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Record W2527307999

Experimental Evaluation of Wireless Communication Channels under Radiation Environment

2014· article· en· W2527307999 on OpenAlexaff
Quan Wang, Liquan Li, Jin Jiang, Ataul Bari, Changjian Deng, Dongyi Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsWestern University
Fundersnot available
KeywordsWirelessTransmitterAntenna (radio)AttenuationSoftware deploymentRadio waveElectromagnetic radiationRadiationNuclear powerComputer scienceElectrical engineeringTelecommunicationsElectronic engineeringEngineeringPhysicsChannel (broadcasting)Optics
DOInot available

Abstract

fetched live from OpenAlex

Deployment of wireless systems in nuclear power plants has attracted a lot of attention recently. However, before wireless systems can be installed in a nuclear power plant, it is necessary to evaluate the effect of radiation environment on electromagnetic wave which is the communication media for all radio wave based wireless systems. This is particular important if the wireless systems are expected to work in a harsh and radioactive environment following a severe accident. This paper presents some results of an experiment for evaluating the effect of radiation on electromagnetic wave. The experiments involve placing transmitter antenna and receiver antenna in a hot cell with variable strength of radiation to study the attenuation effects of the radioactive media. The results indicate that radiation does not effect on the electromagnetic wave propagation. This fact should be considered during the design and deployment wireless systems in a potentially radioactive environment. Keyword: Radiation Environment; Wireless Communication; Electromagnetic Wave; Gamma ray

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.237
Teacher spread0.217 · 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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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