esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Batch Selection for Enhanced Generalization in Imbalanced Datasets
## 5 Results and Discussion
### Comparison with Other Sampling Methods  
We tested our algorithm (without any data-transformation applied) with known online sampling algorithms, which have the ability to cope with dataset imbalance. Specifically, online batch selection [21] was selected because it seems to be the first study that incorporates the adaptive batch selection idea. Another sampling technique that was tested was submodular batch selection [29]. The results on the four imbalanced datasets can be found on Tables 9 and 10 for _F1-score_ and _balanced accuracy_, respectively. We can see that _NBSBS-R_ performs better than the other methods in both metrics in most of the cases.  
Apart from generalization performance, the computational cost of a method is an important characteristic. Below, Table 11 shows the mean training time of the three algorithms that are compared. We can see that our approach is the fastest among the three and in some datasets it finishes training in half the time of the rest. This is being attributed to the fact that the loss that categorizes samples as easy or difficult is an approximation instead of the actual batch loss.  
Figure 1: The mean increase in Balanced Accuracy (on the **top**) and F1-score (on the **bottom**) for the four datasets among all the different data-transformation techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Ozone** & **Adult** & **Credit Card** & **MNIST** \\ \hline Online Batch Selection [21] & 57.12 & 76.88 & 67.75 & 98.64 \\ Submodular [29] & 57.18 & 77.97 & 67.21 & 99.07 \\ NBSBS-R (ours) & **57.26** & **79.83** & **67.95** & **99.08** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Balanced accuracy comparison on the generalization with other online sampling methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Ozone** & **Adult** & **Credit Card** & **MNIST** \\ \hline Online Batch Selection [21] & 58.27 & 76.92 & 68.91 & 98.32 \\ Submodular [29] & 59.22 & 77.46 & 68.87 & **99.09** \\ NBSBS-R (ours) & **59.86** & **78.52** & **69.56** & 99.08 \\ \hline \hline \end{tabular}
\end{table}
Table 9: F1-score comparison on the generalization with other online sampling methods.  
Figure 2: The training curves of the models for three different data-transformation methods (ROS at the **top**, SMOTE at the **middle**, Adasyn at the **bottom**).
# Analysis of Soil-Water Characteristic Curve and Microstructure of Undisturbed Loess
## 3 Results and Discussion  
### SWCCs of Undisturbed Loess Samples  
The SWCCs of undisturbed loess samples at various depths are presented in terms of the volumetric water content (Figure 4). To provide a quantitative representation of the SWCCs, the experimental data points were fitted using mathematical models. The commonly utilized models in this study include the Van Genuchten (VG) model [55],[PERSON] and Xing model [56], and Brooks and Corey model [57]. The VG model has been demonstrated to effectively describe and predict the continuous water tension relationship of undisturbed Jingyang loess under full matric suction values [58]. [PERSON] et al. [56] proposed that when the matric suction of the soil equals 10\({}^{6}\) kPa, the water content of the soil is 0. Thus, we employed the VG model with our experimental data, assuming a residual water content of 0.001 for fitting purposes, to illustrate the relationship between the matric suction and volumetric water content across the range of 0 to 9\(\times\)10\({}^{5}\) kPa. The VG model fitting equation is as follows:  
\[\theta(\psi)=(\theta_{\mathrm{s}}-\theta_{\mathrm{r}})\big{[}1+(a|\psi|)^{n} \big{]}^{1/s-1}+\theta_{\mathrm{r}} \tag{1}\]  
where \(\theta_{\mathrm{s}}\) is the saturated volumetric water content; \(\theta_{\mathrm{r}}\) is the residual volumetric water content; \(\alpha\) and \(n\) are the VG model fitting parameters; and \(\psi\) (kPa) is the matric suction of the sample.
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 2 Materials and Methods  
Machine learning, as a subset of artificial intelligence, is a field of computer science that aims to develop algorithms that can improve through experience and by the use of incremental data [7]. In the past decades, an increase in its research interest led to more and more areas of science finding application within ML algorithms. Application can be found in agriculture [8], industry [9], sensor networks [10], fashion [11; 12] or healthcare [13; 14], just to mention a few examples.  
