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A Two-Arm Archimedean Circularly Polarized Spiral Slot Antenna for IoT Devices in 5G Network

2020· article· en· W3111616384 on OpenAlexaff
Maryam Eshaghi, Rashid Rashidzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSpiral (railway)Spiral antennaAntenna (radio)Circular polarizationPhysicsComputer scienceTurnstile antennaSlot antennaElectrical engineeringDirectional antennaOpticsTelecommunicationsMicrostrip antennaEngineeringCoaxial antennaMechanical engineering

Abstract

fetched live from OpenAlex

The rapid development of fifth-generation (5G) wireless networks will lead to the wide-scale implementation of IoT devices. The main design objective for wireless battery-powered IoT sensors is power consumption and efficiency. In this work, a small size two-arm Archimedean spiral antenna is presented for IoT devices operating in high-frequency networks. An Archimedean circularly polarized spiral slot antenna is optimized for IoT devices using a low loss material. The radiation efficiency, antenna directivity, and the bandwidth have been improved in the 5G band by reducing the antenna size and using Polytetrafluoroethylene (PTFE) as a dielectric layer. COMSOL Multiphysics tools are utilized to design and simulate the antenna at a high-frequency spectrum of 30-70 GHz. The simulation results indicate that the antenna has an insertion loss of -17 dB at 54 GHz and presents an Axial Ratio (AR) difference of 22 dB from 45-65 GHz. The voltage standing wave ratio (VSWR) is less than 2 in the frequency range of 45-50 GHz with a radiation efficiency of 83%.

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: Methods · Consensus signal: Methods
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 integrity0.0010.000
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 head0.023
GPT teacher head0.231
Teacher spread0.207 · 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
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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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