esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data Description  
Three real-world time series tasks were utilized for method evaluation, including sleep stage classification (SSC), human activity recognition (HAR), and epilepsy seizure prediction (ESP). We compared our performances with state-of-the-art approaches on three publicly available datasets. Table 1 summarizes the details of each dataset, e.g., the number of training samples (Train), validation samples (Valid), and testing samples (Test), the length of the sample (Length), the number of sensor channels (Channel), and the number of classes (Class).  
Sleep staging classification is a task that involves classifying EEG signals into one of five sleep stages: wake (W), non-rapid eye movement (N1, N2, N3), and rapid eye movement (REM). For this task, we utilized the Sleep-EDF dataset [23]. The Sleep-EDF dataset contains 42,308 epochs in 39 sleep cassette files collected from 20 subjects aged 25-34. Each subject in the Sleep-EDF database contains 2 day-night PSG recordings, except subjects 13, 36, and 52, whose 1 recording was lost due to device failure. The duration of each epoch is 30 s, and it has been labeled as wake, REM, N1, N2, N3, N4, MOVEMENT, UNKNOWN by experts according to the R&K standard. Specifically, we focused on a single EEG channel (Fpz-Cz) sampled at a rate of 100 Hz, following the methodology established in previous studies [21,24].  
Human activity recognition is concerned with the classification of sensor readings into one of six activities: walking, walking upstairs, walking downstairs, standing, sitting, and lying down. To address this task, we utilized the UCI HAR dataset [25]. Human activity recognition contains 8828 epochs from 30 participants aged 19-48 years. It is concerned with the classification of sensor readings into one of six activities: walking, walking upstairs, walking downstairs, standing, sitting, and lying down. Each sample selects data in a fixed time window of 2.56 s, which is 128 sampling points.  
Epilepsy seizure prediction involves predicting the occurrence of epileptic seizures using EEG recordings. For this task, we leveraged the Epileptic Seizure Recognition (ESR) dataset [26], which consists of EEG recordings obtained from 500 subjects. Each subject's brain activity was recorded for a duration of 23.6 s. As the dataset originally included multiple seizure-related classes, we merged four out of the five classes into a single class as they did not include epileptic seizures. Consequently, the task was transformed into a binary classification problem.
## 3 Modeling of Path Planning
### Global Path Planning  
The global path planning in the navigation layer generates a global network node path for AUV navigation based on the task execution sequence generated by the decision layer. The task execution sequence generated by the decision layer has three disadvantages for the following path planning. Firstly, the generation procedure of task sequence does not consider the influence of environment in detail. This will result in the length of the planned path being larger than the distance of two task points. Secondly, the distances between two task points are usually too long, especially for the scattered tasks in the large-scale marine environment. This characteristic will lead to the long computing time of calculating the optimal path and go against real-time planning and re-planning. Thirdly, the distances between two task points largely differ so that it brings difficulties to the real-time path planning and path re-planning.  
Therefore, in the global path planning stage, this study expressed the task execution sequence as one task network graph \(G^{\text{T}}=(V^{\text{T}},E^{\text{T}})\). \(V^{\text{T}}\) is the task node set of the task network graph (including one start point, one goal point, and lots of task points). \(E^{\text{T}}\) indicates the edge set of the task network graph. Some discrete intermediate points with roughly uniform length are randomly added to the task node set \(V^{\text{T}}\). Combined with the task node set \(V^{\text{T}}\), one path node set \(V^{\text{P}}\) and one path network graph \(G^{\text{P}}=(V^{\text{P}},E^{\text{P}})\) can be generated. The new edge set \(E^{\text{P}}\) can be easily obtained by trying to connect all the added nodes by straight lines. If the straight line connecting two nodes is passed through the land or danger area, the A* algorithm is used to search for the shortest path in the path network graph. The A* algorithm should not take much time to explore the shortest path in this path network graph since it does not concern itself about other constraints, except for the environment. However, it can obtain a discrete path with the shortest distance. The distance between two arbitrary path points is approximately the same that will benefit the real-time path planning and path re-planning. By this way, this global path planning overcomes the above three disadvantages of the task execution sequence generated by the decision layer. As shown in Figure 2, the left picture is the task execution sequence generated by the task planning algorithm, and the right picture is the refined global planning path generated by the discrete path points with uniform spacing distances.
