esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis of the Distribution Parameters of a Metal Dot Array in a SAW Gyroscope
## 4 Conclusions  
In this paper, the influence of distributing metal dot array with different sizes on piezoelectric substrates on the intensity of the SAW gyroscopic effect, and the influence of the rotation speed on the optimum size of the metal dot array are discussed. Based on the differential sensing structure, finite element simulation software was used to simulate the optimum metal dot distribution parameters of ST-X quartz, 128\({}^{\circ}\)YX-LiNbO\({}_{3}\), and X112\({}^{\circ}\)Y-LiTaO\({}_{3}\) piezoelectric substrates at different rotation speeds, and the relationship between the SAW gyroscopic effect and the rotation speed. The research results provide theoretical support for further development of a high-sensitivity SAW angular velocity sensor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(a\) &
\begin{tabular}{c} **Piezoelectric** \\ **Substrate** \\ \end{tabular} & **Width** & \(\kappa^{2}\) & **TCF** & \(g\) \\ \hline \multirow{3}{*}{\(1\times 10^{-3}\)} & ST-X Quartz & \(3/16\lambda\) & \(0.11\) & \(0\) & \(29.5\) kHz \\  & 128\({}^{\circ}\)YX-LiNbO\({}_{3}\) & \(5/16\lambda\) & \(5.4\) & \(-72\) & \(22.4\) kHz \\ \cline{1-1}  & X112\({}^{\circ}\)Y-LiTaO\({}_{3}\) & \(1/16\lambda\) & \(0.71\) & \(-18.2\) & \(5.2\) kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: SAW gyroscopic effect of different piezoelectric substrates.  
Figure 8: Growth rate of the SAW gyroscopic effect at points \(3/16\lambda\) and \(11/16\lambda\).  
Figure 9: The influence of \(a\) value from \(6\times 10^{-3}\) to \(7\times 10^{-3}\) on the SAW gyroscopic effect.  
**Author Contributions:** Conceptualization, W.W., X.S. and L.M.; methodology, software, and writing, H.C.; data analysis, M.L., X.S., L.M. and H.C.; review, X.S. and L.M.; investigation and validation, H.C. and M.L.; funding acquisition, X.S. and W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. 11804201).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Validation and Analysis
### CEEMDAN Decomposition  
GNSS data and acceleration data generated under the conditions of a frequency of 2 Hz and an amplitude of 30 mm (displacement) were selected as the research data for this study. The observation period spans from GPS time 364,060 s to 364,218 s. Figure 7 shows the distribution of displacement and acceleration after processing the original data within the specified time range.  
The GNSS displacement and acceleration data generated by the vibration were decomposed using CEEMDAN, yielding the decomposition results shown in Figure 8. The displacement sequence was decomposed into nine IMF sequences ranging from high to low frequency, along with a residual component. Similarly, the acceleration data were decomposed into fifteen IMF components and their corresponding residuals. After decomposition, the high-frequency vibration information of the displacement data was mainly concentrated in IMF1, exhibiting relatively large amplitudes. As for the acceleration data, high-frequency information was primarily distributed across IMF1 to IMF7, typically con  
Figure 5: The shake table setup and equipment used for data acquisition.  
Figure 6: Acceleration along the X-axis.  
taining noise and rapid variations in the signal, which could reflect the transient response characteristics of the vibration system.  
Figure 8: CEEMDAN decomposition: (**a**) GNSS displacement decomposition; (**b**) acceleration decomposition.  
Figure 7: (**a**) Displacement; (**b**) Acceleration.
# On the Influence of Suspension Geometry on Steering Feedback
###### Abstract  
Feedback through the steering wheel is known as the most important source of information to the driver. The so-called steering feeling, composed of self-aligning actions coming from tyres and suspension geometry all the way through mechanical linkages to the driver's hands, provides vital communication for intuitive driving, and it is therefore utterly important for safety and for a pleasant driving experience as well. Subtle forces and vibrations, due to the interaction between the tyre contact patch and the road surface texture, also play a role, provided they are not heavily filtered or cancelled by the power steering system. Human perception is guided by experience in order to establish correlations between steering feedback and vehicle motion in terms of straight-line stability, cornering speed, tyre adhesion and available friction, vehicle balance, and so on. A front-wheel drive car is potentially a critical vehicle from this point of view, especially when the powertrain can deliver large torque figures, and even more so if a limited-slip differential (LSD) or a similar active device is present in order to improve traction capabilities. Any difference between the two wheels in terms of tractive force can result into the so-called torque steer issue, that is to say, a \"pulling\" sensation on the steering wheel or a shifting of the vehicle from the desired trajectory. This paper analyses the torque steer phenomenon on an all-wheel-drive, full electric sportscar where a significant portion of the torque is transferred to the front axle. The effects of suspension kinematics and the load variation at tyre contact patch level are taken into account. For evaluating the impact of steering feedback, the VI-grade(r) simulation software is adopted and a test campaign on the professional driving simulator available at the University of Brescia has been carried out in order to understand the impact of steering feedback on driver perception and performance.  
