esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Evaluation of the CNN-Based Citation Classification Model  
In this section, we present the findings for both binary and multi-class classification. Table 1 presents the precision, recall, f1-score, and accuracy results for citation context classification using both binary and multiclass schemes. Firstly, we used the entire dataset of 9518 cases for binary classification, with 1007 goes to the \"important\" class and the remaining 8511 to the \"non-important\" class. To address the problem of class imbalance in our dataset, we applied weight balancing to both binary classes, with the \"important\" class receiving more weight. Over the course of 60 epochs, our model improved from 53% accuracy to 98% training accuracy. The CNN model performed well in validation testing, with an accuracy of roughly 90%. Our model achieved a 0.906 accuracy score and a 0.906 f-measure. The precision-recall curve for binary classification is shown in Figure 2a, illustrating that both curves represent a similar stable trend.  
Secondly, we divided our 9518 occurrences into seven categories for the multi-classification (use, extend, background, motivation, compare or contrast, future, none). To address the data imbalance problem, we utilized a customized loss function in training to suppress the well-classified samples and emphasized more on the minority-class samples, which are more difficult to classify. Equation (14) presents the loss function formula, which increases the general cross-entropy function by a factor of \((1-p_{t})^{\gamma}\).  
Our multi-class model began with 0.33 model accuracy and gradually improved to 0.98 over the span of 60 epochs, whereas the multi-class CNN model attained roughly 72% accuracy in validation. The PR curves are shown in Figure 2b, in which both of the precision and recall curves show a consistent increase in precision and recall values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Citation Functions Scheme** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Citation Context Classification & Binary-Class & 0.906 & 0.904 & 0.905 & 0.905 \\ \cline{2-6} Using CNN with FastText & Multi-Class & 0.787 & 0.640 & 0.70 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Precision, recall, f1-score, and the accuracy results for citation context classification using binary and multiclass schemes.  
Figure 2: Precision-recall and validation curves, using CNN model for binary and multi-classification. (**a**) PR curves for binary-classification; (**b**) PR curves for multi-classification.
## 3 Results and Discussion
### Advantages of Tungsten Electrode  
There are three advantages of tungsten electrode elements that enable higher plasma densities and a wider tolerance range:  
1. The tungsten electrode has a high melting point. The melting point of tungsten is 3410 \({}^{\circ}\)C, while the melting point of iron-nickel alloys is between 1430 \({}^{\circ}\)C and 1480 \({}^{\circ}\)C. This high melting point enables the tungsten electrode to withstand higher currents, effectively increasing the plasma density.  
Figure 7: SEM of electrode morphologies: (a) W electrode before discharge; (b) W electrode after discharge; (c) Fe-Ni electrode before discharge; (d) Fe-Ni electrode after discharge.  
2. Tungsten electrodes have a low work function value. Its work function is 4.5 eV, which is lower than that of most metallic materials used for electrodes. This low work function means that less energy is required to move electrons from inside the tungsten electrode to its surface, making it more conducive to improve the performance of the plasma element.
3. As shown in Table 2, tungsten has a higher thermal conductivity and lower specific heat capacity than iron and nickel, which means that tungsten electrodes have a shorter thermal hysteresis and can reach a sufficient temperature in a shorter period of time to bring the element into operation.
## 5 Data Simulation
### GN1B Simulation  
Simulated orbital data with a period of one day were used for the TTL estimation. The initial states are taken from the official GRACE-FO GN1B data and the corresponding time is 23:55:00 on 31 March 2021 in GPS time. The initial orbital elements are listed in Table 1. The gravitational and non-gravitational force models we used for orbit integration are listed in Table 2. The 12 th order Gauss-Jackson integrator was used for orbit integration and the step-size was 1 second.  
Figure 5: Flowchart of the data simulation process. Data in the yellow boxes were obtained from the GRACE-FO RL04 Level-1B data products. We generated the error-free data based on the input and then added some error to obtain the simulated Level-1B data.  
