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Record W4367061531 · doi:10.5281/zenodo.7866779

Design Realization and Tests of a GaN Solid State Power Amplifier with 51dBm Output Power for 17.3-20.2 GHz SatCom Applications

2023· paratext· en· W4367061531 on OpenAlexaff
Rocco Giofrè, Lorena Cabrìa, Mariano Lòpez, Rémy Leblanc, Fabio Vitobello, Paolo Colantonio

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typeparatext
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsNortel (Canada)
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsAmplifierRealization (probability)Power (physics)Electrical engineeringGallium nitrideSolid-stateElectronic engineeringComputer scienceMaterials scienceOptoelectronicsEngineeringTelecommunicationsPhysicsBandwidth (computing)Engineering physicsMathematics

Abstract

fetched live from OpenAlex

In this paper, the design, realization, and tests of the Engineering Model (EM) of the developed SSPA is detailed. Across the entire band, from 17.3 GHz to 20.2 GHz, the SSPA supplies more than 125W output power at only 3dB of compression. Gain and overall efficiency are respectively better than 70 dB and 24% (including the power consumption of the EPC and PSU). It is worth highlighting that these performance levels have been achieved while satisfying space constraints in terms of de-rating and reliability. Moreover, in the same bandwidth the output power at 1dB of gain compression is larger than 100W with a Noise-to-Power Ratio (NPR) better than 13dB. Vibration and vacuum tests as well as thermal cycles are currently ongoing, and results will be presented during the conference. To the best of the authors’ knowledge, this is the first SSPA based on EU GaN-on-Si technology capable of delivering more than 100W output power in the overall Ka-downlink band

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicGaN-based semiconductor devices and materialsFrench-language works237,207