esa-sceva/satcom-chunk-collection huggingface dataset:
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 4 Future Research Prospects  
Industrial processes undergo several different stages for the final production of goods and services. The continuous efforts in the development and integrations of NB-IoT applications in industries are welcomed in the broader world of IoT. More so, since the involvement of NB-IoT in industrial activities requires a huge number of its deployment for process monitoring and data gathering, many of the solutions provided by vendors are yet to fully satisfy the requirements of the industrial world.  
Among the key requirements of industrial settings are: NB-IoT as a Self-Organizing Network, leveraging on edge computing to improve scalability in the deployment of an NB-IoT network, assurance in the security of industrial data generated, and having a suitable NB-IoT model that meets its propagation challenges.
# Atmospheric Turbulence Study with Deep Machine Learning of Intensity Scintillation Patterns
In the computer simulation experiments described in Section 7 we utilized SIM datasets corresponding to the classical [PERSON] turbulence model and its most known modifications ([PERSON] and Andrews models). These models were evaluated in several \"cross-dataset\" modeling and simulation experiments in which a DNN trained at one SIM dataset was challenged to predict the true \(C_{n}^{2}\) values based on scintillation images computed for a different turbulence spectrum model. The results obtained demonstrated high \(C_{n}^{2}\) prediction accuracy and its relatively weak dependence on the examined turbulence models and their major parameters (turbulence inner \(l_{0}\) and outer \(L_{0}\) scales), unless these parameters were artificially altered beyond a range reasonable from a physics viewpoint. This suggests that intensity scintillation patterns corresponding to the examined turbulence spectrum models have nearly identical (undistinguished by the DNN) spatial structures. At the same time, DNN processing of scintillation images obtained using a recently developed turbulence model with noticeable deviation from the Kolmogorov two-thirds power law (non-Kolmogorov turbulence [13; 14]) resulted in large \(C_{n}^{2}\) prediction errors. Similarly, large errors were observed when the DNN trained using non-Kolmogorov turbulence models was contested by the experimental sensing data. In the concluding Section 8 we discuss deep learning's unique potentials for the understanding of atmospheric turbulence dynamics, new DNN-based opportunities for the development of novel atmospheric sensing systems capable of real-time atmospheric turbulence monitoring and rapid forecasting of turbulence's impact on various atmospheric optics remote sensing, imaging, laser communication and laser beam projection systems.
## Materials and Methods
### Neutralization Method
#### 2.2.4 Technological Procedure for Neutralization Treatment on Semi Industrial Plant  
A sample from the Robule Lake was used for the test operation. The AMD was fed to the equipment and pH was set; for the first step on pH 4 and for the second step on pH 7. The wastewater from storage tank for wastewater was transported by a receiving pump with constant flow rate of 20 L min\({}^{-1}\) to the wastewater tank. The wastewater tank was with a level control that switches off/on the receiving pump. The wastewater pump with constant flow rate of 5 L min\({}^{-1}\) was used for wastewater transport to the pH Control tank A. At the same time, to the pH Control tank A was continually added lime milk (2.5 mass %) from the Lime slurry tank by a lime slurry pump. In the pH Control tank A, is installed a pH meter aimed at controlling the first set pH value. Due to the fact that the primary pH value was set on value of 4, according to the pH value during the test, dosing of lime milk was corrected. Suspension from the pH Control tank A overflowed in the Flocculation tank A. Flocculant (0.05 mass %) was transported by a flocculant pump A with a constant flow rate of 0.025 L min\({}^{-1}\) to the Flocculation tank A. Sludge was precipitated in the bottom of Thickener A incrementally during the test. Neutralized AMD that overflowed from the Thickener A was taken for a chemical analysis and by the gravity flow to the pH Control tank B for the secondary pH control (according to the second set pH value of 7). Flocculation was followed in the Flocculation tank B thereafter, and then a solid-liquid separation occurred in the Thickener B. Neutralized AMD that overflowed from the Thickener B was also taken for the chemical analysis and discharged continuously by the gravity as the treated water. Thickener A and Thickener B were discharged one by one, aiming to filtrate the slurry pulp on a filter press. The filtrate was discharged by a portable filtrate pump as the treated water. Dewatered sludge was measured and dried at 40 \({}^{\circ}\)C to constant mass and taken for a chemical analysis.
