esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Types of Cyber-Physical Attacks
#### 3.2.1 Zero-Day Attacks  
The term \"zero-day attack\" refers to a cyber-physical attack that exploits a security vulnerability that has not yet been disclosed publicly [32]. Since such a vulnerability has not been disclosed publicly, there is a high probability that knowledge thereof is only possessed by a selected few individuals who have somehow managed to find such a vulnerability. Although this could mean that the chances of an immediate serious threat are not as high, the probability is equally high that cyber security entities are also not aware of the vulnerability. This means that until such a vulnerability is publicly disclosed (unless it is disclosed privately through confidential channels), there is little to no chance of developing a security patch that would eliminate it. Historic cases indicate that users of commercially popular software, such as Adobe Reader, WinRAR [14] and Microsoft Word, are particularly vulnerable to zero-day attacks without even having the slightest notion that they can easily fall victim to a threat. Consequently, by the time it is disclosed, it could already be too late to rectify any malicious activity executed through such an attack [3; 8; 32].  
The implications of such an attack on an industrial scale could be disastrous to such an industry, particularly if it involves a production line being compromised and the malicious software resisting any attempt to install a security patch that would disable it.
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 5 Sensor Technology in Construction Safety
### Workforce Behaviour Monitoring  
In most cases, the worker's actions are the direct cause of the accident [60], and the identification and prevention of unsafe acts can avoid most of the casualties. Therefore, the digitisation of workers cannot stop at positioning. A digital twin needs to synchronise the behaviour of its physical twin, which results in motion monitoring being crucial. The mainstream method used for automatic action monitoring is extracting the 3D skeleton from images or videos [61]. By comparing the motion of the skeleton with pre-set motion templates, unsafe behaviours can be identified in real-time. The use of 3D cameras has been proven to be accurate and timely for detecting unsafe behaviour in climbing ladders [62] and traversing structural supports [63].  
In addition to the action of workers, a large number of construction injuries are caused by inadequate use of PPE [64]. Based on support vector machine (SVM), [PERSON] et al. [65] developed a method to determine the spatial positions of workers and hard hats through images captured by on-site cameras. By matching the geometric and spatial relationships between the workers and hard hats, individuals without a hard hat can be automatically identified. In terms of working at height, harnesses and anchoring are prescribed as necessities, but people still violate the rules. [PERSON] et al. [66] and [PERSON] et al. [67] detected the implementation of safety prevention measures during aerial work with CNN and regions with CNN (R-CNN). These deep learning algorithms provide an innovative method to extract hidden information from data, facilitating the realisation of the DT concept. The automated detection method proposed by researchers not only reduces labour costs but also improves the scope and accuracy of incorrect PPE usage to assist managers during safety inspections.  
Other forms of unsafe behaviour, such as working under the influence of alcohol or under high fatigue, also negatively affect construction safety as they are mainly manifested in physiological status. Therefore, the use of sensors for physiological condition monitoring was studied [68]. Balance detection tools integrated with IMUs and fuzzy set theory have been developed to detect the balance performance of workers [69]. [PERSON] et al. [70] and [PERSON] et al. [71] introduced an electroencephalogram (EEG) to monitor the stress levels of workers. These monitoring methods provide more detailed data for digital replicas of workers so that a digital twin is able to accurately simulate the physical twin.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## 3 Experiments
### Simulation Experiment
Each scheme with distinct groups was replicated 10 times, and the resulting efficiency statistics are displayed in Tables 2 and 3 as well as Figures 5 and 6. Additionally, in Table 2, we also compare the ground-truth values \(\bigl{(}R_{gt},T_{gt}\bigr{)}\) and the estimated transformation parameters \(\bigl{(}\hat{\mathbf{R}},\hat{\mathbf{T}}\bigr{)}\) through Equations (29) and (30) to get the rotation error \(e_{R}\) and the translation error \(e_{T}\)[71]. For all figures below, the label \"BS\" represents the batch solution results; \"G2\"\(\sim\)\"G200\" represents group segments from 2 to 200, respectively. \[e_{R}=\arccos\Bigl{(}\Bigl{(}\operatorname{tr}\Bigl{(}\mathbf{R}_{gt}\hat{\mathbf{R}}^ {\top}\Bigr{)}-1\Bigr{)}/2\Bigr{)} \tag{29}\]  
