Search Results (1,395)

New-generation communications aim for ubiquitous and pervasive communications with high data rates. Electromagnetic spectrum saturation and increasing data volumes can employ the use of free-space optical communication to ease capacity loads in modern networks. In this writing, we review the impact of the atmospheric channel on the optical signal dynamics for long-range data links between high-speed and maneuverability suborbital platforms in full atmosphere. This work presents the main propagation constraints, such as path loss, turbulence, and aero-optics, which are environment-dependent and geometry-dependent for this worst-case scenario. To carry out our study, we recall experimental results collected in the literature since the early times, showing system constraints and performance limits. This provides a historical timeline perspective. Theoretical models and channel management techniques that appeared through time are briefly summarized, and their impact on link budget and stability on reference link geometries is addressed through analytical simulation. In conclusion, this paper shows that an integrated approach to this kind of link is successful mainly with a convergence of mitigation techniques and tailored engineering, which cannot neglect the knowledge of the operating environment and strongly relies on accurate physics modeling, which remains an area of active open research. Full article

Vertical cavity surface emitting lasers (VCSELs) have shown great potential in high-speed communication, quantum information processing, and 3D sensing due to their excellent beam quality and low power consumption. However, generating high-purity and controllable circularly polarized light usually requires external optical components such as quarter-wave plates, which undoubtedly increases system complexity and volume, hindering chip-level integration. To address this issue, we propose a monolithic integration scheme that directly integrates a custom-designed double-layer asymmetric metasurface onto the upper distributed Bragg reflector of a chiral VCSEL. This metasurface consists of a rotated GaAs elliptical nanocolumn array and an anisotropic grating above it. By precisely controlling the relative orientation between the two, the in-plane symmetry of the structure is effectively broken, introducing a significant optical chirality response at a wavelength of 1550 nm. Numerical simulations show that this structure can achieve a near 100% high reflectivity for the left circularly polarized light (LCP), while suppressing the reflectivity of the right circularly polarized light (RCP) to approximately 33%, thereby obtaining an efficient in-cavity circular polarization selection function. Based on this, the proposed VCSEL can directly emit high-purity RCP without any external polarization control components. This compact circularly polarized laser source provides a key solution for achieving the next generation of highly integrated photonic chips and will have a profound impact on frontier fields such as spin optics, secure communication, and chip-level quantum light sources. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

The efficiency optimization of encryption and decryption algorithms in cloud environments is addressed in this study, where the processing speed of encryption and decryption is enhanced through the application of multi-threaded parallel technology. In view of the high-concurrency and distributed storage characteristics of cloud platforms, a multi-threaded concurrency mechanism is adopted for the direct processing of data streams. Compared with the traditional serial processing mode, four distinct encryption algorithms, namely AES, DES, SM4 and Ascon, are employed, and different data units are processed concurrently by means of multithreaded technology. Based on multi-dimensional performance evaluation indicators (including throughput, memory footprint and security level), comparative analyses are carried out to optimize the design scheme; accordingly, multi-threaded collaborative encryption is realized to improve the overall operation efficiency. Experimental results indicate that, in comparison with the traditional serial encryption method, the encryption and decryption latency of the algorithm is reduced by around 50%, which significantly lowers the time overhead associated with encryption and decryption processes. Simultaneously, the throughput of AES and DES algorithms is observed to be doubled, which leads to a remarkable improvement in communication efficiency. Moreover, under the premise that the original secure communication capability is guaranteed, system resource overhead is effectively reduced by SM4 and Ascon algorithms. On this basis, a quantitative reference basis is provided for cloud platforms to develop targeted encryption strategies tailored to diverse business demands. In conclusion, the proposed approach is of profound significance for advancing the synergistic optimization of security and performance in cloud-native data communication scenarios. Full article

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance. Full article

For enhancing the operations of microgrids, especially in places like Bonavista in Newfoundland and Labrador, accurate short-term wind power forecasting is critically important. This is more so for communities which integrate renewable energy. This paper aims to develop and implement deep learning Long Short-Term Memory (LSTM) models for wind power forecasting for three months ahead based on one year of historical data. With a Mean Absolute Error (MAE) of 0.27 m/s and a Root Mean Squared Error (RMSE) of 0.39 m/s, the model demonstrates high predictive accuracy. Estimated power output was calculated using a standard wind turbine power curve, assuming representative turbine parameters, in order to convert wind speed forecasts into useful power inputs for microgrid operations. The LSTM’s potential and significance in microgrid planning and optimization are highlighted by the results, which show that its yield power estimates closely match actual generation. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

