esa-sceva/satcom-chunk-collection huggingface dataset:
# ERACE: Toward Facilitating Exploit Generation for Kernel Race Vulnerabilities
## 3 Motivating Example and Overview
### Overview  
To meet the requirements mentioned above, we designed an exploitation framework, named ERACE, to assist in the exploitation of a race vulnerability. The top-level design of ERACE can be seen in Figure 5. ERACE contains three components: a race-point detector, vulnerability analyzer, and checkpoint detector. They can satisfy the above three requirements, respectively.  
First, the input of the race-point detector is the kernel source code containing the race vulnerability and the PoC that can trigger the vulnerability. The source code is compiled into an LLVM intermediate representation and an executable binary file bzImage. Then, the multi-thread program in the PoC is converted into two single-thread programs. Next, the two programs in the executable kernel file bzImage run and the corresponding execution path is recorded. At the same time, the LLVM intermediate representation is statically analyzed to identify all the race instruction pairs. Finally, whether the actual execution path executes the race instruction pairs is determined. Therefore, we can accurately locate the race instructions.  
The vulnerability analyzer is responsible for collecting the vulnerability context, identifying the triggering type, and determining the exploitation method. To obtain an accurate and detailed vulnerability context, race points are instrumented to determine their timing relationship. Then, the static analysis is applied to recognize the race variable and its use  
Figure 4: Checkpoint of CVE-2016-8655. P1 and V3 are the two checkpoints.  
Figure 5: Overview of ERACE.  
type and the vulnerability context is recorded at the same time. Based on the use type of the race variable and the vulnerability context, ERACE can determine the triggering type.  
The checkpoint detector is responsible for locating checkpoints and determining the corresponding system call interface. Checkpoints can reflect the success of competing and heap spraying. The principle is to perceive the executing states of the kernel from the user space or confirm that the kernel memory is arranged with user-forged data. First, backward taint analysis is applied to find the kernel data that can be leaked by a leaking anchor. Then, the control-flow analysis is used to check whether there is a system call interface that can reach the leaking anchor without root privileges.  
The last step is to generate the final exploit. First, competing threads are crafted based on the identified race point and related system call. Then, a checking code is crafted based on the identified checkpoint and related system calls. Next, the subsequent exploitation method and structure based on the identified race-triggering type and vulnerability context are determined. The follow-up exploitation phase can be found in existing research [13; 14; 15; 16].
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication
### Smart Grid Communication Technologies  
Communication technologies utilized in SG can as mentioned be wired or wireless. Most power systems use a combination of different wired and wireless technologies, depending on the infrastructure. Several factor that has to be taken into account when deciding on communication technology used in SGs and smart metering. Wireless communication alternatives have some advantages over wired communication, such as low cost and connectivity in inaccessible areas. A number of factors have to be considered for each different case to decide on communication technology. Requirement include aspects such as geographical topography, technical and operational requirements and cost [61]. Wireless communication is less costly to implement in a complex infrastructure and ease of installation in some areas. Wired connection will not necessarily struggle with interference issues as wireless solutions may do. Both types of communication are necessary in SG environments. The technology that fits one environment may not be suitable in a different environment. Tables 6 and 7 gives a summary of wired and wireless communication technologies for SG. Following is a overview of some of the wired and wireless communication technologies used for SGs, together with advantages and limitations.
## 1 Introduction
### Note on Point Target RCS_ versus _Ercs_  
This paper adopts the reasoning laid out in [15]. According to it, a reference point target's brightness in a SAR image is not generally proportional to its radar cross section (RCS), which is a body property depending on frequency and incidence angle. Rather, a target's brightness in a SAR image depends on the measured _complex_ amplitudes and the _weighted averaging_ (over the chirp pulse bandwidth and the azimuth angles) executed by the SAR processor. This other quantity is called _equivalent radar cross section_, or ERCS.  
Targets whose ERCS is accurately known can be taken as a reference to calibrate SAR systems. The aim of the presented case study is to derive the ERCS of DLR's next-generation C-band Kalibri transponder prototype so that in principle it can be taken as a calibration normal for subsequent SAR calibration campaigns.  
In order to avoid confusion between the symbols for (E)RCS and standard deviation, the (E)RCS will not be denoted with the customary letter \(\sigma\) but with its alternative form \(\varsigma\).