When considering the healthcare environment, some studies apply ML algorithms to identify pre-eminent factors and characteristics of patients associated with the lack of attendance to the scheduled appointment [15; 16; 1]. Other studies use statistical predictive models capable of predicting whether a patient will be absent from the appointment based on their historical data [17]. [PERSON] et al. [18] described the development process up to the implementation of the predictive model in a real clinical environment, as well as the insights acquired using the model.  
Despite the relevance of patient absenteeism in medical appointments, very few works use ML algorithms to identify and understand this problem. In this sense, ML was first applied in [19; 20]. In these works, the publicly available dataset was used and guided the overall analysis. However, the lack of available attributes in the dataset did not allow the authors to build a solid predictive model. To the best of the authors' knowledge, a study of the new private dataset with different attributes to analyse the data distinctly and validate new ML models for the no-show problem is still missing in the literature.  
This work presents a statistical analysis to enumerate the potential reasons why patients do not attend appointments. In addition, we apply ML techniques to create models that better fit the absenteeism problem. As we are dealing with a dataset from a real-world environment, we began the data extraction and data analysis only after the research was approved and authorized by the Ethics Committee of the University of Vale do Itajai. Thus, this work provides answers to some of the questions regarding patients' non-attendance, that is:  
* What are the key indicators that signal that a patient will not attend a scheduled appointment?
* What is the probability of a patient not showing up for an appointment?
## 4 Residual Properties Analysis
### Ductility  
Ductility (\(\mu=\Delta_{u}/\Delta_{y}\)) is calculated according to the load-deformation curve. And \(\Delta u\) is the value of deflection at 0.85 times Np, and the maximum deformation is taken to be \(\Delta u\) when the load falls below 0.85 Np. \(\Delta_{y}\) is the value of initial yield deformation and calculated by the equivalent energy method. As shown in Figure 16, the dimension of OAB is equal to the dimension of YUB.  
Figure 15: Deformation corresponding to the peak load. (**a**) rectangular prisms specimens; (**b**) columns; (**c**) bending beams; (**d**) shear beams; (**e**) The influence of temperature; (**f**) The influence of replacement percentage.  
Figure 17 shows the displacement ductility factor of each component after exposure to different elevated temperature. As seen from Figure 17a-e, the displacement ductility coefficient of each component fluctuated slightly up or down as exposure to elevated temperature rises. Generally speaking, the ductility factor of the four types specimens decreases with increasing temperature when \(T\) is between 200 degrees centigrade and 400 degrees centigrade. When \(T\) is equal to 400 degrees centigrade, the displacement ductility factor of test block, column, bending beam and shear beam decrease by 10%, 28%, 44% and 31%, respectively. The displacement ductility factor of each component varies from \(-25\%\) to 4% when \(T\) is higher than 400 degrees centigrade.  
As seen from Figure 17f, the displacement ductility factor of the bending beam increases first and then decreases. And the displacement ductility factor of bending beam increases 63% when the replacement percentage is 70%. And the displacement ductility factor of bending beam decreases 24% when replacement percentage is 100%. Then, the displacement ductility factor of the other three types of components varies slightly with replacement percentage varies.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 3 Method
### Multi-Scale Feature Fusion Pyramid Structure  
The FPN [32] is a top-down feature pyramid fusing feature maps of different scales, which alleviates the contradiction between receptive field and feature extraction and enhances the performance of the detector. Subsequently, researchers proposed PANet [33] based on the FPN. PANet introduces a bottom-up sampling pathway in order to propagate the low-level feature information upwards more efficiently, so as to achieve the fusion of the low-level detail information and the high-level semantic information, and to improve the detection performance of the model. In the task of spatial small target detection, although PANet enhances the extraction of target features by introducing the strategy of top-down and bottom-up paths, there is still the problem of poor detection performance for small targets.  
