esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
The main reason is that the amount of correctly transmitted information increases when the condition of the VLC channel is optimal. The effective bit rates of the PNC-Polar scheme are significantly higher than the other two relay schemes. When the SNR surpasses 9 dB, the traditional relay scheme stabilizes at an efficiency of 0.25, while the DNC relay scheme reaches 0.33, and the PNC relay scheme achieves 0.5. These correspond to 100% and 51.5% increase in transmission efficiency for the PNC and DNC schemes, respectively. Figure 8:2 The effective bit rate per unit time-slot of relay schemes. Figure 8:3 The BER performance of the proposed scheme with code lengths. Figure 20 shows the throughput of the VLC system, employing different collaborative coding schemes. In the experiments, the length of the uncoded sequence is 512, the code rate is 0.5, and the source generates \(10^{3}\) frames of data per second. The correct amount of data transmitted within a certain period of time is counted to calculate the throughput. Overall, the throughput of the designed scheme is 1.5 times higher compared to the DNC-Polar relay scheme. When the SNR is higher than 10 dB, the BERs of the two schemes are close to each other, but the information exchange rate in the PNC-Polar relay scheme is significantly improved by 50%, which balances the transmission reliability and effectiveness in the VLC system.
## 3 Model Building and Algorithm Design
### Model Establishment  
Through the evaluation indicator system \(X=[x_{1}x_{2}x_{3}\ldots x_{i}]\) that affects the mine pressure hazard, the linear regression function that affects the mine pressure hazard can be obtained as shown in Formula (4), which is the input of the logistic regression model.  
\[z=W^{T}X+b=\omega_{1}x_{1}+\omega_{2}x_{2}+\ldots+\omega_{n}x_{n}+b \tag{4}\]  
In the formula, \(x_{i}\), for \(i=1,2,3\ldots n\), represents the \(i\)-th evaluation indicator; \(\omega_{i}\), for \(i=1,2,3\ldots n\), represents the weight of the \(i\)-th evaluation indicator; and \(i=1,2,\ldots,n\), where \(n\) represents the number of extracted evaluation indicators. \(b\) represents the partial regression coefficient. Since the output result of the linear regression is continuous in the real-number field, but the result of the mine pressure hazard is \"danger\" or \"no danger\", which is not a continuous value, it is necessary to introduce the Sigmoid function as the activation function. The Sigmoid function is shown in Formula (5).  
\[h(z)=\frac{1}{1+\exp(-z)} \tag{5}\]  
The Sigmoid function is an S-shaped curve, which can map any real number to a value between 0 and 1, but it does not take 0 and 1.  
In this activation function, when \(z\) approaches positive infinity, the value of \(h(z)\) approaches 1; when \(z\) approaches negative infinity, \(h(z)\) approaches 0. The value range of the independent variable is any real number, and the function value range is [0,1]. Through the Sigmoid function, any input is mapped to the [0,1] interval so as to obtain a predicted value in the linear regression \(z\), and then the value is mapped to the Sigmoid function, thus completing the transformation from the value to the probability of occurrence of mine pressure hazard. The derived prediction model of mine pressure hazard based on logistic regression is shown in Formula (6).  
\[h_{\omega}(x)=\frac{1}{1+\exp(-\omega_{1}x_{1}-\omega_{2}x_{2}-\ldots-\omega_ {n}x_{n}-b)} \tag{6}\]  
In general, we consider that when any input is mapped to the interval [0,1] using the Sigmoid function, and the result \(h_{\omega}(x)\geq 0.5\), the prediction \(y=1\), indicating that there is a danger that the value of mine pressure exceeds the standard. When \(h_{\omega}(x)\leq 0.5\), the prediction \(y=0\), indicating that there is no danger that the value of mine pressure exceeds the standard. By using the conversion of the Sigmoid function, whether the mine pressure value is dangerous is converted into two interval ranges. The conversion value, which can be used to calculate and compare the mine pressure hazard, is obtained.
