esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Energy consumption:  
In Figure 10, we captured the energy level status of all the 1,000 sensors under a single-event scenario at 20, 40, 60, 80,100 time slots, respectively. The results represent the average performance of our proposed network over 100 simulation trials. Obviously, it differs every time, but this makes no difference. For the first 20 time slots, the overall energy level curve is flat, however we still find something special that sensors with index around 610-660 cost more energy than others. This might be caused by the occurrence of the single event. Moreover, it is shown that the average energy consumption increased at every 20 time slots which is due to the expansion of the event area that make more and more sensors involved and become ENs.  
Figure 11: Energy consumption of SS and MS based on the spent time lots under a single-event scenario with totally 1,000 sensors randomly distributed over a 100*100 network area.  
Figure 10: Energy consumption status of 1,000 sensors randomly distributed over a 100*100 square meters network area under single-event scenario, associated with Figure 11.  
The biggest difficulty in object tracking in WSNs is to manage multi-event scenario where events merge into a lesser number of events to form a more complicated network phenomena. We focused on the energy performance of the SS and MS which are more critical than other ordinary sensors for the whole monitoring network.  
In Figure 11, we got a curve in blue for SS to show exponential increase with a sudden weak-downturn at RD assignment and a curve in black for MS to show a calm attitude towards the first approximate 33 time slots, a sudden ascending caused by MSmov and more smooth exponential increase in energy consumption. Moreover we varied the number of sensors with 500 in total, the energy performance was reduced by nearly half proven by Figure 12.  
Figure 12: Energy consumption of SS and MS based on the spent time slots under a single-event scenario with 500 sensors randomly distributed over a 100*100 network area.  
Figure 13: Energy consumption of SS and MS based on the spent time lots under a 3-event scenario with totally 1,000 sensors randomly distributed over a 100*100 network area.  
Figure 13 gives the energy performance of SS and MS based on a 1,000 sensors network with totally three events randomly generated according to the multi-event model. As expected, the energy performance for MS has three sudden ascending steps at three different timings (9, 21, 60 time slots respectively). However, for SS, the overall performance keeps similar due to its requirements on movements.
## 2 Theoretical Background
In recent years, for example, AESS authors focused mainly on security prioritization through additional integrating security meetings into the development [32; 33] and through recommendations for security experts stemming from categories identified in practice [8] and managing security requirements [6; 34; 35]. Furthermore, since user stories can be considered the driving force of AESS, security-related stories (a set of possible scenarios and threats to the end-product), such as safety stories [9], abuser stories [36], misuse and abuse cases [37], and generic security user stories [38], are suggested in the literature. Related to these, some authors additionally propose using security-specific backlogs that may provide a superior overview of security issues and requirements [21; 36]. Others, however, suggest taking a different approach--by measuring and elevating the knowledge [39] and motivation of the development team as the baseline for their technical work. Gamification of security is one such approach. Based on planning poker [40] (heavily influenced by the wideband Delphi method), protection poker [41] and threat poker [42] were proposed as additional elements to be incorporated to the AESS. These aim to help determine the ease of executing the attack and the potential security/privacy impact. On the other hand, authors frequently propose including at least one additional security-specific role. Such roles typically include some variation of a security expert [43], such as a security master [21], security engineer [36], or security champion [44]. Even though these roles are among more common ones, there are also some others, such as security architect [10], penetration tester [14], and security developer [19]. Their primary role is to provide security knowledge and support to the development team. Different AESS approaches often have their own focuses, application levels and characteristics [45; 46; 47]. Nevertheless, certain ideas seem to be reoccurring, and considerable overlaps between different AESS approaches can be noticed. Due to their fundamental differences, it is not an easy task to compare different AESS approaches, and it is much more complex to consolidate them. AESS approaches can, however, be further decomposed. Most AESS approaches involve a set of different activities, such as security intention recap meetings [33], that may be integrated into a software engineering method used in a certain software development enterprise. They often prescribe various artifacts, such as security backlog [22], which may be used during software engineering to enhance security of the developed software. Some AESS approaches also introduce roles, such as the security master [20], that may execute AESS activities and take advantage of AESS artifacts. Similarly to AESS approaches, considerable overlaps may be noticed between different activities, artifacts, and roles found in AESS approaches. However, they may be more directly compared than varying approaches due to their more atomic nature.
