esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 7 Conclusions  
This paper proposes an improved ChOA based on the learning strategy of light reflection (RL-ChoA) based on the original ChOA. An improved Tent chaos sequence was introduced to increase the diversity of the population and improve search accuracy and convergence speed. In addition, a new refraction learning strategy is proposed and introduced into the original ChOA based on the principle of physical light refraction to help the population jump out of the local optimum. First, it uses 23 widely used benchmark functions and [PERSON]'s rank-sum test verify that RL-ChoA has better optimization performance and stronger robustness. Our source code is available on [[https://github.com/zhanguan123-zq/RL-ChoA](https://github.com/zhanguan123-zq/RL-ChoA)]([https://github.com/zhanguan123-zq/RL-ChoA](https://github.com/zhanguan123-zq/RL-ChoA)) (accessed on 20 April 2022).  
In the future, some work needs to be conducted to further improve the algorithm's performance. An adaptive parameter strategy is designed to improve the optimization performance of RL-ChoA. The optimization effectiveness of RL-ChoA is tested on some difficult benchmark functions [75; 76; 77]. Some more multi-dimensional engineering problems are used to verify the engineering validity of RL-ChoA.  
Conceptualization, S.D.; Data curation, K.D. and H.W.; formal analysis, Y.L. and K.D.; investigation, S.D. and Y.L.; Funding acquisition, S.D.; Supervision, S.D.; resources, Q.Z., S.D. and Y.Z.; software, Q.Z.; writing--original draft preparation, Q.Z. and S.D.; writing--review and editing, S.D. and Y.Z.; visualisation, H.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mathbf{d}\) & \(\mathbf{D}\) & \(\mathbf{p}\) & **Mean** \\ \hline GA & 0.0524 & 0.3521 & 11.5980 & 0.032 \\ PSO & 0.0500 & 0.3104 & 14.998 & 0.0131 \\ ChOA & 0.0500 & 0.3159 & 14.3999 & 0.0132 \\ WChOA & 0.0508 & 0.3142 & 14.4314 & 0.0134 \\ RL-ChoA & 0.0613 & 0.6259 & 4.1889 & 0.0129 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of tension/compression spring design.  
Figure 8: Extension/compression spring structure.  
**Funding:** The present work was supported by the National Natural Science Foundation of China (Grant No. 21875271, U20B2021), the Entrepreneurship Program of Foshan National Hi-tech Industrial Development Zone and Zhejiang Province Key Research and Development Program (No. 2019C01060), \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang (Grant No. 2022C01236), International Partnership Program of Chinese Academy of Sciences (Grant No. 174433 KYSB20190019), and Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (Grant No. 2019R01003).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 4 Data Representation Techniques
### Text Data Representation
#### 4.1.7. BERT
They demonstrate in their study that HSBERT provides state-of-the-art results when compared to other models by comparing its performance against the general-domain BERT through extrinsic and intrinsic evaluations. To assess the effectiveness of BERT in detecting cyberbullying in a social media context, [PERSON] et al. [60] used BERT to categorize cyberbullying in two social media datasets. In terms of precision, recall, and F1 scores, their experimental findings are encouraging when compared to the prior research in this area. Overall, these studies demonstrate the effectiveness of using BERT and the combination of BERT with other models for cyberbullying detection, and they highlight the potential for future research in this area. A summary of different word-embedding techniques used in cyberbullying detection is shown in Table 3. We have compared these techniques based on context-sensitive, traditional ML-based, RNN-based, transformer-based, and transfer-learning-based models. We can observe that several word-embedding methods depend on the context. Sincecyberbullying detection is a complex task and it is largely context-dependent, context-sensitive word embedding performs better than context-insensitive word embedding. On the other hand, except for one-hot encoding and TF-IDF [38], which are a kind of mathematical calculation-based vectorization technique, the majority of other approaches are traditional ML-based. However, we find that in some studies, the authors use ELMo [35] to feed data to RNN models. Currently, the state-of-the-art word embedding is BERT [37], which provides satisfactory