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Record W2004882326 · doi:10.1109/pimrc.2010.5671859

Radio channel characterization through leaky feeder for different frequency bands

2010· article· en· W2004882326 on OpenAlexaff
Abdellah Chehri, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNarrowbandWidebandChannel (broadcasting)Power-line communicationComputer scienceElectronic engineeringMobile telephonyTransmission (telecommunications)Electrical engineeringTelephonyRadio frequencyFrequency bandTelecommunicationsRadio spectrumPower (physics)Digital radioMobile radioEngineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

Since several years, communications between fixed and mobile units have been studied in areas in underground tunnels. The leaky feeder (LF) is one of the most useful results of these studies. The LF technology in combination with other communication systems allows us to reduce the transmission power of the mobile entities without sacrificing quality of service. Because the leaky feeder cables are able to transfer both power and RF signal; these technologies are considered as particular case of line power communication. In this paper we present and analyze the results of narrowband and wideband radio channel characterization. The measured frequency bands are selected for uses in several applications (WPAN, CDMA, video and analog telephony). The measurement were performed in underground mine gallery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2010
Admission routes1
Has abstractyes

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