The models in forward procedure in Table 2 were used to generate the error-free orbital states, while the models in the POD procedure were applied to generate the simulated GNI1B data that contain errors. The error-free orbital states were used as observations for the POD procedure since there was no direct simulation of GNSS measurements. The root mean square (RMS) values of the position and velocity residuals were set to several centimeters and tens of microns per second, respectively, by controlling the iteration times in the POD procedure, in which the initial states, the accelerometer scale, and the bias parameters were estimated.
## 1 Introduction
Despite the aforementioned effort, there are still many deficiencies in understanding the effects of yaw operation due to the uncertainties of wind, the complexity of wind turbine control, and the difficulties in carrying out accurate indoor or field tests of the effects of yaw operation on wind turbine aerodynamics and performance [3; 22]. In view of this, numerical research is further conducted in this paper in order to contribute to the following three aspects:  
1. The size of the flow field is critical to the efficiency and accuracy of CFD calculations. With the continual increase of the size of wind turbines, it becomes more and more difficult to accurately predict their dynamics and power generation performance by performing CFD calculations on usual PCs. Then, defining the flow field properly to efficiently and accurately predicting the dynamics and performance of the turbine rotor during yaw operation is an issue that needs to be addressed first. Although the blade loads during yaw operation have been studied many times previously [25; 26], the load control of the hydraulic yaw brake that is used to aid yaw operation has rarely been studied in the literature. However, the load control of the hydraulic yaw brake is critical because the incorrect control of it can cause the failure of yaw operation. Thus, it is of great significance if new knowledge can be developed to aid yaw control. 3. The characteristics of the power produced during yaw operation have been studied before based on wind farm supervisory control and data acquisition (SCADA) data [22; 27]. However, it is still not very clear of the power fluctuation during this process under combined conditions of wind shear, blade deflection, and turbine control. To fill the knowledge gaps identified above, the rest of the paper is organized as follows. In Section 2, the influences of the flow field size on the CFD calculation results are investigated in order to understand an appropriate method for defining the flow field size in the CFD models; In Section 3, following the calculation of the blade loads during yaw and non-yaw operations, mathematical models for predicting the blade loads during yaw operation are developed to aid the load control of the hydraulic yaw brake during yaw operation. In Section 4, the energy capture performances of the wind turbine rotor during yaw operation under different wind shear, blade deflection, and turbine control conditions are investigated to disclose more about the effects of yaw operation. Finally, the paper is completed in Section 5 with a few key conclusions.
## 3 Distributed Cognition-Based Data Analysis and Results
### Evaluation of Model Fitting Indicators  
According to the fitting test results of the measurement model in Table 6, the overall fitting index of the research model performs well: The posterior predicted \(p\)-value (PPP = 0.502) is close to the ideal value of 0.5, indicating that the model can reproduce the observed data well [38]; Although the robust root mean square error (BRMSEA = 0.071-0.074) was slightly higher than the recommended standard (<0.05), its 95% confidence interval indicated a relatively high estimation accuracy [39]. Both the Bayesian Comparative fitting index (BCFI = 0.974-0.975) and the Tucker-Lewis index (BTLI = 0.947-0.950) reached or approached the threshold requirements (\(\geq\)0.95) [40]. The potential scale reduction factor (PSR = 1.000), BTII (0.949), and BNFI (0.966) indicators of the measurement model show good convergence of parameter estimation [41]. However, the PPP value (0.000) and BRMSEA (0.310) deviated significantly from the standard, suggesting that the measurement model might have an excessive constraint problem.  
