esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mass Determination and Material Loss
In cases without filler, groups P4 and P5, using cylindrical and conical tools, respectively, it initially seemed that the cylindrical pin tool caused more chips to be ejected outside the weld. However, the results suggest that this assumption cannot be confirmed, given the close values and high variability observed (\(p>0.05\)). Additionally, many of the chips (burrs) produced by the cylindrical pin tool remained attached to the weld on the retreat side, but outside the weld bead, meaning that they did not contribute to joint strength and worsened the surface's finish (Figure 14a). In the case of a truncated cone tool, the number of burrs is lower (Figure 14b). Although no significant differences in mass variation were found between the two tools (\(p>0.05\)), differences in weld homogeneity were evident. The truncated cone tool produced more uniform welds and achieved better mixing of the material. In Figure 14b, the white material can be seen inside the blue part. Although this work aimed to provide a general understanding of the behavior of the most relevant parameters in the welding process, based on the literature, it is important to further explore and expand on the scope of its application. In this context, the potential implementation of the FSW system in an automated setup, such as a general-purpose robotic arm or a tool specifically designed to weld a particular component, stands out as particularly relevant. This can expand the range of applications and facilitate its transfer to the industrial sector, allowing for tighter control over various parameters and weld quality. Additionally, integrating different sensors and process monitoring could provide  
Figure 14: Weld surface and chips generation observed in specimens (**a**) P5_0 and (**b**) P5_2. Figure 13: Specimens with 15% infill density from groups P1 (**a**) and P2 (**b**) welded at low and high speeds, respectively, showing a visible difference in weld width. valuable insights into the complex phenomena involved, including heat transfer, rheological behavior, and defect formation.
## 3 Results and Discussion
### Numerical Optimisation  
To determine the stability of wastewater digestion as a function of pH, biogas production, and COD removal, without interruption. The mathematical models obtained from the simulated experimental data were found useful as predictive and monitoring tools to detect inhibition threats. The CCD numerical optimization provided a feasible solution to operate the system at optimum conditions. Thus, the optimization criteria were set to maximize the desirability efficiency within the range of the operating parameters. Figure 5 illustrates the selected ramp plot of the optimum parameters identified at a mesophilic temperature (37 \({}^{\circ}\)C) for 28 days. Even though there were 100 optimal solutions obtained, the selection of the optimum combination represents the multi-objective set with an overall desirability of 100%. Additional optimal solutions obtained to maximize the biogas production, COD removal, and pH stability are presented as supplementary data (Table S1). The set of constraints could change based on the process' objectives and desirability. According to the simulated scenario results shown in Table 5, the nanoparticle load (0.46 g), biochar load (0.66 mgVS/L), and pH (7.07) show 100% desirability performance. The response surface (contour and 3-D) plots (Figure 4), in conjunction with the ramp plot (Figure 5), can be used to see the best region and confirm the model-predicted results. The quadratic second-order models' vital maximums, minimums, or saddles are possible definitions for their critical points.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Source**} & \multicolumn{1}{c}{**Sum of Squares**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Mean Square**} & \multicolumn{1}{c}{**F-Value**} & \multicolumn{1}{c}{\(p\)-Value**} \\ \hline Model & 213.29 & 5 & 42.66 & 189.59 & \textless{}0.0001 & significant \\ A-Nano & 9.12 & 1 & 9.12 & 40.52 & 0.0002 & \\ B-Biochar loading rate & 51.87 & 1 & 51.87 & 230.51 & \textless{}0.0001 & \\ AB & 0.9604 & 1 & 0.9604 & 4.27 & 0.0727 & \\ A\({}^{2}\) & 12.35 & 1 & 12.35 & 54.9 & \textless{}0.0001 & \\ B\({}^{2}\) & 30.5 & 1 & 30.5 & 135.56 & \textless{}0.0001 & \\ Residual & 1.8 & 8 & 0.225 & & & \\ Lack of Fit & 1.3 & 3 & 0.4333 & 4.33 & 0.0741 & not significant \\ Pure Error & 0.5 & 5 & 0.1 & & & \\ Cor Total & 215.09 & 13 & & & & \\ \hline Std. Dev. 0.47 & Mean 71.56 & & C.V\% 0.663 & \begin{tabular}{c} Adeq Precision 38.47 \\ Predicted R\({}^{2}\) 0.992 \\ \end{tabular} & \
\begin{tabular}{c} Adeq Precision 38.47 \\ Adjusted R\({}^{2}\) 0.986 \\ \end{tabular} &
\begin{tabular}{c} Adeq Precision 38.47 \\ Predicted R\({}^{2}\) 0.956 \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of variance in COD reduction response.  
Figure 4: Three-dimensional (3D) response surface plots (**a**) pH, (**b**) biogas production, and (**c**) COD reduction.
