esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Higher-Order Sliding Mode Regulation for Boost Converter Output
### Manifold Calculation  
From Equations (5) and (15), one can determine  
\[\pi_{2}(\omega)=\omega_{1}+\omega_{3}. \tag{54}\]  
To calculate \(\pi_{1}(w)\), one reckons \(c(\omega)\) from Equation (14) resulting in the following expression:  
\[c(\omega)=\frac{C}{\pi_{1}(\omega)}\frac{\partial\pi_{2}(\omega)}{\partial \omega}S\omega+\frac{\pi_{2}(\omega)}{R\pi_{1}(\omega)}. \tag{55}\]  
Substituting Equation (55) into Equation (13) yields  
\[\frac{\partial\pi_{1}(\omega)}{\partial\omega}S\omega=-\frac{C\pi_{2}(\omega )}{L\pi_{1}(\omega)}\frac{\partial\pi_{2}(\omega)}{\partial\omega}S\omega- \frac{\pi_{2}^{2}(\omega)}{LR\pi_{1}(\omega)}+\frac{E}{L}. \tag{56}\]  
In fact, the term \(c(w)\) is not explicitly used in the control action of Equation (17). Therefore, \(\pi_{1}(w)\) can be defined as a solution of Equation (52) for given \(\pi_{2}(w)\). In general, finding a solution for PDEs is difficult, but one option is to propose an approximation to the solution [30; 31; 32]. In this case, we propose the following as the approximated solution for \(\pi_{1}(w)\):  
\[\begin{array}{c}\pi_{1}(\omega)=a_{0}+a_{1}\omega_{1}^{3}+a_{2}\omega_{1}^{ 2}+a_{3}\omega_{2}\omega_{1}^{2}+a_{4}\omega_{3}\omega_{1}^{2}+a_{5}\omega_{1 }\omega_{2}^{2}\\ \qquad\qquad+a_{6}\omega_{1}\omega_{2}+a_{7}\omega_{3}\omega_{1}\omega_{2}+a_ {8}\omega_{1}+a_{9}\omega_{3}\omega_{1}+a_{10}\omega_{3}^{2}\omega_{1}\\ \qquad\qquad+a_{11}\omega_{3}^{2}+a_{12}\omega_{2}^{2}+a_{13}\omega_{3}\omega _{2}^{2}+a_{14}\omega_{2}+a_{15}\omega_{3}\omega_{2}\\ \qquad\qquad+a_{16}\omega_{3}^{2}\omega_{3}+a_{17}\omega_{3}+a_{18}\omega_{3} ^{2}+a_{19}\omega_{3}^{3}+\mathcal{O}^{4}(\left\|\omega\right\|_{1}).\end{array} \tag{57}\]  
Multiplying Equation (56) by \(\pi_{1}(w)\) and then substituting Equation (57) into the resulting equation, the values \(a_{i}\) (\(i=0,\,\ldots,\,19\)) can be determined by equalizing the polynomial coefficients in both sides of the equation. All coefficients result equal to zero except for  
\[a_{2}=\frac{1}{RE},\,\,\,a_{6}=-\frac{\alpha C}{E},\,\,\,a_{9}=\frac{2}{RE},\, \,\,a_{15}=-\frac{\alpha C}{E},\,\,\,a_{18}=\frac{1}{RE}. \tag{58}\]  
Note that the bias value for \(\pi_{1}(w)\) is the monomial with \(a_{18}\) as coefficient, i.e., \(b^{2}/(RE)\). This fact was used to obtain the linearized sliding mode Equation (47).
