esa-sceva/satcom-chunk-collection huggingface dataset:
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 1 Introduction
Figure 2 reports two optimal solutions to problem \(P_{m}||MWD\), while the solution in Figure 2a outperforms that in Figure 2b for problem \(P_{m}||SI\). The corresponding values of \(SI\) are equal to \(\sqrt{41}\) and \(\sqrt{45}\), respectively. That is, the solution in Figure 2a has better workload balance than that in Figure 2b. By the above two examples, we believe that although the smoothness index (\(SI\)) is currently used in assembly line processing systems in previous studies, it is the most feasible index to measure the workload balance in identical parallel machine scheduling compared with other existing criteria. Motivated by this, we investigate the identical parallel machine scheduling problem with the objective of minimizing the workload smoothness index, i.e., the \(P_{m}||SI\) problem. The main contributions of this study are as follows. 1. We introduce smoothness index to identical parallel machine scheduling and prove the NP-hardness of the considered problem. Figure 1: Illustration of three optimal solutions to Example 1 for problems \(P_{m}||C_{\max}\) and \(P_{m}||ARPI\). 2. A mathematical model with a minimizing smoothness index is established, and both upper and lower bounds are provided. 3. To quickly find high-quality feasible solutions, an efficient genetic algorithm and an improved list scheduling algorithm are proposed. Numerical experiments show that the proposed genetic algorithm can output high-quality feasible solutions for small-scale instances. For large-scale instances, the proposed improved list scheduling algorithm outperforms the list scheduling algorithm based on LPT, SPT, and RPT rules. Please refer to Section 6 for specific details. The remainder of this paper is organized as follows. Section 2 reviews previous studies on the parallel machine workload balancing problem and smoothness index in the assembly line environment. Section 3 builds a mathematical model for the considered problem and linearizes its objective function. We prove the NP-hardness of the \(P_{m}||SI\) problem and present both upper and lower bounds in Section 4. Section 5 proposes a genetic algorithm and an improved list scheduling algorithm to solve the considered problem. We also conduct numerical experiments to evaluate the performance of proposed heuristic algorithms in Section 6. Finally, Section 7 concludes this work.
# Median Absolute Deviation for BGP Anomaly Detection
###### Abstract  
The stability and reliability of the global Internet infrastructure heavily rely on the Border Gateway Protocol (BGP), a crucial protocol that facilitates the exchange of routing information among various Autonomous Systems, ensuring seamless connectivity worldwide. However, BGP inherently possesses a susceptibility to abnormal routing behaviors, potentially leading to significant connectivity disruptions. Despite extensive efforts, accurately detecting and effectively mitigating such abnormalities persist as tough challenges. To tackle these, this article proposes a novel statistical approach employing the median absolute deviation under certain constraints to proactively detect anomalies in BGP. By applying advanced analysis techniques, this research offers a robust method for the early detection of anomalies, such as Internet worms, configuration errors, and link failures. This innovative approach has been empirically validated, achieving an accuracy rate of 90% and a precision of 95% in identifying these disruptions. This high level of precision and accuracy not only confirms the effectiveness of the statistical method employed but also marks a significant step forward for enhancing the stability and reliability of the global Internet infrastructure.  
anomaly detection; border gateway protocol; median absolute deviation; statistics +
Footnote †: journal:  
Article
## 2 Structure Design of MRE Vibration Isolator
### Mechanical Structure Design  
#### 2.2.1 Choosing Operation Mode  
The working modes of the MRE isolator can be divided into shear, compression, and hybrid [18], as shown in Figure 4. In the shear mode, the direction of motion of the MRE in the isolator is perpendicular to the direction of the applied magnetic field, and the MRE is in a shear stress state. In the tension-compression mode, the motion direction of the MRE in the isolator is parallel to the direction of the applied magnetic field, and the MRE is in the state of an alternating tension-compression cycle. In the mixing mode, the motion direction of the MRE in the isolator is at a certain angle to the direction of the applied magnetic field (between 0\({}^{\circ}\) and 90\({}^{\circ}\)), and the MRE is in a mixed stress state of tension-compression and shear.  
