esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Calibration Model for Quantitative Estimation
#### 3.3.4 C3a-CaS and Al2O3-CaS  
Tables 7 and 8 provide estimated values for binary samples of C3A-CaS and Al2O3-CaS, respectively. In Table 7, the ratio of the intensities of Raman shifts at 756 cm\({}^{-1}\) for C3A, and 416 cm  
Figure 14: Raman spectroscopy estimation for the phase content (wt. %) for C12A7 as a function of the phase content of C12A7 in C12A7-CaS sample.  
Figure 13: Evaluation of relative stabilities (Si) of the calibration variable candidates and mean absolute error values obtained for training and validation sets for C12A7–CaS.  
for MA, for samples of C3A-CaS had the best coefficient of determination, and better MAE values. Similarly, Table 8 shows that the intensities for a Raman shift at 157 cm\({}^{-1}\) for CaS, and 420 cm\({}^{-1}\) for Al\({}_{2}\)O\({}_{3}\), had the best value for the coefficient of determination values, with the lowest MAE compared to the other peaks' ratios for these phases. Figures 15 and 16 also indicate that for all the Raman shift (peaks) identified for CaS, C3A, and Al\({}_{2}\)O\({}_{3}\), the relative stability of the peak ratios for the most intense peaks performed better. Therefore, based on this study, the most intense Raman peaks ratio for C3A-CaS and CaS-Al\({}_{2}\)O\({}_{3}\) with a Raman peak at 756 cm\({}^{-1}\) for C3A, Al\({}_{2}\)O\({}_{3}\) at 416 cm\({}^{-1}\), and CaS at 157 cm\({}^{-1}\) was shown to be most suitable for the quantitative parameters for these samples. Figures 17 and 18 illustrate a linear regression constructed between the relative intensity and phase fraction for C3A in C3A-CaS and the relative intensity and phase fraction for CaS in CaS-Al\({}_{2}\)O\({}_{3}\), respectively.
## 3 Methodology
### Spatial Modeling
#### 3.3.2 Incorporation of Structural Information  
In spectral graph analysis, studying the structural information of the graph usually refers to analyzing the corresponding Laplacian matrix and its eigenvalues. D denotes the diagonal degree matrix, and the Laplacian matrix of a graph and its normalization are defined as \(L=D-A\) and \(L^{sys}=D^{1/2}LD^{1/2}=I_{N}-D^{1/2}AD^{1/2}\), where \(D_{ii}=\sum_{j}^{n}D_{ij}\), and \(I_{N}\) is the identity matrix. The eigen decomposition of normalized Laplacian is \(L^{sys}=U\Lambda U^{T}\), where \(U\) is the Fourier basis, and \(\Lambda=diag[\lambda_{1},\lambda_{2},\cdots,\lambda_{n}]\) is the diagonal matrix of eigenvalues. The GCN model constructs a filter to implement graph convolution operations in the domain of the Laplacian spectrum. The polynomial filtering operation for graph convolution can be formulated as:  
\[g_{\theta}*x=g_{\theta}(L)x=Ug_{\theta}(\Lambda)U^{T}x \tag{5}\]  
where \(g*x\) denotes a graph convolution and \(g_{\theta}(\Lambda)\) denotes the spectral filter. The \(\theta\) denotes the polynomial filter coefficients, and \(g_{\theta}(\lambda)=\sum_{i=0}^{K}\theta_{i}\lambda^{k}\), \(\lambda\in[0,2]\). However, direct eigenvalue decomposition on a larger-scale Laplacian matrix is computationally expensive. The ChebNetII [46] model, a type of graph convolution neural network, is adopted here to incorporate the prior structural information of the input graph. The graph convolution therefore can be formulated as:  
\[\text{GCN}\Big{(}X^{(0)},A\Big{)}=\frac{2}{K+1}\sum_{k=0}^{K}\sum_{j=0}^{K}w _{j}T_{k}(\ddagger_{j})T_{k}(\ddagger)f_{\theta}\Big{(}X^{(0)}\Big{)} \tag{6}\]  
where \(\ddagger=2L^{sys}/\lambda_{max}-I_{N}\), \(\lambda_{max}\) is the largest eigenvalue of \(L^{sys}\), \(K\) is a hyper-parameter, the Chebyshev nodes \(\ddagger_{j}\) for \(T_{k}(\cdot)\) is defined as \(\cos((j+1/2)\pi/(K+1))\), \(w_{j},j\in\{1,\cdots,K\}\) is the learnable parameters, and \(f_{\theta}\) is an MLP. The recursive definition of Chebyshev polynomial can be expressed as \(T_{k}(\ddagger)=2 xT_{k-1}(\ddagger)-T_{k-2}(\ddagger)\), with \(T_{0}(\ddagger)=1\) and \(T_{1}(\ddagger)=x\).