## 3 Results and Discussion
### Changes in pH, TBARS, VBN, and TMC during Storage of Tteokgalbi
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Storage Days**} & \multicolumn{4}{c}{**Treatments 1**} \\ \cline{2-6}  & **CON** & **REF** & **B1** & **B2** & **B3** \\ \hline DPPH radical & 0 & 18.87 \(\pm\) 0.18 \({}^{\text{2.5 MA}}\) & 27.64 \(\pm\) 0.84 \({}^{\text{AA}}\) & 23.02 \(\pm\) 1.14 \({}^{\text{BA}}\) & 23.42 \(\pm\) 0.31 \({}^{\text{BA}}\) & 27.32 \(\pm\) 0.59 \({}^{\text{AA}}\) \\ scavenging & 7 & 13.88 \(\pm\) 1.01 \({}^{\text{GB}}\) & 26.57 \(\pm\) 0.93 \({}^{\text{AA}}\) & 25.50 \(\pm\) 0.89 \({}^{\text{BA}}\) & 24.96 \(\pm\) 0.56 \({}^{\text{BA}}\) & 28.60 \(\pm\) 0.97 \({}^{\text{AA}}\) \\ activity (\%) & 14 & 8.63 \(\pm\) 0.06 \({}^{\text{CC}}\) & 16.22 \(\pm\) 1.13 \({}^{\text{AB}}\) & 11.36 \(\pm\) 0.99 \({}^{\text{BB}}\) & 14.22 \(\pm\) 0.97 \({}^{\text{BB}}\) & 16.09 \(\pm\) 0.93 \({}^{\text{AB}}\) \\ \hline ABTS radical & 0 & 55.41 \(\pm\) 0.60 \({}^{\text{AA}}\) & 70.19 \(\pm\) 0.53 \({}^{\text{AA}}\) & 61.62 \(\pm\) 1.67 \({}^{\text{BA}}\) & 63.99 \(\pm\) 0.84 \({}^{\text{BA}}\) & 70.06 \(\pm\) 1.11 \({}^{\text{AA}}\) \\ scavenging & 7 & 31.14 \(\pm\) 0.9 \({}^{\text{AB}}\) & 47.48 \(\pm\) 1.18 \({}^{\text{AB}}\) & 43.12 \(\pm\) 6.70 \({}^{\text{BB}}\) & 46.08 \(\pm\) 1.09 \({}^{\text{AB}}\) & 46.50 \(\pm\) 0.84 \({}^{\text{AB}}\) \\ activity (\%) & 14 & 10.36 \(\pm\) 0.98 \({}^{\text{CC}}\) & 25.98 \(\pm\) 0.67 \({}^{\text{AC}}\) & 20.33 \(\pm\) 0.78 \({}^{\text{BC}}\) & 21.13 \(\pm\) 0.87 \({}^{\text{BC}}\) & 25.12 \(\pm\) 0.65 \({}^{\text{AC}}\) \\ \hline \hline \end{tabular}  
* Treatments are shown in Table 1. 2 Values are expressed as mean \(\pm\) standard deviation (n = 3). 3 Means \(\pm\) standard deviation with different superscripted small letters within the same row differ significantly by [PERSON]’s multiple range test (\(p\) < 0.05). 4 Means \(\pm\) standard deviation with different superscripted capital letters within the same column differ significantly by [PERSON]’s multiple range test (\(p\) < 0.05). 5 TBA, 2-thiobarbituric acid, 6 VBN, volatile basic nitrogen; 7 NS: not significant; 8 TMC, total microbial counts.  
\end{table} 
Table 11: Changes of DPPH and ABTS radical scavenging activity of Tetokgalbi prepared with different levels of _Boesenbergia pandurata_ (Roxb.) powder during storage at 5 \({}^{\circ}\)C for 14 days.
# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 3 Materials and Methods
### Distributed Inference Pipeline
A Dask cluster is composed of one scheduler node and N worker nodes. By increasing the number of workers, we can scale up the number of maximum tasks executable in parallel, giving our solution great flexibility and scalability. Although not mandatory, such a solution fits well with the serverless infrastructure made available by most cloud providers today. Serverless computing is an execution model in which the cloud provider allocates machine resources on demand, allowing customers to pay only when computational power is needed. Creating a Dask cluster when required and deploying it on a serverless infrastructure dramatically reduces operational costs while maintaining a virtually infinite ability to scale. It is worth remarking that the type of machine used to instantiate Dask workers can be defined during configuration. For example, machines with GPU enabled can be selected to reduce inference time further.