outcomes during model building in cyberbullying-related problems. In our study, we have noticed that BERT, the only transformer-based method, is the most potential word-embedding technique used to deal with text-based cyberbullying problems. We have also observed that all the ML-based word-embedding techniques are pre-trained. BERT is also pre-trained by using unlabeled data collected from the English Wikipedia and BooksCorpus, each of which contains 800 million words. One-hot [74] and TF-IDF approaches are utilized in a few studies in the identification of cyberbullying. However, these techniques perform weaker since they tend to be context insensitive. The study mostly made use of Word2 Vec, GloVe, and ELMo. However, BERT has lately seen sharp growth and outstanding outcomes in its use as a word embedding approach.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 5 An Overview of Attack-Resilient Control and Communication Techniques
### Resilient Control Approaches
#### 5.1.1 Event-Triggered Control
If, on the other hand, the transmission at time \(t_{i}\) is unsuccessful (see Figure 8, right), another transmission may be triggered in the next time instant if \(V(Ax(t_{i}+1)+Bu(t_{i}))>\beta V(x(t_{i}))\). Notice that in this case the state trajectory indicates unstable behavior due to lack of control input in the system. For the probabilistic network characterizations discussed in Section 4, sufficient stability conditions under the event-triggered control framework in [25] are provided in the following result. **Theorem 11** ([25]). : _Consider the linear dynamical system in Equation (1). Suppose that the transmission failure indicator \(\{l(i)\in\{0,1\}\}_{i\in\mathbb{N}_{0}}\) satisfies \(l\in\Lambda_{\rho}\) with scalar \(\rho\in[0,1]\). If there exist a matrix \(K\in\mathbb{R}^{m\times n}\), a positive-definite matrix \(P\in\mathbb{R}^{n\times n}\), and scalars \(\beta\in(0,1)\), \(\varphi\in[1,\infty)\) such that_  
\[(A+BK)^{\mathrm{T}}\,P\,(A+BK)-\beta P\leq 0, \tag{27}\] \[A^{\mathrm{T}}PA-\varphi P\leq 0,\] (28) \[(1-\rho)\ln\beta+\rho\ln\varphi<0, \tag{29}\]  
_then the event-triggered control law in Equations (3) and (26) guarantees almost sure asymptotic stability of the zero solution \(x(t)\equiv 0\) of the closed-loop system dynamics._  
The scalars \(\beta\in(0,1)\) and \(\varphi\in[1,\infty)\) in the conditions in Equations (27) and (28) of Theorem 11 characterize upper bounds on the growth of the Lyapunov-like function \(V(x)=x^{\mathrm{T}}Px\), when the system evolves with the closed-loop dynamics (corresponding to a successful transmission) and open-loop dynamics (corresponding to a transmission failure). Notice that since \(\beta<1\) and \(\ln\beta<0\), if \(\rho\) is sufficiently small, then Equation (29) holds indicating stability. Based on the conditions of Theorem 11, our work [25] also provides a method for designing the scalar \(\beta\) and the positive-definite matrix \(P\) that are utilized in the event-triggering condition in Equation (26). The proof of Theorem 11 is based on a technique similar to that used for obtaining upper bounds of _top Lyapunov exponents_ (see [92; 93]) of stochastic dynamical systems. In [28], we provided a less conservative analysis approach based on a lifting technique. There, the stability of the system is checked by solving a linear programming problem.
## Materials and Methods
### Establishment and Evaluation of the Models
#### 2.4.4 Modeling Performance Evaluation  
To evaluate the performance of the developed models, several effective parameters including the coefficient of determination (\(\text{R}^{2}\), \(\text{R}_{\text{cv}}{}^{2}\), and \(\text{R}_{\text{p}}{}^{2}\) for calibration, cross-validation, and prediction set, respectively), and root mean squared error for calibration, cross-validation and prediction set (RMSEC, RMSECV, and RMSEP) were calculated. In particular, to calculate the practical utility of the prediction models, ratio of the standard deviation (SD) to RMSEP, i.e. residual predictive deviation (RPD), was also employed. If the value of RPD was lower than 1.5, the developed model was unacceptable [26]. On the contrary, a high RPD value (larger than 3) indicated that the model was good or excellent. According to the literature of [PERSON] et al. [27], models that achieved the performance of R\({}^{2}\geq\) 0.70 and RPD \(\geq\) 2.00 would be treated as valid in determining meat quality.
