esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 4 Fusion Mapping on Cloud Server
### Semantic Aggregation
#### 4.3.1 Instantiation  
In this section, we propose a lane instantiation algorithm that is based on clustering and uses logical constraints to address the complexity of lane instantiation. The algorithm consists of a two-stage DBSCAN (density-based spatial clustering of applications with noise) [39] method.  
In the first stage, we expand the LSM lane elements from the vehicle left and right within a certain range (20 cm) to form buffered line objects, which we denote as B. The input dataset is denoted as \(\mathbf{D}=\{\mathbf{b_{1}},\mathbf{b_{2}},\cdots,\mathbf{b_{n}}\}\), where each b is a sample. We use the overlap area between two samples of D as the distance measurement of DBSCAN, expressed as \(\mathbf{distance}\big{(}\mathbf{b_{i}},\mathbf{b_{j}}\big{)}=\mathbf{overlap\_area}\big{(}\mathbf{b_{i }},\mathbf{b_{j}}\big{)}\). The resulting clusters are marked as \(\mathbf{C}=\{\mathbf{c_{1}},\mathbf{c_{2}},\cdots,\mathbf{c_{n}}\}\), as shown in Figure 6b.  
The LSM lane lines with noise can be detected and eliminated using the first stage of clustering. Each cluster generated in the first stage of clustering forms a MultiLine object. In the second stage of clustering, we use the cluster set formed in the first stage as input, and we define the distance measurement of clustering as the Euclidean distance between two MultiLine objects. Considering the logical relationship between lane lines in the real world, a lane line can only be connected to, at most, the following lane lines, such as a new lane instance that is generated at the bifurcation.  
We then adjust the definition of the core object in the DBSCAN algorithm. If the number of samples in the fruit subsample set meets the condition \(\mathbf{MinPts}\big{|}\mathbf{N_{c}}\big{(}\mathbf{c_{j}}\big{)}\big{|}\leq\mathbf{MaxPts} \big{(}\mathbf{MinPts}=\mathbf{1},\ \mathbf{MaxPts}=\mathbf{2}\big{)}\), then we add sample \(\mathbf{c_{j}}\) to the core object set and define the cluster generated by the second stage of clustering as \(\mathbf{C^{\prime}}=\big{\{}\mathbf{c^{\prime}_{1}},\mathbf{c^{\prime}_{2}},\cdots,\mathbf{c^ {\prime}_{n}}\big{\}}\), which is the final instantiation object of the lane, as shown in Figure 4C. This method can deal with any road scene, including branching roads, and has stronger robustness compared to other clustering methods.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 4 Results Analysis
### The Effects of Parameters on the Stability of Slope  
#### 4.2.1 The Effects of Crack Depth and Slope Angle on the Safety Factor  
In order to find out the influence of the crack depth on the safety factor, it is necessary to determine the appropriate depth of a crack by performing a series of stability computations  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Slope** & \multirow{2}{*}{_a/m_} & \multirow{2}{*}{_b/m_} & \multirow{2}{*}{**Radius r/m**} & \multicolumn{2}{c}{**F**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{**F**} \\ \cline{5-8}
**Angle** \(\beta\)\({}^{c}\) & & & & **\(F\)/m** & **Sweden Method** & **Bishop Method** & **Present Study** \\ \hline
24 & 34.77 & 110.51 & 117.75 & 1.044 & 1.102 & 1.113 \\ \hline
21.8 & 40.38 & 115.76 & 125.81 & 1.102 & 1.169 & 1.224 \\ \hline
20.0 & 47.74 & 119.38 & 134.84 & 1.158 & 1.254 & 1.304 \\ \hline
18.4 & 53.09 & 128.99 & 143.96 & 1.220 & 1.300 & 1.389 \\ \hline
17.1 & 59.20 & 134.72 & 152.13 & 1.277 & 1.365 & 1.465 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of safety factor (case 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**slope Angle** & \multirow{2}{*}{_a/m_} & \multirow{2}{*}{_b/m_} & \multirow{2}{*}{**Radius r/m**} & \multicolumn{2}{c}{**F**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{**F**} \\ \cline{5-8} \(\beta\)\({}^{c}\) & & & & **FEM Method** & **Spencer Method** & **Bishop Method** & **Present Study** \\ \hline
35 & \(-\)4.720 & 72.185 & 72.293 & 1.34 & 1.318 & 1.259 & 1.387 \\ \hline
40 & \(-\)3.901 & 66.488 & 66.614 & 1.22 & 1.212 & 1.153 & 1.197 \\ \hline
45 & \(-\)9.041 & 75.044 & 75.611 & 1.12 & 1.115 & 1.06
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Fog Computing (Edge Computing)  
Fog computing, as coined by Cisco, is a particular form of cloud computing, primarily differing in terms of the location of operation. Fog computing, also commonly known as Edge Computing, being operational at the edge of the enterprise networks, extends the cloud computing system. Due to its location being close to the edge of the enterprise network, fog computing provides comparatively better performance in terms of reduced delay, lower latency and jitter--which in return result in improved processing time and near real-time responsiveness of networked user applications. Furthermore, it provides other location-based advantages such as better customization options as well as mobility support. Because the fog computing approach requires the decentralization of a major share of the complete data processing components (such as applications and services, computing and processing power, data analysis and decision making) as well as the data itself, it lessens a substantial volume of network traffic flow, especially in data transmission between the Cloud and IoT devices.  
