esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Data Analysis
### Experiment on Measuring the Pose of a Rigid Body with a Camera
#### 3.3.2 Comparison Experiment of Attitude Measurements  
Binocular cameras have great applications in the field of attitude measurement. A Zed binocular camera was thus used in a comparative experiment. The Zed camera was set to 720p mode with a resolution of 1280 \(\times\) 720 and a frame rate of 30 frames/s. The experiment took the middle frame of the turntable as the object, and the parameter settings of the turntable and high-speed camera were the same as those above. With the three-dimensional turntable in swing motion mode, we used a high-speed camera and a Zed camera to simultaneously record video frames of the dynamic changes of the turntable. The monocular, binocular, and turntable attitude measurements are shown in Figure 8.  
Figure 8 shows that the attitude angle dynamically measured using the monocular camera is consistent with the attitude angles measured using the Zed camera and three-dimensional turntable. Relative to the turntable measurements, the error in the attitude angle measured using the monocular camera was similar to the error in the attitude angle measured using the binocular camera, and the fluctuation was greater than that for the binocular camera. However, monocular cameras have better real-time performance and lower costs than binocular cameras.
## 3 Reliability-Based Topology Optimization
### General Formulation  
RBTO analysis can be interpreted as a problem that finds an optimal topology under probabilistic constraints so that it becomes reliable for these uncertainties. The general form of an RBTO problem is described as follows:  
\[\begin{split} Minimize\quad f(x_{i})\\ \text{subject to }P_{s}(Y)=P\big{[}G\big{(}x_{i},Y_{j}\big{)} \leq 0\big{]}\leq P_{t}\\ 0\leq x_{i}\leq 1\\ i=1,2,\ldots,N\ and\ j=1,2,\ldots,\text{M}\end{split} \tag{12}\]  
where \(Y_{j}\) is the \(j\)-th uncertain variable, \(P_{s}\) is the system's probability of success, \(P_{t}\) is the target probability of success, and \(G\) is the limit state function (performance function). Design variables \(x_{i}\) and M are the numbers of random variables. Applying the performance measure approach (PMA) to Equation (12) for the general case leads to  
\[\begin{split} Min/Max\ \ f(x_{i})\\ \text{subject to }G\big{(}x_{i},Y_{j}\big{)}\geq 0\\ \beta=\beta_{t}\\ 0\leq x_{i}\leq 1\\ i=1,2,\ldots,N\ and\ j=1,2,\ldots,\text{M}\end{split} \tag{13}\]  
where \(\beta_{t}\) is the target reliability index for success.
# Graph Dilated Network with Rejection Mechanism
## 6 Experiments
### Evaluation of Multiple Graph Dilated Convolution Kernels
#### 6.7.2 Evaluation of GraphDRej with the Same Size of Neighborhoods  
We also design another three variants of GraphDRej, denoted as GraphDRej-1-6, GraphDRej-2-3, and GraphDRej-3-2. The details of these versions can be found in Table 5. Generally speaking, although these three variants contain different numbers of layers and different kinds of graph convolution dilated kernels, all of these models capture the same size of neighborhoods, i.e., 6-hop-based neighborhoods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variants** & **GraphDRej-1** & **GraphDRej-2** & **GraphDRej-3** \\ \hline Accuracy & 76.38 & 79.61 & **79.70** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rejection mechanism vs. gate mechanism. We report the accuracy (%) of nodes classification with different numbers of layers on Cora.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variants** & **\#Layers** & \(\gamma\) & **\#Neighborhood** \\ \hline GraphDRej-1 & 2 & [1] & 2 \\ GraphDRej-2 & 2 & [1,2] & 4 \\ GraphDRej-3 & 2 & [1,2,3] & 6 \\ GraphDRej-1-6 & 6 & [1] & 6 \\ GraphDRej-2-3 & 3 & [1,2] & 6 \\ GraphDRej-3-2 & 2 & [1,2,3] & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The statistics for graphDRej variants.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method\textbackslash{}\#layers** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline GG-NN & 67.90 & 73.99 & 76.66 & 77.40 & 76.01 & 30.44 & 30.44 & 30.44 & 30.44 \\ GraphDRej & **73.52** & **78.04** & 79.43 & 79.61 & 80.17 & **80.44** & 80.26 & **79.70** & 78.60 & **80.17** \\ GraphDRej-Gate & 71.31 & 76.94 & **79.70** & **80.17** & **80.81** & **80.44** & **80.90** & 79.43 & **80.54** & 73.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rejection mechanism vs. gate mechanism. We report the accuracy (%) of nodes classification with different numbers of layers on Cora.  
