esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Most previous studies related to saturated soils have just simplified the train loads as moving quasi-static loads despite the fact that the dynamic wheel-rail contact has a great influence on the vibration responses in reality. In addition, the numerical models adopted in most previous studies are based on 2D or 2.5D models, which actually yield a serious limitation in scope due to plane stress/strain assumptions. A novel 3D coupled train-track-soil interaction model, which considers the wheel-rail dynamic contact as well as the saturated soils, was thus developed based on the MBS principle and FEM theory in LS-DYNA. The model was validated against the experimental results obtained from the field measurements. Based on the dynamic responses of the saturated soils, the following novel insights could be drawn:  
1. In the one-layered ground, the maximum pore water pressure occurred at the soil surface and decreased along with soil depth. However, the maximum effective stress occurred at a depth of around 2 m. The total stress exhibited no evident difference between saturated and dry soils, but the displacement of saturated soils was smaller than that of dry soils;
2. When the soil was quite soft, the pore water would carry the majority dynamic loads instead of the soil particles. In contrast, the soil particles would undertake more dynamic loads when the modulus of soils increased;
3. The subgrade exhibited a significant influence on the vibration responses of saturated soils in the multi-layered soils model. The subgrade could reduce the amplitudes of pore water pressure, effective stress, total stress, and displacement of soils. The pile-reinforced ground had lower amplitudes of vibration responses compared with the natural ground;
4. The undrained pore water had an insignificant influence on the train-track dynamic interactions including the wheel-rail contact force and acceleration of the car body.  
Conceptualization, T.L., Q.S., and S.K.; methodology, T.L. and S.K.; software, T.L. and S.K.; validation, T.L., Q.S. and S.K.; formal analysis, T.L.; investigation, T.L., Q.S., and S.K.; resources, Q.S. and S.K.; data curation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, S.K.; visualization, T.L.; supervision, Q.S. and S.K.; project administration, Q.S. and S.K.; funding acquisition, Q.S. and S.K. Funding: This research was supported by the Key Research Development Program of China (No. 2016 YFC0802203-2, No. 2016 YFC0802203-3). The APC is sponsored by the University of Birmingham's Open Access Fund.  
The authors would like to acknowledge the China Scholarship Council for the financial support. The authors sincerely thank the European Commission for H2020-MSCA-RISE Project No. 691135 \"RISEN: Rail Infrastructure Systems Engineering Network,\" which enables a global research network that tackles the grand challenge in railway infrastructure resilience and advanced sensing under extreme conditions (www.risen2 rail.eu) [7].  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Numerical Methods  
The open-source CFD package SimVascular ([[http://simvascular.github.io/](http://simvascular.github.io/)]([http://simvascular.github.io/](http://simvascular.github.io/))) was used as the pre-processor and solver in this study. Blood was assumed as an incompressible Newtonian fluid. The density and dynamic viscosity of the blood were set to 1060 kg/m\({}^{3}\) and 0.004 Pa\(\cdot\)s, respectively. The arterial wall was assumed as the non-slip rigid wall. The 3D flow domain was governed by steady-state Navier-Stokes (N-S) equations (Equations (1) and (2)).  
\[\rho(V\cdot\
abla V)=\ -\
abla p+\mu\
abla^{2}V \tag{1}\]  
\[\
abla\cdot V\ =\ 0 \tag{2}\]  
The volumetric flow rates derived from the time-varying physiological profile (Figure 2) at the five time points were specified on the proximal end of the aortic root, which was mapped onto the inlet plane by using a plug velocity profile [28]. The specific flow rates at t\({}_{1}\) to t\({}_{5}\) were 32 mL/s, 36 mL/s, 204 mL/s, 350 mL/s and 154 mL/s, respectively.  
At the outlets, the resistance of distal vascular systems was considered by coupling 0D lumped parameter models (LPMs) with the 3D aorta model at the efferent arteries. As illustrated in Figure 3, the R\({}_{1}\), R\({}_{2}\), R\({}_{3}\), R\({}_{4}\) represent the resistance of vascular systems distal to the BA, LCCA, LSA and descending aorta (DAO), respectively. The specific values of resistances were calculated according to Murry's law [28]. The compliance components of the 0D models were neglected due to the simulations being conducted under steady flow.  
In the coupling scheme, the 0D LPMs with resistance elements were coupled with the 3D model at the outlet boundaries, including the outlets of descending aorta (DAo), BA, LCCA and LSA. A \"tight coupling\" algorithm [29; 30; 31] was used to transfer the data at the coupling interfaces. At the beginning of the coupling, an initial pressure is applied as the initial condition. Afterward, the area-averaged flow rate at the coupling interface of the 3D CFD model is computed and sent to the LPM to calculate the corresponding cross-sectional static pressure for the next iteration until the convergence is reached for the current time step.  
