esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pore Types and Pore Throat Characteristics
#### 3.2.1 Micron-Scale Pores  
Based on casting thin sections, the main pore types of tight sandstone in the He8 section of the study area are mainly intergranular pores (Figure 3a), grain dissolved pores (Figure 3b), and intragranular dissolved pores (Figure 3c). The occurrence of intergranular pores that exist between quartz grains is triangular and long. The dissolved pores are dominated by particle dissolved pores, and some of rock fragments develop intragranular dissolved pores.  
To gain a whole quantitative analysis of pore and pore throat in tight sandstones, first, all of the casting thin sections of the sample were pretreated following the above analysis process. Correspondingly, binary images of samples were obtained (Figure 3d-f). Furthermore, based on the main functions'size', 'bwarea', 'bwlabel', which analyze the statistics for each pixel of the image, each pixel of the casting thin sections image was also analyzed. The distribution histogram of the pore radius is displayed in Figure 4, as well as the average pore radius of every sample.  
As shown in Figure 4, the blue line represents intergranular pores in tight sandstones with a low pore radius. The yellow line represents intergranular pores and rock fragment intragranular pores. The pore radius is higher than that of a single intergranular pore, and the purple line represents the dissolved pores and intergranular pores. For this combination, the pore radius distribution is generally higher than the other two pore types.  
The pore throat coordination number represents the number of interconnected pores and throats, and is an important parameter for the connectivity of the reaction pores. Similarly, based on 'bwlabel'and 'bwarea' in the Matlab software, every connected area in which the pixel is 255, the number of pores and throats smaller than 2 \(\upmu\)m, and the average pore size were counted and calculated. The number of throat coordination figures is the total number of pores to the total number of connected pores where the distance of connected pores were controlled under 20 \(\upmu\)m. The calculated results are consistent with the porosity and permeability of the samples (Figure 5).  
As shown in Figure 5, the pore throat coordination numbers of the tight sandstone in the He8 section of the study area ranges mainly from 0.5 to 2.5, with an average of 1.36. The left figure shows the relationship between the average coordination number and porosity with a low correlation coefficient, R\({}^{2}\) (0.217). The coordination number and porosity of tight sandstone have a weak positive correlation. The right figure shows the relationship between the average coordination number and the permeability of tight sandstone. The correlation coefficient is 0.856, and the linear relationship is very high. It can be clearly seen that the permeability increases with the increase of the average coordination number, so for tight sandstone, the pore throat coordination ratio is an important influencing factor for the permeability.
## 3 Method  
The urban change detection framework in conflict proposed in this paper is shown in Figure 5, including four parts: optical image preprocessing, SAR image preprocessing, interest information extraction, and urban damaged change detection and assessment.  
First of all, Sen2 Cor, SNAP and ENVI were used to preprocess the collected optical satellite images to form a time-series optical image stack. By observing the spectral response value of the urban burning area, the normalized difference fire index (NDFI) was designed and stacked into the original images as the feature band to reconstruct the spectral dimension of the original images. Then, the CEM algorithm was used to quickly extract the burning area in the reconstructed image and accurately identify the urban burning area with a small amount of manual intervention.  
Figure 4: The type of battlefield images obtained on the Internet.  
SARscape platform was used to correct Sentinel-1A position with precision orbit parameter. After calculating the coherence of SAR image pairs, the coherence map was geocoded by the 1-radian digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM). The Dual polarization normalization coherence index (DPNCI) is designed as an index to evaluate the damaged degree of urban buildings, and its sensitivity to damaged buildings is tested by calculating the change rate of DPNCI of burned buildings. Finally, the spatial location and damage degree of buildings in the photos collected on the Internet are marked one by one, and the optimal threshold for distinguishing different damaged degrees of buildings is determined by combining the OSM data so as to form the analysis and evaluation of the temporal and spatial changes of damaged buildings in Mariupol.
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Research Methodology  
As already stated in the Introduction section, the idea of this paper is to detect malicious websites using the GPSC algorithm. To do this, the publicly available dataset [21; 22; 23] was used which must be transformed into a suitable format so the GPSC algorithm can be applied. Since the dataset has a large imbalance of classes, i.e., a large number of benign and a small number of malicious websites the idea was also to investigate if the application of undersampling and oversampling methods could balance the dataset classes and in the end improve the classification accuracy. After dataset transformation into a usable format, a total of seven different dataset variations were created including the original unbalanced dataset (after dataset transformation into usable format). The remaining six dataset balancing methods are:  
* Random undersampling;
* Oversampling methods;
* Random oversampling;
* Adaptive Synthetic (ADASYN) method;
* Synthetic Minority Oversampling TEchnique (SMOTE);
* Borderline Synthetic Minority Oversampling TEchnique (BorderlineSMOTE), and
* Application of KMeans clustering before oversampling using Synthetic Minority Oversampling Technique (KMeansSMOTE).  
