esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Additive Manufacturing Error  
Figure 7 shows the additively manufactured implant with and without the support structures. Verification results of the reference (nominal CAD) implant are shown in Table 7.  
The mesh sensitivity analysis verified that the fourth iteration of the analysis reached a steady state value of 0.042 mm and 0.062 mm for the mean distance and standard deviation of the AM error, respectively. Figure 8 shows the AM error color plot of the end-use implant on the nominal CAD implant including a histogram, which shows asymmetric and positively skewed deviations.  
A maximum allowable distance of 0.5 mm ensured that all the data points of the surfaces were within the computational range. The grey regions of the color plot depict the surface data of the end-use implant that were not captured by the optical 3D coordinates measuring system. These regions represent locations where the reference points were placed for triangulating the surface geometry. The removal of the reference point surfaces ensured that only the implant surfaces were considered for the surface comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sensitivity Analysis of the End-Use Implant**} \\ \hline \multirow{3}{*}{**Iteration**} & \multicolumn{2}{c}{**CAD Implant Mesh (STL) Properties**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{1}{c}{**Standard**} \\ \cline{2-6}  & **Max. Edge** & **Number of** & **Number of** & **Distance** & **Deviation** \\  & **Length [nm]** & **Triangles [-]** & **Points [-]** & **[mm]** & **[mm]** \\ \hline Original & - & 13368 & 6584 & 0.031 & 0.100 \\ \hline
1 & 0.5 & 13672 & 6736 & 0.034 & 0.101 \\ \hline
2 & 0.4 & 371204 & 185490 & 0.043 & 0.063 \\ \hline
3 & 0.3 & 526942 & 263351 & 0.043 & 0.065 \\ \hline
4 & 0.2 & 1015062 & 507408 & 0.042 & 0.062 \\ \hline
5 & 0.1 & 3660090 & 1829738 & 0.042 & 0.062 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The mesh sensitivity analysis of the nominal CAD implant surface with respect to the optically scanned implant surface.  
Figure 7: The additively manufactured titanium implant with the supports (right) and without the supports (left).  
The magnification error of the additively manufactured implant was evaluated through a series of steps. Firstly, the lattices of the optically scanned implant were removed which eliminated small dimensional features. Then, the base implant was re-oriented to axially align its longest dimension with the global co-ordinate system. Finally, the absolute integrated distance of the base implant was minimized. The removal of lattices decreased the computational time and outlier contribution. The resultant magnification error is presented in Table 8. The compensation of the magnification error decreased the absolute integrated distance by a factor of 3.5 while the mean distance and standard deviations were also decreased.
## 5 Results Analysis
### Meta-Atom Array Configurations
#### 5.2.3 3 \(\times\) 3 Array Analysis  
From Figure 12a, the frequency spans of the negative effective permittivity, permeability and refractive index of the array structure are from 4.58-7.72 GHz and 8.42-10.40 GHz; 7.90-14 GHz; and 4.42-7.48 GHz, 7.83-10.10 GHz and 12.54-14 GHz, respectively. Moreover, in Figure 12b at 90-degree rotation, the frequency spans of the permittivity, permeability and refractive index are from 4-7.19 GHz and 7.55-10.02 GHz; 7.55-14 GHz; and 3.86-6.86 GHz and 7.56-9.45 GHz, respectively. At 180-degree rotation, the frequency spans are from 4.58-7.72 GHz and 8.42-10.41 GHz (permittivity); 7.90-14 GHz (permeability); and 4.42-7.48 GHz, 7.83-10.11 GHz and 10.80-11.32 GHz (refractive index), as explained in Figure 12c. However, Figure 12d shows that the negative permittivity, permeability and refractive index are sequentially from 4-7.19 GHz and 7.55-10 GHz; 7.55-14 GHz; 3.87-6.84 GHz and 7.59-9.45 GHz and left handed properties in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Structure** & **Rotation Angle** & \(\varepsilon\) & \(\mu\) & \(n\) & **EMR (\(\lambda\) / a)** & **Meta-Atom Type** \\ \hline \multirow{4}{*}{2 \(\times\) 2 Array} & 0\({}^{\circ}\) & \(-6.5\) & \(-16.1\) & \(-10.4\) & 8.6 & \multirow{4}{*}{Left-Handed} \\  & 90\({}^{\circ}\) & \(-2.6\) & \(-17.5\) & \(-6.8\) & 4.5 & \\ \cline{1-1}  & 180\({}^{\circ}\) & \(-7.1\) & \(-16.1\) & \(-10.8\) & 8.6 & \\ \cline{1-1}  & 270\({}^{\circ}\) & \(-2.9\) & \(-17.2\) & \(-7.2\) & 4.5 & \\ \hline \end{tabular}
\end{table}
Table 4: Real values of \(\varepsilon\), \(\mu\) and \(n\) at 9.20 GHz.  
