esa-sceva/satcom-chunk-collection huggingface dataset:
# Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines
## 3 Optimization of the Injection Using Double Dead-Time Strategy
 \tag{6}\]  
With this strategy the dead-time effect can be solved. This is the non-ideality that most affects the injected voltage. However, the injection is also affected by the voltage drop at semiconductors and the cable that connects the inverter with the machine. These two variables are introduced in Expression (6) to obtain the final Equation (7). \[ \begin{array}{l}V_{DC_{out}}=\frac{T_{tnt2}\cdot V_{inj1}-T_{tml}\cdot V_{inj2 }}{T_{tnt2}-T_{tml}}-V_{semi}-V_{cable}\end{array}  \tag{7}\]  
To compensate the cable voltage drop, a simple thermal model of the cable can be implemented. Using this model, the value of \(V_{cable}\) is estimated. The value of \(V_{semi}\) is unknown and it depends on the current offset, the current RMS value, the stator frequency and the temperature of the semiconductors. From one side, adjusting the commutation frequency in function of the stator frequency, the dependency on the stator frequency is eliminated. From the other side, the double dead-time injection is made with the same current offset. Thus, a look-up table for different current and temperature values could be implemented to obtain this value. The variation of \(V_{semi}\) does not suffer large variation with respect to the change in the mentioned parameters, so the look-up table does not need to have a big number of measurements. In order to tune this look-up table, the machine is run at different stator current levels with the same current DC value. Measuring the temperature of the machine stator, the value of \(V_{DC_{out}}\) could be estimated for the different working points. Applying the double injection strategy, the look-up table could be tuned using the Equation (8). \[ \begin{array}{l}V_{semi}=\frac{T_{tnt2}\cdot V_{inj1}-T_{tml}\cdot V_{inj2 }}{T_{tnt2}-T_{tml}}-V_{DC_{out}}-V_{cable}\end{array}  \tag{8}\]
# Analysis of AC DC Four-Switch Boost-Buck Battery Charger Converter for EV Applications
## 3 Converter Analysis
### State-Space Averaging and Small-Signal Model Analysis in Buck Mode  
Like the boost mode in CCM, the duty cycle \(d_{2}\) is representing the time of the controlled switch \(Q_{4}\) being on and \((1-d_{1})\) is representing the time of the controlled switch \(Q_{4}\) being off. Figure 3b presents a simplified model of the converter operating in the buck mode. Similar to the modeling procedure for the boost mode, the same approach will be followed to establish the needed transfer functions in the buck mode.  
The average state-space model for the buck mode can be expressed as follows:  
\[\begin{array}{ll}\frac{d{{\bar{t}}_{11}}(t)}{dt}&=\frac{v_{g}(t)-v_{c}(t)}{L _{1}},\\ \frac{d{{\bar{t}}_{22}}(t)}{dt}&=\frac{v_{g}(t)-v_{g}(t-1-d_{2}(t))v_{c}(t)}{L _{2}^{2}}\\ \frac{dv_{g}(t)}{dt}&=\frac{(1-d_{2}(t)){{\bar{t}}_{22}}(t)+i_{11}(t)}{L_{2}}, \\ \frac{dv_{g}(t)}{dt}&=\frac{i_{12}(t)}{\mathbb{C}_{o}}-\frac{\mathbb{C}_{o}(t)}{ R_{o}\mathbb{C}_{o}}.\end{array} \tag{5}\]  
The steady-state values of the converter variables in the buck mode can be obtained by setting the different differential equations in (5) as equal to zero. The steady-state representation of the variables is as follows:  
\[\begin{array}{ll}I_{L1}&=\frac{D_{2}V_{g}}{R_{o}}\left(D_{2}-1\right)\\ I_{L2}&=\frac{D_{2}V_{g}}{R_{o}}\\ V_{C}&=\;V_{g},\\ V_{o}&=\;D_{2}V_{g}.\end{array} \tag{6}\]  
These steady-state values indicate the equilibrium points of the system in the buck mode, where the converter operates under stable and balanced conditions.  
Similar to the boost mode, two transfer functions are expressed as follows:  
\[\begin{split} G_{igd_{lu}}(s)&=\frac{\Delta i_{x}(s)}{ \Delta d_{x}(s)},\\ G_{zd_{lu}}(s)&=\frac{\Delta V_{z}(s)}{\Delta d_{z}(s)}, \end{split} \tag{7}\]  
where \(\Delta d_{2}(s)\) represents the small-signal variation in the duty cycle.
