Multi-Fequency, Pulse-to-pulse Coherent Doppler Sonar Profiler
Bibliographic record
Abstract
Under ideal conditions, pulse-to-pulse coherent Doppler sonar can measure profiles of water velocity with unparalleled accuracy and resolution. However, this technique is limited in application by the occurrence of range and, more critically, speed ambiguities. A simple way to deal with speed ambiguities is to invert velocities using time history or prior knowledge of the flow structure, but these approaches are not always practical or reliable. Another technique is the use of a dual (or multiple) pulse repetition interval: this approach provides a reliable means of improvement but reduces the profile sample rate, and the pulse repetition interval is not always a free parameter (for example in the presence of a boundary). We present a new approach where multiple acoustic frequencies are used simultaneously, allowing a nearly five-fold increase in ambiguity velocity with no reduction in profile sample rate. Results are presented from a prototype multi-static system operating over the frequency range from 1.2 to 2.4 MHz, enabled in part through use of broad-band piezo-composite transducers. The prototype system generates two-component velocity profiles at a rate of 150 profiles/second over a 30 cm range interval with 3 mm range resolution. System performance is demonstrated under laboratory conditions with observations of flow in a turbulent jet.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".