Search

The rapid development of the Internet of Vehicles using centralized systems faces significant challenges, including reliability and security vulnerabilities and high latency. This paper introduces a blockchain-enabled authentication and communication network for scalable IoV to enhance security, reduce latency, and relieve the dependency on centralized infrastructures. The network applies blockchain-enabled domain name services and mutual authentication for fault tolerance consensus, such as PBFT and RAFT, featuring a primary layer of road side units and edge servers for inter-vehicle communication and a sub-layer within each vehicle for intra-vehicle communication. The study evaluates various scenarios and assesses roadside unit availability based on random distribution along vehicle routes. This paper also discusses the legal issues involved in the proposed model, highlighting that the IoV system should be governed by a contract-based decentralized IoV system comprising both smart contracts and traditional contracts. This model offers a novel approach to developing a decentralized, secure, efficient, and ethical IoV ecosystem, advancing autonomous and reliable vehicular networks. [ABSTRACT FROM AUTHOR]

Small-scale motions in turbulent flows play a significant role in various small-scale processes, such as particle relative dispersion and collision, bubble or droplet deformation, and orientation dynamics of non-sphere particles. Recovering the small-scale flows that cannot be resolved in large eddy simulation (LES) is of great importance for such processes sensitive to the small-scale motions in turbulent flows. This study proposes a subgrid-scale model for recovering the small-scale turbulent velocity field based on the artificial neural network (ANN). The governing equations of small-scale turbulent velocity are linearized, and the pressure gradient and the nonlinear convection term are modeled with the aid of the ANN. Direct numerical simulation (DNS) and filtered direct numerical simulation (FDNS) provide the data required for training and validating the ANN. The large-scale velocity and velocity gradient tensor are selected as inputs for the ANN model. The linearized governing equations of small-scale turbulent velocity are numerically solved by coupling the large-scale flow field information. The results indicate that the model established by the ANN can accurately recover the small-scale velocity lost in FDNS due to filtering operation. With the ANN model, the flow fields at different Reynolds numbers agree well with the DNS results regarding velocity field statistics, flow field structures, turbulent energy spectra, and two-point, two-time Lagrangian correlation functions. This study demonstrates that the proposed ANN model can be applied to recovering the small-scale velocity field in the LES of isotropic turbulent flows at different Reynolds numbers. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In response to these challenges, this research presents YOLO-MSD, a multiscale SAR ship detection method. Firstly, we propose a Deep Poly Kernel Backbone Network (DPK-Net) that utilizes the Optimized Convolution (OC) Module to reduce data redundancy and the Poly Kernel (PK) Module to improve the feature extraction capability and scale adaptability. Secondly, we design a BiLevel Spatial Attention Module (BSAM), which consists of the BiLevel Routing Attention (BRA) and the Spatial Attention Module. The BRA is first utilized to capture global information. Then, the Spatial Attention Module is used to improve the network's ability to localize the target and capture high-quality detailed information. Finally, we adopt a Powerful-IoU (P-IoU) loss function, which can adjust to the ship size adaptively, effectively guiding the anchor box to achieve faster and more accurate detection. Using HRSID and SSDD as experimental datasets, mAP of 90.2% and 98.8% are achieved, respectively, outperforming the baseline by 5.9% and 6.2% with a model size of 12.3 M. Furthermore, the network exhibits excellent performance across various ship scales. [ABSTRACT FROM AUTHOR]

For weakly inertial particles subjected to volumetric forces and Stokes drag force in fluid flows, we can solve the simplified particle motion equation using the perturbation method. This method allows us to obtain a recursive formula for the nth-order correction of the asymptotic solution of particle velocity. We verified the error of the asymptotic solution under two typical flow fields: a time-varying uniform flow field with a volumetric force field and a two-dimensional non-uniform cellular flow field. In the former, the relative error of the asymptotic solution of particle velocity and position increases with the Stokes number, and we provided a quantitative analysis of the results. In the latter, we verify and analyze the asymptotic solution from two perspectives: the behavior of a single particle and the collective behaviors of many particles. For asymptotic solutions with maximum velocity and position errors of less than 5%, we select the solution with the lowest order correction and designate it as the optimal asymptotic solution. The order of the optimal asymptotic solution increases with increasing Stokes numbers and motion durations. However, in most cases, for weakly inertial particles [St ∼ O(10−3)], and the time t* ∼ O(10), the first-order asymptotic solution can achieve accuracy, where both St and t* are defined using the flow field characteristic time, Tf = 4π s. The results validate the rationale behind utilizing first-order asymptotic solutions in the fast Eulerian method for turbulent dispersion of weakly inertial particles. [ABSTRACT FROM AUTHOR]

Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identification accuracy, poor timeliness, and limitation to main lobe reconnaissance, an efficient identification method based on sidelobe reconnaissance and machine learning is proposed in this paper. It can identify four classical SAR operating modes, including stripmap, scan, spotlight and ground moving target indication (GMTI). Firstly, the signal models of different operating modes are presented from the perspective of sidelobe reconnaissance. By setting the parameters differently, such as the SAR trajectory height, antenna length, transmit/receive gain and loss, signal–noise ratio, and so on, the feature samples based on multiple parameters can be obtained, respectively. Then, based on the generated database of feature samples, the initialized neural network can be pre-trained. As a result, in practice, with the intercepted sidelobe signal and the pre-trained network, we can precisely infer the SAR operating mode before the arrival of the main lobe beam footprint. Finally, the effect of SNR and the jammer's position on the identification accuracy is experimentally detailed in the simulation. The simulation results show that the identification accuracy can reach above 91%. [ABSTRACT FROM AUTHOR]

We describe a general, physics-based approach to numerical reconstruction of the geometrical structure and mechanical properties of natural sedimentary rock in 3D. Our procedure consists of three main steps: sedimentation, compaction, and diagenesis, followed by the verification of rock mechanical properties. The dynamic geologic processes of grain sedimentation and compaction are simulated by solving a dimensionless form of Newton's equations of motion for an ensemble of grains. The diagenetic rock transformation is modeled using a cementation algorithm, which accounts for the effect of rock grain size on the relative rate of cement overgrowth. Our emphasis is on unconsolidated sand and sandstone. The main input parameters are the grain size distribution, the final rock porosity, the type and amount of cement and clay minerals, and grain mechanical properties: the inter-grain friction coefficient, the cement strength, and the grain stiffness moduli. We use a simulated 2D Fontainebleau sandstone to obtain the grain mechanical properties. This Fontainebleau sandstone is also used to study the initiation, growth, and coalescence of micro-cracks under increasing vertical stress. The box fractal dimension of the micro-crack distribution, and its variation with the applied stress are estimated.

Aiming at the problem of extracting different levels of roads in synthetic aperture radar (SAR) images, an effective multi-scale extraction method for SAR roads from coarse to fine is proposed. Based on Duda operator, the method improves the extraction probability of different levels of roads and increases the link probability of potential road segments through multi-scale extraction and results, integration of road in SAR images. On this basis, the effective extraction of roads in SAR images is realized by combing the hierarchical design of scale filtering, morphological filtering, and fragment linking. The proposed method is tested on real SAR images, and the effectiveness of the method is verified by continuous and complete extraction results of road in SAR images. [ABSTRACT FROM AUTHOR]