esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
## 3 Results and Discussion
### Failure Planes Exhibited  
The deformation behavior of soil samples was assessed using the stress-strain curves. Samples not subjected to wet-dry cycles exhibited shear failure characterized by an approximately 45-degree failure plane. Treated specimens subjected to durability and water immersion tests exhibited mild shear with swelling due to bulging failure resulting from the cyclic absorption of the moisture content. Ductile materials exhibit larger axial strain compared to brittle materials. The stress-strain behavior of expansive soils is dependent on the moisture content. Soils on the dry side of the optimum value display brittle behavior, while soils on the wet side display ductile behavior [64]. Samples that did not undergo durability testing incurred brittle failure, as shown in Figure 8a, since test specimens were prepared with moisture that was less than the optimum values. Durability test samples demonstrated hints of ductile behavior, as seen in Figure 8b, where slight bulging occurred with a distinct failure plane in the middle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Curing Period (Days)** & **UCS (kPa)** & **w (\%)** \\ \hline
7 & 25.04 & 52.92 \\
14 & 21.61 & 54.11 \\
21 & 29.05 & 52.94 \\
28 & 22.01 & 46.62 \\
35 & 27.24 & 47.32 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Average unconfined compressive strength (UCS) and moisture content (w%) of samples subjected to modified water immersion tests.  
The peak normal stress in the untreated sample reached approximately 5% axial strain. The axial strain at peak normal stress was around 8% for the sample subjected to the durability test. The slightly ductile durability test sample absorbed more energy since this material showed a higher percentage of elongation under stress than the brittle material. Moreover, the untreated sample reached a more significant axial stress of 337.63 kPa before failing compared to the test sample subjected to the durability test, which only withstood a load of approximately 28.09 kPa before reaching its failure state.
## 4 Results and Discussions
### Feature Selection Analysis  
To investigate the impact of different feature sets on model performance, this paper constructs five datasets with various feature selection methods including Filter, Wrapper, and Embedded.  
(1) Variance Selection  
Features with low variance usually have little effect on final prediction results. In this study, features with variance below a threshold of three were removed, which includesdraft, trim, wind wave period, combined wave height, wind wave height, wave height, and flow velocity.
2. Correlation Coefficient Selection  
The setting of correlation coefficient threshold is generally based on experience and specific data, aiming to remove features with relatively weak correlations while preserving the features as much as possible. In this study, features with correlation coefficient greater than 0.1 were selected. Based on this criterion, six features--wind wave period, wind wave height, wind speed, turning rate, draft, and flow direction--were removed. Figure 4 shows correlation coefficient between features and fuel consumption.  
(3) Recursive feature elimination  
Recursive feature elimination (RFE) is a feature selection method including a gradient boosting regression model and cross-validation. Four key features including rot, wind direction, merpm, and main engine power are identified by RFE. The specific result of recursive feature elimination is shown in Figure 5.  
It is discernible from the figure that the cross-validation score corresponding to the feature amount of four is the highest. Through outputting the features that are in correspondence with this point, it is ascertained that the steering rate, wind direction, main engine speed, and main engine power have the most significant impact on fuel consumption.  
(4) Feature selection based on LASSO  
Figure 4: The correlation coefficient between features and fuel consumption.  
Figure 5: The result of recursive feature elimination.  
The results demonstrate that hyperparameter adjustment significantly improves model performance across diverse data sets. Enhancements in MAE, MSE, RMSE, and MAPE attest to the efficacy of the hyperparameter adjustment process in elevating the prediction accuracy of SA-LSTM.