In the tension-compression mode, the MRE has a high vertical bearing capacity, but its working displacement range is small, the magnetorheological effect is low, and the plate spacing has a great influence on the magnetic induction intensity and other characteristics of the MRE region. In the shear mode, although the bearing capacity of the MRE is small, its performance controllable range is wider, and the structure design is simpler. In the hybrid mode, the angle between the force direction of the MRE motion and the direction of the applied magnetic field can be designed to make the device more focused on the tension-compression or shear performance. This mode can achieve multi-directional vibration reduction of the device. However, in the design of the hybrid MRE isolator, since the hybrid mode considers both working modes of the MRE, the design and control of the device are more complicated than the shear and tension-compression modes. In the design of a vibration isolator for airborne equipment, due to the small mass of the airborne equipment and the small vibration, it is not necessary to consider the bearing capacity of the device, and the vibration in the vertical direction is mainly isolated. Therefore, the vertical shear mode is selected, and it is not necessary to adopt a more complex mixed mode.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion
### Experiment 1
#### 4.1.1 Scenario 1  
In this scenario, the performance of the intersection GA-based routing algorithm was analyzed to find the compliance path. The proposed algorithm selected the most efficient path with the minimum intersection activation value based on the calculated intersection activation value. Then, the rest of the acceptable paths were considered alternative paths. Figure 6 shows the destinations and the intersection activation values for the available paths towards each destination for different numbers of devices/nodes in the network. Figure 6a-d show that the number of available paths obtained based on the chromosome intersection operation value was increased when the number of nodes was increased for the same destination node. Thus, the algorithm supports a great number of devices and users in the system. However, the available paths based on the ranked intersection activation value (RIAV) present the most efficient path without the constraint priority.  
Figure 6: Available paths towards each destination. (**a**) Available paths with 20 nodes; (**b**) Available paths with 30 nodes; (**c**) available paths with 40 nodes; (**d**) available paths with 50 nodes.  
Figure 7 shows that the cost constraint values for all available paths in the same networks for the maximum cost value in the scenario are less than 70, and the majority of the available paths are less than 50% from the permitted cost value. The objective of this study is to minimize the cost of the selected path. This method reduced the processing of routing selection based on the proposed intersection GA-based routing selection. Compared with the conventional GA-based routing algorithms, the proposed algorithm uses chromosome intersection operation, which can control and avoid invalid paths, thereby improving results and providing an efficient path.  
Figure 7: Available paths in the same networks. (**a**) Available paths with 20 nodes; (**b**) available paths with 30 nodes; (**c**) available paths with 40 nodes; (**d**) available paths with 50 nodes.
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 4 Discussion
### Contribution of Different Factors to NEE  
An accurate assessment of carbon dynamics-controlling factors was vital for preventing grassland degradation [38]. Many studies have been conducted to assess the relative  
Figure 7: The relative impacts of climate change and grazing on NEE of different grassland types: (**a**) Dry Grassland (DG), (**b**) Temperate Grassland (TG), (**c**) Forest Meadow (FM), and (**d**) Central Asia. CA stands for the average of the three sets of simulation results.  
roles of climate change and human activities at regional and global scales [6; 38; 39; 40]. In this research, 100% of the carbon release in Central Asia was caused by grazing, and climate change offset 6.22% on average. Some researchers have already found that grazing was the main cause for desertification [11; 33]; however, in this study, the range and extent of the influence were given.  
The role of climate change and grazing in Central Asian carbon dynamics is varied among different eco-regions. Effects of grazing were more evident on TG, which can be attributed to its relatively high temperature and low precipitation (Figure 3), and the resulting ecological vulnerability.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods
### Artificial Neural Networks
#### 2.2.1 Static Neural Network: Multilayer Perceptron  
In 1958, [PERSON] introduced the perceptron and provided the perceptron convergence theorem [41]. A widely utilized supervised algorithm within feed-forward neural networks (FFNNs) is the multilayer perceptron (MLP). In MLP neural networks, the input parameter (\(\theta\)) progresses unidimensionally from the input layer to the output layer (output results \(\delta\)) via hidden layers [42], as illustrated in Figure 2.  
