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Record W2905466549 · doi:10.1109/antem.2018.8572999

Reflecto-Transmittive Antenna using Active Frequency Selective Surfaces

2018· article· en· W2905466549 on OpenAlexaff
Ghada Hussain Elzwawi, Muhammad M. Tahseen, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsDirectivityReflector (photography)Antenna (radio)OpticsPeriscope antennaPIN diodeAntenna measurementRadiation patternCoaxial antennaAntenna gainAntenna arrayAcousticsMaterials scienceDiodePhysicsEngineeringOptoelectronicsElectrical engineering

Abstract

fetched live from OpenAlex

A novel reflecto-transmittive antenna is presented using active frequency selective surface (AFSS). The reflecto-transmittive screens are shaped resembling to parabolic reflector. The structure consists of an AFSS array, and a patch antenna used a feed. Each element in the array contains of two rectangular shaped patches connected with each other by a high-frequency pin-diode. By switching the diode between ON and OFF states, the reflection and transmission characteristics of the element are controlled. Three AFSS panels are used to investigate the potential of the antenna in reflecting and transmitting the radiation beams. To shape the proposed antenna with parabolic reflector, two panels beside the central are rotated to various angles. The proposed antenna results in maximum directivity of 11.9 dBi for ON state (reflection), while a 7.8 dBi for OFF state (transmission). The maximum directivity is achieved when 15 ô rotation angle is applied to side AFSS panels towards the feed. The antenna performance is evaluated at 2.45 GHz. The performance of the proposed antenna is compared with an antenna designed with PEC in the same environment. The obtained results show a good agreement.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.809

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 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
Published2018
Admission routes1
Has abstractyes

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