esa-sceva/satcom-chunk-collection huggingface dataset:
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 3 Methods
### Improved Combination Prediction Model  
In the traditional combination model, the linear and nonlinear parts of the dam deformation sequence are separately predicted using two models, and the linear and nonlinear prediction values are obtained. Then, the two parts' prediction values are added using a simple linear combination based on Equation (9). However, using a simple linear combination to characterize the relationship between linear and nonlinear prediction values may not be accurate. If the relationship between the linear and nonlinear components is more complex, and a simple combination with static weights is used, it may lower the accuracy of the model and even be lower than the accuracy of a single model.  
To address the issue that the relationship between the linear prediction value \(\hat{L}_{t}\) and the nonlinear prediction value \(\hat{N}_{t}\) in the dam deformation combination model cannot be characterized using a simple linear combination, this section proposes an improved dam deformation prediction model combination method. It uses the powerful self-learning ability of the LSTM model to fit the relationship between the two components dynamically and determine the relationship and weight dynamically rather than using a static addition relationship, thereby achieving more accurate predictions. The specific formula is as follows:  
\[y_{t}=lstm\big{(}\hat{L}_{t},\hat{N}_{t}\big{)} \tag{10}\]
## 3 Results and Discussion of RSM Experiments
### Interaction Effects of the Regression Model  
The perturbation plot (Figure 9) illustrates the differential effects of the three factors (C, E, G) on the angle of repose within their selected ranges. Both rolling friction coefficient (C) and static friction coefficient (E) exhibited positive correlations with the angle of repose, with E demonstrating a steeper slope than C. In contrast, the JKR surface energy (G) displayed a nonlinear relationship: the angle of repose initially increased with rising G but decreased beyond a critical surface energy threshold.  
The repose angle of wheat straw feed arises from a dynamic equilibrium among rolling friction, static friction, and surface energy:  
* Rolling friction governs local particle rearrangement by dictating energy dissipation during granular adjustments [24].
* Static friction at the straw-steel interface modulates substrate support strength, stabilizing interfacial interactions [25].
* JKR surface energy regulates interparticle adhesion, with cohesive forces dominating at finer scales [26].  
Elevated frictional forces amplify the repose angle by restricting particle mobility, while surface energy exhibits a biphasic influence:  
1. Subcritical Regime: Increased adhesion enhances interparticle cohesion, reducing flowability and elevating the repose angle.  
Figure 9: Perturbation.  
2. Supercritical Regime: Excessive agglomeration forms large particle clusters, restructuring the pile geometry and paradoxically improving flowability, thereby decreasing the repose angle. This mechanistic interplay underscores the necessity of balancing frictional and adhesive parameters to replicate realistic biomass dynamics in DEM simulations. Based on the regression equation, the response surface and contour plots of the model were established as illustrated in Figure 10.  
Figure 10: Contour and surface maps of the interaction factors. (**a**) C–E interaction diagram; (**b**) C–G interaction diagram; (**c**) E–G interaction diagram.  
Figure 10a: At fixed G, the angle of repose (\(\theta\)) increases monotonically with rising C and E.  
Figure 10b: When C transitions from low to high levels, \(\theta\) shifts from an initial increase-decrease trend to a sustained increase with rising G. Higher G amplifies the positive correlation between C and \(\theta\).  
Figure 10c: When E transitions from low to high levels, \(\theta\) transitions from increasing to decreasing with rising G. Higher G diminishes the positive correlation between E and \(\theta\)  
All two-factor interaction terms (C\(\times\)G, E\(\times\)G) significantly influenced the angle of repose (\(p<0.05\)), confirming that parameter interdependencies critically shape granular pile dynamics.
# Improvement of Mesh Atomizer Performance by Electrolytic Polishing
## 4 Burr Measurement Experiment of Metal Sheet
### Data Analysis  
A box plot based on the statistical data of the previous experiments is drawn in Figure 5. Figure 5a presents the relationship between the electrolytic polishing time and the diameter of micro-tapered holes of the metal sheet, and Figure 5b presents the relationship between the electrolytic polishing time and the burr height of micro-tapered holes of the metal sheet. Each box plot is composed of 800 groups of data. There is a point in the box that represents the average value of a group of data, and the lines in the diagram are the lines of the mean value. Twenty-five to seventy-five percent of the data are indicated in the box, so the height of the box reflects the fluctuation of the data to some extent. The top and bottom edges represent the maximum and minimum values for that set of data, respectively.  
According to Equation (9) and the measured data in the experiment, the relation between the conical angle of a micro-tapered hole and polishing time and image can be obtained.  
