esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Application of Digital Twin Technique in Steel Structures
## 3 Literature Innovation Discussion
### Application Field
Based on these research results, the digital twin technique proves to be advantageous in fatigue assessment through dynamic load feedback and fatigue safety enhancement, particularly in fatigue-prone infrastructures such as steel turbines, steel pipes, steel bridges, and steel storage structures, where fatigue crack detection, fatigue life estimation, and reinforcement decision-making play important roles in ensuring the safety of citizen lives and properties. Real-time monitoring is another advanced application of the digital twin technique that has the potential to be integrated with other emerging techniques, such as the Internet of Things, big data, cloud computing, and artificial intelligence. The integration enables real-time perception, simulation, prediction, and optimization of physical structure, and, therefore, the early warning of structural conditions can be issued. As shown in the diagram presented in Figure 10 from reference [107], it was constraint monitoring in anti-erosion sensing revertment. Real-time monitoring in this situation was performed, in which the steel wire yield stress can be fully detected by the optical fiber continuous sensor. Many studies have also reported applications of real-time monitoring in structural condition identification [4; 107], steelmaking monitoring [12; 115], large-scale facility manufacturing [17; 104], and structural measurement [31; 103]. It is obvious that the digital twin technique can accurately and timely reflect structural behavior, potential problems, and failure tendency influenced by exterior dynamic loads, combined with data acquisition, transmission, processing, analysis, and model updating, thereby providing a scientific basis for decision-making. The digital twin technique also has other applications in steel structure, such as research on special material properties [60; 75], advanced measurement techniques [18; 79], typical manufacturing processes [29; 109], and specialized industries [53; 119]. As an advanced simulation and management technique for steel structure, the digital twin technique offers significant advantages in areas where traditional methods fall short, poising for widespread adoption in Industry 4.0 intelligent manufacturing and smart city infrastructure. The literature distribution related to the application fields of the digital twin technique is presented in Table 3. It can be concluded that the digital twin technique can be applied in various fields of steel structure, especially in healthy monitoring and condition maintaining. Note that different application fields may be suitable for different digital twin methodologies. Specifically, laser scanning is often used in real-time monitoring, finite element analysis is usually used to explore material deformation, and the BIM model is actually used in onlinesystems for infrastructure management and life cycle forecasting, while machine learning has an advantage in dealing with large amounts of data in figure assessments.
## 4 Evaluation and Results
### True Anomaly Calculation  
\(\sigma\left[\Delta v\right]\) plays an important role in the analytical approximation model of QPE, and Figure 3 presents the results from both the numerical simulation and the analytical approximation model. Figure 3a indicates that the results of the analytical approximation model are almost identical to those of the numerical simulation. However, in the analytical approximation model, at \(90^{\circ}\) and \(270^{\circ}\), \(\sigma\left[\Delta v\right]\) reaches zero according to Equation (28), while the numerical simulation result is not zero at that position. In the proposed model, we assume that there are no errors in the term \(p-\left|\mathbf{R}_{sat,ECI}\right|\) in Equation (27), which leads to a value of zero for \(\sigma\left[\Delta v\right]\) when the satellite is positioned at true anomalies \(90^{\circ}\) and \(270^{\circ}\). In fact, at those positions, the errors in the measurements start playing a major role in the \(\sigma\left[\Delta v\right]\) calculation. However, at those two specific positions, \(\sigma\left[\Delta v\right]\) reaches its minimum, thus it does not introduce much error to the analytical approximation model.  
Figure 2: Velocity vector term: numerical simulation results (blue) and analytical approximation results (red). The figures show that the results from the analytical approximation model are consistent with the numerical simulation results. (**a**,**b**) The expected values have no significant relationship with the variation in the true anomaly and are positioned at a relatively low level, i.e., around zero. The differences between the two sets of results in (**c**,**d**) are due to the ignored terms in the approximation derivations.
