esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Method
#### 3.2.4 Classification  
Each deep activated feature is combined with geometric features and then fed into the classification model. To test the fusion efficiency between deep activated and geometric features, the deep activated features are also tested separately and compared with the fusion features. Most tests were conducted using a well-known \"optimal margin classifier,\" also known as support vector machine (SVM) [31].  
The SVM model aims to find the optimal hyperplane that can best describe the distinction between data, caries, and non-caries in this case. To moderate the number of training points, we apply the Gaussian radial basis function in the classifier. For a given training data \(D=\{(x_{i},y_{i}),\ i=1\dots N\}\) and \(y_{i}\in\{-1,\ 1\}\) the SVM classifier and the mapping function of the Gaussian kernel can be described as follows in Equations (1) and (2):  
\[\min_{\omega,b,\ \xi}\frac{1}{2}||W||^{2}+C\sum_{i}\xi_{i}^{2}\ subject\ to\ y_{i}\Big{(}W^{T}X_{i}+b\Big{)}\geq 1 -\ \xi_{i},\ \xi_{i}\geq 0,\ \forall i \tag{1}\]  
where C > 0 is the selected parameter and \(\xi\) is a set of slack variables.  
\[\text{K}\ (\text{X},\ \text{Y})\ =\ e^{\frac{||X-Y||^{2}}{A}} \tag{2}\]  
where K is the kernel function and A is the constant.  
Furthermore, to guarantee the best classification which fit the features set, we also test the best features set on k-nearest neighbor (KNN) [32], decision tree (DT) [33], Naive Bayes (NB) [34], and Random Forest (RF) [35].  
\begin{table}
\begin{tabular}{c c c} \hline
**Features** & **Name** & **Formula** \\ \hline F1 & Mean & \(\mu=\frac{1}{i}\sum\limits_{(x,y)\in N}I(x,y)\) \\ \hline F2 & Entropy & \(E=-\frac{\sum\limits_{i}\sum\limits_{j}p(i,j)\log p(i,j)}\) \\ \hline F3 & Autocorrelation & \(AutoCor=\sum\limits_{i}\sum\limits_{j}(i)p(i,j)\) \\ \hline F4 & Contrast & \(Cont=\sum\limits_{i}\sum\limits_{j}[i-j]^{2}p(i,j)\) \\ \hline F5 & Correlation & \(Cor=\sum\limits_{i}\sum\limits_{j}\frac{(i-u_{i})(j-u_{j})p(i,j)}{\alpha_{i} \alpha_{i}\alpha_{j}}\) \\ \hline F6 & Cluster prominence & \(Cont=\sum\limits_{i}\sum\limits_{j}\big{(}i+j-\mu_{x}-u_{y}\big{)}^{4}p(i,j)\) \\ \hline F7 & Cluster shade & \(Shade=\sum\limits_{i}\sum\limits_{j}\big{(}i+j-\mu_{x}-u_{y}\big{)}^{3}p(i,j)\) \\ \hline F8 & Dissimilarity & \(Cont=\sum\limits_{i}\sum\limits_{j}[i-j]\cdot p(i,j)\) \\ \hline F9 & Maximum probability & \(MaxProb=\underset{i,j}{\max}p(i,j)\) \\ \hline F10 & Sum of square variance & \(SumVar=\sum\limits_{i}\sum\limits_{j}[i-\mu^{2}|p(i,j)\) \\ \hline F11 & Sum of average & \(SumArg=\sum\limits_{i=2}^{2N_{x}}i\cdot p_{x+y}i\) \\ \hline F12 & Sum of entropy & \(SumEnt=\sum\limits_{i=2}^{2N_{x}}p_{x+y}(i)\log p_{x+y}(i)\) \\ \hline F13 & Sum of variance & \(SumVar=\sum\limits_{i=2}^{2N_{x}}(i-SumEnt)^{2}\cdot p_{x+y}(i)\) \\ \hline F14 & Difference entropy & \(DiffEnt=-\sum\limits_{i=2}^{N_{x}-1}p_{x-y}(i)\log p_{x-y}(i)\) \\ \hline \end{tabular}
\end{table}
Table 2: Geometric features and formula.