## 4 Experimental Validation
### Case 1
#### 4.1.5 Comparative Experiments with Different Models  
In this paper, several widely used bearing fault diagnosis models were selected for comparison with the proposed model, including ResNet18 [28], Vgg16 [29], and a standard 2D-CNN. The 2D-CNN is a standard convolutional neural network architecture consisting of two convolutional modules. Each module consists of a convolutional layer, a batch normalization layer, a ReLU activation function, and a max-pooling layer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Methods** & **0-1** & **0-2** & **0-3** & **1-0** & **1-2** & **1-3** & **2-0** & **2-1** & **2-3** & **3-0** & **3-1** & **3-2** & **Average** \\ \hline Method A & **100** & **99.87** & 99.73 & **100** & 100 & **100** & **98** & **100** & **100** & 86.67 & **100** & **100** & 98.69 \\ Method B & **100** & 93.07 & 80.53 & 100 & 99.87 & 94.4 & 93.07 & 90.67 & 100 & 80 & 86.13 & 99.6 & 93.11 \\ MCR-KAResNet-TLDAF & **100** & **99.87** & **100** & **100** & **100** & 97.6 & **100** & **100** & **94.8** & **100** & **100** & **99.36** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results of different domain adaptation algorithms on the CWRU dataset.  
Figure 9: The average accuracy of different domain adaptation algorithms on the CWRU dataset.  
Based on the basic architectures of these models, we designed the following experiments to evaluate their performance in bearing fault diagnosis tasks. In this experiment, MCR images were used as the input for all models. The domain adaptation algorithm used was the integrated MK-MMD and CDAN+E method.  
The accuracy of the models is shown in Table 8 and Figure 10. The experimental results show that the KAResNet model significantly outperforms several common fault diagnosis models. MCR-KAResNet-TLDAF improved the accuracy by 4.26% compared to the MCR-ResNet18-TLDAF method, 4.55% compared to the MCR-2D-CNN-TLDAF method, and 4.7375% compared to the MCR-Vgg16-TLDAF method. In more complex transfer tasks, such as 0-2, 0-3, 3-0, and 3-1, the KAResNet model surpasses the accuracy limitations of conventional models, achieving higher accuracy rates. Notably, in transfer tasks 0-3 and 3-1, the KAResNet model achieved a 100% accuracy, demonstrating its superior performance in these scenarios. This indicates that the KAResNet model excels at extracting fault features from bearings and can more effectively integrate with domain adaptation algorithms to achieve better alignment between the source and target domains.
## 3 Results and Discussion
### Adsorption Isotherms
The parameters in each isotherm, accompanied with the R\({}^{2}\) and ERR values, are shown in Table 3. From Table 3, the R\({}_{\rm L}\) from the Langmuir isotherm is in a range from 0 to 1, indicating that the adsorption behavior is favorable. On the other hand, the goodness of the fit to each model exhibited its value of R\({}^{2}\) to be more than 0.97. The best fitting model should express its magnitude of R\({}^{2}\) close to unity, describing the concurrence of the experimental data and the one calculated from the model. The magnitude of R\({}^{2}\) from each model was ordered as Koble-Corrigan isotherm (1.000) \(>\) Langmuir isotherm (0.988) \(>\) Freundlich model (0.974), where the number in the parentheses is the value of R\({}^{2}\) of that model. This dictated the goodness of the fit of the experimental data to the Koble-Corrigan isotherm. Moreover, another quality tool for validating a model to the experimental data is the ERR. The adequacy of the fit of the model to the experimental data should show a minimal error distribution between the experimental and calculated data. The magnitude of ERR from the Koble-Corrigan isotherm is less than that of the Langmuir isotherm by around 5.18-folder and that of the Freundlich model by about 13.31-folder. This indicated that the Koble-Corrigan isotherm model fitted very well to the experimental data with its R\({}^{2}\) value being to unity and having a lower ERR. This revealed that the MCC likely had a heterogeneous surface with various active sites, resulting from the synergistic combination of electrostatic attractions and hydrophobic interactions. Some dye molecules were adsorbed on the active sites via electrostatic attractions, whereas some were adsorbed via hydrophobic interactions. On the surface of MCC, there is multilayer adsorption on some active sites and monolayer adsorption on some sites. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Isotherm Model** & \multicolumn{2}{c}{**Parameters**} \\ \hline Langmuir & \(q_{0}\) (mg g\({}^{-1}\)) & 20.20 \\  & R\({}_{\rm L}\) & 0.071 \\  & K\({}_{\rm L}\) (L mg\({}^{-1}\)) & 1.05 \\  & R\({}^{2}\) & 0.988 \\  & ERR (\%) & 6.11 \\ Freundlich & 1/n & 0.651 \\  & K\({}_{\rm F}\) [(mg g\({}^{-1}\))(L mg\({}^{-1}\))\({}^{(1/n)}\)] & 9.84 \\  & R\({}^{2}\) & 0.974 \\  & ERR (\%) & 15.70 \\ Koble-Corrigan & n & 0.992 \\  & K\({}_{\rm KC}\) (L mg\({}^{-1}\)) & 1.21 \\  & R\({}^{2}\) & 1.000 \\  & ERR (\%) & 1.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The isotherm parameters for the adsorption of RR120 on the MCC.
