esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Results
### Controller Performance  
The first controller was an MPC controller without speed compensation (Original), which is indicated by a blue dotted line, and the second controller was an MPC controller with longitudinal speed compensation (proposed), which is indicated by a red solid line.  
Figure 8 shows the simulation results of the tracked path of different controllers. The vehicle longitudinal speed curves are highly similar under the action of two controllers. The deviation occurred near the position where the curvature changes greatly. Both controllers can track the reference path precisely. Figure 9 shows the simulation results of the lateral deviation and the course deviation of different controllers, and the statistics for the results are shown in Table 2. The maximum absolute lateral deviation occurred near the position where the curvature changes greatly. When the reference path curvature changed greatly, the vehicle tracking performance became worse due to the controller, vehicle inertia and other factors. The proposed controller performed better; the performance difference analyzed by these indicators allowed us to find the average value of absolute lateral deviation, the average value of absolute course deviation, the mean squared error of absolute lateral deviation and the mean squared error of absolute course deviation. The proposed controller reduced the average value of absolute lateral deviation by 5% and reduced the average value of absolute course deviation by 4.5%. The tracking error was relatively small for both controllers when the path changed gently, shown in Figures 6 and 9. The tracking error increased significantly when the path changed  
Figure 7: Reference point search method.  
suddenly. However, the proposed controller performed better, especially when the path curvature varies suddenly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \(\overline{|\epsilon|}\) & \(MSE(|\epsilon|)\) & \(\overline{|\Delta\varphi|}\) & \(MSE(|\Delta\varphi|)\) & \(Max(|\epsilon|)\) \\  & **(m)** & **(m)** & **(deg)** & **(deg)** & **(deg)** & **(m)** \\ \hline Original & 0.1624 & 0.1367 & 0.6678 & 1.1014 & 0.5292 \\ Proposed & 0.1544 & 0.1289 & 0.6374 & 1.0571 & 0.5022 \\ \hline \hline \end{tabular} where \(\overline{|\epsilon|}\) is the average value of absolute lateral deviation; \(MSE(|\epsilon|)\) is the mean squared error of absolute lateral deviation; \(\overline{\Delta\varphi|}\) is the average value of absolute course deviation; \(MSE(\left|\Delta\varphi\right|)\) is the mean squared error of absolute lateral deviation; \(Max(|\epsilon|)\) is the maximum of absolute lateral deviation.  
\end{table}
Table 2: Comparison of different controllers.  
Figure 8: The tracked path of different controllers.  
Figure 9: The simulation results of different controllers: Lateral deviation, Course deviation.
## 5 Performance Evaluation
### Impact of the Stretch Factor \(\epsilon\)
The increase of average routing path stretch results in the enlargement of the total traffic in the network, which may increase the energy consumed for packet receiving. In consequence, the network lifetime decreases when \(\epsilon\) varies from 1.5 to 2.5. Beyond 2.5, increasing of \(\epsilon\) not only increases the routing path stretch, but also worsens the load balance, thus the network lifetime is shortened. Finally, it can be seen that the experimental results show the consistency with the theoretical analysis described in Section 4.2. Figure 11: Impact of \(\epsilon\) on load balance. The blue lines represent the maximum packet forwarding ratio of sensor nodes, the red lines represent the percentage of Euclidean routing paths that exceed the network boundary.
# Comparison of Three Biological Control Models of _Pycnoporus__sanguineus_ on Phytopathogenic Fungi
## 2 Materials and Methods
### Model 1: Dual Tests of Antagonistic Activity  
The antagonistic capacity of _P. sanguineus_ against the phytopathogens _B. cinerea_ and _F. oxysporum_ was evaluated through in vitro dual growth tests on PDA medium in large Petri dishes (90 mm in diameter). Mycelium circles of 5 mm in diameter of each species were placed at the end of each dish, 5 mm from the edge. A total of 5 treatments were evaluated, with 5 replications each: _P. sanguineus_ vs. _B. cinerea_, _P. sanguineus_ vs. _F. oxysporum_,as well as _P. sanguineus_, _B. cinerea_, and _F. oxysporum_ grown individually as control tests. The inoculants were isolated from previously mature and growing cultures. The growth rate (GR) was assessed using the formula GR = (FR - IR)/number of days in growth, where GR = the growth rate, FR = the final radius reached by the growing strain, and IR = the initial radius of the growing strain. Simultaneously, the antagonistic capacity of _P. sanguineus_ was assessed by the percentage inhibition of radial growth (PIRG) using the formula PIRG = (R1 - R2)/R1 \(\times\) 100, where PIRG = the percentage of radial growth inhibition, R1 = the radial growth (mm) of the pathogen (_B. cinerea_ or _F. oxysporum_) without the presence of _P. sanguineus_, and R2 = the radial growth (mm) of the pathogen (_B. cinerea_ or _F. oxysporum_) in the presence of _P. sanguineus_. Mycelial growth was measured using a digital Vernier (CD-6 Mitutoyo, Naucalpan de Juarez, Mexico), with measurements taken every 24 h from inoculation until contact occurred between both growing mycelia.
