esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Analysis
### Case Study  
The proposed system is a three-unit series-parallel system, as shown in Figure 1. The deterioration of unit \(i\) follows a gamma distribution, and the shape and size parameters are \(\alpha_{i}\) and \(\beta_{i}\), respectively. The preventive maintenance time and corrective maintenance time of unit \(i\) are exponentially distributed with parameters \(\lambda_{i}\) and \(\mu_{i}\), respectively. The maintenance-related parameters of each unit are shown in Table 4. In addition, the production and cost parameters considered in the proposed model are shown in Table 5 [30].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Unit 1** & **Unit 2** & **Unit 3** & \(r\,i_{\text{max}}=25\) & \(r^{\prime}i_{\text{max}}=50\) & \(r^{\prime}i_{\text{max}}=100\) \\ \hline \(\alpha_{1}=2,\beta_{1}=1.5\) & \(\alpha_{2}=1.5,\beta_{2}=0.8\) & \(\alpha_{3}=1.5,\beta_{3}=0.8\) & 1.0043 & 1.0011 & 1.0003 \\ \(\alpha_{1}=1.2,\beta_{1}=0.8\) & \(\alpha_{2}=1,\beta_{2}=0.6\) & \(\alpha_{3}=1,\beta_{3}=0.6\) & 1.0045 & 1.0012 & 1.0003 \\ \(\alpha_{1}=1.2,\beta_{1}=2\) & \(\alpha_{2}=2,\beta_{2}=2.5\) & \(\alpha_{3}=2,\beta_{3}=2.5\) & 0.9737 & 1.0021 & 1.0007 \\ \(\alpha_{1}=0.7,\beta_{1}=1.2\) & \(\alpha_{2}=1.1,\beta_{2}=0.9\) & \(\alpha_{3}=1.1,\beta_{3}=0.9\) & 1.0134 & 1.0026 & 1.0003 \\ \(\alpha_{1}=0.7,\beta_{1}=2\) & \(\alpha_{2}=0.9,\beta_{2}=1.5\) & \(\alpha_{3}=0.9,\beta_{3}=1.5\) & 1.0231 & 1.0134 & 1.0014 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Value of \(r\) under different parameters.  
Figure 7: The approximate numerical solution of the steady-state probability density function of the joint state of the proposed system \(s(x_{1},x_{2},x_{3})\).  
According to the solving process shown in Figure 6, the genetic algorithm parameters are set as follows: crossover probability \(p_{\sigma\sigma}=0.8\), initial mutation probability \(p_{mut}=0.1\), reduction coefficient \(\delta=0.98\), population size \(N=40\), and iteration number \(L=50\). It should be noted that through the previous calculation, it can be known that the thresholds of unit 2 and unit 3 are roughly the same. Since unit 2 and unit 3 are two identical parallel units, their preventive maintenance thresholds are consistent. Thus, in the final encoding calculation, the two are set to be equal, and the operation is in line with the reality. The optimal strategy for 50 experiments is \(EC(881,6.96,8.25,8.25)=217.5198\). For this enterprise, when the lot size is set to 881, the preventive maintenance threshold of unit 1 is 6.96, and the preventive maintenance thresholds of units 2 and 3 are 8.25, and the total cost rate of long-term production of the system is the lowest at 217.5198. The iteration diagram is shown in Figure 8.
## 5 Experimental Results and Discussion  
Experiments were conducted in order to evaluate the quantitative influence of mechanical errors on the operating performance of the constructed mold/die clamping mechanism. The independent variables for the experiments were the length error of the length adjustable link member (i.e., the mechanical error \(\Delta\tau_{8}\)) and the driving speed of the input link (i.e., the input speed denoted by \(v\)). For comparison purpose, \(\Delta\tau_{8}\) was reasonably set to 0.00, 0.25, 0.50, and 0.75 mm, respectively, and \(v\) was reasonably set to 5, 15, and 25 mm/s, respectively. Hence, twelve sets of the two independent variables (\(\Delta\tau_{8}\), \(v\)) were adopted for conducting twelve series of experiments, respectively. For each series of experiments, the input link was driven forwards from an initial position of \(x=-3.439\) mm to a final position of \(x=51.566\) mm (referring to Figure 6) and then was driven backwards from the final position to the initial position; such a reciprocating motion was repeated for 100 times while the data sets of the continuously varying output positions and input forces were all measured. Therefore, 1200 data sets in total were measured and recorded for further evaluating the operating performance of the constructed mechanism.