If future researchers can measure and calculate the conical angle, even if the experimental conditions are different when conducting similar experiments, then find the corresponding time \(t\) according to Figure 6, and then substitute it into Formula (12) to obtain the atomization amount.  
Figure 4: Burr height of metal sheet: (**a**) Untreated sheet; (**b**) Sheet treated for 1 min; (**c**) Sheet treated for 2 min; (**d**) Sheet treated for 3 min; (**e**) Sheet treated for 4 min; (**f**) Sheet treated for 5 min; (**g**) Sheet treated for 6 min; (**h**) Sheet treated for 7 min.  
Figure 5: The relationship between electrolytic polishing time and metal sheet: (**a**) Relationship between electrolytic polishing time and the diameter of micro-tapered holes; (**b**) Relationship between electrolytic polishing time and burr height.  
According to Equation (12) and the measured data in the experiment, because it is not known how long the electrolytic polishing time \(t\) will be, \(Z\) will significantly change. Hence, \(Z\) will be temporarily treated as a constant and then corrected in accordance with the atomization experiment. A diagram of the relationship between the conical angle and atomization amount is shown in Figure 7. When the electrolytic polishing time is 1 min, the atomization amount decreases somewhat. Nonetheless, overall, the atomization amount \(Q\) increases with the increase in electrolytic polishing time \(t\).
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 3 Methodology
### Anomaly Trajectory Recognition Based on Trajectory Rules
#### 3.3.2 Mathematical Model of Vehicle Driving Behavior  
Taking into consideration the characteristics of highway scenarios and vehicle motion, this study categorizes common highway vehicle motion behaviors into normal and abnormal behaviors. Normal behaviors encompass compliant driving actions such as straight-line driving and lane changing that adhere to road traffic safety regulations. Abnormal behaviors, on the other hand, encompass actions like driving in the opposite direction, speeding, moving at a slow pace, stopping, and making hazardous lane changes. A schematic diagram is illustrated in Figure 3 for reference.  
From the vehicle behavior schematic, it can be observed that different abnormal vehicle behaviors correspond to distinct motion characteristics in vehicle trajectories. For a behavior recognition model, key factors to consider include changes in vehicle position, velocity, as well as vehicle motion direction and angles.  
We define that vehicles driving in the correct direction according to road regulations, moving away from the camera's imaging view, are termed \"downward\", while those approaching the camera are termed \"upward\".  
Based on the aforementioned rules and considering the constraints of the application scenario, a mathematical model for vehicle driving behavior can be established. The mathematical model for recognizing downward vehicle behavior is illustrated in Table 1. With the mathematical model of vehicle behavior in place, we can establish a vehicle behavior detection based on trajectory rules to perform behavior detection on the extracted highway vehicle driving trajectories.  
Figure 3: Schematic diagram of vehicle behavior trajectories for normal behavior and abnormal behavior.
## 4 Results
### Algorithms
#### 4.2.2 Deep Q-Network  
The Deep Q-Network (DQN) is a variation of the Q-learning algorithm that uses a neural network to approximate the Q-function, instead of employing a value-based approach [11]. The training process involves updating the weights of the neural network based on the difference between the predicted and actual rewards obtained by taking certain actions. This allows the agent to improve its decision-making over time and learn optimal strategies for achieving its goals. The key feature of DQN is its use of an experience replay, which stores past experiences in memory and randomly selects them to train the neural network. This prevents it from becoming stuck in local optima and improves sample efficiency.  
Unlike Q-learning, DQN is effective when the state space is large or high-dimensional, such as images or raw sensory inputs, and is well-suited for complex environments with unknown dynamics. However, DQNs can be sample inefficient, requiring extensive interactions with the environment to learn effectively. DQN also suffers from the curse of dimensionality, where the volume of the space increases so fast that the available data become sparse, meaning that it becomes challenging to obtain sufficient samples to accurately represent the underlying distribution of the data.  
In [25], results showed that a Q-learning algorithm with a naive Bayes approach improved the AI bot's \"believability\" against a Greedy-Like Behaviour algorithm. Instead of selecting actions deterministically based on Q-values, a Q-learning algorithm with a naive Bayes approach can use the estimated probabilities to perform probabilistic action selection. This allows for a more diverse exploration of actions, reducing repetitive behaviours.
