esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Studies by Numerical Simulation
### Effect of the Length of the Isolation Layer  
The peak tensile and shear strain responses of the tunnel invert were discussed because the tunnel invert has a more intense strain response than other positions at the same monitoring cross-section from Section 3.2. The peak tensile and shear strain responses are the maximum value of the tensile strain-time history curve and shear strain-time history, respectively.  
Figure 15 plots the peak tensile and shear strain responses at the tunnel invert affected by the isolation layer length under small normal fault creep-slip and subsequent z-direction seismic excitation (El-Centro and Kobe wave). The isolation layer length ranges from 0D (without the isolation layer, D is the fault width) through to 1D, 2D, 3D, 4D, 5D, 6D, and the full loop. It can be observed that the maximum values of peak tensile and shear strains are observed at the fault creep-slip cross-section. The peak tensile and shear strains change sharply at a distance of about 1.5 times the distance to the left and right of the fault creep-slip cross-section. The range where the sharp changes occur does not vary with the  
Figure 14: Comparison results between numerical simulation and test acceleration under different excitations for the model with an isolation layer: (**a**) z-direction input of El-Centro excitation, (**b**) z-direction input of Kobe excitation.  
Figure 13: Comparison results between numerical simulation and test acceleration under different excitations for the model with an isolation layer: (**a**) z-direction input of El-Centro excitation, (**b**) z-direction input of Kobe excitation.  
length of the damping layer or the type of seismic wave. In addition, it can be observed that under fault creep-slip, the Kobe wave excitation induces a sharper change in peak tensile and shear strain responses within the fault zones compared with the El-Centro wave.  
Furthermore, Figure 16 plots the peak tensile and shear strain responses of the tunnel invert at the creep-slip cross-section of the fault versus the length of the isolation layer. The peak tensile and shear strains of the lining invert decrease significantly at first as the length of the isolation layer increases. However, when the length of the isolation layer exceeded 3D, the peak tensile and shear strains decreased very slowly. This means the isolation layer with three times the fault width should be most reasonably deployed under normal fault creep dislocation and subsequent seismic excitation (El-Centro or Kobe wave).
## 3 Initial Results
### Discussions: Towards Real-World Application and Stakeholder Alignment
#### 4.4.3 Alignment with Public Institutions and Law Enforcement  
Fifth, on the law enforcement side, after the victim has been identified and the sextortion crime has been committed, BIAI can support the justice system in several ways, ethically, by following a supranational ethical framework:  
* Transparent legal processes--BIAI may guarantee that legal decisions are transparent and auditable, which will help combat corruption [154]. In turn, corruption, through bribery and the influence of perpetrators, has been proven to affect effective prosecution of sextortion cases, for example, in South Africa with migrants [152].
* Tamper-proof evidence--Blockchain can ensure the integrity of the chain of custody [155], while AI can be employed in the analysis and management of evidence and digital forensics.
* Support for marginalized groups--One of the groups most affected by sextortion is represented by migrants and refugees [152], who are also affected by a lack of easily verifiable identification and may have access to legal aid and support through blockchain-based identity verification.  
Sixth, while the crime has been proven and aid is directed towards the victim, BIAI may also bring efficiency and security. Blockchain-enabled anonymous payment mechanisms, further down the mitigation process, ensure that the aid aimed at victims reaches recipients securely and anonymously without the risk of misappropriation. Blockchain has been proven to improve social welfare programs and promote equitable resource distribution by ensuring that funds are not smuggled into intermediaries. Last, at a more macro level, we must consider a two-pronged perspective: on the one hand, the role of AI in the use of decentralized data to improve decision-making, and, on the other hand, the transformative role of AI and blockchain for significant parts of society. This dual perspective leads us to reassert the need for an integrated approach to ensure the proper and ethical use of BIAI.
## 2 Materials and Methods
### Quality Indicators  
Measurement quality indicators (MQIs) were used for evaluating the contour quality for each test combination and contrast scenario. The basis of this evaluation is a discrete comparison between the distance of contour points in sector \(i\) to the centre of the target, under testing conditions (\(\overline{P_{i}C}\)), and the corresponding reference distance calculated with the CMM. In this work, this comparison was achieved by means of the absolute difference between radial distances for each sector \(i\) (\(D_{i}\)) (Equation (12)).  
\[\left(D_{i}\right)=\left|\overline{P_{i}C}-\overline{P_{i}^{*}C^{*}}\right| \tag{12}\]  
Three MQIs based on \(D_{i}\) were then defined: coverage factor (\(C_{f}\)), mean radial difference (\(\overline{D}\)), and standard deviation of the radial difference (\(\sigma_{D}\)).  
