esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we explore a Tx power-based communication constraint check that can filter remote attacks that aims to disseminate false position information to running vehicles. By using low-power beacons, vehicles can mutually check if the BSM hence the position information therein indeed comes from a physically close neighbor. At least, it would pressure an attacker to come close within the low-power transmission range of the victim vehicles to mount an effective attack. In case the attacker indeed comes in close range, the on-board hardware sensors such as radars, lidar, and cameras with a typically smaller range than V2X can kick in to validate the claimed position.  
Through extensive simulation, we demonstrated that there is value in using the low-power beacon exchanges between vehicles in preventing the harmful impacts from remote false position attacks through V2X communication. Specifically, we confirm that traffic efficiency and comfort of platooning may be decreased due to the remote attack. We show, however, that if we employ the low-power beaconing message check to platooning, we can successfully cope with forged message attacks and can overcome the problem. The additional bandwidth cost is small thanks to the low Tx power, and the Tx power reduction for the additional beacons is easily implementable within the current V2X standard frameworks.  
H.K. conceived and designed the experiments; T.K. performed the experiments and analyzed the data; H.K. wrote the paper. This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 19 CTAP-C151975-01). The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 4 Signal Models
Then, the frequency domain signals from FFT can be defined as \(R_{p,r_{f}}^{m}(k)\), and have the formulation of  
\[R_{p,r_{f}}^{(m)}(k)=\eta(\epsilon_{ RFO})H_{p,r_{f}}^{(m)}(k)S_{p,r_{f}}^{(m)}(k)e^{|2\pi\frac{\pi(N+N_{k}) \epsilon_{ RFO}}{N}}+\Omega(k), \tag{8}\]  
where \(H_{p,r_{f}}^{(m)}(k)\) and \(\Omega(k)\) denote the CFR and the noise of the \(k\)th subcarrier separately. \(\epsilon_{ RFO}\) is the normalized RFO. The constant amplitude and phase deviation caused by RFO are represented by \(\eta(\epsilon_{ RFO})\) which is illustrated in [32]. Considering that the low precise digital controlled crystal oscillators are used by NB-IoT terminals, the challenging problem is how to reduce the RFO to a tolerable scope of uplink transmission within the UTG.
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 3 Experimental Section  
### Experimental Materials and Equipment  
The raw materials were CG-98 hydrogel ultrasonic coupling agent (Hengcan, Jining, China), which consists of surfactants, rust inhibitors, softeners, and wetting agents; HG-99 glycerin ultrasonic coupling agent (Hengcan, Jining, China, ingredient: 95% glycerin); and a three-dimensional graphene structured powder (BET: 1810 m\({}^{2}\)/g, electric conductivity: \(1.0\times 10^{3}\) S m\({}^{-1}\), producer: Graphene Research Center of Guangxi University)  
The experimental equipment were two longitudinal wave sensors with central frequency of 5 MHz, RITEC-RAM-5000 snap-nonlinear high energy ultrasonic testing system, an oscilloscope (Tektronix MDO3022, Beaverton, OR, USA), a microscope (Axio Scope.A1 and Cam MRc 5, Oberkohen, Germany), a thermostatic magnetic stirrer (MYP11-2A), a specially processed ASTM 1020 steel specimen with surface roughness less than 5 \(\upmu\)m [21] and dimensions of 150 mm \(\times\) 30 mm \(\times\) 20 mm, four plastic sheets with thickness of 0.2 mm, a rubber head dropper with volume of 2 mL, and a precision pallet balance (DH-JCS).
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
## 2 Dynamics Modeling of AUV
### Definition of Perturbation of AUV Dynamics  
The dynamics equations of the AUV contain nonlinear terms. Nonlinear terms including underwater disturbances may cause a chattering phenomenon due to the large switching gain used to suppress the nonlinear dynamics when the AUV is controlled using an SMC. The SPO estimates the nonlinear terms of the underwater vehicle dynamics and compensates the estimated nonlinear terms to the control input of the SMC to reduce the  
Figure 1: Coordinate systems representation of AUV.  
chattering phenomenon. The summation of nonlinear terms in (1) to (6) and (9) can be defined as the perturbation terms of SPO, as shown in (11) to (19).  