Figure 2: The volumetric flow rates at the selected times points.  
All the models were discretized by using unstructured 4-node tetrahedral elements with three layers of prism boundary layer. Mesh independence analyses were conducted on the conventional valve model at t\({}_{4}\) with eight different element numbers (Table 1).  
The comparison showed that the differences in flow rates at efferent ends as well as spatially averaged WSS (SAWSS) on ascending aorta were smaller than 2% between cases 7 and 8. Thus, the element number of 34.5 million was adopted in the discretization of all the models. The y\({}^{+}\) \(<\) 2 was achieved by adjusting the initial thickness as well as the growth rate of the boundary layer at this mesh setting, which is capable of capturing the near-wall flow features.
## 3 Results
### Maturity Progress
#### 3.4.2 Maturity at Harvest of VSP and SMPH  
As harvest occurred later in SMPH training system, maturity parameters were analyzed right before harvest on a hundred-berry sample for each training system separately. Berry composition of SMPH treatments was analyzed 9 days (2017), 19 days (2018) and 20 days (2019) later than in VSP training system. Across three seasons significantly lower values for reducing sugars (\(-\)5.6%), extract (\(-\)5.3%) and \({}^{\circ}\)Brix (\(-\)4.9%) was detected in SMPH, compared to VSP. All three thinning treatments increased respective values to a level of VSP (Table S4). Bunch thinning increased sugar concentration (+6%), followed by biotechnological thinning (+3.2%) and shoot thinning (+2.3%). Compared to VSP, higher values of tartaric acid (+4.6%) and significantly lower values of malic acid (\(-\)23.1%) were determined for SMPH, resulting in a significantly lower total acidity (TA). Compared to SMPH, the thinning treatments SMPH GA and SMPH ST showed lower values for malic and tartaric acid and hence lower values for TA across three seasons. SMPH BT decreased malic acid by 18% compared to SMPH and 37% compared to VSP resulting in a significantly lower total acidity. By extending the ripening period in SMPH training system, pH-value did not differ and thinning showed no impact. On average across three seasons, no significant difference in N-OPA concentration was detected between all treatments right before harvest. However, a higher assimilable nitrogen concentration (+12% N-OPA referred to VSP and SMPH) was observed with bunch thinning.
## 3 Proposed Approach
### Data and Pre-Processing
#### 3.1.6 File Transformation  
Files are converted to byte sequences, then into byte frequency vectors. Let \(F=\{b_{1},b_{2},\ldots,b_{n}\}\) be the byte sequence:  
\[f_{k}=\frac{\text{count}(b_{i}=k)}{n},\quad k\in[0,255]\]
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## Appendix A  
The stations HUAD and SHAT in the Guangdong Province of China were selected. The data were collected from 00:00:00 to 23:59:59 (UTC) on day of year 77, 2019 (18 March 2019). The sampling interval was set as 1 s, and the cut-off angle was set at 10\({}^{\circ}\). Figure 11 shows the difference of the unit vectors in adjacent epochs for each station. It can be seen that most of the values are smaller than 1.0 \(\times\) 10\({}^{-4}\), 1.0 \(\times\) 10\({}^{-4}\) and 1.5 \(\times\) 10\({}^{-4}\) in the X, Y and Z components, respectively. Since the positioning accuracy of the network real-time differential positioning is a few decimeters, the first term on the right side of Equation (9) is usually smaller than 0.35 mm, which cannot influence the position variation estimation. Further, the values of GEO are almost equal to zero, the values of IGSO vary from \(-0.5\times\) 10\({}^{-4}\) to \(0.5\times\) 10\({}^{-4}\), and the values of MEO are a little larger. This may be attributed to the different velocity of the satellite relative to the ground.
## 4 Results and Discussion
### Tracking Performance of an SDC System in a Whole Year Operation Cycle under Azimuth Axis Tilt Error
Based on the above analysis, the tracking performance and tracking performance stability of an SDC system are better when \(\phi_{1}\) = 0\({}^{\circ}\) (due south) compared with other azimuth axis tangential angles in a whole year operation cycle. If the tilt error of azimuth axis can be determined in a practical engineering application, then we can get a more specific situation of the tracking performance of the SDC system. For example, during a whole year's operation cycle, on which day is the tracking performance of SDC system the worst? Therefore, we only need to ensure that the day with the worst tracking performance meets the set requirements, and then the SDC system can maintain a good tracking performance in the whole year's operation cycle. Moreover, we only need to calculate the tracking error of a certain day (the worst performance) to predict whether the SDC system will meet the performance requirements in the whole year. This can reduce the calculation amount of the tracking performance evaluation of the SDC system in the whole year operation cycle. A higher tracking accuracy of a tracking system inevitably leads to a higher cost [30]. We only need to perform error correction on the day of the worst tracking performance to ensure the tracking performance of the SDC system. This has significance in reducing the cost of dish solar thermal power generation.
