esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In view of this, the issue of active attitude fault-tolerant stabilization control for rigid spacecraft subject to actuator faults, inertia uncertainty, and external disturbances is investigated in this paper. A novel mixed learning observer (MLO) is proposed to robustly and accurately detect actuator faults, and its stability is proven in detail. Further, based on the mixed learning algorithm, a mixed learning sliding mode controller with a fault-tolerant ability is designed to accurately stabilize spacecraft attitude by compensating for the effect of the jointed disturbances. Finally, a satellite ACS example is simulated to show the effectiveness and superiority of the proposed spacecraft attitude fault-tolerant stabilization control approach. The main contributions of our research work can be highlighted in the following three aspects:  
1. To accurately reconstruct bias actuator faults, we develop a novel mixed learning algorithm by synthesizing the iterative learning algorithm [27] and repetitive learning algorithm [28]. The proposed mixed learning algorithm inherits the advantages of both learning algorithms and can smooth the reconstruction signal, especially when an abrupt disturbance or noise exists. 2. Based on the mixed learning algorithm, a novel MLO is proposed such that it can both estimate attitude angular velocity and reconstruct bias torque resulting from actuator faults, robustly and accurately. Compared with existing work [29; 30], it is more convenient for observer gain parameters to be computed. 3. To guarantee the robust and accurate attitude stabilization, a mixed learning sliding mode control (MLSMC)-based spacecraft active attitude fault-tolerant controller is designed by combining the proposed mixed learning algorithm and the SMC methodology. In the proposed MLSMC scheme, the mixed learning algorithm is first employed to update online and compensate for the jointed disturbance. Compared with the existing SMC [31], the proposed MLSMC approach has the ability to achieve higher attitude stabilization accuracy due to the mixed learning algorithm. The remainder of this paper is organized as follows. In Section 2, spacecraft ACS is modeled, and the problem formulation is presented. Section 3 provides the MLO design for actuator fault reconstruction and its stability analysis. Next, the MLSMC-based spacecraft attitude fault-tolerant stabilization controller design is provided in Section 4. Simulation studies are provided in Section 5. Finally, some concluding comments are drawn in Section 6.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
###### Abstract  
The Moon is the closest celestial body to the Earth. Its rich unique resources are an important supplement to the Earth's resources and have a profound impact on the sustainable development of human society. As large-scale exploration missions gradually progress, demands for communication, navigation, surveying and other services of lunar-space probes have significantly increased. Constellation navigation and orbit determination technology will become an indispensable part of future lunar exploration infrastructure. This article systematically analyzes the current status of lunar relay navigation satellite networks at home and abroad, summarizes the technical principles of single-satellite and constellation navigation and orbit determination, discusses the technical difficulties in lunar navigation constellation orbit determination and navigation, and analyzes possible solutions. Finally, the development trend of research on high-precision orbit determination and navigation methods for lunar navigation constellations is proposed.  
lunar exploration; precise orbit determination; autonomous navigation Article
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 4 Classification Module  
In the classification module, the defects' regions are classified into their specific categories. When the surface film of a metallic component is different, the same defect (damage spot) may have a different color by imaging. So, the color information does not help in the classification of defects. The image's patch of defect regions is firstly converted to gray images in order to reduce the influence of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index of Convolutional Layers** & 3 & 5 & 7 & 9 \\ \hline Atrous Factor & 2 & 2 & 4 & 4 \\ Receptive Field Size & \(7\times 7\) & \(7\times 7\) & \(15\times 15\) & \(15\times 15\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of atrous convolution in the AE network.  
different background colors and lighting. Figure 5 shows the overall architecture of the proposed CNN. All of the grayscale images of the defect regions are resized to 227 \(\times\) 227 for unified input. The proposed CNN contains five convolutional layers and three max pooling layers. The kernel size, the number of kernels, the stride, and the padding for each layer are specified in Table 2. Each convolution layer is followed by a rectified linear unit (ReLU). Moreover, a batch normalization layer is added after the first two convolutional layers for speeding up the training process. It can trim the data in each channel with zero mean and unit variance. In the last layers, all of the units are fully connected to output probabilities for three classes using the softmax function.  