* \(C_{f}\) was defined as the percentage of angular sectors containing dimensional information (at least one valid contour point). If the processed image did not contain any contour point in a particular sector \(i\), then \(D_{i}\) could not be calculated and this sector was excluded from the contour reconstruction. Accordingly, a higher \(C_{f}\) value implied the higher completeness of test target reconstruction, and fewer possibilities of missing small contour abnormalities.
* \(\overline{D}\) was calculated as the mean value of all \(D_{i}\) for a particular test. Accordingly, low values of \(\overline{D}\) indicated the location of contour points calculated under test conditions, such as those obtained with the CMM.
* \(\sigma_{D}\) represented the variability of \(D_{i}\) along the contour. High \(D_{i}\) values indicated that relative differences between correspondent FBS and CMM points varied along the contour.
# BVTED: A Specialized Bilingual (Chinese-English) Dataset for Vulnerability Triple Extraction Tasks
## 5 Conclusions  
In the realm of cyber threat intelligence, cybersecurity vulnerability intelligence plays a crucial role. We developed an ontology for vulnerability knowledge, encompassing 13 entity types and 15 unique relation types, which captures the common knowledge found in vulnerability description texts. This ontology serves as a foundational framework for extracting and structuring information, enabling more effective analysis and utilization of cybersecurity vulnerability data. Leveraging this ontology, we construct a comprehensive dataset for the cybersecurity domain, named BVTED, which supports the task of entity-relation triple extraction. The dataset comprises 27,311 unique vulnerability description sentences with mixed Chinese and English expressions. It contains 97,391 entities and 69,614 relations, including 32,721 Chinese entities, 35,334 English entities and 29,336 entities with mixed Chinese and English.  
To evaluate the dataset's effectiveness, we conduct performance tests using state-of-the-art baselines for named entity recognition and relation extraction tasks. The experimental results confirm the validity and utility of the BVTED dataset. Specifically, our cybersecurity entity extraction model with BiLSTM+CRF and Multilingual BERT achieved an overall F1-score of 0.9667. For relational judgment, the PCNN model attained a precision of 95.35%, a recall of 95.12% and an F1-score of 95.19%. These results substantiate the feasibility and efficacy of utilizing the BVTED dataset for cybersecurity applications.  
This research contributes significantly to the field of CTI analysis by demonstrating the potential of advanced NER and RE techniques in enhancing the accuracy and efficiency of knowledge extraction processes. Despite these achievements, our methodology faces certain limitations. The BVTED dataset's reliance on CNNVD data limits its generalizability, and the high labor and time costs associated with our combined rule-based and manual annotation methods restrict the scalability of our dataset construction. Additionally, the imbalance in data distribution poses challenges for knowledge triplet extraction models. Addressing these issues is crucial for advancing the practical applications of our proposed approach.
# BDS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration
## 1 Introduction
Deploying the Spark framework activity through resilient distributed data can help to speed up deliveries of data within the overlay network and improve network efficiency by reducing the extended delay problem on mesh interconnections at the hop-to-hop ends. A star mesh topology was designed to connect the main fog controller via cloud for faster scaling of the network delay measurements and process validations [36]. The cloud is at the back end of our developed BDPS scheme, and gateways of IoTs are placed at the front end [37]. In the fog layer [38], the RAMA scheduling process runs through BDPS data distribution [39], containers and delivering priority scheduled data [40] from the IoT layer toward the central cloud [41]. After the fog cluster [42; 43] validates the frames [44; 45; 46], the derived data feed to the RDD in memory accelerators to provide efficient data deliveries across the IoT clusters [47; 48] (i.e., gateways). This paper uses three popular shortest-path-routing algorithms (i.e., the Bellman-Ford (BF) algorithm, Floyd-Warshall (FW) algorithm and Dijkstra algorithm (DA)) and a map-reduction data delivery algorithm (i.e., Apache Hadoop (AH) algorithm) based comparison (Table 1) to estimate a performance analysis. The performance analysis is shown by developing a rapid data delivery scheme (i.e., BDPS) across the fog-IoT environment. However, the packet drop ratio, frame delivery ratio, network overhead, computational delay, are throughput are primarily checked to estimate the performance analysis within a hierarchical tree architecture. The main contributions of the research work include the following:  
* We present an efficient Spark-RDD-in-memory-based scheme to mitigate against existing challenges in a scheduled priority batch processing data distribution framework. * We implement the delay efficient high-speed data processing algorithmic scheme for a hierarchical tree-based overlay mesh architecture in the cloud-fog and IoT ecosystem. * We conduct an overall performance analysis of our scheme with shortest path algorithms, such as BF, FW and AH, with efficient delay time analysis (e.g., system run-time tolerance or service load). The remainder of this paper is organized as follows: Section 2 generalizes a discussion about BDPS scheme work procedures within the fog-IoT environment. Section 3 employs an overview of the background study to identify the current solutions and limitations. Section 4 represents the system model and methodology of the developed BDPS scheme. Section 5 illustrates a rigorous experimental result of BDPS and other existing algorithms. Section 6 concludes the paper by providing future insights and outcomes.
