esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multiscale Modeling of Carbon-Fiber-Reinforced Composite
### Microscale Model Simulation Analysis
#### 2.2.2 Influence of Microscale Parameters on Elastic Properties  
The woven yarns in CFRP are composed of fibers and resin, and the proportion of each component greatly influences the elastic properties of the composite. To investigate the impact of the \(V_{\text{f}}\) in the microscale cell on the elastic parameters of the composite material, this study conducts an analysis of microscale cells with different \(V_{\text{f}}\) using the homogenization method based on thermal stress described in Section 2.2.1. The results are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Material Properties** & \(\mathbf{E_{\text{f}}}\)/\(\mathbf{GP_{\text{a}}}\) & \(\mathbf{E_{\text{2}}}\) = \(\mathbf{E_{\text{f}}}\)/\(\mathbf{GP_{\text{a}}}\) & \(\mathbf{G_{\text{12}}}\) = \(\mathbf{G_{\text{f}}}\)/\(\mathbf{GP_{\text{a}}}\) & \(\mathbf{G_{\text{xy}}}\)/\(\mathbf{GP_{\text{a}}}\) & \(\mathbf{\mu_{\text{2}}}\) = \(\mathbf{\mu_{\text{3}}}\) & \(\mathbf{\mu_{\text{3}}}\) \\ \hline Simulation result & 115.12 & 6.04 & 3.44 & 4.8 & 0.34 & 0.25 \\ Experimental result & 113.57 & 6.10 & 3.46 & - & 0.33 & - \\ Error & 1.6\% & 0.98\% & 0.59\% & - & 3.03\% & - \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of simulation results and experimental results.  
As illustrated in Figure 4, the \(V_{\rm f}\) significantly influences the elastic behavior of composite materials. Generally, an increase in \(V_{\rm f}\) leads to a marked rise in the elastic modulus and a concurrent reduction in Poisson's ratio, indicating an enhancement in overall mechanical performance with higher fiber content. Notably, the elastic modulus \(E_{1}\) exhibits a near-linear increment with \(V_{\rm f}\), likely due to the axial properties of fibers being the predominant factor, considering the modulus of the resin is much lower than that of the fibers. On the other hand, both \(E_{2}\) and \(G_{12}\) show an accelerated increase with higher \(V_{\rm f}\). This trend can be explained by the initial dominance of resin properties when the fiber content is low, resulting in lower values for \(E_{2}\) and \(G_{12}\). As the fiber content increases, the influence of fibers becomes more pronounced, leading to the observed enhanced growth rates of \(E_{2}\) and \(G_{12}\) with increasing \(V_{\rm f}\).
## 3 Propagation Model
### Model Requirements
#### 3.1.1 Photon Initial Conditions  
The initial conditions for the light source are simply considered of an ideal light, which is collimated in nature, e.g., a laser diode. All photons are initialized with same locations, directions, and unity weight. However, for a real source, the conditions will vary to some degree and should be included in the light beam profile. The starting \(x,y\) location of the photon is simulated using initial radius \(r_{0}\) and radial angle \(\phi\), such that  
\[x_{0} =r_{0}\cos\phi \tag{1}\] \[y_{0} =r_{0}\sin\phi \tag{2}\]  
and starting direction using initial divergence and radial angles,  
\[\mu_{x} =\sin\theta_{0}\cos\phi \tag{3}\] \[\mu_{y} =\sin\theta_{0}\sin\phi\] (4) \[\mu_{z} =\cos\theta_{0} \tag{5}\]
## 3 Modeling and Simulations of the Assembly Error Effect on the Stability Accuracy
### Modeling and Simulation of the Assembly Error Effects on Stability Accuracy
#### 3.5.2 Modeling and Simulation of Assembly Errors of Run-Outs
\begin{array}{rl}\triangle R_{a}=\left[\begin{array}{cccc}c\triangle\beta _{a}(t)&0&\
