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Record W1969485551 · doi:10.1109/lawp.2014.2329768

A Performance Study of Line-of-Sight Millimeter-Wave Underground Mine Channel

2014· article· en· W1969485551 on OpenAlexaffabout
Mohamad El Khaled, Paul Fortier, Mohamed Lassaad Ammari

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsExtremely high frequencyPath lossMultipath propagationChannel (broadcasting)Line-of-sightTransmitterChannel capacityAcousticsOmnidirectional antennaHorn antennaElectronic engineeringComputer scienceWirelessRemote sensingElectrical engineeringAntenna (radio)TelecommunicationsEngineeringPhysicsGeologySlot antennaAerospace engineering

Abstract

fetched live from OpenAlex

This letter studies the performance of the line-of-sight underground mine channel in the millimeter-wave band. Using a vector network analyzer (VNA) and three carefully chosen antennas, one omni and two horn antennas, we performed measurements at the 40- and 70-m levels in the CANMET mine located in Val-d'Or, QC, Canada. The performance of the channel is studied in terms of its path-loss exponent, shadowing, and capacity. Results prove that the path-loss exponent in an underground mine is smaller than the free-space value. The shadow fading fits very well with the normal distribution. A model of the capacity as a function of the distance between transmitter and receiver is investigated. Channel capacity depends on the multipath characteristics; the capacity found in the narrow environment (70 m) is higher than in the wider environment (40 m). The results also show that it is better to use omnidirectional antennas for a wireless communication system in the millimeter-wave band in an underground mine environment.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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

Citations30
Published2014
Admission routes2
Has abstractyes

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