Our proposed CNN is a compact network that is smaller than the classic classification networks such as GoogleNet [41] and ResNet [42]. This network is more suitable for the metal surface defect inspection tasks for the following two reasons. One the one hand, classical classification networks usually aim at natural images in public datasets, and their training samples far exceed the defect data in industrial inspection. Therefore, our network should be trained from scratch based on industrial defect data, instead of using the classical classification network as a pre-training model. On the other hand, the compact structure of this network reduces the classification time, and is suitable for industrial online inspection.
## 2 Methodology
### Demand Prediction Model
Equation (2) defines the softmax formula for \(\Pi_{k}\). The objective is to minimize the loss, as stated in Equation (3). By accurately representing distributional nuances, our combined model outperforms conventional LSTM-RNN models, revolutionizing the field of taxi demand prediction. \[P(Y=y|X=x)=\sum_{k=0}^{K-1}\Pi_{k}(x)\Phi(y,\mu_{k}(x),\sigma_{k}(x)),\sum_{k=0 }^{K-1}\Pi_{k}(x)=1 \tag{1}\]  
\[SoftMax(\Pi_{k})=\frac{e^{\Pi_{k}}}{\sum_{k=0}^{K}e^{\Pi_{k}}} \tag{2}\]  
Figure 2: The LSTM-RNN layout with the tanh activation function on each RNN layer and the softmax output layer. \[E_{t}=-\ln\left\{\sum_{k=1}^{M}\Pi_{k}(x)g_{k}(y_{t}|x)\right\} \tag{3}\]  
As shown in Figure 3a, the final model is a combination of the two networks described earlier. The model initially extracts essential information from the historical records to forecast temporal data at time \(t\). Before entering the LSTM-RNN, this input undergoes a resampling processing layer. Subsequently, the model produces projected demand values for each location ID in the upcoming period. The MLP-MDN model simultaneously creates a probability distribution representing demand across the map at the specified time. This two-pronged approach not only aids in forecasting demand beyond predetermined zones but also equips the front-end with the capability for two-dimensional visualization.
## 4 Results and Discussion
### A New Method to Compute the Average Free Surface Profile  
We start by considering that using the traditional Manning equation, it is possible to determine the average value of the EGL slope, \(J_{a}\), as follows:  
\[J_{a}=\frac{n^{2}U_{a}^{2}}{R_{a}^{4/3}} \tag{23}\]  
where \(U_{a}\) and \(R_{a}\) are the average values of velocity and hydraulic radius, respectively. For a fixed value of \(n\), the free surface profile can be calculated using the direct step method [37]. In fact, if a downstream flow depth is known, an upstream one can be fixed and the  
Figure 4: Comparison between the measured flow depth and the fitted logarithmic function [1]: (**a**) T5; (**b**) T18. The black bold line indicates the flume bottom; the black line is the calculated free surface; the red symbols indicate the measured free surface.  
distance between the two relative sections, \(\Delta x\), can be calculated using the finite difference of Equation (2), that is:  
\[\Delta x=\frac{\Delta E}{i-J_{a}} \tag{24}\]  
with \(J_{a}\) given by Equation (23) and where \(\Delta E\) is the difference between the downstream and upstream specific energies.  
The direct step method permits the identification, for each test, of the \(n\) value that best allows the experimental free surface profile to be reproduced. As an example, Figure 6 shows for tests T5 and T18 the comparison between the calculated and measured free surface profiles, with the corresponding \(n\) values that best represent them.  
Figure 5: Drag coefficients obtained following the procedure of [PERSON] et al. [1]: (**a**) T5; (**b**) T6; (**c**) T9; (**d**) T12; (**e**) T18; (**f**) T23.  
