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Record W4387753364 · doi:10.5515/kjkiees.2023.34.9.666

Application of the Reaction Theorem to Calculate the Coupling between Array Antennas on an Integrated Mast

2023· article· en· W4387753364 on OpenAlexaff
W.‐Y. KIM, Jeong-Ho Ko, Il‐Suek Koh, K.K. Bae

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsKootenay Association for Science & Technology
FundersAgency for Defense Development
KeywordsAntenna (radio)Interference (communication)Dipole antennaCoupling (piping)PhysicsMathematicsTopology (electrical circuits)Computer scienceEngineeringTelecommunicationsCombinatorics

Abstract

fetched live from OpenAlex

This study applies the reaction theorem to efficiently calculate the interference between antennas. Conventional methods to calculate the interference between antennas required a full-wave analysis when the distance or position between the antennas was changed, increasing the analysis time. By utilizing the reaction theorem for the antenna interference calculations, the individual antennas can be analyzed using the full-wave analysis, and environmental effects, such as antenna placement and platform, can be considered using the reaction theorem. The reaction theorem was applied to formulate the antenna interference problem. The proposed scheme was validated by comparing the results computed utilizing the reaction theorem and those computed by full-wave analysis of the coupling between blade and wire dipole antennas. The effect of the conducting plates was considered by applying the image theory. Finally, the coupling between two array antennas was considered. The array comprised vertical infinitesimal dipoles. Two arrays were placed on the neighboring faces of the integrated mast structure and the reaction between the arrays was calculated as a function of the main beam direction.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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