 15, L15108. DOI: 10.1029/2008GL034688.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B22">
    <label>22.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Landis D.A., Saikin A.A., Zhelavskaya I., et al. NARX neural network derivations of the outer boundary radiation belt electron flux. Space Weather. 2022, vol. 20, iss. 5, e2021SW002774. DOI: 10.1029/2021SW002774.</mixed-citation>
     <mixed-citation xml:lang="en">Myagkova I., Efitorov A., Shiroky V., Dolenko S.A. Quality of prediction of daily relativistic electrons flux at geostationary orbit by machine learning methods. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 2019, pp. 556–565. DOI: 10.1007/978-3-030-30490-4_45.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B23">
    <label>23.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Li W., Hudson M.K. Earth’s Van Allen radiation belts: From discovery to the Van Allen Probes era. J. Geophys. Res.: Space Phys. 2019, vol. 124, iss. 11, pp. 8319–8351. DOI: 10.1029/2018JA025940.</mixed-citation>
     <mixed-citation xml:lang="en">Myagkova I.N., Shirokiy V.R., Shugai Yu.S., et al. Short- and medium-term forecasting of relativistic electron fluxes in the Earth’s outer radiation belt using machine learning methods. Meteorology and Hydrology. 2021, iss. 3, pp. 47–57. DOI: 10.52002/0130-2906-2021-3-47-57.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B24">
    <label>24.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Li X., Baker D.N., Kanekal S.G., et al. Long term measurements of radiation belts by SAMPEX and their variations. Geophys. Res. Lett. 2001, vol. 28, iss. 20, pp. 3827–3830. DOI: 10.1029/2001gl013586.</mixed-citation>
     <mixed-citation xml:lang="en">Novikov L.S., Voronina E.N. Interaction of Spacecraft with the Environment. Moscow: KDU, 2021, 560 p.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B25">
    <label>25.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Ling A.G., Ginet G.P., Hilmer R.V., Perry K.L. A neural network-based geosynchronous relativistic electron flux forecasting model. Space Weather. 2010, vol. 8, iss. 9. DOI: 10.1029/2010SW000576.</mixed-citation>
     <mixed-citation xml:lang="en">Osedlo V.I., Kalegaev V.V., Rubinshtein I.A., et al. Monitoring the radiation state of the near-Earth space on the Arktika-M No. 1 Satellite. Cosmic Res. 2022, vol. 60, iss. 6, pp. 406–419. DOI: 10.1134/S0010952522060089.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B26">
    <label>26.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Lyatsky W., Khazanov G.V. A predictive model for relativistic electrons at geostationary orbit. Geophys. Res. Lett. 2008, vol. 35, iss. 15, L15108. DOI: 10.1029/2008GL034688.</mixed-citation>
     <mixed-citation xml:lang="en">Pilipenko V., Yagova N., Romanova N., Allen J. Statistical relationships between the satellite anomalies at geostationary orbits and high-energy particles. Adv. Space Res. 2006, vol. 37, iss. 6, pp. 1192–1205. DOI: 10.1016/j.asr.2005.03.152.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B27">
    <label>27.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Myagkova I., Efitorov A., Shiroky V., Dolenko S.A. Quality of prediction of daily relativistic electrons flux at geostationary orbit by machine learning methods. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. 2019, pp. 556–565. DOI: 10.1007/978-3-030-30490-4_45.</mixed-citation>
     <mixed-citation xml:lang="en">Potapov A., Ryzhakova L., Tsegmed B. A new approach to predict and estimate enhancements of &quot;killer&quot; electron flux at geosynchronous orbit. Acta Astronaut. 2016, vol. 126, pp. 47–51. DOI: 10.1016/j.actaastro.2016.04.017.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B28">
    <label>28.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Pilipenko V., Yagova N., Romanova N., Allen J. Statistical relationships between the satellite anomalies at geostationary orbits and high-energy particles. Adv. Space Res. 2006, vol. 37, iss. 6, pp. 1192–1205. DOI: 10.1016/j.asr.2005.03.152.</mixed-citation>
     <mixed-citation xml:lang="en">Romanova N.V., Pilipenko V.A., Yadova N.V., Belov A.V. Statistical relationship of the frequency of failures on geostationary satellites with the fluxes of energetic electrons and protons. Space Res. 2005, vol. 43, iss. 3, pp. 186–193.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B29">