## 1 Introduction  
Colored diamonds have always been highly sought after by consumers, and their colors are diverse, including gold, brown, green, and blue. In recent years, with the improvements in high-temperature and high-pressure (HTHP) treatment technology, this technology has also been applied to the field of diamond color modification. In 1999, General Electric (GE) used HTTP treatment technology to turn brown and gray IIa diamonds into colorless to nearly colorless diamonds [1, 2]; in the same year, HTTP treatment technology was also used to turn type Ia brown Nova diamonds into a bright yellow-green color [3, 4]. Since then, more and more scholars have begun to devote themselves to the research of HTTP treatment experiments [3, 5, 6]. With the continuous progress of treatment technology, through HTTP treatment, diamonds can present more vivid colors, such as golden yellow, pink, and so on [7, 8]. The research of the HTTP treatment of diamonds has also been deepening. HTTP laboratory conditions provided sufficient isotropic pressure and potential energy for treating lattice defects in brown-yellow diamonds, which helped the dislocations in type Ia and IIa brown-yellow diamonds to climb, reorganize, annihilate, proliferate, and slip, finally transforming the diamonds into colorless, yellow-green, and blue diamonds [1, 9, 10, 11]. In another study, the formation and evolution of lattice defectsjointly restricted diamond color and luminescence [10]. In other experiments, brown diamonds were changed to yellow after HTHP treatment, mainly related to H3, H4, and N3 centers and isolated nitrogen [12; 13; 14]. Diamonds becoming green or yellow-green after treatment has been shown to be mainly related to H2 and H3 centers [12; 14; 15; 16]; after treatment, the red color is mainly related to high concentrations of (N-V) centers [4; 12; 14; 17; 18]. In other experiments, the color of type IIa diamonds became white, mainly because no new color centers were formed due to vacancies and the disappearing of interstices [10; 14].  
However, previous research on HTHP-treated diamonds has mainly focused on the color-changing mechanism and spectroscopic characteristics; there is relatively little research on their luminescence. Therefore, this paper takes HTHP-treated diamonds as samples and conducts research on their luminescence characteristics by DiamondViewTM, cathodoluminescence, low-temperature photoluminescence (PL), and three-dimensional (3D) fluorescence spectrometry, and hopes to find a non-destructive and rapid method of identification of HTHP diamonds.
## 2 Materials and Methods
### Diagnosis Model Based on XGBoost  
XGBoost (eXtreme Gradient Boosting), proposed by [24], is a highly efficient, flexible and portable distributed ensemble learning algorithm under the Gradient Boosting framework. As an improvement of GBDT (Gradient Boosting Decision Tree), XGBoost has demonstrated its advantages in large-scale data processing; time efficiency in particular. These years, a large number of Kaggle competitors choose XGBoost, ending up with outstanding achievement. In fault diagnosis, XGBoost could be used in discriminating the working states of a sucker rod pump [25], predicting seam tensile strength for Al-Li alloy in laser welding [26] and indicating the health conditions of planetary gearboxes [27].  
Figure 2: Data assembling and labeling method of flight data and BIT record.  
XGBoost is an excellent diagnosis method in the real world due to its excellent ability to handle overfitting and missing values. The objective function of the XGBoost-based diagnosis model is:  
\[Obj^{(t)}=\sum_{i=1}^{n}l\left(y_{i},y_{i}^{(t-1)}+f_{t}(x_{i})\right)+\Omega(f _{t})+c \tag{1}\]  
where \(l\) is the convex loss function that measures the difference between the prediction \(\hat{y}_{i}\) and the target \(y_{i}\), for instance \(x_{i}\); \(\Omega\) penalizes the complexity of the model; \(\hat{y}_{i}^{(t)}\) is the prediction of the \(i\)-th instance at the \(t\)-th iteration; \(f_{t}\) is needed to minimize the objective; and \(c\) is a constant.  
Different from GBDT, XGBoost uses the Taylor Expansion to the loss function to optimize the objective quickly, and controls the complexity by adding a regularization term to cost function, ignoring the constant:  
\[Obj^{(t)}\simeq\sum_{j=1}^{T}\left[\left(\sum_{i\in I_{j}}g_{i}\right)\omega_ {j}+\frac{1}{2}\left(\sum_{i\in I_{j}}h_{i}+\lambda\right)\omega_{j}^{2}\right] +\gamma T \tag{2}\]  
where \(g_{i}=\partial_{\hat{y}(t-1)}l\left(y_{i},\hat{y}^{(t-1)}\right)\), \(h_{i}=\partial_{\hat{y}(t-1)}^{2}l\left(y_{i},\hat{y}^{(t-1)}\right)\) are the first and second order gradient statistics of the loss function; \(T\) is the number of leaves; \(\omega_{j}\) is the weight of leaf \(j\); and \(I_{j}\) is the instance set of leaf \(j\).  