## 3 Results  
Figure 4 presents the von Mises stress distribution of the FE models with the straight-type plate fixation and the horseshoe-shaped plate fixation. In the straight-type plate model and the horseshoe-shaped plate model, a high-stress region was observed at the iliac fixation area. In contrast, low-stress regions were located at the public ramus and ischium fixation areas. For the straight-type plate model, the proportions of the stress concentration in the ilium, public ramus, and ischium were 17.8%, 2.3%, and 0.5%, respectively. For the horseshoe-shaped plate model, the corresponding stress concentration proportions were 19.8%, 2.3%, and 0.6%. In the iliac fixation area, the maximal stress of the horseshoe-shaped plate model (38.6 MPa) was 21% lower than that of the straight-type plate model (48.9 MPa).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Young’s Modulus** & **Poisson’s** & **Tensile Strength** & **Compressive** \\  & **(g/cm\({}^{3}\))** & **(MPa)** & **Ratio (\(v\))** & **(MPa)** & **Strength (MPa)** \\ \hline Cortical bone & 1.64 & 16,700 & 0.26 & 106 & 157 \\ Cancellous bone & 0.16 & 155 & 0.30 & 6 & 6 \\ Ti-6 Al-4V & 4.62 & 96,000 & 0.36 & 1070 & 1070 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used for the finite element simulation.  
Figure 3: Boundary conditions of finite element (FE) models with (**a**) the straight-type plate fixation and (**b**) the horseshoe-shaped plate fixation.  
The horseshoe-shaped plate model was fabricated for the patient's reconstruction surgery, using EBM with the following process parameters: beam power of 3000 W, beam scan speed of 8000 m/s, beam spot size of 0.2 mm, build rate of 55 mm\({}^{3}\)/h, and a build thickness of 50 \(\upmu\)m, on all sides of the scanning strategy. Figure 5 shows the horseshoe-shaped plate model fabricated with medical-grade titanium (Ti-6 Al-4V-ELI per ASTM 136) using a powder-based EBM 3D printer (model: ARCAM A1, Arcam AB, Molndal, Sweden) [26].  
Figure 6 presents the medical images of the pelvis area obtained from the patient after the implantation surgery. The post-operative radiograph (Figure 6a) and 3D CT (Figure 6b) images demonstrated that the pelvis showed a satisfactory implant alignment and no evidence of implant loosening. In the postoperative evaluation, the patient was allowed to walk with underarm crutches two weeks after the surgery, and her feedback was positive.  
Figure 4: The von Mises stress distribution of the finite element (FE) models with (**a**) the straight-type plate fixation and (**b**) the horseshoe-shaped plate fixation.  
Figure 5: 3D-printed titanium periacetabular implant with the horseshoe-shaped plates.
## 4 Simulation Experiments and Analysis  
The effectiveness of the adaptive acceptance circle strategy and the adaptive heading control strategy in improving path-following performance is verified through simulation experiments and is compared with the classical line-of-sight method.  
During the simulation experiments, the initial position of the robot is \(x_{0}=0\) m, \(y_{0}=5\) m, and \(z_{0}=0\) m. Additionally, the initial heading angle is \(\gamma=0^{\circ}\), and the track inclination angle is \(\chi=0^{\circ}\). A comparative study is conducted based on the traditional LOS method and the improved LOS method proposed in this paper to verify the effectiveness of the adaptive acceptance circle and the adaptive heading control strategy in improving the path-following performance. The radius of the adaptive acceptance circle is taken as the circle shown in Equation (5), where a = 2, b = 1, k = 0.2, and the scaling coefficient of the adaptive heading control is \(m\) = 2.5. The control period is T = 0.1 s. In these simulation experiments, three corners are mainly considered, namely, the small corners of the horizontal angle, \(\theta_{a}<90^{\circ}\) and \(\theta_{b}<90^{\circ}\), as well as the larger angle of the horizontal pinch angle, \(\theta_{c}>\pi/2\). Table 2 lists the general parameters for simulation experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter/Unit** & **Value** \\ \hline Fixed forward – looking distance \(\Delta\)/(m) & 0.5 \\ Fixed acceptance circle radius \(R_{0}\)/(m) & 0.5 \\ Start velocity/(m/s) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the simulation experiment.
## 3. Tests and Results  
### Simulation Dataset  
We designed 150 mm \(\times\) 150 mm \(\times\) 6 mm plates with holes (as defects) ranging from 1 mm below the top to 1 mm up the bottom in different diameters and locations. Figure 3 shows the front views of eight specimens with eight kinds of materials and eight defect patterns. Figure 3a-h are panels of aluminum with nine defects, honeycomb with two defects, PVC foam with three defects, epoxy carbon with one defect, resin epoxy with one defect, glass with two defects, E glass fiber with one defect and ABS thermoplastic with three defects, respectively. The defects are randomly located and sized with diameters from  
Figure 2. Michelson-type digital shearography system.  