## 1 Introduction
### Model Evaluation Metrics  
To illustrate the performance of the model, the evaluation metrics selected include the model's recognition accuracy, loss value, recall rate, precision rate, F1-score, and confusion matrix. Accuracy represents the proportion of correctly identified samples among all classification results; the loss value indicates the error of the model; the recall rate is the proportion of correctly predicted positive samples out of all actual positive samples; the precision rate is the proportion of samples with true labels matching a particular state among all predicted samples for that state; the F1-score represents the harmonic mean of precision and recall; and the confusion matrix provides a detailed display of the recognition results for each state, showing correctly identified results, incorrect results, and the specific states with which they were incorrectly identified. The values on the diagonal of the matrix represent the number and proportion of correctly identified instances, while the off-diagonal values indicate the number and proportion of instances incorrectly identified as a particular state. The specific parameter descriptions are detailed in Table 1.  
The formulas involved in these calculations are as follows:  
\[\rm{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN} \tag{25}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual Positive** & **Actual Negative** \\ \hline Predict positive & True Positive (TP) & False Positive (FP) \\ Predict negative & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix description.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{26}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{27}\]  
\[\text{F1}-\text{score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{28}\]
## 2 Related Work
### Log-normal Shadowing Channel Model  
The free space and two-ray ground reflection models were widely used in prior work. According to such channel models, the transmission range of a node is circular, and all of the nodes within the disk are one-hop neighbors. In typical wireless network environments, however, the variations in the received powers as measured by different receivers with the same distance from a same transmitter are random and independent [32], and can be characterized by the log-normal slow fading [16]. Consider a pair of transmitter and receiver where the distance between them is \(d\). According to the log-normal shadowing propagation model, the received power of the intended signal at the receiver in dB, denoted as \(P_{r,dB}\), is represented by [16]:  
\[P_{r,dB}=P_{0,dB}-10\beta\log\left(\frac{d}{d_{0}}\right)+X_{r} \tag{1}\]  
where \(P_{0,dB}\) is the reference power measured at a distance of \(d_{0}\) in dB, \(d_{0}\) is the reference distance, \(\beta\) is the path-loss exponent, and \(X_{r}\) represents a _normal_ random variable with zero mean and standard deviation \(\sigma_{dB}\). That is, \(X_{r}\) is a random variable with the following normal probability density function,  
\[f_{X_{r}}(x)=\frac{1}{\sqrt{2\pi}\sigma_{dB}}\exp\left(-\frac{x^{2}}{2\sigma_{ dB}^{2}}\right) \tag{2}\]  
In (1), the received power consists of two parts: the deterministic attenuation depending on the distance between the transmitter and the receiver, and the probabilistic part \(X_{r}\). The variation of the received power is determined by the standard deviation \(\sigma_{dB}\). Note that typically \(\sigma_{dB}\) ranges from 4 dB to 12 dB in outdoor environments [33]. Due to the probabilistic part \(X_{r}\), the transmission range of a sending node is not circular anymore, as shown in Figure 1. The received power can be expressed in unit of Watts as follows:  
\[P_{r}=P_{0}10^{-\beta\log\left(\frac{d}{d_{0}}\right)+0.1X_{r}}=C\left(\frac{ Y_{r}}{d^{\beta}}\right) \tag{3}\]  
where \(P_{0}\) is the reference power measured at \(d_{0}\) in unit of Watts, \(C=P_{0}d_{0}^{\beta}\) is a constant, and \(Y_{r}=10^{\left(0.1X_{r}\right)}\) represents a log-normal random variable with zero mean and standard deviation \(\sigma=\left(\frac{\ln 10}{10}\right)\sigma_{dB}\).
