esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 1 Introduction  
The Shanghai Synchrotron Radiation Facility (SSRF) is a third-generation synchrotron light source equipped with a 150 MeV linear accelerator, a 3.5 GeV booster, a 3.5 GeV storage ring, beamlines, experimental endstations, and supporting facilities. SSRF opened to users in 2009 with 7 Phase-I beamlines. Another 6 beamlines were built as part of the Follow-up Beamline Program (FBP) in the next few years. In 2016, SSRF launched the largest follow-up beamline construction project--SSRF Phase-II Beamline Project. Table 1 shows the detailed overview of the 16 state-of-the art beamlines of the SSRF Phase-II Beamline Project. When the Phase-II project completes by the end of 2023, Shanghai Light Source will have nearly 40 beamlines in operation supporting users all over the world [1,2]. The SSRF Phase-I and the FBP beamline control system are normally based on the Experimental Physics and Industrial Control System (EPICS) [3, 4, 5], while there is no unified, extensible and interface-standardized framework for the data acquisition system for the experimental endstations. Table 2 shows the development environment of the endstation data acquisition systems at the SSRF Phase-I Project and the FBP beamlines [6, 7, 8, 9, 10, 11, 12]. As can be seen from the table, the development environment is complex and diverse, from c language to Blu-Ice/DCS, which makes maintenance and upgrade of these data acquisition systems extremely difficult and labor-intensive. Compared with the SSRF Phase-I and the FBP, the SSRF Phase-II Project aims to greatly expand the experimental capacity of SSRF, making the resolution of time, space, energy and momentum in SSRF almost reach the limit of the third-generation synchrotron radiation light source. The state-of-the art Phase-II beamlines bring great changes in many aspects, such as sample scan, hardware equipment and data amount. For example, the sample scan expands from one-dimensional and two-dimensional to a multi-dimensional continuous scan. The new generation hardware equipment includes a huge number of area detectors with a larger imaging area and smaller pixels. Additionally, high time resolution accompanied with fast sampling frequency results in a high data-generation rate and high requirements for data transmission peak value. More specifically, beamlines in the SSRF Phase-II Project will have ultra-high spatial resolution of 10 nanometer and ultra-fast time resolution of 100 picosecond. The existing data acquisition system can hardly adapt to the changes mentioned above and meet the requirements of these new beamlines. It is necessary and reasonable to develop a uniform and universal data acquisition system based
## 2 Methodological Background
### Simplified Swarm Optimization (Sso)
#### 2.4.1 Algorithm of SSO  
**STEP S0**. Generate \(X_{0i}\), calculate \(F(X_{0i})\), let \(X_{0i}=P_{i}\) and \(t=1\), where \(I=1,2,\ldots,N_{pop}\).  
**STEP S1**. Let \(I=1\).  
Figure 3: Flowchart of particle swarm optimization (PSO).  
**STEP S2.** Update \(X_{t-1i}\) to \(X_{ti}\), based on Equation (8) and calculate \(F(X_{ti})\):  
\[X_{ij}^{t+1}=\left\{\begin{array}{cl}X_{ij}^{t}&\text{if }\rho e[0,\,c_{w}]\\ p_{ij}&\text{if }\rho e[c_{w},c_{p}]\\ p_{gbestj}&\text{if }\rho e[c_{p},c_{g}]\\ X&\text{if }\rho e[c_{g},\,1\end{array}\right) \tag{8}\]  
**STEP S3.** If \(F(X_{ti})\) is better than \(F(P_{i})\), then let \(P_{i}=X_{ti}\). Otherwise, go to STEP S5.  
**STEP S4.** If \(F(P_{i})\) is better than \(F(P_{gbest})\), then let gbest = \(i\).  
**STEP S5.** If \(I<N_{pop}\), let \(I=I+1\) and go to STEP S2.  
**STEP S6.** If \(t=N_{gon}\) and/or CPU time are met, then halt; otherwise, _let_\(t=t+1\) and go back to STEP S1.  
where  
\(X_{ti}\): \(X_{ti}=(X_{ti1},\,X_{ti2},\,\ldots,\,X_{tiNur})\) is the \(i\)th solution at the \(i\)th generation, where \(I=1,\,2,\,\ldots,\,N_{pop}\) and \(t=1,\,2,\,\ldots,\,N_{gen}\).  
