esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Strong Tracking CKF Based on Multiple Fading Factors
### Multiple Fading Factor Strong Tracking CKF  
The strong tracking filter (STF) can adjust the state prediction covariance matrix \(\mathbf{P}_{k+1|k}\) in real time by introducing the changing fading factor \(\lambda_{k}\), it can keep the filtering residual matrix orthogonal, in order to deal with the problem of the sudden change of the target motion state and improve the robustness. The core idea is to adjust the gain matrix \(K_{k}\) online to meet the following two conditions:  
1. \(E[(\mathbf{x}_{k+1}-\hat{\mathbf{x}}_{k+1})(\mathbf{x}_{k+1}-\hat{\mathbf{x}}_{k+1})^{T}]=\min\);
2. \(E[\mathbf{e}_{k+1+i}\mathbf{e}_{k+1}^{T}]=0\quad k=0,1,2,\cdots\quad j=1,2,\cdots\), where \(\mathbf{e}_{k+1}=\mathbf{z}_{k+1}-\hat{\mathbf{z}}_{k+1|k}\) is the residual at time \(k+1\). That is, it is required that the residual sequences should remain orthogonal to each other at all times. The traditional STF algorithm is based on expend Kalman filter (EKF). In this paper, the STF algorithm and CKF algorithm were combined to establish the STF algorithm for nonlinear systems. The core structure of the algorithm is the calculation of the residual:  
\[\hat{\mathbf{x}}_{k}=\hat{\mathbf{x}}_{k|k-1}+\mathbf{K}_{k}\mathbf{e}_{k} \tag{13}\]  
\[\mathbf{e}_{k}=\mathbf{z}_{k}-\mathbf{h}\Big{(}\hat{\mathbf{x}}_{k|k-1}\Big{)} \tag{14}\]  
The fading factor \(\lambda_{k+1}\) is calculated as follows:  
\[\lambda_{k}=\max\biggl{(}1,\frac{\text{tr}[\mathbf{N}_{k}]}{\text{tr}[\mathbf{M}_{k}] }\biggr{)} \tag{15}\]  
\[\mathbf{N}_{k}=\mathbf{V}_{0,k}-\mathbf{H}_{k}\mathbf{Q}_{k-1}\mathbf{H}_{k}^{T}-\beta\mathbf{R}_{k} \tag{16}\]  
\[\mathbf{M}_{k}=\mathbf{H}_{k}\mathbf{P}_{k|k-1}^{x,l}\mathbf{H}_{k}^{T} \tag{17}\]  
\[\mathbf{H}_{k}=\Big{(}\mathbf{P}_{k|k-1}^{x,l}\Big{)}\Big{(}\mathbf{P}_{k|k-1}^{l}\Big{)} ^{-1} \tag{18}\]  
\[\mathbf{P}_{k|k-1}^{x,l}=\frac{1}{m}\sum_{i=1}^{m}\mathbf{x}_{k|k-1}^{i,l}\mathbf{h}^{T} \Big{(}\mathbf{x}_{k|k-1}^{i,l}\Big{)}-\hat{\mathbf{x}}_{k|k-1}\mathbf{h}^{T}\Big{(}\hat{ \mathbf{x}}_{k|k-1}\Big{)} \tag{19}\]  
\[\mathbf{V}_{0,k}= \begin{cases}\mathbf{e}_{1}\mathbf{e}_{1}^{T},k=0\\ \frac{\mathbf{v}_{0,k}+\mathbf{e}_{k}\mathbf{e}_{k}^{T}}{1+\varepsilon},k>0\end{cases}  \tag{20}\]  
where \(\mathbf{P}_{k|k-1}^{x}\) is the variance matrix of the state prediction error when the fading factor is not introduced and the system noise variance matrix is not considered; \(\mathbf{P}_{k|k-1}^{l}\) is the state prediction error variance matrix considering the system noise variance matrix; \(\beta\geq 1\) is the weakening factor; and \(\varepsilon\) is the forgetting factor, usually taken as \(0.95\leq\varepsilon\leq 0.995\). The nonlinear system variables of hypersonic targets are multi-dimensional and complex, so it is difficult to determine the optimal fading factor, and the accuracy of a single fading factor is limited.