## 2 Models and Principles
### LAC Block
#### 2.2.3 Channel Shuffle  
Group convolution is applied in depthwise separable convolution. However, the group convolution refers to convolution within each group, so there is no information on interaction between groups. For information to sufficiently interact between groups, the author of ShuffleNet [34] proposed to group the results after each group convolution, then exchange the subgroups within their respective groups. This process allows the information between different information groups to blend and enrich the feature information between groups, as shown in Figure 4. The premise of the channel shuffle is to divide the input image into groups by channel. However, the depthwise convolution in the depthwise separable convolution can be regarded as a group convolution with the number of groups equal to that of input channels. Thus, it is possible to combine depthwise convolution with channel shuffle. While reducing the amount of computation and parameters of CNN, the problems that there is no interaction between each channel's information, leading to less learning content being solved. Therefore, channel shuffle enables ShuffleNet to achieve both lightweightness and performance.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 3 The Proposed Signature Scheme
### The Proposed Scheme
#### 3.1.2 Verifying Process  
After decrypting the cipher text \(c\) and retrieving the original message \(m=Dec_{kr}(c)\), the receiver should calculate the value of \(x\) using the sender's public key \(pk_{s}\) as follows:  
\[x=m^{pk_{s}}\mod n^{\prime} \tag{7}\]  
where \(pk_{s}=k_{s}+r\times p\)  
The receiver uses the computed value of \(m\) to detect any manipulation of the cipher text and ensure data integrity. The use of the sender's public key ensures authenticity.  
Then, the check value \(y\) will be calculated:  
\[y=(s_{1}\times s_{2}^{r_{1}})\mod n^{\prime} \tag{8}\]  
Then comes the comparison process:  
\[x\overset{?}{=}y \tag{9}\]  
The workflow of a verification process in the proposed DS scheme is shown in Algorithm 2, which ensures validity (integrity and authenticity).
## 1 Introduction and Related Work
### Related Work
#### 1.2.1 Mathematical Programming Techniques  
To optimize wind-farm layouts, [PERSON] et al. [14] proceed in two steps: the number of turbines is chosen a priori, the positions of the turbines are carefully initialized, and then they are locally tuned by mathematical programming techniques in continuous variables, such as those described in [15]. The algorithms suppose that the variables are ordered and they cannot deal with varying size clouds of points.  
Mixed integer (non-)linear programming methods can be applied by considering a discretization of the domain \(\mathbf{D}\) with a binary turbine presence/absence variable introduced at each discretization node.  
The optimal layout of wind farms is treated in such a way in [16]. The number of wind turbines can vary but the set of possible candidate positions is fixed. The design variables take the form of a graph where the nodes correspond to the positions and the edges to the paths of connection cables between the turbines. Our work, which, beyond wind farms, targets general problems with clouds of points, does not include cable routing variables.  
Figure 1: Two clouds of points for \(d=2\), \(n=20\) on the left and \(n=10\) on the right.  
In [17], optimization problems where the decision variables can be compared to a set of positions with a varying size are tackled. The authors discuss formulations where mixed integer linear problems solvers can be employed. Again, binary values encode the presence of turbines, and there are constraints such as a minimal distance between the points of each set. This formulation does not handle continuous point positions.  
In mathematical programming, a concept related to variables of varying size is that of cardinality and cardinality constraints, as discussed in [18]. In this family of mixed integer non-linear programming approaches, the interval of possible values for \(n\) is divided through inequalities on \(n\) and bounds on achievable objective function in the manner of branch-and-bound and cutting-plane methods. Cutting-plane methods are not general and have been derived for specific (linear) problems. Branch-and-bound methods are general but may turn into a comprehensive enumeration of the \(n\) values if the bounds are not tight enough.