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 1 Introduction
Other researchers, such as [PERSON] et al. [18], introduced methods like leave-one-out cross-validation in XGBoost frameworks, while [PERSON] et al. [19] and [PERSON] et al. [20] adopted gray relational analysis and Pearson correlation screening to refine input variables and boost model generalization. These developments underscore a growing trend toward hybrid intelligent models that combine metaheuristic optimization with neural networks to enhance predictive robustness and adaptability under varying geological and operational conditions. Overall, while current research has achieved certain results, it still presents several limitations: insufficient integration of multiple influencing factors, as most models consider only one or two variables and fail to account for complex interactions among rock types, joint systems, and explosive properties; susceptibility to overfitting, particularly in machine learning models like ANN and SVM when selecting input parameters and tuning hyperparameters; and limited generalization ability, as models that perform well on specific datasets often fail to maintain accuracy under varying geological conditions. To address these challenges, this study proposes a hybrid prediction model that combines the Crown Porcupine Optimization (CPO) [21] algorithm with the BP neural network, referred to as the CPO-BP model, to improve the prediction of blast fragmentation. The CPO algorithm simulates four defensive strategies of the crown porcupine (visual deterrence, acoustic deterrence, odor diffusion, and physical attack), enabling a dynamic balance between global exploration and local exploitation, and effectively avoiding premature convergence. To enhance input optimization and interpretability, the Mean Impact Value (MIV) method is applied to select key influencing factors. The model is trained and validated using 97 sets of real-world blasting data from an iron ore mine in Hunan Province, China. This study aims to investigate whether integrating a biologically inspired optimization algorithm (CPO) with BP networks and feature selection (via MIV) can improve the robustness and prediction accuracy of blasting fragmentation under variable geological conditions. The contributions of this study are threefold:  
1. A novel hybrid model CPO-BP is developed that significantly improves the stability and prediction accuracy of BP networks under complex geological conditions;
2. A real engineering database is constructed, and key variables are selected using the MIV method to optimize the model's input structure;
3. The proposed CPO-BP model outperforms the standard BP model in terms of RMSE, MAE, and MAPE, and demonstrates strong potential for practical application in engineering blasting design.
# Rumor Detection Based on Attention CNN and Time Series of Context Information
###### Abstract  
This study aims to explore the time series context and sentiment polarity features of rumors' life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2 Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors' classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.  
rumor event detection; sentiment polarity; time series algorithm; attention CNN +
Footnote †: journal:  
1
Footnote 1: School of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, China; [EMAIL_ADDRESS]
## 2 Transmit-Array Element Structure
In order to highlight the advantages of this research, Table 2 lists comparative features of several elements in the published literature, including the center frequency, period of the element, thickness of the element, phase modulation, and polariza  
Figure 2: The influence of variations in R_via and R_out on the transmission coefficient, transmission phase, and axial ratio of the lens element over the frequency range of 24.5–27 GHz: (**a**) transmission coefficient of R_via in dB; (**b**) transmission phase of R_via in degrees; (**c**) transmission coefficient of R_out in dB; (**d**) transmission phase of R_out in degrees; (**e**) axial ratio of R_via in dB; (**f**) axial ratio of R_out in dB. tion conversion. It can be seen from the table that only the elements in [10, 24, 25] and our proposed element support both phase modulation and polarization conversion in one unit. However, the element of the proposed element is a subwavelength structure, compared with the elements with half-wavelength size in [10, 24, 25], which means more units can be deployed in the same transmit-array aperture, with higher phase modulation diversity. Figure 3: Phase modulation and polarization conversion properties of the proposed lens element over the frequency range 24.5–27 GHz: (**a**) reflection coefficient in dB; (**b**) transmission coefficient in dB; (**c**) phase shift in degrees; (**d**) axial ratio of the converted right-hand circular polarization in dB; (**e**) phase change for the incident wave varying from 0 degrees to 30 degrees.
# Single-Leg Squat Performance and Reported Pain within Youth Softball Players
###### Abstract  
The purpose of this study was to assess single-leg squat (SLS) performance on reported pain. Forty-two youth softball athletes (\(13.0\pm 2.0\) years; \(162.19\pm 9.75\) cm; \(60.80\pm 14.28\) kg) completed a bilateral SLS and a health history questionnaire in which they indicated if they were currently experiencing any pain/discomfort. Due to the clinical significance of the current study, \(p<0.10\) was classified as nearing significance. A point-biserial correlation was run between the groups (pain and no pain) and all kinematic variables (maximal knee flexion, knee valgus/varus, vertical sacrum displacement, anterior pelvic tilt, and pelvic lateral tilt) at each event (\(45^{\circ}\) descent, maximal knee flexion, and \(45^{\circ}\) ascent), across each phase (descent, ascent), and between legs. Increased vertical sacrum displacement was correlated with pain at \(45^{\circ}\) ascent and at maximal knee flexion in the right SLS. Knee valgus at maximal knee flexion, \(45^{\circ}\) ascent, and in the decent phase of the left SLS was significant. Anterior pelvic tilt in the decent phase of the left SLS and knee valgus and pelvic lateral tilt in the decent phase of the right SLS were found to be significantly correlated with pain. Poor SLS performance was correlated with reported pain.  
