esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of the Landfill Slope with an Engineered Berm Under Composite Failure Mode
## 3 Results
### Parametric Analysis
#### 3.2.1 Parametric Analysis When Over-Berm Failure Occurs  
It can be seen from Figure 17 that as \(H_{\rm B}\) increases, \(FS_{\rm ave}\) increases, and as \(\xi\) increases, \(FS_{\rm ave}\) also increases. When \(H_{\rm B}\) changes from 3 m to 15 m, \(FS_{\rm ave}\) increases by 6.60% when \(\xi=25^{\circ}\); when \(\xi=30^{\circ}\), \(FS_{\rm ave}\) increases by 6.69%; when \(\xi=35^{\circ}\), \(FS_{\rm ave}\) increases by 7.02%; when \(\xi=40^{\circ}\), \(FS_{\rm ave}\) increases by 7.61%. Therefore, it can be found that with the increase in \(\xi\), the influence of \(H_{\rm B}\) on \(FS_{\rm ave}\) gradually becomes stronger.  
It can be clearly observed in Figure 18 that as \(c_{\rm B}\) increases, \(FS_{\rm ave}\) increases, and as \(\delta_{\rm B}\) increases, \(FS_{\rm ave}\) also increases. When \(c_{\rm B}\) varies from 0 to 20 kN/m\({}^{2}\), \(FS_{\rm ave}\) increases by 1.44% when \(\delta_{\rm B}=12^{\circ}\); when \(\delta_{\rm B}=15^{\circ}\), \(FS_{\rm ave}\) increases by 1.51%; when \(\delta_{\rm B}=18^{\circ}\), \(FS_{\rm ave}\) increases by 1.53%; when \(\delta_{\rm B}=21^{\circ}\), \(FS_{\rm ave}\) increases by 1.55%. It can be thus summarized that the influence of \(c_{\rm B}\) on \(FS_{\rm ave}\) slightly increases as \(\delta_{\rm B}\) increases.
# Feature Transformation-Based Few-Shot Class-Incremental Learning
## 4 Simulation Experiments
### Experimental Setup  
We evaluate our method on three widely used benchmark datasets in few-shot class-incremental learning: CIFAR100 [22], miniImageNet [23], and CUB [24]. CIFAR100 contains 100 classes. Following the split protocol in [9], we use 60 classes as base classes and the remaining 40 as novel classes. The novel classes are introduced over 8 incremental sessions, with 5 new classes added per session. The miniImageNet also contains 100 classes. We follow the same partitioning strategy as in [9], using 60 classes as base classes and 40 classes as novel ones. The 40 novel classes are divided into 8 incremental sessions, each adding 5 new classes. CUB contains 200 classes. Following [9], we use 100 classes as base classes and the other 100 as novel classes. These 100 novel classes are divided into 10 incremental sessions, each introducing 10 new classes.  
To ensure fair comparison, we use ResNet-20 as the feature extractor for CIFAR100, and ResNet-18 for miniImageNet and CUB. Both feature extractors include Batch Normalization after each convolutional block and apply Dropout with a rate of 0.5 before the final fully connected layer. The optimizer is Stochastic Gradient Descent (SGD) with a learning rate of 0.1 and momentum of 0.9. The learning rate decays by 0.0005 every 20 epochs, and the total number of training epochs is 100. Cross-entropy loss is used, and the batch size is set to 128. In each incremental session, a 5-shot setting is used, meaning that only 5 labeled samples per new class are available for training. During evaluation, we sample 100 test instances per class from all classes learned so far, and use them to assess the performance of the model after each incremental step.
## 1 Introduction
On this basis, the sparse linear constraint minimum variance (S-LCMV) method was proposed to provide sufficient DoFs to obtain the effective weight coefficients by reducing the number of constraints, thus avoiding the influence of the ill-posed problem on the calculation accuracy. The effectiveness of the proposed method was verified through field experiments on a ULMA in a borehole PEC system designed for the NDE of oil-well casings. The results show that the proposed method was able to eliminate the influence caused by the different TRDs on the inspection effect, and improved the accuracy of the NDE of the downhole casings. In addition, the proposed S-LCMV method had a higher signal-to-noise ratio (SNR) compared to the standard LCMV method and a lower computational cost compared to the DL method, providing practical usability. The rest of this study is organized as follows. Section 2 presents the ULMA-based borehole PEC signal model and the Gauss-Legendre quadrature representation for borehole PEC signals. In Section 3, the sparsity of the PEC signal is analyzed and the cost function of the S-LCMV method is proposed. Simulation and experimental results are provided and analyzed in Section 4. Finally, we conclude the work in Section 5.
