esa-sceva/satcom-chunk-collection huggingface dataset:
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
## 3 The Continuous Wavelet Transform and Generalised Morse Wavelet  
The theory of wavelets and wavelet-based signal processing would be too long to be recalled here; the interested reader is referred to the classic works of [PERSON] & [PERSON] [38], and [PERSON] [39] for the basics concepts, and the book of [PERSON] [40] for a complete discussion. In a few words, the main (but not the only one) feature of any wavelet is its compact support in time. Differently from harmonic functions, which span indefinitely from \(t=-\infty\) to \(+\infty\), these brief oscillations allow capturing time-varying phenomena [41]. For this reason, orthonormal wavelets have been extensively utilised for signal analysis via Wavelet Transform (WT), especially for SHM applications (see, by way of example, [42]; a review about this topic can be found in [43]). In this regard, several variants of WT exist, depending on the shifting and scaling of the basis function, known as the mother wavelet (these points will be further discussed later). The two main forms are Discrete and Continuous WTs; here in this study, the CWT has been applied.
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality
#### 6.1.1 Thd  
In systems associated with power converters, output voltage/current quality is closely linked to THD. The IEEE Std 519-1992 standard recommends harmonic control requirements for electrical systems [133].  
By definition, THD characterizes the effects of harmonics on power system voltage and can be applied to low-, medium-, and high-voltage systems [133]. The THD is defined by Equation (14).  
\[THD_{X}=\frac{\sqrt{\sum_{2}^{\infty}X_{h}^{2}}}{X_{1}} \tag{14}\]  
where \(h\) is the harmonic order, \(X_{h}\) is the amplitude of the harmonic component, and \(X_{1}\) is the amplitude of the fundamental component.  
The IEEE Std 519-1992 standard defines voltage distortion limits at the Point of Common Coupling (PCC) and requires that customers do not introduce excessive current harmonics [133]. For systems where the bus voltage at the PCC is less than or equal to 69 kV, individual voltage distortion must remain below 3%, and the THD must not exceed 5%. The Fast Fourier Transform (FFT) can be used to perform a spectral analysis of electrical signals and thus serves as an effective tool for THD evaluation [134].
# Pediatric Endoscopy, Update 2020
## 4 Esophago-Gastro-Duodenoscopy
### Intervention
#### Hemorrhage and Variesel Bleeding in Children  
Esophageal varacean bleeding in children is rare but can occur, typically in the age range of 7-16 years [61; 62; 63; 64; 65]. Elective treatment of esophageal varices should only be performed in specialized centers. However, an acute varical bleeding requires immediate endoscopic treatment in any hospital. As in adults, ligature therapy is the state of the art method to stop hemorrhage from esophageal varices.  
Liver cirrhosis in children is rare, and a significant amount of children with esophageal and fundus varacean bleeding suffer from portal hypertension due to portal vein thrombosis or thrombosis of the splenic vein [66]. Overall, the endoscopic outcome of non-cirrhotic portal fibrosis is favorable. One-third of patients will have recurrence of esophageal varices and only a small proportion of bleeders will have poor outcome [67].  
Most of the bleedings are self-limiting and substitution therapy with fluids and sometimes blood products are sufficient. Medications like vasopressin, octreotide, and beta receptor blockade can be used accordingly to the therapy in adults (Table 3) [66; 68]. Endoscopic intervention, however, should be first choice of the treatment options. The obliteration therapy with Polidocanol 1% is effective (positive results in 90%-95%) but is not recommended anymore due to complications such as post interventional strictures in 15%. Band ligation therapy has a higher efficiency in relation to post interventional bleeding and has a lower complication rate and lower costs with a higher overall survival [69]. TIPPS (Trans jugular intrahepatic portosystemic shunt) or shunt surgery can be used in therapy-refractory cases. Liver transplantation in children is not an option. In non-cirrhotic portal hypertension, most children will develop a spontaneous shunt over time. In those cases, all therapeutic intentions should aim to bridge the time for nature to take over. The role of beta blockade therapy in children is not established yet.  