A typical MLP comprises interconnected neurons \(n_{ij}\), as shown in Figure 2, or processing elements. These neurons are organized into the following layers: an input layer; one or more hidden layers; and an output layer.  
The performance of MLP in modeling non-linear relationships depends on the activation function used, such as ReLU, sigmoid, or Tanh. The ReLU activation function was employed in this work. ReLU is widely employed to detect the non-linear effect of various factors, defined as follows: \(f(x)\) is zero for \(x<1\), and \(f(x)\) equals \(x\) for \(x\geq 0\).  
MLP neural networks are used to generalize a non-linear function \(y_{j}^{k}\), where \(j\) and \(k\) of Equation (1) denote the generic neuron and hidden layer, respectively, in the tasks of function modeling with one predicted variable. The output is defined by [43]:  
\[y_{j}^{k}=f(\sum_{i=1}^{n}w_{ij}^{k}x_{i}^{k}+b_{i}^{k}) \tag{1}\]  
where \(w_{ij}^{k}\) represents the weight of the connection between the \(i\)th neuron of the previous layer and the current neuron; \(x_{i}^{k}\) is the input from the \(i\)th neuron of the preceding layer, and \(b_{i}^{k}\) stands for the bias linked to the current neuron. The use of MLP allows for the approximation of non-linear mappings, which is useful in overcoming the issue of the  
Figure 2: The structure of the multilayer perceptron.  
non-linear relations among explanatory variables, particularly in the case of concrete dam seepage behavior [9].
## 4 Performance Evaluation
### Random Faults
However, the intensity and  
Figure 8: Recall according to each duration as a function of random fault intensity: (**a**) Cumulative sum of the recall at 0.7 m/s of Jackal UGV; (**b**) Cumulative sum of the recall at 1.0 m/s; (**c**) Cumulative sum of the recall at 1.3 m/s; (**d**) Cumulative sum of the recall at 1.6 m/s. Figure 7: Accuracy according to each duration as a function of random fault intensity: (**a**) Cumulative sum of the accuracy at 0.7 m/s of Jackal UGV; (**b**) Cumulative sum of the accuracy at 1.0 m/s; (**c**) Cumulative sum of the accuracy at 1.3 m/s; (**d**) Cumulative sum of the accuracy at 1.6 m/s. duration become larger, the amount of mistaken data become less. In the case of RT, when the intensity is small (\(\eta_{0}=1\)), the FNs increases regardless of the duration but no faults are detected. When the intensity is medium, the FNs decreases as the duration increases. The FN frequency is very low when the intensity is high and the duration is small. Only when the intensity is large enough to identify the difference, the RT is applied to detect faults. The precision results of each method are shown in Figure 9 according to the magnitude of intensity. At all velocities, AT shows the best performance and 0% of FP occurrence. At 1.0 m/s and 1.3 m/s, there is a significant difference in performance between AT and KT. The difference between the two thresholds at all intensities seems to make an appreciable difference to the performance. As mentioned above, the difference between the first thresholds (\(\rho^{\
u}\)) of AT and KT is the largest at the velocity of 1.3 m/s. Since the small threshold of KT, compared to that of AT, results in the wide anomalous region, in random faults, KT shows low performance with many FPs that occur constantly for all conditions. In contrast, AT does not have any FP at all. At 1.0 m/s, since the second threshold (\(\sigma^{\
u}\)) of KT is too high, KT fails to detect the random faults under the lowest intensity (\(\eta_{0}=1\)) and the shortest duration (\(\delta=5\)). In the case of RT, the performance is better as its velocity increases but the precision is very low. The intensity and the duration are higher simultaneously, a large number of FPs also occurs while TPs are increased. Especially, when the injected faults are similar to the magnitude of the original data (\(\eta_{0}=1\)), the detection performance is zero since almost all injected faults are classified as normal data. This is because all, including the normal and anomalous, data are identified as the normal data when \(\eta_{0}=1\).
