esa-sceva/satcom-chunk-collection huggingface dataset:
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 3 Proposed Graph Convolutional Network with High-Order Features
### Improved Graph Convolutional Network  
The implementation of the graph convolution in the spatial domain is not straightforward. Concretely, the input of every layer in the network is actually a \(C\times T\times N\) tensor, where \(C\), \(T\), and \(N\) are the number of channels, frames, and vertices, respectively. Furthermore, the edge importance matrix was proposed in ST-GCN [6], aiming to distinguish the importance of the edge of skeletons for different actions. The graph convolution operation is formulated as Equation (1) in [6]:  
\[f_{out}^{n}=\sum_{s}^{S_{v}}W_{s}*(f_{out}^{n-1}*A_{S})\odot M_{k} \tag{1}\]  
where the matrix \(A\) is initial adjacency matrix proposed in [6], and S is the subset of matrix \(A\), which is similar to the \(N\times N\) adjacency matrix. \(W_{s}\) is the weight vector of the \(C_{out}^{n}\times C_{out}^{n-1}\times 1\times 1\) convolution operation, where \(*\) denotes the matrix product. \(M\) is the edge importance matrix of \(n*n\), which is dot multiplied by matrix \(A\).  
Equation (1) shows that the edge importance matrix \(M_{k}\) is dot multiplied to \(A_{s}\). That means that if one of the elements in \(A_{s}\) is zero, it will always be zero, which is unreasonable. Thus, we change the computing method. We add another attention matrix \(M_{k1}\) and then multiply matrix \(M_{k}\). In addition, we use the similarity matrix in 2S-AGCN [7] to estimate the similarity of two joints, and determine whether there is a connection between two vertices and how strong the connection is. Finally, Equation (1) is transformed into Equation (2):  
\[f_{out}^{n}=\sum_{s}^{S_{v}}W_{s}*(f_{out}^{n-1}*(A_{S}\oplus M_{k1}\oplus S_{ k}))\odot M_{k} \tag{2}\]  
where \(\oplus\) denotes matrix addition. \(S_{k}\) is the similarity matrix proposed in 2s-AGCN [7]. \(M_{k1}\) is a new attention matrix we added.  
For the temporal domain, since the number of neighbors for each vertex is fixed as two (corresponding joints in the two consecutive frames), it is straightforward to perform the graph convolution similar to the classical convolution operation. Concretely, we perform \(K_{t}*1\) convolution on the output feature map calculated above, where \(K_{t}\) is the kernel size of temporal convolution. Spatial convolution is combined with temporal domain convolution into a graph convolution module. The details are shown in Figure 3:
## 7 Cuckoo Search Algorithm Adapted for Cognitive Radio ad Hoc Networks
### Residual Energy Consumption of the Mobile Nodes  
The mobile nodes are battery powered, and energy efficiency is required for an extended network lifespan because if the energy of a node is depleted, its capability to route data is affected resulting in network bottlenecks, which in turn adversely affects the network lifespan. Let \(P_{max}\) (\(u\)) be the maximum transmission power, then let \(P_{min}(u)\) be the minimum transmission power and let \(P_{u}\) be the transmission power of a node \(u\in V\). The nodes initially transmit with max power \(P_{max}\). Then assume that the transmission power can be regulated between the max and min values such that \(P_{min}(u)<=P_{u}<=P_{max}\). Then, let \(P_{uv}\) be the minimum transmission power required to facilitate communication between node \(u\) and node \(v\). Table 1 presents the simulation parameters.  