Figure 6: Comparison between the measured and the calculated free surface profiles obtained by means of the best Manning coefficient: (**a**) T5; (**b**) T18. The black bold line indicates the flume bottom; the black line is the calculated free surface; the red symbols indicate the measured free surface.  
Figure 7: Drag coefficients \(C_{D-n}\) computed by means of the best Manning coefficient versus \(R_{\mathcal{D}_{n}}\): (**a**) T5; (**b**) T6; (**c**) T9; (**d**) T12; (**e**) T18; (**f**) T23.  
In this way, we obtained a continuous line representing the free surface profile and the \(J\) values for each abscissa. The \(C_{D}\) experimental values, here identified as \(C_{D-n}\), were computed by means of Equation (9) and, when plotted versus \(Re_{D*}\), they appear as in Figure 7.  
In each subplot, \(C_{D}\) presents a monotonic pattern versus \(Re_{D*}\), with the maximum value at the end of the stem array, where the maximum value of \(Re_{D*}\) was found.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Computational Complexity and Encryption Speed  
The computational complexity of the proposed scheme for an image with n number of pixels is depicted in the Table 10. The lifting integer wavelet transform reduces the computational time by almost 50% compared to the traditional wavelet transform and the SPIHT encoding algorithm is the most computationally efficient compression algorithm. Only Kd-tree takes more time when applied on the whole data set, compared to other compression and encryption schemes. However, many researchers have proposed fully parallel Kd-tree implementations in other big-data applications; hence, the processing time can be reduced in the future by implementing a fully parallel Kd-tree for this scheme. The chaotic map encryption is computationally very light weight as like XOR operations mostly. Table 11 lists the compression and encryption speeds of the proposed scheme.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 3 Methods
### Optimization Algorithm
#### 3.4.2 Gradient Estimation  
In stochastic gradient descent, the gradient estimate for each iteration exhibits a degree of randomness and may not align with the optimal gradient for the entire training set. To mitigate this stochasticity and enhance the optimization speed, the average gradient over a recent period of time can be employed as the direction for parameter updates. This technique is referred to as gradient estimation correction and is frequently implemented using methods such as the momentum method.  
The concept of momentum in optimization algorithms is borrowed from classical physics. In physics, momentum refers to an object's tendency to continue moving in its present direction, and it is calculated as the product of the object's mass and velocity. In the realm of optimization algorithms, the momentum method replaces the actual gradient with the previously accumulated momentum. The gradient at each iteration in optimization algorithms can indeed be likened to an acceleration. It signifies the direction and magnitude of changes needed to minimize the loss function and guide the optimization process. In the t-th iteration, Equation (9) is utilized to compute the weighted moving average of the negative gradient, which then serves as the direction for updating the parameters. This approach helps reduce randomness and speeds up the optimization process.  
\[\Delta\theta_{t}=\rho\Delta\theta_{t-1}-\alpha\mathbf{g_{t}}=-\alpha\sum_{\tau =1}^{t}\rho^{t-\tau}\mathbf{g_{t}} \tag{9}\]  
where \(\rho\) is the momentum factor, which is typically set to 0.9, and \(\alpha\) represents the learning rate.  
In this manner, the actual parameter update for each parameter relies on the weighted average of gradients over a recent period. If the gradient direction for a parameter has shown consistency in the recent past, its true parameter update is greater, effectively acting as an accelerator. Conversely, if the gradient directions have been inconsistent in the recent past, the true parameter update becomes smaller, serving as a decelerator to enhance stability. This allows the use of a larger learning rate, potentially resulting in quicker convergence. Nevertheless, directions with larger gradients will exhibit less amplitude per update due to momentum. As a result, the learning rate remains constant when employing the momentum method.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 4 Evaluation Metrics
### F-Measure  
F-measure is another measure derived from precision and recall, and it exhibits the behavior of both recall and precision metrics. F-measures can be a more useful metric than precision and recall; since precision and recall give various information that can complete one another when combined. F-measure will reflect the metric that excels more than the other one. From a probability perspective, F-measure is the number of tests that need to be implemented to detect the first failure [47]. By varying \(\beta\), the value of F\({}_{\beta}\) gives more weight to one metric over the other. Yet, the most common F-measure is known as the consistent mean of precision and recall (F1), where \(\beta=\frac{1}{2}\). Note that the maximum value F-measure can be is 1, which means that all predictions are accurate recommendations. It is useful to understand ranked retrieval. Here, Precision@K is the ratio of the top-k relevant items, and Recall@K is the ratio of relevant items in top-k. Ranked retrieval works under the assumption that the user will examine only the top-k results. Knowing this, [48] shows that as the value of \(k\) increases, F-measure will also increase because of the recall value's upward nature. Equations (2) and (3) show the calculations of both the F\({}_{\beta}\) and F1.  
