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Record W4400411527 · doi:10.1109/ojap.2024.3425225

High Isolation and Band Enhanced Radio Altimeter Antenna for Avionics Applications

2024· article· en· W4400411527 on OpenAlexafffund
Omar M. Khan, Jean‐Jacques Laurin

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBandwidth (computing)BeamwidthAvionicsAcousticsAntenna efficiencyAntenna measurementRadiation patternAntenna (radio)Electrical engineeringPhysicsEngineeringTelecommunicationsAerospace engineering

Abstract

fetched live from OpenAlex

Concept of ringed stacked patch antenna with low side lobe level and enhanced bandwidth is introduced for radio altimeter avionics applications. One dimensional electromagnetic band gap is designed using metallic vias with circular rings along the radiating patches on multilayered substrate for providing high isolation in H-plane and reducing lateral surface currents. A stacked patch antenna configuration is used for enhancing the bandwidth of the antenna. Simulation and analysis are performed for the optimization of the frequency response and sidelobe levels of the proposed antenna. Gain of more than 12 dB with sidelobe levels less than -42 dB were measured for a fabricated prototype. Signal to interference ratio of 85 dB is achieved between two adjacent antennas, which is 5dB better than commercial antennas due to increased gain and reduced sidelobe levels. The antenna is designed for enhanced frequency bandwidth from 4.06 GHz to 4.53 GHz. Computed drag coefficient of the antenna mounted on the fuselage of Cessna 210 aircraft show that the drag is essentially negligible.

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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