esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preparation Methods for the Analysis of SVOCs or Thermally Stable Compounds  
Common SVOCs or thermally stable compounds mostly include pesticides, ERs, carcinogens, such as mycotoxin, process contaminants, among others, which can lead to cancer or impair neurodevelopment in humans. In order to achieve a practical and reliable method, several preparation methods have been developed. Extraction by a solvent is the classic sample preparation technique, which includes liquid-liquid extraction (LLE), soxhlet extraction, solid-liquid extraction (SLE), microwave-assisted extraction (MAE), ultrasonic extraction (USE), accelerated solvent extraction (ASE), and supercritical fluid extraction (SFE) [14; 15; 16; 17; 18; 19; 20; 21; 22]. Improved extraction methods, such as pressurized liquid extraction (PLE), can increase the diffusion rate and solubility of interferences into the matrix [23; 24]. Increasing common clean-up automation and improving instrument design has created a surge in the use of solid-phase extraction (SPE) in a variety of applications has been observed [25; 26]. Based on SPE, novel miniaturized SPE methodologies, such as micro-solid phase extraction (MSPE), dispersive-MSPE (DMSPE), matrix solid-phase dispersion extraction (MSPD), and stir bar sorptive extraction (SBSE), have the advantage of requiring low amount of analytes, sorbent, and organic solvents [27; 28; 29]. Based on miniaturized sorbent-based extraction techniques, several methods have been developed for the analysis of contaminants in real food samples. The development of novel materials, e.g., magnetic molecularly imprinted polymers and other magnetic nanometer materials, with high selectivity to analytes that can at the same time eliminate the interference of the matrix and increase sensitivity and accuracy of the method, is still a promising research field [30; 31].  
As a general trend, ideal sample preparation techniques should be clean, selective, time-saving, cheap, simple, and environmentally friendly [32]. Compared to MS technology, MS/MS has been shown to accurately detect contaminants in multi-residue analysis and has become an analytical reality for food samples. Thus, in this section, we focused on multi-residue analysis suitable for GC-MS/MS developed in recent years and its matrix effect.
###### Abstract  
The security of Internet of Things (IoTs) devices in recent years has created interest in developing implementations of lightweight cryptographic algorithms for such systems. Additionally, open-source hardware and field-programable gate arrays (FPGAs) are gaining traction via newly developed tools, frameworks, and HDLs. This enables new methods of creating hardware and systems faster, more simply, and more efficiently. In this paper, the implementation of a system-on-chip (SoC) based on a 32-bit RISC-V processor with lightweight cryptographic accelerator cores in FPGA and an open-source integrating framework is presented. The system consists of a 32-bit VoxRiscv processor, written in SpinalHPDL, and lightweight cryptographic accelerator cores for the PRINCE block cipher, the PRESENT-80 block cipher, the ChaCha stream cipher, and the SHA3-512 hash function, written in Verilog HDL and optimized for low latency with fewer clock cycles. The primary aim of this work was to develop a customized SoC platform with a register-controlled bus suitable for integrating lightweight cryptographic cores to become compact embedded systems that require encryption functionalities. Additionally, custom firmware was developed to verify the functionality of the SoC with all integrated accelerator cores, and to evaluate the speed of cryptographic processing. The proposed system was successfully implemented in a Xilinx Nexys4 DDR FPGA development board. The resources of the system in the FPGA were low with 11,830 LUTs and 9552 FFs. The proposed system can be applicable to enhancing the security of Internet of Things systems.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
## 4 Discussion  
This contribution studies two critical factors (arc length and SRP model) affecting the BDS-3 satellite's orbit-prediction accuracy. We use precise ephemerides and SLR data to verify the BDS-3 satellite's best-fit arc length. Our results show that the best-fitting arc length for the BDS-3 satellites is at 42-48 h. In addition, the BDS-3 satellite's orbit prediction accuracy does not decrease significantly during the eclipse seasons, which should be attributed to the advanced attitude control mode for the BDS-3 satellite. We also find out that the BDS-3 satellite's orbit prediction accuracy of the ECOMS SRP model during the eclipse seasons is unsatisfactory, especially in the cross-track direction. Therefore, when the BDS-3 satellites are within the eclipse seasons, we recommend using the ECOM9 or ECOM7 SRP model for orbit prediction.  
Since the Earth's orientation parameters also have a significant influence on the orbit-prediction accuracy, we will study the use of real-time earth orientation parameters to analyze orbit-prediction accuracies in the future.
