Search Results (1,488)

A significant bottleneck for the practical deployment of fluid antenna systems (FASs) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel “prediction-axiom” dual-driven paradigm is introduced that fundamentally differs from pure data-driven approaches. The core innovation lies in using an enhanced unified adaptive modeling algorithm (UAMA) not for direct decision-making but as a computational foundation to enable information-theoretic axioms under sparse observation conditions (30% of ports). The UAMA predictor, leveraging spatiotemporal correlations, accurately reconstructs the full channel state from limited measurements. This prediction then empowers an information-theoretic scoring mechanism, which synergizes Fisher information, curvature metrics, and port entropy to transform optimal port selection into a tractable maximization problem. Consequently, the system outage probability remains close to the ideal performance limit achievable under full observability. Tests on diverse antenna systems confirm the algorithm’s high accuracy and robust adaptive capability. This work delivers a reliable, low-cost implementation strategy for 6G dynamic networks, effectively bridging the gap between mathematical theory and practical FAS deployment. Full article

In the field of modern power systems, power supply reliability has become a core indicator for measuring distribution network performance. It serves not only as a fundamental criterion for judging the continuous power supply capacity of distribution networks but also as a key benchmark for evaluating their power quality. Considering the current status of reliability assessment for distribution network power supply, this study conducts an in-depth analysis of a series of key indicators, namely outage duration, outage frequency, the number of affected customers, power supply reliability rate, and the proportion of affected customers. Through a detailed deconstruction of these indicators, an evaluation model for distribution network power supply reliability is established. In the process of model construction, this study innovatively combines the hierarchical recursive weighting method with the entropy measurement weight determination method to accurately define the weights of each evaluation dimension. On this basis, a fuzzy membership quantification strategy is introduced to precisely determine the classification level of distribution networks, and Monte Carlo simulation combined with triangular fuzzy number is used to carry out uncertainty modeling on the reliability score, realizing the transformation from deterministic evaluation to probabilistic evaluation. This strategy is developed to transform qualitative issues into quantitative analysis, effectively clarify the fuzzy and complex interrelationships among multiple influencing factors, and thereby realize a comprehensive evaluation of power supply reliability for distribution networks. To verify the effectiveness and practicality of the proposed method, a distribution network in a specific region is selected as the research object. The aforementioned model and method are applied to assess its power supply reliability, and the precise classification of distribution network levels in this region is successfully realized. This combined model significantly improves the accuracy of evaluation while ensuring the scientific rigor and fairness of the evaluation process. It provides an innovative and practical method for the field of distribution network power supply reliability assessment, and offers substantive reference and support for relevant decision-making and practical operations. Full article

In this study, we statistically analyze the performance of a threshold-based multiple optical signal selection scheme (TMOS) for wavelength division multiplexing (WDM) and adaptive coded modulation (ACM); this is achieved using free space optical (FSO) communication between mobile platforms in maritime environments with fog and 3D pointing errors. Specifically, we derive a new closed-form expression for a composite probability density function (PDF) that is more appropriate for applying various algorithms to FSO systems under the combined effects of fog and pointing errors. We then analyze the outage probability, average spectral efficiency (ASE), and bit error rate (BER) performance of the conventional detection techniques (i.e., heterodyne and intensity modulation/direct detection). The derived analytical results were cross-verified using Monte Carlo simulations. The results show that we can obtain a higher ASE performance by applying TMOS-based WDM and ACM and that the probability of the beam being detected in the photodetector increased at a low signal-to-noise ratio, contrary to conventional performance. Furthermore, it has been confirmed that applying WDM and ACM is suitable, particularly in maritime environments where channel conditions frequently change. Full article

To meet the requirements for the automatic alignment, insertion, and inspection of guide-tube opening pins on the upper core plate in a component pool during refueling outages of nuclear power units, this paper proposes a cognition-enhanced visual-servoing framework that integrates geometric cognition-based compensation, observation-confidence modeling, and constraint-aware optimal control. The framework addresses the key challenge posed by the coexistence of long-term geometric drift and underwater observation uncertainty. Specifically, historical closed-loop data are leveraged to learn and compensate for systematic geometric errors online, substantially improving coarse-positioning accuracy. In addition, an explicit confidence model is introduced to quantitatively assess the reliability of visual measurements. Building on these components, a confidence-driven, finite-horizon, constrained model predictive control strategy is designed to achieve safe and efficient finite-step convergence while strictly respecting actuator physical constraints. Ground experiments and deep-water component-pool validations demonstrate that the proposed method reduces coarse-positioning error by approximately 75%, achieves stable sub-millimeter alignment with an ample engineering safety margin, and effectively decreases erroneous insertions and the need for manual intervention. These results confirm the engineering applicability and safety advantages of the proposed cognition-enhanced visual-servoing framework for underwater alignment tasks in nuclear component pools. Full article

