Satellite–Ground Covert Communications Against an Aerial Warden
Bibliographic record
Abstract
Aerial wardens could pose significant security threats to satellite-ground communications due to their stronger received signals than legitimate ground users. To address this issue, the signals from all jamming satellites in low Earth orbit satellite networks, i.e., full jamming strategy (FJS), are utilized to counter the detection of the aerial warden. However, this worsens the communication quality of ground users. To improve it, we utilize the difference in visible spherical crowns between the ground user and the aerial warden due to the Earth blockage to propose the safeguard-zone strategy (SGS) via merely muting the jamming satellites visible to the ground user. To evaluate the effectiveness of the proposed strategies, we propose a stochastic geometry-based analytical framework to derive the covert probability and connection probability. To capture the trade-off between covertness and reliability, the effective covert rate, defined as the product of transmission rate, covert probability, and connection probability, is also analyzed and optimized. The results validate the accuracy of the analytical expressions and illustrate that SGS outperforms the FJS in the connection probability and effective covert rate with a small loss in covert probability, which can be compensated by increasing the transmit power or the number of jamming satellites.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".