esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to defend the hypersonic glide vehicle (HGV), a cost-effective single-model tracking algorithm using Cubature Kalman filter (CKF) is proposed in this paper based on modified aerodynamic model (MAM) as process equation and radar measurement model as measurement equation. In the existing aerodynamic model, the two control variables attack angle and bank angle cannot be measured by the existing radar equipment and their control laws cannot be known by defenders. To establish the process equation, the MAM for HGV tracking is proposed by using additive white noise to model the rates of change of the two control variables. For the ease of comparison several multiple model algorithms based on CKF are presented, including interacting multiple model (IMM) algorithm, adaptive grid interacting multiple model (AGIMM) algorithm and hybrid grid multiple model (HGMM) algorithm. The performances of these algorithms are compared and analyzed according to the simulation results. The simulation results indicate that the proposed tracking algorithm based on modified aerodynamic model has the best tracking performance with the best accuracy and least computational cost among all tracking algorithms in this paper. The proposed algorithm is cost-effective for HGV tracking.  
Keywords:hypersonic glide vehicle tracking; aerodynamic model; Cubature Kalman filter; interacting multiple model; adaptive grid interacting multiple model; hybrid grid multiple model +
Footnote †: journal: Applied Sciences
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 4 Results and Discussion
### Low Relative Velocity Scenario  
First, we initiated our simulation with a more common scenario where the two vehicles' delta velocity was low. Figure 5 illustrates the results obtained when we employed the default CACC without any additional control method. As the figure shows, the four different modulation schemes exhibited a comparable performance, with the 64 QAM modulation exhibiting a slight discrepancy. This is due to the fact that this modulation is more complex, resulting in a reduced operational distance. The discrepancy can be observed in the relative distance plots as the two vehicles enter the noisy environment. For the QPSK modulation with both coding rates, the packets still arrived, although a few were lost in the vicinity of the maximum noise level. In comparison, the 64 QAM modulation was unable to maintain a robust connection in the presence of noise, necessitating the use of the last known position of the target vehicle as a reference point for the perception module.  
Consequently, the relative distance gradually decreases until it reaches zero, which represents the point of potential collision. However, at this point, the value is not accurate, and the vehicle responds with a delay before accelerating to reach the desired speed. Once the HV has departed from the noisy environment, it receives the most recent information and begins to adjust its velocity to align with the remote vehicle's velocity. In the middle plot, we show SRI throughout the simulation, but the control mechanism is deactivated. The high value can be attributed to the low PDR values.  
Figure 4: Implemented scenario description.  
In Figure 6, we conducted a similar experiment with our proposed control concept. On the fourth plot, we can observe PDR values similar to those in a simple CACC method. The SRI values were slightly lower than those without the suggested control due to the control's impact on the delta velocity between the two vehicles. In the case of QPSK, the change in relative distance was comparable to that in the simple method, but our method was slightly more cautious. Upon evaluating the 64 QAM modulation, it became evident that the SRI values exceeded the desired threshold as soon as the network experienced degradation. Consequently, the control reduced the velocity. Based on the last received message, it appears that the perceived relative distance values are falsely represented. However, when the ground truth is considered, it becomes apparent that the control is almost overly cautious, and the two vehicles are situated at a considerable distance.  
Figure 5: Results from simple CACC for low relative velocity (8.33 m/s = 30 km/h).
## 4 Discussion  
Considering all of the variables together, the uniform discretisation approach with 7 input features, synthetic minority oversampling, and a voxel size of 2.0 mm is best, with consistently reasonably high accuracy compared to other conditions across the classifier types. The choice of classifier is less clear and might be made based on considerations other than accuracy or after further testing. While accuracy should be the key criterion due to the high-value nature of L-PBF parts, computational efficiency should also be considered since the ultimate aim is to achieve in situ monitoring and the adjustment of process parameters to avoid defective parts.  
The maximum achieved test accuracy of around 76% is unlikely to be sufficient for practical application but suggests that the voxelisation-based framework has potential with further refinement of the parameters and expansion of the training dataset. In the histograms of Figures 7-12, it is clear that there is significant overlap between the distributions of porous and non-porous data points for each of the features, indicating that no feature was a very strong predictor of porosity. Given that each data point represents the mean of the signal throughout a single voxel, it is possible that many voxels actually contain a combination of both porous and non-porous signals, which blurs the distinction between the two groups. However, this should then lead to higher accuracy at lower voxel size, which is not found here.  
