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Novel Wideband Antenna for GNSS and Satellite Communications

2020· article· en· W3041593286 on OpenAlexaff
Slobodan Jović, Michel Clénet, Yahia M. M. Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of CanadaRoyal Ottawa Mental Health CentreDefence Research and Development Canada
Fundersnot available
KeywordsDielectric resonator antennaGNSS applicationsPhysicsBeamwidthWidebandAntenna measurementOpticsResonatorAntenna (radio)Computer scienceTelecommunicationsGlobal Positioning System

Abstract

fetched live from OpenAlex

A novel concept of a dual-sense circularly polarized dielectric resonator antenna (DRA) for the Global Navigation Satellite Systems (GNSS) and other satellite communications is presented. The concept introduces a loaded annular dielectric resonator excited using four vertical conformal strips fed in a quadrature. The most unique feature of this antenna is the resonator load, which modifies the boundary conditions of the resonator core. The load is a one-dimensional array of periodic elements that behave like an electromagnetic band-gap structure (EBG). The impact on the antenna performance is multifold, including improved bandwidth, radiation efficiency and reduced mutual coupling between the excitation ports. The experimental results show that this antenna effectively covers both the lower (1165-1300 MHz) and upper (1560-1610 MHz) GNSS bands. The 0 dBic gain and 3 dB axial ratio (AR) bandwidth significantly exceed the 10 dB return loss (RL) bandwidth. The 0 dBic gain and 3 dB AR beamwidth range from 105° to 120° and from 135° to 165°, respectively, within the GNSS bands. The antenna also exhibits a stop-band at 1355 MHz.

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 score0.003

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 head0.045
GPT teacher head0.240
Teacher spread0.195 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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