esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The CPN Model
### The CPN Model
#### 3.2.2 Token Structure  
A token is a dynamic element of a Petri net, and it is used to define the network's state. Unlike the traditional Petri nets, in the colored Petri net formalism each token can belong to a _token type_ and have its own fields. Here, we define two token types, which we call _job_ and _Next_Selected_. A _job_ has the following fields:  
_Job_Id:_ The ID of a job requesting some resources.  
_Job_Priority:_ The priority of a job, which can be 1 for job that request resources from the DSQ(\(Q_{1}\)), 2 for jobs that request resources from \(Q_{2}\), and 3 for jobs that request resources from \(Q_{3}\). The smaller the priority value, the largest the jobs priority for service. This is important for the simulation part of our work, as will be explained in the next section.  
_Arrival_Time:_ The time a job enters the system to request resources.  
_Service_Time:_ The time it takes for a job to take the requested resources from each queue.  
_Type_of_Resources_Requested (ToRR):_ A binary array of \(m\) elements indicating which of the resource types (\(m\) in total) have been requested. A value of 1 indicates a desirable resource, a value of 0 indicates a non-desirable resource. Whenever a job gets a resource type, the corresponding 1 value changes to 0.  
The _job_ tokens are divided into 3 categories based on their **Priority_Id**: Red, that correspond to jobs that request resources from \(Q_{1}\) (DSQ), Green, that that correspond to jobs that request resources from \(Q_{2}\), and Blue, that correspond to jobs that request resources from \(Q_{3}\). A Red token is characterized by a _Job_Priority_ value equal to 1. Similarly _Job_Priority_ values of 2 and 3 characterize the Green and Blue tokens, respectively. Examples follow  
Red: \(<Job\_Id,Job\_Priority,Arrival\_Time,Service\_Time,Type\_of\_Resources\_Requested>\)\(=\)\(<1,1,20,50,[1,1,1]>\)  
Green: \(<Job\_Id,Job\_Priority,Arrival\_Time,Service\_Time,Type\_of\_Resources\_Requested>\)\(=\)\(<5,2,20,50,[0,1,1]>\)  
Blue: \(<Job\_Id,Job\_Priority,Arrival\_Time,Service\_Time,Type\_of\_Resources\_Requested>\)\(=\)\(<8,3,20,50,[0,0,1]>\)  
When a job leaves one queue to enter another, its priority (thus, its color) also changes. This simple transformation is used for the simulator that will be described in the beginning of Section 4.  
The next_Selected token has two fields:  
_Job_Id:_ The ID of the next job to be selected.  
_Arrival_Time:_ The time this selection takes place (this is the moment from which a new allocation can start).
# Co-Authorship Networks Analysis to Discover Collaboration Patterns among Italian Researchers
## 4 Inside the Data
### Multidisciplinarity  
In this subsection, we aim at answering the following questions: _do researchers belonging to different academic disciplines work together?_ and _does the grouping imposed by the Italian law affect the collaboration patterns among researchers?_ To do so, we merge the networks of the different disciplines together and show the resulting networks in Figure 6.  
Figure 5: Correlation among H-index and bibliometric indices in the academic disciplines, from left to right academic disciplines are: MAT/05, SECS-P/01, ING-INF/05, and INF/01.  
Figure 6: Co-authorship network of the four academic disciplines (ING-INF/05 dark orange, INF/01 cyan, MAT/05 is orange, and SECS-P/01 is violet).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Bibliometric Indices** & **ING-INF/05** & **MAT/05** & **SECS-P/01** & **INF/01** \\ \hline documents count & 0.80 & 0.85 & 0.85 & 0.82 \\ citations count & 0.84 & 0.86 & 0.49 & 0.84 \\ coauthors count & 0.30 & 0.23 & 0.27 & 0.37 \\ Year of activity & 0.38 & 0.43 & 0.49 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: [PERSON]’s correlation among H-index and bibliometric indices in the academic disciplines.  