The reliance on global navigation satellite systems (GNSS) for modern vessel poses a critical point of failure. GNSS is vulnerable to jamming, spoofing, and other threats that can increase the risk of accidents. In response, alternative sources of navigational information are being sought. R-Mode offers a promising solution by leveraging terrestrial infrastructure to provide PNT data independently of GNSS. A minimum of three stations in view is needed to obtain a position and timing information. While a single R-Mode station in view cannot provide independent positioning, the received data can still be used to validate a GNSS solutions and detect threats like spoofing or outages. In this study, we introduce a novel approach to validate GNSS positions using R-Mode ranging information from a single station by combining the expected accuracy of the measurements with the geometrical relationship between the GNSS solution and the known R-Mode transmitter location. Our method was tested with real measurements in post-processing, where simulated spoofing events were introduced to mimic real-world scenarios. During these events, the GNSS solution deviated by approximately 100 m from original position. Our technique successfully detected the spoofing instances and raised warnings to increase the awareness of GNSS-based navigation threats. Full article

The integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS) has been widely adopted in railway positioning applications. However, conventional filtering-based approaches are fundamentally constrained by their dependence on instantaneous state estimates while failing to exploit valuable historical measurement information. To overcome this limitation, we develop a factor graph optimization (FGO) framework to enhance data utilization efficiency. During GNSS signal outages, existing implementations typically preserve only SINS factors while excluding GNSS observations, leading to unbounded error growth. To bridge this gap, our novel solution integrates a gated recurrent unit (GRU) with an Improved Bidirectional Long Short-Term Memory (IBiLSTM) network to generate accurate pseudo-GNSS observations through effective learning from both preceding and subsequent GNSS data sequences. Comprehensive evaluation under GNSS-denied conditions demonstrates that our approach achieves significant improvements over conventional neural network-aided methods, with horizontal root mean square error (RMSE) reductions of 49.22% (simulation) and 36.24% (onboard vehicle). Subsequent FGO processing yields additional performance gains, further reducing RMSE by 46.67% (simulation) and 35.31% (onboard vehicle). This innovative methodology effectively maintains positioning accuracy and ensures navigation continuity during GNSS outages, thereby offering a robust solution for train positioning systems in challenging environments. Full article

Accurate and reliable current measurement is a key prerequisite for ensuring the safe operation of power systems. Conventional through-core and wound current transformers require power outage for installation or modification of line structures, which are plagued by high installation difficulty and cost, and fail to meet the digital development needs of smart grids. To address the demand for non-intrusive installation of current transformers, this paper proposes a non-intrusive current transformer with PCB coils in reverse-series connection. First, a magnetic coupling current calculation model is established to design a reverse-series double-layer coil structure, and a mathematical model of the equivalent circuit for the sensing and measurement system is constructed. The influence of circuit parameters on the output response is analyzed, yielding an optimization method for the system operating state and completing the hardware circuit design. Subsequently, a simulation model of the reverse-series double-layer coil is built to calculate and analyze the amplitude-frequency characteristics, steady-state and transient performance, as well as anti-interference capability of the transformer. The results demonstrate that the designed transformer, combined with an active integrating circuit, achieves an upper cutoff frequency of 13,169 Hz and a lower cutoff frequency approaching 0 Hz, which satisfies the requirements of wide-frequency measurement while ensuring high sensitivity and anti-interference capability. Finally, a current-sensing experiment platform is built for comparative verification with conventional invasive current transformers. Experimental results show that after correction with a proportional coefficient of 1.317, the fitting squared error is only 0.0038. The linearity remains excellent under different conditions with a wide dynamic measurement range, and the phase error is less than 15°. Within the range of 2–120% of the rated current, the ratio error is less than 0.9%, indicating high measurement accuracy. This study provides a new high-precision and convenient method for current measurement in smart grids. Full article

As renewable energy sources (RESs) become increasingly prevalent, limitations on connecting new sources arise due to insufficient suitable locations and grid constraints. Existing RES installations introduce challenges such as generation variability, the necessity for costly reserves, and overproduction, which can lead to forced outages. In response, grid operators have adopted more flexible connection policies, notably “cable pooling”, which only restricts the power injected at a given node rather than the total capacity of the connected sources. This article proposes a method for optimal sizing of diverse RES combinations connected to high-voltage networks under cable pooling conditions from an investor’s perspective. The most prominent findings show the existence of a strong relationship between optimal RES sizing and composition on financial objectives, revenue sources, and market prices. Subsequent achievements involve demonstrating that the profitability of energy storage without subsidies is essentially limited to participation in the capacity market and that the reduction of RES generation depends on the investor’s financial objective, not on the market type. Full article

