esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Numerical Investigation of Erosion Wear Characteristics of Hydraulic Spillway**  
**[PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 1,2**  
* **Correspondence: [EMAIL_ADDRESS]**  
**Abstract:** There are many sand-laden waters in China, and the wear of hydraulic structures caused by sand-laden water diversion has been paid more and more attention. Taking the spillway of a reservoir as the research object, the numerical model of erosion wear caused by sediment-laden particle flows on the spillway was established by using the computational fluid dynamics (CFD) method, VOF (Volume of Fluid) multiphase flow model and DPM (Discrete Phase Model). Through the simulation analysis of the spillway's overall erosion, the distribution of the spillway erosion wear was obtained. Then, according to the main wear parts, the single variable, such as sediment diameter, sediment concentration, flow velocity and gate opening degree, was changed to study the erosion amount of the spillway and the distribution law of the spillway erosion parts. The results show that the main erosion sites of the spillway are at the bottom of the gate chamber and the middle section of the spillway. The maximum erosion increases linearly with the increase in sediment concentration. With the increase in sediment concentration, the sediment concentration changed from 1 kg/m\({}^{3}\) to 6 kg/m\({}^{3}\), and the maximum erosion of the spillway increased from 2.58 \(\times\) 10\({}^{-7}\) kg/m\({}^{2}\) to 1.53 \(\times\) 10\({}^{-6}\) kg/m\({}^{2}\). The erosion at the bottom of the spillway and gate leaf increases first and then decreases with the increase in sediment diameter and reaches the maximum value when the particle size is 0.002 mm. The erosion at the bottom of the spillway and the gate leaf increases with different growth trends as the flow velocity increases, when the flow velocity increases from 2 m/s to 9 m/s and the maximum erosion amount at the bottom of the spillway increases from 3.66 \(\times\) 10\({}^{-7}\) kg/m\({}^{2}\) to 1.14 \(\times\) 10\({}^{-6}\) kg/m\({}^{2}\), and the maximum erosion of the gate leaf increased from 1.66 \(\times\) 10\({}^{-8}\) kg/m\({}^{2}\) to 8.98 \(\times\) 10\({}^{-6}\) kg/m\({}^{2}\). The erosion amount at the bottom of the spillway increases with the increase in the gate opening between 0 and 3 m and tends to be stable when the gate opening is greater than 3 m. The maximum erosion position moves to the rear part of the spillway with the change in the gate opening. The change in the gate opening has no obvious effect on the erosion amount of the gate leaf but only changes the area of the gate erosion part. Thus, the erosion wear distribution of spillway under different work conditions is summarized, and the qualitative study between the erosion wear and the distribution of sediment diameter, sediment concentration, flow velocity and gate opening degree is made.  
**Keywords:** hydraulic structures; erosive wear; computational fluid dynamics (CFD); VOF (Volume of Fluid); multiphase; DPM (Discrete Phase Model)
## 4 Rectification with Checkerboard Pattern
### Computation of the Transformation Matrix  
The next step is to determine the rectification transformation from the IR images of the calibration object. Figure 5a shows a calibration infrared image as it is recorded by the IR camera. To create a transformation, first the software marks the corners of the checkerboard grid in the image. Using MATLAB's image processing toolbox, a binary image of the recorded calibration image is computed. By further processing, wrong spots in the binary image are eliminated using morphological operators. MATLAB provides a function to detect checkerboard points in an image, which is often used for camera calibration. This function detects \(n\times m\) checkerboard points in the binary image (see Figure 5b).  
In order to be able to calculate the rectification, a further set of points with \(n\times m\) size and with quadratic shape must be generated. The rectification algorithm will transform the localized distorted \(n\times m\) points to this regular and quadratic pattern. The lowest point on the right side of the original grid is used as a starting point. The mean distance is computed by comparing distances of the lowest row in the original grid. With the starting point and with the mean distance, the new checkerboard points are generated. A geometrical fit of the original and the generated point matrix is computed, using a third order polynomial fitting. The resulting transformation consists of the polynomial coefficients \(A\) and \(B\), which are used to determine \(U\) and \(V\) in the inverse transformation, where \(U\) and \(V\) are the x- and y- coordinates of the points in the original coordinate system [14]. The transformation can now be applied on images of the same size as the calibration image and each pixel is shifted according to the polynomial transformation. Figure 5c shows the rectification of the original grid, after the transformation is applied to the calibration image. This transformation is saved and used on further images of the recorded sequences. The used mean distance is also saved, since it provides the information about the resolution of the transformed image in pixel/mm.
