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Record W2162750800 · doi:10.1109/aps.1997.631683

Stacked square ring resonators for bandwidth enhancment

2002· article· en· W2162750800 on OpenAlexaff
P. Moosavi, L. Shafai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsResonatorBandwidth (computing)StackingSquare (algebra)Materials scienceElectrical impedancePatch antennaMicrostripMicrowaveSplit-ring resonatorMicrostrip antennaRing (chemistry)MetamaterialAcousticsOptoelectronicsPhysicsAntenna (radio)OpticsElectrical engineeringComputer scienceTelecommunicationsEngineeringMathematicsNuclear magnetic resonanceGeometry

Abstract

fetched live from OpenAlex

The advantages of microstrip structures as antenna elements or resonators have aroused much interest in their theoretical and experimental studies. A novel structure that has not been studied well yet is the square ring patch, which is geomechanically an intermediate configuration between a printed loop and a patch. Several interesting features are associated with this patch. Its size is substantially smaller than that of a conventional square patch, and it depends on the ring width. Also, its input impedance is considerably higher, whereas its impedance bandwidth is smaller, in comparison with a similar size patch. Normally, the deviation of its properties from those of a square patch increases by decreasing its width. It is known that stacking similar size patches increases the impedance bandwidth. We investigate this property for two stacked square rings. A number of simulations are carried out, using "Ensemble 4.02 of Boulder Microwave Technologies Inc." to understand the behaviour of stacked rings, and determine suitable parameters. An optimized configuration is also fabricated and tested.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.202
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

Citations5
Published2002
Admission routes1
Has abstractyes

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