esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Prediction
#### 3.2.3 Discussion  
In the above subsection, several ML-based solutions for traffic prediction in SDN have been presented, as summarized in Table 9. Mobile communication faces many challenges when the number of smart devices increases, and hence, the load in the network can lead to network congestion. In this context, ML models can be used to predict the network situation and help the operator to anticipate exceptional resource demand or reduce resources when no longer needed. Traffic prediction is considered a regression problem (i.e., dataset outputs have continuous values). Therefore, supervised learning can be used for traffic prediction. Based on the literature, DL algorithms, especially LSTM, are widely adopted and can be considered a promising solution for network traffic prediction. However, building an LSTM model does not mean successful results, since several factors can affect the performance of the model, such as the model hyperparameters and the size of the dataset used during the training process. Additionally, the model needs continuous training, since the network situation changes quickly. Moreover, the exchange of information between the controller and the forwarding devices can overload the system and can pose security and data privacy problems. Therefore, federated learning seems to be an efficient solution. This method can optimize the communication between SDN and the involved forwarding devices by keeping the data where it was generated. This leads to preserving the bandwidth for the application traffic, reducing costs of data communication, and ensuring data security. Thus, more attention should be given to this research direction.  
Figure 7: A federated learning approach with SDN-enabled edge networks [75].
## 4 Numerical Simulation Model
### Mathematical Model  
According to the assumptions, the control equation of the fluid in the eccentric rotor system can be established:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **Parameter Value** & **Trajectory Equation** & **Velocity Equation** \\ \hline \(O_{1}\) &... & \(x_{B}=e\cdot\sin(\frac{\pi}{2}+\omega t)=e\cos\omega t\) & (3) & \(V_{xB}=-e\omega\cos\omega t\) & (7) \\  & \(y_{B}=e\cdot\sin(\frac{\pi}{2}+\omega t)=e\cos\omega t\) & (4) & \(V_{yB}=-e\omega\sin\omega t\) & (8) \\ \(O_{2}\) & \(\text{LAB}=e\) & \(x_{A}=0\) & \(V_{xA}=0\) & \(V_{xA}=0\) \\  & \(\varphi_{BA}=\frac{\pi}{2}\) & \(y_{A}=2e\cos\omega t\) & (5) & \(V_{yA}=-2e\omega\sin\omega t\) \\  & \(\text{LBC}=e\) & \(x_{C}=-2e\sin\omega t\) & (6) & \(V_{xC}=-2e\omega\cos\omega t\) \\  & \(\varphi_{BC}=-\frac{\pi}{2}\) & \(y_{C}=0\) & \(V_{yC}=0\) & (10) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The trajectory equation and velocity equation of each point on the rotor.  
Mass conservation equation:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\
u)=0 \tag{11}\]  
Among them, \(\rho\) is the fluid density and \(\
u\) is the fluid velocity.  
Momentum conservation equation:  
\[\rho\frac{\partial\
u}{\partial t}+(\rho\
u\cdot\
abla)\
u=-\
abla p+\mu\
abla^ {2}\
u \tag{12}\]  
Among them, \(p\) is the pressure and \(\mu\) is the fluid viscosity.  
Energy conservation equation:  
\[\rho C_{p}(\frac{\partial T}{\partial t}+\
u\cdot\
abla T)=\
abla\cdot k\
abla T \tag{13}\]  
Among them, \(T\) is the temperature, \(C_{p}\) is the constant pressure specific heat capacity, and \(k\) is the thermal conductivity.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
###### Abstract  
Super-Resolution (SR) techniques for image restoration have recently been gaining attention due to their excellent performance. For powerful learning abilities, Generative Adversarial Networks (GANs) have been proven to have achieved great success. In this paper, we propose an Enhanced Generative Adversarial Network (EGAN) for improving its effects for a real-time Super-Resolution task. The main content of this paper are as follows: (1) We adopted the Laplacian pyramid framework as a pre-trained module, which is beneficial for providing multiscale features for our input. (2) At each feature block, a convolutional skip-connections network, which may contain some latent information, was significant for the generative model to reconstruct a plausible-looking image. (3) Considering that the edge details usually play an important role in image generation, a perceptual loss function was defined to train and seek the optimal parameters. Quantitative and qualitative evaluations were demonstrated so that our algorithm not only took full advantage of the Convolutional Neural Networks (CNNs) to improve the image quality, but also performed better than other algorithms in speed and performance for real-time Super-Resolution tasks.  