## 3 Design of the Single Motor Speed Controller  
The block diagram of the dual-motor speed synchronization control system is shown in Figure 1. The current loop input of each motor consists of two components: one is the output signal from the speed loop (\(i_{1}^{t}\) and \(i_{2}^{t}\)), and the other is the compensation term (\(u\)) calculated based on the speed synchronization error. This compensation term is computed by the synchronization controller and fed back into the current loop. It is important to note that the compensation directions for motor 1 and motor 2 are opposite: motor 1 uses negative compensation, while motor 2 uses positive compensation. When motor 1 runs faster than motor 2, the synchronization controller reduces \(u\) provided to motor 1 to decrease its speed, while increasing \(u\) provided to motor 2 to increase its speed. In this way, the synchronization controller can dynamically adjust the torque of the motors in real-time, enabling both motors to quickly recover and maintain synchronization after being subjected to external disturbances.  
Figure 2 illustrates the vector control system structure of a single PMSM in detail. When this vector control strategy is applied to the dual-motor system shown in Figure 1, each motor utilizes the vector control system as depicted in Figure 2. In this control strategy, the stator current of the motor is decomposed into direct-axis and quadrature-axis components. When \(u\) of the speed synchronization error is fed back to the current loop input of each motor, it combines with the original speed loop output (\(i_{1}^{t}\) and \(i_{2}^{t}\)) to form new quadrature-axis current reference values (\(i_{1}\) and \(i_{2}\)). This allows each motor to dynamically adjust its current reference value based on actual operating conditions, thereby achieving more precise speed synchronization control.  
Figure 1: Block diagram of the dual–motor speed synchronization control system.
## 4 Experiments
### Experimental Results
#### 4.2.2 RQ2: Generality Across Various Communication Conditions and FRSs  
In Section 4.2.1, we have demonstrated the applicability of FedRecI2C for multiple FRSs. In practice, it is impractical to include all communication-constrained clients in I2C training during every communication round due to the complexities of real-world conditions. Consequently, we introduce the period parameter \(p\) and client participation ratio \(r\) for I2C, which not only align more closely with real-world communication conditions but also aim to achieve a balance between the convergence speed and additional computation costs. Taking FedMF/PFedMF on MovieLens-1M as an example, we investigate the impact of \(p\) and \(r\) on both the convergence speed and model performance. Figures 5 and 6 depict a scenario where 10% of clients undergo federated training, while the remainder can participate in I2C. In Figure 5, I2C is configured with different \(p\). Specifically, \(p=1\) indicates that I2C runs in every communication round and so forth. In Figure 6, we set different values of \(r\) for clients, selecting \(r=\) (100%, 50%, 25%) communication-constrained clients to perform I2C training. As Figures 5 and 6 show, when the frequency of I2C is higher, and with a larger proportion of clients under communication constraint participating in I2C training, faster convergence is achieved. But the influence of \(p\) and \(r\) on the model's final performance is minimal for FRSs with I2 CG. In contrast to the FRSs (10% clients for FL), the introduction of I2 CG (\(p=1\)) in FRSs leads to the remaining 90% of uploading-constrained clients implementing local training after obtaining item models from the server. This accelerates the training process of these clients. Compared with FRSs (100% clients for FL), implementing I2 CG (\(p=1\)) reduces significant communication costs by sparing the 90% of clients from uploading item models while maintaining a comparable convergence speed, as evidenced by the purple and yellow curves in Figure 5a,b. Convergence acceleration still happens even with \(p=10\) meaning implementing I2 CG every ten rounds or \(r=25\%\) meaning only 25% communication-constrained clients executing I2 CG training, while significantly reducing the associated computation overhead. Adjusting \(p\) or \(r\) can mitigate the additional computation cost associated with I2 CG, which facilitates a tradeoff between additional computation overhead and convergence speed, tailored to specific requirements.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 2 Materials and Methods
### Enhanced YOLOv5 Model
#### 2.2.6 The Structure of CCCS-YOLO  
The paper introduces an improved algorithm called CCCS-YOLO, based on YOLOv5, which enhances the detection of sugar beets and weeds in sugar beet fields. The improvements are made in several aspects, including the C3 module in the neck, the upsampling module, the \(1\times 1\) convolution and IoU in the head, and NMS. These improvements lead to more precise detection. The schematic diagram of the improved CCCS-YOLO is illustrated in Figure 6.  
