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Record W2979704463 · doi:10.1029/2019rs006834

A Wideband Absorber of Load Dipoles on a Dielectric Substrate with Inductive Ground Plane

2019· article· en· W2979704463 on OpenAlexaff
Yumei Chang, Y.L. Chow, Wenquan Che

Bibliographic record

VenueRadio Science · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Waterloo
FundersState Key Laboratory of Millimeter WavesNational Natural Science Foundation of China
KeywordsGround planeDipoleWavelengthOpticsDielectricWidebandMaterials scienceBandwidth (computing)ScatteringDipole antennaPlanarPhysicsOptoelectronicsAntenna (radio)Electrical engineeringTelecommunicationsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract A planar array of dipoles with matched loads in air backed by a ground plane at a quarter wavelength would become an absorber with a −10‐dB bandwidth of about 10%. Obviously, if the air substrate behind the dipoles is replaced by a dielectric substrate, the physical thickness of the absorber, saying a quarter wavelength, can be reduced. Meanwhile, as the incident waves illuminate on the absorber, the reflections are mainly from three ways, which are the air‐dielectric interface, the back scattering of the loaded dipole as a receiving antenna, and the ground plane. This paper shows that with the combination of the three reflections and a suitable dielectric constant together with its dipole separations, the −10‐dB bandwidth of the absorber increases to 20% and beyond. To further decrease the thickness of the dipole‐based absorber, inductive ground plane is introduced to replace the normal ground; phase analyses of the dipole‐based absorber with inductive ground is also given to explain the feasibility of the proposed technique. Both simulations and measurement results show that, with the proposed technique above, not only the thickness of the dipole‐based absorber can be decreased from a quarter wavelength to less than fifth wavelength, but also the −10‐dB bandwidth of the reflection can be enhanced from 20% to 48% or so.

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: Empirical
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 science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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