As for application of the adsorption method to the AMD treatment, the real wastewater treated by the neutralization process was used. To the real AMD water derived from the Robule Lake, the two-step neutralization including treatment at pH 4 and 7 was applied. The treated AMD water had the value of pH 7.2 and included 34 mg L\({}^{-1}\) of Mn\({}^{2+}\) ions. Chitosan (5 g) was introduced into 50 mL of the treated AMD water. It was stirred with a magnet bar for 24 h. After an adsorption removal test, the treated AMD water was filtrated through a PTFE membrane filter with a 0.45 um mesh for a quantitative analysis.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 3 Experimental Validation
### Diagnosis Results and Comparative Analysis
#### 3.2.2 Comparison with Standard CDBN
The feature distribution of the original data is relatively loose, and mixed regions exist without obvious class boundaries. These phenomena are not conducive to the subsequent classification. For instance, features of labels 6 and 7 are mostly muddled; thus, misclassification occurred, as shown in Figure 11. By contrast, the feature distribution is clustered tightly with the improved CDBN, and it has easily observed boundaries and a wider distance, which are helpful for further feature extraction and classification. Reconstruction error is a key indicator for measuring model learning ability. Different optimizers were employed for comparative analysis, and the reconstruction errors by different optimizers are shown in Figure 14. As shown in the figure, during the training, the reconstruction error of the CDBN model with Adam optimizer dropped smoothly and had the smallest reconstruction error. Figure 13: Feature visualization of (**a**) dataset1 using the standard CDBN, (**b**) dataset1 using the improved CDBN, (**c**) dataset2 using the standard CDBN, and (**d**) dataset2 using the improved CDBN.
# Stability Analysis of the Landfill Slope with an Engineered Berm Under Composite Failure Mode
## 2 Computational Model
### Equations of Equilibrium When Over-Berm Failure Occurs
#### 2.2.1 Equations of Equilibrium About Block Wedge  
In accordance with the equilibrium condition of the vertical force acting on the block wedge, i.e., \(\sum F_{\rm Y}=0\), as shown in Figure 3, it can be determined that  
\[N_{\rm B}\cos\xi=W_{\rm B}+F_{\rm B}\sin\xi+E_{\rm VPB} \tag{7}\]  
Substituting Equations (1) and (3) into Equation (7) offers  
\[N_{\rm B}(\cos\xi-\sin\xi\tan\delta_{\rm B}/FS)-C_{\rm B}\sin\xi/FS=W_{\rm B}+ C_{\rm PB}/FS_{\rm V}+E_{\rm HIPB}\tan\phi_{\rm SW}/FS_{\rm V} \tag{8}\]  
According to the equilibrium condition of the horizontal force acting on the block wedge, i.e., \(\sum F_{\rm X}=0\), as shown in Figure 3, the equilibrium equation is as follows:  
\[N_{\rm B}\sin\xi+F_{\rm B}\cos\xi=E_{\rm HPB} \tag{9}\]  
Substituting Equation (1) into Equation (9) and rearranging for \(N_{\rm B}\) yields  
\[N_{\rm B}=\frac{E_{\rm HPB}-C_{\rm B}\cos\xi/FS}{\sin\xi+\cos\xi\tan\delta_{ \rm B}/FS} \tag{10}\]  
Substituting Equation (10) into Equation (8) and rearranging for \(E_{\rm HPB}\) produces  
\[E_{\rm HPB}=\frac{(W_{\rm B}+C_{\rm PB}/FS_{\rm V})(\sin\xi+\cos\xi\tan\delta_ {\rm B}/FS)+C_{\rm B}/FS}{(\cos\xi-\sin\xi\tan\delta_{\rm B}/FS)-(\sin\xi+ \cos\xi\tan\delta_{\rm B}/FS)\tan\phi_{\rm SW}/FS_{\rm V}} \tag{11}\]  
Based on the geometrical characteristics of the engineering berm, the following formulas for \(C_{\rm B}\), \(C_{\rm PB}\) and \(W_{\rm B}\) can be derived:  
\[C_{\rm B}=c_{\rm B}H_{\rm B}/\sin\xi \tag{12}\]  
\[C_{\rm PB}=c_{\rm SW}H_{\rm B}(1+\cos\xi\tan\omega) \tag{13}\]  
\[W_{\rm B}=0.5\gamma_{\rm SW}H_{\rm B}^{2}(1+\cot\xi\tan\omega)\cot\xi \tag{14}\]
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
