esa-sceva/satcom-chunk-collection huggingface dataset:
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 5 Results and Discussions
In our work, the optimum threshold value of ROAD value-based sensing is recognized as the optimum value of operation to obtain data with minimum BER (Figures 21 and 24). As ROAD value-based noise detection is more accurate than the conventional amplitude-based method, it introduces less distortion in the mitigated data and prevents the extra loss of falsely detected data. The performance of the proposed ROAD PB is further compared with GAE enhanced pulse peak processors and the results are as shown in Figure 8.7 The proposed ROAD PB outperformed the GAE enhanced pulse peak attenuators and limiters (Type I and II). Similarly, Figure 26 depicts the performance comparison of the proposed ROAD PB with OBE enhanced pulse peak processors. It has been observed that ROAD PB could outperform OBE PPA. Moreover, ROAD PB has similar or slightly better performance than OBE PPLs for low SNR values. For SNR values 8 dB and above, OBE enhanced pulse peak limiters performed better than ROAD PB. Figure 27 compares the performance of ROAD PB with pulse blanking, GAE PPL (Type 2) and OBE PPL (Type 2). It has been observed that GAE PPL and OBE PPL have an improved performance compared to pulse blanking as data estimation has been performed instead of blanking the noise affected  
Figure 8.8 Probability of missed detection in ROAD statistics-based detection for different threshold value vs. SNR. signal. The threshold value used for all types of pulse peak processors is 0.3 [30]. When ROAD statistics is incorporated with pulse blanking, the performance could be improved better than GAE PPL and comparable performance with OBE PPL. Figure 8.9 Accuracy in ROAD statistics-based detection vs. SNR. Figure 9.0 Probability of detection vs. threshold for SNR = 5 dB. Figure 9.1 Accuracy in ROAD statistics-based detection (SNR = 5 dB) vs. threshold. Figure 9.2 Probability of missed detection in ROAD statistics-based sensing (SNR = 5 dB) vs. threshold. Figure 9.3 Performance of ROAD PB vs. pulse blanking. Figure 9.4 Variation of ROAD PB with different threshold value. Figure 9.5 Performance of ROADPB vs. OBE enhanced pulse peak processors. Figure 9.6 Performance of ROADPB vs. GAE enhanced pulse peak processors.
## 4 Computational Study
### Generation of the Computational Grid for the Darrieus Turbine  
A 3D CFD simulation of a three-bladed Darrieus rotor turbine with three asymmetric blade shapes was performed using ANSYS software. The computational domain was generated such that the boundaries were far enough from the rotor diameter to minimize boundary effects.  
An unstructured triangular grid was used to achieve a grid independent of the calculation parameters. Turbulence was modeled using the Realizable k-epsilon turbulence model using standard boundary functions, and the SIMPLE algorithm was used to couple pressure and velocity.  
The computations were performed under the following conditions: free flow velocity of 3 m/s. A computational mesh was constructed to discretize the computational domain using the ANSYS Meshing routine. This mesh is a finite set of tetrahedral volumes, the number of which is 931.342, as shown in Figure 12. The meshes around the outer edges of the blades are much denser than those away from the rotors in order to accurately capture the boundary layer flow that can be seen in Figure 12.  
Figure 12 shows the computational mesh (without the plate), which is a finite set that covers the area around the wind turbine. The total number of cells used to create the mesh is 846.562. The mesh is shown in the figure and is optimized to accurately simulate the air flow around the windwheel structure.  
Based on the presented computational grid, it can be concluded that the selected configuration provides sufficient detail for accurate modeling of the aerodynamic characteristics of the wind wheel.
## 4 Experiments and Results
### Datasets  
We generated 25 datasets, representing as many combinations of channel scenarios (5 scenarios) and SNR values (5 levels: 20 dB, 15 dB, 10 dB, 5 dB, 0 dB). Each dataset is evaluated under a specific channel scenario and SNR level. In total, we generated 500,000 spectrograms images. Each of our 25 datasets consists of 20,000 spectrogram images, i.e., 10,000 pairs of transmitted and received spectrograms, 2000 for each of the five protocols we considered: IEEE 802.11a, IEEE 802.11 ac, IEEE 802.11 ax, IEEE 802.11n, and LTE. A summary of the implemented noise model scenarios for LTE signals is given in Table 1. The characteristics, the channel conditions, and the received signal for each protocol/RAT are detailed in Appendix A.  