## 3 Results and Discussion
### Effect of Coating Time on the Properties of the Ceramic Membrane  
Figure 7 shows the pore-size distribution of the ceramic membrane with different coating times, prepared by the two-step coating method, and the solid content is 7 wt %. As can be seen, with a coating time of between 5 s and 11 s, the average pore-size of the ceramic membrane decreased. However, when the coating time exceeded 11 s, with a maximum of 17 s, the pore size of the ceramic membrane increased.  
Figure 6: Pore-size distribution of the ceramic membrane with different solid contents.  
The main reason for this phenomenon was that Al\({}_{2}\)O\({}_{3}\) particles repaired the big hole during two-step coating. Thus, the largest hole was reduced, the thickness of the ceramic membrane increased, and the pore-size decreased evenly. However, by extending the coating time, the Al\({}_{2}\)O\({}_{3}\) particles moved far away from the support body, and the wet membrane layer became loose. Meanwhile, it was easy to separate it from the wet membrane layer, using the flowing slurry, for a short period of time. In addition, the Al\({}_{2}\)O\({}_{3}\) deposition on the surface of the wet membrane was lower, and the big hole repair was incomplete. As the coating time increased, the wet membrane far away from the support could be diffused back into the slurry, causing damage to the membrane structure. The thickness of the ceramic membrane decreased, or the surface of the ceramic membrane was uneven. Furthermore, the pore size of the ceramic membrane increased.  
Figure 8 shows SEM images of the ceramic membrane with different coating times. It can be seen from Figure 8 that, with a coating time ranging from 5 s to 11 s, the thickness of the ceramic membrane increased, the surface of the ceramic membrane was smooth, or the distance of the alumina particles decreased. So, the pore size of the ceramic membrane decreased. On the contrary, as the coating time extended from 11 s to 17 s, the thickness of the ceramic membrane decreased, the surface of the membrane was uneven, or the distance of the alumina particle increased. This made the pore size of the ceramic membrane gradually increase.  
Figure 7: Pore-size distribution of the ceramic membrane with different coating times.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 2 System Model
### Circulant Structure of the Transform Matrix  
In this subsection, we will discuss the circulant structure of the transform matrix \(\mathbf{\Phi}\) first. With this feature, low complexity equalization algorithms will be proposed in the following sections. Reminding of (6), the \((m,n)\)th element of \(\mathbf{\Phi}\) is  
\[\Phi(m,n)=e^{-j\frac{\pi}{N}(m-n)^{2}}. \tag{9}\]  
Define \(k=m-n\) and \(\Phi(m-n)=\Phi(m,n)\), we have  
\[\Phi(k)=\Phi(m,n)=\Phi(n,m). \tag{10}\]  
Thus, the transform matrix can be rewritten as  
\[\mathbf{\Phi}=\begin{bmatrix}\Phi(0)&\Phi(1)&\cdots&\Phi(N-1)\\ \Phi(1)&\Phi(0)&\cdots&\Phi(N-2)\\ \vdots&\vdots&\ddots&\vdots\\ \Phi(N-1)&\Phi(N-2)&\cdots&\Phi(0)\end{bmatrix}. \tag{11}\]  
On the other hand, for any integer \(L\in[0,N-1]\),  
\[\begin{split}\Phi(N-L)&=e^{-j\pi\frac{(N-L)^{2}}{N}}\\ &=e^{-j\pi\frac{N^{2}-2 NL+L^{2}}{N}}\\ &=e^{-j\pi N}\times e^{2\pi L}\times e^{-j\pi\frac{L^{2}}{N}}\\ &=e^{-j\pi N}\times e^{-j\pi\frac{L^{2}}{N}}.\end{split} \tag{12}\]  
when \(N\) is odd, \(e^{-j\pi N}\) equals to \(1\), we have  
\[\begin{split}\Phi(N-L)&=e^{-j\pi N}\times e^{-j\pi \frac{L^{2}}{N}}\\ &=e^{-j\pi\frac{L^{2}}{N}}\\ &=\Phi(L).\end{split} \tag{13}\]  
Figure 1: Modulation procedures of the orthogonal chirp division multiplexing (OCDM) (upper) and uniform phase-OCDM (UP-OCDM) (lower).  
Finally, in UP-OCDM, \(\mathbf{\Phi}\) can be expressed as below and is shown to be a circulant matrix.  
\[\mathbf{\Phi}=\begin{bmatrix}\Phi(0)&\Phi(N-1)&\cdots&\Phi(1)\\ \Phi(1)&\Phi(0)&\cdots&\Phi(2)\\ \vdots&\vdots&\ddots&\vdots\\ \Phi(N-1)&\Phi(N-2)&\cdots&\Phi(0)\end{bmatrix}. \tag{14}\]
## 4 Experimental Results and Discussion
### Numerical Simulations and Analysis
#### 4.1.1 CPIMM Simulation  
To validate the effectiveness of CPIMM, the first set of comparisons, including the standard IMM and CPIMM, was simulated. We assumed there were no sensor faults, similar to the Simulation Case 1 in the scenario design section.  
