Search Results (503)

IEEE 802.11ax adopts Orthogonal Frequency Division Multiple Access (OFDMA), which divides a wireless channel into multiple Resource Units (RUs) with different sizes and transmission rates. Most existing studies on uplink random access assume that all stations contend for RUs of the same size and mainly improve channel access by adjusting contention parameters, such as the OFDMA backoff mechanism. As a result, the transmission requirements of stations are not sufficiently considered, leading to inefficient RU utilization. Stations with heavy traffic may not obtain adequate transmission resources, whereas stations with light traffic may occupy unnecessarily large RUs. To address this limitation, this paper proposes a Traffic-Aware Distributed Selection (TADS) scheme for IEEE 802.11ax uplink random access with different RU sizes. Each station estimates its traffic demand based on the mean data rate, delay bound, and buffer status. It also estimates the expected service capacity of each RU size using the transmission capacity and collision probability. The station selects the smallest RU size whose expected service capacity satisfies its traffic demand. If no RU size can satisfy the demand, the RU size with the highest ratio of expected service capacity to traffic demand is selected. When multiple RUs of the selected size are available, the station randomly selects one of them. In TADS, each station determines its RU independently without additional scheduling by the access point. Simulation results show that TADS achieves higher network throughput than conventional random RU selection under data, real-time, and mixed traffic scenarios. The results also show that the performance gain depends on the available RU configuration, with greater benefits when different RU sizes are available. Full article

This paper investigates a wireless ad hoc network node registration and access system based on a hybrid Frame Slotted ALOHA and Time Division Multiple Access mechanism for Unmanned Aerial Vehicle (UAV) charging station cluster management scenarios. Based on combinatorial mathematics and the inclusion–exclusion principle, the exact conditional probability distribution of the number of successfully accessed nodes within a single frame is derived, and a closed-form analytical expression for the total system access delay as a function of node population is established, revealing the existence of an optimal operating point. Building upon this foundation, a Dynamic Access Probability (DAP) mechanism is introduced, which adaptively regulates the number of nodes participating in contention per frame, effectively suppressing collisions and significantly reducing the overall system access delay. To address the difficulty of obtaining the real-time node count in practical systems, a Probability Model-based Maximum Likelihood Estimation algorithm is further proposed, which takes the number of successfully accessed nodes observable in each frame as input to infer the current number of unaccessed nodes. Simulation results demonstrate that the PMLE algorithm achieves superior performance in both estimation accuracy and stability, effectively supporting the accurate operation of the DAP mechanism. Full article

We investigate reactive collision dynamics and effective interaction cross-sections using classical trajectory simulations on a reduced-dimensional reactive potential-energy surface containing reactant and product wells separated by an intermediate barrier region. The simulations are performed over a range of collision velocities for which direct scattering, transient trapping, and reactive trajectories coexist within the same interaction landscape. Trajectories are propagated using a velocity-Verlet integration scheme, while reaction probabilities are analyzed as functions of the impact parameter and initial projectile velocity. The calculated probability distributions exhibit strongly localized reactive windows in phase space separated by extended nonreactive regions, indicating pronounced sensitivity of the dynamics to both collision geometry and initial conditions. Probability maps in the (v_x,b) plane reveal a fragmented phase-space structure and highly nonuniform accessibility of the interaction region across the investigated parameter range. The simulations further show substantial variations in the relative importance of reactive, trapped, and back-scattering trajectories with increasing collision velocity, together with non-monotonic behavior of the effective reactive cross-sections. Despite the intentionally reduced dimensionality of the model, the trajectory ensembles reproduce several characteristic features of complex reactive scattering dynamics, including transient trapping, competing dynamical pathways, and broad residence-time distributions. The present results demonstrate that reduced-dimensional classical trajectory approaches can already capture important phase-space mechanisms governing dynamical accessibility and channel competition in reactive molecular collisions. Full article

