esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Equivalent Circuit Model of the N-TWSRIM
When the traditional multilayer theory is used to calculate the copper layer parameters, the iteration speed is slow. So, to calculate the equivalent circuit parameters of the copper layer, the propagation constant \(K_{cu}\) is used [11], as shown in Equations (14)-(16). \[Z_{cu}=\frac{J\omega_{cu}\mu_{cu}}{K_{cu}}\frac{1}{K_{cu}b_{cu}}\frac{L_{e}K_{e}} {s\tau}\] (14) \[K_{cu}=\big{(}\alpha_{cu}^{2}+\beta^{2}\big{)}^{\frac{1}{2}}\] (15) \[\sigma_{cu}=\big{(}j\omega_{cu}\mu_{cu}\sigma_{cu}\big{)}^{\frac{1}{2}}\] (16) where \(b_{cu}\) is the copper layer thickness, \(\omega_{cu}\) is the copper layer angular frequency, \(\beta=\pi/\tau\), and \(\sigma_{cu}\) is the conductivity of copper. 7. The rotor impedance is calculated by Equation (17). \[Z_{r}=\frac{Z_{cu}Z_{steel}}{Z_{cu}+Z_{steel}}\] (17)
8. We can calculate the stator voltage \(U_{11}\) by the rotor impedance. When the accuracy of \(U_{1}\) and \(U_{11}\) is not satisfied, the loop continues. When the accuracy is satisfied, the values of \(I_{1},\ \mu_{i},\ H_{xi},\ B_{yi}\) are given. The rotor equivalent parameters of the solid rotor motor of the toroidal winding are calculated in cases where the stator winding phase voltage is 220 V and the voltage frequency is 50 Hz. The results are shown in Table 3. Hence, the output torque, stator current, and power factor of the N-TWSRIM can be computed accordingly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Non-Copper Plating Model**} & \multicolumn{3}{c}{**Copper Plating Model**} \\ \hline
**Item** & **Value** & **Item** & **Value** & **Item** & **Value** & **Item** & **Value** \\ \hline \(R_{1}\) & 48.05 & \(X_{1}\) & 6.2 & \(R_{1}\) & 7.89 & \(X_{1}\) & 1.06 \\ \(R_{m}\) & 0 & \(X_{mt}\) & 206.7 & \(R_{m}\) & 0 & \(X_{m}\) & 43.12 \\ \(R_{r}\) & 3.6 & \(X_{r}\) & 1.21 & \(R_{cu}\) & 6.32 & \(X_{ctu}\) & 1.4 \\ \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(R_{steel}\) & 43.36 & \(X_{steel}\) & 29.5 \\ \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(R_{r}\) & 4.81 & \(X_{r}\) & 3.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Equivalent circuit parameters of non-copper and copper plating models (slip = 1).
## 5 Numerical Comparison of the Existing Mass-Rig Systems
### Numerical Modeling of the Inertial Systems
#### 5.1.2 Model II  
One of the mass setups that has been used for shake table tests is to connect and place the additional inertial mass directly on top of the column's head. This configuration is the most realistic option for mimicking the behavior of a single column RC bridge bent, and for that reason, the proposed IMS is compared to it. The numerical model for this setup is illustrated in Figure 14. The same numerical modeling strategy used for the IMS and described previously is used in Model II. The effective inertial mass of the IMS, including half of the column weight, is lumped on top of the column model according to the values presented in Table 1. Additionally, the option of corotational transformation is assigned to the force beam-column element representing the RC column to capture the P-\(\Delta\) effects.
# Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
## 5 Discussion
The review papers, such as [PERSON] and [PERSON]'s study [3], have been further added in this direction to show that it is an active area of interest among researchers. Thresholds were proposed early with metric proposal and definition. For example, some researchers have used percentiles to identify God classes because they were selecting a portion of the system for investigation. However, the connection between metrics and many quality attributes has appeared and the need for advanced threshold techniques became a requirement. Thresholds need to be validated and empirical studies are the most suitable setting. There is a need for comprehensive research on threshold identification by considering all possible techniques and metrics analysis tools to reach valid and more accurate threshold values. Threshold values should be validated theoretically and empirically. In addition, empirical works should include other important quality attributes such as reusability, maintainability, security, and performance. Furthermore, a few research studies have considered multi-value attributes such as bug severity and priority and how they affect threshold identification, and whether the binary classification is better than multivariate classification. * Q5 (Relevant metrics):  
We observed that many of the papers did not report the relevant metrics. However, there are also some studies that clearly explain whether the metrics threshold values were calculated properly or not. Table 15 in the Appendix C shows these metrics per paper. There are plenty of metrics for measuring internal code properties such as size, inheritance, cohesion, and coupling. Coupling metrics were the most correlated with quality attributes. However, how these metrics interact and how they affect threshold derivation was not studied in previous works. There is a need to understand the profound relationship between metrics. Although some studies have considered the profound effect of size, there is a need for more studies on studying, for example, the interaction between coupling and other metrics. For example, it is well-known that low coupling and high cohesion are preferred in systems' quality; however, how each metric affects the thresholds of the other is not considered in the literature.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 5 Discussion
### Quality  
This paper introduces a GPS-based method to plan bus lanes on an urban scale. The final quality is impacted by two main factors.  
The first factor is the specification of traffic parameters. Except for road network data, the road/vehicle traffic parameters used by the model, such as the traffic flow, utilization, and coverage parameters, are specified based on the bus GPS trajectory data. Compared to that for historical experience and questionnaires, traffic parameter assignment based on GPS trajectories provides a more precise spatiotemporal resolution; furthermore, this approach can support high-precision and dynamic objective functions of road utilization and bus punctuality. Although this model does not use passenger flows directly, as is well known, the formulation of bus routes and bus schedules is based on passenger flows. In the model constraints and objective function, some parameters, such as the bus flow volume, bus frequency, and number of buses, which are positively correlated with passenger flows, are considered. Moreover, considering transit systems could be flexible and stretchy [15], although GPS trajectory can specify dynamic traffic information, it needs further research for this method to adapt to dynamic transit systems.  
The second factor is the properties of the evolutionary algorithm. This paper treats bus lane planning as a multiobjective optimization problem and introduces NSGA2 to solve the problem. The algorithm requires 10 min to complete 1000 iterations and derive a series of candidate plans in the test area. It can therefore adapt to rapid changes in urban public transport and be conveniently deployed in other cities. In practice, there are many solution methods that can be further examined to obtain high-quality results. Unlike in single-objective optimization problems, the subgoals are contradictory in the multiobjective case and may produce a set of optimal solutions (Pareto-optimal solutions) [33]. Additionally, other multiobjective optimization methods have been used in existing bus lane planning models. For example, [PERSON] and [PERSON] [13] used the strengthened Pareto evolutionary algorithm 2 (SPEA2) to find the Pareto set for bus lane planning. The common multiobjective optimization methods that have different adaptabilities include particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms [32]. Future work will include comparing these potential solution methods and the corresponding quality.
## 3 Results and Discussion  
### DCMD Performance  
All the heat-pressed and annealed membranes had a salt rejection of 99.99%, while the neat membrane suffered rapid wetting immediately 30 s after the operation started. The wetting of electrospun membranes could be judged based on the steady increase in the flux and the conductivity of the permeate (exceeding 10 mS/cm). Figure 2 shows that optimal heat-pressing improves the flux and wetting resistance. An average of 28.7 LMH was obtained during 10 h of operation, which is much higher than in previous studies under similar conditions. For comparison, [PERSON] et al. achieved a flux of 20.6 LMH with heat-pressed electrospun nanofiber membranes in DCMD [27]. Another study obtained a flux of 22 LMH with heat-pressed, two-layer membranes where the PVDF-HFP concentration was 10% [28]. However, the heat-pressed electrospun nanofiber membrane in the present study still has a noticeable decreasing trend during the 10 h of operation. On the other hand, annealed membranes showed a noticeably higher flux than the heat-pressed membrane, although they shared a close membrane thickness (Figure 2). Moreover, the annealed membrane showed a more stable trend of flux in 10 h of operation than HP, indicating a better wetting resistance. The membrane annealed for two and three days shared a similar performance, and both of them performed better than the membrane annealed for one day. The flux of the commercial membrane (GVHP) is illustrated here for comparison. A2 and A3, having a superb average flux of 35 LMH in 10 h of operation, were 75% higher than GVHP (20 LMH).