The results are reported in Table 7. Although these models capture the same size of neighborhoods, GraphDRej-2-3 outperforms the other two variants significantly, and can better characterize the structure information. It demonstrates the effectiveness of graph dilated convolution kernels, and also shows a well-designed graph dilated convolution architecture is needed.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### Double Spending and Free Rider Attacks  
In this case, the attacker tries to listen to the message sent over the public channel and uses it during another time slot. The attacker can be an EV that has already received an amount of energy and would like to use the message to charge again without paying, i.e., double spending, or a free rider who aims to charge without being authenticated. The proposed system avoids this issue by using time stamps and revocation lists. Each time a hash key is sent to the charging segment, it has to validate \(t_{e}\) by calculating \(|t_{e}-t_{s}|\in\Delta t\), where \(\Delta t\) represents the permitted tolerance time. If the time \(t_{s}\) by which the segment \(s\) receives a key exceeds the limit, the message is invalid. Revocation lists, on the other hand, are particularly beneficial against free riders. This is because once an EV starts receiving energy from the CS, its corresponding pseudonym and session key are added to the revocation list of the charging segment. Accordingly, any EV attempting to reuse the same pseudonym or key would be detected and prevented from charging.
## 1 Introduction
Given the nature of the problem requirements, we use Multi-Objective Evolutionary Algorithm (MOEA) techniques to optimize the UAV node placement considering the following objectives and constraints:  
* Minimization of the number of UAVs needed to service the GN, while ensuring that the QoS requirements (here, measured as the physical data rate) are properly met. * Minimization of the degree of dissatisfaction regarding the required data rate. * The number of available UAVs is limited and must not be exceeded. * The inter-UAV links do not necessarily employ the same technology as GN-UAV links. Inter-UAV links are considered in an abstract way, but constrained to a maximum range. * It is assumed that the throughput values of the links between UAVs are high enough not to constrain end-to-end inter-GN traffic. Only GN-UAV links impose limits to the satisfaction of QoS requirements (end-to-end QoS shall be addressed in future work);
* GN-UAV links are orthogonal. This can be achieved, for example, by assigning different frequencies or orthogonal channel codes. The main contribution of this work is to provide a meta-heuristic algorithm, which takes into account more than one objective and multiple constraints to best position the UAVs. Additionally, to reduce the search space and accelerate the convergence of the algorithm, we propose the use of convex hull algorithms to delimit the total area to be covered into a sub-area inside the convex hull formed by the 2D positions of the GNs as explained later in this paper. The remainder of this paper is structured as follows: Section 2 presents the related work. In Section 3, the system model is presented. Section 4 presents the problem definition and formulation as a Multi-Objective Optimization Problem(MOP). Section 5 presents our MOAEA implementation. The simulation results are presented in Section 6. Section 7 presents the simulation results' discussion, and Section 8 concludes the paper.
