esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Existing Research  
We reviewed previous studies on cellulose and cellulose nanomaterials that are relevant to the field of construction. Table 1 summarizes the review of the existing literature on CNCs.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Authors** & \multicolumn{1}{c}{**Synopsis**} \\ \hline \multirow{4}{*}{[PERSON] et al. [13]} & Investigated the effect of addition of CNCs on cement paste. Highest ball-on-three-ball (B3B) flexural strength (strength increased by \textasci{}30\%\)) was observed after the addition of 0.2 vol\% CNCs and longer curing times. At higher CNC concentrations, an agglomeration of NCs occurred, resulting in a decrease in strength. \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & Use of sisal fibers as a source of CNCs as well as cellulose whiskers and microfibrillated cellulose (MFC). \\  & CNCs, cellulose whiskers, and MFC were obtained from sisal fibers and modified with \(n\)-octadecyl isocyanate. \\  & Efficiency of chemical transformation was found to be highly dependent on the nature of the nanoparticles, with explanations relating to specific areas, peeling ability, and solvent dispersion. Surface modification with \(n\)-octadecyl isocyanate improved the dispersion of nanoparticles in organic solvents. It is possible to form nanocomposite films on a wide range of polymer matrices by casting/evaporation techniques. \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Electrostatic repulsion between cellulose microcrystals (CMCs) is inefficient in nonpolar organic solvents, while strong hydrogen bonding between MCs induces the rapid aggregation of colloidal suspension. As many applications require a good dispersion of the filler phase, this type of aggregation limits the range of solvents that can be used for forming CMC suspensions. The chemical modification of microcrystalline surfaces is an effective approach for improving their dispersion. \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & Unmodified and silylated CNCs (SCNCs) were prepared and used to solution-cast nanocomposite films of poly-L-lactic acid (PLLA). The effects of surface silylation on the morphology, nonisothermal, and isothermal crystallization behaviors, and mechanical properties of nanostructured composites were investigated. Unmodified CNCs formed agglomerates, whereas SCNCs were well dispersed in PLLA. The tensile modulus and tensile strength of PLLA/SCNC nanocomposite films containing only 1 wt% SCNCs were more than 20% higher than those of pure PLLA; this was owing to the crystallinity effects of SCNCs and their good dispersion. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of existing literature on cellulose nanocrystals (CNCs).
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Lower Limit for the Permittivity
#### 3.5.1 Plasma at an Antinode  
The relative frequency shift \(\frac{\Delta f}{f_{1}}\) is investigated for resonant modes where the electric field is large at the position of the plasma. For the TM\({}_{010}\) mode, the response is investigated for four plasma radii (\(r_{1}=10^{-3}\) m-\(10^{-6}\) m) as a function of the change in the relative permittivity of the plasma region. The results are presented in Figure 14. This graph shows that for the plasma radius of \(10^{-3}\) m the shift of the resonance frequency is--as expected--the largest. All four maximum permitted changes in the permittivity lie beyond the reputed limit. The smallest radius allows for a \(\Delta\varepsilon_{\rm r}\) of approximately of \(10^{6}\) or in archic terms \(\frac{\omega_{\rm pe}}{\omega}\sim 1000\) while assuming \(\
u_{\rm m}=0\) Hz. The maximum permitted \(\frac{\Delta f}{f_{1}}\) for all four radii is \(\sim\)\(10^{-2}\) and this means that the linear regime is limited by the total number of electrons in the plasma volume. The fact that the maximum permitted \(\frac{\Delta f}{f_{1}}\) does not depend on the size of the plasma (for \(r_{1}\ll r_{2}\)) is a great advantage for users of the diagnostic method.  
Figure 14: _Cont._  
Figure 15 shows that for a plasma radius of \(10^{-4}\) m the \(\frac{\Delta f}{f_{1}}\) also starts to deviate strongly around \(10^{-2}\) for the TM\({}_{010}\), TM\({}_{020}\), TM\({}_{030}\), and TM\({}_{040}\) mode.  