The integration of inverter-based generation units, such as photovoltaic systems, wind turbines, and vehicle-to-grid (V2G) technologies, has introduced new challenges in maintaining power and frequency stability in modern power systems. Virtual Synchronous Generators (VSGs) have emerged as a promising solution to enhance system stability; however, existing control methods often lack the robustness and flexibility needed to address deliberate and unplanned outages effectively. This paper presents a novel approach for optimizing power control in generation units using a Long Short-Term Memory (LSTM)-based machine learning method. The proposed LSTM-based controller provides a fast and real-time response, ensuring robust and flexible performance under varying operational conditions. Unlike traditional controllers, the proposed method effectively handles nonlinearities and uncertainties associated with inverter-based units. Additionally, it effectively balances technical and economic aspects of power system operation by minimizing oscillations and optimizing resource utilization. The proposed approach is benchmarked against conventional control methods through a detailed simulation-based comparative analysis against a linear Model Predictive Control strategy under identical operating conditions. Simulation results indicate that the proposed controller reduces frequency deviations by up to 66.7%, voltage deviations by 62.5%, and total operational cost by approximately 11.3%, while achieving nearly 90% faster dynamic response, validating its effectiveness for modern power systems. Full article

Voltage stability assessment under transmission contingencies is traditionally performed using severity-based indices evaluated on isolated outage scenarios. While effective for identifying extreme events, such approaches provide limited insight into which transmission corridors structurally govern voltage-stress behavior across the full contingency space. This paper introduces a persistence-based diagnostic framework for voltage stability assessment under exhaustive $N-1$ line contingencies, using the Fast Voltage Stability Index (FVSI) as a base indicator. Rather than ranking lines by instantaneous severity, the proposed methodology identifies dominant transmission lines—defined as those attaining the maximum FVSI in each convergent contingency—and aggregates these outcomes statistically to quantify dominance persistence, conditional severity, and dispersion. A dominance concentration metric ($k_{90}$) is introduced to measure how many transmission corridors are sufficient to explain the majority of dominant voltage-stress events. The framework is applied to IEEE 14, 30, and 118-bus benchmark systems under exhaustive $N-1$ enumeration. Results reveal a clear phenomenon of dominance collapse: as system size increases, dominant voltage-stress outcomes concentrate onto an extremely small set of transmission corridors. While IEEE 14 exhibits partial dominance dispersion ($k_{90}=2$), both IEEE 30 and IEEE 118 demonstrate near-total dominance collapse ($k_{90}=1$), where a single corridor governs more than 90% of dominant FVSI events. The proposed approach is fully deterministic, scalable, and independent of control or optimization assumptions, making it well-suited for planning-stage screening, monitoring prioritization, and pre-filtering of large-scale contingency studies. By shifting voltage stability analysis from severity-only screening to persistence-based structural diagnosis, this work provides new insight into vulnerability concentration in modern transmission networks. Full article

In this paper, the performance of dual-hop relay transmission in modern wireless communication systems is analyzed by considering two fundamental relaying techniques, namely, Amplify-and-Forward (AF) and Decode-and-Forward (DF). The propagation conditions on the source–relay (S-R) and relay–destination (R-D) links are modeled using the κ-μ statistical distribution, which effectively captures the fading characteristics in both line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The analysis focuses on key performance metrics, including the outage probability (P_out) and average bit error probability (P_e), for Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) modulation schemes, assuming transmission via a single relay without a direct S–D link. Closed-form expressions for the considered metrics are derived based on the κ-μ model and verified by numerical evaluation. In addition to classical analytical modeling, a Generative Artificial Intelligence (GenAI)-enabled workflow is incorporated as a supportive tool in order to aid in automated analysis, the interpretation of the results in the context of network management under varying channel and system parameters based on the P_out and P_e calculations with the aim to tackle the underlying complexity and cognitive load of infrastructure adaptation and re-configuration operations. The combined analytical and GenAI-assisted approach provides valuable insights for the optimization, design, and continuous evolution of robust relay-based architectures in next-generation wireless networks. Full article

