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Robust Wakeup, Acquisition, and Alignment for Low Cost, Battery-Limited ACOMMS Modems Operating with High Speed Platforms

2024· article· en· W4406794190 on OpenAlexaboutno aff
Dale Green, Mark F. Baumgartner, Jim Partan, Sandipa Singh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceBattery (electricity)Embedded systemSpeedupComputer hardwareReal-time computingOperating systemPower (physics)

Abstract

fetched live from OpenAlex

Protection of marine mammals, especially “right” whales along the Western Atlantic Ocean, has become a critical imperative for US and Canadian Regulatory agencies and the lobster and crab fishing industries. The historical and continuing practice of deploying sea floor traps connected to surface floats via long rope is one of two primary causes of whale injury and depth. The other is ship strike from speeding vessels. A consensus has been reached within these communities that the ropes must be replaced, but no consensus has yet been reached on precisely how this can be achieved. The Woods Hole Oceanographic Institution (WHOI), has informally developed a concept for the combined use of acoustic communications (acomms) between surface boats and sea floor traps, combined with “cloud-based” connectivity to mitigate mutual interference and noise. The acomms system is based loosely on JANUS [1] in that the modulation follows the precise description developed at CMRE. However, at a center frequency of 25 kHz the wakeup and acquisition in the presence of very high-speed and noisy platforms (up to +/- 7 m/s, or +/-15 kts) requires a significantly different approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.204
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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