esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The Industrial Metaverse depicts the intersection of the physical and digital worlds, resulting in a highly interconnected ecosystem in which virtual and augmented reality interfaces, artificial intelligence, and the Internet of Things (IoT) work together to transform industrial operations [1]. It connects the real world to the virtual world, giving users the feel of the virtual world of augmented (AR) or virtual reality (VR), as shown in Figure 1. In this dynamic environment, wireless sensors serve as the backbone for obtaining real-time data required for operation optimization and safety assurance. However, the widespread deployment of sensors creates serious privacy concerns, particularly about how data are collected, processed, and shared [2].  
Context privacy preservation refers to the protection of sensitive contextual information related to user validation activities. In the Industrial Metaverse, where people interact with a variety of physical and digital elements, context privacy is critical to preventing unwanted access, identity theft, and exploitation of personal information [3]. Furthermore, compliance with data protection standards such as GDPR and CCPA requires the deployment of strong privacy safeguards connected with the events, surroundings, or actions related to data gathering [4]. The addition of wireless sensors for user validation within the Industrial Metaverse access system poses both obstacles and opportunities. However, the opportunities outweigh the obstacles. The challenges include guaranteeing safe communication, reducing the danger of data breaches, and addressing user consent and transparency concerns [5]. However, novel technologies like context-aware encryption, dynamic privacy settings, and biometric authentication provide chances to improve privacy while maintaining usability and efficiency [6]. The challenges of preserving context privacy in wireless sensor networks within the industrial metaverse abound. Examples are data aggregation and fusion. Aggregating data from multiple sensors to derive meaningful insights while preserving the privacy of individual contexts poses a significant challenge. In addition, traditional aggregation techniques may compromise privacy by revealing sensitive information, as the need for real-time processing of sensor data introduces complexities in implementing privacy preservation mechanisms without introducing significant latency or overhead. Dynamic environments characterize industrial settings where contextual factors constantly change. Therefore, adapting privacy preservation techniques to accommodate these changes without sacrificing effectiveness is critical. Ensuring secure data and communication channels between sensors, edge devices, and central processing units is essential to prevent eavesdropping or data interception by malicious entities. Trusted sensors with IP security (IPsec) have to be deployed and utilized within the context privacy framework to guarantee proper user validation.
## 3 Signal Generation Method and Circuit Design Based on Time-Interleaving  
### Signal Generation Method Based on TIDAC  
The TIDAC-based method uses multiple high-resolution DACs and selects different DAC output channels for output through a channel switching circuit. Switch between multiple DAC output channels at higher frequencies and realize a high-resolution and high-speed DA conversion circuit with multiple sampling rates on the basis of the original high-precision DAC. The signal generation circuit structure model based on TIDAC is shown in Figure 4.  
The 4 channels work at the sampling rate of fs, respectively, and the frequency multiplier channel is switched at a frequency of 4 fs to switch among the 4 output channels in turn, realizing high-speed sampling on the original high-precision DAC. After the channel switching, the analog signal is collected and output by the acquisition and holding module at a frequency of 4 fs to reduce the influence caused by the channel switching. Theoretically, the relationship between the 4 channel outputs in TIDAC and the channel switching output is shown in Figure 5.  
Figure 3: Schematic diagram of output stacking method [14].  
In the figure, each channel outputs a level value every sampling period T. When the time belongs to 0\(\sim\)1/4T, the output channel is switched to channel 1, and the level value of channel 1 is output. When the time belongs to 1/4T\(\sim\)1/2T, the output channel is switched to channel 2, and the level of channel 2 is output value. When the time belongs to 1/2T\(\sim\)3/4T, the output channel is switched to channel 3, and the level value of channel 3 is output. When the time belongs to 3/4T-T, the output channel is switched to channel 4, and the level value of channel 4 is output. Then, when the time belongs to T-5/4T, the output channel is switched to channel one again, and the level value of channel one is output, and so on; the multiplexer switches the output evenly among the four channels and is multiplexed. The output waveform after the converter is shown in Figure 3. Comparing these figures, it can be seen that under the same output frequency, the sampling point of the multiplexer output waveform is 4 times the number of sampling points output by a single channel, that is, the sampling rate of the system is relative to the sampling rate of the original DAC. Improved by 4 times.  
