Search Results (147)

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results. Full article

Traditional methods for mission reliability assessment under operational testing conditions exhibit some limitations. They include coarse modeling granularity, significant parameter estimation biases, and inadequate adaptability for handling heterogeneous test data. To address these challenges, this study establishes an assessment framework using a vehicular missile launching system (VMLS) as a case study. The framework constructs phase-specific reliability block diagrams based on mission profiles and establishes mappings between data types and evaluation models. The framework integrates the maximum entropy criterion with reliability monotonic decreasing constraints, develops a covariate-embedded Bayesian data fusion model, and proposes a multi-path weight adjustment assessment method. Simulation and physical testing demonstrate that compared with conventional methods, the proposed approach shows superior accuracy and precision in parameter estimation. It enables mission reliability assessment under practical operational testing constraints while providing methodological support to overcome the traditional assessment paradigm that overemphasizes performance verification while neglecting operational capability development. Full article

This study presents a surrogate modeling framework designed for the rapid yet reliable assessment of sonic boom impacts. The methodology is demonstrated through two case studies: a transatlantic flight from Milan to New York, highlighting the sonic boom impact along the route; and a representative supersonic overflight of Italy, quantifying the population exposure to varying noise levels. Aerodynamic numerical simulations were carried out using an open-source code to capture near-field pressure signatures at three critical mission points. These signatures were used to compute the Whitham F-functions, which were then propagated through a homogeneous atmosphere to the ground using the Whitham equal area rule. The resulting N-waves enabled the computation of aircraft shape factors, which were employed in a regression model to predict the sonic boom characteristics across the full mission profile. Finally, the integration of noise metrics and geographical information system software provided the evaluation of environmental impact and population noise exposure. Full article

An industrial-scale increase in applications of the Internet of Things (IoT), a significant number of which are based on the concept of federation, presents unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. A federation may be created for the duration of a mission, such as military operations or Humanitarian Assistance and Disaster Relief (HADR) operations. These missions often occur in very difficult or even hostile environments, posing additional challenges for ensuring reliability and security. The heterogeneity of devices, protocols, and security requirements in different domains further complicates the requirements for the secure distribution of data streams in federated IoT environments. The effective dissemination of data streams in federated environments also ensures the flexibility to filter and search for patterns in real-time to detect critical events or threats (e.g., fires and hostile objects) with changing information needs of end users. The paper presents a novel and practical framework for secure and reliable data stream dissemination in federated IoT environments, leveraging blockchain, Apache Kafka brokers, and microservices. To authenticate IoT devices and verify data streams, we have integrated a hardware and software IoT gateway with the Hyperledger Fabric (HLF) blockchain platform, which records the distinguishing features of IoT devices (fingerprints). In this paper, we analyzed our platform’s security, focusing on secure data distribution. We formally discussed potential attack vectors and ways to mitigate them through the platform’s design. We thoroughly assess the effectiveness of the proposed framework by conducting extensive performance tests in two setups: the Amazon Web Services (AWS) cloud-based and Raspberry Pi resource-constrained environments. Implementing our framework in the AWS cloud infrastructure has demonstrated that it is suitable for processing audiovisual streams in environments that require immediate interoperability. The results are promising, as the average time it takes for a consumer to read a verified data stream is in the order of seconds. The measured time for complete processing of an audiovisual stream corresponds to approximately 25 frames per second (fps). The results obtained also confirmed the computational stability of our framework. Furthermore, we have confirmed that our environment can be deployed on resource-constrained commercial off-the-shelf (COTS) platforms while maintaining low operational costs. Full article

In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed-integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure-time distribution is established. The joint posterior distribution of regression coefficients is derived using Bayesian theory, which are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments. Full article

