esa-sceva/satcom-chunk-collection huggingface dataset:
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## 3 Results
### Simulations
#### 3.1.2 Carotid Vessel Wall Displacements  
Figure 6 visualizes the estimated inter-frame displacements in the external and internal carotid vessel wall using the four different methods. Major performance difference can be seen for the lateral displacement estimation, with PSF_2 Dcub showing the highest similarity with the ground truth. The RMSE values are visualized in Figure 7. The accuracy of the displacement estimates in the axial direction is higher than in the lateral direction, showing lower RMSE values for all four methods. This can also be appreciated from Figure 6. The largest performance differences between the four methods are observed for the lateral estimates. The PSF_2 Dcub method shows lowest RMSEs for both the diastolic and systolic phase.  
Figure 5: Median absolute bias and SD for the velocity magnitude (**a**, **c**) and angle (**b**, **d**), obtained using a PRF of 12.5 kHz. The performance of the four methods is visualized for a range of beam-to-flow angles.
# Study of Cyclic Plasticity and Creep Ratchet Behavior of PTFE
## 3 Results and Discussion  
### Uniaxial Tensile Tests at Different Temperatures  
Figure 3 depicts the uniaxial tensile curves at five temperatures. The modulus of elasticity was obtained by fitting a straight line to the initial linear part of the stress-strain curve using the least squares method. Since PTFE does not have a readily apparent yield point, the stress value of 0.2% residual deformation is used to define its nominal yield stress, as shown in Table 2. The modulus of elasticity and the nominal yield stress of PTFE, as well as the material's tensile strength, decrease considerably as the temperature rises, as shown in Figure 3. However, this decrease is not completely linear. This may be due to the fact that the crystal structure of the PTFE material changes as the temperature increases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature/\({}^{\circ}\)C** & **Elastic Modulus/MPa** & **Nominal Yield Limit/MPa** \\ \hline
23 & 317 & 5.3 \\
50 & 182 & 3.2 \\
100 & 108 & 1.8 \\
150 & 40 & 1.3 \\
200 & 22 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The elastic modulus and nominal yield limit of PTFE at different temperatures.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 5 Results and Discussion  
This section showcases the results of numerical simulations and experimental analyses conducted using Matlab. The aim is to assess the effectiveness of different algorithms in extracting the damping ratio. Our investigation incorporates established algorithms, including PP, pLSCF, LSRF, and Yoshida, alongside our proposed methods, CTWT-1 and CTWT-2. In our implementation, we utilize the built-in PP and LSRF algorithms in Matlab, as well as the algorithm described in [23] for the Yoshida method and the code provided by [40] for the pLSCF method.
## 4 Empirical Results
### Project Recommendation Results
\begin{table}
\begin{tabular}{l c c c c c c c} ID &
\begin{tabular}{c} **Developer Metrics** \\ **(community approach)** \\ \end{tabular} & \multicolumn{3}{c}{**Context-free similarity**} & \multicolumn{3}{c}{**Context-based similarity**} \\  & **top1** & **top5** & **top10** & **top1** & **top5** & **top10** & **MRR** \\ \hline
1 & commit\_authored & 14.00 & 12.70 & 11.55 & **28.50** & **23.10** & 18.45 & _0.10_ \\
2 & commit\_commented & 22.00 & 19.20 & 16.95 & 26.00 & 20.50 & 18.45 & _0.08_ \\
3 & commit\_committed & 15.50 & 12.90 & 11.55 & **29.00** & 22.60 & 18.50 & _0.11_ \\
4 & issue\_assigned & 7.00 & 8.30 & 7.50 & 26.50 & 17.40 & 14.25 & _0.06_ \\
5 & issue\_closed & 11.50 & 8.10 & 6.25 & 20.00 & 14.80 & 13.15 & _0.05_ \\
6 & issue\_hasPR & **26.00** & 17.90 & 16.20 & **27.40** & 12.90 & 14.10 & _0.25_ \\
7 & issue\_commented & **27.00** & **27.50** & **27.50** & 22.00 & 18.20 & 16.95 & _0.20_ \\
8 & issue\_opened & 23.00 & 20.70 & 18.70 & 26.00 & 22.10 & 18.65 & _0.10_ \\
9 & p\_assigned & 4.00 & 4.20 & 4.30 & 12.50 & 9.30 & 8.50 & _0.04_ \\
10 pr\_commented & 20.00 & 19.60 & 17.85 & 20.00 & 20.10 & 18.60 & _0.07_ \\
