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Dual Linear Polarized Antenna Feed for LEO Satellites

2022· article· en· W4292348732 on OpenAlexaff
Mahmoud Gadelrab, Shoukry I. Shams, A. Sebak

Bibliographic record

Venue2022 International Telecommunications Conference (ITC-Egypt) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsConcordia University
Fundersnot available
KeywordsFeed hornMulti-band deviceHorn antennaCommunications satelliteDirectional antennaBandwidth (computing)TransceiverComputer scienceWirelessSatelliteElectrical engineeringAntenna (radio)Electronic engineeringTelecommunicationsEngineeringOmnidirectional antennaSlot antennaAerospace engineering

Abstract

fetched live from OpenAlex

Low Earth Orbit (LEO) satellites are regaining the interest of the wireless communications society, as they will be heavily utilized in future wireless communication networks. Transceivers of LEO satellites need high gain antennas with stable characteristics within the operating bandwidth. Corrugated horn antennas are the most commonly used feeds for such antennas. In addition, various satellite systems deploy dual-polarized antennas to enable polarization diversity. In this article, dual polarized satellite feeding structure is proposed consisting of an orthomode transducer and a corrugated horn. The proposed design operates in Ka band from (27-30 GHz). The final integrated system achieves a matching level below -15 dB with a stable gain of 14.2 ± 0.8 dB over the operating band 27-30 GHz.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.001

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 head0.041
GPT teacher head0.305
Teacher spread0.264 · 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
Published2022
Admission routes1
Has abstractyes

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