# XLNet-Based Prediction Model for CVSS Metric Values
## 2 Problem Formulation  
Vulnerability metric value prediction is a multi-label text classification problem. A multi-label text classification algorithm is used to obtain possible metric values from vulnerability descriptions. The vulnerability description as textual information cannot be directly input into the classification model. The text information is usually converted into word vector information by the word vector algorithm. Common algorithms include One-hot, TFIDF [32], word2 vec [33], and BERT [34].  
This paper defines the vulnerability metric value generation model as follows:  
\[\hat{y}=f_{1}(\Theta,x) \tag{2}\]  
where \(x\) is a feature extracted from the text information, \(\Theta\) is an adjustable model parameter, \(f_{1}(\cdot)\) is the structure of the generative model required to determine a prediction function, \(\hat{y}\) denotes the probability that the value of the vulnerability metric to be predicted is within \([0,1]\).  
\[y_{t}=f_{2}(\hat{y}) \tag{3}\]  
\(f_{2}(\cdot)\) is used to transform the probabilistic form of the metric values into a textual form, \(y_{t}\) denotes the transformed metric values.  
In this paper, the dataset of vulnerability metrics prediction is denoted as \(D=\{X,Y\}\), where \(X=[V_{1},V_{2},\cdots,V_{n}]\), and \(V_{i}\) is the description of the \(i\)th vulnerability. Accordingly, \(Y=\left[y^{(1)},y^{(2)},\cdots,y^{(n)}\right]\) is the truth-value label vector, while \(y^{(i)}\) is the label of \(V_{i}\). In this paper, a multi-label classification text problem is investigated. To get the probability of each label value, we split the multi-label multi-classification text problem into a multi-label text binary classification problem, where \(y^{(i)}=[y_{1},y_{2},\ldots,y_{m}]\), \(m=\sum_{i}^{k}c_{i}\), \(c_{i}\) refers to the number of categories owned by the \(i\)th metric, \(k\) represents the total number of metric values to be predicted, and \(y_{i}\in\{1,0\}\) denotes whether the \(i\)th metric value exists. To achieve vulnerability metric prediction, \(X\) is converted into the feature matrix \(x=[x_{1},x_{2},\ldots,x_{n}]\), where \(x_{i}\in\ R^{n}\) are the features extracted from \(V_{i}\), while \(n\) is the number of feature dimensions. As stated in (2), the expected result for \(x\) is \(\hat{y}=f_{1}(\Theta,x)\), where \(\hat{y}=[\hat{y}_{1},\hat{y}_{2},\cdots,\hat{y}_{n}]\), \(\hat{y}_{i}=[y(1),y(2),\cdots,y(m)]\), \(y(i)\) is the predicted probability corresponding to each metric value. Since vulnerability metric value generation is a multi-label binary classification task, the objective of prediction model parameter optimization is to lower the model's loss function according to the machine learning convention. Therefore, the mission of this study is to construct a better \(f_{1}(\cdot)\) and to find the appropriate \(\Theta\) in order to achieve the task of predicting vulnerability metric values with better performance.