Figure 11: Amplitude of the permittivity, permeability and refractive index at: (**a**) 0\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 180\({}^{\circ}\), (**d**) 270\({}^{\circ}\) rotations of the 2 \(\times\) 2 array structure.
# The Impact of Node Location Imperfections on the Reliability of Single-Layer Steel Domes
## 2 Reliability and Elasticity Indexes
The hyperplane is described by this equation:  
\[\mathrm{l}(\mathbf{Z})=-\left.\alpha^{\mathrm{ T}}\cdot\mathbf{Z}+\beta\right. \tag{7}\]  
where \(\alpha=-\left.\frac{\mathrm{VG}(\mathbf{z})}{\left\|\mathrm{VG}(\mathbf{z}) \right\|}\right|_{\mathbf{z}=\mathbf{z}^{*}}\) and \(\beta=\mathrm{sign}[\mathrm{l}(0)]\)\(\delta^{*}\) is the Hasofer-Lind reliability index, related to \(\delta^{*}\), i.e., the hyperplane distance \(\mathrm{l}(\mathbf{z})=0\) from the origin of the coordinate system and \(\alpha\)-unit vector, the direction of which is opposite to the \(G(\mathbf{z})\) function gradient at the design point. Thus, the FORM changed the problem of failure probability integration into an issue that concerned finding the design point, the calculations for which could be performed using an optimization algorithm with inequality limitations [47; 48]. An advantage offered by the FORM is the possibility of determining sensitivity to the change of parameters without making additional calculations. The fact that sensitivity of the reliability index is known becomes of key importance for reliable optimization, forecasting trends in the optimization process, and for initial considerations when the stochastic model is developed. If the sensitivity of the reliability index to random variable \(X_{i}\) is low compared to other variables, it is possible to acknowledge that the effect of this variable on the failure probability value is small. Consequently, in subsequent computations it can be treated as a deterministic variable. For the sake of interpretation and comparison, a normalized sensitivity measure, namely the elasticity index \(\mathrm{E}_{\beta}(\mathrm{p}_{\mathrm{i}})\), was introduced. The elasticity index \(\mathrm{E}_{\beta}(\mathrm{p}_{\mathrm{i}})\) can be defined as the percentage change in the reliability index\(\beta\) (Figure 3). This article assumes that the \(\mathrm{p}_{\mathrm{i}}\) parameter changes by 1%. We can determine this value depending on our needs. Figure 2: The approximation of the first order of the probability value. The indicator \(\mathrm{E_{\beta}(p_{i})}\) allowed us to describe the sensitivity of the reliability index to changes in all random parameters \(\mathrm{p_{i}}\)[49]. It can be written as follows:  
\[\mathrm{E_{\beta}(p_{i})}=\ \frac{\partial\beta}{\partial p_{i}}\cdot\frac{p_{i}}{\beta} \tag{8}\]  
For examples:  
with standard deviation:  
\[\mathrm{E_{\beta}(\sigma_{i})}= \frac{\partial\beta}{\partial\sigma_{i}\cdot\beta} \tag{9}\]  
or with mean value:  
\[\mathrm{E_{\beta}(\overline{Xi})}= \frac{\partial\beta}{\partial\overline{X_{i}}}\cdot\frac{ \overline{Xi}}{\beta} \tag{10}\]  
In a stochastic model, the variable \(\mathrm{p_{i}}\) can determine a standard deviation \(\sigma_{i}\) or a mean value \(\overline{X_{i}}\). In this article, both variables are used.
## 3 Materials and Methods
### VOCs Analysis and Optimization  
#### 3.3.1 Analysis of Volatile Metabolites Using HS-SPME-GC-MS  
For the analysis, 750 mg of copoazu pulp, previously grounded with liquid nitrogen, was weighed and transferred to a 20 mL headspace vial. The vial was tightly sealed with a silicon septum and pre-equilibrated in an oil bath at 60 \({}^{\circ}\)C for 6 min. An 80 \(\upmu\)m \(\times\) 10 mm DVB/CWR/PDMS fiber was then inserted into the vial's headspace to extract the volatile metabolites for 30 min at 60 \({}^{\circ}\)C. Subsequently, the fiber was desorbed at 250 \({}^{\circ}\)C for 5 min in the injection port of the GC-MS in splitless mode, following previously reported conditions [62].  