## 4 Numerical Simulation Results and Analysis
### Estimation of Turbulence Layer Strength Based on TILAS Measurements  
Consider analogous dependencies of the TILAS received power signal variance \(\sigma_{l}^{2}\) (Equation (3))on the turbulence layer position (Figure 5a), as well as the turbulence layer strength (Figure 5b). As seen, \(\sigma_{l}^{2}\) demonstrates comparable level of variability for both \(z\)/\(L\) and \(C_{n}^{2}\). Besides, the function \(\sigma_{l}^{2}(z/L)\) in Figure 5a is not monotonic; strongest signal fluctuations correspond to the turbulence layer placed in the middle of the propagation path. Therefore, the power signal variance is not suitable to be used to define the layer position. However, \(\sigma_{l}^{2}(C_{n}^{2})\) in Figure 5b is monotonically increasing with the increase of the turbulence strength (at least inside the examined interval of \(C_{n}^{2}\)). At the same time, as was confirmed by our numerical analysis, this variance does not depend on the speed of the target, \(v_{T}\). Therefore, if the turbulence layer position \(z\)/\(L\) is already defined from the correlation times estimations and the TILAS parameters and the distance to the target are known independently, the set of functions \(\sigma_{l}^{2}(C_{n}^{2})\) computed for different layer positions can be used for retrieval of the turbulence layer strength.  
Figure 4: **(a)** Dependencies of the correlation time \(\tau_{\text{cor}}\) on the relative position \(z\)/\(L\) of a single turbulence layer of different turbulence strengths, \(C_{n}^{2}~{}=~{}10^{-14}\,\text{m}^{-2/3}\) (solid line), \(10^{-15}\,\text{m}^{-2/3}\) (dashed line), and \(10^{-16}\,\text{m}^{-2/3}\) (dotted line). **(b)** Dependencies of the correlation time \(\tau_{\text{cor}}\) on \(C_{n}^{2}\) for different relative positions of the layer, \(z\)/\(L\) = 0.1, 0.3, 0.5, 0.7, and 0.9, denoted by blue, red, green, purple, and sky-blue lines, respectively. Other parameters are the same as in Figure 3.  
The value of the TILAS received power signal variance for the distributed turbulence case with \(C_{n}^{2}\ =\ 2\times 10^{-15}\,\mathrm{m}^{-2/3}\) and the same Fried parameter as for the single layer model with \(C_{n}^{2}\ =\ 10^{-14}\ \mathrm{m}^{-2/3}\) is close to those obtained for the turbulence layer placed around 0.3 z/L or 0.7 z/L (see dash-dotted and solid lines in Figure 5a). This suggests that the layers positioned close to the middle of the propagation path contribute the most to the power signal variance.
## 7 Numerical Examples
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(l\) & \(nel\) & \(nno\) & \(e_{l}^{1}\) & \(\dfrac{e_{h}^{1}}{e_{2h}^{1}}\) & \(r_{1}\) & \(e_{l}^{2}\) & \(\dfrac{e_{h}^{2}}{e_{2h}^{1}}\) & \(r_{2}\) \\ \hline
3 & 128 & 81 & \(4.878\cdot 10^{-2}\) & \(-\) & \(-\) & \(3.902\cdot 10^{-1}\) & \(-\) & \(-\) \\
4 & 512 & 289 & \(1.222\cdot 10^{-2}\) & \(3.992\) & \(1.997\) & \(1.955\cdot 10^{-1}\) & \(1.996\) & \(0.997\) \\
5 & 2048 & 1089 & \(2.654\cdot 10^{-3}\) & \(4.604\) & \(2.203\) & \(8.492\cdot 10^{-2}\) & \(2.302\) & \(1.203\) \\
6 & 8192 & 4225 & \(5.15\cdot 10^{-4}\) & \(5.151\) & \(2.365\) & \(3.297\cdot 10^{-2}\) & \(2.575\) & \(1.365\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relative errors \(e_{l}^{1}\) and \(e_{l}^{2}\) in the \(L_{2}\) and \(H^{1}\) norms, respectively, for mesh sizes \(h_{l}=2^{-l}\), \(l=3,\ldots,6\), for \(m=2\) in (78). Figure 3: Convergence of the relative \(L_{2}\) and \(H^{1}\) norms computed via (79). Computed hybrid FE/FD versus exact solutions with \(m=8\) in (78) at the time \(t=0.25\) are presented in Figure 4, where \(|E_{h}|\) is computed using the domain decomposition algorithm (Algorithm 2) for different meshes with sizes \(h_{l}=2^{-l},l=3,4,5,6\). The top figures of Figure 4 present hybrid FE/FD meshes which were used for computations; common hybrid FE/FD solution in \(\Omega\) is presented in the middle figures; the bottom figures show only FD solution as part of the common hybrid solution in \(\Omega_{FDM}\). Interpreting these figures, we observe smooth behavior of the hybrid solution across finite element/finite difference boundary, as was predicted in theory. Furthermore, through these figures, as well as tables and Figure 3, we observe that with increasing \(l\) in \(h_{l}=2^{-l},l=3,4,5,6\), the computational errors approach the second order convergence in \(L_{2}\)- and first order in \(H^{1}\)-norm for \(m=2,4,6,8\). Therefore, we can conclude that the finite element scheme in the hybrid FE/FD method, considered in \(\Omega_{FEM}\), behaves like a first-order method in the \(H^{1}\)-norm and a second-order method in the \(L_{2}\)-norm. These results are all in good agreement with the analytic estimates derived in Sections 5 and 6, as well as with results presented for finite element method in [44] for the whole \(\Omega\).
