esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 State Machine for the Robot
### Time Cost for Stair-Climbing  
The motion during stair-climbing can be illustrated as shown in Figure 8. Since the stairs have distances in two directions, namely, the horizontal direction \(y\) and the vertical direction \(z\), the side view of the stairs can be depicted as shown in Figure 8a. The contact point between the front wheel and the ground is the origin of the coordinate, and the stairs are assumed to be of uniform width \(w\) and height \(h\). To better express the process of climbing stairs, the position-time plots in the horizontal and vertical directions are given, as shown in Figure 8b,c, respectively. Furthermore, the \(z(t)\)-axis of graph (b) is aligned with the \(z\)-axis of graph (a), and the \(y(t)\)-axis of graph (c) is aligned with the \(y\)-axis of graph (a) to show a clear positional relationship. Hence, with a \(t\)-axis, the change of the robot's position on  
Figure 7: The state machine of moving downstairs.  
the stairs can be shown directly. Thus, the robot's horizontal velocity \(v_{y}\) and vertical velocity \(v_{z}\) are constant values, as shown in Figure 8d. Then, the time to climb one stair is calculated as follows:  
\[T=T_{y}+T_{z}, \tag{3}\]  
where \(T_{y}=w/v_{y}\), \(T_{z}=h/v_{z}\).  
For a stair with \(n\) steps, the distances in \(y\) and \(z\) are described as follows:  
\[y=\left\{\begin{array}{cc}v_{y}(t-nT)+nv_{y}T_{y}&nT\leq t\leq nT+T_{y}&(n \in N)\\ nv_{y}T_{y}&nT+T_{y}\leq t\leq(n+1)T&(n\in N)\end{array}\right. \tag{4}\]  
\[z=\left\{\begin{array}{cc}v_{z}(t-nT)+nv_{z}T_{z}&nT+T_{y}\leq t\leq(n+1)T& (n\in N)\\ nv_{z}T_{z}&nT\leq t\leq nT+T_{y}&(n\in N)\end{array}\right. \tag{5}\]  
For Equations (4) and (5), on the one hand, they are established without considering the time spent for the posture adjustment; on the other hand, in the practical design, both \(v_{y}\) and \(v_{z}\) depend on the EH power, and the power should be determined from the trade-off between the speed and the stability of stair-climbing. According to Equations (4) and (5), the relationship between \(n\) steps and the number of stair-climbing action cycles can be obtained, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stair Steps** & **Number of Stair-Climbing Action Cycles** \\ \hline
1 step & 2 \\
2 steps & 4 \\
3 steps & 5 \\
4 steps & 6 \\ \(n\) steps & \(n+2\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The relationship between \(n\) steps and the number of stair-climbing action cycles.  
Figure 8: The motion curves of stair-climbing: (**a**) the model of the staircase; (**b**) the position-time plot in the vertical direction; (**c**) the position-time plot in the horizontal direction; (**d**) the velocity-time plot of the robot’s moving in the direction \(y\) and \(z\).
# Blind Image Deblurring Based on Local Edges Selection
## 2 Method
### Image Deblurring Model  
As is mentioned above, the edges of images are less sparse when the image become blurred. Based on the changes in sparsity, in some methods, the first-order or higher-order gradient operators are added to the deblurring model in likelihood terms [19]; some methods also find new criteria for selecting informative edges [20], but adding too many partial derivative operators will increase computational complexity and reduce efficiency [21]. In addition, sometimes the failure of extracting blurred image edges will cause ringing artifacts. Since the proposed edges selection method can better detect the edges of the image, we use it to build the deblurring model.  
The proposed image deblurring model is defined as follows:  
\[\underset{x,H}{\text{argmin}}\big{\|}y-H\otimes x\big{\|}_{2}^{2}+\big{\|} \hat{y}-H\otimes\hat{x}\big{\|}_{2}^{2}+\alpha Q_{TV}(x)+\beta\|H\|_{2}^{2} \tag{11}\]  
where \(\hat{x}\) and \(\hat{y}\) are the local edges of clear image and blurred image. \(Q_{TV}(x)\) is the optimized total variation term and Section 2.4 covers it in detail.  
