esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 3 Proposed work  
### DWT-Based Signal Decomposition  
Wavelet or scaling functions play a direct role in the computation of signal expansion coefficients. They reflect the relationships between the expansion coefficients on a lower and higher scale [16]. Here, Haar scaling and wavelet functions are used to improve performance with respect to 5G LTE signals. The 5G signal is decomposed into  
\[\phi(t)=\sum_{a=0}^{M-1}h(a)\sqrt{2}\phi(2t-a)=\sum_{a}^{M-1}h(a)\phi_{1,n}(t) \tag{1}\]  
where \(M\) is the number of decomposition coefficients. For the Haar wavelet, \(N=2\), the above relation is expanded as follows:  
\[\phi(t) =h(0)\sqrt{2}\phi(2*t)+h(t)\sqrt{2}\phi(2t-1)\] \[=h(0)\phi_{1,0}(t)+h(1)\phi_{1,1}(t) \tag{2}\]  
Here, \(h(0)=\frac{1}{\sqrt{2}}\) and \(h(1)=\frac{1}{\sqrt{2}}\) are normalized Haar coefficients. Thus, the Haar wavelet is extended to the \(jth\)-level decomposition, which is expressed as follows:  
\[\phi(2^{j}t-k)=\sum_{b=2k}^{2k+M-1}h(b-2k)\sqrt{2}\phi(2^{j+1}t-b) \tag{3}\]  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Related Work** & **Year** & **Methodology** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Hybrid Deep Learning Model for Spectrum Sensing in 5G Networks [23]} & \multirow{2}{*}{2021} & Combined CNN and long short-term memory (LSTM) networks to capture both spatial and temporal features in the received signal & Improved accuracy and adaptability to changing signal characteristics & Increased model complexity \\ \cline{1-1} \cline{2-5}  & & & Implemented transfer learning from pre-trained models in similar tasks, adapting knowledge to spectrum sensing & Reduced need for extensive labeled data & May suffer from domain shift issues \\ \cline{1-1} \cline{2-5}  & & & Investigation of challenges and opportunities in applying deep learning to spectrum sensing for 5G and beyond & Highlights specific issues related to 5G and LTE technologies & Does not provide detailed methodologies \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1  
where \(b=2k+n\). In the above equation, \(b\) is a variable that depends on the level of decomposition [15; 16; 20]. For simplicity, we assume that \(j=0,k=3\), and \(M=2\) for \((\textit{Haar})\), so the above equation is modified as follows:  
\[\phi(2^{0}t-3) = \sum_{b=6}^{7}h(b-2k)\sqrt{2}\phi(2^{1}t-b)\] \[\phi(t-3) = h(0)\sqrt{2}\phi(2t-6)+h(1)\sqrt{2}\phi(2t-7) \tag{4}\]  
In the above equation, the Haar values are replaced, and the equation becomes  
\[\phi(t-3)=\phi(2t-6)+\phi(2t-7) \tag{5}\]  
Figure 1 shows a hierarchy that indicates how the input image is decomposed: it can be convolved with filters of different sizes to improve the classification performance. The proposed wavelet-based convolutional neural network helps to improve the classification results through the application of filtering and downsampling. After feature extraction, we apply the quantum cat swarm optimization algorithm to achieve feature selection from the extracted features.
## 4 Example Analysis
### The Comparison of the Reliability Evaluation Results  
The number of critical states of system \(S\) is set to 3000. As shown in Table 1, the values of the hidden states number \(N\) of various PV outputs are set, and the relative error, the mean value, and the offline calculation time between the PCE methods and the SE method are calculated.  
When \(N=1\), which corresponds to setting the PV output to a Gaussian mixture distribution, Table.1 demonstrates that when \(N>1\), the calculated relative error is less than when \(N=1\), proving that the GMM-HMM method utilised in this paper can effectively increase the precision of the analytical modelling. Moreover, it is discovered that the relative error of the calculated EENS decreases as \(N\) increases. When \(N=6\), the accuracy improvement brought by increasing the PV output is not substantial, but the offline running time increases significantly, so this paper sets \(N=5\) as the number of PV output hidden states.  
