Bibliographic record
Abstract
Satellite constellations have become a promising solution for future Terabits/s connectivity. However, conventional radio frequency (RF) communication links are unable to achieve such high throughput due to the limited spectrum available. Free-space optical (FSO) communication has recently acquired a growing interest in the satellite communication (Satcom) community as a potential solution. Nonetheless, FSO communication is sensitive to the adverse effects of beam scintillation, beam-wander-induced pointing errors, free-space loss, and weather conditions. To mitigate these effects, we present a new space-air-ground (SAG) FSO transmission system with a strategically deployed high-altitude platform relay. It can effectively remedy the effect of atmospheric impairments on FSO transmission, particularly at high zenith angles. Then, we integrate the proposed SAG-FSO link and traditional space-ground FSO/RF links to further improve system performance and reliability. We carry out a thorough performance analysis of the resulting hybrid SAG-FSO/RF communication systems. Tractable analytical expressions for the symbol error and outage probabilities are derived. The numerical results highlight the significant potential of the proposed, highly innovative Satcom systems over existing solutions. Furthermore, Monte-Carlo simulations are performed to validate the accuracy of the analytical results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".