esa-sceva/satcom-chunk-collection huggingface dataset:
# On Evaluating IoT Data Trust via Machine Learning
## 6 Discussion
### ML Models  
The considered supervised ML-based approaches offer the best performance in classifying data trust using both RWI and Drift methods for synthesizing untrustworthy data and both correlation and DST feature sets. The best-performing algorithm is MLP with an accuracy of 91% when trained on RWI synthetic data and evaluated on datasets containing either RWI or Drift synthetic data, as seen in Figures 5 and 6.  
The considered unsupervised ML-based approaches perform relatively poorly in comparison with the supervised ML-based approaches. The considered semi-supervised ML-based approach utilizing the label propagation algorithm performs similarly to the supervised ML-based approaches. This is encouraging as semi-supervised learning is more practical in IoT applications given that real-world IoT data are usually partially or sparsely labeled.  
Figure 8: UMAP visualization of the trustworthy and untrustworthy decision regions in the correlation feature space determined by the MLP algorithm trained on a dataset containing untrustworthy data synthesized via the Drift method.  
Figure 9: UMAP visualization of the unique trustworthy and untrustworthy decision regions in the correlation feature space determined by the MLP algorithm trained on datasets augmented by untrustworthy data generated via the RWI or Drift method.
## 3 Optical Properties of Laccase
### SERS (Surface-Enhanced Raman Scattering) Spectroscopy  
Raman spectroscopy has many advantages, such as rapid responses and the need for a very small sample volume, but the low intensity of Raman scattering is clearly a limit for many practical applications, and, therefore, finding means of enhancing the Raman process is often beneficial. In the 1970s, it was discovered that when molecules are adsorbed onto or near to corrugated metal surfaces, such as silver or gold nanoparticles [88], the intensity of the Raman signal coming from the molecules is greatly enhanced (by factors up to \(10^{8}\) or even larger). It is now well recognized that this enhancement is mainly  
Figure 8: Resonant Raman spectra of lacase from _Rhus vernicifera_ in H\({}_{2}\)O at room temperature (300 K upper spectrum) and in the frozen state (200 K lower spectrum) (reprinted from Ref. [83] under PMC open access conditions).  
Figure 9: Resonant Raman spectrum of _Pleurotus ostreatus_ lacase obtained with an excitation of 633 nm (reprinted with permission from Ref. [65]).  
due to surface plasmons generated in the metal resulting in an evanescent field that interacts with and amplifies the Raman modes (electro-magnetic enhancement) [89; 90; 91]. Since its discovery, SERS has been employed in a plethora of cases, also enabling single-molecule (SM) SERS, and its use is continuously boosted by the ability to realize very specific corrugated structures. SERS has also become very popular in laccase-related studies [88; 89; 90; 91; 92].  
In a study conducted by [PERSON] et al. [93], vibrational spectroscopies, particularly SERS, were studied to evaluate the immobilization of laccase on gold and silver surfaces coated with self-assembled monolayers of thiols. This immobilization was verified by Raman spectra of the colored oxidation product of syringaldazine (4-hydroxy-3,5-dimethoxyybenzaldehydeazine). In Figure 10, some of the results obtained by [PERSON] et al. [93] are reported. In particular, on the left, there is the absorption spectrum of the oxidized form of syringaldazine, with a maximum signal at about 530 nm. On the right, the SERS spectra are presented when laccase is absorbed on the Ag surface (a) and after the addition of syringaldazine on the Ag/laccase surface (b). The Raman and SERS spectra reported in Ref. [93] were obtained with 514.5 nm laser excitation. The results reported on the right of Figure 10 confirm the advantages offered by SERS. In this figure, SERS spectra confirmed the immobilization of laccase on silver surfaces coated with self-assembled monolayers of thiols differently from Raman spectra, which did not give evidence of the laccase immobilization.  
Further examples of the use of Raman and SERS spectroscopy for the study of immobilized lactose are cited in the Supplementary Materials.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 4 Numerical Examples
### A cantilever Beam Initially Curved to \(\pi\)/4 Subjected to a Concentrated Tip Load  
As shown in Figure 8, a cantilever beam initially curved to an arch of radius 100 m with 45-degree bending was subjected to a concentrated end load. This example was originally proposed by [PERSON] [1] and has also been adopted by a number of other researchers [2; 3; 12; 13; 21]. The beam has a unit square cross section and lies in the X-Y plane with initial tip position 70.71 m, 29.29 m, 0 m of the fixed X-Y-Z global coordinates. The material was linear and elastic, with the following properties: Elastic modulus, E = 10\({}^{7}\) N/m\({}^{2}\), and Shear modulus, G = 5 \(\times\) 10\({}^{6}\) N/m\({}^{2}\). In the above reference, the beam was modeled by straight and curved beam elements with the nodes on the centroidal axis of the beam. In the present study, DRK was fulfilled by nodal points to simulate a beam undergoing large deformation.  