# Deep Learning Methods in Image Matting: A Survey
## 5 Evaluation Indicators
### Single-Point Evaluation  
For single-point evaluations, the discrepancy between the ground truth alpha mattes and alpha mattes estimated by the algorithms is calculated for each pixel using the mean square error (\(MSE\)) and sum of absolute difference (\(SAD\)). To mathematically represent the calculation of the discrepancy between the estimated alpha matte and ground truth alpha matte for pixel \(i\), the \(MSE\) is expressed as  
\[MSE=\frac{1}{N}\sum_{i}^{N}(\hat{\alpha}_{i}-\widetilde{\alpha}_{i})^{2}. \tag{2}\]  
The smaller the \(MSE\), the better the model fits the dataset. The \(MSE\) has strong robustness to changes in brightness and contrast, but it gives higher weight to errors with larger pixel values, which may lead to less accurate evaluation results.  
The sum of absolute value errors is denoted by  
\[SAD=\sum_{i}^{N}|\hat{\alpha}_{i}-\widetilde{\alpha}_{i}|. \tag{3}\]  
where \(N\) represents the number of unknown pixels in the unknown region of the trimap, while \(\hat{\alpha}_{i}\) and \(\widetilde{\alpha}_{i}\) denote the alpha values of pixel \(i\) in the estimated and ground truth alpha mattes, respectively.  
The smaller the value of the \(SAD\), the higher the degree of fitting between the model and the dataset. The \(SAD\) is sensitive to changes in brightness and contrast and therefore is not suitable for evaluating image quality in complex scenes.  
Single-point evaluation indicators such as \(MSE\) and \(SAD\) are simple to use and have low computational costs. However, they may not accurately capture the subjective perception of image quality by the human eye, which limits their ability to reflect the true performance of algorithms in practical applications.
## 4 Results and Analysis
### WASN-Based Analysis in Real-Life Scenarios
#### 4.3.1 The Recognition Accuracy Before and After Compression  
To explore the influence of ADPCM on recognition, we used raw data with PCM and compressed data with ADPCM for the test. We manually selected 1742 car horns and 1579 background noises from the database to build our dataset that occupies the capacity of 511 M. We divided the dataset as 70% training, and 30% test set for each species, as shown in Table 6. The recognition result is shown in  
Figure 10: Example of the place where the sensor nodes are being deployed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples Size** & **Samples Length (ms)** & **Time-Consuming of Feature Extraction (ms)** & **Time-Consuming of Recognition (ms)** & **Time-Consuming of Extraction and Recognition (ms)** \\ \hline
10,000 & 139,120,15 & 182,100,1 & 2340 & 18,233,41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time-consuming of feature extraction and recognition.  
Table 7. Next, we compressed the data of the dataset by the ADPCM method, and the compressed dataset occupies the capacity of 116 M. We used the compressed dataset to test again with the same conditions, and the recognition result is shown in Table 8.  
The recognition precision of the car horn is 93.8% (\(491/(491+32)\times 100\%\)) in Table 7 and is 91.8% (\(480/(480+42)\times 100\%\)) in Table 8. It is reduced by 1.9% after the ADPCM compression, which was considered as a small influence on recognition. In addition, the capacity of raw dataset was about four times as big as the capacity of compressed dataset. Obviously, the compression with ADPCM reduces the capacity of transmission while ensuring the high recognition accuracy, thus we considered it as suitable for our system.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 5 Performance Optimization of the PPWJ Device
### Experiment on Rock Breaking by PPWJ
#### 5.2.2 Results and Discussion  
The comparison of jet erosion on granite by the original device and the optimized device is shown in Figure 14. Under the action of the jet generated by the optimized device, the diameter of formed erosion pit \(D_{\text{P}}\) is 53 mm and the volume \(V_{\text{P}}\) is 2.44 mm\({}^{3}\), which are 1.4 and 1.5 times those of the erosion pit formed by the jet generated by the original device, respectively. As the nozzle diameter increases, the high-pressure water flow through the nozzle grows, which will make more energy act on the rock without reducing pulse pressure. In addition, as the pulse frequency increases, the rock is subjected to greater impact, resulting in the generation of higher momentary pressures on the rock surface and transmitting stress waves into the rock interior. Therefore, at a constant pulse pressure, the increase in nozzle diameter and pulse frequency is conducive to producing more intensive rock breakage, and thus verified the feasibility of the optimization method.  