# An Arch-Bridge Topology-Based Expressway Network Structure and Automatic Generation
## 1 Introduction
However, unlike urban roads, expressways have long road sections and wide spans, and small-sample floating cars cannot achieve full-domain coverage, resulting in a severe lack of data for some road sections. Therefore, there is an urgent need to develop a method that can quickly generate the entire domain road network topology of expressways. To address the aforementioned challenges, we conducted research on the ETC system in a province of China with a user penetration rate exceeding 80%, meaning that over 80% of the vehicles on the expressway were using the ETC service. Specifically, we proposed a novel arch-bridge topology-based expressway network structure. Furthermore, by conducting in-depth mining analysis of ETC data features and designing candidate topology optimization rules, we could achieve the rapid automatic generation of the entire domain road network topology of expressways. Theoretical analysis and experimental results show that our method has good robustness. In addition, based on the expressway network topology structure of the ETC system, real-time monitoring of all sample vehicle information can be achieved, making it possible to finetune the management of expressways. The main contributions of this study are as follows:  
1. We proposed a novel arch-bridge topology structure with ETC gantries as key nodes, which can simplify the complex road network topology to the smallest unit by redefining the topology of expressways. 2. Through deep mining and the analysis of ETC data characteristics, we designed excellent optimization rules for targeted anomalous topologies such as loop, reverse, missing, and opposite topologies. 3. The validity of the proposed method is verified by using real ETC transaction data with a user penetration rate of over 80%, which can achieve the effective and cost-effective generation of the entire domain of an expressway road network topology. The remaining parts of this work are organized as follows. In Section 2, we first review relevant research on road network topology generation. In Section 3, we provide relevant definitions. In Section 4, we introduce the candidate topology generation and optimization method. In Section 5, we conduct experiments and analyze the results. Finally, we give the conclusion of this work and look forward to future research.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing  
In order to verify the validity of the proposed method for a faulty rolling bearing vibration signal, the test data from the bearing database of Case Western Reserve University are selected for analysis. The experimental device is shown in Figure 9. At the output terminal of the motor, one acceleration transducer is vertically installed on the shell of a supporting bearing to collect data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sequence Number** & **NSVR** \\ \hline
1 & 6.254 \\
2 & 2.561 \\
3 & 2.033 \\
4 & 1.875 \\
5 & 1.262 \\
6 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ratios of neighboring singular values.  
Figure 8: The Hilbert envelope spectra of the extracted fault signal after kernel-independent component analysis (KICA).  
The rolling bearing used in the test is SKF6205. The motor speed is 1180 rpm, that is, the rotation frequency \(f_{r}=19.7\) Hz. The sampling frequency \(f_{s}=8192\) Hz and the number of sampling points is 8192. The inner and outer rings of the rolling bearing are respectively machined with tiny pitting pits of 0.28 mm in depth and 0.54 mm in diameter to simulate the faults of an inner ring and an outer ring. The fault frequency of the inner ring can be expressed as:  
\[f_{ic}=\frac{1}{2}N(1+\frac{d}{D}\cos\alpha)f_{r}. \tag{12}\]  
The fault frequency of the outer ring can be expressed as:  
\[f_{oc}=\frac{1}{2}N(1-\frac{d}{D}\cos\alpha)f_{r}, \tag{13}\]  
where \(N\) is the number of rollers, \(d\) is the diameter of the rollers, \(D\) is the pitch diameter of the bearing, and \(\alpha\) is the bearing's contact angle. According to Equations (12) and (13), \(f_{ic}=106\) Hz and \(f_{oc}=70\) Hz.  
The time-domain waveform and amplitude spectra of a healthy signal and faulty signal are shown in Figure 10.  
Figure 10: The time-domain waveform and amplitude spectra of a healthy signal and faulty signal. (**a**) The time-domain waveform of a healthy signal. (**b**) The amplitude spectra of a healthy signal. (**c**) The time-domain waveform of a faulty signal. (**d**) The amplitude spectra of a faulty signal.  
Figure 9: The test rig of a faulty rolling bearing.  
The fault features of the inner ring and outer ring cannot be found from the time-domain waveform and amplitude spectra in Figure 10. Therefore, the effectiveness of feature extraction via wavelets, EMD, VMD, EEMD and the proposed method are compared in the following section.
# A Survey of UV Filters Used in Sunscreen Cosmetics
## 4 Materials and Methods  
From October to December 2023, the INCI compositions of sunscreen products available online on the Polish market were collected. The survey involved 150 randomly chosen preparations with UV filters for adults and 50 for children. The adult products included 71 international brands, with one to eight products from each brand, while the child products were obtained from 33 international brands, with one to three products per brand.  