First, in the neck network's C3 module, the Faster-Block module is added. This module utilizes depthwise separable convolution to make the entire network more lightweight. It reduces the parameter count and computational complexity while facilitating feature fusion in the backbone network. Additionally, the CARAFE operator is introduced in the neck to replace the original nearest-neighbor interpolation for upsampling. This enhances the network's receptive field and feature fusion capabilities while achieving a more lightweight design compared to standard upsampling.  
Second, in the head section, the Context Aggregation module is used to replace the ordinary \(1\times 1\) convolution block. This module employs context information fusion for multi-scale feature fusion. It allows the network to acquire more useful and efficient features, better differentiate between backgrounds and targets, and strengthen the detection performance.  
Lastly, in the improved CCCS-YOLO, the original YOLOv5's IoU and NMS are replaced with more efficient and accurate EIoU and Soft-NMS. This combination, known as Soft-NMS-EIoU, enhances the loss function between predicted boxes and target boxes and the non-maximum suppression calculation, resulting in further improvements in detection performance.  
Below is a diagram illustrating the structure of the improved CCCS-YOLO:
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 5 Simulation Results and Discussion
### Effectiveness Analysis
#### 5.1.3 Ramp Input Response  
In order to further verify the effectiveness of the proposed proportional valve control method, a gradually varying ramp input was used. The response results of the DPV are shown in Figure 14.  
It can be observed from Figure 14 that, compared with the existing method, the proposed control method effectively suppresses the oscillation under the slowly varying input signal and exhibits a better position-tracking performance. The maximum displacement tracking error is 0.1 mm when the maximum stroke of the spool is 7 mm. As shown in Figure 14c, when the spool moves forward at a uniform speed, HSV 1 and 3 are in the working state, and HSV 2 and 4 are in the non-working state. This control method is energy-saving and efficient, which can reduce the wear of the HSV and improve the service life. As is evident from Figure 14d, as the stroke of the spool increases at a constant speed, the pressure p5 in the left chamber increases at a constant speed. With the sudden increase in the valve port area at 10.239 s, the flow force increases in a stepwise manner, and the pressure p5 in the control chamber also increases, and the main spool oscillates with a small  
Figure 13: Sinusoidal response of the proportional valve: (**a**) Sinusoidal response of the main spool; (**b**) displacement tracking error; (**c**) control input of HSVB; (**d**) pressure of the two control chambers of the main spool.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **ISE Value** \\ \hline PID of half-bridge control & 490.493 \\ Proposed & 57.691 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance indices for the studied valve subjected to the sinusoidal input response.  
amplitude and then quickly resumes smooth operation. This satisfies Equation. 1, which proves the effectiveness of the proposed control method.
## 4 Calibration and Application Experiments
### Stability Tests  
The stability of the proposed precision level was tested in a non-temperature-controlled environment. While measuring the stability of the precision level, it is directly mounted on an optical bench. The one-hour stability data of the developed level sensor are shown in Figure 11. The drift of the reading is less than 1 arc-sec within one hour. The stability of the proposed dual-axis precision level is satisfactory. It can also be seen from the stability data that the short-term reading jitter is only about 0.1 arc-sec, which can also represent the resolution of the proposed sensor.  
Figure 11: Stability data of the proposed precision level.  
Figure 10: The response time of the proposed precision level: (**a**) all the response raw data; (**b**) partial view.
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 5 Discussion  
### Factors Affecting the Task Performance  
The size of the target was a major factor affecting the performance of tasks on other devices, such as mobile and touchpad [43; 44; 58]. In previous research, it was confirmed that the size of the target is a factor affecting the ER, and most research shows that the larger the target size, the faster the input task can be performed. In this study, we found a significant difference in task completion time between the two levels of target size. On the other hand, it is not a statistically significant factor in the ER, although more errors occurred when a small target was pressed. Therefore, if we extend the range of the target size further, it is expected that we can find the size of the target that can achieve minimum usability in terms of time and error.  
The method of selecting the target also showed statistically significant results. The subjects could process tasks on average 500-ms faster than the gaze-selection when using manual-selection. Therefore, the manual-selection method was more advantageous for the content needing fast and continuous selection. However, the results in Figure 8 implies that the target selection task can be performed regardless of the size of the target in gaze selection, and the accuracy was affected more sensitively by the target size in the case of manual selection. It is noteworthy that, whereas the same operation was performed, the effect of the target size was small in the case of the gaze selection.  
