Revolutionizing nanosatellites’ data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction
- DOI
- 10.1371/journal.pone.0347344
- Published
- 2026-04-30
- Container
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0347344,
title = {Revolutionizing nanosatellites’ data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction},
author = {Sara Karim and Ekramul Haque Tusher and Abdur Rahman and Riadul Islam Rabbi and K. O. Anwar and Khair Razlan Othman},
year = {2026},
journal = {PLOS One},
doi = {10.1371/journal.pone.0347344},
url = {https://doi.org/10.1371/journal.pone.0347344}
}RIS
TY - JOUR TI - Revolutionizing nanosatellites’ data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction AU - Sara Karim AU - Ekramul Haque Tusher AU - Abdur Rahman AU - Riadul Islam Rabbi AU - K. O. Anwar AU - Khair Razlan Othman PY - 2026 JO - PLOS One DO - 10.1371/journal.pone.0347344 UR - https://doi.org/10.1371/journal.pone.0347344 ER -
APA
Karim, S., Tusher, E. H., Rahman, A., Rabbi, R. I., Anwar, K. O., & Othman, K. R. (2026). Revolutionizing nanosatellites’ data integrity with SEEnet: A real-time ensemble learning approach for Single-Event Effect (SEE) prediction. PLOS One. https://doi.org/10.1371/journal.pone.0347344
Source records
- crossref · retrieved 2026-11-08T02:44:53.914Z