Bibliographic record
Abstract
A small unmanned aerial vehicle (UAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes within the atmospheric boundary layer (ABL). These gusts have major impacts on the flight characteristics and performance of modern small unmanned aerial vehicles. Hence, this project was set to investigate the power spectral density of gusts observed at low altitudes by measuring the gusts with an aerial platform. The small UAV carried an air-data system including a fivehole probe that was adapted for this specific application. The air-data system measured the local wind gusts with an accuracy of 0.5 m/s by combining inputs from a five-hole probe, an inertial measurement unit, and Global Navigation Satellite System (GNSS) receivers. Over 20 flights were performed during the development of the aerial platform. Airborne experiments were performed to collect gust data at low altitudes between 50 m and 100 m. The result was processed into turbulence spectrum and the measurements were compared with the MIL-HDBK-1797 von K´arm´an turbulence model and the results have shown the model underpredicted the gust intensities experienced by the flight vehicle. The anisotropic properties of low-altitude turbulence were also observed when analyzing the measured gusts spectra. The wind and gust data collected are useful for verifying the existing turbulence models for low-altitude flights and benefit the future development of small UAVs in windy environment.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".