\[F_{\beta}=\frac{precision\ \times recall}{(1-\beta)\times precision+\ \beta\times recall} \tag{5}\]  
\[F_{1}=\frac{2\times precision\ \times recall}{precision+\ recall} \tag{6}\]  
Figure 4: Simultaneous optimization of the precision and recall.  
Figure 3: ROC (Receiver operating characteristic) curve for the recall against fallout.
## 5 Conclusions  
This study presented a new mathematical model for 3Z system with three zones among zone-based registration methods using a semi-Markov process approach. Assuming a hexagonal zone, the performance of 3Z was compared with those of previous 1Z and 2Z systems. That is, different from other previous studies, in this study, we analyzed performances of 1Z, 2Z, and 3Z for a hexagonal-shaped zone for the first time by exactly modeling two types of 3Z structures in more realistic wireless network environments. Based on the numerical results, when the call-to-mobility ratio (CMR) is smaller (that is, when the number of inter-zone movements of the user equipment (UE) is relatively high compared to the UE's call occurrence), the performance of 3Z is more superior to 1Z and 2Z. Moreover, as the probability of returning to the zone previously registered increases and the number of times it moves to other zones increases, the registration cost eventually decreases, resulting in the better performance of 3Z. However, when the CMR is large (in this study, in the situation where the UE remains in the zone for more than twice the time interval of the call occurrence), the performance of 3Z is superior to that of 1Z and similar to that of 2Z. Finally, it was observed that efficient radio resources could be operated if the zone-based registration scheme was applied differentially in consideration of UE's mobility (CMR). The results of this study can be used to design the optimal zone-based registration operation for each UE. The 3Z system can be easily implemented without substantial hardware development using the 1Z scheme that is widely used in most wireless communication networks.  
The 3Z system is also be expected to be an algorithm to efficiently control movement of devices such as autonomous vehicles, drones, robots and moving IoT that will appear in 5G/6G networks in the future. Future studies should present a new model with improved performance by automatically configuring 3Z RA without needing additional registration using prior information on the zone _id_ structure when the UE moves. Moreover, a comparative analysis could be conducted with previous studies, considering a square-shaped zone.  
Modeling and data analysis, H.-S.J. and J.-H.B.; Writing-original draft, J.-H.B.; Writing-review & editing H.-S.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Research Base Construction Fund Support Program funded by Jeonbuk National University in 2023. It was also supported by grants (No.2021 RIF1A1049933 and No.2022R1F1A1074141) of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT), Republic of Korea.  
Not applicable.  
We comply with MDPI Research Data Policies.  
The authors sincerely thank the anonymous reviewers for their hard work and efforts to review this paper.  
The authors have no conflict of interest relevant to this study to disclose.