## 1 Introduction
Thus, the question of which approach or software to use became important and unneglectable due to the rush developments of the used methods and approaches used, as well as the software based on them by virtue of a linear increase in advances in computer systems. The researchers tried to investigate the efficiency of each approach and describe their limitations, applications, and in some works even recommendations. For example, in [33; 34], the authors provided an analytical comparison of wire/surface modeling, while in [35; 36; 37; 38], the authors decided to discuss the differences numerically. But to the best of our knowledge, there is no general work which provides comprehensive overview on the history of each approach, its applications, limitations, and working algorithms. Nor is there any work with analytical and numerical comparisons of their efficiency using own advanced and accelerated implementations on the example of different antenna types, as well as a mesh generator based on their hybridization. The aim of the article is to compare these two MoM-based algorithms and analyze their capabilities in modeling different types of antennas. The rest of this article is organized as follows. Section 2 describes two algorithms under study and the features of their software implementation. Section 3 presents the results of analyzing several antennas using these algorithms. Section 4 discusses the obtained results and formulates several practical recommendations for antenna modeling. Section 5 provides the conclusions.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
## 2 Physarum Polycephalum and Tasks Inspired by It
### Network Development Model of Physarum Polycephalum  
The mathematical model of Physarum's adaptive network construction mechanism was developed based on intelligent behavioral observations in laboratory experiments [23]. In the network model, edges represent plasmodial tubes in which protoplasm flows, and nodes are junctions between tubes. The model considers feedback loops between the thickness of each tube and the internal protoplasmic flow. High rates of streaming stimulate an increase in tube diameter, whereas tubes tend to decline at low flow rates. Suppose the pressure at nodes \(i\) and \(j\) are \(P_{i}\) and \(P_{j}\), respectively, and the two nodes are connected by a cylinder of length \(L_{ij}\) of radius \(r_{ij}\). Then, considering the flow is laminar, the flux through the tube following the Hagen-Poiseuille equation is given by  
\[Q_{ij}=\frac{\pi r_{ij}^{4}\big{(}P_{i}-P_{j}\big{)}}{8\epsilon L_{ij}}=\frac{ D_{ij}\big{(}P_{i}-P_{j}\big{)}}{L_{ij}}\ \, \tag{1}\]  
where \(\epsilon\) is the viscosity of the fluid and \(D_{ij}=\frac{\pi r_{ij}^{4}}{8\epsilon L}\) is a measure of the conductivity of the tube. As the length \(L_{ij}\) is a constant, the behavior of the network is described by the conductivities of the tubes. If the flux amount \(I_{0}\) flows from the source node \(N_{1}\) and the sink node is \(N_{2}\) then,  
\[\sum_{i}Q_{i1}+I_{0}=0\ \ \ \ \text{for Source Node 1} \tag{2}\]  
\[\sum_{i}Q_{i2}-I_{0}=0\ \ \ \ \text{for Sink Node 2}\]  
\[\sum_{i}Q_{ij}=0,\ \text{inflow and outflow must be balanced for }(i\ \
eq\ 1,2).\]  
To accommodate the adaptive behavior of the plasmodium, the conductivity of each tube evolves according to  
\[\frac{dD_{ij}}{dt}=f\big{(}|Q_{ij}|\big{)}-\mu D_{ij}, \tag{3}\]where \(f\big{(}|Q_{ij}|\big{)}\) describes the expansion of tubes in response to the flux, and \(\mu\) is the decay rate of the link [24]. The equation implies that the conductivity of a particular tube becomes nil if there is no flux; thus, the tube will gradually disappear. The function \(f(|Q|)=\frac{|Q|^{\gamma}}{\big{(}1+|Q|^{\gamma}\big{)}}\) describes a sigmoidal response, where \(\gamma\) is a parameter that controls the nonlinearity of feedback (\(\gamma>0\)). Considering the above features, the basic steps of the computational model of Physarum are as follows:  
Step 1: Initialize the environment.  
Step 2: Measure flux through individual links, based on edges' pressures.  
Step 3: Update the conductivity of individual links, based on flux through it.  