Assuming \(I_{L}\) and \(I_{R}\) are the instance sets of nodes after the split, \(I=I_{L}\cup I_{R}\), we could choose the best split point by splitting the gain of leaf nodes as:  
\[Gain=\frac{1}{2}\left[\frac{\left(\sum_{i\in I_{L}}g_{i}\right)^{2}}{\sum_{i \in I_{L}}h_{i}+\lambda}+\frac{\left(\sum_{i\in I_{R}}g_{i}\right)^{2}}{\sum _{i\in I_{R}}h_{i}+\lambda}-\frac{\left(\sum_{i\in I}g_{i}\right)^{2}}{\sum _{i\in I}h_{i}+\lambda}\right]-\gamma \tag{3}\]  
There are two split methods in building trees that could be selected: a basic exact greedy algorithm with high accuracy, and a quick approximate method with a histogram algorithm (supported afterwards). In our fault diagnosis model, the exact greedy algorithm was chosen to scan all possible splitting points, finding the optimal splitter point with the largest gain. The grid search approach was employed in our model to tune the hyperparameters of XGBoost.
# Image Watermarking Scheme Using LSB and Image Gradient
## 4 Experimental Results
### Comparison with Other Paper  
In this section, we compare the results of our suggested techniques with other techniques. In [28], watermarking was achieved using DWT, DCT, and image gradient. DWT separates the image into multiple bands for insertion. The gradient is used to give a topological map of the image. The method in [13] is blind, whereby the watermark is embedded in the blue component of the RGB image in the spatial domain. The imperceptibility analysis of the methods in [13; 28; 29] for the baboon image is shown in Table 5. Figure 6 shows different types of images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attacks** & **Strengths** & **NC** \\ \hline Salt and Pepper (S\&P) & 0.1 & 0.9918 \\ S\&P & 0.2 & 0.9850 \\ S\&P & 0.5 & 0.9853 \\ S\&P & 1.0 & 0.9918 \\ Speckle & 0.04 & 0.9937 \\ Gaussian & 0.05 & 0.9918 \\ JPEG & 75\% & 0.9976 \\ JPEG & 20\% & 0.9966 \\ JPEG & 10\% & 0.9937 \\ Median Filter & [3 3] & 0.9962 \\ Median Filter & [5 5] & 0.9966 \\ Rotation & \(-\)50 & 0.9966 \\ Translation & [50 50] & 0.9955 \\ Cropping & 25\% & 0.9993 \\ Cropping & 50\% & 0.9988 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized correlation strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IQA** &
\begin{tabular}{c} **Suggested** \\ **Technique** \\ \end{tabular} & [28] & [13] \\ \hline PSNR & 53.18 & 42 & 49.89 \\ SSIM & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Imperceptibility analysis.  
Figure 6: \(Cont\).  
Noise insertion, smoothening, cropping, and contrast enhancement are considered common image processing distortions. A comparison of these factors with the method suggested by [PERSON] et al. (2018) is shown in Table 6. Figure 7 indicates the comparison of SSIM and PSNR.  
Table 7 shows that suggested method has more robustness than the method proposed by [PERSON] et al. (2013).  
In image watermarking, a geometric attack is basically a displacement of a pixel by a random amount. In other words, the original watermark is present, but bits are displaced. The development of such a technique is compulsory in geometric distortion correction. Table 8 illustrates the attacks and normalized correlation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Attacks**} & \multirow{2}{*}{**Strengths**} & **Normalized** & **Correlation** \\ \cline{3-4}  & & **Suggested Technique** & [28] \\ \hline S\&P & 0.01 & 0.9970 & 0.6833 \\ S\&P & 0.03 & 0.9948 & 0.4013 \\ Gaussian Noise & 0.001 & 0.9910 & 0.9036 \\ Gaussian Noise & 0.003 & 0.9904 & 0.6974 \\ JPEG & 60\% & 0.9916 & 0.9713 \\ \hline \end{tabular}
\end{table}
Table 6: Normalized Correlation Against Image Processing Attacks.  
Figure 6: (**a**) Original baboon image; (**b**) watermark image; (**c**) scramble image; (**d**) watermarked baboon image.  
Figure 7: PSNR and SSIM comparison.
