esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Control for Biped Robot with Incremental Learning Mechanism
## 3 Online Control System Design
### Weighted Neural-Network Estimator  
From Equations (5)-(7), it is noted that both ZMP stability and yaw moment are related with the position, velocity and acceleration of each link. According to [24; 25] and results in [28], the locomotion stability can be ensured by regulating robot joints. Thus, the following mapping function is considered  
\[\Delta\mathbf{q}=f(\mathbf{q},\dot{\mathbf{q}},\bar{\mathbf{q}},\Delta X_{z},M _{z}) \tag{9}\]  
where \(f(\bullet)\) is a non-linear mapping function, \(\Delta\mathbf{q}=[\Delta q_{1},...,\Delta q_{n}]^{T}\) denotes the corrections of all joints; \(\Delta X_{z}\) is ZMP error, \(M_{z}\) denotes yaw moment. By approximating the non-linear mapping function \(f(\bullet)\), \(\Delta\mathbf{q}\) can be obtained.  
In our scheme, the random vector functional-link neural networks(RVFLNNs) [29] is adopted to estimate \(f(\bullet)\). Different from traditional Neural Networks, RVFLNNs effectively eliminates the disadvantage of the long training process and provides a fast learning property by designing a flatted network with randomly generated weights and biases.  
Given N training sample sequences \(\{\mathbf{X}_{i},t_{i}\}_{i}^{N}\), let \(\hat{c}_{i}\) represent the appropriate weight of the \(i\)th sample. Thus, the approximation task is formulated as the following optimization problem  
\[\arg\min_{W}:\sum_{i=1}^{N}\hat{c}_{i}(A_{i}W_{i}-T_{i})+\lambda\|\mathbf{W} \|_{2}^{2} \tag{10}\]  
where \(\hat{c}_{i}\) denotes the weight of the \(i\)th sample, \(\mathbf{A}=[A_{1},...,A_{N}]=[\mathbf{K}^{n}\,\mathbf{H}^{m}]\) represents the input matrix, \(\mathbf{K}^{n}=[\mathbf{K}_{1},...,\mathbf{K}_{n}]\) is the input node set, \(\mathbf{K}_{i}=\varphi(\mathbf{X}\mathbf{W}_{ei}+\beta_{ei})\) is the input node, \(\mathbf{X}\) is the input sample data, and \(\mathbf{H}^{m}=[\mathbf{H}_{1},...,\mathbf{H}_{m}]\) is the enhancement node set, \(\mathbf{H}_{i}=\varphi(\mathbf{K}^{n}\mathbf{W}_{hi}+\beta_{hi})\) is the enhancement node; \(\beta_{ei}\) and \(\beta_{hi}\) are bias; \(\mathbf{W}_{ei}\) and \(\mathbf{W}_{hi}\) are weight matrices; \(\varphi(\bullet)\) is sigmoid function; \(\mathbf{T}=[T_{1},...,T_{N}]\) is the desired output matrix, \(\lambda\) is a penalty coefficient. Moreover, \(\mathbf{W}\) is the connecting weight matrix, which can be computed by \(\mathbf{W}=[W_{1},...,W_{N}]=\mathbf{A}^{+}\mathbf{T}\).  
By applying the Moore-Penrose inverse, we have  
\[\mathbf{A}^{+}=\lim_{\lambda\to 0}(\lambda\mathbf{I}+\hat{C}\mathbf{A} \mathbf{A}^{T})^{-1}\mathbf{A}^{T}\hat{C}^{T} \tag{11}\]  
where \(\mathbf{A}^{+}\) is the pseudo-inverse matrix of \(\mathbf{A}\), \(\hat{C}=[\hat{c}_{1},\hat{c}_{2},...,\hat{c}_{n}]\) is the weight matrix of sample data.
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 3 Results  
### Fabrication of MCPs with Different OARs  
In this study, three types of MCPs with varying OARs and channel geometries were fabricated and tested: a circular MCP, a tapered circular MCP, and a hexagonal MCP, with OARs of 65.6%, 67.2%, and 49.8%, respectively. Among these, the circular MCP represents a conventional structure, while the other two types were used to address specific challenges: the tapered circular MCP is designed to increase the OAR, and the hexagonal MCP is designed to mitigate potential edge discharge risks in circular apertures. As shown in Figure 2, these two special MCPs show different surface morphologies and channel geometries. In addition to the differences in appearance, we also want to further evaluate their detection performance.  
