esa-sceva/satcom-chunk-collection huggingface dataset:
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Summary  
The framework presented in this section enables autonomous MANO functionalities for smart city applications in diverse IoT scenarios. First, architectural challenges of the fog computing paradigm have been presented. Then, a system overview of our proposed framework has been detailed. Our work provides MANO capabilities to 5G networks, which according to our knowledge are still not yet fully explored in literature since previous and ongoing research has not yet delivered an integrated and autonomous solution for the resource management and orchestration issue in smart cities. Our approach follows the guidelines of the ETSI NFV MANO architecture and the standards of ETSI oneM2M regarding device management and security aspects, by extending it with additional software components that will offer not only high computing performance, but also monitoring and data analysis functionalities to provide secure and reliable communications in smart cities. Then, a novel application layer P2P fog protocol based on the advantages of the OSPF routing protocol has been introduced to provide exchange of application service provisioning information between the multiple FNs in the fog layer and the CN in the cloud layer. Instead of forwarding routing tables across the network, the objective of the foreseen fog protocol is to forward application service information, such as micro-service allocations and migration requests, enabling fast decisions related to the resource provisioning in smart cities. Finally, a hierarchical and distributed data monitoring and analysis approach based on our fog computing framework has been detailed. The proposed framework enables low-latency and scalable data analysis operations in order to detect abnormal events in a timely manner to apply appropriate actions in real time. In the next section, an anomaly detection use case is presented, which has been used to validate our distributed data analysis approach in 5G smart cities for the proposed fog-based MANO framework.
## 3 Mathematical Modeling
### Turbulence Modeling  
Compared with LES, the \(k\)-\(\epsilon\) turbulence model requires fewer computing resources, shorter computing time, and has stability, economy, and calculation accuracy, which is more suitable for the calculation and solution of related problems in this paper. Therefore, the \(k\)-\(\epsilon\) turbulence model is chosen.  
Turbulent kinetic energy k Equation [33; 34]  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\bigg{(}\sigma_{k}l_{eff}\frac{\partial k}{ \partial x_{j}}\bigg{)}+G_{k}+G_{b}-\rho\varepsilon-Y_{M}+S_{k} \tag{5}\]  
where \(k\) is the turbulent kinetic energy, \(\sigma_{k}\) is the turbulent Planck number of turbulent kinetic energy, \(\mu_{eff}\) is modified kinetic viscosity, \(G_{k}\) is the turbulent energy due to mean velocity gradient, \(G_{b}\) is the turbulent energy due to buoyancy, specific dissipation rate, and \(i,j\) are index symbols in tensor, value ranges are (1,2,3).  
The specific dissipation rate \(\varepsilon\) Equation [33; 34]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho\varepsilon u_{i})=\frac{\partial}{\partial x_{j}}\bigg{(}\sigma_{k}l_{ eff}\frac{\partial\varepsilon}{\partial x_{j}}\bigg{)}+G_{1\varepsilon}\frac{ \varepsilon}{\overline{k}}(G_{k}+G_{3\varepsilon}G_{b})-G_{2\varepsilon} \rho\frac{\varepsilon^{2}}{\overline{k}}-R_{\varepsilon}+S_{\varepsilon} \tag{6}\]For incompressible fluids, \(G_{b}\)= 0, \(Y_{M}\)= 0, \(S_{k}\)= 0 and \(S_{\epsilon}\)= 0. The source term does not need to be considered in the calculations in this paper. Equations (5) and (6) are simplified as follows [33; 34]:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{i} )=\frac{\partial}{\partial x_{j}}\left(\sigma_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}}\right)+G_{k}-\rho\epsilon \tag{7}\]  
\[\frac{\partial}{\partial t}(\rho\epsilon)+\frac{\partial}{\partial x_{i}}( \rho\epsilon u_{i})=\frac{\partial}{\partial x_{j}}\left(\sigma_{\epsilon}\mu_ {eff}\frac{\partial\epsilon}{\partial x_{j}}\right)+G_{1\epsilon}\frac{ \epsilon}{k}G_{k}-G_{2\epsilon}\rho\frac{\epsilon^{2}}{k}-R_{\epsilon} \tag{8}\]  
\[\mu_{eff}=\mu+\mu_{t},\ \mu_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon},\ G_{k}=- \rho u_{t}^{\prime}u_{j}^{\prime}\frac{\partial u_{j}}{\partial x_{i}},\ R_{ \epsilon}=\frac{C_{\mu}\rho\eta^{3}(1-\eta/\eta_{0})}{1+\beta\eta^{3}}\frac{ \epsilon^{2}}{k},\ \eta=S_{\epsilon}^{k},\ S=\sqrt{2S_{ij}S_{ij}},\ S_{ij}=\frac{1}{2} \left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{ i}}\right).\]  
\[G_{1\epsilon}=1.42\ \mathrm{J/kg},\ G_{2\epsilon}=1.68\ \mathrm{J/kg},\ C_{\mu}=0.0845,\ \sigma_{k}=1.0,\ \sigma_{ \epsilon}=1.3,\ \eta_{0}=4.38,\ \beta=0.012.\]
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 5 Experimental Setup