## 3 CDHS Controller with Luenberger Observer
### CDHS Control Layer
#### 3.3.2 CDHS Controller  
Due to the limited computing power of the processor used in a space manipulator, a simplified model must be used. Given that the stiffness of the joint's output unit is significantly greater than that of the transmission unit, the stiffness and damping of the output and transmission units can be combined. Assuming constant joint transmission stiffness and only considering viscous friction, the joint's dynamic equation can be simplified to a transfer function model, where the motor input angle is \(\theta_{i}\), the joint output angle is \(\theta_{o}\), the joint equivalent stiffness is \(k_{t}\), the equivalent inertia is \(J_{t}\), and the viscous friction system is \(f\). The transfer function model is as follows:  
\[\frac{\theta_{o}(\mathrm{s})}{\theta_{i}(\mathrm{s})}=\frac{k_{t}}{J_{t}s^{2}+ fs+k_{t}} \tag{33}\]  
The undamped oscillation frequency \(\omega_{n}\) and damping ratio \(\xi\) of the system can be calculated using the following equations:  
\[\omega_{n}=\sqrt{\frac{k_{t}}{J_{t}}}\xi=\frac{f}{2\sqrt{J_{t}k_{t}}} \tag{34}\]  
According to the above formula, it can be observed that an increase in load inertia leads to a decrease in system damping and a decrease in undamped oscillation frequency, which may result in joint motion overshoot or instability. To address this issue, a closed-loop damping control system is introduced via speed damping feedback, and a position variable gain controller is added to adjust the closed-loop stiffness of the system. Here, \(k_{1}\) represents the position control gain, \(k_{s}\) represents the damping control gain, and the transfer function of the joint closed-loop control system is as follows:  
\[\frac{\theta_{o}(\mathrm{s})}{\theta_{i}(\mathrm{s})}=\frac{k_{t}k_{1}}{J_{t} s^{2}+(f+k_{t}k_{1}k_{s})s+k_{t}k_{1}} \tag{35}\]  
The new system undamped oscillation frequency \(\omega_{n}\) and system damping \(\xi\) are calculated as follows:  
\[\omega_{n}=\sqrt{\frac{k_{t}k_{1}}{J_{t}}}\xi=\frac{f}{2\sqrt{J_{t}k_{t}k_{1}} }+\frac{k_{s}}{2}\sqrt{\frac{k_{t}k_{1}}{J_{t}}} \tag{36}\]  
The above formula shows that the speed damping feedback introduces an additional term of \(\frac{k}{2}\sqrt{\frac{k_{t}k_{1}}{J_{t}}}\) compared to the original system. By setting a desired reference inertia \(J_{exp}\), for the joint, the speed damping feedback coefficient \(k_{s}\) can be adjusted to maintain a constant closed-loop damping of the joint.  
\[k_{s}=\frac{f}{\hat{k}_{t}k_{1}}\Bigg{(}\sqrt{\frac{\hat{f}_{t}}{J_{exp}}}-1 \Bigg{)} \tag{37}\]  
In the same way, changing the position gain \(k_{1}\) can make the system undamped oscillation frequency \(\omega_{n}\) stable at the expected value \(\omega_{nexp}\).  
\[k_{1}=\frac{\hat{f}_{t}\times\omega_{nexp}{}^{2}}{\hat{k}_{t}} \tag{38}\]
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### Variety Bias Problem and the Solution  
People tend to choose as many attributes as possible to build decision tree models. If \"Number\" can be used as a candidate attribute, there will be four feature attributions (Number, Color, Body Shape, Hair Type) and one class attribution (characteristic) in Table 1. The selection of the optimal attribution based on Equations (2) and (12) presented above is the feature attribution \"Number\", the detailed computation process is shown in the following. The decision tree model is presented in Figure 3. From Figure 3, we find that there is no sense to predict new samples for this decision tree, because each piece of sample data has their own number.  
In order to avoid the multi-value bias problem, the C4.5 method used gain ratio instead of the information gain equation, which is a good solution to the multi-value bias problem, but more logarithmic expressions are brought into the computation process by gain ratio, which will influence the running time according to Section 3.1. This paper proposed a new method that introduces different weights for each attribute, which is not only helpful for the selection of the optimal attribution, but also brings no computation pressure. The detailed computation process based on the information gain equation in ID3 is shown in Equations (13)-(16). Equation (17) is the new information gain equation, and the computation process based on Equations (17) is also briefly listed in Equations (18)-(21). The decision tree built by the improved information gain equation is showed in Figure 4. Obviously, this decision tree has better prediction ability for new unknown samples than that of decision trees in Figure 3.
