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Record W4327701551 · doi:10.1117/12.2657144

Design and analysis of on-chip optical phase array systems for satellite communications

2023· article· en· W4327701551 on OpenAlexaff
Hugh Podmore, Akash Chauhan, Brett Poulsen, Michael Zylstra, Xiaochen Xin, Mackenzie Essington, Ahmed Y. Elsharabasy, Md Ruhul Fatin, Nicholas Zonta, Greg Iu, Alan Scott, Tamara Djokic, Jayshri Sabarinathan, Winnie N. Ye, Amr S. Helmy, Regina Lee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of TorontoCarleton UniversityYork UniversityHoneywell (Canada)
Fundersnot available
KeywordsBeam steeringSynchronizingComputer scienceFree-space optical communicationOptical communicationCommunications satelliteTelecommunicationsSatelliteAerospace engineeringElectronic engineeringEngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

The demand for optical technologies in space is growing rapidly driven by the advent of low-earth orbit satellite “mega-constellations” providing global communication services. Free space optical communications between satellites in low earth orbit presents a number of technology challenges related to maintaining stable links between two satellites separated by thousands of kilometers. One principal challenge is the development of mechanically robust, mass-producible beam-steering technologies with low SWaP, and recurring cost. One potential solution to this challenge is to replace costly mechanical steering mechanisms with beam-steering elements such as on-chip optical phase arrays. This work presents ongoing research towards the development of an on-chip wide-steering optical phase array for inter-satellite communications. The presentation will cover the system architecture, component design, and control algorithms for synchronizing many emitters into a single output beam.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.095
GPT teacher head0.334
Teacher spread0.239 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicInterconnection Networks and SystemsFrench-language works237,207