Then, based on the proposed deblurring model, the image deblurring process is shown in Algorithm 1. The blurred image \(y\) and the blurring kernel size are the input, the output values of the algorithm are the clear image \(x\) and blurring kernel \(H\). In the initial stage of the algorithm, the clear image \(x\) and blurring kernel \(H\) are unknown, so we need to initialize them. In the proposed method, we initialize clear image \(x\) by setting \(x=y\). The blurring kernel \(H\) is assumed to be a sparse matrix and a few pixels are nonzero [21] in the initialization. In subsequent calculations, the value of \(x\) and \(H\) are constantly updated in each iteration until the algorithm stops. In each iteration, we obtain the renewed local edges of the intermediate clear image \(x\) by the proposed local edges selection method, then the intermediate clear image and blurring kernel are renewed respectively. When the stopping criterion is satisfied, we can get the final results of the clear image and blurring kernel. The stopping criterion is introduced in Section 2.5.  
```
1. Input: blurred image \(y\), kernel size [m,n]
2. Initialization: \(x=y\); \(H=\) zeros [m,n], \(H\) (1,1) = 1; KS = 0;
3. if KS \(\leq\) 0.95 Estimate intermediate clear image \(x\) by the method in Section 2.4;
4. Estimate intermediate blurring kernel \(H\) by the method in Section 2.3;
5. Update kernel similarity (KS) by the method in Section 2.5;
6. or else break;
7. Output: \(x,H\)
```  
**Algorithm 1** The blind image deblurring process
## 2 Preliminaries  
### Reinforcement Learning  
An RL task can be modeled as an MDP [24, 46], which consists of a 4-tuple \(\langle\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R}\rangle\), where \(\mathcal{S}\) is a finite set of states, \(\mathcal{A}\) is a finite set of actions, \(\mathcal{P}:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\mapsto[0,1]\) is a transition function which describes the probability of the next state \(s^{\prime}\in\mathcal{S}\), after taking action \(a\in\mathcal{A}\) in state \(s\in\mathcal{S}\), and \(\mathcal{R}:\mathcal{S}\times\mathcal{A}\times\mathcal{S}\mapsto\mathbb{R}\) is a reward function which returns a scalar value conditioned on two consecutive states and the intermediate action. A policy function \(\pi(a|s)\) describes the probability distribution of choosing \(a\) in state \(s\). An optimal policy can be learned by maximizing the state-value function or the action-value function. The state-value function is usually defined as \(V^{\pi}(s_{t})\)=\(\mathbb{E}_{\pi}\Big{[}\sum_{i=t}^{H}r^{i-t}r_{t}|s=s_{t}\Big{]}\), which represents the expected rewards for visiting state and following policy \(\pi\) thereafter. Similarly, the action-value function is defined as \(Q^{\pi}(s_{t},a_{t})=\mathbb{E}_{\pi}\Big{[}\sum_{i=t}^{H}\gamma^{i-t}r_{t}|s=s _{t},a=a_{t}\Big{]}\), which describes the expected rewards for taking action \(a\) when visiting state \(s\) and keeping policy \(\pi\) throughout, where \(0\leq\gamma\leq 1\) is a discount factor, and \(H\) is the finite horizon of the episode.  
In this paper, we use advantage actor-critic (A2C) [7] to learn the response policy. The opponent's policy will remain fixed until the agent learns the optimal response policy. Suppose there is a policy network \(\pi_{\theta}\) with parameter \(\theta\) and a value network \(V_{\phi}\) with parameter \(\phi\). A2C updates the network parameters as follows:  
\[\min_{\theta,\phi}\mathbb{E}_{B}\Big{[}-\hat{A}\log\pi_{\theta}(a|s_{t})+ \frac{1}{2}\big{(}r+\gamma V_{\phi}(s_{t+1})-V_{\phi}(s_{t})\big{)}^{2}\Big{]} \tag{1}\]  
where \(B\)=\(\{(s_{t},a_{t},r_{t},s_{t+1})\}_{t=0}^{t=H}\) is the batch of transitions, and \(\hat{A}\) is the basic advantage term.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection  
The performance of EEG recognition systems is based on the selection of a feature extraction technique and a classification algorithm. In our study, we investigated the possibility of detecting two preference states, namely pleasant and unpleasant, by using EEG and classification algorithms. To this end, we performed rigorous offline analysis to investigate the computational intelligence for the preference detection and classification. We used deep learning classification from the DEAP dataset to explore how to employ intelligent computational methods in the form of classification algorithms. This could effectively mirror the preference states of the subjects. Furthermore, we compared our classification performance with those of the KNN and RF classifiers. We built our model in the open source programming language Python and used the Scikit-Learn toolbox for machine learning, along with SciPy for EEG filtering and preprocessing, MNE for EEG-specific signal processing, and the Keras library for deep learning.  
