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Record W2547426063 · doi:10.1109/icwcuca.2012.6402506

Large-scale characterization of an underground mining environment for the 60 GHz frequency band

2012· article· en· W2547426063 on OpenAlexaff
Chanez Lounis, Nadir Hakem, G.Y. Delisle, Yacouba Coulibaly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsPath lossComputer scienceFrequency bandWirelessDelay spreadChannel (broadcasting)Real-time computingRange (aeronautics)Non-line-of-sight propagationElectromagnetic environmentSoftware deploymentElectronic engineeringScale (ratio)Radio propagationLine-of-sightOrthogonal frequency-division multiplexingRemote sensingAntenna (radio)TelecommunicationsEngineeringGeologyAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In order to improve the mining communication applications such as video with high data rates, a characterization of the underground mining channel was done. The use of the IEEE.802.15.3c standard with an OFDM modulation scheme for the 60 GHz can allowobtaining a data rate range from 31.5 Mbps to 5.67 Gbps. This paper provides the propagation characteristics of the mine, which is a complex electromagnetic environment, necessary to the deployment of networks in the IEEE.802.15.3c or the IEEE.802.11ad standard. The experimental results were obtained during an extensive measurement campaign over a frequency range of 61 GHz to 63 GHz in an underground mining environment. These results allow the extracting of the large scale parameters such as the path loss exponent which help to design wireless communication systems. The line of sight (LOS) measurements were performed in the middle of the gallery. Finally, a comparison of the results obtained in a mining environment and in a laboratory is done. The path loss exponents are less than 2 in both scenarios as the environments have dense concentration of scatterers.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score0.228

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 head0.022
GPT teacher head0.218
Teacher spread0.196 · 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 designBench or experimental
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

Citations7
Published2012
Admission routes1
Has abstractyes

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