Figure 6: Convergence comparison with different number of the nodes and order of basis.  
Figure 7: Load versus displacement curves for the cantilever curved beam under an end moment.  
Figure 9, solved by 31 nodal points with 10 loading increments, shows the evolution of tip displacement as a function of the applied load modulus. The number of iterations is fewer than five within each increment. Table 1 shows the free end position of the initial curved cantilever beam under out-of-plane force. A comparison of the free end position from these references is also given in Table 1. The results show that the present theory can provide accurate solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Nelm** & **Nnode** & **X** & **Y** & **Z** \\ \hline Present & & 31 & 47.89 & 15.96 & 53.10 \\ [PERSON] and [PERSON] [1] & - & 47.20 & 15.90 & 53.4 \\ [PERSON] and [PERSON] [2] & 8 (linear, straight) & 47.23 & 15.79 & 53.37 \\ [PERSON] and [PERSON] [3] & 8 (linear, straight) & 47.04 & 15.96 & 53.50 \\ [PERSON] et al. [13] & 8 (linear, curved) & 46.89 & 15.61 & 53.60 \\ [PERSON] et al. [21] & 4 (CR-Incremental) & - & - & 53.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Free-end position of the cantilever 45-degree under out-of-plane force.  
Figure 8: Cantilever beam 45-degree bend with an upward concentrated load.  
Figure 9: Load versus displacement curves of tip for the cantilever curved beam under an end force.
## 2 Analytical Solution  
### Analytical Model  
An analytical solution is easier and faster in completing an analysis than a numerical simulation analysis method. Due to the substantial similarity between the welding process and the SLM forming process, many researchers have adopted [PERSON]'s equation [PERSON] (1986) in analytical analysis. This equation assumes that the laser beam is a point light source, only considers heat conduction, ignores heat loss and does not consider the powder layer.  
\[T=T_{0}+\frac{AP}{2\pi kr}\exp\left[-\frac{u(\xi+r)}{2\alpha}\right] \tag{1}\]  
where \(T_{0}\) is the temperature at locations far from melt pool, \(A\) is the absorptivity of material, \(P\) is the laser power, \(k\) is thermal conductivity, \(u\) is the laser speed, \(\alpha\) is thermal diffusivity. The size and shape of the melt pool is defined by \(\xi\) and \(r\).  
[PERSON] (1986) presented the estimation equation of the melt pool width for materials with low thermal diffusivity (such as In718) by deriving from [PERSON]'s equation.  
\[W\approx\sqrt{\frac{8}{\pi e}\cdot\frac{AP}{\rho C_{p}u(T_{m}-T_{0})}} \tag{2}\]  
where \(\rho\) is density, \(C_{p}\) is specific heat, \(W\) is the width of melt pool, \(T_{m}\) is the melting temperature.  
However, Gaussian surface heat source is often used as a high-energy laser beam for calculation and analysis in SLM calculation and simulation. [PERSON] (1986) derive a new calculation equation by introducing the Gaussian distribution, and [PERSON] (1986) simplified it by normalizing.  
\[q(r)=\frac{2 AP}{\pi a^{2}}\exp\left(-2\frac{r^{2}}{a^{2}}\right) \tag{3}\]\[T=\frac{AP}{\pi k}\sqrt{\frac{D}{\pi}}\int\limits_{0}^{\infty}\frac{exp\left[-\frac{ z^{2}}{4 Ds}-\frac{y^{2}+(x-s)^{2}}{(4 Ds+a^{2})}\right]}{(4 Ds+a^{2})\sqrt{s}}ds\]  
where \(r\) is the distance from any point in the laser spot coverage area to the spot center (m) and \(a\) is the laser spot radius (m).  
\[g=\frac{T}{T_{s}};T_{s}=\frac{AP}{\pi\rho C_{p}\sqrt{\alpha uu^{3}}};p=\frac{ \alpha}{ua};x^{\prime}=\frac{x}{a};y^{\prime}=\frac{y}{a};z^{\prime}=\frac{z}{ \left(\frac{aa}{u}\right)^{0.5}};t=\frac{s}{(a/u)}\]  
\[g=\int\limits_{0}^{\infty}\frac{exp\left[-\frac{z^{\prime 2}}{4t}-\frac{y^{ \prime 2}+(x^{\prime}-t)^{2}}{(4 pt+1)}\right]}{(4 pt+1)\sqrt{t}}dt \tag{4}\]  
According to the above equation [15], the thermal history can be calculated. Moreover, the melt pool configuration and size can be calculated by replace \(T\) with \(T_{m}\).