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 3 Controllability of Take-Off in Jumping Animals and Robots
### Take-off Angle  
Overall, animals have excellent ability to control the take-off angle, but there are some special issues. Due to the their morphological constraints, click beetles can only jump with a constant take-off angle (\(79.9^{\circ}\pm 1.56^{\circ}\), \(n=9\) beetles) that directs 98% of the jumping force vertically against gravity [44]. So, click beetles are able to adjust the take-off velocity but they cannot control the take-off angle.  
Locusts, instead, can adjust the take-off angle by acting on the initial position of hind legs: the angle of elevation is equal to the slope of the line drawn from the distal end of the tibia through the proximal end of the femur, and it is not affected if the two hind legs extend asynchronously [210].  
Most miniature jumping robots cannot adjust the take-off angle automatically. For example, the 7 g locust-inspired jumping robot developed by [PERSON] et al. [88] and the variable gear transmission ratio kangaroo-inspired jumping robot proposed by [PERSON] et al. [211] use the variation of the feet bar length and gear transmission ratio separately to adjust the take-off angle within a certain range. These robots have to be reassembled manually to change their mechanism parameters. The first generation of locust-inspired jumping robots proposed by [PERSON] et al. can adjust the take-off angle within a small range by adjusting the mass center position through the counterpart fixed on an added tail [101]. Only the third generation of jumping robots proposed by [PERSON] et al. [145] can realize the adjustment of take-off angle within a wide range. The angle of the six-bar jumping mechanism relative to the ground can be adjusted acting on the position of the support leg [146].
## 2 Numerical Analysis Model
### Governing Equations of Analysis Model  
Analyzing the hydrogen charging process comprehensively required simulating both the turbulent flow within the tank, driven by the momentum and buoyancy of the incoming hydrogen, and the resultant temperature increase and heat transfer due to hydrogen compression. Accordingly, this study employed a set of governing equations, including a continuity equation for compressible flow simulation, a momentum equation incorporating gravity's effect, and a shear stress transport (SST) turbulence model to simulate turbulent flow. The adoption of the turbulence model aligned with its prevalent use in numerous prior investigations into the charging dynamics of high-pressure hydrogen [8; 11; 14]. Additionally, an energy equation was applied to model heat transfer within the tank. The computational analysis utilized the coupled algorithm within a pressure-based solver framework. The governing equations used in this study are as follows.  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\rho\overset{\rightarrow}{u} \right)=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial\left(\rho\overset{\rightarrow}{u}\right)}{\partial t}+\
abla \cdot\left(\rho\overset{\rightarrow}{u}\overset{\rightarrow}{u}\right)=\rho \overset{\rightarrow}{g}-\
abla P+\
abla\cdot\left(\mu\
abla\overset{ \rightarrow}{u}\right) \tag{2}\]  
Energy equation:  
Figure 3: Computational domain of the tube skid.  