## 4 Discussion
### Bodily Movement with Clear Aligners  
Producing a force system conducive to bodily movement with clear aligners presents a significant biomechanical challenge, particularly in the absence of auxiliaries such as composite attachments or pressure points [21; 22]. When an aligner is fitted onto a tooth to be displaced lingually, the aligner generates multiple contact points along the facial surface of the crown. This leads to a resultant lingual force and a moment of force at the tooth's center of resistance, causing the tooth to tip with the center of rotation near the center of resistance. This initial tipping movement induces elastic deformation of the aligner, forming new contact points which generate a moment of couple, counteracting the moment of force. The magnitude of this counter-moment depends on the aligner's precise fit with the tooth; if the fit is suboptimal due to a lack of retention, the counter-moment becomes insufficient, resulting in excessive crown movement relative to the root.  
In the present study, none of the aligners generated a force system suitable for bodily movement of the maxillary left central incisor. This result was in accordance with previous in vitro studies [21; 22], which found that effective control of incisor torque could not be accomplished using aligners when bodily movement was prescribed. Interestingly, in the present study, at 0.50 mm activation, ATMOS generated a mean M/F ratio of 8.61 mm, which is close to 10 mm, the M/F ratio for bodily movement [19]. This value also agrees closely with the mean M/F ratios determined by [PERSON] and [PERSON] [23] for 0.5 mm palatal tipping using Duran foils. However, this finding should be interpreted with caution, as the initial force and moment levels generated to achieve the M/F ratio were extremely high (7.70 N and 66.22 Nmm, respectively) and beyond the physiological range for safe tooth movement. Further studies are needed to determine the clinical conditions and aligner design features, such as composite attachments or pressure points, which may be required to reliably achieve bodily movement while maintaining optimal force levels.
## 3 Results and Discussion
### The pH, Water Holding Capacity (WHC), and Cooking Loss
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Treatments** & **Day 0** & **Day 3** & **Day 7** \\ \hline \multirow{4}{*}{\(L^{\star}\)} & Control & 56.78 \(\pm\) 0.79 \({}^{\text{Ca}}\) & 55.84 \(\pm\) 0.63 \({}^{\text{Ba}}\) & 56.50 \(\pm\) 3.50 \({}^{\text{Ba}}\) \\  & 7.4 MPa & 62.02 \(\pm\) 0.72 \({}^{\text{Ba}}\) & 61.97 \(\pm\) 1.55 \({}^{\text{A}}\) & 58.51 \(\pm\) 1.32 \({}^{\text{AB}}\) \\  & 11.4 MPa & 67.09 \(\pm\) 2.1 \({}^{\text{Aa}}\) & 64.89 \(\pm\) 1.77 \({}^{\text{Aa}}\) & 63.62 \(\pm\) 2.03 \({}^{\text{Aa}}\) \\  & 15.4 MPa & 63.41 \(\pm\) 0.91 \({}^{\text{Ba}}\) & 62.55 \(\pm\) 0.72 \({}^{\text{Aa}}\) & 61.53 \(\pm\) 0.74 \({}^{\text{AB}}\) \\ \hline \multirow{4}{*}{\(a^{\star}\)} & Control & 12.23 \(\pm\) 0.67 \({}^{\text{Aa}}\) & 12.64 \(\pm\) 0.29 \({}^{\text{Aa}}\) & 10.72 \(\pm\) 1.31 \({}^{\text{Aa}}\) \\  & 7.4 MPa & 10.00 \(\pm\) 0.28 \({}^{\text{Ba}}\) & 10.98 \(\pm\) 0.92 \({}^{\text{Aa}}\) & 9.81 \(\pm\) 0.34 \({}^{\text{Aa}}\) \\  & 11.4 MPa & 8.28 \(\pm\) 0.15 \({}^{\text{Ca}}\) & 8.70 \(\pm\) 1.22 \({}^{\text{Ca}}\) & 8.21 \(\pm\) 1.10 \({}^{\text{Ba}}\) \\  & 15.4 MPa & 9.68 \(\pm\) 0.50 \({}^{\text{Ba}}\) & 10.02 \(\pm\) 0.72 \({}^{\text{Bc}}\) & 9.29 \(\pm\) 0.78 \({}^{\text{Aa}}\) \\ \hline \multirow{4}{*}{\(b^{\star}\)} & Control & 12.52 \(\pm\) 0.39 \({}^{\text{Aa}}\) & 12.38 \(\pm\) 0.52 \({}^{\text{ABa}}\) & 9.57 \(\pm\) 2.05 \({}^{\text{Aa}}\) \\  & 7.4 MPa & 11.59 \(\pm\) 0.72 \({}^{\text{Aab}}\) & 13.30 \(\pm\) 0.32 \({}^{\text{Aa}}\) & 11.18 \(\pm\) 1.12 \({}^{\text{Ab}}\) \\ \cline{1-1}  & 11.4 MPa & 13.18 \(\pm\) 0.63 \({}^{\text{Aa}}\) & 10.80 \(\pm\) 0.97 \({}^{\text{Bb}}\) & 11.95 \(\pm\) 1.10 \({}^{\text{Aab}}\) \\ \cline{1-1}  & 15.4 MPa & 12.87 \(\pm\) 1.73 \({}^{\text{Aa}}\) & 11.61 \(\pm\) 1.13 \({}^{\text{ABa}}\) & 12.33 \(\pm\) 1.61 \({}^{\text{Aa}}\) \\ \hline \end{tabular}  
Means with different small letters of the same treatment (effect of time) are significantly different (\(p<0.05\)). Means with different capital letters of the varied treatment (effect of treatment) are significantly different (\(p<0.05\)).  