Both methods have a low error probability of less than 1%, but the target selection method has a statistically significant effect on the errors. The manual-selection method produced 9-times more errors than the gaze-selection method. As mentioned in Section 4.1, the manual-selection method had an advantage in terms of the task completion time. However, it was not in terms of accuracy. Factors such as hand trembling of participants and cursor deviation caused by head movement when pressing the touchpad may have affected the errors. Therefore, the gaze-selection method should be used in cases where accurate selection is required. Considering that most errors occurred in the gaze-selection method were wrongly selected the previous target without the subject noticing that the target had changed, it can be judged that the gaze-timing on the target may not be sufficient in certain situations or more clear feedback may be needed. These findings support the process of designing interfaces to perform accurate and fast tasks.
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 1 Introduction
For instance, if the power calculation reveals that the satellite will not have enough energy to perform a specific duty at the necessary frequency, the engineer may need to lower the number of payloads or modify the design to improve energy efficiency. Knowing the estimated power is also essential for scheduling satellite tasks. Offline scheduling algorithms can use this information to find the optimal times to activate and deactivate payloads to maximize available energy usage. This can ensure that the satellite can complete all its responsibilities within the allotted time. This paper's objectives are to provide a methodology as well as ready-to-use problems of the Offline Nanosatellite Task Scheduling (ONTS) optimization problem. With the use of actual input data for power harvesting calculations based on the FloripaSat-I mission, the study seeks to provide realistic means to compare different solution techniques for the OINTS issue. The study's examples and methods are created to help researchers assess their approaches and contrast their findings with those in the published works. By providing a standard place to begin future investigations and creating a baseline for the OINTS problem, this study's ultimate purpose is to advance the area of research.
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 3 Design of Composite Layered Cross-Coupling Controller  
Usually, the sources of machining errors in multi-axis linkage CNC systems mainly manifest in single-axis tracking errors and multi-axis linkage contour errors. The design method of the single-axis controller was provided in the second section. Although the performance of single-axis tracking has a certain impact on the final machining accuracy, the contour error has a more significant impact. Therefore, this section proposes a composite layered cross-coupling controller design, which divides the spatial contour into upper and lower layers. Firstly, cross-coupling control is applied to each layer, and finally, the two planes are combined to form a spatial contour model for control. Although the cross-coupling control algorithm of traditional multi-axis systems ensures the overall contour error of the system, it cannot guarantee the contour accuracy of a single plane, neglecting the collaborative problem generated by multi-plane coupling and limiting the flexibility of cross-coupling controller design. Cross-coupling controllers are established for the upper and lower layers to ensure the overall contour accuracy and single-plane contour accuracy of the multi-axis motion system based on the construction of a layered contour error model. The structure diagram of the composite layered cross-coupling control algorithm is shown in Figure 6.  
In the above, \(x_{d}\), \(y_{d}\), and \(z_{d}\) are the input desired trajectories, respectively, \(x\), \(y\), and \(z\) are the output desired trajectories, respectively, \(e\) is the tracking error, and \(C\) is the cross-coupling coefficient. From Figure 6, it can be seen that the composite layered control system first ensures the contour accuracy of the XY layer. Based on its coupling results, a virtual axis is established, and the Z-axis is used to achieve spatial contour accuracy control. This system ensures the contour accuracy of a single plane and improves the overall contour accuracy.
###### Abstract  
The environmental concern about waste generation and the gradual decrease of oil reserves has led the way to finding new waste materials that may partially replace the bitumens used in the road paving industry. Used motor oil from vehicles is a waste product that could answer that demand, but it can also drastically reduce the viscosity, increasing the asphalt mixture's rutting potential. Therefore, polymer modification should be used in order to avoid compromising the required performance of asphalt mixtures when higher amounts of waste motor oil are used. Thus, this study was aimed at assessing the performance of an asphalt binder/mixture obtained by replacing part of a paving grade bitumen (35/50) with 10% waste motor oil and 5% styrene-butadiene-styrene (SBS) as an elastomer modifier. A comparison was also made with the results of a previous study using a blend of bio-oil from fast pyrolysis and ground tire rubber modifier as a partial substitute for usual PG64-22 bitumen. The asphalt binders were tested by means of Fourier infrared spectra and dynamic shear rheology, namely by assessing their continuous high-performance grade. Later, the water sensitivity, fatigue cracking resistance, dynamic modulus and rut resistance performance of the resulting asphalt mixtures was evaluated. It was concluded that the new binder studied in this work improves the asphalt mixture's performance, making it an excellent solution for paving works.  