Keywords:injury-prevention screening; lumbopelvic stability; overhead throwing +
Footnote †: journal: Applied Sciences
## 4 Results
### Answers to the Questions of Context and Research Approach
#### 4.1.2 Data Sources  
Considering the seven data sources (ACM, IEEE, Science Direct, Scopus, Springer, Wiley, and WOS), the initial search yielded a total of 3776 articles, where the sources that provided the greatest numbers of articles came from ACM and Science Direct, with Springer and IEEE in second place at 13%. Then, the application of filters 1 and 2 generated an approximate reduction of 22% of the articles, with the articles from Springer (0), Wiley (126), and WOS (11) substantially decreasing.  
In addition, the application of filters 3 and 4 and their respective criteria generated a 97% reduction with respect to the total obtained with the initial search, ultimately ending in 81 articles. It must be mentioned that after applying filter 4, only articles from ACM (31), Science Direct (4), IEEE (26), and Scopus (20) remained (see Figures 3 and 6).  
Concerning the quartiles of scientific journals, 62.5% belonged to quartile 2, while 25% belonged to quartile 1, and they were mainly distributed in SCIE-type editions. The rest of the articles were equally distributed between quartiles 3 and 4 (see Figure 7a,b).  
Figure 5: Evolution of the subject.  
In addition, the countries with the most conferences were the USA, China, and India (between 10% and 12% each). In the second place, it was possible to find conferences in Canada, Brazil, and South Korea, which were equally distributed (7.7%), followed by Europe, with Portugal, France, and Spain (4.6%) (see Figure 10(a)). The conferences with the most articles studied corresponded to ASE (IEEE International Conference on Automated Software Engineering) and ICPC (International Conference on Program Comprehension), with four articles each (see Figures 8 and 10(b)).  
difficulties in terms of the replicability of the studies (see the results of questions RQ-ML4 and RQ-ML5).  
A small number of articles (3) proposed recommendations or guidelines for carrying out certain ML-supported maintenance tasks.  
that it was a proof of concept, and there were unclear areas that were confused with experience reports.
## 2 Operation Principle for J-DBN-Based Equalization  
### Traditional DBN Method  
The DBN can be approximated as a stack of restricted Boltzmann machines (RBMs) [28], as shown in Figure 1a. An RBM is a generative stochastic network containing a visible layer \(v=\left\{v_{i}\right\}_{i=1}^{N}\) and a hidden layer \(h=\left\{h_{j}\right\}_{j=1}^{M}\) with the parameters \(\theta=\left\{W,d,c\right\}\). The energy function and the likelihood function of the RBM can be stated as  
\[E(v,h)=-\sum_{i,j}v_{i}W_{ij}h_{j}-\sum_{i}d_{i}v_{i}-\sum_{j}c_{j}h_{j}, \tag{1}\]  
\[P(v,h)=\frac{1}{Z}\exp(-E(v,h)), \tag{2}\]  
where \(v_{i}\in\left\{0,1\right\}\), \(h_{j}\in\left\{0,1\right\}\), \(W=\left(W_{ij}\right)\in R^{N\times M}\) are the weights connecting the visible layer and hidden layer, \(d\) and \(c\) are the bias terms of the visible and hidden layers, and \(Z\) represents the partition function. Moreover, the probabilities \(P(v|h)\) and \(P(h|v)\) can be calculated by  
\[P(v_{i}=1|h)=\sigma\left(\sum_{j}W_{ij}h_{j}+d_{i}\right), \tag{3}\]  
\[P(h_{j}=1|v)=\sigma\left(\sum_{i}W_{ij}v_{i}+c_{j}\right), \tag{4}\]  
where \(\sigma(\cdot)\) is the sigmoid function defined as \(\sigma(x)=1/\left(1+e^{-x}\right)\).  
The DBN with \(\lambda\) hidden layers contains \(W_{1},W_{2},\ldots,W_{\lambda}\) connection weight matrices and \(\lambda+1\) biases \(d_{0},d_{1},\ldots,d_{\lambda}\), where \(d_{0}\) is the bias of the visible layer \(v\). Therefore, the output probability can be calculated by the hidden vector: \(\Phi=\sigma(W_{\lambda}h_{\lambda-1}+d_{\lambda})\).  
