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Path Loss Analysis for Array Antenna systems in Underground Mine at 3.5GHz

2023· article· en· W4386523913 on OpenAlexaff
Saif Eddine Hadji, Mourad Nedil, Mohamed Lamine Seddiki, Ismail Ben Mabrouk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsPath lossTransmitterAntenna (radio)Antenna height considerationsAcousticsRangingLine-of-sightShadow (psychology)Log-distance path loss modelLine (geometry)Channel (broadcasting)EngineeringElectronic engineeringGeologyElectrical engineeringTelecommunicationsPhysicsWirelessAerospace engineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

In this paper, path loss characteristics of the underground mine channel for a 64-element uniform rectangular array (URA) antenna system at 3.5GHz are presented and analyzed. Measurements were conducted in an underground mine environment, including both line-of-sight (LoS) and best-line-of-sight (Best-LOS) scenarios, with the transmitter-receiver (Tx-Rx) separation distance ranging from 5m to 70m. The log-distance path loss model was used to evaluate the path loss performance from the measurement data. The obtained path loss exponents were found to be 1.56 and 1.76, with shadow fading standard deviation of 1.34dB and 2.32dB for LoS and Best-LOS, respectively. These results indicate that the propagation characteristics in an underground mine have a waveguide effect on the propagated signal. As a result, the 3.5 GHz frequency band could be a suitable candidate for 5G communications in underground mine environments.

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: none
Teacher disagreement score0.616
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.244
Teacher spread0.208 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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