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<p>This study introduces a hybrid statistical–machine learning framework to evaluate the impact of the May 2024 geomagnetic storm on the power subsystem of the MisrSat-2 satellite. The proposed framework integrates a multi-tiered statistical approach, employing CUSUM for change point detection, z-score for outlier identification, and event-based analysis, with robust validation through Welch’s t-tests, bootstrapping, and Benjamini–Hochberg false discovery rate (BH-FDR) control. On 10 May, near the storm’s onset, the solar arrays showed modest current deviations, after validation, 13 events solar panel-1 and 17 events solar panel-2 were retained, with the largest cluster between 07:05 and 09:25&#xa0;UTC. whereas the battery subsystem remained stable and buffered fluctuations, maintaining bus integrity. Event-based analysis confirmed that all deviations were small (&lt; 4%) and within design tolerances. Radiation degradation modeling with EQUFLUX predicted only 0.32% cumulative loss for May 2024, align with the absence of measurable radiation-driven signatures in telemetry. Extending beyond descriptive detection, a Mixture of Experts (MoE) machine learning framework achieved superior predictive accuracy (R<sup>2</sup> = 0.921, MAE = 0.063&#xa0;A) compared to baseline models providing interpretable validation of statistical findings. The novelty of this research lies in its integrative approach, merging physics-based modelling, robust statistical methods, and interpretable machine learning to provide a scalable framework for anomaly diagnostics and mission assurance in dynamic space environments.</p>

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A hybrid statistical–machine learning framework for evaluating geomagnetic storm effects on MisrSat2 satellite power subsystems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Marwa S. Mostafa,
  • Mohammed Abu Bakr Ali,
  • N. Hesham,
  • Yassin Mounir Yassin,
  • Asmaa Ahmed,
  • Dalia Elfiky

摘要

This study introduces a hybrid statistical–machine learning framework to evaluate the impact of the May 2024 geomagnetic storm on the power subsystem of the MisrSat-2 satellite. The proposed framework integrates a multi-tiered statistical approach, employing CUSUM for change point detection, z-score for outlier identification, and event-based analysis, with robust validation through Welch’s t-tests, bootstrapping, and Benjamini–Hochberg false discovery rate (BH-FDR) control. On 10 May, near the storm’s onset, the solar arrays showed modest current deviations, after validation, 13 events solar panel-1 and 17 events solar panel-2 were retained, with the largest cluster between 07:05 and 09:25 UTC. whereas the battery subsystem remained stable and buffered fluctuations, maintaining bus integrity. Event-based analysis confirmed that all deviations were small (< 4%) and within design tolerances. Radiation degradation modeling with EQUFLUX predicted only 0.32% cumulative loss for May 2024, align with the absence of measurable radiation-driven signatures in telemetry. Extending beyond descriptive detection, a Mixture of Experts (MoE) machine learning framework achieved superior predictive accuracy (R2 = 0.921, MAE = 0.063 A) compared to baseline models providing interpretable validation of statistical findings. The novelty of this research lies in its integrative approach, merging physics-based modelling, robust statistical methods, and interpretable machine learning to provide a scalable framework for anomaly diagnostics and mission assurance in dynamic space environments.