This study assumes the utilization of the IEEE 802.11b interfaces that are operating within the ad hoc network at 11 Mbps utilizing the distributed coordination function (DCF),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Simulation Values** \\ \hline Mobile nodes & 10, 20, 30, 40, 60, 80, 90, 100 \\ Network topology & 1000 m \(\times\) 1000 m \\ Channel type & Channel/wirelessChannel \\ Radio-propagation model & Propagation/TwoRayGround \\ Simulation time & 100 s \\ MAC time & Mac/802\_11 \\ Interface queue type & Queue/DropTail/PriQueue \\ Traffic source & File transfer protocol \\ Antenna model & Antenna/OmniAntenna \\ Routing protocols & OCS-AODV, CS-DSDV, ACO-AODV, \\ Simulator & NS 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
with ready to send (RTS)/clear to send (CTS) handshake packets. We demonstrate the normal power (\(X_{m}\)) consumed by the interface as:  
\[X_{m}=t_{sl}*X_{sl}+t_{ud}*X_{ud}+t_{Rv}*X_{Rv}+t_{Wk}*X_{Wk}\dots \tag{13}\]  
where \(t_{sl}\), \(t_{ud}\), \(t_{Rv}\), and \(t_{Wk}\) are the negligible portions of the time epochs spent by the interfaces in one of the potential states: sleep, idle, receive, and transmit. These epochs fulfill the condition. Similarly, \(X_{sl}\), \(X_{ud}\), \(X_{Rv}\), and \(X_{Wk}\) represent energy consumed in the four states, considering \(X_{m}\) and the underlying energy of the node (\(E\)). We can compute the node lifetime (\(T_{v}\)), which addresses the duration before the energy of the node is depleted:  
\[T_{v}=\frac{E}{X_{m}}\ \dots \tag{14}\]  
It also shows energy utilization at the four states: idle, sleep, received, and transmit. We can compute the energy management (\(X_{m}\)) and the node lifetime (\(T_{v}\)), which addresses the time before the energy is depleted [23]. Managing the battery usage among nodes enables the estimation of the power consumption of the nodes.
## 2 Materials and Methods
### Winter Wheat Planted Area Data from the USDA NASS Cropland Data Layer Dataset (CDL)  
The annual CDL dataset at a 30 m spatial resolution includes more than one hundred crop types in the CONUS [15]. The CDL dataset is produced by using a machine learning method, in situ reference data, and multi-date satellite images to identify crop types. The training data used in the crop classification include the National Land Cover Dataset (NLCD) and the USDA Farm Service Agency (FSA) Common Land Unit (CLU) data. The CLU attributes include the crop types and acreages reported by producers to the FSA county offices in the early growing season. The CDL classifier utilizes multiple temporal remote sensing images from multiple sensors, including Landsat TM/ETM+, AWiFS, Deimos-1, UK-DMC-2, and MODIS images [15]. The spatial resolution of the CDL dataset is 30 m from 2010-2018 and 56 m from 2008-2009. In 2018, the CDL data for 2008 and 2009 were reprocessed to 30 m. The CDL data from 2008 to 2018 at a 30 m spatial resolution were used, which ensures the spatial resolution is consistent during the study period. The CDL dataset provides detailed spatial information for individual crop types, and high classification accuracies (>90%) for major crop types (e.g., winter wheat, soybean, and maize) were reported [15]. Numerous agriculture-related studies and land cover change studies have used the CDL dataset [36; 37; 38; 39]. CropScape, which is a web-based data portal, provides interactive tools for people to download, visualize, and analyze the CDL data in a more effective and efficient way [40]. The annual total planted area of winter wheat in each county was calculated from CropScape. The national total areas in each year were then calculated as the total of annual planted crop areas over all the counties in the nation. Figure 1: Annual maps of county-level winter wheat over CONUS for year 2010: (**a**) CDL-derived planted area (plt_CDL), (**b**) NASS planted area (plt_NASS), (**c**) NASS harvested area (harv_NASS), (**d**) NASS grain yield, (**e**) NASS grain production, and (**f**) annual averaged GPP\({}_{\text{VPM}}\) of winter wheat. Gross Primary Production Estimates for Winter Wheat from the Vegetation Photosynthesis Model (Gpp\({}_{\text{VPM}}\))  
The VPM estimates the daily GPP from the amount of PAR absorbed by chlorophyll (APAR\({}_{\text{chl}}\)) and the light use efficiency [41; 42]. GPP derived from the eddy-covariance tower sites (GPP\({}_{\text{EC}}\)) was used to assess daily GPP\({}_{\text{VPM}}\) in different croplands, including winter wheat [43; 44], soybean [45; 46], paddy rice [47], maize [45; 48], and sugarcane [49]. Strong temporal consistency between GPP\({}_{\text{EC}}\) and GPP\({}_{\text{VPM}}\) was reported in the aforementioned publications, with R\({}^{2}\) values ranging from 0.70 to 0.98. The driving data of the VPM include vegetation indices (land surface water index (LSWI) and EVI) and meteorological data (air temperature, shortwave radiation). The theory, model structure, and parameters of the VPM were documented in [30; 31].
