Design and Evaluation of a New Localization Scheme for Underwater Acoustic Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underwater acoustic sensor networks are quite different from terrestrial wireless sensor networks. Localization for underwater applications is different due to the bandwidth limited acoustic communication, sparsely distributed network deployment, and more expensive and powerful sensor nodes. In this paper, we propose a new scheme to achieve better localization accuracy for underwater acoustic sensor networks. Instead of using the commonly adopted circle-based event detection and least squares algorithm based location estimation, the proposed scheme utilizes the hyperbola-based approach for event localization and a normal distribution for estimation error modeling and calibration. Our analysis and simulation results indicate that the performance of the proposed scheme is clearly better than those from the least squares location estimation based localization schemes.
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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 | 0.000 | 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 it