Search Results (17)

In contrast to terrestrial networks, the rapid movement of low-earth-orbit (LEO) satellites causes frequent changes in the topology of intersatellite links (ISLs), resulting in dynamic shifts in transmission paths and fluctuations in multi-hop latency. Moreover, limited onboard resources such as buffer capacity and bandwidth competition contribute to the instability of these links. As a result, providing reliable quality of service (QoS) for time-sensitive flows (TSFs) in LEO satellite networks becomes a challenging task. Traditional terrestrial time-sensitive networking methods, which depend on fixed paths and static priority scheduling, are ill-equipped to handle the dynamic nature and resource constraints typical of satellite environments. This often leads to congestion, packet loss, and excessive latency, especially for high-priority TSFs. This study addresses the primary challenges faced by time-sensitive satellite networks and introduces a management framework based on software-defined networking (SDN) tailored for LEO satellites. An advanced queue management and scheduling system, influenced by terrestrial time-sensitive networking approaches, is developed. By incorporating differentiated forwarding strategies and priority-based classification, the proposed method improves the efficiency of transmitting time-sensitive traffic at multiple levels. To assess the scheme’s performance, simulations under various workloads are conducted, and the results reveal that it significantly boosts network throughput, reduces packet loss, and maintains low latency, thus optimizing the performance of time-sensitive traffic in LEO satellite networks. Full article

In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. Full article

Object detection in drone aerial imagery has been a consistent focal point of research. Aerial images present more intricate backgrounds, greater variation in object scale, and a higher occurrence of small objects compared to standard images. Consequently, conventional object detection algorithms are often unsuitable for direct application in drone scenarios. To address these challenges, this study proposes a drone object detection algorithm model based on YOLOv5, named SMT-YOLOv5 (Small Target-YOLOv5). The enhancement strategy involves improving the feature fusion network by incorporating detection layers and implementing a weighted bidirectional feature pyramid network. Additionally, the introduction of the Combine Attention and Receptive Fields Block (CARFB) receptive field feature extraction module and DyHead dynamic target detection head aims to broaden the receptive field, mitigate information loss, and enhance perceptual capabilities in spatial, scale, and task domains. Experimental validation on the VisDrone2021 dataset confirms a significant improvement in the target detection accuracy of SMT-YOLOv5. Each improvement strategy yields effective results, raising the average precision by 12.4 percentage points compared to the original method. Detection improvements for large, medium, and small targets increase by 6.9%, 9.5%, and 7.7%, respectively, compared to the original method. Similarly, applying the same improvement strategies to the low-complexity YOLOv8n results in SMT-YOLOv8n, which is comparable in complexity to SMT-YOLOv5s. The results indicate that, relative to SMT-YOLOv8n, SMT-YOLOv5s achieves a 2.5 percentage point increase in average precision. Furthermore, comparative experiments with other enhancement methods demonstrate the effectiveness of the improvement strategies. Full article

An electrically controlled rotor (ECR), also known as a swashplateless rotor, eliminates the swashplate system to implement the primary control via the trailing-edge flaps (TEFs), which can result in enhancements in rotor performance, as well as substantial reductions in weight, drag, and cost. In this paper, the unsteady aerodynamic characteristics of the airfoil with TEF of a sample ECR under unsteady freestream condition are investigated. The CFD results are obtained with sliding and overset grid techniques that simulate the airfoil pitching and flap deflection. Comparative analysis of the aerodynamic characteristics under steady and unsteady freestream conditions at different advance ratios is conducted. At various advance ratios, the lift and drag coefficients are higher at a small angle of attack under unsteady freestream condition; however, it is the opposite at a large angle of attack. The peak values of the lift and drag coefficients show an increased trend with the increase in the advance ratio. On the contrary, the pitch moment and flap hinge moment coefficients demonstrate minor variation under unsteady freestream condition. Furthermore, the aerodynamic characteristics of airfoils become more unsteady with variation in the freestream. Therefore, the lift and drag coefficients of the ECR airfoil with TEF show significant differences between steady and unsteady freestream conditions; however, the pitch moment and the flap hinge moment coefficients show little difference. Full article