## 2 Information Fusion Process Based on D-S Evidence Theory
### Calculation Process for MIFMS
Therefore, during the fire detection, for multi-sensormeasurement data using D-S evidence theory, the analysis of information should be carried out simultaneously in the space and time domain fusion, as shown in Figure 2.  
Suppose there are \(M\) sensors to detect the scene (in the same recognition framework \(\Theta\)); \(m_{s}^{k}(o_{i})\) is the \(s\)-th sensor in the \(k\)-th measurement cycle calculating the basic probability assignment of the target pattern; here, \(s=1\), 2, L...M, \(k=1\), 2, L...M. \(m_{s}(o)\) is the sum of basic probability of mode \(o\) from the \(s\)-th sensor in the \(N\)-th measurement cycle after fusion of the target. The calculation process is as follows:  
1. The time domain information fusion should be carried out for each sensor: \[m_{s}(o_{i})=\frac{\sum\limits_{\alpha\cdot c_{\alpha_{j}}-o=1\leq s\leq N}m_{s }^{k}(o_{i})}{1-k}\] (12)
2. The time domain information fusion of each sensor should update the fusion results for space and time. \[m\left(o_{i}\right)=\frac{\sum\limits_{\alpha\cdot c_{\alpha_{j}}-o=1\leq s\leq N }m_{s}(o_{j})}{1-k}\] (13) for which \(K=\sum\limits_{\alpha_{i}\sim o_{j}}\prod\limits_{\mu\in 1\leq s\leq N}m_{s} \left(o_{j}\right)\).
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### WMN Node Placement and Routing
#### 2.2.2 Topology Optimisation  
Most recent work [29] on WMN topologies tends to focus on approximate optimisations and has delivered improvements using heuristics, meta-heuristics, and hybrids. Heuristic systems tend to be more historic, and although recent work was carried out on clustering algorithms for MRs [35] and in tree-based approaches [36], it otherwise tends to focus on gateway placement. Meta-heuristics-based techniques have proven the most popular, and recent studies [37; 38] have focused on using hill-climbing and simulated annealing (SA), respectively, for MR router placement through various environments.  
Approaches that utilise hybrid techniques are still quite limited, but the recent work presented in [39] examined the use of particle swarm optimisation and genetic algorithms for node placement.  
Overall, the problem concerns determining the optimal number of nodes required to provide connectivity and their link and meshing configuration, while also considering the physical and financial constraints of the deployment. However, as the metrics become more numerous, we also see multi-objective approaches introduced that consider the trade-offs between competing requirements, such as cost and complexity, coverage and connectivity, and load balancing and interference management, and performance characteristics such as throughput, delay, and overall capacity [40].
## 3 Results
### Application Tests
#### 3.3.2 Performance Tests  
It is important to include performance tests in the development process for several reasons. They are useful to detect possible bugs of the computer implementation (and not only the formulation) and inefficiencies. Specifically, tests showing the actual orders of the computational complexity of the code are very useful. In this context, performance tests help to know the limitations of the code and the situations in which it operates optimally. Additionally, performance tests are useful after refactoring the code.  
In our case, we show the advantages and the limitations of the method making larger and larger grids. The outcome of the tests we should expect is that the introduction of the method is not worth it for small grids and it becomes more and more advantageous when the grid is larger. Without loss of generality, the problem that we solve is the propagation of a plane wave (expression (21)) whereas now we use 8324 tetrahedra and 53,124 unknowns for the unit cell. We use a personal workstation, equipped with a Linux distribution and with a six-core Intel Core i7-3970 and 32 GB of RAM, to obtain the results in this section.  