###### Abstract
With regard to the second problem, different organisations such as the World Health Organisation [18; 19] and the European Commission [20] suggest the implementation of strategies to effectively address sedentary lifestyles and their consequences on public health and the education and training system. In recent years, different strategies have been developed in schools to meet physical activity recommendations, such as physical education classes, active transport, active recesses, and active breaks [7; 21]. Classroom physical activity programmes based on active breaks (AB) are presented as an intervention strategy to increase physical activity and/or decrease sedentary behaviour, and seem to be the key alternative to reach up to 50% of the daily physical activity recommendations established by the WHO [22], to reduce sedentary lifestyles and improve the level of physical fitness, classroom behaviour, and executive functions of schoolchildren [23]. These AB are understood as physical activity breaks of five to 15 min, at any level of intensity (although predominantly moderate and vigorous physical activity), which are guided by teachers of any subject during the normal course of the class and can be implemented in any school context as it does not require a specialised space, material or staff [7; 24]. The time available for these AB is discrete and limited, so several additional times during the school day are necessary for children to decrease sedentary time and reach the recommended physical activity levels [22; 24]. Therefore, physical activity in the classroom is a teaching strategy that teachers can use to educate children to maintain an active lifestyle within school hours [21; 23]. The incorporation of physical activity in the classroom can take place during class sessions (intra-class) or between class changes (inter-class), either by integrating physical activity into lessons (physically active classes) or through short bursts of curriculum-related physical activity (curriculum-focused active breaks) or non-curriculum-related physical activity (active breaks) [23]. Despite the increasing scientific evidence suggesting the use of TPSR model and AB in the classroom [14; 23] and the fact that the scientific-educational community considers the design, development, and implementation of multicomponent and interdisciplinary educational programmes to be fundamental, there is no longitudinal programme that combines the TPSR with the AB. Therefore, this study seeks to explain the rationale and protocol of an educational programme called \"ACTIVE VALUES\" as an intervention strategy for the reduction of sedentary lifestyles and the promotion of values education in schools. It is hoped that this programme will contribute to developing values of responsibility and autonomy in young people, as well as promoting healthy lifestyle habits and an active lifestyle.
## 5 Experimental Results and Analysis
### Hyperparameter Selection  
In the proposed DARIoU loss function, two hyperparameters, denoted by \(\lambda\) and \(\mu\) control the overall and local weight of the penalty term, respectively. To investigate their impact on model performance, we conducted ablation experiments on the DIOR-R dataset, testing various values for \(\lambda\) (0.3, 0.7, 1.0, and 1.5) and \(\mu\) (0.05, 0.1, 0.3, 0.7, and 1.0). The results, shown in Table 7, indicate that the model performs optimally when \(\lambda\) = 0.3 and \(\mu\) = 1.0, providing the best balance of performance.  
Figure 11: Training trajectories of AP50 (HRSC2016) and mAP (DIOR-R) for DARIoU and baseline. DARIoU shows faster convergence and higher stability compared to Baseline, with an improved final performance on both datasets.  
Figure 12: Comparison of the visualization results between baseline and our method on DIOR-R.
## 4 Wind-Induced Fragility Analysis of Corroded Transmission Tower
### Construction of Fragility Model  
The wind-induced fragility function reflects the probability that the structure reaches or exceeds a certain limit state under extreme wind loads, and the failure probability when the wind load effects \(S_{D}\) exceed the structural bearing capacity \(R_{C}\) can be determined by the following formula:  
\[P_{f}=P_{r}(\frac{R_{C}}{S_{D}}\leq 1) \tag{4}\]  
In the above equation, assuming that both \(S_{D}\) and \(R_{C}\) obey lognormal distribution, and selecting the maximum displacement at the top of the tower as the index of the load effects, the above equation can be transformed into:  
\[P_{f}=\Phi\left[\frac{\ln(m_{D})-\ln(m_{C})}{\sqrt{\beta_{C}^{2}+\beta_{D}^{2} }}\right] \tag{5}\]  
where: \(\Phi[\bullet]\) denotes the standard normal distribution function, \(m_{D}\) is the maximum displacement at the top of the transmission tower under the given wind loads, \(m_{C}\) is the structural bearing capacity under different limit states, \(\beta_{C}\) denotes the logarithmic standard deviation of the structural bearing capacity and the value can be taken from the literature [10]. \(\beta_{D}\) denotes the logarithmic standard deviation of the wind load effects.  