An electrically controlled rotor (ECR), also known as a swashplateless rotor, is an active rotor system that reduces the vibration load of the rotor through active control while achieving primary control by using a trailing edge flap system instead of a swashplate. In this study, the control effect of a 2Ω higher-order harmonic input on the vibration load of an ECR is investigated. First, an analytical aeroelastic model of the ECR is established based on Hamilton’s principle and an unsteady aerodynamic model with a flapped airfoil. On this basis, the use of higher-order harmonic flap control to reduce the vibration load of the ECR is investigated. The effect of the 2Ω higher-order harmonic flap control on the 2Ω vibration load of the example ECR is analyzed by sweeping the amplitude and phase of the higher-order harmonic flap control. The effect of higher-order harmonic flap control on the primary control of the ECR is also analyzed. The results show that the 2Ω higher-order flap deflection has the most significant control effect on the 2Ω vertical vibration load of the hub, that there is coupling between the higher-order flap deflection and the primary control of the ECR, and that the higher-order flap deflection disrupts the original equilibrium of the ECR. Full article

The operation process of a virtual power plant is affected by many uncertainties, and how to ensure its comprehensive operation quality is a pressing challenge. For the virtual power plant incorporating electric vehicles, the interval number is used to describe the stochastic fluctuation of system uncertainties, and the optimization objectives are to (1) improve the operating economy, environmental protection, and grid load smoothing, (2) build a multi-objective interval optimal dispatching model considering the constraints of power balance and equipment operating characteristics, (3) solve the Pareto solution set by adopting the improved NSGA-II algorithm incorporating extreme scenario analysis, and (4) determine the optimal dispatching solution by the hierarchical analysis method. The median values of the determined optimal target intervals are 6456.11 yuan, 9860.01 kg, and 2402.56 kW. The algorithm shows that the proposed optimal dispatching strategy can effectively improve the economy of the virtual power plant and ensure that environmental protection and grid load smoothing requirements are met. Full article

As an important tool to control CO₂ emission, carbon emission trading (CET) has been highlighted in prior studies for its positive effects on firms. However, we are concerned about the role of the CET in corporate financing. Through a quasi-natural experiment from China’s CET pilot, regarded as the start-up stage of China’s emission trading system, we investigate the manufacturing corporate financing (i.e., debt and commercial credit financing). The results show that the firms in China’s CET market have less debt financing. Additionally, in the heterogeneity analysis, we found that (1) the CET is negatively related to corporate financing when their financing constraints are weak, whereas it only reduces long-term debt for the firms with strong financing constraints. (2) The impact of the CET on corporate financing is not significant for the firms located in first-tier cities in China, but in other cities, the CET negatively influences firms’ long-term debt and contributes to commercial credit financing. (3) The CET only plays a negative role in long-term debt and a positive role in commercial credit financing for firms in high energy-consuming industries. This study enlightens the government to improve the emission trading system and increase financing support to manufacturing firms in the CET market. Full article

Automatic charging for electric vehicles has broad development prospects for meeting the personalized service experience of users while overcoming the inherent safety hazards. An identification and positioning approach suitable for engineering applications is the key to promoting automatic charging. In this paper, a low-cost, high-precision method to identify and position charging ports based on SIFT and SGBM is proposed. The feature extraction approach based on SIFT is adopted to produce the difference of Gaussian (DOG) for scale space construction, and the feature matching algorithm with nearest-neighbor search, which is a kind of machine learning, is utilized to yield the map set of matching points. In addition, the disparity calculation is conducted with a semi-global matching algorithm to obtain high-precision positioning results for the charging port. In order to verify the feasibility of the method, a complete identification and positioning experiment of charging port was carried out based on OpenCV and MATLAB. Full article

With the increase of fire problems of new energy vehicles (EVs), more and more attention has been paid to charging safety. Firstly, the charging safety problems and protection strategies in the power grid are summarized from the grid side, the charging equipment side, the vehicle side, and the operation platform side, and a solution for the vehicle side charging safety protection is proposed. Secondly, with regards to building a charging early warning protection system architecture, a real-time protection strategy for EV charging is proposed; a battery temperature difference, battery voltage ramp rate, and current ramp rate are proposed; and a double-layer protection model of an active protection layer and a big data protection layer is established based on the real-time monitoring of 27 parameters. Finally, by building a physical simulation platform of the early warning system, the simulation and verification are carried out based on the BYD Han model. The system was demonstrated in the State Grid Tianjin Electric Power Company of China. The results show that the system can realize the charging real-time early warning and deal with it in time when the battery charging is abnormal, which has practical application value for the popularization and development of EVs. Full article

With the continuous development of society and the economy and the popularization of the environmental protection concept, more and more people have begun to turn to electric vehicles. The application of electric vehicles can effectively avoid the damage caused by automobile fuel emissions to the surrounding environment and promote the development and utilization of new energy. However, the development of the electric vehicle industry has led to frequent accidents related to charging safety. In order to prevent accidents related to the charging safety of electric vehicles and ensure proper safety of passengers and people, the charging safety and charging safety protection methods of electric vehicles have become the research priorities for scholars. This paper reviewed the existing research results on the charging safety of electric vehicles, analyzed the influencing factors of the charging safety of electric vehicles, summarized the charging safety protection methods, and forecast the future research direction of charging safety, which has reference value and reference significance for the charging safety research of electric vehicles. Full article

