Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of TLS Encrypted Communication Behavior Based on Bipartite Graph and Traffic Information Embedding

Currently, encrypted traffic using the TLS protocol is widely used, and there are a lot of malicious and unknown behaviors. It is an important goal in the research field to detect the traffic behavior of TLS and maintain network security and stability. This article is based on TLS encrypted traffic, taking the multi-session communication traffic between the client and the server as the object, and carrying out traffic analysis on the generated bipartite graph. Different from the existing works, this paper focuses on the multiple attributes of TLS traffic and transforms it into a bipartite graph with rich attributes, and breaks the bottleneck of single session traffic detection, avoiding simple graph mining mode analysis. The results of anomaly detection and application classification experiments based on real data sets show that similar client application objects and application behaviors show similar traffic characteristics. Finally, we discussed the importance of using the behavioral similarity of client response objects to identify and detect abnormal traffic in network applications.
Energy Efficient Resource Allocation and Trajectory Optimization in UAV-Assisted Mobile Edge Computing System

Mobile edge computing(MEC) has been considered as a promising technology with the ever-increasing computation demands, which offloads computation-intensive tasks to MEC servers to meet the low latency and high bandwidth requirements of the tasks. But considering the dynamic UEs, nonuniform distribution of task requests and the limitation of the dynamic of the fixed deployment of the edge severs, we investigate a Unmanned Aerial Vehicles (UAVs)-assisted edge computing system in this paper, where each UAV is equipped with server to assist local edge servers. Utilizing the mobility of UAVs provides flexible edge computing services. In this model, tasks are executed on the local edge server first. When the computing resources of the local edge server cannot meet the computational requirements of the task, the system will dispatch the UAVs. Task will be offloaded to UAV for execution. Considering that the endurance of UAVs is a tough problem under the current technical level. Our aim is to minimize the energy consumption of UAVs under the premise of satisfying the UEs demands as much as possible to achieve a higher resource utilization rate. We propose Tasks Offloading Policy Algorithm(TOPA) and Online UAVs Dispatching Base on the Shortest Distant Algorithm(ODSH). Simulation results show the effectiveness of the proposed of algorithms.
Hybrid Energy Storage System Based on Li-Ion and Li-S Battery Modules and GaN-Based DC-DC Converter

Lithium-ion (Li-ion) batteries are still the best technology to power the Electric Vehicle (EV), due to their high power and energy density. However, the use of these batteries can be limited in cars with a high demand for peak power and very high energy density. One way to improve the performance of the Li-ion battery and reduce its weight is to associate this battery with another technology of higher specific energy as a second energy source, e.g. Lithium-sulfur (Li-S). The development of Hybrid Energy Storage Systems (HESSs) is a promising solution optimizing the energy management of EVs. In this paper, we present experimental results obtained with a high specific energy and power capability HESS prototype, composed of i) a Lithium-Titanate-Oxide battery to ensure high power capabilities, ii) a Li-S battery to improve specific energy, and iii) a power converter based on Gallium Nitride (GaN) devices to link both battery modules, minimizing at the same time system weight, volume and power losses. The developed GaN-based power converter achieves high efficiency (96.5%) operating at 300 kHz with a reduced size (0.4 L). Besides, the behavior of the developed HESS prototype is experimentally evaluated under standard automotive profiles, for different driving scenarios. The HESS prototype achieves an increase of energy density and specific energy of +5.56% and +28.21%, respectively, compared to a battery system composed only of Li-ion cells. The limitation of the developed system for automotive application are highlighted, and the critical research needs are clearly identified to accelerate the implementation of such systems on commercial EV solutions.
An Abstract Domain to Infer Linear Absolute Value Equalities

The classic linear (technically, affine) equality abstract domain, which can infer linear equality relations among variables of a program automatically, is one of the earliest and fundamental abstract domains. As a lightweight relational abstract domain, it has been widely used in program analysis. However, it cannot express non-convex properties that appear naturally due to the inherent disjunctive behaviors in a program. In this paper, we introduce a new abstract domain, namely the abstract domain of linear absolute value equalities, which generalizes the linear equality abstract domain with absolute value terms of variables. More clearly, we leverage the absolute value function to design the new abstract domain for discovering linear equality relations among values and absolute values of program variables. The new abstract domain can be used to infer piecewise linear behaviors (e.g., due to conditional branches, absolute value function calls, max/min function calls, etc.) in a program. Experimental results of our prototype are encouraging: In practice, the new abstract domain can find interesting piece-wise linear invariants that are non-convex and out of the expressiveness of the linear equality domain.
Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies.
Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods

RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time.
Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.
Recovery of Tropical Cyclone Induced SST Cooling Observed by Satellite in the Northwestern Pacific Ocean

Based on the satellite observed sea surface temperature (SST), the recovery of SST cooling induced by the tropical cyclones (TCs) over the northwestern Pacific Ocean is investigated. The results show that the passage of a TC induces a mean maximum cooling in the SST of roughly −1.25 °C. It was also found that most of this cooling (~87%) is typically erased within 30 days of TC passage. This recovery time depends upon the degree of cooling, with stronger (weaker) SST cooling corresponding to longer (shorter) recovery time. Further analyses show that the mixed layer depth (MLD) and the upper layer thermocline temperature gradient (UTTG) also play an important role in the SST response to TCs. The maximum cooling increases ~0.1 °C for every 7 m decrease in the MLD or every 0.04 °C/m increase in the UTTG. The combined effects of MLD and TC intensity and translation speed on the SST response are also discussed.
Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs

Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to the potentially complex texture from older ice floes (i.e., floating chunks of sea ice) and surrounding slush ice, making ice floes less distinctive from the surrounding water. We propose a pipeline using a U-Net variant with a Resnet encoder to retrieve ice floe pixel masks from very-high-resolution multispectral satellite imagery. Even with a modest-sized hand-labeled training set and the most basic hyperparameter choices, our CNN-based approach attains an out-of-sample F1 score of 0.698–a nearly 60% improvement when compared to a watershed segmentation baseline. We then supplement our training set with a much larger sample of images weak-labeled by a watershed segmentation algorithm. To ensure watershed derived pack-ice masks were a good representation of the underlying images, we created a synthetic version for each weak-labeled image, where areas outside the mask are replaced by open water scenery. Adding our synthetic image dataset, obtained at minimal effort when compared with hand-labeling, further improves the out-of-sample F1 score to 0.734. Finally, we use an ensemble of four test metrics and evaluated after mosaicing outputs for entire scenes to mimic production setting during model selection, reaching an out-of-sample F1 score of 0.753. Our fully-automated pipeline is capable of detecting, monitoring, and segmenting ice floes at a very fine level of detail, and provides a roadmap for other use-cases where partial results can be obtained with threshold-based methods but a context-robust segmentation pipeline is desired.
Spatiotemporal Variability of Dune Velocities and Corresponding Uncertainties, Detected from Optical Image Matching in the North Sinai Sand Sea, Egypt

Understanding the relationship between climatic conditions and dune ecosystems requires the large-scale monitoring of spatiotemporal patterns of dune velocities. Due to their large extent and remoteness, dune fields are ideal for remote sensing techniques. Dune velocities in the Sand Sea North Sinai are characterized by large spatial and temporal variability. To this end, a total of 265 pairs from four Landsat-8 images from April 2013 to April 2018 were automatically matched with the COSI Corr engine to determine the dune velocities. These pairs were selected so that differences in the solar angles were small and spanned at least one year. This helps to reduce shadowing in the deformation fields and the error budget in converting displacements to annual velocities. To improve spatial coverage and reduce measurement uncertainty, the fusion of individual offset maps is considered feasible. We compared the performance of two methods (i.e., inversion and temporal median fusion) in performing the fusion of individual velocities, and the two methods showed good agreement. The fusion of individual velocities allowed us to estimate the final velocities for about 98.8% of the dune areas. Our results suggest that the magnitudes and directions of dune migration at Sand Sea are spatially and temporally variable. The geometric mean of the active features associated with 12 regions in the Sand Sea ranged from 1.65 m/y to 3.52 m/y, with median directions from 56.19° to 173.11°. The stable regions allowed us to estimate the 95% confidence intervals of the final velocities and extend these calculations to the dune targets. The median uncertainties were 0.10 m/y and 0.25 m/y for the stable and moving targets, respectively. We estimated the coherence of the final velocity vector, which can be considered as an indicator of the homogeneity of migration directions between the offset maps. We compared the final Landsat-8 velocities with those from Sentinel-2 to validate the results and found a good agreement in the magnitudes and directions. The process of selecting high-quality pairs and then fusing the individual maps showed a high performance in terms of spatial coverage and reliability of the extracted velocities.
Exploiting Structured CNNs for Semantic Segmentation of Unstructured Point Clouds from LiDAR Sensor

Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and computer vision. Due to the unstructured nature of point clouds, designing deep neural architectures for point cloud semantic segmentation is often not straightforward. In this work, we circumvent this problem by devising a technique to exploit structured neural architectures for unstructured data. In particular, we employ the popular convolutional neural network (CNN) architectures to perform semantic segmentation of LiDAR data. We propose a projection-based scheme that performs an angle-wise slicing of large 3D point clouds and transforms those slices into 2D grids. Accounting for intensity and reflectivity of the LiDAR input, the 2D grid allows us to construct a pseudo image for the point cloud slice. We enhance this image with low-level image processing techniques of normalization, histogram equalization, and decorrelation stretch to suit our ultimate object of semantic segmentation. A large number of images thus generated are used to induce an encoder-decoder CNN model that learns to compute a segmented 2D projection of the scene, which we finally back project to the 3D point cloud. In addition to a novel method, this article also makes a second major contribution of introducing the enhanced version of our large-scale public PC-Urban outdoor dataset which is captured in a civic setup with an Ouster LiDAR sensor. The updated dataset (PC-Urban_V2) provides nearly 8 billion points including over 100 million points labeled for 25 classes of interest. We provide a thorough evaluation of our technique on PC-Urban_V2 and three other public datasets.
Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data

Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of a 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories.
Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Remote sensing image scene classification acts as an important task in remote sensing image applications, which benefits from the pleasing performance brought by deep convolution neural networks (CNNs). When applying deep models in this task, the challenges are, on one hand, that the targets with highly different scales may exist in the image simultaneously and the small targets could be lost in the deep feature maps of CNNs; and on the other hand, the remote sensing image data exhibits the properties of high inter-class similarity and high intra-class variance. Both factors could limit the performance of the deep models, which motivates us to develop an adaptive decision-level information fusion framework that can incorporate with any CNN backbones. Specifically, given a CNN backbone that predicts multiple classification scores based on the feature maps of different layers, we develop a pluginable importance factor generator that aims at predicting a factor for each score. The factors measure how confident the scores in different layers are with respect to the final output. Formally, the final score is obtained by a class-wise and weighted summation based on the scores and the corresponding factors. To reduce the co-adaptation effect among the scores of different layers, we propose a stochastic decision-level fusion training strategy that enables each classification score to randomly participate in the decision-level fusion. Experiments on four popular datasets including the UC Merced Land-Use dataset, the RSSCN 7 dataset, the AID dataset, and the NWPU-RESISC 45 dataset demonstrate the superiority of the proposed method over other state-of-the-art methods.
Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.