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Record W2887629066 · doi:10.1002/mop.31315

Design of a broadband microwave absorber from wideband receiving antenna array

2018· article· en· W2887629066 on OpenAlexaff
Yumei Chang, Wenquan Che, Y.L. Chow

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Jiangsu Province
KeywordsDipole antennaWidebandOpticsMicrowaveBroadbandAcousticsAntenna arrayPhysicsAntenna (radio)EngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract In some extent, microwave absorbers and receiving antennas have some similarities, for both of them aim at receiving the ambient electromagnetic wave around. The essential difference between them is that the receiving antenna must transmit the EM wave to the loading system, but the microwave absorber just need to dissipate them. In this work, we studied a novel broadband microwave absorber, which is achieved from a wideband folded‐dipole antenna array backed through a quarter‐wavelength spacer by a metal ground. By studying the impedance locus of the folded dipole array on Smith chart, the authors first obtained a wideband folded‐dipole array antenna, and then changed it to an absorber by replacing the excitations with lump resistors. Both simulation and measurement results show that the folded‐dipole array‐based microwave absorber can achieve a wide bandwidth of 100% at the normal incident waves as in mode of antenna array, while the center frequency is at 5.5 GHz. Meanwhile, the −10 dB bandwidth of the absorber can still be over 90% at an oblique incident waves of 30°.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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