steering feeling; torque steer; electric cars +
Footnote †: journal: Applied Sciences
## 2 Problem Description and Assumptions
### Control Model Formulation
#### 2.3.1 Production and Subcontracting Strategies  
In this section, we present a proposed extension to the hedging point policy, which included the contribution of unreliable subcontracting. The proposed policy used the age \(a(t)\) of the machine at time \(t\) to indicate the adequate time to trigger subcontracting participation. Equations (10) and (11) are convenient representations of the proposed production-subcontracting control policy.  
If \(a(t)<A_{sub}\):  
\[\left\{\begin{array}{c}u_{1}(t)=\left\{\begin{array}{cc}\frac{u_{max}}{1- \beta\left(a\right)}&if\ x(t)<Z^{*}\ y\ \ \alpha_{1}(t)=1\\ \frac{d}{1-\beta\left(a\right)}&if\ x(t)=Z^{*}\ y\ \alpha_{1}(t)=1\\ 0\ if\ x(t)>Z^{*}\ or\ \alpha_{1}(t)=0\\ u_{2}\left(t\right)=0\ \forall\ x(t),\ \alpha_{1}(t),\ \alpha_{2}(t)\\ \end{array}\right. \tag{10}\]  
If \(a(t)\geq A_{sub}\):  
\[\left\{\begin{array}{c}u_{1}(t)=\left\{\begin{array}{cc}u_{max}&if\ x(t)<Z ^{*}\ and\ \alpha_{1}(t)=1\ and\ \alpha_{2}(t)=0\\ Max\Big{(}\frac{u_{max}}{1-\beta\left(a\right)}-u_{2}^{*}\Big{)}&if\ x(t)=Z^{*} \ and\ \alpha_{1}(t)=1\ and\ \alpha_{2}(t)=1\\ \frac{d}{1-\beta\left(a\right)}&if\ x(t)=Z^{*}\ and\ \alpha_{1}(t)=1\ and\ \alpha_{2}(t)=0\\ Max\Big{(}\Big{(}\frac{d}{1-\beta\left(a\right)}-u_{2}^{*}\Big{)},0\Big{)}&if\ x(t)=Z^{*}\ and\ \alpha_{1}(t)=1\ and\ \alpha_{2}(t)=1\\ 0&if\ x(t)\leq\ Z^{*}\ and\ \alpha_{1}(t)=0\ and\ \forall\alpha_{2}(t)=0\\ 0&if\ x(t)>Z^{*}\\ u_{2}\left(t\right)=\left\{\begin{array}{cc}u_{2}^{*}&if\ x(t)\leq\ Z^{*} \ and\ \alpha_{2}(t)=1\ and\ \forall\alpha_{1}(t)\\ 0&if\ x(t)\leq\ Z^{*}\ and\ \alpha_{2}(t)=0\ and\ \forall\alpha_{1}(t)\\ 0&if\ x(t)>Z^{*}\\ \end{array}\right.\end{array}\right. \tag{11}\]  
For practical purposes, \(A_{sub}\) is the critical age where subcontracting is triggered and \(Z^{*}\) is the optimal production threshold. To an extent, Equation (10) represents a classical HPP strategy in the production unit, and it indicates that subcontracting is not requested when the age of the machine is less than \(A_{sub}\) since, in such a period, the machine is operational in acceptable conditions and the effects of deterioration are not so severe. By applying Equation (11), one could control the participation of the production unit and subcontracting when the age of the machine surpasses \(A_{sub}\). In such an equation, internal production and subcontracting depend on their stochastic modes \((\alpha_{1}(t),\alpha_{1}(t))\), the inventory level \(x(t)\), and the age of the machine \(a(t)\).
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Generative Adversarial Networks
#### 2.3.2 Discriminator  
The discriminator is a binary classifier that distinguishes between real data (from the training set) and fake data (produced by the generator). Its job is to correctly label real data as real and generated data as fake. The discriminator receives inputs from both the real dataset and the generator's output and must decide whether each input is genuine or artificially created.  