## 3 Results
### Consistency of Precise Products from Different Analysis Centers
#### 3.3.1 Gps  
The epoch-wise differences of precise satellite orbit and clock corrections among different analysis centers (ACs) for GPS satellites are first derived, and then we compute the root mean square (RMS) statistics of the epoch-wise orbit and clock differences over all the available satellites and days. The epoch-wise orbit and clock differences between GFZ and WHU on DOY 122 of 2020 are plotted in Figure 3. Different satellites are represented by different colors. The orbital differences for most of the satellites are smaller than 5 cm, except for three satellites (G18/G24/G30), which can reach approximately 0.3 m. The epoch-wise differences in clock offsets are usually less than 0.5 ns, and several satellites show very small clock differences (G01/G04/G06/G09/G25/G26/G27/G32). The obtained results about RMS statistics are provided in Table 2. To comprehensively reflect the precise product differences among different ACs, Table 2 also lists the SISRE statistics. The radial orbit differences are usually at a level of approximately 2 cm, except for those between GFZ and CNES, and WHU and CNES, which are 2.9 and 2.5 cm, respectively. As for the clock differences, the numerical values can be as small as 0.06 ns between ESA and WHU, while the corresponding differences range from 0.08 to 0.13 ns for the other five cases. The along-track and cross-track orbit differences show comparable performance for the three post-processed precise products, and are only 1.6 cm between ESA and WHU and about 2.5 cm between GFZ and ESA or WHU. The orbit differences are increased to 3.2-4.1 and 2.4-3.5 cm in the along-track and cross-track directions between CNES and the other three ACs, respectively. In terms of the SISRE, the consistency of precise satellite orbit and clock products can be up to 1.2 cm between ESA and GFZ or WHU, whereas the corresponding consistency between GFZ and WHU degrades to 3.5 cm. The SISRE differences between real-time precise products and three post-processed precise products vary in a range of 2.4 to 2.7 cm. Overall, for GPS satellites, the consistency of precise products shows best performance between ESA and WHU. In contrast, GFZ and WHU precise products exhibit a worse consistency, and it is also the case for the real-time and post-processed precise products.
## 4 Analysis of Test Results
### Dynamic Displacement
#### 4.3.2 Relationship between Vibration Acceleration and Velocity and Load  
The change in dynamic displacement amplitude at different depths with respect to vehicle speed is depicted in Figure 20. The figure reveals that the surface dynamic displacement increases with higher velocities. In contrast, the deep-layer dynamic displacement remains relatively unaffected by velocity changes. This phenomenon occurs because, at a depth of 0.9 m, the dynamic displacement amplitude has already undergone significant attenuation, resulting in a smaller magnitude after attenuation, thus rendering the changes less pronounced.  
The variation of the dynamic displacement magnitude with the load at different depths is illustrated in Figure 21. It is evident from the figure that the dynamic displacement magnitude at both the surface and deeper depths experiences a substantial increase with the rising load when the vehicle speed is set at either 90 km/h or 100 km/h. In the deeper layers of the roadbed, the impact of speed on the dynamic displacement magnitude change is relatively minor. However, at greater depths within the roadbed, there is a pronounced positive correlation between the load and dynamic displacement magnitude. Hence, the load exerts a more substantial influence on the dynamic displacement magnitude than velocity.  
Figure 19: Acceleration distribution along depth for different loads (v = 100 km/h): (**a**) Variation of the dynamic displacement with the depth; (**b**) Variation of the attenuation coefficient with the depth.  
Figure 18: Distribution of dynamic displacement along the depth under different loads (v = 90 km/h): (**a**) Variation of the dynamic displacement with the depth; (**b**) Variation of the attenuation coefficient with the depth.  
The variation in the dynamic displacement attenuation coefficient with vehicle speed is depicted in Figure 22. With a 30 t axle load, the dynamic displacement attenuation coefficient at depths of 0.9 m and 1.6 m decreases as the vehicle speed increases. This phenomenon is attributed to the faster decay of high-frequency vibrations in the medium. However, with a 40 t axle load, this trend becomes less prominent, suggesting that the load exerts a more significant influence on the dynamic displacement attenuation characteristics in the roadbed.  
Figure 21: Variation of dynamic displacement amplitude with load at different depths: (**a**) 90 km/h; (b) 100 km/h.  
Figure 22: Dynamic displacement attenuation coefficient with velocity: (**a**) 30 t; (**b**) 40 t.  
