Search Results (203)

Airport apron buses, which shuttle passengers between terminal gates and remotely parked aircraft, are vital for efficient ground operations. However, flight delays and disruptions make demand for apron-bus services uncertain and time-varying, creating substantial challenges for planning driver capacity and work schedules. To tackle this challenge, we develop an Integrated Stochastic-Flexible Planning Model (ISFPM), formulated as a mixed-integer linear program (MILP), that simultaneously optimizes workforce sizing, duty scheduling, and roster assignment for apron-bus drivers. The objective is to minimize the sum of labor costs and the expected penalty for understaffing. This penalty is evaluated under the assumption that driver demand in each period follows a Poisson-binomial distribution, which is derived from probabilistic models of flight delays. For computational efficiency, we reformulate the expected penalty term using continuity-corrected normal approximations based on the cumulative distribution function (CDF). Furthermore, we incorporate practical workforce flexibility features, including hourly-granularity duty start times and heterogeneous workday patterns across roster groups. The computational study is based on Beijing Capital International Airport and accompanied by deidentified replication materials. Across 100 materialized baseline scenarios, the ISFPM uses 148 drivers instead of the 175-driver deterministic fixed-shift benchmark, reducing average management cost by 21.9% and passenger waiting time by 85.6%. Across 18 representative-day scenarios with correlated and severe disruptions, it reduces mean management cost by 15.8% and passenger waiting time by 53.6%. The disruption-scenario analysis also indicates that deterministic flexible staffing attains the lowest waiting time at a higher cost, while common apron travel-time shocks reduce the service advantage of the ISFPM. Full article

This study evaluates the performance of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) version 7 (IMERG v07) datasets (IMERG-Early, IMERG-Late, and IMERG-Final) in estimating precipitation at hourly and daily scales using PIERS station data from Texas collected from October 2023 to September 2025. Four stations were analyzed to evaluate precipitation occurrence, total precipitation, mean precipitation, 99th percentile extreme precipitation, and precipitation intensity. Model performance was assessed using both categorical and continuous statistical metrics, along with probability density function (PDF) and cumulative distribution function (CDF) analyses. IMERG datasets detected 53–75% of hourly precipitation events and 71–87% of daily precipitation events. IMERG-Late and IMERG-Final exhibited comparable performance in detecting precipitation events at the hourly timescale, whereas IMERG-Late performed better at the daily timescale across most stations and performance metrics. The ability of IMERG products to simulate precipitation totals was station-specific and depended on the temporal resolution level. At the hourly scale, IMERG-Final performed best at PIERS0035 and PIERS0034; IMERG-Early performed best at PIERS0030, and IMERG-Early and IMERG-Late performed equally well at PIERS0032. On the daily scale, IMERG-Early provided the best results at PIERS0030 and PIERS0032, whereas IMERG-Final performed better at PIERS0035 and PIERS0034. The performance of the IMERG datasets also varied across stations and temporal scales in simulating mean precipitation and different precipitation percentiles. Overall, although IMERG-Final is the calibrated dataset and is generally recommended for research applications, this study found that IMERG-Early and IMERG-Late outperformed IMERG-Final at specific temporal scales, for certain precipitation metrics, and at particular stations. The results further demonstrate that IMERG dataset performance varies across temporal scales, precipitation metrics, and observation sites. Full article

The Crab pulsar produces extremely intense, short-duration radio bursts known as giant pulses (GPs). We introduce a cumulative-energy diagnostic to quantify the apparent spectral extent of individual Crab giant pulses across the ultra-wideband low-frequency (UWL) receiver band, aiming to build a reproducible method for describing the frequency-domain concentration of emission and to characterize the observed spectral diversity of Crab GPs. Using UWL receiver on the Murriyang (Parkes) radio telescope, we present a systematic study of GPs from the Crab pulsar (PSR J0534+2200). We introduce an empirical classification scheme based on the cumulative distribution function of the pulse energy as a function of observing frequency. We use this diagnostic to separate events with apparent spectral concentration from events with broader spectral coverage. Under this empirical classification scheme, most detected events show apparent spectral concentration within a limited frequency range. Events classified as apparently spectrally concentrated contain most of their measured relative energy within limited frequency ranges, whereas broadband events show more extended spectral coverage. We emphasize that this classification describes the observed spectral extent and should not by itself be interpreted as proof of intrinsically narrow-band emission. Spectral fitting shows that most apparently spectrally concentrated (ASC) GPs have negative spectral indices, while a few events exhibit positive slopes, indicating substantial spectral diversity within the sample. The 3$\sigma$ widths of ASC main pulse GPs appear to cluster around two characteristic ranges, although this feature should be interpreted with caution given the finite time resolution of the data. The energy distribution of ASC main pulse GPs is broadly consistent with a log-normal functional form at low-to-intermediate energies and resembles a power-law-like tail at the high-energy end. The waiting-time distribution can be described by a Weibull function, while a sliding-window comparison with Monte Carlo realizations of a Poisson distribution shows no statistically significant deviation from temporal independence over the present 18.9-min observing span. The CDF-based classification method developed here is transferable to other wideband receiver data, provided that careful consideration is given to the instrumental bandpass, frequency-dependent sensitivity, RFI masking, and signal-to-noise thresholds. These results provide observational constraints on the phenomenology of Crab GPs and may be useful for future studies of pulsar coherent emission and related radio transients. Full article