The discriminator is typically implemented as a neural network, which can consist of fully connected layers or convolutional layers, depending on the task (e.g., image, text, or audio recognition). It assigns a probability score to each input, indicating whether it believes the input is real (closer to 1) or generated (closer to 0). The discriminator's training process involves minimizing its classification error by correctly identifying real versus fake data.  
The discriminator's objective function is to maximize the likelihood of correctly classifying real and fake samples. This can be expressed as follows:  
\[L_{D}=-\mathrm{E}_{x\sim p_{data}(x)}[\log(D(x))]-\mathrm{E}_{z\sim p_{z}(z)}[ \log(1-D(G(z)))]\]  
where \(D(x)\) is the discriminator's prediction for a real sample \(x\), and \(D(G(z))\) is its prediction for a generated sample \(G(z)\).  
The discriminator aims to maximize its ability to distinguish between real and fake data while minimizing the generator's chances of fooling it. As the discriminator improves, it becomes more challenging for the generator to produce convincing fake data.
## 4 Case Study
### Simulation Results and Discussion
#### 4.2.1 Scenario I: Ideal Case  
In this scenario, it is assumed that 12 sorts of cables in total can be used. The optimized layouts are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Units**} & \multicolumn{3}{c}{**Scenario I**} & \multicolumn{3}{c}{**Scenario II**} \\ \cline{3-8}  & & **MBA** & **BA\_FREE** & **BA\_MIN** & **MBA** & **BA\_FREE** & **BA\_MIN** \\ \hline \multirow{2}{*}{Tenching cost of cables} & [REUR] & 1126.94 & 1091.77 & 1063.08 & 1128.29 & 1136.05 & 1130.98 \\  & [REUR] & 2625.46 & 2883.08 & 2645.9 & 2803.31 & 2790.14 & 2664.48 \\ PLC & [REUR] & 2161.84 & 1928.86 & 2333.12 & 2009.57 & 1977.65 & 2136.07 \\ Total cost & [REUR] & 5914.24 & 5903.72 & 6042.09 & 5941.17 & 5903.84 & 5931.53 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results. PLC: power loss cost.  
Figure 7: _Cont._  
In Figure 7, the red stars indicate the positions of WTs while the green square shows the OS.  
It can be seen that thicker cables are adopted if the MBA and BA_FREE methods are used, which agrees with the fact that MBA_MIN merely used the thinnest cables that satisfy the current constraint. It can also be seen that the cable connection layouts found by BA_FREE and BA_MIN are with crossed cables, while no crossed cables exist in the layout found by MBA, as illustrated in Figure 7a. The statistic results for running each model using BA are shown in Figure 8.  
In Figure 8, the pink diamond, blue cross, and red star represent the results obtained by BA_MIN, BA_FREE, and MBA, respectively. It can be seen that the layouts found by MBA and BA_FREE are always better than the layouts found by BA_MIN in 10 trails. The fitness value corresponds to the CPU time of the best trial for each method, as shown in Figure 9.  
It can be seen in Figure 9 that BA_MIN converged earliest among the three models, which stabilized after 150 s, while MBA and BA_FREE stabilized at 300 s and 680 s, respectively. Though this situation varies in each trail, the algorithm can stabilize after 750 s even in the worst case. In the following scenario, the available cable types are reduced to approximate the real case.  
Figure 8: Ten trails for layout optimization by different algorithms (Scenario I).  
Figure 7: Optimized cable connection scheme in Scenario I. (**a**) Optimized layout obtained by MBA; (**b**) optimized layout obtained by BA_FREE; (**c**) optimized layout obtained by BA_MIN.
## 2 Material and Methods
### Effects of the Analyte Concentration on Density and Sound Speed  
Equation (4) reveals the direct relationship of the resonant frequency with the density (\(\rho_{s}\)) and sound speed (\(c_{s}\)) of the test solution if other parameters are defined as constant values. Obviously, the density of the test solution will change with the analyte concentration. The effect of analyte concentration on sound speed in the test solution needs to be explained. The sound speed in glucose solution, as well as the density, was observed to increase as glucose concentration increased [20]. The adiabatic compressibility of the glucose solution (\(\beta_{s}\)) can be expressed as  
\[\beta_{s}\ =\ \beta_{0}(1-\Phi) \tag{8}\]  
where \(\beta_{0}\) is the adiabatic compressibility of the water molecule and \(\Phi\) is the fractional volume of the glucose in the solution.  