A study suggested that antibiotic use within 48 h of admission in cirrhotic children with acute UGIB might have a positive impact on the percentage of children free of bacteremia and the readmission rate [70].
## 3 ABER Performance of Transmit Aperture Selection under Jamming
Thus, the effective threshold value for the received signal becomes  
\[y_{th}=\sqrt{E_{x}}\bigg{(}\frac{h_{m}}{2}\bigg{)}. \tag{20}\]  
Now, considering both the transmitted signals to be equi-probable, we can determine the BER of the system in the presence of an active jammer from (9) as follows:  
\[BER_{\text{Jactive}}(h_{m})=\rho\bigg{[}0.5\int_{y_{th}}^{\infty}f_{Y}(y|x=0)dx +0.5\int_{0}^{y_{th}}f_{Y}(y|x=1)dx\bigg{]}. \tag{21}\]  
Taking into account the distribution of additive jamming noise and the presence of information signals, we can derive the BER contributed by the jammer using (17) in (21) as follows:  
\[BER_{\text{Jactive}}(h_{m})=0.5\,\rho\,\zeta_{j}^{2}\bigg{(}\frac{h_{m}\sqrt{ \rho\gamma}}{2A_{0}}\bigg{)}^{\zeta_{j}^{2}}\Gamma\bigg{(}-\zeta_{j}^{2},\frac {h_{m}\sqrt{\rho\gamma}}{2A_{0}}\bigg{)}. \tag{22}\]  
The final expression of BER imparted by the jammer and Gaussian noise for the given FSO system can be obtained from (10) and (22):  
\[BER(h_{m})=0.5\,\rho\,\zeta_{j}^{2}\bigg{(}\frac{h_{m}\sqrt{\rho\gamma}}{2A_{ 0}}\bigg{)}^{\zeta_{j}^{2}}\Gamma\bigg{(}-\zeta_{j}^{2},\frac{h_{m}\sqrt{\rho \gamma}}{2A_{0}}\bigg{)}+\ (1-\rho)\,Q\bigg{(}\frac{h_{m}\sqrt{\kappa\gamma}}{2} \bigg{)}. \tag{23}\]  
Now, the ABER can be calculated from (23) by integrating it over the PDF of the related RV, i.e.,  
\[ABER = \int_{-\infty}^{+\infty}\bigg{(}0.5\,\rho\,\zeta_{j}^{2}\bigg{(} \frac{h_{m}\sqrt{\rho\gamma}}{2A_{0}}\bigg{)}^{\zeta_{j}^{2}}\Gamma\bigg{(}- \zeta_{j}^{2},\frac{h_{m}\sqrt{\rho\gamma}}{2A_{0}}\bigg{)}+\ (1-\rho)\,Q\bigg{(}\frac{h_{m}\sqrt{\kappa \gamma}}{2}\bigg{)}\bigg{)} \tag{24}\] \[\times f_{(h_{m})}(h_{m})dh_{m}\] \[= ABER_{\text{Jactive}}+ABER_{\text{Jide}},\]  
where  
\[ABER_{\text{Jactive}}=\int_{-\infty}^{+\infty}0.5\,\rho\,\zeta_{j}^{2}\bigg{(} \frac{h_{m}\sqrt{\rho\gamma}}{2A_{0}}\bigg{)}^{\zeta_{j}^{2}}\Gamma\bigg{(}- \zeta_{j}^{2},\frac{h_{m}\sqrt{\rho\gamma}}{2A_{0}}\bigg{)}f_{(h_{m})}(h_{m}) dh_{m} \tag{25}\]  
is the ABER contributed by the jammer, and  
\[y_{th}=\sqrt{E_{x}}\bigg{(}\frac{h_{m}}{2}\bigg{)}-\frac{\rho}{1-\rho}\frac{N_{0 }}{N_{j}}\sqrt{2\pi N_{0}}. \tag{19}\]0  
represents the ABER imparted by the Gaussian noise. To solve (25) and (26), we need the PDF for \(h_{m}\) which is the maximum of \(N\) fading coefficients for the channel suffering from saturated AT and PE conditions altogether.