This paper investigates a class of Hadamard variable-order fractional differential equations in which the fractional order is determined by the cumulative distribution function (CDF) of a continuous random variable. The proposed framework establishes a novel connection between probability theory and variable-order fractional calculus by allowing the memory index of the fractional operator to evolve according to a prescribed distribution law. To facilitate the analysis, the CDF-based variable order is considered through a piecewise-constant representation on a finite partition of the interval, which transforms the original problem into a family of Hadamard fractional differential equations of constant order on successive subintervals. Existence and uniqueness results are established by converting the differential problem into an equivalent fractional integral equation and applying the Banach contraction principle in suitable Banach spaces. Sufficient conditions ensuring the well-posedness of the problem are derived in terms of explicit bounds involving the fractional order and the nonlinear term. In addition, the Ulam–Hyers stability of the proposed model is investigated, and stability criteria are obtained under the same analytical framework. To illustrate the applicability of the theoretical results, a numerical example involving a CDF-generated variable-order function is presented. The example verifies the assumptions of the existence, uniqueness, and stability theorems and demonstrates the effect of piecewise-constant approximations of the cumulative distribution function on the resulting numerical solutions. The obtained results show that the proposed CDF-based Hadamard variable-order framework provides a mathematically consistent setting for studying fractional differential equations whose memory characteristics depend on probabilistic distributions. Full article

In order to reduce aviation’s negative environmental effects and support international efforts to battle climate change, the International Civil Aviation Organization (ICAO) seeks to cut greenhouse gas (GHG) emissions. About 2–3% of the world’s CO₂ emissions come from aviation, and at high altitudes, the fraction of other GHGs that significantly alter the atmosphere is considerably greater. In this study, hourly wind speed data at 100 m height from ECMWF’s fifth-generation reanalysis (ERA-5) were used over a period of 40 years (1985–2025). Hourly assessments of wind speeds at 40 m and 80 m heights are conducted in ERA-5, with biases at specific ground locations rectified via the Global Wind Atlas (GWA). This research estimates and analyzes many factors, including Weibull statistical parameters, daily and monthly wind speed variations, cumulative distribution function (CDF), and atmospheric turbulence intensity. The energy generation from several wind turbine types at different elevations was assessed. The findings indicate that the examined location revealed fair potential for the construction of large-capacity wind energy units at heights equal to or above 80 m. Turbines that are less than 50 m tall are spread out at least 10 km around the airport runway. While turbines that are less than 150 m tall are spread out at least 15 km away from the airport runway. Full article

Underwater surveillance systems require a multi-AUV-based multi-hop underwater data collection system to deliver data from sensor nodes distributed over a wide underwater area to a buoy. In this system, the node-visit of the data collection AUV and the relay transmission to the relay AUV jointly affect the end-to-end Age of Information (AoI) from each node to the buoy. This paper proposes a discrete soft actor–critic (SAC)-based integrated scheduling policy that jointly determines the node-visit and relay transmission of the data collection AUV within a single decision-making process. The proposed method represents the end-to-end information update process from data collection to buoy update in the state design and derives a relationship showing that the accumulated node-wise max AoI increment corresponds to the Mean Peak AoI and uses this increment as the decision step reward. Computer simulation results show that the proposed method achieves a lower Mean Peak AoI and higher Delivery than conventional methods that determine node visits and relay transmissions separately, including Visit-Only SAC, Age-Greedy threshold, and Age-Gain TX. The cumulative distribution function (CDF) analysis also shows more stable performance across episodes. Full article

This article introduces the two-sided length-biased Wald (TSLBW) distribution, an extension of the length-biased Wald (LBW) distribution, designed to model processes in which failure or event completion can originate from both sides. The TSLBW distribution is particularly suited to reliability engineering, survival analysis, and material failure studies where crack propagation or failure initiation may occur simultaneously from two directions. Notably, the distribution is right-skewed and non-symmetric, yet it arises naturally from the symmetric propagation of cracks from both sides, bridging distributional asymmetry with the underlying symmetry of the failure initiation process. This interplay between symmetry and asymmetry motivates its study within the broader framework of statistical modeling. We derive the fundamental properties of the TSLBW distribution, including its probability density function (PDF), cumulative distribution function (CDF), moment-generating function (MGF), and hazard rate function, and establish closed-form expressions for its mean and variance. Parameter estimation is addressed through both the method of moments (MOM) and maximum likelihood estimation (MLE), offering practical tools for real-world applications. Simulation studies and numerical experiments are conducted to evaluate the performance of the proposed distribution and its estimators. The results demonstrate that the TSLBW distribution provides improved flexibility for modeling lifetime data governed by two-sided failure mechanisms, making it a valuable addition to the statistician’s toolkit for reliability inference. Full article