It should be noted that there are other oversampling/undersampling methods; however, they were omitted from further investigation since they did not balance the dataset. The procedure of training and testing the GPSC was performed on each dataset variation and can be divided into two main approaches i.e.,  
* Train/test GP symbolic classifier with random hyper-parameter search method; and
* Train/test GP symbolic classifier with random hyper-parameters search and with 5-fold cross-validation on train dataset part.  
The schematic overview of the research methodology is shown in Figure 1.
## 3 Methods
### V-RTI Validation: Ground-Truth Comparisons
#### 3.3.2 RTI vs. V-RTI  
In order to visually compare equivalent portions of an object that has been digitized using both the RTI and V-RTI techniques, a small test object was selected as a case study. Thus, since it was not possible to perform traditional RTI on the wall of the Ellesiya chapel, the case study presented here involved a small painting on copper measuring 9 \(\times\) 12 cm. The painting was notable for its intricate details, which allowed for a direct comparison between the two methodologies [43].  
RTI acquisition involved 64 shots, resulting in a final resolution of 3648 \(\times\) 5472 px. Based on focus distance (56 cm), camera lens (100 mm), object size (9 \(\times\) 12 cm), and sensor dimension (24 \(\times\) 36 mm), the resulting GSD was 0.004 (cm/px).  
The chosen frames highlighted variations in brush strokes, surface irregularities, and disparities in pigment application. Three distinct visualizations were generated to facilitate a comparative analysis of the same areas represented with both techniques. The visualization methods employed for this comparison were Diffuse Gain, Coefficient Unsharp Mask, and Normal Map. Parameters such as lighting angle, resolution, and export settings were identical for each frame.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 4 Case Study and Discussion
### CPPS Controllability under Different Redistribution Strategies
#### 4.3.1 Case A: IEEE 39-Node System  
Using the IEEE 39-node system as an example, a comparison was made between the reallocation strategy proposed in this paper and strategy I and strategy II. The simulation results are shown in Figure 11. Under a degree attack on the network, the driver node density of strategies I and II increased significantly after the first attack, reaching about 0.8. However, before the network of the redistribution strategy in this paper was attacked for the third time, the driver node density of the network increased significantly. The density is kept below 0.3. At this point, the controllability of the network is much greater than that of the network under other redistribution strategies. It is worth noting that under betweenness attacks, after two betweenness attacks, the redistribution strategy of this paper still maintains the driver node density of the network below 0.1, which means that most nodes in the network are still in a normal state, while the other two redistribution strategies drive the node density to increase to about 0.8 after one betweenness attack, indicating that most nodes in the network are in an isolated state at this time.  
Figure 11: (**a,b**) The \(n_{d}\) change curves of the CPPS with different load redistribution strategies in two different attack strategies, respectively. CPPS is coupled through the IEEE 39-node system and the WS network.
## 3 The Inverse Problem
### Implementation of Inverse Geometry Problem  
The process for solving the IGPs is as follows:  
1. Initialize the guess of the unknown boundary and set the iterative time \(it=0\).
2. Solve the forward problem by FVM: solve Equations (1) and (2) using the FVM and get the calculated temperature \(T_{i}\) at the inspection surface.
3. Calculate the objective function Equation (7) and judge if the specified stopping criterion is satisfied (Equation (8)). If not, go to step 4.
4. Calculate the optimal positions \(y_{t}^{n+1}\) of the unknown boundary according to the temperature measurement points' coordinates using Equation (11).
5. Set \(it=it+1\) and repeat steps 2-5 until the stop criterion is satisfied.
## 2 Materials and Methods
### Design of Trajectories  
To allow geo-acquisitions or tribology measurements, the test rig proposed and described here must be properly programmed to perform acquisitions following specific trajectories properly chosen for the specific application. These acquisition paths can be defined and transferred to the Cartesian robot through a G-code program. Therefore, the imposed trajectories are implemented point-by-point by programming the position of the Cartesian robot's end-effector in the workspace.  
The acquisition paths can be designed as planar trajectories in the plane of the worktable, as Figure 6 shows, or can be set as three-dimensional curves. In the case of the soil analogs' geo-acquisitions, the most suitable trajectories to allow spectral analyses are planar ones, which are repeated at different heights along the z-axis.  
