esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results Analysis
### Experiment Analysis of the Compensation System Response  
Figure 8 shows the DC bus voltage response waveform of the compensation system. The figure shows that the bus voltage is kept at 785 V when the system is in a steady state. In other words, the compensation system is not working at this moment. The voltage at the bus starts to rise sharply when the system is subjected to the 400 kW impact load. This process indicates that the super capacitor is discharging outward through the DC/DC converter. Due to the virtual inertia of a super capacitor being much smaller than the natural gas generator, super capacitors can discharge a large amount of energy in a very short period of time. After about 4 s, the natural gas generator speed increases to the rated speed, and the compensation system gradually exits the compensation process. At this time, although the output current of the compensation system can be turned off directly by the IGBT, the voltage out of the bus still drops slowly due to the existence of inertia.  
Figure 7: The waveform of power response (**a**) Power response of generator, (**b**) Power response of compensation system, (**c**) Power response of impact load, (**d**) Response summary.  
In a word, the super capacitor compensation system can effectively improve the load characteristics of natural gas generators, especially in the face of rough operating conditions such as impact loads. Finally, the experimental results in Figures 9 and 10 show that the experiments and simulations have the same results. However, it should be added that although this paper has achieved the realization of power compensation in the proposed microgrid. However, compared with the literature [24; 25; 26], the proposed control strategy is not able to achieve inertia response and voltage support in the grid. On the contrary, the proposed control strategy has the advantage of maintaining a fast power balance after the microgrid is connected to the impact load compared with the above references. In  
Figure 11: Generator transient response during impact load loading (**a**) Angular frequency and acceleration, (**b**) Electromagnetic and mechanical power.  
Figure 12: Compensation system transient response during impact load loading (**a**) Inductor currents, (**b**) Supercapacitor Current and Voltage.  
Figure 10: The waveform of AC current (**a**) Whole time, (**b**) Loading moment.  
addition, the strategy has the same disadvantage as the literature [29; 30], i.e., large current harmonics.
# TransPGC: Point Cloud Geometry Compression Based on Transformers
###### Abstract  
Due to the often substantial size of the real-world point cloud data, efficient transmission and storage have become critical concerns. Point cloud compression plays a decisive role in addressing these challenges. Recognizing the importance of capturing global information within point cloud data for effective compression, many existing point cloud compression methods overlook this crucial aspect. To tackle this oversight, we propose an innovative end-to-end point cloud compression method designed to extract both global and local information. Our method includes a novel Transformer module to extract rich features from the point cloud. Utilization of a pooling operation that requires no learnable parameters as a token mixer for computing long-distance dependencies ensures global feature extraction while significantly reducing both computations and parameters. Furthermore, we employ convolutional layers for feature extraction. These layers not only preserve the spatial structure of the point cloud, but also offer the advantage of parameter independence from the input point cloud size, resulting in a substantial reduction in parameters. Our experimental results demonstrate the effectiveness of the proposed TransPCC network. It achieves average Bjontegaard Delta Rate (BD-Rate) gains of 85.79% and 80.24% compared to Geometry-based Point Cloud Compression (G-PCC). Additionally, in comparison to the Learned-PCGC network, our approach attains an average BD-Rate gain of 18.26% and 13.83%. Moreover, it is accompanied by a 16% reduction in encoding and decoding time, along with a 50% reduction in model size.  
point cloud geometry compression; transformers; convolution +
Footnote †: journal: _Algorithms_
# A Metamaterial Solution for Soundproofing on Board Ship
## 3 Metamaterial Solution: Materials, Methods, and Design
### Numerical Method  
The STL performances of the Metasolution were numerically evaluated and optimized by means of a plane-wave tube model. The model was implemented by adopting the Acoustics Pressure Module coupled with the Solid Mechanics Module of COMSOL Multiphysics v5.6.  
With this numerical approach, a single unit cell is modeled, saving a significant amount of computational effort. The performances of a complete periodic panel of size \(800\times 800\) mm were derived by applying the Bloch-Floquet boundary conditions [26].  
The Bloch-Floquet wave vector corresponds to the wave vector of the incident wave  
\[k_{in}=\left[k_{x;in},k_{y;in},k_{z;in}\right]=\left[k_{0}\sin\theta\cos\Phi, k_{0}\sin\theta\cos\Phi,k_{0}\cos\Phi\right] \tag{1}\]  
where \(k_{0}\) is the wave number of the fluid, \(\theta\) is the incidence angle, and \(\Phi\) is the azimuth angle of the applied incident wave.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**E [GPa]** & **v [-]** & **\(\rho\) [kg/m\({}^{3}\)]** & **\(\eta\) [-]** \\ \hline
210 & 0.3 & 7850 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steel physical properties.  
