esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling of the Three-Dimensional (3D) Chest Wall for a Pectus Excavatum Patient  
#### 2.2.1 Collection of CT Images for PE Patients  
The first step for deriving the 3D chest wall of a PE patient is to collect the medical imaging data of the PE patients. We collected computed tomography (CT) images of 15 PE patients from the Pusan National University Hospital, and classified them based on the chest wall shape into symmetrical and asymmetrical types. Ten patients with a symmetrical depression based on the center of the sternum were included in the symmetric group, and five patients with a depression deviated from the center of the sternum or an asymmetrical depression were included in the asymmetric group.  
In general, CT, magnetic resonance imaging (MRI), ultrasound, and other methods can be adopted to fabricate the 3D surface and finite element (FE) model for several organs of the human body. For example, [PERSON] et al. [9] have developed a novel algorithm that automatically extracts the facial surface from ultrasound images. Using this method, they fabricated a 3D foetal face model without any human intervention or training procedure.  
However, in this study, the CT image was adopted from among various medical images. The reason is that in the case of MRI, the image can be distorted due to the patient's breathing, and in the case of ultrasound, it is not easy to generate a 3D surface and FE model using general-purpose medical image processing software such as Materialise MIMICS due to the noise.
## 2 Antenna Design and Configuration  
The printed geometry of the proposed monopole-based MIMO antenna and the design parameters are illustrated in Figure 1. The final geometrical design parameters are given in Table 1. This antenna consists of two \(50\)\(\Omega\) microstrip-fed pentagonal radiators associated with two rectangular parasitic elements placed symmetrically on the substrate. A standard \(1.6\) mm thick FR4 dielectric substrate with a \(4.3\) relative dielectric constant and a \(0.025\) loss tangent is used. Three CSRR slot structures are etched into the middle of the L-shapeddefective ground plane for bandwidth enhancement and coupling reduction. The proposed design has a compact overall size of 30 \(\times\) 20 \(\times\) 1.6 mm\({}^{3}\).
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 2 Data
### Problem Definition  
In this section, the definitions of important symbols are introduced and the definition of the research task is formally presented.  
**Definition 1** (Metro Station).: _For a city subway system, each station serves as the spatial unit for metro passenger flow prediction. We define \(\mathbf{V}=\{\mathbf{v}_{1},\mathbf{v}_{2},\mathbf{v}_{3},\dots,\mathbf{v}_{n}\}\) to represent the set of subway stations, wherein \(n\) is the station number. In addition, \(A\ \in\mathbb{R}^{n\times n}\) is an adjacency matrix representing the node's proximities between any pair of nodes._  
**Definition 2** (Time Interval).: _For the temporal dimension, the entire time period is divided into time intervals of equal length, i.e., \(\mathbf{T}=\{t_{1},t_{2},t_{3},\dots,t_{m}\}\). At each time segment, we calculate the inflow or outflow of traffic from each site at different time intervals, such as 15, 30 and 60 min._  
**Definition 3** (Passenger Flow).: _The passenger flow matrix \(\mathbf{F}\in\mathbb{R}^{n\times m}=\{X_{t},X_{t-1},X_{t-2},\cdots,X_{t-m+1}\}\), whereby \(n\) is the station number that is ordered according to the metro line number, \(m\) denotes past time intervals used to predict passenger traffic at the next time interval, and \(X\in\ \mathbb{R}^{n\times 1}\) is the inflow vector in a specific time interval._  
**Problem 1** (Problem Statement).: _Given the above definitions, our research problem can be defined according to (1), whereby \(f(\cdot)\) is the mapping function to be learnt using the proposed deep-learning framework._  
\[X_{t+1}=f(A;\ X_{t},\ X_{t-1},\ X_{t-2},\cdots,\ X_{t-m+1}) \tag{1}\]
## 2 Materials and Methods
### Encapsulation Efficiency Evaluation  
Samples for calculating encapsulation efficiency were prepared following a previously established procedure by [PERSON] et al. [25]. Briefly, 15 mg of encapsulates was diluted with 3 mL of a 50:8:42, \(V/V/V\) ethanol/acetic acid solution, vortex mixed, and filtered through a 0.45 \(\upmu\)m PTFE filter. Folin-Ciocalteu method was then applied to calculate the total phenolic content (TPC). 100 \(\upmu\)L of Folin-Ciocalteu reagent, 40 \(\upmu\)L of prepared solution, and 3160 \(\upmu\)L of distilled water were combined. After 4 min of incubation, 300 \(\upmu\)L of 20% (\(w/V\)) sodium carbonate was added and the mixture was incubated at 40 \({}^{\circ}\)C for 30 min. Gallic acid solutions (50-1000 mg/L) were used as a standard for establishing the calibration curve. The absorbance was measured at 765 nm in triplicates, and results are expressed as gallic acid equivalents. For the determination of surface phenol (SPC) content, 24 mg of encapsulates were washed for 5 min using 3 mL of ethanol/methanol (1:1, \(V/V\)) solution. The solution was filtered through a 0.45 \(\upmu\)m PTFE filter and measured using the previously described Folin-Ciocalteu method.  
