esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Implementation and Validation  
As mentioned earlier, the current study implements SVM, MLP, and LR models in Waikato Environment for Knowledge Analysis (Weka Version 3.8.2, developed by the University of Waikato, New Zealand [77]. Using the random partition algorithm in Weka, the sampling data was divided into training and validation data subsets in the ratio 70% (41 points) and 30% (17 points), respectively. In practice, there is no generally acceptable mechanism for partitioning a sampling dataset; the choice varies in the literature, usually depending on the quantity and quality of the sample data [61]. Before using the data for modeling, it was subjected to a correlation-based feature selection (CFS), employing the greedy stepwise search algorithm 10-fold cross-validation method to reduce the error rate, increase efficiency, and to achieve a better performance [78; 79]. CFS is an efficient feature selection technique; the greedy algorithm adds either the most favorable feature or deletes the most unfavorable one in each round [80]. In the current study, the process ranks attributes in their order of influence on the model. The resulting models were executed in ArcGIS 10.5 and the output raster data was reclassified into five suitability classes using the quantile classification method [13] to produce the final hospital suitability maps. The quantile method is an efficient classification technique that measures equivalent representation of each class by statistically evaluating the range of raster values in the input layer [81].  
Validation is essentially a necessary step in any predictive modeling task. In this study, the data was divided into 70% training dataset and 30% test dataset for validation. For every classifier, a 10-fold cross-validation was executed. The cross-validation process splits the dataset into 10 subgroups; 9 subgroups utilized for training and the remaining subgroup for testing. In each step of the 10-fold processing stage, a different segment is utilized for testing the accuracy and the final result represents the average of the 10 results [82]. The performance of each model was assessed using the correlation coefficient (R2), and the associated quantitative metrics, root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE) for the cross-validation result. In addition, the overall performance of the models was validated using the receiver operating characteristics (ROC) accuracy assessment parameter, specifically, sensitivity, specificity, and area under the curve (AUC) measures.
## 3 Machine Learning Models
### Support Vector Machines (SVM)
#### 3.1.1 Financial Data  
In the scientific literature related to time series forecasting applications, SVM and ARIMA techniques are contrasted in analyzing data from a wide variety of sources (Table 1). In the economic domain, the work of [PERSON] et al. [20] compared the capabilities of classical ARIMA and SVMs in predicting the daily price of gold. The results of the study show that the prediction using SVMs outperforms ARIMA based on the root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics, with RMSE = 0.028 and MAPE = 2.5 for SVM and RMSE = 36.18 and MAPE = 2897 for ARIMA, respectively. This particular research also supports the popularization of the use of SVM techniques in the price prediction of any product due to the accuracy of their results.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Tube Placement  
When placing tubes on the destination area, it is important for them to have a consistent orientation so that they can be further processed more easily.  
During this phase, it is assumed the gripper is holding a single tube. The points belonging to the tube are used to compute the object's principal axes using PCA (Principal Component Analysis), by performing eigendecomposition on the points' normalized covariance matrix [20]. The principal component with the highest eigenvalue provides a reliable estimate of the tube's orientation (i.e., if the tube is a perfect cylinder, then the first principal component will correspond to the cylinder's axis). To ensure that the tube is placed with the correct orientation, the gripper performs a rotation to align the principal component with a predefined axis describing the desired orientation.  
Optionally, to prevent the gripper from performing dangerous rotations, the principal direction vector can be projected in the xOy plane so that the alignment rotation only contains a yaw component.  
Figure 7 presents an example of a coordinate frame that reflects the tube's orientation. The unit vector for the \(x\)-axis (in red), \(\hat{x}\), is oriented according to the principal direction vector's projection on the xOy plane while the unit vector for the \(z\)-axis (in blue), \(\hat{z}\), is equal to \([0,0,1]^{T}\). The vector for the  
Figure 6: Visual representation of the torque vector.  
\(y\)-axis (in green) is simply equal to \(\hat{z}\times\hat{x}\). The tube is placed with a movement for the gripper that allows this coordinate frame to be aligned with a predefined frame in the destination area.