The searched UV filters were listed in Annex VI to Regulation (EC) No 1223/2009 of the European Parliament and Council of 30 November 2009 on cosmetic products.  
The collected data were processed using Microsoft Excel. The data collected from the INCI compositions of preparations are series of dichotomies that determine for each preparation and filter whether the filter is included in the preparation or not. There is no numerical measure here, such as concentration or dose. Quantitative analysis of such data encounters great difficulties, so only basic statistical analyses were performed on them.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling  
With the introduction of network models such as PointNet [6], PointNet++ [27], and DGCNN [28], irregular point clouds can be directly used for training. To benefit from this approach, the application of deep learning to point clouds has gradually become a popular research topic, and point cloud upsampling models with deep learning have also achieved a variety of results. Deep learning-based point cloud upsampling can be divided into supervised point cloud upsampling and unsupervised point cloud upsampling.
# The Role of a Simple Inerter in Seismic Base Isolation
###### Abstract  
The present study investigates the role of a simple inerter in supplemental devices for possible implementation in the mature seismic base isolation technique. Firstly, the response of the base-isolated structure with an optimally tuned mass damper inerter (TMDI) is investigated to see the tuning effects. The time required to tune the TMDI was found to be significantly longer than the duration of a strong-motion earthquake. There was still a reduction in the response of the isolated structure, which is primarily due to the added damping and stiffness (ADAS) of TMDI and not because of the tuning effects. Hence, it is proposed that the corresponding ADAS of the TMDI be directly added to the isolation device. Secondly, the response of the base-isolated structures to the fluid inerter damper (FID) is studied. It was observed that the inerter of the FID does not influence the displacement variance of an isolated structure under broadband earthquake excitation. It implies that the response of the isolated structure to FID is primarily controlled by its counterpart fluid damper (FD). The performance of optimal TMDI, ADAS, FID, and FD to mitigate the seismic response of the flexible multi-story base-isolated structure under real earthquake excitations is also investigated. In terms of suppressing the displacement and acceleration responses of the isolated structure, it has been found that TMDI and ADAS perform similarly. Comparing the response of the isolated structure with FID and FD demonstrated that the inerter in the FID has detrimental effects on the isolated structures, in which the top floor's acceleration and base shear are substantially increased.  
floor accelerations; fluid inerter damper; real earthquake; seismic base isolation; stationary excitation; tuned mass damper inerter
## 2 Materials and Methodology  
### Data and Variables  
#### 2.1.1 Data Description  
To validate our research, we sought out the widely recognized HighD dataset, which is an open-source dataset widely used in the field. The dataset has been extensively utilized for various studies, including car-following [29; 30], lane-changing [31], and trajectory prediction [32]. One of the key advantages of the HighD dataset is its high-quality data and diverse range of scenarios. The HighD dataset comprises post-processed trajectories of 110,000 cars and trucks extracted from drone video recordings captured during the years 2017 and 2018 on German highways. The recordings were conducted at six different locations (refer to Figure 1) along a road segment of approximately 420 m (refer to Figure 2). Each vehicle in the dataset has a median visibility duration of 13.6 s.  
Figure 1: The recording locations in the HighD dataset.  
The dataset provides valuable insights through two primary data files per recording. The first file contains statistical information on the driving behavior and attributes of all vehicles present during the recording period. This includes vehicle identifiers (which remain consistent within the data file), vehicle dimensions, types, and lane positions, as well as maximum acceleration and inter-vehicle distances during operation. Please refer to Table 3 for key indicators. The second data file contains detailed motion information for each vehicle, including speed, acceleration, distance to the preceding vehicle, and preceding vehicle identifiers, recorded at each frame. This information enables a comprehensive understanding of the real-time driving states of vehicles. Please refer to Table 3 for a detailed breakdown.  
In summary, the HighD dataset offers a comprehensive and reliable resource for studying highly automated driving systems. The HighD dataset's rich features and diverse scenarios make it a valuable asset for our research.