VOCs were separated using a capillary GC column composed of 5% Phenyl/95% Dimethyl Polysiloxane (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; SGE, Austin, TX, USA) and analyzed with a Gas Chromatograph HP 6890 Series equipped with an Agilent Mass Selective Detector 5973 (Agilent Technologies, Palo Alto, CA, USA). The optimized heating ramp began at 35 \({}^{\circ}\)C for 0.5 min, then increased at 1 \({}^{\circ}\)C/min to 40 \({}^{\circ}\)C, raised to 60 \({}^{\circ}\)C at 1.5 \({}^{\circ}\)C/min,finally ramped at 5 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C, and held for 1 min. High-purity helium (99.999%) was used as carrier gas at a flow rate of 1.3 mL/min. The mass spectrometer was operated under the following conditions: electronic ionization mode; ion source temperature, 230 \({}^{\circ}\)C; electron energy, 70 eV; quadrupole mass range, 30-300 amu; transmission line temperature, 230 \({}^{\circ}\)C; detector voltage, 1.4 kV; and quadrupole temperature, 150 \({}^{\circ}\)C. VOCs were tentatively identified by comparing the mass spectra of detected metabolites with those in the NIST 17.0 and GNPS libraries. Standards, including a terpene mixture (Terpene mixture1, 21 components, 2500 \(\mathrm{\SIUnitSymbolMicro g}\)/mL in hexane) and an allergen mix (Fragrance Allergen Mix A1), were used at a concentration of 600 ppm for reference. The relative percentage content of each compound in copoazu samples at three ripening stages was calculated using the peak relative area normalization method.
## 3 Results and Discussion
### Mixing Behavior Comparison between Laser-Arc Hybrid and Laser Cold-Wire Processes  
In square butt welds with narrow root gap (0.4 mm) the difference in mixing between the upper and lower halves of the joint thickness was more discernible in the weld samples produced with laser cold-wire welding than with laser-arc hybrid welding. When the root gap was 0.8 mm, the weld made with laser cold-wire welding and leading wire feeding still showed inhomogeneous mixing between the upper and lower halves of the joint. With trailing wire feeding, on the other hand, more even mixing behaviour was found. Corresponding samples for laser-arc hybrid welding and 0.8 mm root gap showed more even mixing behaviour with leading wire feeding compared to the laser cold-wire process. Trailing wire feeding with 0.8 mm root gap produced the best mixing of the laser-arc hybrid test welds and, especially at the weld root, the average mixing-% was somewhat higher than in corresponding laser cold-wire test welds. With the V-groove welds, the laser cold-wire process with trailing wire feeding exhibited superior mixing behaviour in terms of more homogenous filler metal distribution from the weld surface to the weld root. The V-groove welds with laser-arc hybrid welding, in turn, showed high average mixing-% values in all four measurement lines (ML1-ML4) from the weld surface to root but the overall homogeneity of the fusion zone mixing was only moderate.  
In the case of the laser-arc hybrid process, the electromagnetic force, that is, the Lorentz force, is known to act in the weld pool due to the electric current and the magnetic field induced. The Lorentz force is one of the driving forces for fluid flow in the weld pool during arc welding. The Lorentz force pushes liquid metal downward from the surface of the centre axis of the melt pool and liquid metal is circulated along both sides of the weld pool boundaries and rises back to weld surface [23]. In the laser-arc hybrid welds, the Lorentz force could have been augmenting weld metal mixing with convective fluid flow, which would cause a stirring effect in the melt pool.
## 2 Materials and Methods
### Data Processing  
#### 2.4.1 UAV Data Processing  
1. Point Cloud and DSM Data  
The coordinates of UAV images and GCPs (checkpoints) were imported into DJI Terra 3.4.4 software. Ground control points were marked in the images; then, point cloud and DSM data were generated through calculation of aerial triangulation, 2D reconstruction and 3D reconstruction.
2. DSM Volume Difference Calculation  
The acquired DSM data were imported into ArcGIS 10.2 software, followed by fill and excavation analysis in the software's 3D Analyst Tools. The DSM data generated under different flight proximity conditions were superimposed with the DSM data for the control group to analyze differences in terms of volume changes.
3. Point Cloud Data Index Calculation  
The acquired point cloud data were imported into Point Cloud Magic V2.0 (PCM V2.0) and Cloud Compare 2.12 software to denoise the point cloud and crop the main study area, superimpose the point cloud of the cropped main study area, and calculate the distance from point cloud to point cloud.
