Fixed-Wing UAV Aided Full-Duplex Amplify-and-Forward Relaying With Constant Ambient Wind
Bibliographic record
Abstract
This paper studies a fixed-wing unmanned aerial vehicle (UAV) aided full-duplex amplify-and-forward relaying in the presence of a constant ambient wind, where the UAV flies along a predetermined ground track and provides relaying service to two ground users so as to meet their demand of data transmission. According to aerodynamics and the wind triangle, engine power of the UAV under wind disturbance is evaluated. It is shown that the UAV’s engine power is related only to the vertical component of the wind-speed while not to the horizontal component when the UAV performs a level flight at a constant air-speed. On this basis, an optimization problem corresponding to the adjustment of the UAV’s air-speed, flight time and attitude (namely, crab and pitch angles) is studied. According to the wind triangle, three sub-problems are derived from the initial problem and solved by three propositions proposed, which leads to a novel optimization method. Computer simulation results confirmed that the developed optimization method can allow the UAV to fly according to the predetermined ground track and provide satisfactory relaying service for the two ground users through the adjustment of the UAV’s air-speed and flight time as well as its crab and pitch angles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".