Step 4: Return the network with links having appropriate conductivity.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 2 Materials and Methods  
### Model Implementation: From GNSS Doppler Observations to VTEC and VTEC Rate  
GNSS Doppler measurements are the time derivatives of the carrier phase; thus, the corresponding observation equations contain time derivatives of geometric terms and biases. Ionospheric errors become functions of the TEC and TEC rate; at the same time, no ambiguity parameters need to be estimated since the equations contain only time derivatives of continuous variables. In order to implement a model based on Doppler observations for real-time estimation of ionospheric parameters, the following aspects need to be considered:  
* Writing observation equations in terms of the VTEC and VTEC rate;
* Parameterizing the VTEC and VTEC rate as functions of latitude and longitude;
* Implementing the Kalman filter.  
In the following, we analyze each of these aspects in detail.
###### Abstract  
Very short-term load demand forescasters are essential for power systems' decision makers in real-time dispatching. These tools allow traditional network operators to maintain power systems' safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point forescasters, these tools do not provide complete information because they do not estimate the deviation between actual and predicted values. Therefore, the aim of this paper is to develop a very short-term probabilistic prediction interval forecaster to reduce decision makers' uncertainty by computing the predicted value's upper and lower bounds. The proposed forecaster combines an artificial intelligence-based point forecaster with a probabilistic prediction interval algorithm. First, the point forecaster predicts energy demand in the next 15 min and then the prediction interval algorithm calculates the upper and lower bounds with the user's chosen confidence level. To examine the reliability of proposed forecaster model and resulting interval sharpness, different error metrics, such as prediction interval coverage percentage and a skill score, are computed for 95, 90, and 85% confidence intervals. Results show that the prediction interval coverage percentage is higher than the confidence level in each analysis, which means that the proposed model is valid for practical applications.  
smart grid; energy demand; very short-term forecaster; probabilistic prediction interval +
Footnote †: journal: Applied Sciences
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 4 Adaptive Channel Estimation Scheme Based on Preamble
### Estimation of Average SNR  
In order to implement the adaptation algorithm, a process for estimating the average SNR is needed. It is very difficult to obtain reliable SNR values in mobile systems; thus, various SNR estimation methods have been proposed to address this problem [41; 42; 43; 44]. In [44], the preamble is employed to estimate the average SNR. This technique has the advantages of being applicable to OFDM-based systems and shows high accuracy [29].  
In this paper, we assume that the average SNR estimation is perfect to verify the adaptation algorithm.
## 3 Results and Discussion
### Phenolic and Flavonoid Contents and Antioxidant Capacity
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & **ABTS** & **DPPH** \\  & **(EC\({}_{50}\)\(\mu\)g\(\cdot\)mL\({}^{-1}\))** & **(EC\({}_{50}\)\(\mu\)g\(\cdot\)mL\({}^{-1}\))** \\ \hline JWE & 3.89 (\(\pm\)0.22) \({}^{\text{a}}\) & 30.17 (\(\pm\)8.89) \({}^{\text{a}}\) \\ \hline JWF1 & 3.30 (\(\pm\)0.11) \({}^{\text{a,b}}\) & 18.85 (\(\pm\)4.96) \({}^{\text{b}}\) \\ \hline JWF2 & 2.15 (\(\pm\)0.65) \({}^{\text{b}}\) & 5.18 (\(\pm\)3.58) \({}^{\text{c}}\) \\ \hline JWF3 & 1.22 (\(\pm\)0.10) \({}^{\text{b}}\) & 6.20 (\(\pm\)2.06) \({}^{\text{c}}\) \\ \hline Standard Trolox & 1.05 (\(\pm\)0.26) \({}^{\text{b}}\) & 3.05 (\(\pm\)0.19) \({}^{\text{c}}\) \\ \hline \hline \end{tabular} Mean (\(\pm\)SD) for triplicates. Means followed by different letters, in the same column, differ from each other by the Tukey _postluc_ test (ANOVA) (\(p<\) 0.05).  
\end{table} 
Table 2: Antioxidant capacity of jabuticaba wine extract (JWE) and fractions (JWF1–JWF3).