## 3 Results and Discussion
### Classification
#### 3.2.2 Results of the Fusion Classification Study  
Table 6 gives the resulting average 4-fold cross-validation accuracies for the MLP classifier with the spectral modes fused at the input layer. As with the single-mode study,  
Figure 13: Plot of 4-fold cross-validation accuracies for each of the five classifiers as a function of the number of selected wavelengths in the single-mode classification study for (**a**) VNIR, (**b**) fluorescence, and (**c**) SWIR data. The red dashed line in each plot marks the benchmark accuracy obtained by MLP using all wavelength in the spectrum. Error bars mark the range of accuracies for the four folds.  
the value in the \"Benchmark\" column is the accuracy obtained by fusing all wavelengths from the various modes. We present results from the fusion of all three modes as well as results of fusion without the SWIR mode. This latter iteration was included due to the poor performance with the SWIR data in the single-mode study. By fusing the modes, MLP is able to produce classification accuracies that exceed the highest accuracies from the single-mode study by \(>\)10% for \(k\) = 3 and by \(>\)4% at \(k\) = 7. An accuracy of \(>\)90% is obtained even with only three wavelengths. The fusion accuracies with all three spectral modes exceed the accuracies without the SWIR data only by 1-2 percentage points for the \(k\) = 3, 4, 5, 6, 7 cases (and is lower for the benchmark case), indicating that SWIR, in fact, does not contribute independent information for species classification. Figure 14 shows the confusion matrix for the fusion of all three modes with \(k\) = 7. Note that although the rates of correct classification are \(>\)99% for many species and \(>\)90% for 20 species, the large, concentrated misclassification errors seen in the single-mode study were found here as well. Tuna has the lowest classification accuracy at 61.8%, with 27.8% of the misclassifications  
species suggest the need for an expansion to the proposed methodology to enable high classification accuracy for large numbers of fish species. We are currently investigating a cascading multiple-model approach that will be the subject of a future publication. Future work with a larger dataset will also include hyperparameter optimization to identify the optimal MLP architectures for each of the single-mode and fusion cases.
## 2 Materials and Methods
### Properties of the Rigid Catenary Section and the Supports  
Figure 3 shows the details and geometric characteristics of a section of Furrer rigid beam, which is used in Spanish railways. The figure shows a normal section and a section with a joint flange. It can be seen that the normal beam is composed of two elements: the body of the beam itself, which is made of aluminum; and the contact wire, made of copper, through which the electric energy flows [20; 21]. Obviously, Figure 3 consists of two elements: an aluminum beam and a copper wire. As we have two materials, we  
Figure 2: Beam element with bending stiffness.  
should have two moments of inertia and two elastic moduli. However, according to [24], it is possible to obtain an equivalent single beam representing the composite beam. This approach is considered in this paper in order to simplify the model.  
As in the treatment by finite elements, according to Equation (3), we assume that the beam is composed of a single material. It is necessary to determine the equivalent values for the section of the area, the position of the center of gravity, the moment of inertia, and the density, as if this section is formed by a single material. Thus, assuming that the entire section is exclusively aluminum, we first define the relationship between the elastic modulus or [PERSON]'s modulus, where the subscript 1 corresponds to copper, and subscript 2 to aluminum. This notation is also adopted for the remainder of the properties, and thus we have:  
\[m=\frac{E_{1}}{E_{2}} \tag{5}\]  
In Equation (5), \(m\) describes the relationship between the moduli. To simplify the notation, the subscripts are excluded.  