## 4 Numerical Analysis and Discussion
### Parametric Study  
To obtain an optimal design of the PTMD, the effect of the mass ratio between the PTMD and the transmission tower, the effect of the ground motion intensity, the effect of the gap between the mass block and the limiting device, and the effect of the incident angle of the seismic ground motion are investigated, respectively. The El Centro earthquake is selected in this section. Unless mentioned otherwise, the peak ground acceleration of the El Centro earthquake is adjusted to 400 gal, and the mass ratio and gap are 2% and 0.02 m, respectively.  
Figure 9: Dynamic response under the El Centro earthquake conditions. (**a**) Longitudinal displacement; (**b**) Longitudinal acceleration; (**c**) Transverse displacement; (**d**) Transverse acceleration; (**e**) Axial internal force.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options
### Overview of the Different EMS Integration Options  
In Section 4, we point out that the requirements of the _PTB-A 50.8_ are only mandatory for billing-relevant data processed by the SMGW. The _PTB-A 50.8_ does not address the processing and provision of measurements that are not billing-relevant. Hence, for an automated energy management based on local meter measurements, only the weaker constraints of the _TR-03109-1_[7] remain. Billing is still possible via setting up an interval for collecting measurements that still register the measurements  
Figure 3: Integration options for Energy Management Systems (EMSs) using the _Smart Meter Gateway_ only for providing a transparent data tunnel, but not for the collection of meter measurements. Controllable Local Systems that are only monitored and controlled from inside the Home Area Network (HAN) are marked with an *.  
at the quarter hour marks (hour marks respectively for gas), e.g., by using an interval of 1 min. Then, a (additional) TAF that complies with the _PTB-A 50.8_ can be configured for billing purposes.  
A summary of all presented options can be found in Table 2. In regards to DSM, the options providing direct or indirect access to CLSs are relevant. For these, an active EMP and a transport channel for the communication between the SMGW and the EMP is required. Subsequently, we provide a more detailed assessment of the options. It has to be noted that the best EMS integration option is specific to the concrete energy management use case.
## 3 Methods
### Numerical Simulation  
Numerical simulation technology has advanced quickly in recent years and is frequently used to simulate how construction operations affect the surrounding environment. The software called 3 DEC is a computational analysis program based on the discrete element method as the basic theory, used to describe the mechanical behavior of discrete media. It adopts the finite difference method consistent with FLAC and adds the simulation of discontinuous mechanical behavior of the contact surface in mechanics. Therefore, 3 DEC not only has universal mechanical analysis in the field of continuum mechanics, but also can simulate the motion process of discontinuous media, especially suitable for the analysis of static and dynamic response problems of discrete media under the action of carriers. Currently, 3 DEC numerical simulation as a kind of block discrete element numerical simulation, is widely used in rock mechanics and geotechnical engineering induced by coal mining [31].  
Discrete element analysis uses the dynamic relaxation discrete element method, a numerical technique that can be used to solve dynamic corresponding issues and converts nonlinear static problems into dynamic ones (Figure 3). This method's main idea is to integrate the essential damping equation gradually. Mass damping and stiffness damping  
Figure 2: Field measurement of WCFZ.  
are typically employed to absorb the system's kinetic energy in order to guarantee the acquisition of a quasi-dynamic solution. The system's vibration will cease as soon as possible and the function will converge with the static value when the damping coefficient is below a particular critical point. The 3 DEC 5.2 software mainly solves the behavior of the blocks and the behavior of discontinuities in a discontinuing numerical model through Equations (1) and (2), and then simulates the discontinuous motion of the blocks [32].  
Plane motion equation:  
\[\ddot{x}_{i}+a\dot{x}_{i}=\frac{F_{i}}{m}-g_{i} \tag{1}\]  
Rotation equation:  
\[\ddot{\omega}_{i}+a\dot{\omega}_{i}=\frac{M_{i}}{I} \tag{2}\]  
where \(\ddot{x}_{i}\) is acceleration of the center of mass, \(\dot{x}_{i}\) is velocity of the center of mass, \(a\) is the viscous damping constant, \(F_{i}\) is the resultant force at the center of the bolock, \(m\) is the mass of the block, \(g_{i}\) is acceleration due to gravity, \(\ddot{\omega}_{i}\) is the angular acceleration of a certain axis of the block, \(\dot{\omega}_{i}\) is the angular velocity, \(M_{i}\) is the the bending moment; \(I\) is the moment of inertia.  