\[\frac{\partial\left(\rho c_{p}T\right)}{\partial t}+\
abla\cdot\left(\rho c_{p}T \overrightarrow{u}\right)=\
abla\cdot\left(k\
abla T\right) \tag{3}\]  
Transport equation for \(k\) of the \(k\)-\(\omega\) SST model:  
\[\frac{\partial\left(\rho\kappa\right)}{\partial t}+\
abla\cdot\left(\rho k \overrightarrow{u}\right)=\
abla\cdot\left(\Gamma_{k}\
abla\overrightarrow{ u}\right)+G_{\kappa}-Y_{k} \tag{4}\]  
Transport equation for \(\omega\) of the \(k\)-\(\omega\) SST model:  
\[\frac{\partial\left(\rho\omega\right)}{\partial t}+\
abla\cdot\left(\rho \omega\overrightarrow{u}\right)=\
abla\cdot\left(\Gamma_{\omega}\
abla \overrightarrow{u}\right)+G_{\omega}-Y_{\omega}+D_{\omega} \tag{5}\]
## 4 Design Procedure and Application Example  
This section illustrates the design of both the power stage and the SMC to ensure the desired operation of the battery-supporting system. The first characteristic concerns the battery voltage, which is selected as 48 V due to the wide use of such a voltage level in DC loads and microgrids. The second characteristic is the \(C_{2}\) nominal voltage, which is selected as 48 V to require a nominal duty cycle near 50%; however, any other value can be selected. Concerning the load transients, this example considers fast current changes up to 70 A/ms with magnitudes of 2 A, where the battery will be protected for frequencies higher than 500 Hz; again, any other values can be used depending on the load and battery characteristics. In addition, the maximum deviation of the \(C_{2}\) voltage must be limited to 3% for 100 ms, thus avoiding a large discharge or overcharge of the capacitor. Finally,the maximum switching frequency is selected as 120 kHz, which is a conservative value for commercial applications. Table 2 summarizes the previous characteristics.
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
###### Abstract  
The research on damage detection in wind turbine blades plays an important role in reducing the risk of shut down in wind turbines. Rapid and accurate damage identification by using efficient detection models is the focus of the current research on damage detection in wind turbine blades. To solve the problems of the complex structure of the model and high time consumption in deep learning models, an improved broad learning system (BLS) model using the algorithm of chunking based on non-local means (NLMs) was proposed, which was called the CBNLM-BLS. The chunked, in-parallel accelerated integral image approach was used to optimize the NLM to speed up the BLS. Experiment results showed that the proposed model achieved a classification accuracy of 99.716%, taking 28.662 s to detect damage in the wind turbine blades. Compared with deep neural network models, such as ResNet, AlexNet and VGG-19, the proposed CBNLM-BLS had higher classification accuracy, shorter training time and less complex model construction and parameters. Compared with traditional BLSs, the CBNLM-BLS had less time complexity. It is of great significance to identify damage in wind turbine blades more efficiently.  
wind turbine blade; damage detection; neural network; BLS; NLM +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model. _Appl. Sci._**2022**, _12_, 5164. [[https://doi.org/10.3390/app12105164](https://doi.org/10.3390/app12105164)]([https://doi.org/10.3390/app12105164](https://doi.org/10.3390/app12105164))
# Lavandula Species, Their Bioactive Phytochemicals, and Their Biosynthetic Regulation
## 5 Biosynthesis Regulation of Lavelender Bioactive Phytochemicals
### Biosynthesis of Secondary Metabolites in Lavelender  
Most of the metabolites present in essential oil originate from 5-carbon precursors: isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). These  
Figure 3: Understanding the complex gene–terpenoid networks is important in the context of improving the phytochemistry of _Lavandula_ species through targeted breeding of new varieties (Certekova).  