eg s\triangle\beta_{a}(t)\\ 0&1&0\\ s\triangle\beta_{a}(t)&0&c\triangle\beta_{a}(t)\end{array}\right]\left[\begin{array} []{cccc}1&0&0\\ 0&c\triangle\alpha_{a}(t)&s\triangle\alpha_{a}(t)\\ 0&-s\triangle\alpha_{a}(t)&c\triangle\alpha_{a}(t)\end{array}\right],\\ \triangle R_{p}=\left[\begin{array}{cccc}c\triangle\beta_{p}(t)&s\triangle \beta_{p}(t)&0\\ -s\triangle\beta_{p}(t)&c\triangle\beta_{p}(t)&0\\ 0&0&1\end{array}\right]\left[\begin{array}{cccc}1&0&0\\ 0&c\triangle\alpha_{p}(t)&s\triangle\alpha_{p}(t)\\ 0&-s\triangle\alpha_{p}(t)&c\triangle\alpha_{p}(t)\end{array}\right],\end{array}  \tag{19}\]  
The angular velocity vectors of the azimuth and the pitch in \(\{i\}\) affected by errors of run-outs are denoted as \(\triangle\overrightarrow{\omega}_{ia}\) and \(\triangle\overrightarrow{\omega}_{ip}\), and can be calculated by using Equation (20):  
\[\triangle\overrightarrow{\omega}_{ia}=R_{b}^{a}\triangle R_{a}\overrightarrow{ \omega}_{ib}+\overrightarrow{\omega}_{bqr}\triangle\overrightarrow{\omega}_{ip }=R_{a}^{p}\triangle R_{p}\triangle\overrightarrow{\omega}_{ia}+ \overrightarrow{\omega}_{ap}, \tag{20}\]  
Figure 11: The assembly errors of azimuth run-outs. Based on analysis above, the signals of the gyroscopes affected by \(\triangle\overrightarrow{\sigma}_{a}\) and \(\triangle\overrightarrow{\sigma}_{p}\) are simulated in Simulink as shown in Figure 12. Taking the azimuth, for example, based on Equation (18) and Equation (20), \(\overrightarrow{\omega}_{ib}\) and the motor rotation angle \(\theta_{a}\) are set as the inputs; the maximum tilting oscillations including \(\triangle\alpha_{am}\) and \(\triangle\beta_{am}\), and the transformation matrix \(\triangle R_{a}\) are imbedded in the middle; the output is \(\triangle R_{a}\overrightarrow{\omega}_{ib}\), which will be used in other modules. The value of \(\triangle\alpha_{am}\) and \(\triangle\beta_{am}\) can be set as needed in the future simulation experiments. The value of \(\theta_{a}\) can be obtained from the motor module in Section 3.4. The simulation module for the pitch frame is similar.
## 3 Analytical Methods  
### Zircon U-Pb Dating and Trace Element Analyses  
Zircon U-Pb dating and trace element analysis were simultaneously performed using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) at the State Key Laboratory of Geological Processes and Mineral Resources (SKLGPMR), China University of Geosciences (Wuhan, China). The experimental setup employed a GeoLas 2005 laser ablation system (Coherent, Inc., Santa Clara, CA, USA) coupled with an Agilent 7500a ICP-MS (Agilent Technologies, Inc., Santa Clara, CA, USA). Analytical procedures followed the methodology described by [PERSON] et al. (2008) [PERSON] et al. (2008) [PERSON] et al. (2008), with helium serving as the primary carrier gas and argon as the make-up gas, mixed through a T-connector prior to plasma introduction. To enhance analytical performance, nitrogen was introduced into the central plasma gas flow (Ar + He) to improve sensitivity and resolution [38]. Each measurement  
Figure 3: **(A)** Field photograph of biotite schist and granitic gneiss from the Sulu orogenic belt, with yellow five-pointed stars indicating sampling locations; **(B,C)** Photomicrographs of biotite schist, biotite + quartz + calcite; **(D,E)** Photomicrographs of granitic gneiss, quartz + K-feldspar + biotite. Abbreviations: Qz (quartz), Bt (biotite), Cal (calcite), Ap (apatite), Kfs (K-sfeldspar), Sph (sphene), Alm (allanite).  
cycle consisted of 20-30 s of background acquisition followed by 50 s of sample ablation. Signal acquisition and instrumental control were managed using Agilent Chemstation software (OpenLab CDS ChemStation Edition), while subsequent data processing, including background correction, signal integration, mass bias correction, and quantitative calibration for both trace elements and U-Pb isotopes, was conducted using ICPMSDataCal v.11.8 [39].  