## 4 Results
### Single-Factor SVRM Prediction Results  
First, only the average monthly displacement rate of the landslide from April 1998 to December 2005 was used as a factor to build the single-factor SVRM model. The number of total data is 93. The initial 62 datasets were used as the learning samples, and the remaining 31 datasets were applied as the testing samples. A single-factor SVRM model was built to predict the development of the landslide, and the parameters (C and \(\gamma\)) of the model were defined using the grid-search method, which was proposed by [PERSON] and [PERSON] (2001) (Figure 5) [41]. This enabled us to obtain the most appropriate values for the C (i.e., 0.5, Equation (3)) and \(\gamma\) (i.e., 2.8284, Equation (8)) parameters. The use of these parameter values will produce a SVRM model with the smallest mean square error (MSE). The predicted result of the single-factor SVRM model is shown in Figure 6.  
Figure 6 shows that the predictive curve of the single-factor SVRM model and the monitoring curve of the landslide display the same trend and are in accordance. However, there are some differences between the predicted and monitored values of the individual points, particularly for point no. 5 (Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Correlation**} \\ \cline{2-3}
**Time** & **Between Landslide Displacement** & **Between Landslide Displacement and** \\  & **and Rainfall** & **Reservoir Water Level** \\ \hline
1998 & 0.840 & 0.347 \\
1999 & 0.878 & 0.337 \\
2000 & 0.887 & 0.336 \\
2001 & 0.871 & 0.336 \\
2002 & 0.913 & 0.337 \\
2003 & 0.910 & 0.337 \\
2004 & 0.929 & 0.336 \\
2005 & 0.911 & 0.336 \\
1998–2005 & 0.894 & 0.338 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The grey correlation analysis results between landslide displacement and rainfall/reservoir water level.
# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 2 Material
### GPP  
The PEM used to estimate the daily GPP (g m\({}^{-2}\) d\({}^{-1}\)) images is  
\[\text{GPP}=\text{PAR}f_{\text{APAR}}\ \epsilon_{\text{max}}\ \epsilon_{\text{W}}\ \epsilon_{\text{T}} \tag{1}\]  
where PAR (MJ m\({}^{-2}\) d\({}^{-1}\)) accounts for the photosynthetically active radiation in the 0.4-0.7 \(\upmu\)m spectral range. It was computed as 46% of the daily solar irradiance, which was given by the MSG DIDSSF product (LSA-203) [29] reprocessed and available from 2004 up to the present at [[https://landsaf.ipma.pt/](https://landsaf.ipma.pt/)]([https://landsaf.ipma.pt/](https://landsaf.ipma.pt/)) (accessed on 13 January 2022). This product was very satisfactorily validated in the study area and is used as the input of the MSG/SEVIRI GPP product LSA-411 [30].  
The daily \(f_{\text{APAR}}\) (fraction of absorbed photosynthetically active radiation) at 1 km spatial resolution was obtained by applying the algorithm proposed in [31] to MODIS products MCD43A1 and MCD43A2. This algorithm is actually used to derive the SEVIRI/MSG \(f_{\text{APAR}}\) product (LSA-425) [32]. In this study, the \(f_{\text{APAR}}\) TS were filtered and reconstructed using an optimized locally weighted regression and smoothing scatterplots (LOESS) method [33].  