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 4 Case Study
### Workpiece Position Optimization  
As discussed in earlier sections, the positioning of the workpiece can affect robotic surface machining, leading to the use of an optimization algorithm to determine the optimal workpiece position. Also, given the numerous constraints, finding a workpiece position  
Figure 5: Region-based workpiece subdivision results with optimal machining directions for the initial workpiece position: perspective view for WP1 (**a**) and WP2 (**c**), top view for WP1 (**b**) and WP2 (**d**) with the maximum feasible speeds and VVF for each region.  
manually that satisfies all the constraints through trial and error proved challenging. The PSO algorithm was utilized to minimize the optimization problem described in (24), enhancing the robot's operational efficiency under the optimization constraints shown in Table 4. Additional workpiece placements were also analyzed to validate the proposed objective function's effectiveness, confirming our optimization approach's robustness and practical applicability.  
Figure 6 shows the different placements of the two workpieces in the working area. Figure 6a represents the placements for WP1, where green is the optimal position found by the optimization algorithm and orange is the initial middle position as in Section 4.2, whereas blue and violet are randomly picked poses; the same goes for WP2 in Figure 6b. Actual positions in the form \(x[mm];y[nm];\gamma[^{\circ}]\) are presented in Table 5.  
A machining time comparison was made for region-based machining paths, with a stepover of 1 mm for all positions; the results are shown in Table 5. As we can see, the best results were observed in the optimal position identified by the PSO, where the machining time improved by approximately 4% for WP1 and 16% for WP2 compared to the second-best initial positions, which correlates with the calculated workpiece MTIs. This modest improvement can be attributed to the relative proximity of all the tested positions; the random positions were near a global minimum. This suggests that the minor improvements are because no other random position, significantly further from the global minimum, could be found manually, confirming our challenges and limitations of manual positional adjustments. The final region-based subdivision resulted in the optimal positions, which are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline
**Optimization Constraint** & **Value** \\ \hline UR5e robot joint limits & \(360^{\circ}\leq\theta_{i}\leq 360^{\circ},\ i=1,\dots,6\) \\ Working area & \(-350\) mm \(\leq x\leq 350\) mm \\ \(-300\) mm \(\leq y\leq-700\) mm \\ Workpiece rotation & \(-180^{\circ}\leq\gamma\leq 180^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 4: Optimization constraints.  
Figure 6: Different workpiece positions for WP1 (**a**) and WP2 (**b**).
# Determining the Compaction Temperature of Warm-Mix Anti-Rutting Asphal Mixture
## 3 Viscosity-Temperature Curve of Warm-Mix Anti-Rutting Asphalt
### Optimum Dosage of Anti-Rutting Agent  
In order to study the effect of warm-mix agent on the compaction characteristics of anti-rutting asphalt mixture, it is necessary to determine the optimum dosage of anti-rutting agent. The addition of the warm-mix agent reduces the viscosity of the anti-rutting asphalt mixture. In order to ensure good compaction characteristics and maintain good rutting resistance at high temperatures, the focus is to determine the optimum dosage of anti-rutting agent through the results of the softening-point test and viscosity test. The softening point and viscosity data of warm-mix anti-rutting asphalt at different anti-rutting agent contents were obtained through tests, as shown in Tables 3 and 4. The softening-point test results show that the maximum increase in the softening point was observed when the anti-rutting agent dosage was increased from 5% to 6%, and in the viscosity test, the increase in anti-rutting agent significantly increased the viscosity of asphalt.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Anti-Rutting Agent**} & \multicolumn{3}{c}{**Viscosity**} \\ \cline{2-4}  & **1\% Sasobit** & **2\% Sasobit** & **3\% Sasobit** \\  & **Warm-Mix Agent** & **Warm-Mix Agent** & **Warm-Mix Agent** \\ \hline
4\% & 1.067 & 0.983 & 0.889 \\
5\% & 1.258 & 1.180 & 1.112 \\
6\% & 1.432 & 1.276 & 1.188 \\
7\% & 1.548 & 1.351 & 1.234 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Warm-mix anti-rutting asphalt viscosity test results (135 °C, Pa-s).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Anti-Rutting Agent**} & \multicolumn{3}{c}{**Softening Point**} \\ \cline{2-4}  & **1\% Sasobit** & **2\% Sasobit** & **3\% Sasobit** \\  & **Warm-Mix Agent** & **Warm-Mix Agent** & **Warm-Mix Agent** \\ \hline
4\% & 63.8 & 72.4 & 75 \\
5\% & 66.6 & 73.5 & 78.2 \\
6\% & 72.3 & 80.3 & 83.2 \\
7\% & 74.6 & 83.8 & 85.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Softening-point test results of warm-mix anti-rutting asphalt (°C).  
