Dynamic soaring surveillance in a gradient wind field
Why this work is in the frame
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Bibliographic record
Abstract
Small unmanned aerial vehicles face energy challenges in performing energy-intensive tasks such as surveillance. This paper is inspired by the dynamic soaring strategy to exploit kinetic energy from wind gradients. By incorporating the soaring strategy into the surveillance framework, a dynamic soaring surveillance approach is proposed. In this approach, an energy-efficient trajectory is designed based on a specified Dubins path. At the initial and final points of the trajectory between the neighboring visit, UAV’s total energy (potential and kinetic energy) remains as close as possible. As a result, the UAV can take the advantage of the wind instead of consuming its on-board power to perform the surveillance task. Therefore, the endurance performance may be extended to allow for longer and wider surveillance with a limited on-board power supply. In this paper, the proposed energy-efficient planning algorithm is presented, followed by simulation results to demonstrate its effectiveness.
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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 | 0.000 |
| 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 | 0.000 | 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 it