Air Traffic and Usage Predictions in Avionic Communications using Attention Based VAEGAN Model
Bibliographic record
Abstract
The need for uninterrupted connection is an enabler for enhanced connectivity in aircrafts. Satellite based communication in aircrafts exhibits high latency and can have limited data rates. Furthermore, the increasing demand for air travel can strain the capacity of satellite communication systems, necessitating the development of more robust traffic prediction methods. This necessity is particularly pronounced in the realm of business aviation, given the irregular traffic patterns compared to scheduled commercial flights. In this paper, we present an attention-based VAEGAN model designed to forecast the number of active tails within satellite beams. We extend the capabilities of our proposed model to predict the volume of upstream and downstream usage within these satellite beams. To validate our model, we employ real avionics data collected from the two most heavily traversed flight routes. Finally, we perform a comparative analysis, benchmarking the performance of existing machine learning-based techniques with our proposed model. The findings indicate that the proposed VAEGAN model exhibits superior performance in forecasting irregularities in the timeseries pattern, specifically in forecasting unusual highs or lows in the number of aircrafts within the satellite beam, outperforming alternative models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".