esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In their electrostatic actuator design, they used a single electrode with high voltage inputs and a moving mass in order to increase vibration intensity. By exploiting the beat phenomenon, created by two high voltage input signals that were applied to the electrode, they demonstrated the feasibility of the actuator for large display applications [11]. As an alternative to the single electrode ERA, this study proposes a new haptic module that incorporates two electrodes, referred to as the dual-electrode ERA design. In the proposed ERA, the additional electrode can increase the output vibration intensity when compared to that of a comparable ERA with a single electrode. Figure 1 illustrates the electrode configurations for both electrostatic actuators. Figure 1 shows the single-electrode actuator geometry with an electrode below the mass, and Figure 1 shows the dual-electrode actuator geometry with an electrode on each side of the mass. In the proposed two-electrode design, the electrodes are powered one at a time in an alternating fashion. This ensures that the forces generated by the upper and lower electrodes do not interfere. This was expected to significantly increase the vibration intensity of the actuator compared to using only a single electrode. The primary goal of this study was to experimentally evaluate the effects of electrode configurations in increasing vibration intensity and to compare the performance of a single-electrode ERA to the proposed dual-electrode ERA. To this end, the dual-electrode actuator and custom controller were designed and fabricated along with a single-electrode ERA. Using the actuators, several tests were performed to identify and compare the actuators' actual parameters to their design parameters. Following the actuation characterizations, the single-electrode and newly proposed dual-electrode actuators' vibration performance was experimentally evaluated. Peak acceleration measurements  
Figure 1: Electrode configuration of: (**a**) single-electrode actuator; (**b**) proposed dual-electrode actuator. were used to compare the performance of the actuators with both types of electrode configurations in producing vibration intensities. The next section provides the working principle of the proposed dual-electrode actuator. After describing the construction of the actuators and the custom high voltage amplifiers in Section 3, the paper presents experimental results.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 3 System Model
### Acoustic Propagation Model  
The temperature, pressure, and salinity of seawater dictate the speed at which sound travels in underwater environments. This relationship can be expressed as a function that incorporates the following variables [38]:  
\[\begin{array}{l}c=1448.96+4.519T-5.304\times 10^{-2}T^{2}+2.374\times 10^{-2}T^{3 }+1.304(S-35)+1.63\times 10^{-1}D\\ +1.675\times 10^{-7}D^{2}-1.025\times 10^{-2}T(S-35)-7.139\times 10^{-13}TD^{3} \end{array} \tag{7}\]  
The propagation speed of sound in meters per second is represented by '_c_'; the temperature, measured in degrees Celsius, is denoted by '_T_'; the salinity, measured in parts per thousand, is represented by '_S_'; and the depth, in meters, is denoted by '_D_'. It is important to mention that the speed of sound increases with higher values of temperature,depth, and salinity. Equation (7) is applicable if the conditions are satisfied as follows: \(0\leq T\leq 30,\ 30\leq S\leq 40,\ 0\leq D\leq 8000\).
# Coordinated Control Method of Bus Signal Priority and Speed Adjustment Based on Stop-Skipping
## 2 Analysis of Scheduling Strategy
### Combined Scheduling Strategy  
This study was conducted in three stages:  
(1) Make decision to stop-skip based on historical data.  
Bus operation has the characteristics of high departure frequency and short arrival interval during the peak period, and the strategy of stop-skipping helps to optimize the bus stops [30; 31]. By setting the objective function and constraint conditions, the decision of stop-skipping can be obtained according to the historical average passenger flow.  
(2) Make signal-priority decisions at intersections based on online data.  
The intersection is the bottleneck of the urban transportation system, and the traffic signal control has a great impact on the public transportation system [32]. Figure 2 shows the signal-priority control system. The decision trigger detects the vehicle information at the previous stop of the intersection, and makes signal-priority decisions according to the time when the bus arrives at the intersection.  
(3) Coordinated control combined with speed adjustment.  
According to the signal-priority triggering mechanism, the corresponding signal-priority scheme is adopted. Usually the signal-priority methods include extending the duration of the green lights, turning on the green lights in advance and shortening the duration of the red lights [33], combined with speed adjustment for cooperative control for time intervals that are not suitable for signal priority.