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
###### Abstract  
The exponential growth in data traffic in the real world has drawn attention to the emerging computing technique called Fog Computing (FC) for offloading tasks in fault-free environments. This is a promising computing standard that offers higher computing benefits with a reduced cost, higher flexibility, and increased availability. With the increased number of tasks, the occurrence of faults increases and affects the offloading of tasks. A suitable mechanism is essential to rectify the faults that occur in the Fog network. In this research, the fault-tolerance (FT) mechanism is proposed based on cost optimization and fault minimization. Initially, the faulty nodes are identified based on the remaining residual energy with the proposed Priority Task-based Fault-Tolerance (PTFT) mechanism. The Minimum-Cost Neighbour Candidate Node Discovery (MCNCND) algorithm is proposed to discover the neighbouring candidate Fog access node that can replace the faulty Fog node. The Replication and Pre-emptive Forwarding (RPF) algorithm is proposed to forward the task information to the new candidate Fog access node for reliable transmission. These proposed mechanisms are simulated, analysed, and compared with existing FT methods. It is observed that the proposed FT mechanism improves the utilization of an active number of Fog access nodes. It also saved a residual energy of 1.55 J without replicas, compared to the 0.85 J of energy that is used without the FT method.  
fault tolerance; fog computing: Internet of Things; neighbour node discovery; pre-emptive forwarding +
Footnote †: journal:
## 4 Results and Discussion
### Modelling Results
#### 4.2.3 Comparison of Two Baseline Models with the BAS-RF  
The precision of the BAS-RF model, compared with the other two (LR and MLR), is shown in Figure 13 through boxplots and Taylor diagrams. The difference between the predicted and actual sample values is indicated in the boxplot. In Figure 13, the RF model had a lower median value (the red line) and a more condensed interquartile range (the space between the upper and lower blue lines). This phenomenon indicated a lower prediction error than the other two baseline models. In addition, the upper limit of the RF was lower than the LR and MLR, although one outlier was observed, illustrating the high accuracy of the RF model. In Figure 13, the RF model was also more accurate in predicting ASR values than LR and MLR because of its condensed interquartile range although several outliers appeared. Moreover, three model evaluation indices (standard deviation, \(RMSE\), and \(R\)) are integrated into the polar coordinates, as shown in Figure 13,d. The two RF models were both in the closest position to the \"actual\" points with the uppermost R-value, minimum \(RMSE\), and lowest standard deviation values. Owing to the highest prediction accuracy and lowest error among the three ML models, the BAS-RF model is the most suitable to predict the FS and ASR values of WGP modified mortar.  
Figure 12: Scatter plot of simulated and actual values of (**a**) FS and (**b**) ASR.
## 2 Three-Dimensional Dubins Path
### Three-Dimensional Dubins Path  
A direction vector at each waypoint is defined as the direction to the next waypoint. With this definition, three sequential waypoint tuples \(\{\mathbf{p}_{i},\mathbf{p}_{i+1},\mathbf{p}_{i+2}\}\) and their associated two-direction vectors \(\{\mathbf{e}_{i},\mathbf{e}_{i+1}\}\) are all on the same plane \(plane_{i}\), and the next consecutive waypoint tuple \(\{\mathbf{p}_{i+1},\mathbf{p}_{i+2},\mathbf{p}_{i+3}\}\) and their associated directions \(\{\mathbf{e}_{i+1},\mathbf{e}_{i+2}\}\) are also located on another plane \(plane_{i+1}\). The \(plane_{i+1}\) is tilted from \(plane_{i}\) about the waypoint segment line as amount of \(\gamma=\cos^{-1}(\mathbf{e}_{n,i}\cdot\mathbf{e}_{n,i+1})\).  
Because the waypoint segment line is the intersection line of both planes, the circular arcs on \(plane_{i}\) and \(plane_{i+1}\) have the same tangent at the waypoint \(\mathbf{p}_{i+1}\), which is a direction vector along the segment line. Therefore, the Dubins path on both planes is smoothly connected at the waypoint \(\mathbf{p}_{i+1}\), as shown in Figure 8. This is how to extend the 2D Dubins path to a 3D path in our method.  
The advantage of the proposed method is that the problem of the 3D Dubins path becomes just designing 2D Dubins paths on every plane, and the smooth connection between adjacent planes is automatically achieved. This way, it preserves the same complexity and the shortest path property of the 2D Dubins path.
## 4 Deployment of Cloud-Native Core Network in ComEdge
### Example Analysis
In the above processes, network functions that interact frequently with the terminal, such as AMF and UPF, are deployed on almost every edge node, while network functions related to authentication, such as UDM and AUSF, are concentrated on one node. This ensures load balancing between nodes and effectively controls the control signaling overhead of the system. Figure 4: Partial 5G Signaling Process. It should be noted that the number of users and the volume of requests in the wireless coverage areas of various edge nodes are dynamically changing. The above three examples of edge core network deployments are all static solutions and cannot adapt to dynamic real-world situations. In the following subsection, we introduce a DQN-based CNF deployment algorithm.