A terrain-based comparison of chaos modulation in wireless acoustic sensor networks
Bibliographic record
Abstract
The operating terrain, in which wireless sensor networks are deployed to function, has a potentially significant impact on the network performance. This is due to the inherent non-ideal channel conditions present in the operating environment such as multipath, noise and propagation delays. However, most simulations ignore these non-idealities thus yielding very optimistic results. This paper incorporates channel non-idealities such as propagation delays into a simulated wireless sensor network, and evaluates the effect on network performance. Given their inherent wideband characteristic, which makes them robust to non-ideal conditions such as multipath, chaos-based modulation schemes are a possible alternative to conventional spread spectrum techniques. By incorporating these non-ideal conditions, and evaluating the network performance when deployed in non-terrestrial terrains such as underwater acoustic localization, this paper contributes a more realistic simulation framework of the sensor network performance using the metrics of throughput and end-end delay, and a comparison of the results when different chaos modulation schemes are applied is presented.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".