# A Star-Identification Algorithm Based on Global Multi-Triangle Voting
## 3 Simulations and Results  
Simulation experiments including different levels of position noise, magnitude noise, and different numbers of false stars have been completed to test the proposed star-identification algorithm. In this paper, the following two judgment conditions need to be met simultaneously for a successfully identified star image: (1) neither true stars are identified incorrectly, nor false stars are identified as a guide star. (2) more than two true stars are correctly matched in the identification result. Moreover, two classic star-identification algorithms belonging to different types were selected as comparison algorithms, namely, the geometric voting algorithm and the grid algorithm, because they have been proven to be excellent in several space missions, and we validated the proposed algorithm by comparing the identification rate, run time, and database size with these methods. In simulations, the key parameters of the star sensor are given in Table 1, and 9040 stars brighter than 6.5 Mv in the SAO J2000 star catalog were chosen to generate simulated star images. In addition, all the algorithms in this section were written in MATLAB R2021 and implemented on a personal computer with a Core 3.4 GHz.
## 4 Numerical Simulation of Slope Stability under Different Vegetation
### Vegetated Slope Stability Analysis
#### 4.3.3 Plastic Strain  
The equivalent plastic strain (EPS) and maximum equivalent plastic strain under different conditions were calculated by the SRM, as shown in Table 6 and Figure 7. It can be seen that the maximum plastic strain causing slope instability occurred at the foot of the slope and was mainly concentrated in that area, indicating a shallow failure. Compared with the bare slopes, the root-soil composites of C.d, M.m and the G.s mixture in the different growth periods reduced the maximum EPS by 11.5 to 38.5%, 16.3% to 53.8% and 21.2% to 63.5%, respectively. The reduction in EPS increased with the growth of the roots, demonstrating that the root-soil composites of C.d, M.m and G.s had a significant effect on reducing the plastic strain of the slope.  
When comparing the slip surfaces of the bare, C.d, M.m, and G.s mixture slopes, it was found that the plastic zone gradually moved to the depth of the slope as the growth period increased. The concentrated area of plastic deformation gradually moved away from the foot of the slope. These results show that the root system of the G.s mixture can strengthen the control of soil deformation in shallow slopes more effectively, make the soil deformation area move towards the deep layers, and increase the shallow slope stability.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Indices** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** & **C13** \\ \hline Maximum equivalent plastic strain & 1.04 & 0.92 & 0.87 & 0.70 & 0.64 & 0.87 & 0.79 & 0.61 & 0.48 & 0.82 & 0.79 & 0.58 & 0.38 \\ Reduction by proportion/\% & - & 11.5 & 16.3 & 32.7 & 38.5 & 16.3 & 24.0 & 41.3 & 53.8 & 21.2 & 24.0 & 44.2 & 63.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum equivalent plastic strain at slope failure.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Conditions** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** & **C13** \\ \hline Maximum horizontal displacement/\(10^{-3}\) & 4.00 & 3.83 & 3.69 & 3.40 & 3.28 & 3.39 & 3.05 & 2.70 & 2.51 & 2.61 & 2.53 & 2.34 & 2.11 \\ Maximum horizontal displacement reduction/\% & & 4.3 & 7.8 & 15.0 & 18.0 & 15.3 & 23.8 & 32.5 & 37.3 & 34.8 & 36.8 & 41.5 & 47.3 \\ Maximum vertical displacement/\(10^{-2}\) & 5.57 & 5.39 & 5.28 & 5.05 & 4.87 & 5.29 & 5.14 & 4.82 & 4.77 & 5.09 & 4.88 & 4.71 & 4.66 \\ Maximum vertical displacement reduction/\% & & 3.2 & 5.2 & 9.3 & 12.6 & 5.0 & 7.7 & 13.5 & 14.3 & 8.6 & 12.4 & 15.4 & 16.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Maximum horizontal and vertical displacements under different conditions.  
Figure 6: Nephograms of horizontal displacement under different conditions (unit: m).
# Augmented Reality-Based Interface for Bimanual Robot Teleoperation
## 1 Introduction  
### Motivation  
There is currently a proliferation of applications based on robotic systems that require interaction or cooperation with users [1, 2, 3]. This is due, on the one hand, to the fact that the complete automation of such applications is not yet well resolved due to their complexity, need for adaptability to changes, decision making, etc., and, on the other hand, to the benefits of human and robot cooperation for certain applications [4].  
