esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Manufacturing System Context
### Two Machines in Line  
The simplest multi-machine configuration is a manufacturing system composed of two machines in line. This setup is defined as a flow shop with two machines. Regarding the context of the two-machine sequence-dependent permutation flow shop, the compromise between the minimization of energy consumption and of the makespan was studied in [31]. Addressing this aim and finding the Pareto frontier, they proposed a mixed integer linear multi-objective optimization model and developed a constructive heuristic method to solve it. In subsequent work, this study is further analyzed and a new constructive heuristic method was implemented with the purpose of outperforming the previous one [32]. The single job two machines scheduling problem was studied in [33]. In their work, the authors considered the possibility of dividing the job into several sublots with each characterized by different processing speeds. With these considerations, the aim of the paper is to obtain the optimal schedule (the size of the sublots and the processing speeds) that allows for minimizing total energy consumption. [PERSON] et al. [34] analyzed energy-saving through scheduling in a flow shop composed of two machines. In the paper, a mixed integer linear programming with two objectives is proposed; the mathematical model was solved with an exact method in order to minimize the makespan and total energy consumption. The optimization process provides the production and preventive maintenance planning that results in a minimization of the considered targets. Considering that electricity cost can be an alternative way to address energy efficiency, the energy costs are usually higher during the power peak period; thus, avoiding high power consumption when electricity prices are high leads to less electric line saturation. [PERSON], [PERSON] et al. [35] provided a two-machine permutation flow shop scheduling model in order to minimize the total electricity cost, considering the electrical cost during the time variable. The authors used two heuristic algorithms to obtain the optimal schedule and, in addition, proposed an iterated local search method. In another approach discussed in [36], the minimization of the total electricity cost was achieved considering the minimum makespan fixed and under time-of-use electricity tariffs; six heuristic algorithms, three based on [PERSON]'s rule and three based on [PERSON]'s algorithm, were proposed and tested. [PERSON]. [PERSON] [37] studied energy consumption optimization problems in a two machines serial line considering the Bernoulli reliability model.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Security Analysis Comparison  
According to the findings presented in Table 3, SL_GAS fulfills all the security criteria outlined in Section 3.3. It provides essential security features, such as PFS, protection against IP spoofing attacks, key leakage attacks, session unlinkability, and untrackability. Following each successful authentication, the secret key and other parameters, including identities, random values, and identifiers, are modified, resulting in added advantages for our suggested protocol. The SL_GAS authentication technique provides identity privacy  
Figure 7: (**a–c**) Verify the results of SL_GAS under different settings in the Scyther tool. Remark: There has been an established level of security for these two main components of SL_GAS, namely aggregated MAC with tags and detecting functionality, as well as ensuring broadcast encryption. Formal verifications can be abstract terms. Thus, the emphasis is placed on the security of SL_GAS.  
preservation and safeguards against insider and traceability attacks, features that are not found in other authentication schemes, as seen in Refs. [11; 16; 20]. All the related schemes do not provide unlinkability, intractability, and PFS, and they cannot resist key leakage attacks. In addition, both schemes of Refs. [16; 20] are vulnerable to Sybil and privileged insider attacks. None of them was created according to the CK or eCK-adversary models. In contrast, SL_GAS offers security in both the CK and eCK-adversary models, as well as SK security and verification. These features are not included in the authentication systems mentioned in Refs. [11; 16; 20]. Overall, SL_GAS is fortified against security breaches and is capable of fulfilling all security criteria.
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 4 Results
### Robustness Analysis
By allowing one second-order section to have time-varying coefficients  
Figure 8: Comparison of classical time-invariant filter and proposed time-varying SOS filter under step input with added 110 Hz interference. The 110 Hz component lies just above the 100 Hz cutoff, creating a near-edge suppression challenge. The time-varying design shows faster and cleaner convergence. Figure 9: Comparison of a classical IIR notch filter with the proposed time-varying methodology. (red), the filter settles faster and more cleanly, with less oscillatory behavior. These results demonstrate that the proposed time-varying SOS technique can be seamlessly integrated into notch-type IIR filters, enabling them to reduce transient times while maintaining precise attenuation at the targeted notch frequency.
