Orbital Debris Outage Risk Analysis in Inter-Satellite Links With Multicasting
Bibliographic record
Abstract
Inter-satellite links (ISLs) are crucial for enabling high-speed data transmission and advancing non-terrestrial missions. This study explores inter-satellites link with multicasting (ISL-M) under the threat of space debris to evaluate the associated orbital debris outage risk (ODOR). Necessary components for an ISL-M are established. An approximation of the ODOR is derived in terms of the outage probability. Our findings show that an exterior object causing shadowing can also be sensed by an observer due to the suppressed line-of-sight path gain. ISL-M not only enables orbital debris detection with a low complex estimators based on received signal strength such as likelihood ratio, but also reduces ODOR by increased spatial diversity among ISLs. In our demonstration,$\sim 17$dBFS reference power losses are observed due to the obstacle blockage depending on its position. On the other hand,$\sim 2$dBFS deviation sensed by an external observer due to the obstacle. The numerical results show that ISL-M enables outage resilient communication within$[0, 0.5]$probability of debris occurrence for different shadowing severity. This capability lays the foundation for integrated sensing and communication systems in space under threat of debris.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".