## Materials and Methods
### Methods
#### 2.2.3 Spatial Evaluation  
As described previously, a spatially explicit evaluation of the extracted historical urban extents is difficult due to the lack of reference data. The historical built-up property records (BUPR) surfaces from the HISDAC-US provide an estimate of the historical building density distributions across space in urban, but also in rural, areas and are available at a half-decadal temporal resolution. As our urban area extraction approach is assumed to be responsive to densely built-up urban areas only, a direct (i.e., binary) comparison of urban grid cells extracted from the historical map with any built-up reference grid cell (i.e., containing at least one structure) is not suitable, as it would underestimate the accuracy of our approach. Thus, we decided to carry out Receiver-Operator-Characteristic (ROC) analysis [90], which is a performance measurement tool for a (binary) classifier that yields a continuous output as a function of thresholds that are applied in order to predict class membership [91]. Herein, we use ROC analysis to test whether there is a building density threshold in the BUPR surfaces that successfully reproduces the urban/non-urban labels extracted from the historical maps. This threshold may possibly vary between maps and study areas.  
We conducted such an analysis for the US study areas where HISDAC-US is available. To do so, we resampled the building densities from the BUPR layers to the RGB\({}_{100}\) grids (see Figure A2b). As these historical map composites consist of individual maps produced in slightly different years (see Table 1), we created a BUPR composite that reflects the BUPR distribution in each map quadrangle in or close to the production year of the underlying historical map. For example, if a map sheet was created in 1898, we used the BUPR estimates in 1900 for the grid cells within the area covered by the map sheet.
## 2 Materials and Methods
### Electron Probe Microanalyzer Analysis  
The impact of NCP pre-treatment on the effect of dental fluoride, i.e., APF application was determined by pre-treating the prepared enamel specimens with NCP for 3 min, followed by APF alone. The distance between the enamel specimen and NCP was 10 mm. Each specimen (\(n\) = 9) was treated with APF and NCP once daily for 4 days. Subsequently, the teeth were brushed with distilled water, and treated with artificial saliva at room temperature for 30 min, and dried. The mesh experiment was conducted to confirm the working element of plasma, which is important for the effect of NCP on F\({}^{-}\) application.  
The dielectric mesh was made from cotton mesh, so that most of the NCP working elements can pass through it, though the flow rate of NCP can be limited. The electric grounded mesh, on the other hand, was created utilizing a copper mesh that was directly connected to the ground panel of the power supply device, therefore eliminating the impacts of charged particles from NCP (Figure 3) [30]. Each prepared enamel sample was divided into four experimental groups as follows (\(n\) = 9): APF treatment alone, APF+ NCP, and treatment with NCP equipped with a cloth mesh at the end of the NCP device (APF+ NCP dielectric mesh (DE)), and treatment with NCP equipped with an electric grounded mesh at the end of the NCP device (APF+ NCP grounded electric mesh (EG)). After APF and NCP treatment, the teeth were brushed with distilled water, treated with artificial saliva at room temperature for 30 min, and dried. After all treatments were completed, the F\({}^{-}\) content of each sample was analyzed using field emission electron probe microanalyzer (FE-EPMA) equipment (JXA-8530F, JEOL, Tokyo, Japan) capable of qualitative and quantitative analysis of the microelements. The measurements were conducted by randomly designating the site of each specimen to ensure the accuracy and objectivity of the results [25].  
Figure 3: Charged particles of NCP. (**a**) NCP; (**b**) NCP dielectric mesh (DE); (**c**) NCP grounded electric mesh (EG). NCP: no-ozone cold plasma.  
Figure 2: Schematic diagram of the NCP device used in the experiment. NCP: no-ozone cold plasma.
## 3 Proposed System and Methodology
### Protocol Identification  
We aim to apply ML in identifying the unlicensed radio technology specifically for spectrum sharing. Some signal processing functions can be learned within the physical layer as discussed in [13]: ML is used only for modulation classification of single-carrier modulation schemes using the CNN on radio frequency time-series data [13]. There are other ML methods applied for classifying radio signals such as SVMs [44], small feed-forward neural networks, and random forests [64].  