At first, we limit the analysis to the depth-zero networks Figure 6a, which only include Italian academic researchers. As shown in the plot, the number of collaborations across the disciplines under study is very low. More in detail, there is almost no cooperation between researchers in MAT/05, SECS-P/01, and the computer-science related ones, while researchers ING-INF/05 and INF/01 have coauthored more papers, which is expected since the strong overlap in the research topics [44]. We later extend the networks by including the direct coauthors, i.e., by merging the depth-one networks. The resulting network, depicted in Figure 6b, shows that there is some (indirect) collaboration among groups, and a very strong overlap in the groups of INF/01 and ING-INF/05. (Let us note that in depth-1 and depth-2 some authors can be coauthors of researchers belonging to different academic disciplines, so they are present in both networks; we represent them as blue points). By merging the networks at depth-two, the size of the largest cluster grows a lot, which shows that other researchers (e.g., researchers from the same field but working for private institutions or living abroad) bridge the various groups. Please note that we omit the resulting figure since the network would be too large and dense.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 4 Results
### Forcing Frequency Analysis
For the baseline, maximum efficiency and maximum lift cases, this is presented in non-dimensional form and every 10% of the chord in Figure 12. The set of profiles for both the maximum efficiency and maximum lift cases show only positive velocity values up to around \(x/C=0.7\), entitling no boundary layer separation appears until this streamwise position. For the maximum efficiency case, at around \(x/C=0.7\), negative averaged velocities start appearing close to the wall, returning to a completely positive profile at \(x/C\approxeq 0.8\), which matches perfectly well with the small laminar bubble observed in Figure 11a. A slightly different behaviour is observed for the maximum lift case, where Figure 12 shows how the averaged velocities close to the wall become negative for the streamwise positions ranging from \(x/C=0.7\) and \(x/C=1\). A direct connection between the boundary layer thickness just presented and what it is observed in Figure 11b can be made, the elongated and downstream growing vortex is clearly observed in both figures. When observing the negative velocity distributions associated with the maximum lift case, the maximum lift case is not observed in the same figure. Figure 11: Streamlines of the averaged flowfield for the optimal and maximum lift configurations. (**a**) Streamlines of the averaged flowfield for the optimal configuration. (**b**) Streamlines of the averaged flowfield for the maximum lift configuration. Figure 10: Pressure coefficient comparison for the baseline case and maximum efficiency and maximum lift configurations. to the maximum lift case from Figure 12b, and when comparing them with the velocity distributions corresponding to the baseline case, it can be concluded that the intensity associated to the maximum lift case vortex is much smaller than the one corresponding to the baseline case, therefore the maximum lift vortex must rotate with a low angular velocity. The vortex generated for the maximum efficiency configuration at around \(x/C=0.7\) has a larger vorticity magnitude than the maximum lift one, but still lower than the vorticity associated to the baseline case vortex. For the baseline case, negative averaged velocity values can be observed from the streamwise position \(x/C=0.1\) up to \(x/C=1\), indicating a rapid separation of the boundary layer that extends for the whole airfoil. In reality, the negative averaged velocities cannot be clearly seen at streamwise position \(x/C=0.1\) in Figure 12a, but the separation point can be easily localized around \(x/C=0.1\) in Figure 12a. At \(x/C=1\), slightly positive averaged velocities can be observed close to the wall due to the appearance of a counter rotating vortex generated at the trailing edge, as observed in Figure 8.  
Figure 12: Mean velocity profiles for the baseline, maximum E and maximum L cases, from \(x/C=0.1\) to \(x/C=1\). (**a**) Velocity profiles of the mean velocity from \(x/C=0.1\) to \(x/C=0.5\). (**b**) Velocity profiles of the mean velocity from \(x/C=0.6\) to \(x/C=1\).