## 4 Simulation Examples
### RADP-Based Sliding-Mode Fault-Tolerant Controller in Presence of Input Delay  
Factors that cause delays are numerous in the ACS, such as the response time of actuators and sensors, faults of actuators and sensors, signal sampling and transmission, etc. The effects will be reflected in the input delay [34]. The existence of the input delay will cause the degradation of control performance and even destroy the stability of the system. To verify whether the proposed controller can cope with the time delay uncertainty, in the above simulation example, an unknown constant time delay is added into the original control system:  
\[J\dot{\omega}=-\omega^{\times}J\omega+u(t-\tau)+f_{a}, \tag{45}\]  
where the time delay is set as \(\tau=0.1\) and other parameters are the same as the values set above. The simulation results are shown as Figures 5-7.  
Figure 4: Evolution of the critic neural network weights.  
Figure 5: Comparison of attitude of ACS without and with input delay. (**a**) Evolution of attitude of the ACS. (**b**) Evolution of attitude with input delay.  
Figure 3: Evolution of control inputs of the ACS under different control schemes in the presence of mismatched uncertainties and actuator faults. (**a**) Traditional SMC-based FTC. (**b**) The proposed FTC with RADP-based sliding surface.  
By comparing the simulation results before and after adding the input delay, it can be found that the proposed control scheme can make the time delay system bounded, but the control performance has dropped. If a better control performance is pursued, special design for time delay is essential.
## 2 Experimental Method and Results
The total dispersion was varied from negative to strong positive by changing the length of the UHNA4 between 0 and 4.8 m, and the mode-locking properties were examined. The corresponding repetition rate was 36.3-19.4 MHz. In order to examine the role and effectiveness of the spectral filter, a SMF with the same length of the birefringent spectral filter was used to keep the magnitude of the net cavity dispersion and repetition rate. Figure 2: Configuration of wavelength tunable spectral filter based on birefringent fibers. Figure 4a,b show the variation of optical spectra when film A was used. The magnitude of net cavity dispersion was changed from \(-0.319\) ps\({}^{2}\) to \(0.1010\) ps\({}^{2}\). Mode-locking was obtained in these wide dispersion conditions without the spectral filter. When the developed filter was applied, a wavelength tuning operation was achieved in an anomalous dispersion region. Since there was a limitation of bandwidth by the spectral filter, mode-locking was not obtained around the zero-dispersion region. Figure 4c,d show the variation of optical spectra when film B was used. Here, the mode-locking operation was confirmed for net cavity dispersion from \(-0.319\) ps\({}^{2}\) to \(0.1010\) ps\({}^{2}\). Stable mode-locking was obtained in this wide dispersion range. Since the magnitude of absorption was larger in film B compared to film A, film B was useful to suppress the additional cw oscillation. A wavelength tunable operation was obtained in the anomalous dispersion region. Compared with the previous work that used a Tm-doped fiber, the same output power was obtained with much lower pump power [15]. The estimated pumping efficiency was improved by \(\sim\)3 times, compared to that of a Tm-doped fiber laser using the same SWNT film. Next, we discuss the dependence on net-cavity dispersion.
## 5 Experiment and Analysis
### Evaluation of SAPRM  
#### 5.2.1 Performance of SAPRM  
To validate the performance of the SAPRM in the DSKG construction for quality engineering of continuous casting, we compared with TPLinker [27], PRGC [11], PFN [28] and TablERT-CNN [29] algorithms, and the results are shown in Table 4. The symbol \(\dagger\) means all models use BERT-BASE-CHINESE as encoder.  
For NER task, SAPRM outperforms PFN in both precision and F1 score. For RE task, SAPRM significantly outperforms the other models in terms of precision, recall, and F1 score. Specifically, compared to PFN, SAPRM achieves a 1.72% improvement in NER precision, a 2.69% improvement in RE precision, and a 4.31% improvement in RE F1 score.  
In the task of continuous casting quality engineering knowledge extraction, our SAPRM achieves the best performance. Therefore, these prove that our SAPRM is an effective approach for quality engineering knowledge extraction task of continuous casting.