## 2 Materials and Methods  
### Embedded Sensor Method  
The embedded sensor method utilizes damped sensors to measure the vibration energy values. In this study, the ZBL-G1000 detector and SDA1003 damping vibration sensor from the Beijing ZBL Science and technology Co., Ltd. (Beijing, China) were used to detect the fullness of grouting. The principle is based on the damped vibration amplitude attenuation, wherein the sensor vibrates under the applied signal and experiences the effects of friction and medium resistance, causing the amplitude to decay over time. By analyzing the different vibration amplitude decay patterns, the surrounding medium environment of the sensor can be inferred, and the grout filling degree within the sleeve can be assessed.  
According to the Chinese technical specification for inspection of sleeve grouting quality of precast concrete structures (T/CECS 683-2020) [27], the grout filling degree can be evaluated based on the following criteria: when the vibration energy value is more than  
Figure 1: Research flowchart of this study.  
100, the grout filling is considered complete; when the vibration energy value is between 100 and 150, the grout filling is essentially complete; and when the vibration energy value is >150, the grout filling is incomplete.  
The testing procedure for the embedded sensor method is illustrated in Figure 2. Figure 2 includes (1) weighing grouting material, (2) mixing grouting material, (3) sealing the lower end of the sleeve, (4) grouting, (5) testing, and (6) data acquisition. First, the damped sensor is inserted through the grout outlet, ensuring that the sensor reaches the bottom of the sleeve and is tightly sealed with a rubber plug. After the grouting is completed, the signal cable is connected to the instrument's sensor interface at one end, and the two clips are attached to the signal lines of the pre-embedded sensor within the tested sleeve. By clicking the sampling button or the sampling key on the main interface, the system automatically starts continuous sampling, and the waveform area displays the collected dynamic waveform. Upon completion of the sampling, the data are stored, and the next sleeve can be tested. This process is repeated until all the sleeves have been tested, and then the stop button is clicked.
## 4 Results
### Findings of the NN Model  
In the NN model with a single hidden layer and 200 epochs, we conducted experiments with learning rates of 0.1, 0.01, and 0.005, as presented in Table 8. We found that a learning rate of 0.1 yielded the best accuracy, so we used it for all the cases.  
Table 9 shows the effect of an epoch count on the NN model with one, two, and three hidden layers, using a learning rate of 0.1. The results indicated that the NN model, featuring a single hidden layer and trained across 800 epochs with a learning rate of 0.1, attained the highest accuracy of 92.86%. Moreover, the NN model demonstrated superior accuracy in both training and testing compared to all the other neural network models examined.  
Figure 7 illustrates the ROC curve for a model using one hidden layer trained over 800 epochs. We computed the accuracy of the single-hidden-layer NN model over 800 epochs using 10-fold cross-validation (K = 10). The model achieved an average accuracy of 90%. This finding provides strong evidence of the one-hidden-layer NN model's effectiveness in lung cancer prediction. It highlights the potential of DL techniques to improve diagnosis and the necessity of further exploring model architectures and hyperparameters for optimal performance.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Learning Rate** & **Accuracy** \\ \hline
0.1 & **0.929 * \\
0.01 & 0.881 \\
0.005 & 0.905 \\ \hline \hline \end{tabular}  
* 0.1 has performed the best accuracy of 92.86% out of three.  
\end{table}
Table 8: Values of accuracy corresponding to its learning rate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Hidden Layer** & **Epochs** & **Accuracy** & **Accuracy (Train)** & **Accuracy (Test)** \\ \hline \multirow{3}{*}{1} & 200 & 0.881 & 92.74\% & 88.1\% \\  & 400 & 0.857 & 93.59\% & 85.71\% \\  & 800 & **0.929 * & 92.74\% & **92.86\% *** \\ \hline \multirow{3}{*}{2} & 200 & 0.881 & 92.74\% & 88.1\% \\  & 400 & 0.786 & 87.61\% & 78.57\% \\  & 800 & 0.786 & 87.61\% & 78.57\% \\ \hline \multirow{3}{*}{3} & 200 & 0.905 & 89.74\% & 90.48\% \\  & 400 & 0.927 & 89.74\% & 92.76\% \\ \cline{1-1}  & 800 & 0.925 & 89.74\% & 92.76\% \\ \hline \hline \end{tabular}  
* These are the best accuracy out of all others.  