Figure 4: Signal generation circuit structure model based on TIDAC.  
Figure 5: The relationship between the output of each channel and the output of the multiplexer.
# Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model
## 3 Proposed Method
### Decision Tree  
The decision tree (DT) model [19] can be used to solve both classification and regression problems. In the present study, the model was used to solve the regression problem of predicting the turning precision based on the values of the four machining parameters. As shown in Figure 7, the DT structure consisted of three types of nodes: a root node, multiple interior nodes, and several output nodes. The root node represented the entire sample, while the interior nodes represented the features of the data set and the leaf nodes represented the regression outputs. Moreover, the branches between the nodes represented the decision rules (formulated as True or False). Briefly, each data point in the dataset was run through the tree until it reached an output node. The final prediction result was then obtained by computing the average value of the dependent variable at each node.  
In this study, the depth of the DT structure was set as 10, the parameter controlling the randomness of the estimator was set as 0, and the feature split randomness criterion was set as \"best\" (meaning that the DT chose the split preferably based on the most important feature).
## 5 Experiment
### Experimental Platform and Environment  
The experiment was carried out on a robot platform based on Turtlebot 2, equipped with a notebook, a LiDAR and an RGB-D camera. The notebook has Intel Core i5 processor and 8G memory, running on Ubuntu 14.04 + ROS Indigo system. The robot platform is shown in Figure 6:  
The LiDAR is RPLIDAR-A2 produced by SLAMTEC company. It is a low-cost LiDAR for service robotics, which has a 360\({}^{\circ}\) coverage field and range of up to 8 m. The key parameters are listed in Table 1. It should be noted that the parameters are taken under the ideal situation, and being a low-cost LiDAR, the data collected from it is usually much poorer than expensive ones in a real scene.  
The RGB-D camera is Xtion-pro produced by ASUS company. The effective range of depth measurement is 0.6-8 m, the precision is 3 mm, and the angle of view of the depth camera can reach horizontal 58\({}^{\circ}\)and vertical 45.5\({}^{\circ}\).  
With the robot platform, we have collected several typical indoor databases in the \"rosbag\" format, which is easy to read for ROS. For each database, it contains sensor data obtained by the robot while it is running in a real scene, including odom data, laser scan, color image and depth image. Figure 7 shows  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measuring Distance** & **Ranging Accuracy** & **Angle Range** & **Angle Resolution** & **Frequency** \\ \hline
0.15–12 m & 0.001 m & 0–360\({}^{\circ}\) & 0.9\({}^{\circ}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The key parameters of RPLidar-A1.  
Figure 6: The robot platform for the experiment.  
one example of the database. Where Figure 7a displays the robot in the environment; Figure 7b shows the scan data obtained in the place; Figure 7c is the RGB image; Figure 7d is the depth image.  
Different experiments were taken to evaluate the performance of mapping and relocation. For the mapping, the methods for comparison include Karto-SLAM [3], orb-SLAM [9] and this paper. For the relocation with s map, we compared our method with the Adaptive Monte Carlo Localization (AMCL) [34]. In the experiments, the parameter \(\beta\) of formulas (6) and (8) is set to 0.2, by considering the precision and reliability of the two main sensors in the environment.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 2 Related Work  
There have been some works on the detection and defense methods against adversarial attacks in speaker verification systems [21; 22; 23]. For example, separate neural networks have been proposed to detect the appearance of adversarial samples in [24; 25]. Generative adversarial network (GAN) based and variational autoencoders (VAE) based defenses have been studied in [26]. A voting method has been used to defend against adversarial examples in [27]. The implementation of these detection or defense methods, however, is not simple nor light-weight, and requires significant computations. Moreover, it is not clear how these detection and defense methods perform against the state-of-the-art adversarial attacks such as FakeBob.  