super-resolution; deep-learning; generative adversarial networks Article
# Experimental Study of a Piezoelectric De-Icing System Implemented to Rotorcraft Blades
## 6 Full-Length Tail Rotor Leading Edge Numerical Case
### Frequency and Direct Steady-State Dynamical Analysis  
A frequency analysis was first conducted to calculate the first resonant modes of the iced leading edge. As for the small blade setup, the first modes were targeted to minimize power consumption (Equation (1)). Modes 2, 3 and 4 (Figure 15) were selected for their lower frequency and mode shape similar to the successful resonant modes with small blade setup and proper anti-node size for the actuators. Mode 1 was not selected since its mode shape did not correspond to the resonant modes that yielded de-icing with the small blade. While it is possible that this mode is effective, it was preferable to focus on a mode shape similar to those proven effective during experimental testing.  
Figure 14: Ice layer part with linear thickness increase.  
For each of those modes, PI876.A15 piezoelectric actuator patches were positioned at the center of each anti-node on the upper and lower surface. The direct steady-state dynamic analysis was conducted for each mode with the optimal phasing of the actuators. For each mode, the voltage peak to peak required to obtain 2.72 MPa of shear stress S12 was determined using the numerical simulation. This corresponded to the average shear stress calculated with the numerical model at complete de-icing of the blade for the small blade setup. Table 9 presents the resonant mode frequencies as well as the voltage values required for each mode to obtain sufficient stress for de-icing in the static case (only the piezoelectric actuator de-icing system) and for the case when a centrifugal force is added. The centrifugal force is considered at 600 RPM, the same as for the small-scale tests and conservative tail rotor rotation speeds, the estimated accumulated ice mass of 25 g and a radius of 0.55 m.  
Figure 15: Full-length tail rotor leading edge resonant modes 2 (a), 3 (b) and 4 (c).
# A Robust and Effective Two-Factor Authentication (2 FA) Protocol Based on ECC for Mobile Computing
## 3 Proposed Protocol  
To meet the security requirements for a 2 FA protocol for mobile users and service providers, we employed the following five core approaches in the proposed protocol:  
1. The user only sends \(ID_{i}\) to the service provider and the protocol uses fuzzy verification technology to design password login verifiers in the registration phase to resist attacks from privileged insiders;
2. To resist password-guessing attacks where the adversary leverages the verifier to guess the password, we used the \"Fuzzy-Verifiers\" and \"Honeywords\" technologies [42] to set the verifiers of the password \(PW_{i}\); i.e., \(A_{i}=H(ID_{i}\parallel PW_{i}\parallel a_{i})\) mod \(n_{0}\), \(RPW_{i}\ =\ H(ID_{i}\parallel PW_{i})\) mod \(n_{0}\);
3. In terms of guaranteeing efficiency and forward secrecy [53], we applied lightweight ECC to ensure the 2 FA protocol's efficiency and, in addition to the long-term key, we added a secret value that cannot be obtained by the adversary in the calculation of the session key to ensure forward secrecy;
4. To resist key-compromise impersonation attacks, we included a secret parameter \(r_{i}\) that can be stored with the service provider securely (e.g., stored in an auxiliary server, as with [17]). Consequently, \(\mathcal{A}\) is unable to acquire the value of \(V_{i}\) with \(r_{i}\) to forge the login request message \(M_{1}\);
5. To ensure the user's un-traceability, a dynamic \(M_{2}\) computed with the dynamic parameters \(K_{2}\) and \(V_{i}\) prohibits the adversary from tracing the unchanged identity of the user.  
Next, this paper describes the 2 FA protocol in detail, including the system setup phase, the registration phase, the following login and authentication phase, and, lastly, the password update phase.
