esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
1. In this paper, the line of action of trochoidal roller pinion rack is firstly studied, and the equations of the line of action are deduced by using its tooth profile formation principle and meshing characteristics. It is found that the line of action of trochoidal roller pinion rack is a curve, not a straight line.
2. The kinematics simulation is carried out by Adams software, and the positions of five special meshing points are extracted and compared with the theoretical values. The error is less than 1%, which proves the correctness of the equations of the line of action.
3. It is found that the number of roller pins \(z\) and the module \(m\) have influences on the end point and length of the line of action. The rack tooth profile offset coefficient \(c_{y}\) and the diameter coefficient of roller pin \(c_{f}\) have influences on the start point, end point and length of the line of action. The addendum coefficient of rack \(c_{h}\) only affects the length of the line of action.
4. The calculation formula of meshing stiffness per unit tooth width of the trochoidal roller pinion rack is derived. Since the trochoidal roller pinion rack has time-varying pressure angle, its meshing stiffness is transformed into torsional stiffness of the roller pinion. The meshing stiffness is obviously divided into single tooth meshing area and double tooth meshing area. In the double tooth meshing area, the meshing stiffness increases first and then decreases, while in the single tooth meshing area, the meshing stiffness gradually decreases.
5. The number of roller pins \(z\) has influence on the value of meshing stiffness and the meshing range of single and double tooth meshing areas. The module \(m\) and the rack tooth profile offset coefficient \(c_{y}\) mainly affect the value of meshing stiffness. The diameter coefficient of roller pin \(c_{f}\) has influence on the value of meshing stiffness and the meshing range of double tooth meshing areas. The addendum coefficient of rack \(c_{h}\) only affects the meshing range of single and double tooth meshing areas.  
Conceptualization, G.L.; methodology, H.W.; computation and analysis, L.L.; writing--original draft preparation, L.L.; writing--review and editing, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Plan of China (2019 YFB2004700).  
The authors declare no conflict of interest.
## 2 Identifying Steering Feel Factors  
### Steering Feel Performance Index  
In this study, we utilized the indices commonly selected in previous research defining steering feel. All the indices were measured by the driver or a steering robot while inputting the steering angle. First, two indices could be observed when the steering angle was used as the input, and the corresponding steering torque was measured. As depicted in Figure 1a, the steering torque at a \(0^{\circ}\) steering angle represents an important aspect related to perceived differences and the change in steering torque with respect to the steering angle. Additionally, the slope of the steering torque at \(0^{\circ}\) was defined as the stiffness.  
Second, by considering the relationship between steering torque and lateral acceleration, multiple indices can be examined, including steering returnability, off-center steering effort, and torque build-up linearity, as shown in Figure 1b. In [15], lateral acceleration and yaw velocity were defined as representing the responsiveness of the vehicle, as illustrated in Figure 2. However, most researchers consistently consider lateral acceleration, but only a few studies take yaw rate into account. According to [19], all the information obtainable from the yaw rate can also be derived from lateral acceleration. Consequently, to verify the reliability of our simulation model and assess any improvements in steering feel, we employed steering angle, steering torque, and lateral acceleration.
# A Real-Time Sound Field Rendering Processor
## 4 Performance Estimation
### Performance of the Prototype Machine
#### 4.1.3 Throughput  
The throughput denotes the number of grids updated per second, and is calculated by Equation (15).  
\[D_{throughput}=\frac{N_{grid}}{T_{total}}*N_{time\_step} \tag{15}\]  
Here, \(N_{grid}\) is the number of grids, \(N_{time\_step}\) is the number of time steps, and \(T_{total}\) is the calculation time. In the software simulation, the incident sound wave has 9,022,848 data, and each datum will be input into the system as an incidence. Nevertheless, the time steps are 9,022,848. From Table 2 and Figure 10, when six processor cores are applied, the throughput in the software simulation is about 194 (\(32\times 32\times 16/(762.25/9,022,848)\)) M grids/s. In the prototype machine, the computations at each time step are completed in 64 (4 \(\times\) 4 \(\times\) 4) cycles. Thus, the throughput is about 51.2 (\(32\times 32\times 16/(64\times 1/0.2)\) G grids/s. Even if the clock frequency of the prototype is much lower than the PC, the throughput is much higher. Compared with the PC-based simulation, the prototype machine achieves about 263.9 times gain in throughput.
