Search Results (24)

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

Dry-farmed vineyards of Vitis vinifera L. cv. País in central–southern Chile represent one of the oldest viticultural systems in the Americas; however, objective compositional evidence supporting valley-scale typicity remains limited. This single-vintage study evaluated whether dry-farmed País wines from the Maule and Itata valleys exhibit compositional and sensory differences under standardized winemaking conditions. Ten monovarietal wines (2018 vintage; n = 5 per valley) were produced by controlled microvinification and analysed for general chemistry, phenolic composition, polysaccharides, chromatic attributes (CIELAB), and volatile compounds (SPME–GC–MS), together with descriptive sensory analysis by a trained panel. Total phenols (~1.2 g GAE L⁻¹), anthocyanins (~130 mg malvidin-3-glucoside equivalents L⁻¹), and tannins were low and comparable between valleys. However, differences were observed in specific compositional domains: Maule wines showed higher flavanols, polysaccharides, and aldehydes, whereas Itata wines exhibited higher ester levels. Sensory evaluation revealed differences in colour intensity, floral aroma, retronasal red-fruit notes, and astringency. Multivariate analysis (PCoA) revealed a structured but partial separation between valleys; however, this pattern was not supported by PERMANOVA, indicating limited statistical evidence for multivariate differentiation. These findings, based on a single vintage, suggest subtle compositional and sensory differences rather than strong valley-level typicity. Full article

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified. Full article

This paper systematically analyzes security vulnerabilities that may occur during the OpenCV library and IP camera linkage process for the YOLO v10-based IP camera image processing system used in the disaster safety management field. Recently, the use of AI-based real-time image analysis technology in disaster response and safety management systems has been increasing, but it has been confirmed that open source-based object detection frameworks and security vulnerabilities in IP cameras can pose serious threats to the reliability and safety of actual systems. In this study, the structure of an image processing system that applies the latest YOLO v10 algorithm was analyzed, and major security threats (e.g., remote code execution, denial of service, data tampering, authentication bypass, etc.) that might occur during the IP camera image collection and processing process using OpenCV were identified. In particular, the possibility of attacks due to insufficient verification of external inputs (model files, configuration files, image data, etc.), failure to set an initial password, and insufficient encryption of network communication sections were presented with cases. These problems could lead to more serious results in mission-critical environments such as disaster safety management. Full article

Accreting white dwarf binaries (AWDs) comprise cataclysmic variables (CVs), symbiotics, AM CVns, and other related systems that host a primary white dwarf (WD) accreting from a main sequence or evolved companion star. AWDs are a product of close binary evolution; thus, they are important for understanding the evolution and population of X-ray binaries in the Milky Way and other galaxies. AWDs are essential for studying astrophysical plasmas under different conditions along with accretion physics and processes, transient events, matter ejection and outflows, compact binary evolution, mergers, angular momentum loss mechanisms, and nuclear processes leading to explosions. AWDs are also closely related to other objects in the late stages of stellar evolution, with other accreting objects in compact binaries, and even share common phenomena with young stellar objects, active galactic nuclei, quasars, and supernova remnants. As X-ray astronomy came to a climax with the start of the Chandra and XMM-Newton missions owing to their unprecedented instrumentation, new excellent imaging capabilities, good time resolution, and X-ray grating technologies allowed immense advancement in many aspects of astronomy and astrophysics. In this review, we lay out a panorama of developments on the study of AWDs that have been accomplished and have been made possible by these two observatories; we summarize the key observational achievements and the challenges ahead. Full article

Synthetic aperture radar (SAR) is emerging as a valuable dataset for monitoring crops globally. Unlike optical remote sensing, SAR can provide earth observations regardless of solar illumination or atmospheric conditions. Several methods that utilize SAR to identify agriculture rely on computationally expensive algorithms, such as machine learning, that require extensive training datasets, complex data pre-processing, or specialized software. The coefficient of variation (CV) method has been successful in identifying agricultural activity using several SAR sensors and is the basis of the Cropland Area algorithm for the upcoming NASA-Indian Space Research Organization (ISRO) SAR mission. The CV method derives a unique threshold for an AOI by optimizing Youden’s J-Statistic, where pixels above the threshold are classified as crop and pixels below are classified as non-crop, producing a binary crop/non-crop classification. Training this optimization process requires at least some existing cropland classification as an external reference dataset. In this paper, general CV thresholds are derived that can discriminate active agriculture (i.e., fields in use) from other land cover types without requiring a cropland reference dataset. We demonstrate the validity of our approach for three crop types: corn/soybean, wheat, and rice. Using data from the European Space Agency’s (ESA) Sentinel-1, a C-band SAR instrument, nine global AOIs, three for each crop type, were evaluated. Optimal thresholds were calculated and averaged for two AOIs per crop type for 2018–2022, resulting in 0.53, 0.31, and 0.26 thresholds for corn/soybean, wheat, and rice regions, respectively. The crop type average thresholds were then applied to an additional AOI of the same crop type, where they achieved 92%, 84%, and 83% accuracy for corn/soybean, wheat, and rice, respectively, when compared to ESA’s 2021 land cover product, WorldCover. The results of this study indicate that the use of the CV, along with the average crop type thresholds presented, is a fast, simple, and reliable technique to detect active agriculture in areas where either corn/soybean, wheat, or rice is the dominant crop type and where outdated or no reference datasets exist. Full article