## 1 Introduction  
As a kind of clean energy source, photovoltaic (PV) capacity has grown significantly in recent decades. The development of PV power generation systems encounters a serious problem concerning their operating safety under lightning threat. PV panels are usually installed in large exposed areas and away from tall objects; therefore, they are especially prone to lightning strike [1; 2; 3]. After a PV bracket system is struck by lightning, current surges will propagate along its conducting branches and flow into the ground. A transient magnetic field is produced around the current-carrying branches that is strong enough to interfere with proper operation of the sensitive electronics in the control units. Moreover, there are conductor loops formed by the aluminum alloy profiles, busbars and connecting wires in the PV bracket system. When the transient magnetic field penetrates through the conductor loops, the induced voltages are produced and often cause serious damage to PV modules, inverters, cables, etc. [4; 5; 6; 7; 8]. This leads to both high repair cost and great loss of profit for PV power stations [9]. To meet the increasing demand for lightning protection design of PV installations, it is necessary to calculate the transient magnetic field and induced voltage in PV bracket systems under lightning stroke. Previous studies have mainly concentrated on the circuit simulation of lightning transients in PV bracket systems. The circuit models have been built for calculating the lightning transient responses in PV bracket systems [10; 11; 12], from which the distributions of transient currents and potentials have been obtained in PV bracket systems. However, an appropriate algorithm has not been found in the literature for calculating the transient magnetic field around the current-carrying branches in PV bracket systems. The previous calculation of the transient magnetic field was usually based on oversimplified procedures [3; 6; 13], in which an adequate consideration had not been given to the complicated spatial location of the current-carrying branches. Although the approximate procedures were employed to estimate the induced voltage [3; 4; 5; 6; 14; 15], they lacked a high calculation precision on account of their deficiency in exactly solving the transient magnetic field. In view of the imperfection in the previous studies, an efficient method is proposed in this paper for predicting the magnetic field distribution and induced voltage in PV bracket systems. The method provides a set of differential formulas for calculation of the transient magnetic field. These formulas are suitable to different spatial locations of the current-carrying branches. The induced voltage is obtained by evaluating the time derivative of the magnetic linkage through a conductor loop. The experimental measurement of the induced voltages is made in a reduced-scale PV bracket system and the calculated results are compared with the measured ones for confirming the validity of the proposed method. A numerical example is also furnished to examine the practical applicability of the proposed method.
## 2 Energy Model
### Solar Power Model  
For a satellite, energy is the basic guarantee of normal operation. Especially for communication satellites, the energy problem is particularly important, since the transmission  
Figure 1: Walker-delta constellation schematic.  
power is related to the data rate. Carrier-noise-ratio (\(CNR\)) is another important indicator of the communication system. It determines not only the capability of receiver but also the output power of the transmitter. \(CNR\)[22] can be described as  
\[CNR=\frac{EIRP}{k\cdot BW}\cdot\frac{g_{r}}{t_{s}}\cdot\frac{1}{l} \tag{1}\]  
\[EIRP=p_{t}+g_{t} \tag{2}\]  
where \(p_{t}\) and \(g_{t}\) are the output power and antenna gain of transmitter, \(g_{r}\) and \(t_{s}\) are the antenna gain and noise temperature of receiver. \(l\) is the path loss in space; \(k\) is Boltzmann constant. \(BW\) is the bandwidth of the channel, which can be described as:  
\[BW=(1+\alpha)\cdot R_{d} \tag{3}\]  
where \(\alpha\) is the roll-off factor of the pattern filter; \(R_{d}\) is the data rate of the system.  
For a particular communication mode, \(CNR\) is determined by requirements. In order to increase the data rate, it is necessary to provide higher output power. Therefore, under a fixed traffic density, the energy balance and routing paths are closely related.  
Solar energy system is the power provider for a satellite. There are two different orbit states, namely: \"day side\" and \"night side\" for walker-delta constellations. In the former, the satellite charges in sunlight; in the latter, the satellite discharges in shadow, as shown in Figure 2.  
In \"day side\", charging power (\(CP\)) is related to satellite solar panel design and relative attitude; it can be described as:  
\[CP=\sin\theta\cdot S\cdot\eta\cdot SC \tag{4}\]  
where \(\theta\) is the angle between the light line and the vertical line, \(S\) is the area of the solar panel, \(\eta\) is the energy conversion efficiency and \(SC\) is the solar constant.  
According to the public information of some commercial constellations, the charging power of Starlink and OneWeb can be obtained, as shown in the Table 1[23].  