The analysis of the simulation results can provide support for the optimization of the structural parameters of the PPWJ device. However, the research on the structure of the PPWJ device and its application in rock breaking is still in the exploratory and experimental stages. Regardless of the mechanical properties of the rock, it is confirmed that increasing the nozzle diameter and pulse frequency can improve rock breaking efficiency. On the one hand, some other hydraulic parameters are associated with rock breaking efficiency such as stand-off distance, erosion time, and erosion angle, and still need more work. On the other hand, the thermal effects and friction effects were not considered in theoretical analysis and simulation, which will be further improved in the future research.  
Figure 13: Experimental system of rock breaking by PPWJ.
# Mechanical Property of Sn-58 Bi Solder Paste Strengthened by Resin
## 3 Results and Discussion  
### Spreading Performance of Solder Paste  
The spreading performance of solders reflects the wettability and solderability to some extent. The spreading status of Sn-58 Bi solder paste at different resin contents are shown in Table 2. Solder paste with epoxy resin and copper substrate exhibited good wettability. The effects of different epoxy resin contents of Sn-58 Bi solder paste on the spreading area and wetting angle are shown in Figure 2. Five data points were tested for one parameter and averaged. Standard deviations were used to calculate the stability of experimental data. The spreading area of the epoxy resin Sn-58 Bi solder paste had a maximum at 5 wt.%, and the wetting angle was reduced by 5\({}^{\circ}\) compared with that of the Sn-58 Bi solder paste, which indicated that the addition of epoxy resin promoted the wettability of the solder paste.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resin Content** & **0 wt.\%** & **3 wt.\%** & **5 wt.\%** & **7 wt.\%** & **9 wt.\%** \\ \hline Spreading area & & & & \\ (mm\({}^{2}\)) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spreading status of solder paste at different resin contents on Cu substrate after reflow.  
Figure 1: Reflow profile.  
The wettability test was observed to obtain the following types of solder paste spreading, as shown in Figure 3. Spreading morphological characteristics are summarized in Figure 3a-c; the cross-sections of different types of solder paste spreading are shown in Figure 3d-f. Figure 3a,d illustrate that the molten flux spread around and completely separated from the Sn-58 Bi solder paste, while Figure 3b,c,e,f indicate that the molten flux and epoxy resin covered all or part of the Sn-58 Bi solder. Combined with the curves in Figure 2 and Table 2, the spreading area of the flux containing epoxy resin was reduced with increasing resin content. The solder and flux influenced the wettability of solder paste. The wettability was determined using an intermetallic compound (IMC) that formed Cu\({}_{6}\)Sn\({}_{5}\) between the Sn-58 Bi melting solder and the Cu substrate [21; 22]. Meanwhile, the flux improved the wettability mainly by removing the oxide film on the surface of the Cu substrate, reducing the surface tension and preventing reoxidation. In this study, epoxy resin can reduce the surface tension between the Sn-58 Bi solder and the Cu substrate, which helps to increase the spread area.
## 2 Literature Review
### Plausible Effects of Ignoring \(K_{III}\) on CP and Keq  
Ignoring mode III has an impact on the equivalent SIF (Keq) and the fracture path, according to theoretical models based on Westergaard's stress function.  
By representing displacement as vectors, [PERSON] [25] was able to derive an equivalent SIF model, which was applied to [PERSON]'s strip yield model. [PERSON] postulated that crack length increases as a factor of SIF to the fourth power when the displacement (or the addition of them) in a strip ahead of the crack tip reaches a critical amount. The model is shown in Equation (7).  
\[K_{Tunaka}=\sqrt[4]{K_{I}{}^{4}+8K_{II}{}^{4}+\frac{8K_{III}{}^{4}}{1-\
u}} \tag{7}\]  
On the other hand, [PERSON] [25] proposed a stress-based criterion for a three-point bending test coupon loaded under mode I with an inclined-to-normal crack, which is represented in Equation (8). It is evident that the impact of \(K_{III}\) on [PERSON]'s Keq is completely linear.  
\[K_{P3D}=\frac{K_{I}(1+2\
u)+\sqrt{K_{II}{}^{2}(1-2\
u)^{2}+4K_{III}{}^{2}}}{2} \tag{8}\]  
[PERSON] et al. [3] proposed an implicit crack path model, which included \(K_{III}\) that [PERSON] et al. [37] later fit results into the expression shown in Equation (9).  