Then, the sound speed (\(c_{s}\)) in glucose solution can be calculated using [21]  
\[c_{s}=\frac{1}{\sqrt{\beta_{s}\rho_{s}}} \tag{9}\]  
Using Equations (8) and (9) and referring to the existing data in [21], sound speed in glucose solution with different glucose concentrations is calculated as shown in Table 1. The values of density are also listed in Table 1, which reveal that both the density and sound speed increase with higher glucose concentration.  
\begin{table}
\begin{tabular}{c c c} \hline
**Glucose Concentration** & **Density (kg/m3)** & **Sound Speed (m/s)** \\ \hline
0 wt.\% (Water) & 998 & 1481.5 \\
5 wt.\% & 1010.5 & 1498.1 \\
10 wt.\% & 1023.8 & 1518.5 \\
15 wt.\% & 1038.0 & 1543.6 \\ \hline \end{tabular}
\end{table}
Table 1: Density and sound speed at different glucose concentration.  
Figure 2: Duct linked with a Helmholtz resonator as side-branch.
## 3 Methods
### Investigation of Missing MODIS FRP Due to Sampling Limitations
#### 3.2.3 Comparison of FRP on a Continental Scale and Various Grid Sizes  
To determine the overall missing MODIS FRP over Africa, we compared daily sum FRP from the extracted daytime MODIS and VIIRS I-band contemporaneous active fire detections. There was a total of 44 days from January 8 to December 26, 2017 when the two sensors sensed actively burning fires contemporaneously (within \(\pm\)5 min) in the same region across Africa. Note that the difference between MODIS and VIIRS overpass time was longer than 5 min during the rest of days. For each of the 44 days, FRP of the contemporaneous active fire detections was summed up separately for MODIS and VIIRS. Then, the daily sum FRP from the two sensors were statistically compared using the reduced major axis regression (RMA) method that minimizes errors of both dependent and independent variables [53]. Their FRP correlation was characterized using Pearson's correlation coefficient (**r**).  
We further compared MODIS and VIIRS FRP at a set of grid sizes to explore the spatially detailed difference in FRP from the two sensors. Specifically, the extracted contemporaneous fire detections were first gridded into the seven grids of 0.05\({}^{\circ}\), 0.1\({}^{\circ}\), 0.25\({}^{\circ}\), 0.5\({}^{\circ}\), 1\({}^{\circ}\), 2.5\({}^{\circ}\), and 5\({}^{\circ}\), separately, for MODIS and VIIRS. Then, grids with valid fire detections (FRP \(>\) 0 MW) were spatially matched between twosensors. This is because, even within an observing time difference of 5 min, fires sensed by one sensor (e.g., VIIRS) could be missed by the other sensor (e.g., MODIS), and vice versa [32]. Grids where both MODIS and VIIRS contemporaneously detected fires were considered as the matched grids; otherwise, grids, where only one of the two sensors observed fires, were grouped as the not-matched grids. In the not-matched grids, fire detections observed by one sensor were considered as missing fire observations by the other sensor. Subsequently, the percentage of these missing fire detections and their contribution to the sum FRP were calculated for MODIS and VIIRS, respectively. Meanwhile, for a matched grid, grid-level FRP was obtained separately for VIIRS and MODIS by summing up FRP within a given grid. Finally, the ratio of VIIRS grid FRP to MODIS grid FRP was calculated using Equation (2):  
\[\varphi=\frac{FRP_{V}}{FRP_{M}} \tag{2}\]  
where \(\varphi\) is the grid FRP ratio, and FRP\({}_{\text{V}}\) and FRP\({}_{\text{M}}\) are VIIRS and MODIS grid FRP, respectively.  
The variation in grid FRP from MODIS and VIIRS data was examined for the seven grid scales. The finest grid resolution was set as 0.05\({}^{\circ}\) as it is approximately a MODIS pixel (\(\sim\)5 km) at the scan edge along the equator.
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 2 Background  
### Intrusion Detection  
Intrusion detection research has traditionally been based on analysis strategies, which can be divided into anomaly-based and misuse-based detection [8]. Anomaly-based detection checks for any deviations from important patterns compared to normal patterns and can analyze network patterns statically and dynamically. Misuse-based detection uses rules to interpret events that best describe abnormal behavior within the system. Feature extraction is a critical step in intrusion detection, and it can reduce the size of the raw data, increase model training performance, and improve classifier accuracy.  
