Search Results (802)

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

Cloud-based AI systems increasingly rely on Retrieval-Augmented Generation (RAG) to handle complex, knowledge-intensive queries. However, query decomposition for multi-hop retrieval—traditionally powered by large language models (LLMs)—incurs significant latency and cost, rendering it impractical for large-scale, cost-sensitive cloud deployments. We propose ToR-Lite, a lightweight, generative LLM-free semantic query decomposition framework designed to enhance multi-hop retrieval efficiency in cloud-based AI systems. ToR-Lite employs a novel Word-Window Splitting algorithm that detects semantic breakpoints via sliding window embeddings, effectively decomposing complex queries without expensive LLM inference. Experiments on the MultiHop-RAG benchmark (n = 2255) demonstrate that ToR-Lite achieves +6.03 pp Hits@10 and +0.89 pp Exact Match improvements over the Baseline, while operating 3.18 times faster than LLM-based Adaptive ToR. Retrieval performance correlates monotonically with decomposition granularity: three sub-query decompositions (#Dq = 3) yields a +7.00 pp Hits@10 improvement, confirming that semantic granularity is a key driver of retrieval performance. Comparison with rule-based Baselines confirms that these gains derive from the precision of semantic boundary detection rather than decomposition quantity alone. ToR-Lite delivers nearly twice the retrieval improvement per unit of computational cost, offering a practical and cost-effective solution for latency-sensitive cloud AI deployments. Full article

This study investigated interlimb asymmetries in lower limb performance using both vertical and horizontal jump tests in elite young basketball players. Specifically, it aimed to determine whether (1) unilateral jump performance and (2) the magnitude of interlimb asymmetry differed across maturity groups, whether (3) limb dominance influences performance, and whether (4) asymmetry direction is consistent across tests. One hundred elite male basketball players (U13 to U19) were categorised into three maturational stages: Pre-PHV (n = 19), Circa-PHV (n = 29), and Post-PHV (n = 52). Each athlete performed the following unilateral tests with both the dominant and non-dominant leg: single-leg hop, triple hop for distance, 6 m timed hop, single-leg countermovement jump (SL-CMJ), and single-leg drop jump (SL-DJ) from a 30 cm box. The Bilateral Strength Asymmetry (BSA) index was computed for each test. All tests showed significant differences between Pre-PHV and Circa-PHV groups (p < 0.001), whereas only the 6 m timed hop differed between Circa-PHV and Post-PHV (p < 0.01). BSA did not differ significantly across maturation stages in any test, except for the single-leg hop. Agreement in asymmetry direction between test pairs was slight to fair (kappa ≤ 0.29). BSA values remained largely stable across maturational stages, suggesting that interlimb asymmetries are established before PHV, likely during childhood. Limb dominance did not affect jump performance, and asymmetry direction varied between tests, confirming they are not interchangeable. Full article

This paper considers geometry-aware ground user clustering and Cluster Center Optimization for beam pointing and scheduling in adaptive multibeam Geostationary Earth Orbit (GEO) satellite networks. By grouping ground users, beams can be directed toward user clusters to maximize satellite throughput. We propose GeoClust, a polynomial-time geometric user clustering algorithm for adaptive multibeam GEO satellite networks, using a geometric set-cover approach that explicitly balances link signal-to-interference-plus-noise ratio (SINR) and hopping overhead. The algorithm employs a Boyle–Dykstra projection-based cluster center update within an alternating optimization framework, combined with nearest-center membership updates, to enforce the cluster-radius constraint while ensuring feasibility and provable convergence. It also achieves near-linear throughput scaling with increasing number of RF chains. Numerical evaluations on real-world population data show that, under heavy traffic conditions, our approach more than doubles the zero outage and median user rates compared to benchmark schemes. Full article

Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature–classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect different feature combinations, classifiers, and subjects. In this work, we provide, to our knowledge, the first systematic robustness map of a conventional sEMG pipeline under controlledclipping and single-sensor failure. sEMG from nine subjects performing a multi-session, multi-gesture protocol is windowed (250 ms, 50 ms hop) and represented using four common time-domain features (Root Mean Square, Variance, Zero Crossing, and Waveform Length). We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (${10}^{-6}$–${10}^{-1}$) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This design enables explicit characterization of the following: (1) accuracy recovery as clipping weakens for each feature pair; (2) dependency of robustness on which channel fails; and (3) differences among Support Vector Machine, Linear Discriminant Analysis, and Random Forest under identical degradations. The results show that lightweight feature pairs (Root Mean Square + Waveform Length, Variance + Zero Crossing, and Waveform Length + Zero Crossing) coupled with Random Forest form a consistently robust operating point, with performance recovering as clipping weakens and remaining resilient under single-channel dropout. Beyond robustness, the conventional pipeline trains substantially faster than representative deep learning baselines under a unified end-to-end timing definition, supporting real-time recalibration and repeated robustness sweeps in wearable deployments. Full article