\[e_{T}=\|T_{gt}-\hat{\mathbf{T}}\| \tag{30}\]  
Figure 4: Stanford point cloud dataset visualized registration results. (**a**) Bunny based on Batch solution; (**b**) Monkey based on Batch solution; (**c**) Dragon based on Batch solution; (**d**) Bunny based on HiGoReg method; (**e**) Monkey based on HiGoReg method; (**f**) Dragon based on HiGoReg method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Schemes**} & \multirow{2}{*}{**Voxel Grid (m)**} & \multirow{2}{*}{**RMSE (m)**} & \multicolumn{3}{c}{**Errors**} \\ \cline{5-7}  & & & & & \(e_{R}\) **(rad)** & \(e_{T}\) **(m)** \\ \hline \multirow{2}{*}{Bunny} & Batch & 0.001 & \(9.5738\times 10^{-13}\) & \(3.9633\times 10^{-8}\) & 0 & \(7.7008\times 10^{-8}\) \\  & HiGoReg & 0.001 & \(9.5738\times 10^{-13}\) & \(3.9633\times 10^{-8}\) & 0 & \(7.7008\times 10^{-8}\) \\ \hline \multirow{2}{*}{Monkey} & Batch & 0.001 & \(4.3130\times 10^{-7}\) & \(2.0391\times 10^{-8}\) & 0 & \(1.0277\times 10^{-7}\) \\  & HiGoReg & 0.001 & \(4.3130\times 10^{-7}\) & \(2.0391\times 10^{-8}\) & 0 & \(1.0277\times 10^{-7}\) \\ \hline \multirow{2}{*}{Dragon} & Batch & 0.001 & \(3.8049\times 10^{-8}\) & \(8.5133\times 10^{-6}\) & 0 & \(6.3609\times 10^{-8}\) \\  & HiGoReg & 0.001 & \(3.8049\times 10^{-8}\) & \(8.5133\times 10^{-6}\) & 0 & \(6.3609\times 10^{-8}\) \\ \hline \multirow{2}{*}{Large-scale} & Batch & 0 & \(4.0863\times 10^{-6}\) & \(7.4580\times 10^{-4}\) & \(3.2452\times 10^{-5}\) & 0 \\  & HiGoReg & 0 & \(4.0863\times 10^{-6}\) & \(7.4580\times 10^{-4}\) & \(3.2452\times 10^{-5}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated point cloud dataset registration results. Figure 5: Statistics on computing time cost of Stanford point cloud datasets under equal weighting. (**a**) Bunny; (**b**) Monkey; (**c**) Dragon. “Running index” denotes the index of execution counts, where each execution sequentially completed the batch solving process and HiGoReg algorithm workflows under different groups, with a total of 10 executions.
# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 1 Introduction  
The goal of path planning is to provide a path that is safe from obstacle collisions and to guide any object through the most appropriate path that has the shortest distance [1; 2; 3; 4; 5]. To accomplish this goal, a robot must collect surrounding data to generate an environment map.  
An environment map refers to a map that features data on the surroundings in which a mobile robot stands. Although a robot uses sensors to generate an environment map, the data acquired through these sensors are always affected by elements of the surrounding environment. For this reason, such data always contain inaccuracies. Therefore, these inaccuracies in the data must be considered when creating environment maps.  
Sensors that are often used to detect distances from obstacles include ultrasound sensors [6], laser sensors [7; 8], etc. Among these, ultrasound sensors use the elapsed time it takes for a sound wave to come back to the sensor after hitting the obstacle to detect distance. However, because ultrasound sensors use sound as a medium, the measurable distance is short, and detecting distance is difficult if the direction of the obstacle and the sound wave do not align vertically. Laser sensors measure an object's distance away from obstacles using lasers [9; 10]. The properties of laser sensors include their ability to direct straight beams, they are capable of measuring long distances, and they are more accurate compared to other sensors [11]. However, due to these properties, a drawback of laser sensors is that they cannot accurately detect transparent obstacles. Therefore, a mobile robot misoperates in an environment that includes transparent obstacles [6; 12].  
When a laser beam meets a transparent obstacle, as shown in Figure 1, reflection, refraction, and penetration occur, and they cause the inaccurate detection of transparent obstacles.  
The reflection noise that occurs at the opposite side of the transparent obstacle could be found from the results of measuring an object's distance away from the transparent obstacle using an actual laser range finder (LRF). Accordingly, in this paper, a state-of-the-art algorithm that can detect the boundaries of transparent obstacles from reflection data obtained solely by the LRF has been proposed.