Equation (6) shows the area of the equivalent section \(A_{\alpha j}\); the new position of the center of gravity of section \(G\) with respect to the position of the center of gravity of the aluminum section \(G_{2}\,y_{\alpha j}\); the moment of inertia of the equivalent section with respect to the \(x\)-axis that passes through \(G\)\(I_{x\alpha j}\); and the density of the equivalent section:  
\[A_{\alpha j}=A_{2}+mA_{1},\ \ \ y_{\alpha j}=\frac{mA_{1}y_{1}}{A_{2}+mA_{1}},\ \ I_{x \alpha j}=I_{x2}+mI_{x1},\ \ \rho_{\alpha j}=\frac{\rho_{1}A_{1}+\rho_{2}A_{2}}{mA_{1}+A_{2}} \tag{6}\]  
The details of the section of the rigid catenary section are shown in Figure 3, and the values for the Furrer beam are taken from reference [20; 21]. After applying Equations (5) and (6) for the materials and sections of the beam, respectively, moments of inertia for the normal section of \(I_{x\alpha j}\) = 4,228,994 mm\({}^{4}\) and the flanged section of \(I_{x\alpha j}\) = 5,421,787 mm\({}^{4}\) are obtained. More details can be found in reference [20]. In addition, the spans of the beam are delimited by the supports, the detail of which is shown in Figure 3. The effect of the supports on the dynamic equations is assumed to be equivalent to that of a linear spring with stiffness \(k_{s}=1\times 10^{5}\) N/m.
# The Future of Factories: Different Trends
## 3 Characteristics of Digital Factory
### Relationship between Digital Factory and Virtual Factory  
The European concept of _Virtual Factory_ is a major expansion upon virtual enterprises in manufacturing. The virtual organization approach integrates collaborative business processes from different enterprises to simulate, model, and test different design options to evaluate performance, thus to save time-to-production [31].  
Both _digital_ factory and _virtual_ factories share common reference models for realizing a Factory of the Future. In a digital factory, decision-making technologies play a key role in real plant simulation and optimization.  
Similar applications are found in a digital factory, where its implementation extends to design and production processes across multiple departments within the company boundaries. This trend emerges in companies that implement a digital factory to support a collaborative process among departments. For example, the China Aerospace Science and Technology 211 company adopts a full step-by-step design process, ranging from 3D-to-process to 3D-to-site and 3D-to-factory. It can be outlined that the adoption of 3D models drives process collaboration through the entire development process. Therefore, three-dimensional modeling is a key enabler of a collaborative process, promoting the sharing of product data flexibly within the digital factory boundaries.  
Similarly, 3D virtual environments and discrete event simulation models are proposed for modeling, simulating, and evaluating manufacturing assets [31] in a virtual factory. In contrast, while a digital factory is likely to define its operational boundaries inside the company, a virtual factory extends the factory's capabilities across multiple organizations to provide a unified virtual environment to test, model, and simulate factory layouts and processes [32].  
Ultimately, there is a strong overlap between the digital factory and the virtual factory. While the digital factory provides cooperation within departments, the virtual factory  
Figure 5: Industrial Internet Consortium alignment between RAMI 4.0 and IIRA [30].  
extends this cooperation among multiple enterprises. Therefore, for the rest of the chapter, we will refer to a virtual factory as an extension of the digital factory. The concept of collaborative manufacturing is also an important characteristic of cloud manufacturing described in the next section.
## 3 Fracture Parameters for 3D Crack
### Numerical Analysis of 3D Crack
#### 3.2.2 Computational Analysis  
The data processing for \(K\) and \(G\) outputted by the I-II-III mixed mode crack simulation is more complex than for the I-II mixed mode crack--not only for the value \(K_{III}\) added, but also for much more data on the crack tip line. To obtain the fracture parameter \(K\), the present study is described as follows:  
1. Determine \(K\) and \(J\) for kink cracks repeatedly, varying the kink length from 0.1 mm to 1 mm with four data points (\(a=0.1\) mm, \(0.3\) mm, \(0.5\) mm, \(1\) mm). Then, compute the energy release rate as the kink propagation vanishes on the curve of \(K\) and \(J\) versus kink length through fitting each node on the crack tip line.
