Analysis of Experimental Data Fusion Schemes for Underwater Communication over a Hydrophone Array
Bibliographic record
Abstract
Underwater communication across long distances remains a challenging task, primarily due to factors such as noise, geometric spreading, and multi-path propagation. These causes of signal distortion often result in significant condtion variations, making reliable communication difficult. In an effort to address this challenge, Defence Research and Development Canada (DRDC) participated in a sea-trial experiment during the summer of 2021 to test the effectiveness of UWSPR, a communication scheme designed for achieving reliable and narrow band communication. The experiment involved transmitting 16 UWSPR one-minute frames near a marginal ice zone, while an array of hydrophones placed 2.3 kilometers away from the projector recorded the transmissions. In this paper, the results of the analysis of the recordings are presented. The recordings were demodulated using a novel multi-frame and multichannel strategy that combines energy from several channels, where each hydrophone and acoustic frequency pair defines a channel. A performance assessment of the demodulated signals is provided, demonstrating the effectiveness of the enhanced UWSPR scheme in achieving reliable communication in the challenging underwater environment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".