\end{table}
Table 9: The learning rate of 0.1 influences the accuracy via modifications in the hidden layer.
[27] proposed a reduction factor for the bearing capacity and ductility of eccentrically compressed rectangular steel piers compared to centrally compressed ones. The factor's size is only related to the eccentricity. [PERSON] et al. [28] also established the reduction factor expressions for steel piers with circular sections. Thus, from the above studies, a certain understanding exists about the seismic performance of eccentrically compressed steel piers in the in-plane and out-of-plane directions. Specifically, the structural bearing capacity and ductility performance in the two different directions are obviously different, and the calculation formulas of the limiting state can be directly converted from the results of the centrally compressed steel piers. However, actual bridge structures are subjected to earthquakes in both horizontal directions simultaneously. The additional bending moment further deteriorates the seismic performance of eccentrically compressed steel piers under horizontal bidirectional earthquakes. The single-degree-of-freedom system model obviously cannot be used for seismic response analysis under horizontal bidirectional earthquakes. In contrast, the fiber-element calculation model can take into account the biaxial bending effect. Therefore, the fiber model is a reasonable choice to implement the horizontal bidirectional seismic response calculation in engineering. To improve the bridge seismic design theory, it is necessary to establish an accurate and practical calculation model based on fiber elements for the seismic response analysis of eccentrically compressed steel piers. This study is conducted based on the fiber model proposed in the literature [18; 19], which considers the local instability effect of steel plates and the horizontal bidirectional seismic input. For steel piers without eccentric compression, the effectiveness of the improved fiber model has been verified through the comparison with FE analysis [18] and steel pier tests [19]. In this paper, the fiber-element calculation models for eccentrically compressed steel piers are established, performing pseudo-static analysis under the in-plane or out-of-plane horizontal cyclic loads. The applicability of the improved fiber model is studied by comparing the results with those obtained from a refined hybrid-element model. Furthermore, the calculation accuracy of the fiber-model calculation method was verified by the horizontal bidirectional seismic response analysis. This study expands the application range of the improved fiber model considering the local instability effect, and provides a calculation method for the elastoplastic seismic response analysis of eccentrically compressed steel piers with high precision and efficiency.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 4 Simulation and Analysis
### Simulation Results
#### 4.2.2 Simulating a Health Assessment in Case of Failure  
Three points of time (\(t_{1}\), \(t_{2}\), \(t_{3}\)) are selected: the initial moment of autonomous splicing, the moment of splicing progress, and the moment of splicing completion. Then, the variable-weight Equation (22) is used to perform fault analysis for the swarm. The simulation conditions are as follows.  
1. The center of the communication range of the initial distribution is the origin of the coordinate system.
2. The small sphere and the square hexagonal prism represent the unstitched and the stitched satellites, respectively, and the large transparent sphere represents the star swarm.
3. Red indicates malfunction, and yellow and green indicate subhealth and health status, respectively.  
For the three selected moments \(t_{1}\), \(t_{2}\), \(t_{3}\), the health status assessment is carried out assuming that there is no satellite failure, eight satellite failures, and twenty satellite failures, and the results are obtained as shown in Figures 7-9.  
Figure 5: Interplanetary communication link reliability model.  
Figure 6: Swarm health curve model.  
Figure 8: Health assessment results at moment t\({}_{2}\): (**a**) No satellite failure case; (**b**) Eight satellite failure case; (**c**) Twenty satellite failure case.  
Figure 7: Health assessment results at moment t\({}_{1}\): (**a**) No satellite failure case; (**b**) Eight satellite failure case; (**c**) Twenty satellite failure case.  
