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Record W2158262607 · doi:10.1109/aero.2002.1035656

Antenna and phased array implementations using engineered substrates

2003· article· en· W2158262607 on OpenAlexaff
L. Shafai, Cyrus Shafai, Mojgan Daneshmand, P.L. Bugyik

Bibliographic record

VenueProceedings - IEEE Aerospace Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhased arrayPhase shift moduleMaterials scienceDielectricMicrostripMicrostrip antennaAntenna (radio)OptoelectronicsSubstrate (aquarium)Etching (microfabrication)Extremely high frequencyPhase (matter)Electronic engineeringOpticsElectrical engineeringInsertion lossEngineeringPhysicsNanotechnology

Abstract

fetched live from OpenAlex

Beneficial effects of engineered substrates in design of antennas and phased arrays are demonstrated. Two different applications are considered, one with adaptive electrical and mechanical parameters and the other with properties tailored for special applications such as photonic band gap (PBG) devices. For substrates with adaptive parameters a novel technique for generating inter-element phase shifts in microstrip phased arrays, without the use of phase shifter is introduced. The concept is examined using a circular microstrip patch array. Both cavity model and moment method solution, are used and the possibility of modifying the far field phase by changing the patch or substrate parameters is established. Then, array beam scan by modifying the antenna parameters is demonstrated. For substrates with tailored parameters a perforated dielectric is fabricated. Preferential etching of the substrate is used to generate reduced local dielectric constant in the vicinity of millimeter wave elements. Substrates possessing multiple local or graded dielectric constants are possible.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.250
Teacher spread0.218 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

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