\end{table} 
Table 2: Effects of three levels (7.4, 11.4 and 15.4 MPa) of SC-CO\({}_{2}\) at 31 \({}^{\circ}\)C for 10 min on \(L^{\star}\), \(a^{\star}\) and \(b^{\star}\) values of raw chicken meat stored at 4 \(\pm\) 1 \({}^{\circ}\)C for seven days. in protein and fat that are heat-induced. Furthermore, fluctuations in the yield of cooked meat are an economic problem for meat processors. Temperature and time are key factors in the heat and mass transfer processes, protein denaturation, and, in some cases, protein solubilization occurring during cooking [28].
## 2 Methodology
### Machine Learning Based Localization
#### 2.1.1 Feature Extraction  
As the data were categorized into H or L cases, first, the strain data of all sensors were normalized concerning the highest strain value of data over all the trials. With this normalization, it could be assumed that the data are stored from an equal energy level. Therefore, the parameters of the data will be affected because of the loosening of bolts only.  
_Approximate entropy (ApproximateEnt)_: The ApproximateEnt is a function that generates a delayed-reconstruction: \(y_{1:n}\) corresponding to '\(n\)' data points and embedding dimension '\(d\)'. The number at a point '\(i\)' within the selected range is calculated [38]:  
\[n_{i}=\sum_{i=1,i\
eq j}^{n}1\Big{(}\big{\|}y_{i}-y_{j}\big{\|}_{\infty}<r\Big{)} \tag{1}\]  
where '\(r\)' depicts the radius of similarity. The approximate entropy is computed as  
\[ApproximateEnt=\varphi_{d}-\varphi_{d+1} \tag{2}\]  
Figure 1: Working procedure of the hybrid ML and MU method.  
where,  
\[\varphi_{d}=(n-d+1)^{-1}\sum_{i=1}^{n-d+1}\log(n_{i}) \tag{3}\]  
_Shannon entropy (ShannonE\({}_{n}\))_: In ShanonEn calculation, the power of every frequency (\(j\)) is multiplied by the logarithm of the inverse power of each frequency and calculated by summing across all frequencies (\(J_{f}\)) as described in Equation (5) [39].  
\[j_{f}=\frac{J_{f}}{\sum J_{f}} \tag{4}\]  
\[ShannonE_{n}=\sum_{f}j_{f}\log\left(\frac{1}{j_{f}}\right) \tag{5}\]  
_Reniy entropy (RenyE\({}_{n}\)):_ [PERSON]'s entropy is used for the estimation of the spectral complexity of time-series which is defined as [39]:  
\[RenyiEn(\beta)=\frac{1}{(1-\beta)}\log\left(\sum_{f}P_{f}^{\beta}\right), \beta>0,\beta\
eq 1 \tag{6}\]  
In this study, \(\beta=2\) is considered to compute _[PERSON]'s quadratic entropy_ which represents the uncertainty level about the event, '\(f\)'.  
_Mean Absolute deviation (MAD)_: It is used to address the statistics of various fundamental processes. Here, MAD is defined as [40]:  
\[MAD=\frac{1}{m}\sum_{m=0}^{m-1}|\mu_{i}[M]-\kappa_{i}| \tag{7}\]  
where '\(\kappa_{i}\)' represents the mean of the \(i\)th Fourier-intrinsic-band-function, '\(\mu_{i}[M]\)' and \(0\leq M\leq m-1\).  
Four other statistical features, namely, _standard deviation_, _variance_, _skewness_, and _kurtosis_ are also considered in this paper as described in [41; 42; 43].  
The feature sets for all the unloosed and loosened cases were kept separately and provided as input for the SVM algorithm to train and validate the SVM model. After, the trained and validated model was employed to test the data sets. In this context, the test data sets were given as input for the SVM model.