Figure 4: (**a**) Elementary cell detail extracted from periodic Metasolution and (**b**) characteristic dimensions.  
According to [6], the diffuse field transmission coefficient for a finite-dimensions panel is:  
\[\tau_{diff,fin}=\left(\frac{\int_{0}^{\theta_{lin}}\tau_{fin}(\theta)\cos\theta \sin\theta d\theta}{\int_{0}^{\theta_{lin}}\cos\theta\sin\theta d\theta}\right) \tag{2}\]  
The \(\tau_{fin}\) term is the transmission coefficient calculated as:  
\[\tau_{fin}(\theta)=\tau_{inf}(\theta)\sigma_{R,avg}\cos\theta \tag{3}\]  
where \(\tau_{inf}\) is the transmission coefficient for a panel with infinite dimensions, which can be easily determined from the numerical model with imposed periodic boundary conditions on the unit cell. Considering the relation between the STL for an infinite panel in a diffuse sound field and the transmission coefficient:  
\[STL_{inf}(\theta)=10\log_{10}\left(\frac{1}{\tau_{inf}(\theta)}\right) \tag{4}\]  
and combining Equations (2)-(4), the STL for a finite panel in a diffuse sound field can be derived:  
\[STL_{diff,fin}=-10\log_{10}\left(\tau_{diff,fin}\right)=-10\log_{10}\left( \frac{\int_{0}^{\theta_{lin}}\tau_{inf}(\theta)\sigma_{R,avg}\cos^{2}\theta \sin\theta d\theta}{\int_{0}^{\theta_{lin}}\cos\theta\sin\theta d\theta}\right) \tag{5}\]  
where \(\sigma_{R,avg}\) is the averaged geometrical efficiency, calculated as a function of the incidence angle for the different frequencies as:  
\[\sigma_{R,avg}=\left(\frac{R\epsilon\{Z_{r}\}}{Z_{0}}\right) \tag{6}\]  
and \(Z_{0}\) is the characteristic impedance of air. \(Z_{r}\) is the radiation impedance of the acoustic panel, which is evaluated according to the methodology proposed in [27].
# Enhanced Label Noise Filtering with Multiple Voting
## 6 Conclusions and Future Works  
In mislabeled data detection, the multiple-voting based filter (MVFilter) is generally superior to the conventional single-voting based filter (SVFilter). However, one important unsolved issue in the MVFilter is how to choose the optimal decision point (ODP) to maximize its noise detection performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{5}{c}{C\({}_{01}\)C\({}_{10}\) (10\% Noise Ratio)} \\ \hline  & 1:1 & 1:3 & 1:5 & 1:10 & 1:20 & 3:1 & 5:1 & 10:1 & 20:1 & Ave. \\  
In this paper, a novel approach was proposed to solve this issue. This approach implicitly computed the ODP by estimating the mislabeled data distribution in the noisy training dataset. Our approach took a noisy dataset and a cost matrix as input, then output an ODP, which aimed to minimize the expected cost of errors. Note that minimizing cost was one important contribution of this work, because most existing works were not aware of the importance of cost. They just implicitly assumed that all errors were equally costly, but in most real applications, this is far from the case.  
A set of experimental evaluations was conducted, which proved the effectiveness of our approach. With the aid of our approach, an MVFilter could effectively reduced the cost. In particular, in the difficult noise detection environment (when the noise ratio was high or cost was big), the advantages of our approach were more obvious. Furthermore, the proposed methodology could also be extended to a multi-class problem. One possible strategy is the naive way to divide the multi-class problem into several two-class problems, and then, the proposed approach can solve each two-class problem.  
Although the clean dataset (i.e., validation cases) and cost matrix are available in most cases, the prior information of noise ratio is not easily available; therefore, the current solution needs to be improved to alleviate the prior information requirement. Therefore, in future work, we will focus on developing more elegant approaches to further improve the current proposed approach.