### Real-World Datasets  
We comprehensively tested our algorithm on the following different real-world datasets, also shown in Table 1. Among the various real-world datasets, the following are some that showed a weak/hidden community structure. More specifically, we chose networks like those that are a part of Facebook's social network, such as Political pages or the DBLP network where each layer revolts a set of communities grouped by a common attribute, i.e., nodes with common annotations are part of one community, and by combining the set of communities, it becomes an annotated layer. These layers cover all nodes in the relative networks. The graphical representation of the difference in the number of communities is given in Section 6.1 Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **Dataset** & **Edges** & **Vertices** \\ \hline Social & Dolphins & 159 & 62 \\  & FB-Pages-Politician & 42 k & 6 k \\ Collaboration & Citeseer DBLP & 4.5 k & 3.6 k \\  & & 1 M & 317.1 k \\  & Enron Email & 200 k & 158 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Real-world network datasets used in an experiment of the HCD method.  
* Dolphins: An undirected social network of frequent associations between 62 dolphins in a community living off Doubtful Sound, New Zealand, as compiled by [PERSON] et al. (2003). This network has 62 nodes with 159 edges. Among them, nodes represent dolphins, and the sides indicate frequent contact between dolphins.
* Enron Email: This is an email message dataset between employees of the Enron
* Corporation [20], mostly senior management of Enron, organized into folders. It contains messaging data belonging to 158 members of the corporation. The relationship among the members can be defined by the existence of their message communications or maybe some other similar attributes.
* CiteSeer: This is a citation network of computer science publications [21] containing 3312 vertices and 4536 edges. Here, the relationship attributes can either be defined by citation or by the content similarity.
* DBLP: The DBLP [22] computer science bibliography provides a comprehensive list of research papers in computer science with approx. 317 k vertices and 1 M edges. A vertex stands for an author, and an edge is formed if two authors write a research paper together or share the same research interest. Using this dataset, we can make a general multi-layer graph whose layers are more than two, where each layer corresponds to the co-authorships in the different venues (conferences or journals).
* FB-Pages-Politicians: Data collected about Facebook pages (November 2017). These datasets represent blue-ticked verified Facebook page networks of different politicians [23]. Nodes represent the pages, and edges are mutual likes among them.
## 1 Introduction
### Research Contribution  
The contribution of this research is reflected in the theoretical and practical sense, where the focus is on improving the decision-making process in the selection of suppliers of agricultural equipment and machinery. First, the research contributes to the development of the theoretical framework of MCDM decision-making, where integration with IFS is carried out in the context of supplier selection. In this way, modeling is performed under conditions of uncertainty and insecurity in DM. This approach overcomes the limitations of the classic fuzzy approach. In addition, the literature in the MCDM field in the field of agricultural production and supply chain is enriched. In addition, this paper contributes to the development of group decision-making, where Bonferroni mean operators are used to align DM opinions. Unlike classical means, this operator takes into account the comparison of ratings and their mutual interactions, and with this operator, it is possible to reduce the influence of extreme DM ratings. The values obtained with this operator are, thus, more aligned compared to classical means. Second, the research has a practical contribution, where the model used can serve as a useful DM tool in the Agriculture 4.0 system. This model enables a systematic and sustainable approach to supplier selection. Through defined criteria, economic, technical, environmental and social aspects are included, which allows the decision-making model used to make a more transparent and objective selection of suppliers. This is especially present in developing and transition countries. Third, the research promotes the concept of sustainable supplier selection in the area of procurement in the Agriculture 4.0 system through the inclusion of environmental and social criteria. This contributes to the integration of sustainability among agricultural producers, thereby strengthening awareness of the importance of socially responsible business in rural areas. Fourth, the decision-making model can serve as a basis for future applications where, with certain adjustments, it can be used in other areas of decision-making. This allows for the expansion of the application of IFS wherever there is a certain degree of uncertainty and subjectivity in decision-making.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 3 Phase Modulation in Graphene Metasurfaces