Machine learning methods for intrusion detection no longer rely on rule matching but instead build classification models to distinguish between normal and malicious traffic [9]. Support vector machines (SVMs) have been widely used for intrusion detection, and by combining SVM with feature selection techniques, the feasibility and effectiveness of intrusion detection can be enhanced [10]. Clustering is a simple and low-cost technique that can adapt to the real-time requirements of intrusion detection by clustering unmarked data based on data similarity [11].  
Deep learning has become the most popular choice for intrusion detection models due to its ability to learn potential complex patterns and network behavior from massive data, making it ideal for distinguishing between normal traffic and network attacks [12]. Techniques such as 2D CNN and Flow-Image have been used to map network flows to 2D image arrays and encode them for processing by popular CNNs like ResNet50 and GoogLeNet [13,14]. Hybrid detection systems that combine classic autoencoders and deep neural networks have also been proposed [15].  
Although intrusion detection technology has matured, attacks are becoming more covert and diversified, requiring continued research and development in this field to protect network systems.
## 3 Results and Analysis  
### Basic Mechanical Properties of PVC Membrane  
The basic uniaxial tensile properties of PVC membranes are shown in Figure 5. Since the differences among the three groups of test results are very small, and the errors are all within 5%, we show a group of data that is closest to the mean. It can be seen from the figure that, with the increase of strain, the stress of the membrane stretched along with the fiber direction increases faster than that stretched along the 45-degree angle of the fiber. The former breaks at a strain of 40%, while the latter breaks at a strain 2.5 times higher than the former. The former has a tensile strength of 67.3 MPa, while the latter has only 0.55 times the former, i.e., 37 MPa.  
It can be seen from the strain cloud and failure pattern of uniaxial tension PVC membrane during the loading process that the strain of PVC membrane changes with the increase of load level. For stretching along the fiber direction, the fibers in the PVC membrane are all broken simultaneously, which indicates that the PVC membrane is a brittle fracture. For stretching along with the 45-degree inclination of the fiber, the film's rupture cut is 45 degrees inclined to the stretching direction. Some of the fibers were pulled out and broken, indicating shear failure.  
For specimens stretched along the fiber direction, the strain cloud pattern is more uniform. Excessive local strain only occurs in the end clamping area. It indicates that the fibers at the cross-section of this sample all bear a high tensile force. For specimens stretched at an angle of 45 degrees along with the fiber, the strain inside the specimens varies unevenly. It indicates a relative slip between the fibers in the membrane, and they were under the combined stress of shear and tension. This loading method results in low tensile strength and high strain of PVC membranes. The above two samples' strain distributions will further affect the deformation properties of the attached thin-film cells.
## 5 Evaluation and Performance
### Results and Discussion  
The simulation outcome of our proposed approach has been compared against various network performance metrics. The metrics used in this evaluation mainly include network throughput, energy consumption, and number of alive nodes after each round [28; 29]. The detailed discussion on the results retrieved for these metrics has been provided below.
## 3 Fault Diagnosis
### FD Methods
#### 3.1.3 Data Driven-Based Method
 \tag{17}\]  
SVM is a widely used machine learning method in pattern recognition, especially for FD of centrifugal pumps [105], rolling bearing [106], gearbox [107], wind turbine [108], marine diesel engine [109]. [PERSON] et al. used SVM to realize fault diagnosis of marine main engine cylinder cover [95]. One-class support vector machine (OC-SVM) was firstly used to divide the input data of marine turbocharger system into two parts, normal and abnormal, and only the fault data are used to identify specific fault types [110]. By this means, automatic fault diagnosis of the system was realized. Compared with the ANN method, SVM-based FD models have better model interpretability performance and could easily realize the optimal global solution and obtain high diagnosis accuracy [80]. The SVM method also has disadvantages. Firstly, the method is mainly suitable for small sample data. With the improvement of ship intelligence, the method will not be suitable for big data. Furthermore, the performance of the diagnostic model is affected by kernel parameters, and its selection has a great impact on the accuracy of the model. Finally, complicated architectures are needed when facing the multi-class classification problem, and a single SVM model could not meet the requirements. The performance of data-driven FD approaches mainly relies on the quality and quantity of state parameters. Insufficient data has become the main factor limiting the wide application in MSAE. Therefore, the data-driven FD method is applied to some equipment and components with relatively sufficient data. Whenever the historical data could not contain complete status information, approaches based on physical model-based or knowledge-based methods come into play.