## 7 Histopathological Findings  
A histopathological examination should be performed to distinguish AGEP from other pustular eruptions. The skin biopsy should include a pustule. Typically, the biopsy shows spongiform subcorneal and/or intraepithelial pustules, an oedemaous papillary dermis and perivascular infiltrates with neutrophils and some eosinophils (Figure 3). In some cases, necrotic keratinocytes and leucocytochastic vasculitis can also be found. Histopathologically, it can be difficult to differentiate AGEP from GPP. The presence of eosinophils, necrotic keratinocytes, a mixed interstitial and mid-dermal perivascular infiltrate and absence of tortuous or dilated blood vessels favors AGEP, while the presence of psoriasisiform acanthosis is characteristic of GPP [24; 54].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **AGEP** & **Generalized Pustular Psoriasis** \\ \hline \begin{tabular}{l} **History of psoriasis** \\ **(family/personal)** \\ \end{tabular} & usually lacking & often present \\ \hline \begin{tabular}{l} **Distribution pattern** \\ \end{tabular} & initially predominance in the folds & more generalized \\ \hline \begin{tabular}{l} **Onset of pustules** \\ \end{tabular} & fast (hours or few days after use of medication) & slower \\ \hline \begin{tabular}{l} **Duration of pustules** \\ \end{tabular} & \begin{tabular}{l} Shorter (rapid resolution in a few days, max. 15 days, \\ after drug suspension) \\ \end{tabular} & longer \\ \hline \begin{tabular}{l} **Size of pustules** \\ \end{tabular} & tiny (pinhead) & larger \\ \hline \begin{tabular}{l} **Duration of eruption/fever** \\ \end{tabular} & shorter (resolution in a few days after drug suspension) & longer \\ \hline \begin{tabular}{l} **History of drug reaction** \\ \end{tabular} & usual & uncommon \\ \hline \begin{tabular}{l} **Recent drug administration** \\ \end{tabular} & very frequent & less frequent \\ \hline \begin{tabular}{l} **Arthritis** \\ \end{tabular} & rare & about 30\% \\ \hline \
\begin{tabular}{l} **Histology** \\ \end{tabular} &
\begin{tabular}{l} single-cell necrosis of keratinocytes, edema of papillary \\ dermis, vasculitis, exocytosis of eosinophils \\ \end{tabular} & papillomatosis, acanthosis, tortuous or dilated vessels \\ \hline \hline \end{tabular}
\end{table}
Table 1: Factors favoring the diagnosis of AGEP over pustular psoriasis.  
Figure 3: Typical histological features of AGEP are indicated.
# Modeling of Muscle Activation from Electromyography Recordings in Patients with Cerebral Palsy
## 3 Results  
The EMG signals collected for full cycling revolutions were converted into muscle activation patterns following the mathematical transformations described in the Methods section. Examples of two such data sets are presented in Figure 3, and in Figure 4 for a healthy participant and for a patient with CP.  
Comparison of top and central panels in both Figures 3 and 4 show that signals \(E(t)\) and \(n(t)\) have similar shapes. They are separated only by the delay time (\(d\)) considered in the conversion from \(E(t)\) to \(n(t)\), as shown in more detail in Figure 5.  
The nonlinear transformation delivered \(a(t)\) vs. \(n(t)\) plots that were similar for the patient and control groups, as illustrated by the individual data sets in the bottom panels of Figures 3 and 4.  
However, muscle activation assessed at \(0^{\circ}\) crank angle position of the cycling path was much more variable in patients, as indicated by the norm distance values in Figure 6. This deviation occurred for all four muscles and was larger for higher MAS levels.  
Figure 3: EMG signal \(E(t)\) (1 st row) collected from lower extremity muscles during cycling on an ergometer and mathematical transformations (middle and bottom row) applied to \(E(t)\) as schematically described on the left border of the figure. The first and third panels (from left) illustrate an example from a healthy participant, and the second and fourth panels from a participant with left hemiparesis and spasticity MAS level 3. Block (**a**) refers to left biceps femoris (LBF); block (**b**) refers to left rectus femoris (LRF).  
Figure 4: Similar to Figure 3 but for two other muscles under consideration. Figure panels correspondence as in Figure 3. Block (**a**) for left tibialis anterior (LTA); block (**b**) for left gastrocnemius (LGT).  
Figure 5: Time difference between EMG recording (solid line) and input spread function \(n(t)\) (dashed line, input for motor response) against time. The two functions are separated by electromechanical delay time \(d\).