Cloud computing is not redundant, however, as batch processing jobs are still heavily generated by the scientific and business world. These types of computing tasks are best processed in the Cloud. Fog computing is ideal where data processing needs to be executed at the point of its generation. Thus, data analytics and knowledge processing and generation can occur at or near the data source. Fog computing, due to its localized nature, also allows for better locally optimized applications. To meet future performance requirements of integrated IoT and Fog Computing, the architecture of Fog Computing needs to meet the strict tolerances and requirements of energy savings, data throughput and latency constraints at both the node and system level. Hence, Fog computing still needs to evolve further to meet performance requirements of IoT over the use of contemporary Fog nodes.
# A Transplantatable Frequency Selective Metasurface for High-Order Harmonic Suppression
###### Abstract  
A transplantable frequency selective metasurface element (FSMSE) for high-order harmonic suppression (HS) is presented in this paper. The proposed harmonic free FSMSE can be integrated with arbitrary frequency selective surfaces (FSSs) operating at the same frequency band. The designed HS-FSMSE is applied to two different types of FSSs for verification of harmonic suppression, respectively. One is a multilayer sub-wavelength patch-grid FSS that has weak resonant behavior, and the other is a complementary resonant loop FSS which has strong resonant behavior, especially for high-order harmonic waves. By integrating with HS-FSMSE, the two kinds of FSSs operating at 10 GHz are free of harmonic transmission bands up to 30 GHz. The simulation and measurement results show feasibility of the harmonic suppression FSMSW and good polarization and angle stabilities.  
frequency selective metasurface element (FSMSE); harmonic suppression; transplantable 1
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Hybrid-I  
The C/S and P2P architectures can be combined with a hybrid architecture to overcome the weakness of one architecture by the strength of another. According to the ways of combination, hybrid architectures can be divided into two classes. The first one, denoted by Hybrid-I (Figure 8c), leverages the P2P computing techniques to the C/S architecture for computation offloading, since P2P resources can be easily scaled with user population. In Hybrid-I virtual worlds, e.g., [91], clients disseminate updates to each other through P2P communication to save a server's outgoing bandwidth and thus its operating cost. Cloud gaming can also exploit P2P techniques to reduce game latency [69] and service operating costs [100; 138].  
The Hybrid-I architectures have different requirement satisfactions, depending on how clients participate in a simulation. This section introduces some typical examples. Firstly, in [139], the cloud servers form a P2P publish-subscribe overlay for the communication between game servers. Clients do not involve in any simulation. Then, in [91], a central server is employed to control and store game state, while clients are only in charge of message dissemination to save the server's outgoing bandwidth. Their requirement satisfaction is similar to that of C/S architecture, except that the latter design provides more cost-efficient scalability solutions, because the efficiency of message dissemination scales with the user population. They also share the same single point of failure with C/S architecture due to the existence of a central point for content storage, including the limitations in content persistency, data security, and legal protection.  