Another likely contributing factor is the inherent difficulty of predicting porosity from photodiode signals alone. The photodiodes measure the total intensity of certain wavelengths emitted from the region of the melt pool, so they may not detect smaller-scale variations within this region that would better indicate porosity.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Data Processing Algorithm Comparison Test  
Because of changes in the refractive index of air and other factors in a long distance of 30 m, the laser beam will slightly deflect during the propagation, and the position of the laser spot on the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Displacement of Micro** & **Abscissa Value of Laser** & **\(\Delta\)x (mm)** & **\(\Delta\)X (pixel)** \\
**Motion Platform (mm)** & **Spot Center (pixel)** & **\(\Delta\)x (mm)** & **\(\Delta\)X (pixel)** \\ \hline
0 & 636.39 & — & — \\
10 & 567.28 & 10 & 69.11 \\
20 & 498.04 & 10 & 69.24 \\
30 & 428.44 & 10 & 69.60 \\
40 & 360.53 & 10 & 67.91 \\
50 & 291.98 & 10 & 68.55 \\
60 & 223.58 & 10 & 68.40 \\
70 & 154.93 & 10 & 68.65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(x\)-axis conversion coefficient calibration experimental data.  
Figure 16: Conversion coefficient calibration experiment. 1. image acquisition module; 2. movement direction; 3. micro motion platform; 4. laser.  
filter imaging screen will vibrate. Given the isotropy of laser spot vibration, this experiment only addressed the \(x\)-axis coordinates of the spot, and the unit is pixels.  
As shown in Figure 17, the laser spot position information of 1000 spot images was processed, wherein the abscissa axis was the acquisition time. As can be seen from the diagram, the vibration range of laser spot was big; therefore, when the laser propagation distance is long, the laser spot center data at a single time point could not be used to represent the monitoring reference. The position probability distribution of the 1000 spot images is shown in Figure 18. Although the laser spot was in vibration, the position probability distribution was approximately in accordance with Gaussian distribution; therefore, stable data was found to represent the monitoring reference by processing a set of data over a period of time.  
Given the characteristics of the dataset, the following methods were used to process the data: (1) calculation of the extremum after Gauss fitting, (2) calculation of the expectation from original data, (3) calculation of the expectation after low-pass filtering, and (4) acquisition of the maximum probability data from data probability distribution map. The results are shown in Table 2. As can be seen, the fourth method has the highest accuracy; therefore, this method was used to process the laser spot position information.  
Figure 17: Laser spot position information of 1000 spot images.  
Figure 18: Position probability distribution.
## 3 In Situ AE Monitoring in Salt Mines
### Salt Mine Asse in Germany
Figure 5 displays AE activity above the roof of the chambers and in the southern flank above the two chambers in a rotated coordinate system. The location of the AE events are marked as black dots in the four crosscuts as shown in Figure 3. In Crosscut 1 and 2 (left-hand side) and Crosscut 3 and 4 (right-hand side), the AE events were recorded by the AE system in the western and eastern chamber, respectively. The width of the crosscut is about 12 m.  
The observed AE activity is not homogenously distributed but clusters clearly, in this way outlining planes of activity. In the chamber's roof most events outline planes oriented east-west and dipping to the south according to the salt's layering. The events extend up to 15 m from the roof into the salt rock, but at greater depth no activity is recorded, which demonstrates that currently no active damage process is occurring. In the roof of the chambers, all events occur in a homogenous part of the younger salt rock (Leine formation) and most likely correspond to damage processes owing to stress re-distribution. A geomechanical survey confirmed an increased permeability in the area of AE clusters, but did not show macroscopic damage. The strongest AE activity is observed on the southern flank of the salt dome. The events outline a plane of activity that is oriented roughly east-west and dipping to the south. The AE events seem to follow the rock salt-anhydrite-sandstone rock boundary, which is subject to significant geomechanical processes including the loss of integrity owing to a barrier thickness of only 15 m in the upper mine. Events are observed as far as 70 m above the network. Within this plane, AE events cluster on vertical structures. Figure 5: Location of AE events (black dots) in projection onto four vertical crosscuts in a rotated coordinate system above the chamber and along the anhydrite-host rock boundary at the southern flank [38]. All dimensions are given in meter. Overall, the authors conclude that although a singular AE event is too small to have a damage potential, the AE events are clearly able to outline in high-resolution areas and, even more precisely, the exact position, extension, and orientation of potential damage zones.
