esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reliability Evaluation for Machine Center Subsystems Using the Improved Average Rank Method
### Reliability Model and Parameter Estimation for Machining Centers  
For machining centers, it is often assumed that their failure time follows Weibull distribution. The Weibull distribution has a concise expression, which facilitates subsequent maintenance-related calculations. However, before making such an assumption, validation is required, such as the correlation coefficient and K-S test validation mentioned in Section 4 of this paper. The cumulative distribution function, failure probability density function, reliability function, and failure rate function of the Weibull distribution are shown as follows:  
\[F(t)=1-\exp\left[-\left(t/\eta\right)^{\beta}\right] \tag{3}\]  
\[f(t)=\frac{\beta t^{\beta-1}}{\eta^{\beta}}\exp\left[-\left(t/\eta\right)^{ \beta}\right] \tag{4}\]  
\[R(t)=1-F(t)=\exp\left[-\left(t/\eta\right)^{\beta}\right] \tag{5}\]  
\[\lambda(t)=\frac{\beta}{\eta}\left(\frac{t}{\eta}\right)^{\beta-1} \tag{6}\]  
After obtaining the empirical distribution function through Equation (2), and bringing it and the failure interval time \(t_{i}\) into Equation (3), the Weibull distribution parameter estimation can be obtained by using the least square method.  
The left side of Equation (3) is changed to \(1-F(t)\) and two logarithmic operations are performed on both ends of the equation to obtain:  
\[\ln\{-\ln[1-F(t)]\}=\beta\ln t-\beta\ln\eta \tag{7}\]  
Let \(y=\ln\{-\ln[1-F(t)]\},\ x=\ln t,\ A=-\beta\ln\eta,\ B=\beta\), then  
\[y=A+Bx \tag{8}\]  
According to the least square method, the estimated point estimation value of \(A\) and \(B\) is:  
\[\left\{\begin{array}{l}\hat{B}=\frac{\sum_{i=1}^{n}x_{i}y_{i}-n\overline{x_{ i}}}{\sum_{i=1}^{n}x_{i}^{2}-n\overline{x}^{2}}\\ \hat{A}=\overline{y}-B\overline{x}\end{array}\right. \tag{9}\]  
where \(\overline{x}=\sum\limits_{i=1}^{n}x_{i}/n\) ; \(\overline{y}=\sum\limits_{i=1}^{n}y_{i}/n\), and \(n\) is the number of samples.  
Thus, the parameters of the Weibull distribution can be determined by:  
\[\left\{\begin{array}{l}\hat{\eta}=\exp\left(-\frac{\hat{A}}{\hat{B}}\right) \\ \hat{\beta}=\hat{B}\end{array}\right. \tag{10}\]
## 2 Study Area and Data  
### Study Area  
Sichuan Province is located in the southwest of China, situated in the upper reaches of the Yangtze River. It spans between \(26^{\circ}03^{\prime}\) to \(34^{\circ}19^{\prime}\) north latitude and \(97^{\circ}21^{\prime}\) to \(108^{\circ}12^{\prime}\) east longitude, bordered by seven provinces including Chongqing, Guizhou, Yunnan, Tibet, Qinghai, Gansu, and Shaanxi. Sichuan Province exhibits significant differences in its eastern and western topography, with a complex and diverse terrain characterized by a west-high-east-low topographical feature. It encompasses mountains, hills, plains, basins, and plateaus, covering a total area of 486,000 square kilometers. The province is administratively divided into 21 prefecture-level administrative regions, 183 county-level divisions, and 3101 township-level divisions. According to the Seventh National Population Census of China, the permanent population of Sichuan Province in 2020 was 83.67 million, ranking fifth in the nation in terms of total population. In recent years, Sichuan has experienced rapid socio-economic development, but it has also faced frequent geological disasters. This poses a significant challenge for urban management and emergency disaster preparedness in densely populated areas. Therefore, this study selected Sichuan Province as the study area (Figure 1).  
Figure 1: Population distribution map of the study area in 2020.
## 4 Results  
Using the knowledge summarized in the previous chapters, calculations of the evolution of system unavailability were performed for several scenarios described in this chapter. First, calculation was performed for a separate part of each infrastructure, i.e., the power grid and the communication network separately.  