## 4 Performance Evaluation of Improved SOSK-Means
### Experiment 3
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**ISOSK-Means**} & \multicolumn{2}{c}{**PSODE**} & \multicolumn{2}{c}{**FADE**} & \multicolumn{2}{c}{**IWODE**} \\  & **Mean** & **Std Dev** & **Mean** & **Std Dev** & **Mean** & **Std Dev** \\ \hline
**Aggregation** & 0.7262 & 0.0014 & 0.5958 & 0.0391 & **0.5948** & 0.0424 & 0.6015 & 0.051 \\
**Breast** & 0.8121 & 0.0000 & 0.7256 & 0.1115 & 0.9378 & 0.2051 & 0.7965 & 0.1177 \\
**Bridge** & 0.6464 & 0.0009 & 0.7141 & 0.1007 & **0.6405** & 0.0709 & 1.1397 & 0.0666 \\
**Compound** & 0.4985 & 0.0021 & **0.4983** & 0.0092 & 0.5273 & 0.0576 & 0.50004 & 0.0312 \\
**D31** & 0.8125 & 0.0070 & 0.8021 & 0.0407 & **0.7788** & 0.0376 & 0.7972 & 0.0514 \\
**Flame** & 0.7760 & 0.006 & 4.4069 & 4.6725 & 0.6975 & 0.0477 & **0.6749** & 0.0391 \\
**Glass** & 0.6547 & 0.1727 & 0.5966 & 0.0672 & 0.5971 & 0.0996 & **0.5562** & 0.1384 \\
**Housec5** & **0.5377** & 0.0123 & 2.2408 & 4.0861 & 0.5467 & 0.0287 & 0.6865 & 0.0229 \\
**Housec5** & 0.4919 & 0.5305 & 0.5022 & 0.0315 & **0.4707** & 0.0383 & 0.6344 & 0.0408 \\
**His** & 0.6165 & 0.0132 & 0.5811 & 0.021 & 0.585 & 0.035 & 0.8384 & 0.0916 \\
**Jain** & 0.6507 & 0.0008 & **0.6399** & 0.015 & 0.6451 & 0.0062 & 0.65 & 0.0058 \\
**Leaves** & 0.7618 & 0.0745 & 0.758 & 0.1514 & **0.7483** & 0.1539 & 1.5116 & 0.0396 \\
**Letter** & 0.9683 & 1.0545 & 0.9121 & 0.0628 & **0.8665** & 0.0663 & 1.2057 & 0.0571 \\
**Joensuu** & 0.5073 & 0.0038 & 0.5094 & 0.0098 & 0.51 & 0.0327 & **0.4972** & 0.0041 \\
**Finland** & **0.4427** & 0.0011 & 0.4465 & 0.006 & 0.4686 & 0.0547 & 0.4864 & 0.0371 \\
**Path-based** & 0.6669 & 0.0050 & **0.6526** & 0.0166 & 0.6561 & 0.0178 & 0.6567 & 0.0156 \\
**R15** & 0.7400 & 0.0171 & 0.6423 & 0.0722 & 0.6399 & 0.0984 & **0.5996** & 0.0894 \\
**Spiral** & 0.7386 & 0.0033 & 0.7441 & 0.0136 & **0.7373** & 0.0156 & 0.7758 & 0.0143 \\
**T4.sk** & **0.0227** & 0.0000 & **0.0227** & 0 & 0.04234 & 0.0882 & 0.0928 & 0.0515 \\
**Thyroid** & 0.6039 & 0.0182 & 0.5024 & 0.0196 & **0.4955** & 0.0241 & 0.9893 & 0.1077 \\
**Wolbc** & **0.0508** & 0.0000 & **0.0508** & 0 & **0.0508** & 0 & 0.0814 & 0.0263 \\
**Wine** & 1.0053 & 0.0228 & 0.8891 & 0.0601 & **0.8648** & 0.0973 & 1.2312 & 0.0525 \\
**Yeast** & 0.7489 & 0.0682 & 0.7193 & 0.0677 & 0.6375 & 0.1344 & **0.5949** & 0.1144 \\
**Average** & **0.6331** & 0.0198 & 0.8805 & 0.2608 & 0.7234 & 0.0615 & 0.8961 & 0.0464 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of ISOSK-means with other hybrid metaheuristic algorithms on 23 real-life datasets.
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 1 Introduction
### Contributions  
In this work, we propose a novel approach for volumetric instance segmentation in NDT by combining SAM with FFN. Our contributions include the following:  
**1. Evaluation of SAM on NDT data-sets**  
We assess the performance of SAM on data-sets from the field of non-destructive testing and demonstrate its effectiveness in accurately segmenting instances in challenging CT imaging scenarios.  
**2. Implementation and evaluation of various methods to combine image-based SAM for the application with volumetric data-sets**  
We implement and evaluate different techniques to integrate and fuse the output of the image-based SAM approach for the application of volumetric data-sets, hence enabling the segmentation of three-dimensional objects using FFN's spatially adaptive capabilities.  
**3. Extending SAM for objects of arbitrary size through tile-based approaches**  
We propose a tile-based approach that leverages FFN's capabilities to segment objects of arbitrary size. By initially dividing the input volumes into tiles and then applying SAM on each tile individually, we achieve accurate and efficient segmentation results for objects of any size.