# Radio Channel Scattering in a 28 GHz Small Cell at a Bus Stop: Characterization and Modelling
## 2 Experimental Set-Up
### Data Processing  
Off-line processing was carried out to first demodulate the IF signal to baseband and then to perform the cross-correlation with an exact replica of the transmitted pseudo-random binary sequence. The result was the time-varying complex impulse response of the radio channel h(t,\(\tau\)) with a delay resolution of \(\Delta\tau\) = 1.11 ns (1/900 MHz), which let us resolve a propagation path with a length difference of 0.33 m. The impulse response was obtained every \(\Delta\)t = (\(2^{12}-1\))/900 MHz = 4.55 \(\upmu\)s, which corresponded to a sampling frequency of around 220 KHz. Increasing the sequence length would result in an improved dynamic range, but would reduce the sampling rate in the time domain. This fast sampling rate in the time domain allowed analysis of the temporal variations of some channel characteristic functions, such as the DS.
## 1 Introduction  
As an organically interwoven system of people, government organizations and businesses, modern cities have become the engine that drives economic growth and technological revolution for each nation [1]. Furthermore, the global population is expected to reach nearly 10 billion by 2050 [2], with more than 68% of that population estimated to inhabit cities [3]. Such an inexorable urbanization will lead to an ever-growing functional demand on a whole host of critical infrastructure systems (CISs) [4]. To catch up with these soaring functional demands, more CISs are expected to be constructed, which will further accelerate climate change, insomuch as 70% of the global greenhouse-gas (GHG) emissions emanate from the construction and operation of CISs [5]. Catalysed by climate change, natural hazards would thus become even more frequent and destructive in the coming decades, which will, in turn, pose a substantially graver challenge to the safety of CISs [6, 7], and ultimately, the resilience and sustainability of future cities [8].  
Meanwhile, owing to the latest breakthrough in the Internet of Things (IoT) and machine learning [9, 10], digital twin (DT) technology has emerged as a new milestone of the digital revolution [11]. Conceptually, DT is a model that resembles a real-world physical system, ideally, in a real-time pattern. Therefore, DTs could not only work collaboratively, but also inform and enhance the original physical systems [11]. It is noteworthy that, in the domain of civil engineering, digital tools have long been employed to visualize and design building structures and CISs [12]. More recently, these software tools have been further integrated into whole building information models [13, 14] and/or city information models [15], thereby enabling the life-cycle management of those physical assets [16]. Justifiably, such progresses have already laid the groundwork for establishing the DTs of modern CISs as an ideal testbed (since the configuration and size of buildings and CISs will not be fast-evolving; furthermore, their operational conditions are often substantially easier to measure and quantify compared to most of the socio-economic systems). Against this backdrop, pilot research has been undertaken recently to chart a viable pathway towards DT-driven sustainable cities.
## 2 Modeling and Dynamics of the Hydraulic Manipulator  
Several applications in the machine tool/handling industry have utilized hydraulic systems in past. These applications include the handling of hazardous material, heavy industry, oil and gas exploration, etc. Servo systems can be designed to estimate different parameters, such as acceleration, force, temperature profile, and voltage/current, among others. The advantageous features of hydraulic actuators include robust response, broad range and limited stroke, etc., in addition to zero backlash, less wear and tear, and accurate control. Generic schematic diagrams of a 3-DOF hydraulic manipulator utilized for dismantling nuclear power plants are shown in Figures 1 and 2. Hydraulic systems used for dismantling nuclear power plants include a couple of hydraulic cylinders for the two links and an AC servo-motor for the base. The dismantling process requires a greater force to produce vertical moment as compared to the force required to produce horizontal moment. The hydraulic manipulator is modeled using the signal compression method [39].  
The modeling equations of a manipulator depend upon localization, different forces, and acceleration properties, which can be mathematically written as,  
\[T=A(\theta)\ddot{\theta}+B\big{(}\theta,\dot{\theta}\big{)}+g(\theta) \tag{1}\]  
Figure 1: Schematic diagram of the hydraulic manipulator.  
Figure 2: Schematic diagram of the base, 2\({}^{\rm nd}\) link and end effector.  