We increase the size of the problem using a 1D periodic type expansion (i.e., \(1\times M\) configuration, with \(M\) as a variable number of cells) and a 2D periodic type expansion (\(M\times M\) configuration) to detect possible differences between the two. In Figure 8, we  
Figure 6: Convergence error.  
show the time that we need to solve the problem for the whole code (preprocessing, assembly, solving, and postprocessing) using the substructuring technique of one Schur complement of Section 2.2.3 and without using any substructuring technique (i.e., using the full mesh approach of Section 2.2.1). We can observe that the difference in the computational complexities between the two approaches is larger for the \(1\times M\) case: we obtain \(\mathcal{O}(M)\) and \(\mathcal{O}(0.67M)\) for the full mesh and one Schur complement approaches, respectively, whereas for the \(M\times M\) configuration we have \(\mathcal{O}(1.7M)\) and \(\mathcal{O}(0.83M)\) for full mesh and the one Schur approaches, respectively. Moreover, the use of the one Schur approach proposed in this paper is more advantageous for a small number of domains (almost from the beginning with \(M=2\)).  
Figure 8: Performance tests. CPU comparison for (**a**) \(1\times M\) configuration, and (**b**) \(M\times M\) configuration.  
Figure 7: Smoke test with a plane wave, where (**a**) is the analytic solution \(\mathbf{E}^{\text{ana}}\), (**b**) is the solution provided by the code \(\mathbf{E}^{\text{sol}}\), and (**c**) is the pointwise error between (**a**,**b**).
## 5 Routing Layer in WMSN  
Routing layer in wireless sensor network aims to deliver the sensed data from the sources to the sink node taking into account several design considerations, such as energy efficiency, link quality, fault tolerance, and scalability. Although there are many routing protocols proposed for the traditional WSN, the design of routing protocols for WMSN is still an active research area. We believe that the new characteristics and constraints due to the multimedia content handling over the network make the proposed routing protocols for WSNs not directly applicable for WMSNs. The multimedia nature of the collected information (video streaming, still images, audio) adds more constraints on the design of the routing protocols in order to meet the application-specific QoS requirements and network conditions.  
There are many traffic classes in WMSNs and can be categorized into three main classes or services depending on their QoS requirements: 1) Event-driven service which is delay intolerant and error intolerant but it requires less bandwidth, so a path with a little traffic and high signal to noise ratio is attractive for this kind of service. 2) Data query service is error intolerant but query-specific delay tolerant applications, so a path with significant congestion and a high signal to noise ratio may be used for this service. 3) Stream query service which is delay intolerant but query-specific error tolerant application (in a sense packet losses can be tolerated to a certain extent), so a path with less traffic and relatively lower signal to noise ratio is better for this type of service.
## 5 Delay Caused by Using PMUs  
PMUs have been found in many applications in distribution systems. The use of PMUs can introduce a delay in real-time applications [29]. The sources of PMU measurement delay include PMU processing, phasor data concentrator (PDC) processing, communications protocols, and networks being used to transfer measurement data. The delayed measurements can greatly affect the accuracy of real-time state estimation and control schemes, particularly in the presence of faults. For example, in wide-area voltage control [30], the voltage data sent by PMUs are used to compute the current reference. With the delayed PMU data, the controller receives the delayed voltage signal and processes the real-time voltage. This results in an error in the current reference, and consequently the obtained voltage profile oscillates. The performance of the wide-area voltage control is degraded. The PMU processing, PDC processing, and propagation delays are constant. On the other hand, the serial and routing delays are considered to be stochastic delay. The mean (\(M\)) and standard deviation (\(\sigma\)) values of the serial delay are set at 20 \(\upmu\)s and 8.944 \(\upmu\)s, respectively [31]. The \(M\) and \(\sigma\) of the routing delay are found by means of a Monte Carlo simulation with a routing delay equation. The routing delay (\(\tau_{rout}\)) is expressed as  
\[\tau_{rout}=\frac{\lambda}{\lambda(\zeta-\lambda)}, \tag{13}\]  
where \(\lambda\) is the incoming data rate (bits/s) and \(\zeta\) is the serving data rate (bits/s). The \(M\) and \(\sigma\) of the coming data rate are 12,000 bits/s and 5366.56 bits/s, respectively [31]. The serving data rate is defined as 10 Mbits/s. After the Monte Carlo simulation randomly samples the incoming data rate 100,000 times, this results in an \(M\) and \(\sigma\) of the routing delay of 0.1 \(\upmu\)s and 0.05404 ns, respectively. \(M\) and the variance (\(\sigma^{2}\)) are used as the inputs of a Gaussian probability distribution function, which generates random values of serial and routing delays [32]. These delays are assigned to every sending packet.