# Analysis of the Influence of Segmented Rollers on Slab Bulge Deformation
## 4 Comparisons of Slab Bulge Deformations with Different Segment Rollers
### Bulge Deformation on the Narrow Side  
The simulation result of the slab bulge deformation along the narrow side is displayed in Figure 8. The horizontal bulge deformation of the slab's narrow side was uneven, and the bulge deformation of the narrow side with the three-segment rollers was relatively close to that of the rigid rollers. The bulge deformation on the narrow side with the three-segment rollers reduced from 0.38 to 0.23 mm, a decrease of 39.5%. The bulge deformation on the narrow side with the two-segment rollers reduced from 0.39 to \(-\)0.008 mm, which was down by 102%. The bulge deformation on the narrow side with the rigid solid rollers reduced from 0.39 to 0.25 mm, dropping by 35.9%. The bulge deformation on the narrow side with the elastic solid rollers reduced from 0.37 to \(-\)0.08 mm, a decrease of 121.6%.  
Figure 7: Bulge deformation of the slab on the wide side with different casting rollers.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 4 Plant Extracts and Phytochemicals against Viruses Causing Foodborne Diseases  
### Norovirus  
Norovirus is single-stranded, positive-sense RNA and non-enveloped virus which is also known as winter vomiting bug [184]. It is a major cause of acute gastroenteritis affecting people of all ages. The transmission of the virus occurs via contaminated food. After entry into the body, the virus replicates in the intestines. It takes one to two days for symptoms to appear. The major symptom reported is gastroenteritis [185]. Other symptoms include watery diarrhea, abdominal cramps, nausea and vomiting. The infection is self-limiting, and recovery occurs within two to three days. There is no specific treatment for norovirus infection. Supportive therapy includes maintaining fluid and electrolyte balance [186]. A combination of juice and polyphenols from pomegranate is effective for eradicating norovirus infection [187]. Chinese galls and pomegranate significantly reduces the attachment of norovirus P protein to their receptors (human histo-blood group antigens) which is an important step for this pathogen virulence. Along with other phytochemicals, tannic acid is responsible for this blocking action [188]. Similarly, grape (_Vitis vinifera_ L.) seed extract is used in GIT diseases and has been proved to have potential anti-norovirus activity [189]. Allspice oil and lemongrass oil can reduce viral infection by degrading the capsid of the virus [190]. Curcumin, a natural compound from _Curcuma longa_ L. and juice of mulberry (_Morus alba_ L.) were tested for anti-noroviral activity and were found to have good inhibitory activity on norovirus.
# Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation
###### Abstract  
The reduction in mass and surface of antennas and scattering structures is an important task in designing electromagnetic devices. To solve this task, we present a novel algorithm based on the optimal current grid approximation for generating sparse scattering structures and evaluating their effectiveness. The main contributions of the work include analyzing the performance of the optimal current grid approximation and its modifications in generating sparse antennas, as well as demonstrating the effectiveness of the algorithm in generating sparse scattering structures using a scattering plate and corner reflectors as an example. To validate the accuracy of the algorithm, we compared the scattering properties of the obtained sparse scattering structures. The results show that the algorithm worked accurately and effectively in generating sparse scattering structures while reducing their mass and surface.  
Keywords:sparse antenna; sparse scatterer; optimal current grid approximation; method of moments +
Footnote †: journal: _Algorithms_
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 2 Methods
### Cross-Power Spectrum Matrix Dimensional Reduction  
After the screening in the previous section, a clear area of the cross-power spectrum matrix is obtained, which can be used to estimate the displacement well. Since the two-dimensional cross-power spectrum matrix is difficult to do the phase unwrapping, thus, dimensional reduction processing has to be performed.  
According to the basic principles of phase correlation, the rank of the cross-power spectrum matrix is one under the ideal conditions [47], so SVD strategy can be used to make a rank-one estimation, and then the dimensionality of cross-power spectrum matrix is reduced from 2 to 1. SVD is performed on the screened cross-power spectrum matrix, its first singular value is taken, and the corresponding left and right singular vectors are used to represent the ideal cross-power matrix. Assuming the area matrix selected in the previous section is A, SVD decomposition on this matrix is performed according to Equation (5):  
\[\text{A}=\text{U}\times\text{diag}(\sigma_{1},\sigma_{2},\cdots,\sigma_{\text {k}},0,\cdots)\times\text{V} \tag{5}\]  
where \(\text{U}=[\text{u}_{1},\cdots,\text{u}_{\text{k}}|\text{u}_{\text{k}+1}, \cdots,\text{u}_{\text{m}}]\), \(\sigma_{1}>\sigma_{2}>\cdots\), \(\text{V}=[\text{v}_{1},\cdots,\text{v}_{\text{k}}|\text{u}_{\text{k}+1}, \cdots,\text{v}_{\text{n}}]\).  