Encapsulation efficiency (EE) was calculated using the following equation: Equation (3):  
\[EE[\%]=\frac{\mathit{TPC}-\mathit{SPC}}{\mathit{TPC}} \tag{3}\]
# PTG-HEFA Hybrid Refinery as Example of a SynBioPTx Concept--Results of a Feasibility Analysis
## 3 Results and Discussion
### Economic Assessment  
Based on the results of the technical assessment, specific production costs were calculated. Referring to Figure 4, the bandwidth of total production costs is from 1295 to 1800 EUR t\({}^{-1}\) for HEFA-SPK, which is well above the current market price of fossil jet fuel of around 425 EUR t\({}^{-1}\). Costs can be lowered to 1210 EUR t\({}^{-1}\) for shifting the product values to diesel; this is due to the smaller production of short-chained, low-value naphtha and fuel gases.  
When analysing the impact of the specific cost components to the total costs, feedstock costs dominate and the contribution from the PTG part is significant. Costs of capital and operation seem negligible in that context. The scenario of steam reforming with naphtha and fuel seems to be more favorable than PTG in terms of costs. This is due to the fact that only small amounts of revenue are lost but large investments in electrolysers and hydrogen storage and therefore also electricity costs are saved.  
A more detailed discussion of the results, an exemplarily sensitivity analysis and comparisons with results of similar investigations for HEFA fuels are presented in [12].  
Figure 3: Specific GHG emission per scenario for HEFA-SPK and HEFA-diesel, and GHG mitigation potential compared to fossil reference of the European Renewable Energy Directive (RED).
## 1 Introduction  
For a long time, satellite navigation and positioning relied mainly on the American GPS. Later, the Russian GLONASS was used as an additional system. Currently, thanks to the two new Galileo and BeiDou, positioning without GPS is possible. The Galileo system has already passed the initial phase of orbital validation (In-orbit Validation-IOV), which included testing and checking of four operational satellites and related terrestrial infrastructure. Currently, Galileo is in the reaching of Full Operational Capability (FOC) phase. The completed Galileo constellation will consist of 30 satellites located on the Middle Earth Orbits (MEO) [1]. The constellation of 18 Galileo satellites at the end of 2016, and then thanks to the provision of precise products in the real time by IGS (International GNSS Service) Real Time (RT) measurements have become possible. These measurements are important not only for surveys, but also for many communities, including those dealing with geographic information systems, precise navigation and many others. It is the first civilian global positioning system, expected for a long time not only in Europe but also in other parts of the world. It uses experience of its predecessors: American GPS and Russian GLONASS and therefore has several important advantages over them. Galileo uses, among others, a new more accurate model of ionosphere (NeQuick) and instead of one signal in one frequency, it generates signals in two frequencies called data and pilot. Data-includes navigational messages. Pilot-is used to determine the pseudoranges. In addition, all Galileo satellites are equipped with retroreflectors to track them by satellite laser ranging (SLR), which allows the assessment of the quality of the orbit in space.  
So far, we find in papers the results of research, which refer to the use of newly built navigation systems Galileo and BeiDou in combination with GPS and GLONASS. Research in this area has been published by many scientists from around the world, they relate to precise GNSS positioning in real time or in postprocessing [2; 3; 4; 5; 6]. There are also research projects in this area, such as Multi-GNSS Experiment (MGEX) [7; 8]. Another direction is the integrated, compatible GNSS used in precise point positioning in real time using corrections provided by satellite from large-scale satellite networks [9; 10; 11].  