## 2 Data Acquisition and Preprocessing
### Data Preprocessing
#### 2.2.2 Data Correlation Analysis  
Before model training, correlation analysis of the data features is the key to determining the speed of model training and convergence. In this paper, the DO was used as the dependent variable feature to be measured, and pH, water temperature, conductivity, salinity, and total dissolved solids were used as the independent variable features. In order to determine which independent variable features have a greater impact on the DO, PPMCC was used to determine the linear correlation between each feature and the DO. PPMCC is a measure of the strength of linear correlation between two variables \(x\) and \(y\). It is usually used in regression analysis and numerical prediction with the following formula:  
\[r_{x,y}=\frac{cov(X,Y)}{\sigma x\sigma y} \tag{3}\]  
where \(cov(X,Y)\) is the covariance of variables \(x\) and \(y\), \(\sigma x\)_and_\(\sigma y\) are the standard deviations of \(x\) and \(y\), respectively. The value of \(r\) is between \(-1\) and \(1\). The closer to \(-1\), the more negatively correlated \(x\) and \(y\) are, the closer to \(1\), the more positively correlated \(x\) and \(y\) are, while near \(0\) indicates that \(x\) and \(y\) are not correlated. In general, if \(|r|\geq\ 0.8\), the two variables can be considered highly correlated; if \(0.5\ \leq|r|<\ 0.8\), the two variables can be considered moderately correlated; if \(0.3\ \leq|r|<\ 0.5\), the two variables can be considered to be lowly correlated; if \(|r|\ <\ 0.3\), the two variables can be considered largely uncorrelated [32].
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 3 Hardware Design  
To optimize the extraction of the head wave of the UILWD echo signal, a real-time head-wave extraction method was designed, consisting of two parts--echo reception pre-processing and echo digital signal processing based on FPGA. The former is mainly a hardware circuit, whereas the latter is mainly a processing algorithm.
# Forecasting Students Dropout: A UTAD University Study
## 2 Background Theory and Literature Review
They modeled the problem using 3 ML methods: Gradient Boosted Tree (GB), XGB, and ANN, obtaining accuracy in the range 68.0% to 85.8%. Studies using \"external\" features could also be found. [PERSON] et al. [13] used a model with non-academic features. They concluded that the number of family members, interest in further studies, and the relationship with lectures are features that influenced the dropout. [PERSON] et al. [14] studied the importance of extracurricular activities to predict dropout in students from two Bachelor of Science degrees (Engineering and Business), showing that extracurricular activities are excellent dropout predictors. The following works revisions the students' dropout prediction. [PERSON] et al. [15] revised machine learning algorithms to predict academic dropout in developing countries. They conclude that many researchers ignore data that is unbalanced, leading to improper results. On the other hand, their main focus is providing early prediction instead, including ranking and forecasting mechanisms on addressing the dropout student's problem. [PERSON] et al. [16] searched scientific indexed publications in higher education to analyze the retention and dropout of higher education students. They identified the data and techniques used and proposed a classifier using several categories considering several student and external features. Table 1 sums up the models, features, metrics used, and the results obtained. Column 1 identifies the work, and column 2 indicates the features used. The \"Marks\" feature means that the work uses the curricular units marks. The academic feature indicates the use of academic data like attendance, GPA, and marks. The \"socioeconomic\" feature indicates the use of social and economic data, the \"Institutional\" refers to data related to the study plan and the university, \"Personal\" refers to personal data like address, age, and gender, \"Demography\" indicates the inhabitant number of a residential area and other demographic data, \"Motivation\" refers to the driving force behind students actions and other psychological phenomena. \"Sports\" indicates that students practice sports activities. \"High School\" comprises data related to the student's high school and their marks obtained. \"Activity\" is the data obtained through interaction with computer learning systems, e.g., time spent by a student for a day. \"Attendance\" is the school attendance. \"Knowledge\" is the knowledge degree that the students have. Finally, \"Volunteer\" indicates if the students practice volunteering. The column methods enumerate the methods used in the works. The \"Result\" column indicates the respective metric value (e.g., ACC: accuracy, AUC: area under the curve).
