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Record W4385894644 · doi:10.5515/kjkiees.2022.33.6.415

Design of a Parallel Resonance Based Frequency-Selective Rasorber

2022· article· en· W4385894644 on OpenAlexaff
Geonyeong Shin, Young-Wan Kim, Se-Hwan An, Ji-Han Joo, Ick‐Jae Yoon

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsRadomeEquivalent circuitMaterials scienceRLC circuitLC circuitResonatorAbsorption (acoustics)OpticsFrequency bandTransmission (telecommunications)AcousticsCenter frequencyOptoelectronicsResonance (particle physics)Electrical engineeringBand-pass filterPhysicsEngineeringAntenna (radio)Capacitor

Abstract

fetched live from OpenAlex

In this study, we propose a rasorber in the Ka-band as a means of reducing the radar cross section (RCS) and compare its characteristics and performance with those of state-of-the-art studies. A rasorber is a combination of a radome and an absorber. It implements the radome function while absorbing external electromagnetic waves. Frequency-dependent frequency-selective rasorber (FSR) is the most commonly used type of rasorber. An FSR, comprising lossy and lossless resonators, is designed in this study to have an electromagnetic transmission characteristic at 35 GHz through a parallel LC tank circuit. The lossy resonator located in front of the losselss resonator exhibits a broadband electromagnetic absorption characteristic due to an additional series RLC circuit. The center frequency of the transmission band is tuned by adjusting the resonant frequency of the LC tank circuit. Additionally, the proposed FSR is designed with a transmission band located at the end of the absorption band. It has a transmission frequency of 35 GHz with an absorption rate of 80 % or more at 7.8∼28.5 GHz including the X, Ku, and K-bands.

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 score0.119
Threshold uncertainty score0.336

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 head0.010
GPT teacher head0.204
Teacher spread0.193 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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