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Record W4400351402 · doi:10.1109/tsg.2024.3424150

Integrated Satellite-Terrestrial Network Framework for Next Generation Smart Grid

2024· article· en· W4400351402 on OpenAlexaff
Yang Shen, Quan Zhou, Yao Wen, Zhikang Shuai, Z. John Shen

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2024
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsSimon Fraser University
FundersNational Natural Science Foundation of China
KeywordsSmart gridSatelliteComputer scienceGridTelecommunicationsDistributed computingSystems engineeringEngineeringElectrical engineeringAerospace engineeringGeography

Abstract

fetched live from OpenAlex

Satellite Internet as a representative of 6G offers a globally resilient service of communication, navigation, timing and remote sensing at multiple temporal and spatial scales. Facing the 6G era, the next generation smart grid (NGSG) will be constructed with the assistance of a unified space-ground network. This accounts for the unified network’s attributions of wide coverage, large base of sources and loads, and the complex environment. Hence, an integrated satellite-terrestrial network (ISTN) framework is proposed in this letter. It aims at empowering the NGSG under the massive renewable energy sources integration, which includes conditions of an intelligent urban grid, a reliable remote area grid, and a resilient grid to emergency. Heterogeneous data such as remote sensing and navigation signals are aggregated and leveraged in the proposed ISTN framework. Information entropy theory is used to coordinate the space- and ground-based networks elaborately, maximizing the utilization of network resources pointedly and efficiently. A cyber-physical platform is used based on an actual LEO satellite Internet test constellation to validate the proposed ISTN, which reveals the superiority and promising future.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.083
GPT teacher head0.283
Teacher spread0.201 · 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 teacher head, not a consensus.

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

Citations35
Published2024
Admission routes1
Has abstractyes

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