# An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
## 2 Real-Time Contrast Control Chart
### Random Forests  
The random forests algorithm is very effective and useful for both prediction and classification problems. It was originally developed by [PERSON] [PERSON] (1984); [PERSON] (1985); [PERSON] (1986). A decision tree-based random forests is a robust model for outliers and is able to handle various data types, interaction between variables, and nonlinearity. It is also used in RTC control charts because of its ability to effectively obtain a probability estimation. Random forests should set two parameters as a classifier that constructs an ensemble using the bagging (bootstrap aggregation) of a large number of decision trees. First, the number of decision trees constituting an ensemble should be determined. Second, the number of randomly chosen variables should be determined. In the case of decision trees, the overfitting problem does not occur even if a sufficiently large number of decision trees is set. For this reason, in this study, the number of decision trees was set to 500. Also, the number of randomly selected variables was set to the square root of the number of variables [PERSON] (1984); [PERSON] (1985); [PERSON] (1986). Observations that are not used for bagging in the learning process of an individual decision tree are called out-of-bagging (OOB) observations. These are used to estimate the probability of anomalies and enable the fault identification of anomalies. The probability of the OOB observations for each observation \(x_{i}\) belonging to class \(k\) being correctly predicted by the original class is defined as  
\[\hat{p}_{k}(x_{i})=\frac{\sum_{j\in OOB_{i}}I[\hat{y}(x_{i},\;\;t_{j})=k]}{|OOB _{i}|},k\;=\;0,\;1 \tag{6}\]  
In addition, the predicted class is 0 when \(\hat{p}_{0}(x_{i})\) less than control limit and 1 otherwise. An indicator function \(I(\cdot)\) returns 1 for true or 0 for false for the argument and \(\hat{y}(x_{i},t_{j})\) represents the predicted class. Here, \(t_{j}\) is the \(j^{th}\) decision tree among the decision trees in the random forests. \(OOB_{i}\) is the set of decision trees that do not use observation \(x_{i}\) in the learning process using bagging. An additional consideration is the class-imbalance problem. Because \(N_{0}\) is set such that it is very large compared to \(N_{\text{w}}\) when the classifier learns, there is the problem of predicting the class of all the observations to the class of \(N_{0}\). To overcome this problem, the downsampling of \(N_{0}\) can be applied. If downsampling is performed in the process of learning an individual decision tree, it is possible to improve the classification speed of random forests by improving the computation speed and decreasing the correlation between decision trees [PERSON] (1984); [PERSON] (1985); [PERSON] (1986). In this study, the size of the downsampling is the same as the sliding window size.
# Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
## 3 Results
### Resource Description Framework Knowledge Graph  
#### 3.2.1 Representing Questionnaires and Responses  
A hypothetical HRQoL questionnaire corresponding to DAG #6 (Figure 1(6)) was used as an example, and was represented according to the proposed methodology as a semantic model (i.e., as an RDF KG) based on DDI-RDF. Figure 4 illustrates a representation of a fragment of this hypothetical questionnaire in RDF Turtle serialization.  
The namespace prefixes at the top of the Turtle excerpt correspond to existing semantic models available on the Web; the \"disco\" prefix corresponds to DDI-RDF, while prefix \":\" refers to the base prefix corresponding to a fictional namespace devised for the purposes of this example.  
With regard to the structure of the KG, we note the following points:  
* The excerpt shown in the figure only includes three sample hypothetical questions for illustration purposes. The representation of an actual HRQoL questionnaire would include all the questions.
* In order to also represent the order of questions in the questionnaire, every question contains information about the previous one via predicate \":isAfter\".
* The association of questions to facets (see Section 2.2.3) is materialized via DDI-RDF predicate \"disco:concept\".
* The association of facets to domains is materialized via SKOS predicate \"skos:broader\", which represents a narrower-broader interrelationship.  
In a similar fashion, the patients' responses were serialized in RDF Turtle, as seen in Figure 5.  
The excerpt represents a sample hypothetical patient and their responses to questions \"O\" and \"P\", which are specified in Figure 4. In this case, the responses assume integer values ranging from 1 to 4.  