11 pr\_merged & 16.00 & 12.80 & 11.80 & 28.00 & 20.70 & 18.90 & _0.09_ \\
**12 pr opened** & **26.10** & 19.20 & 16.00 & **27.50** & 17.40 & 17.20 & _0.48_ \\
13 & comments & **27.00** & 19.60 & **27.60** & 22.00 & 19.30 & 16.95 & _0.36_ \\
14 & code contributions & 23.00 & 22.20 & 21.00 & 24.00 & 20.50 & 18.25 & _0.09_ \\
15 & commit\_related & 23.00 & 18.40 & 17.75 & 26.00 & 19.40 & 19.00 & _0.08_ \\
16 & pr\_related & 25.00 & 23.60 & 22.05 & 22.00 & **23.90** & _20.10_ & _0.15_ \\
17 & issue\_related & 25.00 & **24.40** & **23.35** & 18.50 & 17.00 & 15.20 & _0.11_ \\
18 & binary\_commit\_related & 24.00 & 19.30 & 19.10 & **27.50** & 23.00 & **22.00** & _0.17_ \\
19 & **binary\_related** & 23.50 & 22.50 & 22.35 & **25.50** & 22.50 & **22.10** & _0.34_ \\
**20 binary\_issue\_related** & 25.50 & **25.50** & 22.50 & 15.00 & 17.70 & 16.40 & _0.30_ \\
21 & binary\_code\_contributions & 24.00 & **24.40** & **23.55** & 22.00 & 20.70 & 18.05 & _0.12_ \\
**22 binary\_comments** & **27.00** & **24.80** & **23.35** & 17.50 & 18.80 & 17.60 & _0.26_ \\
23 & 0\_issue\_commented & 22.00 & 21.20 & 19.25 & 15.00 & 13.00 & 12.25 & _0.06_ \\
24 & code\_2 comment & 22.50 & 19.80 & 17.85 & 27.50 & 22.20 & 19.40 & _0.11_ \\
25 & Sun\_metric & 8.00 & 7.60 & 7.75 & 14.50 & 11.50 & 9.90 & _0.04_ \\ \hline \end{tabular}
\end{table} 
Table 9: Developer metrics scores according to evaluate with the community relation approach. metric represent many features about a developer, we believe that projects developed in the languages in which the developer specializes are better explored. In addition, in this approach, the _binary_issue_related_ metric leads among the fusion metrics. The issue that is the primary function of project management operations is the most crucial feature in terms of developer-project correlation. Lastly, the results of _Sun_metric_ drew attention here, unlike with the previous approach.
###### Abstract  
Ojective: Sialic acid [SA] represents a critical mucosal membrane component maintaining mucosal integrity. This investigation stratified adult subjects based on clinical parameters of periodontal health to examine salivary sialic acid [SA] as a health measure and develop a corresponding rapid visual chair-side assay. Methods: Adults [\(n=90\)] were enrolled and clinically stratified into healthy [\(n=30\)], gingivitis [\(n=29\)] or periodontitis [\(n=31\)] groups. Saliva from subjects was evaluated for SA using the Ninhydrin method. A novel rapid SA spot test was developed utilizing filter paper discs soaked in a sialidase substrate. Substrate-laden disks were incubated at room temperature with saliva produced a blue color with increasing color intensities due to higher sialidase activity. Subjects were recalled weekly for clinical and salivary assessments. Results: Average baseline salivary SA in healthy, gingivitis and periodontal disease groups were 64, 95 and 102 \(\upmu\)g/mL, respectively with significant differences (<0.05). Differences in SA concentrations among control and test groups were maintained throughout the study. Similarly, the differences in the color intensities in the rapid visual chair side spot test were also observed during the entire study period. Conclusions: Increasing levels of salivary SA were observed from healthy to periodontal disease with these differences remaining consistent over the study. These results corresponded with the chair-side visual assay, which is suitable for patient education or monitoring.  
chair-side test; periodontitis; gingivitis; healthy; mucosal barrier; patient engagement +
Footnote †: journal: Medical Medical College, ISS Academy of Higher Education & Research (Deemed to Be University), Mysuru 570 015, India 6
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 5 Conclusions  
This paper proposed a novel hybrid data-fusion damage detection system which contained three intelligent models. The study shows that the integration of FD and PNN based on data fusion has great potential in the structural damage detection. An arch bridge model and experimental test were used to validate the efficiency and robustness of the proposed system.  