## 3 Experimental Results and Simulation Analysis
### Simulation Optimization Analysis
#### 3.3.3 The Impact of Pipeline Layout on the Flow Field of Cable Chambers  
A flow field analysis was conducted on the original pipeline layout and the optimal typical interface at Y = 50 mm, and the coordinates on the map represent the X and Z directions of the cable room. The central cavity is a cable hole with a diameter of 110 mm, and it is solid in the positive Y direction. At this point, the three separate cables are wrapped in insulated sleeves and there is no significant change in surface temperature. The negative Y direction leads to the exterior of the cable room, with a sleeve diameter of 100 mm. The humidity supply inlet is a circular ring with an inner diameter of 100 mm and an outer diameter of 110 mm, designed to simulate the pores of the cable hole. By comparing the temperature distribution, relative humidity distribution, and velocity distribution cloud maps of typical cross-sections between Layout 1 and Layout 4 are obtained, as shown in Figures 12 and 13. Comparing the temperature distribution cloud maps in Figures 12 and 13, on a typical cross-section, the overall temperature of Layout 1 is lower at 301 K, while the local temperature in the upper left corner is slightly higher at 302 K; Layout 4 has an overall temperature of 302 K, with a more uniform distribution. Comparing the temperature  
Figure 11: (**a**) Distribution of relative humidity data at 100% source humidity; (**b**) distribution of dew point temperature data at 100% bottom inlet humidity.  
Figure 10: (**a**) Distribution of relative humidity data at 85% bottom inlet humidity; (**b**) distribution of dew point temperature data at 85% bottom inlet humidity.  
distribution cloud maps in Figures 12b and 13b, on a typical cross-section, the overall relative humidity of Layout 1 is above 60%, with higher humidity in the central area near the cable trench; The overall relative humidity of Layout 4 is above 50%, and the fluctuation area near the cable trench is smaller. Comparing the temperature distribution cloud maps in Figures 12c and 13c, on a typical cross-section, Layout 1 has a larger range of velocity fluctuations, with velocities varying around 0.3 m/s in the middle, upper right corner, lower right corner, and directly below; the velocity distribution of Layout 4 is more uniform, with only the middle part and the lower part having a speed close to 0.3 m/s, while there is little change in other parts.  
Overall, the flow field distribution in Layout 4 is more stable compared to Layout 1, which affirms the positive significance of optimizing the pipeline layout.
# Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
###### Abstract  
Modal analysis is widely used for addressing NVH--Noise, Vibration, and Hardness--in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical problem. When this basis is used for expressing the displacement field of a dynamical problem, the model equations become uncoupled. Moreover, a reduced basis can be defined according to the eigenvalues magnitude, leading to an uncoupled reduced model, especially appealing when solving large dynamical systems. However, engineering looks for optimal designs and therefore it focuses on parametric designs needing the efficient solution of parametric dynamical models. Solving parametrized eigenproblems remains a tricky issue, and, therefore, nonintrusive approaches are privileged. In that framework, a reduced basis consisting of the most significant eigenmodes is retained for each choice of the model parameters under consideration. Then, one is tempted to create a parametric reduced basis, by simply expressing the reduced basis parametrically by using an appropriate regression technique. However, an issue remains that limits the direct application of the just referred approach, the one related to the basis ordering. In order to order the modes before interpolating them, different techniques were proposed in the past, being the Modal Assurance Criterion--MAC--one of the most widely used. In the present paper, we proposed an alternative technique that, instead of operating at the eigenmodes level, classify the modes with respect to the deformed structure shapes that the eigenmodes induce, by invoking the so-called Topological Data Analysis--TDA--that ensures the invariance properties that topology ensure.  
modal analysis; topological data analysis; NVH; structural dynamics +
Footnote †: journal: Applied Sciences
## 3 Results
### Endpoints Localization  
Based on the methods discussed before, the vibration signals of the manipulator can be divided into segments of single actions, which makes it possible to score parts of the moving process. Commonly, a single instruction controls the single-step action of the robotic arm. Examples are given to illustrate the effect of endpoints localization, as shown in Figures 7 and 8. The robot is asked to complete the palletizing task with different set parameters.  