Figure 2 shows samples of the captured spectrograms for some combination of channel scenarios and SNR levels.
## 2 Theoretical Background
### Deep Neural Network  
An ANN is a system for simulating a network of neurons that comprise a human brain for a computer to learn things and make decisions in a human-like manner. [PERSON] [25] first proposed an ANN model that learns the weight of connection based on the concept of artificial neurons. In the initial model, learning is possible only when the learning object is classified into a linear model, and there is a disadvantage that the amount of calculation is rapidly increased according to the size of the neural network. These disadvantages can be solved by using the backpropagation algorithm developed by [26], and the multilayer neural network that uses the nonlinear activation functions. An ANN with several hidden layers is called a DNN, and the structure of an existing multilayer neural network and DNN is the same. However, various optimization methods are used to overcome the disadvantages of existing multilayer neural networks in a DNN. A limitation of the existing multilayer neural network is the overfitting (i.e., overtraining) problem, that is, predicting the data used for training is accurate, but the accuracy for new data excluded in training is very low because only the training data are learned excessively. [PERSON] et al. [27] demonstrated that the overfitting problem can be reduced and several problems with backpropagation can be solved when performing proper initialization through a restricted Boltzmann machine (RBM), which is the method for setting the initial value of the weights. More recently, it was discovered that one could train deep supervised nets by proper initialization, just large enough for gradients to flow well and activations to convey useful information [28]. Another limitation of the conventional backpropagation algorithm is the vanishing gradient problem which is where the gradients vanish as the activation functions are repeatedly used between the layers. This problem is not a fundamental problem with neural networks, and it depends on the choice of the activation function. Figure 3 shows the activation functions commonly used in neural networks.  
The activation function that is typically used in the ANN is a sigmoid function, and the vanishing gradient problem occurs because the derivative value is smaller than 1, and these small gradients are multiplied during backpropagation. This problem is aggravated with the increase in the number of layers in the architecture, and the optimization performance may deteriorate given the difficulty in modifying the weights [29]. Afterward, a hyperbolic tangent activation function that extends the sigmoid function range from \(-1\) to 1 has been proposed but a vanishing gradient problem remains. To overcome this problem, [PERSON] and [PERSON] [30] proposed a rectified linear unit (ReLU) function that treats all values as 0 when \(x\) is less than 0 and uses \(x\) as a value that is larger than 0. ReLU is currently the most frequently used activation function in the DNN because it can overcome the disadvantages of sigmoid function and can also be effective in alleviating the overfitting problem.
## 4 Results
### Model Evaluation  
#### 4.3.1 XGBoost  
Figure 7 and Table 9 present the results of using the XGBoost algorithm to predict the internal temperature of a greenhouse one hour in advance.  
This algorithm achieved similar results compared to R\({}^{2}\); however, a single metric cannot evaluate a model's performance. Hence, combining metrics for this analysis demonstrated that XGBoost delivered stable and favorable results for temperature forecasting. It is worth noting that the summer season (Figure 7d) exhibited the most unfavorable metrics because of constant temperature fluctuations, such as temperature spikes above 50 \({}^{\circ}\)C. In contrast, winter (Figure 7b) yielded the best results, while fall (Figure 7a) and spring (Figure 7c) produced similar results.
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulation Environment Setup

Table 2: Parameters for the simulations. Figure 1: Used network topologies (NDN testbed and Abilene). (**a**) The NDN testbed topology [63], 43 nodes. (**b**) The Abilene network topology [54], 11 nodes. only the replacement strategy in this work. For each scenario, 20 simulations are performed with the duration of 240 s each. Five metrics are considered for the performance evaluation: (1) the CHR, given by (3), which measures the performance of the system for a given caching policy; (2) the number of messages (traffic) in the network; (3) the delay between the time that a request is issued and the time (including time for re-transmissions) that the corresponding content is received on the requester; (4) the number of re-transmissions; and (5) the number of upstream hops. \[Cache\ Hit\ ratio=\frac{\sum_{n=1}^{N}hit_{j}}{\sum_{n=1}^{N}(hit_{j}+miss_{j })} \tag{3}\]
## 3 System Implementation Based on LPA Synchronization Algorithm
### Multi-h CPM Receiver Implementation
#### 3.2.1 Digital Down Conversion (DDC)  
The DDC unit is used to move the received intermediate frequency (IF) signal to the baseband and consists of a direct digital synthesis (DDS), a mixer, and two consecutive filters. DDS outputs the synchronized carrier at IF 70 MHz, which is updated by the estimated carrier phase \(\phi_{n}\). The mixed complex signal is filtered by two low-pass FIR filters to eliminate the noise and interruption.