## 4 Results and Discussion
### Stability Analysis of Control System  
#### 4.2.1 Nyquist Stability Criterion  
In this work, a mixed feedforward/feedback control mechanism is proposed, and its performance was assessed in two typical scenarios: a step load change and a linear load change. However, since load following is a nonlinear tracking process, the stability of this control scheme has to be verified carefully. For the power control system, since the open-loop transfer function does not have a pole on the right-hand side of the Gauss plane (P = 0) and it does not encircle the point (\(-1+\mathrm{j0}\)) (\(Z=0\)), according to the Nyquist stability criterion (N = 0), this power control system is stable in theory, as shown in Figure 7. For the steam temperature control system, since the open-loop transfer function does not have a pole on the right-hand side of the Gauss plane (P = 0) and it encircles the point (\(-1+\mathrm{j0}\)) once clockwise and encircles the point (\(-1+\mathrm{j0}\)) once counterclockwise (\(Z=1-1=0\)), according to the Nyquist stability criterion (N = 0), this steam temperature control system is stable in theory, as shown in Figure 8. As the two control systems are proven to be stable,the stability of the whole control scheme proposed in this work is verified according to the Nyquist stability criterion.
## 2 Problem Formulations
### Extended State Observer  
By employing the extended state observer (ESO) referring to [32] with u and x as inputs and state variables, the observed system variable can be defined as follows:  
\[\begin{split}\dot{\hat{x}}_{1}=\hat{x}_{2}+\kappa_{i1}(x_{i1}-\hat{ x}_{i1})\\ \dot{\hat{x}}_{2}=\hat{x}_{i3}+\kappa_{i2}(x_{i1}-\hat{x}_{i1})+u+ \hat{f}_{i}\\ \hat{x}_{i3}=\kappa_{i3}(x_{i1}-\hat{x}_{i1})\end{split} \tag{9}\]  
where \(\hat{\mathbf{x}}=[\hat{x}_{i1},\hat{x}_{i2},\hat{x}_{i3}]^{T}\in R^{3\times 1}\) and \(\kappa_{ij}\) with \(i,j=1,2,3\) are observer gains to be specified. The gains are selected as:  
\[[\kappa_{i1},\kappa_{i2},\kappa_{i3}]=\left[3\omega_{0},3\omega_{0}^{2},\omega _{0}^{3}\right] \tag{10}\]  
where \(\omega_{0}>0\) is the observer bandwidth.  
By defining \(\widetilde{x}_{ij}=x_{ij}-\hat{x}_{ij}\) for \(i,j=1,2,3\), the observer estimation error can be presented as follows:  
\[\begin{split}\dot{\widetilde{x}}_{11}=\widetilde{x}_{i2}-3\omega _{0}\widetilde{x}_{i1}\\ \dot{\widetilde{x}}_{22}=\widetilde{x}_{i3}-3\omega_{0}^{2} \widetilde{x}_{i1}+\widetilde{f}_{i}\\ \dot{\widetilde{x}}_{i3}=-\omega_{0}^{3}\widetilde{x}_{i1}+\delta _{i}\end{split} \tag{11}\]  
where \(\delta_{i}\) is the differential of the lumped uncertainties.  
Let \(\chi_{ij}=\frac{\widetilde{x}_{ij}}{\omega_{0}^{j-1}}\), \((i=1,2;j=1,2,3)\); then, the observer estimation error in (11) can be represented as follows:  
\[\dot{\mathbf{x}}_{i}=\omega_{0}A\mathbf{x}_{i}+B\frac{\widetilde{f}_{i}}{ \omega_{0}}+C\frac{\delta_{i}}{\omega_{0}^{2}} \tag{12}\]  
where \(\mathbf{x}_{i}=[\chi_{i1},\chi_{i2},\chi_{i3}]^{T}\in R^{3\times 1}\), \(A=\begin{bmatrix}-3&1&0\\ -3&0&1\\ -1&0&0\end{bmatrix}\), \(B=\begin{bmatrix}0&1&0\end{bmatrix}^{T}\), \(C=\begin{bmatrix}0&0&1\end{bmatrix}^{T}\).  
