Through-Cloud Relaying to Mitigate Cloud-Induced Attenuation in Optical Satellite Downlinks
Bibliographic record
Abstract
High-speed optical satellite-ground feeder links are expected to play a significant role in providing efficient backhauling for the next generation of mobile networks. In addition, their capability to serve rural and remote areas where establishing fiber backhauling is prohibitively expensive is especially attractive. However, attenuation from clouds is a major weather-related impairment that can degrade and often disrupt these optical satellite feeder links. The most common approach to mitigate the impact of clouds is to deploy multiple ground stations to achieve ground station diversity. However, this solution is often not possible due to the infeasibility and high cost of installing fiber cables between various ground stations. In this paper, we propose the use of high-altitude platform (HAP) stations and unmanned aerial vehicles (UAVs) as decode-and-forward relays through clouds. The effects of clouds and wind on the proposed approach are analyzed, and a heuristic optimization algorithm is presented to maximize link availability with the minimum number of UAVs. Using the ERA5 database, which provides wind and cloud measurements, we evaluate the performance of the proposed approach in northern Canada.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".