Figure 1: Schematic diagram of the platform used to measure the capture efficiency of MCPs.
## 2 Materials and Methods
### Studies on UDMH Degradation in Soil  
The investigated soil was collected in the Beijing Olympic Park, China, 5-10 cm below the ground surface. This natural soil was \"clean\", and then was contaminated in the laboratory. Before being used in subsequent experiments, the soil samples were passed through a 2-mm sieve, stirred well, and air dried. Detailed physico-chemical properties of the soil are listed in Table 1. The degradation experiment with isolated bacteria was performed in sterile soil (SS) and non-sterile soil (_n_SS). The SS were autoclaved for 1 h at 121 \({}^{\circ}\)C and 0.15 MPa to remove the native soil organisms. The non-organic matter (_n_OM) soil was SS with the organic matter removed. It was an SS sample with 30% H\({}_{2}\)O\({}_{2}\) added in the ratio of 1:10 (soil:water), and evaporated to dryness in a 85 \({}^{\circ}\)C water bath. Then 1 mol\(\cdot\) L\({}^{-1}\) ammonium acetate was added in the ratio of 1:10 (soil:water) and shaken for 2 h. After it was centrifuged, the supernatant was discarded. It was washed twice with deionized water, dried overnight in an 80 \({}^{\circ}\)C oven, and refined into powder [25].  
Because of the low chemical stability of UDMH, a reliable determination of the spiked concentration in the soil could not be obtained [26]. UDMH may react with oxygen in the atmosphere, organic matter, and transition metal ions. In particular, UDMH may be tightly bound with the soil solid, which was a form including the UDMH adsorbed by the mineral phase and chemically adsorbed by the soil's organic matter [27]. In order to get a relatively stable UDMH concentration, the added solution made a water seal by saturating the soil samples. A 10 mL solution containing 1000 ug UDMH was sprayed on the surface of each 5 g soil sample. A micro-syringe was used to spray the surface of the soil so that very small droplets were dispensed to ensure thorough mixing.  
Next, different kinds of solution were introduced into SS with added UDMH solution (in quadruplicate). The first SS of the quadruplicate samples with 10 mL of added water was kept as the control. The second had 10 mL of broth medium added to investigate the effect of the nutritive medium on biodegradation. The third and the fourth had 10 mL incubated bacterium solution of M12 and P4 strain added, respectively. The same series was applied to _n_SS, which was contaminated with UDMH to study the degradation potential of autochthonous microorganisms. A 5 g sample of _n_OM contaminated with UDMH had 10 mL of water added, which was used to find out the effect of organic matter on degradation.  
All the soil samples were thoroughly mixed and incubated in a darkened thermostatic chamber maintained at 25 \(\pm\) 2 \({}^{\circ}\)C and relative humidity (RH) 90% for five days. Additional water would be supplied if water losses exceeded 2%.
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 2 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Reference** & **Feature** & **Target** & **Performance** \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Fuzzy model** \\ **[**11**]** \\ \end{tabular} } & [11] & Gray level co-occurrence matrix & Ceramic & Recognition rate: 92.31\% \\  & [13] & Mathematical morphological & Billet & Accuracy: 87.5\% \\  & [15] & Fractal model & Steel & Accuracy: 88.33\% \\  & [16] & Gabor filter & Steel billet & Thin crack: 91.9\% and corner crack: 93.5\% \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **Fuzzy model** \\ **[**17**]** \\ \end{tabular} } & [3] & Bivariate color histogram & Particleboards & Can effectively detect and localize defect \\  & & Color coherence vectors & NWPU-RESISC45 & Accuracy: 96.66\% \\  & & combined with texture features & data sets & Accuracy: 96.66\% \\  & & as a basis & Cemertitious & ERT can be efficient for situ monitoring \\  & & Color histogram & materials & and defect detection of cement mortar \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Fuzzy model** \\ **[**9**]** \\ \end{tabular} } & [6] & Fourier image & Magnet & Can automatically detect surface-cutting defects in magnets \\  & & Comparison of the whole & & Can detect various types of non-repeating patterns in the electronic industry, even \\  & & Fourier spectra between the & pattern images & those as small as one pixel wide, making \\  & & image & & it useful for identifying defects \\  & & A circle Hough transformation, & Compact camera & Able to identify defects in complicated \\  & & weighted Sobel filter, and polar & lens & circular inspection areas and has been \\  & & transformation & & proven to be highly effective \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent applications using machine-learning-based vision algorithms for detecting surface defects in industrial products, categorized into three categories based on texture, color, and shape features.