Figure 17: Cross-section of a corroded angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Steel Type** & **Angle Size** & **30 Years** & **60 Years** & **90 Years** \\ \hline Q345 & \(L90\times 8\) & 7.44 & 6.98 & 6.51 \\  & \(L100\times 8\) & 7.44 & 6.98 & 6.51 \\ \hline  & \(L40\times 4\) & 3.44 & 2.98 & 2.51 \\  & \(L45\times 4\) & 3.44 & 2.98 & 2.51 \\ Q235 & \(L50\times 5\) & 4.44 & 3.98 & 3.51 \\  & \(L56\times
# Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation
## 4 Simulation Results
It is concluded that the ANNBC has a very small steady-state error, which can well solve output constraints, model uncertainties and external disturbances, and meet the performance requirements of UAV for fast, accurate and anti-interference landing. Figure 5: Response curves of positions tracking of the BDSC. Figure 6: Response curves of attitude tracking of the BDSC. In this paper, the maximum and minimum of the control inputs are set, respectively, as \(U_{1M}=9,U_{1m}=4,U_{2M}=5,U_{2m}=-10,U_{3M}=2,U_{3m}=-5,U_{4m}=-30,U_{4M}=30\). The curves of the control inputs without anti-saturation strategy \((v_{1},v_{2},v_{3},v_{4})\) and with the anti-saturation strategy \((U_{1},U_{2},U_{3},U_{4})\) are shown in Figure 9a-d. The UAV control system is a typical nonlinear system. As the control input is limited by physical factors, the control input will show input saturation. To verify the effectiveness of the anti-saturation strategy, the control input without anti-saturation strategy and the control input with anti-saturation strategy are simulated for comparison. It can be seen that the control input of each channel is always within the set constraints under the anti-saturation strategy. Control inputs without anti-saturation processing will be too large or too small at first. With the anti-saturation processing, the control input is strictly limited to a constrained range and still has good tracking performance. Figure 8: Curves of attitude tracking errors of the ANNBC. Figure 7: Curves of position tracking errors of the ANNBC.
## 3 Results and Discussion
### Cider VOC Profiles Resulting from Different Yeast Strains  
A total of 27 VOCs were identified in four matured ciders that were identically prepared, save for the yeast strain used for fermentation, as shown in Tables 1 and 2. These tables list all analytes in order of their relative peak area (RPA, obtained through integration) and include retention times (RT), percentage correlation with NIST spectral database (NIST %), odor descriptors, and perception thresholds (PT) from previous sensory panel studies [21; 30]. Table 1 lists all major analytes observed (relative peak area > 0.05), and Table 2 lists minor analytes (relative peak area < 0.05). Unlike previous yeast strain comparison work, which analyzed for VOCs after 10 days [20], these ciders were taken through a six-month maturation process prior to analysis. In addition, unique to this study were the _S. cerevisiae_ yeast strains chosen for analysis (traditionally used in beer fermentation), which we will reference herein as: Edinburgh, Belgian II, French Saison, and Abbey.
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 3 Background
### Generative Adversarial Network  
A Generative Adversarial Network (GAN) [10] is a generative model for synthesizing artificial images. GAN consists of two key components: a generator and a discriminator. The generator learns to generate fake images by fooling the discriminator, while the discriminator learns to distinguish them from real images. The objective of GAN can be written as follows:  
\[\min_{G}\max_{D}\mathbb{E}_{x\sim x}[\log D(x)]-\mathbb{E}_{z\sim\mathcal{N}(0,1 )}[\log(1-D(G(z)))], \tag{2}\]  
where \(D\) and \(G\) represent the discriminator and the generator, respectively. \(x\) is real data, and \(G(z)\) is a generated image using Gaussian noise \(z\).  
As an extension of GAN, the conditional generative adversarial network (cGAN) [9] generates images based on specific conditions, such as semantic segmentation maps or other references. Unlike vanilla GAN, cGAN can generate more-realistic and -varied images. The loss function for cGAN is very similar to that of vanilla GAN, with the main difference being the addition of conditional inputs to both the generator and the discriminator.  
\[\min_{G}\max_{D}\mathbb{E}_{x\sim X}[\log D(x,c)]-\mathbb{E}_{z\sim\mathcal{N}( 0,1)}[\log(1-D(G(z,c),c))], \tag{3}\]  
where \(c\) is the conditional input. In many real-world scenarios, including virtual try-on, cGAN is frequently utilized to synthesize images meeting their conditions or references.
