Abstract Reliable identification of tactical engagement transitions in continuous underwater telemetry is critical for automated monitoring and situational awareness, yet it remains challenging due to severe bandwidth constraints, environmental noise, and complex multivariate sensor dynamics. This study develops... Reliable identification of tactical engagement transitions in continuous underwater telemetry is critical for automated monitoring and situational awareness, yet it remains challenging due to severe bandwidth constraints, environmental noise, and complex multivariate sensor dynamics. This study develops and evaluates a robust multimethod change point detection framework designed to accurately identify these operational transitions. We applied seven complementary statistical approaches, CUSUM, PELT, binary segmentation, Bayesian inference, kernel detection, ruptures style segmentation, and multivariate Mahalanobis analysis to a comprehensive 12,000-observation telemetry dataset. Dimensionality reduction via principal component analysis and multivariate distributional metrics were utilized to capture coordinated, system level state shifts. Results demonstrated exceptional cross-method convergence and temporal precision. Six of the seven methods localized the tactical engagement onset exactly at the reference boundary, while the online CUSUM monitor detected the transition within a negligible five-second delay. Furthermore, segmentation methods successfully recovered the complete engagement interval, accurately identifying both onset and termination boundaries. Multivariate and kernel analyses confirmed that the transition represents a profound, systemwide distributional shift rather than an isolated sensor anomaly. By integrating diverse detection principles, the proposed framework provides strong cross validation, ensuring the identified boundaries are genuine features of the underlying telemetry dynamics rather than algorithmic artifacts. Ultimately, this multimethod approach offers a highly reliable, automated mechanism for distinguishing routine patrol from event related tactical states, significantly enhancing real time decisionmaking, reducing operator workload, and improving mission reliability in complex underwater sensing environments. Read More Read Less

Abstract The rapid expansion of wireless communication networks has necessitated a massive deployment of antenna infrastructure, yet the longitudinal and spatial temporal dynamics of antenna structure registrations remain poorly understood. This study addresses this gap by analyzing 124,811... The rapid expansion of wireless communication networks has necessitated a massive deployment of antenna infrastructure, yet the longitudinal and spatial temporal dynamics of antenna structure registrations remain poorly understood. This study addresses this gap by analyzing 124,811 Antenna Structure Registration (ASR) records in the United States from 1996 to 2012. Moving beyond simple aggregate counts, we employ an integrated analytical framework combining Seasonal Trend decomposition using Loess (STL), nonparametric trend testing (Mann Kendall, Sen's slope), multiple structural break detection algorithms (PELT, Bai Perron, Joinpoint, Bayesian), Markov switching regime analysis, and state level spatial temporal modeling. Results show a strong, statistically significant monotonic upward trend (+3.66 registrations/month) with weak seasonality. Crucially, the registration process is highly non-stationary, characterized by multiple structural breaks converging around 2011-2012 and two persistent latent regimes (low activity and high activity) with transition persistences exceeding 94%. We also identified 33 temporal anomalies, mostly clustered in the later observation period, indicating episodic acceleration rather than smooth growth. Conventional SARIMA forecasting demonstrated significant limitations (MAPE = 76.8%) in capturing this structural instability. Spatially, infrastructure deployment is highly concentrated, with Texas, California, and Florida accounting for over 20% of all registrations, though late period expansion was broadly distributed across leading states. Ultimately, this study demonstrates that antenna infrastructure evolution is a complex, regime dependent, and geographically heterogeneous process, establishing a foundational framework for future spatial temporal and regime aware infrastructure modeling. Read More Read Less

Abstract Modern telecommunication networks are characterised by multidimensional, dynamically varying performance conditions that no single key performance indicator (KPI) can adequately capture. This study develops and empirically validates the Telecommunication Network Quality Index (TNQI), a composite measure that integrates... Modern telecommunication networks are characterised by multidimensional, dynamically varying performance conditions that no single key performance indicator (KPI) can adequately capture. This study develops and empirically validates the Telecommunication Network Quality Index (TNQI), a composite measure that integrates three heterogeneous network performance indicators signal strength (dBm), latency (ms), and packet loss (%) into a unified 0-100 quality scale. Using a dataset of 3,000 temporally ordered network observations collected between 1 January 2023 and 5 May 2023 across five network technologies (4G LTE, 5G NR, Fibre Optic, Microwave, and Satellite), the study employs directional min-max normalisation, equal weight aggregation, Pearson and Spearman correlation analysis, weighted reliability scoring, and quartile-based classification. The correlation analysis reveals near zero associations among the three KPIs (|r|ï‚£ 0.019), confirming that signal quality, network responsiveness, and packet delivery reliability represent statistically independent dimensions of communication performance. The overall mean TNQI was 50.39 (SD = 16.66; range: 2.12-94.41), indicating moderate average communication quality with substantial heterogeneity. Network-type comparisons yielded relatively modest differences, with Satellite recording the highest mean TNQI (52.19) and 5G NR the lowest (49.34). A complementary weighted network reliability score (30% signal, 35% latency, 35% packet loss) and an inverse network degradation index (NDI = 100 - TNQI) provide operational monitoring perspectives. Quartile-based classification partitions observations into Critical, Poor, Good, and Excellent states. The findings demonstrate that multidimensional integration through TNQI provides a more comprehensive and interpretable representation of network quality than any individual KPI, establishing a reproducible analytical foundation for network monitoring, technology comparison, temporal degradation tracking, and future early warning applications. Read More Read Less