\(F()\): the fitness function value of the solution.  
\(P_{i}\): \(P_{i}=(p_{i1},p_{i2},\,\ldots,\,p_{iNur})\) is the current pbest with respect to the \(i\)th solution, where \(I=1,\,2,\,\ldots,\,N_{pop}\).  
\(N_{pop}\): the number of SSO solutions in each generation.  
\(\rho\): random number uniformly distributed in \([0,\,1]\).  
\(C_{w}\), \(c_{p}\), \(c_{g}\): parameters that represent the probabilities of the new variable value generated from the current solution, pbest, and gbest.  
Pbest, gbest: pbest represents the best individual solution achieved so far; gbest represents the best value of all solutions so far.  
\(N_{gen}\): total number of independent generations in SSO.
## 4 Findings  
### Organizational Dynamics Analysis  
We aimed to forecast manpower changes in five years from 2019 to 2024. We found that approximately 21% of researchers are retiring due to age limits. This had tremendous implications in the functioning of a research institution. Currently, most of the 25 public research organizations had a reverse pyramid structure, where the composition of senior researchers takes the \"top heavy\" part. This also implies that in the next five years, if no special changes happen, younger Ph.Ds. will occupy the top positions, which will bring the organizational shape back to the \"normal\" pyramid type until the same organizations turn into a reverse pyramid shape in the future. This point necessitates a retooling of manpower structure in these institutions in the 4 th industrial revolution era so that research organizations may not naturally follow the cyclical changes from normal pyramid to reverse pyramid shape. In fact, empirical findings have confirmed the point.  
Another way of interpreting the implication for the organizational shape is based on the data this study analyzed. While it is difficult to determine who--Senior researchers or junior researchers--Contribute more to organizational performance in a cold context, because different ways of proving will always override the opposite hypothesis, Table 3 shows an indirect implication.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**1 st Stage** & **2 nd Stage** & **3 rd Stage** \\ \hline \multirow{6}{*}{Scientific/Engineering Excellence} & \multicolumn{2}{c}{Necessity for research} & \multirow{2}{*}{\begin{tabular}{c} \\ Levels of research objectives \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Articles/patens per researcher \\ Profitability of outcomes \\ Growth potentials o research \\ Spin-off potentials \\ \end{tabular} } \\  & \multicolumn{2}{c}{Innovation strategy} & \multirow{2}{*}{\begin{tabular}{c} R\&D intensity \\ R\&D manpower \\ R\&D cooperation \\ R\&D investment \\ \end{tabular} } \\  & \multicolumn{2}{c}{Efficiency in budget} & \multirow{2}{*}{\
\begin{tabular}{c} Organizational efficiency \\ R\&D budget efficiency \\ \end{tabular} } \\ \hline \multirow{6}{*}{Efficiency in Man power \& social values} &
\begin{tabular}{c} Socioeconomic perspective \\ Public interest perspective \\ Future change Adapatability \\ Infra structure \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: AHP weighting elements.  
The upper part of Table 3 shows that seniority matters, because senior researchers excelled in both articles and patents. However, considering the bottom part of the table expressed in \"pure\" money terms, there was no difference across the three groups. It could mean that we need researchers from all three age brackets to achieve better results. This coincides with the academic finding of the \"I\" shape organization model for research institutes.
