Development of a small and transportable de-icing/anti-icing drone-mounted system. Part 1: System design
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.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".