While many approaches can be found in the literature proposing solutions for complex applications, where human and robot have to cooperate, many of these solutions do not take into account that human-robot interaction has to be natural and intuitive for the human [5, 6, 7, 8]. Otherwise, the benefits that such cooperation may bring a priori will be negatively affected, and the cooperative solution adopted may be rejected.  
Based on the complexity presented by the interaction of users with the so-called bimanual robotic systems, this work develops a novel methodology for the design of interfaces based on augmented reality so that this interaction is natural and intuitive for the user.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline
### The Three-Dimensional Cross-Mapping Method Based on Equal Division of Sub-Matrix with Dual-Channel
#### 3.1.3 Address Mapping Strategy
\begin{cases}a^{\prime}=floor(a/2)\times 2=floor(mod(x,N_{a}/2)/2)\times 2\\ b^{\prime}=floor(b/4)\times 4=floor(mod(y,N_{r})/4)\times 4\end{cases}  \tag{11}\]  
By substituting the above formula into the following formula, the actual physical storage address of DDR3 SDRAM corresponding to point \(p^{\prime}(a^{\prime},b^{\prime})\) can be obtained. \[ \begin{cases}i=floor((m\times N+n)/B_{n})\\ j=floor(a^{\prime}/2)\times 2N_{r}+2b^{\prime}+mod(a^{\prime},2)=a^{\prime} \times N_{r}+2b^{\prime}\\ k=mod(m+n,B_{n})\end{cases}  \tag{12}\]  
According to the above discussion, the current data position coordinates are obtained by adding a certain offset to the coordinates of the previous data. For the range direction, in the continuous access process starting from the data point \(A(x,y)\), it is necessary to sequentially obtain the physical address of \(A(x,y)\), \(A(x,y+4)\), \(A(x,y+8)\), etc. To make the address calculation easier, we use the DDR3 physical address \(D\) corresponding to \(A\) to calculate the DDR3 physical address \(D1\) corresponding to \(A1\). Therefore, during continuous access in the range direction, only the coordinates of the initial data point in the two-dimensional matrix are calculated to the actual physical address of DDR3, and the address of the data point for subsequent access is quickly calculated based on the physical address of the previous data point. The continuous access method in the azimuth direction is similar to that in the range direction.
## 2 Related Work
### Explainability  
Explainability, as a concept, involves interpreting the outcome of a Machine Learning model via the detection of the importance of the input features to the output. Recently, as AI and ML tasks have been integrated into numerous industries and also into daily activities, Explainable Artificial Intelligence (XAI) has drawn major attention globally. However, ML models are treated by several approaches as \"black boxes\", focusing exclusively on the input and output of these models for interpretation without considering the inner structure. One such method is _LIME_ (Local Interpretable Model-Agnostic Explanation), which is able to generate explanations for any type of classifier by the method of forming local perturbations across every prediction. _LIME_ creates a large number of data points by sampling locally around an instance. These data points are utilized to identify the changes concerning the classifier's output. The produced explanations highlight the importance of the features in an understandable and interpretable fashion. On the other hand, _SHAP_ (SHapley Additive exPlanation) uses game theory concepts to measure each feature's contribution to the prediction. Specifically, it focuses on assigning a value to each feature, which portrays the change in the expected model prediction when conditioning that feature.  
Depending on whether an XAI model generates explanations for each instance of the dataset individually or general patterns with high-level insights are produced, these methods can be distinguished into two main categories, namely, instance-level and model-level methods. Instance-level methods generate for each example individual explanations that are usually more understandable, as real input instances are provided. However, these approaches require more human oversight, due to the individuality of the explanation generated. On the other hand, model-level methods do not rely on specific inputs to provide interpretable explanations; instead, they utilize the total of the training instances to optimize a target prediction.
## 4 Computational Study
### System of Equations Describing the Air Flow Around the Windwheel Model  
The static pressure contours and velocity vector distribution fields were calculated using CFD numerical analysis (k-epsilon methods).  
In this study, the high-order spectral difference method with dynamic unstructured grids was used for numerical simulation. The equations describing the fluid motion are presented as non-stationary Navier-Stokes equations in conservative form, which can be expressed as follows:  
\[\frac{\partial Q}{\partial t}+\frac{\partial F}{\partial x}+\frac{\partial G}{ \partial y}+\frac{\partial H}{\partial z}=0 \tag{1}\]  
where \(Q=\left(\rho,u,v,w,E\right)^{T}\) are conservative variables; \(\rho\) is the density of the liquid; \(u,v,\omega\) are components of velocity in the Cartesian coordinate system; \(E\) is the total initial energy; and \(F\), \(G\), and \(H\) are total flows, including both inviscid and viscous vector flows, i.e., \(F=F^{i}+F^{v}\), \(G=G^{i}+G^{v}\), and \(H=H^{i}+H^{v}\).  