In conclusion, the research model has passed the tests of the vast majority of fitting criteria, supporting the structural rationality of the theoretical assumptions. However, further optimization of the measurement model is needed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Fit Indices**} \\ \cline{2-4}  & **PPP** & **BRMSEA (CI)** & **BCFI (CI)** \\ \hline \multirow{2}{*}{Measurement Model} & \multirow{2}{*}{0.000} & 0.310 & \multirow{2}{*}{0.975} \\  & & [0.310, 0.310] & & \\ \multirow{2}{*}{Research Model} & \multirow{2}{*}{0.502} & 0.073 & \multirow{2}{*}{[0.974, 0.975]} \\  & & & [0.071, 0.074] & \\ \multirow{2}{*}{Recommendation Criteria} & Close to 0.5 & \textless{}0.05 & \textgreater{}0.95 \\ \multirow{2}{*}{Assessment} & Good & Good & Good \\ \hline \multirow{2}{*}{Measurement Model} & **BTLI (CI)** & **BNFI (CI)** & **PSR** \\ \hline \multirow{2}{*}{Measurement Model} & \multirow{2}{*}{0.949} & 0.966 & 1.000 \\  & [0.947, 0.950] & [0.965, 0.966] & 1.000 \\ \multirow{2}{*}{Recommendation Criteria} & \(\geq\)0.95 & \(\geq\)0.90 & \(\leq\)1.01 \\ \multirow{2}{*}{Assessment} & Good & Good & Good \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bayesian model fit and convergence criteria.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Construct** & **LF** & **PF** & **EF** & **PU** & **PEU** & **PE** & **SA** & **WTCTU** \\ \hline LF & 0.765 & & & & & & \\ PF & 0.607 & 0.671 & & & & & \\ EF & 0.459 & 0.537 & 0.413 & & & & \\ PU & 0.628 & 0.541 & 0.404 & 0.906 & & & \\ PEU & 0.662 & 0.572 & 0.43 & 0.664 & 0.852 & & \\ PE & 0.317 & 0.319 & 0.323 & 0.316 & 0.317 & 0.564 & \\ SA & 0.636 & 0.549 & 0.41 & 0.638 & 0.673 & 0.316 & 0.888 & \\ WTCTU & 0.606 & 0.521 & 0.386 & 0.623 & 0.641 & 0.245 & 0.639 & 0.994 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of discriminate validity.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 6 Results
### Optimised Critical Scenarios for the Pedestrian Step-Out Scenario  
The optimisation as described in Section 5.3 was run for \(N=100\) iterations. This yielded 100 critical concrete scenarios in \(\mathbf{\mathcal{D}_{crit}}\), which are close to the critical TTC of \(ttc_{target}=1.5\) seconds. The initial data \(\mathbf{\mathcal{D}_{I}}\), constructed via LHC sampling to optimally fill the parameter space, had a size of 100 concrete scenarios and was executed in simulation. From \(\mathbf{\mathcal{D}_{I}}\), the classification data set \(\mathbf{\mathcal{D}_{C}}\) and the regression data set \(\mathbf{\mathcal{D}_{R}}\) were constructed and then fed into Algorithm 1. In every iteration of Algorithm 1, a new candidate scenario pool of size \(n_{cand}=1000\) was used.  
To account for variations in the sampling, the optimisation was repeated three times with different initialisation seeds. The optimised concrete scenarios of one of the experiments are visualised in Figure 4. The concrete scenarios are coloured according to their actual minimum TTC measure, as evaluated in the simulation. In the three repetitions, between 71%-75% of the concrete scenarios in \(\mathbf{\mathcal{D}_{crit}}\) are within 0.5 s of the target TTC of 1.5 s, while only 9%-12% had a TTC higher than 2 s. Between 15%-18% of the concrete scenarios that were found had a TTC between 0 and 1 s.  
Furthermore, we evaluated the Mean Absolute Error (MAE) and Root-Mean-Squared Error (RMSE) of the optimised scenario with respect to the target TTC. The resulting set of MAE = {0.086, 0.146, 0.114}, measured in seconds, shows very low deviation from the TTC target of 1.5 s. The RMSE was calculated to {1.516, 1.426, 1.297} seconds. The RMSE is more sensitive to outliers than the MAE. This can be observed here, as there are a few larger outliers in the data set of optimised scenarios. The largest outlier scenario had a minimum TTC of 11 s, as can be seen from Figure 4. The inclusion of these scenarios might be due to a slightly inaccurate prediction in the GP models, or they were included by the heuristic on purpose to explore the scenario space. This is the case, if one of the scenario candidates has a high predictive variance. It is then included by the heuristic according to Equation (22) to explore the scenario space and improve the GP models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameters** & **Lower Limit** & **Upper Limit** \\ \hline \(S_{1}\) [km/h] & 10 & 50 \\ \(S_{2}\) [km/h] & 2 & 15 \\ \(d\) [m] & 5 & 40 \\ \(SR\) [m] & 10 & 100 \\ \(H\) [deg] & 45 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters for the pedestrian step-out scenario and their ranges.  