The most relevant work to our approach was recently presented in [28]. This work also studied how to use small noise to counteract query-based black-box adversarial attacks. There are some key differences between their work and our work: (1) The work in [28] focuses on the image classification problem, whereas we study speaker verification systems. Images and audios are different signals and have distinct characteristics. As shown in [7], many defense systems that perform well for images cannot be applied to audios. (2) Image classification and speaker verification usually apply different machine learning methods. For example, an image classifier uses the classic convolutional neural network (CNN) model, whereas a speaker verification system applies the GMM. The same defense mechanism may have very different performance on distinct machine learning models. (3) The work in [28] aims at untargeted adversarial attacks, whereas our work focuses on targeted adversarial attacks.
## 4 Data Smoothing Algorithm
### Software Scheme of Track Geometry State Detector  
The data processing software of the track geometry state detector system was divided into four software modules: The sensor original data preprocessing module, the inertial navigation initial alignment module, the inertial/GNSS/odometer Kalman filter integrated navigation and reverse smoothing module, and the track geometry parameter calculation module, as shown in Figure 6.
# Integrated Design of Hydraulic Coupling Bolts for Large Shaft Systems
## 2 Calculation of Tightening Load  
### Second Tightening Load  
The sequence of calculating the tightening load starts with determining the second tightening load with great attention to the axial tensile stress in the bolt, which is a primary concern for operational safety during the overall tightening process. The bolt material was a low-alloy steel known as SNCM439, which contains elements, such as chromium (Cr), molybdenum (Mo), and nickel (Ni). The mechanical properties for analysis were obtained from the tensile test and are shown in Table 1. Figure 4 schematically illustrates the design of the bolt intended for use in a large shaft system, based on the existing hydraulic coupling bolt with the M72 specification, while the detailed dimensions are not disclosed in this paper. The second tightening load (\(F_{2}\)) was determined by considering factors, such as yield stress (\(\sigma_{y}\)), stress concentration factor (\(K_{\text{t}}\)), and safety factor (\(SF\)). The stress concentration factor was estimated based on the minimum cross-section shown in Figure 4, using FE analysis (see Section 2.3). The \(SF\), which is dependent on the operating conditions, was set to the value provided by the manufacturer. The formulas for calculating the second tightening load are shown below.  
\[F_{2,\text{max}}=A_{\text{min}}\cdot\sigma_{y} \tag{1}\]  
\[\sigma_{\text{nom}}=\frac{F_{2,\text{ar}}}{A_{\text{min}}} \tag{2}\]  
\[K_{\text{t}}=\frac{\sigma_{\text{max}}}{\sigma_{\text{nom}}} \tag{3}\]  
Figure 3: Flowchart of the entire design process.  
\[F_{2} =\frac{F_{2,\text{max}}}{K_{\text{t}}\cdot SF} \tag{4}\] \[P_{2} =\frac{F_{2}}{A_{\text{t}}}\cdot 10 \tag{5}\]  
where \(F_{2,\text{max}}\) is the maximum allowable load during the second tightening process, \(A_{\text{min}}\) is the minimum cross-section area, \(\sigma_{\text{nom}}\) is the nominal stress, \(F_{2,\text{ar}}\) is the second tightening load that was arbitrarily applied to compute the stress concentration factor, \(\sigma_{\text{max}}\) is the maximum stress obtained through the FE analysis at the minimum cross-section, \(P_{2}\) is the hydraulic tensioner pressure required for the second tightening load, and \(A_{\text{t}}\) is the inner area of the tensioner where the hydraulic pressure acts. Initially, the maximum allowable load was computed using Equation (1). Subsequently, the stress concentration factor was determined by combining Equations (2) and (3) with the FE analysis results from Section 2.3. Following this, Equation (4) was employed to calculate the second tightening load, which was ultimately converted into the corresponding hydraulic pressure through Equation (5). Table 2 presents the calculated results for the mentioned parameters. Based on these calculations, the second tightening load (\(F_{2}\)) was 825 kN, which is considered suitable for maintaining the structural strength of the hydraulic coupling bolt during its operation.
_applied sciences_  
_Article_  
**Prediction of Grazing Incidence Focusing Mirror Imaging Quality Based on Accurate Modelling of the Surface Shape Accuracy for the Whole Assembly Process**  
[PERSON] 1, [PERSON] 1,*\(\copyright\), [PERSON] 1\(\copyright\), [PERSON] 2, [PERSON] 2,* and [PERSON] 1  
1 School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;  
[EMAIL_ADDRESS] (E.L.); [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (T.S.)  