## 3 Proposed System
### PMIPv6/ AAND--Various Multi-Homing Topologies
#### 3.4.1 Topology 1--PMIPv6/ AAND  
The MN and the CN are annexed to the same MAG in Topology 1. The localized path between MN and CN has already been established. The packets are now being translated from MN to CN. If the MN and CN interface is modified, then the AAND fields hold the connection layer identifier (LL-ID) information, and those fields make the MN mobility continuous. Figure 7 displays the PMIPv6/ AAND Multihoming Topology 1.  
Figure 6: PMIPv6/ AAND—Architecture with Multiple Virtual Interface Technology.  
Figure 7: Topology 1—Multi-Homing of PMIPv6/ AAND.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model
### Scheduling Algorithms
#### 4.4.1 Conventional Scheduling Policies  
In this case, for any given frame, the number of slots allocated to active users must satisfy the following condition [28]:  
\[N_{S}\ =\sum_{k=1}^{K}N_{S}^{(k)}n^{(k)} \tag{35}\]  
where \(N_{S}^{(k)}\) represents the number of slots allocated to a user using MCS\({}_{k}\), and \(n^{(k)}\) is the number of active users using MCS\({}_{k}\).  
Note that \(N_{S}^{(k)}\) depends on the scheduling algorithm, and the number of active stations must satisfy:  
\[n\ =\sum_{k=0}^{K}n^{(k)} \tag{36}\]  
The way in which the scheduler allocates the slots \(N_{S}^{(k)}\) is detailed in the following pseudocode:  
Let \(K_{F}\) [0...KJ] be the set of MCS used by the active mobile stations.  
1. Resource Fairness \[\begin{array}{l}N_{S}^{(k)}\ =\ for\ k\ =\ 0\\ N_{S}^{(k)}=\frac{N_{S}}{\sum_{k=1}^{K}n^{(k)}}\ \text{for}\ k\
eq 0\end{array}\]
2. Throughput Fairness \[\begin{array}{l}if\ k\ =\ 0\\ \\ \  
\(N_{S}-N_{S}^{(F)}\) unused slots per frame. Let \(N_{S}^{(u)}\) represent the number of slots reserved for each mobile station (MS), which is given by the following pseudocode:  
The number of slots required for each active station \(N_{S}^{(u)}\) in a frame to reach \(DBR\) is calculated considering the following condition:  
\[\text{if }m_{k}^{(u)}\
eq 0\] \[N_{S}^{(u)}=\frac{\frac{min\left(x_{\alpha n}^{(u)},DBR\times T_{ F}\right)}{m_{k}^{(u)}}}{else}\] \[N_{S}^{(u)}=0\] \[\text{\emph{end}}\]  
where \(x_{\alpha n}^{(u)}\) is the random value representing the ON data volume downloaded by MS\({}_{u}\), and \(m_{k}^{(u)}\) is the number of bits per slot for MS\({}_{u}\) using MCS\({}_{k}\) during a TDD frame.  
The total number of slots required by nnn active users during a TDD frame is  
\[N_{S}^{(F)}=\sum_{u=1}^{m}N_{S}^{(u)} \tag{37}\]  
The following procedure will calculate the degradation factor \(D\):  
\[\begin{array}{l}\text{if }N_{S}^{(F)}\ \leq\ NS\\ D\ =\ 1\\ else:\\ D\ =\ \frac{N_{S}}{N_{S}^{(F)}}\\ end\end{array}\]  
Finally, the number of slots to be allocated to each MS in a frame is  
\[N_{S}^{(u)}\ =\text{D}\ \times\ N_{S}^{\prime(u)}\]
# Photovoltaic Cell and Module I-V Characteristic Approximation Using Bezier Curves
## 5 Discussion  
In all studied cases, the \(x\) coordinates can be evenly spaced. Both for the PV cells and for the PV modules, the first Bezier cubic curve was very close to a straight line and ended at \(x_{03}=0.5V_{oc}\), while the middle curve ended at \(x_{13}=0.75V_{oc}\). The relative error is less than or equal to 1.18% for all the studied PV devices, while neglecting the values in the \(0.94V_{oc}-V_{oc}\) region, where the absolute error is tens of mA and the relative error is misleading, (Figures 11 and 17).  
The differences between the results obtained with the proposed method and the least squares method are negligible (less than 1% for all coordinates). \(R_{s}\) and \(R_{sh}\) can be easily derived from the manufacturer's datasheet, using for example the method proposed by [PERSON] et al. [5]. In most cases \(R_{sh}\) is close to \(R_{sho}\) (Table 7). Larger differences occur for the series parasitic resistance: for the same PV cell. For different irradiances and temperatures, Section 4.4 provides all the necessary formulae.  