The above equations can be established in three coordinate axis directions, so three plane motion and rotation equations can be established for each block.
## 3 Results and Discussion

Table 3: Tentative assignments of the most important regions in the SERS spectrum of blood serum for the discrimination between AD and OD, as determined by GA.  
After GA, ANNs were used to build three different models. The first model was a binary model to distinguish between all AD and HC samples. The second and third models were tertiary models built to distinguish between mild AD, moderate AD, and HC samples and between HC, all AD, and OD samples. The spectral features selected by GA for the best discrimination between AD and HC samples was used as the input dataset for all ANN networks. Since ANN is a supervised method, it is important to characterize the modeled system sufficiently with input and output data. The common approach is to partition the dataset into three parts: the training, test, and validation sets. For the algorithm, the inputs were provided as data points of the Raman spectra (240 values of wavenumbers, selected by GA) and the assigned classes (AD, OD, and HC) were the identity labels. The output layer contained two and three neurons for the binary and the tertiary models, respectively. The network weights within the network are trained for multiple different configurations of ANNs based on the resilient backpropagation algorithm with weight backtracking of the adjustable parameters in order to minimize differences between the network output and the known labels of the samples. The threshold for the partial derivatives of the error function was set to a stopping criteria of 0.01. All hyper-parameters, i.e., parameters whose values are set before the learning process begins, influence how the weights (parameters) between the neuron connections will be trained by the algorithm. The hyper-parameters are identified by a trial-and-error process, and performance of the algorithm on the test dataset is used to determine the optimal ANN architecture. The network that provided the lowest prediction errors on the test set as compared to the others was selected as the final optimal ANN network. The confusion table seen in Table 4 summarizes the results that were achieved when using the optimal ANN network structures during the testing phase. The confusion table depicts to which class every spectrum from the test set was predicted as belonging and compares the predictions to the actual class. After the training process was finished, all network weights were fixed to their single optimized value.
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 3 Network Topology Optimization Method
### The Structural Parameters of Network Topology  
It is indicated in [24] that the algebraic connectivity of network topology \(\lambda_{2}(\mathcal{G})\) is affected by structural parameters of network topology. Therefore, it is reasonable to take the structural parameters as the basis of a network topology optimization method.  
The structural parameters of network topology, such as node's degree (\(d\)), the average path length (\(l\)), and clustering coefficient (\(c\)), play a key role in the recent development of complex network theory.  
The average value of the shortest path between any two sensor nodes in a network topology, \(l\), determines the effective \"size\" of the network.  
The clustering coefficient of node \(i\), \(c_{i}\), is defined as the ratio between the actual number of links, \(E_{i}\), and the maximum number of links, \(d_{i}(d_{i}-1)/2\), between neighboring nodes, and \(c_{i}=\dfrac{2E_{\mathcal{N}_{i}}}{d_{i}(d_{i}-1)}\). The average of \(c_{i}\) over all the sensor nodes, \(c\), measures the extent to which a network is clustered.  
**Proposition 1**.: _In an undirected and connected topology, \(\lambda_{2}(\mathcal{G})\leq\dfrac{ld_{\min}}{I-1}\) and \(\lambda_{I}(\mathcal{G})\geq d_{\max}+1\), where \(d_{\min}\) and \(d_{\max}\) are the minimum and maximum degrees of topology \(\mathcal{G}\), respectively._  
Proof of Proposition 1.: See [25] (p. 300) and [26] (p. 224). \(\square\)  
**Proposition 2**.: _The algebraic connectivity \(\lambda_{2}(\mathcal{G})\) is inversely proportional to the average path length \(l\), and \(\lambda_{2}(\mathcal{G})\leq\dfrac{1}{l}\)._  
**Proof of Proposition 2.** See [27] (p. 2141). \(\square\)  
**Proposition 3.**_The larger the clustering coefficient \(c\), the more closely sensor nodes are connected._  
**Proof of Proposition 3.** See [28] (p. 50). \(\square\)