## 5 Experimental Results
### Parameter Comparisons of Different Methods  
The number of parameters of our proposed model is compared with other referenced models. Figure 5 presents a histogram of the parameters of the models. By comparing the results of histogram, it can be found that the parameters of our model are 30.02 million. Although the parameters of the classic Attention-Wave-U-Net [14] and the causal DEMUCS [15] are less than that of SECS-U-Net, the scores of metrics in Tables 3 and 4 show that their overall enhancement performance is inferior to SECS-U-Net. Furthermore, it also indicates that the parameters of our model only account for 29.99% of that of the recently proposed SASEGAN [7]. In addition, compared with the latest Sinc-SEGAN [9], the parameters of SECS-U-Net are 32.99% of its. In total, the results in Figure 5 show that our proposed model achieves better denoising performance with fewer parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSNR** & **SAR** & **SDR** \\ \hline Noisy & 1.68 & 8.90 & 9.07 \\ SEGAN [4] & 7.15 & 17.19 & 16.05 \\ Attention-Wave-U-Net [14] & **10.05** & **20.64** & **19.24** \\ DEMUCS (causal) [15] & 8.53 & 18.34 & 17.25 \\ SASEGAN [7] & 8.40 & 19.85 & 17.91 \\ \hline SECS-U-Net (\(L_{alpha}\)) & **9.54** & **19.92** & **18.91** \\ \hline \hline \end{tabular}
\end{table}
Table 4: SSNR, SAR and SDR of the proposed method and the reference methods (the bold indicates our metric scores and the metric scores that exceed ours in the reference models).  
Figure 4: The spectrograms of speech enhanced by **(c)** Attention-Wave-U-Net [14]. **(d)** DEMUCS (causal) [15], **(e)** SASEGAN [7] and **(f)** SECS-U-Net (\(L_{alpha}\))on open test set. The (a) and (b) are spectrograms of clean and noisy speech, respectively.
## 2 Criticality Indexes
### Economic Index  
The reduced water supply will incur an economic loss on the user side. Water has different economic value depending on the user. For municipal users, the supply shortage will disrupt daily activities and commerce. Meanwhile, for an industrial area, the water shortage will decrease the product amount or halt the manufacturing process, resulting in higher economic loss compared to the municipal area. Here, an economic value loss (\(EVL\)) index is developed to quantify the economic loss due to a disruption event of a WDN. Equation (2) represents the equation of the \(EVL\) index.  
\[EVL_{i}=1-\frac{\sum_{j=1}^{N}Q_{e,j}\times Tr_{i}\times u_{j}}{\sum_{j=1}^{N} Q_{n,j}\times Tr_{i}\times u_{j}}, \tag{2}\]  
Here, \(EVL_{i}\) is the economic value loss index with the isolation of pipe \(i\); \(Tr_{i}\) is the time to repair the pipe \(i\); and \(u_{j}\) is a unit value of water at node \(j\).  
In this study, the unit values of water are 0.1577 $/m\({}^{3}\) for the municipal sector and 0.2293 $/m\({}^{3}\) for the industrial sector [19]. \(Tr_{i}\) is the average time spent to repair a pipe depending on the pipe diameter and is calculated using a regression curve proposed by [PERSON] and [PERSON] [20]. Equation (3) expresses the equation of \(Tr_{i}\) in hours.  
\[Tr_{i}=6.5D_{i}^{0.285}, \tag{3}\]  
Here, \(D_{i}\) is the diameter of pipe \(i\) in inches.
###### Abstract
Following this trend, this study will use machine learning algorithms including decision trees (DT) and random forests (RF) algorithms, which can generate explainable results, to predict freshmen's academic performance. Except for DT and RF, multilayer perceptron (MLP) [10] will be performed as our comparison base. The prediction of first-year student academic achievements has received substantial attention in educational practice and theory [11]. Previous works used some variables, such as resilience, engagement [12], scores of quizzes and assignments [13; 14], students' academic self-concept [15], motivation, social relationships [16], and participation [11], to construct prediction models of first-year academic achievements. However, the information on these variables in the research can only be obtained during the course or in the progress of the semester. Some information also needs to be obtained through survey questionnaires and interviews. This is not enough to improve students' learning performance in time, which is especially true for those students who are performing poorly due to economic factors. In Taiwanese vocational universities, the majority of students are economically disadvantaged. They often need to rely on government tuition, miscellaneous fee waivers, and student loans to register. In addition, they must work part-time every month to support themselves and their family's living expenses. In addition to lack of interest, the biggest reason for dropping out is due to poor learning results. Therefore, the models established in published works [11; 12; 13; 14; 15; 16] and the prediction results are often less time-sensitive. A predictive model needs to be established before the semester begins to provide student counseling, financial assistance, and supplements. The annotation of teaching resources could be more accurate and more immediate. Therefore, this study attempts to use family background variables, including department, gender, address, admission status, Aboriginal status, child of new residents, family children ranking, on-campus accommodation,main source of living expenses, student loan, tuition waiver, parents' average income, status, occupations, and education. These variables can be obtained before the start of the semester, in order to construct predictions before the freshmen students start to learn, and thus buy more time for student guidance or investing learning resources in technological and vocational education. In sum, this paper aims to build a prediction model that can be used to predict freshmen students' learning performance based on decision trees and random forest algorithms. The sample was 2407 freshmen who enrolled in 12 departments of a university in Taiwan. From this constructed model, we can determine which students will succeed and which students indicate to be poor; the university is then able to offer them necessary assistance before they start their sophomore year. Based on experimental results, we can highlight some factors, which highly affect the first-year undergraduates' learning performance.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Experimental Preparation  
The hardware environment used for the experiments is Intel Core i5-8600k with 16 GB RAM and RTX2060; the software environments are Python 3.7, Tensorflow1.12 (GPU), PyCharm 2019.2, Keras2.2.4, and MATLAB 2016a.  