We are now ready to discuss the protocol identification stage to be deployed after the CDAE stage for signal denoising. The architecture of our classifier is a Convolutional Neural Network, fed with with the features learnt by the CDAE in signal denoising process. The basic block of any CNN is the convolution, a simple application of a _filter_ to an input that results in an activation. CNN filters are locally connected to capture correlations between different data regions in the image and output a feature map. The convolutional structure reduces the number of model parameters significantly and provides a robust recognition of affine invariance [6]. Powerful CNN models developed for imaging applications include ResNet, Inception-V4 [65], and GoogLeNet [66]. These models mostly differ from one another in terms of the CNN depth (Different inception and residual techniques were also proposed to overcome overfitting and gradient vanishing problems that are typical of \"deep\" CNNs. A detailed discussion of these techniques is outside the scope of this paper.).  
CNNs were also adapted for video action recognition using 3D CNNs [67]. While our intuition supported the notion that CNNs could perform well on the snapshots/images of wireless spectra, we did not jump to conclusions. Instead, we implemented several ML models and compared their protocol identification performance across different SNR values and propagation models.  
Our resulting pipeline, which composes a DAE and a CNN classifier, includes a series of convolutional layers, a _maxpooling_ layer, a fully connected layer, _dense_ layer, and a _softmax_ activation layer to perform the classification. The input to our CNN-based classifier is the DAE weights' matrix (compressed representation), and the output is the class type of the signal. Our overall architecture for signal classification is illustrated in Figure 5. A further detailed explanation is given in Section 4.5.  
Figure 5: Signal classifier architecture composed of multiple convolutional layers. The classifier’s input is the compressed representation of the denoising autoencoder, and the output is the class.
## Theoretical Framework  
### Density Functional Theory  
Density functional theory (DFT) has long been the pillar of ground-state energy and density profile calculations in condensed matter science, widely used both by physicists, chemists and material researchers to study theoretically various properties of many-body systems, and in particular, it is an approach for the description of ground-state properties of metals, semiconductors and insulators. The success of density functional theory not only encompasses standard bulk materials, but also complex materials, such as proteins and nanostructures [21; 22].  
In 1965, [PERSON] and [PERSON] proposed a practical way to implement DFT and made a significant breakthrough when they showed that the problem of many weakly-interacting electrons in an external potential can be mapped exactly to a set of non-interacting electrons in an effective external potential. The effective potential in this non-interacting particle system (the Kohn-Sham system) can be shown to be completely determined by the electron density of the interacting system and is for this reason called a density functional theory [23].  
The Kohn-Sham (KS) equation looks like a simple one-particle Schrodinger equation, and it can be described by the following equation:  
\[[-\frac{\hbar^{2}\
abla^{2}}{2m}+V_{ext}(r)+V_{Hartree}+V_{xc}(r)]\Phi_{j}(r)= \epsilon_{j}\Phi_{j}(r)\]  
where \(\Phi_{j}(r)\) is KS wave functions and \(V_{ext}\) is the external potential acting on the interacting system. Furthermore, \(V_{Hartree}\) is the Hartree part of the Coulomb electron-electron interaction. The exchange-correlation potential, \(V_{xc}(r)\), which stems from the many-body effects, describes the effects of the Pauli principle and the Coulomb potential beyond a pure classical electrostatic interaction of the electrons. Possessing the exact exchange-correlation potential means that we solve the weakly many-body problem exactly. A common approximation is the so-called local density approximation (LDA), which locally substitutes the exchange-correlation energy density of an inhomogeneous system by that of an electron gas evaluated at the local density.
## 3 Numeric Examples
### Numerical Examples for the EFM  
3.2.1. The Numerical Application of the EFM to the 3-3 Type SP  
From Figure 3, the interval of \(\lambda\) values that lead to real roots for \(\mu\) and \(\
u\) are found as \(0.573\leq\lambda\leq 0.722\). \(\lambda\) is altered within the obtained interval, and the error function is plotted versus \(\lambda\), Figure 5. From this figure, the minima of the error function are obtained.  