# An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
## 3 Computational Experiments
### Damping Forcing Term  
Next, we consider a one-dimensional LL equation on domain \(\Omega=(0,2\pi)\) with a damping forcing term. The exact solution and corresponding forcing term are given as follows:  
\[\mathbf{m}^{\epsilon}(x,t) = \begin{pmatrix}u^{\epsilon}(x,t)\\ v^{\epsilon}(x,t)\\ w^{\epsilon}(x,t)\end{pmatrix}=\begin{pmatrix}\operatorname{sech}(at)\cos(x) \\ \operatorname{sech}(at)\sin(x)\\ \tanh(at)\end{pmatrix},\] \[\mathbf{f}(x,t) = \mathbf{m}_{t}^{\epsilon}(x,t)+\mathbf{m}^{\epsilon}(x,t)\times \mathbf{m}_{xx}^{\epsilon}(x,t)=\begin{pmatrix}(v^{\epsilon}(x,t)-\alpha u^{ \epsilon}(x,t))w^{\epsilon}(x,t)\\ (u^{\epsilon}(x,t)-\alpha v^{\epsilon}(x,t))w^{\epsilon}(x,t)\\ \alpha[1-(w^{\epsilon}(x,t))^{2}]\end{pmatrix}.\]  
Figure 11a-c show the snapshots of the computational results of \(\mathbf{m}(x,t)\) at \(t=0.0001\), \(0.0007\), and \(0.001\), respectively. Figure 11d-f display the numerical approximations of \(u(x,t)\), \(v(x,t)\), and \(w(x,t)\) with the corresponding exact solutions at \(t=0.01\), respectively. Figure 11g-i illustrate the difference of \(u(x,t)\), \(v(x,t)\), and \(w(x,t)\) with the matching exact solutions at \(t=0.01\), respectively. Here, we use \(N_{x}=100\), \(h=2\pi/N_{x}\), \(\alpha=1000\), \(tol=10^{-5}\), and \(\Delta t_{\max}=0.5h^{2}\).  
Lastly, we simulate the case where the forcing term is constant, \(f=(0,\ 0,\ 1)^{T}\) with the same computational domain as the last numerical simulation. Tolerance \(10^{-7}\) is applied. The constant forcing term represents the constant one directional external magnetic field that is applied to the dynamics of magnetization. The initial condition is given as \(\mathbf{m}(x,0)=(\cos(x)/\sqrt{2},\ \sin(x)/\sqrt{2},\ 1/\sqrt{2})^{T}\). Figure 12a-c show the snapshots of  
Figure 11: Simulation of damping forcing term. Numerical solution at (**a**) \(t=0.0001\), (**b**) \(t=0.0007\), and (**c**) \(t=0.001\). Numerical and analytic solutions of (**d**) \(u\), (**e**) \(v\), and (**f**) \(w\) at \(t=0.001\). The differences between the exact solution and the numerical solutions (**g**) \(u\), (**h**) \(v\), and (**i**) \(w\) at \(t=0.001\).  
