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Design and Analysis of a Stand-Alone 5G Private Network with SNPN Architecture for Critical Applications in the Rheinhafen-Dampfkraftwerk Karlsruhe Power Plant

2025· article· W4417132708 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.

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicIoT Networks and Protocols
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsScalabilitySoftware deploymentNode (physics)ArchitectureMIMONetwork architecturePort (circuit theory)

Abstract

fetched live from OpenAlex

This work explores the deployment and analysis of a Stand-Alone Non-Public Network (SNPN) 5G architecture for the Rheinhafen-Dampfkraftwerk Karlsruhe power plant. By leveraging dual-frequency bands-N78 (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3. 6 ~ G H z}$</tex>) for wide-area, reliable coverage and mmWave (26.5GHz) for high-capacity, low-latency applications-the proposed network addresses the connectivity challenges of industrial environments. Advanced MIMO antenna configurations and a hybrid macro-micro node topology ensure robust coverage and high data rates in critical areas such as the port and substation. Enhanced backhauling strategies and security frameworks further support the network's scalability and reliability. The results demonstrate that private 5G networks, tailored to the needs of industrial facilities, can significantly enhance operational efficiency, enable real-time monitoring, and support IoT integration. This study establishes a framework for deploying scalable and secure 5G solutions in similar industrial settings.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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