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 2 Detection System in the Peruvian Navy
### Phase 2: Tracking-Prediction  
The second phase comprises two processes: (1) tracking and (2) prediction. In this phase, the extracted plots are monitored. The movements made along the track are evaluated to determine its course and velocity. Predictive algorithms are used to obtain the future position; the correlation between the past, current, and arrival times at the defined target points or contact points is analyzed.  
The objective of the our work is to prove that a new algorithm model based on deep learning can achieve better performance than the existing CFAR algorithm. Consequently, it improves the efficiency of the radar detection system. Currently, the CFAR algorithm  
Figure 2: Processes of plot extractor phase flow chart.  
is operational. In addition, radar vessels present the infrastructure designed to execute real-time operations. The state of the art concerning artificial intelligence-based models is focused on selecting models that would provide higher accuracy performance in marine contexts over speed. We review new approaches to object detection, which offer better performance, increasing the ability to obtain more plots (recall) with a high degree of precision (accuracy). Therefore, we prioritize models that provide higher performance in accuracy over speed.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 4 Results
### Evaluation Metrics
#### 4.1.1 Metrics for a Binary Classification Problem  
A confusion matrix is a table or matrix that summarizes the prediction results of a classification problem. It is not a metric itself but it helps to visually understand the metrics and types of errors the model is making. Table 1 represents the confusion matrix for the case of a binary classification problem, where two classes have been considered: _positive_ and _negative_. The observations are distributed in two rows and two columns. The rows represent the actual classes, while the columns represent the predicted classes. The observations in the diagonal represent the correct decisions, while the elements in the antidiagonal represent the misclassifications.  
More precisely, the four elements in a confusion matrix of a binary classification problem are:  
* True positive (tp): the number of positive observations predicted as positive;
* True negative (tn): the number of negative observations predicted as negative;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\  & & **Positive** & **Negative** \\ \hline \multirow{3}{*}{**Variable**} & **Positive** & True positive & False negative \\  & & (tp) & (fn) \\ \cline{1-1}  & **Negative** & False positive & True negative \\ \cline{1-1}  & & (fp) & (tn) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of a binary classification problem.  
Figure 5: Flowchart summarizing the proposed damage diagnose strategy.  
False positive (fp): the number of negative observations wrongly predicted as positive;
* False negative (fn): the number of positive observations wrongly predicted as negative.  
The five metrics for the binary classification problem are then defined in Table 2 in terms of the elements of the confusion matrix. The F\({}_{1}\) score is a particular case of the F\({}_{\beta}\) score defined in [40] when \(\beta=1\).
# The Study of Tooth Erosion Tested by the Color Digital Holography (CDH) Detection System
## 4 Discussion  
### The Effect of Acid Etching Times on the Tooth Erosion  
[PERSON]'s modulus is an important index for quantitative evaluation of enamel erosion, and is helpful in the pathological study of tooth erosion, external treatment, and the development of acid-resistant dental materials [42]. [PERSON]'s modulus of samples could be calculated by Equation (6) based on the results of the deformation in Figure 5.  