\[\theta_{R3D}=\mp\left[140^{\rho}\frac{|K_{II}|}{K_{I}+|K_{II}|+|K_{III}|}-70^{\rho} \bigg{(}\frac{|K_{II}|}{K_{I}+|K_{II}|+|K_{III}|}\bigg{)}^{2}\right] \tag{9}\]  
Figure 2a compares [PERSON]'s Keq for different Poisson ratios with the case of leaving out \(K_{III}\). For the cases where it is accounted for, \(K_{III}\) is equal to \(K_{I}\) to keep the comparison under proportional load, but varying Poisson's ratio from 0.2 for concrete, 0.3 for metals, and 0.4 for plastics. One can see the proportionality between them, and that difference lowers as \(K_{III}\) augments, and leaving \(K_{III}\) out caps out the equivalent SIF. Moreover, the equivalent SIF shown in Equation (8) is larger for the larger Poisson ratios, which means the out-of-plane displacements should also be larger. Moreover, the effect of \(K_{III}\) in the crack kinking angle is examined in Figure 2b for the [PERSON] et al. model Equation (9). In that case, there is a clear linear dependence, and the planar crack kinking angle is affected by the value of SIF mode III. Finally, not including mode III can underestimate the fracture toughness [15]. This is because mode III can influence stress distribution at the crack tip, thus affecting crack stability.  
The literature review did not reveal an implementation of the LSM for mode III SIF calculation from full-field displacement experimental data. Only in [28] is it hinted that it was used to calculate \(K_{III}\) from numerical data with no further details. So, this paper presents a method to calculate mode III SIF from displacement fields by fitting them to [PERSON] series for an infinite cracked plate and solving the overdetermined equations system with the LSM.
# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 4 Algorithms
### Obstacle Avoidance Algorithm (VRAOA)
Finally, for each current visiting point \(P_{c}\), select the next point with the smallest angle and update \(P_{c}\) (line numbers 23-24). The polygon is complete when \(P_{c}\) returns to \(P_{s}\), as shown in Figure 1. The final polygon around the obstacle ABCDEFGHIJKLMNA (blue line) is ABDHJLNA (orange line). Figure 3: Sample inputs and outputs of VRAOA. (**a**) Input sample 1. (**b**) Output sample 1. (**c**) Input sample 2. (**d**) Output sample 2. (**e**) Input sample 3. (**f**) Output sample 3. (**g**) Input sample 4. (**h**) Output sample 4. ```
Input: the coordinate and edge sets \(C\) and \(E\)

Output: A convex polygon
```
1: select starting and ending point \(P_{s}\)
2: set current visiting point \(P_{c}=P_{s}\). 3:for each \(P_{c}\) in \(C\)do
4:if\(x_{c}<x_{s}\)then\(\triangleright\)\(x_{c}\): x-axis coordinates of \(P_{c}\), \(x_{s}\): x-axis coordinates of \(P_{s}\). 5:\(\vec{u}=<0,1>\)\(\triangleright\)\(\vec{u}\): a unit vector which is in the direction of y-axis. 6:else
7:\(\vec{u}=<1,0>\)\(\triangleright\)\(\vec{u}\): a unit vector which is in the direction of x-axis. 8:endif
9:if\(P_{c}=P_{s}\)then
10: calculate vector \(\vec{v}_{i}\) from \(P_{c}\) for all \(i\) in \(C\) except \(P_{c}\). \(\triangleright\) Vector value \(\vec{v}_{i}=<v_{ix},v_{iy}>\), Where \(v_{ix}=x_{i}-\)\(x_{c}\) and \(v_{iy}=y_{i}-y_{c}\). Here \((x_{c},y_{c})\) and \((x_{i},y_{i})\) is the coordinates of \(P_{c}\) and any other point, respectively. 11:else
12: calculate vector \(\vec{v}_{i}\) from \(P_{c}\) to all the other points in \(C\) and \(P_{s}\) except \(P_{c}\) and the previously visited points in the polygon. 13:endif
14:for each \(i\) in \(C\)do
15: Calculate the rotation angle \(\theta_{i}\) from \(\vec{u}\) to other vectors \(\vec{v}_{i}\). Rotation angle \(\theta_{i}=cos^{-1}\left(\frac{\vec{u}.\vec{v}_{i}}{|\vec{u}|+|v_{i}|}\right)\)\(\triangleright\) Vector values of \(\vec{u}\) and \(\vec{v}_{i}\) are \(<u_{x},u_{y}>\) and \(<v_{ix},v_{iy}>\), respectively. Vector dot product, \(\vec{u}.\vec{v}_{i}=(u_{x}*v_{ix}+u_{y}*v_{iy})\), magnitude of the vectors, \(|\vec{u}|=\sqrt{u_{x}^{2}+u_{y}^{2}}\) and \(|\vec{v}_{i}|=\sqrt{v_{ix}^{2}+v_{iy}^{2}}\), respectively. 16:if\(\vec{u}=<0,1>\) and \(v_{ix}\geq 0\)then
17:\(\theta_{i}^{\prime}=360^{\circ}-\theta_{i}\)
18:else if\(\vec{u}=<1,0>\) and \(v_{iy}<0\)then
19:\(\theta_{i}^{\prime}=360^{\circ}-\theta_{i}\)
20:else
21:\(\theta_{i}^{\prime}=\theta\)
22:endif
23:endfor