The truncation order of PCE is set to 2, and \(q=0.5\) in the HTPCE method.  
Table 2 provides a comparison of the mean value and calculation time of the predicted 4-day reliability levels obtained by the SE method, the PCE method, and the HTPCE method.  
The dynamic reliability levels at each hour of the forecasted 4 days calculated by the PCE method, SE method, and HTPCE method for the power system are further compared in Figure 5. Figure 5 also shows the predicted 4-day PV output.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _err_ & \(\overline{EENS}\) & **Offline Time/s** & **Online Time/s** \\ \hline PCE\({}_{N=5}\) & 0.40\% & 173.82 & 13,589.80 & 0.130 \\ HTPCE\({}_{N=5}\) & 0.92\% & 173.58 & 11,869.97 & 0.113 \\ SE & - & 174.1 & 34,587.58 & 360.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the reliability calculation results of the three methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _err_ & \(\overline{EENS}\) & **Offline Time/s** & **Online Time/s** \\ \hline PCE\({}_{N=5}\) & 0.40\% & 173.82 & 13,589.80 & 0.130 \\ HTPCE\({}_{N=5}\) & 0.92\% & 173.58 & 11,869.97 & 0.113 \\ SE & - & 174.1 & 34,587.58 & 360.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of PCE methods under different hidden states numbers.  
Figure 5 shows that the calculation results of the three methods are almost equal. Simultaneously, it can be observed that the higher the PV output, the lower the risk of load loss, and the higher the level of power system reliability. Compared to the SE method, the PCE method has a relative error of only 0.40%, but its offline calculation time is 39.29% of the SE method. This demonstrates that the PCE method can reduce calculation time while maintaining accuracy.  
In comparison to the HTPCE and SE methods, the relative error is 0.92%. Even though the error is greater than the PCE method, this method still meets the requirements of engineering practise. Simultaneously, the offline calculation time using the HTPCE method is only 34.31% of the SE method, demonstrating the efficacy of the HTPCE method proposed in this paper.
# Dynamic Simulation of Underground Cable Laying for Digital Three-Dimensional Transmission Lines
## 5 Discussion  
To demonstrate the reliability of this article, we first model the target equipment and construction environment using BIM software. We utilize ADAMS virtual prototype technology to implement cable modeling through the Bushing force connection method and macro command language, establishing a three-dimensional realistic cable laying system for the transmission line. This dynamic simulation method has been applied in overhead line construction; however, there are currently insufficient dynamic simulations for cable laying operations. Therefore, this article proposes a simulation method for the transmission line cable laying construction process based on BIM technology. After the simulation is completed, we perform error analysis by comparing the cable axial tension and laying speed with theoretical calculation values to verify the consistency between the model and theory. Finally, by analyzing the tension fluctuations during cable laying and the pulley load-bearing capacity, we achieve control over the construction process and safety reconnaissance. From the result, it can be seen that the max error between the real value and simulation value is 5.4%, the mean error is 2.75%, and the min error is 0.8%, which indicates that the model is correct and the results have a certain degree of reliability. This research can provide a theoretical basis for practically eliminating safety hazards in cable laying projects and holds certain engineering value.
## 1 Introduction
### Time-Space Coupling Method of Temperature Field Based on Gauss-Newton Method
According to the determined values of \(R\) and \(Fo\), these undetermined parameters will obtain specific values. With reference to [28], the specific values of the uncertain parameters in Equation (10) under the conditions \(\lambda=1.2\ \mathrm{W}/(\mathrm{m}\cdot\mathrm{K})\), \(a=0.8\ \times 10^{-6}\ \mathrm{m}^{2}/\mathrm{s}\ (a=\lambda/\rho c)\) are obtained, and the results are shown in Table 2.  
