Development of a small and transportable de-icing/anti-icing drone-mounted system. Part 2: Prototype testing and proof of concept
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
During winter, aircraft de-icing and anti-icing is a significant part of the operations at large airports. These mandatory operations ensure that the aircraft is clean of any contaminations during take-off. However, these operations are complex due to the heavy equipment required. They cannot be performed at most small airports and remote northern locations. As part of Canada’s Department of National Defense Innovation for Defense Excellence and Security research program, a study to design a de/anti-icing system mounted onto a drone has been initiated. This system should greatly increase the flexibility of these operations, allowing them to be easily performed when no other infrastructure is available. The developed system can even be transported inside the aerial vehicle itself for operations at its initial location and at its destinations. This paper presents the second part of this study in which the system is first tested under laboratory controlled conditions to demonstrate its ability to perform de/anti-icing operations with a real fluid. Then, a flight test is performed for a proof-of-concept in which the system successfully cleans a commercial box truck from contaminants.
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 | 0.000 | 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