24: Choose the smallest \(\theta_{i}^{\prime}\) among all \(i\). 25: Update the current visiting point \(P_{c}\) with the point that has the smallest \(\theta_{i}^{\prime}\). 26: If the updated \(P_{c}=P_{s}\), then stop the algorithm. 27:endfor ```
**Algorithm 1** Vector rotation angle-based obstacle avoidance (VRAOA) algorithm
## 4 Discussion
It was conducted at a single center, which means the number of adult participants was restricted, and the study included both males and females, as well as obese and non-obese individuals. We did not find an association between HGS and NAFLD in males. It is essential to publish not only positive research results but also negative ones. Our population differs from that referenced in [PERSON] et al. [35]. The study evaluated an Asiatic population. Additionally, the authors used the hepatic steatosis index as a surrogate marker for NAFLD, while we used TE assessment. There are no studies on the association between HGS and NAFLD in European males. Despite a small sample size, we believe that our findings could be valuable in designing future research for both genders. Furthermore, we did not evaluate IR and inflammation status, serum levels of IGF-1, or myostatin. Due to the lack of muscle mass measurements, we were unable to verify the presence of sarcopenia. We did not perform any liver biopsies, mainly due to the apparently healthy study population, the high cost, and the associated risk. Due to the study design, we were unable to identify a causal relationship. Additional data from across EU countries is needed to confirm our findings. In addition, it is imperative to conduct prospective studies and intervention trials to ascertain a causal connection between impaired muscle function and fatty liver disease. The major strength of this study is that it was a cohort of adults, who are the subjects at greatest risk of metabolic disorders and sarcopenia, and that we used TE to detect NAFLD instead of steatosis scores [33]. Moreover, TE exhibits a high level of sensitivity and specificity in evaluating liver steatosis and fibrosis [34] and is commonly employed in population-based research due to its non-invasive nature and accessibility [35]. Finally, AUROC are 81-84% for \(\geq\)S1 (steatosis in at least 5-10% of hepatocytes), 85-88% for \(\geq\)S2 (33%), and 86-91% for S3 (66%) steatosis. The reported sensitivities for \(\geq\)S1, \(\geq\)S2, and \(\geq\)S3 are 60-75%, 69-84%, and 77-96%, respectively [81]. Some studies suggest that skin capsular distance (SCD) can affect the accuracy of the CAP score by overestimating this measurement, especially in those with a large SCD (\(\geq\)25 mm) [82; 83]. However, the FibroScan Model 502 and later models have an automated probe selection tool in their software that suggests the choice of the appropriate probe (M and XL) for each patient according to the real-time evaluation of the SCD [84]. Additionally, as in prior studies [32; 33; 35; 36], we utilized HGS to assess muscle function, which bolsters the statistical robustness of our findings. Consequently, the primary outcome of this study highlights that diminished muscle function may represent a risk factor for NAFLD among aging adults who appear to be in good health.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### Shading Faults  
Shading faults in solar modules occur when shadows cast on the surface of the solar panels affect part of their surface, preventing the shaded areas from effectively converting sunlight into electrical energy. This could be due to buildings, trees, or other obstacles obstructing sunlight at certain times or seasons. Shading faults reduce the overall performance of the solar panels, thereby decreasing the output power [16].  
The simulation of shading faults is conducted by connecting three solar cell modules and providing different levels of irradiance to each module as shown in Figure 7. The irradiance for the shaded parts ranges from 100 W/m\({}^{2}\) to 500 W/m\({}^{2}\). The simulation also varies the shaded area coverage, with small portions covering 5% to 10% and larger areas covering up to 50%, to achieve different degrees of shading fault simulation as shown in Figure 8.  
Figure 5: Aging current–voltage curve simulation circuit diagram with series resistor.  
Figure 6: Aging current–voltage curve.  
Figure 7: Shading fault current–voltage curve simulation circuit diagram.