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Record W2946919241 · doi:10.1049/pbte086e_ch18

A Modular Architecture for Low Cost Phased Array Antenna System for Ka-Band Mobile Satellite Communication

2019· book-chapter· en· W2946919241 on OpenAlexaff
Wael M. Abdel‐Wahab, Hussam Al‐Saedi, M. Raeiszadeh, Ehsan Haj Mirza Alian, G. Chen, Ahmad Ehsandar, Naimeh Ghafarian, Heba El-Sawaf, Ardeshir Palizban, Mohammad‐Reza Nezhad‐Ahmadi, S. Safavi‐Naeini

Bibliographic record

VenueInstitution of Engineering and Technology eBooks · 2019
Typebook-chapter
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhased arrayAntenna (radio)Modular designAntenna arrayPhased-array opticsEngineeringElectronic engineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

A low-cost and low profile phased array antenna system based on a modular approach is being developed for Ka-band mobile SATCOM applications. A Transmitter (TX) antenna array has been designed to provide the required circular polarized (CP) radiation beam with sufficient gain and a radiation pattern that satisfies the standard (FCC) emission mask. In the proposed modular approach, the intelligent active phased subarray (module) is designed and used as a building block to construct the whole array in any form and any size in any customized platform. In this design, the size of the intelligent subarray is chosen to be 4×4 with half-wavelength separation between the elements for both TX (30 GHz), and RX (20 GHz) array antennas. Larger array antenna with a few thousands of radiating elements can be formed by assembling as many modules as required on one platform. This paper discusses the modular approach aspects and its application in large scale phased array antenna implementation. Super TX / RX array modules of 256 elements are assembled and tested successfully to validate the proposed modular architecture. The measured radiation patterns at 30/ 20 GHz shows that the antenna's main beam can be steered to ± 70° in both azimuth and elevation directions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.189
Teacher spread0.182 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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