The bending test is commonly used to evaluate the low-temperature crack resistance of asphalt mixtures, with the maximum flexural-tensile strain as the evaluation index. The specimen is a beam specimen with dimensions of 250 \(\times\) 30 \(\times\) 25 mm. By applying a vertical force at the mid-span of the specimen, the maximum strain at the time of failure is measured. This study used a bending test to examine the low-temperature crack resistance of the anti-rutting asphalt mixture.  
Table 5 shows the results of the low-temperature maximum flexural-tensile strain of the asphalt mixture with different dosages of an anti-rutting agent without a warm-mix agent. However, when the anti-rutting agent is more than 6%, the maximum flexural-tensile strain is lower than the 2000 \(\mu\varepsilon\) required for the base asphalt mixture in the construction specification technology (JTG F40-2004). Ultimately, the optimum dosage of the anti-rutting agent is determined to be 6%.
# Image-Based Malware Detection Using \(\alpha\)-Cuts and Binary Visualisation
## 3 Materials and Methods
### Creation of Image Datasets
#### 3.3.4 The Mapping  
The H-Index Space-Filling Curve  
In mathematical analysis, a Space-Filling Curve (SFC) is a continuous curve that \"maps a one-dimensional space to a higher dimensional space\" [68]. The first SFC was discovered by [PERSON] in 1890, who was inspired by [PERSON]'s work on the infinite number of elements in the unit interval. More specifically, SFCs, which are considered to be special cases of fractals, formed the basis for [PERSON]'s work in [69]. Ever since, more SFCs have been discovered, with Hilbert's curve being by far the most popular one in engineering applications. Some of these applications include signal processing [70], computer graphics [71], and cloud storage [72].  
In our previous work, two SFCs were tested for CNN-aided malware detection, namely the Hilbert curve, discovered by [73], and the H-indexing (or H-order, or simply the H-curve), discovered by [74]. Out of these two curves, the H-curve provides a better locality property. In other words, points that are close together in the one-dimensional space, tend to stay close together when mapped in a higher dimension. For this reason, the H-curve was chosen to be the mesh indexing of our experiments. Moreover, for the purpose of this study, we used 256 \(\times\) 256 images, or the seventh iteration of the H-curve mapping. It is worth noting that the H-curve is a Hamiltonian curve, which in our method starts and ends on the top left of the image. Figure 3 shows the third iteration of the H-curve mapping.  
\begin{table}
\begin{tabular}{c c} \hline
**FIS** \\ \hline
**Type** & Mamdani \\
**Inputs** & Left Similarity, Right Similarity \\
**Ouput** & Colour Tone \\
**Implication** & min \\
**Aggregation** & max \\
**Defuzzification** & centroid \\ \hline \end{tabular}
\end{table}
Table 2: A summary of the FIS that adjusted the colour tone of each pixel and we first implemented in [17].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rule** & **Left Similarity** & **Right Similarity** & **Colour Tone** \\ \hline
1 & Different & - & Light \\
2 & Similar & - & Medium \\
3 & Same & - & Dark \\
4 & - & Different & Light \\
5 & - & Similar & Medium \\
6 & - & Same & Dark \\ \hline \end{tabular}
\end{table}
Table 3: The rules of the FIS. The neighbouring similarity of the pixels to the left and the right affects the colour tone.