the computational results of \(\mathbf{m}(x,t)\) at \(t=0.001,\ 0.007\), and \(0.01\), respectively. Figure 12d-f display the numerical approximations of \(u(x,t)\), \(v(x,t)\), and \(w(x,t)\) with the corresponding exact solutions at \(t=0.01\), respectively. We can see the numerical results are stable and therefore capable of simulating this application.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 3 Result and Discussion
### Methylene Blue Adsorption
#### 3.2.3 Kinetic Studies  
To determine the kinetic mechanism of the removal process, pseudo-first-order and pseudo-second-order, Elovich, and Weber-Morris adsorption kinetic models were used for the adsorption of MB on YL-M-L-AC. The schemes of the kinetic models are presented in Figure 9 and the calculated parameters are listed in Table 4. Taking into account the R\({}^{2}\) values and the ARE, it was found that the pseudo-second-order kinetics model (R\({}^{2}\) = 0.9989, ARE = 1.14%) is more suitable for MB adsorption on YL-M-L-AC. The rate of the MB removal is controlled by chemical reactions that may involve electron contributions or covalent interactions between the adsorbent and the adsorbate [77; 78]. However, the fit values obtained for the pseudo-first-order model (R\({}^{2}\) = 0.9761, ARE = 6.22%) are still suitable for describing the sorption kinetics of MB on YL-M-L-AC. These values explain the surface processes including chemisorption and physisorption during the adsorption of the dye onto the biocompatible [79]. The values of q\({}_{1}\) and q\({}_{2}\) obtained from the pseudo-first- and second-order models are 30.46 mg/g and 32.37 mg/g, respectively, and are very close to the actual value of the sorption capacity obtained during the experiment. The last model with a high fit was the [PERSON] kinetic model (R\({}^{2}\) = 0.9439, ARE = 9.90%), the fit of which was confirmed when examining systems with heterogeneous surfaces [80]. In addition to adsorption on the surface, processes such as chemical sorption, ion exchange, precipitation of substances, and diffusion were also observed. The obtained parameters \(\alpha\) and \(\beta\) allowed the conclusion that in the removal of MB on YL-M-L-AC, the adsorption process is the dominant process over the desorption process (\(\alpha>\beta\)) [81].
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 4 Modeling Description
### Improved Similarity Calculation  
The residual of vibrations component in \(\mathbf{X}_{obs}\) and \(\mathbf{X}_{est}\), including the average value for residual sequence, are usually used for judging whether the incipient failures occur in previous works. However, the information contained in the other variables, including temperature of end-side bearings, current air volume of unit, operation frequency of driving motor, and current of driving motor, would be neglected, but some vibrations failure might be first reflected by indirect parameters. Therefore, except the analysis the residual of vibrations directly, a comprehensive similarity calculation based on other components in observation vector is proposed, and the difference between components is considered.  
The similarity could be formulated as,  
\[S(X_{obs},X_{est})=\frac{1}{1+\sqrt{\sum\limits_{i=1}^{n}R_{i}[X_{obs,i}(t(k))- X_{est,i}(t(k))]^{2}}} \tag{17}\]  
Figure 3: Procedure for constructing memory matrix D.  
where \(\mathbf{X}_{obs,i}\) and \(\mathbf{X}_{est,i}\) are the component \(i\) in observation vector and estimation vector, respectively, and \(R_{i}\) is corresponding weight coefficient for \(i\) component in vectors, which could be obtained according to the result of grey relational analysis in Table 4.  
\[R_{i}=\frac{r_{i}}{\sum\limits_{j}^{n}r_{j}} \tag{18}\]  
where \(n\) is the number of components that are selected in to memory matrix.
# Adaptive Multi-Level Search for Global Optimization:
## 4 Adaptively Integrated Swarm Intelligence-Metamodelling Framework  
### Methodology of the ASIM Framework  
In this paper, an adaptively integrated swarm intelligence-metamodelling (ASIM) framework is proposed to perform a search for the optimal solution in two levels.  
In the first level of optimization, also known as exploration, a number of entities are initially placed in the search space of the particular optimization problem with respective positions \(\mathbf{x}_{i}^{t}\) and velocities \(\mathbf{v}_{i}^{t}\). Each particle \(i\) has its movement controlled by Equation (10). The final global best solution is obtained only if the objective no longer improves or after a certain number of iterations. However, distinguished from the conventional PSO, each particle also gains an insight within its neighborhood. That forces each particle to refine their personal best position by exploiting their neighborhood, which is known as the second level of optimization. In this local level search, an adaptive metamodel is built by MAM within a trust region surrounding the particle, and then the personal best solution \(\mathbf{x}_{i,MAM}\) obtained by MAM is regarded as a local refinement in position. Following that, the personal and global best position \(\mathbf{pbest}^{t},\mathbf{gbest}^{t}\) is determined and updated until the termination criterion is satisfied. To sum up, the surrogate helps guide the search direction of each particle and assists in refining the current overall best position until the final global best solution is found. Eventually, the swarm as a whole moves close to a global optimum of the objective function. The flowchart of the ASIM framework is depicted in Figure 2.  