**Assumption 2:** the function, \(f_{0}\), is a Lipschitz function.
## 3 The Navigation Strategy with Path Priority  
This section introduces the main algorithm of this paper called the navigation strategy with path priority (NSPP) to solve the problem. The NSPP contains five steps as follows and is summarized as the flowchart in Figure 1.  
1. [label=Step 1]
2. Giving the flat space map for the robot moving includes obstacles and all wheeled robots initially.
3. Setting the path-priority order for each robot and applying the GVD map division to each robot according to its path-priority order.
4. Finding an efficient path for any robot from the starting point to the target point.
5. Designing the velocity control strategy for all robots.
6. To avoid any collision, the robot moving should follow the path-priority order defined in Step 2 to determine whether it moves continuously or stops and waits to avoid a collision.  
The following figure summarizes the above steps and is explained in detail below.
## 3 Inherent Optical Properties (IOPs)
### Ocean IOPs--Bio-Optical Models
#### 3.6.1 The CCRR Water Impurity IOPs  
Here we first describe a bio-optical model used in the CoastColour Round Robin (CCRR) effort [58]. The CCRR bio-optical model consists of the three input parameters chlorophyll concentration (CHL), mineral concentration (MIN), and \(\alpha_{\mathrm{CDOM}}(443)\), which are allowed to vary. According to this decomposition into three basic components, the \"mineral particle\" component can include also non-algae particles whose absorption does not covary with that of the algae particles [58].
## 4 Simulation
### Influence of Mounting Errors of Ball Bar on Circular Trajectories  
Considering the influence of mounting errors of the T-side ball, the center offset of the T-side ball (\(x_{T},y_{T},z_{T}\)) with respect to the tool spindle axis is added to the initial center coordinate \(T_{\text{T}}\) in the Equation (1), expressed as the following Equation (15). In the same way, the center offset of the W-side ball (\(x_{W},y_{W},z_{W}\)) with respect to the C axis origin is added to the initial center coordinate \(W_{\text{C}}\) in the Equation (13), expressed as the following Equation (16). The simulation is performed in two coordinate systems at the condition that the offset is 20 \(\upmu\)m and the obtained results are shown in Table 4.  
\[T_{\text{T}}=\left[\begin{array}{c}x_{T}\\ y_{T}\\ z_{T}\\ 0\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ -R_{\text{B}}\\ 1\end{array}\right] \tag{15}\]  
\[W_{\text{C}}=\left[\begin{array}{c}x_{W}\\ y_{W}\\ z_{W}\\ 0\end{array}\right]+\left[\begin{array}{ccc}1&0&-\beta_{\text{CZ}}&-\delta x_{ \text{CZ}}\\ 0&1&\alpha_{\text{CZ}}&-\delta y_{\text{CZ}}\\ \beta_{\text{CZ}}&-\alpha_{\text{CZ}}&1&0\\ 0&0&0&1\end{array}\right]W_{\text{C}}^{\prime} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \(x_{W}\) & \(y_{W}\) & \(z_{W}\) & \(x_{T}\) & \(y_{T}\) & \(z_{T}\) \\ \hline \multirow{4}{*}{Cylindrical coordinate system} & B axis Radial & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & B axis & & & & & \\ \cline{2-6}  & Z-direction & & & & & \\ \hline \multirow{4}{*}{Cylindrical coordinate system} & C axis & & & & & \\ \cline{2-6}  & C axis & & & & & \\ \cline{2-6}  & Tangential & & & & & \\ \cline{2-6}  & C axis Axial & & & & & \\ \hline \multirow{4}{*}{C Cartesian coordinate system} & C axis  
In Table 4, a dotted circle represents theoretical trajectory when there is no mounting errors of ball bar and the red circle represents changed trajectory affecting by one mounting error for each measurement. The blank indicates the trajectory has not changed. It is found that the eccentricity of circular trajectories in these two coordinate systems are strongly affected by the mounting errors of the T-side ball. Therefore, it is crucial to coincide the center of the T-side ball to the tool spindle before conducting measurements. However, the mounting errors of the W-side ball do not affect the eccentricity in Cartesian coordinate system for the B axis measurements, and in cylindrical coordinate system for the C axis measurements. Therefore, to eliminate the influence of mounting errors of the W-side ball on the eccentricities of circular trajectories, the B axis measurements in Cartesian coordinate system and C axis measurements in cylindrical coordinate system are proposed to identify the geometric deviations of multi-tasking machine tools.
