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Record W4313195381 · doi:10.1109/tcomm.2022.3228611

Future Space Networks: Toward the Next Giant Leap for Humankind

2022· article· en· W4313195381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Communications · 2022
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsPolytechnique MontréalSt. Francis Xavier UniversityCarleton University
FundersNational Research Council CanadaMitacs
KeywordsStandardizationSoftware deploymentSpace (punctuation)Communications satelliteTelecommunicationsSatelliteSystems engineeringComputer scienceSpace technologyArchitectureSpace explorationEngineeringAerospace engineeringSoftware engineering

Abstract

fetched live from OpenAlex

Due to the unprecedented advances in satellite fabrication and deployment, innovative communications and networking technologies, ambitious space projects and programs, and the resurgence of interest in satellite networks, there is a need to redefine space networks (SpaceNets) to incorporate all of these evolutions. This paper introduces a vision for future SpaceNets that considers advances in several related domains. First, we present a reference architecture that captures the various network entities and terminals in a holistic manner. Based on this, space, air, and ground use cases are studied. Then, the architectures and technologies that enable the envisaged SpaceNets are investigated. In so doing, we highlight the activities and projects of different standardization bodies, satellite operators, and national organizations towards the envisioned SpaceNets. Finally, the challenges, potential solutions, and open issues from communications and networking perspectives are discussed.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.282
Teacher spread0.206 · 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