Modeling and Simulation for Assessing the Risk of Near Mid-Air Collisions in Unmanned Aerial Systems
Bibliographic record
Abstract
The use of Unmanned Aerial Systems (UASs) is expanding speedily.This results in a need to integrate UAS traffic into non-segregated airspace.However, this integration introduces a risk of a mid-air collision between a UAS and a manned aircraft in the airspace.To deal with this issue, we present a UAS traffic simulation model (or UAS model) to assess the risk of a near mid-air collision (NMAC) between a UAS and another manned aircraft operating in Canada's Northern airspace.In this thesis, we present two implementations of the UAS model.The first implementation is a proof-of-concept model which involved the Cell-DEVS formalism and software, while the second implementation is a more complete model implemented with the Processing software.Our results show that there is a low probability of an NMAC occurring between a UAS and an aircraft in the airspace region being considered in this work.However, more simulations need to be run to achieve more precise and accurate results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".