## 5 Performance Evaluation
### Comparison of Algorithm Efficiency  
From the perspective of the actual running process of an intelligent application system, as mentioned in Section 3, the usability of each edge device may keep changing. It is necessary to dynamically divide and deploy a DNN according to the latest status of edge devices when a request arrives. In this scenario, the algorithm's execution time will be accumulated to the actual system response time. Therefore, this section compares the algorithm running time of exhaustive method, basic genetic algorithm, and the proposed method in different deployment scenarios. Table 3 shows the detailed comparison results.  
The above table shows that the running time of all three algorithms increases significantly with the growing number of devices or DNN layers. However, the improved GA needs the least time to obtain a better solution. For example, in partitioning AlexNet, the improved GA needs about 1.84\(\times\), 1.26\(\times\), and 166.74\(\times\) shorter time than the exhaustive method in each scenario. In partitioning ResNet110 into three parts, the improved GA can save 712.13\(\times\) running time compared to applying the exhaustive method and nearly 3\(\times\) running time compared to applying the basic GA. It can be seen that when the problem size gets larger, the propose GA has better execution efficiency.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### MFPR  
MFPR [35] is a QoS-aware routing scheme that uses memetic flower pollination (MFP) to determine an optimal energy efficient forwarding set of underwater sensor nodes in a multiple sink architecture (Figure 8). The scheme performs its operations in two phases: (a) MFP identifies the set of nodes forming the optimal route from source node to the sink. The preliminary population uses optimal forwarding nodes to create a possible set of relays forming a route from the source node to the destination node. The MFP routes the populated paths according to best fitness value. (b) A flower pollination algorithm (FPA) switches between global and local pollination, in order to relay packets on reliable routes. MFPR employs grant free pattern division multiple access (GFPDMA), which reduces latency and transmission delay. MFPR delivers packets optimally with high energy efficiency and low end-to-end delay, while achieving longer network lifetime and high packet delivery ratio. Moreover MFPR deploys replaceable nodes near the sink to overcome the voids created due to the quicker dissipation of the top layer node. However this seems to move the same problem one hop down.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 4 Smart City De-Anonymization and Re-Identification Use Cases
### Wearable Devices  
Wearable devices have grown in popularity in recent years, with a wide range of applications from fitness tracking to medical monitoring. However, these devices also pose significant privacy risks, such as wireless eavesdropping, flawed protocol design, software vulnerabilities and side-channel attacks [131]. Wireless eavesdropping refers to unauthorized individuals or entities intercepting and listening in on wireless communications between the wearable device and other devices, such as smartphones or servers, which can lead to the exposure of sensitive personal information, such as location data or health information.  
Flaws in the design and implementation of the device's software and protocols can also pose a significant privacy risk. These flaws can create vulnerabilities that can be exploited by attackers to gain access to personal information or take control of the device. For example, a flaw in the Bluetooth protocol used by a wearable device could allow an attacker to connect to the device without the user's knowledge or consent, potentially giving the attacker access to sensitive data.  
Side-channel attacks are another type of attack that can be used against wearable devices. These attacks exploit weaknesses in the device's hardware or software to extract sensitive information, such as encryption keys, without directly accessing the device. For example, a side-channel attack could analyze the power consumption of a wearable device to extract encryption keys used to protect the device's data.  
Moreover, sensor data can also be used to re-identify individuals and a wide range of their behaviors and psychological states, as demonstrated in the usage of electrocardiograms (ECGs) and respiration sensors in medical services [132]. Additionally, the geolocation data on these devices can also be subject to re-identification, as stated in Section 4.2.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 3 The Proposed Method
### Computational Complexity Analysis  
For one video clip \(V\) in the dataset, as described in Sections 3.2-3.4, \(n_{j}\times M\) spatial features are extracted for all the persons, which has a computational complexity of \(O(n_{j}M)\). After the spatial features are imported into GRU and the average pooling layer, \(M\) spatio-temporal features are generated. Compared with the number of spatial features, the number of spatio-temporal features decreases \(n_{j}\) times, and the computational complexity reduces to \(O(M)\). Suppose the dataset contains \(N_{V}\) video clips, the total number of spatio-temporal features is \(N_{V}\times M\), and the computational complexity is \(O(N_{V}M)\). Due to the large number of spatio-temporal features in the dataset, searching for a target person by directly computing the similarity between spatio-temporal features increases the computational cost. Thus, all the spatio-temporal features are organized by LSH, and the query time for top-\(K\) results reduces to \(O((N_{V}M)^{1/K})\) for the Hamming distance, thereby reducing the computational cost of the search process and improving search efficiency. In Section 4.4, the experimental results verify the search efficiency of the proposed method.