6G promises ultra-low latency, high data throughput, and seamless global connectivity. However, providing uninterrupted connectivity in remote and underserved regions remains a critical challenge for Terrestrial Networks (TNs), where the cost of deploying infrastructure is difficult to justify against sparse user density. Standardized under 3GPP Release 17, Non-Terrestrial Networks (NTNs) have emerged as a viable solution to close this digital divide. Among NTN platforms, High-Altitude Platform Stations (HAPS) occupy a strategic middle ground, as they deliver lower propagation delays than Low-Earth Orbit (LEO) satellites while achieving far broader coverage than TN-based Base Stations (BS). Despite these advantages, battery-powered Internet of Things (IoT) devices communicating via HAPS face a fundamental energy efficiency (EE) challenge: transmit power must be carefully managed to maximize data throughput while preserving battery life and minimizing packet queuing delays. To address this, we propose a Q-learning-based Reinforcement Learning (RL) framework. The RL agent observes the instantaneous battery level and queue state of the IoT device, and dynamically selects optimal power levels from a discrete action space across successive time slots. Unlike traditional heuristic algorithms, such as Round Robin (RR), Max Single-to-Noise Ratio (Max-SNR), and fixed-power allocation, which rely on static rules or greedy channel-based decisions, the proposed Q-learning agent learns adaptive, long-term optimal policies through direct interaction with the environment, without requiring explicit mathematical modeling of the channel or traffic dynamics. Extensive simulations demonstrate that the proposed framework achieves up to 40% higher average EE compared to all benchmark schemes, maintains consistently lower power consumption, and exhibits superior statistical reliability as evidenced by a right-shifted Cumulative Distribution Function (CDF) of EE. These results demonstrate Q-learning as a promising candidate for scalable, energy-aware power control of next-generation HAPS-assisted IoT deployments in 6G NTN ecosystems. Full article

In this paper, we propose a 1-bit algorithm using spatial information to improve the accuracy of Kalman filter (KF)-based location estimation. The proposed algorithm aims to improve position estimation accuracy by re-estimating values outside a feasible region as being at the boundary of that region, based on the information that the user is present within that feasible region. This approach enhances position estimation accuracy without significantly increasing complexity. This paper discusses two methods for applying the 1-bit algorithm and verifies their performance by comparing Time of Arrival (ToA), the ToA-based KF, and the ToA-based KF with the 1-bit algorithm through simulations under three scenarios. Performance analysis was conducted from two perspectives: cumulative distribution function (CDF) and average position error (APE). The ToA-based KF with a 1-bit algorithm demonstrated the best performance. The proposed approach improved performance without high computational complexity and is suitable for real-time applications, making it applicable to indoor positioning, robot navigation, and wireless sensor networks that require high positioning accuracy. Full article

This article presents a simple, original data augmentation algorithm for Received Signal Strength Indicator (RSSI), dedicated to indoor localization systems. The aim of the research was to develop a synthetic data generation method to serve as a regularization technique, making models more robust against measurement noise. The proposed approach combines propagation modeling using polynomial regression with the individual statistical characteristics of each Access Point (AP), accounting for signal fluctuations and a probabilistic signal outage mechanism. The effectiveness of the proposed solution was experimentally verified by evaluating K-NN and MLP neural network models in both classification and regression variants. The study was conducted on datasets with different measurement grid granularities, demonstrating the algorithm’s ability to improve the generalization properties of estimators, even with a limited number of samples in the training set. The results showed that the use of augmentation reduced the Mean Absolute Error (MAE) by an average of approximately 20% for the dense training set and about 17% for the sparse set. Within the evaluated test environment, models trained on the augmented sparse measurement grid, which contained 67% fewer physical calibration points (30 points compared to the dense grid’s 92), reached a precision comparable to models trained on the dense real-world dataset. Analysis of histograms and Cumulative Distribution Functions (CDF) of the error confirmed the preservation of the signal’s statistical integrity and the effective mitigation of gross errors. The proposed solution constitutes an efficient and easy-to-implement alternative to complex generative models (e.g., GANs). These findings serve as a successful proof-of-concept and pilot study, laying the foundation for further development and validation in larger, more complex spatial environments. Full article