To address this problem, a new multi-scale feature fusion pyramid structure is proposed in this paper to enhance the detection of small targets in space, and two versions with four detectors and three detectors are provided, named MFFPN-L (Multi-scale Feature Fusion Pyramid Networks-Large) and MFFPN-S (Multi-scale Feature Fusion Pyramid Networks-Small), respectively. The feature pyramid proposed in this paper consists of three modules, i.e., the CST module, the CSE module, and the ISPP module. The pyramid structure is shown in Figure 2.  
Figure 1: Images of parts of the database.  
In this paper, we present MFFPN-L, and the proposed multi-scale feature fusion pyramid structure is shown in Figure 2b. We were inspired by PANet to construct the feature pyramid with top-down and bottom-up features, where C3, C4, C5, and C6 are the input images that have been downsampled by the backbone network. Due to the presence of a large number of very small debris, small celestial objects, and other targets in the space small target detection task, we added a detection head for tiny targets to the traditional three detection heads to improve the detection performance for small targets. Considering the existence of interference from weak illumination and the star map background, we used the CSE module in the top-down path to improve the extraction ability of the target region through the attention mechanism and reduce the interference of illumination and star map background in target detection. Then, to improve the detection ability of small targets through multi-scale information, F3, F4, F5, and F6 were input into the ISPP module, and the improved spatial pyramid pooling was used to enhance the detection ability of small targets by fusing the features of multiple scales while increasing the sensory field acquisition. Finally, the CST module was used in the bottom-up path to extract local information in the image using the CNN, and then, positional coding was introduced by Transformer to strengthen the connection of contextual information and improve the sensory field and feature extraction ability of the model.
## 3 Test Results and Discussion  
### Afterberg Limits Tests  
This study explored the effects of different concentrations of MgCl\({}_{2}\) solution (4%, 9%, and 14%) on the liquid limit (LL), plastic limit (PL), and plasticity index (PI) of the soil (Figure 2). The results demonstrate a consistent decline in these parameters with an increase in MgCl\({}_{2}\) concentration. The reduction is significant in soil mixtures treated with a 14% MgCl\({}_{2}\) solution, showcasing decreases of 14% in the liquid limit, 9% in the plastic limit, and 19% in the plasticity index. These results align with previous research by [PERSON] (1977) [55], suggesting that the decrease is likely due to a thinning of the diffuse double layer (DDL). This process is believed to be driven by the cation exchange with divalent magnesium ions and a subsequent rise in electrolyte concentration, given the complete solubility of MgCl\({}_{2}\) in water.
## 6 Multi-Service Dependency  
Looking to find the relevant work in other areas, such as multi-service dependency which could possibly be used to extract more complex dependency between services that can influence service behaviour has resulted in the following insights. As an example shown in Figure 3 taken from [63], we can see different dependencies that can influence the behaviour of service S3.  
Figure 3: Multi-service dependency between different services [63].  
To extract the multi-service dependency model of distributed services, and get the total ordering of the events, the first step is the synchronisation of the services' clock. In [64], [PERSON] examined the concept of one event happening before another in a distributed system and defined a partial ordering of the events. To synchronise a system of logical clocks, a distributed algorithm was introduced, which also can be used to totally order the events. [PERSON] then derived a bound on how far out of the synchrony the clock can become.  
We categorise the related work below based on the type of data that they use to investigate the dependencies. Some of the studies use network-level data, some use application-level data, and others utilise both of them. Depending on criteria such as test goals, needs, and systems and resources access level, different methods are followed. Based on the captured data, they extract dependencies between distributed systems or components at different levels of granularities, and, in most cases, build a dependency graph to show the dependencies, their strength and direction of influence.
## 3 Model Reconstruction and Finite Element Analysis
### FEA Simulation  
To obtain the effective elastic modulus value of the micron silver sintered joints under thermal shock, the simulation loading conditions were set as below: one side of the reconstructed model was subjected to a fixed constraint and the opposite side was put into stress \(\sigma_{\text{i}}\), shown in Figure 10. The input Young's modulus was set from nanoindentation result of 25 Gpa. The density and the Poisson's ratio were 7.8 g/cm\({}^{3}\) and 0.25. For the FEA model with length \(L\), the displacement on the force surface and equivalent stress of each node are extracted, and the effective elastic modulus is calculated by Equations (12) and (13).  