### Wavefront Shaping in Transmission
The metasurface works best at the CNP and shows reduced intensity with additional biasing due to increased loss in graphene. Reproduced with permission from [52], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2015. **(f)** Schematic of the active metasurface composed of a single layer of graphene deposited on a U-shaped metallic aperture where the spatial phase function is defined by the orientation of the U-shaped aperture. **(g)** Variation of the efficiency of anomalous refraction with biasing voltage and frequency. **(f)** Electric field distribution at two different gate voltages when the metasurface is designed for anomalous refraction. The metasurface works best at the CNP and shows reduced intensity with additional biasing due to increased loss in graphene. Reproduced with permission from [52], Copyright WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2015. **(g  
2017. (**g**) Monolayer graphene attached to gold aperture antennas with different orientations for geometric phase response and different lengths for resonance phase response. The resonance phase is tunable by voltage to carefully compensate the variation of the phase function at different frequencies. (**h**,**i**) The phase profile is dynamically tuned to satisfy two parabolic functions at two Fermi levels, leading to different focal lengths with comparable focusing efficiency. Reproduced with permission from [53], Copyright Chinese Laser Press, 2018. As the geometric phase does not change with the Fermi level, the active tunability of the device is limited to intensity modulation at a fixed frequency. In more scenarios, one wants to tune the focal point or the steering angle dynamically with the maximum possible intensity. For this purpose, [PERSON]'s group proposed a strategy in 2017 to achieve a transmissive metalens with a dynamically-tunable focal length [54]. The experimental demonstration was done very recently [53]. The structure consists of monolayer graphene on top of gold aperture antennas with different orientations and lengths (Figure 3g). The phase profile is determined by both the geometric phase and resonance phase, the latter of which is adjustable by the Fermi level of graphene. With a careful design, the phase profile can be dynamically tuned to satisfy two parabolic functions at two Fermi levels, leading to an electrically-tunable focal length in a large range of 4.45\(\lambda\) in experiments with comparable efficiency, as shown in Figure 3h,**i**.
###### Abstract  
The fraction of absorbed photosynthetically active radiation (fAPAR) is a key parameter for estimating the gross primary production (GPP) of trees. For continuous, dense forest canopies, fAPAR, is often equated with the intercepted fraction, fIPAR. This assumption is not valid for individual trees in urban environments or parkland settings where the canopy is sparse and there are well-defined tree crown boundaries. Here, the distinction between fAPAR and fIPAR can be strongly influenced by the background and large illumination variations due to multi-scattering and shadows of buildings. This study investigates the radiative budget of PAR bands using a coupled leaf-canopy radiative transfer model (PROSPECT-DART), considering a suite of tropical tree species over a wide range of assumed leaf chlorophyll contents. The analyses simulate hyperspectral images (5 nm bandwidth) of individual tree crowns for the selected background (concrete vs. grass) and illumination conditions. We then use an artificial neural network-based method to partition sunlt vs. shaded leaves within each crown, as the latter have lower fAPAR and fIPAR values. Our results show fAPAR of sunlit leaves decreases with the ratio of diffuse to direct scene irradiance (SKYL), while SKYL has minimal influence for shaded leaves. Both fAPAR and fIPAR decrease at more oblique solar zenith angles (SZA). Higher values of fAPAR and fIPAR occur with concrete backgrounds and the influence of the background is larger at higher diffuse ratio and solar zenith angles. The results show that fIPAR is typically 6-9% higher than fAPAR, and up to 14% higher for sunlit leaves with a concrete background at SKYL = 0. The differences between the fIPAR and fAPAR also depend on the health condition of the leaves, such as chlorophyll content. This study can improve the understanding of urban individual trees fAPAR/fIPAR and facilitate the development of protocols for fAPAR field measurements.  
fIPAR; fAPAR; urban individual tree; discrete anisotropic radiative transfer model (DART); illumination; urban background +
Footnote †: journal: Remote Sensing
## 2 Misalignment Error Analyses  
We define two orthogonal coordinate systems to describe the misalignment error as follows: the INS coordinate system \((o-x_{i}y_{i}z_{i})\) and the magnetometer coordinate system \((o^{\prime}-x_{m}y_{m}z_{m})\). As shown in Figure 1, the three-axis misalignment between a magnetometer coordinate system and an INS coordinate system caused by misalignment error is reflected.  
Figure 1: The diagram of misalignment error between a magnetometer and an inertial navigation system (INS).  