For U-Pb isotopic analysis, zircon 91500 served as the primary reference material, with duplicate measurements performed following every five unknown sample analyses. Instrumental mass fractionation and time-dependent drift were corrected through a linear interpolation of standard measurements following [PERSON] et al. (2010) [39], utilizing the recommended isotopic ratios from [PERSON] et al. (1995) [40]. The associated uncertainties were systematically propagated to the final sample results. Data reduction included concordia diagram construction and weighted mean age calculation using Isoplot/Ex_ver3 [41]. Trace element quantification employed USGS reference glasses BCR-2G and BIR-1G for calibration, with values sourced from the GeoReM database, combined with internal standardization protocols [39].
# Low-Cost 400 Gbps DR4 Silicon Photonics Transmitter for Short-Reach Datacenter Application
## 4 DR4 Transmitter Test
The power consumption of each MZM was 0.032 W ((1.8 V/2)\({}^{2}\)/50 ohm \(\times\) 2) in the PN junction and 0.03 W in the thermal phase shifter. The power consumption of each driver was 0.4 W. Therefore, 0.462 W \(\times\) 4 = 1.848 W power is used for modulators and drivers, which dominates the power consumption on the transmitter side. According to 400 G transmitter power budget estimations of 4.6-5.5 W [31], there is enough of a margin for the micro controller and DSP.
## 5 Energy Consumption on Active Tilting System
### Mechanism of Energy Balance
#### 5.1.2 Energy Consumption by Cornering Drag in Case of No Tilting  
Generally, centripetal forces for vehicles are given as lateral force (_SF_) by tire slip angle (_SA_) when vehicles turn. Useful cornering force (\(\gamma\)) for turning is represented as \(SF\times\cos\ (SA)\). Orthogonal \(SF\times\sin\ (SA)\) is the cornering drag component, and energy is consumed wastefully.  
Although the vertical load of each tire is always changing, for simplicity, the static load is substituted and the tire cornering power (\(K\)) is defined at this load, as shown in Figure 29. \(Y\) is obtained from a dynamic simulation and \(SA\) is obtained by dividing \(Y\) by \(K\).  
Figure 28: Energy consumption mechanism.  
Figure 29: Energy consumption caused by cornering drag.  
Total consumed energy rate caused by cornering drag is represented by the product of the cornering drag component and the longitudinal distance of the vehicle, as shown in Equation (12). Then, the accumulated consumed energy is the sum of the consumed energy rate, as shown in Equation (13).  
\[\Delta E\ =\ Y_{fl}\times\sin\!\left(\frac{Y_{fl}}{K_{fl}}\right)\times v\times \Delta t+Y_{fR}\times\sin\!\left(\frac{Y_{fR}}{K_{fR}}\right)\times v\times \Delta t+Y_{r}\times\sin\!\left(\frac{Y_{r}}{K_{r}}\right)\times v\times\Delta t \tag{12}\]  
\[E\ =\ \Sigma\ \Delta E \tag{13}\]  
E: energy  
Y: cornering force  
K: cornering power  
v: vehicle velocity  
t: time
## 4 Proposed Method: XQIFFT
### Logarithmically-Scaled QIFFT  
A variant of the QIFFT approach is available in the literature which is calculated through a parabolic fit on a logarithmically-scaled magnitude spectrum, which we denote the \"logarithmically-scaled QIFFT\" or _LQIFFT_.  
