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Record W3160082263 · doi:10.18280/i2m.200207

Design of Antenna Array for Ku-Band Wireless Application

2021· article· en· W3160082263 on OpenAlexvenueno aff
Sarmistha Satrusallya, Mihir Narayan Mohanty

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCommunications satelliteMicrostrip antennaPatch antennaBandwidth (computing)Computer scienceElectronic engineeringAntenna gainAntenna height considerationsTelecommunicationsAcousticsElectrical engineeringAntenna (radio)EngineeringSatelliteAntenna factorPhysics

Abstract

fetched live from OpenAlex

The antenna is the back bone of communication. In recent time, it needs to communicate in many ways along with different types of data such as voice, video, text etc. A long distance communication satellite needs a seamless transmission. The antenna design for satellite communication is to be array type to avoid the communication failure. This insists to work with array antenna to fulfil the seam less communication through satellite. Further the antenna design depends on the geometry of the patch, the placement of the patch for better gain and bandwidth. In this paper, authors have chosen the circular patch due to its single degree of freedom. The antenna is compact and is of 30X30mm2 where substrate thickness is considered as 1.6mm. The central patch is of rectangular shape with two slots. Slot is made because of better bandwidth. It is cut diagonally at the corner. As a result, the bandwidth is increased to 2.4GHz with a gain of 5.68dB. The substrate is considered to be FR4 Epoxy. The proposed design satisfies the compactness along with the satellite communication band with satisfactory gain. Simulation results compromise with the measured value. A 3X3 array of circular parasitic elements is considered. It is found that the antenna performs well at 14GHz that is meant for Ku band. The radius of the parasitic patch is considered as 4mm to satisfy the less space with good performance.

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: none
Teacher disagreement score0.933
Threshold uncertainty score0.482

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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