Stacking data analysis method for Langmuir multi-probe payload
There are numerous small-scale electron density irregularities in the ionosphere. The coordination of multiple needle Langmuir probes (m-NLPs) enables in situ measurement of electron density with high spatial resolution. However, the theoretical analysis method based on orbital motion-limited (OML)...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2025.1614225/full |
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| author | Jin Wang Jin Wang Duan Zhang Qinghe Zhang Qinghe Zhang Xinyao Xie Fangye Zou Qingfu Du Qingfu Du V. Manu Yanjv Sun |
| author_facet | Jin Wang Jin Wang Duan Zhang Qinghe Zhang Qinghe Zhang Xinyao Xie Fangye Zou Qingfu Du Qingfu Du V. Manu Yanjv Sun |
| author_sort | Jin Wang |
| collection | DOAJ |
| description | There are numerous small-scale electron density irregularities in the ionosphere. The coordination of multiple needle Langmuir probes (m-NLPs) enables in situ measurement of electron density with high spatial resolution. However, the theoretical analysis method based on orbital motion-limited (OML) theory cannot accurately estimate electron density, even at higher resolutions, due to limitations in satellite measurements. In addition, due to the influence of satellite charging and flight wake, the currents collected between multi-probes have low consistency, introducing significant error into the measurement results. This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. The integrated characteristics of the stacking model make full use of the advantages of various models such as multilayer perceptron (MLP), support vector regression (SVR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). The combination of integrated machine learning methods and IRI models greatly improves the accuracy of electron density measurements obtained by m-NLPs. The results indicate that even with poor consistency among the currents collected by multiple probes, the coefficient of determination (R2) of the prediction results using this method can reach 0.9553, which is 0.5079 higher than that of the traditional diagnostic method. |
| format | Article |
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| institution | Kabale University |
| issn | 2296-987X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj-art-dfd4a42e4edb4386aa4fc24ad8e81d6b2025-08-20T03:35:54ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2025-08-011210.3389/fspas.2025.16142251614225Stacking data analysis method for Langmuir multi-probe payloadJin Wang0Jin Wang1Duan Zhang2Qinghe Zhang3Qinghe Zhang4Xinyao Xie5Fangye Zou6Qingfu Du7Qingfu Du8V. Manu9Yanjv Sun10State Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaState Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaState Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaWeihai Key Laboratory of Microsatellites Payload Development and Geospace Environment Exploration, Institute of Space Sciences, Shandong University, Weihai, ChinaThere are numerous small-scale electron density irregularities in the ionosphere. The coordination of multiple needle Langmuir probes (m-NLPs) enables in situ measurement of electron density with high spatial resolution. However, the theoretical analysis method based on orbital motion-limited (OML) theory cannot accurately estimate electron density, even at higher resolutions, due to limitations in satellite measurements. In addition, due to the influence of satellite charging and flight wake, the currents collected between multi-probes have low consistency, introducing significant error into the measurement results. This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. The integrated characteristics of the stacking model make full use of the advantages of various models such as multilayer perceptron (MLP), support vector regression (SVR), K-nearest neighbors (KNN), and light gradient boosting machine (LightGBM). The combination of integrated machine learning methods and IRI models greatly improves the accuracy of electron density measurements obtained by m-NLPs. The results indicate that even with poor consistency among the currents collected by multiple probes, the coefficient of determination (R2) of the prediction results using this method can reach 0.9553, which is 0.5079 higher than that of the traditional diagnostic method.https://www.frontiersin.org/articles/10.3389/fspas.2025.1614225/fullLangmuir probestackingmachine learningplasma diagnosisionospheric irregularity |
| spellingShingle | Jin Wang Jin Wang Duan Zhang Qinghe Zhang Qinghe Zhang Xinyao Xie Fangye Zou Qingfu Du Qingfu Du V. Manu Yanjv Sun Stacking data analysis method for Langmuir multi-probe payload Frontiers in Astronomy and Space Sciences Langmuir probe stacking machine learning plasma diagnosis ionospheric irregularity |
| title | Stacking data analysis method for Langmuir multi-probe payload |
| title_full | Stacking data analysis method for Langmuir multi-probe payload |
| title_fullStr | Stacking data analysis method for Langmuir multi-probe payload |
| title_full_unstemmed | Stacking data analysis method for Langmuir multi-probe payload |
| title_short | Stacking data analysis method for Langmuir multi-probe payload |
| title_sort | stacking data analysis method for langmuir multi probe payload |
| topic | Langmuir probe stacking machine learning plasma diagnosis ionospheric irregularity |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2025.1614225/full |
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