Underwater channel characterization for shallow water multi-domain communications
Bibliographic record
Abstract
The underwater acoustic channel is a difficult communication medium due to its variable link quality which depends on location, time and the environment.This paper reports on underwater channel characterization for shallow water (< 100 m) in the Atlantic offshore of Halifax, Canada. The underwater channel characteristics drives the level of multi-domain robot collaboration possible to characterize a floating target both above- and below-water. RF communications is used between the topside unmanned surface vehicle and unmanned aerial vehicle. Underwater, acoustic modems are used between the submerged part of the unmanned surface vehicle and the unmanned underwater vehicles. The outcome is a multi-domain picture of the floating target from the sensors on these collaborating robots.Before deployment, simulations were performed with a tool that integrates BELLHOP and newly developed complementary analysis capabilities in a MATLAB framework to determine operational ranges from range-dependent attenuation. The main contributions are an underwater acoustic environment-informed approach to placing mobile communicating nodes in a network, the network’s range predictions, channel analysis and a user-friendly GUI to manage the BELLHOP inputs and outputs. The approach and tools are validated in simulations and verified in-water. Transmission losses (TL) and underwater channel characteristics for illustrative cases are 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".