## 3 Results
### Spatial Representativeness with Target Vegetation Type  
For the whole study period from 2003 to 2010, the distribution of the FTVT varied greatly along the wind direction. The FTVT was 0.89 when the wind direction was southwest but reduced to 0.65 when the wind direction changed to northeast due to the larger fraction of double-cropped rice in the east part of the flux site (Figure 6).  
Influenced by the monsoon climate, the source areas of the EC measurements were mainly concentrated in the southeast part in summer, and there was more coniferous plantation. In the non-growing season, however, the main wind direction was north and northwest. The source areas were mixed with more double-cropped rice. Therefore, the FTVT exhibited a single-peak seasonal variation from 2003 to 2010, which was higher in the growing season (May to September) than that in the non-growing season (Figure 7). The mean value of the FTVT ranged from 0.8254 to 0.8391, with a seasonal variation ranging from 0.0783 to 0.1416. Meanwhile, the FTVT from July 2006 was lower than the other years (Table 1). Compared with the other years, the source area of that month in 2006 was biased to the northeast, where more double-cropped rice was mixed in.  
Figure 5: Changes of FTVT and EVI in a circular window with an increasing size from 300 to 3000 m.  
Figure 6: Distributions of yearly FTVT, along with wind directions from 2003 to 2010.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 5 Applications  
### Commercial Instruments  
The echelle system has been extensively used in commercial instruments because of its parallel measurement in a broadband spectral range with a high resolution and its compact size. The mainstream instruments include the inductively coupled plasma optical emission spectrometer (ICP-OES), atomic absorption spectrometry (AAS), and laser-induced breakdown spectroscopy (LIBS).  
The combination of the echelle and cross-dispersion is the optimum optical scheme for ICP-OES that measures a large number of spectral lines simultaneously [65; 66; 67; 68; 69]. The spectral resolution is normally a few picometers. An echelle polychromator for ICP-OES designed by [PERSON] in 1993 had a spectral range of 167-782 nm and a resolution of 0.006 nm (full width at half-maximum, FWHM) at 220 nm [66]. [PERSON] designed one that measured wavelengths from 130 nm to 800 nm, with a spectral bandwidth of 0.006 nm at 134.724 nm [65]. The near-infrared Echelle microwave-induced plasma atomic emission spectrometry (NIR-Echelle-MIP-OES) studied by [PERSON] can be used for high-repetitive, high-resolved, and simultaneous spectra acquisition between 640 nm and 990 nm [67]. [PERSON] proposed a wide spectral coverage approach with a rotating prism. The spectral range can reach 180-900 nm. Combined with ICP-OES, the wavelength precision is better than 0.01 nm [15]. [PERSON] developed an approach in 2019 that can be applied to ICP-OES, and its imaging system includes primary, secondary, and tertiary tilted mirrors. The spectral resolution is up to 2.3 picometers/pixel [70].  
The early AAS used hollow cathode lamps as the light source [71] and replaced the lamps to measure different chemical elements. Using continuous light as the light source directly is a challenge because it needs an optical scheme with a higher efficiency and a detector with more sensitivity [72]. The echelle cross-dispersed system provides a good solution [73]. For example, the electrothermal atomic absorption spectrometry (ETAAS) system developed by [PERSON] determined simultaneous multi-elements and increased the radiation throughput [74]. [PERSON] developed an echelle spectrometer as the research tool for the structured background in flame atomic absorption spectrometry (flame-AAS) with a spectral range of 200-465 nm [75].  