Figure 10 shows the unavailability progression of the power grid (a) and the communication network (b) over a 5-year duration. It can be seen from this figure that the communication network unavailability over a 5-year horizon is almost twice as high as that of the power grid.  
Figure 11 shows graphically the aforementioned evolution of the unavailability comparison for systems S1 and S2. Furthermore, the unavailability calculated for the complete interconnected infrastructures represented by the SS system is plotted in this figure. The input for this calculation was the AG shown in Figure 7 in the previous section. From this comparison, it can be seen that in a holistic view of interconnected infrastructures, the impact of both infrastructures on the contingency of the overall system, which increases significantly with interconnection, must be considered.  
It has already been mentioned in this paper that the unavailability calculation can also be used for optimization tasks. This fact was demonstrated in another calculation where the S1 system was improved by placing a new transformer, T2 (connected in parallel to transformer T1 in Figure 4).  
The calculation of the evolution of the unavailability of such an improved system (S1 improved) is shown in Figure 12, where comparison with the original S1 and S2 systems can be seen (a).  
Subsequently, the unavailability calculation of the whole system (denoted as SS improved) is also updated, and comparison with the original SS system is made (b). This comparison shows an improvement in the unavailability of the S1 improved system as the overall unavailability dropped to \(2.48\cdot 10^{-4}\) (originally \(4.2\cdot 10^{-4}\)).  
All computations were numerically computed using the high-performance programming language MATLAB on computing equipment with the following parameters: Intel (R) CoreTM i7-3770 CPU @ 3.4 GHz 3.9 GHz, 8.00 GB RAM.  
Figure 11: Comparison of unavailability evolution of systems SS, S1 and S2 for the case with original parameters.  
Figure 10: Unavailability evolution of the power grid (**a**) and the communication network (**b**) within the mission time of 5 years.
## 1 Introduction
### Models and Relations between them  
We note that a single, universal, technology- and cost- independent model is missing in the state of the art, so for each protocol and problem we precisely state the particular modeling assumptions (weaker or stronger) needed. Also, we provide the following hierarchy of models and relations between them.
## 3 Materials and Methods
### Traffic Direction
#### 3.3.1 DBSCAN Algorithm  
The DBSCAN algorithm forms clusters based on density, making it effective at identifying clusters of different shapes and detecting noise, which is advantageous for maintaining the quality of analysis. Unlike other clustering methods, DBSCAN does not require pre-defining the number of clusters. [PERSON] et al. [42] applied DBSCAN to identify ship traffic patterns of fixed regions and frequent routes, contributing to maritime ship traffic analysis. [PERSON] et al. [43] further developed this approach by incorporating vessel speed, direction, and location attributes into the DBSCAN process, offering a more comprehensive view of vessel movements. [PERSON] et al. [44] extracted ship voyages from AIS data and applied the longest common subsequence (LCS) algorithm and DBSCAN clustering algorithms, categorizing ships with similar routes to visualize ship traffic flow. The DBSCAN algorithm  
Figure 3: Colormap mapping method. (**a**) Single-color gradient mapping method for maritime traffic density maps. (**b**) Multi-color gradient mapping method for maritime traffic density maps.  
requires two key parameters: epsilon and MinPts. Epsilon defines the neighborhood radius around a data point, whereas MinPts determines the minimum number of points required to form a cluster. Given that the results of the DBSCAN algorithm depend on these parameters, selecting the optimal values is crucial for meaningful and accurate results. To set the optimal parameters, epsilon is determined using the k-distance graph, while MinPts is selected using the Silhouette Index, a clustering performance evaluation technique. The k-distance graph enables an effective visual understanding of density variation in the data, identifying an epsilon value that reflects the data's structure rather than arbitrarily assigning a value based on data characteristics. Various approaches can identify the epsilon value in the k-distance graph. Typically, points before a sharp rise on the graph represent high-density areas with relatively small distances between points, while points after the sharp rise indicate low-density areas with large distances between points. Therefore, we selected points on the k-distance graph before or just after the sharp rise as the epsilon value. Subsequently, the two most important parameters of the DBSCAN algorithm were determined by selecting the highest-performing MinPts value using the Silhouette Index, which is a clustering performance evaluation technique.
