Low-orbit satellite signal interference detection based on fast regional convolutional network and its multidimensional evaluation method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To counter threats to low-orbit communication satellites from hacker attacks and spectrum interference, this study develops an adversarial sample detection model using a variational self-encoder and a fast region-based convolutional network for spectrum interference detection. The proposed model achieves 97.68% accuracy and an F1 score of 96.86% in intrusion traffic detection, with AUC values above 95% for various network attacks. For single-tone interference, it attains 98.65% accuracy, 96.21% recall, and 93.14% precision, converging within 200 iterations with an average recognition accuracy of 95.47%. These results confirm the model’s ability to detect adversarial threats and interference, enhancing satellite communication security.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it