## 4 Analysis of Numerical Results
### Influence of Tunnel Excavation Surface Spacing  
During tunnel construction, tunnels that are dug one after another are prone to mutual disturbance. In order to speed up the construction process, the two tunnels are often excavated to be kept at a certain distance from each other, so that the structural and rock interaction between the two tunnels should be kept within an acceptable range. At the same time, the two shield machines in the actual construction of the twin tunnels usually do not go hand in hand, because the disturbance to the surrounding rock is too large and the stress at the palm surface is sharply concentrated, which may cause too much deformation of the tunnel and jam the shield machine.  
In this section, we analyze the spatial effects of tunnels in the special terrain conditions of river tunnels, numerically simulate shallow buried tunnels with different excavation surface distances between the left and right lines, and propose a reasonable distance between excavation surfaces. In the simulation, it is assumed that the right line is the first hole and the left line is the later hole. Five working conditions of tunnel excavation surface spacing (0 m, 25 m, 50 m, 75 m and 100 m) were selected for the simulation of stepwise excavation, numbered 1-5 as shown in Table 2.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 4 Simulation Results  
In order to verify the effectiveness of the algorithm, a multi-UAV cooperative search simulation environment was established in MATLAB. The information in the search area was completely unknown, and the purpose of the search was to identify all targets and possible trends in the sea area. The effectiveness of the algorithm was verified by comparative simulation, which was aimed at the targets of the independence and the formation, respectively. The relevant parameters in the simulation were set as follows: the weighted parameters were \(w_{1}=0.25\), \(w_{2}=0.15\), \(w_{3}=0.1\), \(w_{4}=0.5\), the decision-making period was 20 s, and the speed of the UAV was 30 m/s. The speed of the warships was 9 kn. The sensor parameters were \(p_{D}=0.9\) and \(p_{F}=0.1\), and the search map parameters were \(\tau=0.98\), \(\tau_{c}=0.9\), and \(\tau_{H}=0.9\).
## 3 EMI NO\({}_{2}\) Retrieval Algorithm
### AMF Calculation
The model outputs include the NO2 profile (\(n_{l}\)) of the volume mixing ratio, temperature profile (\(T_{l}\)) in the unit of K, hybrid pressure level in the unit of Pa, and the tropopause layer index (\(l_{tp}\)) is also provided to separate the stratosphere and troposphere. For each individual pixel, the surface albedo is obtained from the climatological monthly Lambertian equivalent reflector (OMLER) data at 440 nm [55]. The surface elevation is obtained from the GMTED2010 data, which have a uniform spatial resolution of 30 arc seconds. During the AMF calculation, the scattering weight is interpolated according to the observation condition and the pressure grid of the a priori profile. Figure 5 shows the cloud fraction and tropospheric AMF for OMI, EMI, and TROPOMI, respectively. The pattern of the tropospheric AMF spatial distribution from EMI is consistent with OMI and TROPOMI. The area averaged tropospheric AMF of EMI (Figure 5e) is 1.20, while the corresponding value of OMI (Figure 5d) and TROPOMI (Figure 5f) is 1.25 and 1.33, respectively. For cloud-free pixels, the area averaged AMF of EMI is 1.48% higher than OMI and 6.1% lower than TROPOMI. These discrepancies could mainly result from discrepancies in the a priori NO2 profiles.