# Energy Sector Development: System Dynamics Analysis
## 2 Research Method
### Delimitations  
In our modelling, we make the following delimitations:  
* Nuclear power is excluded because it is not included in total Australian energy production. It is only produced for export and it seems unlikely to be used to generate power in the near future due to public opposition and high capital cost.
* Oil is excluded as Australia's oil production already reached its peak in 2000, and reserves are declining with time [28]. In addition, most of Australia's oil production is exported because the characteristics of Australian oil are not suited to Australia's refineries [29]. However, CO\({}_{2}\) emissions resulting from imported and exported oil are considered in the model.
* LPG is excluded as it depends on oil production, which is already excluded, and on natural gas production, which is already included.
* Solar hot water is excluded from the total supply of solar power due to slow growth and small capacities. Strong growth has been demonstrated for photovoltaic cells, which are included in the model.
* Biogas and biofuels are excluded from the total supply of biopower due to slow growth and small capacities. Strong growth and big amounts are only available for wood, wood waste and bagasse, which are included in the model. Although biomass releases CO\({}_{2}\) emissions resulting from burning, it is excluded from the CO\({}_{2}\) emissions model, as they are carbon-neutral energy resources. In other words, they captured already a nearly equivalent amount of CO\({}_{2}\) through photosynthesis during their lifecycle.
* Geothermal is excluded as there has been no growth since 2004 with very small generated energy (0.5 GWh) since that time.
* Due to the data availability and small capacities of wind and solar power, historical data started from 2005, 2010 for wind and solar, respectively.
## 5 Results from BEM Computations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \begin{tabular}{c} **CLEAN** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **st8** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)1** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **st9** \\ **(N)** \\ \end{tabular} & \begin{tabular}{c} **\(\Delta\)2** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **st8 st9** \\ **(N)** \\ \end{tabular} &
\begin{tabular}{c} **\(\Delta\)3** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{**NTM75**} & Seed 1 & 187,634 & 188,168 & 0.284 & 188,158 & 0.279 & 188,166 & 0.283 \\  & Seed 2 & 160,211 & 160,670 & 0.287 & 160,662 & 0.282 & 160,661 & 0.281 \\  & Seed 3 & 155,155 & 155,599 & 0.286 & 155,594 & 0.283 & 155,590 & 0.280 \\ \hline \multirow{4}{*}{**NTM7.5**} & Seed 1 & 398,168 & 399,301 & 0.285 & 399,283 & 0.280 & 399,299 & 0.284 \\  & Seed 2 & 352,561 & 353,563 & 0.284 & 353,545 & 0.279 & 353,562 & 0.284 \\  & Seed 3 & 342,486 & 343,461 & 0.285 & 343,449 & 0.281 & 343,452 & 0.282 \\ \hline \multirow{4}{*}{**NTM10**} & Seed 1 & 570,742 & 572,303 & 0.273 & 572,283 & 0.270 & 572,295 & 0.272 \\  & Seed 2 & 534,179 & 535,603 & 0.267 & 535,584 & 0.263 & 535,595 & 0.265 \\ \cline{1-1}  & Seed 3 & 511,960 & 513,324 & 0.266 & 513,302 & 0.262 & 513,307 & 0.263 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean values of the thrust calculations for the cases with MTs in comparison with the case with no device implemented. \begin{table}
\begin{tabular}{c l c} \hline \hline
**Variable** & **Name** & **Units** \\ \hline \(V_{o}\) & wind speed & m/s \\ \(a_{i}\) & axial induction factor & - \\ \(a^{{}^{\prime}i}\) & tangential induction factor & - \\ \(r_{i}\) & airfoil spanwise position (radius) & m \\ \(w\) & rotor rotational speed & rad/s \\ \(\rho\) & air density & Kg/m\({}^{3}\) \\ \(\Phi\) & flow angle & rads \\ \(c_{i}\) & airfoil chord length & m \\ \(dT\) & thrust on the annular element & N \\ \(dM\) & root bending moment & Nm \\ \(B\) & number of blades & - \\ \(R\) & rotor radius & m \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Thrust and bending moment calculations variables. bending moment in the root of the blade has been found. All the increments in the bending moment due to the MTs implementation are acceptable taking into account the increase in the average power output production presented in Figure 14.