## 2 Background
### Multi-Page Architecture of MLC NAND Flash  
MLC NAND flash memory adopts a multi-page architecture [20] to reduce program disturbances and improve the program speed at the same time. Under this multipage architecture, every two pages form a pair of programmable pages called shared pages. These two pages are further defined as the Least Significant Bit (LSB) and the Most Significant Bit (MSB) pages. For a pair of shared pages, allocation of the data in LSB or MSB pages determines the distortion level of the MLC NAND flash which can directly affect the reliability of the corresponding physical page. Normally, the cell of MLC NAND flash is firstly programmed into two low voltage states, then other two voltage states which represent the information of the MSB page are generated by the second programming process as shown in Figure 2b.  
Figure 1: (a) Program disturbances cause the bit errors in a NAND flash memory cell. (b) Leakages of current causes the bit errors in a NAND flash memory cell.  
Therefore, the MSB page has much lower program distortion as that of the LSB page and much higher reliability because it can directly avoid distortions induced by the first step of the programming process.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 3 Deep Learning-Based Channel Estimation
### Fully Connected Deep Neural Network-Based Channel Estimation
From a set of initial values, the weights and biases are updated by minimizing the loss function (31) with forward and backward propagation [15].
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Usability
[54] looks at user preferences in using VR interfaces for teleoperating robots in combat or hostile environments. Although their work is done entirely in simulation, they found that in a 10 person user study, that 90% of the users preferred the immersive VR interface compared to a traditional non-immersive, keyboard, mouse, and monitor interface.
## 5 Biosensor Application of Electrodeposited Composite Nanostructures
### Composite Electrodes with Chitosan  
Chitosan is the deacetylated form of natural chitin; it has excellent film-forming ability, is biocompatible, and is well-suited for the immobilization of enzymes [137]. A composite structure of  
Figure 10: SEM images of Prussian blue and chitosan (PB–CS) nanocomposite film (**A**) and AuNPs–CS/PB–CS nanocomposite film (**B**). (**C**) Cyclic voltammetries (CVs) obtained from different modified electrodes in five mM of [Fe(CN)\({}_{6}\)]\({}^{3-}\)/[Fe(CN)\({}_{6}\)]\({}^{4-}\) containing \(0.1\) M of phosphate buffer solution (PBS) at a scan rate of \(20\) mV s\({}^{-1}\). (a) Bare Au, (b) PB–CS/Au, (c) AuNPs–CS/PB–CS/Au, and (d) CS/\(\beta\)-G-GOD/AuNPs–CS/PB–CS/Au electrode. Reproduced with permission from ref. [134], Copyright 2014, Elsevier. \(\beta\)-G-GOD: glucose oxidase.  
zirconia (ZrO)-coated SiO\({}_{2}\) nanoparticles and chitosan was formed on a gold electrode surface and used as the support for a lactate biosensor [138]. The modified electrode was formed by applying 20 potential cycles between \(-\)0.2 to +0.4 V in a suspension containing the nanoparticles in one M of KCl and 0.2% chitosan. Lactate oxidase was immobilized on this structure by cross-linking with glutaraldehyde. A linear relation between oxidation current and lactate concentration was observed over the range 0.1 to 4000 mM. The analytical recovery of lactate spiked into blood serum was noted as 96.6% to 99%. The biosensor was used to detect elevated lactate concentrations in patients with lactoacidosis.  
A glucose sensor based on a composite film of gold nanoparticles, ionic liquid, and chitosan entrapping glucose oxidase was prepared using electrodeposition [139]. Gold nanoparticles with a size of 20 to 40 nm were deposited in 0.5 M of H\({}_{2}\)SO\({}_{4}\) and 10 mM of HAuCl\({}_{4}\) onto a gold surface by stepping the potential from 0.8 V to 0.2 V (versus Ag/AgCl) and then holding for 10 s. Then, a composite film containing glucose oxidase and chitosan was deposited from a solution of 0.5% chitosan, 20 mM of benzoquinone, 1% 1-butyl-3-methyl-imidazolium tetrafluoroborate (BMIM-BF4) as the ionic liquid and 5.0 mg mL\({}^{-1}\) of glucose oxidase. The benzoquinone served as a proton trap that increased the pH near the gold surface, thus leading to the deprotonation of chitosan and its deposition. In the final step, a layer of Nafion was cast over the composite film. Glucose was detected via the amperometric detection of H\({}_{2}\)O\({}_{2}\) at 0.6 V with a detection limit of 1.5 \(\upmu\)M and a linear range from three \(\upmu\)M to nine mM.  
