Air-to-Ground Cellular Communications for Airplane Maintenance Data Offloading
Bibliographic record
Abstract
Airplane sensors and on-board equipment collect an increasingly large amount of maintenance data during flights that are used for airplane maintenance. We propose to download part of the data during airplane’s descent via a cellular base station (BS) located at the airport. We formulate and solve an offline optimization problem to quantify how much data can be offloaded in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-dedicated band</i> while ensuring that the interference power at the terrestrial BSs in the vicinity of the airport remains below a maximum allowable threshold. Our problem allows for adaptive tuning of transmit power, number of frequency channels to be used, and beamforming according to the position of the plane on the descent path. Our results show that, when the BS at the airport and the plane are equipped with uniform planar arrays, during the last 5 minutes of descent, in the microwave band the plane can offload up to 5GB of maintenance data in a 20 MHz band with a transmit power of 1 W or 40 W. In the mmWave band, the plane can offload up to 24 times more data in a 1 GHz band, with a transmit power of 40 W (using most of the bandwidth) and below 4 times with a transmit power of 1 W (effectively using a maximum of 202 MHz due to bandwidth tuning). Beamforming, power and bandwidth tuning are all crucial in maintaining a good performance in the mmWave band while in the microwave band, dynamic tuning of bandwidth does not improve the performance noticeably.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".