waste motor oil; styrene-butadiene-styrene; bitumen modification; bio-binder; asphalt mixtures; performance
## 4 Results and Discussion  
### Optimization of Modeling Results  
#### 4.1.1 Verification of Model Accuracy and Data Normality  
The modeling results, obtained after reanalysis following the removal of interaction terms with insufficient statistical significance in the final model, are presented below. The predictive capability of the final model was exceptionally high, as evidenced by an R  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Symbol**} & \multicolumn{4}{c}{**Range of Levels**} \\ \cline{3-5}  & & **Low** & **Center** & **High** \\  & & **(\(-\)1)** & **(0)** & **(1)** \\ \hline Spray pressure (bar) & X1 & 1 & 2 & 3 \\ Spray speed (mm/min) & X2 & 50,000 & 75,000 & 100,000 \\ Spray distance (mm) & X3 & 200 & 300 & 400 \\ Number of sprays (count) & X4 & 1 & 3 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Four factors of the first optimization experiment.  
value of 0.9712 and an adjusted R\({}^{2}\) value of 0.9560. This indicates that the model accurately represents the experimental data. Additionally, the root mean squared error (RMSE) value was calculated to be 166.08. This indicated that the model's predicted values deviate from the observed values by approximately \(\pm\)166.08 units on average. This metric provides a quantitative measure of typical prediction error and further supports the assessment of the model's accuracy. The ANOVA results for the initial dust dispersion model are presented in Table 4  
As shown in Figure 5, In the normal probability plot for the model, the data points generally align with the reference line. Furthermore, the histogram of residuals does not exhibit any discernible bias (e.g., left-right asymmetry) or multimodal distribution. Consequently, it was confirmed that the assumption of normality is largely satisfied.
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 3 Results and Discussion  
### Carbon Steel Weld Defect Data Collection  
The highest amplitude A-scan signal in the defect signal was selected as the data sample for subsequent research. In a series of detection experiments, 150 datasets were collected, with 30 sets each for crack defects, lack of fusion defects, lack of penetration defects, porosity defects, and slag inclusion defects. Using the measurement results from specimen 1# as a reference, the visual characteristics of the five common defects were analyzed.  
(1) Crack Defect  
In the A-scan view, the waveform amplitude of the crack defect signals was broad, often exhibiting multiple peaks, and the echo waveform was unstable with significant fluctuations. In the S-scan view, large areas of overlapping color blocks were commonly observed. The spectrum of the crack defects is illustrated in Figure 6.  
Figure 5: Denoising flowchart of the SSA-VMD-WPD method.  
In the A-scan view, the waveform of lack of fusion defect signals was generally single-peaked. In the S-scan view, a lack of fusion defects often occurred at the groove, with the defect orientation commonly parallel to the groove, as shown in Figure 7.  
In the A-scan view, the waveform of the lack of penetration defect signals typically showed a single peak with a large echo amplitude and narrow waveform, which was stable. In the S-scan view, a lack of penetration defects often occurred at the toe of the weld seam, as shown in Figure 8. Observing the S-scan view in the figure, the lack of penetration defect was located at the toe of the X-groove weld seam.  
Figure 6: Crack Defects. (The red boxes showed the crack defects at the corresponding signals).  
Figure 7: Unfused Defects. (The red boxes showed the unfused defects at the corresponding signals).  
(4) Porosity Defect  
Based on the A-scan signal of the porosity defects, the waveform of the porosity defect signals generally had a smaller echo amplitude, showing a single and independent peak. The spectrum of porosity defects can be seen in Figure 9.  
(5) Slagging defects  
The echo waveform of the slag defect signal was unstable and irregular in shape. The spectrum can be seen in Figure 10.  
Figure 8: Unwelded Defects. (The red boxes showed the unwelded defects at the corresponding signals).  
Figure 9: Porosity Defects. (The red boxes showed the porosity defects at the corresponding signals).