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 2 Star Pattern and Catalog
### Lookup Table Generation  
The identification algorithm is divided into two parts: initial matching and similarity calculation. In the initial matching process, we use the distance ratio between the reference star and neighboring stars, as well as the angles between adjacent neighboring stars, as features for subsequent matching. A lookup table (LUT) is generated by precomputing and storing the features of star patterns, including distance ratios and angles, enabling efficient retrieval during the matching process.  
According to the feature \(\{r_{k},\theta_{k}\}\), we construct a two-dimensional lookup table to store stars that share this feature together, thereby reducing redundant matches and further improving the efficiency of initial matching. In this lookup table, the distance ratio \(r_{k}\) and angles \(\theta_{k}\) between neighboring stars are discretized as indices \(\{i_{k},j_{k}\}\), while the stored values consist of the stars ID \(id_{star}\) that possess this feature and the corresponding positions \(k_{start}\).  
\[i=\lceil\frac{r_{k}}{Interval_{r}}\rceil \tag{4}\]  
\[j=\lceil\frac{\theta_{k}}{Interval_{\theta}}\rceil \tag{5}\]  
\[LUT(i,j)=\{id_{star},k_{start}\} \tag{6}\]  
where \(\lceil x\rceil\) denotes the smallest integer greater than a given value, and the intervals for distance and angle partitioning are represented by \(intervals_{r}\) and \(intervals_{\theta}\), respectively. Each time an initial match is performed, the search is reduced to a sufficiently small area. Subsequent matching is then carried out within this reduced search space.
## 3 Results  
Now, the numerical simulation of the tensile test is performed for the constants given in Table 4. The profiles of specimen measured from the numerical simulation and the quasi-static test are compared in Figure 13. The GTN model does not apparently take account of the effect of strain rate. However, the model, depending on the loading rate and the predefined solution time, calculates the strain rate and consider its effect on the voids growth.  
In this work, the constants of GTN model are determined using the results of quasi-static tensile test. The constants are then used for simulation of dynamic test. The profile of the specimen after fracture is compared with that obtained from the experiment in Figure 14. The error in predicting the reduction of diameter of the specimen after failure is worked out to be only (1.5%) which is reasonable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(f_{\epsilon}\) & \(f_{0}\) & \(f_{n}\) & \(f_{f}\) & **Error \%** \\ \hline
0.154 & 0.012 & 0.048 & 0.246 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The final constants of GTN damage model obtained using the Kriging method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Initial Samples**} & \multicolumn{4}{c}{**Constants of GTN Damage Model**} & \multirow{2}{*}{**Error \%**} \\ \cline{2-2} \cline{4-5}  & \(f_{\epsilon}\) & \(f_{0}\) & \(f_{n}\) & \(f_{f}\) & \\ \hline
15 & 0.150 & 0.012 & 0.047 & 0.246 & 0.11 \\
10 & 0.154 & 0.012 & 0.048 & 0.246 & 0 \\
9 & 0.155 & 0.012 & 0.049 & 0.245 & 0.60 \\
8 & 0.151 & 0.011 & 0.048 & 0.250 & 0.95 \\
7 & 0.154 & 0.012 & 0.048 & 0.251 & 0.59 \\
6 & 0.145 & 0.013 & 0.048 & 0.254 & 2.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of Kriging method with different numbers of initial samples.  
Some damage models such as [PERSON] and Tracey suggest that there is a direct relation between voids' growth rate and magnitude of stress triaxiality. Moreover, according to [PERSON] theory, the stress triaxiality parameter is maximum in the center of the sample notch. Hence, considering these two principals, it can be assumed that the voids' coalescence and consequently the rupture of the sample initiates from the center of samples. This is consistent with the results obtained from Gurson model in the current study. According to [PERSON] theory [33], the stress triaxiality component is maximum in center of the necked section of specimen. Thus, it may be assumed that voids' initiate and coalesce from the center of notches. In numerical simulation using GTN model this phenomenon was well predicted. Figure 15 illustrates estimated positions for coalescence of voids in numerical simulation.  