These two examples are the signals collected from the six-step movement of the robotic arm at different set speeds and accelerations. The proposed scheme can well identify the locations of endpoints, and the parts of the vibration signals corresponding to the selected actions can be easily obtained and further scored.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(50, 60)** & **(60, 70)** & **(70, 80)** & **(80, 90)** & **(90, 100)** \\ \hline \(|\overline{a_{main}}|\) & 0.0902 & 0.0745 & 0.0685 & 0.0555 & 0.0503 \\ \(\delta_{a_{min}}\) & 0.1385 & 0.1109 & 0.1006 & 0.0672 & 0.0553 \\ \(|\overline{a_{vib}}|\) & 0.0193 & 0.0147 & 0.01153 & 0.0099 & 0.0066 \\ \(\delta_{a_{vib}}\) & 0.0291 & 0.0240 & 0.0193 & 0.0161 & 0.0104 \\ \(\alpha_{p}\) & 0.0901 & 0.7803 & 0.7241 & 0.4493 & 0.3326 \\ \(|\overline{w}|\) & 5.1312 & 4.8916 & 4.7796 & 4.7771 & 2.7194 \\ \(\delta_{w}\) & 10.0246 & 8.2129 & 7.6831 & 7.6668 & 4.4152 \\ \(w_{p}\) & 66.7387 & 52.7907 & 48.0674 & 41.3878 & 31.3652 \\ \(AP_{min}\) & 0.07504 & 0.0548 & 0.0501 & 0.0272 & 0.0162 \\ \(AP_{vib}\) & 0.0028 & 0.0019 & 0.0012 & 0.0009 & 0.0003 \\ \(jerk\) & 0.1160 & 0.0882 & 0.0655 & 0.0533 & 0.0350 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Feature statistics for score segments.  
Figure 6: ROC curve.  
Figure 8: Velocity 100%, acceleration 100% vibration signal endpoints identification.  
Figure 7: Velocity 40%, acceleration 40% vibration signal endpoints identification.
## 4 Discussion
### Characteristics of Fresh and Packed Nettle Leaves
#### 4.4.6 Antioxidant Capacity  
The bioactive compounds analyzed in this work are known for their strong antioxidant properties that are beneficial both for plants and human organisms. As shown in Figure 8, the highest antioxidant capacity measured by FRAP was in leaves that were packed in LDPE after 14 days of storage. It is well mentioned that samples packed in BOPP also had a strong antioxidant capacity. Similar results were shown for the preservation of photosynthetic pigments, total phenolic compounds, and AsA (Sections 4.4.3-4.4.5), so this correlation indicates that they were indeed responsible for the strong antioxidant capacity. The positive correlation between the content of polyphenolic compounds as strong antioxidants and their antioxidant potential is confirmed by numerous studies [97; 98; 99].  
The retention of antioxidant compounds in nettle leaves was due to the reduced levels of O\({}_{2}\) and the increased levels of CO\({}_{2}\) in LDPE and BOPP. These observations are similar to other examples in the scientific literature, where authors showed that the antioxidant capacity of lettuce was the highest under high CO\({}_{2}\), [89] and of baby spinach in the atmosphere with reduced O\({}_{2}\) and increased CO\({}_{2}\) as well [10]. The lowest antioxidant capacity measured by FRAP was after 14 days in PA/PE. Oxygen exposure can cause the oxidation of molecules characterized by a strong antioxidant potential, while, in contrast, lower levels of O\({}_{2}\) can reduce oxidative stress and attenuate the production of ROS (reactive oxygen species) while maintaining the antioxidant status of plant tissues [100]. Some common packaging materials, especially BOPP and LDPE, can provide effective barrier and permeability properties, ensuring a decrease in O\({}_{2}\) content and thus helping to maintain antioxidant levels, as suggested by our results obtained by FRAP. Elevated CO\({}_{2}\) can stimulate the production of certain antioxidants in plants as it acts as a signaling molecule, triggering the expression of genes involved in antioxidant defense mechanisms [101], which may be the reason for the higher antioxidant capacity of nettle leaves packed in LDPE and BOPP measured by the FRAP method. The results of antioxidant capacity measured by ABTS and FRAP showed some differences. These deviations can be explained by the fact that the FRAP assay is more sensitive to compounds of hydrophilic nature, which were in the majority in this study, especially polyphenols, while the ABTS method detects molecules of lipophilic nature in addition to hydrophilic ones, which were not strongly presented in nettle leaves.
# Finite Difference/Collocation Method for a Generalized Time-Fractional KdV Equation
## 2 Preliminaries of Fractional Calculus and Jacobi Polynomials  
### Generalized Fractional Calculus  
In this part, we introduce the definitions of new generalized fractional integrals and derivatives. To guarantee that the new generalized fractional integral and derivative are well-defined, we need to assume that the weight function is positive and the scale function is monotonically increasing on the domain.  