To enhance line capacity in high-speed railways without new infrastructure, virtual coupling train sets (VCTSs) enable reduced inter-train distances via real-time communication and cooperative control. However, unknown disturbances and model uncertainties challenge VCTS performance, often causing chattering, slow convergence, and poor disturbance rejection. This paper proposes a novel finite-time extended state observer-based nonsingular terminal sliding mode (FTESO-NTSM) control strategy. The method integrates a nonsingular terminal sliding mode surface with a hyperbolic tangent-based reaching law to ensure fast convergence and chattering suppression, while a finite-time extended state observer estimates and compensates for lumped disturbances in real time. Lyapunov analysis rigorously proves finite-time stability. Numerical simulations under different initial statuses are conducted to validate the effectiveness of the proposed method. The results show that the maximum observation error achieves 0.0087 kN. The speed chattering magnitudes reach 0.00087 km/h, 0.0017 km/h, 0.0026 km/h, and 0.0034 km/h for the leading train and three followers, respectively. Furthermore, the convergence time of the followers is 56 s, 130 s, and 76 s, respectively. The results highlight that the proposed method can significantly improve line capacity and transportation efficiency. Full article

Existing State-of-the-Art (SOTA) methods for situational awareness typically rely on high-bandwidth transmission of raw data or computationally intensive models, which are often impractical for resource-constrained edge devices in unstable communication environments. To address these limitations, this paper introduces a comprehensive framework for Regional Risk Monitoring utilizing unmanned swarm systems. We propose an innovative knowledge distillation approach (SIKD) that leverages both soft label dark knowledge and inter-layer relationships, enabling compressed models to run in real time on edge nodes while maintaining high accuracy. Furthermore, recognition results are fused using Bayesian inference to dynamically update the regional risk level. Experimental results demonstrate the feasibility of the proposed framework. Quantitatively, the proposed SIKD algorithm reduces the model parameters by 52.34% and computational complexity to 44.21% of the original model, achieving a 3× inference speedup on edge CPUs. Furthermore, it outperforms state-of-the-art baseline methods (e.g., DKD and IRG) in terms of convergence speed and classification accuracy, ensuring robust real-time risk monitoring. Full article

Existing vehicle-mounted satellite terminals primarily rely on mechanical or purely analog electronically steered antennas. They lack protocol-level relay capability and usually provide only short-range hotspot connectivity. These limitations make it difficult for such systems to deliver stable, high-throughput satellite access for personal mobile devices in dynamic vehicular environments, especially in remote regions without terrestrial networks. This paper proposes an intelligent vehicle repeater for satellite networks (IVRSN) that builds a dedicated satellite–vehicle–device relay architecture. It enables reliable broadband connectivity for conventional mobile terminals without requiring specialized satellite hardware. The IVRSN consists of three key technical components. Firstly, a dual-mode relay coverage mechanism is designed to support energy-efficient in-vehicle access and extended out-of-vehicle coverage. Secondly, a DoA-assisted, attitude-compensated hybrid beamforming scheme is developed. It combines subspace-based direction estimation with inertial sensor measurements to maintain high-precision satellite pointing under vehicle dynamics. Finally, a bidirectional protocol conversion module is introduced to ensure compatibility between ground wireless protocols and satellite link-layer formats with integrity-checked data forwarding. Compared to existing solutions, the proposed IVRSN provides higher stability and broader device compatibility, making it a feasible solution for high-speed, high-quality communications in remote or disaster regions. Full article

Due to changing weather conditions, productivity needs to be enhanced and resources must be used more efficiently in agriculture. Precision agriculture relies on systems that can gather real-time environmental data to address these issues. However, the high cost of commercial weather stations often limits their adoption in rural areas. This study introduces a low-cost weather station designed for precision agriculture applications. The system consists of three main modules. The first module is the weather station, which gathers data on temperature, relative humidity, barometric pressure, solar radiation, wind speed and direction, and precipitation. It then transmits this data via LoRa communication to the local console module. This console receives the data, displays it on a screen, and sends it through Wi-Fi to the cloud server module. The cloud server presents the information via an interactive interface and is responsible for storing, processing, and analyzing the data records collected. The system was installed in the municipality of Ojocaliente, Zacatecas, Mexico, where performance and validation tests were conducted over a one-month period using sensors and reference measurements to evaluate the accuracy and stability of the data. The results showed high operational reliability and a strong correlation between the recorded values and the reference data. This confirms that the proposed solution provides a scalable, low-cost, and reliable alternative for environmental monitoring in precision agriculture. Full article