    <label>29.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Potapov A., Ryzhakova L., Tsegmed B. A new approach to predict and estimate enhancements of “killer” electron flux at geosynchronous orbit. Acta Astronaut. 2016, vol. 126, pp. 47–51. DOI: 10.1016/j.actaastro.2016.04.017.</mixed-citation>
     <mixed-citation xml:lang="en">Son J., Moon Y.-J., Shin S. 72-hour time series forecasting of hourly relativistic electron fluxes at geostationary orbit by deep learning. Space Weather. 2022, vol. 20, iss. 10, e2022SW003153. DOI: 10.1029/2022sw003153.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B30">
    <label>30.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Son J., Moon Y.-J., Shin S. 72-hour time series forecasting of hourly relativistic electron fluxes at geostationary orbit by deep learning. Space Weather. 2022, vol. 20, iss. 10, e2022SW003153. DOI: 10.1029/2022sw003153.</mixed-citation>
     <mixed-citation xml:lang="en">Stepanova M., Pinto V., Antonova E. Regarding the relativistic electron dynamics in the outer radiation belt: a historical view. Rev. Modern Plasma Physics. 2024, vol. 8, iss. 25. DOI: 10.1007/s41614-024-00165-4.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B31">
    <label>31.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Stepanova M., Pinto V., Antonova E. Regarding the relativistic electron dynamics in the outer radiation belt: a historical view. Rev. Modern Plasma Physics. 2024, vol. 8, iss. 25. DOI: 10.1007/s41614-024-00165-4.</mixed-citation>
     <mixed-citation xml:lang="en">Sun X., Lin R., Liu S., et al. Modeling the relationship of ≥2 MeV electron fluxes at different longitudes in geostationary orbit by the machine learning method. Remote Sensing. 2021, vol. 13, iss. 17, p. 3347. DOI: 10.3390/rs13173347.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B32">
    <label>32.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Sun X., Lin R., Liu S., et al. Modeling the relationship of ≥2 MeV electron fluxes at different longitudes in geostationary orbit by the machine learning method. Remote Sensing. 2021, vol. 13, iss. 17, p. 3347. DOI: 10.3390/rs13173347.</mixed-citation>
     <mixed-citation xml:lang="en">Vernov S.N., Grigorov N.L., Logachev Yu.I., Chudakov A.E. Measurements of cosmic radiation on an artificial Earth satellite. Reports of the Academy of Sciences. 1958, vol. 120, iss. 6, pp. 1231–1233.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B33">
    <label>33.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Wei L., Zhong Q., Lin R., et al. Quantitative prediction of high-energy electron integral flux at geostationary orbit based on deep learning. Space Weather. 2018, vol. 16, iss. 7, pp. 903–916. DOI: 10.1029/2018SW001829.</mixed-citation>
     <mixed-citation xml:lang="en">Wei L., Zhong Q., Lin R., et al. Quantitative prediction of high-energy electron integral flux at geostationary orbit based on deep learning. Space Weather. 2018, vol. 16, iss. 7, pp. 903–916. DOI: 10.1029/2018SW001829.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B34">
    <label>34.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">Williams D.J., Arens J.F., Lanzerotti L.J. Observations of trapped electrons at low and high altitudes. J. Geophys. Res. 1968, vol. 73, iss. 17, pp. 5673–5696. DOI: 10.1029/ja073i017p05673.</mixed-citation>
     <mixed-citation xml:lang="en">Williams D.J., Arens J.F., Lanzerotti L.J. Observations of trapped electrons at low and high altitudes. J. Geophys. Res. 1968, vol. 73, iss. 17, pp. 5673–5696.DOI: 10.1029/ja073i017p05673.</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B35">
    <label>35.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">URL: https://swx.sinp.msu.ru/ (дата обращения 10 сентября 2024 г.).</mixed-citation>
     <mixed-citation xml:lang="en">URL: https://swx.sinp.msu.ru/ (accessed September 10, 2024).</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B36">
    <label>36.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">URL: http://www.swpc.noaa.gov/ (дата обращения 10 сентября 2024 г.).</mixed-citation>
     <mixed-citation xml:lang="en">URL: http://www.swpc.noaa.gov/ (accessed September 10, 2024).</mixed-citation>
    </citation-alternatives>
   </ref>
   <ref id="B37">
    <label>37.</label>
    <citation-alternatives>
     <mixed-citation xml:lang="ru">URL: https://rscf.ru/project/22-62-00048/ (дата обращения 10 сентября 2024 г.).</mixed-citation>
     <mixed-citation xml:lang="en">URL: https://rscf.ru/project/22-62-00048/ (accessed September 10, 2024)</mixed-citation>
    </citation-alternatives>
   </ref>
  </ref-list>
 </back>
</article>