Collision statistics provide a finite-resolution view of information by measuring how often independent samples fall on the same state and form the basis of integer-order Rényi entropies. Here, we use low-order Rényi entropies to characterize finite-resolution approximations to Shannon entropy and mutual information. Specifically, we determine what population information is captured by finite collision moments, we quantify how the resulting targets differ from their Shannon counterparts, and we analyze how accurately they can be estimated from finite samples. We use the interpolation remainder to identify structural approximation error induced by extrapolating from integer-order Rényi entropies to the Shannon point. We separate this deterministic error from finite-sample estimation error: increasing sample size improves estimation of a finite-resolution target but does not eliminate its deterministic difference from Shannon entropy or mutual information. Finally, we show that finite collision moments do not generally identify Shannon entropy, and that increasing collision order shifts sensitivity toward high-probability events. Our numerical experiments illustrate the approximation–estimation trade-off and evaluate collision-based approximations alongside plug-in and Miller–Madow estimators. Together, these results provide a principled way to use low-order coincidence structure as finite-resolution information, while making explicit what finite collision moments can and cannot reveal about Shannon entropy and mutual information. Full article

The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network. Full article

Ensuring information integrity requires not only reducing decoding errors but also reducing the probability that corrupted data are accepted as valid. This research presents a hybrid integrity protection system that incorporates seeded universal hash verification, algebraic manipulation detection (AMD), and a binary Reed–Muller outer code. Transmission over the binary symmetric channel $\mathrm{B}\mathrm{S}\mathrm{C}\left(p\right)$, outer encoding using $\mathrm{R}\mathrm{M}(r, m)$, bounded-distance decoding, an ${\epsilon }_{\mathrm{A}\mathrm{M}\mathrm{D}}$-secure AMD layer, and a seeded 2-universal hash family with $\mathcal{l}$-bit output define the model used in the analysis. Under explicitly stated freshness and conditional-independence assumptions, the system-level undetected error probability is upper-bounded by the residual decoder-miscorrection probability multiplied by the AMD acceptance bound and the seeded universal hash collision bound. A conservative alternative is also provided for settings in which the required conditional independence cannot be guaranteed. In this context, an explicit upper bound for the undetected error probability is derived. The outcome makes clear the different functions of outer coding and post-decoding verification and results in a direct dependency on the parameters r, m, p, and l. Finite-length Monte Carlo validation for a concrete instantiation based on RM(2, 5) complements the theoretical study and verifies that the hybrid construction offers a lower empirical undetected error probability compared to the comparable outer-only, AMD-only, and hash-only variations. The study does not propose new coding or verification primitives. Its contribution is a finite-length layered acceptance model and a Reed–Muller-specific undetected error analysis that incorporates the code weight distribution and bounded-distance decoding regions. The resulting spectrum-based bound distinguishes decoder miscorrection from the broader event of exceeding the guaranteed correction radius and is evaluated together with post-decoding verification and redundancy overhead. The model’s formal manipulation detection and collision guarantees are provided by AMD and universal hash layers, while Reed–Muller code parameters and their standard distance formulas are conventional. Full article

We present the first study of leading subjet (LSJ) radial distributions as a novel jet substructure observable in high-energy nuclear collisions using Pythia8 for p + p collisions and the Linear Boltzmann Transport (LBT) model for Pb + Pb collisions. In p + p collisions, the LSJ is concentrated near the jet core, with multi-subjet events exhibiting enhanced probability compared to single-subjet events except in the innermost core of the jet. In Pb + Pb collisions, the inclusive sample exhibits pronounced suppression without significant radial broadening, while single-subjet events display weak modification throughout the jet cone. Multi-subjet events exhibit suppression at small radii followed by enhancement at large radii, indicating a medium-induced redistribution of energy toward the jet periphery. This broadening is masked in the inclusive distribution due to the dominance of single-subjet events. The increased transverse momentum balance between the LSJ and subleading subjet (SLSJ) in two subjet events drives the jet axis away from the LSJ, providing a mechanistic explanation for the observed radial broadening. These results establish the LSJ radial distribution as an independent and complementary probe of medium-induced jet modifications. Full article