In this section, we discuss our methodology along with some implementation details of the proposed system for EEG-based preference detection. We begin with describing the benchmark dataset and ground truth of the preference labeling. Next, we explain the feature extraction. Finally, we illustrate the DNN classification model.
## 2 Epoxy-Based Composites  
### Epoxy-Based Composites Reinforced with Recycled Aggregates  
Because of their environmental benefits, natural fibers are increasingly being used in various engineering applications such as automotive, infrastructure, aerospace, and packaging. Compared to glass fiber and carbon fiber composites, less research has been carried out on their reuse and recycling. [PERSON] and [PERSON] [21] used different fiber loadings of 19, 28, 40, and 46 wt.% to make recycled cellulose fiber (RCF)-reinforced epoxy composites. The results revealed that flexural strength, flexural modulus, fracture toughness, and impact strength increased when the fiber content rose. With 46 wt.% fiber, the maximum mechanical characteristics were obtained. Water absorption influence on the mechanical and physical characteristics of RCF/epoxy composites has also been studied. The maximum water absorption and diffusion coefficient increased as the fiber content increased. As moisture absorption increased, the flexural strength, flexural modulus, and fracture toughness decreased. However, with water absorption, the impact strength was observed to rise somewhat [21; 22]. Using paper sheets made from recycled cardboard boxes, Soya oil-based resin and cellulose fibers were successfully used to manufacture the  
Figure 1: (**a**) NFRCs characteristics in construction and automotive applications. (**b**) Steps involved in NFRCs fabrication.  
composite structures. Epoxy composites made from recycled aggregate generally have two-fold benefits: first, wastage is reduced; second, a high-strength composite can be obtained from these wastes.
## 3 Results and Discussion
### The Fourier Transform Infrared (FTIR) Test
#### 3.2.2 Aliphatic Hydrocarbons  
Aliphatic hydrocarbons arise within C-H stretching bands and can occur in the range of 3055-2730 cm\({}^{-1}\). Methylene symmetric C-H stretching occurred at 2920 and 2851 cm\({}^{-1}\). These bands are considered part of the saturates fraction, where both bands are assigned to the alkyl functional group [6; 37]. Figure 5a shows the FTIR spectrum results of bands. The absorbance peaks at wave numbers 2920 and 2851 cm\({}^{-1}\) were recorded for all samples before and after ageing and are illustrated in Figure 5b. Before ageing, the recorded absorbance at both wave numbers was almost the same for all binders. No changes were recorded due to adding NC particles or mixing factors (temperature and duration). However, ageing influences the absorbance at both wave numbers; a slight reduction was detected after STA and LTA, as shown in Figure 5b.  
After STA, the absorbance at 2920 cm\({}^{-1}\) for 0 NCMB, 2 NCMB and 4 NCMB was 0.225, 0.227 and 0.226, respectively, as presented in Figure 5b. The absorbance at 2851 cm\({}^{-1}\) for 0 NCMB, 2 NCMB and 4 NCMB was 0.149, 0.156 and 0.153, respectively. It is clear that the STA procedure has little effect on these bands. After LTA, the reduction in absorbance peak value for both wave numbers continued, and variation in their absorbance was detected clearly between the modified binders. The 0 NCMB was more sensitive, wherethe highest reduction was recorded compared to 2 NCMB and 4 NCMB. The absorbance at 2920 cm\({}^{-1}\) for 0 NCMB, 2 NCMB and 4 NCMB was 0.194, 0.199 and 0.208, respectively, and the absorbance at 2851 cm\({}^{-1}\) for 0 NCMB, 2 NCMB and 4 NCMB was 0.130, 0.134 and 0.138, respectively.  
The reduction in absorbance reflects the reduction in the concentration (density of a material) of the C-H band, which may lead to reduction of its mass. However, no changes were detected on its wave numbers, which mean that both bands are inert bands. The variation in absorbance reduction may be used to rank the sensitivity of the bitumen; 0 NCMB was more sensitive compared to 2 NCMB and 4 NCMB, which highlights the benefit of the addition of NC. As per previous studies, the same conclusion was stated, as the bands at 2920 and 2851 cm\({}^{-1}\) decreased slightly after the ageing procedure. The reduction was in mass, but no changes were detected after ageing due to chemical inertness, as was stated previously [31; 32; 37].  
Figure 4: (a) FTIR spectrum results of band at wave number 1694 cm\({}^{-1}\); (b) absorbance value for carboxylic acid at wave number 1694 cm\({}^{-1}\).