# Impact of VR Application in an Academic Context
## 2 Materials and Methods
### EduAssistant Experiential Learning Instrument Based on VR  
To accomplish the study's objectives, we designed a VR application using the following technologies: Unity, C#--.NET framework, Agora.io REST API, Steam VR, and Open XR. The application is called EduAssistant. We analyzed VR and PC-based technologies and found that VR provides a more immersive and engaging experience for the participant, which can improve their motivation and engagement with the activity [17; 18; 19; 20; 21]. [PERSON] et al. conducted a study in which participants were randomly assigned to one of three experimental conditions: a VR condition using an Oculus Rift headset, a desktop 3D, or a 2D condition using a standard computer monitor. The authors reported that participants in the VR condition had significantly higher accuracy and faster reaction times compared to the other conditions. Additionally, participants in the VR condition reported higher levels of presence and engagement with the task.  
To ensure the students were engaged with the immersive reality, hardware equipment was also required. Valve Index provided the hardware, which consists of a headgear with IPS (in-plane switching), fast switching, two base stations for motion monitoring, and two controllers for VR interaction. EduAssistant has the following key features: a virtual amphitheater, a whiteboard, a video conference system, and voice search recognition. The search feature is based on a dictionary customized for Workflow Management course (Politehnica University of Bucharest, Faculty of Automatic Control and Computer Science). This Workflow Management course provides the knowledge required for the development of the conceptual-theoretical and methodological framework of process analysis based on activity flows, as well as for the comparative analysis of the performances and costs offered by various iterations of activity planning for the realization of a specific business processes.  
Discrete event dynamic systems using Petri nets are studied in the course. The system workflow is reflected in Figure 2. In the modelling phase, prior to choosing the learning material for VR application, it was essential to hold discussions with expert teachers from Politehnica University of Bucharest and to actively participate in the Workflow Management course.
## Preliminary Investigations  
### Conceptual Considerations  
A fundamental requirement for our noise control system was the ability to suppress broadband noise. Although tonal noise is more disturbing than a broadband noise of equal power, the cancellation of only the tonal components in an office results in minor overall suppression. Broadband noise usually has significant power around the 1-kHz frequency where passive shielding is still not appropriate, so we have aimed to control the noise field up to 1 kHz.  
If random noise is to be suppressed, feedforward control is the most suitable solution [1]. Feedforward control needs reference signals correlated with the primary noise. If there are multiple noise sources, each source should be monitored by its own reference microphone. This principle can be maintained if a dedicated noise control is designed specifically for a given location. However, in our case, neither the position of the chair, nor the position of the primary sources, nor the room were known in advance. Nevertheless, high correlation between the error signal and the primary noise can also be ensured if the reference microphone is close to the error microphone. To retain the causality of the control filter, the direct sound from the primary source shall reach the reference microphone earlier than the error microphone. This requirement is not necessary for tonal disturbance control, but essential in the case of random noise. As the primary sources can appear in various positions in the room, more than one reference microphone is to be used, in order to cover the possible directions. Generalizing the idea, several reference microphones should surround the chair, allowing the suppression of the primary noise generated at any position in the room. Furthermore, this arrangement is capable of controlling multiple primary sources. This concept has been tested intensively by experiments, which are introduced below.  
Concerning the chair itself, the two loudspeakers have to be built into the headrest, similarly to other solutions; see, e.g., [6]. The main problem is the acquisition of the error signals. As previously mentioned, physical microphone-based sensing has been chosen. This solution allowsone to spend most of the computational capacity on the calculation of the adaptive control filters. As the users' ears are in different positions relative to the headrest, the positions of the error microphones have to be set individually. To this end, flexible gooseneck microphones are installed. Now, the size of the zones of quiet is left to be determined. As was cited in the Introduction, for diffuse sound fields, about a 10-dB reduction can be achieved inside a sphere of a diameter of one tenth of the wavelength. This diameter is 3.4 cm at 1 kHz, which can be accommodated by physically adjusting the microphone positions, without discomfort to the user. The zones of quiet have to be estimated for the direct sound field, as well; hence, simple simulations have been carried out, the results of which are also reported in this section.