Figure 2: Satellites charging and discharging.  
Starlink apply a bigger solar panel to increase the charging power. However, this will cause a lot of occlusion issues. OneWeb applied a solar array drive assembly (SADA) to increase the energy conversion efficiency. A smaller solar panel can reduce manufacturing and carrying costs, but suffers from a lack of power. Therefore, an efficient energy balance strategy is very important for NTSN.
# Effect of High-Speed Train-Induced Wind on Trackside UAV Thrust Near Railway Bridge
###### Abstract  
Imaging devices attached to unmanned aerial vehicles (UAVs) are used for crack measurements of railway bridges constructed for high-speed trains. This research aims to investigate track-side wind induced by high-speed trains and its effect on UAV thrust near the railway bridge. Furthermore, the characteristics of train-induced wind in three axial directions along a track, wind velocity, and the effect of train-induced wind on the UAV thrust were analyzed. This was achieved by installing 3-axis ultrasonic anemometers and a UAV thrust measurement system on top of a PSC box girder bridge. The changes in the train-induced wind velocity were monitored along the train travel, width, and height directions. The train-induced wind was measured at distances of 0.8, 1.3, 2.3, and 2.8 m away from the train's body to analyze wind velocity based on distance. It was found that the maximum wind velocity decreased linearly as the distance from the train's body increased. The UAV thrust increased by up to 20% and 60%, owing to train-induced wind when the leading and trailing power cars of a high-speed train passed, respectively. Thus, it is necessary to conduct further research to develop robust control and a variable pitch-propeller that can control thrust.  
railway bridge; health monitoring; UAV; train-induced wind +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Total Phenols and Cinnamic Acids Content  
The different first hydrolysis step led to different concentrations and yields of total phenols and cinnamic acids based on spectrophotometric analyses (Table 2). Total phenols content of the samples was evaluated with two different methods. The Folin Index is widely adopted to study natural antioxidants and estimate the total phenols content even though it actually measures the capacity of a compound to reduce the Folin's reagent, therefore it already gives an estimation of the antioxidant capacity. On the other hand, the 280 index is based on the characteristic absorption of the benzene rings of the majority of phenols at 280 nm, and is less influenced by the oxidative status of the analyzed molecules, but it is also related to the release of acid soluble lignin and sugar degradation products.  
It is important to underline that the pH of the acid liquor obtained from the auto-hydrolysis reactor was mildly acidic (\(4.28\pm 0.05\)), when compared to the liquor coming from the autoclave acid hydrolysis (\(0.31\pm 0.01\)). Even though only distilled water was used, the acetic acid released during the auto-hydrolysis process from delignification is the catalyzer of the reaction and the reason for this acidity [42]. The total phenols yield, calculated considering the exact volume of the recovered liquor, was higher for auto-hydrolysis, from 2.4 to 4.3 times based on the 280 Index and Folin index, respectively, and 11 times for total cinnamic acids. This might be due to the higher efficiency of this kind of hydrolysis together with a lower severity that could have avoided the degradation of the released phenolic compounds. On the other hand, there might also have been an effect of the higher liquid-to-solid ratio applied.
In the optimization process, conventional approaches, like the \"one variable at a time\" method, are inadequate for detecting the inter-relationship of multiple parameters. The response surface methodology (RSM) is a commonly used and efficient biotechnological optimization technique that employs a complete quadratic polynomial to demonstrate the correlations [26; 27]. The response surface methodology (RSM) is a set of mathematical or statistical methods for planning experiments, visualizing models, and figuring out how the variables affect each other by setting up multivariable systems to get meaningful answers [28]. Central composite design (CCD), which is a statistical approach that appropriately includes component interaction and influence, is employed in RSM. It is frequently used for the optimization of fermentation nutrient conditions [29]. Basically, the CCD model is a fundamental component of the response surface methodology. This form of optimization model is more accurate and does not need a three-level factorial experiment to construct a second-order quadratic model [30]. In this design, the center points are supplemented with a collection of axial points known as star points. Using this architecture, it is possible to rapidly estimate first- and second-order terms. Many forms of central composite designs and their importance in various experimental designs have been explored in detail. Yet, computations based on the determination of alpha (\(\alpha\)) and axial points have not been studied in detail [31]. In this work, we screened and optimized the medium nutrients in a submerged fermentation so that _B. velezensis_ BP-1 could synthesize potent antifungal compounds. The optimized fermentation broth was tested in vitro and in vivo against _P. aradichiola_ for the biological control of peanut web blotch.