\[E_{\text{i}}=\frac{F_{i}L}{Ax_{i}}, \tag{12}\]  
\[E=\frac{\sum\limits_{i}E_{\text{i}}}{n}=\frac{\sum\limits_{i}\left[\sigma_{ \text{arev}}/\left(\frac{dL_{i}}{L}\right)\right]}{n}, \tag{13}\]  
where \(E_{i}\) is the effective elastic modulus obtained by fixing X, Y and Z planes, respectively, to eliminate the calculation error caused by structural randomness. \(Fi\) is tension, \(x_{i}\) is displacement and \(A\) is the section area. \(n\) is the number of simulation tests, and \(\sigma_{\text{arev}}\) is the average tensile stress which is calculated as Equation (14). \(dL_{i}\) is the average displacement in the stress direction.  
\[\sigma_{\text{arev}}=\frac{\sum\limits_{i}\sigma_{i}\times V_{i}}{V_{medium}}, \tag{14}\]  
\(\sigma_{i}\) is the equivalent stress in the \(i^{th}\) node. \(V_{i}\) is the volume of element in which node \(i\) resides. \(V_{medium}\) is the total volume of all nodes in elements.  
The calculation of average stress can reduce the stress inequality caused by complex structure, which is equivalent to the effective stress of sintered joints. Moreover, it depends on the microstructural characteristics rather than stress concentrations of individual points.  
Table 3 shows the model size, the number of elements and calculation time during simulation. After generating FE models, the solving process generally requires 20 to 30 min. The cumulative time  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thermal Shock (Cycles)** & 50 & 100 & 150 & 250 \\ \hline
**KL divergence** & 0.11 & 0.12 & 0.12 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kullback–Leibler (KL) divergence between autocorrelation functions.  
Figure 10: Simulation loading diagram. Green represents a fixed constraint, and red represents applied tensile stress. The element type is Solid 226.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### Model 2: CNN-LSTM with Significant Features (\(t\)-Test)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Value** & **Description** \\ \hline \multirow{5}{*}{CNN} & Filters & 64 & Number of filters for spatial feature extraction \\ \cline{2-4}  & Kernel size & \(3\times 3\) & \begin{tabular}{c} Defines the size of the convolution window \\ \end{tabular} \\ \cline{2-4}  & Activation function & Rectified linear unit & \begin{tabular}{c} Enables the learning of complex spatial features \\ \end{tabular} \\ \cline{2-4}  & Padding & Same & Preserves the input size \\ \cline{2-4}  & Pooling size & \(2\times 2\) & \begin{tabular}{c} Reduces the dimensionality of the input feature map \\ \end{tabular} \\ \hline \multirow{5}{*}{LSTM} & Units & 50 & \begin{tabular}{c} The number of neurons in each \\ LSTM layer \\ \end{tabular} \\ \cline{2-4}  & Return sequences & True & \
\begin{tabular}{c} Keeps sequential outputs for \\ the attention mechanism \\ \end{tabular} \\ \cline{2-4}  & Dropout rate & 0.3 &
\begin{tabular}{c} Reduces overfitting by \\ randomly disabling neurons \\ during training \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Integrated convolutional neural network (CNN) and long short-term memory (LSTM) settings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Channel** & **Channel** & **ERP Segment** & **Features** \\ \hline \multirow{2}{*}{0} & FP1 & N100 &
\begin{tabular}{c} Mean, peak, area under the \\ curve (AUC) \\ \end{tabular} \\ \hline \multirow{2}{*}{0} & FP1 & N200 & Mean, peak, AUC \\ \hline \multirow{2}{*}{1} & FP2 & N100 & Mean, peak, AUC \\ \hline \multirow{2}{*}{2} & F3 & P300 & Peak latency, AUC \\ \hline \multirow{2}{*}{2} & F3 & Late positive potential (LPP) & Peak latency, AUC \\ \hline \multirow{2}{*}{3} & F4 & P300 & Peak latency, AUC \\ \hline \multirow{2}{*}{3} & F4 & LPP & Peak latency, AUC \\ \hline \multirow{2}{*}{6} & Fz & P300 & Peak latency, AUC \\ \hline \multirow{2}{*}{6} & Fz & LPP & Peak latency, AUC \\ \hline \multirow{2}{*}{9} & O2 & P300 & Peak latency, AUC \\ \hline \multirow{2}{*}{9} & O2 & LPP & Peak latency, AUC \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Selected channels and features by event-related potential segments.