The following categories of data were analyzed for this study:  
1. The data used for the EALL are the monitoring data of a dam, selected from 13 November 2011 to 16 May 2018, the positive plumb line downstream displacement data, temperature data, and water level data from PL4 measurement point of 19# dam section, totaling 7227 items. The data are firstly preprocessed by pandas. The outliers are removed from the data according to the 3\(\sigma\) criterion, the data are temporally complemented according to the resample method, then the data are interpolated and complemented by the Lagrange Interpolation Polynomial to obtain the final preprocessed data.
2. The data used in Experiment 1 on gray wolf optimization are the Sphere function, Rastrigin function, and Grienwank function in the CEC2014 benchmark function set. Benchmark function is used to test whether an algorithm has high performance and high efficiency, the exploration ability, convergence speed, and optimization-seeking accuracy of the optimization algorithm can be evaluated by selecting appropriate benchmark functions for testing.
3. The experimental data based on the feasibility and performance of the STAGCN-IEALL model are selected from five measurement points (PP1-1, PP2-1,..., PL5,) of five positive vertical lines (PP1-PL5) of the downstream displacement data, which are arranged in the dam segment of No. 1, 7, 12, 17, and 19, respectively. The time span of each measurement point is selected from 13 November 2011 to 16 May 2018, while the deformation data and the corresponding temperature, water level, and other four characteristics data are selected for each measurement point. The dataset contains a total of 60,225 entries.
## 5 Results and Discussion
### The Influence of Sample Scale  
For the SVM method, the parameters are optimized before model training, so the calculated value of OA with a certain training sample scale is considered as the best accuracy directly, while, for the KNN method, the maximum value of OA obtained by different numbers of nearest neighbors--\(K\) is selected as the best accuracy. The data are illustrated in Figure 16. Then, a curve fitting for the best accuracy data is performed to quantitatively evaluate the performance and potential of KNN and SVM in data classification.  
Based on Figure 16, a natural exponential function is chosen as the general model:  
\[f(x)=M\cdot e^{-R\cdot x}+OA_{opt}. \tag{17}\]  
The fitting model is a monotone increasing function with an upper bound. It contains three coefficients: \(OA_{opt}\) is the upper bound of the function, which characterizes the maximum accuracy of the classification method, \(R\) determines the increasing rate of the function, and \(M\) determines the location of the zero crossing point when \(OA_{opt}\) is certain. The curve fittings were processed by the Curve Fitting Toolbox in MATLAB (version 3.5.2, the MathWorks, Inc. Natick, MA, USA) [63].  
The fitting results are shown in Table 1, and Figures 17 and 18. Based on the existing data and fitting results, it can be conjectured that the highest accuracy of KNN can reach about 96.18% OA with sufficient training samples, and is superior to that of SVM, which is about 95.14% OA. The coefficient \(R\) of the two methods (0.007754 for KNN and 0.01294 for SVM) illustrate that, when the training data are limited, the SVM method can achieve a relatively high accuracy rate as the increasing number of training points is more conducive to SVM. However, for the KNN method, it will need more training data to reach the same level. According to the coefficient, the accuracy rate of KNN will drop to 0 earlier than SVM when sample data are reducing.  
Figure 16: The best accuracy of KNN and SVM.  
\(OA_{opt}\) is the upper bound of the fitting function, \(R\) is the increasing rate of the function, and \(M\) is the location of the zero crossing point.