Simulations for three moments show that the swarm health decreases with the number of simultaneous failed satellites. At the initial moment, the swarm health \(H_{swarm}=0.8763\) for no satellite failure drops to 0.5375 for eight satellite failures and drops to 0.4493 for twenty satellite failures. At the splicing moment, the swarm health \(H_{swarm}=0.8579\) for no satellite failure drops to 0.5243 for eight satellite failures and drops to 0.4377 for twenty satellite failures. After the stitching is complete, the swarm health \(H_{swarm}=0.8578\) for no satellite failure drops to 0.5276 for eight satellite failures and drops to 0.4432 for twenty satellite failures.  
Figure 10a,b show the swarm health curves from the satellite's failure and communication link failure, respectively. It can be seen from the figures that the swarm health decreases instantaneously after a fault launch, and the satellite's fault exhibits a more fluctuating effect on the swarm health than the communication link fault. Swarm health is more dependent on the satellite's health. In fact, by establishing the above health state model and the fault model, the impacts of other faults on the swarm can be analyzed, which represents a good guideline for swarm health state assessment.  
Figure 9: Health assessment results at moment t\({}_{3}\): (**a**) No satellite failure case; (**b**) Eight satellite failure case; (**c**) Twenty satellite failure case.
# Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection
## 3 The Proposed Algorithm  
### Framework of the Algorithm  
Figure 1 presents the flowchart of our proposed method. It is summarized as follows:  
1. To reduce the computational complexity and satisfy the requirement of DSST for the input image size, the original image is first preprocessed.
2. After preprocessing, the test and training images are discrete separable shearlet transformed, and all DSST coefficients are extracted as the original expression feature set.
3. The improved normalized mutual information feature selection method proposed in this paper is used to find the optimal feature subset of the original feature set. The feature subset retains the key classification information of the original set.
4. After the feature extraction and selection, the feature space is reduced by employing linear discriminant analysis (LDA).
5. The support vector machine (SVM) is used to recognize the expressions.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 3 Two-Stage Quantization Method
### Clustering
#### 3.2.1 K-Means
PSO's performance can be dynamically adjusted by the acceleration constants \(c_{1},c_{2}\) and the non-negative inertia factor \(\omega\). In Equation (5), pb is the individual optimal position and gb is the global historical optimal position. These factors affect the updating of velocity \(v_{i}\) and position \(x_{i}\) for each particle through Equations (4) and (5). \[v_{i}(t+1)=\omega v_{i}(t)+c_{1}\text{ random }(0,1)(pb_{i}(t)-x_{i}(t))+c_{2} \text{ random}(0,1)(gb-x_{i}(t)) \tag{4}\]

\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{5}\]

The silhouette coefficient is a way of evaluating the clustering effect. Since the weights of the neural network have no practical significance, this paper uses the silhouette coefficient as the fitness function of PSO-K-means to evaluate its performance. The value range of the silhouette coefficient is \([-1,1]\). The best value is \(1\), and the worst value is \(-1\). Values close to \(0\) represent overlapping clusters. Negative values usually indicate that the samples were assigned to the wrong cluster [32]. For this paper, we define the following symbols:

* \(\text{a}_{\text{i}}\) denotes the average intra-cluster distance;
* \(\text{b}_{\text{i}}\) denotes the average nearest-cluster distance;
* \(\text{S}_{\text{i}}\) denotes the silhouette coefficient of one sample;
* SC denotes the silhouette coefficient of the cluster. The silhouette coefficient for a sample is: \[\text{S}_{\text{i}}=\frac{\text{b}_{\text{i}}-\text{a}_{\text{i}}}{\text{ max}(\text{a}_{\text{i}},\text{b}_{\text{i}})}\] (6) The overall silhouette coefficient of one cluster is: \[\text{SC}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\text{S}_{\text{i}}\] (7)```
0: Dataset D = \(\{Z_{1},Z_{2}\dots Z_{m}\}\), cluster centroids k
0: Result set \(\{R_{1},R_{2}\dots R_{k}\}\)
1: Initialize the particle's individual position and individual velocity
2: Initialize k cluster centroids \(\{R_{1},R_{2}\dots R_{k}\}\)
3:repeat
4:for\(0<\mathrm{i}<\mathrm{m}\)do
5:\(\mathrm{d}\left(Z_{\mathrm{i}},\mathrm{M}_{\mathrm{j}}\right)=\sqrt{\sum_{n=1}^ {\mathrm{N}}\left(Z_{\mathrm{in}}-\mathrm{M}_{\mathrm{jn}}\right)^{2}}\)