## 4 Discussion
### Climatic and biotic Control over RE
This study indicates that EVI is the most important environmental factor affecting the spatial pattern of RE in northern China's grasslands (Figure 8c), which is in accordance with previously reported results [16; 12]. As a vegetation index commonly used for estimation of vegetation productivity, EVI is strongly associated with GPP in space and time [76; 77]. GPP is the main supply for autotrophic respiration, and determines its intensity [24]. Decomposition of organic matter by microorganisms, including heterotrophic soil respiration, is also supported by photosynthesis. As shown in Figure S3c, the rate of increase for EVI in temperate grasslands from 2001 to 2015 was substantially greater than that in the alpine grasslands. This illustrates the faster increase in productivity of temperate grasslands, although the rate of increase for RE in temperate grasslands over 15 years was slower than that in the alpine grasslands (Figure 6c). This may be due to the greater uptrend of carbon sequestration in temperate grasslands [78]. The soil carbon substrate supplies various components of the RE, and determines the distribution of nutrients in aboveground and underground plant tissues, as well as the phenological patterns of vegetation growth [67]. Soil organic carbon (SOC) is one of the main forms of soil carbon that affects spatiotemporal patterns of respiration [1; 79]. With a relatively large soil carbon pool and gradually melting permafrost, alpine grasslands on the TP have the potential to release significant amounts of CO\({}_{2}\)[80]. Our results also demonstrate that the RE and its rate of increase on the TP are larger than those in the IM (Figure 6c). SoilTex indirectly influences RE, primarily by controlling water and nutrient availability [81; 82]. Clay and silt soils have higher soil water-holding capacities and longer water-holding times [83]. This provides more available water for vegetation growth, microbial decomposition, and respiration. Soils with higher clay and silt content can also store more SOC and nutrients for respiration processes [14; 27]. The negative impacts of sand content on respiration could be attributed to larger soil porosity, resulting in lower soil water availability. This may constrain microbial activity and vegetation growth, leading to reduced respiration. The effects of biotic and climatic factors on RE are strongly interlinked. Adequate hydrothermal conditions increase vegetation growth and autotrophic respiration [84]. These hydrothermal and vegetation conditions further regulate the SOC and affect the soil heterotrophic respiration [85]. Biotic and climatic factors synergistically stimulate RE, but at different spatial and temporal scales, and one of these variables may be particularly significant.
# Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method
## 2 Physical Model and Simulation Set-Up  
As shown in Figure 1, horizontal drilling includes a horizontal section and an inclined section. This can be modelled by an annulus structure with an inner pipe supplying air as the carrier fluid to bring out the debris in the annular space. Attached at the end of the inner pipe is the drill bit, where air exits the inner pipe and flows into the annulus space. This is also the place where the solid particlesenter the annulus space and are carried away by air; thus, it can be regarded as the inlet boundary for our numerical simulation to be introduced in the subsequent sections.  
To simulate the flow in the annulus space, GAMBIT software is used to create the computation domain, shown in Figure 2, and to generate the model and mesh for Fluent calculation. The real configuration is modelled as a set of two coaxial pipes of different diameters, the inner pipe and the outer pipe. The carrier fluid, namely air, enters the inner pipe as indicated as \(A\) in Figure 2, and exits the inner pipe at the other end as indicated as \(B\). The axial distance between the inner and outer pipe at the end \(B\) is 100 mm. The left end of the outer pipe is blocked, representing the rock/earth being drilled. On exiting the inner pipe at point \(B\), the carrier fluid will take a U-turn to enter the annulus space, as indicated by the arrows. Solid particles will flow into the annular pipe at the point marked by \"Solid inlet\" and be carried away by the air flow. The solid inlet is a circle of 10 mm in diameter, in which the center is located at the height of 115.5 mm on the left end of the outer pipe. The inner pipe is 500 mm in length and 75 mm in diameter, while the outer pipe is 520 mm in length and 156 mm as the inner diameter.  
A computational grid is built using a combination of Cooper and TGrid, as shown in Figure 3 as an example for the U-turn region. The number of a computational element is 746, 847.  