Figure 14: Investigation of the linearity of the relative resonance frequency shift \(\frac{\Delta f}{f_{1}}\) as a function of the change of the relative permittivity \(\Delta\varepsilon_{\rm r}\) of the TM\({}_{010}\) and TM\({}_{110}\) mode for four plasma radii. The vertical dashed line at \(-\Delta\varepsilon_{\rm r}=1\) indicates the expanded putative limit of the Microwave Cavity Resonance Spectroscopy (MCRS) diagnostic method and the circles indicate where the response is no longer linear.  
Figure 15: _Cont._
###### Abstract  
In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFFSMC can realize a \"seamless\" air-water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.  
hybrid aerial underwater vehicles; trans-medium; nonsingular fast terminal sliding-mode control; adaptive technology 2023  
H  
HAUVs Facing Uncertainties and Unknown Environmental Disturbances
## 4 Performance Evaluation and Discussion
### Example 1: Signal Fading Rapidly and Acceleration Scenario
From 121 s to 180 s, the Doppler rates for PRN 14 and PRN 19 are 3 Hz/s and 23 Hz/s, respectively. The KF tracking and the PLL tracking cannot follow the sudden changes caused by the Doppler frequency variation and signal strength attenuation. KF tracking fails because it cannot update the \(\mathbf{Q}_{k}\) matrix by the adaptive algorithm. The PLL tracking fails because the tracking loop's bandwidth is fixed and cannot adapt to the dynamic and weak signal. For the STKF tracking, although it introduces the fading factors, without the system stability detection and adaptive \(\mathbf{Q}_{k}\), false fading factors can be obtained in the STKF, which leads to wrong estimations. Then, the STKF performance performs worse than the KF. Hence, STKF is difficult to ensure accurate fading factors for all situations [33]. Accordingly, the STKF tracking for PRN 14 is better than KF tracking, but the STKF for PRN 19 is worse than KF. With the system stability detection and \(\mathbf{Q}_{k}\) adaptation, the proposed AKF tracking method can keep tracking well during the whole process. Figure 6 shows the adaptive factor of the proposed AKF algorithm. As we can observe, when the signal strength decreases quickly while the velocity is accelerating, the adaptive factor \(\lambda_{k}\) will be turned to a larger value. However, as the signal strength increases, the system keeps track of the signal well and the adaptive factors need not be used. This indicates that the adaptive process noise covariances play a significant role in adapting to the signal strength and dynamic variation. According to the results, the proposed AKF approach is effective in resisting sudden changes from the power fading and high dynamic of the navigation signal. The proposed AKF tracking shows higher robustness compared with other tracking algorithms. This superiority can improve the position accuracy of dynamic receivers under signal fading environments.
# Artificial Intelligence in Adaptive and Intelligent Educational System: A Review
## 3 Reinforcement Learning in Adaptive and Intelligent Educational System (RLATES)  
Distance learning has attracted increasing attention in recent years. When students and teachers are unable to attend classes face to face in the same classroom, distance learning is essential. Sometimes, learning through resources on the web (text, video, pictures, voice, etc.) or online tutorials provided by teachers can fulfill the basic requirements of distance learning. However, when questions are encountered, students are unable to accurately find the answers through online resources, and the one-to-many teacher-student sessions ultimately do not fulfill one function, which is adaptive instructions [38]. The reason why one-to-one lessons are more effective and more satisfying than small group lessons is that one-to-one lessons allow for personalized teaching strategies, but because of the high financial cost of one-to-one lessons, it is impossible to extend this approach to all groups of students. Based on this situation, the Adaptive Intelligence Educational System was designed to allow students to develop their own personalized teaching strategies if they have access to a computer, thus allowing them to benefit from a one-to-one teaching model at a relatively low cost and allowing each student to have their own virtual teacher.  
The principle of AIESs is to resequence all course knowledge modules based on student characteristics, and a variety of machine learning techniques are used in the system to learn student characteristics [39]. According to the principles of AIESs, if reinforcement learning is introduced into the system, it allows the student to interact with the system and allows the system model to continuously improve its learning, thus enhancing its performance. The type of system that introduces reinforcement learning into AIESs is called RLATES.  
RLATES comprises two models, the knowledge model and the pedagogical strategy model [11]. In the knowledge model, the content of the teaching is decided, for example, which chapters of the textbook will be covered and which format (video, audio, text, or pictures) will be used for delivery. In the pedagogical strategy model, the teaching strategy is developed, which determines how the material will be delivered.  