The effect of acid-etching time on the enamels is shown in Figure 6. It can be found from Figure 6a that the three curves almost have no change with the increase in load. This is because the values on the right side of Equation (6) are constant under the condition of elastic deformation. In addition, the data points of [PERSON]'s modulus in the figure are the average values of five samples in every group, and the Shapiro-Wilk (SW) normal distribution test of [PERSON]'s modulus was carried out with the load increasing [43]. The results of \(p=0.37216\) for Group B, \(p=0.68516\) for Group E, and \(p=0.2348\) for Group G show they meet the normal distribution at the 0.05 level [44], It was also found that the fluctuation in the three curves with the increase in load is very small. Figure 6a also shows that there is a great difference in [PERSON]'s modulus among the three curves. Figure 6b shows that the average values of [PERSON]'s modulus in Group B, Group E, and Group G were 100.7, 83.0, and 25. 7 GPa, respectively. Thus, [PERSON]'s modulus of cattle enamel decreased with increased acid-immersion time. It can be concluded the tooth erosion of cattle enamel increases with increasing acid-immersion time, and the acid-immersion time has a great influence on the tooth erosion.  
The variance homogeneity of [PERSON]'s modulus for the three was tested; the value \(p=0.175>0.05\) shows that the variances of the three groups were equal and significant. Therefore, a single-factor analysis of variance (ANOVA) for multiple comparisons was used to analyze the differences among these groups [44]. Post hoc analysis uses the Tukey method; as shown in Table 2, the values of significance were less than 0.05. Previous studies have shown that, with the increase in acid-erosion time, the resistance of tooth enamel to deformation decreases significantly [15; 45], and Young's modulus of enamel also decreases.  
Figure 6: [PERSON]’s modulus Change curve of Group B, Group E, and Group G for (**a**) and [PERSON]’s modulus average value of Group B, Group E, and Group G for (**b**).  
Based on [PERSON]'s modulus of tooth enamel samples of different acid-etching times and the ANOVA results in Table 2, it can be concluded that the erosion time has a great impact on enamel erosion, but the decrease in [PERSON]'s modulus is not proportional to the increase in time. This is because, after the surface layer of tooth enamel is eroded by acid, the trace elements in the solution increase, and the rate of deep acid erosion decreases [45; 46].
# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
###### Abstract  
Emotion recognition using an odor-induced electroencephalogram (EEG) has broad applications in human-computer interaction. However, existing studies often rely on subjective self-reporting to label emotion, lacking objective verification. While the \(\beta\)/\(\alpha\) ratio has been identified as a potential objective indicator of arousal in EEG spectral analysis, its value in emotion recognition remains underexplored. This study ensured the authenticity of emotions through self-reporting and EEG spectral analysis of 50 adults after inhaling sandalwood essential oil (SEO) or bergamot essential oil (BEO). Classification models were built using discriminant analysis (DA), support vector machine (SVM), and random forest (RF) algorithms to identify low or high arousal emotions. Notably, this study introduced the \(\beta\)/\(\alpha\) ratio as a novel frequency domain feature to enhance model performance for the first time. Both self-reporting and EEG spectral analysis indicated that SEO promotes relaxation, whereas BEO enhances attentiveness. In model testing, incorporating the \(\beta\)/\(\alpha\) ratio enhanced the performance of all models, with the accuracy of DA, SVM, and RF increasing from 70%, 75%, and 85% to 75%, 80%, and 95%, respectively. This study validated the authenticity of emotions by employing a combination of subjective and objective methods and highlighted the importance of \(\beta\)/\(\alpha\) in emotion recognition along the arousal dimension.  