Figure 14: A comparison between the profiles of specimen measured from numerical simulation and dynamic test.  
Figure 13: A comparison between the profiles of specimen measured from numerical simulation and quasi-static test.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods  
### Data Preprocessing  
As Figure 2 shows, data preprocessing includes two streams: processing Sentinel-2 LIC images and Landsat-8 surface reflectance products to generate fused time series with a spatial resolution of 10 m, and processing NPP VIIRS nighttime light data to define the boundary of urban and rural areas.
## 5 Conclusions  
In this study, we evaluated the CLM4.5-simulated SLA against the observed data collected from China and examined the impacts of SLA variation on GPP simulation using the CLM4.5 model. The results showed that CLM4.5 overestimated the default SLA values at the top of canopy for 11 PFTs and the canopy gradient of SLA for temperate BDT and temperate NET. The higher default SLA values in temperate NET, temperate BDT, and C3 grass caused an underestimation in the modeled GPP at the QYZ, CBS, and HBG sites compared with the results from the mean SLA observations. Substantial SLA variation could cause great changes in modeled GPP, especially for temperate NET and temperate BET. Our study suggested that the interspecific variation in SLA within a given PFT and its responses to environmental changes should be considered in terrestrial biosphere models to reduce the uncertainties in GPP and LAI estimations. More efforts are needed to make full use of the plant trait database to understand the underlying mechanisms of trait variation and to promote model development so as to enhance the prediction ability of ecosystem responses to future climate changes.  
Figure 8: The SLA values of different PFTs under three soil moisture regimes (**a**) and different N addition levels (**b**). The different lowercase letters indicate significant differences at 0.05 level. More details about the species can be found in Table S2 [58; 59; 60; 61; 62; 63; 64; 65].  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f14010164/s1](https://www.mdpi.com/article/10.3390/f14010164/s1)]([https://www.mdpi.com/article/10.3390/f14010164/s1](https://www.mdpi.com/article/10.3390/f14010164/s1)), Table S1: The SLA values for different plant functional types between the CLM4.5 model and the observation data in China; Table S2: Plant SLA information in multiple environmental factor experiments.  
**Author Contributions:** Conceptualization, L.Z. and Y.Z.; Methodology, Y.Z. and L.Z.; Software, Y.Z., P.L. and Y.L.; Validation, P.L. and Y.Z.; Formal Analysis, Y.Z., L.Z. and X.R.; Resources, Y.Z., P.L., H.H. and Y.M.; Writing--Original Draft Preparation, Y.Z.; Writing--Review and Editing, L.Z., X.R. and H.H.; Funding Acquisition, L.Z. and H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China (31971512, 42030509, 42141005, 31988102).  
**Data Availability Statement:** The China plant SLA dataset supporting the results is publicly available. This dataset can be found here: [[https://www.scidb.cn/s/IBVUZj](https://www.scidb.cn/s/IBVUZj)]([https://www.scidb.cn/s/IBVUZj](https://www.scidb.cn/s/IBVUZj)) (accessed on 6 August 2022).  
**Acknowledgments:** We thank all related staff of Qianyanzhou site, Changbaishan site, Dinghushan site, and Haibei site from ChinaFLUX and CERN for their contributions, from observations to data processing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 1 Introduction
With the improved structure of the network model, the revised algorithm can perform better target recognition than the traditional machine vision algorithm by virtue of its superior feature learning ability [15; 16; 17; 18; 19; 20]. Aware of the fact that the YOLO algorithm is equipped with a satisfying detection precision and detection speed in defect recognition, [PERSON] et al. [21] introduced a confidence loss function and a cross attention network based on a gradient-harmonized mechanism into the YOLOv4 model framework, with which they detected defects on ceramic substrates. [PERSON] et al. [22] adopted Canopy with K-means clustering to improve the K-means clustering in YOLOv4 and then acquired the prior bounding box, thereby detecting the defects of the bare PCB board. [PERSON] et al. [23] applied YOLOv5 to the detection of appearance defects in cigarettes, such as spots, wrinkles, and the wrong teeth on the surface. However, there is still room for improvement in the precision of defect recognition and real-time detection speed when it comes to hidden defect features, multi-scale defects, and defects with similar features. Therefore, this paper proposed an illumination method with total reflection lighting, which aims to accurately distinguish dust and point defects according to their characteristic differences in the imaging field. YOLOv5, a deep learning network with excellent performance, was employed to improve the method, allowing, in its application, the detection of scratches, point defects, stains, and dust on mobile phone flat glass, as well as industrial real-time detection.