# Strans-YOLOX: Fusing Swin Transformer and YOLOX for Automatic Payement Crack Detection
## 4 Experiments and Results
### Experimental Results and Analysis
This demonstrates that the improved NMS is more effective than the traditional NMS in dealing with the error suppression caused by occlusion and overlapping objects. We also explore the influence of different values of \(\alpha\) on the detection results. As shown in Table 4, the performance of STrans-YOLOX varies with the values of \(\alpha\). With an increase in the values of \(\alpha\), the mAP first increases and then decreases. When \(\alpha\) is 1, the performance is equivalent to using the traditional NMS. When \(\alpha\) exceeds 1, the mAP decreases significantly. The reason for this may be that the box suppression is loose when \(\alpha\) is too small, which leads to false detection. When \(\alpha\) is large, the box suppression is strict, resulting in missing detection. Therefore, the value of \(\alpha\) is finally set as 0.75 based on the experimental results. (3) Visual Detection Results on the Pavement Crack Dataset  
To visually assess the utility of STrans-YOLOX, we pick some representative images of the detection results obtained using different models, which are depicted in Figure 8, where (a) is the original crack images to be detected, (b) is the detection results based on RetinaNet, (c) is the detection results based on YOLOv4, (d) is the detection results based on YOLOX, and (e) is the detection results based on STrans-YOLOX proposed in this paper. From the detection results, it is clear that some models have missing detection and false detection in complex scenes, and our proposed model can accurately detect and locate multi-class and multi-scale crack objects. In addition, the heatmap visualization results obtained using different detection networks are illustrated as Figure 9. The figure demonstrates that STrans-YOLOX can accurately focus on regions of interest in complex scenes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) & **0.5** & **0.6** & **0.7** & **0.75** & **0.8** & **0.9** & **1** & **2** & **3** \\ \hline
**mAP/\%** & 63.19 & 63.63 & 63.62 & 63.73 & 63.71 & 63.62 & 63.23 & 58.93 & 52.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effects of different values of \(\alpha\) on the detection results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP (D00)/\%** & **AP (D10)/\%** & **AP (D20)/\%** & **Map/\%** \\ \hline YOLOX & 59.92 & 50.98 & 70.18 & 60.36 \\ YOLOX + Swin Transformer & 60.01 & 53.48 & 72.59 & 62.03 \\  & (\(\uparrow\)0.09) & (\(\uparrow\)2.5) & (\(\uparrow\)2.41) & (\(\uparrow\)1.67) \\ YOLOX + Swin Transformer & 61.44 & 55.63 & 72.63 & 63.23 \\  + GAGM & (\(\uparrow\)1.43) & (\(\uparrow\)2.15) & (\(\uparrow\)0.04) & (\(\uparrow\)1.2) \\ YOLOX + Swin Transformer & 62.11 & 55.78 & 73.30 & 63.73 \\  + GAGM + \(\alpha\)-IoU-NMS & (\(\uparrow\)0.67) & (\(\uparrow\)0.15) & (\(\uparrow\)0.67) & (\(\uparrow\)0.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of the ablation experiments. Figure 8: The visual detection results of different models.
## 2 Results and Discussion
### Biofilm Formation Increases as the Concentration of SK-03-92 Increases  
With an increase in _srtA_ transcript abundance shown by the qRT-PCR results, an increase in biofilm formation would be expected following SK-03-92 treatment. To further analyze the effects of the increase in _srtA_ transcription, a biofilm assay in microtiter plates was performed after SK-03-92 drug treatment (Figure 3). Wild-type JE2 and MW2 cultures were tested following SK-03-92 drug treatment (range 0.5-0.64 \(\mathrm{\SIUnitSymbolMicro m}\)/mL). The JE2 culture grown without drug showed an OD\({}_{570}\) of 2.41, whereas the MW2 culture had an OD\({}_{570}\) of 2.50.  