After SVD processing, the phase of corresponding left and right singular vectors \(\text{u}_{1}\) and \(\text{v}_{1}\) is calculated, and one-dimensional phase unwrapping is performed according to Equation (6). In this equation, \(\overline{\varphi}_{\text{i}}\) is the phase after unwrapping; \(\varphi_{\text{i}}\) is the currently processed phase; \(\text{mod}\{\text{a},\text{b}\}\) means taking the remainder of dividing a by b; and \(\varphi_{0}\) is the first phase in the sequence. The winding phases and unwrapping phases of \(\text{u}_{1}\) and \(\text{v}_{1}\) are shown in Figure 5, where Figure 5a is the winding phase and unwrapping phase of \(\text{u}_{1}\), and Figure 5b is the winding phase and unwrapping phase of \(\text{v}_{1}\):  
\[\overline{\varphi}_{\text{i}}=\varphi_{0}+\sum_{\text{j}=0}^{\text{i}-1} \text{mod}\{\big{(}\varphi_{\text{j}+1}-\varphi_{\text{j}}\big{)},2\pi\} \tag{6}\]  
Figure 4: Cross-power spectrum matrix based on PSD screening.
# Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism
## 3 MATT-CNN+BiGRU Feature Fusion Model
### The Brief Introduction of MATT-CNN+BiGRU
#### 3.1.1 Principal Component Analysis  
Principal component analysis, also known as (PCA), is a dimensionality reduction method based on mathematical statistics. PCA uses the idea of dimensionality reduction to first standardize data in high-dimensional data and, then, the covariance matrix of the target matrix and its corresponding eigenvectors are obtained. Finally, the initial data is transformed into a linearly independent representation of arbitrary dimensions by linear transformation, thereby transforming multiple indicators (high dimensions) into a few major feature components. The basic principle of PCA is:  
Let the raw data \(A_{m\times n}\) set be arranged as a matrix, then each row element of the matrix is zero-averaged and the calculation expression is as shown in Equation (1):  
\[A_{ij}=\frac{a_{ij}-\overline{a}_{i}}{S_{i}}, \tag{1}\]  
where \(a_{ij}\) represents the element of the \(i\)th row and \(j\)-column of the matrix \(A_{m\times n}\), and \(\overline{a}_{i}\) represents the \(i\)th row of the matrix \(A_{m\times n}\). The average value, \(S_{i}\) represents the standard deviation of the \(i\)th row of the matrix \(A_{m\times n}\). Following zero-mean normalization, the covariance matrix \(S\) of the matrix \(\mathrm{X}\) is obtained. Equation (2) is as follows:  
\[S=\frac{1}{\mathrm{n}-1}\sum\
olimits_{k=0}^{j}{(a_{j}-\overline{a})(a_{j}- \overline{a})^{T}}, \tag{2}\]  
where \(\mathrm{n}\) represents the number of samples in the above formula and, after obtaining the covariance matrix \(S\), the eigenvalue \(\lambda_{1}\geq\lambda_{2}\geq\ldots\geq\lambda_{n}\) and the eigenvector \(d_{1},d_{2}\ldots d_{n}\); are obtained accordingly.  