The error function has two minima about \(\lambda=0.66\) and \(\lambda=0.72\). In this case, \(\mu\) and \(\
u\) are calculated from Equations (8) and (9), corresponding to these values of \(\lambda\). Given these triplets of (\(\lambda\), \(\mu\), \(\
u\)), the real solution set is searched using a suitable algorithm (for example, the Newton-Raphson iteration method or fsolve, a built-in function in MATLAB)). The results obtained are given in Table 10. From the comparison of Tables 4 and 10, it is immediately noticeable that both methods provide the same results.
## 2 Experimental Studies
### Penetration Diffusion Laws of Cement Grout in Gravel Soil  
The penetration grouting experiments were carried out according to the designed experiment scheme, shown in Figure 1. The penetration diffusion progress of the cement grout in the gravel soil can be divided into three stages.  
1. Beginning stage: Most of the cement grout diffused rapidly in the gravel soil from the grouting hole along the downward (especially vertical downward (gravity)) direction. A certain amount of cement grout diffused horizontally and laterally around the grouting hole, and only a very small amount of cement diffused to its upper area. In this stage, the cement grout diffusing along the downward direction was much larger than the cement diffusing around the grouting hole horizontally and laterally, both in terms of diffusion speed and diffusion volume.
2. Intermediate stage: both the diffusion speed and diffusion volume of cement grout in gravel soil along the downward direction gradually decreased; while the amount of cement grout diffusing horizontally and horizontally around the grouting hole slowly increased, and a certain amount of cement diffused into the gravel soil at the upper part of the grouting hole.
3. End stage: Only a very small amount of cement grout penetrated and diffused into the gravel soil at the lower part of the grouting hole. The speed and volume diffusing in the horizontal and lateral part around the grouting hole gradually decreased, while most of the cement penetrated and diffused in the upward direction at this stage.  
The diffusion flow pattern and diffusion morphology of cement grout in gravel soil are shown in Figure 2, respectively.  
Figure 1: Part of the penetration grouting experiments device.
## 3 Conclusions  
An adaptive neural fault-tolerant control scheme was developed for a UAV helicopter SISO non-affine nonlinear yaw control system capable of dealing with unknown external disturbances, actuator faults, input saturation, and full-state constraints. By combining a Taylor series expansion technique with a robust sliding mode filter, the actual control input was explicitly defined. An RBFNN was employed to approximate the unknown nonlinear function. In comparison with prior research, this paper leads to the following salient conclusions:  
(i) A symmetric barrier Lyapunov function with a smooth structure was designed to prevent the system from exceeding all state constraints.  
(ii)To lighten the online computational burden, the Euclidean norm of the unknown neural network weight vector was estimated instead of the ideal weight vector. The number of learning parameters was reduced along with the complexity of the calculations.  
(iii) The compound bounded disturbances and unknown parameters were estimated by adaptive technology. Moreover, we employed the limit of \(g\rho\) to handle actuator fault tolerance, making it unnecessary to determine the bound value of \(g\rho\) in the process of stability proofing. This broadens the range of applications.  
(iv) By incorporating an anti-saturation compensator, the signal difference between actual control input and saturation actuator output was analyzed for its effect on system control so that the control input met the input constraint requirement.  
The future investigative directions are to verify the feasibility of the UAV helicopter's longitudinal dynamics with existing time-varying state constraints, unknown control directions, and input saturation issues. From the perspective of the control system, we will design the overall solution of UAV helicopter based on the research in the paper and navigation instructions. In the future, the proposed control scheme should also extend its generality and adaptability to the MIMO systems.  
Conceptualization, Q.Z.; methodology, Q.Z. and X.C.; software, D.X.; validation, Q.Z. and X.C.; writing--original draft preparation, Q.Z., X.C. and D.X.; supervision, D.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by National Natural Science Foundation of China (61503156, 61403161), Shandong Provincial Natural Science Foundation (ZR2019 MF015) and the Key Research and Development Plan of Shandong Province of China (2017 GGX30121).  
