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Record W2568203081

Transmission Line Model of a Hertzian Dipole Antenna with FSS Superstrates and AMC Ground Plane to Extract Its Far-field Radiation Properties

2007· article· en· W2568203081 on OpenAlexaff
Alireza Foroozesh, L. Shafai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGround planeTransmission lineOpticsMaterials scienceDielectricAntenna (radio)Dipole antennaDipolePermittivityPlane waveElectrical conductorOptoelectronicsAcousticsPhysicsEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Highly reflective surfaces as antenna superstrates have applications in the gain enhancement [1]. These surfaces can be realized by different means using high permittivity or high permeability materials, or highly reflective frequency selective surfaces (FSSs) [1-6]. On the other hand, one can make these high-gain antenna designs low-profile, using artificial ground planes [5-6]. A full wave analysis of actual antennas, which will have truncated FSS superstrates and artificial magnetic conductor (AMC) ground plane will be time-consuming and will need a large amount of computer memory due to the metallic patches or different dielectric layers. The transmission line equivalent network (TEN) models have been successfully employed to extract far-field radiation properties of Hertzian dipoles over conventional ground planes (PEC) with superstrates [2-4]. It was proposed in [6] that TEN models can also be used in designing antennas with artificial ground planes. However, only full-wave analysis of a few antennas having ideal (lossless, angularand polarization-independent) reactive impedance surfaces and a truncated FSS superstrate was reported. In this paper, TEN models are used to obtain radiation properties of a Hertzian dipole with angular-dependent FSS superstrates and AMC ground planes. The results are compared with selected ideal cases.

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 score0.291
Threshold uncertainty score0.387

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 head0.227
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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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