esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 3 Path-Tracker Design
### Proposed Path Tracker for 4 WS Vehicle  
We propose a 4 WS-based path tracker to improve the path-tracking performance of autonomous vehicles in urban environments with many non-standard sections. Compared to a dynamics-based path tracker, the geometry-based path tracker used in this paper guarantees stable performance in low-speed environments and has the advantage of simpler parameter settings, though it has the drawback of reduced responsiveness to sudden path changes [22; 23]. However, in this paper, we aim to overcome the limitations of the existing algorithm by using the characteristics of the 4 WS system.  
Figure 3: Architecture of the Stanley method.  
Figure 2: Schematic diagram of the Stanley method.  
The proposed path tracker for 4 WS-based autonomous vehicles generates steering commands \(\delta_{f}\) and \(\delta_{r}\) for independent lateral control of the front and rear wheels. Ultimately, the vehicle's lateral movement is controlled to ensure that the autonomous vehicle follows the target path without deviation [24; 25]. The basic control mechanism of the proposed path tracker is shown in Figure 4. The gain parameters of the proposed path tracker presented in this chapter determine the characteristics of the path tracker as described in Table 1. These gain parameters should be tuned considering the characteristics of the path and the driving platform.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### User Filtering Mechanism  
To screen LTE users for admission control of the unlicensed band, it is necessary to sort the LTE users by their access priority. The LTE users who need to transmit timing-sensitive packets, such as voice over IP (VoIP), are assigned the highest priority. The next priority is for LTE users located at the cell's edge or with an inferior signal to interference plus noise ratio (SINR). According to the above priority classification, all LTE users are roughly classified into different classes. and we assume there are \(C\) priority classes. Then in each class, the priority of each user will be ordered by its minimum bandwidth requirement. After the LTE user precedence is settled, the upper bound of \(\delta\) can be adjusted accordingly.  
Firstly, the upper bound of \(\delta\) is initialized to 1. In the contention-free period, the Wi-Fi QSTAs have the highest priority to access the channel. Therefore, before considering the LTE users, the upper bound of \(\delta\) should be subtracted by \(\frac{T_{QSTA}\cdot N_{QSTA}}{L}\), which is the ratio occupied by Wi-Fi QSTAs. After that, for each additional LTE user \(j\), the upper bound of \(\delta\) must be recalculated by subtracting \(\frac{s_{UE}^{STE}}{\mathbb{E}_{\big{(}s_{j}^{ITE}\big{)}}\cdot L}\), which is the resource quota used by LTE user \(j\). If the value of the upper bound is not less than the value of the lower bound, the user can access the unlicensed band and continue the filtering scheme to find the next user in the same class. As illustrated in Figure 8, when considering the LTE users by the priority order one by one, the upper bound of \(\delta\) will approach the lower bound. If no other users are allowed to access the unlicensed band in the class, then we switch to the next priority category and continue the filtering process. If all categories have been filtered, no additional LTE users can be selected. The remaining LTE users are not allowed to access the unlicensed band, and the upper bound of the \(\delta\) is obtained.  
Figure 7: The occupation time proportion for user filtering mechanism.