LIBS requires broadband and high-resolution performance. In 1998, [PERSON] first proposed an approach using an echelle and an intensified charge coupled device (ICCD) in LIBS, which can simultaneously measure spectral lines in a wide wavelength range and improve the performance of analyzing samples [76]. Since then, the echelle scheme has been widely used in LIBS [77; 78]. [PERSON] [79] and [PERSON] [80] applied the echelle dispersion technique to LIBS successfully. A setup for LIBS and Raman spectroscopy measurements in a single unit using an echelle spectrograph system has been reported in recent years to measure minerals, archaeological artifacts, and other complex samples with minimum sample damage or consumption [81].
## 3 System Model  
The block diagram of the transmit antenna selection system based on LDC (LDC-TAS) [44; 45] is shown in Figure 1, it is easily found that there is a feedback circle link between the receiver with the transmitter in the system. It is supposed that being \(M_{t}\) transmitting antennae and \(M_{r}\) receiving antennae in it, but just M transmit antennae are selected by the transmitter to transmit. \(\mathbf{H}\) being a \(M_{r}\times M_{t}\) channel coefficient array whose elements are absolutely independent and its distribution is \(CN(0,1)\). At the same time, supposing the receiver has known an ideal CSI (channel status information), and given the channel state \(\mathbf{H}\) and \(\{\mathbf{M}_{n}\}_{n=0}^{N-1}\), then the \(M_{r}\times M_{t}\) channel array \(\mathrm{Hs}\) can be selected by one function \(\hbar\) below:  
\[\hbar:\mathbb{C}^{M_{r}\times M_{t}}\to\hat{H}=[H_{1},H_{2},H_{3},\ldots H_{P}]. \tag{5}\]
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 1 Introduction
In contrast to pure pursuit, the Stanley method will not \"cut corners\" but rather overshoots turns [11]. The others are model-based, represented by state-feedback control (SFC), linear quadratic regulator (LQR), and model predictive control (MPC). Model-based methods mainly based on dynamic models are often used for stability control of high-speed vehicles, whose disadvantages include poor real-time performance and difficulty in obtaining kinetic parameters accurately [10]. In this paper, we adopt the Stanley method, which provides balance stability, real-time feasibility, and tracking accuracy, modified for low-speed 4 WS vehicles. The proposed path tracker is designed to have low computational complexity by utilizing geometric relationships between vehicle and reference path suitable for low-speed driving instead of a dynamic model [12]. It improves response performance by applying the geometric error relationship between the vehicle and the path, as well as the curvature of the driving path and the vehicle's turning radius, as correction parameters. To evaluate the performance of the proposed path tracker, simulations and real-car-based performance tests were conducted, demonstrating the validity of the results.
## 3 System Model and Problem Formulation
### Offloading Location  
For the task offloaded by \(n\)-th ST, its access satellite can choose to locally process or transparently forward to its connected TC. We denote the offloading location of \(n\)-th ST as \(x_{n}\), where \(x_{n}=1\) and \(x_{n}=0\) respectively denote the SatEC server and TC.
# Investigation on Aerodynamic Noise Characteristics of Coaxial Rotor in Hover
## 2 Methodology
### Acoustic Prediction Model  
The acoustic prediction model for a coaxial rotor is built based on that for a single rotor [27; 28], where Farassat 1A formulations [20] are adopted. For an impermeable data surface, such as the blade surface, the acoustic pressure time history is given by  
\[p^{\prime}(x,t)=p_{T}^{\prime}(x,t)+p_{L}^{\prime}(x,t) \tag{2}\]  
\[4\pi p_{T}^{\prime}(x,t)=\int_{f=0}\left[\frac{\rho_{0}\hat{v}_{n}}{r(1-M_{r}) ^{2}}+\frac{\rho_{0}v_{n}\hat{r}_{i}\hat{M}_{i}}{r(1-M_{r})^{3}}\right]_{ret}dS +\int_{f=0}\left[\frac{\rho_{0}c_{0}v_{n}\big{(}M_{r}-M^{2}\big{)}}{r^{2}(1-M_{ r})^{3}}\right]_{ret}dS \tag{3}\]  
\[4\pi p_{L}^{\prime}(x,t)=\frac{1}{c_{0}}\int_{f=0}\left[\frac{\dot{p}cos\theta }{r(1-M_{r})^{2}}+\frac{\hat{r}_{i}\dot{M}_{i}pcos\theta}{r(1-M_{r})^{3}} \right]_{ret}dS+\int_{f=0}\left[\frac{p(cos\theta-M_{i}n_{i})}{r^{2}(1-M_{r})^ {2}}+\frac{pcos\theta\big{(}M_{r}-M^{2}\big{)}}{r^{2}(1-M_{r})^{3}}\right]_{ ret}dS \tag{4}\]  
where \"\(\cdot\)\" above the variable represents the derivative of restarted time. \(r\) is the distance between the source and the observer. \(M_{r}\) is the component of the surface source Mach number in the radiation direction. \(cos\theta=n_{i}\hat{r}_{i}\), \(M_{r}=M_{i}\hat{r}_{i}\), and \(\theta\) indicate the local angle between the surface normal and radiation direction. \(p\) is the blade surface gage pressure. The subscript \"\(ret\)\" stands for the retarded time (\(\tau\)), which is obtained from the solution of the equation: \(t-\tau-r/c_{0}=0\).  