## 3 Results and Discussion
### Extraction and Analysis of Streak Structures
According to turbulence statistical theory, the position corresponding to the first peak in the correlation function when \(\Delta z^{+}=0\) can be regarded as the spanwise average spacing of the streak structures. Figure 10 presents the autocorrelation function curves of the streamwise fluctuating velocity along the spanwise direction for the four types of walls under two flow fields, and the spanwise spacing of the streak structures for each type of wall is obtained and marked in the figure. In the clean water flow field, the dimensionless spanwise spacings for the 30\({}^{\circ}\) convergent riblet, smooth flat plate, streamwise riblet, and 30\({}^{\circ}\) divergent riblet are 62.01, 66.31, 70.45, and 87.97, respectively. In the liquid-solid two-phase flow field, the spacings are 83.84, 86.04, 90.02, and 103.73, respectively. These data match the previous analysis made on the streak structures well, indicating that both drag-reduction riblet walls and the particulate phase effectively suppress the motion of the streak structures in the spanwise direction near the wall.
## 3 Results and Discussion
### Electronic Nose Detection
#### 3.1.3 Dimensionality Reduction of Data Based on PCA Algorithm
(**a**) Gas samples from oysters in an environment of 4 \({}^{\circ}\)C. (**b**) Gas samples of oysters in an environment of 12 \({}^{\circ}\)C. (**c**) Gas samples of oysters in an environment of 20 \({}^{\circ}\)C. (**d**) Gas samples of oysters in an environment of 28 \({}^{\circ}\)C.  
At 28 \({}^{\circ}\)C, the within-class distances in the scatter plots for the first two days are large and exhibit significant variation, suggesting that the amplitude of volatile gas changes in oyster samples is greater during this initial period. It can be observed that as the temperature increases, the amplitude of gas variation in the oyster sample also increases. To achieve more accurate test results, an alternative method should be employed to analyze the oyster data.
## 2 Ad hoc On-Demand Distance Vector Routing and Hotline Based Routing
### Hotline-Based Reliable Routing  
Recent research in WSNs to increase their reliability and life time, has given birth to many new and efficient ideas, and one such idea is the hotline-based reliable routing mechanism. It is well suited for real-time applications such as intrusion detection and battlefield surveillance applications, _etc._ The best case assumption for such a network is that the event should be detected with the help of just one packet transmission. To make such a packet driven reliability we use the concept of high-reliability hotline links between sensor gateways. In a large WSN the numbers of gateways is far less than the number of nodes, by using the concept of hotline-connected gateways, reliable and energy efficient routing in WSNs is possible [5].
###### Abstract  
A multiband coplanar waveguide (CPW)-fed antenna loaded with metamaterial unit cell for GSM900, WLAN, LTE-A, and 5G Wi-Fi applications is presented in this paper. The proposed metamaterial structure is a combination of various symmetric split-ring resonators (SSRR) and its characteristics were investigated for two major axes directions at (x and y-axis) wave propagation through the material. For x-axis wave propagation, it indicates a wide range of negative refractive index in the frequency span of 2-8.5 GHz. For y-axis wave propagation, it shows more than 2 GHz bandwidth of near-zero refractive index (NZRI) property. Two categories of the proposed metamaterial plane were applied to enhance the bandwidth and gain. The measured reflection coefficient (S11) demonstrated significant bandwidths increase at the upper bands by 4.92-6.49 GHz and 3.251-4.324 GHz, considered as a rise of 71.4% and 168%, respectively, against the proposed antenna without using metamaterial. Besides being high bandwidth achieving, the proposed antenna radiates bi-directionally with 95% as the maximum radiation efficiency. Moreover, the maximum measured gain reaches 6.74 dBi by a 92.57% improvement compared with the antenna without using metamaterial. The simulation and measurement results of the proposed antenna show good agreement.  