Figure 23 displays the change in the dynamic displacement attenuation coefficient with the load. In the figure, it is evident that, at speeds of 90 km/h and 100 km/h, the dynamic displacement attenuation coefficient for each layer in the roadbed increases with the load. This indicates that a greater load results in a slower attenuation of the dynamic displacement amplitude and a deeper impact.
All the measurements were performed in a setup depicted in Figure 3. An DSOX 4104A oscilloscope (Keysight, Santa Rosa, CA, USA) used in the experiments has a built-in precise sine generator, whereas for the purpose of the precise noise and spectrum analysis a Tektronix MDO3024 was used, which allows performing high accuracy measurements with spectral averaging functionality. It should be mentioned that the experimental setup in which the stimulation of the QTF was done with an acoustic signal produced with a speaker instead of a photoacoustic signal produced by a light beam passing between the QTF prongs and absorbed by the gas was used for this purpose. Due to such an approach the stimulation of the QTF was much stronger and making the measurements results virtually insensitive to external acoustic noise. Moreover, every replacement of the op-amp may slightly change the position of the QTF and if photoacoustic stimulation was used a realignment \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Op-Amp** & **Gain-Bandwidth** & **Input Bias** & **Input Voltage Noise** & **Input Current Noise** & **Input Impedance** \\
**Model** & **Product [MHz]** & **Current [nA]** & **Density [nV/Hz\({}^{1/2}\)]** & **Density [pA/Hz\({}^{1/2}\)]** & **(Differential) [Ohm]** \\ \hline OP27 & 8.0 & 15 & 3.0 & 0.4 & \(4\times 10^{6}\) \\ OP2227 & 8.0 & 2.5 & 3.0 & 0.4 & \(10^{7}\) \\ T1071 & 3.0 & 0.065 & 18.0 & 0.01 & \(10^{12}\) \\ OP42 & 10.0 & 0.13 & 12.0 & 0.007 & \(10^{12}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected parameters of the op-amps used in the measurements [7; 8; 9; 10]. Figure 3: Setup used in the measurements. of the beam will be necessary. Unfortunately, every such readjustment might result in slightly different measuring conditions, while in the case of the purely acoustic stimulation produced with a speaker such realignments were not necessary and the measuring conditions can be considered as identical for all the investigated op-amps and their configurations. It was verified experimentally that under the mentioned acoustic stimulation of the tuning fork slight changes of the QTF position did not show any noticeable influence on the output signal level.
## 2 Basic Knowledge
### The Sine Cosine Algorithm and Its Basic Principles  
The sine cosine algorithm (SCA) is a new meta-heuristic optimization algorithm based on mathematical trigonometric functions proposed by [PERSON] in 2016. The SCA utilizes the mathematical properties of the sine and cosine functions, performs a global search and local search of the algorithm by adding adaptive parameters to change the amplitude of the sine and cosine functions, and finally finds the optimal solution. The update formula of the position of the individual in the SCA is described as follows [43].  
\[X_{i}^{t}=\left\{\begin{matrix}X_{i}^{t}+r_{1}\times\sin(r_{2})\times\left|r_ {3}\times P_{best}^{t}-X_{i}^{t}\right|,&r_{4}<0.5\\ X_{i}^{t}+r_{1}\times\cos(r_{2})\times\left|r_{3}\times P_{best}^{t}-X_{i}^{t} \right|,&r_{4}\geq 0.5\end{matrix}\right. \tag{12}\]  
\[r_{1}=a\left(1-\frac{iteration}{Maxiteration}\right) \tag{13}\]  
\[r_{2}=rand(0,\;2\pi) \tag{14}\]  
\[r_{3}=rand(0,\;2) \tag{15}\]  
\[r_{4}=rand(0,\;1) \tag{16}\]  
In Equation (12), \(X_{i}^{t}\) represents the position of the \(i\)-th individual at the \(t\)-th iteration; \(P_{best}^{t}\) represents the optimal individual in the population at the \(t\)-th iteration; \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\) are four key parameters in the sine cosine algorithm, where \(r_{1}\) controls the individual search direction and the degree of influence of the step size in the iteration process, \(r_{2}\) controls the search distance of the individual in the iterative process, \(r_{3}\) is a random weight coefficient, which determines the optimal individual's impact on the current influence degree of the individual, and \(r_{4}\) is the conversion switch of the sine mechanism or cosine mechanism of the control algorithm. In Equation (13), \(a\) is a constant (generally 2); _iteration_ is the current iteration number; _Maxiteration_ is the maximum iteration number.  