**Definition 1**.: _([24]) The left-generalized fractional integral of order \(\alpha>0\) of a function \(u(t)\) with respect to a scale function \(z(t)\) and a weight function \(w(t)\) is defined as_  
\[\left({}^{*}I_{a+}^{\alpha}u\right)(t)=\frac{\left[w(t)\right]^{-1}}{\Gamma( \alpha)}\int_{a}^{t}\frac{w(s)z^{\prime}(s)u(s)}{[z(t)-z(s)]^{1-\alpha}}ds, \tag{1}\]  
_provided the integral exists. The superscript \"\({}^{*}\) indicates the integral here is a new generalized fractional integral._  
**Definition 2**.: _([24]) The left first-order generalized derivative of a function \(u(t)\) with respect to a scale function \(z(t)\) and a weight function \(w(t)\) is defined as_  
\[\left(D_{[z,w,L]}u\right)(t)=[w(t)]^{-1}\left[\left(\frac{1}{z^{\prime}(t)}D_{ t}\right)\left(w(t)u(t)\right)\right], \tag{2}\]  
_provided the right side of the equation is finite._  
**Definition 3**.: _([24]) The left-generalized derivative of order \(m\) of a function \(u(t)\) with respect to a scale function \(z(t)\) and a weight function \(w(t)\) is defined as_  
\[\left(D_{[z,w,L]}^{m}u\right)(t)=[w(t)]^{-1}\left[\left(\frac{1}{z^{\prime}(t )}D_{t}\right)^{m}\left(w(t)u(t)\right)\right], \tag{3}\]  
_provided the right side of the equation is finite, where \(m\) is a positive integer._  
**Definition 4**.: _([24]) The left Caputo-type generalized fractional derivative of order \(\alpha>0\) of a function \(u(t)\) with respect to a scale function \(z(t)\) and a weight function \(w(t)\) is defined as_  
\[\left({}^{*}D_{a+}^{a}u\right)(t)=\left({}^{*}I_{a+\dot{\dot{\gamma}}z,w}^{m- \alpha}D_{[z,w,L]}^{m}u\right)(t), \tag{4}\]  
_provided the right side of the equation is finite, where \(m-1\leq\alpha<m\), \(m\) is a positive integer._  
Similarly, for one function containing two variables, that is, \(u(x,t)\), its (left) Caputo-type generalized fractional partial derivative with respective to \(t\) is defined as [25]  
\[\left({}^{*}D_{a+}^{a}u\right)(x,t)=\frac{[w(t)]^{-1}}{\Gamma(1-\alpha)}\int_ {a}^{t}\frac{1}{[z(t)-z(s)]^{\alpha}}\cdot\frac{\partial[w(s)u(x,s)]}{\partial s }ds,\,0<\alpha<1. \tag{5}\]  
**Remark 1**.: _Left Riemann-Liouville-type generalized fractional derivatives can be defined if we switch the order of integral and differential. When the scale function is selected as \(z(t)=t\) and the weight function is a nonzero constant, the operators defined in (1), (4) and left Riemann-Liouville-type generalized fractional derivatives are reduced to left Riemann-Liouville fractional integrals, left Caputo fractional derivatives and left Riemann-Liouville fractional derivatives, respectively._
## 1 Introduction
The printed elements demonstrated stability under thermal stresses of up to 215 \({}^{\circ}\)C, confirming that the printing direction and material choice have a direct impact on applicability [8]. Measurement techniques based on digital image correlation (DIC) play a key role in testing the mechanical and geometric reliability of resin printing. The GOM ARAMIS system enables non-contact, high-resolution deformation measurements, allowing not only the validation of numerical simulations but also the spatial mapping of the effects of the printing strategy. [PERSON] et al. [9] performed fracture mechanics tests on 3D-printed polyamide specimens of PRNT and SENT types, using the GOM ARAMIS system to monitor crack growth and displacement fields. The measured data were used to plot crack growth curves that were independent of geometric configuration--confirming material-dominated fracture behavior [9]. A similarly detailed picture was provided by the work of [PERSON] et al. [10], who investigated CFRTP composites with different fiber orientations. Using the ARAMIS system (GOM, Braunschweig, Germany), they were able to identify not only deformation fields but also damage zones resulting from manufacturing defects (e.g., voids) [10]. [PERSON] et al. [11] investigated the effect of raster direction on fracture behavior on PLA specimens fabricated by FDM technology and validated the accuracy of the finite element models by ARAMIS measurements. The shear behavior at a 45\({}^{\circ}\) orientation could only be adequately described by a nonlinear model; the necessary parameters were also obtained from DIC data [11]. [PERSON] et al. [12] investigated the mechanical behavior of a topologically optimized 3D-printed cantilever load-bearing element made of PET-G and ABS. The deformation of the printed specimens was measured using the ARAMIS system, and the resulting displacements showed good agreement with the FEA simulations, with deviations ranging from 4% to 12%. The study demonstrated the applicability of DIC measurements in validating the mechanical performance of optimized molds [12]. Overall, the mechanical reliability of resin 3D printing is highly dependent on the printing direction, layer thickness, resin composition, and post-curing conditions. Digital image correlation measurements with the GOM ARAMIS system provide a reliable solution for accurately mapping the effects of these parameters. The resulting spatial deformation data are crucial for predicting structural performance and validating finite element models, thereby playing a vital role in the development of advanced, load-bearing printed components.