\(\epsilon_{\text{W}}\) is the water stress coefficient \(C_{\text{WS}}\) proposed by [34] and applied satisfactorily over Mediterranean regions [12; 35; 36]. It is calculated as a linear relationship with the water balance between actual and potential evapotranspiration (AET and PET, respectively):  
\[C_{\text{WS}}=0.5+0.5\ \text{AET}/\text{PET} \tag{2}\]  
If AET is not available and precipitation is lower than PET then it can be assumed that AET is equal to precipitation. In this study, the accumulated precipitation and PET are usedbecause it demonstrated a better performance in the study area [36]. The accumulation period depends on the vegetation type [28]. PET (mm\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\)) is calculated, according to [37] parameterization, from the daily global irradiation \(R_{\rm g}\) (k) m\({}^{-2}\) d\({}^{-1}\)), as given by the LSA-203 product, and the daily \(T\) (in \({}^{\circ}\)C):  
\[\rm{PET}=R_{\rm g}\ (0.025\ T+0.08)/2450 \tag{3}\]  
\(\varepsilon_{\rm max}\) (g M\({}^{-1}\)) is the ecosystem light use efficiency under optimal conditions and depends on the vegetation type. The vegetation type map described in Section 2.2 was used to assign the \(\varepsilon_{\rm max}\) value to each pixel according to the literature [11]. Finally, \(\varepsilon_{\rm T}\) is the Tmin_scalar used in MOD17 product [4], a linear ramp function of minimum air temperature that reduces \(\varepsilon_{\rm max}\) accounting for low temperatures, depending on the vegetation type.  
This methodology was previously tested in the study area against eddy covariance observations obtaining coefficients of determination between 0.5 and 0.96 in [12], where more details on the estimation of GPP from remotely sensed data can be found.
## 4 MPPT Control Design
### Current Regulation
#### 4.2.1 PI Controller  
The PI controller is a control loop feedback system that attempts as much as possible to continuously calculate the difference between the desired and actual (measured) outputs. The control function of the PI controller is given in Equation (20), while its synoptic diagram is shown in Figure 10, where \(e(t)\) is the error, and \(K_{p}\) and \(K_{i}\) are respectively the proportional and integral coefficient terms.  
\[s=K_{p}e(t)+K_{i}\int e(t)dt. \tag{20}\]  
Although the PI controller is commonly used in a wide range of applications, several drawbacks such as the difficulties of finding out the constants \(K_{p}\) and \(K_{i}\) are causing a big issue [67; 68; 69; 70]. Moreover,  
Figure 10: Synoptic diagram of the PI controlller.  
Figure 9: PEMFC PI characteristics and tracking trajectory.  
its sensibility dealing with the load variations has caused researchers to look for another controllers that can provide robustness against load variations. In this paper, the method used for determining the values of \(K_{p}\) and \(K_{i}\) is known as the \"Ziegler-Nichols tuning method,\" discovered and developed by [PERSON] and [PERSON]. It is an online method that is usually used when there is a lack of knowledge of the model parameters [71]. In order to apply this method, three main steps should be accomplished.  
1. The first step is to switch off the integral and derivative gains (\(K_{i}=0\) and \(K_{d}=0\)).
2. The second step is to increase the \(k_{p}\) gain from a low/zero value until the first sustained oscillation occurs (Figure 11). The reached gain at the sustained oscillation is noted as a critical value \(k_{cr}\), while the period of these oscillations is measured as \(P_{cr}\).
3. Finally, taking into account the type of the used controller, \(K_{p}\) and \(K_{i}\) can be calculated using the formula given in Table 2.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 1 Introduction
The traffic is routed and inspected simultaneously to detect the QoS requirements, providing the necessary information to create a specific QoS path end-to-end. In addition, the architecture reduces the overload on the controller by decreasing the number of messages that must to be processed. As a proof of concept, a prototype with the proposed logic using Open vSwitches, Mininet, and Ryu Controller has been built. The quantitative result shows an SDN architecture that reduces problems related to the controller high response times, and it is able to route QoS traffic on demand immediately. Additionally, two levels for QoS traffic detection are implemented, one on the edge of mobile network at evolved NodeB (eNB) or next generation NodeB (gNB) and another in centralized specialized elements. The comparative analysis shows a flexible and scalable traffic detection logic, which distributes traffic processing at the borders of the mobile network and, at the same time, reduces the centralized processing requirement, increasing the solution's robustness. As the architecture is created using OpenFlow, it can take advantage of the traffic engineering possibilities that the SDN implementation provides. The structure of this paper is as follows. Section 2 presents the considerations of the proposed architecture and the related works. Section 3 provides a complete description of the proposed architecture, starting with the initial configuration, and focused on the 4G eNB or 5G gNB logic proposal, traffic detection tables, and specific Evolved Packet System (EPS)-bearer/QoS-flow creations. In Section 4, the prototype is described and operation examples are provided. Section 5 provides quantitative and qualitative analysis comparing the characteristics of the proposed architecture with current Evolved Packet Core (EPC) and other proposals. Finally, Section 6 presents the main conclusions of this work.