In this paper, we construct a new 1′ $\times$ 1′ global seafloor topography model, BAT_VGG2021, using the satellite altimetric vertical gravity gradient anomaly model (VGG), SIO curv_30.1.nc, and ship soundings. Approximately 74.66 million single-beam depths and more than 180 GB of multibeam grids were downloaded and adopted from the National Centers for Environmental Information (NCEI), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Geosciences Australia (GA). The SIO curv_30.1.nc model was used to predict seafloor topography at 15~160 km wavelengths, and ship soundings were used to calibrate topography to VGG ratios. The accuracy of the new BAT_VGG2021 model was assessed by comparing it with ship soundings and existing models. The results indicate that the standard deviation of differences between the predicted model and ship soundings is about 40~80 m, and ~93% of the differences are within 100 m, similar to that of the SIO topo_20.1.nc model. The new BAT_VGG2021 model shows better accuracy than the DTU18BAT, ETOPO1, and GEBCO_08 models, and has been improved significantly from our last model, BAT_VGG2014. Full article

With the development of electric vehicles in China, the fault monitoring and warning systems for the charging process of electric vehicles have received the industry’s attention. A method for the monitoring and warning of electric vehicle charging faults based on a battery model is proposed in this paper. Through online estimation of the state of charge of the power battery model and battery electromotive force, parameters such as battery state of charge, voltage, and temperature can be adjusted in real time to simulate the charging response of the power battery, which can simulate power batteries of different types, specifications, and parameters. During the charging process, CAN (Controller Area Network) bus monitoring technology is used to receive and analyze the charging information of the charger, as well as the battery charging information and battery charging demand information. The charging response information simulated by the battery model is compared with the battery charging state information, and the charging state information of the charger is compared with the battery charging demand information to determine whether the charging process is normal. When it is judged that a charging fault occurs, a fault warning signal is sent. This method can identify more than 10 types of faults, including the failure of the BMS (Battery Management System) function. The comparison and analysis of actual charging accident data and power battery model data verifies the feasibility of the charging fault monitoring method proposed in this paper. Full article

Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation of SWE, the complete snowfall period over a winter season is split into snow accumulation, transition, and melting period in accordance with the variation characteristics of snow depth and SWE. By extensively using in situ snow depth and SWE observations recorded by snow telemetry network (SNOTEL) and regression analysis, three empirical models are developed to describe the relationship between snow depth and SWE for the three periods, respectively. Based on the snow depth fusion model and the SWE empirical models, an SWE estimation algorithm is proposed. Three data sets recorded in different environments are used to test the proposed method. The results demonstrate that there exists good agreement between the in situ SWE measurements and the SWE estimates produced by the proposed method; the root-mean-square error of SWE estimations is smaller than 6 cm when the SWE is up to 80 cm. Full article

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R² = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval. Full article

Global navigation satellite system (GNSS) multipath signals received by a geodetic-quality GNSS receiver can be used to estimate the water content of soil around the antenna. The direct signals from satellite to GNSS antenna are the most valuable signals in geodetic measurement, such as positioning, navigation, GNSS control network, deformation monitoring, and so on. However, the GNSS antenna also captures the reflected signals from the ground, which contain information of surrounding environment, so that useful information about the reflection surface can be inferred by analyzing the reflected signal. This technique is termed as GNSS-interferometric reflectometry. The signal-to-noise ratio (SNR) data recorded by a receiver contains SNR component of reflected signals, which is related to the soil moisture of the ground. The changes of soil moisture content will cause the change of soil permittivity and reflectivity which are the key factors that make further change of the SNR of reflected signals. We used the measured data to evaluate the correlation between amplitude of multipath induced SNR time series and real soil moisture. An improved soil moisture estimation algorithm based on multipath induced SNR amplitude data is proposed in this paper. The performance of the proposed soil moisture estimation method is evaluated using the 15-month data recorded by PBO H2O GNSS station and a 14-day experiment in Wuhan, China. The experimental results show that the estimated soil moisture has a strong correlation with the real soil moisture and the estimation accuracy in terms of root-mean-square error (RMSE) is 0.0345 cm³cm⁻³ and 0.0339 cm³cm⁻³, respectively. Meanwhile, the application scope of the method is given. Full article