esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment and Characterization  
The chemicals (analytical grade) in the experiment need not be purified any more. The synthesis of VO\({}_{2}\)(B) nanobelts is divided into the following steps. Firstly, 0.585 g of NH\({}_{4}\)VO\({}_{3}\), 1.103 g of H\({}_{2}\)C\({}_{2}\)O\({}_{4}\).2H\({}_{2}\)O (NH\({}_{4}\)VO\({}_{3}\):H\({}_{2}\)C\({}_{2}\)O\({}_{4}\).2H\({}_{2}\)O = 1:1.75, abbreviate V:C = 1:1.75) were blended together with 35 mL distilled water. After stirring for 30 min, the mixture was kept at 180 \({}^{\circ}\)C for 48 h in a 50-mL Teflon-lined stainless steel autoclave. Subsequently, the products were washed and dried at 75 \({}^{\circ}\)C for 12-24 h.  
X-ray powder diffraction (XRD) was tested in Panalytical X'Pert powder MPD with Cu K\(\alpha\) radiation. Fourier transform infrared spectroscopy (FT-IR) was applied to measure the products with Nicolet 6700. The Raman spectra were carried out with a 532-nm excitation line (Thermo Scientific, MA, USA). Morphology and dimensions were measured through scanning electron microscopy (SEM) on QUANTA50, and transmission electron microscopy (TEM) on FEITECNAIG220. The surface area was determined on ASAP-2020 through the Brunauer-Emmet-Teller (BET) method.  
A three-electrode cell was employed in the electrochemical test. The working electrode was prepared via the following steps. Firstly, mix 10 wt% of carbon black, 10 wt% of polyvinylidene difluoride, and 80 wt% of VO\({}_{2}\)(B) together, and then put N-methyl-2-pyrrolidone into the mixture. Next, the slurries were coated on a nickel foil. After heating at 80 \({}^{\circ}\)C for 24 h, these nickel foils were pressed on a Ni grid under the pressure of 10 MPa. An amount of 1.0 mol-L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) aqueous solution was chosen as the electrolyte. The electrochemical characteristics using CV and GCD were performed on CHI 660D. The specific capacitance (C, F:g\({}^{-1}\)), energy density (E, W:h:kg\({}^{-1}\)) and the power density (P, W:kg\({}^{-1}\)) are determined through Equation (1) to Equation (3).  
\[\mathrm{C} = \frac{\mathrm{I}\cdot\mathrm{\Delta t}}{\mathrm{m}\cdot\mathrm{ \Delta V}} \tag{1}\]  
\[\mathrm{E} = \frac{1}{7.2}\mathrm{C}\cdot(\mathrm{\Delta V})^{2} \tag{2}\]  
\[\mathrm{P} = \frac{3600\mathrm{E}}{\mathrm{\Delta t}} \tag{3}\]  
where, I (A) denotes the discharge current. \(\mathrm{\Delta t}\) (s) denotes the discharge time. m (g) denotes the mass of the VO\({}_{2}\)(B). \(\mathrm{\Delta V}\) (V) denotes the potential drop during the process of discharge.
## 3 Steering Control Test on the Curved Section  
### Active Steering Bogie  
To analyze the steering performance according to the steering angle in the curve section, the test run was carried out using the active steering bogie prototype. An active steering bogie prototype was developed for subway EMU trains running on urban railway areas [14; 15]. Figure 4 shows the configuration of the active steering bogie and Figure 5 shows the active steering bogie prototype installed on the test train.  
Figure 3: Radial steering position.  
An active steering bogie adopts a radial steering position control strategy based on real-time curvature radius estimation [9; 10; 11]. The active steering system of the bogie is composed of a sensor unit, a control unit and a driving unit [14].  
The sensor unit estimates the radius of curvature of the curve in real time, the controller calculates the target steering angle for active steering, and the steering driving unit conducts steering the wheelset according to the controller command.  
The minimum radius of the curved section that can be detected by the active steering bogie is R80 with a radius of curvature of 80 m and the maximum controllable steering angle is 0.5 degrees. In other words, steering control is possible up to R250, a radius of curve of 250 m, the smallest curve on commercial lines.  