In [106], a central server stores application states and relays client messages for security checks. Clients compute and disseminate the game state with P2P techniques. The requirement satisfaction of this approach lies between the C/S architecture and the P2P architecture. Specifically, the P2P clients allow it to have similar requirement satisfaction and limitation to the P2P architecture. The centrally controlled game state allows it to have the similar requirement satisfaction and limitations to the C/S architecture in persistency, credibility, and security, while P2P clients allow it to have the similar requirement satisfaction and limitations to the P2P architecture with respect to the rest issues.  
In [140], moreover, P2P clients manage application states for each other, while a central server only keeps the sensitivity data (e.g., user profile) and provides the utility functions (e.g., authentication). This design is similar to the P2P architecture, except that the satisfaction of the data security requirement can approach that of the C/S architecture because of the centralized storage of sensitive data.
## 2 Materials and Methods
### Assessment of In-House tcdB RT-PCR  
To determine the analytical specificity of the _tcdB_ DFS RT-PCR, a panel of bacteria (Supplementary Table S1) was obtained from the Alberta Precision Laboratories-Provincial Laboratory for Public Health (ProvLab) Quality Control Department (Edmonton, AB, Canada). These organisms were grown in their respective culture media under appropriate growth conditions. Nucleic acid was extracted by suspending a single colony of bacteria in 100 \(\upmu\)L rapid lysis buffer (100 mM NaCl, 10 mM Tris-HCl pH 8.3, 1 mM EDTA pH 9.0, 1% Triton X-100) and incubated for 15 min at 95 \({}^{\circ}\)C using a VWR Standard Heatblock (VWR Scientific, Mississauga, ON, Canada). Following incubation, the cell suspension was subjected to centrifugation of 13,000\(\times\)_g_ for 15 min and the supernatant containing extracted nucleic acid was removed and stored at \(-\)70 \({}^{\circ}\)C until used.  
The analytical sensitivity of the RT-PCR was determined using serial dilutions of a toxicigenic _C. difficile_ strain isolated from a clincal specimen. The isolate was grown on Brain Heart Infusion (BHI) agar (ProvLab, Edmonton, AB, Canada) and a sweep of the culture was added to BHI broth and incubated for 6 h at 35 \({}^{\circ}\)C. The broth was then diluted to an optical density of 0.4, and ten-fold serial dilutions from 10\({}^{-1}\) to 10\({}^{-8}\) were prepared. Aliquots of 100 \(\upmu\)L from dilutions 10\({}^{-5}\) and 10\({}^{-6}\) were inoculated onto BHI agar (ProvLab, Edmonton, AB, Canada) in triplicates and incubated for 48 h in anaerobic conditions to numerate the colony forming units/mL (CFU/mL), and RT-PCR was performed for all dilutions of cell suspensions using nucleic acids extracted by the NucleiSENS\({}^{\text{\textregistered}}\) easyMAG\({}^{\text{\textregistered}}\) instrument (bioMerieux) as outlined in Section 2.2, with the modification of using 200 \(\upmu\)L of cell suspension in 800 \(\upmu\)L of NucleiSENS\({}^{\text{\textregistered}}\) Lysis Buffer (bioMerieux). Both the bacterial culture, to determine the CFU/mL, and the RT-PCR assays (both performed in triplicates) were repeated in 10 different experiments on separate days to establish the reproducibility of the assay. The limit of detection was determined based on the results of the sensitivity assay as described above. The positive cut-off for the RT-PCR assay was set to the Ct value obtained where a positive amplification curve was seen in 95% of the PCR runs in 10 replicates (Supplementary Table S2).
## 1 Introduction
The limited size and power availability necessitate highly efficient and miniaturized components. Thermal management becomes critical as active components may generate heat that must be dissipated within the small satellite body. Additionally, the complexity of integrating these advanced antenna systems with existing CubeSat platforms requires careful consideration to ensure compatibility and reliability. Despite these challenges, the potential benefits of enhanced communication capabilities make the pursuit of beam-steering and beamforming technologies for CubeSats a worthwhile endeavor. As the demand for more sophisticated CubeSat missions continues to grow, developing and implementing advanced antenna systems will be crucial in expanding the horizons of small satellite applications.