compounds are synthesized in either the 2-C-methyl-D-erythnitol-4-phosphate pathway (MEP) or the mevalonate pathway (MVA). MEP is localized in plastids and generates IPP and DMAPP, which later serve for producing monoterpenes, dieterpenes, and tetraterpenes. The first enzyme of this pathway is deoxy-D-xylulose 5-phosphate synthase (DXS). The MVA metabolic pathway takes place in cytosol and peroxisomes. Its products are further involved in the synthesis of sesquiterpennes and triterpen. The first step of the MVA pathway is catalyzed by the enzyme 3-hydroxy-3-methylglutaryl-CoA synthase (HMGS) and the second by the enzyme 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) [8; 70; 71; 72]. While the _DXS_ gene was highly expressed in the glandular trichomes of the flowers of _L. angustifolia_, the transcripts of the _HMGR_ were scarcely detectable. This may indicate the importance of the MEP pathway in the process of secondary-metabolite production [8]. This hypothesis is also supported by another study in which an increased activity was observed in most of the genes involved in the MEP pathway during the flowering period of _L. angustifolia_, whereas genes involved in the MVA pathway were expressed in a smaller quantity [73]. Several sources, however, point to a mutual connection between MVA and MEP pathways [74; 75].  
IPP and DMAPP are, in the following step, condensed by prenyltransferases to form geranyl diphosphate (GPP) and farnesyl diphosphate (FPP). These molecules can then be used to synthesize a wide variety of terpenoids through the action of TPSs. Hydrolysis of carbocation intermediates derived from GPP produces monoterpenoids, while hydrolysis of FPP produces sesquiterpenoids [75; 76]. There are several types of TPSs that significantly contribute to the structural diversity within this class of terpenoids. These enzymes remove the pyrophosphate from linear structures such as GPP and FPP, thereby creating many cyclic or linear products, which can subsequently be subjected to further modifications by hydroxyles, reductases, or cytochrome P450 (CYP) oxygenases [77; 78; 79; 80].
###### Abstract  
Heavy metals in water sources can threaten human life and the environment. The analysis time, need for chemical reagents, and sample amount per analysis assist in monitoring contaminants. Application of the Fourier Transform Infrared (FT-IR) Spectroscopy for the investigation of heavy metal elements has significantly developed due to its cost effectiveness and accuracy. Use of chemometric models such as Partial Least Square (PLS) and Principle Component Regression Analysis (PCA) relate the multiple spectral intensities from numerous calibration samples to the recognized analytes. This study focused on the FT-IR calibration and quantification of heavy metals (Ag, Cd, Cu, Pb and Zn) in surveyed water sources. FT-IR measurements were compared with the atomic absorption spectrometer (AAS) measurements. Quantitative analysis methods, PCA and PLS, were used in the FT-IR calibration. The spectral analyses were done using the Attenuated Total Reflectance (ATR-FTIR) technique on three river and four borehole water sources sampled within two seasons in QwaQwa, South Africa (SA). The PLS models had good R\({}^{2}\) values ranging from 0.95 to 1 and the PCA models ranged from 0.98 to 0.99. Significant differences were seen at 0.001 and 0.05 levels between the PLS and PCA models for detecting Cd and Pb in the water samples. The PCA models detected Ag concentrations more (<0 mg L\({}^{-1}\) on selected sites). Both the PLS and PCA models had lower detection only for Zn ions mostly above 45 mg L\({}^{-1}\) deviating from the AAS measurements (<0.020 mg L\({}^{-1}\)). The FT-IR spectroscopy demonstrated good potential for heavy metal determination purposes.  
FT-IR calibration; chemometric models; heavy metals; water resource contamination +
Footnote †: journal: Applied Sciences
## 2 Theoretical Derivation of Analytical Solutions for Blasting Stress Fields in Adjacent Tunnels
### Multi-Borehole Stress Field Superposition Based on Stress Wave Propagation Time Intervals  
In multi-borehole blasting, stress field superposition must account for time interval effects caused by differences in stress wave propagation paths (see Figure 5). These delays result from variations in propagation distances and anisotropic wave velocities in the rock mass, necessitating precise phase corrections for accurate dynamic stress predictions.  