The steering driving unit is adopting an electro-mechanical type actuator, with a maximum steering thrust force of more than 50,000 N. Table 1 shows the specifications of the active steering system of the active steering bogie  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Specification** \\ \hline Detectable curve radius & min 80 m \\ Steering angle & 0.5 degrees \\ Supply voltage & three phase 380 VAC \\ Maximum current (per vehicle) & 16 A \\ Thrust force(per driving unit) & above 50,000 N \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the active steering system.  
Figure 4: Configuration of the active steering bogie.  
Figure 5: Prototype of the active steering bogie.  
The steering controller is mounted on the body to perform steering control of the front and rear steering bogies. The control algorithm of the steering controller is shown in the block diagram in Figure 6. The controller for steering estimates the radius of curvature through the signal detected in real time from the curvature sensor and calculates the target steering angle. Figure 7 shows the mechanism by which the drive unit works to achieve the target steering angle. This control method has been experimentally verified through a test run using a test train equipped with an active steering bogie [15].
## 2 The Information Bottleneck Method and Coarsely Quantized Information Bottleneck Signal Processing
### The Information Bottleneck Method
As we will discuss later in Section 3, the lookup table interpretation is just one possibility for implementing deterministic information bottleneck compression mappings in communication receivers. We want to mention here that we only formally consider discrete observed variables \(\mathsf{Y}\) in this article. The reason for this is that every digital communication receiver needs to quantize the continuous received signal with a limited number of bits per sample before conducting further digital signal processing steps, as will be explained in more detail later. Before we continue, an important concept from [35] shall be revisited. As the mutual information relation between \(\mathsf{X},\mathsf{Y}\) and \(\mathsf{T}\) in the information bottleneck method is quite abstract, it is reasonable to introduce a compact graph notation that allows one to visualize the intended mutual information relations between the variables involved in an information bottleneck problem. In [35], information bottleneck graphs were introduced for that purpose. Information bottleneck graphs are extended factor graphs that aim to compactly visualize the intended mutual information relations of the information bottleneck method. In an information bottleneck graph, a compression mapping \(p(t|y)\) designed with an information bottleneck algorithm is visualized as a trapezoid node that is labeled with the respective relevant random variable. The compressed variable is connected to the shortest side of the trapezoid. The other connected variables form the observation. This concept allows one to cover compression mappings with a single scalar input \(y\) or multiple scalar inputs \(y_{n}\), \(n\in\{0,1,\ldots,N-1\}\). Figure 3 shows an example for a compression mapping \(p(t|y)\) with one scalar input on the left. On the right of the figure, an example for a compression mapping \(p(t|y_{0},y_{1},y_{2},y_{3},y_{4})\) with five scalar inputs is shown. A compression mapping \(p(t|y_{0},y_{1},\ldots,y_{N-1})\) with \(N\) scalar inputs can equivalently be seen as a compression mapping \(p(t|\mathbf{y})\) for a row vector \(\mathbf{y}=[y_{0},y_{1},\ldots,y_{N-1}]\). In this case, the observed random variable \(\mathsf{Y}\) is a random vector, but the principle of the information bottleneck method remains unchanged, and the compression mapping \(p(t|\mathbf{y})\) can still be designed with the available information bottleneck algorithms. Problems arise, however, for a large number of inputs \(N\), as the number of possible conditions \(\mathbf{y}\) might grow massively with \(N\). Nonetheless, Figure 3 illustrates that the intended information relations can be visualized very compactly in information bottleneck graphs. Information bottleneck graphs will be used extensively in Section 2.3.
## 6 User Interface
#### Trend Monitoring  
The system detects that the antenna test results deviate from typical values when one of the moving average indicators exceeds the defined threshold value. In this case, the user is notified of the occurrence of an alarm displayed in the application. This procedure is based on the fact that these indicators are directly related to the represented values, so if one of the moving averages exceeds the defined thresholds, it means that there was a number of undesirable values outside the normal range. The algorithm generates three types of warnings, depending on the moving-average curve that exceeds the outlined limits (Figure 8):  
* The cyan-blue moving-average curve represents the curve with the highest volatility as it considers sets of three past values to calculate the arithmetic mean and is the most influential curve.