In the past three decades, the city of Addis Ababa, a capital city of Africa, has grown significantly in population, facilities, and infrastructure. The area involved in the recent urbanization is prone to slow natural subsidence phenomena that can be accelerated due to anthropogenic factors such as groundwater overexploitation and loading of unconsolidated soils. The main aim of this study is to identify and monitor the areas most affected by subsidence in a context, such as that of many areas of emerging countries, characterized by the lack of geological and technical data. In these contexts, advanced remote sensing techniques can support the assessment of spatial and temporal patterns of ground instability phenomena, providing critical information on potential conditioning and triggering factors. In the case of subsidence, these factors may have a natural or anthropogenic origin or result from a combination of both. The increasing availability of SAR data acquired by the Sentinel-1 mission around the world and the refinement of processing techniques that have taken place in recent years allow one to identify and monitor the critical conditions deriving from the impressive recent expansion of megacities such as Addis Ababa. In this work, the Sentinel-1 SAR images from Oct 2014 to Jan 2021 were processed through the PS-InSAR technique, which allows us to estimate the deformations of the Earth’s surface with high precision, especially in urbanized areas. The obtained deformation velocity maps and displacement time series have been validated using accurate second-order geodetic control points and compared with the recent urbanization of the territory. The results demonstrate the presence of areas affected by a vertical rate of displacement of up to 21 mm/year and a maximum displacement of about 13.50 cm. These areas correspond to sectors that are most predisposed to subsidence phenomena due to the presence of recent alluvial deposits and have suffered greater anthropic pressure through the construction of new buildings and the exploitation of groundwater. Satellite interferometry techniques are confirmed to be a reliable tool for monitoring potentially dangerous geological processes, and in the case examined in this work, they represent the only way to verify the urbanized areas exposed to the risk of damage with great effectiveness and low cost, providing local authorities with crucial information on the priorities of intervention. Full article

Lake Kasumigaura, one of Japan’s largest lakes, presents significant challenges for remote sensing due to its eutrophic conditions and complex optical properties. Although the Global Change Observation Mission-Climate (GCOM-C)/Second-generation Global Imager (SGLI)-derived inherent optical properties (IOPs) offer water quality monitoring potential, their performance in such turbid inland waters remains inadequately validated. This study evaluated five established IOP retrieval algorithms, including the quasi-analytical algorithm (QAA_V6), Garver–Siegel–Maritorena (GSM), generalized IOP (GIOP-DC), Plymouth Marine Laboratory (PML), and linear matrix inversion (LMI), using measured remote sensing reflectance (Rrs) and corresponding IOPs between 2017–2018. The results demonstrated that the QAA had the highest performance for retrieving absorption of particles (a_p) with a Pearson correlation (r) = 0.98, phytoplankton (a_ph) with r = 0.97, and non-algal particles (a_nap) with r = 0.85. In contrast, the GSM algorithm exhibited the best accuracy for estimating absorption by colored dissolved organic matter (a_CDOM), with r = 0.87, along with the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Additionally, a strong correlation (r = 0.81) was observed between SGLI satellite-derived remote-sensing reflectance (R_rs) and in situ measurements. Notably, a high correlation was observed between the a_ph (443 nm) and the chlorophyll a (Chl-a) concentration (r = 0.84), as well as between the backscattering coefficient (b_bp) at 443 nm and inorganic suspended solids (r = 0.64), confirming that IOPs are reliable water quality assessment indicators. Furthermore, the use of IOPs as variables for estimating water quality parameters such as Chl-a and suspended solids showed better performance compared to empirical methods. Full article

This paper provides a comprehensive overview of mission-profile-based stress testing methods for AC electric motor drives, focusing on the impact of stress factors on long-term performance. Special attention is given to the categorization of stress factors that make up the mission profile and their influence on the reliability and lifespan of motor drives. The paper reviews existing testing methods, discussing their applications and highlighting their focus on individual components, such as electric motors and power converters, rather than the entire drive system. Methods aimed at improving the reliability and extending the lifespan of motor drives are also presented, alongside an overview of state-of-the-art testing approaches. Relevant research is examined to outline current practices in the field. Moreover, through this survey, attention is drawn to the importance of considering component interactions in future testing protocols, with the aim of supporting more realistic system-level assessments and improving overall drive performance. Full article

