Development of a small and transportable de-icing/anti-icing drone-mounted system. Part 1: System design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The icing of aircraft on the ground is an important flight safety issue. Aircraft must be de-iced and anti-iced to remove and protect the aircraft from freezing and frozen contamination, respectively, before and during takeoff. Winter de-icing and anti-icing operations are nonetheless costly, require a significant amount of time, and rely on extensive infrastructures. The essential equipment is often not available at smaller airports and remote locations, thereby preventing departures under a range of winter conditions. For sites located in northern Canada, this limitation results in frequent takeoff delays or cancellations during a significant portion of the year. As part of Canada’s Department of National Defence Innovation for Defence Excellence and Security research program, this study aimed to develop a practical solution to mitigate these limitations. This solution involves mounting a ground de-icing/anti-icing system onto a drone for a system that can be readily acquired and stored at smaller airports and remote locations or even be transported within the aircraft itself to ensure the possibility of performing de-icing/anti-icing operations at sites lacking the standard infrastructure. This paper presents the conception and design of a drone-based system that should allow winter operations at small and remote airports where it is not yet available. To do so, a spraying system satisfying the industry requirements is designed and integrated to a selected drone. The calculations were theoretically confirmed as a concept, and a prototype was built to perform laboratory and flight test in the next part of the study.
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.
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 | 0.001 | 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