* The purple curve represents the average volatility and considers sets of five past values to calculate the average, and a medium concern alarm is triggered.
* The yellow curve represents the least volatile moving average considering sets of twenty past values for calculating the average being the least influential, requiring a more significant number of values outside the expected limits to be considered a trend. It represents the most severe alarm.  
Alternatively, if the algorithm does not register any abnormality in the data, it returns a message suggesting that everything conforms with the expected values. For the case under study, the user interface generated the warning represented by Figure 9. This type of monitoring applies the analytical tool of creating manufacturing sustainability [15].  
Although the application developed allows a detailed analysis of the behavior of the values tested on the assembly line complemented by feedback through different alarms based on the graphical behavior, the last part of this manuscript focuses on the development of a system capable of predicting the behavior of the values tested. A system capable of predicting the system's future behavior becomes necessary since the algorithm developed for the graphical interface needs the data to be stored in the database to conclude whether there is a trend, which means that the said trend may already be occurring. The following section uses an LSTM model to predict, based on past data, the behavior of the assembly line.  
Figure 9: Warning generated by the monitoring graph (purple moving average curve).
## 4 Experimental Results
### Preliminary System Evaluation on Real-World Data
In the current use-case, this curve is affected by the involved system parameters, namely time window (_tw_) that controls observation length, similarity index threshold (_tsi_) that estimates the relation of the retrieved articles, and count threshold (_cnt_) that summates the number of the retrieved documents within the wanted _tsi_ values. This was initially checked through empirical observations, with trial-and-error testing to indicate an appropriate time window of _tw_ = 1800 s (i.e., half an hour). Hence, it was decided to further elaborate on the behavior of the classifier through the Precision-Recall curve concerning the change of the similarity threshold (Figure 7) and the count threshold (Figure 8). It is expected that greater threshold values will have a great impact on Precision, as the detection algorithm will be \"stricter\". An attempt to evaluate the behavior of the detection system was also made by changing the time window from 600 to 6000 s while keeping the similarity threshold at 0.85 and the count threshold at 5, but the results remained the same. Overall, based on the evaluation results of the trained models, the parameter that has the greatest impact on the classifier's behavior is the similarity threshold. Setting the similarity threshold to 0.75, a Precision value of 0.857 and a Recall value of 0.125 were achieved on the three-vote unanimous set. This leads to the conclusion that most of the retrieved articles were truly classified as breaking news, whilst the probability of a breaking article to be retrieved by the system was kept low. This prototype system performs remarkably better on the two-vote majority set, on which setting the similarity threshold to 0.75 a precision value of 1.0, and a recall value of 0.241 is achieved. Figure 8: Precision–Recall curve keeping the window at 1800 s and the similarity threshold at 0.85, while increasing the count threshold from 3 to 13 with a step of 1: (a) three-vote unanimous set; (b) two-vote majority set. Figure 7: Precision–Recall curve keeping the window at 1800 s and the count threshold at 5 while increasing the similarity threshold from 0.55 to 1.0 with a step of 0.5: (a) three-vote unanimous set; (b) two-vote majority set. Recalling the conducted state-of-research review, related works, which deal either with hot topic detection or breaking news detection, evaluate their results with the same metrics, achieving average Precision and Recall values of 0.84 and 0.83, respectively [36; 40]. It should be highlighted that the accuracy of the systems that try to solve these problems is still relatively low at the level of 80% [36; 40; 41]. Given that the current work faces a broader/more demanding problem, and the proposed solution fertilizes the ground for future ML/DL elaborations (the full potential of which have not been explored at this point), we can support the idea that the results achieved are mostly adequate, strongly validating the targeted proof of concept.