## 5 Use Case and Experiments
This step corresponds to the service chain execution in the service engine layer mentioned in Section 4.3. The above steps were implemented and used for damage estimation in the Wenchuan earthquake. The basic functions of PGIS such as task analysis, managing sensors for data acquisition, service composition, service chain construction and execution were implemented. Preliminary experimental  
Figure 11.0 GISC of change detection. Figure 11.1 The resulting GISC after the automatic service composition. results show that the PGIS is capable of providing valuable geospatial services to support decision making in disaster estimation and mitigation, and the important properties of service-oriented geospatial information services including data interoperability, flexibility, and reusability can be achieved. It should be noted that the complete implementation of an actual PGIS application is a huge task since it covers various aspects as described in this paper. The next step of the implementation will focus on the autonomy of the system, self-adaptation of the services, and the efficient aggregation and cooperation of the service chain. More geospatial and socioeconomic data from the Wenchuan earthquake will be employed for experimental analysis. More detailed geospatial services (e.g., the situations and analysis about building collapse, landslide dam, and vegetation destruction due to the earthquake) will be implemented and integrated in the PGIS system. The performance and efficiency of using the PGIS for decision making purposes will be quantitatively evaluated.
## 4 Classification
### Machine Learning Classification Using Multiple Features  
Supervised learning algorithms were implemented using MATLAB (2018a). Our preliminary analysis on classification used more than 10 classifiers and their derivatives based on linear and nonlinear kernel functions. Considering overall classification accuracies and physical interpretation, results were obtained for five classifiers: Linear Discriminant (LD), medium Decision Tree (DT), linear Support Vector Machine (SVM), weighted K-Nearest Neighbours (KNN) and Neural Network (NN). The LD is a simple classifier that uses a linear combination of selected features to test whether the dataset is linearly separable. Since LD does not consider nonlinear metrics, LD directly separated the dataset using feature amplitude range. The medium DT used up to 20 splits and Gini's diversity index as the split criterion. Our previous analysis showed that the overall classification accuracy was notably lower if the number of splits was smaller than 10 or larger than 40. Linear kernel functions were used to build the SVMs. The linear SVMs used a one-versus-one strategy for multiclass classification and chose the label that was selected by the most linear SVM classifiers. The weighted KNN used 10 nearest neighbours to determine the host's label. The number of neighbours was optimized in previous pilot studies. The Euclidean distance used by the weighted KNN described the degree of sample aggregation. The decision boundaries were physically meaningful in terms of clustering tightness. The NN used 100 neurons in one hidden layer and the scaled conjugate gradient back-propagation method in updating parameters. Different neuron numbers were tested before, and the number of 100 neurons was shown to have a good classification performance in terms of classification accuracy and computation time. The performance of the NN was evaluated using the cross-entropy method. A five-fold cross-validation was done for all classifiers, with the exception of the NN, to prevent model overfitting. The average accuracy was determined. For the NN classifier, the dataset was randomly divided into three subsets: training (n = 70%), validation (n = 15%) and testing (n = 15%). The data randomization was repeated six times for the NN classification, and the average accuracy was calculated for presentation. Classification was performed on the full feature set and its nine subsets to investigate the priority and the contribution of each feature to the overall accuracies. A leave-one-feature-out (LOFO) strategy was used to build the first seven feature subsets. The \(H_{1\text{-}4}\) + SE subset was a composite of spectral features. The \(J_{r}+S_{r}\) subset was built using two vocal stability metrics.  
Figure 6: The averages and standard deviations of \(H_{1}\)–\(H_{4}\), SE, jitter and shimmer based on 14 participants’ “pure” NSA samples for the pilot study. The blue, red and green colours represent the modal, pressed and breathy voice types, respectively.