The iteration principle of the SCA is shown in Figure 3[44], in which \(r_{1}=2\), \(r_{3}=1\). It can be seen from the figure that when \(1<|r_{1}\sin(r_{2})|<2\) and \(1<|r_{1}\cos(r_{2})|<2\), the algorithm performs a global search; when the value of \(r_{1}\sin(r_{2})\) or \(r_{1}\cos(r_{2})\) belongs to the interval \([-1,\;1]\), the algorithm performs a local search in the explored space. The purpose of a global search is to quickly browse the search space and lock the range of the optimal solution, while that of a local search is to find the optimal solution within the locked range.
# Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination
## 2 Dynamic Modeling
### Dynamic Modeling of Motorized Spindle with Spindle Inclination Fault  
As the inclination angle is relatively small in practical work, once it is ignored, it may lead to further serious failure problems such as the spindle hitting the film or misalignment. Therefore, it is necessary to study the influence of the inclination angle on the dynamic characteristics of a motorized spindle.  
As shown in Figure 4, when the motorized spindle inclines, an inclination angle of magnitude is generated between the axis and the horizontal line. Because it is small,  
\[\begin{cases}\sin\alpha=\alpha\\ \cos\alpha=1\\ \end{cases} \tag{14}\]  
Due to the existence of the inclination angle, the actual value of the rotation angle at the right end of the spindle is the calculated rotation angle plus the inclination angle caused by the inclination, and the deflection will also be increased in the same way. The bending deformation analysis of the elastic shaft shows that  
\[\begin{cases}y_{i+1}=y_{i}+\theta_{i}l_{i}+\frac{M_{i+1}^{L}l_{i}^{2}}{2 EI_{i }l_{i}}-Q_{i+1}^{L}\bigg{(}\frac{l_{i}^{3}}{3E_{i}l_{i}}+\frac{k_{i}l_{i}}{G_{i} A_{i}}\bigg{)}+\alpha l_{i}\\ \theta_{i+1}=\theta_{i}+\frac{M_{i+1}^{L}l_{i}}{E_{i}l_{i}}-Q_{i+1}^{L}\frac{l ^{2}}{2E_{i}l_{i}}+\alpha\\ M_{i+1}^{L}=M_{i}^{R}+Q_{i}^{R}l_{i}\\ Q_{i+1}^{L}=Q_{i}^{R}\end{cases} \tag{15}\]  
Figure 4: Stress diagram of massless elastic shaft under spindle inclination fault.  
where \(\alpha\) is the small angle at which the axis of the motorized spindle is inclined. The equation can be obtained by changing the above formula into matrix form:  
\[\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}_{i+1}^{L}=\begin{bmatrix}1&l&\frac{l^{2}}{2 EI}&\frac{l^{2}}{6 EI}(1-v )\\ 0&1&\frac{l}{EI}&\frac{l^{2}}{2 EI}\\ 0&0&1&1\\ 0&0&0&1\end{bmatrix}_{i}\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}_{i}^{R}+\begin{bmatrix}\alpha I\\ 0\\ 0\end{bmatrix}_{i} \tag{16}\]  
Therefore, the transfer matrix of the whole unit is  
\[\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}_{i+1}^{L}=\begin{bmatrix}1+\frac{l^{2}}{6 EI}(1-v)\big{(}m \omega^{2}-k_{r}\big{)}&l+\frac{l^{2}}{2 EI}\big{(}k_{\theta}-(J_{d}-J_{p}\frac{ \Omega}{\omega})\omega^{2}\big{)}&\frac{l^{2}}{2 EI}&\frac{l^{2}}{6 EI}(1-v)\\ \frac{l^{2}}{2 EI}(m\omega^{2}-k_{r})&1+\frac{l}{EI}\big{(}k_{\theta}-(J_{d}-J_{ p}\frac{\Omega}{\omega})\omega^{2}\big{)}&\frac{l}{EI}&\frac{l^{2}}{2 EI}\\ l(m\omega^{2}-k_{r})&k_{\theta}-(J_{d}-J_{p}\frac{\Omega}{\omega})\omega^{2}&1 &l\\ m\omega^{2}-k_{r}&0&0&1\end{bmatrix}_{i}\begin{bmatrix}y\\ \theta\\ M\\ Q\end{bmatrix}_{i}^{L}+\begin{bmatrix}\frac{l^{2}}{2 EI}(1-v)\\ \frac{l^{2}}{2 EI}\\ 1\end{bmatrix}_{i}U_{i}\omega^{2} \tag{17}\]  
As with other conditions, Equation (17) is written as \(\mathbf{Z}_{i+1}^{L}=\mathbf{T}_{i}\mathbf{Z}_{i}^{L}+\mathbf{B}_{i}\), where \(\mathbf{T}_{i}\) is the transfer matrix between units and \(\mathbf{B}_{i}\) is the unbalance vector.