Figure 12: Computational grid for the mathematical model under study.  
To describe the wind flow around the sample, the following system of equations was used:  
\[\frac{\partial u_{j}}{\partial x_{j}}=0 \tag{2}\]  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho u_{i}u_{j}}{\partial x _{j}}+\frac{\partial\rho}{\partial x}=\frac{\partial\tau_{ij}}{\partial x_{j}} \tag{3}\]  
where \(\tau_{ij}=(\mu+\mu_{t})\bigg{[}\frac{\partial u_{ij}}{\partial x_{i}}+\frac{ \partial u_{i}}{\partial x_{j}}\bigg{]}\) is the stress tensor.  
The turbulence model used was the Realizable k-epsilon model. The turbulence energy transfer is described by the following Equation (4):  
\[\frac{\partial\rho k}{\partial t}+\frac{\partial\rho ku_{j}}{\partial x_{j}}= \frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}} \bigg{)}\frac{\partial k}{\partial x_{j}}\bigg{]}+G_{k}-\rho\varepsilon \tag{4}\]  
The transfer of turbulent energy for the dissipation rate is described by the following Equation (5):  
\[\frac{\partial\rho\varepsilon}{\partial t}+\frac{\partial\rho\varepsilon u_{ j}}{\partial x_{j}}=\frac{\partial}{\partial x_{j}}\bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{\partial x_ {j}}\bigg{]}+\rho\varepsilon\bigg{(}C_{1}S-C_{2}\frac{\varepsilon}{k+\sqrt{ \varepsilon}\varepsilon}\bigg{)} \tag{5}\]  
Turbulent Kinetic Energy Production is 2 \(G_{k}=\mu_{t}S^{2}\), where \(S=\sqrt{2S_{ij}S_{ij}}\) is the modulus of the strain rate tensor (6):  
\[C_{1}=\max\bigg{(}0.43\frac{\eta}{\eta+5}\bigg{)},\ \eta=S\frac{k}{\varepsilon},\ C_{2}=1.9 \tag{6}\]  
The coefficient of turbulent viscosity is \(\mu_{t}=\rho C_{\mu}\frac{  
Boundary conditions on the walls of a wind turbine:  
\[u_{i}=U(t,x,y,z) \tag{11}\]  
where \(U(t,x,y,z)\) is the speed of movement of the wall in accordance with the speed of rotation of the working cylinder around its own longitudinal axis.
## 5 Discussion
### Quantitative Relationship between Parameters and FMU  
In the assessment of motor function in clinical settings, including the adduction of the shoulder joint and the extension of the elbow joint, etc., the quantitative parameters should also be taken into consideration in the robotic assessment. By performing multivariable linear regression between the quantitative parameters, including the movement range and the movement smoothness in the circular trajectory training, and FMU, we obtained the fitting function of the quantitative assessment method. The fitting function exhibited a good fit with \(R^{2}=0.91\), showing that FMU were positively correlated with movement range and movement smoothness. It was proven that the fitting function could be used as a quantitative assessment for upper limb motor function in clinical rehabilitation training.  
Due to differences in brain lesion profiles, patients show different defects in movement behavior. For example, high muscle tension will lead to abnormal contracture of biceps and affect the active range of movement. Muscle atrophy caused by impaired motor function will result in a decrease in muscle strength and the ability to control movement. Patients with varied weak movement items should be assigned to the corresponding strengthened training items. Large joint training should be emphasized for patients with limited active range of movement. Patients with large trajectory deviation should strengthen fine movement training, while patients with bad movement smoothness should strengthen muscle coordination training. In clinical application, through establishing the correlation between the characteristic parameters calculated in the robot-assisted training and the clinical FMU, the corresponding individualized rehabilitation training strategy can be developed so as to improve the patients' motor function to the greatest extent. In addition, for patients who undergo short hospitalization, it is difficult to detect the minor changes of motor function using clinical scales such as FMA. Therefore, it is necessary to further explore the quantitative assessment method, which can be used to reflect subtle changes of motor function.  
One of the limitations of this research was that the sample size was rather small. Although the specific statistical analysis method was used to test whether the statistical significance of the quantitative parameters in the clinical experiment have existed, more patients will be recruited to verify the validation of the quantitative assessment method in the future.