Background/Objectives: Telerehabilitation is intended for remote treatment, but studies on group training after stroke performed in the patient’s home have not yet been conducted. The purpose of this study was to evaluate adherence rates, safety, usability and enjoyment and preliminary clinical effects of home-based remote group training for balance and mobility in chronic stage after stroke. Methods: Community-dwelling patients in chronic stage after stroke who walked independently and had mild balance deficits participated. Over a 6-week period, they completed 60-min sessions of balance and mobility training twice a week in a group from their home. Adherence rates, adverse events and technical problems were recorded. Participants’ satisfaction was assessed using Modified Physical Activity Enjoyment Scale. The primary outcomes were Mini-Balance Evaluation Systems Test (mini-BESTest), 5TSTS and 10MWT and secondary outcomes were limits of stability, weight-bearing symmetry and Activities-Specific Balance Confidence Scale (ABC Scale), measured before, immediately after and six weeks after remote group training. Results: Participants expressed a very high level of satisfaction with training. There were no adverse events and dropouts, but some minor technical challenges. Results showed significant improvements in primary outcomes (mini-BESTest, 10MWT fast walking speed, 5TSTS; all p < 0.001), however there were no significant improvements in secondary outcomes (weight-bearing symmetry, limits of stability and ABC Scale). All improvements persisted six weeks after training. Conclusions: Remote group training at home is feasible, safe and efficient to improve balance and mobility in patients in the chronic phase after stroke. Full article

This study investigates how next-generation digital infrastructures—terahertz (THz) communication and AI-driven network orchestration—shape perceived service quality, luxury perception, and loyalty within the context of luxury hospitality. An empirical survey was conducted among 693 guests at Torre Melina Gran Meliá (Barcelona) between June 2024 and June 2025. Using a refined 38-item Likert-scale instrument, a three-factor structure was validated: (F1) Network Performance (speed, stability, coverage, seamless roaming, and multi-device reliability), (F2) Luxury Perception (modernity, innovation, and brand image), and (F3) Service Loyalty (satisfaction, return intentions, recommendations, and willingness to pay a premium). The results reveal that superior network performance functions both practically and symbolically. Functionally, it enables uninterrupted video calls, smooth streaming, low-latency gaming, and reliable multi-device usage—now considered essential utilities for contemporary travelers. Symbolically, high-performing and intelligently managed connectivity conveys technological leadership and exclusivity, thereby enhancing the hotel’s luxury image. Collectively, these effects create a “virtuous cycle” in which technical excellence reinforces perceptions of luxury, which in turn amplifies satisfaction and loyalty behaviors. From a managerial perspective, advanced connectivity should be viewed as a strategic investment and brand differentiator rather than a cost center. THz-ready, AI-orchestrated networks support personalization, dynamic bandwidth allocation (i.e., real-time adjustment of network capacity in response to fluctuating user demand), and monetizable premium service tiers, directly strengthening guest retention and brand equity. Ultimately, next-generation connectivity emerges not as an ancillary amenity but as a defining pillar of luxury hospitality in the emerging 6G era. Full article

Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF attenuation in conductive seawater, which strictly limits range coverage. In this article, we focus on a comprehensive review of different antenna types for future underwater communication and sensing systems, evaluating their performance and suitability for Autonomous Underwater Vehicles (AUVs). We critically examine and compare distinct antenna technologies, including Magnetic Induction (MI) coils, electrically short dipoles, wideband traveling wave antennas, printed planar antennas, and novel magnetoelectric (ME) resonators. Specifically, these antennas are compared in terms of physical footprint, operating frequency, bandwidth, and realized gain, revealing the trade-offs between miniaturization and radiation efficiency. Our analysis aims to identify the benefits and weaknesses of the different antenna types while emphasizing the necessity of innovative antenna designs to overcome the fundamental propagation limits of the underwater channel. Full article

With the rapid development of the low-altitude economy, efficient energy supply and communication systems have become key demands for low-altitude vehicles and Internet of Things (IoT) devices. This study investigates wireless powered communication systems (WPCSs) for low-altitude economy, focusing on the energy consumption characteristics of different power supply schemes under various practical application scenarios. Through simulation experiments, we compared the energy efficiency of traditional battery power, simple wireless power supply, optimized wireless power supply, and the proposed efficient WPCS (with dynamic adjustment) under changes in transmission distance, communication frequency, weather conditions, flight speed, and task complexity. The results show that traditional battery power schemes exhibit high energy consumption in long-distance transmission and high-frequency communication scenarios, while the proposed efficient WPCS significantly reduces the rate of energy consumption increase through dynamic energy transmission adjustment mechanisms, demonstrating the lowest energy consumption levels across all test scenarios. This research provides important theoretical and practical references for the design of WPCS in the low-altitude economy, highlighting the significant advantages of dynamic adjustment mechanisms in improving energy efficiency and offering technical support for the sustainable development of the low-altitude economy. Full article