## 3 Matched Plasma Samples
### Determination of Selected Phenolics in CSF and Plasma Samples  
We determined 12 phenolics in CSF and plasma specimens with a combination of solid-phase extraction technique with high-performance liquid chromatography, with electrochemical (HPLC-ECD) or ultraviolet-visible (HPLC-UV-Vis, diode array detector wavelengths 210, 280, and 325 nm) detection, as previously described [28; 29]. Vanillic acid, dihydrocaffeic acid, caffeic acid (CA), and HVA were determined by HPLC-ECD, while 3-hydroxyphenyl acetic acid (3 HPAA), hippuric acid, 3-hydroxyhippuric, 4-hydroxybenzoic acid, chlorogenic acid, ellagic acid, and urolithin A were determined by HPLC-UV-Vis. All procedures related to polyphenols determination were the same for CSF and plasma samples. The instrumentation of HPLC-ECD and HPLC-UV-Vis and chromatographic conditions and parameters were described in detail elsewhere [28; 29]. All chemicals, buffers, and water used throughout the study were of high-performance liquid chromatography (HPLC) grade.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Demographic/Clinical** & **Whole Group \({}^{\ddagger}\)** & **Multiple Sclerosis** & **Other Neurological** \\  **Variables** & & & **Disorders \({}^{\ddagger}\)** \\ \hline Number of subjects & 28 & 15 & 13 \\ \hline Sex F/M & 18/10 & 13/2 & 5/8 \\ \hline Age [years] & 46 \(\pm\) 16 (44; 29) & 40 \(\pm\) 14 (23; 4) & 53 \(\pm\) 15 (30; 9) \({}^{\star}\) \\ \hline plasma CRP [mg/L] & 4.1 \(\pm\) 6.1 (1.5; 5.2) & 1.7 \(\pm\) 1.7 (0.2; 0.2) & 7.5 \(\pm\) 8.2 (0.5; 1.0) \({}^{\star}\) \\ \hline \multicolumn{4}{c}{Cerebrospinal fluid samples} \\ \hline Cell count (cells/\(\upmu\)L) & 22 \(\pm\) 41 (6; 9) & 10 \(\pm\) 11 (2; 1) & 31 \(\pm\) 52 (1; 0) \\ \hline Erythrocytes (cells/\(\upmu\)L) & 118 \(\pm\) 127 (3; 74) & 123 \(\pm\) 199 (4; 82) & 39 \(\pm\) 59 (1; 35) \\ \hline Total protein (g/L) & 0.56 \(\pm\) 0.36 (0.45; 0.19) & 0.44 \(\pm\) 0.12 (0.29; 0.04) & 0.65 \(\pm\) 0.38 (0.30; 0.12) \\ \hline Glucose (mg/dL) & 74 \(\pm\) 22 (68; 13) & 67 \(\pm\) 6 (69; 7) & 81 \(\pm\) 29 (68; 34) \\ \hline \end{tabular} \({}^{\dagger}\) initially, thirty patients were recruited, however, two of them dropped out due to the blood CSF samples. \({}^{\ddagger}\) 4 patients had polynetropathy, 3 mononeuroopathy, 2 meningitis, 1 brain tumor, 1 epilepsy, 1 amyotrophic lateral sclerosis and 1 suffered from [PERSON]’s ataxia. Three patients with polyneuropathy also had diabetes mellitus. \({}^{\star}\)\(p<\) 0.05 versus sclerosis multiplex subgroup.  
\end{table}
Table 1: Basic characteristic of studied patients with neurological disorders and samples of cerebrospinal fluid.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology
### Validation of the Decametric LAI Product and Reference LAIs
#### 3.1.1 Validation and Recalibration of the Sentinel-2 LAI Product  
The Sentinel-2 LAI time series were validated over the entire growing season through direct comparison with LAINet LAI measurements having a similar footprint. Then, a linear regression relationship between the Sentinel-2 and LAINet LAI was fitted and used to recalibrate Sentinel-2 LAI to generate recalibrated SL2P-based LAI.
# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
Semi-supervised learning can use the information in a small number of labeled samples and find patterns from a large number of unlabeled samples, and then perform classification, avoiding the use of only a small number of labeled samples for training, which may result in over-fitting. However, as information continues to increase, the training data and testing data will also not satisfy the same-distribution hypothesis. In view of the different characteristics of transfer learning and semi-supervised learning, this paper combines the two methods to propose an unknown radar emitter identification method based on semi-supervised and transfer learning. Firstly, we construct the support vector machine model based on transfer learning, using the information of labeled samples in the source domain to train in the target domain, which can solve the problem that the training data and the testing data do not satisfy the same-distribution hypothesis. Then we design a semi-supervised, co-training algorithm, using the information of unlabeled samples to enhance the training effect, which can solve the problem that insufficient labeled data results in inadequate training of the classifier. Finally, we combine the transfer learning method with the semi-supervised learning method for the unknown radar emitter identification task. Our major contributions are summarized as follows: (1) Focusing on the actual application scenarios to study radar emitter identification, and simultaneously solving the problem that training data and testing data do not satisfy the same-distribution hypothesis and the problem of insufficient labeled data, which provides a good thinking way for future research in this area; (2) proposing a method combining support vector machine based on transfer learning with semi-supervised co-training algorithm; (3) verifying the interaction between the transfer learning method and the semi-supervised learning method for unknown radar emitter identification task.
# Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
## 3 Contrastive Learning-Based Intrusion Detection Model for Industrial Control Systems
### Data Pre-Processing
#### 3.1.2 PCA Downscaling  
Principal Components Analysis (PCA) is often used for data dimensionality reduction. By using PCA data dimensionality reduction, the data is reduced to lower dimensions, which can reduce the storage space needed for the data, save time for model training, remove redundant attributes, and improve the accuracy of detection.
# Immediate Settlement of Ring Footings Resting on Inhomogeneous Finite Stratum
## 3 Numerical Analysis  
### Geometry and Parameters  
This study has considered ring footings placed on a layered system in which an elastic layer of finite thickness overlying a rigid rough base, as illustrated schematically in Figure 1. The ring footing is specified with inner and outer radii \(R_{i}\) and \(R_{0}\), respectively. In this study, five inner-to-outer radius ratios (\(R_{i}/R_{0}\) = 0, 0.2, 0.4, 0.6, and 0.8) are investigated, which covers most problems of practical interest [17]. The footing roughness is assumed to be perfectly rough, which does not permit any relative movement at all along the soil-footing interface. A similar assumption was used in the literature [18] for elastic settlement of shallow footings. The soil is modeled as an isotropic inhomogeneous continuum which obeys [PERSON]'s law. The soil deformation is represented by two parameters, i.e., elastic modulus \(E_{s}\) and [PERSON]'s ratio \(\
u_{s}\). The \(E_{s}\) was idealized as varying linearly with depth:  
\[E_{s}=E_{0}+nz \tag{3}\]  
where \(E_{0}\) is the elastic modulus at the surface, \(n\) is the rate of increase of modulus with depth \(z\). For a footing of diameter \(D\) (=\(2R_{0}\)), the degree of soil nonhomogeneity beneath the footing can be characterized by the dimensionless ratio \(nD/E_{0}\), indicating that this is particularly important for large footings. As studied by [PERSON] and [PERSON] [19], examinations are made for the \(nD/E_{0}\) values of 0.01 to 100. As \(nD/E_{0}\approx 0\), the modeling approaches the pure homogeneous cases. The Poisson's ratio is assumed to be constant with depth, and the values of \(\
u_{s}\) ranging from 0 to 0.495 are prescribed to simulate the compressible and incompressible soils. The relative stiffness \(K\) between the soil and footing for a ring footing is quantified by [20]:  
\[K=\frac{E_{f}}{E_{0}}\Big{(}1-\
u_{s}^{2}\Big{)}\bigg{(}\frac{t}{R_{av}} \bigg{)}^{3} \tag{4}\]  
where \(E_{f}\) and \(t\) are the elastic modulus and thickness of the footing, respectively. \(R_{av}\) is the average radius of the footing:  
\[R_{av}=\frac{R_{0}+R_{i}}{2} \tag{5}\]  
From Equations (4) and (5), it is obvious that the decrease in the value of \(R_{i}/R_{0}\) increases the footing stiffness, at constant values of other parameters. The footing stiffness typically lies in the range of \(0.01<K<100\)[4], and the following categories can be defined: perfectly flexible (\(K<0.01\)), intermediate stiffness (\(0.01\leq K\leq 10\)), and perfectly rigid (\(K>10\)) [4]. To obtain solutions to layered media, the relative thickness of finite layer defined as the ratio of the upper layer thickness to the footing diameter \(H/D\) is introduced, encompassing values of \(H/D\) varying from 0.1 to 100 [21].