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 3 Traditional Algorithm  
### _F Computation in Multi-View Geometry_  
The most basic \(F\) computation is based on the linear equations  
\[Af=0 \tag{1}\]  
where  
\[f=\left[F_{11}\ F_{12}\ F_{13}\ F_{21}\ F_{22}\ F_{23}\ F_{31}\ F_{32}\ F_{33} \right]^{T} \tag{2}\]\[A=\begin{bmatrix}x_{1}^{{}^{\prime}}x_{1}&x_{1}^{{}^{\prime}}y_{1}&x_{1}^{{}^{ \prime}}&y_{1}^{{}^{\prime}}x_{1}&y_{1}^{{}^{\prime}}y_{1}&y_{1}^{{}^{\prime}}&x_ {1}&y_{1}&1\\ x_{2}x_{2}&x_{2}y_{2}&x_{2}&y_{2}x_{2}&y_{2}y_{2}y_{2}&y_{2}&x_{2}&y_{2}&1\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ x_{n}^{{}^{\prime}}x_{n}&x_{n}^{{}^{\prime}}y_{n}&x_{n}^{{}^{\prime}}&y_{n} ^{{}^{\prime}}x_{n}&y_{n}^{{}^{\prime}}y_{n}&y_{n}^{{}^{\prime}}&x_{n}&y_{n}&1 \end{bmatrix} \tag{3}\]  
where \(u=[x~{}y~{}1]^{T}\) and \(u^{\prime}=[x^{\prime}~{}y^{\prime}~{}1]^{T}\) are the coordinates of the matching feature on two-view images. It is usually called the eight-point method because at least eight matching point pairs are needed to determine this equation. Of course more than eight matching point pairs can compose an over-determined system of equations to minimize \(|Af|\) that could produce more robust result.  
In RANSAC framework, increasing iteration times \(n_{I}\) is to find a better \(F\) with less error. But as the \(n_{I}\) increases, the errors of projective distance or metric distance become smaller and smaller at beginning of the iterations, then keep almost at same level after beginning iterations.  
If constraint \(\det(F)=0\) is enforced to Equation (1), only seven matching pairs are necessary to solve this equation [26]. So this is usually called seven-point method. With more constraints being added in Equation (1), less matching pairs become indispensable. In the extreme occasion as mentioned in Section 2, only two matching pairs are needed [17].  
Normalization [6; 27] of matching feature coordinates is an essential step and recommended to reduce the final projection error. The suggested normalization is a translation and scaling of each image so that the centroid of the matching feature points is at \([0,0]\), the origin point of the coordinates, and the root mean square (RMS) distance of the points to the origin point is \(\sqrt{2}\). Usually, the last step is to optimize \(F\) for to minimize some sort of average error by nonlinear optimization method. The error is always the squared distance between a point and its epipolar line, which will be computed for both points of one match and averaged over all \(N\) matches.
# Key Roles of Plasmonics in Wireless THz Nanocommunications--A Survey
## 5 Plasmonic THz Wireless Nanoscale Link Components  
The main components of a typical WiNoC layout are basically the THz band antenna along with transceiver components, used as a feeding element. Specifically, the transceiver is held responsible for preparing the information for outgoing transmissions to antenna, and for demodulating incoming transmissions from antenna respectively.
## 2 Materials and Methods
### Multispectral Image Analysis
#### 2.4.3. SAVI (Soil-Adjustment Vegetation Index)  
Similar to NDVI, SAVI is used to measure the vegetation spectrum. It was introduced by [PERSON] [31] and is expressed mathematically by Equation (3). The reflection spectra of soil depend on its characteristics, including moisture, organic content, composition, and iron content. In areas of low vegetative cladding density (<40%) with exposed soil surfaces, the reflectance rates of red and near-infrared spectra can affect the vegetation index. Red and near-infrared wavelengths can be problematic when performing analyses depending on soil presence that can reflect the light of other wavelengths. SAVI can be used to compensate for this phenomenon and to adjust soil brightness when the vegetative sheath is low [32]. The soil brightness correction factor (\(L\)) is used as a value of 0.5 in most land cover types. This index is mainly useful in areas of concentrated vegetation.  