Themal, vacuum and force loadings with strength in every level were simulated in ANSYS to obtain more deformation data. Figure 4 shows two deformation distributions and shearography images of the ABS thermoplastic panel in Figure 3 with thermal loads of 30 \({}^{\circ}\)C and 50 \({}^{\circ}\)C. The distributions in Figure 4a,c are similar, but the maximum deformations are distinct. With different thermal loads, two different shearography images of 640 \(\times\) 640 pixels are simulated in Figure 4b,d. Figure 5 shows those of the honeycomb panel in Figure 3b with force loads of 5N and 20N. Two different shearography images of 640 \(\times\) 640 pixels are also simulated in Figure 5b,d. Apparently, when the load is in a small strength, the defect deformations are small and butterfly patterns of some defects are vague.  
Figure 4: _Cont._  
Figure 3: Eight simulation specimens (units: mm): (**a**) aluminum; (**b**) honeycomb; (**c**) PVC foam; (**d**) epoxy carbon; (**e**) resin epoxy; (**f**) glass; (**g**) E glass fiber; (**h**) ABS thermoplastic.  
Besides loading settings, shearing settings can also enrich the simulation dataset. Figure 6 shows simulated shearography images of 640 \(\times\) 640 pixels in four shearing directions, i.e., horizontal, vertical and two diagonals. The butterfly patterns are distinct in the different shearing directions.  
Figure 4: Deformation distributions and shearography images with thermal loads on 30 \({}^{\circ}\)C and 50 \({}^{\circ}\)C: (**a**) deformation, 30 \({}^{\circ}\)C; (**b**) shearography image, 30 \({}^{\circ}\)C; (**c**) deformation, 50 \({}^{\circ}\)C; (**d**) shearography image, 50 \({}^{\circ}\)C.  
Figure 5: Deformation distributions and shearography images with force loads of 5N and 20N: (**a**) deformation, 5N; (**b**) shearography image, 5N; (**c**) deformation, 20N; (**d**) shearography image, 20N.  
With all the settings in the parameters of object, defect, load and shearing, we prepared a simulation dataset with 5117 shearography images of 640 \(\times\) 640 pixels for deep learning. Figure 7 shows part of the simulated dataset. The simulated samples are rich in diversity.
## 4 Analysis of the Effect of TBM Crossing Fault Fracture Zone Under Different Treatment Methods
### Analysis of Calculation Results
#### 4.3.1 Study of the Distribution Pattern of Plastic Zones  
The depth of plastic zone development under four operating conditions is calculated and compared. Figure 13a shows that the plastic zone depth at different positions is compared visually. The results indicate that in the three pipe shed support conditions, the plastic zone is more pronounced at the arch waist and mainly occurs near the interface between the pre-grouting range and the soil. In contrast, under the surface pre-grouting condition, the surrounding rock plastic zone is less developed, with a significantly smaller depth compared to the other conditions. This suggests that tunnel excavation disturbance to the surrounding rock is effectively reduced, and surface pre-grouting provides better reinforcement.  
Figure 11: Schematic diagram of TBM excavation support construction stage division. (Left: Schematic of the relationship between pipe shed grouting stiffness and surrounding rock deformation. Right: Schematic of the interaction between surface grouting, surrounding rock, and support). (**a**) Stages of over-reinforcement. (**b**) Surface grouting reinforcement stage classification.  
Figure 12: Schematic diagram of the TBM excavation monitoring program.  
Additionally, the results show that condition II, involving pipe shed grouting, caused substantial soil damage, especially at the bottom of the arch, where a large plastic zone formed. This suggests that the single-layer pipe shed support is insufficient to prevent plastic deformation of the surrounding rock in fault fracture zones due to soil collapse. Therefore, enhanced support is necessary for smooth TBM passage. In condition I, by adding chemical grouting at the palm face and installing inclined small conduits at the shield's tail, the bearing capacity of the support structure was improved. This limited the collapse and damage of surrounding rock in the fault fracture zone, yielding favorable results.  
However, it should be noted that the grouting area increases the load on the lower soil, leading to some expansion of the plastic zone at the bottom of the arch. This indicates that, when designing support schemes, the combined effects of pipe sheds and grouting must be considered to optimize the support structure's performance.  
Comparing the four working conditions, surface pre-grouting results in the smallest plastic zone, with a development depth between 1.54 and 1.9 m. In contrast, the use of single-layer pipe shed support causes the greatest damage, with a maximum plastic zone depth of 4.55 m. After double-layer pipe shed and chemical grouting treatment, the geological conditions improve, and the plastic zone development is less pronounced, with a depth between 3 and 4 m.