## 4 Methodology  
The gantry anomaly dynamic detection algorithm proposed in this paper mainly includes three parts: graph convolutional neural network, attention mechanism, and GRU, and the overall framework is shown in Figure 2. The algorithm first uses the GCN model to model the transaction data and adjacency of the gantry, learns the connection relationship between the gantry nodes and the influence of the transaction data through the GCN, and fuses the node features with the graph structure to generate a new node representation. Then, time-series modeling is performed with the GRU model to capture the time-dependence of the same gantry transactions, which allows for dynamic adjustment of the hiding state and the detection of various anomalies through the computation of reset gates and update gates. Finally, the classification results are obtained through the fully connected layer combined with the SoftMax function.
## 2 Materials and Methods  
The CVD-grown SiC films (Figure 1) are produced in a thickness range of 20-600 nm, using a low deposition temperature (T\({}_{\rm D}\)) range (650 \({}^{\circ}\)C \(\leq\)\(T_{\rm D}\)\(\leq\) 800 \({}^{\circ}\)C) in a hot-wall quartz tube CVD reactor (Figure 1A). The deposition employed a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C\({}_{3}\)H\({}_{12}\)Si\({}_{3}\)) [7; 35]. Prior to deposition, Si (100), undoped (intrinsic) double-polished Si, and Si (111) substrates were cleaned in 1% diluted HF (DHF) for 10 min to remove native oxide from the surface. Additionally, in situ cleaning and passivation were performed at 800 \({}^{\circ}\)C for 30 min in a forming gas ambient (comprising 5% H\({}_{2}\) and 95% Ar). In situ post-deposition annealing was performed using three different processes: annealing [A1] in forming gas at 850 \({}^{\circ}\)C; [A2] in Ar at 900 \({}^{\circ}\)C; and [A3] in forming gas at 1100 \({}^{\circ}\)C for 1 h. To optimize uniformity and minimize surface roughness, we have explored combinations of deposition temperatures and process pressures, targeting a surface-reaction-limited growth process where film growth is predominantly governed by the T\({}_{\rm D}\). In this growth process regime, achieved by reducing the pressure of the CVD system, a reduced flux and high diffusivity of the reactive species can be attained, thus yielding smoother, more conformal films with fewer defects. Conversely, the mass-transport-limited regime introduces undesirable flow-dependent effects that can degrade film quality (e.g., thickness variation, increased defectivity, and roughness) [36]. To this end, films deposited at T\({}_{\rm D}\)\(\geq\) 750 \({}^{\circ}\)C were grown at 200 mTorr (limited by our CVD pumping apparatus under the investigated conditions) with growth rates of 2-11 A/s, while films grown at lower temperatures (T\({}_{\rm D}\)\(\leq\) 700 \({}^{\circ}\)C) required higher pressures, for example, 1.5 Torr for the 650 \({}^{\circ}\)C deposition temperature, and exhibited much lower growth rates (\(\sim\)0.5 A/s) (Figure 1A). Furthermore, to optimize both deposition and post-deposition parameters for achieving high-quality SiC films, we have conducted extensive characterization using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM) (Figure 1B-E). Additionally, spectroscopic ellipsometry (SE) is employed to determine the optical properties of the films, specifically the index of refraction (\(n\)), extinction coefficient (\(k\)), and absorption coefficient (\(\alpha\)), along with their spectral dispersion.  
The films are modeled using a bilayer structure consisting of a SiC film on top of a c-Si or a SiO\({}_{2}\)/c-Si substrate. A discussion of the SE models in relation to our study is provided in the next section. To further study the absorption characteristics in the 1550 nm regime, we have performed resonant micro-photoluminescence excitation (PLE) spectroscopy and mapping using a tunable pulsed excitation laser and a time-gated detection system.