# A New Enhancement Edge Detection of Finger-Vein Identification for Computer System
## 3 Proposed Finger-Vein Identification System
### Bilateral Filter  
In Figure 2a, many denoising methods compromise image edge details so high frequency information is lost. A bilateral filter is a nonlinear filter that measures the spatial domain and gray levels to preserve edges, as demonstrated in Equation (1). A bilateral filter has two components, one of which relates to the pixel distance, for which the weight is product of the spatial nearest factor and similarity factor. The spatial nearness factor is a Gaussian filter coefficient and increased pixel distance decreases the weighting. An increase in the \(\delta_{r}\) value increases image smoothing, as described by Equation (2). Equation (3) describes the relationship between the luminescence similarity factor and the spatial pixel difference. The greater the spatial pixel difference, the lower is the weighting for the luminesce similarity factor. An increase in the \(\delta_{d}\) value increases the smoothing effect for pixels with the same gray level so less edge data is preserved. A bilateral filter is used with a Sobel operator to improve edge detection, as shown Figure 2b.  
\[f_{\text{BF}}(x,y)=\frac{\sum_{i}\sum_{j}f(i,j)w(w,y,i,j)}{\sum_{i}\sum_{j}w(x, y,i,j)} \tag{1}\]  
\[r(x,y,i,j)=exp\left(-\frac{\left|f(x,y)-f(i,j)\right|^{2}}{2\delta_{r}^{2}}\right) \tag{2}\]  
\[d(x,y,i,j)=exp\left(-\frac{(x-i)^{2}+(y-j)^{2}}{2\delta_{d}^{2}}\right) \tag{3}\]  
\[w(x,y,i,j)=exp\left(-\frac{\left|f(x,y)-f(i,j)\right|^{2}}{2\delta_{r}^{2}} \right)-\frac{\left|f(x,y)-f(i,j)\right|^{2}}{2\delta_{d}^{2}} \tag{4}\]  
Figure 1: Flowchart for the proposed system.
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### Equilibrium Analysis for S-ALOHA System
#### 5.2.3 Equilibrium Analysis in the Non-Saturation State for \(Q\geq 9\)  
For \(Q\geq 9\), there are two load boundaries \(0<\rho_{1}<\rho_{2}<\infty\). Actually, as shown in Figure 4a, \(s/\rho_{1}\) is the tangent of \(h(s)\) on \((s_{0},+\infty)\) and \(s/\rho_{2}\) is the tangent of \(h(s)\) on \((0,s_{0})\). Let \(s_{2}\) denote the point of tangency of \(s/\rho_{2}\) and \(h(s)\), we can derive that:  
(i) for \(\rho>\rho_{2}\), there is only a single equilibrium point on \((s_{2},+\infty)\);  
(ii) for \(\rho<\rho_{2}\), there is only a single equilibrium point on \((0,s_{2})\) and there are two equilibrium points on \((s_{2},+\infty)\) for \(\rho_{1}>\rho>\rho_{2}\) and one equilibrium point on \((s_{2},+\infty)\) for \(\rho>\rho_{1}\).  
As shown in Figure 4b, if we can prove that \(s_{2}\geq 1\), there must be precisely a single equilibrium point on (0,1) for \(\rho<\rho_{th}\).  
As shown in Figure 4b, we can have the tangent \(\varphi(s)\) of \(h(s)\) at \(s=1\):  
\[\phi(s)=h^{\prime}(1)s-h^{\prime}(1)+h(1) \tag{42}\]  
The vertical intercept of \(\varphi(s)\) is computed as:  
\[\begin{array}{ll}\phi(0)&=-h^{\prime}(1)+h(1)\\ &=Q(1-e^{-1})^{Q-1}\\ &>0\end{array} \tag{43}\]  
Figure 4: Function \(h(s)\) for \(Q=20\), \(s_{0}\) is the inflexion point of \(h(s)\): (**a**) Function \(h(s)\); (**b**) Illustration for proof of \(s_{2}\geq 1\).  
Figure 3: Function \(h(s)\) for \(Q=5\), \(s_{0}\) is the inflexion point of \(h(s)\).  
So we must have \(s_{2}\geq 1\). That is to say, there is only a single equilibrium point on (0,1) if \(\rho<\rho_{th}\). For \(\rho>\rho_{th}\), all the solutions for Equation (35) are on (1,+\(\infty\)), which means that the system is in a saturation state.  