## 3 Materials and Methods
### Finite Element Model Development
#### 3.1.4 Meshing Strategies  
To ensure the fidelity of the workpiece meshing process, an initial convergence study was undertaken. Upon observing an initial rise in the measured von Mises maximal stress with increasing mesh density, subsequent increases in mesh density yielded diminishing returns, culminating in a stress plateau at an element size of 0.04 mm. This convergence was achieved within a calculation time of approximately 5 min. To further refine the investigation into the depth distribution of residual stresses, the thickness of the workpiece was segmented into three partitions, as illustrated in Figure 4. Notably, Partition 1 maintained a fixed element size of 0.16 mm. Conversely, a progressive meshing strategy was implemented for the second and third partitions, facilitating a gradual decrepitation density outside the region of primary interest (along the -Y direction). Within these regions, the element size progressively increased from 0.16 mm to 0.2 mm and eventually to 0.12 mm. As depicted in Figure 4, The meshing of the workpiece employed tetrahedral elements, yielding a three-dimensional structure comprising 7023 nodes and 25,014 elements with reduced integration. In contrast, the deep rolling sphere was meshed using 2880 linear quadrilateral elements of type R304. This meticulous approach to meshing ensures a refined  
Figure 3: Deep rolling basic setup (**a**) frictional contact. (**b**) spherical joint.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Yield Strength**} & \multirow{2}{*}{**Strain**} & \multirow{2}{*}{**Exponent**} & **Strain** & **Thermal** & \multirow{2}{*}{**Stdening**} & \multirow{2}{*}{**Tmelt** (**C\({}^{\circ}\)**)} \\  & & & **Ardening** & & **Rate** & **Softening** & **(C\({}^{\circ}\))** \\ \cline{6-7}  & & **Coefficient** & & **Coefficient** & & **Exponent** & \\ \hline \hline UIC 860 Grade & \multirow{2}{*}{438} & \multirow{2}{*}{2208} & \multirow{2}{*}{0.2} & \multirow{2}{*}{0.012} & \multirow{2}{*}{0.8} & \multirow{2}{*}{1370} \\  900A & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Johnson–[PERSON] material parameters for railway UIC 860 Grade 900A.  
representation of the geometric and mechanical complexities inherent to the deep rolling process, thereby enhancing the accuracy and reliability of subsequent analyses.
## 3 Results and Discussion
### Determination of Method Operable Design Region
#### 3.3.2 Chromatographic Conditions  
The CG-MS(BP20)-MODR definition was performed based on CMA responses from the interactions between the oven temperature (35, 60, and 85 \({}^{\circ}\)C), ramp speed (40, 50, and 60 \({}^{\circ}\)C/min), and flow rate (0.8, 1.0, and 1.3 L/min). A 3\({}^{3-1}\) level FFD was used to perform this study (Supplementary Material Table S6). The Pareto ranking analysis for TPA, IP, PR, TF, and SF is shown in Figure S4 (Supplementary Material), respectively. None of the CMPs had a significant influence on the CMA response. A desirability plot was performed overlaying all CMAs, with the following values being defined for each one (respectively TPA, RSD, PR, TF, and SF): desirable (3.68 \(\times\) 10\({}^{8}\), 1.0, 1.0, and 1.0), acceptable (2.0 \(\times\) 10\({}^{8}\), 10.0, 5.0, 0.9, and 0.9), and unacceptable (3.20 \(\times\) 10\({}^{7}\), 14.0, 2.0, 0.5, and 0.5). The desirability plots of the interaction between the change level of continuous CMPs (flow, ramp, and oven temperature) on CMA responses are shown in Figure 2b, respectively. From the results, it was possible to verify that the higher CMA values were influenced by the flow rate and oven temperature, with two clear desirable areas.  
One in the lower temperature range (30-40 \({}^{\circ}\)C) is associated with higher flow rates (1.2-1.4 L/min), and the other at a higher temperature range (70-80 \({}^{\circ}\)C) is associated with mild flow rates (1.0-1.1 L/min). Ramp temperature did not influence the CMAs much. The ramp temperature vs. flow rate plot shows a clear area between 1.0 and 1.1 L/min, throughout the ramp temperature range. Similar results are presented in the ramp vs. oven temperatures, with the highest desirability between 75 and 90 \({}^{\circ}\)C, throughout the ramp temperature range. Taking into consideration the equipment and material consumption, the optimum point for GS-MS(BP20)-MODR was defined with an oven temperature of 80 \({}^{\circ}\)C, a flow rate of 1.0 L/min, and a ramp temperature of 50 \({}^{\circ}\)C/min. Data verification was performed through analysis of the agreement between the predicted and observed values, which are described in Table S7 (Supplementary Material. According to the results, it was found that all the values observed in the experiments were close to the predicted values. However, the results showed slight differences between the observed and the predicted values in the CMA responses. Such differences might be due to the extended range values for the CMPs and small variations on the optimum point, which produced variability in the CMA responses. Therefore, the parameters at the optimal point were studied through a robustness test (see Section 3.4).  