# Dynamic and Stability Analysis of Multibolt Plane Joints under Normal Forces
## 4 Force-Displacement Characteristic of the Bolt Joint Interface  
To analyze the dynamic performance of the system, the stiffness should be obtained. On the basis of Equation (14) in Section 3, kinetic equation without inertia force and damping force illustrates the relationship between the normal external load \(F_{n}\) and \(\Delta x\) under slow load rate, which is given by  
\[F_{n}(\Delta x)=\left\{\begin{array}{ll}(K_{\textit{bases}}+K_{\textit{bolts }})\Delta x-K_{\textit{bases}}f^{-1}(\Delta x),&\Delta x\leq\Delta x_{ \textit{hypre}}\\ K_{\textit{bases}}\Delta x-K_{\textit{bases}}f^{-1}(\Delta x)+F_{\textit{pre}},& \Delta x>\Delta x_{\textit{hypre}}\end{array}\right.. \tag{20}\]Furthermore, with the ignorance of unevenness, Equation (17) can be simplified as  
\[F_{n}(\Delta x)=\left\{\begin{array}{ll}K_{bolts}\Delta x-Mg-F_{pre},&\Delta x \leq\Delta x_{apre}\\ (K_{buses}+K_{bolts})\Delta x,&\Delta x_{apre}<\Delta x\leq\Delta x_{bpre}\\ K_{buses}\Delta x+F_{pre},&\Delta x>\Delta x_{bpre}\end{array}\right.. \tag{21}\]  
Taking the derivative of \(F_{n}\) with respect to \(\Delta x\), the total stiffness \(K_{n}\) is written as  
\[K_{n}(\Delta x)=\left\{\begin{array}{ll}\frac{K_{base}K_{s}\left[f^{-1}( \Delta x)\right]}{K_{base}+K_{s}\left[f^{-1}(\Delta x)\right]}+K_{bolts},& \Delta x\leq\Delta x_{bpre}\\ \frac{K_{base}K_{s}\left[f^{-1}(\Delta x)\right]}{K_{base}+K_{s}\left[f^{-1}( \Delta x)\right]},&\Delta x>\Delta x_{bpre}\end{array}\right.. \tag{22}\]  
When roughnesses are overlooked, the expression of \(K_{n}\) is transformed into  
\[K_{n}(\Delta x)=\left\{\begin{array}{ll}K_{bolts},&\Delta x\leq\Delta x_{ apre}\\ K_{buses}+K_{bolts},&\Delta x_{apre}<\Delta x\leq\Delta x_{bpre}\\ K_{buses},&\Delta x>\Delta x_{bpre}\end{array}\right.. \tag{23}\]  
The influence of different manufacturing methods on the static behavior of the system is shown in Figure 3. As demonstrated in Figure 3a, the slope of curves increases except for that of ideal surfaces, indicating a hardening nonlinear behavior when \(\Delta x>0\) and a softening nonlinear behavior when \(\Delta x<0\). This phenomenon is more apparently for scraping/scraping joint which has a smoother equivalent surface in \(-0.5\times 10^{-5}\) m \(<\Delta x<0.5\times 10^{-5}\) m. With the decrease of the displacement in the negative direction, these curves are closer to each other. It is caused by the interface separation of the contact pairs. Figure 3b shows that the stiffness without the consideration of asperities is piecewise-linear over the whole displacement range and greater than others when \(\Delta x>0\). All the curves intersect
## 4 Results
Moreover, the wind energy potentials of the districts were also calculated to compare between the districts and give efficient information to decision makers. Table 6 demonstrates the wind energy potential of each district, according to different wind speed threshold values. In a study conducted by the Turkish Atomic Energy Agency on alternative energy sources, the lowest wind speed that can be used for electricity generation was determined to be 6 m/s [50]. According to the International Electrotechnical Commission (IEC) standard, the minimum technical wind speed is expressed as 6 m/s by [PERSON] et al. [42]. For these reasons, the minimum speed was determined to be 6 m/s in this study. Other threshold values were determined in order to compare the wind energy potentials of the districts. The wind speed values were divided into certain ranges and the number of cells in the specified ranges and corresponding total areas were determined in order to more clearly understand the wind energy potential of the Balikesir Province at a height of 100 m (Table 7). The wind energy potential values in Tables 6 and 7 were derived from the calculation process carried out by using the database files of the point-based shapefile of the wind speed maps. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name of District**} & \multicolumn{3}{c}{**Wind Energy Potential (MW)**} \\ \cline{2-4}  & **>6 m/s** & **>7 m/s** & **>8 m/s** \\ \hline Ayvalik & 548.49 & 0.00 & 0.00 \\ Balya & 5758.53 & 3780.93 & 2416.78 \\ Bandrma & 4957.45 & 4839.97 & 3668.77 \\ Bigadic & 848.04 & 0.00 & 0.00 \\ Burhaniye & 1625.17 & 929.52 & 265.30 \\ Dursunbaye & 334.24 & 23.90 & 0.00 \\ Edemit & 2031.30 & 236.08 & 83.45 \\ Erdek & 1639.09 & 1466.05 & 427.49 \\ Gómez & 674.11 & 0.00 & 0.00 \\ Gómez & 2010.05 & 275.84 & 0.00 \\ Havran & 2986.36 & 1451.84 & 51.75 \\ Ivrnidi & 1294.61 & 56.03 & 0.00 \\ Kepsut & 0.00 & 0.00 & 0.00 \\ Manyas & 1679.05 & 0.00 & 0.00 \\ Marmara & 664.25 & 553.82 & 0.00 \\ Merkez (Alteyjul-Karesi) & 875.36 & 64.96 & 0.00 \\ Savasetpe & 0.00 & 0.00 & 0.00 \\ Sundrgi & 0.00 & 0.00 & 0.00 \\ Susurluk & 1101.49 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The wind energy potential of districts. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wind Speed Range**} & **Number of** & **Total Area** & **Wind Potential (P)** \\
**(m/s)** & **Cells** & **(km2)** & **(MW)** \\ \hline
2.5–4.5 & 42,794 & 1711.76 & 1345.22 \\
4.5–6.0 & 157,450 & 6298.00 & 16,702.09 \\
6.0–7.0 & 76,122 & 3044.88 & 15,325.79 \\
7.0–7.5 & 14,768 & 590.72 & 4127.50 \\
7.5–8.0 & 7616 & 304.64 & 2601.31 \\
8.0–8.5 & 66
# Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem
## 3 Results
### Comparison of the Diel Patterns of CO\({}_{2}\) Fluxes  
To investigate the influence of different vegetation activities on carbon fluxes, we first compared the monthly diel variations of CO\({}_{2}\) fluxes in 2019 and 2020 (Figure 5). NEE and GPP illustrated similar patterns with clear diel cycles in the growing season (i.e., March to July 2019 and February to April 2019, respectively) and constrained diel variations in the non-growing season. At the beginning of the growing season (e.g., March and April 2019; February and March 2019), NEE exhibited a U-shaped diel variability with the peak CO\({}_{2}\) uptake around 11:00-12:00 Pacific Standard Time (PST). In the late months of the growing season (e.g., May to July 2019; April 2019), the magnitude of NEE reached its maximum value in the morning, then decreased slightly and reached a plateau in the early afternoon. The different diel patterns of NEE in the early and later growing seasons suggest different responses of vegetation types (grasses and shrubs) to environmental drivers. Regarding the monthly diel variations of ecosystem respiration, the calculated R\({}_{\text{eco}}\) followed similar diel patterns with minimum and maximum values appearing in the early morning and late afternoon, respectively. The difference between the maximum and minimum values was less than 0.3 umol m\({}^{-2}\) s\({}^{-1}\). In months with a larger SWC and high temperature, R\({}_{\text{eco}}\) usually had larger values and larger amplitudes of diel variations.  