To sum up, if the ACB scheme is enabled, we can derive the following conclusions for S-ALOHA system under \(Q\geq 1\)  
1. If \(L\leq~{}L_{th}^{S-ALOHA}\), i.e., \(\rho\geq\rho_{th}\), the system would surely converge to a saturated equilibrium state. Therefore, the equilibrium point cannot be computed by Equation (35) directly and the ACB scheme would ensure that \(s=1\). Then the average success probability and resource utilization ratio can be computed as: \[\begin{array}{ll}P_{S}&=\frac{L/\delta}{L}\\ U&=\frac{\delta\Delta P_{S}}{L}=e^{-1}\end{array}\] (44)
2. If \(L>~{}L_{th}^{S-ALOHA}\), i.e., \(\rho<\rho_{th}\), the ACB scheme would ensure the system converges to an equilibrium state which is non-saturate. The single unique equilibrium point \(s\in\) (0,1) can be computed by Equation (35). In the non-saturation state we can have: \[\begin{array}{ll}P_{S}&=1-(1-e^{-s})^{Q}\\ U&=\frac{\delta\Delta P_{S}}{L}\end{array}\] (45)
# Learning Kernel Stein Discrepancy for Training Energy-Based Models
## 3 Maximum Mean Discrepancy and Kernel Stein Discrepancy
### Maximum Mean Discrepancy: A Two-Sample Test Method  
From the perspective of hypothesis testing, the most popular solution paradigm of density distribution is the two-sample test. During the two-sample test, equivalent samples are picked from two density distributions separately. Then, we verify whether \(p(\theta;z)\) equals \(q(x)\) by calculating the values of the statistics. Due to the spatial mapping capability, Maximum Mean Discrepancy (MMD) [21] is a valid and widely used two-sample test method. MMD maps the samples into high-dimensional even infinite feature space RKHS (also named Reproducing Kernel Hilbert Space) to measure their distance. For all \(f\in\) RKHS, \(E_{x}f=\left\langle f,\mu_{p}\right\rangle\) is called the mean embedding of \(p\), among \(\mu_{p}\in\text{ RKHS}\). If the distance between the mean embeddings of two density distributions is less than a certain threshold value, we can be sure that the two sets of samples are generated from the same distribution, namely \(q(x)=p(\theta;z)\).  
MMD employs kernel functions to transform spatial coordinate bases. We denoted that \(k(x,\ x^{\prime})\) is a positive definite kernel and also a function: \(\chi\times\chi\to R\). \(\mathcal{H}_{k}\) is the Reproducing Kernel Hilbert Space (RKHS) defined by \(k(x,\ x^{\prime})\). RKHS is an inner product space with reproducibility, separableness, and completeness. In density distribution, a finite set of samples \(\left\{x_{i}\right\}_{i=1}^{n}\subset\chi\subset R^{d}\) are sampled from an unknown density distribution \(q(x)\). Another set of samples are defined as \(\left\{y_{j}\right\}_{j=1}^{m}\subset Y\subset R^{d}\) (also named fake samples), and \(y_{j}\) is an independent identically distributed sample from the density distribution \(p(\theta;z)\). The square of MMD can be represented by the mean embeddings \(\mu_{p}\) and \(\mu_{q}\), and they can be written as follows:  
\[MMD_{k}^{2}=\left\|h_{p}-\mu_{q}\right\|_{\mathcal{H}_{k}}^{2}=E_{q}[k(x_{i}, x_{i^{\prime}})]-2E_{q,p}\left[k(x_{i},y_{j})\right]+E_{p}\left[k(y_{j},y_{j} \right)\right] \tag{4}\]
## 2 Methodology  
### Vine Copula Models  
A copula function can be used to model multivariate distributions with given univariate margins. For a random vector \(X=(X_{1},\ldots,X_{d})\sim H\) along with \(H_{i},\ i=1,\ldots d\), [PERSON]'s theorem ([PERSON] 1959) can be written as:  
\[H\ (X_{1},\ldots,X_{d})=C(F_{1}((X_{1}),\ldots,F_{d}(X_{d})), \tag{1}\]  
where \(H\) is an \(n\)-dimensional distribution with marginal \(F_{i},i=1,\ 2\ldots,d\). \(C\) is a \(d\)-dimensional copula linked to form a joint distribution. [PERSON]'s theorem with a bivariate copula is implemented as:  
\[H(u_{1},u_{2})=C(F_{1}^{-1}(u_{1}),F_{2}^{-1}(u_{2})), \tag{2}\]  
where \(u_{1},u_{2}\in[0,1]\) and \(F\) is the distribution of invertible margins \(F_{1}\) and \(F_{2}\).  