Figure 4: Serialization of a fragment of a health-related quality of life questionnaire as a Resource Description Framework Turtle.
# Designing an Interactive Visual Analytics System for
## 3 Comprehensive Hourly Precipitation Dataset  
### Data Collection  
As existing HPD records frequently contain gaps (missing measurements, inconsistent recording periods, or no data due to temporary station outages), we created a comprehensive long-term HPD by integrating multiple data sources to construct a complete dataset. Cooperative Observer Program (COOP) Hourly Precipitation [38] is used as the primary data source because it provides well-organized hourly precipitation measurements. The COOP HPD contains quality-controlled precipitation amounts (hourly accumulation for rain and snow) [32] from approximately 2000 observing stations across the United States and several U.S. territories in the Caribbean and Pacific, collected through the National Weather Service (NWS) Fischer-Porter Network [39]. As the COOP program was phased out in 2013, we incorporated Local Climatological Data (LCD) [40] as our secondary source. LCD is an active, reliable repository of weather and climate data summarizing conditions from airports and prominent weather stations managed by the NWS, Federal Aviation Administration (FAA), and Department of Defense (DOD). While LCD primarily provides monthly summaries of daily temperature extremes and averages, it also includes hourly precipitation data from approximately 1000 U.S. stations.  
Although the two datasets contain substantial hourly precipitation records, no common unique identifiers exist for direct alignment. Therefore, we implemented a semiautomatic mapping process by creating a unified weather station list and cross-referencing attributes between the datasets with the list to ensure accurate unification into our comprehensive HPD repository. A detailed explanation of how the unified weather station list is included in Appendix A.
# Investigation on the Application of Artificial Intelligence in Prostodontics
## 4 Discussion  
In this comprehensive assessment of AI technologies utilized in prosthodontics, the predictive power and identification potential of AI for application in automated diagnostics were proven [72; 73; 74]. Due to the rapid evolution of digital technologies, in the past 2-3 years, the time frame for this investigation was set to the last four years. This systematic study was not intended to serve as a comprehensive history of AI in dentistry but rather to highlight applications for prosthetic AI. The findings demonstrated that CAD/CAM systems, implant prostheses, and studies of orofacial anatomy were just a few examples of the ways AI was used in prosthodonics. The application of AI in prosthodontics was the subject of only 24 qualifying studies. Although dynamic caries detection was the focus of artificial intelligence dental image analysis diagnosis for some time, the discipline moved to other areas of interest, demonstrating the utilization of AI technology in prosthodontics, and the large and complex field of prosthodontics in dentistry may profit from routine AI technology application [75; 76; 77].  
For a prosthetic reconstruction to be effective, it must have a synoptic treatment idea, sufficient backward planning, and clean practical execution, including dental laboratory processes. Using prosthetic AI, it was possible to diagnose periodontal deficient premolars and molars with 90% and 95% accuracy, respectively [78; 79; 80; 81]. However, because of theredundancy of the imaging characteristics and the visual field of periapical radiographs, this method cannot distinguish between early lesions or offer a conclusive diagnosis of periodontal disease.
## 2 Working Principle of Electromagnetic-Ring Balancing Actuator
### Basic Structure of the Balancing Actuator
#### 2.2.1 Structure Design  
As shown in Figure 2, the balancing actuator comprises a rotatory part and a stationary part. The rotatory part rotates with the hollow rotor during operation and decreases the unbalance vibration through the inner mass distribution. The stationary part stays still during operation, receives electrical signals from the power supply unit and generates the magnetic field to drive the action of the rotatory part. The stationary part consists of two coils with respective coil cases, one center stator and several bolts to connect them. The central stator is machined with a through hole which is the wire channel to connect the power supply unit with the two coils. The rotatory part consists of an actuator case, two cover plates, four iron tooth rings, two counterweight discs with counterweight, two ball bearings and so forth. The actuator case is used to connect the actuator with the hollow rotor of the spindle and can be designed according to the size of the inner hole. Because the inner hole of the rotor used in the balancing experiment is conical, the case is also conical and the conical degrees are the same. The other components of the rotatory part are symmetrical in pairs and all of them are connected with the actuator case. For example, four iron tooth rings can be divided into two pairs, each pair contains an outside ring and an inside ring and their specific working principle is introduced below. The two inside rings are machined with a through hole to connect the center stator. Because they have relative movement during operation, an air gap is needed to prevent friction and two ball bearings are adopted to guarantee their long-cycle operation.