Figure 5: Comparison of the monthly diurnal average (**a**) net ecosystem CO\({}_{2}\) exchange (NEE), (**b**) gross primary production (GPP), (**c**) ecosystem respiration (R\({}_{\text{eco}}\)), (**d**) soil respiration (R\({}_{\text{soil}}\)), (**e**) sensible heat flux (H), and (**f**) latent heat flux (LE) in 2019 and 2020. The shaded area denotes the standard errors of the 30-min mean fluxes.
## 4 Discussion
### Optimal Welding Conditions for T-Joint  
The effects of beam inclination angle, beam offset from the flange and the focal point position relative to the surface of the material were studied to gain insight into the applicability of each setup under industrial conditions. Table 5 summarizes the findings.  
The optimal parameters for all set-ups were inclination angle 6\({}^{\circ}\), beam offset from the flange 1 mm, and focal point position \(-\)4 mm below the surface of the material. In all of the setups, the axis of the weld was aligned along the direction of the beam propagation. For this reason, when the beam was aimed past the root of the joint, the formed molten pool did not follow the joint plane, resulting in a lack of fusion at the back of the weld. However, the applicability of a beam inclination angle of 6\({}^{\circ}\) in industrial applications may be limited because of a danger of collision of the laser welding head while maneuvering in restricted space, especially in situations where the incident beam side of the flange exceeds the focal length of the laser.  
From the industrial point of view, the most versatile solution for T-joints of the three process fibers tested would be the fiber with a core diameter of 600 \(\upmu\)m. This setup produced top beads superior in quality to the two other setups studied. The welds made with the 200 \(\upmu\)m and 300 \(\upmu\)m process fibers were deep yet extremely narrow at the deepest section of the weld and prone to undercut at the surface. A setup with a 600 \(\upmu\)m fiber results in a more stable process that has a greater tolerance for beam displacement and smaller probability for seam imperfections.
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 2 Microgrid Operation Structure
### Model of Distributed Generations  
#### 2.1.1 Photovoltaic Array  
As one of the main sources of RE, solar power can harness sunlight in real time and convert it into electricity for load use. The model for the output power of a PV is shown as follows:  
\[P_{\text{PV}}=P_{m}\frac{I}{I_{\text{STC}}}[1+\alpha(T-T_{M})] \tag{1}\]  
where \(P_{\text{PV}}\) is the current output power of the PV, \(P_{m}\) is the output power of the PV under standard test conditions, \(I\) is the current solar irradiance, \(I_{\text{STC}}\) is the solar irradiance under standard test conditions, \(\alpha\) is the temperature coefficient of the PV, \(T\) is the current temperature of the PV, and \(T_{M}\) is the temperature of the PV under standard test conditions.
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
## 5 Conclusions  
Three grain sizes of the clean Nakdong river sand were poured into a small compaction mold and subsequently applied with surcharge. The series of EMI sensing experiments were performed on the three different grain sizes of sands. Four levels of surcharge (6, 11, 17, 23 kPa) were applied to the surface of the sand. The results were compared in terms of the peak frequency and conductance and may be listed as follows:  
1. With an increase in surcharge levels, the peak frequency demonstrated the corresponding increase, but the peak conductance was found to decrease.
2. The unit weight of the sand demonstrated a strong correlation with the measured peak frequency and conductance, except for the peak conductance of large grain sized sand.
3. The relationship between the peak frequency or conductance and unit weight of sand could be found useful in predicting the degree of soil compaction, loosening, or cavitation in the field.  
Figure 10: Effect of unit weight increase on peak frequency and conductance for three sand types: (a) Relationship between the peak frequency and the unit weight; (b) relationship between the peak conductance and the unit weight.  
