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Record W1920006418 · doi:10.1109/ccst.1995.524725

Advances in ported coaxial cable technology

2002· article· en· W1920006418 on OpenAlexaff
R.W. Clifton, B.G. Rich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsSenstar (Canada)
Fundersnot available
KeywordsPortingCoaxialComputer scienceReliability (semiconductor)Coaxial cableEngineeringSystems engineeringElectrical engineeringEmbedded systemTelecommunicationsOperating systemSoftware

Abstract

fetched live from OpenAlex

Ported coaxial cable technology was first developed in the mid-1970's in response to a growing need for high performance covert sensors for perimeter intrusion detection. Technology development over the years has lead to a number of different sensor products that have seen wide-spread application. The original ported coaxial cable sensors went through several generations of improvements-primarily in response to experience gained from thousands of installations worldwide. These early generations were essentially refinements to the original products, intended to incrementally improve detection and invalid alarm performance and also to increase reliability. For perspective, this paper provides a very brief historical review of the ported coaxial cable sensors that have been commercialized to date and introduces two new and innovative sensor configurations that have recently emerged from research and development programs at Senstar Corporation. The first is a new "single-cable" networked sensor introduced to the market as S/spl infin/Trax which significantly reduces installation costs and complexity by eliminating the need for a second buried cable-while retaining the detection zone characteristics and performance established by the original sensors. The second is a "totally covert" configuration that takes advantage of recent advances in semiconductor technology to further simplify installation and also offers a growth path for joint domain and multiple sensor integration.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.345

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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 designNot applicable
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

Citations2
Published2002
Admission routes1
Has abstractyes

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