Figure 1b,c represent the \(j\)-th neuron of the feedforward and reconstruction architecture. The RBM utilizes the stochastic approximation method to update the parameters \(\theta=\left\{W,d,c\right\}\) by maximizing the likelihood \(P_{\theta}(v)\).
# Lithium Concentrations in Saline Brines of the Shu-Sarysu Depression
## 2 Geological Characteristics of the Study Area
The Shu-Sarysu province, known for its rare-metal underground brines, shares its geographical boundaries with the corresponding tectonic depression. The most promising brines, enriched with microelements, are linked to the aquifer system found in Devonian and Carboniferous deposits, situated beneath loose Mesozoic-Cenozoic rocks at depths ranging from 1700 to 2900 m. Similarly, the Moyynkum region, also abundant in rare-metal waters, occupies the same depression, which was shaped by the presence of a thick layer of Middle-Upper Paleozoic deposits exceeding 900 m in thickness. Through testing wells located within specific oil and gas formations at depths between 874 and 2111 m, diverse brines with salinity levels ranging from 130 to 316 g per liter were discovered. Concentrations of rare elements vary over a very wide range (mg/L): potassium 660-2111, strontium 182-4800, iodine 5-40, bromine 31-2080, lithium 12-37.5 [7; 10]. In the Neogene, aquifer systems at significant depths come into direct contact with felsic intrusions of the ridge, intersected by numerous disjunctive faults with a northwestern strike. Through these faults, hydrocarbonate sodium waters infiltrate into the  
Figure 2: Hydrographic network of the rivers Shu and Sarysu, Scale 1:1,500,000 [12]. Neogene aquifers and groundwater, even at significant depths (Figure 3). On the other hand, morphologically, this area represents an alluvial-lacustrine (delta) plain where the groundwater level is very close to the surface (ranging from 0.5 to 1 m). These groundwater sources undergo intense processes of evaporative salt concentration. There are numerous lake basins and salt flats where surface waters concentrate to form brines, and in shallow closed basins, even to the formation of self-precipitating salts.
## 3 Methodology Description
### Numerical Approach
Connectivity between nodes is avoided, allowing a faster and more efficient computation, even though a truncation error is assumed. Lately, some features have been added, and the Shepard factor is now computed geometrically through a semi-analytical approach, improving the stability and robustness of the simulations [34]. Generally, no-slip boundary conditions should be used for the tank walls. However, when the Reynolds number is around \(10^{5}\) and greater, the wall boundary layer is thinner than the resolutions tested in the present work. Then, free slip boundary conditions are used on the tank walls instead, even though the expected difference is small, as has been already discussed in [22]. Time integration is performed by means of a predictor-corrector scheme. Due to the violence of the impacts expected during the simulation, the Courant-Friedrichs-Lewy (CFL) condition is set to 0.1. Several time step constraints are imposed in order to consider all the phenomena involved, taking the minimum of them at every time step:  
\[ \begin{split}\Delta t\leq\text{CFL}\frac{h^{2}}{v}\qquad\qquad \Delta t\leq\text{CFL}\min_{i}\left(\frac{h}{c_{s}+h\max_{j}\pi_{ij}}\right) \\ \Delta t\leq\text{CFL}\min_{i}\left(\frac{h}{\parallel\mathbf{u}_{i} \parallel}\right)\qquad\quad\Delta t\leq\text{CFL}\min_{i}\sqrt{\frac{h}{ \parallel\frac{\mathbf{Du}_{i}}{\parallel Dt}\parallel}}\end{split}  \tag{10}\]  
Simulations have been carried out with the open-source SPH software AQUAgpusph developed by CEHINAV-UPM. As a reference, see, e.g., [26; 33; 34; 35; 36] for applications done with this software. For further information, and to download the official release of the code, it can be accessed at [[http://canal.etsin.upm.es/aquagpusph/](http://canal.etsin.upm.es/aquagpusph/)]([http://canal.etsin.upm.es/aquagpusph/](http://canal.etsin.upm.es/aquagpusph/)). Last access 4 August 2022.