Keywords: coplanar waveguide (CPW) antenna; near-zero refractive index (NZRI); wideband; DNG metamaterial; multiband +
Footnote †: journal: Sensors
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 3 A Vertex-Cover-Based Approach
### Index Construction
#### 3.2.1 Vertex Cover Computation
The cost of initializing the hash table, \(H\), in line 2 is also \(O(|V|)\), due to sequentially scanning \(A\) once. Since the cost of each iteration to process a vertex in lines 4-15 is \(|out_{G}(u)|+|in_{G}(u)|\), for all the vertices, the time cost is \(\sum_{u\in V}\!|out_{G}(u)|+|in_{G}(u)|=2|E|\). Hence, for Algorithm 1, the time complexity is \(O(|V|+|E|)\). It is worth noting that _kReach_[10] also computes a vertex cover in linear time \(O(|V|+|E|)\). Here, we give an intuitive comparison between the two approaches. The common feature is that they both can obtain a vertex cover in linear time \(O(|V|+|E|)\) and that the result of both the approaches is a two-approximate minimum vertex cover. The difference lies in two aspects. First, as shown in Example 2, the size of the vertex cover, \(S\), of _kReach_ could be large in practice due to the fact that it randomly selects an edge, rather than a vertex, in each iteration. We see in Section 4 that our approach usually generates a vertex cover much smaller than that of _kReach_. Second, as pointed by [16], processing vertices in descending order with respect to their degrees is usually a better choice to generate an index with a smaller size; however, _kReach_ cannot guarantee that the vertices in \(S\) always have a larger degree. For example, for \(G\) in Figure 1 and for \(S=\{a,b,c,d,e,g,i,j\}\), we know that \(j\in S\) and that the degree of \(j\) is one. As a comparison, \(f\
otin S\), but the degree of \(f\) is two, which is greater than that of \(j\).
## 3 Obstacle Avoidance Model by Harmonic Potential Field with Curvature Constraint
### Pure Pursuit Controller for Mobile Robots
Curvature then could be further related to the robot's velocity and angular velocity. Therefore, the angular velocity of a robot moves along a path at a constant forward speed \(U\) could be computed from the path curvature via the following relation (Equation (12)):  
\[\kappa=\frac{1}{r_{c}}=\frac{\omega}{U}=\frac{\Delta\theta}{U\cdot\Delta t} \tag{13}\]  
where \(\kappa=\mathrm{y}_{g,Robot}/L^{2}\)[20]. To satisfy the maximum allowable curvature, we regularize the signed curvature as  
\[\kappa_{constraints}=Sign(\kappa)\cdot\kappa_{\max}\quad if\quad\kappa>\kappa_ {\max} \tag{14}\]  
Thus, the motion in the local frame of the robot can be applied to command the motion controller via the inverse kinematics of Equation (1). The displacement \(\Delta\mathbf{X}_{g}\) in the global frame can be derived by exploiting the displacement \(\Delta\mathbf{x}_{Robot}\) in the local frame  
\[\Delta\mathbf{X}_{g}=\mathrm{R}(\theta+\Delta\theta)\Delta\mathbf{x}_{Robot} \tag{15}\]  
A pure pursuit algorithm summarizing this subsection is shown in Algorithm 2. ```
Input: robot initial pose, target streamline path, look-ahead distance, maximum allowable curvature Output: status, pursuit path
```  
**While (not timeout or status) do**  
let \(\mathrm{R}(\theta)\) represent the transformation to robot coordinate  
pClosest-- {(x,y)! min[! (x,y) -poseRobot! ] and (x,y) in  
pursuitpath} \(\mathbf{X}_{g}\)\(\leftarrow\) {(x,y)! min[! (x,y) - pClosest! ] and (x,y) in pursuit path after pClosest} \(\mathbf{x}_{g,Robot}\)\(\leftarrow\) \(\mathrm{R}(\theta)\) (\(\mathbf{X}_{g}-\mathbf{X}_{Robot}\)) (9)  
Calculate the curvature \(\kappa\)\(\leftarrow\)\(\mathrm{y}_{g,Robot}/L^{2}\)  
Regularize the curvature constraints (Equation (14))  
Set the steering angle of the robot (Equation (13))  
Update robot's heading direction, poseRobot  
if poseRobot and curvature == streamline path do  status\(\leftarrow\)TRUE  store path into pathArray if collide with obstacle do  status\(\leftarrow\)FALSE endwhile ```  
**Algorithm 2** The pure pursuit streamline path.
## 3 Results and Discussion  
### Performance of the Perovskite Solar Cell under Initial Conditions  
Prior to the optimization of each factor affecting the efficiency of PSCs, the initial conditions for the PSC device were set: an ETL TiO\({}_{2}\) layer thickness of 100 nm, a PAL MAPbI\({}_{3}\) layer thickness of 600 nm, and an HTL Cu\({}_{2}\)O layer thickness of 400 nm. Figure 2 shows the current density-voltage (\(J\)-\(V\)) graph of the initial PSC. The maximum optical conversion efficiency was calculated using Equations (6) and (7) [60] while considering the initial conditions as 13.79%.  
