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Record W7142841720 · doi:10.63125/vntbqq40

The Role of Cloud-Native Infrastructures in Supporting Autonomous and Uncrewed Systems (UXS) in Operations

2025· article· W7142841720 on OpenAlex
Shamsul Arifeen, Md. Morshedul Islam

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
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsOralys (Canada)
Fundersnot available
KeywordsLikert scaleReliability (semiconductor)Scale (ratio)Flexibility (engineering)Descriptive statisticsRegression analysisScalabilityData collectionOperational efficiency

Abstract

fetched live from OpenAlex

This study examined the role of cloud-native infrastructures in supporting autonomous and uncrewed systems (UXS) in operations, addressing the problem that many UXS deployments depend on rigid, centralized, or insufficiently scalable infrastructures that struggle to support dynamic workloads, mission reconfiguration, fault tolerance, communication continuity, and secure data exchange in complex operational environments. The purpose of the research was to determine whether key cloud-native capabilities significantly enhance UXS operational effectiveness across case-based settings. A quantitative, cross-sectional, case-study-based design was adopted, using purposive sampling and a structured five-point Likert scale questionnaire administered to professionals engaged in logistics, industrial inspection, maritime surveillance, emergency/security, and ICT/infrastructure contexts. Out of 180 distributed questionnaires, 156 were returned, and 150 valid responses were used for analysis, producing an 83.3% usable response rate. The independent variables were scalability, flexibility, reliability, and security with data management, while the dependent variable was UXS operational effectiveness. Data were analyzed through descriptive statistics, Cronbach’s alpha, Pearson correlation, and multiple regression. The findings showed consistently high perceptions across all major constructs, with mean scores of 4.18 for scalability, 4.09 for flexibility, 4.23 for reliability, 4.15 for security and data management, and 4.21 for UXS operational effectiveness. Reliability coefficients were strong, ranging from 0.81 to 0.88. Correlation results indicated significant positive relationships with operational effectiveness, led by reliability (r = .71), followed by security and data management (r = .67), scalability (r = .64), and flexibility (r = .58), all significant at p < .01. Regression analysis further showed that the model explained 65.9% of the variance in UXS operational effectiveness (R² = .659, F = 69.98, p < .001), with reliability emerging as the strongest predictor (β = .31, p < .001), followed by security and data management (β = .26, p = .002), scalability (β = .24, p = .003), and flexibility (β = .17, p = .021). The study implies that organizations should treat cloud-native infrastructure as mission-critical operational architecture for improving resilience, coordination, adaptability, and readiness in UXS environments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.004
GPT teacher head0.250
Teacher spread0.246 · 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