Enhanced FSO (Free Space Optical) Data Backhaul for Satellite IoT Networks in the Presence of Adverse Weather Conditions
Bibliographic record
Abstract
For decades, satellites have facilitated remote Internet of Things (IoT) services. However, the recent proliferation of increasingly capable and numerous sensors has led to a substantial growth in the volume of data. But legacy Radio frequency-based systems have limited capacity. Hence, free space optical (FSO) communication systems have been proposed, as they allow for high data rates. However, FSO communications are vulnerable to adverse weather. We investigated the potential benefits of using high-altitude platform systems (HAPS) to improve the performance of these networks. In addition, we also developed strategies that use predicted weather conditions to improve the HAPS-enabled networks' performance. Subsequently, we developed a reinforcement learning-based (RL) solution to improve the energy efficiency of satellite-to-ground FSO downlink operations in the presence of adverse weather. We compared the RL solution to a set of simple threshold solutions and found notable performance increases for some network configurations.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".