## 5 Empirical Model  
### Baseline Model  
To confirm the influence of a high-speed rail service supply on urban green technology innovation capability, we developed the following econometric model:  
\[\ln\text{GreenPatenti}_{\text{i,t}}=\alpha_{0}+\alpha_{1}\text{lnHSRreq1}_{\text {i,t}}+\alpha_{2}\text{X}_{\text{i,t}}+\mu_{\text{i}}+\gamma_{\text{t}}+ \varepsilon_{\text{i,t}}\]  
where i and t denote city and year, respectively; In GreenPatenti,t is the explained variable representing the logarithm of the number of green patents of city i in year t; lnHSRreq1i,t is the main explanatory variable, standing for the frequency of high-speed rail service in city i in year t; \(\text{X}_{\text{i,t}}\) is a series of control variables, involving government support, financial development, the public cultural infrastructure, the level of communication technology, the vitality of the economic system, and the degree of environmental pollution; \(\mu_{\text{i}}\) indicates the city fixed effect, controlling the city characteristic factors which do not change with time; \(\gamma_{\text{t}}\) denotes the year fixed effect, which is employed to control the urban heterogeneity; and \(\varepsilon_{\text{i,t}}\) is the random error term.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable Name** & **Number of Observation** & **Mean** & **Standard Deviation** & **Min** & **Max** \\ \hline Green technology innovation & & & & & \\ (InGreenPatent) & 2840 & \(-\)2.450055 & 1.583217 & \(-\)6.310173 & 2.618316 \\ High-speed rail service & & & & & \\ (lnHSRreq1) & 2840 & 0.7096632 & 1.469769 & 0 & 6.381816 \\ Government support & & & & & \\ (sci\_GDP) & & & & & \\ Financial development & 2840 & 0.1438235 & 0.1501863 & 0 & 1.996286 \\ (Fin\_GDP) & & & & & \\ Public cultural infrastructure & & & & & \\ (ln books\_100\_1) & & & & & \\ Level of communication & & & & & \\ technology & 2840 & 1.9142 & 0.9666132 & 0 & 10.74006 \\ (ln mobile1) & & & & & \\ Vitality of the economic system & & & & & \\ (umemp\_Pop) & & & & & \\ Degree of environmental & & & & & \\ pollution & & & & & \\ (gyfs\_GDP) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical description of variables.
# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 3 Experiments
### Evaluating Indicator  
We chose PSNR (Peak Signal-to-Noise Ratio) [29] and SSIM (Structure Similarity Index Measure) [30] to evaluate the quality of the image after denoising by the improved network. PSNR (unit dB) is an objective evaluation index used to determine the degree of image distortion; the larger its value, the better the quality of the image. For an image with a pixel size of \(m\times n\), the PSNR is calculated as shown in Equations (14) and (15):  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left[Y(i,j)-X(i,j)\right]^{2} \tag{14}\]  
\[PSNR=10\times\log_{10}(\frac{MAX^{2}}{MSE}) \tag{15}\]  
where \(Y(i,j)\) and \(X(i,j)\) denote the pixel values at the corresponding positions of the ground truth and denoised images respectively, \(MSE\) is the mean square error between the two and \(MAX\) is the maximum value of the pixels between the noisy image and the denoised image.  
SSIM is based on the human eye's visual perception of an image and measures the similarity between the ground truth and the denoised image in terms of brightness, contrast and structure. The value of SSIM ranges from \(-1\) to \(1\), and the closer it is to \(1\), the better quality of the denoised image is indicated, and its specific formula is shown in (16):  
\[SSIM(X,Y)=\frac{(2\mu_{x}\mu_{y}+c_{1})+(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+ \mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{16}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the denoised image and the clear image, respectively, \(\sigma_{x}\) and \(\sigma_{y}\) are the variances of the two, \(\sigma_{xy}\) is the covariance of the two, and \(c_{1}\) and \(c_{2}\) are the constants introduced to avoid the denominator being zero.