It is worth noting that there are three rules applied to compare solutions during the optimization process:  
1. Any feasible solution is preferred to any infeasible solution;
2. Among feasible solutions, the one with a better objective function value is preferred.
3. Among infeasible solutions, the one having a fitness value with smaller constraint violations is preferred. In the current implementation, the fitness function is defined by  
\[\begin{array}{ccc}\mathit{Fitness}(\mathbf{x})&=&\begin{cases}f(\mathbf{x})&\text{ if }\mathbf{x}\text{ is feasible}\\ f(\mathbf{x})\times\prod[g_{j}(\mathbf{x})]^{2}&\text{elseif }f(\mathbf{x})\geq 0\\ f(\mathbf{x})+[f(\mathbf{x})]\times\prod[g_{j}(\mathbf{x})-1]^{2}&\text{elseif }f(\mathbf{x})<0\end{cases}\end{array} \tag{11}\]
## 3 Improved Snake Optimization Algorithm for Melting Point Optimization Solution
### Improved Snake Optimization Algorithm
#### 3.2.3 Dynamic Weights Cauchy-Gauss Mutation  
In the snake optimization algorithm, after the mating behavior of male and female snakes is completed, the female snake will lay eggs and hatch, resulting in new offspring. The new snakes will replace inferior individuals to supplement the population and initiate a new cycle of iterations. In the new iteration, to prevent the algorithm from falling into local optima and accelerate convergence as much as possible, this paper introduces a dynamic weighted Gaussian mutation strategy to enhance the algorithm's ability to escape local optima.  
The Gaussian distribution is symmetric about the mean. This implies that data on both sides of the mean exhibit similar distribution characteristics. Therefore, the use of Cauchy mutation to enhance global exploration capability and utilizing Gaussian mutation to enhance local exploitation capability near the current best solution is proposed. The modified Cauchy-Gaussian mutation formula is shown in Equations (23)-(25):  
\[mutation(m)=m\times[1+\alpha_{1}Cauchy(0,1)+\alpha_{2}Gauss(0,1)] \tag{23}\]  
\[\alpha_{1}=1-\left(\frac{t}{T_{\max}}\right)^{c} \tag{24}\]  
\[\alpha_{2}=\left(\frac{t}{T_{\max}}\right)^{c} \tag{25}\]  
In the equations, \(m\) represents the position of the best individual in the current iteration round, \(mutation(m)\) represents the position of the individual after mutation, \(Cauchy(0,1)\) is a random variable following the standard Cauchy distribution, and \(Gauss(0,1)\) is a random variable following the standard Gaussian distribution. \(\alpha_{1}\) and \(\alpha_{2}\) are adaptive adjustment factors, and \(c\) is a parameter controlling the rate of change.  
As the number of iterations increases, \(\alpha_{1}\) gradually decreases from 1 to close to 0, while \(\alpha_{2}\) increases from 0 to 1. In the early stages of iteration, the adaptive adjustment factor allows Cauchy mutation to play a greater role, primarily expanding the population's search range. In the later stages of algorithm iteration, the effect of Cauchy mutation weakens, and Gaussian mutation takes over, allowing individuals to search in a small range, enhancing the population's local search capability, and enabling the algorithm to converge efficiently and rapidly.
## 4 Results of Parameter Identification and Optimization
### Results of Parameter Identification
The research results suggest improvements in the design of pumps and cylinders and the installation of accumulators to absorb pressure fluctuations. It is also important to implement advanced control systems for pressure regulation and pulsation compensation. The dependence of the energy efficiency and power output of large-sized cylinders on pressure pulsations during operation with axial piston pumps is significant. Reducing pressure pulsations through design improvements, damping devices, and operational adjustments can enhance the overall performance and efficiency of hydraulic systems. This, in turn, leads to more stable and reliable operations, extending the lifespan of the equipment and reducing energy consumption.