## 3 Controller Design and Analysis
\begin{array}{l}V=|s|+\frac{1}{2\eta_{1}}tr\left(\widetilde{\underline{W}_{4 }^{T}\widetilde{\underline{W}}_{4}}\right)+\frac{1}{2\eta_{2}}tr\left( \widetilde{\widetilde{W}_{4}^{T}\widetilde{\underline{W}}_{4}}\right)+\frac{1}{2 \eta_{3}}tr\left(\widetilde{\underline{W}_{4c}^{T}\widetilde{\underline{W}}_{4c }}\right)+\frac{1}{2\eta_{4}}tr\left(\widetilde{\widetilde{W}_{4c}^{T} \widetilde{\underline{W}}_{4c}}\right)+\frac{1}{2\eta_{5}}tr\left(\widetilde{ \underline{B}_{4}^{T}\widetilde{\underline{B}}_{4}}\right)\\ +\frac{1}{2\eta_{6}}tr\left(\widetilde{\underline{B}_{4}^{T}\widetilde{ \underline{B}}_{4}}\right)+\frac{1}{2\eta_{7}}tr\left(\widetilde{\underline{W}_{ 3}^{T}\widetilde{\underline{W}}_{3}}\right)+\frac{1}{2\eta_{8}}tr\left( \widetilde{\underline{W}_{3}^{T}\widetilde{\underline{W}}_{3}}\right)+\frac{1}{2 \eta_{9}}tr\left(\widetilde{\underline{C}_{2}^{T}\widetilde{\underline{C}}_{2}} \right)+\frac{1}{2\eta_{10}}tr\left(\widetilde{\underline{C}_{2}^{T}\widetilde{ \underline{C}}_{2}}\right)+\frac{1}{2\eta_{11}}tr\left(\widetilde{\underline{B}_{2 }^{T}\widetilde{\underline{B}}_{2}}\right)\\ +\frac{1}{2\eta_{12}}tr\left(\widetilde{\overline{B}_{2}^{T}\widetilde{ \underline{B}}_{2}}\right)+\frac{1}{2\eta_{13}}tr\left(\widetilde{\underline{W}_{5}^{T} \widetilde{\underline{W}}_{5}}\right)+\frac{1}{2\eta_{14}}tr\left(\widetilde{ \widetilde{W}_{5}^{T}\widetilde{\underline{W}}_{5}}\right)+\frac{1}{2\eta_{15}} \widetilde{\underline{R}_{1}^{2}}+\frac{\text{C}bk_{2}}{2}{\left(\int\text{sgn}(s) dt\right)}^{2}\end{array}  \tag{39}\]  
Taking the derivative of Equation (39) yields the following:  
\[\begin{array}{l}\dot{V}=\text{sgn}(s)\cdot\dot{s}+\frac{1}{\eta_{1}}\dot{ \widetilde{\underline{W}}_{4}}^{T}\widetilde{\underline{W}}_{4}+\frac{1}{\eta_{ 2}}\dot{\widetilde{\underline{W}}_{4}}^{T}\widetilde{\underline{W}}_{4}^{T} \widetilde{\underline{W}}_{4}+\frac{1}{\eta_{3}}\dot{\widetilde{\underline{W}}_{4c}}^{T} \widetilde{\underline{W}}_{4}+\frac{1}{\etaBy substituting (31)-(38) into (40) and ensuring the conditions in Theorem 1 are met, the following can be obtained:  
\[\dot{V}=C\text{sgn}(s)\cdot\Delta_{0}-Cbk_{1}^{*}|s|^{\frac{1}{2}}\leq-Cbk_{1}^{* }|s|^{\frac{1}{2}}+C|\Delta_{0}| \tag{41}\]  
Assuming that the \(|\Delta_{0}|\) derived above has an upper bound, i.e., \(|\Delta_{0}|<\Delta_{\text{max}}\), the following can be obtained:  
\[ \begin{cases}\dot{V}\leq 0\ \ |s|\geq(\frac{\Delta_{\text{max}}}{bk_{1}^{*}})^{ 2}\\ \dot{V}>0\ \ |s|<(\frac{\Delta_{\text{max}}}{bk_{1}^{*}})^{2}\end{cases}  \tag{42}\]  
It can further be derived that \(s\) will eventually converge to the following:  
\[-(\frac{\Delta_{\text{max}}}{bk_{1}^{*}})^{2}\leq s\leq(\frac{\Delta_{\text{ max}}}{bk_{1}^{*}})^{2} \tag{43}\]  
When the value of optimal sliding mode gain \(k_{1}^{*}\) is appropriately chosen, the sliding mode surface will eventually converge to a small domain, which can indicate that the system is stable. \(\square\)