Recently, as the satellite data service market has grown significantly, satellite demand has been rapidly increasing. Demand for geostationary satellites with weather observation, communication broadcasting, and GPS missions is also increasing. Completing the charging process of the Li-ion battery during the sun period is one of the main tasks of the electrical power system in geostationary satellites. In the case of the electrical power system of low Earth orbit satellites, the Li-ion battery is connected to the DC/DC converter output, and the charging process is completed through CV control. However, in the case of the regulated bus of the DET type, which is mainly used in the electrical power system of geostationary satellites, a Li-ion battery is connected to the input of the DC/DC converter. Therefore, a method other than the CV control of the DC/DC converter is required. This paper proposes a control algorithm for tapering charging of the Li-ion battery in the regulated bus of the DET type for Li-ion battery charge completion operation required by space-level design standards. In addition, the proposed control algorithm is verified through an experiment on a geostationary satellite’s ground electrical test platform. The experiment verified that it has a power conversion efficiency of 99.5% from the solar array to the battery. It has 21 tapering steps at the equinox and 17 tapering steps at the solstice. Full article

With the advancement of computer technology, transformer models have been applied to the field of computer vision (CV) after their success in natural language processing (NLP). In today’s rapidly evolving medical field, radiologists continue to face multiple challenges, such as increased workload and increased diagnostic demands. The accuracy of traditional lung cancer detection methods still needs to be improved, especially in realistic diagnostic scenarios. In this study, we evaluated the performance of the Swin Transformer model in the classification and segmentation of lung cancer. The results showed that the pre-trained Swin-B model achieved a top-1 accuracy of 82.26% in the classification mission, outperforming ViT by 2.529%. In the segmentation mission, the Swin-S model demonstrated improvement over other methods in terms of mean Intersection over Union (mIoU). These results suggest that pre-training can be an effective approach for improving the accuracy of the Swin Transformer model in these tasks. Full article

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated. Full article

A total of 248 UAV RGB images were taken in the summer of 2021 over a representative pistachio orchard in Spain (X: 341450.3, Y: 4589731.8; ETRS89/UTM zone 30N). It is a 2.03 ha plot, planted in 2016 with Pistacia vera L. cv. Kerman grafted on UCB rootstock, with a NE–SW orientation and a 7 × 6 m triangular planting pattern. The ground was kept free of any weeds that could affect image processing. The photos (provided in JPG format) were taken using a UAV DJI Phantom Advance quadcopter in two flight missions: one planned to take nadir images (β = 0°), and another to take oblique images (β = 30°), both at 55 metres above the ground. The aerial platform incorporates a DJI FC6310 RGB camera with a 20 megapixel sensor, a horizontal field of view of 84° and a mechanical shutter. In addition, GCPs (ground control points) were collected. Finally, a high-quality 3D photogrammetric reconstruction process was carried out to generate a 3D point cloud (provided in LAS, LAZ, OBJ and PLY formats), a DEM (digital elevation model) and an orthomosaic (both in TIF format). The interest in using remote sensing in precision agriculture is growing, but the availability of reliable, ready-to-work, downloadable datasets is limited. Therefore, this dataset could be useful for precision agriculture researchers interested in photogrammetric reconstruction who want to evaluate models for orthomosaic and 3D point cloud generation from UAV missions with changing flight parameters, such as camera angle. Full article

The proliferation of reusable space vehicles has fundamentally changed how assets are injected into the low earth orbit and beyond, increasing both the reliability and frequency of launches. Consequently, it has led to the rapid development and adoption of new technologies in the aerospace sector, including computer vision (CV), machine learning (ML)/artificial intelligence (AI), and distributed networking. All these technologies are necessary to enable truly autonomous “Human-out-of-the-loop” mission tasking for spaceborne applications as spacecrafts travel further into the solar system and our missions become more ambitious. This paper proposes a novel approach for space-based computer vision sensing and machine learning simulation and validation using synthetically trained models to generate the large amounts of space-based imagery needed to train computer vision models. We also introduce a method of image data augmentation known as domain randomization to enhance machine learning performance in the dynamic domain of spaceborne computer vision to tackle unique space-based challenges such as orientation and lighting variations. These synthetically trained computer vision models then apply that capability for hardware-in-the-loop testing and evaluation via free-flying robotic platforms, thus enabling sensor-based orbital vehicle control, onboard decision making, and mobile manipulation similar to air-bearing table methods. Given the current energy constraints of space vehicles using solar-based power plants, cameras provide an energy-efficient means of situational awareness when compared to active sensing instruments. When coupled with computationally efficient machine learning algorithms and methods, it can enable space systems proficient in classifying, tracking, capturing, and ultimately manipulating objects for orbital/planetary assembly and maintenance (tasks commonly referred to as In-Space Assembly and On-Orbit Servicing). Given the inherent dangers of manned spaceflight/extravehicular activities (EVAs) currently employed to perform spacecraft maintenance and the current limitation of long-duration human spaceflight outside the low earth orbit, space robotics armed with generalized sensing and control and machine learning architecture have a unique automation potential. However, the tools and methodologies required for hardware-in-the-loop simulation, testing, and validation at a large scale and at an affordable price point are in developmental stages. By leveraging a drone’s free-flight maneuvering capability, theater projection technology, synthetically generated orbital and celestial environments, and machine learning, this work strives to build a robust hardware-in-the-loop testing suite. While the focus of the specific computer vision models in this paper is narrowed down to solving visual sensing problems in orbit, this work can very well be extended to solve any problem set that requires a robust onboard computer vision, robotic manipulation, and free-flight capabilities. Full article