2 Beijing Institute of Control Engineering, Beijing 100190, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (Z.M.)  
**Abstract**: The key indicator of a grazing incidence focusing mirror's imaging quality is its angular resolution, which is significantly influenced by its surface shape distribution error. In this paper, we propose a method for the prediction of grazing incidence focusing mirror imaging quality based on accurate modelling of the surface shape accuracy for the whole assembly process. Firstly, the three-dimensional surface shape distribution error of the inner surface of the focusing mirror is reconstructed based on measured point cloud data, and the changes in the surface shape induced by suspension gravity and the adhesive curing shrinkage force are obtained through simulation, and then an accurate geometric digital twin model based on the characterisation of its surface shape accuracy is established. Finally, a study on the quantitative prediction of the angular resolution of its imaging quality is performed. The results show that the surface shape error before assembly has the greatest influence on the imaging quality; the difference in angular resolution between the two suspension methods under the influence of gravity is approximately 2.1\({}^{\prime\prime}\), and the angular resolution decreases by about 4.2\({}^{\prime\prime}\) due to adhesive curing. This method can provide effective support for the prediction of the imaging quality of grazing incidence focusing mirrors.  
**Keywords: grazing incidence focusing mirror; surface shape accuracy; imaging quality**
# A Macroscopic Traffic Model based on Driver Reaction and Traffic Stimuli
## 1 Introduction
This can result in negative speeds when the traffic volume is large, which is impossible [23; 24]. [PERSON] argued that the speeds in different lanes are not the same in multi-lane traffic and this difference allows vehicles to travel faster than the average speed of all lanes. [PERSON] and [PERSON] [25] improved the PW model by introducing a monotonically increasing function of density such that changes occur at or below the average speed. However, this can result in large acceleration and deceleration when the density is high, which is unrealistic [26]. [PERSON] [8] improved the PW model by incorporating driver presumption, which is based on changes in the equilibrium velocity. However, in the [PERSON] model, a driver adjusts to the traffic density instantaneously and driver physiology is not considered. [PERSON], [PERSON] and [PERSON] [27] introduced a diffusion term to mitigate the unrealistic acceleration and deceleration in the PW model. However, this model cannot characterize abrupt changes in density. Interactions between vehicles on a road are not adequately characterized by the PW model [28]. Changes in density produce changes in the equilibrium velocity distribution, which results in driver reaction to align to the forward vehicles. Thus, in this paper, a new anticipation term is proposed. The performance of the proposed and PW models was evaluated over a 300 m circular road with an inactive traffic flow bottleneck to illustrate the improvements in behavior. The rest of this paper is organized as follows. The proposed model is presented in Section 2 and the Roe decomposition for numerical evaluation is given in Section 3. Section 4 presents a stability analysis of the model. The performance of the proposed and PW models is investigated in Section 5 and some concluding remarks are given in Section 6.
## 4 Experiment and Results
### Performance Measures
#### 4.3.2 F1-Score  
The \(F1\)-_score_ represents the weighted average of _precision_ and _recall_, and it is represented by Equation (42). It considers both false positive and false negative by the model.  