OnVeMCS 1.1 is a standalone software for probabilistic human health risk assessment of pollutants in soil, sediment, water, air, and food, enabling Monte Carlo simulation (MCS) of risks across multiple exposure pathways. The hazard index (HI) and cancer risk metrics (TCR/ILCR) for ingestion, inhalation, and dermal contact are quantified using the standard dose/concentration approach. Users can manually enter analyte concentrations with various probability distributions or import them from Excel templates, and select scenario-specific exposure factor sets for residents (children and adults), outdoor and indoor workers, and food consumers. The software supports both one-dimensional (1D) and two-dimensional Monte Carlo simulation (2D MCS) modes. The results are presented through a variety of plots, including histograms and cumulative distribution functions (CDFs), pathway/analyte contribution charts, sensitivity analysis plots, nested CDFs, and uncertainty ribbons. The software also allows the overlay of two or more outputs and the inclusion of regulatory thresholds (HI = 1; TCR/ILCR = 10⁻⁶–10⁻⁴). The results are exported to a multi-sheet Excel workbook containing raw arrays, summary tables, exceedance probabilities, and sensitivity data. OnVeMCS operates quickly, with even 2D MCSs being completed in several seconds. OnVeMCS is distributed as a single Windows installer file with data examples and is free for the academic community. Full article

Currently, solar photovoltaic (PV) systems are a priority for end-use decarbonization, aimed at reducing reliance on fossil fuels. However, PV systems are typically exposed to outdoor conditions, making them more susceptible to aging and damage. In this paper, a predictive maintenance approach that integrates digital twin technology with the copula-based model is proposed. This integration enables accurate simulation of the PV system’s condition and precise representation of the correlation between the power output of the digital twin and that of the actual system. Given the power output of the digital twin, predictive maintenance is performed based on the conditional cumulative distribution function (CDF) of the actual power output, which is derived from the copula model. A comprehensive case study was conducted to evaluate the performance of the proposed approach named OCAD (Optimal Copula-based Anomaly Detector), which achieved an accuracy of 92.51% and an F1-score of 92.13%. This significantly outperforms conventional models, including SVM, KNN, and ANN, demonstrating the effectiveness of the proposed predictive maintenance strategy. Full article

Motivated by the requirements for wide field-of-view (FOV) reception in underwater wireless optical communication (UWOC) systems, this study investigates the performance of multi-faceted receivers based on various regular polyhedral geometries. A truncated Gumbel minimum distribution model with geometric boundary constraints is proposed in order to characterize the statistical properties of the minimum incidence deflection angle associated with the selected receiving facet. Numerical simulations demonstrate that the proposed model effectively captures the angular response characteristics of multi-faceted receivers, with the root mean square error (RMSE) of the fitted cumulative distribution function (CDF) below $2.2\times {10}^{-2}$ for all regular polyhedral structures. Furthermore, this paper evaluates the effects of different polyhedral structures and receiver FOVs on the bit error rate (BER) and outage probability. The results further show that system performance does not vary monotonically with the number of receiving facets. Under the constraints of the same total effective detection area and unified system parameters, the dodecahedral structure achieves the best performance in terms of average BER and outage probability, followed by the cube, whereas the icosahedral structure exhibits the worst performance. Taking typical link distances of 35–40 m as an example, the average BER of the dodecahedral structure is approximately one order of magnitude lower than that of the icosahedral structure. These findings provide design guidance for the structural design and parameter optimization of multi-faceted receivers in UWOC systems. Full article

Accurate streamflow information is critical for early flood and drought warning. However, global hydrological forecasting systems are affected by residual errors in meteorological forcing, model structure, and routing, which propagate into simulated streamflow. Within the GEOGLOWS River Forecast System (RFS), ERA5 runoff biases are routed into streamflow simulations. The most effective operational bias-correction method, MFDC-QM, requires local discharge observations and cannot be applied consistently in ungauged basins. This study evaluates a pre-routing, grid-scale runoff bias-correction framework that adjusts ERA5 runoff before routing by combining Flow Duration Curve (FDC) mapping and Sparse Cumulative Distribution Function (CDF) matching, using GSCD as a spatially distributed reference runoff data. Baseline GEOGLOWS RFS, pre-routing correction, and MFDC-QM were compared for 1980–2025 using 16,517 gauging stations, Kling–Gupta Efficiency (KGE), and paired significance tests. Globally, the median KGE increased modestly from 0.16 to 0.22, compared with 0.48 for MFDC-QM. Results demonstrate a clear regional dependence: pre-routing correction produced statistically significant gains in South America and Africa (p < 0.05), where ERA5 runoff exhibits stronger residual biases, but had limited effects in Europe and North America, where dense hydrometeorological networks likely impose stronger observational constraints on the underlying reanalysis. These patterns show that pre-routing correction is most valuable where residual forcing bias is large and observational constraints are limited, complementing observation-based post-processing in ungauged, data-limited regions. Full article

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article