Highway–rail grade crossing (HRGC) safety has improved substantially over recent decades through engineering upgrades, active warning systems, crossing closures, enforcement, and public education. Recent national trends, however, suggest that these gains may have slowed, raising the question of whether HRGC safety has entered a persistent plateau. This study investigates whether the historical decline in U.S. HRGC incidents has transitioned into a statistically distinct safety regime and whether the factors associated with casualty occurrence have changed following that transition. An analytical framework integrating regime-transition analysis, cross-regime casualty comparison, and explainable machine learning was applied to nationwide Federal Railroad Administration incident records from 1976 to 2025. Trend analysis, complementary stationarity diagnostics, residual diagnostics, information criteria, and sensitivity analysis consistently identified 2012 as the onset of a statistically stationary safety plateau. Comparison of casualty outcomes showed no meaningful change in either the probability of casualty occurrence or the distribution of injury and fatality outcomes following the transition. Explainable random forest models further demonstrated substantial temporal stability in the factors associated with casualty occurrence. Train speed, vehicle occupancy, driver presence, and highway-user actions remained the dominant predictors across both safety regimes, with driver presence ranking among the most influential characteristics during the plateau period. These findings indicate that the current safety challenge is not the emergence of new collision mechanisms but the persistence of well-established operational and behavioral risk factors. Future reductions in HRGC casualties will likely require targeted engineering improvements, advanced warning technologies, connected-vehicle and vehicle-to-infrastructure systems, artificial intelligence-enabled monitoring, and focused public education to address the persistent residual risks sustaining the national safety plateau. Full article

In deep-space exploration and remote sensing, characterizing radiative transfer in complex planetary atmospheres is fundamental for robust target detection and optical navigation. On the Martian surface, intense scattering and attenuation by dust aerosols pose severe environmental interference, challenging star sensors used for high-precision navigation. To address this, this study develops a spectral radiative transfer model based on the Null Collision Monte Carlo Method to characterize the optical background of the dusty Martian atmosphere. Mie scattering theory is employed for dust particles, while gas molecular absorption is modeled via line-by-line integration. The simulated sky radiance is validated against Mars rover Navcam observations, yielding an average relative error of 7.83% between the modeled and observed radiance values across scattering angles greater than 5°. Building on this, an imaging link model evaluates surface-based detection performance, including signal-to-noise ratio, detection success probability, and star count. Optical parameters—aperture, field of view, and integration time—are optimized for nighttime and dawn-dusk modes. Spatio-temporal assessments are conducted globally across Martian years, focusing on the Zhurong landing site and Tianwen-3 candidates. Finally, an Earth-environment equivalence experiment using a 60% transmittance filter verifies the design’s robustness. This work confirms the feasibility of star-sensor-based attitude determination on Mars. Full article

The expansion of wind energy, while essential for the energy transition, poses serious collision risks to birds, particularly due to the rotating blades of wind turbines. When conducting environmental impact assessments, accurate quantification of these risks is challenging, especially offshore, and it relies heavily on collision risk models. This study investigates the influence of wind turbine geometry and operational parameters on avian collision probability, utilising the Band model in combination with four well-documented reference wind turbines. By systematically varying turbine characteristics and bird flight height distributions, the analysis reveals that both turbine design and the vertical distribution of bird flight critically affect collision risk estimates. Generally, turbines with higher power ratings and larger rotor diameters exhibit lower collision probabilities, which is attributed to their lower rotor speed. The study further introduces an adaptation of the standard Band model, which incorporates a more detailed blade geometry, including local airfoil thickness and twist. With a few exceptions, this updated model increases the predicted collision probabilities. By basing the analysis on open source reference wind turbines, the present study establishes transparent methodologies and improves reproducibility and benchmarking in collision risk assessments. The results highlight the need for species- and site-specific modelling, as well as the value of refined turbine representations, to support effective mitigation strategies and nature-inclusive wind farm planning. Full article

Underwater acoustic networks are critical for applications such as environmental monitoring, infrastructure inspection, and autonomous underwater operations. In these scenarios, autonomous underwater vehicles (AUVs) increasingly operate in cooperative swarms, requiring efficient route-discovery and multi-hop communication. However, acoustic underwater communication imposes extremely constrained conditions, including low data rates, long propagation delays, limited bandwidth, and unstable channels. These constraints make routing overhead a dominant factor limiting network performance. In this work, we propose a systematic redesign of the Ad hoc On-Demand Distance Vector Routing (AODV) protocol tailored for ultra-low-rate underwater acoustic networks. The proposed approach combines three complementary design strategies: (i) architectural simplification through a layer-2 AODV variant that eliminates layer-3 overhead, (ii) protocol-level compression using the Static Context Header Compression and Fragmentation (SCHC) standard to minimize packet size, and (iii) acoustic channel-aware parameter tuning to account for long propagation delays and limited channel capacity. The approaches are designed and implemented in ns-3.43 using the underwater acoustic network (UAN) model and evaluated under reachability and scalability scenarios against the default AODV protocol. The results show that the proposed protocols reduce the hello message loss percentage from over 10% to below 3%, decrease route creation time from around 35s to around 15s, and significantly improve the packet delivery ratio under increasing traffic load. In high-load scenarios, the proposed methods consistently outperform default AODV due to reduced channel occupancy and lower collision probability. Full article