where \(\theta=\) angle of joints, \(A(\theta)=\) mass/inertia matrix, \(B\left(\theta,\dot{\theta}\right)=\) centrifugal/coriolis torque, \(g(\theta)=\) joint space gravity torque and \(T=\) vector of joint torques. Equations for different dynamical properties of links can be written as:  
\[\left(J_{s1}+\Delta J_{s1}\right)\ddot{\theta}_{1}+(D_{s1}+\Delta D_{s1}+\beta _{1})\dot{\theta}_{1}+0.5M_{s1}L_{1}g\sin\theta_{1}+\tau_{e1}=T_{1} \tag{2}\]  
\[(J_{s2}+\Delta J_{s2})\ddot{\theta}_{2}+(D_{s2}+\Delta D_{s2})\dot{\theta}_{2}+ M_{s2}L_{2 cm}g\cos\theta_{2}+\beta_{2}\dot{x}+\tau_{e2}+\lambda=T_{2} \tag{3}\]  
\[(J_{s3}+\Delta J_{s3})\ddot{\theta}_{3}+(D_{s3}+\Delta D_{s3})\dot{\theta}_{3}+ 0.5M_{s3}L_{3}g\sin\theta_{3}+\tau_{e3}=T_{3} \tag{4}\]  
where \(J_{s1}\)\(J_{s2}\)\(J_{s3}\), \(D_{s1}\)\(D_{s2}\)\(D_{s3}\), \(M_{s1}\)\(M_{s2}\)\(M_{s3}\), and \(T_{1}\)\(T_{2}\)\(T_{3}\) denote the inertia, damping, mass and torques of the base, 2 nd link, and end effector, respectively. \(\Delta\) denotes the uncertainty term, and \(\beta_{1}\) and \(\beta_{2}\) represent the viscosity of the cylinders. \(L_{1}\) and \(L_{3}\) are the lengths of the base and the end effector, \(L_{2}\) represents the length from the joint to the center of mass (COM) of the 2 nd link. \(\lambda\) in Equation (3) denotes a dynamic torque effect at joint 2 by hydraulic cylinder motion attached to link 2 to rotate the end effector of the manipulator, \(\dot{\theta}_{1}\) and \(\dot{x}\) are the velocities of the 1 st and 2 nd cylinders, respectively. \(\tau_{e1}\), \(\tau_{e2}\) and \(\tau_{e3}\) denote the reaction torque generated by contact with the environment and joints 1, 2 and 3, respectively.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 3 Materials and Methods
### Jigsaw Transform and Cellular Automata
#### 3.2.3. ECA Applied on Jigsaw Transform  
For our work, we used the Jigsaw transform in combination with the elementary cellular automaton described in Section 3.2.2. Given that the JST has a limited key space, the ECA previously described was used to help achieve a broader key space. We used the JST with \(\mathcal{M}=5\times 5\) subsections, storing the index of each subsection in a \(5\times 5\) matrix to be able to reverse the algorithm. We can encrypt this matrix using our ECA with an arbitrary number of iterations and considering values in the range from 1 to 25. We chose a value of \(k=17\) for our work. The key space of the ECA applied on the Jigsaw transform would be \(K=(\mathcal{M}!)(Y)(K^{\times Y})\) in general; substituting for the variables we chose for our work, we obtained a key space of \(K\leq(25!)(5)(25^{5\times 5})=1.2689\times 10^{89}\). We used this algorithm to encrypt our watermark image, as described later in Section 4; given that we used \(5\times 5\) subsections, we can achieve full image encryption of the watermark image by using the JST along with 5 iterations of the ECA to modify all the rows of the JST index matrix. Since the main theme of this work was watermarking, a thorough analysis of the JST with the ECA for image encryption is beyond the scope of this article but can be studied in future work.
# FPGA Applied to Latency Reduction for the Tactile Internet
## 4 PHANToM Omni Device Model (MD & SD)
### Inverse Kinematics  
In inverse kinematics (IK), the relationship between the joint angles and the Cartesian system is reversed, that is, given the spatial position of the tool it may be possible to determine the joint coordinates. The solution to this process is not as straightforward as in the direct kinematics. In direct kinematics, the position of the tool is determined solely by the displacements of the joints. In inverse kinematics, equations are composed of nonlinear calculations formed by trigonometric functions. Depending on the manipulator structure, multiple solutions may be possible for the same tool position, or there may be no solution  
Figure 3: PHANToM Omni structure—MD and SD.  
Figure 2: PHANToM Omni—MD and SD.  
for a particular set of tool positions. Based on the works [40; 41; 42], the value of \(\theta_{1}\) can be defined through the equation expressed by  
\[\theta_{1}=-\text{atan}2(x,z+L_{4}) \tag{18}\]  
where \(x\) and \(z\) represent coordinates in the Cartesian plane and \(L_{4}\) corresponds to the size of the the arm segments, as shown in Figure 3.  
