Abstract Characterizing long range dependence (LRD) and statistical self similarity in complex systems relies heavily on accurately estimating the scaling parameters of fractional Gaussian noise (fGn). However, empirical observations are inherently finite, and short observation windows severely constrain... Characterizing long range dependence (LRD) and statistical self similarity in complex systems relies heavily on accurately estimating the scaling parameters of fractional Gaussian noise (fGn). However, empirical observations are inherently finite, and short observation windows severely constrain frequency resolution, introducing spectral leakage and estimation variance that obscure intrinsic fractal scaling. This study systematically investigates the finite sample effects on the spectral characterization and power law convergence of fGn. We developed a controlled, scalable benchmark using synthetic fGn realizations across four dyadic lengths (N = 64 to 65,536). By employing dual spectral estimators Welch’s averaged periodogram and the raw Fast Fourier Transform we tracked the evolution of the spectral exponent, goodness of fit, and confidence intervals. Our results demonstrate a non-linear transition from noise dominated, white like spectra in short records to asymptotic power law scaling in longer sequences. For the shortest realizations, estimation variance dominates, yielding statistically fragile exponents. Conversely, as sample size increases to N  4096, the spectral exponents stabilize near 0.68 (corresponding to a persistent Hurst exponent of ~0.84), with R² values exceeding 0.75 and significantly narrowed confidence intervals. The concordance between smoothed and unsmoothed spectral representations confirms the robustness of this convergence. Ultimately, this work quantifies the minimum observational requirements necessary to reliably distinguish true fractal scaling from finite data artifacts, providing a rigorous reference framework for the spectral analysis of longmemory stochastic processes in complex systems. Read More Read Less

Abstract Contactless radar sensing has emerged as a transformative modality for non invasive, continuous vital signs monitoring. However, a rigorous multi-domain characterization of raw radar displacement signals under both normal and disordered breathing conditions remains a fundamental requirement... Contactless radar sensing has emerged as a transformative modality for non invasive, continuous vital signs monitoring. However, a rigorous multi-domain characterization of raw radar displacement signals under both normal and disordered breathing conditions remains a fundamental requirement for developing reliable clinical tools. This study presents a comprehensive multi domain analysis of radar derived vital signs, focusing on the contactless monitoring of resting respiration and the detection of apneic events. Utilizing the VITALSENSE 120 dataset, which comprises synchronized continuous wave radar recordings (sampled at 333.33 Hz) and clinical grade reference physiological signals, we systematically evaluated the radar displacement signal across multiple analytical domains. Results indicate that resting respiration exhibits stable periodic oscillations with a dominant respiratory frequency concentrated in the 0.1–0.5 Hz band, alongside strong temporal synchronization with reference signals. Conversely, apneic events are distinctly characterized by significant spectral power reduction in the respiratory band, marked envelope reduction indicating diminished thoracic displacement, and pronounced autocorrelation reduction reflecting the loss of respiratory rhythm periodicity. Furthermore, cross-domain analysis reveals a sharp drop in the correlation coefficient between the radar displacement and reference respiratory signals, while residual cardiac induced micromovements remain detectable. This multi-domain evidence base confirms the physiological interpretability of radar observations and quantifies the discriminative signatures necessary for separating spontaneous breathing from apnea, laying a solid foundation for robust, explainable AI/ML pipelines in remote healthcare. Read More Read Less

Abstract The evolution of sixth generation (6G) networks relies heavily on Terahertz (THz) communications and Cloud-IoT architectures, which introduce complex trade-offs between communication quality, network stability, and security overhead. Evaluating these heterogeneous environments requires comprehensive frameworks capable of handling... The evolution of sixth generation (6G) networks relies heavily on Terahertz (THz) communications and Cloud-IoT architectures, which introduce complex trade-offs between communication quality, network stability, and security overhead. Evaluating these heterogeneous environments requires comprehensive frameworks capable of handling multiple, often conflicting performance criteria. This study proposes a novel hybrid Multi Criteria Decision-Making (MCDM) framework to evaluate THz enabled Cloud IoT networks. Using a comprehensive dataset of 4,000 observations encompassing environmental, communication, and blockchain-based security metrics, we integrate both subjective and objective weighting mechanisms. Specifically, the Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM), and CRITIC are utilized to derive criterion weights, which are subsequently applied in TOPSIS and VIKOR ranking algorithms. The results signal a high degree of concordance among the weighting schemes. AHP and CRITIC prioritize core communication metrics like signal strength and bandwidth, while EWM highlights the critical statistical variability of blockchain validation latency. Extensive sensitivity analyses confirm the framework’s structural robustness against weight perturbations. Furthermore, TOPSIS and VIKOR rankings consistently identify optimal network configurations that effectively balance high capacity links with low latency and robust trust scores. These findings emphasize that optimizing next generation systems requires a holistic approach to dynamically adapt to multi dimensional constraints. Ultimately, this hybrid MCDM approach offers a rigorous, multi perspective decision support tool for network architects, ensuring the optimal deployment of intelligent, secure, and high performance 6G THz Cloud IoT environments. Read More Read Less