Figure 4: Combinations of scenario parameters of critical scenarios for the pedestrian step-out scenario.
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation
### Long-Channel Threshold Voltage Model
#### 2.2.2 Threshold Voltage  
As shown in Equation (26), the surface potential can be minimized along the y-direction. The surface potential at \(y=y_{0}\) is referred to as the threshold surface potential, and the corresponding gate-source voltage is the threshold voltage. By taking the derivative of Equation (26) with respect to y and finding the zero-crossing point, the surface potential at that point will be the minimum surface potential:  
\[V_{Smin}=V_{SL}^{\prime}+[2(V_{bi}-V_{SL})+V_{DS}]\frac{\sinh(L/2l)}{\sinh(L/l) }, \tag{27}\]  
When \(V_{Smn}=\phi_{s}\) and \(V_{gs}=V_{th}\), substituting this condition into Equation (27) and rearranging, we can obtain:  
\[V_{th}=V_{TH0}-\Delta V_{th}, \tag{28}\]  
where \(\Delta V_{th}=\frac{2(V_{bs}-\phi_{s}+V_{1})+V_{DS}}{2\cosh(L/2l)-2}\). In the expression for \(\Delta V_{th}\), the first term in the numerator can be attributed to the short-channel effect (SCE), which represents the influence of the change in channel length on the threshold voltage when \(V_{DS}\) is constant. The second term in the numerator can be attributed to the drain-induced barrier lowering (DIBL) effect, which represents the influence of the change in \(V_{DS}\) on the threshold voltage as the channel length varies; then, \(\Delta V_{th}=\Delta V_{th}(SCE)+\Delta V_{th}(DIBL)\). Considering the impact of the process, we introduce process parameters [14]\(K_{5}\), \(K_{6}\), \(K_{7}\), \(K_{8}\), \(K_{9}\), and \(K_{10}\), which are obtained through parameter extraction. Therefore, the influences of the SCE and DIBL effect on the threshold voltage can be expressed as:  
\[\Delta V_{th}(SCE)=-\frac{0.5K_{5}}{\cosh\left(K_{6}\frac{L_{eff}}{l_{c}} \right)-1}(V_{bi}-\phi_{s}+V_{1}), \tag{29}\]  
\[\Delta V_{th}(DIBL)=-\frac{0.5}{\cosh\left(K_{7}\frac{L_{eff}}{l_{0}}\right)-1} (K_{8}+K_{9}V_{bs})V_{ds}, \tag{30}\]  
where \(X_{dep0}=\sqrt{\frac{2\varepsilon_{SiGe}}{qN_{SiGe}}}\phi_{s}+\frac{\varepsilon_ {SiGe}}{\varepsilon_{SSl}}\left(\frac{\varepsilon_{SiGe}}{\varepsilon_{SSl}}- \frac{N_{SiSi}}{N_{SiGe}}\right)T_{SSi}^{2}-\frac{\varepsilon_{SiGe}}{ \varepsilon_{SSl}}T_{SSi}\) in Equation (29), \(K_{6}\) is substituted for \(\eta^{-1/2}\), \(l_{0}=\sqrt{\frac{\varepsilon_{SiGe}T_{ex}X_{dep0}}{\varepsilon_{ex}}}\), and \(l_{t}=\sqrt{\frac{\varepsilon_{SiGe}T_{ex}X_{dep0}}{\varepsilon_{ex}}}(1+K_{10} V_{bs})\).