## 3 Numerical Model
### Model Parameter Settings  
Based on the ABAQUS 2020 finite element numerical simulation software, the method of extended finite element method (XFEM) was applied to simulate the cross-layer propagation law of fracture. The established two-dimensional numerical simulation model is shown in Figure 3, with a width and height of 40 m \(\times\) 40 m. Quadrilateral elements were used with each element having a width and height of 0.5 m, resulting in a total of 6400 elements in the model. The model was divided from top to bottom into 5 layers, namely: upper old fine-grained sandstone 15 m, direct upper siltstone 4 m, pseudo upper mudstone 1 m, coal seam 6 m, and lower mudstone 14 m. The horizontal well was positioned within the fine sandstone roof 1 m above the coal seam. The diameter of the borehole was 0.20 m. XFEM elements were inserted as initiation points for fracturing, simulating a downward perforation depth of 0.5 m. Water was chosen as the fracturing fluid with a pumping rate of 0.5 m\({}^{3}\)/min, fluid viscosity of 1.0 Pa-s, and a fracturing time of 100 s. The selection of parameters was based on the measured rock mechanics parameters, permeability, and in situ stress data from the Panxie mine in the Huainan Mining Area.  
Figure 3: Schematic diagram of numerical simulation model for fracturing and fracture perforation.  
To accurately represent the cohesive characteristics of the interface, zero-thickness cohesive element layers were used in the numerical simulation model to characterize its properties. A total of four zero-thickness cohesive element layers were inserted into the model to represent the coal-rock interface. Due to the development of coal cleats and the smooth surface treatment, the bonding strength on the coal side was significantly lower than that on the mudstone and sandstone side. Obtaining rock samples containing the coal-roof interface is challenging in actual ground drilling processes, especially for coal seams with fragmented and soft structures. Based on previous research by scholars [27; 28; 29], the following interface strengths were set from top to bottom: a tensile strength of 4 MPa and shear strength of 9 MPa for the fine-grained sandstone to siltstone interface; a tensile strength of 3 MPa and shear strength of 7 MPa for the siltstone to mudstone interface; and a tensile strength of 1 MPa and shear strength of 2 MPa for the coal to mudstone interface. Observation points A and B were respectively located at the injection point of the sandstone roof and the middle of the coal seam to track the variation in rock fracture width during the hydraulic fracturing process. Fracturing patterns formed by the model at different time steps were extracted to analyze the propagation laws of cross-layer fractures. These parameters are listed in Table 2.
## 3 Results and Discussion
Several Raman spectra were collected on different portions/grains of the C nanostructures in order to obtain appreciable reproducibility. Notably, the presence of the main Raman features from CuO (295 cm\({}^{-1}\)) [26] and CuS (473 cm\({}^{-1}\)) [27; 28; 29; 30] was never detected. The red rectangle in Figure 5 delimits a region between 250 and 820 cm\({}^{-1}\), enlarged in the inset, which shows a flat area without features, where typically the structures of Cu oxides and sulphides are found [26; 27; 28; 29; 30]. This observation is in perfect agreement with all the described data, confirming that the copper nano-roses exposed to air have the composition of metallic Cu\({}^{0}\) and thus are self-protected against oxidation from the external atmosphere. One hypothesis that could explain this important result lies in the possibility of interpreting the petals of the nano-roses as consisting of stacked layers of Cu [111] planes, such as [43], graphitic-like arranged, and thus resistant to possible oxidation. Figure 4: Selected energy dispersive XRD pattern collected upon carbon nanostructures, clearly indicating the presence of crystalline metallic Cu and graphite. The red circle evidences a region magnified in the inset, where the signature of crystalline CuO and CuS would be expected. No Bragg reflections related to those compounds are present. Figure 5: Raman spectrum from C nanostructures. In the inset, the region evidenced by the red rectangle is highlighted.
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 3 SBNN
### Searched Normal Cell and Reduction Cell
#### 3.2.3 Patch Shift Processing  
SAR ship images, which contain coherent speckle noise, always have a poorer quality than natural optical images. Hence, it is more difficult to train a binary classification network about the distribution of the feature maps. ReAct Sign and React PReLU put effort on reshaping the distribution channel-wisely. However, those functions do not cover any adjustment at spatial level. Inspired by resent researches on spatial attention of neural networks [30; 31; 35], we propose a spatial representation reinforcement method named as patch shift processing, which is illustrated in Figure 5. Firstly, the proposed patch shift processing divides the input feature maps into \(p\times p\) patches. Then, a learnable parameter is given to each patch in each channel. Finally, the features are reinforced by adder operators. This processing is trained with other weights in the network through backpropagation. Important areas can be highlighted and information irrelevant to the target is reduced in feature maps.  
In Figure 5, the sample which is directly binarized without the processing has too few useful information to be learnt. On the contrary, the processed image gives a high-quality 1-bit feature map with a clear shape of the ship after binarization. Considering the training difficulty and the computational complexity, we set \(p\) as 12 and place the proposed patch shift processing after linear layers in each cell.  
Figure 4: The demonstration of 4 different types of binary convolution in SBNN.