Plot the relationship curve between the dimensionless radius \(R\) and parameters \(\mathrm{E}\), \(\mathrm{G}\), and \(\mathrm{H}\) based on the data in Table 2, as illustrated in Figure 2. It is evident that the dimensionless radius \(R\) exhibits an exponential function relationship with parameter \(\mathrm{E}\), a linear relationship with parameter \(\mathrm{G}\), and an exponential function relationship with parameter \(\mathrm{H}\). Figure 2 shows that the relationship between dimensionless temperature, dimensionless time (\(Fo\)), and dimensionless radius (\(R\)) is \(\Theta=f(Fo,R)\). Therefore, Formulas (7) and (8) can be coupled, that is,  
\[\Theta=\frac{-\mathrm{exp}\big{(}\Lambda\times R^{\mathsf{B}}\big{)}}{\mathrm{ O}\times R\times Fo^{\mathrm{P}}+\mathrm{exp}(\mathrm{S}\times R^{\mathsf{Q}})}+\mathrm{C} \tag{11}\]  
As the dimensionless radius \(R=0\) at the wall surface of the surrounding rock yields no solution to Equation (11), the applicable range of the coupling formula is \(R>0\). In scenarios where excavation and ventilation are not conducted, the rock maintains its original thermal \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensionless Radius/\(R\)** & \(\mathrm{E}\) & \(\mathrm{G}\) & \(\mathrm{H}\) \\ \hline
1 & 0.9529 & \(-\)0.735 & 1.1236 \\
2 & 0.1827 & \(-\)0.987 & 0.2942 \\
3 & 0.0491 & \(-\)1.2149 & 0.1023 \\
4 & 0.0147 & \(-\)1.4448 & 0.0389 \\
5 & 0.0045 & \(-\)1.681 & 0.0152 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The values of undetermined parameters \(\mathrm{E}\), \(\mathrm{G}\), and \(\mathrm{H}\) under various \(R\) conditions. balance, and the temperature of the rock everywhere is the VRT. By inserting this value into Formula (11), C = 1 can be obtained as follows:  
\[\Theta=\frac{-\text{exp}\big{(}\text{A}\times R^{\text{B}}\big{)}}{\text{O}\times R \times Fo^{\text{P}}+\text{exp}(\text{S}\times R\text{Q})}+1,R>0 \tag{12}\]  
The Gauss-Newton iteration form of Formula (12) is  
\[ \begin{array}{ll}\mathbf{a}_{\text{A,B,O,P,Q}\text{S}}(^{l+1})=&\mathbf{a}_{\text{A, B,O,P,Q}\text{S}}(^{l})\\ &-(\mathbf{J}(\mathbf{a}_{\text{A,B,O,P,Q}\text{S}}(^{l}))^{\text{T}}\mathbf{J}(\bm {a}_{\text{A,B,O,P,Q}\text{S}}(^{l}))\\ &+\mu_{l}\mathbf{I})^{-1}\mathbf{J}(\mathbf{a}_{\text{A,B,O,P,Q}\text{S}}(^{l}))^{ \text{T}}\mathbf{r}(\mathbf{a}_{\text{A,B,O,P,Q}\text{S}}(^{l}))\end{array}  \tag{13}\]  
where A, B, O, P, Q and S are the parameters to be solved in the binary function, \(l\) is the number of iterations, \(\mathbf{J}(^{l})\) is the Jacobi matrix of \(\mathbf{r}\) at the \(l\)th iteration, \(\mu_{l}\) is the damping factor, and \(\mathbf{I}\) is the unit matrix.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
#### 2.2.4 Random Forest  
Random Forest (RF) classifier is an ensemble algorithm [93] that matches multiple decision-tab classifiers on different data sub-samples, using average data to enhance predictive accuracy and control of fitting [94]. Ensemble algorithms combine more than one algorithm of the same or different kinds for classifying data [95; 96; 97; 98; 99]. RF was commonly used in the literature for the development of cyberbullying prediction models; examples are the studies conducted by [97; 98; 99]. Consequently, RF consists of several trees used randomly to pick the variables for the classifier data. In the following four simplified steps, the construction of the RF takes place. In the training data, N is the number of examples (cases) and M the number of attributes in the classifier.  
* In the training data, N is the number of examples (cases), and M is the number of attributes in the classifier.
* Selecting random attributes produces a set of arbitrary decision tresses. For each tree, a training set is selected by selecting n times out of all existing N instances. The remaining instances in the training set are used by predicting their classes to estimate the tree's error.
