In recent decades, the reliability and safety of large engineering structures have become a critical issue due to failures caused by undetected micro-level deformations. Fiber-optic strain sensors, especially Fiber Bragg Grating (FBG) and interferometric systems, are widely used in structural health monitoring (SHM); however, their standard sensitivity is often insufficient for early detection of nano-strain level damage. This paper presents a comparative analysis and system-level optimization of the main sensitivity enhancement methods, including mechanical amplification, functional coatings and composite embedding, interferometric schemes, and advanced spectral signal processing. Analytical modeling and numerical simulations were performed. It is shown that flexure-beam amplifiers provide a stable sensitivity gain of 2.1–4.8, whereas lever-type mechanisms achieve higher amplification (5.6–9.3) at the cost of dynamic degradation. Functional coatings increase the strain transfer coefficient from 0.62 to 0.68 to 0.91–0.97, but introduce temperature-induced errors up to 1.5–2.0 µε. Interferometric systems can detect strains at the 10⁻⁸ level but exhibit high temperature cross-sensitivity. Advanced spectral processing reduces the Bragg wavelength estimation error by 8–15 times, improving the equivalent strain resolution to (2–5) × 10⁻⁸. Based on these results, an optimized integrated approach combining moderate mechanical amplification (2.5–3.5), improved strain transfer (η ≈ 0.85–0.92), and efficient spectral processing is proposed. This improves the equivalent strain resolution from 1 × 10⁻⁶ to (1.5–3.0) × 10⁻⁸ while keeping temperature-induced errors within 15–25% and limiting the computational load increase to 2–3 times. The proposed solution is suitable for long-term monitoring of large engineering structures.

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure.

Fibre-optic gyroscope (FOG) navigation systems are widely used in autonomous, aerospace, and terrestrial applications due to their high stability and independence from external navigation signals. While significant progress has been made in improving FOG performance at the sensor level, fewer studies have investigated the system-level impact of residual gyroscope errors, particularly during periods of GNSS unavailability. This study addresses that gap by analysing the accuracy and reliability of navigation in FOG-based inertial navigation systems under various operational modes: GNSS-aided, unaided, and intermittently aided. A mathematical model of navigation is implemented in a MATLAB/Simulink environment to assess error propagation. Performance is evaluated using practical metrics such as heading drift, position error accumulation, and recovery efficiency after GNSS signal restoration. Simulation results show that during unaided navigation, heading errors reach approximately 2–3 degrees over 600–900 seconds, while position errors grow to 40–80 meters during 300–600 seconds of GNSS outage. Upon GNSS reacquisition, error reduction of 80–90% is observed within 30–60 seconds. These results demonstrate that system-level modelling can significantly enhance navigation reliability without requiring modifications to the FOG hardware.

Respiratory sound analysis is becoming increasingly popular as a non-invasive method for detecting adventitious sounds and aiding in the diagnosis of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. However, studies in this field differ significantly in datasets, recording devices, annotation techniques, preprocessing pipelines, model architectures, validation strategies, and evaluation metrics. This scoping review examines current research on artificial intelligence-based respiratory sound analysis, with a focus on datasets, acquisition and annotation practices, signal processing, feature representations, machine learning (ML) and deep learning (DL) methods, and evaluation protocols. The search identified 1056 database records, and 89 reports were included for the final evidence mapping. The reviewed datasets support event-, cycle-, recording-, and patient-level tasks and differ considerably in population, scale, acquisition hardware, annotation granularity, and label structure. The findings also show that acquisition and annotation are closely linked, creating potential device-, recording-site-, and label-related confounding. Methodologically, the literature can be summarized in four broad stages: handcrafted feature-based ML; deep spectrogram learning; representation learning and multimodality; and deployment-oriented, robustness-focused systems. Despite recent achievements, the field remains limited by small and imbalanced datasets, annotation uncertainty, device and population variability, inconsistent data splitting, and limited external validation. More reliable clinical use will require standardized acquisition and annotation, quality-controlled preprocessing, patient-independent evaluation, task-appropriate metrics, transparent reporting, and validation across independent devices, datasets, and clinical populations.

The object of this research is resource management and energy consumption processes in optical fiber communication networks with access–metro–core architectures. The study addresses the problem that conventional static and semi-dynamic control methods are unable to simultaneously ensure energy efficiency and QoS stability under conditions of exponentially growing and highly variable traffic. To solve this problem, an AI-based integrated control model was developed that combines traffic prediction, dynamic resource allocation, spectrum management, and power optimization within a unified framework. Traffic prediction is performed using LSTM–BiRNN neural networks (1.2–1.8 million parameters, 300–500 thousand records), while control decisions are generated by an Actor–Critic reinforcement learning algorithm. Simulation results obtained in the Python 3.12 and OptiSystem 17.0 environments demonstrate that, in the Access segment (1–10 Gb/s), latency is stabilized within 1–10 ms; in the Metro segment (40–120 Gb/s), energy consumption is reduced by 18–27%; and in the Core segment (400–1000 Gb/s), the efficiency of RSA algorithms increases by 22–35%. When the EDFA output power is maintained within +17 to +23 dBm, amplifier power consumption decreases by 10–15%, resulting in overall network energy savings of 20–40%. The obtained results are explained by the synergy of accurate traffic prediction provided by the LSTM–BiRNN model and proactive real-time decision-making enabled by the Actor–Critic algorithm. The distinctive feature of the proposed approach is the simultaneous optimization of energy efficiency and QoS across all access, metro, and core segments within a single integrated architecture. The results can be practically applied in the design and modernization of optical fiber communication networks, as well as in the deployment of energy-efficient intelligent network management systems.

Spacecraft and launch-vehicle structures require lightweight multipoint monitoring under combined mechanical, thermal, and environmental loads. This study presents an analytical fiber Bragg grating (FBG) sensor-network workflow integrating reference-grating temperature compensation, regional strain assessment, opposite-surface curvature sensing, wavelength-division-multiplexing allocation, and strain-to-shape reconstruction. The deterministic compensation case is used only as a self-consistency check, whereas practical robustness is assessed through 10,000 Monte Carlo trials incorporating packaged-coefficient mismatch, temperature nonuniformity, wavelength noise, strain-transfer variation, drift, and calibration uncertainty. The calibrated estimator achieved a median strain mean absolute error of 1.73 με and a 95th-percentile error of 4.22 με. The defined finite-element benchmarks produced a maximum engine-mount truss strain of 456.2 με under the defined loads and a median full-field panel-reconstruction normalized root-mean-square error of 1.29% for 18 sensing locations with 2 με noise. Conservative WDM analysis yielded 54, 13, and 16 channels for three operating envelopes, and the prescribed random-vibration spectrum produced 6.78 g_rms. These results demonstrate a reproducible numerical proof of concept and define practical limits for compensation, spectral allocation, curvature interpretation, and inverse reconstruction; they do not constitute experimental validation or flight qualification.