Based on the single-hole stress field derived above, the total stress field at a given observation point under multi-borehole blasting is obtained by superposition:  
\[\begin{bmatrix}\sigma_{r}\\ \tau_{Z}\\ \tau_{\theta}\end{bmatrix}=\sum_{j=1}^{n}\begin{bmatrix}\sigma_{rj}(t-t_{j}) \\ \tau_{Zj}(t-t_{j})\\ \tau_{\theta j}(t-t_{j})\end{bmatrix} \tag{25}\]  
where \(n\) is the number of boreholes; \(t_{j}\) is the time required for stress waves generated from the \(j\)-th borehole to reach the observation point, given by \(t_{j}=R_{j}/C_{p,s}\); \(R_{j}\) is the distance from the \(j\)-th borehole to the observation point (m); and \(C\) is the corresponding P-wave or S-wave propagation velocity (m/s).
## 2 Review of the Analytical Solutions for the Vertical Response of FREIs
### Analytical Formulation of the \(\psi\) Function  
The analytical equation of the \(\psi\)_function_ can be obtained using Equations (5) and (10):  
\[\psi(\delta_{H})=\frac{\delta_{c}^{c}}{\delta_{c}^{c}+\delta_{v}^{s}}=\frac{1 }{1+\frac{\pi G_{c}F^{\delta_{H}=0}A_{c}^{2}}{4F_{c}F_{c},tr}\Big{[}\frac{\pi f _{c}-\sin(\pi f_{v})}{1-\cos(\pi f_{v})}\Big{]}\frac{\delta_{H}^{2}}{H}} \tag{14}\]  
Considering that \(S_{2}=A_{c}/t_{r}\) and setting \(K_{H,\alpha}=G_{c}A_{c}/H\) (the horizontal stiffness of an equivalent beam), Equation (14) becomes  
\[\psi(\delta_{H})=\frac{1}{1+\frac{\pi E_{c}^{\delta_{H}=0}}{4F_{c}F_{c},tr} \Big{[}\frac{\pi f_{c}-\sin(\pi f_{v})}{1-\cos(\pi f_{v})}\Big{]}\delta_{H}^{ 2}S_{2}K_{H,eq}} \tag{15}\]  
As the \(\psi\)_function_ quantifies the reduction of the vertical stiffness and the effective compressive modulus with the horizontal deformation, a larger reduction \(\psi(\delta_{H})\) results in lower values of the vertical response parameters of the bearings. It is also worth adding that the ratio of the vertical stiffness to the horizontal stiffness of an elastomeric bearing needs to be large enough to support the weight of the structure and to avoid rocking motion [8]. Using the \(\psi\)_function_, this ratio can be obtained as  
\[\frac{K_{V}^{\delta_{H}}}{K_{H}^{\delta_{H}}}=\psi(\delta_{H})\cdot\frac{K_{V} ^{\delta_{H}=0}}{K_{H}^{\delta_{H}}} \tag{16}\]  
where \(K_{H}^{\delta_{H}}\) is the horizontal secant stiffness value at a generic horizontal displacement. Equation (16) shows how the ratio of stiffness at a generic horizontal displacement \(\delta_{H}\) can be obtained through the \(\psi\)_function_ starting from the ratio between the vertical stiffness under pure compression and the secant horizontal stiffness at \(\delta_{H}\). Using the definition of vertical stiffness under pure compression given in Equation (1), Equation (16) becomes:  
\[\frac{K_{V}^{\delta_{H}}}{K_{H}^{\delta_{H}}}=\psi(\delta_{H})\cdot S_{2}\cdot \frac{E_{c}^{\delta_{H}=0}}{K_{H}^{\delta_{H}}} \tag{17}\]  
This ratio can be calculated using Equation (15) for the \(\psi\)_function_, Equations (3) or (4) for the effective compressive modulus and one of the different analytical solutions proposed for the horizontal stiffness of U-FREIs [46; 47; 48; 49; 50; 51; 52].