# Analytical and Graphical Profiling of Thread-Milling Cutters for Forming Internal Threads
## 5 Research Methodology and Experimental Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Name of the Parameter**} & **Designation** & \multicolumn{2}{c}{**Value**} \\ \hline \hline \multicolumn{5}{c}{Thread parameters M20 \(\times\) 2} \\ \hline Thread pitch & P, mm & 2 \\ \hline Outer diameter & D, mm & 20 \\ \hline Medium diameter & D\({}_{2}\), mm & 18.701 \\ \hline Inner diameter & D\({}_{1}\), mm & 17.835 \\ \hline Profile angle & \(\alpha_{\ast}\)\({}^{\circ}\) & 60 \\ \hline \multicolumn{5}{c}{Topical parameters M10 \(\times\) 1.5} \\ \hline Outer diameter of the milling part of the thread-milling cutter & d\({}_{0}\) & 7.876 mm \\ \hline \multicolumn{5}{c}{Topical surface profile parameters} \\ \hline \hline \multicolumn{5}{c}{Topical surface center trajectory radius} & A\({}_{0}\), mm & 4.062 \\ \hline \multicolumn{5}{c}{Points from the tool surface profile} & point 1 & point 3 & point 5 \\ \hline \multicolumn{5}{c}{Results obtained using the analytical method} \\ \hline Radius of the profiled circle & r\({}_{01}\), mm & 3.9485 & 3.4606 & 3.1354 \\ \hline Distance from point 1\({}^{\prime}\) to point i along the “y” axis of the profiling circle & h\({}_{01}\), mm & 0 & 0.4878 & 0.8131 \\ \hline Distance from point 1\({}^{\prime}\) to point i along the “z” axis of the profiling circle & z\({}_{01}\), mm & 0 & 0.2813 & 0.4688 \\ \hline Tool profile angle & \(\alpha_{0i_{\ast}}\)\({}^{\circ}\) & 0 & 59.9292 & 59.9260 \\ \hline Deviation in profile angle & \(\Delta\alpha_{0i_{\ast}}\)\({}^{\circ}\) & 0 & 0.0708 & 0.0740 \\ \hline \multicolumn{5}{c}{Results obtained using the graphical method} \\ \hline Radius of the profiled circle & r\({}_{01}\), mm & 3.9468 & 3.4590 & 3.1340 \\ \hline Distance from point 1\({}^{\prime}\) to point i along the “y” axis of the profiling circle & h\({}_{01}\), mm & 0 & 0.4878 & 0.8128 \\ \hline Distance from point 1\({}^{\prime}\) to point i along the “z” axis of the profiling circle & z\({}_{01}\), mm & 0 & 0.2813 & 0.4688 \\ \hline Tool profile angle & \(\alpha_{0i_{\ast}}\)\({}^{\circ}\) & 0 & 59.9328 & 59.9450 \\ \hline Deviation in profile angle & \(\Delta\alpha_{0i_{\ast}}\)\({}^{\circ}\) & 0 & 0.0672 & 0.0550 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of the profile of the tool surface intended for forming an internal thread M20 \(\times\) 2, obtained analytically and graphically.
