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Record W3002394035 · doi:10.1051/epjconf/202022508007

Comparative Evaluation of Three Wireless Sensor Network Transceivers in a High Radiation Environment

2020· article· en· W3002394035 on OpenAlexafffund
Qiang Huang, Jin Jiang, Yongqiang Deng

Bibliographic record

VenueEPJ Web of Conferences · 2020
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear EngineeringOhio State University
KeywordsWireless sensor networkWirelessTransceiverNuclear power plantWireless networkRadiator (engine cooling)EngineeringEmbedded systemComputer scienceElectrical engineeringTelecommunicationsComputer networkPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents on the results of radiation studies for three commonly used wireless sensor nodes based on the following protocols: ZigBee, WirelessHART, ISA 100.11a, and network devices built with commercial off-the-shelf (COTS) components. The level of radiation considered is at par with that experienced at Fukushima Daiichi Nuclear Power Plant after the accident. An experimental setup is developed to monitor behaviors of each wireless device and network real-time under the 60Co gamma radiator at The Ohio State University Nuclear Reactor Lab (OSU-NRL). The experimental results have indicated that the performance of the communication channels and wireless signal parameters do not degrade significant under such radiation. However, all the tested devices and networks can only survive for several hours under the high dose rate condition (20 K Rad/h). The results of these experimental studies have provided useful references to those who design and manufacture COTS-based wireless monitoring systems for use in high level radiation 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 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.051
GPT teacher head0.253
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 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

Citations3
Published2020
Admission routes2
Has abstractyes

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