\[\Psi_{u}=\left(X_{u|u|}^{\prime}u|u|-X_{U|U|}^{\prime}L|U|-(W-B)sin\theta\right) /\left(m-X_{u}^{\prime}\right)+d_{u}, \tag{11}\]  
\[\Psi_{v}=\left((W-B)cos\theta sin\phi-muv\right)/\left(m-Y_{\hat{v}}^{\prime} \right)+d_{v}, \tag{12}\]  
\[\Psi_{w}=\left((W-B)cos\theta cos\phi-muq\right)/\left(m-Z_{\hat{w}}^{\prime} \right)+d_{w}, \tag{13}\]  
\[\Psi_{p}=\left((z_{B}sin\phi-y_{B}cos\phi)Bcos\theta\right)/\left(I_{xx}-K_{ \hat{p}}^{\prime}\right)+d_{p}, \tag{14}\]  
\[\Psi_{q}=\left(x_{B}Bcos\theta cos\phi+z_{B}Bsin\theta\right)/\left(I_{yy}-M_{ \hat{q}}^{\prime}\right)+d_{q}, \tag{15}\]  
\[\Psi_{r}=\left(x_{B}Bcos\theta sin\phi+y_{B}Bsin\theta\right)/\left(I_{zz}-N_ {r}^{\prime}\right)+d_{r}. \tag{16}\]  
\[\Psi_{\phi}=qsin\phi tan\theta+rcos\phi tan\theta+d_{\phi}, \tag{17}\]  
\[\Psi_{\theta}=qcos\phi-rsin\phi+d_{\theta}, \tag{18}\]  
\[\Psi_{\phi}=qsin\phi/cos\theta-rcos\phi/cos\theta+d_{\phi}, \tag{19}\]  
where \(\mathbf{D}=\left[d_{u},d_{v},d_{w},d_{p},d_{q},d_{r},d_{\phi},d_{\phi},d_{\phi}\right]\) is the unknown disturbances of the underwater environment.
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
###### Abstract  
The packets carried along a pre-defined route in underwater sensor networks are very vulnerable. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate's location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS) and position random candidates selection (PRCS). PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.  
underwater sensor networks; opportunistic routing; candidates selection; candidate coordination 18 1293; doi:10.3390/s18041293
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Avoid-Leader-Zone  
The Avoid-Leader-Zone perceptual schema behavior produces a behavioral costmap that inserts a high-cost ring of a preset radius around a robot's leader. This ring creates a zone around the leader that the follower vehicles will not enter, in effect enforcing a following distance between the vehicles. To do this, the behavior takes the position of its leader as an input. If network communications are available, the position is provided wirelessly by the lead vehicle. If the convoy is under a jamming attack, the target goal \(\mathbf{T}\), as provided by the Maintain-Formation behavior is used instead. The costmap coordinates that comprise the ring are calculated using basic trigonometric functions to obtaining coordinates of a scaled unit circle with an offset center, as follows:  
\[x_{i}=floor\left(\frac{a}{f}coscos\ \left(\frac{i}{n}2\pi\right)\ \right)+l_{x}, \tag{5}\]  
\[y_{i}=floor\left(\frac{a}{f}sinsin\ \left(\frac{i}{n}2\pi\right)\ \right)+l_{y}, \tag{6}\]  
where \([x_{i},y_{i}]\) are costmap indices for each point in the ring; \(i=1,\,\ldots,\,n\) costmap cells, with \(n\) representing the number of total number costmap cells the ring should contain; \(a\) is the radius of the leader's following distance zone; \(f\) is the costmap cell resolution; and \([l_{x},l_{y}]\) is the costmap index of the leader. Figure 11 shows a robot and the leader zone produced by the Avoid-Leader-Zone behavior.  
Figure 10: Costmap inflation in the Avoid-Obstacle-Proximity behavior: (**a**) the costmap produced from LiDAR sensing a construction cone; (**b**) the costmap produced from creating a buffer zone of inflated costs around the construction cone.
## 3 Results
### DEM Calculation of the Test Areas Using the Modified Wedge Filtering Method  
For the calculation of the new filter method, the VB.NET program mentioned in the article \"Wedge-Filtering of Geomorphologic Terrestrial Laser Scan Data\" [14] was further developed.  