## 3 Results and Discussion
### Electrochemical Analysis
#### 3.3.2 Polarization Curves
Detailed values for I\({}_{\text{corr}}\), E\({}_{\text{corr}}\), and R\({}_{\text{p}}\) are presented in Table 5.  
\[\text{R}_{\text{p}}=\frac{\beta_{\text{a}}\beta_{\text{c}}}{2.303\text{I}_{ \text{corr}}(\beta_{\text{a}}+\beta_{\text{c}})} \tag{6}\]  
Figure 9: N80 steel corrosion rate as measured by means of LPR across a range of immersion times. Blue spots represent the durations of the immersion experiments (i.e., 0, 3.5, 7, 14, and 28 days). When considering the immersion time, the N80 steel sample displayed the highest corrosion current density (I\({}_{\text{corr}}\)) of 3.70 \(\times\) 10\({}^{-7}\) A\(\cdot\)cm\({}^{-2}\), the most negative corrosion potential (E\({}_{\text{corr}}\)) of \(-\)0.541 V, and the lowest polarization resistance (R\({}_{\text{p}}\)) of 9.15 \(\times\) 10\({}^{5}\)\(\Omega\cdot\)cm\({}^{2}\) at an immersion time of 3.5 days. These results indicate the poor corrosion resistance of N80 steel under this condition. Between the time periods of 3.5 days and 14 days, there was a gradual increase in the polarization resistance (R\({}_{\text{p}}\)) of the N80 steel samples, suggesting an improvement in the safeguarding impact of corrosion products on the steel substrate and an enhancement in the samples' ability to withstand corrosion. However, at 28 days, the R\({}_{\text{p}}\) started to decline, indicating a reduction in the protective effect of corrosion products and an aggravation of the corrosion severity. As the temperature increased, the R\({}_{\text{p}}\) of the sample initially increased, followed by a subsequent decrease, with the change exceeding one order of magnitude. Consequently, the corrosion resistance demonstrated an initial improvement and then a weakening trend. This corresponded exactly to the results obtained through calculation of the corrosion rate.
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 3 Hydrogels
### Classification of Hydrogels  
Hydrogels can be classified according to several criteria such as the nature of their macromolecular structure, on the final form of the system, or on the nature of the crosslinks [58]. Based on the nature of their macromolecular structure, hydrogels can be classified as natural, synthetic or hybrid.  
Natural polymeric hydrogels are gelsmade from polymers that have a natural origin such as collagen, fibrin, chitosan, hyaluronic acid or alginate, and have been successfully employed for tissue engineering applications due to their biocompatibility, biodegradability and non-toxicity [59]. However, their mechanical strength is poor and their composition may vary from batch to batch. On the other hand, synthetic hydrogels are synthesized using synthetic polymers such as methacrylated gelatin, pluronic, or polyethylene glycol (PEG). Synthetic hydrogels have gradually replaced over the last years natural ones as it is easier to control physical and chemical properties than for natural polymers (i.e., matrix elasticity, ligand density and porosity) [60]. However, normal cellular processes, such as morphology changes, migration and proliferation, are inhibited in synthetic hydrogels unless they are designed to degrade over time. In fact, non-degradable hydrogels present typically a pure elastic mechanical behavior, while reconstituted ECMs, such as collagen or fibrin, are viscoelastic and exhibit a partial stress relaxation when a constant stress is applied [61] (Figure 2A). This explain the great effort made in the last years to develop a new class of synthetic hydrogels capable to not only reproduce the molecular stiffness but also the strain-stiffening and the extent of bundling, such as the thermo-responsive biomimetic polyisocyanopeptide (PIC) hydrogels [62].  
Simultaneously, the lack of adequate mechanical strength of most natural polymers, have made researchers to develop hybrid scaffolds by combining various hydrogels with other components to improve their resistance to stress. For this, nanomaterials, biological factors, and combinations of biopolymers (e.g., proteins and polysaccharide) are added to hydrogels to produce Interpenetrating Networks (IPNs) and Soft Network Composites (SNCs), often referred as composite or hybrid hydrogels [63].
# Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors
## 4 Results
### Result of Travel Mode Detection  
Based on the parametric analysis, we showed the result of travel mode detection under the optimal hyperparameters on the test data. Figure 8 shows the performance of our method in recognizing each type of travel mode.  
Figure 8: The performance of the proposed method.  
Figure 7: The performance of different travel modes with different sensor combinations.  