Figure 1: Horizontal drilling.  
Figure 2: Structure of annular pipe.
## 6 Conclusions  
A new signal processing algorithm for white light interferometry has been proposed. The goal of signal processing algorithm was to find the time delay (phase shift) between two fringe scans which makes it possible to measure the absolute optical path length of a sensing interferometer. This new signal processing algorithm can be used for absolute temperature measure measurement by mapping the zero order fringe peak position of cross-correlation \(i(n)\) to the time delay between two fringe scan. Cross-correlation between two fringe scans utilizes all the photons in fringe scans effectively and the uncertainty of the zero order fringe peak mis-identification was reduced. Monte-Carlo simulations showed that the proposed signal processing algorithm identified the zero order fringe peak with a miss rate of 3x10\({}^{4}\) at 31 dB shot noise and the extrapolated miss rate at 35 dB shot noise was 3x10\({}^{8}\). Also resolution of less than 10\({}^{-3}\) fringe was obtained at 35 dB shot noise (Figure 12). The fine tuning of signal processing algorithm requires some prior knowledge on the coherence length \(L_{C}\) of the SLD and sample rate \(f_{S}\). But the performance of proposed signal processing algorithm turned out to be not sensitive to the estimation error in \(\hat{L}_{C}\) and \(\hat{f}_{S}\) (within the range from \(-6\%\) to \(6\%\) estimation error of \(\hat{b}\) ). Also the proposed signal processing reached the theoretical limit of fringe order detection over the higher _SNR_ range (30-40 [dB]). The proposed signal processing algorithm uses a software approach which is potentially inexpensive, simple and fast. As a whole, the proposed signal processing algorithm has proven to be a high precision signal processing algorithm for AFWLI phase (time) delay estimation.
## 4 Synthetic Examples Tests
### Example II: A Single Prism Model in a High Noise Background  
The true model in example II is the same as the model in example I. A 20% Gaussian random error was added to the observed data to form the synthesized data under the high noise background. The initial conditions of inversion were the same as those of example I, and the inversion results are shown in Figure 4.  
As shown in Figure 4a,c, under the background of high noise, the model of separate inversion is distorted on the boundary. Compared with separate inversion, joint inversion can suppress the effect of high noise and recover the shape and value of abnormal bodies better, especially near the boundary of anomalies (Figure 4b,d). The cross-gradient value of the joint inversion is significantly reduced compared with the separate inversion (column 3 in Figure 4).  
The _RMS_ values of the resistivity method and TDIP obtained in separate inversion were 1.762 and 1.821, respectively, and that in joint inversion were 1.764 and 1.801, respectively. The variation in _RMS_ values of the resistivity method and TDIP is not obvious, because _RMS_ represents the data misfit, but the response data cannot reflect the real information of the underground model in the high noise background. Therefore, \(\zeta\) is more suitable for evaluating inversion results than the _RMS_.  
The values obtained for separate inversion were \(\zeta_{p}=17.89\) and \(\zeta_{\eta}=0.65\), while those for joint inversion were \(\zeta_{p}=3.59\) and \(\zeta_{\eta}=0.44\). The \(\overline{t}\) in separate inversion was \(4.67\times 10^{-4}\), while it was \(3.73\times 10^{-4}\) in joint inversion. The values of \(\zeta\) and \(\overline{t}\) were smaller in the joint inversion than in the separate inversion, especially the value of \(\zeta_{\rho}\), which decreased significantly.
## 3 Results
### Microscope Detection with Change of Flow Rate  
Table 2 illustrates a sample of data collected in terms of number of beads over a fixed period of time (30 s). This time is fixed by Matlab to count the number of the total beads passed within the microfluidic channel, as shown in Figure 3. The data shown in Table 2 demonstrates an overview of the highest probable chances microspheres are going to travel inside the channel at specific flow rates. The evidence of the movement detection allows an analysis of where most beads flow in terms of the 0.8 mm diameter channel displayed in Table 2. The microfluidic channel diameter is then divided into two sections on Table 2 (outside to middle, middle to inside) to demonstrate where the bead is more likely to travel.  