Figure 8a shows a screenshot of the VB.Net program, in which a small section of the test area view is seen from the side (blue arrow in Figure 7). The vegetation points are still in green colour. In Figure 8b, these vegetation points are no longer shown for the same section. The points, which are shown in brown colour, remain, which were classified as soil points in the filter process and give a homogeneous surface structure.  
Steps of Calculation  
* Calculate the horizontal and vertical angles as well as the length distances from the recording centre to every single point.
* Determine the average raster spacing of the horizontal-vertical angle grid.
* Eliminate measuring errors, as described in Section 2.3.1, Point (a). An angle of \(179^{\circ}\) was chosen as the threshold value.
* Reduce to 2.5D, as described in Section 2.3.1, Point (b). With this step, approximately 40% of the points are eliminated from the further calculation.
* Perform HOVE-Wedge Filtering as described in Section 2.3. This process is carried out iteratively, until no further point is eliminated. The wedge angles (see Section 2.3.3), horizontal and vertical angles of the HOVE-grid (see Section 2.3.1) and point density (points within a certain angular segment, see Section 2.3.2) have influence in Equation (8): \[\begin{split}\left((\theta_{wedge}-180)\times F+(\theta_{vert}-180 )\times F+(\theta_{hori}-180)\times F\right)\times-density>\lambda_{TLV}\\ \rightarrow\text{$classification$ as non-ground point}\end{split}\] (8) where \(\theta_{wedge}\) is the difference of wedge angle; \(\theta_{vert}\) is the difference of vertical angle from HOVE-Raster; \(\theta_{hori}\) is the difference of horizontal angle from HOVE-Raster; \(F\) is the weight factors; \(\lambda_{TLV}\) is the threshold limit value; and _density_ is the value of point-density from HOVE-Raster.  
As threshold limit value, we used 200. With the factors, it would be possible to weight the difference. In the calculation, all factors were set to one.  
Figure 8: Screenshot of the computer program with vegetation points (**a**); and screenshot of the computer program only with ground points (**b**).
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### COVID-19 Infection Detection Using Deep Learning
#### 3.3.3 Adam Optimizer  
The Adam optimizer, short for Adaptive Moment Estimation, is an efficient optimization algorithm widely utilized for classification-based problems in deep learning [36]. Itcombines the benefits of two other popular optimization techniques, namely \"RMSProp\" and \"gradient descent with momentum\" [35; 36].  
The core idea behind the Adam optimizer lies in estimating the first-order moments (mean) and second-order moments (uncentered variance) of the gradients. These moments, denoted as \(m_{t}\) and \(s_{t}\), respectively, are initialized as zeros and then updated during each iteration using exponential moving averages. Equations (3) and (4) illustrate the update rules for \(m_{t}\) and \(s_{t}\).  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})\frac{\delta L}{\delta w_{t}} \tag{3}\]  
\[s_{t}=\beta_{2}s_{t-1}+(1-\beta_{2})\bigg{(}\frac{\delta L}{\delta w_{t}} \bigg{)}^{2} \tag{4}\]  
Here, \(\beta_{1}\) and \(\beta_{2}\) are the decay rates of the gradients, typically set to \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). These decay rates control the exponential moving averages and help the optimizer to remain regulated and unbiased.  
To mitigate the bias introduced by the initialization of \(m_{t}\) and \(s_{t}\) as zeros, bias-corrected estimates \(\hat{m_{t}}\) and \(\hat{s_{t}}\) are computed as shown in Equations (5) and (6).  
\[\hat{m_{t}}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{5}\]  
\[\hat{s_{t}}=\frac{s_{t}}{1-\beta_{2}^{t}} \tag{6}\]  
With the bias-corrected estimates, the Adam optimizer updates the model's weights using the general equation presented in Equation (7):  
\[w_{t}=w_{t-1}-\eta\left(\frac{\hat{m_{t}}}{\sqrt{s_{t}}+\epsilon}\right) \tag{7}\]  
where \(w_{t}\) denotes the weights at a given point in time \(t\); \(w_{t-1}\) denotes the weights at a given point in time \(t-1\); \(\eta\) represents the learning rate, typically set to 0.001; and \(\epsilon\) is a small constant (e.g., \(10^{-8}\)) to avoid division by zero when \(\hat{s_{t}}\) approaches zero.  