## 3 Results and Discussion
### Purification of APs from KPH
#### 3.2.4 Purification of APs from GC-3 by Reversed-Phase HPLC (RP-HPLC)  
Finally, GC-3 was purified using a Zorbax C-18 column on an Agilent 1260 HPLC system and the elution profile is shown in Figure 4. The eluted APs were collected on their chromatographic peaks. In the end, six APs from GC-3, with a retention time (RT) of 9.05 min (P1), 11.36 min (P2), 12.63 min (P3), 13.25 min (P4), 16.78 min (P5), and 17.22 min (P6), were prepared for molecular structural identification and bioactivity analysis.
## 5 Analysing General XAI
#### XAI in Terms of Design Space  
The prime reason of adding explanation to the dimensions of the design space defined in Section 4 () Prediction, \(\mathbf{\Theta}\) Optimisation/Automation, \(\mathbf{\Theta}\) Analysis, and \(\mathbf{\Theta}\) Mod elling/Simulation), is to increase trust of humans on the decision making process of AI/ML models [265] and improve human decisions or predictions based on the models [266]. Moreover, human decisions can be often driven by biases and heuristics that may present some limitations under certain working conditions [267]. However, abundant relevant information also does not necessarily assist people in making proper decisions. In human decision-making for the stated dimensions, the heuristics and biases are mostly controlled by statistical constraints [268]. Regardless of the domain expertise, people generally can not produce fully optimal decisions [269]. On the contrary, XAI can be beneficial to human decision-making by increasing the trust on the automated systems. This can be investigated through human experiments testing AI systems with explanations compared with traditional AI systems alone for different dimensions of the design space. Literature indicates that mostly four different forms of explanations are generated to \"explain\" to the human the decisions of the AI/ML models as well as the process of deducing a decision for the tasks from different dimensions of the design space. Those four forms of explanations have the following format: Numeric, rules, textual, and visual explanations. Some researches [270, 271, 272] investigated the use of a combination of formats to make the explanation more understandable and user friendly. The available methods for adding explainability of these forms to the existing and proposed AI/ML models are clustered on the basis of the categories of tasks; prediction, optimisation/automation, analysis, and modelling/simulation from the publications on AI in ATM. In addition, the available explainable methods are grouped based on the explanation generation scope and stages. The summary of the clustering is represented in Table 6. The bottom row of the table presents the total number of articles that contribute to each of the components of explainability and categories of tasks from the design space. It is evident that most of the methods were deployed to add explainability to prediction tasks whereas the tasks of modelling/simulation were investigated least with a view to add explanations. On the other hand, for specific methods, the Adaptive Neuro-Fuzzy Inference System (ANFIS) [273] was observed to be utilised to generate all four types of explanations in the tasks containing optimisation/automation and analysis. However, Sequential Rule Mining (SRM) [274] algorithms were found to be exploited in all task categories of design space except prediction. Among other methods available for adding explainability to intelligent systems, Local Interpretable Model-Agnostic Explanations (LIME) [270, 272, 275, 276] and Shapley Additive Explanations (SHAP) [271, 272, 277] are worth mentioning due to their wide acceptability among researchers.
## 2 Related Work
### Self-Calibration  
Another approach to reducing distortions in images is based on self-calibration also called autocalibration. Self-calibration results in rectifying images without the use of any predefined, exemplary pattern. Images are transformed only on the basis of their content. Self-calibration is performed in stereo cameras mainly to reduce extrinsic distortions caused by the fact that side camera is aimed at slightly different direction than a reference one and side camera may be rotated with respect to a reference one.  
Algorithms for performing self-calibration are based on features extraction and finding the same keypoints in both images. One of the most well-known algorithms for identifyingkeypoints is Scale-invariant feature transform (SIFT) [53]. This algorithm was used for self-calibrating stereo cameras by [PERSON] et al. [54]. SIFT was a base for developing commonly used Speeded up robust features (SURF) algorithm [55]. However, other methods for identifying keypoints are also used for performing self-calibration of stereo cameras. [PERSON], [PERSON] andSmach compared seven techniques for features extraction: SIFT, SURF, BRISK, FAST, FREAK, MinEigen and MSERF [56].  