Figure 2: The effects of all CMPs through the desirability surface contour method. (**a**) The desirability plots for HS-SPME(DVB/CAR/PDMS), and simultaneous analysis of the TPA and IP results. (**b**) The desirability plots for CG-MS(BP20), and simultaneous analysis of the TPA, IP, PR, TF, and SF results.
## 4 Results
### Testing Workstation and Applications  
All results were received using prototype application Terax 2.0 compiled in Microsoft Visual Studio 2013. The software was partly written in C# 5.0, C++11, and CUDA 3.5 for Microsoft Windows x64 platform. However, all algorithms were tested on the GPU implementation. For random sequences, the Mersenne Twister algorithm in 64-bit variant was used. The testing workstation was based on the Asus Maximus VI Formula motherboard equipped with Intel Core i7-4771 CPU, 32 GB Kingston Hyper-X DDR3 RAM, and Asus GTX780-DC2 OC-3 GD5 Graphics Card with nVidia GeForce GTX 780 GPU. The visualisation was performed in Terragen 3.4 Professional Edition with Animation (Planetside Software LLC) without any of the application-specific modifiers except colour shaders.  
Figure 21: Examples of butte-like assets obtained by the proposed techniques (grid resolution: \(1024\ \times\ 1024\)). (**a**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 300\); total faults: 100 along x-axis; \(mat=3\). Erosive model was obtained for \(E=0.45\) with caprock and \(\varrho=1\). (**b**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 150\); total faults: 999 along x-axis; \(mat=4\). Erosive model was obtained for \(E=0.45\) without caprock and \(\varrho=25\). (**c**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 250\); total faults: 350 around model centre; \(mat=4\). Erosive model was obtained for \(E=0.30\) with caprock and \(\varrho=1\). (**d**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 250\); total faults: 150 around model centre; \(mat=4\). Erosive model was obtained for \(E=0.35\) with caprock and \(\varrho=10\). (**e**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 250\); total faults: 75 along x-axis; \(mat=4\). Erosive model was obtained for \(E=0.65\) with caprock and \(\varrho=5\). (**f**) Fault radius: \(1\ \leqslant\ r\ \leqslant\ 250\); total faults: 250 along x-axis; \(mat=3\). Erosive model was obtained for \(E=0.35\) without caprock and \(\varrho=5\).
## 3 Control System Design
### 2 WS Controller  
In this study, a model predictive control algorithm is selected for the 2 WS controller design. MPC is known for its ability to perform predictive control and handle constraint control problems, which can ensure accurate lateral tracking control by optimizing the real-time slip angle of the front wheel [39].  
The state equation is expressed as Equation (12):  
\[\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array} \tag{12}\]  
where the state vector is \(x=\left[Y,\psi,v_{y},r\right]^{T}\), the control input \(u=\left[\delta_{f}\right]^{T}\), the control output \(y=\left[\psi,Y\right]^{T}\).  
\[A=\begin{bmatrix}0&V&1&0\\ 0&0&0&1\\ 0&0&-\frac{C_{f}+C_{f}}{mV}&\frac{l_{f}C_{f}-l_{f}C_{f}}{mV}-V\\ 0&0&\frac{l_{f}C_{f}-l_{f}C_{f}}{l_{z}V}&-\frac{l_{f}^{T}C_{f}+l_{f}C_{f}}{l_{ z}V}\end{bmatrix},B=\begin{bmatrix}0\\ 0\\ 0\\ -\frac{C_{f}}{l_{z}}\\ -\frac{l_{f}C_{f}}{l_{z}}\end{bmatrix},C=\begin{bmatrix}0&1&0&0\\ 1&0&0&0\end{bmatrix},D=\begin{bmatrix}0\\ 0\\ 0\end{bmatrix}\]  
For nonlinear model predictive control problems, fast optimization is crucial, especially in real-time applications in intelligent vehicles [40; 41]. To improve the optimization speed, the nonlinear predictive model is often transformed into a linear model to reduce complexity and enhance computational efficiency. The linear motion programming model is obtained by linearizing the equation of state, as shown in Equation (13).  
\[\dot{\xi}=A_{k}\xi+B_{k}u \tag{13}\]  
where \(A_{k}\) and \(B_{k}\) are the Jacobian matrix relative to the state variables and the control input, respectively.  
