The object of the research is the developed automated computational model (AI-driven system) for real-time monitoring and analysis of road traffic, focusing on the identification and assessment of dangerous situations (traffic violations, congestion, and accident risks). This paper examines how the increased number of people moving to cities and their vehicles increases the likelihood of traffic accidents on public roads. It is also noted that traditional inspections are carried out very slowly and do not fully detect violations of traffic rules. To overcome these limitations, it is proposed a novel automated computational model for vehicle and accident tracking, based on UAVs combined with computer vision and artificial intelligence technologies. The proposed model allows for real-time threat detection and evaluation. The study, modeled in the MATLAB environment using real traffic data from drone-captured video. This model demonstrates significant improvements in operational metrics, an average detection achieved accuracy 89% for vehicles and critical events (e. g., congestion, deviations). The model successfully visualizes risk areas with heat maps and predicts short-term traffic pattern changes, increasing the reliability of traffic management and expanding the possibilities of traffic risk forecasting. The results obtained during the simulation can be used in practice by transport services, road, and maintenance organizations, particularly at difficult intersections and on highly accident-prone highways in urban, heavily built-up areas.

Perovskite solar cells (PSCs) have quickly achieved certified energy conversion efficiency reaching a certified record of 27.3% for single-junction cells, while having a low mass, thin-film form factor and high specific power, which are attractive for space energy systems. However, their long-term reliability in extraterrestrial environments is not adequately ensured by terrestrial qualification routes, and standardized space-related test protocols remain insufficiently developed. This review critically summarizes the current understanding of the degradation of PSCs under the influence of key environmental factors in space—ionizing and non-ionizing radiation, thermal vacuum exposure and thermal cycling, and ultraviolet radiation AM0, as well as atmospheric oxygen in low orbits. The central task of the work is to develop and justify the need to create specialized PSCs test protocols for space applications, since existing ground standards do not reflect the multifactorial nature and extreme orbital loads. It has been shown that thermal vacuum accelerates ion migration, interphase reactions, and degassing, while AM0 UV and atomic oxygen introduce additional photochemical and oxidative mechanisms of destruction; at the same time, stressors often act synergistically and are not detected by single-factor tests. Next, the limitations of the current IEC and ISOS are discussed and an approach to their expansion is formulated through the ISOS-T-Space and ISOS-LC-Space protocols, which integrate high vacuum, AM0 lighting, extended temperature ranges and controlled particle irradiation. It is concluded that the development and interlaboratory validation of such space-oriented protocols is a key condition for the correct qualification of PSCs and targeted optimization of materials and interfaces to meet the requirements of space energy.

Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances.

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals.

Perovskite solar cells (PSCs) have emerged as a disruptive photovoltaic technology for aerospace and extreme environment applications, driven by their substantial power-to-weight ratio and mechanical flexibility. However, continuous operation under harsh conditions, characterized by the AM0 spectrum, deep vacuum, extreme thermal cycling, and ionizing radiation, exposes the fundamental thermodynamic instability of traditional organic–inorganic hybrid perovskites. This comprehensive review systematically synthesizes 131 recent studies to provide a holistic framework for designing ultrastable, radiation-hardened PSCs. We critically examine the underlying degradation mechanisms, including vacuum-induced volatile desorption, UV-triggered halide segregation, and thermomechanical fracture at buried interfaces. To overcome these critical barriers, we highlight advanced engineering strategies: the transition to all-inorganic CsPbX₃ and lead-free double/chalcogenide perovskites (e.g., Cs₂SnI₆, CaHfS₃), the implementation of dopant-free inorganic transport layers coupled with self-assembled monolayers (SAMs) for cascade band alignment, and the integration of polymeric scaffolds for fracture energy toughening. Furthermore, we emphasize the imperative shift toward solvent-free vacuum deposition techniques (ALD, PLD). A distinctive focus of this review is the integration of Artificial Intelligence; specifically, we evaluate Deep Learning architectures, such as Long Short-Term Memory (LSTM) networks, for predictive State of Health (SOH) monitoring, underscoring the vital transition from simulated to empirical datasets. Finally, coupled with Material Flow Cost Accounting (MFCA), this review outlines a strategic roadmap for the commercialization and deployment of autonomous, self-diagnosing photovoltaic platforms in next-generation satellite and deep-space missions.

The article presents a comprehensive review of modern approaches to the development and use of fiber-optic gyroscopes in inertial navigation systems, especially in the absence or distortion of satellite signals. The operating principles of interferometric and resonant fiber-optic gyroscopes based on the Sagnac effect, their design features, advantages and disadvantages compared to traditional mechanical gyroscopes are considered in detail. Particular attention is paid to the results of modeling an interferometric fiber-optic gyroscope in the MATLAB environment, taking into account the effect of the length and diameter of the optical coil on sensitivity, the level of random interference and zero instability. Integration of fiber optic gyroscopes into autonomous navigation systems remains a challenging engineering task requiring design optimization, signal processing algorithm improvement, adaptation to multi-sensor systems, and increased real-time data collection efficiency. The presented results and conclusions require high accuracy and reliability of navigation for applications in aviation, marine transport, space industry, geodesy, and robotics.