## 6 AOG Downtime Reduction and Implementation Results
### SmartLynx Inventory Management control
Conversely, higher MTBF values allow reduced safety stock, enhancing inventory precision and minimizing excess stock. While the Z-score remains constant for a given service level, a higher MTBF reduces demand variability (\(\sigma\)d), lowering safety stock requirements.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 2 SAS Theoretical Background
### Monodisperse Inhomogeneities  
In the very simple case of identical inhomogeneities, Equation (3) yields a much simpler form:  
\[\frac{d\Sigma}{d\Omega}(Q)=NV^{2}(\Delta\rho)^{2}|F(Q)|^{2} \tag{4}\]  
\(N\) is the number density of the inhomogeneities, i.e., the number of inhomogeneities per unit volume; \(V\) is the volume of a single inhomogeneity; \(|F(Q)|^{2}\) is the form factor of the inhomogeneity.  
At small values of \(Q\), [PERSON] showed that \(|F(Q)|^{2}\) can be approximated by a Gaussian form [27], and the SCS takes the following form:  
\[\frac{d\Sigma}{d\Omega}(Q)\cong\frac{d\Sigma}{d\Omega}(0)exp(-\frac{Q^{2}R_{g }^{2}}{3}) \tag{5}\]  
where \(R_{g}\) is the gyration radius of the single inhomogeneity, defined as  
\[R_{g}^{2}=\frac{1}{V}\int_{V}r^{2}dV \tag{6}\]  
and the SCS at \(Q=0\) is given by  
\[\frac{d\Sigma}{d\Omega}(0)=(\Delta\rho)^{2}NV^{2} \tag{7}\]  
In the case of a sharp interface between the inhomogeneities and the matrix, or solvent, the asymptotic behaviour of \(|F(Q)|^{2}\) can be approximated by the so-called Porod approximation [28]:  
\[\frac{d\Sigma}{d\Omega}(Q)\cong\frac{2\pi(\Delta\rho)^{2}NS}{Q^{4}} \tag{8}\]  
where \(S\) is the surface of an inhomogeneity, and therefore, \(S_{T}=NS\) represents the total surface of the inhomogeneities per unit volume.
## 2 Methods
### Entropy Method  
The basic idea of the entropy weight method is to determine the objective weight according to the size of the index variability [52]. Generally, if the information entropy of a certain index is smaller, indicating that the degree of variation in the index is greater, more information is provided, meaning that the greater the role it can play in the comprehensive evaluation, the greater the weight. On the contrary, the greater the information entropy of an index, meaning a smaller degree of variation in the index value, the smaller the amount of information provided is, and the smaller the role played in the comprehensive evaluation and the smaller the weight is [53]. The detailed processing is as follows:  
The first is to determine the risk evaluation factors, and select appropriate evaluation indicators according to the actual situation of the experimental research area. The algorithm of the mathematical model of the entropy method is as per Formula (5).  
Standardized data matrix:  
\[A_{ij}=\frac{B_{ij}-\min\bigl{\{}B_{ij},\cdots,B_{nj}\bigr{\}}}{\max\bigl{\{}B _{ij},\cdots,B_{nj}\bigr{\}}-\min\bigl{\{}B_{ij},\cdots,B_{nj}\bigr{\}}} \tag{5}\]  
where \(A_{ij}\) is the standardized value representing the \(j\)th indicator data for the \(i\)th district and county, \(i=1,2,\ldots,n;j=1,2,\ldots,m\), and \(B_{ij}\) is the \(i\)th unit and the \(j\)th metric data raw value.  
According to the entropy method, we can determine the weight of the factor and then compute the \(j\)th index of information entropy values \(E_{j}\) and \(F_{j}\) and evaluation weight \(W_{j}\).  
\[\left\{\begin{array}{l}E_{j}=-\frac{1}{\ln m}\sum_{i=1}^{m}A_{ij}\ln A_{ij}\\ F_{j}=1-E_{j}\\ W_{j}=\frac{F_{j}}{\sum_{i=1}^{m}F_{j}}\end{array}\right. \tag{6}\]  
As the evaluation factor grading standards were fuzzy, in order to reasonably describe the grading boundaries, the membership degree was adopted by establishing the fuzzy relation matrix R. Combined with the risk assessment index weight matrix \(W\), the fuzzy comprehensive evaluation matrix \(C\) was obtained.  
\[R=\begin{bmatrix}A_{11}&A_{12}&\cdots&A_{1m}\\ A_{21}&A_{22}&\cdots&A_{2m}\\ \vdots&\vdots&\ddots&\vdots\\ A_{n1}&A_{n2}&\ldots&A_{nm}\end{bmatrix} \tag{7}\]  
\[C=W\times R \tag{8}\]  
The risk degree is calculated using the formula, that is, \"degree of risk (R) = hazard (H) \(\times\) vulnerability (V)\".