Figure 1: Demonstration of the MQIFFT.  
The LQIFFT uses (13) and (14) with the following nonlinear scaling and unscaling functions:  
\[\mathrm{f}(\Theta) =\log(\Theta)=\Phi \tag{15}\] \[\mathrm{f}^{-1}(\Phi) =\exp(\Phi)=\Theta\,. \tag{16}\]  
The intuition leading to the use of logarithmic scaling in the QIFFT is that since the Fourier Transform (FT) of a continuous-time Gaussian window is also a Gaussian, the FT of a sinusoid will have a Gaussian-shaped main lobe. Since the logarithm of a Gaussian is exactly a parabola, using a parabolic fit on a logarithmically-scaled magnitude response of a Gaussian-windowed signal would give a perfect estimate of \((\mathcal{K},\mathcal{X})\). Considering that many popular smoothing window have a shape that can be seen as more closely approximating the shape of a Gaussian than a parabola, the logarithmic scaling was introduced in [12; 31], and was shown to outperform the QIFFT on a linear scale for a variety of typical windows (Rectangular, Hann, Hamming, Blackman-Harris, Kaiser-Bessel). Note that changing the base of the logarithm has no impact on the resulting estimation--we arbitrarily choose the natural logarithm.
# Simultaneous Measurement of Refractive Index and Flow Rate Using a Co\({}^{2+}\)-Doped Microfiber
## 2 Materials and Methods  
### Fabrication of the Co-MFBGS  
In the proposed Co-MFBGS, the sensing device is a section of Co\({}^{2+}\)-doped optical fiber (CDOF) with 8.4/125 \(\upmu\)m core diameter and cladding diameter, and the length is 20 mm. At a laser wavelength of 1480 nm, the section of CDOF absorption coefficient is approximately equal to 31.3 dB/m. The CDOF with a length of 20 mm was spliced to two sections of 1m length single-mode optical fibers, one at each end.  
The fabrication of the proposed Co-MFBGS has two steps. First, an FBG with a length of 15 mm was written in the section of CDOF using a KrF excimer laser (248 nm) with a phase mask. Second, the CDOF with an FBG was immersed into a 20% aqueous hydrofluoric acid solution for 105 min, then removed and rinsed with ultrapure water to clean off the acid solution. Thus, a Co-MFBGS with a diameter of 16.7 \(\upmu\)m (Figure 1a) was fabricated. With the same process, a Co-MFBGS with a diameter of 6.4 \(\upmu\)m (Figure 1b) was also fabricated.  
The reflection spectra of the Co-MFBGS with diameters of 16.7 and 6.4 \(\upmu\)m were investigated, as shown in Figure 1c. It is clear that the Co-MFBGS with a diameter of 16.7 \(\upmu\)m has a core mode and two cladding modes, but the Co-MFBGS with a 6.4 \(\upmu\)m diameter has only a core mode, the wavelength range from 1525 to 1565 nm. Accordingly, the 16.7 \(\upmu\)m Co-MFBGS was selected as the test sensor for the rest of the experiments. As  
Figure 1: **(a,b)** Microscope images of the Co-MFBGS; **(c)** the reflection spectra of Co-MFBGS with diameters of 6.4 and 16.7 \(\upmu\)m, and the inset shows the spectral characteristics of the Co-MFBGS.  
shown in Figure 1c, the wavelength separation of \(\lambda_{a}\) (core mode) and \(\lambda_{b}\) (cladding mode) is 7.1 nm. The spectral characteristics of the Co-MFBGS were analyzed by using the numerical mode simulation software COMSOL, and the reflection spectrum compositions of the core mode and one cladding mode, and amplitude distributions of the transverse electric field, are as shown in the bottom of Figure 1. This indicates that on the Co-MFBGS surface, the evanescent field of the cladding mode is stronger than that of the core mode.