The authors declare no conflict of interest.  
Figure 9: The control input with saturation and actuator faults.
###### Abstract  
The hybrid system of wave energy converters (WECs) using coastal structures is an attractive issue in terms of a decrease in construction costs and an improvement of the ability to capture wave energy. Most studies on the utilization of reflected waves from structures, which is one of the hybrid systems, are limited to mathematical analysis based on linear theories. Therefore, this paper presents fundamental experimental results in the presence of a reflection wall simplified as a coastal structure behind a bottom-hinged flap-type WEC under unidirectional regular waves. The behavioral characteristics and the power generation efficiency \(k_{e}\) of the flap were investigated, focusing on wave steepness, initial water depth, and distance from the reflection wall. The results show that the condition of the initial water depth being smaller than the flap height is more effective in terms of avoiding unstable rotating of the flap. The maximum \(k_{e}\) appeared slightly far from the node position of the standing waves because the flap shape and the power take-off (PTO) damping induce the phase difference between the reciprocating behavior of the flap and the period of the standing wave. The results imply that the optimum position of a WEC is dependent on WEC shape, PTO damping, and installation water depth.  
wave energy converter; power generation efficiency; bottom-hinged flap-type WEC; standing wave; reflection wall +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
[PERSON] [20] developed a stochastic traffic model to optimize the headway of connected and automated vehicles (CAVs), allowing them to pass through unsignalized intersections continuously. [PERSON] et al. [21] designed an adaptive cruise control (ACC) system for autonomous vehicles that can effectively reduce vehicle collisions and delays at intersections. [PERSON] [22] proposed a speed control method for vehicles at unsignalized intersections in a connected vehicle environment to improve traffic efficiency. In summary, there is extensive research on speed guidance and trajectory optimization for purely CAVs, but there is relatively little research on speed guidance strategies for mixed traffic flow. [PERSON] [23] et al. proposed a joint optimization framework at intersections for mixed traffic flows with the goal of reducing traffic emissions. [PERSON] [24] et al. proposed a new robust advisory control framework based on model predictive control (MPC) to optimize the fuel efficiency of CAVs in heterogeneous urban traffic flows. In this control framework, CAVs are considered to provide advisory commands to HDV to follow, in order to improve their own speed and overall fuel economy. [PERSON] [25] et al. proposed a two-layer hierarchical longitudinal control method for travel time and trajectory along multiple intersections on arterial roads under mixed traffic of CAVs and HDVs. [PERSON] [26] et al. proposed a trajectory prediction-based DRL method for mixed traffic environments. Based on existing research results, this paper combines factors such as vehicle reaction time and the driving style of HDV when following CAVs to establish a car-following model for mixed traffic flow considering multiple factors. Secondly, considering that most existing research on speed guidance strategies focuses on single-lane driving environments under fully CAV conditions, this study constructs a passage model for mixed traffic flow in a two-lane driving environment. Additionally, recognizing that current research on platoon speed guidance strategies often involves excessive assumptions, resulting in an overly idealized research environment, this study considers the uncertainty interference of leading vehicles during the driving process. Various interference factors are studied, ultimately establishing a speed guidance model for mixed traffic flow platoons. The specific arrangement of the paper is as follows: The second section presents the establishment of the car-following model for mixed traffic flow. The third section presents the passage model at signalized intersections. The fourth section provides the speed guidance strategy considering the uncertainty interference of leading vehicles during the driving process. The fifth section includes the experimental design and result analysis to verify the effectiveness of the model guidance.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Modeling of LoRa/LoRaWAN  
This section focuses on the LoRa modulation scheme and the LoRaWAN protocol taking into account the variation effect of the spreading factor, the coding rate and the bandwidth, which are critical parameters permitting a trade-off between energy consumption and nominal data rate [22]. Table 5 summarizes the characteristics of three LoRaWAN modes that can be used with the _SX1272_ transceiver.