Demodulation of a Hydroacoustic Sensor Array of Fiber Interferometers Based on Ultra-Weak Fiber Bragg Grating Reflectors Using a Self-Referencing Signal
Bibliographic record
Abstract
We report on current theoretical and experimental results of hydroacoustic sensing array based on ultra-weak fiber Bragg gratings, using a modified phase generated carrier (PGC) demodulation method with a self-referencing signal. The self-referencing signal is obtained by a sensor isolated from acoustic signals and other environmental disturbances. We report improvements over the conventional PGC methods. Using our demodulation method and with nonsensitized bare fiber (reference sensor0) keeping separate from water, experiment testing demonstrates a minimum detectable hydroacoustic pressure of 2239 μPa/√Hz. The properties of our demodulation method are also compared with those of the conventional PGC algorithms. Both simulation and experiments indicate that our demodulation method is immune to the drifts of modulation depthCand of carrier frequency, and the detectable frequency range can be increased by five times compared to using conventional PGC methods with the same carrier frequency. The sensor array exhibits a particularly large response in the very low frequency region, which is of great importance for underwater seismic detection and submarine applications.
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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 | 0.001 |
| 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 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".