## 2 Materials and Methods
### Device Fabrication  
For the PSC device fabrication, first, pre-etched fluorine-doped tin oxide (FTO) coated glass substrates were cleaned rigorously using soapy water, deionized water (DI), ethanol and isopropanol (IPA) for 20 min each under sonication. After cleaning, the substrates were N\({}_{2}\) blow-dried to remove the residual solvents from the substrates and then thermally treated in a convection oven at 70 \({}^{\circ}\)C for 30 min. Cleaned substrates were further treated in a UV-ozone plasma for 20 min before starting the thin film deposition of the solar cell stacking layers. For the fabrication of the n-i-p structure, the cleaned FTO substrates were spin coated with a 0.15 M solution of titanium diisopropyloxide bis(acetylacetonate) in ethanol at 3000 rpm for 20 s to form a compact titanium dioxide (c-TiO\({}_{2}\)) electron transport layer (ETL) and subsequently annealed at 510 \({}^{\circ}\)C for 30 min. For the perovskite absorber layer, a perovskite precursor solution was prepared with an equimolar precursor ratio of the organic halide (MAI = 1 M, 1.2 M, 1.5 M, 1.6 M) and the metal halide (PbI\({}_{2}\) = 1 M, 1.2 M, 1.5 M, 1.6 M) in a fixed solvent formulation of DMF:DMSO (4:1); all four solutions were stirred at 65 \({}^{\circ}\)C overnight. The perovskite films were then deposited by spin coating these 4 precursor solutions on top of the c-TiO\({}_{2}\) layer via a spin coating program comprising of 8 s at 1000 rpm, with a subsequent ramp to 5100 rpm for 15 s. An amount of 0.1 mL of toluene, used as the anti-solvent, was dripped dynamically onto the underlying spinning substrate, for the last 5 s of the second stage ramp to 5100 rpm. The stack was then thermally treated at 125 \({}^{\circ}\)C for 20 min. For the hole transport layer (HTL), the spiro-OMeTAD (86 mg mL\({}^{-1}\) in chlorobenzene) was doped with three solutions, all used in conjunction, consisting of 34 \(\mu\)L of TBP, 20 \(\mu\)L of Li-TFSI and 11 \(\mu\)L FK209. For the latter two solutions, i.e., Li-TFSI, a mother solution of 500 mg/mL in acetonitrile was used, while the FK209 required a mother solution of 300 mg/mL in acetonitrile. This spiro-OMeTAD solution was spin coated onto the underlying perovskite layer at 4000 rpm for 20 s to form the HTL in our PSC. Finally, an ~80 nm thick Au top electrode was deposited using e-beam evaporation at a pressure of ~10\({}^{-5}\) Torr.