A non-enzymatic glucose sensor was prepared by one-step electrodeposition from a solution of HAuCl\({}_{4}\) and chitosan in 0.5 M of acetic acid onto a GCE electrode [140]. The electrodeposition took place at \(-\)1.0 V (versus SCE) for 60 s, producing a film of Au nanoparticles in a chitosan film. For the optimized conditions, the detection limit for glucose detection was 0.37 mM with a linear range from 0.4 to 10.7 mM, which was based on the peak currents in the cyclic voltammograms in 0.10 M of NaOH.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
###### Abstract
The dual Ku-band antennas were mounted outside the capsule, and the baseline length was only 2.3 m, limited by the capsule size. The radar look angle ranges from 1\({}^{\circ}\) to 8\({}^{\circ}\), to ensure a swath width of at least 40 km. Based on the acquired data, [PERSON] et al. has evaluated the relative altimetric accuracy of InIRA to be \(\sim\)3 cm at 10 km resolution ([PERSON] _et al._, 2017). Though the baseline of InIRA is even shorter than KaRIn, it has proved the great potential in OST altimetry. Since SWOT KaRIn and Tiangong-2 InIRA both suffer from short baseline deficiency, in reference to the successful practice from TerraSAR-X/TanDEM-X (TSX/TDX) ([PERSON] _et al._, 2017), we propose the Large Baseline Imaging Radar Altimeter (LB-IRA) from satellite formation, whose baseline length is no longer restrained by a single platform. However, large baseline also aggravates the signal decorrelation, preventing altimetric accuracy from being further improved. We utilize the cross-interferometry method, of which a small carrier frequency shift (CFS) is introduced in between two transmitted signals to compensate for the severe baseline decorrelation. The principle of cross-interferometry was first proposed by [PERSON] ([PERSON], 2016), and further verified by ERS-ENVISAT terrain datasets ([PERSON], 2016; [PERSON], 2016; [PERSON], 2016). As for LB-IRA, a helix orbit is carefully selected to prevent severe time decorrelation from along-track baseline while maintaining a minimum safety distance. Furthermore, in order to prevent radio interference while maintaining a sufficient swath width, the idea of matching CFS with signal bandwidth is introduced for the first time, innovated by the principle of frequency division multiplexing. The numerical results indicate that the LB-IRA system can achieve \(\sim\)1 cm relative accuracy on a 1 km resolution grid. The paper is organized as follows: Section 2 briefly introduces the IRA altimetry principle and defines the concept of relative/absolute altimetric accuracy, and Section 3 discusses the helix orbit configuration in consideration of arranging proper cross-track/along-track baseline. The principle of cross-interferometry, and the design principle of several key parameters in consideration of CFS, is discussed in Section 4, and the methodology for system parameter selection and phase noise suppression are explained in Section 5. Section 6 presents and analyzes the numerical results of the altimetric accuracy based on the system parameters of LB-IRA, and the last section (Section 7) summarizes the whole paper.
## 3 Concept
### Propulsion
#### 3.3.2 Motor Type  
With the long stator confirmed, the large airgap is another challenge. To allow for realistic construction tolerances, an airgap in the range of one to two centimeters is necessary. A linear synchronous motor with strong excitation in the pod can cope best with this problem by increasing motor efficiency through stronger magnetic excitation. A long stator induction motor would come with extensive heat in the pod and decreased efficiency due to the large airgap. A long stator reluctance motor, on the other hand, would come with plenty of laminated iron in both the pod and the track, increasing the system cost.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The sample comprised medically fit children aged between 9 and 11 years at the time of sampling. The children who participated in the study had erupted permanent incisors and molars, as well as primary molars and canines in both upper and lower arches. In order to avoid the confounding effect of molar relationship on bite pattern, only children with Class I occlusion were included in the study. Children with partially erupted permanent teeth or shed deciduous teeth were excluded from the study. Children with history of dental abscesses, restored teeth, or history of TMJ trauma were excluded from the study. Children with gross facial asymmetry or deviation of the jaw on mouth opening were also excluded from the study. All included participants were categorized as having a short, average, and long face based on previously established criteria based on the Mandibular Plane Angle (MPA) (1-3). A search of the cephalometric records of age appropriate children from the databases of three different tertiary care centers was undertaken to screen a total of 90 children (30 per group) having a short ((MPA < 28\({}^{\circ}\)), normal (28\({}^{\circ}\)\(\leq\) MPA \(\leq\) 39\({}^{\circ}\)), and long (MPA > 39\({}^{\circ}\)) facial types.