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Record W2282170575 · doi:10.1109/lawp.2015.2457338

Broadband Sounding Rocket Antenna for Dual-Band Telemetric and Payload Data Transmission

2015· article· en· W2282170575 on OpenAlexaff
Jérémie Prades, Anthony Ghiotto, Éric Kerhervé, Ke Wu

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPayload (computing)Antenna (radio)BroadbandOmnidirectional antennaRetransmissionRocket (weapon)Transmission (telecommunications)Data transmissionMulti-band deviceElectrical engineeringEngineeringComputer scienceElectronic engineeringAcousticsAerospace engineeringTelecommunicationsPhysicsComputer network

Abstract

fetched live from OpenAlex

This letter presents and investigates a broadband sounding rocket antenna developed for dual-band data transmission of telemetric and payload systems. This single-element antenna permits the use of both the well-established 869.4-869.65 MHz and 2.4-2.4835 GHz frequency bands available in Europe to implement a redundant transmission of telemetric and tracking data desired for reliability. Furthermore, the available spectrum in the upper band offers a high-speed wireless link for transmitting payload data. To preserve the mechanical integrity and aerodynamics of rocket, the antenna is integrated in its nose cone. The proposed low-cost and low-weight structure is based on printed circuit board (PCB) pieces assembled in a LEGO-like manner to form a broadband three-dimensional (3-D) biconical antenna, which is fed by a diplexer integrated in its base. A prototype has been fabricated and validated using a reduced-length fuselage in a near-field measurement system.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 head0.041
GPT teacher head0.249
Teacher spread0.209 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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