According to [PERSON]'s theorem [17], the rotation transformation between two orthogonal coordinate systems can be described by a \(3\times 3\) rotation matrix \(\mathbf{R}\). Then, the misalignment error calibration can be transformed into solving the rotation matrix \(\mathbf{R}\).  
\[\left[\begin{array}{c}X_{i}\\ Y_{i}\\ Z_{i}\end{array}\right]=\mathbf{R}\cdot\left[\begin{array}{c}X_{m}\\ Y_{m}\\ Z_{m}\end{array}\right] \tag{1}\]  
We set a matrix \(\mathbf{S}\) as follows:  
\[\mathbf{S}=\left[\begin{array}{ccc}0&-s_{z}&s_{y}\\ s_{z}&0&-s_{x}\\ -s_{y}&s_{x}&0\end{array}\right] \tag{2}\]  
Then, we can express the rotation matrix \(\mathbf{R}\) as:  
\[\mathbf{R}=(\mathbf{I}-\mathbf{S})^{-1}\cdot(\mathbf{I}+\mathbf{S}) \tag{3}\]  
where \(\mathbf{I}\) denote the unit matrix.
###### Abstract  
Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2 FP-OHDBN) model. The presented AST2 FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2 FP-OHDBN model initially normalizes the traffic data using min-max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2 FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2 FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634.  
Keywords:intelligent transportation system; smart cities; traffic flow prediction; deep learning; hyperparameter tuning; autonomous driving +
Footnote †: journal: _Sciences_
## 4 Performance Evaluation
### Simulation Metrics and Parameters  
Next, we define the performance metrics that appear in the simulation experiments. We simulate some core routing performance such as network lifetime, throughput, packet delivery rate, and end-to-end average delay. In addition, we add coverage rate to judge the advantage of the algorithm in network coverage, the following are some definitions of these evaluation metrics.  
Network lifetime: defined as the number of rounds of packet transmission iterations when the percentage of dead nodes in the network reaches 5%, the coverage-related algorithms need to meet the coverage rate threshold. The reason why 5% is defined is because the network has high reliability and coverage rate at this time.  
Throughput: defined as the total number of packets successfully received by the sink during the network lifetime.  
Packet delivery ratio(PDR): defined as the ratio of the total number of packets successfully received by the sink to the total number of packets that the network should generate during the network lifetime. Equation (8) represents this calculation.  
\[Pdr=\frac{Rec}{Gen} \tag{8}\]End-to-end average delay: defined as the ratio of the sum of the time incurred by all the nodes from the sending of the data to the reception of the data by the sink to the number of nodes. Equation (9) exhibits this calculation.  
\[D_{ave}=\frac{D_{all}}{Num} \tag{9}\]  
Network coverage rate: defined as the ratio between the range covered by the working nodes and the monitoring area. Equation (10) shows this calculation.  
\[Cov=\frac{C_{w}}{S^{2}} \tag{10}\]  
Coverage rate threshold: defined as the minimum network coverage rate required to achieve network operation, its value is usually given by actual engineering.  
Table 2 gives the parameter settings of the simulation experiment, we use MATLAB 2018a as the simulation experiment platform, MATLAB contains the advantages of programming efficiency, perfect functions, and powerful calculation ability.
## 4 Architecture of Software Receiver
### Multi-Thread Programming and Real-Time Execution Flow  
The system program schedules the threads to execute the software receiver for fully exploiting the resource of CPU. Figure 11 shows the execution flow plan for the receiver using 4 antennas and 5 time taps. To achieve real-time capability, the execution flow must be finished within 1 ms for 1 ms input signals. The _MultiAntsThread_ masters the whole flow to synchronize the threads using event objects. At first, _MultiAntsThread_ sets events to receiver threads and an adaptation thread for initiating the individual receiver operation and weight updating. For the composite beamformed signal, _RcvrThread_ performs the weight-and-sum using weight from previous iteration, software correlator, acquisition/tracking and positioning. The positioning result will be given by this thread. For each antenna element, each _RcvrWoNavThread_ performs software correlation and acquisition/tracking only. The ICPs are measured by these threads. For adaptation, the _AdaptApplebaumThread_ first sets events to multiple _CalculateCovarThread_ to calculate the elements of the covariance matrix, and then perform the MVDR algorithm to update the weights. Finally, the _MultiAntsThread_ uses the differential ICPs to build the steering vectors.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods
### Octree Encoding/Decoding
If _leaf_size_ is set too large, query operation will be slowed down, if it is too small, the octree will be deeper dramatically and influence the efficiency. Octree is more suitable for regular data, which is a well-known fact. However, the Point Cloud obtained from sensors (LiDAR, RGB-D camera) which are mounted on robot or UAV is always irregular.