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

Objective: This randomized controlled trial aimed to investigate how volume-matched unilateral and bilateral jump training affects physical performance in prepubertal and pubertal male youth soccer players and to examine whether maturational status influences these training adaptations. Methods: Sixty-five male soccer players (age 10.5 ± 2.9 years; height 136.7 ± 17.8 cm; body mass 32.8 ± 8.6 kg; maturity offset −1.6 ± 1.0 years) completed an 8-week training program (two sessions/week). Participants were randomly assigned to a bilateral jump group (n = 22), unilateral jump group (n = 22), or control group (n = 21). Performance was evaluated in a single testing session, which included horizontal jump tests (bilateral standing long jump and single-leg hop distance), linear sprint tests over 10 m (acceleration) and 30 m (maximal sprint performance) using timed trials, and change-of-direction (COD) ability assessed via a standardized timed COD test. Results: Significant main effects of time, maturation, and time × group interactions were observed for all outcomes (p ≤ 0.013). A maturation × group interaction was found for bilateral jump performance (p = 0.045), a group effect for 10 m sprint time (p = 0.015), and a time × maturation × group interaction for COD performance (p < 0.001). Both training groups had improved jump performance (jump distance) and 10 m sprint time across maturity levels, while no changes were observed in the control group. For 30 m sprint time, improvements were observed in both training groups in prepubertal players, whereas only the unilateral group showed improvements in pubertal players. COD performance (completion time) improved in the unilateral group at both maturity levels and in the bilateral group at the pubertal level. Conclusions: Structured jump training enhances horizontal jump distance, sprint performance, and COD ability in youth soccer players. Adaptations appear to be influenced by training modality and maturation, although these effects may vary depending on the specific performance task. Full article

Nd₂NiO_4+δ was investigated as a Ruddlesden–Popper (RP) cathode material for intermediate-temperature solid oxide fuel cells (IT-SOFCs), with particular emphasis on its electrochemical performance and oxygen reduction reaction mechanism. The material was synthesized via a polymeric sol–gel route derived from Pechini’s method and evaluated in symmetric cells using Ce_0.9Gd_0.1O_2−δ (GDC) as the electrolyte. X-ray diffraction confirmed the formation of a single RP phase and good chemical compatibility with GDC after thermal treatments at 800 °C. Cathode layers with thicknesses of 8–12 µm were deposited by dip-coating. Electrical conductivity measurements revealed a thermally activated semiconducting behavior governed by Ni²⁺/Ni³⁺ small-polaron hopping, with an activation energy of ~1.08 eV. Electrochemical impedance spectroscopy showed a strong temperature dependence of the area-specific resistance, decreasing from 9.18 Ω·cm² at 600 °C to 0.39 Ω·cm² at 800 °C. Distribution of relaxation times (DRT) analysis enabled the identification of the dominant electrochemical processes, indicating that oxygen surface exchange reactions are more favorable than charge transfer at the cathode–electrolyte interface, which remains the main limiting step. These results demonstrate that Nd₂NiO_4+δ is a promising cathode for IT-SOFC operation, while further optimization of the electrode–electrolyte interface is required to enhance its oxygen reduction kinetics. Full article

For the first time, hybrid nanocomposites based on poly(2,5-dichloro-3,6-bis(phenylamino)-p-benzoquinone) (PCPAB) and multi-walled carbon nanotubes (MWCNTs) were obtained and the influence of the preparation method on their structure and functional properties was demonstrated. The nanocomposites were obtained both by ultrasonic mixing of PCPAB and MWCNTs, and via in situ oxidative polymerization of CPAB in the presence of MWCNTs or MWCNTs with the addition of ZnO. The formation of hybrid nanocomposites occurs due to non-covalent interaction (π-stacking) between the graphene structures of the MWCNT surface and the phenyl rings of PCPAB. It was found that during the in situ oxidative polymerization of CPAB in the presence of MWCNTs, the growth of polymer chains occurred in close proximity to the filler surface, which led to the formation of a polymer coating. ZnO particles, localized on MWCNTs, on the one hand, prevent their aggregation, and on the other hand, create additional polymerization reaction centers due to the coordination of the Zn-O bond at the H and O atoms of the monomer. An increase in the concentration of reaction centers as a result led to a 2–2.5-fold reduction in the induction polymerization period. According to SEM data, in this case, a more ordered and denser polymer layer is formed due to intermolecular complexation between the main and side chains of the growing polymer with the participation of Zn²⁺ ions formed as a result of the transformation of ZnO to ZnCl₂ in the acidic reaction medium of polymerization. The results of the study of the frequency dependences of conductivity indicate a hopping mechanism of conductivity of nanocomposites. The electrical conductivity of nanocomposites depends on their production method and the MWCNT content and varies between 0.5 and 1.1 S∙cm⁻¹, which is 6–12 times higher than the conductivity of the original polymer. Thermogravimetric analysis revealed that the nanocomposites exhibit enhanced thermal stability compared to PCPAB. The best results were shown by nanocomposites with a higher content of MWCNTs, for which the residual mass at 450 °C was 51–53%. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article