## 4 Results and Analysis
### Simulation Results and Analysis
#### 4.1.1 Tracking Performance Analysis  
Figure 12 provides simulation waveforms of the DC bus voltage and the d-axis components of grid current during the system startup process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **Symbols** & **Values** \\ \hline DC link voltage & \(V_{\rm dc}\) & 480 V \\ Nominal/Rated power & \(P\) & 1.1 kW \\ Frequency of the grid voltage & \(f\) & 50 Hz \\ Grid side inductance & \(L_{S}\) & 3 mH \\ Converter side inductance & \(L_{c}\) & 6 mH \\ Filter capacitor & \(C_{f}\) & 10 μF \\ Grid voltage & - & - \\ \hline \hline \end{tabular} NOTE please, the standard testing conditions: solar irradiance is 1000 W/m\({}^{2}\), and module temperature for simulating is set to 25 \({}^{\circ}\)C.  
\end{table}
Table 1: Power System Indicators.  
Figure 11: Disturbance-rejecting Abilities under Various Factors. (**a**) Disturbance-rejection with an increasing \(\omega_{\rm o}\). (**b**) Disturbance-rejection with an increasing \(k\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Values/Units** \\ \hline Open circuit voltage & \(V_{\rm o}\) & 36.5 V \\ short circuit current & \(I_{\rm s}\) & 9.1 A \\ Operating temperature range & \(R_{\rm t}\) & \(-\)40–80 \({}^{\circ}\)C \\ Maximum output power & & 245 W & \\ Maximum power point voltage & \(V_{\rm max}\) & 29.1 V \\ Maximum power point current & \(I_{\rm max}\) & 8.43 A \\ \hline \hline \end{tabular}
\end{table}
Table 2: PV Array Technical Parameters.  
The Grid-tie inverter with improved ADRC in the outer loop can make the d-axis current component enter the steady state within 0.03 s. On the contrary, traditional ADRC has a d-axis current regulation time of approximately 0.05 s.
## 3 Numerical Simulation and Discussion  
### Simulation Code  
Different from the theoretical analysis shown in Section 2, here, we try to evaluate the performance of the above WTIPM solutions in mid-IR OPCPA through numerical simulations: one is the strict WTIPM solution at \(\lambda_{\rm p}\) = 2.06 \(\upmu\)m and \(\lambda_{\rm s}\) = 5 \(\upmu\)m, while the other is the approximate WTIPM solution at \(\lambda_{\rm p}\) = 1.65 \(\upmu\)m and \(\lambda_{\rm s}\) = 3.3 \(\upmu\)m, which in fact is a solution of third-order WIPM. Both solutions belong to the Type-I PM in the XZ plane of LiGaS\({}_{2}\) crystal, but the former one is a noncollinear solution and the latter is a collinear solution. In this section, we consider the effects of dispersion, walk-off, diffraction, absorption, and Gaussian pulse and beam profiles. Under the slowly varying envelope approximation, the coupled-wave equations that govern the mid-IR OPCPA are as follows [31],  
\[\frac{\partial A_{\rm i}}{\partial z}+\rho_{\rm i}\frac{\partial A_{\rm i}}{ \partial x}-\frac{j}{2k_{\rm i}}\left(\frac{\partial^{2}A_{\rm i}}{\partial x ^{2}}+\frac{\partial^{2}A_{\rm i}}{\partial y^{2}}\right)+\  
gain bandwidth of each solution given in Section 2 (i.e., \(\Delta\lambda=1\) m for strict WTIPM and \(\Delta\lambda=2.1\) m for third-order WIPM), which is large enough to measure the amplification bandwidth of OPCPA. This broadband seed was chirped into a Gaussian pulse with a pulse duration of 10 ps (FWHM) and a beam radius of 5 mm (FWHM). The pump had the same pulse and beam profiles as the chirped signal. Before OPA, the peak intensities of the pump and chirped signal pulses were 5 GW\(\cdot\)cm\({}^{-2}\) and 0.05 GW\(\cdot\)cm\({}^{-2}\), respectively.  