## 2 The Experimental Data
### Stratified Water and a Constant Velocity Gradient in the Sub-Bottom Layer  
For the more realistic case, when the acoustic velocities of both the water and bottom media vary with depth (Figure 6), no analytical expression is applicable to calculate the travel-time curves. These calculations require numerical modeling, e.g., of ray travel times. In our case, the BELLHOP [57] ray-tracing program, which is freely available at the Ocean Acoustic Library as a part of the Acoustic Toolbox, was used.  
Calculations were done in two steps. First, the travel times of the direct arrival and the seafloor reflection were calculated. The model consisted of a water layer of thickness \(H_{1}\) with a seasonally changing oceanographic stratification, bottoming at a flat horizontal seafloor. Afterwards, the arrival times of the bending refraction and the sub-bottom reflection from the top of the salt layer, at the base of the sub-bottom sequence, were calculated. In BELLHOP, after reflection at the boundary, the beam is only reflected and does not penetrate further into the second medium. Thus, in order to calculate the bending refraction and the sub-bottom reflection, the seafloor must be removed from the model. For this, the water layer of thickness \(H_{1}\) and the bottom layer of thickness \(H_{2}\) were combined into one layer of thickness \(H_{1}+H_{2}\), keeping the variability of the sound speed. Ray travel distances were calculated from ray coordinates that were obtained using BELLHOP. After calculating the rays' travel paths and distances, the rays' travel times were calculated by dividing the travel distance by the sound speed at every given depth. Note that at this step we are interested only in ray travel times and not in sound pressure. That is why neither the densities nor the attenuation coefficients of the media are needed.  
Comparing the modeling results that were carried out with a constant-velocity water layer with the results of using the summer- and winter-velocity profiles (Figure 2) reveals some differences: (1) Changes in the offsets at which specific rays bend and reach the seafloor, particularly at low grazing angles. For example, a ray with a grazing angle of29\({}^{\circ}\) bonded and reached the surface at offsets of 7250, 7150 and 6900 m when modeled with a constant velocity, winter and summer water profiles, respectively. In contrast, a ray with a grazing angle of 16\({}^{\circ}\) bonded at offsets of 7300, 6200 and 5100 m, respectively. (2) Differences in arrival travel times. Primarily, differences of up to 0.082 s were observed in the arrival times of the direct wave at a specific offset between the constant-velocity and summer-velocity profiles. Within the range of our modeled travel times (to 7200 m) the differences in the travel times of reflected and bending rays were small. However, more substantial time differences may be encountered at larger offsets. (3) The first angle at which rays start bending beneath the seafloor is reduced in the realistic-velocity case, with respect to the constant-velocity case.
## 4 Experiments and Discussion  
In experiments, we use the ECG data obtained from the website in [33; 34] to simulate the proposed method for each ECG signal with a length of 4096 samples represented by 16-bit. Since the 5-level Haar DWT is performed on the ECG signal, the lowest-frequency sub-band in level 5 has 128 coefficients. The embedding strength \(\varepsilon\) is set to 2000 and 4000 for \(n=2\) and \(n=4\), respectively. Experimental results and discussion are listed in the following. Figure 4: Architecture of the proposed intelligent patients’ confidential data communication system. Without generality, the superiority of the proposed method is judged by signal to noise ratio (SNR) and similarity, which are formulated as follows:  
\[\text{SNR}=-10\log_{10}\left(\frac{\sum\limits_{i=1}^{n}{(\hat{s}_{i}-s_{i})}^{2 }}{\sum\limits_{i=1}^{n}{s_{i}^{2}}}\right) \tag{14}\]  
\[\text{Similarity}\big{(}S,\hat{S}\big{)}=\frac{\sum\limits_{i=1}^{n}{s_{i} \hat{s}_{i}}}{\sum\limits_{i=1}^{N}{\hat{s}_{i}^{2}}} \tag{15}\]  
where \(s_{i}\) and \(\hat{s}_{i}\) denote original ECG signal sample point and embedded ECG signal sample point. The higher the SNR and the similarity, the smaller the distortion of the embedded signal, that is to say, the higher the SNR and the similarity, the better the quality of the embedded signal. Our proposed method embeds patients' confidential data into their ECG signals with minimal distortion and then enables each hidden ECG signal to be of good quality. For example, Figure 3a demonstrates the original ECG signal without patients' confidential data, and Figure 3b demonstrates the hidden ECG signal with patients' confidential data using the proposed embedding technique in DWT 5-level decomposition. In Figure 5c,d, the blue curve represents the original ECG signals and the green curve represents the optimal-embedded ECG signals in the case of embedding strength \(\varepsilon=4000\) and \(n=4\). Figure 5e shows that they are almost indistinguishable visually. Figure 5: \(Cont.\)  