The SK-03-92 drug had a biphasic effect on the wild-type strains. At low concentrations, the drug reduced biofilm formation as exhibited by the 0.5 and 1 \(\mathrm{\SIUnitSymbolMicro m}\)/mL data points that were significant for both strains that were tested (\(p<0.05\)). As concentrations of SK-03-92 increased, the OD570 readings increased, plateauing at 32 \(\mathrm{\SIUnitSymbolMicro m}\)/mL for both strains. Strain MW2 showed significant increases in biofilm formation going from 0 \(\mathrm{\SIUnitSymbolMicro m}\)/mL to 8-64 \(\mathrm{\SIUnitSymbolMicro m}\)/mL SK-03-92 concentration (\(p<0.05\)). A similar finding was observed when _Candida_ species grown as a biofilm were exposed to varying concentrations  
Figure 2: Absorbance (254 and 280 nm) of the formic acid extracted nucleotide pool of log-phase _S. aureus_ ATCC 29213 after 20 min with either (**A**) no treatment, (**B**) 60 \(\mathrm{\SIUnitSymbolMicro m}\)/mL muiprocin, or (**C**) 16 \(\mathrm{\SIUnitSymbolMicro m}\)/mL SK-03-92. Arrow denotes the (p)ppGpp peak.  
of echinocandin [75]. _Candida_ treated with low drug concentrations killed the fungal cells but at concentrations higher than the MIC showed there was an increase in the cell density of the biofilms. Echinocandin acting on the _Candida_ species has the same effect as our SK-03-92 drug, triggering upregulation of a specific gene that increases biofilm formation.  
Under normal growth conditions, biofilm formation is not necessary for a cell, but under stressful environmental conditions, such as exposure to the SK-03-92 drug, biofilm formation would greatly benefit the _S. aureus_ population. Formation of a biofilm would benefit cells by allowing for the formation of persister cell populations [76]. When biofilms form, the cells at the base of the biofilm slow or stop most cell metabolism and go into a dormant state, allowing the organisms to survive in the presence of a drug, for example SK-03-92. In addition, cells in a biofilm often undergo quorum sensing, which also can lead to the emergence of persister cells [77].
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 2 Methodology
### Automatic Selection of HDTs Data Points for Initiating the Iteration Process of Prediction  
As per the proposed framework, after the construction of HDTs, the next step was the automatic selection of the HDTs data points for initiating the iteration process of prediction. According to [17], there is a research gap on from which points of HDTs the iteration process should start for producing the prediction points. As per the general experience, the vibration of in-operation mechanical equipment increases with the passage of time. Therefore, it was decided that only those data points of the HDTs should be selected for commencing the iteration process, which have a trend of increasing slopes. This concept gave a logical sense that HDT's data points owning a patch of successive increasing slopes can be expected to progress towards the upward threshold line (like a natural vibration trend). To comply with this idea, a moving window size of 20 data points of each HDT was selected for calculating the slopes. In the case of channel 2, a standard was made that at least every 6 th consecutively increasing slope point would be utilized for initiating the iteration process, as shown in Figure 5a. Since the neural networks require a suitable amount of data for their training and learning stage, 200 data points of both the channels' HDTs were kept excluded during the data points selection process.  
By following the approach stated above, the corresponding data points were found to be 258, 288, 312, 364, 416, and 458 for the channel 2 HDT. For the first iteration process, Section 1 of HDTs data points, i.e., from 1 to 258, was selected, as shown in Figure 5b. Similarly, for the second iteration process, Section 2 of data points, i.e., 1 to 288, was selected. Accordingly, the 6 th section of data points, i.e., 1 to 456, was picked up to initiate the 6 th iteration process for producing the prediction points.  
Similarly, in the case of channel 4, every 4 th (or above) sequentially increasing slope point was utilized for starting the iteration process, as shown in Figure 5c. The corresponding slope points values were found to be 456, 495, 565, 655, 720, 824, and 834 for the channel 4 HDT. Same as earlier, by utilizing the selected data points, seven sections were formed, which were utilized for initiating the iteration process, as shown in Figure 5d.  
Figure 4: Constructed health degradation trends for (**a**) channel 2 and (**b**) channel 4.  
Figure 5: _Cont_.