In this paper, precise relative positioning, performed by RTK method based only on signals from Galileo satellites is carried out. The research was carried out in three main aspects. The first included determining the availability and continuity of positioning. The second concerned the convergence time as the speed of obtaining a precise position with given accuracy. The third aspect included research on determining the real accuracy of relative positioning in real time. The research was carried out using signals of Galileo satellites, from December 2017-January 2018.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 2 Materials and Methods
### Electro-Optical Property Characterization  
The alternating electric field with a square wave function was provided by a function generator (33220A, Agilent Technologies Inc., Santa Clara, CA, United States). The electric signal from the function generator was amplified through a high-voltage linear amplifier (F20A, FLC Electronics AB, Partille, Sweden). The output voltage was measured by an oscilloscope (DSOX3032T, Keysight Technologies, Santa Rosa, CA, United States). Transmittance of the samples were measured by spectrometer (HR2000+, Ocean Optics Inc., Largo, FL, United States). The wavelength of 550 nm was select to evaluate the transmittance. A halogen lamp (LS-3000, Keyence (CHINA) Co., Ltd., Shanghai, China) was used to provide white light. Response time of the samples were measure by photodiode (FSD1010, Thorlab Inc., Newtown, NJ, United States) with time resolution of 65 ns. The output signal of photodiode was amplified and then transformed by an acquisition card (USB6002, National Instruments, Austin, TX, United States), then collected by computer with a LabVIEW program. A 5 W White LED was used to provide white light. The experiment setup is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Materials** & **Concentration** \\ \hline Negative LC & 96.5 wt.\% \\ RM82 & 3 wt.\% \\ IR651 & 0.5 wt.\% \\ Silver nanowires & 0.01, 0.02, 0.05, 0.1 wt.\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proportion of materials required for the experiment.  
Figure 3: Materials used for PSLC compositions.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Double-Balanced Down-Conversion Mixer Noise Calculation and Simulation
#### 4.2.1 Noise Due to Transconductor Stages  
Figure 11 shows the noise model of the RF transconductance stage. From that, the equivalent noise at the input is as follows:  
\[\overline{v_{ni}^{2}}=\frac{1}{(gain)^{2}}.\overline{l_{no}^{2}}=\frac{1}{ \frac{\pi_{n}^{2}\pi_{n}^{2}}{(\tau_{b}+R_{s}+z_{+})^{2}}}.\Big{[}i_{sc}^{2}+g _{m}^{2}v_{n}^{2}\Big{]} \tag{9}\]  
In simplified terms, we obtain the following:  
\[\overline{V_{n}^{2}}=4 KTr_{b}+\frac{2 qI_{c}}{\overline{s_{m}^{2}}}=4 KT\bigg{(} r_{b}+\frac{1}{2g_{m}}+\frac{g_{m}}{2\beta}\bigg{)} \tag{10}\]  
\[\overline{l_{n}^{2}}=2 qI_{B}=4 KT\bigg{(}\frac{g_{m}}{2\beta}\bigg{)} \tag{11}\]  
1. Flicker noise \(\Big{(}\Big{/}\Big{/}\Big{)}\) of the transconductor: In the baseband, 1/f noise is present, but it does not occur at the frequency of the RF signal. Thus, it gets up-converted to the LO frequency and the odd harmonics of the LO signal. Ideally, the flicker noise of the transconductor transistor is shifted out of the baseband, and this should not impact the SNR at the mixing output [1; 18].  
2. White Noise of Transconductor Figure 12 shows the spectral content of several signals in the single-balanced mixer. The signal resulting from LO mixing comprises odd harmonics of the fundamental frequency \(f_{LO}\). The output current of the RF stage at small-signal levels exhibits noise. We assume the power spectral density of noise from the transconductance stage is flat with frequency, i.e., white noise--associated with the transconductor stage [1; 3; 18].  
Figure 11: Noise model of the transconductance stage [25].  
Hence, mixer communication is considered to be similar to a square wave, with the LO frequency and its odd harmonics converting the corresponding parts of the white noise to the IF [1; 18]. This is expressed as follows:  
\[\overline{V_{n,o,RF}^{2}}=n.\left(\frac{2g_{\mathrm{m1}}R_{L}}{\pi}\right)^{2} \left(4 KT\left(r_{b1}+\frac{1}{2g_{\mathrm{m1}}}\right)\right) \tag{12}\]  
And factor n from [1] is as follows:  
\[\mathrm{n}=2\left(1+\frac{1}{3^{2}}+\frac{1}{5^{2}}+\cdots\right)=\frac{\pi^{ 2}}{4} \tag{13}\]  
The double-sideband mixer would contribute half of the value indicated in Equation (13) due to the absence of an image sideband. Examining the sidebands of as many harmonics as needed in a mixer circuit is necessary for analyzing the frequency-dependent noise in the RF stage. Calculating the power spectral density is challenging because of inadequate parasitic modeling at very high frequencies.