6: Calculate the distance
7: Determine the centroids
8: Update the velocity and position of each particle
9: Update fitness
10: Update pbest and gbest
11:endfor
12:until until SC = M
13: use K-means to cluster weights
14:return Result set
```  
**Algorithm 2** Hybrid PSO-Kmeans.
# Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine
## 3 Establishment of Predictive Model for Loose Zone of Surrounding Rock in Roadway  
According to the theory of WRVM, a predictive model of loose zone of surrounding rock in roadway using WRVM has been established. As shown in Figure 3, the concrete steps are:  
(1) Determining input and output. Predicting the roadway surrounding rock loose zone, the main factors having an impact on the thickness of roadway surrounding rock loose zone are selected as the evaluation index, that is, the input of the prediction model (i = 1,2... D). The thickness of loose zone is the output of the prediction model Y.  
(2) Preprocessing sample data. In order to eliminate the influence of inconsistent evaluation index dimension on data analysis, it needs to standardize the samples, that is, to divide the statistical data by the standard deviation of the data after zero-mean. It is to ensure that the mean value of each statistical data after processing is 0 and the variance is 1.  
(3) Establishing the thickness prediction model of loose zone. The use of the cross-validation method [63] for the optimization of the kernel function parameters. The commonly used cross-validation methods include leave-out method, K-fold cross-validation and leave-one validation. Leave-out method was applied in this study to randomly separate the training samples into two parts. The first part is applied as training set to train the model. Another is used as validation set to validate the parameters and model. Therefore, the core parameters which render the smallest verification set RMS error can be selected. Using all training samples, the predictive model of roadway surrounding rock loose zone using WRVM has been established.  
(4) Predicting loose zone thickness based on the model. Equation (6) can be used to predict the thickness of the loose zone after establishing the prediction model of the main factors and the loose zone thickness of roadway surrounding rock. Among them, \(\mu_{*}\) is the model predictive value, \(\sigma^{2}_{*}\) the variance information of the current predictive value.  
(5) Analyzing model accuracy. After establishing the prediction model of surrounding rock loose zone, the accuracy of the model is evaluated by Equations (11)-(13).
## 3 Results
### Principal Component Analysis  
The potentiometric head data of 195 observation wells (selected from sampling) were analyzed using PCA in IBM SPSS (version 22.0) software. The major output of the PCA results were two matrices i.e., \"total variance explained\" and \"rotated loading factor\" matrix. The total variance explained matrix provided statistics about the variance of the data explained by each component. In our case, the first two components of PCA explained more than 98% of the variation of the groundwater head in the area. Hence, only the first two components were selected for further analysis. The rotated loading factor matrix gave a score between 0 and 1 based on the importance of a given well in explaining the groundwater head variation in the study area. In our case, 135 of the 195 wells scored greater than or equal to 0.90 in the rotated loading factor matrix. It was found that these 135 wells could represent the study area with an average error of 0.318 m and standard deviation of 0.95 m (Table 1). The number of observation wells was reduced from 195 to 135 (i.e., about a 31% reduction) with an insignificant  
Figure 8: Residuals (observed minus simulated heads) at the end of the model calibration.  
loss of information. This reduction is less compared to that shown in previous literature, in which the reduction in randomly installed monitoring wells was more than 70% [3; 23]. The smaller reduction in observation wells occurred because the network was spatially optimized using a hexagonal pattern before the application of PCA.