The Adam optimizer efficiently combines the benefits of RMSProp and gradient descent with momentum, leading to faster convergence and better generalization in various deep learning tasks, including COVID-19 detection from chest X-ray images.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 1 Introduction
[16] used the symmetry relationship between mirror projection images with a 180-degree difference to construct a cost function for correcting the deflection angle and lateral offset of the axis of rotation. In 2013, [PERSON] et al. [17] corrected the geometric parameters of the cone beam CT system using projection and symmetry. This method proves that the sum of the projection images centered on the axis of rotation is symmetrical, and the geometric parameters of the five systems are corrected by minimizing the symmetry error of the projection and the rotation. In 2018, [PERSON] et al. [18] proposed a self-calibration method for geometric parameters based on the immovability of the rotating axis. This method uses the center-row and center-column of the symmetrical projection image to construct a similarity function to achieve geometric parameter correction. In 2019, [PERSON] et al. [19] proposed a method based on sinogram symmetry to determine parameters by calculating the minimum value of the variance between the left-half of the virtual detector and the right-half of the projected data sum. These methods realize parameter calibration based on projected image and have low computational complexity. However, the direct use of the projection image grayscale value for geometric parameter calculation is susceptible to noise and cone angle of the cone beam, which reduces calculation accuracy. In this study, a method based on projected feature matching is proposed to achieve geometric parameter self-calibration. Firstly, the feature-point-based image alignment algorithm in feature matching is advantageously utilized to realize the mirror projection image for feature-point extraction and matching. Secondly, the feature triangles are designed based on the immovable position of the rotation axis to realize fine screening of the feature-point pairs and reduce the impact of mismatched points on the calculation accuracy. Third, the random sample consistency algorithm (RANSAC) [20] is used to fit the position of the rotation axis. Finally, calibration of the lateral offset of the rotation axis and the deflection angle of the rotation axis is achieved. Simulations and actual data show that the method achieves good results in calibrating geometric parameters and can effectively eliminate the blurring and ghosting caused by geometric artifacts. The rest of this paper is organized as follows. The geometric model of the nanodomography and the proposed method are introduced in detail in Section 2. In Section 3,the feasibility and accuracy of the method are verified by simulation and nanotomography experimentation and compared with other methods. Finally, concluding remarks are provided in Section 4.
## 5 Results and Discussions  
### Experimental Evaluation  
In order to evaluate the model's performance and the benchmark models, we consider the following five indicators in our experiment: accuracy, recall rate, F1 score and the AUC. Each indicator's best results are highlighted in bold. Tables 2-5 show the results of the experiment.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Cardio** & **Credit Card** & **Satellite** & **MUSK** & **Arrhythmia** \\ \hline PCA & 0.886 & 0.831 & 0.683 & 0.892 & 0.808 \\ OC-SVM & 0.927 & 0.811 & 0.710 & 0.90 & 0.809 \\ ABOD & 0.923 & 0.907 & 0.745 & 0.735 & 0.647 \\ HBOS & 0.854 & 0.862 & 0.746 & 0.716 & 0.698 \\ GAN & 0.697 & 0.659 & 0.427 & 0.796 & 0.656 \\ MOGAAL & 0.730 & 0.734 & 0.797 & 0.814 & 0.702 \\ DSEBM & 0.837 & 0.883 & 0.705 & 0.899 & 0.773 \\ AE & 0.798 & 0.867 & 0.764 & 0.931 & 0.816 \\ VAE & 0.803 & 0.901 & 0.766 & 0.969 & 0.895 \\ VAEGA & 0.851 & 0.950 & 0.792 & 0.961 & 0.879 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy results of outlier detection for 10 models on 5 datasets.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Cardio** & **Credit Card** & **Satellite** & **MUSK** & **Arrhythmia** \\ \hline PCA & 0.832 & 0.892 & 0.675 & 0.881 & 0.805 \\ OC-SVM & 0.975 & 0.878 & 0.893 & 0.866 & 0.808 \\ ABOD & 0.948 & 0.562 & 0.972 & 0.726 & 0.801 \\ HBOS & 0.899 & 0.913 & 0.895 & 0.869 & 0.847 \\ GAN & 0.618 & 0.752 & 0.776 & 0.767 & 0.776 \\ MOGAAL & 0.792 & 0.854 & 0.971 & 0.880 & 0.854 \\ DSEBM & 0.942 & 0.84 & 0.6375 & 0.847 & 0.762 \\ AE & 0.840 & 0.922 & 0.950 & 0.922 & 0.812 \\ VAE & 0.840 & 0.958 & 0.962 & 0.933 & 0.871 \\ VAEGA & 0.966 & 0.966 & 0.970 & 0.957 & 0.863 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC of 10 models for outlier detection on 5 datasets.  