In order to achieve the vehicle closest possible approximation to the desired path, the design problem of the MPC path tracking controller is formulated as a general optimization problem. The objective is to find an optimal control input vector that minimizes the tracking error between the predicted outputs and the reference values. Since intelligent vehicles may encounter sudden changes in control variables during operation, it is necessary to incorporate relaxation factors in the objective function. Therefore, the objective function must satisfy Equation (14).  
\[\min[J(\xi(k),\Delta U(k)]=\sum_{i=1}^{N_{p}}\left\|y(k+i|k)-y_{r}(k+i|k) \right\|_{Q}^{2}+\sum_{i=0}^{N_{c}-1}\left\|\Delta u(k+i|k)\right\|_{R}^{2}+ \rho\epsilon^{2} \tag{14}\]  
where \(N_{p},N_{c}\) denote the prediction time domain and the control time domain, respectively; \(Q\) denotes the state weighting matrix; \(R\) denotes the control input weighting matrix; \(\epsilon\) denotes the relaxation factor; \(\rho\) denotes the relaxation factor weight coefficient.
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Comparative Experiment
#### 4.5.1 EO-1 Dataset  
The visual results of the proposed fusion method and six comparison methods on the EO-1 dataset are shown in Figure 4. The pseudo-color composite bands are Red: 29, Green: 13, and Blue: 16. As illustrated in the figure, the proposed method yields the best visual results. The GFPCA and SFIM methods demonstrate good color fidelity, but the spatial details in these fusion results are insufficiently clear. The HySure and CNMF methods provide sharp spatial details but suffer from low color fidelity, especially in the lake area. A zoomed-in view of the lake region reveals significant color deviations after fusion with the HySure and CNMF methods. The DRCNN fusion method performs moderately, as shown in image (g), where significant shape distortion is observed along the coastline. In comparison to the proposed method, the DRSAN method produces lower resolution and poorer spectral fidelity.  
Table 3 presents the evaluation metrics of different methods on the EO-1 dataset. From the table, it is clear that the proposed method achieves the best performance in terms of the Q value and CC value, and it also demonstrates superior performance in terms of ERGAS, SAM, and RMSE when compared to the other seven methods. When compared  
Figure 5 shows the spectral reflectance differences of different methods on the EO-1 dataset compared with the baseline (black dashed line), reflecting the spectral fidelity of the fusion results. Each subplot in the figure represents the spectral reflectance differences at four randomly selected locations for the different fusion methods. The coordinates of the four randomly selected pixels are as follows: (a) (129, 226), (b) (138, 72), (c) (89, 90), and (d) (158, 76). The closer the spectral reflectance difference curve is to the baseline (0), the better the fusion effect. Visually, it is evident that the proposed method exhibits the smallest deviation from the baseline in terms of spectral reflectance difference, indicating the best spectral fidelity of the fused image. The spectral fidelity of SFIM and DRSAN is second best. The spectral reflectance difference curves of other methods fluctuate more, indicating poorer spectral fidelity.
## 2 Materials and Methods
### Immunobloting  
Whole-cell extracts were prepared from differentiated Caco-2/HT-29-MTX cultures by cell scraping in radioimmunoprecipitation assay (RIPA) buffer (Cell Signalling Technology, Danvers, MA, USA, #9803) supplemented with phenylethylsulfofluoride (PMSF), sodium orto-vanadate (final concentrations 1 mM) and a protease inhibitor cocktail (Cell Signalling Technology, USA, #5871). The protein concentration was determined using a QuantiPro BCA kit (Merck Life Sciences, USA) and the lysate samples (15 \(\upmu\)g of protein) were resolved through routine SDS-PAGE electrophoresis. The immunoblotting procedure was carried out as previously described [32], using the following primary antibodies for detection: intestinal alkaline phosphatase (Novus Biologicals, Centennial, CO, USA, #NBP2 37389); villin 1 (Novus Biologicals, #NBP1 85336), ATP citrate lyase (Cell Signalling Technology, #4332), fatty acid synthase (CST, #3180), lipin 1 (CST, #14906), long chain acyl-CoA synthetase 1 (CST, #9189) and long chain acyl-CoA dehydrogenase 9 (CST, #9796), all in a 1:1000 dilution. To confirm equal loading, an anti-\(\upbeta\)-actin mouse monoclonal antibody (1:4000 dilution, Sigma-Aldrich, Steinheim, Germany, #A2228) was used. The luminescent signal was recorded using the ChemiDocTM system (Bio-Rad, Hercules, CA, USA) and the densitometric analysis of the protein bands was performed using ImageJ Fiji. The studied protein band intensities were divided by the corresponding \(\upbeta\)-actin band intensity and this ratio was set to 100% for the controls.
