Effect of Human Presence on UWB Radiowave Propagation Within the Passenger Cabin of a Midsize Airliner
Bibliographic record
Abstract
We have characterized the effect of human presence on path gain and time dispersion over ultrawideband (UWB) channels within the passenger cabin of a typical midsize airliner. We measured a few hundred channel frequency responses over the range 3.1-6.1 GHz between various locations within a Boeing 737-200 aircraft, with and without volunteers occupying the passenger seats. The links were deployed in a point-to-multipoint configuration with the transmitting antenna along the centre-line of the forward part of the cabin at either the ceiling or headrest level and the receiving antenna at the headrest or armrest level at selected locations throughout the rest of the cabin. As the density of occupancy increased from empty to full, path gain dropped by no more than a few dB on the ceiling-to-headrest paths but dropped by up to 10 dB on the ceiling-to-armrest and headrest-to-armrest paths. The gain reduction reached its maximum at the mid-point of the cabin and decreased thereafter. In all cases, increasing the density of occupancy caused the distance dependence of the rms delay spread to decrease greatly, the decay rate of the scattered components in the power delay profile (PDP) to almost double and the number of significant paths to drop by almost half. The results suggest that human presence substantially affects both path gain and time dispersion within the aircraft and should therefore be considered when assessing the performance of in-cabin wireless systems.
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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 | 0.001 |
| 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".