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 3 System Modeling
More specifically, \(\overline{L}\) can be denoted as the quantity:  
\[\overline{L}=\frac{\sum_{v\
eq l_{u}\in S_{c}}L_{u,p}}{m}, \tag{1}\]  
where \(m\) is the number of consumers. Industrial applications are typically time-critical, and consequently, the industrial operator requires a maximum data access latency threshold \(L_{\max}\). This is an important constraint in the network, and the implementation of a data delivery strategy should ensure that the average multi-hop  
Figure 2: Example of data access latency. In this case, the data access latency is \(L_{u,p}=l_{u,v_{1}}+l_{v_{1},v_{2}}+l_{v_{2},p}+l_{p,v_{2}}+l_{v_{2},p_{1}}+ l_{v_{1},u}\). access latency does not exceed the threshold value. In other words, the following inequality should hold: \(\overline{L}\leq L_{\text{max}}\). Note that this formulation is amenable to different purposes. If \(L_{u,p}\) is the mean latency between \(u\) and \(p\), the above inequality guarantees that the average of the mean latencies is below \(L_{\text{max}}\). If it is the worst-case latency between \(u\) and \(v\), the inequality provides a guarantee on the average worst-case latency. In the following, we show that the DML can be used in both cases.
## 2 Materials and Methods
### Data Analysis Methods
#### 2.5.5 Establishment of the Regression Model  
The sample set was divided into a calibration set and a prediction set using the Kennard-Stone (KS) algorithm in a ratio of 2:1 [29]. Least squares support vector regression (LS-SVR) is a renovation combined the least square method (LS) and the traditional support vector regression (SVR). LS-SVR employs a least squares linear system as the loss function, which reduces the complexity of the model operations while retaining the advantages of the SVR model [30; 31]. LS-SVR uses radial basis function (RBF) as the kernel function and a grid search (GS) algorithm to find the optimal of model regularization parameters gamma (\(\gamma\)) as well as radial basis function sig2 (\(\sigma^{2}\)) bandwidth. The LS was used to calculate the regression coefficients and deviations value, and the final prediction model was obtained [32; 33].  
After the regression model was established, the predictive performance of the model was evaluated by correlation coefficient (R), root mean square error (RMSE), and residual predictive deviation (RPD). Generally, the larger the R and RPD, the smaller the RMSE, and the closer the R to 1 (RMSE to 0), indicating the better predictive performance of the model [34; 35]. These model performance indexes were defined using Equations (4)-(6) as follows:  
\[\mathrm{R}=\frac{\sum_{i=1}^{n}\left(y_{i,c}\ -\ \overline{y}_{i,c}\right)\left(y _{i,p}\ -\ \overline{y}_{i,p}\right)}{\sqrt{\sum_{i=1}^{n}\left(y_{i,c}\ -\ \overline{y}_{i,c}\right)^{2}}\sqrt{\sum_{i=1}^{n}\left(y_{i,p}\ -\ \overline{y}_{i,p}\right)^{2}}} \tag{4}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i,c}\ -\ y_{i,p}\right)^{2}}{n\ -\ 1}} \tag{5}\]  
\[\mathrm{RPD}=\frac{SD_{v}}{SEP}=\frac{1}{\sqrt{1\ -\ \mathrm{R}^{2}}} \tag{6}\]  
where \(y_{i,c}\), \(y_{i,p}\) are the actual measured chemical values and model predicted chemical values of the \(i^{th}\) sample, respectively; \(\overline{y}_{i,c}\), \(\overline{y}_{i,p}\) are the average of the actual measured chemical values and the average of the model predicted chemical values of the samples, respectively; \(SD_{v}\), \(SEP\) are the standard deviation of sample content and the predicted standard deviation of the prediction set, respectively; \(n\) is the number of samples.  
The above spectral data processing method were performed using Matlab 2016 a (The MathWorks, Natick, MA, USA). The Unscrambler X10.1 (CAMO, Process, AS, Oslo, Norway) was also used for spectral pretreatments. The data was illustrated by graphs using Origin 2017 C (OriginLab, Northampton, MA, USA).
