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Record W2170507507 · doi:10.1109/ccece.2002.1015251

Determining wind velocity and the speed of sound with redundant transponders for a spread spectrum acoustic ranging system

2003· article· en· W2170507507 on OpenAlexaff
G. Drysdale, R.J. Palmer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsRangingAcousticsMultipath propagationSpeed of soundMultipath interferenceWind speedBandwidth (computing)Spread spectrumComputer scienceUnderwater acoustic communicationGlobal Positioning SystemPhysicsUnderwaterTelecommunicationsGeologyCode division multiple access

Abstract

fetched live from OpenAlex

Acoustic spread spectrum signals offer advantages over radio frequencies when implementing a ranging system. Global Positioning System (GPS) receivers become less accurate when shadowed by buildings and obstacles (see Nebot, E.M. and Durrant-Whyte, H., Robotics and Autonomous Systems, vol.26, p.81-97, 1999). Sound waves offer a larger bandwidth which, when used in conjunction with spread spectrum, can create better immunity to multipath interference. A drawback to transmitting acoustic signals in a ranging system is the fact that the speed of sound can vary. Its speed changes in accordance with the temperature, density and speed of the medium in which it travels. When transmitting an acoustic signal through air, the wind affects the propagation velocity. By accounting for a changing speed of sound, a more accurate acoustic ranging system can be achieved. This paper introduces an approach to calculating both the wind velocity and the propagating speed of sound by using redundant transponders.

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: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.273

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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