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Record W2964843535 · doi:10.5515/kjkiees.2019.30.6.467

Development of Wide-Band Planar Active Array Antenna System for Electronic Warfare

2019· article· en· W2964843535 on OpenAlexaff
Jae-Duk Kim, Sang-Wang Cho, Sam Yeul Choi, Doo Hwan Kim, Heui Jun Park, Dong Hee Kim, Jonghyun Lee, Inseon Kim, Chang Hoon Lee

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2019
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsNexen (Canada)
FundersAgency for Defense Development
KeywordsAttenuator (electronics)Planar arrayAmplifierElectrical engineeringOpticsBeam waveguide antennaTransmitterCoplanar waveguideAntenna (radio)PhysicsOptoelectronicsEngineeringBandwidth (computing)Dipole antennaPeriscope antennaMicrowaveTelecommunications

Abstract

fetched live from OpenAlex

This paper describes the development and measurement results of a wide-band planar active phase array antenna system for an electronic warfare jamming transmitter. The system is designed as an 8×8 triangular lattice array using a 45° slant wide-band antenna. The 64-element transmission channel is composed of a wide-band gallium nitride(GaN) solid state power amplifier and a gallium arsenide(GaAs) multi-function core chip(MFC). Each GaAs MFC includes a true-time delay circuit to avoid a wide-band beam squint, a digital attenuator, and a GaAs drive amplifier to electronically steer the transmitted beam over a ±45° azimuth angle and ±25° elevation angle scan. Measurement of the transmitted beam pattern is conducted using a near-field measurement facility. The EIRP of the designed system, which is 9.8 dB more than the target EIRP performance(P), and the ±45° azimuth and ±25° elevation beam steering fulfill the desired specifications.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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
Published2019
Admission routes1
Has abstractyes

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