For multivariate analysis, C and D Vine copulas, which were initially proposed by [PERSON] (1996), were utilized to estimate dependency. \(X=x_{1},x_{2},x_{3}\) are the factors within the marginal distribution functions. For instance, C Vine copulas can be formed as Equation (3):  
\[f(x_{1},x_{2},x_{3})=f_{1}(x_{1})f(x_{2}|x_{1})f(x_{3}|x_{1},x_{2}) \tag{3}\]  
followed by [PERSON]'s theorem (Equation (1)) such that  
\[f(x_{2}|x_{1})\ =\frac{f(x_{1},x_{2})}{f_{1}(x_{1})} \tag{4}\]  
\[f(x_{2}|x_{1})=c_{1,2}(F_{1}(x_{1}),F_{2}(x_{2}))f_{2}(x_{2}), \tag{5}\]  
and  
\[f(x_{3}|x_{1},x_{2})=\frac{f(x_{2},x_{3}|x_{1})}{f(x_{2}|x_{1})} \tag{6}\]  
\[f(x_{3}|x_{1},x_{2})=c_{2,3|1}(F(x_{2}|x_{1}),F(x_{3}|x_{1})).c_{1,3}(F_{1}(x _{1}),F_{3}(x_{3}))f_{3}(x_{3}). \tag{7}\]
## 2 Simulation Model of ICE-ORC System  
### ICE Modeling  
A four-cylinder turbocharged diesel engine was selected for this study. The engine type was BJ493 ZLQ3, which was manufactured by Beijing Foton Co., Ltd. (Beijing, China). The main technical parameters are listed in Table 1.  
The engine was modeled by a zero-dimensional model in the software GT-POWER. Normally, three-dimensional simulation can give the detailed turbulent flow and combustion process inside the cylinder [40]. However, it will take a long time to run the simulation. In this study, only the transient variations of the temperature and the mass flow rate of the exhaust gas were considered and zero-dimensional simulation was enough. Therefore, GT-POWER was used to model the diesel engine. Figure 1 shows the engine model in GT-POWER, mainly consisting of the intake system, the four cylinders, the crankshaft, the exhaust system, and the turbocharger. The complicated working processes inside the cylinder could be represented as a function of the mass of the working fluid, the in-cylinder pressure and temperature. In this model, the Weibo function was used to determine the heat release rate and the working processes for the four strokes were expressed by ordinary differential equations with regard to the crank rotation angle. Some assumptions were assumed and listed as follows:  
1. The thermodynamic properties of the working fluid such as the internal energy and the specific heat capacity are functions of the temperature and the components.
2. The fluctuations of the pressure and temperature inside the intake system are neglected.
3. The thermodynamic state inside the cylinder is considered homogeneous and the influence of the residual combusted gas inside the cylinder is ignored.  
The compressor and the turbine should operate in the regions with high efficiencies to ensure a good match of the turbocharger with the engine. The operation trajectories of the compressor and the turbine are shown in Figure 2 under the full-load conditions. Most of the operation points are in the high-efficiency regions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Displacement & 2.771 & L \\ Rated power & 85 & kW \\ Max. torque & 280 & N\(\cdot\)m \\ Bore & 93 & mm \\ Stroke & 102 & mm \\ Compression ratio & 17.4 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main technical parameters of the BJ493 ZLQ3 engine.  
The simulated data were validated by the measured data under the full-load conditions. The detailed descriptions can be found in [PERSON] et al. [41]. At the rated power point, the simulated engine power was slightly greater than the measured one. The differences between the simulated results and the measured results for the engine power, the torque, and the fuel consumption were less than 7.8%, indicating that the accuracy of the built engine model could fulfil the requirement. The simulated engine power increased as the engine speed rose under the full-load conditions. The engine torque first increased and then decreased while the fuel consumption rate showed an opposite trend.