2. Plot the non-dimensional value (\(K_{I}/\sqrt{EG},\ K_{II}/\sqrt{EG},K_{III}/\sqrt{EG}\)) for each kink angle (\(\theta=30^{\circ},\ 60^{\circ},\ 90^{\circ},120^{\circ},\ 150^{\circ}\))
3. Determine the parameters of inclined ellipses that fit the above data points using a fitting method for quadric surfaces in space by MATLAB, and the corresponding ellipse equation is presented as follows: \[\frac{\left(\frac{K_{I}}{\sqrt{EG}}cos\alpha+\frac{K_{II}}{\sqrt{EG}}sin \alpha\right)^{2}}{a^{2}}+\frac{\left(\frac{K_{II}}{\sqrt{EG}}cos\alpha-\frac {K_{I}}{\sqrt{EG}}sin\alpha\right)^{2}}{b^{2}}+\frac{\left(\frac{K_{III}}{ \sqrt{2\mu G}}\right)^{2}}{C^{2}}=1\] (11) where \(a\), \(b\), \(c\), \(\alpha\) are the semi-major axis, semi-middle axis, semi-minor axis and inclination of ellipse, respectively.
4. Obtain the coefficients of quadratic of energy release rate in terms of stress intensity factors, and defined as \[G=\frac{1}{E}\Big{[}a_{11}K_{I}^{2}+a_{12}K_{I}K_{II}+a_{22}K_{II}^{2}+a_{33} (1+v)K_{III}^{2}\Big{]}\] (12)  
Figure 15: Crack model with mixed mode I-II-III in ABAQUS. (**a**) A 3D model with a mixed mode I-II-III crack; (**b**) The finite element model.  
The results of fitting the non-dimensional value are shown in Figure 16; a series of conclusions can be summarized as follows:  
1. The non-dimensional value (\(K_{I}/\sqrt{EG},\ K_{II}/\sqrt{EG},K_{III}/\sqrt{EG}\) ) transformed from the data output by ABAQUS forms a series of ellipsoids.
2. At each presupposed extended angle (30\({}^{\circ}\)\(\sim\)150\({}^{\circ}\)), the scattered points of dimensionless data output by ABAQUS under different loadings are all on the surface of the same tilted ellipsoid.
3. The coefficients of quadratics for energy release rate in terms of stress intensity factors are calculated for each kink angle based on the ellipsoid equations.
## 3 Model Predictions  
The micromechanics multiscale model developed in Section 2 is herein exploited in order to determine the critical ITZs and to predict the strength of recycled concrete for a variety of different recycled concrete compositions. First, a specific recycled concrete composition for benchmarking purposes is defined. The benchmark recycled concrete contains 35% of new cement paste (\(f_{ncpm}^{con}\!=\!0.35\)), all aggregates used are recycled (\(f_{ra}^{a}=1\)), the recycled aggregates consist of 35% of old cement paste (\(f_{ocp}^{ra}\!=\!0.35\)), the recycled aggregates are evenly distributed among the three classes considered (\(f_{1}\!=\!f_{\Pi}\!=\!1/3\)), both old and new cement paste exhibit a \(w/c\)-ratio of \(0.5\,[(w/c)_{ocp}\!=\!(w/c)_{ncp}\!=\!0.5]\), the old cement paste is completely hydrated (\(\xi_{ocp}\!=\!\xi_{ocp}^{ult}\!=\!1\)), while, for the new paste, we consider a hydration degree typically reached after two weeks (\(\xi_{ncp}\!=\!0.7\)), and we finally consider that none of the ITZs are weakened by means of increasing its porosity (\(F_{por}\!=\!1\)). The benchmark composition and maturity parameters are used to construct the phase volume fractions according to Equations (16)-(19), which allow for evaluating the stress concentration relations (3)-(11). Solving the maximization problem (13) shows that the ITZs around old plain aggregates are the critical ITZs (\(\mathcal{I}_{\text{crit}}\!=\!\mathcal{I}_{opa}^{ncp}\)) for the defined benchmark composition. The most unfavorable hydrate stresses occur at polar angles \(\psi_{\text{crit}}\!\approx\!33^{\circ}\), and the critical hydrate is oriented in the direction of the macroscopic uniaxial loading (\(\theta_{\text{crit}}\!=\!0\)). The related strength of recycled concrete then follows from evaluating the Drucker-Prager failure criterion (1) for the critical ITZ and the critical position and orientation angles and amounts to \(\Sigma^{ult}\approx 37\,\text{MPa}\).