## 2 Preliminaries: Main Tools and Requirements
### Classical Recursive Controllers  
In sequential computing, the conventional step-size selection scheme is of the form \(h_{n+1}=\rho_{n}\cdot h_{n}\), i.e., the step-size sequence is generated recursively along the solution trajectory. Taking logarithms, we obtain a linear recursion,  
\[\log h_{n+1}=\log h_{n}+\log\rho_{n}, \tag{2}\]  
i.e., we have a simple summation process, referred to as an integral (or I) control. The quantity being summed is the control error \(\log\rho_{n}\). In the simplest case, one takes  
\[\rho_{n}=\left(\frac{\|e_{n}\|}{\textsc{tol}}\right)^{1/p}, \tag{3}\]  
where \(e_{n}\) is the local error estimate. This changes (2) into  
\[\log h_{n+1}=\log h_{n}+\frac{1}{p}\big{(}\log\|e_{n}\|-\log\textsc{tol}\big{)},\]  
where the deviation \(\log\|e_{n}\|-\log\textsc{tol}\) is the control error. The step size keeps changing unless the control error is zero. Note that, in order to generate a smooth step-size sequence, we basically advocate changing the step size every step, no matter how small the change is. Thus, older schemes of step-size halving/doubling typically generate nonsmooth step-size sequences, although it may not be possible to avoid such sequences in connection with multigrid-type step-size refinement.  
There are many more advanced alternatives to the elementary controller (3). Some more general recursive controllers have the form  
\[h_{n+1}=\rho_{n}^{\delta_{0}}\rho_{n-1}^{\delta_{1}}\rho_{n-2}^{\delta_{2}} \cdot h_{n}, \tag{4}\]  
where the \(\rho_{i}\) is given by (3) and the coefficients \(\beta_{0},\beta_{1},\beta_{2}\) can be chosen to improve both stability and regularity, notably by using techniques from digital filtering (see [6; 7]). Depending on the resulting structure, the controllers may be referred to as proportional-integral (PI control) or proportional-integral-derivative (PID) control. An important aspect is being able to reduce the gain of the controller, as the elementary controller (3) is sometimes prone to generating oscillatory (nonsmooth) step-size sequences. There are also further possibilities that account for previous step-size sequences. The design of the controller should be matched to the type of method and the problem class. Thus, stiff integration can benefit from special implicit methods with dedicated time-step adaptivity.  
In the present context, experiments are based on variants of (3) due to the special requirements associated with multigrid refinement.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Analysis of the Accuracy of Parallel Ultra-Rapid POD
#### 3.3.1 Improvement of Accuracy with Increased Update Frequency by the Parallel POD  
The update frequency of the original ultra-rapid orbit is 6 h, and the update frequency can be increased to 1 h after applying the parallel POD. The accuracy of orbit with 6-h update frequency and 1-h update frequency are compared in this section. Observation data from DOY 182 to 189, 2018 are selected for multi-GNSS ultra-rapid POD. The accuracy of GPS, GLONASS, Galileo, and BDS predicted orbit with parallel and non-parallel POD are analyzed, which are shown in Figures 7-10.  
From Figures 7-10, it can be seen that the accuracy of the predicted orbit of the 6-h update frequency fluctuates at different extrapolation times. The accuracies of orbit at 0 o'clock, 6 o'clock, 12 o'clock and 18 o'clock are the highest, and then gradually decrease. After the update frequency is increased to 1-h by parallel approach, the accuracy of predicted orbit is higher and more stable at different extrapolation time. The statistical results of the accuracy of GPS/GLONASS/Galileo/BDS orbit are shown in Table 7. Selecting the precise orbit of IGS and CODE analysis center as reference, the along, normal, radial and 1D mean RMS values of predicted orbit of 1-h update frequency and 6-h update frequency are compared.  
It can be seen from the table that the accuracy of GPS, GLONASS, Galileo, and BDS 1-h updated orbits are improved in normal, radial and along direction, and the radial direction is improved the most compared to the 6-h updated orbit. The RMS of the predicted orbit of GPS, GLONASS, Galileo and BDS are 33.4%, 31.4%, 40.1% and 32.8% higher than those of the original 6-h results, respectively.