The two main conclusions of the study are:  
1. The proposed system has excellent damage detection capability, noise-tolerance and robustness. Its highlight is that it can offer three intelligent models, which can be selected according to a variety of actual situations.
2. The numerical simulation and experimental results show that, after the data-fusion process, both the eigen-level model and decision-level model in the proposed system can offer more precise and reliable detection results than that of a single PNN classifier.  
However, the comparison between the work and SVM (support vector machine), decision tree, random forest, as well as deep learning-based method, should be done in the future. Moreover, multi-type-sensor system, multi-source information fusion algorithm and multi-signature extraction technology should also be investigated in real civil engineering in the future.  
Both authors took part in the discussion of the work described in this paper. [PERSON] wrote the paper; [PERSON] revised and finalized the paper. Both authors have read and agreed to the published version of the manuscript.  
The work is supported by PhD Start-up Fund of Natural Science Foundation of Liaoning Province, China (Grant No. 20170520246), National Thirteenth Five-Year Key Research Projects of China (Grant No. 2020 YFD1100403), and Fujian Science and Technology Planning Project (Grant No. 2017Y0412).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would also like to thank the anonymous referees for their constructive comments and suggestions.  
The authors declare no conflict of interest.
###### Abstract  
In this study, we present a novel design methodology for unit cells in chessboard metasurfaces with the aim of reducing the radar cross-section (RCS) for linearly polarized waves. The design employs rotational symmetry and incorporates ten continuous parameters to define the metasurface units, enabling the creation of flexible 2D structures. The geometrical parameters of the two units are then optimized using a simulated annealing (SA) algorithm to achieve a low RCS chessboard metasurface. Following optimization, the properties of the metasurface were experimentally verified. The experimental results show a significant RCS reduction of 10 dB within the 7.6-15.5 GHz range, with the peak reduction reaching-28 dB at normal incidence. For a bistatic RCS, the metasurface effectively scatters incident waves into four distinct lobes. The proposed method offers an alternative strategy for the inverse design of low RCS metasurfaces and can be extended to applications in polarization control, phase gradient manipulation, and transmissive metasurfaces.  
radar cross-section (RCS); inverse design; artificial magnetic conductor (AMC); simulated annealing; broadband Article
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments
### Averaged Blockage Probability  
In realistic deployments, the number of UEs can be large. In such cases, we might be interested in the average blockage probability between BS and a randomly chosen UE in the coverage area or between two randomly chosen UEs. These results can be obtained by assuming a certain random distance between communicating entities. For BS-to-UE communications, uniform distribution of UEs in the coverage area of BS is often assumed. By considering a square factory compartment with side \(R\), for BS to UE communications, this average blockage probability is immediately given by  
\[p_{B}=\int_{0}^{R}\int_{0}^{R}\frac{1}{R^{2}}p_{B}(x,y)dxdy. \tag{11}\]
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 1 Introduction
The MSSA algorithm [12] for solving multi-objective problems has some drawbacks, such as premature convergence and susceptibility to getting stuck in local optima. Therefore, further improvements are needed for the MSSA algorithm. The enhanced algorithm (EMSSA) is then applied to the study of sensor node deployment in complex environments. The primary contributions of this research are summarized as follows. * Considering network coverage, node utilization, and network energy balance, a multi-objective optimization deployment model is proposed for WSNs coverage optimization. * To deploy sensor nodes in effective positions, we propose a grid-based approach for monitoring areas with obstacles. * Based on the multi-objective optimization model proposed in this paper, we have designed an enhanced multi-objective salp swarm algorithm (EMSSA) based on multiple strategies. * Finally, we applied the proposed algorithm in a complex environment, providing different effective coverage optimization configurations by adjusting coverage thresholds for node deployment. The remaining of the paper is organized as follows. Recent relevant works in the literature are discussed in Section 2. In Section 3, we provide a detailed description of our proposed multi-objective optimization deployment model. The algorithm is explained in Section 4. Subsequently, we discuss our simulation experiments and their results in Section 5, while the Section 6 concludes the paper and provides future research directions.
# Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line
## 4 Hybrid Task Allocation Method Based on Task Binding Strategy and IPSO
### Initial Population Generation
#### 4.3.2 Heuristic Rules  
In order to improve the quality of the initial population, four heuristic rules are proposed. Based on each heuristic rule, a heuristic individual is generated.  
The four heuristic rules are as follows:  
(a) Task group with a closer end time is preferred  
\[TG_{n}\uparrow=t_{TG_{n}}^{e} \tag{16}\]  
where \(t_{TG_{n}}^{e}\) denotes the end time of the best delivery time interval of task group \(TC_{n}\), \(\uparrow\) denotes ascending sort.  
(b) Task group with more task groups bound is preferred  
\[TG_{n}\downarrow=N_{B,TG_{n}} \tag{17}\]  
where \(N_{B,TG_{n}}\) denotes the number of task groups bound to task group \(TG_{n}\), \(\downarrow\) denotes descending sort. It should be noted that \(N_{B,TG_{n}}\) contains the number \(N_{B,TG_{n+1}}\) if \(TG_{n+1}\) is bound to \(TG_{n}\).  
(c) Task group with more tasks bound is preferred  
\[TG_{n}\downarrow=\sum_{T_{i}\in TG_{n}}N_{B,T_{i}}+N_{TG_{n}} \tag{18}\]  
where \(N_{B,T_{i}}\) denotes the number of tasks bound to task _Ti_. It should be noted that \(N_{B,T_{i}}\) contains the number \(N_{B,TG_{n+1}}\) if \(TG_{n+1}\) is bound to \(TG_{n}\).  
(d) Task group requiring more AGVs is preferred  
\[TG_{n}\downarrow=\sum_{T_{i}\in TG_{n}}N_{G,T_{i}} \tag{19}\]  
According to the priority of task groups generated, tasks in task groups are allocated to AGVs in sequence. The allocation follows that the AGVs chosen can handle the task earlier than other AGVs. Every time a task is allocated, the delivery time of the task needs to be calculated, and the available time of the AGVs chosen needs to be updated.
## 2 Detection Methods  
The coaxiality error can be understood as the maximum variation between two axis lines. Therefore, the key to calculating the coaxiality error value lies in obtaining the equations of the measured axis and the reference axis. During the assembly process of automobile brake piston components, inevitable assembly errors occur between the spring and the cylinder. This error causes a variation between the axis of the spring and the axis of the cylinder, which is denoted as \(\theta\).  
The mathematical model of an automobile brake piston component is shown in Figure 2a. The blue line represents the axis of the spring (measured axis), while the green line represents the axis of the cylinder (reference axis). The origin of the coordinate system is at the junction between the spring and the cylinder, denoted as O, the coordinate system OXYZ is established, and the Z axis is the reference axis.  
An industrial camera is positioned horizontally in front of the measured part (the position relationship between the industrial camera and the automotive brake piston component is shown in Figure 2b). During the rotation of the automobile brake piston component around the Z-axis, there is a moment when the blue axis is located exactly on the YOZ plane. At this time, the image captured by the industrial camera is the maximum variation of the spring part relative to the reference axis, denoted as \(\theta_{\text{max}}\). Moreover, the more images are collected, the closer the value of \(\theta_{\text{max}}\) is to the real value. At this time, the distance between the center of the circle on the surface of the spring part and the reference axis is the coaxiality error, which is denoted as \(\Delta_{\text{x}}\), and its mathematical calculation formula is as follows:  
\[\Delta_{\text{x}}=\text{L}\ \times\ \sin\theta_{\text{max}} \tag{1}\]  
The flow chart of the detection method is shown in Figure 3. The first step is to obtain the edge contour of the unit pixel width of the target region through a series of image preprocessing algorithms, then extract the region of interest of the target region, and divide the automobile brake piston component into the spring ROI and the cylinder ROI. The second step is to optimize the edge of the region of interest of the spring, and then extract the geometric feature points of the two parts, so as to calculate the equation of the measured  
Figure 2: Schematic diagram of the coaxiality detection principle: (**a**) the mathematical model; (**b**) the position relationship between the measured part and the industrial camera.  
axis and the reference axis. The third step is to utilize the two equations obtained from the calculation, calculate the variation \(\theta\), and then extract the maximum variation \(\theta_{\text{max}}\) among a series of images, so as to calculate the coaxiality error value.
