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Record W4402996344 · doi:10.26866/jees.2024.5.r.251

Millimeter-Wave Far-Field Range Antenna Measurement System for a W-Band Monopulse Antenna

2024· article· en· W4402996344 on OpenAlexaff
In‐June Hwang, Jong-Gyun Baek, J.E. Kim, Jeong-Il Park, Dae-Chan Kim, Heeduck Chae, No-Weon Kang, Chihyun Cho

Bibliographic record

VenueJournal of Electromagnetic Engineering and Science · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsNexen (Canada)
FundersAgency for Defense DevelopmentKorea Research Institute of Standards and Science
KeywordsAntenna (radio)Extremely high frequencyMonopulse radarNear and far fieldPhysicsElectrical engineeringOpticsTelecommunicationsAcousticsEngineeringRadar

Abstract

fetched live from OpenAlex

In this study, a millimeter-wave far-field range antenna measurement system is proposed for W-band monopulse antenna measurements. Since the W-band monopulse antenna installed in radar systems has many input/output ports, multiple calculations are required to measure all the ports. We propose a system structure capable of the simultaneous measurement of multiple channels to quickly and accurately measure the performance of a monopulse antenna. The proposed measurement system includes a multi-channel rotary joint, a pair of diplexers, and multiple vector network analyzer extenders. In addition, an RF sub-system is implemented to reliably measure the phase of the W-band radiation pattern. It utilizes a shared local oscillator source and external power amplifiers for the transmit and receive paths. Employing the proposed multi-channel antenna measurement system, the sum/difference channels of a monopulse antenna were simultaneously measured according to the azimuth angle. In addition, a comparison of the far-field measurements provided by the proposed system and the near-field measurements was conducted.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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