To calculate the other two joints \(\theta_{2}\) and \(\theta_{3}\) it is necessary to perform intermediate calculations. Thus, one can obtain \(R\), \(r\), \(\beta\), \(\gamma\) and \(\alpha\) through the equations  
\[R=\sqrt{x^{2}+(z+L_{4})^{2}}, \tag{19}\]  
\[r=\sqrt{x^{2}+z+L_{4})^{2}+(y-L_{3})^{2}}, \tag{20}\]  
\[\gamma=\text{acos}\left(\frac{L_{1}^{2}-L_{2}^{2}+r^{2}}{2L_{1}r}\right), \tag{21}\]  
\[\beta(n)=\text{atan}2(y-L_{3},R), \tag{22}\]  
and  
\[\alpha=\text{acos}\left(\frac{L_{1}^{2}+L_{2}^{2}-r^{2}}{2L_{1}L_{2}}\right). \tag{23}\]  
After performing the intermediate calculations it is possible to calculate \(\theta_{2}\) through the equation  
\[\theta_{2}=\gamma+\beta. \tag{24}\]  
Finally, the value corresponding to the \(\theta_{3}\) joint can be obtained through the equation  
\[\theta_{3}=\theta_{2}+\alpha-\frac{\pi}{2}. \tag{25}\]
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
## 3 Results and Discussion
### Establishment of Prediction Models and Comparison of Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**XGBoost**} & \multicolumn{4}{c}{**CatBoost**} \\ \hline \multirow{3}{*}{\begin{tabular}{c} **Physicochemical** \\ **Indicators** \\ \end{tabular} } & **Feature** & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{3-10}  & **Filtering Methods** & **R2c** & **RMSEc** & **R2p** & **RMSEp** & **R2c** & **RMSEc** & **R2p** & **RMSEp** \\ \hline \multirow{8}{*}{\begin{tabular}{c} _SSC_ \\ \end{tabular} } & FV & 0.9986 & 0.2175 & 0.8992 & 0.2175 & 0.9918 & 0.3605 & 0.9116 & 0.3605 \\  & RFECV & 0.9940 & 0.3529 & 0.8950 & 0.3529 & 0.9729 & 0.6611 & 0.8682 & 0.6611 \\  & PI-RF & 0.9975 & 0.2595 & 0.9240 & 0.2595 & 0.9822 & 0.5269 & 0.9152 & 0.5269 \\  & PI-LR & 0.9958 & 0.3120 & 0.8958 & 0.3120 & 0.9702 & 0.6879 & 0.8743 & 0.6879 \\  & GA & 0.9980 & 0.2411 & 0.9040 & 0.2411 & 0.9858 & 0.4746 & 0.9031 & 0.4746 \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} _Acidity_ \\ \end{tabular} } & FV & 0.9812 & 0.2410 & 0.9171 & 0.2410 & 0.9973 & 0.0875 & 0.9397 & 0.0875 \\  & RFECV & 0.9649 & 0.3256 & 0.8960 & 0.3256 & 0.9845 & 0.2137 & 0.8786 & 0.2137 \\  & PI-RF & 0.9757 & 0.2712 & 0.9233 & 0.2712 & 0.9949 & 0.1211 & 0.9382 & 0.1211 \\  & PI-LR & 0.9687 & 0.3057 & 0.9069 & 0.3057 & 0.9895 & 0.1748 & 0.9226 & 0.1748 \\  & GA & 0.9760 & 0.2680 & 0.9257 & 0.2680 & 0.9947 & 0.1237 & 0.9471 & 0.1237 \\ \hline \multirow{8}{*}{
\begin{tabular}{c} _pp_ \\ \end{tabular} } & FV & 0.9998 & 0.0012 & 0.9016 & 0.0012 & 0.9577 & 0.0117 & 0.8740 & 0.0195 \\  & RFECV & 0.9986 & 0.0024 & 0.9016 & 0.0012 & 0.9188 & 0.0163 & 0.8740 & 0.0195 \\ \cline{1-1}  & PI-RF & 0.9997 & 0.0014 & 0.9092 & 0.0014 & 0.9483 & 0.0129 & 0.8708 & 0.0199 \\ \cline{1-1}  & PI-LR & 0.9982 & 0.0027 & 0.8859 & 0.0027 & 0.9033 & 0.0176 & 0.8056 & 0.0244 \\ \cline{1-1}  & GA & 0.9996 & 0.0015 & 0.9030 & 0.0015 & 0.9464 & 0.0132 & 0.8748 & 0.0194 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: XGBoost and CatBoost modeling results. The results are presented as the mean of each fold. The differences in R\({}^{2}\) values between the calibration and prediction sets for the three predictive models are 0.0735, 0.0476 and 0.0905, respectively. Overall, the robustness of the three models is good, and there is no issue of overfitting leading to inadequate generalization ability. However, in general, the prediction performance for _SSC_ and _Acidity_ is better than that for _PP_. This difference may be related to the reason for the dielectric behavior, which is mainly caused by free water propagation and ion migration within the passion fruit [12].