Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new method for selecting an ellipse-shaped geographical area and constructing a routing grid that circumscribes the contour of the designated area. The resulting grid describes the set of points used by the flight trajectory optimization algorithms to determine an aircraft’s optimal flight trajectory as a function of given particular atmospheric conditions. This method was developed with the intent of its employment in the context of Flight Management System trajectory optimization algorithms, but can be used in Air Traffic Management environments as well. The routing grid limits the trajectory’s maximal total ground distance (between the departure and destination airports), maximizes the geographical area (for a better consideration of the wind conditions) and minimizes the number of grid nodes. The novelty of the proposed method resides in the fact that it allows a distinct and independent parameterization and control of the ellipse’s total surface, and the required size of the take-off/landing procedure maneuvering areas at the departure/destination airports. The ellipse contour constructed using this method is, therefore, well adapted to the particular configuration of the trajectory for which the optimization is performed. Each design variables’ influence is presented, as well as a set of routing grids generated for trajectories corresponding to different total flight distances, and were further compared with real flight trajectory data retrieved using the website Flight Aware.
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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 | 0.001 |
| 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