Atomic interferometric gravity gradient measurement enables atomic interference by manipulating atoms with lasers of specific frequencies. Thus, the frequency and phase−locking performance of the laser system exerts a significant impact on key experimental parameters, including the loading rate and ultimate cooling temperature of atomic clouds, the state selection efficiency of Raman transitions, the contrast of atomic interference fringes, and the level of detection noise. As atomic interferometric gravity gradient measurement transitions from static laboratory measurements to mobile field operations, conventional laser frequency and phase−locking methods struggle to meet the demand for rapid re−locking after device movement and cannot achieve timely system recovery in the event of laser unlocks. This work proposes an automatic laser frequency and phase−locking system that can detect real−time deviations in laser frequency and phase and implement rapid and precise corrections. Meanwhile, by utilizing the reference signal source in the optical phase−locked loop, the system realizes laser frequency hopping to satisfy the diverse laser frequency requirements across all stages of atomic interferometric gravity gradient measurement. Full article

Conventional message-driven frequency-hopping (MDFH) systems are vulnerable to partial-band jamming, particularly when the jamming simultaneously affects both active and idle subcarriers, which disrupts energy-based detection. To address this limitation, this paper proposes a novel randomized intra-group subcarrier selection with joint suppression (RIJS-MDFH) scheme. In this framework, subcarriers are dynamically organized into configurable groups, and active carriers are randomized within each group. This structure decouples the jamming signal into distinct mainlobe and sidelobe components. The mainlobe is mitigated via rate-adaptive channel coding, whose rate is matched to the jamming bandwidth and the subcarrier mapping configuration. The sidelobe is suppressed using a filter-bank-based technique, effectively accelerating its roll-off. Simulation results demonstrate that the proposed scheme significantly outperforms existing MDFH systems in anti-jamming robustness under identical partial-band jamming conditions. At the same time, it preserves high spectral efficiency through flexible parameter adjustment. The work confirms that jointly addressing both jamming components enables reliable communication under low signal-to-jamming ratios, overcoming a key weakness of conventional MDFH designs. Full article

Background and Objectives: Throughout the return-to-play process after anterior cruciate ligament reconstruction (ACLR), clearance criteria and limb symmetry indices (LSI) play an important role in clinical decision-making by helping evaluate patient readiness and informing safe activity progressions, with the goal of reducing re-injury risk. How clearance criteria are implemented in research studies to evaluate patient readiness, specifically in force plate jumping studies, is currently unknown. This scoping review was a focused examination of clearance criteria and limb symmetry indices in studies performing force plate-based jumping assessments with ACLR patients. The research questions guiding this scoping review were as follows: (1) What clearance criteria are reported in studies involving primary ACLR patients who participate in jumping assessments on force plates? (2) What LSI are reported in force plate studies, and what level of symmetry is deemed acceptable to allow for safe participation of ACLR patients who participate in jumping assessments of force plates? Materials and Methods: Nine databases were searched on 7 or 8 September 2024 for three concepts: ACLR, force plates, and movement properties. Inclusion criteria were as follows: (a) primary ACLR patients at least 6 months post-surgery; (b) performing a countermovement or drop jump; (c) collecting at least one kinetic parameter using a force plate. Clearance criteria was operationally defined as a time from surgery boundary, functional or performance-based testing criteria, medical evaluation, or completion/participation in a rehabilitation program. Results: Thirty-five studies were included. Time from surgery was the most frequently reported clearance criteria (26/35; 74.3%), followed by medical evaluation (18/35; 51.4%), and completion of rehabilitation (10/35; 28.6%). Use of LSI as clearance criteria was limited (5/35; 14.3%). Minimum required LSI ranged from 85 to 90% in quadriceps strength and hop testing. Conclusions: Clearance criteria varied by jump type and post-surgical time frame when the participant was tested. Standardized rehabilitation was common prior to 2 years post-surgery, whereas medical clearance was common after 2 years post-surgery. Single leg jumps typically required 2–3 clearance criteria, whereas double leg jumps required 1–2 clearance criteria. Limb symmetry indices were used in combination with two other clearance criteria in studies with single-leg countermovement or drop jumps. Improvements in clearance criteria and adverse event reporting may help improve patient safety and interpretation of findings across studies. Full article