## 2 Simulation Model
### Fire Dynamics Simulation  
This study employs the Large Eddy Simulation (LES) method to divide the study domain into several control volumes with consistent physical parameters, enabling a more accurate simulation of fluid flow, heat transfer, and combustion chemical reactions during a fire event. Equations (1)-(5) describe a series of partial differential equations that govern the physical processes of flow, heat transfer, and combustion in the fire scenario. By incorporating the boundary and initial conditions of the specific fire problem and using Computational Fluid Dynamics (CFD) methods for numerical solution, the model can provide results for a given fire scenario.  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\rho V=0 \tag{1}\]  
In the equations, \(\rho\) represents the gas density, \(V\) is the velocity vector, where \(V=(u,v,w)\), and \(t\) is the simulation time.  
\[\frac{\partial(\rho V)}{\partial t}+\
abla(\rho VV)+\
abla P=\rho g+\
abla T \tag{2}\]  
In the equations, \(P\) represents pressure, \(g\) is the gravitational acceleration, and \(T\) is the viscous stress tensor.  
\[\frac{\partial}{\partial t}(\rho h)+\
abla\cdot(\rho gV)=\frac{dp}{dt}+\dot{q} -\
abla\cdot q+\varnothing \tag{3}\]  
In the equations, \(h\) represents specific enthalpy, \(\dot{q}\) is the volumetric heat source, \(q\) is the radiative heat flux vector, and \(\varnothing\) is the dissipation function.  
\[\frac{\partial}{\partial t}(\rho Y_{i})+\
abla\cdot(\rho Y_{i}V)=\
abla\cdot( \rho D_{i}\
abla Y_{i})+\dot{m_{i}} \tag{4}\]  
In the equations, \(Y_{i}\) is the mass fraction of component \(i\), \(D_{i}\) is the diffusion coefficient of component \(i\), and \(\dot{m_{i}}\) is the generation or dissipation rate of component \(i\) per unit volume.  
\[P=\frac{\rho RT}{M}=\rho RT\sum_{i}\left(\frac{Y_{i}}{M_{i}}\right) \tag{5}\]  
In the equations, T is the gas temperature, R is the universal gas constant, R = 8.314 J/(mol\(\cdot\)K), M is the molecular weight of the gas in kg/mol, and M\({}_{\rm i}\) is the molecular weight of component \(i\).
## 2 Air-to-Space Channel Models
### Additional Theoretical A2S Studies  
For the sake of completeness, additional theoretical studies described recent advances that do not elaborate on channel models. The optimization of the UAV trajectory and deployment was addressed in [30; 31], aiming at achieving efficient transmission efficiency in the A2S link. Furthermore, adaptive channel-tracking algorithms and beam-tracking for the A2S channel were investigated in [32; 33; 34]. Finally, the link-level performance regarding the packet backlog, delay, and throughput as a function of the UAV number and link utilization was examined theoretically in [35], in scenarios in which UAVs accessed satellites.  
Finally, Table 1 summarizes all the related research regarding the A2S channel, including experimental, theoretical, and simulation studies.