In most cases \(V_{mp}>P_{13x}\). The method was also valid for Onyx Ref [10], Onyx Ref [30], and 6.5 Wp L Cell, where the previous relation was not satisfied.  
In relation to the Maximum Power Point Tracking (MPPT), it must be emphasized that the Bezier curves are inherently smooth. This reduces the risk for the algorithm of becoming stuck in some false area/minimum of the curve. Furthermore, as the generation of the Bezier curves is so easy, the MPPT simulation can be further simplified.
# New Insights on Volatile Components of _Vanilla planifolia_ Cultivated in Taiwan
## 2 Results
### Comparison of Volatile Components of Taiwan-Cultivated Vanilla from Different Regions  
It has been previously determined that the characteristics of vanilla depend not only on genetic characteristics but also on the unique climate, cultivation conditions, and technical methods of each country or region [11]. Therefore, differences in the volatiles of vanilla pods of different origins have also been discussed in recent years. [PERSON] et al. [27] speculated that vanilla aroma would be affected by geographic origin and may then indirectly affect other vanilla products such as chocolate or milk. Sensory analysis also confirmed that the vanilla pods from different regions were different.  
The experiments in our study were conducted at Taoyuan Station on pods obtained from seven other regions in Taiwan (Taoyuan Xinwu, Longtan, Hsinchu Xinpu, Nantou Mingjian, Yunlin Sihu, Taitung Station, and Taoyuan Station), and we compared the volatile components of vanilla pods obtained from these different regions. The volatile components and their relative percentage are listed in Table 5. The difference in the content is clear in regard to vanillin. The samples from Taitung Station and Taoyuan Longtan possessed higher amounts of vanillin (83%), and the vanillin percentage of samples from Nantou Mingjian was lower (67%). When comparing the total volatile contents and primary component content of vanilla cultivated in Taiwan among different regions, we observed that the percentage of vanillin was highest in pods from Taitung Station and Taoyuan Longtan. This indicates that the total volatile content was affected by vanillin. However, differences still remain due to variations in the other components.
# A Novel Test Rig for the Basic Nonlinear Characterization of Bolted Joints
## 4 Measurement of the Bolt Tension
### Correlation of the Axial Load and Torque  
A torque wrench was used to apply the preload (Figure 9), as it was applied for the dynamic test. In order to obtain repeatable results, the head of the screw was kept in the same position on the metal block using a wrench fixed in a specific position. The axial loads measured by the strain gauges were compared with theoretical values obtained by using the analytical formulation where the tightening torque is considered, and their correlation was analyzed to assess the reliability of results.  
The axial load generated by the torque-wrench is influenced by the friction occurring at the head and the threads, and only a limited percentage of the tightening torque \(T\) is spent to generate the tension preload assembly \(F_{exp}\). The relations occurring between \(T\) and \(F_{exp}\) can be expressed as follows [32]:  
\[T=T_{th,P}+T_{th,F}+T_{b}=F_{exp}\left[\frac{P}{2\pi}+\frac{d_{2}}{2 cos(\alpha /2)}\mu_{th}+\frac{D_{b}}{2}\mu_{b}\right] \tag{3}\]  
Figure 8: The bending vibration, (**a**) two-beam joint (472 Hz), (**b**) one monolithic beam (854 Hz).  
Figure 9: Setup to apply the tension preload through the torque wrench.  
where \(T_{th,p}\) is the thread pitch torque, \(T_{th,F}\) the thread friction torque, and \(T_{b}\) the nut friction torque, while the average diameter \(D_{b}\) of the bolt head contact interface can be expressed as:  
\[D_{b}=(d_{out}+d_{in})/2 \tag{4}\]  
with \(d_{out}\) and \(d_{in}\) representing the nut outer and inner diameters, respectively.  
Given the value of the measured torque \(T\), \(F_{exp}\) can be obtained from Equation (3) by using the parameters listed in Table 1.  
Considering the presence of possible uncertainties in the bolt tension calculation, mainly due to the definition of the friction coefficient, the bolt tension cannot be represented by a single value corresponding to the applied tightening torques, but an interval of values is needed (i.e., the grey region in Figure 10). The strain gauge measurements reported in Figure 10 show a well-defined linear variation of the tension preload within the expected theoretical interval, even after 8 consecutive tests. This result gives confidence to the testing practice, which can therefore be considered consistent and repeatable. In order to improve the test repeatability, the tensile load on the bolt is applied with reference to measured strains instead of the applied torque.