Bibliographic record
Abstract
Array processing techniques such as beamforming or matched field processing require accurate knowledge o f the location o f individual elements in the array.For horizontal arrays laid on the ocean floor, relative arrival times measured across the array from nearby implosive sources are often used to aid in estimating the sensor positions.However, the inverse problem of determining the sensor positions from the relative arrival times is both nonunique and ill-conditioned.Standard grid search techniques rely on very accurate measurements of the source locations and some knowledge of the array.This paper shows how simulated annealing can be used to solve the inverse problem with limited knowledge of the array or source locations.Synthetic studies show that relative sensor locations can be exactly found while tests with real data show an improvement in array gain comparable to the theoretical limit obtained from a perfectly known array. RÉSUMÉLes techniques de traitement de signal de réseau, tel la conformation du faisceau et le traitement de champs appariés nécessitent une connaissance précise de la location des éléments individuels du réseau.Pour des réseaux horizontaux déployés sur le fond marin, les temps d 'arrivée relatifs des signaux provenant de sources implosives proches, mesurés le long du réseau, sont souvent utilisés pour aider à l'estimation de la position des capteurs.Par contre, le problème inverse de la détermination de la position des capteurs à par tir des temps d'arrivée relatifs est non-unique et mal défini.Les techniques de recherche sur une grille stan dard dépendent de la mesure très précise des positions de la source, et d 'une première approximation de la position du réseau.Cet article démontre comment le traitement thermique simulé peut être utilisé pour résoudre le problème inverse avec une connaissance limitée de la position du réseau et de la source.Des études avec des données synthétiques démontrent que la position relative des capteurs peut être établie avec précision, et des essais avec des données réelles produisent une amélioration du gain de réseau comparable à la limite théorique pour un emplacement de réseau parfaitement connu.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.004 |
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".