A High-Fidelity Multi-Model Benchmark Dataset for General Aviation Anomaly Detection Generated via Physics-Based Fault Injection.
- DOI
- 10.1038/s41597-026-07733-y
- Published
- 2026-06-25
- Container
- Sci Data
- Publisher
- Not recorded
- Open access
- yes
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uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1038/s41597-026-07733-y,
title = {A High-Fidelity Multi-Model Benchmark Dataset for General Aviation Anomaly Detection Generated via Physics-Based Fault Injection.},
author = {Lu J and Fang Y and Huang Z and Zhang Y and Fan S and Wu X.},
year = {2026},
journal = {Sci Data},
doi = {10.1038/s41597-026-07733-y},
url = {https://doi.org/10.1038/s41597-026-07733-y}
}RIS
TY - JOUR TI - A High-Fidelity Multi-Model Benchmark Dataset for General Aviation Anomaly Detection Generated via Physics-Based Fault Injection. AU - Lu J AU - Fang Y AU - Huang Z AU - Zhang Y AU - Fan S AU - Wu X. PY - 2026 JO - Sci Data DO - 10.1038/s41597-026-07733-y UR - https://doi.org/10.1038/s41597-026-07733-y ER -
APA
J, L., Y, F., Z, H., Y, Z., S, F., & X., W. (2026). A High-Fidelity Multi-Model Benchmark Dataset for General Aviation Anomaly Detection Generated via Physics-Based Fault Injection.. Sci Data. https://doi.org/10.1038/s41597-026-07733-y
Source records
- europe-pmc · retrieved 2026-09-26T17:37:16.629Z
- doaj · retrieved 2026-09-26T17:37:16.631Z