\[FF=\frac{V_{max}J_{max}}{V_{\rm{ac}}J_{sc}} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **TiO\({}_{2}\)**[55; 56] & **MAPbI\({}_{3}\)**[57] & **Cu\({}_{2}\)O**[58] \\ \hline Thickness (nm) & 100 & 600 & 400 \\ Bandgap (eV) & 3.26 & 1.55 & 2.17 \\ Electron affinity (eV) & 4.2 & 3.9 & 3.2 \\ Dielectric permittivity (relative) & 10 & 32 & 6.6 \\ CB effective density of states (cm\({}^{-3}\)) & \(2.2\times 10^{18}\) & \(2.8\times 10^{20}\) & \(2.5\times 10^{20}\) \\ VB effective density of states (cm\({}^{-3}\)) & \(1.8\times 10^{18}\) & \(3.9\times 10^{20}\) & \(2.5\times 10^{20}\) \\ Electron mobility (cm\({}^{2}\)/Vs) & 20 & 11.8 & 80 \\ Hole mobility (cm\({}^{2}\)/Vs) & 10 & 11.8 & 80 \\ Shallow uniform acceptor density \(N_{A}\) (cm\({}^{-3}\)) & 0 & \(1\times 10^{13}\) & \(3\times 10^{18}\) \\ Shallow uniform donor density \(N_{D}\) (cm\({}^{-3}\)) & \(1\times 10^{17}\) & \(1\times 10^{13}\) & 0 \\ Defect density \(N_{t}\) (cm\({}^{-3}\)) & \(1\times 10^{15}\) & \(3\times 10^{14}\) & \(1\times 10^{15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The initial physical parameters of TiO\({}_{2}\), MAPbI\({}_{3}\), and Cu\({}_{2}\)O used in the ITO/TiO\({}_{2}\)/MAPbI\({}_{3}\)/Cu\({}_{2}\)O/Au PSC.  
Figure 1: (**a**) Basic structure of an all-inorganic multi-layer perovskite solar cell (PSC); and (**b**) the energy band alignment of an all-inorganic PSC.  
\[PCE=\frac{V_{oc}J_{sc}FF}{P_{in}} \tag{7}\]  
Here, \(FF\) is the fill factor, \(V_{max}\) is the maximum voltage, \(J_{max}\) is the maximum current density, \(V_{oc}\) is the open-circuit voltage, \(J_{sc}\) is the short-circuit current density, \(PCE\) is the power conversion efficiency, and \(P_{in}\) is the incident power of the sun (Table 2).
# Enhanced Path Planning by Repositioning the Starting Point +
### Motivation and Contribution
* Geometric Median Optimization Discovery: Identification of the geometric median approach as the optimal solution for starting point repositioning, achieving identical results to brute-force methods while maintaining computational efficiency. * Comprehensive Experimental Validation: Execution of extensive testing across 520,000 scenarios (65,000 unique configurations with multiple approaches), demonstrating statistically significant improvements ranging from 4% to 22% in total distance reduction. * Performance Metric Development: Creation of starting point distance (SPD) and Starting Point Factor (SPF) metrics to quantify the impact of starting point optimization on overall mission efficiency. Scalability Analysis: Systematic evaluation of optimization effectiveness across varying numbers of drones (2-10) and stations (5-100), revealing predictable performance patterns for mission planning applications. * Simulation Framework: Development of a graph-based framework modeling flight planning using mTSP to identify distance-minimizing solutions. The remainder of this paper is structured as follows: Section 2 examines related work on the mTSP and its variants, the local search algorithm and the search operators used in the current implementation. Section 3 presents the proposed framework, while Section 4 discusses the experimental setup, results, and evaluation methodologies. Finally, Section 5 provides concluding remarks and future research directions.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 3 Cross-Layer CS-MAC Design
### Compressive Complex Requests during Wireless Transmission
The process continues that the second node sends \(x_{2}\) to the destination to calculate \(h_{12}\) and \(h_{22}\). Then, the third and the fourth node send \(x_{3}\) and \(x_{4}\) in the same way until all elements in the measurement matrix \(\mathbf{H}_{2\times 4}\) are obtained. Under the condition that the sensor nodes are moving, the channel matrix can still be considered the measurement matrix. Because the channel coefficients from the sensor nodes to the \(M\) antennas of the destination are random and independent, it matches the restricted isometry property (RIP) condition. However, the obtained channel matrix might be a little different from the exact measurement matrix. Therefore, to avoid the possible error in the measurement matrix by the moving the sensors, the training sequence need to be transmitted frequently.