As it could be seen in Table 2, between the outside of the meander 0.8 mm to the middle sector at 0.4 mm, there is a higher probability that the bead will travel between the flow rates of 45-105 \(\mu\)L/min.  
Figure 7 shows results from the data collected over three tests for each flow rate to determine the recovery rate, which is the number of beads detected in the region in question over the first 100 beads detected within the channel. As demonstrated in Figure 4, 69% of beads can be recovered from the sample between the outside of the meander 0.8 mm to the middle sector at 0.4 mm. On the other side, from the middle of the channel at 0.4 mm to the inside 0.0 mm the probability that the bead will flow between these points is at the flow rate between 135-225 \(\mu\)L/min. This provides a recovery rate of 88%, conveying that the increase of flow rate increases the consistency of where the microsphere will travel. However, this will not allow an easy detection, as the bead's velocity will increase too, as explained in Section 3.4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Diameter** & **45** & **75** & **105** & **135** & **165** & **195** & **225** \\
**(mm)** & \(\mu\)**L/min** & \(\mu\)**L/min** & \(\mu\)**L/min** & \(\mu\)**L/min** & \(\mu\)**L/min** & \(\mu\)**L/min** & \(\mu\)**L/min** \\ \hline
**0.7–0.8** & 2 & 1 & 1 & 1 & 0 & 1 & 0 \\
**0.6–0.7** & 0 & 1 & 4 & 0 & 1 & 0 & 1 \\
**0.5–0.6** & 1 & 0 & 4 & 0 & 0 & 0 & 0 \\
**0.4–0.5** & 2 & 2 & 2 & 0 & 0 & 0 & 0 \\
**0.3–0.4** & 1 & 0 & 2 & 1 & 2 & 1 & 1 \\
**0.2–0.3** & 0 & 2 & 0 & 3 & 0 & 0 & 1 \\
**0.1–0.2** & 0 & 2 & 1 & 3 & 1 & 1 & 0 \\
**0.0–0.1** & 0 & 0 & 1 & 1 & 2 & 4 & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Number of beads counted after 30 s. Terms of specific flow rates against specific areas of microfluidic channels at the same time for each test.  
Figure 7: Recovery rates within the microfluidic channel.  
From these results, fabrication of a microchannel can be inserted for the pathogen to be removed from the sample. Being able to do this will allow microfluidic technology to essentially replace most filtration methods used to remove waterborne pathogen such as EPA _Cryptosporidium_ detection method [5].
## 4 Experiments 1
### Design  
The evaluations of the 3D user interface focus on three main aspects [26]: object manipulation [21; 27], viewpoint manipulation [28] and application control [29]. Since the AR Pointer is proposed for the efficient registration and manipulations of 3D virtual objects, our experiment was conducted with a focus on the first aspect only. Also, interviews were carried out about three categories: speed, intuition and ease of learning. A total of 26 participants participated in this experiment 1. One group was an experienced group of 3D graphics tools, aged between 25 and 33 years old (mean [M] + standard deviation [SD] = 28.2 + 2.2, male = 10, female = 3, one left-handed). The other group was an inexperienced group of 3D graphics tool, aged between 25 and 31 years old (mean [M] + standard deviation [SD] = 27.8 + 1.9, male = 9, female = 4, all right-handed). Each group performed four tasks: registration, translation, scaling and rotation of 3D virtual objects. Also, each task was performed four times with proposed AR Pointer interactions and keyboard and mouse combinations, respectively. In this experiment, we implemented the furniture layout application prototype shown in Section 3.3 and used it for the experiment. Completion time was calculated according to how quickly the furniture was moved to the suggested location in the room and transformed into the correct scale and pose.  
The subjects manipulated the objects using the interfaces of the Unity Engine [20]. They could register and manipulate the objects while continuing to view the actual surrounding environment through the PC screen. The objects could be manipulated using the mouse in the scene view or by entering the number through the keyboard in the inspector view. The basic usage of the authoring tool (Unity Engine) was briefly explained about 5 min before the experiment started. The experiment proceeded in two stages. First, a ground truth object is randomly generated in the Unity test scene. The subjects had to register the object at the corresponding location (registration experiment). In the next step, a guide object was created at a new location and the re-registration (translation) task is performed by selecting the object registered in the previous step and then manipulate it to orientation (rotation) and size (scaling). The first experiment was performed in the Unity Engine, which is the authoring tool, without wearing the HMD. When manipulating objects using the AR Pointer, subjects still had to check their work on a PC monitor. The first experiment was usually completed within 5 min.