# Safety and Efficacy of Modern Stents in Patients with Metabolic Syndrome
## 3 Results
### Clinical Outcomes  
Patients were followed up for one year from the index ACS. The cumulative incidences for MACE and each of its components are shown in Table 7. MACE had occurred in 20.52% versus 24.52% of patients underwent 2 nd generation DES and other stents placement procedures, respectively. From the total, 4.41% of all patients enrolled died within one year of the acute coronary event, and 3.43% of deaths were due to cardiac diseases. There were no significantly differences in terms of death rates between groups. Graphical representation of the leading causes of cardiac deaths in each group is shown in Figure 1.  
Ischemic driven TLR was performed in 7.35% of all patients, with no statistically significant differences in new revascularizations between the 2 nd generation DES and the other stents cohorts (\(p\) > 0.05). Despite these results, most patients requiring TLR had bare metal stents and 1 st generation DES implanted at the index procedure (9.43%). On the other hand, higher rates of non-TLR were recorded in the 2 nd generation DES group.  
Cerebrovascular ischemic events and bleeding events are described in Table 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Medication at Discharge**} & **2 nd Generation DES** & **Other Stents** & \multirow{2}{*}{_p_**-Value**} \\  & **(_n_ = 151_)** & **(_n_ = 53_)** & \\ \hline Acetylsalicylic Acid (A), \(n\) (\%) & 148 (98.01) & 52 (98.11) & 0.96 \\ Clopidogrel (C), \(n\) (\%) & 29 (19.20) & 8 (15.09) & 0.51 \\ Ticagrelor (T), \(n\) (\%) & 121 (80.13) & 45 (84.90) & 0.44 \\ DAPT, A + C, \(n\) (\%) & 27 (17.88) & 8 (15.09) & 0.64 \\ DAPT, A + T, \(n\) (\%) & 120 (79.47) & 44 (83.01) & 0.58 \\ Beta blocker, \(n\) (\%) & 115 (76.15) & 43 (81.13) & 0.46 \\ Statin, \(n\) (\%) & 150 (99.33) & 52 (98.11) & 0.44 \\ ACEI, \(n\) (\%) & 118 (78.14) & 42 (79.24) & 0.87 \\ ARB, \(n\) (\%) & 10 (6.62) & 3 (5.66) & 0.81 \\ \hline \hline \end{tabular} Abbreviations: DES, drug eluting stent; DAPT, dual antiplatelet therapy; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker.  
\end{table}
Table 6: Medication at discharge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Clinical Outcomes at 12**} & **2 nd Generation DES** & **Other Stents** & \multirow{2}{*}{_p_**-Value**} \\
**Months** & **(_n_ = 151_)** & **(_n_ = 53_)** & \\ \hline MACE & 31 (20.52) & 13 (24.52) & 0.54 \\ All-cause mortality & 5 (3.31) & 4 (7.54) & 0.20 \\ Cardiac mortality & 3 (1.98) & 4 (7.54) & 0.05 \\ TLR & 10 (6.62) & 5 (9.43) & 0.50 \\ Non-TLR & 8 (5.29) & 2 (3.77) & 0.66 \\ No-R & 8 (5.29) & 2 (3.77) & 0.66 \\ Coronary event-free survivor & 128 (84.76) & 45 (84.90) & 0.60 \\ Stroke & 3 (1.98) & 1 (1.88) & 0.96 \\ Bleeding events & 5 (3.31) & 2 (3.77) & 0.87 \\ \hline \hline \end{tabular} Abbreviations: DES, drug eluting stent; MACE, major adverse cardiac events; TLR, target lesion revascularization; Non-TLR, non-target lesion revascularization; No-R, no-revascularization.  
\end{table}
Table 7: One-year clinical outcomes.