Currently, most rescue robots are mainly teleoperated and integrate some level of autonomy to reduce the operator’s workload, allowing them to focus on the primary mission tasks. One of the main causes of mission failure are human errors and increasing the robot’s autonomy can increase the probability of success. For this reason, in this work, a stair detection and characterization pipeline is presented. The pipeline is tested on a differential drive robot using the ROS middleware, YOLOv4-tiny and a region growing based clustering algorithm. The pipeline’s staircase detector was implemented using the Neural Compute Engines (NCEs) of the OpenCV AI Kit with Depth (OAK-D) RGB-D camera, which allowed the implementation using the robot’s computer without a GPU and, thus, could be implemented in similar robots to increase autonomy. Furthermore, by using this pipeline we were able to implement a Fuzzy controller that allows the robot to align itself, autonomously, with the staircase. Our work can be used in different robots running the ROS middleware and can increase autonomy, allowing the operator to focus on the primary mission tasks. Furthermore, due to the design of the pipeline, it can be used with different types of RGB-D cameras, including those that generate noisy point clouds from low disparity depth images. Full article

The establishment of steady-state continuous crop production during long-term deep space missions is critical for providing consistent nutritional and psychological benefits for the crew, potentially improving their health and performance. Three technology demonstrations were completed achieving simultaneous multi-species plant growth and the concurrent use of two Veggie units on the International Space Station (ISS). Microbiological characterization using molecular and culture-based methods was performed on leaves and roots from two harvests of three leafy greens, red romaine lettuce (Lactuca sativa cv. ‘Outredgeous’); mizuna mustard, (Brassica rapa var japonica); and green leaf lettuce, (Lactuca sativa cv. Waldmann’s) and associated rooting pillow components and Veggie chamber surfaces. Culture based enumeration and pathogen screening indicated the leafy greens were safe for consumption. Surface samples of the Veggie facility and plant pillows revealed low counts of bacteria and fungi and are commonly isolated on ISS. Community analysis was completed with 16S rRNA amplicon sequencing. Comparisons between pillow components, and plant tissue types from VEG-03D, E, and F revealed higher diversity in roots and rooting substrate than the leaves and wick. This work provides valuable information for food production-related research on the ISS and the impact of the plant microbiome on this unique closed environment. Full article

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle’s sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people’s daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G’s advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development. Full article

Phosphorus (P) fertiliser is applied regularly to the nutrient-poor sandy soils in southwestern Australia to elevate and/or maintain pasture production. This study aimed to characterise differential growth, root carboxylate exudation, and mycorrhizal responses in three temperate perennial pasture grasses at variable P supply. Tall fescue (Festuca arundinacea L. cv. Prosper), veldt grass (Ehrharta calycina Sm. cv. Mission), and tall wheatgrass (Thinopyrum ponticum L. cv. Dundas) with five P rates varying from 0 to 100 mg P kg⁻¹ soil were evaluated in a controlled environment. Rhizosphere carboxylate exudation and mycorrhizal colonisation were assessed. Veldt grass produced the maximum shoot dry weight, highest agronomic phosphorus-use efficiency at low P supply, as well as the highest specific root length and shoot P content at all P rates. Across species, the maximum shoot weight was obtained at 20 and 50 mg P kg⁻¹ soil, which differed significantly from the two lowest P rates (0 and 5 mg P kg⁻¹ soil). Phosphorus application influenced carboxylate exudation, with plants exuding acetate only in the zero P treatment, and citrate and malonate in the P-supplemented treatments. In all three species, acetate and malonate were the major carboxylates exuded (37–51% of the total). Only tall wheatgrass released trans-aconitate. Citrate and malonate concentrations in the rhizosphere increased with P supply, suggesting their important role in P acquisition. Phosphorus applications reduced arbuscular mycorrhizal colonisation and increased root diameter as the P rate increased. Root carboxylate exudation in low-P soil played a role in mobilisation of P via P solubilisation, but the role of exuded carboxylate in soils well supplied with P might be diminished. Full article