## 4 Results
### DEAP Dataset  
A comprehensive comparison of the proposed method's classification performance with KNN and CNN models in terms of average accuracy for the valence and arousal classes on the DEAP dataset is presented in Table 1. The results demonstrate that the accuracy of the proposed method 1 outperforms CNN, while method 2 achieves higher accuracy by integrating dimensionality reduction and outperforms both KNN and method 1. However, method 3, which employs both dimensionality reduction and hyperparameters tuning, achieves the highest performance, with an accuracy of 64.06% and 57.42% for valence and arousal, respectively, as shown in Figure 5. The confusion matrix for the proposed method 3 illustrates the classification performance on the DEAP dataset, as shown in Figure 5a,b. The results suggest that the model performs well in correctly identifying low and high valence. The model has demonstrated a solid ability to accurately predict the valence class, with a true positive and negative rate of 64%. However, for the arousal class, the model exhibits a higher rate of false positives, with 48% of low-arousal trials being classified as high arousal.  
### DEAP Dataset  
The performance comparison of the three proposed models on the DREAMER dataset is depicted in Figure 6. The confusion matrices for both valence and arousal classes are presented in Figure 6a,b. The results reveal a consistent pattern across both classes, with higher accuracy achieved in classifying high arousal and high valence, reaching 64% and 67%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Proposed Methods**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{1}{c}{**CNN**} \\ \cline{2-6}  & **M1** & **M2** & **M3** & **K = 5** & **EEGNet** \\ \hline Valence & 58\% & 63.5\% & 64\% & 59\% & 57.24\% \\ Arousal & 55.48\% & 57.1\% & 57.4\% & 58\% & 56.21\% \\ \hline \hline \end{tabular} This table shows the classification results for the proposed methods compared to KNN and CNN (state-of-the-art machine learning algorithms for emotion recognition) on the DEAP dataset. The proposed methods are M1: MDRM, M2: MDRM plus PCA, M3: MDRM plus PCA plus hyperparameter tuning.  
\end{table}
Table 1: DEAP classification results.  
Figure 5: DEAP dataset classification results: (**a**) classification result comparison of the proposed methods. (**b**) Valence classification confusion matrix (method 3). (**c**) Arousal classification confusion matrix (method 3).  
This finding suggests that the models may overemphasize certain features associated with high arousal and valence, leading to a potential bias in the classification results.  
The accuracies achieved by the proposed methods are compared to those of CNN and KNN classification approaches in Table 2. The results indicate that the proposed method 3 achieves superior performance, achieving accuracies of 56% for valence and 58.64% for arousal, outperforming the state-of-the-art techniques.
## 3 Improved Network for Component Detection
### Baseline Methods  
The cutting-edge approaches in deep learning for target detection could be sorted into two main kinds: those that rely on region proposals and those that use regression. Region proposal-based methods, also referred to as two-step detection techniques, segment the detection process into two separate stages: the initial extraction of potential regions through algorithmic means, followed by a further refinement of these proposed bounding boxes. As a result, these methods tend to offer superior detection precision. Notable examples of such methods are the region-based Convolutional Neural Network (R-CNN) [34], FastRCNN [35], and Faster R-CNN [36]. In contrast, regression-based methods, also known as one-stage detection techniques, dispense with the need for pre-selected regions. They are capable of identifying the target's category and pinpointing its location in a single step, leading to rapid inference times. The YOLO series [37] and RetinaNet [38] are among the most prominent examples. This research endeavors to facilitate the swift and automated detection of subsidence funnels on extensive interferogram datasets. While two-stage methods are accurate, their intricate training processes and substantial computational demands pose challenges to real-time detection. Consequently, four exemplar one-stage detection methods were chosen for this investigation, YOLOv3 [39], YOLOv5 [40], YOLOv6 [41], YOLOv8 [22], and YOLOv11 [42], with the goal of enhancing the experimental workflow's efficiency.  
The YOLO (You Only Look Once) groups have emerged as a prominent family of object detection algorithms, with each iteration bringing significant advancements. YOLOv3, released in 2016, marked a milestone with its end-to-end architecture and ability to detect multiple objects simultaneously. Subsequently, YOLOv5, introduced in 2019, offered improved speed and accuracy with its more efficient design and multi-scale training approach. Building upon the success of YOLOv5, YOLOv6 introduced a novel backbone structure and data-efficient training strategies in 2021, aiming to find a middle ground between speed and precision. Then, YOLOv8 continued to push the boundaries with further enhancements in detection performance and efficiency, solidifying YOLO's position as a leading algorithm within the realm of computer vision. Finally, YOLO11 is a cutting-edge, state-of-the-art object detection model, built upon previous versions of YOLO and incorporating new features and improvements to further enhance its performance and flexibility. Additionally, this study has opted for the lighter versions of the YOLO series models. In particular, YOLOv8 and YOLOv11 were chosen with the \"n\" variant, YOLOv5 and YOLOv6 were selected with the \"s\" variant, and YOLOv3 was chosen with the \"tiny\" variant.