## 2 Materials and Methods
### Deep Neural Network Architecture  
From each patient, ten repetitions per gesture class were used for the EMG dataset. With a sampling rate of 1 kHz, the signal length of a single gesture attempt was 750 samples. After filtering raw EMG signals with a Butterworth bandpass filter (20-300 Hz), labeling individual gestures (including attempted movements), and normalizing to mean absolute values (with no other feature extraction methods applied), we deployed a 1D CNN-LSTM network for hand gesture classification [22; 23; 24]. This preprocessing stabilized the learning and validation curves during initial model testing [16]. The network architecture is detailed layer by layer (see Figure 5).  
The sequence input layer was configured with an input size of 4 channels, representing features from the 4-channel EMG input. Two 1D convolutional layers were employed, each with a filter size of 6, using 24 filters in the first and 48 in the second layer. Based on empirical tuning, CNN layers were set to use causal padding to maintain the temporal causality of EMG signals, which is crucial given the limited EMG data. Each convolution operation was followed by a rectified linear unit (ReLU) activation and layer normalization to introduce non-linearity and stabilize gradient flow during the network's learning [11]. Subsequently, a global average pooling layer was added to summarize the temporal dimension of each feature map, reducing computational complexity while preserving key temporal features of the EMG signal in subacute stroke datasets.  
Our hybrid network architecture consisted of two LSTM layers for sequence-to-label classification [23]. The first layer had 64 units, and the second had 32 units. Each unidirectional layer was paired with a dropout layer to mitigate overfitting, with rates of 0.3 for the 64-unit layer and 0.2 for the 32-unit layer [24]. The output from the LSTM layers was then passed to a fully connected layer with neurons equal to the number of gesture classes [26]. Finally, a Softmax layer converted scores into probabilities, which were processed by a classification layer for hand gesture recognition [22].  
For the training configuration, we employed the Adam optimizer with an initial learning rate of 0.002, using a piecewise schedule that reduced the rate by a factor of 0.1 every 100 epochs [11; 12; 23]. A gradient threshold of 1 was applied to prevent gradient explosion, and a mini-batch size of 32 was used. The network was trained for up to 300 epochs, with validation checks performed every 150 iterations. To ensure randomness, the training data was shuffled at the beginning of each epoch. All hyperparameters were selected empirically, with reference to related studies [9; 10; 11; 12; 16; 20; 21; 22; 23; 24; 25; 26].
## 2 Basic Theory
### Model Construction  
This paper first determines the feature set of the candidate influence factors of the distributed energy system and uses KPCA method to deal with the feature dimensionality reduction, and then uses the fireworks algorithm to optimize the KELM, thus the optimal value of the penalty coefficient \(C\) and the kernel parameter \(\sigma\) is obtained. Finally, the characteristic data after KPCA reduction are input to get the prediction result. The proposed composite prediction framework is shown in Figure 1.  
The following are the specific steps of the prediction model.  
1. Initial input variable selection and data processing. The influence factors of the distributed energy system load are determined by the literature data analysis, and the candidate input variables \(C=\{Ci,\,i=1,\,2,\,\ldots,\,n\}\) are formed, and quantify and normalize the input data (\(Ci\)).
2. KPCA feature reduction. After step (1), a matrix is formed based on the input data, the nonlinear mapping function selects the Gauss kernel function \(k(x,y)=\exp(-\frac{\|x-y^{2}\|}{2\sigma^{2}})\). After the KPCA nonlinear transformation in the Section 2.1, the kernel principal component is retained when the cumulative variance contribution rate \(\tau\) is greater than 90 %, and finally a new input variable matrix is formed.
3. Initialize the FWA parameter. After many tests, the maximum quantity of iterations is \(Maxgen=500\), the quantity of population is \(PopNum=30\), the quantity of spark determines the constant \(M=100\), and the radius of explosion determines the constant \(\hat{R}=150\).
4. Get the best values of C and \(\sigma\) in KELM. Firstly, \(C\) and \(\sigma\) will be randomly assigned, and then the fitness of each generation will be compared to select the best parameters. Judge whether each iteration satisfies the stop condition of the algorithm. If yes, the parameter is the global optimal parameter. If not, start a new cycle until the global optimal parameter is found.  
Figure 1: The proposed composite prediction framework.  
Simulation prediction. According to the prediction model above, the short-term load of distributed energy system is forecasted, and the results of load forecasting are analyzed and evaluated.