# Quantitative Description for Sand Void Fabric with the Principle of Stereology
## 2 Existing Theories of the Void Fabric Tensors  
The distribution equation of void space is determined based on the principle of stereology. The equation is determined by the average porosity (scalar) and the symmetric fabric tensor that trace is 0 [35]. [PERSON] [35] used the REV (Representative Element Volume) method to detect the void fabric distribution quantitatively. As shown in Figure 1, the first step is to select a sample space with enough particles to perform mathematical statistics. The second step is to determine a measurement sphere. Finally, the length \(L\) in the sphere is the scan line, and the length of the scan line truncated by the void is the measured void length. By rotating the scan line throughout the sphere, the void distribution in the sphere is measured. Therefore, the void distribution in spherical coordinates is \(l_{l}(\alpha,\beta)=\sum_{l}(\alpha,\beta)\), and the ratio of scan lines to void is as follows:  
\[L_{\text{L}}(\alpha,\beta)=\frac{l_{l}(\alpha,\beta)}{L}=\frac{l_{l}(\alpha, \beta)}{2R} \tag{1}\]  
where \(L_{\text{L}}(\alpha,\,\beta)\) is the porosity of the scan line. \(L\) is the length of the scan line. \(R\) is the radius. \(\alpha\) and \(\beta\) are independent integral variables of the integral interval.  
Then, the average porosity in a unit sphere is as follows:  
\[n_{m}=\frac{1}{4\pi}\int_{0}^{2\pi}\int_{0}^{\pi}L_{\text{L}}(\alpha,\beta)\sin \alpha\text{d}\alpha\beta \tag{2}\]  
where \(n_{m}\) is the average porosity per unit measured through the \(m\)-th scan line. Equation (2) expresses in the Cartesian coordinate system as follows:  
\[n_{m}=\frac{\int\int_{Syz}l_{x}\text{d}y\text{d}z+\int\int_{Szx}l_{y}\text{d} z\text{d}x+\int\int_{Sxy}l_{z}\text{d}x\text{d}y}{8\pi} \tag{3}\]  
where \(l_{x}\), \(l_{y}\), and \(l_{z}\) are the distribution functions of the void density on the corresponding coordinate plane, respectively. \(s_{yz}\), \(s_{zx}\), and \(s_{xy}\) are the integral intervals on the corresponding plane, respectively.  
The void distribution equation is expressed as average porosity and a symmetric fabric tensor that's trace is 0 [23; 24; 25] and reads:  
\[N(\overrightarrow{n})=n_{m}(1+N_{ij}n_{i}n_{j}+N_{ijkl}n_{i}n_{j}n_{k}n_{l}+\cdots) \tag{4}\]  
Figure 1: The unit test sphere.  
where \(N\Big{(}\begin{pmatrix}\rightarrow\\ \overrightarrow{n}\end{pmatrix}\Big{)}\) is the average void distribution function, \(\overrightarrow{n}\) is the unit vector, \(N_{i_{1}i_{2}\ldots i_{n}}\) is a void tensor that's trace is 0 and is reads:  
\[N_{i_{1}i_{2}\ldots i_{n}}=\frac{2n+1}{2^{n}}\bigg{(}\begin{array}{c}2n\\ n\end{array}\bigg{)}\Omega_{i_{1}i_{2}\ldots i_{n}} \tag{5}\]  
where \(\Omega_{i_{1}i_{2}\ldots i_{n}}\) is the tensor defined by [PERSON] [23; 24; 25] and reads:  
\[\Omega_{i_{1}i_{2}\ldots i_{n}}=\frac{1}{4\pi n_{m}}\int_{\Omega}n_{i_{1}i_{2} \ldots i_{n}}n_{in}L_{\mathrm{L}}(\alpha,\beta)\mathrm{d}\overrightarrow{n} \tag{6}\]  
The key of Equation (4) to describe the void distribution is how to measure \(n_{m}\) and \(N_{i_{1}i_{2}\ldots i_{n}}\).
