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Record W3029786268 · doi:10.18280/ejee.220203

Management and Security Analysis of Blockchain Shard Storage for Monitoring Data on the State of Smart Substations

2020· article· en· W3029786268 on OpenAlexvenueno aff
Chenxi Jia, Hongyuan Ding, Chuanjin Zhang, Xing Zhang

Bibliographic record

VenueEuropean Journal of Electrical Engineering · 2020
Typearticle
Languageen
FieldComputer Science
TopicEnvironmental Engineering and Cultural Studies
Canadian institutionsnot available
FundersJiangsu Provincial Department of EducationScience and Technology Support Program of Jiangsu ProvinceGovernment of Jiangsu Province
KeywordsBlockchainState (computer science)Computer securityComputer scienceBusiness

Abstract

fetched live from OpenAlex

The ubiquitous power Internet of Things (UPIoT) provides a crucial platform for energy reform. The key features of blockchain, namely, decentralization, openness, and transparency, are in line with the spirits of the UPIoT. By integrating blockchain and the UPIoT, this paper analyzes the security strategy of the state monitoring system (SMS) for smart substations, constructs a blockchain network for smart substations, and designs a shard storage and management for the proposed model. Unlike the existing plans, our plan shards the transaction data to be stored in the blockchain. The sharding both localizes the data storage system, and maintains the scalability of the security system. Security analysis shows that our plan keeps the information secure, reliable, and private, while reducing the storage occupation of each node. The research results promote the application of blockchain in smart substations.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.212
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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