A Dual-Prediction Framework for High-Energy Proton Flux Driven by Graph Neural Networks
Predicting high-energy proton flux is essential for radiation-effect protection of satellite devices. We introduce a dual-prediction framework based on Graph Neural Networks (GNN) to model proton flux from both non-time-series and time-series perspectives. In the non-time-series approach, the model...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2025-01-01
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| Colecção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/11214361/ |
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