## 3 Results and Discussion  
### Simulation Results  
The results of numerical simulation used to verify the reconstruction method described in Section 2.3 are shown in Figure 4. The software used for the simulation was MATLAB and the data accuracy was 64 bits. In order to clarify the performance of the method combining 2D CS-SDOCT with ISAM, we directly simulated steps 2-5 described in Section 2.3. The simulated sample consisted of a row of scattering particles with diameters of 2 \(\upmu\)m, which were evenly arranged laterally at the depth of 500 \(\upmu\)m from the focus, as shown in Figure 4a. The other simulation conditions were the same as those used in Figure 1b,c.  
\begin{table}
\begin{tabular}{c c} \hline
**System Parameters** & **Specification** \\ \hline Axial resolution (air/tissue) & 8.79 \(\upmu\)m/6.85 \(\upmu\)m \\ Optical power of light source & 6 mW \\ Theoretical maximum imaging depth (air/tissue) & \(3.25\times 2\) mm/2.24 \(\times\) 2 mm \\ Maximum imaging width & 13.8 mm \\ Working distance of sample arm & 15.4 mm \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the SD-OCT system.  
Curves in Figure 4b-e are lateral distribution of signals at the depth of 500 \(\upmu\)m. The green and blue curves in Figure 4b are the reconstructed structural signals of the SD-OCT and ISAM using 100% B-scan data, respectively, as can be seen in the lateral resolution being significantly improved in signals of ISAM. It should be noted that only the results in the range of \(x\in[-30\mu m,90\mu m]\) are displayed in Figure 4b,e to clearly show the correction effect of the ISAM on the lateral resolution. Figure 4c shows the under-sampled B-san spatial signal \(\mathbf{a}(x_{c},500)\) with a sampling rate of 50%. The signal \(\mathbf{a}(Q_{//c},500)\) in Figure 4d was obtained by performing the \(DFT\) on the lateral component \(x_{c}\) of \(\mathbf{a}(x_{c},500)\), namely \(\mathbf{a}(Q_{//c},500)=\mathbf{F^{H}}\mathbf{a}(x_{c},500)\). It can be seen that the signal \(\mathbf{a}(Q_{//c},500)\) in the \(Q_{//}\)-domain had better compressibility, i.e., sparser, than the signal \(\mathbf{a}(x_{c},500)\) in the spatial domain. The red curve in Figure 4e is the result after proceeding steps 2-5 including step 4, while the black curve is the result of not performing step 4. We found that the lateral resolution of the result plotted with the red curve was improved by approximately 2 times, and the improvement effect was similar to that of the results obtained with 100% data shown in Figure 4b.  
Consequently, it can be concluded that the feasibility of the proposed method of combining 2D CS-SDOCT with ISAM is verified by the simulations.
## 1 Introduction
The need to consider security as a new dimension during the whole design process of embedded systems has already been stressed [3; 6], and there have been some initial efforts towards design methodologies to support security [7; 8; 9], but to the best of our knowledge no attempt has been made to exploit the special characteristics of sensor networks. Applications built on sensor networks--SCADA systems being no exception--have to live with the fact that privacy and integrity cannot be preserved in every node of the network. This poses restrictions on the information a single node can manage, and also in the way the applications are designed and distributed in the network. Of course, the inherent insecurity of embedded systems should not prevent us from striving to avoid compromises. We should guarantee that a massive attack can not be fast enough to escape the detection, isolation, and recovery measures. Therefore we should design the nodes as secure as the available resources would allow. In spite of the disadvantages of sensor networks from the security point of view, they provide one important advantage for fighting against attacks: redundancy. A sensor network usually has a high degree of spatial redundancy (many sensors that should provide coherent data), and temporal redundancy (habits, periodic behaviors, causal dependencies), and both can be used to detect and isolate faulty or compromised nodes in a very effective manner. In previous work [10], we proposed the use of reputation systems and non-deterministic routing to secure applications based on wireless sensor networks. In this article, focused on sensor networks for SCADA systems, we propose the use of reputation systems enhanced with distributed agents based on unsupervised learning algorithms (specifically, self-organizing maps) in order to achieve more resistance to previously unknown attacks. Separate detectors are further organized in a distributed system using the idea of immune system organization. In Section 2. we review some of the most relevant previous approaches. Section 3. describes our approach in detail. In Section 4., some experimental data and algorithms description is shown and discussed. Finally, in section 5., we draw some conclusions.