Note that, for the loading noise in Equation (4), the far field terms are written together (the first one), which has \(1/r\) order. In addition, the near field terms (the second one) have \(1/r^{2}\) order. The current research is more interested in the far field noise. Thus, the far field terms will be given more attention in the following analysis.  
Figure 2 shows the acoustic radiation hemisphere and its Lambert projection map used in this paper. The radius of the hemisphere is 5R, and the center of the hemisphere is the coordinate origin, which is located at the center of the hub and between the twin rotors. The seven observation points set in this paper are marked in the projection map, and their coordinate values are given in Table 3.  
Figure 2: Schematic diagram of acoustic radiation hemisphere and Lambert projection of rigid coaxial rotor. (**a**) Acoustic radiation hemisphere; (**b**) Lambert projection map.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### _Informal Analysis_
#### 5.2.7 Ephemeral Secret Leakage Attack  
If the temporary secret random values \(r_{1}\), \(r_{2}\), and \(r_{3}\) are leaked, the attacker may attempt to calculate the value of \(R_{i-j-cs}\). However, the attacker cannot know the long- and short-term keys of the network simultaneously (as seen in the threat model assumptions); thus, even if the attacker knows the random values \(r_{1}\), \(r_{2}\), and \(r_{3}\), they cannot determine the values of \(N_{i}\) and \(N_{j}\). Therefore, the attacker cannot compute the session key, comprising the long-term keys \(N_{i}\) and \(N_{j}\) and short-term key \(R_{i-j-cs}\). Therefore, the proposed protocol resists ephemeral leakage attacks.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 3 Adaptive Energy Managements: Requirements, Components, and Mechanisms
### Components of the NFV-Enabled Ecosystem and Proposed Energy Management Mechanisms  
#### 3.3.1 Energy Supplier  
In the ecosystem for energy management, the ES is equipped with advanced control and monitoring systems, performs the energy-mixing process (Process to obtain energy for direct use, combining different primary energy sources [25]), and it may be composed of several suppliers or sub-suppliers. For analytical simplicity, in the proposal, the ES is regarded as a single entity and it s characterized by its power production capacity. The power level delivered by the ES, or available power in the system, defined as \(P_{ES}\) has an initial time \(T_{init}^{P_{ES}}\) and a duration equal to \(m\). The \(P_{ES}\) is _de facto_ the power received at the point of consumption, regardless of losses, and it is a combination of the power obtained from renewable sources (\(P_{R}\)), such as hydroelectric, solar, and wind, and from non-renewable sources (\(P_{NR}\)), such as coal, natural, and gas, as indicated in Equations (1)-(3), respectively.  
\[P_{ES}=P_{R}+P_{NR} \tag{1}\] \[P_{R}=P_{Es}\,\times\,w_{R}\] (2) \[P_{NR}=P_{Es}\,\times\,(1-w_{R}) \tag{3}\]  
where the weight \(w_{R}\in[0,1]\), controls the contribution of the renewable energy in the total generated power \(P_{Es}\). Thus, the \(w_{R}=1\) if the provisioning is exclusively from green sources.