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Record W4281793232 · doi:10.18421/tem112-54

Comparative Analysis of Methods for Calculating the Energy Flux Density used in Assessing the Permissible Level of Environmental Pollution by Electromagnetic Radiation

2022· article· en· W4281793232 on OpenAlexaboutno aff
Viliam Ďuriš, В. Н. Иванов, Sergey G. Chumarov

Bibliographic record

VenueTEM Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Signal Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsIsotropyFlux (metallurgy)Range (aeronautics)Antenna (radio)Electromagnetic radiationRadiationPower densityEnvironmental scienceOpticsPower (physics)PhysicsTelecommunicationsEngineeringMaterials scienceAerospace engineering

Abstract

fetched live from OpenAlex

In this article, the results of measurements and calculations of the power flux density in the frequency range 670 MHz-766 MHz (46th and 57th television channels) of digital broadcasting of the DVBT2 standard were presented. Experimental data on the power flux density were obtained by the selective electromagnetic field meters NARDA SRM-3000 with an isotropic antenna. The radiating system consists of 16 panel antennas (four-storey panel antennas) with a circular radiation pattern in the horizontal plane. Calculations were carried out in the ПК АЭМО (Software package for electromagnetic environment analysis) and MMANA programs. The data obtained were compared with the permissible exposure limit (PEL) of energy flux density (EFD) set by health and safety rules and standards (2.1.8/2.2.4.1383-03), which operate on the territory of Russia.The values obtained are significantly less than the acceptable levels. In the USA, Japan, European countries, Canada, China, and the PEL of energy flux density in the frequency range 300 MHz – 300 GHz are higher than in Russia.

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)0.012
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.055
GPT teacher head0.365
Teacher spread0.310 · 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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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