The rapid evolution and deployment of 5G networks have introduced complex security challenges due to their reliance on dynamic network slicing, ultra-low latency communication, decentralized architectures, and highly diverse use cases. Traditional perimeter-based security models are no longer sufficient in these highly fluid and distributed environments. In response to these limitations, this study introduces SecureChain-ZT, a novel Adaptive Zero Trust Policy Framework (AZTPF) that addresses emerging threats by integrating intelligent access control, real-time monitoring, and decentralized authentication mechanisms. SecureChain-ZT advances conventional Zero Trust Architecture (ZTA) by leveraging machine learning, reinforcement learning, and blockchain technologies to achieve autonomous policy enforcement and threat mitigation. Unlike static ZT models that depend on predefined rule sets, AZTPF continuously evaluates user and device behavior in real time, detects anomalies through AI-powered traffic analysis, and dynamically updates access policies based on contextual risk assessments. Comprehensive simulations and experiments demonstrate the robustness of the framework. SecureChain-ZT achieves an authentication accuracy of 97.8% and reduces unauthorized access attempts from 17.5% to just 2.2%. Its advanced detection capabilities achieve a threat detection accuracy of 99.3% and block 95.6% of attempted cyber intrusions. The implementation of blockchain-based identity verification reduces spoofing incidents by 97%, while microsegmentation limits lateral movement attacks by 75%. The proposed SecureChain-ZT model achieved an authentication accuracy of 98.6%, reduced false acceptance and rejection rates to 1.2% and 0.2% respectively, and improved policy update time to 180 ms. Compared to traditional models, the overall latency was reduced by 62.6%, and threat detection accuracy increased to 99.3%. These results highlight the model’s effectiveness in both cybersecurity enhancement and real-time service responsiveness. This research contributes to the advancement of Zero Trust security models by presenting a scalable, resilient, and adaptive policy enforcement framework that aligns with the demands of next-generation 5G infrastructures. The proposed SecureChain-ZT model not only enhances cybersecurity but also ensures service reliability and responsiveness in complex and mission-critical environments. Full article

Traditional remote sensing techniques face notable limitations in accurately estimating forest canopy height. Optical data often suffer from vegetation occlusion, while radar systems, though capable of penetrating foliage, show reduced accuracy in complex terrains. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR mission, offers high-resolution measurements that address these challenges. However, the complexity of waveform processing and the influence of geolocation uncertainty demand rigorous assessment. This study employs GEDI Version 2.0 data, which demonstrates substantial improvement in geolocation accuracy compared to Version 1.0, and integrates airborne laser scanning (ALS) data from the Changbai Mountain forest region to simulate GEDI waveforms. A Monte Carlo-based approach was used to quantify and correct geolocation offsets, resulting in a reduction in the average relative error (defined as the mean of the absolute differences between estimated and reference canopy heights divided by the reference values) in canopy height estimates from 11.92% to 8.55%. Compared to traditional correction strategies, this method demonstrates stronger robustness in heterogeneous forest conditions. The findings emphasize the effectiveness of simulation-based optimization in enhancing the geolocation accuracy and canopy height retrieval reliability of GEDI data, especially in complex terrain environments. This contributes to more precise global forest structure assessments and provides a methodological foundation for future improvements in spaceborne LiDAR applications. Full article

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R²), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R² assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R² < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R², and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m³/s. The test results were 0.681, 0.91, 0.828, and 41.237 m³/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article

This study proposes an advanced space radiation shielding design method that integrates multi-objective optimization with reliability evaluation to mitigate the impact of harsh space radiation environments on electronic systems. A genetic algorithm is employed to optimize multi-layer shielding configurations with respect to radiation dose reduction, mass efficiency, and structural thickness. To ensure practical applicability, a reliability evaluation framework incorporating uncertainty factors is developed, where shielding designs are considered acceptable when the risk confidence level (CL) remains below 5%. A case study simulating long-duration deep space missions demonstrates that the optimized five-layer shielding configuration reduces the radiation-induced failure rate by approximately 57%, enhancing the long-term reliability of core electronic components to 0.94 over a five-year mission. These findings validate the effectiveness of the proposed approach in supporting the development of reliable, lightweight radiation shielding for future space missions. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

The use of novel technologies for low-emission and more efficient aviation requires not only the achievement of a given technology readiness level, but also their integration into aircraft concepts. Furthermore, the assessment of unconventional configurations requires robustness considerations already in the conceptual aircraft design phase. In this context, the next developmental milestone of the Advanced Morphological Approach (AMA) as a conceptual aircraft design method is presented by introducing design parameter uncertainties for disruptive technologies. The purpose of this work is the integration verification of Bayesian networks (BNs) into the AMA process for semi-quantitative system modeling and uncertainty propagation. This allowed for the visualization of uncertainties in the solution space, and therefore the depiction and initial estimation of configuration robustness. The verification is demonstrated on an existing conceptual design use case of a regional aircraft for 50 passengers, similar to the ATR 42-600. It investigated hybrid-electric and fuel-cell-based hybrid propulsion systems for 2030, 2040, and 2050 as potential years of entry into service. A BN-based system model has been developed by verifying its quality, adding parameter uncertainty and three energy price scenarios. The executed Bayesian inference propagated the uncertainties through the system and allowed for the visualization of a solution space. The presented uncertainties for the mission energy, mission energy price, and emission criteria for each design solution yield a more reliable basis for robustness analysis and decision-making. Full article