Figure 5 gives the true and estimated trajectories of the maneuvering target. As seen in Figure 5, both CPIMM and IMM can track the trajectory of the target.  
Figure 5: True and estimated trajectories of the maneuvering target for CPIMM for Case 1.  
The comparisons of the RMSE position and velocity curves of the two filtering algorithms are shown in Figures 6 and 7, and the corresponding statistical data of RMSE are listed in Table 2. The filtering precision of the IMM is lower than the CPIMM, although the IMM is not divergent. The CPIMM greatly increases the filtering precision compared with IMM. In addition, this means that the tracking performance of CPIMM is higher than that of the IMM, because the cubature principle is used to increase the estimation values in the interaction process of the IMM.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 4 Prediction of Linear Guide Preload--ANN Modeling
### Evaluation of ANN Model
Therefore, the stage rigidities predicted from the vibration signals measured by the ANN models are comparable to the rated values, with a better prediction accuracy of approximately 96.5-97.8%. However, the stage for data collection in the evaluation phase was assembled with sliding blocks rated at different preloads. For example, the stage in case (V) was equipped with two high preload blocks on one rail and two low preload blocks. For the stage in case (VI), three blocks were medium preloaded, and one was at a lower preload. Compared with case (VI), the dynamic characteristics of the sliding blocks in stage case V were incontinent, which may affect the stage vibrating with more fluctuation. As shown in Figure 16, the data points of the vibration occurring in case (V) scattered in a wider range without a significant tendency with the feed rate, which probably caused the predicted stage rigidity to deviate more from the rated values. Consequently, it yielded lesser accuracy in prediction as compared to the other cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ANN Model**} & \multicolumn{5}{c}{**Test Cases of Stage**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Case IV**} & \multicolumn{2}{c}{**Case V**} & \multicolumn{2}{c}{**Case VI**} \\ \cline{2-7}  & **RMSE** & **MAPE** & **RMSE** & **MAPE** & **RMSE** & **MAPE** \\ \hline MLP 8-16-1 & 122.5 & 5.96\% & 257.8 & 15.0\% & 133.9 & 6.83\% \\ MLP 8-18-1 & 140.6 & 7.55\% & 326.9 & 15.4\% & 138.6 & 7.37\% \\ MLP 8-28-1 & 117.0 & 5.74\% & 326.9 & 16.8\% & 180.8 & 9.68\% \\ MLP 8-30-1 & 181.0 & 8.6\% & 333.0 & 16.8\% & 180.8 & 7.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical values of neural network models for verification dataset.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 2 Prediction of Tool Wear Based on Simulation
### Boundary Conditions and Friction Models  
In the micro-milling process, the micro-milling cutter rotates at the same time it completes the feed, and the workpiece is fixed on the table. Constrain the non-machined surface of the workpiece, set the Rotation and feed of the cutter and complete the setting of the boundary conditions, as shown in Figure 6. Cutting heat efflux is accomplished by chip, workpiece, cutter and environment diffusion. Set thermal boundary between the workpiece and cutter. The heat transfer coefficient between the cutter and workpiece is set as 107 kW/(m\({}^{2}\cdot\)K), and the heat transfer coefficient between the environment and the workpiece is set as 45 kW/(m\({}^{2}\cdot\)K).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **A (Mpa)** & **B (Mpa)** & **C** & **n** & **m** & **T\({}_{\text{melt}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{room}}\) (\({}^{\circ}\)C)** \\ \hline LF 21 & 34.4 & 114.2 & 0.2062 & 0.2762 & 0.018 & 643 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of Johnson–Cook constitutive model of aluminum alloy LF 21.  
Figure 5: Meshing of the workpiece and enlargement of workpiece edge.  
