Vertical Avoidance and Recovery Analysis of a General Aircraft in Near Mid-Air Collision Scenarios Using Design and Analysis of Computer Experiments
Bibliographic record
Abstract
Scenarios with mid-air collision events require complex studies where the aircraft may fly on the edge of their flight envelope. This problem is not only limited to the maneuver since the recovery might lead the system to an unstable state if the performance is critical. Design and analysis of computer experiments (DACE) by using the uniform design (UD) experimental method represent a tool to study the performance of the aircraft without the full analysis of the dynamics of flight. In a specific simulation context described in this paper, encounters between two representative general aircraft, a Cessna 172 and a Twin Otter, in a Phi (φ) maneuver are simulated. From the encounters, a diving avoidance maneuver is developed in a mid-air collision circumstance. The recovery is later observed and analyzed when the aircraft remains in a safe area while it waits for the thread to be removed. Assuming that the computer model is accurate and the simulation stable, the metamodel using UD provides an optimal combination of commands for all the scenarios with a minimum discrepancy. The implications of this paper are seen in the flexibility of this method owing to its adaptability to fit any computer model and simulation scenario. This feature is currently being used to study unmanned aerial vehicles and their interactions with other human-piloted aircraft in the same environment with the purpose of developing critical avoidance maneuvers.
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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) | 0.000 | 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) | 0.001 | 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".