# On the Role of Clustering and Visualization Techniques in Gene Microarray Data
## 2 Clustering for Microarray Gene Expression Data
### Model-Based Clustering  
In this framework [43; 44; 45; 46], a statistical basis is provided to model the cluster structure of gene expression data and it is assumed that the data come from a multivariate finite mixture model of the general form  
\[f(\mathbf{t})=\sum_{k=1}^{K}\pi_{k}f_{k}(\mathbf{t};\theta_{k}), \tag{2}\]  
where \(f_{k}\) are the component distributions, \(\pi_{k}\) and \(\theta_{k}\) are the component probabilities (the probability that an observation belongs to the \(k\)th component) and the component parameters, respectively. Roughly speaking, the general procedure is as follows: given a data set \(S=\{\mathbf{t}_{1},\mathbf{t}_{2},\ldots,\mathbf{t}_{N}\}\), determine how many clusters \(K\) one wants to fit to the data, choose parametric models for each of these \(K\) clusters (for example, multivariate Normal distributions in which the mean, \(\mu_{k}\), and the covariance, \(\Sigma_{k}\), are the parameters), and eventually employ an EM (Expectation Maximization) [47] scheme for determining, from data, parameters and probabilities for each component. When EM convergence occurs, a sample is assigned to the component having maximum conditional probability.  
Gene expression data are typically extremely related, so there may be cases where there is a strong correlation between a single gene and two distinct clusters. Thus, model-based clustering probabilistic nature is especially appropriate for gene expression data. Model-based clustering, however, assumes that your data fits a given distribution, even though, often, this assumption does not hold. Modeling gene expression data are a continuing endeavor by many scientists, and there is presently no well-established model of representation. For instance, [PERSON] et al. [46] studied several data transformation used in practice to assess, among a number of gene expression data sets, the level to which the data fits a multivariate Gaussian model. The authors ascertained that in all cases there is a poor fit with the Gaussina model and there is no uniform rule to suggest a transformation that would best improve this fit.
## 5 Robust Optimization Model of Multiple Regional IESs and the Process of Solving  
In view of the randomness and volatility of renewable energy output, multiple uncertain factors, such as renewable energy output and load prediction, need to be considered in the dispatching of multiple regional gas-electric IESs.  
The uncertainty set model of new energy output prediction error is established; the adjustment factor is introduced to characterize the new energy output prediction error power; and the uncertainty deviation \(z_{ij}\), the polyhedron uncertainty parameter set \(U\), and the uncertainty deviation set \(Z\) are defined.  
\[d_{t}\in\Big{[}\overline{d}_{t}-\hat{d}_{t},\overline{d}_{t}+\hat{d}_{t}\Big{]},\overline{d}_{t}>0,\hat{d}_{t}>0 \tag{35}\]  
\[z_{t}=\frac{\overline{d}_{t}-\hat{d}_{t}}{\hat{d}_{t}}\in[-1,1] \tag{36}\]  
\[U=\left\{d_{t}\left|d_{t}=\overline{d}_{t}+\hat{d}_{t}z_{t},\forall t\in T\right\}\right. \tag{37}\]  
\[Z=\left\{z_{t}\middle|\sum_{t}\left|z_{t}\right|\leq\Gamma,\left|z_{t}\right| \leq 1,\forall t\in T\right\} \tag{38}\]  
where \(\overline{d}_{t}\) represents the predicted value of renewable energy output and load during the period \(t\) within a day; \(\hat{d}_{t}\) represents the maximum deviation of renewable energy output, that is, the precision of renewable energy output and load; \(z_{t}\) represents the degree of deviation of actual renewable energy output and load from the predicted renewable energy output and load; and \(\Gamma\) represents the uncertain budget, which can be understood as the extent to which uncertainties affect decision-making at the same time. There is no deviation if \(\Gamma=0\) and the corresponding robust optimization model is equivalent to the deterministic model.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Analysis of Numerical Simulation Results
#### 3.4.2 Anchor Axial Force Analysis  
The curve in Figure 16 reflects the variation in axial force along the self-drilling anchor axis under different anchor displacements. As shown in Figure 16, the axial stress of the self-drilling anchor decreases gradually with increasing anchorage depth, with the limit of axial stress occurring at approximately 5.75 m. It can also be observed that when the applied load is less than 190 kN, the axial force curve of the self-drilling anchor follows a convex shape, indicating that the load decreases more gradually at the front end of the anchor, while the decay is more significant at the rear end. When the pullout load exceeds 190 kN, the curve begins to show a concave shape, with a significant reduction occurring in the 0-2 m range. This behavior is more intuitively reflected in the axial force contour plots of the anchor at different displacements in Figure 17. In these plots, the axial force is predominantly largest near the load application end and gradually decreases  
Figure 15: Displacement contour map at the ultimate uplift bearing capacity.  