However, RLATES is not available for teaching directly from the beginning. At the early stage, the model needs to first be trained by feeding it with training data so that the system learns which teaching strategy to use when confronting students with different characteristics. Therefore, the whole experimental process should be separated into two phases when designing the system, the training phase and the teaching phase [10]. Only after the model has been successfully trained can it be implemented into real teaching.
## 4 Finite Element Model and Solution of the Nonlinear Discrete Equations  
### The Beam Finite Element  
We use an isoparametric interpolation with the geometry \(\mathbf{r}_{0}\) and the kinematic fields \(\mathbf{u}\) and \(\mathbf{\varphi}\) interpolated with the same quadratic Lagrangian shape functions. By introducing the approximation functions in Equation (20), we obtain  
\[\mathbf{\psi}(s)=\mathbf{N}_{\psi}(s)\mathbf{u}_{e}\]  
where vector \(\mathbf{u}_{e}=\{\mathbf{u}^{i}_{,}\mathbf{\varphi}^{i}_{,}\mathbf{u}^{m},\mathbf{\varphi}^{m}, \mathbf{u}^{j},\mathbf{\varphi}^{j}\}\) collects the FE DOFs corresponding to the initial, mid-span, and end node of the element and \(\mathbf{N}_{\psi}(s)\) is the interpolation matrix. The element DOFs \(\mathbf{d}_{e}\) also include the two Gauss points stresses, assumed as independent variables in the spirit of the MIP formulation [12] so giving the  
\[\mathbf{d}_{e}=\begin{bmatrix}\mathbf{t}_{1}\\ \mathbf{t}_{2}\\ \mathbf{u}_{e}\end{bmatrix}\]  
The discrete form of the residual force vector of the element is  
\[\mathbf{r}_{e}\equiv\frac{\partial\Phi(\mathbf{d}_{e})}{\partial\mathbf{d}_{ e}}=\begin{bmatrix}(\mathbf{\rho}_{1}-\bar{\mathbf{\rho}}_{1}-\mathbf{F}_{\mathbf{\rho}} \mathbf{t}_{1})\mathbf{w}_{1}\\ (\mathbf{\rho}_{2}-\bar{\mathbf{\rho}}_{2}-\mathbf{F}_{\mathbf{\rho}}\mathbf{t}_{2})\mathbf{w} _{2}\\ \sum_{g}\mathbf{B}_{g}^{T}\mathbf{t}_{g}\mathbf{w}_{g}\end{bmatrix}\quad\text{with} \quad\mathbf{B}_{g}=\mathbf{L}_{g}\mathbf{N}_{\psi}(s_{g}). \tag{23}\]  
Vector \(\mathbf{r}_{e}\), which represents the residual of the equilibrium equations, can be decomposed in the following form, useful for the application of the Newton method  
\[\mathbf{r}_{e}=\mathbf{s}_{e}-\mathbf{p}_{e}\quad\text{with}\quad\mathbf{s}_{ e}\equiv\begin{bmatrix}(\mathbf{\rho}_{1}-\mathbf{F}_{\mathbf{\rho}}\mathbf{t}_{1}) \mathbf{w}_{1}\\ (\mathbf{\rho}_{2}-\mathbf{F}_{\mathbf{\rho}}\mathbf{t}_{2})\mathbf{w}_{2}\\ \sum_{g}\mathbf{B}_{g}^{T}\mathbf{t}_{g}\mathbf{w}_{g}\end{bmatrix}\quad\mathbf{p} _{e}\equiv\begin{bmatrix}\bar{\mathbf{\rho}}_{1}\mathbf{w}_{1}\\ \bar{\mathbf{\rho}}_{2}\mathbf{w}_{2}\\ \mathbf{0}\end{bmatrix} \tag{24}\]  
It is important to note that in the proposed formulation vector \(\mathbf{p}_{e}\), i.e., the load coming from the measured strain, is constant.  