The feature vector is obtained by arranging the feature values from largest to smallest to obtain the matrix \(P\), and the reduced-dimensional data \(Y\) is obtained by Equation (3):  
\[Y=PA. \tag{3}\]  
Finally, the contribution rate \(V_{i}\) of each feature root is calculated according to Equation (4):  
\[V_{i}=\frac{\lambda_{i}}{\sum_{1}^{n}{\lambda_{i}}}. \tag{4}\]
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Feature Analysis
#### 3.2.1 RadViz  
The RadViz method is a visualization technique that is used to represent the relationship between multidimensional data points in a two-dimensional space. It maps each data point onto a circle with a set radius and then draws lines from the center of the circle to each point. The position of each data point on the circle is determined by a weighted average of its values in each dimension. The formula for calculating the position of a data point on the circle is as follows:  
\[x_{i}=\frac{\sum_{j}(w_{j}\cdot v_{ij})}{\sum_{j}w_{j}} \tag{7}\]  
where \(x_{i}\) is the position of the data point on the circle, \(v_{ij}\) is the value of the data point in the \(j_{th}\) dimension, and \(w_{j}\) is a weight that determines the importance of the \(j_{th}\) dimension. The weights are determined by the user and are used to emphasize certain dimensions over others. Once the positions of the data points on the circle are determined, lines are drawn from the center of the circle to each point. The length of each line is proportional to the distance between the data point and the center of the circle, which represents the overall relationship between the data points [24].  
The RadViz method provides an intuitive visualization of how different dimensions contribute to the overall relationship between the data points, making it a useful tool for exploratory data analysis and for communicating complex relationships between data points.
# Techniques and Paradigms in Modern Game AI Systems
## 5 Paradigms and Trends
### Common Paradigms
#### 5.1.1 AlphaGo Series  
AlphaGo, AlphaGo Zero, and AlphaZero follow a common paradigm to solve classic board games, where the policy and value models are trained and combined with MCTS in real-time gameplay. While AlphaGo uses supervised learning and policy gradient to train the networks, AlphaGo Zero and AlphaZero adopt MCTS as policy improvement operators in the RL training. MCTS is heavily used both as planning and learning algorithms for two reasons. First, MCTS can be used here because these classic board games are all perfect-information games where the environment model is known, and a fast response of actions is not required. Second, board games like Go, Chess, and Shogi have such large state space that a perfect policy or value model is infeasible, but a relatively small time horizon that real-time planning algorithms like MCTS can improve the performance of an imperfect model by a large margin. The feasibility and superiority of the MCTS application are the keys to the success of the AlphaGo series in perfect-information games.  
However, this paradigm is limited to perfect-information games because there are some limitations to the use of MCTS. First, an explicit environment model is required to predict the next state under current action. Second, the action space should be discrete and limited so that the branching factor of the search tree is under control. Third, MCTS only expands trees with limited depth and is inefficient to bootstrap values from the end of the episode to the early stages of the game when the time horizon is long. Though MuZero [86] overcomes the first limitation by training an additional representative model to simulate the environment and achieves success on Atari 2600 video games, the latter two limitations make it unable to apply to games with imperfect information or long time horizons.
## 5 Experiments
### MFD along Air Gaps  
To measure the MFD along the air gaps, the sensitive part of the gaussmeter probe is placed in the air gap with the meter fixed on the moving subassembly. In this way, the MFD along the air gap can be sampled at equal intervals as the moving subassembly is controlled to travel slowly from one end to the other.  
Measurement results of MFD along the upper and lower air gaps are shown in Figure 13. Compared with the results shown in Figure 9, the measured MFD is about 20% smaller than the theoretical model and FEM results. The main reason is that the remanence value of PM material is not exactly the same as assumed in the models, and magnetic flux leakage at the flank sides of the RCDMC is inevitable. Benefiting from the strict vacuum anneal process [20], the actual permeability of yokes is higher than the tested result of the specimen part in Figure 7, and the measurement result of uniformity of MFD is better than the theoretical model and FEM results. Due to the fringe effect, the MFD increases rapidly in the regions of 20 mm from both ends of the air gaps, which is consistent with the FEM results. Differing from the theoretical and FEM analysis results, the two MFD measurement curves along the upper and lower air gap respectively are not perfectly symmetrical, and there are differences between the two curves. This is due to the unequal thickness of the two air gaps in the prototype caused by  
Figure 12: Prototype of RCDMC assembled in a horizontal ULF vibration exciter. Reproduced with permission from [19], IEEE, 2019.  
machining and assembly errors, while material distortions also have a major impact on that. The average MFD along the upper and lower air gaps is 102.44 mT and 102.79 mT, the non-uniformity of MFD is 0.21% and 0.26%, and AWHD is 0.24% and 0.27%, respectively.