## 2 Materials and Methods
### The Experimental Process  
The experiment was conducted using a randomized crossover design. This study was conducted in the Biomechanics Laboratory at Ningbo University. Participants' height and weight were assessed using a measuring tape and a calibrated scale, respectively. A motion capture system consisting of eight infrared cameras (Vicon Metrics Ltd., Oxford, UK) was employed to track movement trajectories at a frequency of 1000 Hz. Concurrently, a three-dimensional force plate (AMTI, Watertown, MA, USA) collected kinetic data at a frequency of 200 Hz. These systems were synchronized to allow for simultaneous data collection. An infrared timing light (Brower Timing System, Draper, UT, USA) was utilized to monitor the running time for each trial. The infrared light was placed at both ends of the force platform, spaced 3.3 m apart, and the running speed was calculated accordingly. Before the formal experiment, each subject performed five trial runs in neutral shoes to determine their average self-selected speed, which served as the baseline. During tests with different shoe conditions, a 5% speed variation was allowed to control the self-selected speed. Prior to the commencement of the official experiment, participants were required to warm up by running at a self-selected pace on a treadmill for 10 min, followed by a 5 min period to familiarize themselves with the experimental procedure and environment. Participants wore uniform compression garments and leggings.  
Following previous research protocols, 38 reflective markers were placed on the participants' trunks and lower limbs [25] (as shown in Figure 2a). After marker application, participants were instructed to stand on the force plate with feet together, maintaining a standard anatomical posture and facing forward to collect static data. During the dynamic phase of the experiment, participants were guided to run at a self-selected speed on a 10 m track with a 3D force plate positioned in the center. To accurately record the speed of each run, an infrared timing light was placed at the midpoint of the track, spaced 3.3 m apart. Throughout the experiment, participants selected their comfortable running pace and were required to step on the force plate with their right foot during the run, completing five valid trials per foot. A valid trial was defined as the heel making initial contact followed by toe-off, with the entire foot remaining within the boundaries of the force plate throughout the process. To ensure the accuracy of the data, participants were explicitly instructed to maintain their natural running posture and avoid any intentional alteration of their posture. A 30 s rest interval was provided between each trial to allow participants to adjust.  
Figure 1: (a) Schematic diagram of the stratification of experimental shoes. (b) Schematic diagram of the insole structure.
# The Impact of the S-Adenosylmethionine Analogue Sinefungin on Viral Life Cycles
## 2 Materials and Methods
### Molecular Validation  
The inhibition observed was confirmed through a molecular analysis (qPCR). The test was set up as the viral yield reduction assay (as described in Section 2.4), with an incubation time of 10 h for HSV-1 and 6 h for SARS-CoV-2. For HSV-1, the expression of the late gene UL27 (F: 5'-GCCTTCTTGCCCTTGC-3'; R: 3'-CGCTCGTGCCCCTTCTTCTT-5') was analyzed; for SARS-CoV-2, the expression of the S gene (F: 5'-AGGTTGATCACAGGCAGACT-3'; R: 3'-GCTGACTGAGGGAAGGAC-5') encoding the spike protein was evaluated. Threshold cycle (Ct) values were normalized to the reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH, F: 5'-CCTTCATTGAGCTCCAT-3', R: 3'-CGTACATGGGGAGCGTC-5') and calculated using the 2-\(\Delta\Delta\)Ct method.