## 3 Method
### Proposed LSTM Network  
#### 3.4.1 Overview of LSTM  
To overcome the difficulties in training the RNN model due to gradient vanishing [38] and error blowing up, the LSTM, in which nonlinear units are replaced in conventional RNNs, were proposed. Figure 4 shows the typical structure of an LSTM cell. An LSTM cell contains a self-connected memory cell and three gates, namely the forget-gate, the input-gate, and the output-gate [40]. The three gate units are the essential components to learn the long-term patterns by preventing memory contents from irrelevant inputs and outputs. The input-gate controls the flow of input activations into the cell-memory, forget-gate controls how much information will flow from the cell-memory, and the output gate controls the output flow of cell activations into the rest of the network. The cell computes the cell output \(h_{t}\) and the updated cell-memory output \(c_{t}\) from the previous cell outputs (\(h_{t-1}\)), the previous cell-memory outputs (\(c_{t-1}\)), and the sequential input \(x_{t}\) at time step \(t\) as follows:  
\[f_{t}=\sigma_{g}(W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}), \tag{9}\]  
\[i_{t}=\sigma_{g}(W_{xf}x_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}), \tag{10}\]  
\[o_{t}=\sigma_{g}(W_{xo}x_{t}+W_{ho}h_{t-1}+W_{co}c_{t-1}+b_{o}), \tag{11}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\sigma_{h}(W_{xx}x_{t}+W_{hc}h_{t-1}+b_{c}), \tag{12}\]  
\[h_{t}=o_{t}\odot\sigma_{h}(c_{t}), \tag{13}\]  
where \(W_{xf}\), \(W_{hf}\), \(W_{xi}\), \(W_{hi}\), \(W_{xo}\), and \(W_{ho}\) are the weight matrices from the input to the forget-gate, from the previous output to the forget-gate, from the input to the input-gate, from the previous output to the input-gate, from the previous output to output-gate, respectively, \(W_{cf}\), \(W_{ci}\), \(W_{co}\) are the diagonal weight matrices for peephole connections, and \(b_{f}\), \(b_{i}\) and \(b_{o}\) are the bias vectors of the forget-gate, the input-gate, and the output-gate, respectively. \(\odot\) denotes the Hadamard product, and \(\sigma_{x}\) and \(\sigma_{g}\) denote the hyperbolic tangent and sigmoid functions, respectively. \(i_{t}\),\(f_{t}\), and \(o_{t}\) are the input-gate, forget-gate, and output-gate, respectively.
## 3 Numerical Simulation
### ALE Fluid-Structure Interaction Algorithm
#### 3.1.3 ALE Description  
In terms of computation, the ALE Description integrates the features of Lagrangian Description and Eulerian Description, where the incorporated features of the Lagrangian Description deal with the movement at the boundary of the structures, in order to effectively analyze the movement at the boundary of structures. Regarding air and charge, the features of the Eulerian Description in terms of computation have been used to enable the Element Mesh to be independent from the material substance to be analyzed. In the ALE Description, the Space Mesh can be adjusted by moving any nodes in the process of finding a solution, as based on the defined parameters, to prevent the mesh from severe deformation. Moreover, substances can flow among the meshes, which is very beneficial in analyzing the issue of significant deformation. Such an algorithm can be used to overcome computation failure, as resulted from the severe deformation of the mesh, and avail the dynamic analysis of Fluid-Structure Interaction; i.e., the Eulerian Description is used for fluids, while the Lagrangian Description is used for solids, as shown in Figure 11. It can be seen from Figure 11 that the material points move along with the continuous deformation of the object. In computation, the computation of the time step of one or more Lagrangian meshes will be initially executed by the ALE Description. At this moment, the mesh units will be deformed along with the flow of materials, and computation of the ALE time step is subsequently executed; its computation modes can be divided into a Smooth Step and Advection Step. The Smooth Step reorganizes the meshes internally in order to maintain the boundary conditions of the object after deformation; thus, the mesh extension relation will remain unchanged. The AdvectionStep inputs the unit variables (density, energy, stress tensor, etc.) of the deformed meshes and node velocities into reorganized meshes.  