Similarly, the local stiffness matrix is  
\[\mathbf{K}_{e}\equiv\frac{\partial^{2}\Phi(\mathbf{d}_{e})}{\partial\mathbf{ d}_{e}^{2}}=\begin{bmatrix}-\mathbf{F}_{\rho}\mathbf{w}_{1}&\mathbf{0}&\mathbf{B}_{1} \mathbf{w}_{1}\\ \mathbf{0}&-\mathbf{F}_{\rho}\mathbf{w}_{2}&\mathbf{B}_{2}\mathbf{w}_{2}\\ \mathbf{B}_{1}^{T}\mathbf{w}_{1}&\mathbf{B}_{2}^{T}\mathbf{w}_{2}&\sum_{g}\Xi_{g}\mathbf{ w}_{g}\end{bmatrix} \tag{25}\]  
where  
\[\Xi_{g}=\mathbf{N}_{\psi}^{T}(s_{g})\mathbf{\Gamma}(\mathbf{t}_{g})\mathbf{N}_{ \psi}(s_{g}). \tag{26}\]
## 1 Introduction
This paper aims to develop a new high-precision quasi-3D shear deformation theory, called the spectral displacement formulation (SDF) [35], for the static bending analyses of FGM plates. The displacement fields in the thickness direction are expanded using the Chebyshev series. Similar to the CUF, the SDF is capable of hierarchical refinement to approach exact 3D solutions, and it can satisfy traction-free boundary conditions without additional modifications. Compared with commonly used shear deformation theories, such as CPT, FSDT, HSDTs, and most of the quasi-3D HSDTs, the SDF supplies higher accuracy, especially for the strain and stress analyses. The SDF is extended from the CPT and naturally free from shear locking. This feature guarantees that the SDF has versatility for thin and thick FGM plates and offers a flexible balance between accuracy and computational cost that can be adjusted by changing the truncation numbers. A non-uniform rational B-splines (NURBS)-based isogeometric analysis (IGA) method [36] is implemented to satisfy the in-plane C\({}^{1}\)-continuity requirement of the SDF and further enhance the present method's applicability to complex plate geometries. In view of the remarkable precision and versatility, the SDF-based IGA method has significant potential in various applications, e.g., strain-based or stress-based reliability analysis, optimization design, fatigue analysis, and fracture analysis [37, 38]. This paper is organized as follows: Section 2 details the basic assumptions of the SDF and presents the integral governing equations based on d'Alembert's principle and the principle of virtual work. Section 3 briefly introduces the concept of NURBS and derives the discrete governing equations. Section 4 presents numerical examples and discussions. The conclusions are summarized in Section 5.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 3 Proposed System Explanation
### Subsequence Dynamic Time Warping for Tunnel Environment Positioning  
Dynamic time warping (DTW) is a popular algorithm for determining the optimal alignment of two time series data. DTW enables optimal alignment regardless of the varying lengths and speeds of the two sequential data. In this study, the optimal alignment signifies the minimal difference between the two selected sequences comprising the estimated pair. For a result pair estimated through DTW, the first and last indices of each pair correspond to the first and last values of the input data, respectively. However, the data measured in certain applications may be a portion of the data already stored. The sequence data \(X\) and \(Y\) to be compared are defined as follows,  
\[\begin{split} X:=\{x_{1},x_{2},\dots,x_{L}\}\\ Y:=\{y_{u1},y_{u2},\dots,y_{uL}\}\end{split} \tag{5}\]  
where \(L\) indicates the length of \(X\), and \(uL\) denotes the length of \(Y\). As \(Y\) is only a fraction of \(X\), physically, the index of \(Y\) can be evaluated as,  
\[1\leq u1\leq uL\leq L \tag{6}\]  
Subsequence DTW is an algorithm that determines the optimal pair in a given situation. In addition, the value of the first row is continuously initialized in calculating the accumulated cost matrix using subsequence DTW. The optimal pairs for DTW and subsequence DTW are depicted with a red line in Figure 8a,b, respectively. The endpoints of \(X\) and \(Y\) are the same in Figure 8a.  