# Amortized Bayesian Meta-Learning with Accelerated Gradient Descent Steps
## 3 Meta-Learning with Amortized Bayesian Inference
### Fast Transductive Inference with the Synthetic Inference Network
We generate the new \(\theta_{t}^{\prime}\) sampled from \(\mathcal{I}\) and then assign it to \(\theta_{0}\) for a middleware descent output. By applying a modest number of descent steps, where \(K_{0}<<K\), the learned latent variable \(\theta_{t}\) of the meta-learner can converge to the optimal point. Figure 3:0 The proposed multi-task learning network structure of SIN. ```
0: the dataset \(\mathcal{D}\); learning rate \(\eta\); iteration number \(K\); number of tasks \(T\). 1: Initialize meta-model \(\theta=\{\mathbf{\mu}_{\theta},\mathbf{\sigma}_{\theta}^{2}\}\), \(p(\theta)\sim(8)\). 2: Initialize the transductive amortization network \(\phi(d^{S})\) and the pre-trained feature network \(f\). 3: Initialize loss function \(\mathcal{L}^{(t)}=\inf\) for \(T\) tasks of SIN. 4:while not converged do
5:for\(t=1\) to \(T\)do
6: Sample a task \(t\)'s data \(\mathbf{x}^{t}\) with its related support set \(\mathcal{D}_{S}^{(t)}\) and query set \(\hat{\mathcal{D}}_{Q}^{(t)}\). 7: Compute \(\theta_{0}^{t}=\phi(f(\mathbf{x}^{t}))\), where \(\mathbf{x}^{t}\in\mathcal{D}_{S}^{(t)}\). 8:if\(\hat{\mathcal{L}}^{(t)}<\xi\)then
9: Compute \(\theta_{t}^{\prime}\) sampled from the output \(\hat{\mathcal{I}}\) of SIN, assign \(\theta_{t}^{0}=\theta_{t}^{\prime}\) sampled from \(\hat{\mathcal{I}}\). 10:for\(k=1,\cdots,K^{\prime}(K^{\prime}\ll K)\)do
11: Compute \(\theta_{t}^{k}\) via (4) with learning rate \(\eta\)
12:endfor
13:else
14:for\(k=1,\cdots,K\)do
15: Compute \(\theta_{t}^{k}\) via (4) with learning rate \(\eta\)
16:endfor
17:endif
18: Implement the VBEM method for \(\theta_{t}^{k}\) to compute the inferred parameter \(\mathcal{I}^{(t)}\). 19: Update \(\mathbf{w}\leftarrow\mathbf{w}-\eta\
abla_{\mathbf{w}}\mathcal{L}(\mathcal{I}^{(t)})\) with \(K_{0}\) (\(K_{0}<K\)) iterations, where \(\mathbf{w}\) denotes the weight of SIN. 20:endfor
21: Update \(\phi\leftarrow\phi-\eta_{\phi}\mathrm{KL}(q_{\phi(d^{S})}(\psi)||p(\psi|\theta))\). 22: Optionally, fine-tune \(f\gets f+\eta\
abla_{f}\log p_{f}(d_{t}|\psi_{t})\). 23:endwhile
```  
**Algorithm 1** Amortized Bayesian meta-training with SIN. To summarize the architecture of the proposed ABML-SIN model, we provide a clear learning structure of our model, depicted in Figure 3:1 We can see that the architecture of ABML-SIN is embedded into the meta-learner module. It helps the meta-learner to quickly infer the learned latent posterior \(\theta_{t}\) for task \(t\) based on the prior \(\theta_{0}\). ABML-SIN provides an amortized synthetic inference network (ASIN) to use the pre-trained student network for fast \(\theta_{t}\) output. For different task numbers \(t=0,\cdots,T\), the inference network outputs different posterior values \(\theta_{t}\) related to the prediction results.
###### Abstract  
With the existing reliability evaluation methods, it is difficult to realize the overall evaluation of multi-voltage levels distribution systems, and sense-control terminal faults are not considered in the process of reliability evaluation. Therefore, a reliability evaluation method for multi-voltage levels distribution networks considering the influence of the fault of the sensing and control terminal is proposed. Firstly, based on the component reliability model, system state selection method, and reliability index, a reliability evaluation method for distribution networks considering sense-control terminal faults has been proposed. Secondly, the features of a multi-voltage levels distribution network are analyzed. The sequential Monte Carlo simulation method and the failure mode effects analysis method are combined, and a collaborative evaluation method for power supply reliability of multi-voltage levels distribution systems is proposed. Eventually, the effectiveness of the proposed method is proved by an example comparison, and the impact of the terminal fault on the reliability of distribution network is analyzed. The reliability evaluation results can provide technical support for the planning of the distribution network.  
distribution network; reliability evaluation; sense-control terminal; fault handling