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Record W2507816682 · doi:10.1109/antem.2016.7550115

High gain antipodal fermi-linear tapered slot antenna (AFLTSA) array fed by SIW for MMW applications

2016· article· en· W2507816682 on OpenAlexaff
Shraman Gupta, M. Akbari, Abdel-Razik Sebak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsSide lobeAntenna measurementAntipodal pointRadiation patternAntenna arrayAntenna gainDipole antennaArray gainOpticsAntenna (radio)Antenna factorAntenna efficiencyPhysicsOptoelectronicsTelecommunicationsComputer scienceMathematics

Abstract

fetched live from OpenAlex

A 32.5 GHz antipodal fermi-linear tapered slot antenna (AFLTSA) 1×8 array for millimeter wave applications is presented in this paper. The substrate integrated waveguide (SIW) feeding structure is used to excite this proposed antenna. The proposed antenna has a sine corrugation to improve the radiation characteristics of the antenna. The simulated results of the single element yields a wide bandwidth between 30-40 GHz with a high gain of 12.15 dB and a side lobe level better than 17.85 dB in E-plane. This antenna is further simulated for 1×8 antenna array which yields a high gain of 20.1 dB with a side lobe level better than 22.5 dB in E-plane. The objective of this work is to have a high gain and lowside lobe level for this proposed array structure. The work is simulated using AnsoftHFSS and CST Microwave Studio software.

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 score0.003

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)0.0010.001

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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