The coupled-wave equations (Equations (7)-(9)) were solved by the split-step Fourier transform and fourth-order Runge\(-\)Kutta methods, while the thermal transfer equations (Equations (10)-(12)) were solved by the finite difference method. Calculations of coupled-wave and thermal transfer equations were conducted in an alternating manner until a stable temperature distribution in the crystal was reached.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 4 Practical Considerations and Experimental Results  
In this section, the implementation and error modeling of the standard DF-PPP is demonstrated using static and kinematic data. In both scenarios, only observations from GPS and GLONASS systems were available from the GNSS receivers; therefore, only these two constellations were utilized in the implemented PPP solution.  
Essential correctional components--namely the final satellite orbit, clock, and DCB corrections--were sourced from the CODE analysis center, part of the IGS MGEX project. The satellite orbit corrections were provided every 5 min, whereas the satellite clock corrections every 30 s.  
The Extended Kalman Filter (EKF) was employed as the navigation filter for static and kinematic datasets. However, some practical aspects must be considered when processing static versus kinematic data. The PPP processing was performed with an in-house MATLAB code. It is worth mentioning that the results in this section are examples of what can be expected of the standard DF-PPP performance in static and kinematic modes. The performance metrics cannot be statistically generalized, as this requires longer and more diverse data, which is beyond the scope of this article.
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 3 Methodology  
### Data Collection  
This section provides an overview of the data used in the study, which centers on predicting collision severity. The data source is the collision incidents data from the Virginia Department of Transportation (VDOT). The dataset contains several attributes, including the collision severity level, road user types, road geometry, weather conditions, and behavioural aspects of road users. This publicly accessible dataset spans from 2019 to 2023, comprising approximately 500,000 collision incidents [29]. To ensure the reliability and accuracy of the model, the study executed a rigorous series of data processing steps. The obtained dataset contains categorical attributes. Dummy coding was used for the categorical variables and their boundaries are presented by the variable categories. Data cleaning was conducted for missing values, where missing values were eliminated given the large number of observations [30]. Data processing was conducted using R-software (RStudio Team, 2023) [31]. The crash severity level is presented using the KABCO scale. The crash severity level was classified into four categories: (a) Property Damage Only (PDO), designated as O; (b) minor injuries designated as B + C (similar to [32]); (c) major injury designated as A; and (d) fatalities denoted as K.
## 3 Results
### Methodological Quality Assessment (Newcastle-Ottawa Scale)  
The methodological quality assessment using the _Newcastle-Ottawa Scale_ (NOS) revealed that a subset of studies achieved the highest possible score of nine points, indicating strong methodological quality across all domains [33; 69; 80; 97; 90]. These studies showed robust participant selection with clearly defined eligibility criteria and representative cohorts [69; 90], appropriate control or comparison groups [80; 97], and validated exposure or outcome assessments [33]. They also ensured comparability by adequately controlling for confounding factors [80; 97] and conducted rigorous follow-up procedures with low risk of attrition [33; 69]. The majority of studies scored between six and eight points, suggesting moderate methodological quality with some limitations. In the Comparability domain, most studies demonstrated inadequate confounder control and failed to match groups or adjust for key outcome-influencing variables [25; 26; 28; 34; 35; 38; 39; 40; 41; 42; 43; 46; 62; 67; 81; 85; 94], resulting in descriptions that were too brief or incomplete for meaningful interpretation.  
In the Exposure or Outcome assessment domain, several studies lacked long follow-up periods or failed to clearly describe outcome ascertainment methods, which may limit the reliability of their findings [31; 65; 77; 83; 84; 86]. Their loss to follow-up or lack of outcome validation was evident. A smaller number of studies scored the lowest on the NOS, with only five points, indicating substantial methodological limitations [68; 74]. These studies often suffered from weaknesses in the Selection domain, such as non-representative cohorts or unclear inclusion criteria [68; 74], and from inadequate exposure assessment. A detailed breakdown of NOS domain scores and overall quality ratings for each study is provided in Table S3: Methodological Quality Assessment (_Newcastle-Ottawa Scale_).