Validation of wind measurements from a multirotor RPAS-mounted ultrasonic wind sensor using a ground-based LiDAR system
Bibliographic record
Abstract
The aim of this research was to establish the validity of wind measurements from on board a multirotor Remotely Piloted Aircraft System (RPAS) for the purposes of wind monitoring applications. A custom-built hexacopter RPAS recorded wind speed and direction by means of an onboard ultrasonic wind sensor, whilst operating in the inherently highly stochastic nature of open field atmospheric conditions. Experimental data were collected during open field hovering flights subject to different ambient conditions with free stream horizontal wind speeds reaching up to 12 m/s. Flights were conducted at different altitudes above ground level and in proximity to a Light Detection and Ranging (LiDAR) remote wind measurement unit that was used as a low-resolution reference meteorological station. Very good correlation was obtained between the RPAS and LiDAR unit for both wind speed and wind direction measurements across all hovering flight altitudes. The RPAS-based wind speed measurements were found to have a consistent 1 m/s positive offset, whilst the RPAS-based wind direction readings had a 6.16° negative offset. These were potentially caused by differences in the localized wind fields between the LiDAR and RPAS measuring positions, as well as by localized RPAS rotor-induced air flows for wind speed measurements and potential slight misalignments in the instruments’ reference datum for wind direction readings.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".