The positioning system is, thus, governed by a matrix containing the coordinates of the end-effector's positions including the stop times for each desired acquisition. The desired paths can be defined at different heights on the z-axis for comparing the planar acquisitions at different distances from the samples.  
The project trajectories of the end-effector in the workspace are defined also over time: as an example, Figure 7 shows the x position of the end-effector over time. The program  
Figure 5: The proposed test rig.  
Figure 6: Project trajectories in the plane of the worktable.  
for the positioning of the handling system can be properly modified at each acquisition, based on the defined best acquisition paths.  
The prismatic joints of the robot that allow the translation in the x and z directions are consecutive, but the joint for the translation in the y direction moves separately with respect to the others. To solve this problem, the governance of the robot was split into two parts according to the block diagram reported in Figure 8.  
To simulate the motion of the _Exoland Simulator_ and its ability to follow given paths, a specific project trajectory was considered with the main characteristics reported in Table 1. With this kind of trajectory, the system was properly programmed and the end-effector moved along this path, registering the differences between the assigned and executed trajectory along the three axes, over time.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 7 Conclusions  
This paper proposes a GNSS spoofing identification and smooth localization method for GNSS/Vision SLAM system. The time and localization results are offset of the GNSS data through the visual SLAM data, and it is identified whether the time and localization results of the GNSS exceed the time and localization verification threshold. According to the identified verification flags, different smoothing localization strategies are selected, and the localization results are optimized and adjusted using the sliding window to obtain smoothing localization results.  
The selection of the time verification and localization verification threshold of the PTC-SLM method is related to the sensor performance (frequency and accuracy). For the selected sensors used in the KITTI datasets, we analyzed the selection of the time and localization verification threshold of the PTC-SLM method, and verified that the selection of the threshold is related to the sensor frequency and sensor accuracy.  
In addition, two scenes in the KITTI datasets are selected for simulation and evaluation. In the simulation, we limited the size of the offset of the applied GNSS spoofing. By  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Max/m** & **Mean/m** & **Median/m** & **Min/m** & **Rmse/m** & **Std/m** \\ \hline GNSS & 98.237 & 3.152 & 1.158 & 0.189 & 11.802 & 11.373 \\ VINS-Fusion & 85.983 & 3.883 & 1.912 & 0.030 & 10.441 & 9.693 \\ PTC-SLM-Forward & 78.804 & 2.941 & 1.193 & 0.067 & 10.399 & 9.975 \\ PTC-SLM & 18.34 & 1.548 & 1.182 & 0.064 & 2.477 & 1.934 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Error parameters of the four methods in urban scene.  
analyzing the performance comparison of the methods in different scenes, it is proved that the PTC-SLM method can identify the GNSS forwarding spoofing attacks and GNSS progressive spoofing attacks, and obtain smoothing localization results.  
Conceptualization, [PERSON], H.W. and T.L.; Data curation, [PERSON] ([PERSON]), D.J. and H.L.; Formal analysis, [PERSON], [PERSON] ([PERSON]) and D.J.; Investigation, [PERSON]([PERSON]) and T.L.; Methodology, [PERSON], H.W., D.J. and [PERSON]([PERSON]); Resources, T.L.; Software, [PERSON] ([PERSON]) and H.L.; Supervision, H.W. and T.L.; Validation, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Visualization, [PERSON] and D.J.; Writing--original draft, [PERSON], [PERSON] and H.L.; Writing--review & editing, [PERSON], [PERSON] ([PERSON]), [PERSON] ([PERSON]) and T.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Youth Innovation Promotion Association (E03315010D).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### Evaluation Metrics  
We use several common metrics to assess the performance of our model, including mean intersection over union (mIoU), pixel accuracy (Acc), and mean accuracy (mAcc). These metrics offer quantitative measures of the alignment between predicted segmentation masks and ground truth masks. The formulas for calculating these metrics are as follows:  
\[mIoU=\frac{1}{p_{c}}\sum_{i}\frac{p_{ii}}{g_{i}+\sum_{j}p_{ji}-p_{ii}} \tag{21}\]  
The mean intersection over union (mIoU) is a widely used metric for assessing the overall accuracy of semantic segmentation models. It calculates the average intersection over union for each class. Here, \(p_{c}\) represents the total number of classes, \(g_{i}\) indicates the number of pixels with ground truth label \(i\), and \(p_{ij}\) represents the number of pixels predicted as class \(j\) with ground truth label \(i\). The mIoU score ranges from 0 to 1, with higher values indicating better segmentation performance.  
