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Record W2138285417 · doi:10.1109/tap.2002.800732

Efficiency and gain of slot antennas and arrays on thick dielectric substrates for millimeter-wave applications: a unified approach

2002· article· en· W2138285417 on OpenAlexaff
George V. Eleftheriades, Meide Qiu

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2002
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExtremely high frequencyDielectricSlot antennaSurface waveAntenna (radio)RadiationOffset (computer science)Radiation patternDipole antennaOpticsPhysicsElectromagnetic radiationRadiation propertiesAcousticsMaterials scienceComputer scienceOptoelectronicsTelecommunications

Abstract

fetched live from OpenAlex

A unified approach for representing the surface-wave fields and for computing the corresponding radiation efficiencies from arbitrarily shaped slot-type antennas on thick dielectric substrates at millimeter-wave (mm-wave) frequencies is presented. In this approach, two different representations of the surface-wave fields, i.e., the one based on the idea that the surface-wave power can be associated with a substrate radiation pattern and the other based on the cylindrical wave expansion, are linked together in a unified way. The developed theory is then applied for investigating the surface wave characteristics of printed twin offset slots and uniform linear slot arrays. Based on the developed theory, new insight into the mechanism with which surface-wave power is coupled by the slot pair is presented. Subsequently, the optimum number of elements and corresponding interelement spacing for linear, uniformly excited, slot dipole arrays is explored for achieving maximum radiation efficiency and gain. In order to provide guidance on practical mm-wave integrated slot antenna array design, supportive numerical results are presented for Substrates of dielectric constant /spl epsi//sub r/ = 4.0 and /spl epsi//sub r/ = 12.0.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.668

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 studies0.0000.000
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 head0.024
GPT teacher head0.207
Teacher spread0.183 · 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

Citations30
Published2002
Admission routes1
Has abstractyes

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