From Figure 8, we can see that the proposed method shows excellent performance in recognizing all travel modes. It can be found that the F value of all five types of travel mode recognition is above 97%. The same goes for precision and recall. The effect of travel mode \"car\" is the best, with precision, recall rate and F value reaching more than 98.5%. Walking mode has the lowest accuracy among the 5 types of travel modes, which may be caused by the randomness and confusion of walking behavior. The F-measure values for all the activities are equal or higher than 0.98 except bike, whose F-measure is 0.97.  
Table 4 shows the confusion matrix of the proposed method on the test data with the optimal hyperparameters. Table 4 shows that the algorithm can recognize these five types of travel mode with few confusions between different types of travel modes.
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 4 Control Strategy of the MCAT  
### Dynamic Characteristics of the AC Side in the MCAT  
When neglecting the fluctuation of the voltage of the MMC sub-module capacitor, the internal AC fundamental equivalent circuit of the MCAT in the steady-state is shown in Figure 3[19; 20; 21]. Here, \(u_{\text{diffdj}}\) and \(u_{\text{diffdj}}\) are the \(dq\) axis components of the differential mode voltage \(u_{\text{diffj}}\) output by MMC\({}_{j}\) on the valve side[18], \(i_{\text{dj}}\) and \(i_{\text{dj}}\) are the \(dq\) axis components of the current \(l_{j}\) at MMC\({}_{j}\) valve side, and \(u_{\text{bust}}\) and \(u_{\text{bustq}}\) are the \(dq\) axis components of B\({}_{\text{com}}\) voltage \(u_{\text{bus}}\). \(L_{0}\) and \(R_{0}\) are the MMC\({}_{j}\) arm inductor and resistor respectively[22; 23; 24; 25].  
\(C_{j}\) and \(L_{j}\) are series resonance in the fundamental circuit, and the circuit equation can be listed from Figure 3:  
\[\left\{\begin{array}{l}u_{\text{diffdj}}=(R^{\prime}+L^{\prime}\frac{\text{ d}}{\text{d}t})i_{\text{dj}}-\omega_{0}L^{\prime}i_{\text{dj}}+u_{\text{bustd}}\\ u_{\text{diffdj}}=(R^{\prime}+L^{\prime}\frac{\text{d}}{\text{d}t})i_{\text{dj}} +\omega_{0}L^{\prime}i_{\text{dj}}+u_{\text{bustq}}\end{array}\right. \tag{25}\]  
where \(L^{\prime}=L_{0}/2\), \(R^{\prime}=R_{0}/2\). [PERSON]'s current law (KCL) for B\({}_{\text{com}}\) is written as:  
\[\left\{\begin{array}{l}\sum\limits_{j=1}^{M+2N}i_{\text{dj}}=0\\ \sum\limits_{j=1}^{M+2N}i_{\text{dj}}=0\end{array}\right. \tag{26}\]  
where \(i_{\text{dj}}\) and \(i_{\text{dj}}\) are obtained by Equation (25). Considering that \(L^{\prime}\) is much larger than \(R^{\prime}\) and substituting \(i_{\text{dj}}\) and \(i_{\text{dj}}\) into Equation (26), the following equation is obtained as:  
\[\left\{\begin{array}{l}u_{\text{bustd}}=\sum\limits_{j=1}^{M+2N}u_{\text{ diffdj}}/(M+2N)\\ u_{\text{busq}}=\sum\limits_{j=1}^{M+2N}u_{\text{diffqj}}/(M+2N)\end{array}\right. \tag{27}\]  
Equation (27) shows that the AC bus voltages \(u_{\text{bustd}}\) and \(u_{\text{busq}}\) can be regulated by MMC.  
Figure 3: Equivalent circuit of the inner AC circuit of the MCAT.