A vast amount of research regarding calibration and autocalibration is focused on calibrating stereo cameras and camera rigs for the purpose of using them with autonomous cars. [PERSON] et al. applied for this purpose A-KAAZE-feature extraction [57]. [PERSON], [PERSON] and [PERSON] used feature SMRT extraction technique for calibrating multi-camera rig [58]. [PERSON] et al. applied a similar approach for the rig mounted on a car [59].
## 3 Results
### LM/Esthetic Plane Analysis  
The LM study of [PERSON] [24] is built on the esthetic plane, which is a line drawn tangent to the tip of the nose and soft tissue pogonion [24] (Figure 1). A profile is studied to be preferably beautiful when the LM-2 is approximately 2.0 mm, and the LM-1 is about 4.0 mm posterior to the esthetic plane [13]. In this study, LM was posterior to the esthetic plane in 64.52% (NC), 96.55% (BCLP), 97.56% (UCLP), 55.56% (UCLA), and 69.23% (UCL) with respect to LM-1, whereas 54.84% (NC), 41.38% (BCLP), 36.59% (UCLP), 0% (UCLA), and 23.08% (BCLP) were recorded for the LM-2. The LM touching and anterior to the esthetic plane were 6.45% and 29.03% (NC), 3.45% and 0% (BCLP), 0% and 2.44% (UCLP), 22.22% and 22.22% (UCLA), and 7.69% and 23.08% (UCL) for the LM-1, and 0% and 45.16% (NC), 0% and 58.62% (BCLP), 2.44% and 60.98% (UCLP), 0% and 100% (UCLA), and 7.69% and 69.23% (UCL) for the LM-2, respectively (Table 2). Ricketts norms for LM-1 and LM-2 (LM-1/LM-2, \(-4\) mm/\(-2\) mm) were the closest to the values, and we did not find any for the groups (LM-1/LM-2, \(-1.56\) (NC), \(-6.93\) (BCLP), \(-6.59\) (UCLP), \(-2.42\) (UCLA), and \(-2.26\) (UCL) mm/0.49 (NC), 0.98 (BCLP), 0.45 (UCLP), 2.52 (UCLA), and 2.76 (UCL) mm). In relation to LM-1, significant disparities were found among groups. No significant gender and side disparities (Tables 3 and 4) and among all groups were found in relation to LM-2 (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **Side** & **Mean** & **SD** & \multicolumn{2}{c}{**95\% CI**} & \multicolumn{1}{c}{_p-Value_} \\  & & & & **Lower** & **Upper** & \\ \hline LM-1 & Right & \(-\)5.208 & 4.706 & \(-\)2.575 & 2.203 & 0.877 \\  & Left & \(-\)5.022 & 4.683 & \(-\)2.005 & 0.410 & 0.134 \\ LM-2 & Right & 0.480 & 3.734 & \(-\)3.005 & 0.410 & 0.134 \\  & Left & 1.777 & 3.043 & & & \\ NLA & Right & 82.949 & 13.428 & \(-\)2.823 & 10.988 & 0.242 \\  & Left & 78.866 & 13.665 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Side disparities for Upper lip to E plane (LM-1), Lower lip to E plane (LM-2), and Nasolabial angle (NLA) among non-cleft, unilateral cleft lip and palate (UCLP), unilateral cleft lip and alveolus (UCLA), and unilateral cleft lip (UCL) individuals (N = 63, Right = 27, and Left = 36).  
Figure 2: Multiple group comparison results of Upper lip to E plane (LM-1) (**A**), Lower lip to E plane (LM-2) (**B**), and Nasolabial angle (NLA) (**C**) in non-cleft, unilateral cleft lip and palate (UCLP), bilateral cleft lip and palate (BCLP), unilateral cleft lip and alveolus (UCLA), and unilateral cleft lip (UCL) individuals. (_p_ \(<\) 0.05 = *, \(p\) \(<\) 0.01 = **, \(p\) \(<\) 0.001 = **).