## 2 Connection Options and Topology Optimization after Site Densification
### Rain and Atmospheric Attenuation
Equation (11) has no closed-form solution. After inserting the rain and atmospheric attenuation formulas (Equations (12)-(17)) into Equation (11), the maximum hop length \(d\) can be calculated numerically for a given \(R_{0.01\%}\) rainfall rate, polarization (either V or H), and frequency \(f\). \[d_{\text{eff}}=\frac{d}{1+d/d_{0}}. \tag{13}\]0  
where \(f\) is in GHz and \(d\) is in km units. The \(SG\), \(G_{\mathrm{ant}}\), and all the attenuation terms are in dB. Index V/H shows that parameters \(k\) and \(\alpha\) are different for the two polarizations. In Section 5, systematic calculation results are given for typical system values considered in dense access networks.
## 5 Conclusions  
The beam diameter has a strong influence on the PBF-EB process. Despite keeping line energy, area energy, return time, or lateral velocity constant, changes in melt pool characteristics and microstructure are observed. The gas porosity varies between 0.5 and 0.1% and the aluminum content is in a range of 0.8-2.1%. These changes are attributed to the power dependence of the beam diameter, which is currently not considered when calculating line energy or area energy. The widening of the electron beam with increasing beam power is affecting both, the power distribution of the beam as well as the resultant peak temperature in the powder bed. In consequence, the varying melting conditions have a strong impact on melt pool geometry and microstructure.  
Knowledge about the beam diameter and its dependence on the beam power is mandatory to control the PBF-EB process in detail. Quantifying the size and the shape as well as the power distribution of the electron beam is necessary for the scaling of process parameters from simply shaped geometries with uniform scan lengths to complex parts with various scan lengths. In addition, the determination of the system-dependent beam  
Figure 8: Calculated beam power density (Equation (6), black squares), beam diameter (Equation (5), red dots), and Gaussian beam power distribution (Equation (7), grey lines) plotted over the beam power.  
diameter is highly recommended when transferring process parameters between different PBF-EB machines to provide almost equivalent process conditions necessary for on-demand microstructure and properties.  
Conceptualization, M.R. and M.F.; methodology, M.R.; software, C.B.; validation, M.R. and C.B.; formal analysis, M.R. and C.B.; investigation, M.R. and C.B.; resources, M.F.; data curation, M.R. and C.B.; writing--original draft preparation, M.R.; writing--review and editing, M.F., C.B. and C.K.; visualization, M.R. and M.F.; supervision, M.F. and C.K.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
The Chair of Materials Science and Engineering for Metals gratefully acknowledges the German Research Foundation--Project ID 61375930--SFB 814--\"Additive Manufacturing\" TP B02 for financial support.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We are grateful to [PERSON] from the Chair of Materials Science and Engineering for Metals (FAU, Erlangen, Germany) for conducting the EPMA measurements and [PERSON] from Neue Materialien Fuerth (Fuerth, Germany) for conducting the heat treatments.  
The authors declare no conflict of interest.
## 5 Numerical Analysis of a CVJ
### Axial Movement  
Due to the periodic, alternating track arrangement, the inner race in the CVJ moves axially under deflection during rotation, since a resulting force effect in this direction is produced by the individual track forces. The magnitude of the oscillation that occurs depends on the external load factors. It can be generally stated that it increases proportionally with the deflection angle and rotational speed. For the following analysis, as before, a rotational speed of 300 rpm, a deflection angle of 2\({}^{\circ}\) and a load torque of 2000 Nm were used.  
In Figure 14a, the oscillation paths of a ball within the track coordinate system and the inner race with respect to the inertial system are compared. It is noticeable that the inner race, at approximately 58 \(\upmu\)m, has a much smaller oscillating motion than the ball. However, even an initially low amplitude can become critical for the system behaviour in the resonance case, which is why the phenomenon of axial movement in the joint cannot be neglected in the evaluation of the joint. This would not be possible with analytical methods, which underlines the necessity of a numerical joint analysis. It can also be seen that the inner race (red line) performs a four times oscillating movement around the zero position during one revolution.  
When transforming the time signal of the axial movement of the inner race via FFT [28] into the frequency domain, the significant frequencies become obvious. The resulting frequency spectrum is shown in Figure 14b. For the rotational speed of 300 rpm, the first order is 5 Hz. As was already evident from the previous time observation of the inner race movement, the fourth order appears to be the most significant frequency of the movement process. This is due to the geometry of the joint (see Figure 1), which has a 90\({}^{\circ}\) symmetry due to the alternate track arrangement and the number of balls, so that the joint has an identical starting position after a quarter turn. Further peaks (12 th order, 20 th order, 28 th order, etc.; see