\[SAVI=\frac{(1+L)(NIR-Red)}{(NIR+Red+L)} \tag{3}\]
## 4 Experiments and Results
### Validation  
To show the feasibility of the proposed Sensitivity Analysis method for borehole state classification tasks, we performed several experiments on real-world datasets. First, we used the EventTracker Sensitivity Analysis for a reduction of the number of possible features mentioned in Section 1.4 to a manageable, \"important\" subset. Later, the obtained reduced feature set (features ranked due to their \"importance\") was used with standard methods to solve the borehole operational state classification task.
###### Abstract  
Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.  
6  
Machine learning computational time; feature selection; ROC curves; AUC scores; LASSO; ElasticNet
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 1 Introduction
The sampled echo signals were divided into distance gates matching the pulse width, and the corresponding power spectrum of the echo signal was obtained for each beam after signal processing. Then, the consistency checked method was used to eliminate the polluted echo signals in order to overcome the influence of the interference from the individual beams on the calculation results [33]. Therefore, compared with the three-beam and five-beam methods, the precision of the eight-beam coherent Lidar is higher. This paper is organized as follows. In Section 2, the analysis method is described. In Section 3, results and discussions are listed. Finally, the conclusions are presented.
## 4 Models and Algorithm Design
### Algorithm with Multiple Uncertainties
#### 4.5.1 Line Losses  
Line losses cannot be avoided when transporting energy using cables over long distances. In addition, each voltage transformation to another level also brings with it certain losses. Line losses will be marked as \(P_{\text{loss}}\) and are added to the total required load \(P_{D}\). This will compensate them. Formula (3) then takes the form:  
\[\sum_{i=1}^{N_{c}}P_{c_{i}}+\sum_{i=1}^{N_{k}}P_{c_{i}}+\sum_{i=1}^{N_{\text{ per}}}P_{w_{i}}+\sum_{i=1}^{N_{b}}P_{b_{i}}+P_{g}=P_{D}+P_{D}\cdot P_{\text{loss}}. \tag{34}\]  
Line losses are minimal compared to the load in the networks. That is why they are often neglected in EDP [10], but to obtain a more accurate model it is advisable to consider them. These losses are often thought of as 5% (0.05) of the total demand \(P_{D}\)[13]:  
\[P_{D}=P_{D}+P_{D}\cdot P_{loss}=P_{D}\cdot(1+P_{loss})=P_{D}\cdot(1+0.05)=1.05 \cdot P_{D}.\]  
Moreover, subsequently, the same rule will apply to the individual local demands \(P_{D_{i}}\):  
\[P_{D_{i}}=P_{D_{i}}+P_{D_{i}}\cdot P_{loss}=P_{D_{i}}\cdot(1+P_{loss})=P_{D_{ i}}\cdot(1+0.05)=1.05\cdot P_{D_{i}}.\]  
This modification is reflected in the initialization of the algorithm for \(P_{i}(0)\):  
\[P_{i}(0)=\begin{cases}P_{i}^{\text{max}}&\text{if}\quad P_{i}^{\text{max}}<P_{ D_{i}},\\ D_{i}&\text{if}\quad P_{i}^{\text{min}}\leq P_{D_{i}}\leq P_{i}^{\text{max}}, \quad\forall i\in N.\\ \\ P_{i}^{\text{min}}&\text{if}\quad P_{D_{i}}<P_{i}^{\text{min}},\end{cases}\]
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 3 Coexistence of a Goal-Oriented and a Data-Oriented Communication System
### Evaluation of the System without Optimization  
In this section, we evaluate the performance of the overall system, in which GO and DO systems coexist. To do so, Monte Carlo experiments are conducted, considering different realizations of wireless channels, computation delays and pattern inference requests (i.e., number of patterns generated at each time slot). Results are obtained from \(T=\) 50,000 independent realizations of such parameters. The following assumptions hold for communication, computation, and inference settings.