olfactory stimulation; EEG; machine learning; emotion recognition; \(\beta\)/\(\alpha\) ratio +
Footnote †: journal: applied sciences
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Indexes for Evaluation  
In order to provide quantitative measurement of the performance of our proposed model, we follow the performance evaluation procedures employed in the video quality experts group (VQEG) [29]. We test the proposed IQA metrics using Spearman's rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) as evaluation indexes. SROCC, PLCC and RMSE are respectively defined as  
\[SROCC=1-\frac{6\sum_{i=1}^{N}\left(R_{s_{i}}-R_{p_{i}}\right)^{2}}{N\left(N^{ 2}-1\right)} \tag{13}\]  
\[PLCC=\frac{\sum_{i=1}^{N}\left(s_{i}-\overline{s}\right)\left(p_{i}-\overline{ p}\right)}{\sqrt{\sum_{i=1}^{N}\left(s_{i}-\overline{s}\right)^{2}\left(p_{i}- \overline{p}\right)^{2}}} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(p_{i}-s_{i}\right)^{2}} \tag{15}\]  
where \(N\) is the number of images, \(s_{i}\) and \(p_{i}\) represent the \(i\)-th scores given by subjective evaluation and objective evaluation, \(\overline{s}\) and \(\overline{p}\) represent mean subjective quality score and mean objective predicted score, and \(R_{s_{i}}\) and \(R_{p_{i}}\) represent the rank order number of \(\overline{s}\) and \(\overline{p}\), respectively. SROCC is a nonparametric measure of rank correlation to statistically assess how well the relationship between the subjective quality score and the objective predicted score can be monotonically described, while PLCC is a measure of the linear correlation between them. RMSE represents the square root of the quadratic mean of the differences between the subjective quality scores and the objective predicted scores, and is a frequently used measure. By using these three statistical measures, we can easily analyze the consistency between the subjective quality score and the objective predicted score, which indicates the capability of IQA methods.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 1 Introduction
The above-mentioned works only considered appliances with low energy consumption and assumed that the management system did not significantly impact the daily bill. In some, many user comfort constraints were not considered, resulting in inefficiency of the developed systems and their inability to maintain user comfort. These issues were considered in the physical system developed in the present study through the use of support circuitries that provide online information regarding inputs related to user comfort and lifestyle, such as room temperature. The main contributions of the work can be summarized, as follows:  
* Design and implementation of a complete home energy management system consisting of multiple smart sockets, a programmable air conditioner (AC) remote control, and room condition monitoring nodes for the acquisition of comfort data to be inputted to the main controller. All of these components are wirelessly connected by a Zigbee communication protocol, eliminating the need for complex wiring. * Consideration of user comfort as the first priority of the energy management process. This makes it easier to integrate the system with the daily routine of the consumer without affecting their lifestyle. * The development of a rule-based algorithm for reducing the energy consumption within a 24-h period on a repeatable basis.
## 3 Results
### Performance Measures  
We measured the accuracy, sensitivity, and specificity using a confusion matrix to evaluate the proposed PCNN. The experimental models for predicting the BCR were as follows: support vector machine (SVM), Bayesian network (BN), random forest (RF), XGBoost (LR), XGBoost (Tree), neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM), and the proposed PCNN. The compared models were implemented using SPSS IBM Modeler 14.2 (IBM, Armonk, New York, USA), and default model parameters were used. The parameters of the PCNN were as follows: learning rate = 0.1, activation function = sigmoid, learning algorithm = backpropagation, batch size = 10, and epochs = 1000.  
As the training and testing sets had a low class imbalance and used small amounts of data, we conducted experiments using 66:33 sets (674:347 patients). Table 4 presents the experimental results of the accuracy, sensitivity, and specificity. In the training set, the tree-based algorithms XGBoost (Tree) and RF had the highest accuracy (94.60%) and specificity (100%). The proposed PCNN achieved better performance (93.6%, 97.60%, and 92.28%) than other algorithms such as SVM and NN. The performance of XGBoost (Tree) and RF in the testing set was low (72.14% and 72.05%, respectively). As the boosting technique is used in these methods, they are not suitable for datasets that have not been trained, owing to overfitting problems. The proposed model achieved exceptionally high performance (74.70%, 77.57%, and 72.15%) and was the most effective.  
Subsequently, the performance was measured using 10-fold cross-validation, which is a suitable method for measuring small datasets. The accuracy, sensitivity, and specificity were measured, and the measurement results are displayed in Table 5. The proposed PCNN model achieved the highest performance (87.16%, 90.80%, and 85.62%). XGBoost (Tree) achieved high performance in the sensitivity (86.60%, 91.43%, and 84.94%).  
Figure 2: PCNN. PSA, prostate-specific antigen; BMI, body mass index; GS, Gleason score (sum); PT, pathology T stage; ECE, extracapsular extension; SV, seminal vesicle; PNI, perineural invasion; LNM, lymph node metastasis, SM, surgical margin.