## 5 Experimental Verification and Result Analysis  
### Simulating Scenarios and Experimental Setup  
The environment of the simulated case models and randomly selects three scenarios based on the mining SAR environment model. In order to verify the ability of the UGV agent to avoid obstacles online, the scene is randomly set by a map generation code (based on the code in [19]), and only kept consistent in the comparative experiment. Because the experiment involves online path-planning verification, the UGV does not conduct offline learning in advance. All algorithms in this paper are coded and implemented in Python version 3.9. The training environment for this model is Intel Core i7-4710 MQ CPU @2.50 GHz four cores Chinese-made laptop.
## 2 Structure Configuration and Finite Element Model Validation  
### Geometric Description  
Based on the field blast tests carried out by [PERSON] et al. [23; 24], Figure 1 shows a typical set-up for the field blast test just before detonation. The target structures adopted for the current validation were trapezoidal (TZ) and triangular (T) corrugated sandwich panels. They consist of front, and back plates and a corrugated folded shape core layer in between made of annealed 304 stainless steel alloy. The tested panels were clamped along the four edges, providing a \(288\times 300\) mm\({}^{2}\) exposure area to the shock wave, as shown in Figure 1. In the present study, only the exposure area of the panel was modeled, as the outer region of the panels was used for clamping purpose as depicted in Figure 1. The geometric parameters of the panels are demonstrated in Table 1. The blast wave was generated by the detonation of a 55 g cylindrical TNT charge located over the center of the panel with a certain stand-off distance (_SoD_). The _SoD_ is the distance between the center of the explosive and the front face of the target. Here, three different _SoD_s of 50 mm, 100 mm and 150 mm were adopted to validate the deformation and failure mechanisms of the sandwich panel under air blast loading. The _SoD_s of 100 and 150 mm were used to capture the localized plastic deformation. However, the blast intensity under the _SoD_ of 50 mm was to simulate the fracture and failure of the panel.  
Figure 1: Schematics of (**a**) an experimental set-up, (**b**) trapezoidal (TZ)-panel cross-section unit cell, and (**c**) triangular (T)-panel cross-section unit cell (reproduced from refs. [23; 24]).
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 5 Numerical and Simulation Results
Given sufficiently high ANR, all the ASER curves achieve the non-informative error performance, which confirms Proposition 5. However, the performance difference between different AN designs is not significant. This can be explained by the fact that the ASER expression in Equation (25) does not consider the phase of the AN, and the degree of freedom of the phase design for the AN is not utilized. Furthermore, it is interesting to compare the SER performance of the optimal AN designs in Sections 3 and 4. Figure 11 compares the ASER performance of various AN designs. In the figure, the \"Instantaneous CSI-Based Sim.\" curve is plotted by computing \(\mathcal{E}_{h,g}[\overline{\text{SER}}_{\text{max}}]\) using the Monte Carlo method, and the curve \"Statistical CSI-Based Ana.\" is plotted using the same method as in Figure 10. The curve \"Gaussian Sim.\" is plotted by numerically computing \(\mathcal{E}_{h,g}[\text{SER}(z)]\), with Gaussian-distributed AN. From Figure 11, we observe that the instantaneous CSI-based AN  
Figure 10: ASER performance of 4-QAM and 16-QAM modulations as a function of \(E_{z}/N_{0}\), where \(E_{m}/N_{0}=10\) dB, \(\sigma_{h}=1\), and \(\sigma_{g}=1\). design yields a higher ASER than the statistical CSI-based AN design, which is consistent with our intuition. However, the performance difference between these two designs is not quite significant. Without optimal phase and power designs, the Gaussian distribution performs worse than both CSI-based AN designs.
# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 2 Methods
### Improvement of the DGAIL Algorithm
#### 2.1.2 Activation Function of the Discriminator D  
Commonly used activation functions include sigmoid, tanh, ReLU, and leaky-ReLU. The sigmoid function and tanh function are more traditional activation functions, but they may encounter the problem of regional saturation when transmitting information in the neural network, resulting in the phenomenon of gradient disappearance. The ReLU function solves the problem of vanishing gradients, but there is a \"dead zone\" when the input is negative. The leaky-ReLU function optimizes this zone by assigning a nonzero slope to all negative values of the input, solving the \"dead zone\" problem of the ReLU function. The ReLU activation function used by the discriminator D in the original GAIL method reduces the influence of the \"dead zone\" on the training results, as shown in Figure 2. This paper selects leaky ReLU as the activation function between the input layer and the hidden layer.