## 4 Simulation Results and Discussion  
### Case Study  
The considered MG test system shown in Figure 5 is used to demonstrate the effectiveness of the proposed strategy. The test system includes a PV plant and a WT unit as renewable resources, a diesel engine as a DG, EVs and loads. Moreover, a dump load bank is considered for dumping the surplus energy produced by the RESs' units in isolated mode operation. Based on an economical assessment performed by HOMER Pro(r), the optimal design of MG's sources (installation capacities of resources) is obtained for a region in the east of Iran (32.8649\({}^{\circ}\) N, 59.2262\({}^{\circ}\) E). With respect to this assessment and considering the technical constraints, the installed capacity of PV, WT and DG are calculated as 300 kW, 375 kVA and 300 kVA, respectively.  
The forecasted load, output power of WT, PV and the summation power of WT and PV (\(P_{\text{Ren}}\)) in a typical day are depicted in Figure 6. It is also assumed that there are 100 EVs plugged into two different parking lots (in residential and office buildings) and their arrival times are modeled based on a Gaussian distribution with \(\mu=19\) and \(\delta^{2}=10\)[21].  
Figure 5: Single line diagram of the examined MG.  
Here, the minimum and maximum limit of \(SOC\) are considered as 40% and 90% of the total battery capacity, respectively [22]. The connected EVs are assumed to be charged at different current ratings (i.e., 2, 10 or 30 Ampere), and to be discharged either with 2 or 10 Ampere [23].  
Moreover, it is assumed that the charge/discharge processes in both parking lots is the same and the number of EVs in each parking lot is evaluated based on the available profiles depicted in Figure 7. Due to EVs travelling, the number of EVs in both parking lots is a percentage of the total number of EVs.
## 5 Simulation and Results
Some recent and advanced filters can have a better response than the particle filter, but among all the filters compared, the PF provides a better response. Figure 12: Integer and fractional order controller comparison for different state estimation methods for Joint 1. Figure 13: Comparison of filter responses with fractional order control for Link 1. Quantitatively, the performance may vary for different control techniques in the case of IOPID and FOPID controllers (Figures 11 and 12), but qualitatively, the PF has a better response. This is because the PF does not generalize all of the data; rather, it operates on the areas of data that are known as particles, which are both time-consuming and computationally intensive processes. A higher number of particles does not necessarily lead to a better performance, because two consecutive data points may have similar characteristics and they need to be operated together. Therefore, the number of particles is an important factor. In comparison to the PF, the UKF has a smaller number of particles, also known as sigma points. Another factor that leads to the better performance of the PF is the distribution of data. The KF, EKF and UKF consider the distribution of data as Gaussian, but there may be a case in which data is not distributed in a Gaussian form. The PF provides the flexibility to choose the type of distribution that better fits the data points. We have provided a review and results of different techniques for state estimation with the application of FO calculus. Overall, the combined algorithm offers improved performance for the 2 DSFL and 2 DSFJ concerning conventional control in terms of parameters such as tracking accuracy and the orientation of the manipulator.
## 2 Research Questions and Hypothesis  
### Research Questions  
This paper proposes a topology for implementing the functions of an STPC and examines its integration into PDSs. Like other power electronics equipment, an STPC generates harmonics due to its use of semiconductor switching devices. Therefore, it is necessary to study PQ, including harmonics. In addition, the topology and control method of the STPC should be designed, and both computer simulations and field demonstrations are required for on-site demonstration. The detailed research questions of this paper are presented in Table 2.  
There are few instances where power conversion equipment is directly connected to PDSs. Regarding the equipment described above, an Energy Storage System (ESS), Power Conditioning System (PCS), and Uninterruptible Power Supply (UPS) are used for load equipment, a Static Synchronous Compensator (STATCOM) is utilized in PTSs, and solid-state transformers are employed in PDSs. However, they have not yet been widely adopted in PDSs due to high production costs and size constraints.