## 3 Results and Discussion
### Antimicrobial Activity of Synthesized AgNPs  
Pathogenic microbes exist in all environments, causing many problems and negatively affecting economies and public health [62; 63]. Silver nano-forms are successfully applied to inhibit pathogens that are commonly found in medical, industrial, and agricultural fields [21; 24]. In the present study, the antimicrobial activities of AgNPs extracellularly produced by supernatants of _S. obliquus_ cultivated with nitrogen sources of different nitrates and sulphate were studied. The targeted microorganisms were: 2 G+ bacteria (_St. aureus_ ATCC- 47077 and _B. cereus_ ATCC- 12228), 2 G- bacteria (_E. coli_ ATCC  
Figure 5: HRTEM of AgNPs fabricated by supernatant of _S. obliquus_ cultivated in media containing different nitrogen sources (**A**: KNO\({}_{3}\); **B**: NH\({}_{4}\)NO\({}_{3}\); **C**: NaNO\({}_{3}\); **D**: (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)).  
25922 and _Ps. aeruginosa_) as well as yeasts _C. albicans_ ATCC- 10231 and _Sac. cerevisiae_ ATCC- 9763. Figure 6 shows the inhibitory effects of the different produced AgNPs. All of the synthesised AgNPs exhibited antimicrobial activities against all of the tested microorganisms except that generated by ammonium sulphate against _C. albicans_. However, the AgNPs produced by the microalgal supernatants of KNO\({}_{3}\) or NaNO\({}_{3}\) treatment were superior in inhibiting the growth of all of the tested pathogens. This antimicrobial superiority of AgNPs from KNO\({}_{3}\) or NaNO\({}_{3}\) was consistent with the small size of these synthesized nanoparticles. The largest size of synthesized AgNPs was recorded for the ammonium sulphate supernatant treatment, which exhibited no activity against _C. albicans_ as well as the lowest microbial activity against other targeted microbes. The obtained results indicated that the microbial activity of the synthesized AgNPs was related mainly to the produced particles' size, which was a function of the nitrogen source of the original culture. Strong correlations between AgNP size and antimicrobial activities were suggested by [PERSON] et al. [PERSON] et al. (2012) and [PERSON] et al. [PERSON] et al. (2014). In the present study, AgNPs synthesized using cultural filtrates of _S. obliquus_ could be successfully used as an antimicrobial agent; however, additional studies are needed in order to assess their environmental and health impacts prior to actual applications.
## 1 Introduction
The preliminary results of INER-OC4 regarding the analysis procedure of jacket support structure have been presented in this work. The NREL (National Renewable Energy Laboratory) 5 MW OWTG FAST model, the OC4 jacket substructure model and some environmental data (i.e., wave and current) from the Taiwan Central Weather Bureau are engaged in the intensive simulations. The proposed analysis procedure is depicted in Figure 1. OC4 Phase I is the major reference of INER-OC4. The employed jacket support structure model in this work is designed according to the OC4 Phase I. After the details of jacket substructure are determined, the essential information, i.e., the mass and modal analysis, are evaluated and then compared with those by other OC4 research teams. The tasks of modeling and model check will continue until no significant difference observed. Once the model check passed, the global analysis and local analysis will be engaged sequentially. In the global analysis, freeware FAST 7.02 [16] is employed to investigate the dynamic response of OWTG supported by jacket substructure. However, only the monopile support structure can be analyzed by FAST 7.02 [17]. The purpose of global analysis is therefore to build the equivalent monopile model for jacket support structure and evaluate the aerodynamic response of the OWTG supported by this simplified substructure under various Design Load Cases of IEC 61400-3. The NREL FAST code is employed to analyze the dynamic response of the OWTG system under severe design load cases (DLCs) of IEC 61400-3. In the local analysis, the major object of investigation is the jacket support structure. The model of jacket support structure is firstly established with the aid of software SAP2000. The aerodynamic loadings on OWTG model from global analysis and the environmental loadings (actual data of wave and currents in Taiwan) are included in the following simulations as well. The aim of local analysis is to compute and check the strength of member and joint of jacket substructure. These simulation results are finally examined by the API (American Petroleum Institute) RP 2A-WSD [18] and NORSOK (Norwegian Petroleum Industry) N-004 [19] standards. Totally, six ultimate DLCs of IEC 61400-3 are investigated and the results are revealed in this work. If the designed jacket support structure cannot fulfill the strength requirement, the procedure will return to the initial step.