Imputation Models for Pitch-Angle-Resolved Medium-Energy Electron Flux Data in LEO
Year: 2026
Type: software
Abstract: This archive accompanies the manuscript “Neural Network Imputation of the Pitch-Angle-Resolved Medium-Energy Electron Flux Data in LEO.” It provides the materials required to demonstrate and verify the inference process of eight trained neural network models developed for imputing missing local pitch angle electron flux measurements from the Medium-Energy Electron Detector (MEED) aboard the Fengyun-3E satellite.The archive includes trained models for the E7 and E8 energy channels (E7, 280–410 keV; E8, 410–600 keV) and the PA1, PA2, PA8, and PA9 local pitch angle bins (0°–20°, 20°–40°, 140°–160°, and 160°–180°, respectively), together with the corresponding preprocessing objects, Python inference scripts, example input data, and reference outputs. The example data cover 23 October 2023 from 00:00 to 24:00 UTC. Each generated output file contains UTC, observations, and model outputs. The archived reference outputs allow users to verify that the inference results are reproduced within the expected floating-point tolerance.The package was tested in an independent Python 3.11 environment using CPU inference. Detailed installation, execution, model selection, and verification instructions are provided in the README file.The source code and trained models are released under the MIT License. The example input data and reference outputs are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users should cite both the associated manuscript and this Zenodo archive when using these materials.
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203603598