The friction regions between the cutter and the workpiece, the cutter and chips are divided into bond zone and slip zone, corresponding to shear friction and Coulomb friction, respectively. The two friction models are shown in Equations (3) and (4), respectively. The contact between the cutter and the workpiece is set as shear friction, and the friction coefficient is set as 0.45. The contact between the workpiece and the chips is set as Coulomb friction, and the friction coefficient is set as 0.2:  
\[\text{Shear friction}:\ \tau_{f}=\lambda k\ \mu p_{i}\geq\lambda k \tag{3}\]  
\[\text{Coulomb friction}:\ \tau_{f}=\mu p_{i}\ \mu p_{i}<\lambda k \tag{4}\]  
where, \(\tau_{f}\) is frictional stress; \(\lambda\) is shear friction coefficient; \(k\) is shear yield stress; \(\mu\) is Coulomb friction coefficient; \(p_{i}\) is the contact surface pressure.
## 3 System Model
### GSTBC-EFOGSM \(R=3/4\)  
In the case of the GSTBC-EFOGSM scheme with a coding ratio of R = \(3/4\) and time slot T = 4, the information bits were partitioned into three distinct groups and modulated into three EFOGSM symbols \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\) and \(\mathbf{x}_{3}\). Where \(\mathbf{x}_{k}\) is:  
\[\mathbf{x}_{k}=[0,\ldots,0,\ \mathrm{P}_{M},S_{M/2},\ 0,\ldots,0]^{T},\ \mathrm{k}=1,2,3 \tag{3}\]  
These symbols were then subjected to further processing to improve the overall reliability of the system. Then, the transmitted signal was obtained by OSTBC coding. The  
Figure 2: System models of the GSTBC-EFOGSM.  
GSTBC-EFOGSM scheme with the transmitted signal \(\mathbf{S}_{3/4}\in\mathbf{R}^{N_{t}\times 4}\) could be obtained by using \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\) and \(\mathbf{x}_{3}\) as follows:  
\[\mathbf{S}_{3/4}=\begin{bmatrix}\mathbf{x}_{1}&\mathbf{\overline{x}}_{2}& \mathbf{x}_{3}&\mathbf{0}\\ \mathbf{x}_{2}&\mathbf{x}_{1}&\mathbf{0}&\mathbf{x}_{3}\\ \mathbf{x}_{3}&\mathbf{0}&\mathbf{\overline{x}}_{1}&\mathbf{\overline{x}}_{2 }\\ \mathbf{0}&\mathbf{x}_{3}&\mathbf{x}_{2}&\mathbf{x}_{1}\end{bmatrix}_{N_{t}\times 4} \tag{4}\]  
In the equation above, by using an overline notation, the complement of a signal \(\mathbf{x}_{k}\) is defined by \(\mathbf{\overline{x}}_{k}=[0,\ldots,0,\ P_{M}+M+1,S_{M/_{2}}+M+1,\ 0,\ldots,0]^{T},\ k=1,2,3\)[14].
# Influence of Dielectric Plate Parameters on the Reflection Coefficient of a Planar Aperture Antenna
## 2 Analysis of the Parameters of the Dielectric Plate on the Antenna Aperture
### Permittivity and Electrical Conductivity of the Dielectric Plate
Because the moisture of the material has an effect on the imaginary part of the complex permittivity, in addition to the previously mentioned parameters, S\({}_{11}\) is also affected by moisture (electrical conductivity) and temperature [15]:  
\[\rm S_{11}=\frac{\sqrt{\frac{(\mu\
u-j\mu^{\prime\prime})}{(\epsilon^{\prime \prime}-\epsilon^{\prime\prime})}}tanh\left(j\frac{2\pi f\rm d}{c}\sqrt{(\mu^{ \prime}-j\mu^{\prime})(\epsilon^{\prime}-j\epsilon^{\prime\prime})}\right)-1} {\sqrt{\frac{(\mu\
u-j\mu^{\prime\prime})}{(\epsilon^{\prime}-j\epsilon^{ \prime\prime})}}tanh\left(j\frac{2\pi f\rm d}{c}\sqrt{(\mu^{\prime}-j\mu^{ \prime\prime})(\epsilon^{\prime}-j\epsilon^{\prime\prime})}\right)+1}, \tag{12}\]  
where S\({}_{11}\) is the reflection coefficient, \(\epsilon\)\(\prime\) and \(\epsilon^{\prime\prime}\) are the real and the imaginary part of the complex electric permittivity, \(\mu\)\(\prime\) and \(\mu^{\prime\prime}\) are the real and the imaginary part of the complex magnetic permeability, f is the frequency of an electromagnetic wave, c is the speed of light, and d is material thickness.
###### Abstract  
Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed \"DataMatrix-ArUco-Hybrid\" (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called \"FiMa-Reader\", which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots' positioning with high precision in various indoor lighting conditions.  
mobile robot; smart factory; target docking; fiducial markers; visual guidance +
Footnote †: journal: Applied sciences