## 3 The System Configuration and Operating Process
### Single-Level System Configuration and Operating Process  
Figure 2(a) shows that the single-level system consists of a wind farm, a hybrid storage system, a grid, and an MPC controller. The single-level MPC operating process is presented as follows:  
First, the MPC obtains the available information, such as the current wind power, forecasted wind power, energy levels in the two energy storage systems, grid plan and working states of the two energy storages (pumped mode or generation mode, charging mode or discharging mode).  
Second, the MPC makes optimal decisions based on this information and then implements the actions based on these decisions. Note that only the current time optimal decisions can be made.  
The two above-mentioned steps are repeated when the next scheduling time arrives.  
Figure 2: Wind power spectrum density curve.
## 4 Case Studies
### Method  
To estimate the performance of KNN, LDA, and Perceptron, we used repeated k-fold cross-validation, with \(k=5\) and 100 repetitions for each algorithm. For the KNN algorithm, a number of 23 close neighbors was found to be the best choice for the Cleveland dataset, while for Banknote and Wisconsin datasets, 4 and 5 close neighbors were the best choices, respectively.  
We evaluated if the data was correctly classified based on the number of true positives (\(TP\)), false negatives (\(FN\)), false positives (\(FP\)), and true negatives (\(TN\)). The accuracy and error of each system are then defined as:  
\[Acc=\frac{TP+FN}{TP+TN+FP+FN} \tag{6}\]  
\begin{table}
\begin{tabular}{c l l c} \hline \hline
**Method** & \multicolumn{1}{c}{**Advantages**} & **Disadvantages** & **Ref** \\ \hline \multirow{4}{*}{KNN} & - Easy to understand and implement & - High computational cost & \multirow{4}{*}{[52,53]} \\  & - Fast training & - Poor run time performance & \\  & - Robust to noisy traning data & - Sensitive to local structure of the data & \\  & - Good behaviour dealing with several labels & & \\ \hline \multirow{4}{*}{LDA} & - Low computational cost & - Requires normal distribution & \multirow{4}{*}{[54,55]} \\  & - Easy to implement & - Linear decision boundaries & \\  & - Discriminate different groups & - Limited to two classes & \\  & - Visual representation makes clear understanding & & \\ \hline \multirow{4}{*}{Perceptron} & - Easy training and set up & - Only works on linearly separable data & \multirow{4}{*}{[56]} \\  & - Yields the decision function directly through training & - Boundaries are only allowed to be hyperplanes & \\ \cline{1-1}  & - Limited to binary data & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of the methods.  
\[error=Acc=\frac{FP+FN}{TP+TN+FP+FN}=1-Acc\] (7) Other metrics that were used to evaluate the performance of the algorithms were:
* Sensitivity (\(TPR\)): represents the probability of detecting the condition when it is present. \(TPR=\frac{TP}{TP+FN}\)
* False negative rate (\(FNR\)): represents the probability of not detecting the condition when it is present. \(FNR=\frac{FN}{TP+FN}\)
* False positive rate (\(FPR\)): represents the probability of detecting the condition when it is not present. \(FPR=\frac{FP}{TN+FP}\)
* Specificity (\(TNR\)): represents the probability of not detecting the condition when it is not present. \(TNR=\frac{TN}{TN+FP}\)
* Positive predictive value (\(PPV\)): represents the probability of the patient really having the condition when the test is positive. \(PPV=\frac{TP}{TP+FP}\)
* Negative predictive value (\(PPV\)): represents the probability of the patient not having the condition when the test is negative. \(PPV=\frac{TN}{TN+FN}\)  
Apart from these metrics, the area under the ROC curve was also used to evaluate the performance of the algorithms. Furthermore, a MANOVA analysis was carried out to see if there was any statistical difference between the algorithms (\(p=0.05\)).