In order to determine the capability of searching abnormal subspace using the trained GA layer based on the outlier detection model, we compare it to the ground truth for the outliers detected by the outlier detection model. Obtaining the ground truth requires traversing all low-dimensional subspaces in the dataset and sorting the outlier degrees. Outlier degrees correspond to accurate abnormal subspaces and are given by the outlier degree in the top 1. In Table 5, a subspace in the ground truth of abnormal subspaces is shown. Table 5 shows the three-dimensional subspace component [V4, V5, V10]. A relatively high outlier degree in the data with serial number of 86 suggests an abnormality may exist in the V4, V5, and V10 feature subspace. In Table 6, a comparison of the accuracy is made between the ground truth and the abnormal subspaces of the outliers set searched by the genetic algorithm.
# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 4 The Experimental Investigation
### Preparation of Tattoo Dataset  
The analysis of related works has shown that the availability of publicly available tattoo datasets is not very high. In some of the studies, several authors have created their datasets, but the datasets are not publicly available. Additionally, as mentioned, the Tatt-C dataset was frequently used in related studies, but this dataset has been discontinued and can no longer be downloaded. Other equally popular datasets, such as the NTU Tattoo dataset and WebTattoo, require a special agreement to be used for research purposes, which has not been obtained. Therefore, in this paper, only one publicly available deMSI ([PERSON] et al., 2016 [27]) dataset was chosen for model training. The sample of the dataset is presented in Figure 2.  
Figure 1: Main principle of automated suspect identification model development based on tattoo photos.  
Figure 2: Sample of the deMSI tattoo dataset used in the research.  
To facilitate the development and testing of their proposed method, the authors have assembled their dataset by collecting and manually labeling 450 tattoo images from the ImageNet database. Each of the collected images contains one or more tattoos. The authors of this dataset used the ConvNet model in their research and, therefore, annotated each tattoo using a series of connected line segments. However, in the case of this study, such an annotation will not be suitable for our research because the chosen object detection model for tattoo detection is YOLOv5. The chosen model accepts bounding box annotations for each object, where each object in an image is surrounded by a rectangular box that can be described by the coordinates of its top-left corner and its width and height. Therefore, the images in the dataset have been manually annotated. In this study, a dataset was created that contained a total of 1000 images.
# Chaotic Orthogonal Composite Sequence for 5G NR Time Service Signal Capture Algorithm
## 1 Introduction
The final simulation results show that this combination spread spectrum sequence can be effectively applied to the parallel FFT capture algorithm, significantly optimizing the number of searches and capture duration while successfully capturing the 5G NR time service signal. The 5G NR Time Service Signal plays a crucial role in modern communication systems and numerous other fields. It provides a precise time synchronization reference, which is particularly crucial for determining the location of positioning devices, especially in Time Difference of Arrival (TDOA) positioning technologies. In 5G networks, precise timing helps optimize the utilization of network resources, enhance data transmission efficiency, and reduce potential data conflicts. For critical infrastructure such as power grids and traffic control systems, precise timing is fundamental to ensuring stable system operation. In the realm of smart manufacturing and industrial automation, precise timing enables the synchronization of robots, sensors, and other automated devices, thereby improving production efficiency and reliability. Industries like finance and network security also benefit from precise timing, as it aids in preventing fraudulent activities, ensuring transaction orderliness, and integrity. In emergency situations such as natural disasters or security incidents, precise timing plays a crucial role in enabling rapid responses and effective resource coordination. The 5G NR Time Service Signal is one of the key technologies unlocking the full potential of 5G networks, significantly enhancing communication network performance and providing robust support for digital transformation across various sectors, thereby driving technological advancement and innovation throughout society.