## 4 Model Development
### Elapsed Time Threshold Determination
#### 4.3.1 First Estimation Based on Rule 1  
From a spatial point of view, the metro-to-bus interchange behavior involves metro alighting station/time, and bus boarding station/time. The distance between the metro alighting station and the bus boarding station can be expressed as follows [32]:  
\[\begin{array}{l}d_{i,j}=R\times\arccos(\sin(lat_{1})\cdot\sin(lat_{2})+\cos( lat_{1})\\ \cdot\cos(lat_{2})\cdot\cos(lon_{1}-lon_{2}))\ast\pi/180\end{array} \tag{3}\]  
where, \(lat_{1}\) and \(lon_{1}\) denote the latitude and longitude of the metro station, respectively; \(lat_{2}\) and \(lon_{2}\) represent the latitude and longitude of the bus station, respectively; and \(R\) is the radius of the earth. In the study, \(R\) was set to 6378.137 km.  
Generally, the passenger interchange behavior between metro stations and other feeding traffic modes occurs in the service area of metro stations [23]. In other words, the transfer distance between the metro alighting station and bus boarding station should not exceed the user-defined distance threshold as follows:  
\[d_{ij}\leq D_{i} \tag{4}\]  
where, \(d_{ij}\) denotes the transfer distance between the metro alighting station \(i\) and the bus boarding station \(j\); \(D_{i}\) is the maximal allowable travel distance of transfer trips at metro station \(i\), depending on the public transportation network and travelers' personal characteristics.
###### Abstract  
This study describes the implementation of Internet of Things (IoT) sensors in flow meters installed in drinking water systems and in fog catchers built in low-income, high-altitude communities in the Andes region of Ecuador, taking studies at the University de las Fuerzas Armadas ESPE as our reference. The influence and management of these intelligent sensors are analyzed, as well as a basic review of the materials and methods used in their implementation. The importance of validating the accuracy and reliability of IoT sensors compared to professional devices is highlighted, especially in mountain areas with difficult access. Additionally, the cost-benefit of using IoT sensors in fog catchers and drinking water distribution networks is mentioned, which depends on several factors such as the scale of the project, the objectives to be achieved concerning monitoring, and the available resources. Finally, it is highlighted that using Internet of Things (IoT) sensors in construction and water collection systems has proven beneficial in detecting possible effects on its operation and determining consumption and supply flows for a given population.  
Internet of Things (IoT); smart IoT sensors; fog catcher tower; drinking water network; cost-to-profit; non-revenue water (NRW)
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.8 Antimicrobial Activity of the Hydrolysates

Table 4: Antibacterial activity against gram-positive and gram-negative bacterial strains (expressed as the diameter of the inhibition zones) of different concentrations (50–800 ug/mL) of black, red and white kidney bean protein hydrolysate (BKH, RKH and WKH) obtained after 30 min Alcalase hydrolysis at 37 \({}^{\circ}\)C.  
Figure 3: Free radical scavenging activity (**A**) and antioxidant activity (**B**) of protein Alcalase hydrolys of black, red and white kidney bean protein (BKH, RKH, WKH). Protein Alcalase hydrolysis was conducted at 37 \({}^{\circ}\)C, pH 6 for 30 min. Means with different lowercase letters indicate significant differences at \(p\leq 0.05\) by LSD. The data show that BKH has the lowest MIC values (25-45 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro 2}\)/mL) against the six studied bacteria, representing only less than 50% and 25% of the RKH, and WKH respective values, respectively. At higher concentrations, BKH could completely inhibit the bacteria recording MBC in the range 50-85 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro 2}\)/mL against 110-175 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro 2}\)/mL in the case of WKH. [PERSON], et al. [43] reported similar levels of IZDs of 20.26 mm and 19.23 mm, of red kidney beans hydrolysate against _Escherichia coli_, and _Pseudomonas aeruginosa_, respectively. [PERSON] and [PERSON] [75] found that white kidney bean protein hydrolysate produced by 4 h papain-hydrolysis inhibited the growth of tested bacteria with IZDs in the range of 70-90 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro 2}\)/mL. The antibacterial action of peptides depends on bacterial species. First, the peptides may electrostatically bind to the bacterial membranes. Therefore, the intercellular structures and processes, such as cell wall synthesis, DNA, RNA, and protein synthesis could be affected and the plasma membrane may be interrupted [84].