On the other hand, the first index of \(Y\) (\(u1\)) matched \(a\), and the last index of \(Y\) (\(uL\)) matched \(b\) in Figure 8b. The measured magnetic field values were stored in the user buffer to estimate the vehicle position inside the tunnel. As the data in the user buffer became a part of the DB upon comparison, the position was estimated by applying sequence DTW. However, the computational cost becomes exceedingly large upon comparing the entire DB and user data due to the storage of numerous RPs in the DB. Therefore, in this study, only a portion of the DB--defined as minor DB--was utilized for position estimation using the optimal pair result obtained earlier. The minor DB was selected by adding the RP margin index to the previous pair index. The margin index to be additionally imported  
Figure 8: Accumulated cost matrix and optimal pair for (**a**) dynamic time warping and (**b**) subsequence dynamic time warping.  
was defined as \(R\). The construction of the minor DB is illustrated in Figure 9. The red line indicates the minor DB data and its sequence can be defined as  
\[X_{m}:=\{x_{a-R},x_{a-R+1},x_{b+R}\} \tag{7}\]  
Finally, the result of sequence DTW can be expressed as  
\[(a^{*},b^{*})=\underset{(a,b)}{\text{arg min}}(sDTW(X_{m},Y)). \tag{8}\]  
The final estimated position of the vehicle is the position of the RP corresponding to the \(b^{*}\) index.
## 4 Hybrid Workflow
### Cad-Mesh Connection  
One of the critical points in the workflow is the transfer of information from CAD to the mesh. This requires a routine to encode the shape variation defined in the geometry, making it usable for mesh deformation. To facilitate this, a tool based on the OpenCascade geometrical modeler [61] was developed to create a point cloud on the surface of the baseline geometry and a series of corresponding points on the CAD variant.  
Several challenges arise in this operation. A fundamental aspect is the recognition of homologous surfaces and edges. Using scriptable CAD editors offers a significant advantage in this regard. The same operations are executed sequentially by changing the input parameters, ensuring that the order of the generated surfaces and edges remains consistent. For the successful coupling of homologous entities on different CADs, it is essential that the CADs are iso-topological, so that the homologous points on the homologous surfaces and edges can be defined. We adopt an evolution of the approach proposed in [62]: the surfaces of the baseline geometry are covered with a regular grid of points in the two-dimensional U-V space, and similarly, the edges are covered with a set of one-dimensional points evenly spaced. The final position of the points on the edges is imposed by prescribing the same parametric positions; the final positions of the points inside the surfaces are obtained by transforming the U-V parametric position of each node according to a 2D RBF deformation defined by the surface parametric position of the points on the edges in the original and deformed shape. Since the number of points is the same between the two clouds, the grids can be put into one-to-one correspondence. Additionally, managing the interface zone between two surfaces is crucial. To ensure a good mesh quality and proper overlap of the deformed mesh with the corresponding geometry, points on edges and surfaces are independently defined, and a buffer is provided between them; surface interior points that are within a buffer distance to the edges are removed. The interior point spacing is constant both in the u and v directions and computed at the center of each surface, imposing individually a du and a dv that produces the desired spacing in the 3D space. This approach does not require an external meshing tool and allows to obtain good-quality, uniformly spaced clouds suitable for RBF mesh morphing in 3D.
## 2 Methods
### Bayer Pattern Conversion  
As mentioned, the Bayer pattern push-broom imaging of OVS satellites differs from the conventional linear push-broom mode because The imaging data in the push-broom direction is the imaging data of the same detector in the same band, while the Bayer pattern push-broom obtains data in multiple bands. In relative calibration, the imaging data of each detector within each band must be accurately traced to calibrate the radiometric response model of each detector within each band. Therefore, reconstructing the order of each detector within the Bayer pattern sensor and converting it into a virtual linear array sensor is necessary to separate the RGB three-band components from the Bayer pattern.  
For OVS satellites using push-broom imaging with a Bayer pattern array, a \"GBRG\" imaging detector set of the Bayer pattern is equivalent to two green band detectors, one blue band detector, and one red band detector, for a total of four imaging detectors performing push-broom imaging simultaneously. Therefore, two complete rows of the Bayer pattern are converted into one row to form a detector arrangement comparable to the linear array push-broom sensor and referred to as a \"virtual linear array push-broom sensor\" (Figure 6). The number of probes within the virtual linear sensor imaging detector \(N\) is twice the number of probes within the Bayer pattern detector \(M\), where the number of detectors in the R and B bands is half of \(M\), and the number of detectors in the \(G\) band is equal to \(M\), as shown in Equation (1).  
\[N=2\times M\text{,}N_{G}=M\text{,}N_{R}=M/2\text{,}N_{B}=M/2\text{.} \tag{1}\]  
Figure 6: Schematic of Bayer pattern conversion at a single integration.