Figure 5: _Cont._  
Moreover, as shown in Table 1, the main drawback in methods in [22; 23; 28] is that the SNR, which represents the quality of each embedded ECG signals, decreases significantly with the increase of the embedding strength \(Q\). Since SA is applied to optimize the quality of each embedded ECG signal, we improve the disadvantage that the quality of each embedded ECG signal greatly decreases with increasing the embedding strength \(\varepsilon\). In other words, the quality of each embedded ECG signal is improved by the increase of the embedding strength \(\varepsilon\). Figure 5: Comparison between original ECG signal and embedded ECG signal for dataset ID 1; (**a**) Original ECG signal; (**b**) Hidden ECG signal; (**c**) Original ECG between 0.09 and 1.09 (s); (**d**) Hidden ECG between 0.09 and 1.09 (s); (**e**) Waveform comparison between 0.09 and 1.09 (s). words, our method maintains a good SNR (i.e., good quality) with sufficient hidden capacity for each embedded ECG signal regardless of the increase in the embedding strength \(\varepsilon\).
# Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm
## 2 Prior Research
Simulation experiments show that the proposed algorithm successfully optimizes the trajectory while satisfying the constraints and is unlikely to converge to the local minimum. In [15], the redundancy strategy can be selected for a single subsystem. The author applies the genetic algorithm to solve the redundant allocation problem of the series-parallel system. In [16], the author uses genetic algorithm to design a multi-task linear continuous time system robust controller with guaranteed cost. In [17], the authors used genetic algorithms to obtain the best solution to minimize the annual value of the power system during the analysis. In [18], the author applied genetic algorithm and particle swarm optimization algorithm to the selection of the optimal parameter combination of laser micro-marking. By using genetic algorithm and particle swarm optimization, the optimal setting of input constraints was predicted. From the application research results of particle swarm optimization and genetic algorithm, the particle swarm optimization algorithm has good local search performance, and the genetic algorithm has good global search ability. Therefore, this paper combines the two, and the proposed hybrid algorithm improves the performance of the algorithm based on the performance of particle swarm and genetic algorithm. Compared with previous research, the hybrid algorithm proposed in this paper has better accuracy in community detection. And with the increase of network scale, the research method in this paper is also significantly better than the traditional method in terms of calculation speed.
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
## 3 System Model
### Deep Learning
After necessary steps, deep learning models are trained for each QoS _target_ (steps 4 to 12). Having trained the model to a sufficient accuracy, the _recommendations_ are transmitted back to the user premises in the form of _target:value_ and _parameter:value_ pairs. ```
1 functionPDR_EC_predict(data);
2 Input:data from end-users in the form of parameters:[\(f_{m,1},f_{m,2},...,f_{m,10}\)], and targets:[\(PDR,EC\)]
3 Output:recommendations in the form of \(recomm\_parameters:[f_{n,1},f_{n,2},...,f_{n,10}\)], and pred_targets:[\(pPDR,pEC\)]
4 fraindata:random70% of data;
5 festdata:remaining30% of data;
6 foreachtargetintargetsdo
7 initialize deep learning model;
8 repeat
9 compute forward pass on _traindata_ by _equations_5 and 6;
10 compute backward pass on _traindata_ by _equations_7, 8, 9 and 10;
11 perform gradient descent and updat weights by by _equations_11 and 12;
12 untilconvergence;
13 compute predictions for testdata.target;
14
15 endfor
16 populate recommendations based on predictions;
17 dissiminate recommendations to end-users
```  
**Algorithm 1**The deep learning based procedure to predict \(PDR\) and \(EC\)
## 5 Example Analysis
### Summary  
Based the analysis of example 1 and example 2, it is concluded that although the slip surface searched by this method is not exactly the same as that searched by Geo software, the form of the slip surface is very similar, and the starting position and end position of the slip surface are similar. The stability factor calculated by this method is smaller than that calculated by the two finite element software, the specific reasons are as follows. When using PLAXIS 3D to calculate the stability factor of filled-slope reinforced by frame with prestressed anchor-plates, in addition to the reinforcement effect of the anchor-plates on the filled-slope, the frame beam also plays a certain role in reinforcing the slope, while the stability calculation method proposed in this paper only