* M random variables are chosen for the nodes of each tree to base the decision at that node. In the training package, the most exceptional split is determined using specific m attributes. Each tree is built entirely and not pruned, as can be done in the development of a regular tree classifier.
* This architecture produces a large number of trees. For the most common class, those decision trees vote. Such processes are denominated RFs. RF builds a model consisting of a group of tree-structured classifiers, where each tree votes for the most popular class [93]. The one selected as the output is the most highly voted class.
## 5 Discussion
Nevertheless, the largest difference between the two trajectories is about 10 cm, which is within the error envelope detected earlier in our previous studies. The trajectory along the X, Y, and Z directions for Test #2 can be seen in Figure 16. This figure shows the decreasing altitude (see the Z component) as expected for landing aircraft. Also note that the CV model depicted with red in the Figure 16 is a line; however, it is very unlikely that this motion component behaves such a way. The CT solution depicted with blue follows a more realistic trajectory. Unfortunately, reference solution, for instance, from GPS, is not available for the quantitative analysis of the methods. However, the residuals from the CT algorithm can describe the \"internal\" accuracy between the measured cube velocities and the underlying motion model. The average and standard deviation of the absolute residuals from the CT algorithm are presented in Table 4.  
The statistics of the average absolute residuals (0.06-0.69 m/s) are similar to those we experienced earlier for simpler motions. The X velocity errors are dominant for both cases, but significantly larger than the Y component in the case of the Taxiing data; note that the X axis is the direction of landing and taxiing. The accuracies of the Y and Z components are similar. The standard deviation is also relatively low (0.08-0.17 m). Note that the residuals are velocities, and do not describe the positioning accuracy directly. The sampling frequency of the sensors is 10 Hz, and thus, the positioning error is supposed to be around one to seven centimeters based on these velocity residuals. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{3}{c}{**Average of the Absolute Residuals**} & \multicolumn{3}{c}{**Standard Deviation of the Absolute Residuals**} \\ \cline{2-7}  & **X (m/s)** & **Y (m/s)** & **Z (m/s)** & **X (m/s)** & **Y (m/s)** & **Z (m/s)** \\ \hline
**\#1 Taxing** & 0.69 & 0.06 & - & 0.44 & 0.00 & - \\
**\#2 Landing** & 0.67 & 0.44 & 0.51 & 0.17 & 0.08 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative statistics of the CT method.
## 4 Results: Threat Analysis
### Threat 3: Transaction Manipulation by \(D\)  
As the data warehouse responsible for handling user-submitted transactions, \(D\) plays a critical role in the proposed framework. It receives transaction hash values and encrypted transaction content from users, relays the hash values to \(t_{M}\) for inclusion in \(\mathbb{PC}_{M}\), and later discloses the actual transactions in \(\mathbb{\widehat{PC}}_{M}\) after the corresponding segment is finalized. Given this central role, \(D\) may be tempted to manipulate the transaction flow in ways similar to \(t_{M}\), such as selectively publishing, delaying, or reordering transaction content. The proposed framework prevents such behavior through the design features described below:* **Rejection, Delay, and Reordering:** Since transactions are uploaded in encrypted form and anonymously, \(D\) cannot infer their content or origin, and is required to disclose them in the order of hash registration immediately after the corresponding \(\mathbb{PC}_{M}\) segment is anchored to \(\mathbb{RC}\), making any attempt to selectively delay, reorder, or omit transactions both auditable and easily detectable.
* **Collusion with \(t_{M}\)**: A potential threat is that \(D\) could collaborate with \(t_{M}\) to selectively delay, reorder, or censor transactions. However, because users upload only hash values and encrypted transaction content anonymously, \(D\) cannot associate transactions with specific users or interpret their semantics prior to registration.  
Although \(D\) performs a non-privileged and auditable role, further resilience could be achieved by designing the system to allow competition or redundancy among multiple candidate entities. Rather than relying on a single designated data warehouse, the platform could adopt a modular architecture where any qualified provider may fulfill \(D\)'s role as long as it conforms to the disclosure protocol. If any instance of \(D\) is found to be unreliable or malicious, another compatible provider can be deployed without disrupting the system. This approach enhances accountability through substitutability and aligns with the fault-tolerant principles used in decentralized infrastructure design.