The widespread adoption of solar energy storage systems is transforming the global energy landscape, enabling more efficient use of renewable resources and enhancing energy resilience. The integration of residential batteries significantly enhances energy efficiency and sustainability by facilitating the storage of surplus renewable energy, providing reliable backup during power outages, and optimising energy consumption. This study explores the factors influencing end-user adoption of batteries, utilising the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a guiding framework to analyse adoption behaviours and determinants. This study employs a qualitative approach using semi-structured interviews with stakeholders divided into three categories: regulatory authorities, industry experts, and end-users. This study highlights key factors influencing battery adoption, such as energy independence, grid reliability, and environmental impact, while addressing challenges like regulatory inconsistencies and installer training. Study extends UTAUT2 to residential battery adoption, emphasising performance expectancy, facilitating conditions, and price value in decision-making and makes a methodological contribution by validating deeper qualitative insights into renewable technology adoption. The practical implications emphasise the need for designing targeted policies, such as subsidies and net metering, alongside developing user-centric systems that enhance affordability, usability, and consumer awareness to facilitate residential battery adoption. Full article

Precise orbit determination for multi-spacecraft deep space missions faces challenges including long communication delays, sparse tracking, dynamic model uncertainties, and inefficient data fusion. Presenting a hybrid estimation architecture, this study integrates onboard autonomous navigation with ground-based batch processing of delayed measurements. The framework makes three key contributions: (1) a delay-aware fusion paradigm that dynamically weights space- and ground-based observations according to real-time Earth–Mars latency (4–22 min); (2) a model-informed online calibration framework that jointly estimates and compensates dominant dynamic error sources, reducing model uncertainty by 60%; (3) a lightweight hierarchical architecture that balances accuracy and efficiency for resource-constrained “one-master-multiple-slave” formations. Validated through Tianwen-1 mission data replay and simulated Mars sample return scenarios, the method achieves absolute and relative orbit determination accuracies of 14.2 cm and 9.8 cm, respectively—an improvement of >50% over traditional centralized filters and a 30% enhancement over existing federated approaches. It maintains 20.3 cm accuracy during 10 min ground-link outages and shows robustness to initial errors >1000 m and significant model uncertainties. This study presents a robust framework applicable to future multi-agent deep space missions such as Mars sample return, asteroid reconnaissance, and cislunar navigation constellations. Full article

In this study, we emphasize that the maximum sum rate can be achieved through AI-based subchannel allocation, while taking into account all users’ quality of service (QoS) requirements in data rates for hybrid beamforming systems. We assume a limited number of radio frequency (RF) chains in practical hybrid beamforming architectures. This constraint makes subchannel allocation a critical aspect of hybrid beamforming in massive multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiple access (MIMO-OFDMA), as it enables the system to serve more users within a single time slot. Unlike conventional subcarrier allocation methods, we employ a deep reinforcement learning (DRL)-based algorithm to address real-time decision-making challenges. Specifically, we propose a dueling double deep Q-network (Dueling-DDQN) to implement dynamic subchannel allocation. Simulation results demonstrate that the performance of the proposed algorithm gradually approaches that of the greedy method. Furthermore, both the average sum rate and the average spectral efficiency per user improve with a reasonable variation in outage probability. Full article

For the purpose of fulfilling the dual requirements of persistent cellular network connectivity and flight safety for cellular-connected Unmanned Aerial Vehicles (UAVs) operating in dense urban airspace, this paper presents an A*-oriented comprehensive path-planning scheme for multiple connected UAVs that integrates a radio map and complex network. Existing research often lacks rigorous processing of environmental map data, while the traditional A* algorithm struggles to simultaneously handle constraints such as obstacle avoidance, flight maneuverability, and multi-UAV path conflicts. To overcome these limitations, this study first constructs a path-planning model based on complex-network theory using environmental data and the radio map, clarifying the separation of responsibilities between environment representation and algorithmic search. On this basis, we proposed an improved A* algorithm for multi-UAV scenarios termed MURM-A*. Simulation results demonstrate that the proposed algorithm effectively avoids collisions with obstacles, adheres to UAV flight dynamics, and prevents spatial conflicts between multi-UAV paths, while achieving a joint optimization between path efficiency and radio quality. In terms of performance comparison, the proposed algorithm shows a marginal difference but ensures operational validity compared to traditional A*, exhibits a slightly increase in flight time but achieves a substantial reduction in radio-outage time compared to the Deep Reinforcement Learning (DRL) method. Furthermore, employing the path-planning model enables the algorithm to more accurately identify environmental information compared to directly using raw environmental maps. The modeling time is also notably shorter than the training time required for DRL methods. This study provides a well-structured and extensible systematic framework for reliable path planning of multiple cellular-connected UAVs in complex radio environments. Full article