The Fluid-Structure Interaction analysis of a blast meets the features of ALE computation, which consider the method of liquid description and are able to complete the dynamic analysis of Fluid-Structure Interaction. Therefore, in this study, charge and air materials are set as ALE meshes, while the walls, ground, and other materials are set as Lagrangian meshes, in order to facilitate the dynamic analysis of Fluid-Structure Interaction. Regarding the establishment of analysis meshes, liquid meshes and solid meshes are established by overlaying the meshes, where the number of solid meshes shall be at least two times as many as the fluid meshes, as shown in Figure 12.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 5 Conclusions  
In this study, to model the air transportation system more accurately from physical facilities to traffic applications, three-layer networks, including air route networks, airline networks, and flight operation networks, were built, where the air route networks were regarded as the physical layer, and the airline networks and flight operation networks were the application layers. Moreover, a powerful tool, complex network theory, was used to analyse the topology characteristics of the three-layer networks. In addition, to reduce the congestion of the air route network induced by the air traffic flows of application layers, a simulated annealing-based method was proposed to optimize the routing paths for the airlines based on the mapping relations between three-layer networks, where an elaborate method for perturbing solutions, named Selection after Remove (SAR), is adopted. Experimental results show that, compared with the default routing paths, the shortest routing paths, and random routing paths, the proposed routing paths' optimization strategy can reduce the maximum traffic flows of the air route network by 2.4%, 4.6%, and 4.8%, respectively, making air traffic distribution well-balanced, which shows that the proposed optimization method is an effective way of alleviating the traffic congestion of the air route network. In other words, there is an opportunity for advancements in air transportation system, especially in air-routing-allocation programs for flight plans. For future work, we plan to investigate the improvement of the proposed methods under more datasets from other countries besides China.  
Figure 7: The traffic flows distributed on air route network at different time intervals.  
**Author Contributions:** Conceptualization, H.D., M.H., Q.X., Y.T. and J.Y.; methodology, H.D. and M.H.; software, H.D., Q.X. and Y.T.; validation, H.D., M.H. and J.Y.; formal analysis, H.D.; investigation, H.D.; resources, M.H.; data curation, J.Y.; writing--original draft preparation, H.D. and J.Y.; writing--review and editing, H.D. and M.H.; visualization, H.D. and Q.X.; supervision, M.H.; project administration, M.H.; funding acquisition, H.D., M.H., Y.T. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2018 YFE0208700) and the Science and Technology Project of Jiangsu Province (No.BZ2020001).  
Not applicable.  
The authors declare no conflict of interest.  
The following variables are used in this manuscript:  
\(G_{ARN}\) Graph of the air route network  
\(G_{FN}^{i}\) Graph of the flight operation network at time \(t\)  
\(G_{AN}\) Graph of the city-pair airline network  
\(r_{ij}\) air route segment connecting city \(i\) and city \(j\)  
\(l_{ij}\) city-pair airline connecting city \(i\) and city \(j\)
## 4 Experiments
### Robustness Evaluation  
In addition to the previous comparisons, we conduct further evaluations to assess the robustness of our method. To achieve this, we subject the images from the NIST dataset to various image attack methods. These methods consist of resizing, JPEG compression with the quality factor \(\eta\) and Gaussian blur with the kernel size \(\kappa\). The parameters used and the performance of manipulation localization, measured by the F1-score and AUC, are presented in Table 4. Our model performs well under a variety of distortions. Especially on compressed images, the F1-score is only 0.4% lower than without the distortion when  
Figure 4: Visual comparisons of original models. From left to right, we present forged images, the predictions of RRUnet, PSCCNet and ours, GT masks.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Number** & **F1** & **AUC** \\ \hline CASIA [28] & 500 & 78.1 & 88.7 \\ NIST [29] & 300 & 84.8 & 92.2 \\ RTD [31] & 220 & 69.2 & 71.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1 scores and AUC (%) of manipulation detection results on various datas.  
the quality factor is 100 and 1.0% lower than without the distortion when the quality factor is 80. It is evident that our network demonstrates robustness against different image attack methods.
# A Method Based on an Autoencoder for Anomaly Detection in DC Motor Body Temperature
###### Abstract  
Anomaly detection has an important role in industrial systems. Abnormal situations occurring in a system cause anomalies, and the anomalies reduce system performance over time, and may also make the system malfunction. Therefore, the correct and timely detection of anomalies is of critical importance for predictive maintenance. In this study, an autoencoder-based method is proposed for anomaly detection in DC motor body temperature. The performance of the method was examined on a dataset that was created specifically for this study. In the experiments, the three-sigma outlier method was also applied on the same dataset for the same purpose and its performance results are used for comparison. The performance results of both methods are represented in terms of three measures, namely, accuracy, recall, and precision. The experimental study showed that the proposed method achieved over 96% ratios for all three measures, and it can be successfully used for anomaly detection in DC motor body temperature. Additionally, it can be concluded that the proposed system can be preferred for anomaly detection in time series data collected from different types of sensors when the performance results are taken into consideration.  
anomaly detection; DC motor body temperature; autoencoders; neural networks Article