\[F1-score=\frac{\left(2*(Precision*Recall)\right)}{Precision+Recall}. \tag{42}\]
## 3 Materials and Methods
### Dataset Descriptions
#### 3.1.2 Our Own Recorded SSMVEP Dataset  
Furthermore, an SSMVEP dataset was recorded in a confined lab using nine healthy experiment participants (eight male subjects and one female subject, between the ages of 25 and 35); all the experiment's subjects were recruited and randomly selected to facilitate the investigation. A Simulink model was interfaced with a gtec EEG recording system via MATLAB to enable the acquisition of our own recorded SSMVEP dataset. In this case, the EEG recording system consisted of a gNautilus headset and a gNautilus base station that established wireless communication between the two components. The gNautilus headset consisted of 16 electrodes and a transmission module that made it possible to transmit raw EEG signals to the Simulink model, which were then saved to the MATLAB workspace for further processing. All sixteen electrodes were dedicated only to EEG signal acquisition and positioned on the surface of the scalp according to a 10-20 positioning system. Therefore, the EEG signals were recorded at a sampling rate of 250 Hz. At the beginning of the experiment, the subjects were seated facing a projecting monitor, at which point visual cues were displayed on said computer monitor. Four objects corresponding to four SSMVEP classes were sequentially displayed on the monitor [27]. The objects were flickering at different frequencies (29 Hz, 13.3 Hz, 17 Hz, and 21 Hz) and also moving in four different directions (left, right, up, and down), equivalent to the four SSMVEP classes [4; 12]. Moreover, whenever an object was projected on the monitor, a beeping sound was used to notify the participants to focus their attention on the flickering object. Each SSMVEP class was recorded for 300 s, resulting in 7500 samples, as illustrated in Figure 3.
## 4 Uniaxial Tension Test Results and Analysis
### Influence of \(V_{f}\) on Strain and Stress  
All specimens with PVA fibers showed a good toughness performance. Even \(V_{\mathrm{f}}\) of the specimen was only 0.5%, its \(\varepsilon_{\mathrm{f}}\) could reach 0.77%. When the specimen F2 failed, its \(\varepsilon_{\mathrm{f}}\) reached up to 6.39%. As shown in Figure 10, \(\varepsilon_{\mathrm{f}}\) increased significantly with the increase of \(V_{\mathrm{f}}\). Unlike \(\varepsilon_{\mathrm{f}}\), \(\varepsilon_{\mathrm{c}}\) changed little when \(V_{\mathrm{f}}\) increased. The relation between \(\varepsilon_{\mathrm{c}}\) and \(V_{\mathrm{f}}\) exhibited an approximate horizontal line.  
As shown in Figure 11, both \(\sigma_{\mathrm{c}}\) and \(\sigma_{\mathrm{f}}\) increased clearly with the increase of \(V_{\mathrm{f}}\). But the growth of \(\sigma_{\mathrm{f}}\) was faster than the one of \(\sigma_{\mathrm{c}}\). After \(V_{\mathrm{f}}\) increased from 0.5% to 2%, \(\sigma_{\mathrm{f}}\) increased by 1.68, 2.01, and 2.87 times, while \(\sigma_{\mathrm{c}}\) increased by 1.48, 1.7, and 2.06 times. Because the small curve segment exactly represents the pullout or snap process of some PVA fibers, the more curve segments exhibits and the greater pulling capacity needs. So \(\sigma_{\mathrm{f}}\) of the specimen F2 was the biggest. \(\sigma_{\mathrm{f}}\) of the specimen F0.5 was the smallest.  
Figure 10: Influence of \(V_{\mathrm{f}}\) on tensile strain.  
Figure 9: Stress–strain curves under uniaxial tension.  
These above results regarding the crack patterns, stress-strain curves, influence of \(V_{\rm f}\) on the strain and stress of PVA-FRCM under uniaxial tension were consistent with the previous research results [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] which showed that remarkable tight-cracking, strain-hardening and super-high tough behaviors processed if PVA fibers were added into the matrix of cement-based composites.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System
### NEFOCAST's Service Center  
Measurements of SmartLNB power levels on both the forward and the return links are periodically collected by the NEFOCAST service center, which interacts with the satellite hub management system using a dedicated application programming interface (API). These data are processed to get the signal attenuation on the path between each SmartLNB and the satellite, which will be then translated into rain rate estimates. The NEFOCAST Service Center also stores data coming from the rain gauges of its network. It is possible to compare rain rates observed by the Smart LNBs with the values measured by the rain gauges.  
Data coming from the sensor network are further processed using a specific algorithm that generates real time rain field maps. These output data (both precipitation maps and rainfall estimated by ground stations) will be available to service integrators through a public API. It will then be possible to show them in end-user applications or integrate them in other more complex systems (e.g., weather forecast, logistics, business strategy engines, etc.). In both cases, these activities are developed by VASPs. In the context of the experimentation an existing alerting application, developed by one of the project partners, has been selected to be used for data presentation and alerting.