Space debris presents a growing operational risk to spacecraft, especially in low Earth orbit, where collisions can generate further debris and increase future collision probability. Active debris removal and in-orbit servicing require robust close-range perception, but on-board systems are constrained by power, memory, processing capability and the need for reliable real-time operation. This paper investigates convolutional object detection for on-board space debris detection using the SPARK 2022 spacecraft detection dataset. A YOLOv3 detector is fine-tuned and used to evaluate post-training compression through static quantisation and pruning. A lightweight architectural variant, YOLO-DWSC, is also introduced by replacing the YOLOv3-tiny backbone convolutions with depthwise separable convolutions while retaining the detection head. The full-precision YOLOv3 model achieves 0.972 mAP₅₀ and 0.884 mAP_50:95, while 8-bit static quantisation reduces model size from 405 MB to 102 MB with only a small reduction in mAP₅₀, although tighter localisation accuracy is more affected. YOLO-DWSC is much smaller and faster, reaching 256.4 FPS on the tested GPU at 43 MB, but with reduced accuracy. We present this work as a controlled case study rather than an attempt at state-of-the-art SPARK 2022 performance. The original challenge test labels were unavailable, and the experiments therefore use a class-balanced re-split of the labelled data. The results should consequently be interpreted as internally controlled comparisons of compression behaviour, not as leaderboard-comparable benchmark results. Pruning and a two-pass refinement method are also evaluated. The results indicate that simple compression methods can be useful for broad region-of-interest detection, but they also show that claims about on-board deployment require caution. Speed benefits are hardware- and runtime-dependent, and safety-critical proximity operations require evaluation criteria better aligned with full-object containment. Full article

Traffic accidents significantly impact societies and economies. The risk of collision is highest in urban areas, leading to devastating loss of life and escalating socioeconomic costs. In this context, numerous studies have focused on accurately predicting accident risk, severity, and duration using various methodologies. This paper presents an overview of traditional statistical models for accident prediction and a comprehensive systematic review of the literature on statistical modeling, machine learning (ML), and deep learning (DL) techniques employed in this field. Different methodologies and techniques are compared by categorizing studies that adopt similar approaches and analyzing them comparatively. Furthermore, a distinction is made between temporal and spatiotemporal models to describe how these approaches influence the accuracy of future predictions regarding accident occurrence and the duration of impact. This review distinguishes itself from similar works by not only comparing models and approaches, but also by analyzing how external features, such as meteorological data, road geometric design, and land usage, affect the probability of accidents and the models’ accuracy in forecasting road safety. The study explores the performance levels and limitations associated with a set of forecasting approaches, offering an analytical discussion of their differences and similarities, and potential future developments in this research space, including the use of hybrid models and reinforcement learning (RL). The results of this review indicate that DL models tend to be better suited to complex forecasting problems due to their superior ability to represent features and extract non-linear spatiotemporal correlations. This article concludes by describing various directions for further research, ranging from optimizing model architectures to integrating real-time big data into proactive prediction systems. Full article

Achieving safe and efficient navigation in cluttered and dynamic environments remains an open challenge for mobile robots, especially when perception and actuation are uncertain. Standard navigation stacks typically handle obstacle avoidance through fixed safety margins or costmap inflation layers. While effective in simple settings, these approaches are difficult to tune in practice: conservative inflation can prevent traversal through narrow passages, whereas less conservative settings may lead to unsafe behavior. Moreover, they usually encode risk only as a function of obstacle proximity. We propose a unified probability-inspired risk-cost map that integrates perception uncertainty, actuation uncertainty, dynamic obstacle prediction, and occlusion-aware memory into a single spatial representation. The resulting risk map is used by a local path-modification module that adapts a reference global path using the proposed risk map and interfaces with a standard Model Predictive Path Integral (MPPI) controller. The proposed method is compatible with standard navigation pipelines. We validate the resulting framework in Gazebo simulations under different sensing and actuation uncertainty conditions and in environments containing unknown static and dynamic obstacles. The results show that the proposed method is more robust than conventional costmap-based baselines, resulting in fewer aborted goals in cluttered environments and substantially fewer collision events when dynamic obstacles are present. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article