## 3 Optimization of Discharge Chamber Size and Magnetic Field
### Effects of Three Classical Axial Magnetic Field Distributions on Ionization Characteristics
When the ionization process within the discharge chamber reaches a steady state at 0.1 ms, the distribution of electron density over the whole volume is depicted in Figure 10. The following conclusions can be drawn: 1. Regardless of the variation in the magnetic field distribution, the plasma will eventually be concentrated in the center of the discharge chamber, with the maximum electron density near the discharge chamber proper and the minimum electron density at the walls. However, the distribution and concentration of the plasma are different, which is consistent with the results of the antenna source simulation [28]. The saddle-shaped magnetic field has a more vital magnetic field confinement force than the flat and Gaussian-distribution magnetic fields. It allows for the plasma to be contained in a smaller space, reducing the likelihood of ion recombination on the discharge chamber wall. 2. It is more likely that dense plasma will form with the saddle-shaped magnetic field distribution. Figure 10 shows that, compared to the other two cases, the saddle-shaped magnetic field produces a significantly higher electron density. This result could be explained by the saddle-shaped axial magnetic field distribution, which has a high magnetic field at both ends and a low magnetic field in the middle. It will constrain the electrons to move back and forth between the magnetic mirrors, thereby improving the ionization efficiency. At the same time, the saddle-shaped magnetic field is more extensive than other magnetic fields through the resonance plane of the region with high plasma density, thereby increasing the probability of electrons gaining energy. Thus, it can be inferred from the ionization conditions of the discharge chamber under those, as mentioned above, three classical axial field distributions, that the saddle-shaped magnetic field has good aggregation ability and a larger resonance surface, which enhances the efficiency of electron ionization and decreases the ionization of ions on the cavity wall. As a result, the saddle-shaped magnetic field will perform better for the ion source employing it under the same circumstances since it is more favorable for the production of plasma [26; 29; 30; 31].
## 5 Data Analysis and Method Implementation  
This section discusses the available datasets and implements different ML techniques to verify their predictive accuracy against the unseen test examples. Figure 8 shows the different stages of the methodology implemented to build the ML classifiers. Each of the four stages exhibits the necessary steps connected from data pre-processing to creating the complete ML model. They are further elaborated in the below sections.  
Figure 8: Methodology Flowchart: The given flowchart shows the steps involved to build and evaluate different classifiers on the given input dataset.
# Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
## 4 Numerical Experiments
### Parameter Tuning and Setting  
In order to complete the description of our algorithms, the parameters are discussed in this section. Table 1 contains the definitions of all parameters and their best values. Some numerical experiments were tested to find the suitable values of these parameters. Parameter values were set to be as independent from the problem as possible. Despite the the theoretical part of EDAs parameters being studied before--for example, in [27]--the number of population size \(R\) values is still a main factor that varies from problem to problem. In fact, trading off between the population size and the number of generation is a main issue.  
Before choosing a suitable value for parameter \(R\), a comparison between different values of \(R=100\), \(R=200\), and \(R=500\) was applied for both types of problems (global optimization, simulation-based optimization). The number of function evaluations was set to be fixed at 500,000 in all of these experiments. Table 2 shows that for the global optimization problem, increasing the population size does not have a positive effect on most problems. This means that the search process is more qualified with an extra number of generations. For the simulation-based optimization problem, Figure 1 shows an almost identical performance when applying different values of \(R=100\), \(R=200\), \(R=500\), and \(R=1000\). Some functions need more exploration of the search space (increase \(R\)), such as \(f_{3}\) and \(f_{5}\), but \(R=100\) is still the best choice for rest of the functions.  
For parameters \(m\) and \(L\), which follow parameter \(R\), setting higher values yields higher computational times without any significant improvement. For sample size parameters, the settings follow the recommended values in [10; 13].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Definition** & **Best Value** \\ \hline \(R\) & The population size & 100 \\ \(m\) & No. of selected individuals & \(0.15\times R\) \\ \(L\) & The solution generating parameter in Algorithm 7 & 10 \\ \(N_{\text{min}}\) & The initial value for the sample size & 10 \\ \(N_{\text{max}}\) & The maximum value for the sample size & 10,000 \\ \(\alpha\) & The radius parameter & \(N_{k}/N_{\text{max}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters definition and values.