considers the effect of the anchor-plates. When using Geo to calculate the stability factor of filled-slope reinforced by a frame with prestressed anchor-plates, the software is a two-dimensional finite element software, and the anchor-plate can only be simulated as an anchor during simulation, which is different from the algorithm in this paper, so the calculated stability factor of the slope is quite different. Although the stability factor of slope calculated by this method is smaller than that calculated by finite element software, it can provide a certain safety reserve for practical engineering when designing a filled-slope reinforced by a frame with prestressed anchor-plates. Therefore, this stability calculation method of a filled-slope reinforced by a frame with prestressed anchor-plates proposed in this paper is reasonable and suitable for the case of arbitrary arc slip surface in the filled-slope reinforced by a frame with prestressed anchor-plates, and it provides some guiding values for the design of practical engineering.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 3 Surface Correlation-based Fingerprinting Method
### Surface Correlation  
The Surface Correlation is the process of determining a location by calculating the similarity between the measured and reference RSS patterns. The similarity is expressed as a Surface Similarity Distance (SSD) and calculated as follows:  
\[\rho_{i}=\sum_{i=1}^{L-w}\sum_{j=1}^{w}\left\{\frac{\sqrt{\sum_{k=1}^{m}\left(S_{ jk}^{\text{Meas}}-S_{i+j-1\&}^{DB}\right)^{2}}}{m}\right\} \tag{4}\]  
where \(i\) is the epoch of RP on a generated reference trajectory and \(m\) is the number of BS. \(\rho_{i}\) is the SSD for \(i\)th calculation, \(S^{\text{Meas}}\) is the measured _Surface_, and \(S^{DB}\) is the RSS pattern on the reference trajectory. \(L\) is the number of RPs in the reference trajectory and \(w\) is the number of samples on the _Surface_ during the time of walking distance \(D\), that is, the number of estimated steps. To calculate the similarity distance, the number of RPs selected in the reference trajectory should be equal to the number of samples in the measured _Surface_ during the period of walking distance \(D\), which is not usually possible. This is because the step length of a pedestrian and the interval between the RPs is not equal. To solve this problem, we generate a virtual _Surface_ equal to the number of RPs within distance \(D\) through linear spacing as shown in Figure 4. The virtual _Surface_ is utilized for the calculation of SSD as shown in Figure 5a. The \(i\)th RP coordinate with the minimum \(\rho_{i}\) is considered as the current location as shown in Figure 5b.
### HGV maneuver Intention Prediction
In addition,using multilayer LSTM networks to predict the target maneuver type is also a current maneuver intention prediction method with strong adaptability and high prediction accuracy [18]. The LSTM network can be used to approximate the dynamic relationships between aerodynamic parameters and displacements by aerodynamic analysis of HGVs at variable Mach numbers and mean angles of attack. In summary, the establishment of the maneuver intention model for HGVs is mainly based on maneuver intent type modeling. The consideration of longitudinal and lateral maneuver magnitudes mainly relies on the artificial classification of trajectories into several classes, which are then recognized by LSTM networks [27]. A multi-channel neural network is a parallel connection of multiple single-channel neural networks. Each channel extracts different patterns in the time sequences, and then the outputs of multiple channels are fused together to achieve the recognition and prediction of complex temporal relationships [19]. In this paper, in solving the recognition and prediction problem of maneuver intention sequences of HGVs, the number of stacked network layers is adjusted on the basis of multilayer LSTM networks to improve the recognition and prediction accuracy. Then, multiple LSTM networks are connected in parallel by using multi-channel neural networks. Each channel extracts the temporal association information of different time steps, and then the features of multiple time steps are fused to obtain rich trajectory features, and then the maneuver intention results are obtained by the fully connected network.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 1 Introduction
Experiments show that TCTE not only effectively improves the user's download rate and edge node throughput, reduces download rate fluctuations, reduces user download delay, and improves fairness, but also has universal applicability. The remainder of this paper is organized as follows. Section 2 discusses the existing literature on the topic. Section 3 depicts the NDC transmission process and the name resolution service. Section 4 describes our proposed algorithm. Section 5 evaluates the effectiveness of our proposed algorithm. Section 6 concludes our work.