\[Acc=\frac{1}{g}\sum_{i}p_{ii} \tag{22}\]  
Pixel accuracy (Acc) is a straightforward metric that measures the ratio of correctly predicted pixels to the total number of pixels in the image. It is calculated by summing the diagonal elements of the confusion matrix (\(p_{ii}\)) and dividing it by the total number of ground truth pixels (\(g\)). Acc provides a global measure of segmentation accuracy but does not consider the per-class performance.  
\[mAcc=\frac{1}{p_{c}}\sum_{i}^{c}Acc_{i} \tag{23}\]  
Mean accuracy (mAcc) is the average accuracy across all classes. It is calculated by dividing the sum of pixel accuracy for each class (\(Acc_{i}\)) by the total number of classes (\(p_{c}\)). mAcc takes into account the performance of the model on individual classes and provides a more comprehensive evaluation of segmentation accuracy.  
mIoU, Acc, and mAcc are commonly used evaluation metrics in semantic segmentation tasks. They provide different perspectives on the performance of segmentation models, allowing us to assess accuracy at both the pixel and class levels. These metrics will be utilized to evaluate the effectiveness of our proposed approach in the experimental results section.
# An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction
## 3 Results
### Fragmentation Model with Shear Effect
As for particle size d\({}_{20}\), Figure 9 (circular marker) illustrates that for a 0.8 MPa stress, the adjusted model predicts a d\({}_{20}\) value of 0.649 cm, very close to the experimental value of 0.624 cm. At higher stresses, the accuracy was maintained, with predictions of 0.530 cm for 3 MPa compared to 0.514 cm experimentally, and 0.498 cm for 5 MPa compared to 0.485 cm obtained in physical experiments. The errors corresponding to the particle size d\({}_{50}\) also reflect the accuracy of the fitted model. Under a stress of 0.8 MPa, the model predicted a value of d\({}_{50}\) with an error of only 3.1%. At 3 MPa, the error was reduced to 2.3%, and at 5 MPa, it decreased even more to 2.0%, highlighting the progressive improvement in the model under conditions of greater stresses (Figure 9, diamond marker). For particle size d\({}_{80}\) presented in Figure 9 (triangular marker), at 0.8 MPa, the model predicts a d\({}_{80}\) of 1.447 cm, with an error of 2.0% compared to the experimental value of 1.42 cm. At 3 MPa, the error is only 1.9%, with a prediction of 1.29 cm versus the experimental 1.27 cm. Finally, at 5 MPa, the model predicts a d\({}_{80}\) of 1.248 cm, with an error of only 1.6% compared to the experimental value of 1.23 cm.
## 5 Conclusions  
The conclusions of this study can be summarized in the following key points:  
* This study demonstrates that integrating \(p\)-refinement techniques into finite element modeling enables an efficient analysis of axially cracked pipes reinforced with composite patches, significantly reducing computational costs while maintaining high accuracy.
* The proposed model successfully integrates ESL and LW theories within a single element framework, minimizing DOFs while ensuring accurate results. Benchmark problems and -fracture analyses using the VCCT validated the accuracy and reliability of the proposed model, showing excellent agreement with numerical and analytical solutions.
* Parametric studies reveal that patch material, size, thickness, and adhesive properties significantly affect reinforcement performance. Stiffer patch materials lead to greater reductions in SIFs, with patch size and thickness also playing crucial roles in reinforcement effectiveness.
* The proposed method provides a computationally efficient and robust approach for analyzing cracked cylindrical structures with composite reinforcements, contributing to damage-tolerant design strategies for pressurized cylindrical structures. These findings have potential applications in aerospace, marine, and mechanical engineering.
* This study provides a numerical modeling framework that enhances structural reinforcement techniques for pressurized cylindrical structures. The results contribute to the development of more efficient and accurate design methodologies, offering valuable insights for engineers and researchers involved in structural integrity assessment and reinforcement design.
* These findings reinforce the significance of computationally efficient fracture analysis and reinforcement strategies, ensuring that complex cracked structures can be analyzed with high accuracy while maintaining their practical feasibility.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea, NRF-2020R11A3061349.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The author declares that the research was conducted in the absence of commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DOF & Degrees of freedom \\ FEM & Finite element method \\ ESL & Equivalent single-layer \\ LW & Layer-wise \\ SIF & Stress intensity factors \\ VCCT & Virtual crack closure technique \\ \end{tabular}
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 2 Related Work
### Multi-Sequence Frequency Hopping
In complex jamming environments, there is not only reactive jamming, but there are also other types of non-intelligent jamming, such as multi-tone jamming and sweep frequency jamming. DFH can avoid some sweep jamming through frequency hopping, but due to the fixed frequency hopping pattern of DFH, sweep jamming may still cover the frequency used by the target signal under certain conditions.