## 2 Related Work
### Graph Neural Network-Based Methods  
As one of the commonly used deep learning models, graph neural network (GNN) is specially designed for processing graph structure data by message passing mechanisms to capture the information of node features and their neighbors in the graph. There is abundant graph structure information in natural language text, such as syntax and semantic dependency trees, so the graph neural network can be used to effectively mine such information to improve the classification effect of the model. Because of the flexibility of the graph structure, graph neural networks perform very well in dealing with different granularities of text classification problems, which makes graph neural networks powerful tools for dealing with various text classification tasks [28].  
TextRank [29] is one of the earliest text classification models based on graph structure. This method represents natural language text as a graph structure, \(G=(V,E)\), where \(V\) represents a set of nodes and \(E\) represents a set of edges between nodes. [PERSON] et al. [17] proposed a deep learning model of Graph-CNN. The core idea of the model is to first convert text into a word graph, then use graph convolution operations for information representation of the word graph, and finally obtain document labels through classifiers. [PERSON] et al. [18] took words and documents as nodes at the same time, and constructed a heterogeneous word-text graph to represent text data, to realize the classification of documents. In 2019, [PERSON] et al. [30] completed the construction of the text graph structure through the dependency tree structure in the statement, and then realized the classification calculation of the statement through GNN. By combining the attention mechanism with GNN, [PERSON] et al. [31] proposed the heterogeneous graph attention network, which not only integrates the framework of different types of additional information to model short texts, but also uses the dual attention mechanism to learn the importance of adjacent nodes and the importance of different node types. [PERSON] et al. [32] proposed the HyperGAT to model text through documentation-level hypergraphs and proposed a hypergraph model based on a double attention mechanism to support the representation learning of text hypergraphs. As a niche field, the industry classification of water enterprises is rarely studied. Although the above method, based on a graph neural network, has achieved good results in various text classification tasks, due to the characteristics of enterprise naming, it is difficult to be directly applied to the industry classification of water enterprises.
## 4 Research Context  
The appropriate research context was set up to answer the main research questions and test the hypotheses proposed in the previous sections. The CRISS platform was provided within the Horizon 2020 project to selected primary and secondary schools in six European countries--Croatia, Greece, Italy, Romania, Spain, and Sweden. In each country, a project partner monitored the platform's whole process of use by teachers and students for several months. For ease of use, the interface of the CRISS platform was translated into target languages as well as scenarios, tasks, and activities. The latter were also adapted to fit the country-specific context and different educational levels (primary or secondary).  
The CRISS platform is a cloud-based platform consisting of teaching methodologies and services that enable the acquisition of DC in schools. It is based on the validated CRISS DC Framework [38] aligned with the European DigComp. It also follows the \"integration pedagogy\" concept introduced by [39] as a valid approach for developing the competence assessment that focuses on learning (mastering) DC. In the CRISS DC Framework, DCs are divided into 12 sub-competences and form five areas. Each student should produce certain evidence according to a defined set of performance criteria to achieve an individual sub-competence. Moreover, each performance criterion consists of indicators that provide measurements or conditions required to analyze the evidence regarding performance criterion and competence attainment.  
Teachers can plan students' learning within the CRISS platform by choosing activities and tasks from the repository and applying them according to their teaching practice. They also evaluate students' activities and tasks performed within the platform and provide them with feedback. Each successfully evaluated activity brings students closer to attaining a certain sub-competence. An overview of a CRISS DC certification process integrated into the CRISS platform is presented in Figure 11 of Appendix A.  
DC can be evaluated through human or technological interventions on the CRISS platform. Human interventions are performed by teachers and students using various adaptable tools (e.g., checklists, rubrics, scales) implemented within the platform. These tools are designed to be easily used by the teacher and for students' self and/or peer evaluation. Furthermore, the CRISS platform automatically performs a technological intervention by tracking students' activities, based on which it collects relevant information.