# Dynamic Response Analysis of Parallel Twin Tunnels Under Different Train Loads
## 1 Introduction
[15] analyzed the mutual influence between adjacent tunnels in saturated soil using 3D FEM, observing strong dynamic interactions between adjacent tunnels influenced by tunnel diameter. They further emphasized that these interactions are localized and highly frequency dependent. [PERSON] et al. [16] conducted a 1:40 scale model test to examine the deformation characteristics, crack behavior, ultimate bearing capacity, and internal forces of large-section tunnels under different train loads. They observed that cracks primarily developed at the invert and spring of twin tunnels with increasing load. These studies highlight the need for further investigation into the impacts of overloaded trains in twin-tunnel systems on adjacent tunnels. In summary, current research primarily focuses on the static and dynamic responses of single-tunnel structures under varying load conditions and the static and dynamic responses of twin-tunnel structures under conventional train loads. However, limited investigation has been conducted on the responses of closely spaced parallel twin tunnels under different train load conditions. This study employs Particle Flow Code (PFC2D) to establish a twin-tunnel model. Eight distinct load conditions (\(F_{i}\) = 62.4-131.5 kN, \(i\) = 1, 2, 3..., 8) are simulated to explore the effects of train overload on adjacent tunnel structures. This study comprises two primary sections. The first section analyzes the static and dynamic responses of the twin-tunnel structure under different train loads, focusing on the displacement and velocity of tunnel sleepers and liners. The second section examines the effects of different train loads on the surrounding environment, emphasizing displacement, velocity, porosity, and strain in the surface above and around the tunnel. The innovation of this paper is the exploration of the static and dynamic response characteristics of super-close twin tunnels under different train loads by DEM.
## Materials and Methods
### GC\(\times\)GC-TOFMS Instrumentation  
Experiments were performed on a LECO Pegasus(r) 4D GC\(\times\)GC-TOFMS system (LECO Corp., St. Joseph, MI, USA). This instrument consisted of an Agilent 7890B GC (Agilent Technologies, Palo Alto, CA, USA) incorporating LECO's thermal modulator (dual-stage quad-jet) and a secondary oven mounted inside the primary GC oven. The column set consisted of a 60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m DB-FFAP (Agilent Technologies, Palo Alto, CA, USA) primary column and a 1.5 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m Rxi-17 Sil MS secondary column (Restek, Bellefonte, PA, USA). Ultrahigh purity helium was used as the carrier gas at a constant flow of 1.00 mL/min. The separation was performed using the following temperature program: 45 \({}^{\circ}\)C kept for 3 min, ramped at 4 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C and held for 2 min; reaching 200 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min for 20 min. The secondary oven was operated at 5 \({}^{\circ}\)C higher than the primary oven throughout. The modulator was offset by +20 \({}^{\circ}\)C in relation to the primary oven. A modulation period of 4 s (hot pulse of 0.80 s) was used.  
The TOFMS parameters included ion source of 230 \({}^{\circ}\)C and transfer line of 240 \({}^{\circ}\)C, electron energy of -70 volts, acquisition of 1430, mass range of 35-400 amu, and acquisition rate of 100 spectra/s.
# The Outdoor Field Test and Energy Yield Model of the Four-Terminal on Si Tandem PV Module
## 4 Discussion
### Further Performance Improvement  
Although the 4T configuration effectively improves the seasonal loss of multi-junction modules, there still is some annual drop. It is because the top cell has two junctions, and its spectrum mismatching drops the output of the top cell.  
Further improvement of the mismatching was proposed in several articles. One is fine-tuning of the bandgap energy [106]. The second is enhancing radiative coupling that was shown in the concentrator PV application [107] and non-concentrating application [1].  
Figure 8: Comparison of the annual output between 4T configuration and 2T configuration affected by the variation of the atmospheric parameter. The red solid trend line in the bottom charts corresponds to the normalized energy yield of 4T, and the black dashed trend line corresponds to that of 2T configuration.