UWB Radiowave Propagation within the Passenger Cabin of a Boeing 737-200 Aircraft
Bibliographic record
Abstract
Past efforts to develop measurement-based models of the UWB propagation channel have focused on residential, office, and industrial environments. However, with its confined volume and cylindrical structure, the geometry of the passenger cabin of a jet aircraft is fundamentally different from those environments considered previously. Here, we characterize large-scale aspects of UWB propagation within the passenger cabin of a typical mid-size airliner. Our measurement database consists of hundreds of frequency responses over the range 3.0 - 10.6 GHz that we collected aboard a Boeing 737-200 aircraft. The data were collected in a point-to-multipoint configuration in which a biconical UWB transmitting antenna was mounted at one of three locations near the cabin ceiling and an identical receiving antenna was mounted at headrest, armrest, and footrest level at over 50 locations throughout the cabin. We have accounted for the effects of human presence by collecting this data with the cabin empty, with passengers occupying half of the seats, and with passengers occupying virtually all of the seats. Our initial data reduction efforts have focused on the manner in which human presence and/or receiving antenna mounting location affects five large scale aspects of UWB propagation, i.e., those that affect coverage and reliability: (1) the distance dependence of path loss, (2) the frequency dependence of path loss, (3) the ratio of the energy in the line-of-sight component to the scattered components of the channel impulse response, (4) the RMS delay spread, and (5) the locations and distribution of the poles of the corresponding autoregressive frequency domain model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".