## 2 Materials and Methods
### Procedure and Data Mining Algorithms
#### 2.2.2 Algorithms for Comparison  
Two commonly used data mining methods were used for comparison with the developed models. The support vector machines (SVM) method has been widely applied in remote sensing studies [57] to classify land cover or use targets, extract biophysical parameters in different spatial resolutions, and perform landslide analyses [40; 41]. The core role of the SVM classifier is to determine the largest margin of the classifier based on factor transformations with linear and nonlinear functions. These transformations are known as the kernel function. Equation (11) describes a concept of the SVM-based classification with a \"sign\" operator for the binary classification, where l, y, and k(xi,xj) indicate the number of used factors (support vectors), the outputted label, and a kernel function, respectively, and a and b represent the coefficients of the margin. The kernel functions used in this study were linear, polynomial, and radial basis functions.  
\[\mathrm{f(x)}=\mathrm{sign}\!\left[\sum_{i=1}^{1}a_{i}y_{i}\mathrm{,k}\!\left( x_{i},\;x_{j}\right)+\mathrm{b}\right] \tag{11}\]  
Logistic regression (LR) is a typical and traditional statistical method used to model landslide events [20; 58]. The computation describes the relationship between the dependent and independent variables as defined in Equation (12), where p refers to the likelihood or probability, a is a constant, b is the regression coefficient, and x represents independent variables or used factors:  
\[\mathrm{p}=\frac{\exp(a+b_{1}x_{1}+b_{2}x_{2}+\ldots+b_{n}x_{n})}{1+\exp(a+b_{ 1}x_{1}+b_{2}x_{2}+\ldots+b_{n}x_{n})} \tag{12}\]
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## Nomenclature  
For clarity, the main variables and parameters used in this paper are listed below:  
General Symbols and Indices  
\(t,k\)  
Time index and iteration index, respectively.  
\(i,j\)  
Indices for Distributed Renewable Energy Generation Units (DREGUs).  
\(N\)  
Total number of DREGUs in the microgrid.  
Graph Theory  
\(G=(V,E,A)\)  
Directed graph representing the communication network.  
\(A=[a_{ij}]\)  
Adjacency matrix of the graph.  
\(D=\text{diag}(d_{i})\)  
In-degree matrix of the graph.  
\(L\)  
Laplacian matrix of the graph, \(L=D-A\).  
\(O=\text{diag}(o_{i})\)  
Pinning gain matrix connecting DREGUs to the virtual leader.  
Microgrid Model and Variables  
\(x_{i}\)  
State vector of the \(i\)-th DREGU.  
\(u_{i}(t,k)\)  
Control input voltage for the \(i\)-th DREGU.  
\(y_{i}(t,k)\)  
Measured output voltage of the \(i\)-th DREGU.  
\(y_{dj}(t,k)\)  
Sensor input voltage including noise.  
\(y_{si}(t,k)\)  
Saturated output voltage from the sensor.  
\(y_{0}(t)\)  
Predefined reference voltage (virtual leader).  
\(w_{i}(t,k)\)  
Bounded disturbance or measurement noise.  
\(s\)  
Saturation threshold of the voltage sensor.  
DAILC Algorithm  
\(\varphi_{i}(t,k)\)  
Pseudo-Partial Derivative (PPD) of the \(i\)-th DREGU.  
\(\phi_{i}(t,k)\)  
Estimated value of the PPD.  
\(\phi_{i}(t,k)\)  
Estimation error of the PPD, \(\phi_{i}=\varphi_{i}-\phi_{i}\).  
\(e_{i}(t,k)\)  
Local neighborhood pinning error.  
\(e_{si}(t,k)\)  
Tracking error for the saturated output, \(e_{si}=y_{0}-y_{si}\).  
\(\eta_{1},\lambda_{1}\)  
Step size and penalty factor for the control law update.  
\(\eta_{2},\lambda_{2}\)  
Step size and penalty factor for the PPD estimation.  
\(e\)  
Reset threshold for the PPD estimation algorithm.