## 4 Equations of State for Modeling the Solubility of Acid Gases  
Many equations of state have been used by researchers to model the solubility of acid gases in ILs. Two of the most widely used EoS are the Soave-Redlich-Kwong (SRK) and the Peng-Robinson (PR) EoS, which are defined with Equations (10) and (15), respectively [35]. The results of these two EoS are used to contrast those obtained by using the proposed models.  
Noting that the calculation of solubility using EoS is related to the calculation of the mole fraction, the equations below express how to determine the mole fraction.  
Soave-Redlich-Kwong (SRK) EoS:  
\[P\ =\ \frac{RT}{v-b}-\frac{a(T)}{v(v+b)} \tag{10}\]  
where \(T\), \(P\), \(v\), and \(R\) indicate the temperature, pressure, molar volume, and gas constant respectively, and \(a\) and \(b\) represent the EoS variables.  
For computing the variables, a and b in the case of mixtures, the classic van der Waals one-fluid rules of mixing is used [58]:  
\[a\ =\ \sum_{i\ =\ 1}^{N}\sum_{j\ =\ 1}^{N}x_{i}x_{j}\sqrt{a_{i}a_{j}}\Big{(}1-K _{ij}(T)\Big{)};\ b\ =\ \sum_{i\ =\ 1}^{N}x_{i}b_{i} \tag{11}\]  
with \(x_{i}\) and \(x_{j}\) denote the mole fractions of components \(i\) and \(j\), and \(N\) indicates the number of the mixture's components. The parameter \(K_{ij}(T)\) is a parameter of binary interaction, that enlarges the molecular interactions between molecules \(i\) and \(j\). For pure materials, the parameters can be computed as follows:  
\[a_{i}(T)\ =\ 0.42747\frac{R^{2}{T_{c,i}}^{2}}{P_{c,i}}\alpha_{i}(T_{R}) \tag{12}\]  
\[b_{i}\ =\ \frac{0.08664 RT_{c,i}}{P_{c,i}} \tag{13}\]  
\[\alpha_{i}(T_{R})\ =\ \Big{[}1+\Big{(}0.480+1.574w_{i}-0.176w_{i}^{\ 2}\Big{)}\Big{(}1-\sqrt{T_{R}}\Big{)}\Big{]}^{2} \tag{14}\]  
with \(T_{Ri}\ =\ \frac{T}{T_{ci}}\), denotes the reduced temperature. We note that the above description is valid for a system of arbitrarily many components. In our system, it was assumed that we had two components where CO\({}_{2}\) was one and the IL the other.  
Peng-Robinson (PR) EoS:  
\[P\ =\ \frac{RT}{v-b}-\frac{a(T)}{v(v+b)+b(v-b)} \tag{15}\]  
with:  
\[a_{i}(T)\ =\ 0.457235\frac{R^{2}{T_{c}}_{i}{}^{2}}{P_{c,i}}\ast\Big{[}1+\Big{(}0.37 464+1.54226w_{i}-0.26992w_{i}{}^{2}\Big{)}\Big{(}1-\sqrt{T_{R}}\Big{)}\Big{]}^{2} \tag{16}\]  
\[b_{i}\ =\ \frac{0.077796 RT_{c,i}}{P_{c,i}} \tag{17}\]  
For mixtures, the parameters a and b are determined using the same formulas as in SRK EoS. The parameter of binary interaction (\(K_{ij}\)) is the only adjustable parameter for both EoS, SRK, and PR. This parameter is determined using a genetic algorithm (GA) with the next objective function [58]:  
\[f\ =\ \sum_{i\ =1}^{N}\Bigg{(}\frac{x_{solute}^{exp}-x_{solute}^{cal}}{x_{ solute}^{exp}}\Bigg{)}^{2} \tag{18}\]  
with \(x_{solute}^{exp}\) and \(x_{solute}^{cal}\) indicate the experimental and EoS-calculated mole fractions of the solute respectively.