**Author Contributions:** Conceptualization, S.-S.P. and J.-C.L.; methodology, J.-S.L. and D.-E.L.; formal analysis, S.-S.P. and J.-C.L.; writing original draft preparation, J.-S.L.; writing review and editing, S.-S.P. and J.-C.L.; supervision, S.-S.P.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2018R1A5A1025137).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 4 Results and Discussion  
### In Key Management Protocols  
The key management protocol in WSNs plays a vital role in ensuring the security of the system to build and to maintain security, especially in environments with special security risks and limited resources. The choice of key management protocol for WSNsdepends on many factors, especially on the computing and communication capabilities of sensor nodes according to the purpose of each specific application.  
In this subsection, several key management protocols for sensor networks are considered. The analysis and comparative results show that all the proposed key management protocols have some certain advantages, and all the studies prioritize the ability to balance memory, processing, and communication factors (Table 2). Currently, some proposed key management protocols assume that the base station is trusted (LEAP). However, there might be some situations (e.g., on the battlefield) where a base station security needs to be considered. Therefore, it is necessary to research the design of key management protocols that consider the security of base stations. Additionally, revoking existing nodes and adding new nodes are other challenges when designing key management for WSNs.
### Analytical Forming Force Prediction Models  
Iseki's Model: [PERSON] proposed the first analytical ISF force model [11], assuming plane-strain conditions, uniform sheet deformation, and neglecting friction for its derivation. Indeed, its origin can be traced back to the basic relation between tensile force, stress, and cross-section area, \(T=\sigma A\) as,  
\[T=\left(\frac{2}{\sqrt{3}}\right)^{(n+1)}K\;\varepsilon_{x}^{n}\;d_{t}\;t_{0} \;exp(-\varepsilon_{x}), \tag{1}\]  
where \(T\) is the tensile force aligned with the wall direction, \(K\) is the strength coefficient, and \(n\) is the strain-hardening exponent of the power law utilized to model the material work hardening, \(d_{t}\) is the tool diameter (as shown in Figure 1), and \(t_{0}\) is the initial blank thickness. \(\varepsilon_{x}\) is the strain in the wall direction and can be estimated using the alternative form of the conventional sine law,  
\[\varepsilon_{x}=\ln\left(\frac{1}{\cos\alpha}\right) \tag{2}\]  
where \(\alpha\) is the forming angle.  
Finally, the force in the direction of the tool axis, \(F_{z}\), can be computed from the tensile force, T, as  
\[F_{z}=T\sin\alpha. \tag{3}\]  
Aerens' Model: [PERSON] et al. [12] proposed an equation to predict the axial forming force of truncated Uniform Wall Angle (UWA) cones.  
\[F_{Zs}=0.0716\;R_{m}\;t_{0}^{1.57}\;d_{t}^{0.41}\;\Delta h^{0.09}\;\alpha\; \cos\alpha \tag{4}\]  
Such equation is a correlation originating from the results of an experimental campaign conducted on five different metallic alloys, where \(R_{m}\) is the tensile strength of the formed material and \(\Delta h\), the scallop height, is computed from the distance between two forming layers. Notice that the experimentally observed dependence of the axial force on the wall angle was approximated as \(\alpha\)cos\(\alpha\) in Equation (4).  
Axial force parallel to the tool axis and the predominant SPIF force component of UWA geometries may be estimated with the previous analytical models. However, other  
Figure 1: Schematic representation of the SPIF process.  
promising analytical resources for estimating radial, tangential, and axial forces have been recently introduced [13; 14; 15]. Furthermore, machine learning algorithms are improving the accuracy of predictions for maximum forming force across various conditions [16].  
The previous resources provide valuable insights for designing and safely utilizing SPIF equipment. However, several applications need computing force evolution during the entire forming process. This critical information can only be obtained through simulation. With accurate transient force predictions, simulations become valuable tools for applications such as (a) the compliance compensation of industrial robots [17; 18], (b) the development of failure control strategies [19], or (c) the identification and prevention of other imperfections, such as the pillow effect [20].