toward the far end along the anchor axis, following the same distribution pattern as the curve. Additionally, the axial force curve demonstrates that as the pullout load increases, relative slippage occurs, and the interface between the anchor and the surrounding soil will primarily rely on residual bond strength and friction to maintain its resistance. This results in the axial force shifting rapidly toward the deeper part of the anchor body, leading to a relatively rapid decay of the axial force.  
Figure 16: Distribution curve of axial force of anchor rod body.  
Figure 17: Contour plot of anchor axial force at different displacements.
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
###### Abstract  
Software-defined networking (SDN) decouples the control plane and data plane through OpenFlow technology and allows flexible network control. It has been widely applied in different areas and has become a focus of attention in the future network. With SDN's development, its security problem has become a necessary point of research to be solved urgently. In this paper, we propose a novel attack, namely, the packet injection exploiting attack. By maliciously injecting false hosts into SDN network topology, attackers can further use them to launch a denial of service (DoS) attack. The consequences affect the throughput and processing capabilities of the controller, severely consume data plane resources, and ultimately affect the entire network. To prevent the packet-injection exploiting attack, we designed PIEDefender, an efficient, protocol-independent component built on SDN controllers to detect and mitigate attacks effectively. We implement the PIEDefender prototype on the Floodlight controller and assess the effectiveness in the software environment. Experimental results show that PIEDefender achieves a 97.8% injection detection precision and a 97.96% DoS detection precision, incurring an average CPU consumption of 10%. The evaluation demonstrates that the PIEDefender can effectively mitigate the attack against SDN with limited overhead.  
software-defined networking (SDN); OpenFlow; packet injection exploiting attack; false hosts; denial-of-service (DoS); detection; defense +
Footnote †: journal: Applied  
Article
# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 4 Experimental Results and Analysis
### Ablation Test  
In order to study the performance gains of each module in the TSIN, a comprehensive ablation experiment was performed as shown in Table 3.  
The Behavior Encoder can capture the dependency between various behaviors, but Time Encoders cannot make good use of the advantages of multivariate time series. The processing method makes multivariate time series become like univariable time series, losing the connection between various features. Therefore, the effect of most Behavior Encoders is better than that of Time Encoders. The gating mechanism can integrate the features of the two channels well through adaptive weights. If the features encoded by the two encoders are simply connected, the accuracy of the prediction will be reduced to varying degrees.  
Different time series data may have different propensities towards temporal and behavioral characteristics. For example, on the dataset UWave, models with only Time Encoders had superior performance to other models; on the ArabicDigits data set, with both Time Encoder and Behavior Encoders, the model without Intermediate Fusion Module had the highest accuracy.
## 2 Theoretical Background
### Fractal Dimension
#### 2.3.2 Katz' Fractal Dimension (\(Kfd\))  
To obtain the \(KFD\) value of a signal, the process is as follows [34]:  
1. Calculate the maximum Euclidean distance, \(d\), between the first sample, \(x_{1}\), and the sample \(x_{k}\) of a signal \(X\) with \(N\) samples, with \(k=1,\ldots,N\).
2. Obtain the sum of the Euclidean distances between consecutive samples, \(L\), of the signal \(X\) and its average, \(a\), as shown in Equations (23) and (24). \[L=\sum_{k=2}^{N}dist(x_{k}-x_{k-1})\] (23)  
\[a=\frac{L}{N-1} \tag{24}\]
3. The value of \(KFD\) is defined by Equation (25). \[KFD=\frac{\log(L/a)}{\log(d/a)}\] (25)