## 4 Discussion
### Improvement of RV Reverse Remodeling Systolic Function after CCM  
Although several studies reported improvements in symptoms, exercise capacity, and LV global and regional LV contractility, whether CCM would induce favorable changes in RV systolic function was largely unknown.  
As guidelines [13] recommended, RV size was measured from a four-chamber view in the context of the left ventricle. Both basal and mid-cavity diameters were reduced, suggesting a favorable RV remodeling; however, the severity of tricuspid regurgitation  
Figure 5: Effects of CCM on right ventricular arterial coupling. * = \(p\) < 0.05.  
remained stable, maybe because of the presence of leads passing from the right atrium to the RV through the valve.  
In this study, we found that all three RV systolic function indices (TAPSE, S wave, RVFWLS) improved after 6 months of CCM therapy.  
TAPSE is a measure of longitudinal contraction of the RV. It requires no geometric assumptions, it has the advantage of being obtained even with poor images, and it can be measured on all ultrasound machines [14].  
TAPSE has prognostic value in a variety of conditions that may affect the RV, such as HFrEF [15] and pulmonary hypertension [16; 17].  
TDI-derived lateral tricuspid annulus S-wave velocity is easy to measure, reliable, and reproducible. Like TAPSE, it is a unidimensional measure and evaluates RV longitudinal function [16].  
Unlike wall motion displacement (TAPSE) or velocity (S wave), RV free-wall myocardial strain (myocardial deformation) is unaffected by the motion of the entire heart and allows distinction between active and passive myocardial tissue movement. RVFWLS is an angle-independent speckle-tracking echocardiography (STE)-derived parameter that is very useful for the evaluation of RV contractile function. It has prognostic value for cardiovascular morbidity and mortality [18; 19].  
Therefore, as previously demonstrated for LV global and regional systolic function [20], in our study the CCM also seems to improve RV systolic function, increasing global RV contractility, not just limited to the septal wall where CCM signals were delivered.  
Similar to the findings in our study, in an animal model of chronic right ventricular dysfunction, CCM has been shown to improve global cardiac function significantly inboth ventricles [21].  
CCM seems to be useful in HFrEF patients to improve both LV and RV systolic function, probably given its proven effects on regional and global myocardial contractility [20] and given the key role played by the interventricular septum in the contractility of both ventricles via ventricular interdependence [15; 22; 23]. Furthermore, [PERSON] et al. showed better positive biventricular echocardiographic results in patients with lower LVEF, probably because the intensity of LVEF reduction appears to be correlated partly with impaired neuro-humoral activation associated with HF, assuming that the underlying mechanism could be \"the worse the LVEF at baseline, the more can be repaired\" [24].
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 6 Service Management Framework
### System Architecture and Queuing System Model

Table 1: Summary of notations. of those jobs sent to the cloud, in that it models the portion of the data analyzed and processed in the cloud. The cloud tier is structured with a number of homogeneous servers, each of which entails a queue with infinite capacity to buffer incoming fog jobs for execution in its computing resource. Factors incurred due to server failures and server-to-server communications are not considered. The _M/M/N_ queuing model is adopted to design the cloud computing system, where \(N\) represents the number of cloud resources that exist in the system, as shown in Figure 2. A system-queue, called a cloud dispatcher _cd_, receives fog environment jobs and allocates them for execution on cloud resources for further processing. The arrival behavior of fog jobs to the dispatcher _cd_ of the cloud tier is modeled as a Poisson process. The time between consecutive arrivals of such fog jobs follows an exponential distribution with a particular arrival rate. The service demand of each fog job in a cloud resource is assumed to be known in advance based on prediction methods applied on incoming workload history to estimate a job's execution time, and thus it is modeled from an exponential distribution with a service rate \(\mu\)[65,66]. Fog jobs allocated by the cloud dispatcher _cd_ to resource queues are allowed to be both reordered in the same queue and migrated from one queue to another. A fog job can be executed by only one cloud resource at a time. A cloud resource can execute only one fog job at a time. Cloud resources are available to provide services at any time. The service discipline of such fog jobs in cloud resources is non-preemptive; a fog job cannot be interrupted once it starts data execution in a cloud resource. Figure 1: Cloud–fog architecture.