## 5 Conclusions  
This paper proposes a knowledge distillation-based framework for facial feature reconstruction, effectively recovering critical features lost due to occlusion. Experimental results show that the method significantly enhances the robustness of age estimation models under various occlusion conditions. Future work will focus on reducing data biases,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**AFAD**} & \multicolumn{2}{c}{**CACD**} & \multicolumn{2}{c}{**IMDB-WIKI**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-7}  & **MAE** & **CS(\%)** & **MAE** & **CS(\%)** & **MAE** & **CS(\%)** & **MAE** & **CS(\%)** \\ \hline Human workers & 8.53 & 48.26 & 8.95 & 46.18 & 9.58 & 44.37 & 9.02 & 46.27 \\ DLP-CNN [48] & 7.86 & 56.58 & 7.98 & 52.33 & 8.92 & 49.37 & 8.25 & 52.76 \\ Pix2 Pix [49] & 6.18 & 65.22 & 6.33 & 63.36 & 6.81 & 61.62 & 6.44 & 63.40 \\ DeepFill v2 [50] & 5.92 & 69.39 & 6.06 & 67.86 & 6.61 & 63.83 & 6.20 & 67.03 \\ LCA-GAN [1] & **4.72** & **79.69** & 5.21 & **75.25** & 5.98 & 70.83 & 5.30 & **75.26** \\ MCGRL [6] & 5.11 & 78.86 & 5.48 & 74.22 & 5.81 & 71.09 & 5.47 & 74.72 \\ KD-CORAL (ours) & 4.83 & 77.11 & **5.15** & 74.29 & **5.71** & **72.16** & **5.23** & 74.52 \\ KD-DLDL (ours) & 5.04 & 75.59 & 5.59 & 73.33 & 6.04 & 70.97 & 5.56 & 73.30 \\ KD-MWR (ours) & 5.28 & 74.68 & 5.66 & 72.85 & 6.37 & 70.03 & 5.77 & 72.52 \\ \hline \hline \end{tabular} Note: The best performance is highlighted in bold.  
\end{table}
Table 9: Cross-dataset evaluation method to verify the generalization performance of the model (training data: MORPH-2). We count cumulative scores for age errors within a 5-year range.  
handling more complex occlusion patterns, and extending the framework to tasks such as facial expression recognition and pose estimation. Additionally, developing a more representative occlusion dataset will be key to improving practical applications.  
Conceptualization, Q.Z.; methodology, Q.Z.; software, Q.Z. and S.Y.; formal analysis, Q.Z. and S.Y.; investigation, Q.Z.; resources, Q.Z.; data curation, S.Y.; visualization, Q.Z.; writing--original draft preparation, S.Y.; writing--review and editing, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shandong Provincial Natural Science Foundation, China (No. ZR2023 MF106).  
Ethical review was waived as this study solely utilized publicly available anonymized datasets.  
Not applicable. This study exclusively utilized publicly available anonymized datasets with no personally identifiable information.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Results and Discussion
### Experiment Set Up  
Dataset is split into train set and test set in the ratio of 0.7 and 0.3, respectively. The proposed RFMLP is evaluated in terms of accuracy, precision, recall, and F1 score. Experiments are carried out using a 2 GB Dell PowerEdge T430 GPU on 2x Intel Xeon 8 Cores 2.4 GHz machine with 32 GB DDR4 random access memory (RAM). Machine learning and deep learning models are coded in Python programming language in Anaconda Jupyter notebook editor.
## 5 Estimation Equation for Maximum Load Bearing Capacity Considering Buckling Position
### Applicability of Estimation Equations
#### 5.3.1 Comparison with Numerical Analyses  
As representative examples, Figure 14 shows the results of the numerical analysis and the values obtained from the estimation equations accounting for buckling locations for models C-009-020 and C-009-040. The vertical axis, horizontal axis, and legend are the same as in Figure 11. It can be seen that using the two estimation equations that consider buckling position yields better estimates of the maximum load bearing capacity than shown in Figure 11c,d, which use one estimation equation for the same conditions.  
Figure 15 plots the results from the numerical analysis and the estimation equations for all cases. The vertical axis shows the estimated value of \(P/P_{\rm u}\), while the horizontal axis shows the numerical value of \(P/P_{\rm u}\). The solid line in the figure indicates an error of 0%. The dashed lines indicate a range of error within \(\pm 5\%\), and the red dashed lines indicate a range of error within \(\pm 10\%\). Figure 15 shows that the difference between the numerical and estimated values was about 5% in all cases. Moreover, it can be seen that the errors that exceeded 10% in Figure 12, based on one estimation equation, have improved. Therefore, the maximum load bearing capacity can be accurately estimated within the scope of this study by using two estimation equations that account for the position where buckling occurred.  
Figure 14: Maximum load bearing capacity and modified estimation equations for models C-009-020 and C-009-040: (**a**) Model C-009-020; (**b**) Model C-009-040.