esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The method of describing a set of n points with elliptical areas allows us to replace any number of points with an ellipse, representing the area of their occurrence in a single plane (Figure 1). The described method should be applied to sets of points in which the number of radius vectors of the points \(R_{i}\) (1) larger than the mean length of the radius vector \(\overline{R}\) (2) is close to the number of vectors smaller than the mean length of the radius vector. This dependence can be verified by determining the value of the distribution uniformity ratio \(\Delta_{P}\) (3) of the analyzed points \(Pi\) (\(x_{i};y_{i}\)) with respect to the geometric center of gravity of the analyzed set \(\overline{P}(\overline{x};\overline{y})\) (4, 5). For the method described, the value of \(\Delta_{P}\) should be close to 0.5.  
\[R_{i}=\sqrt{x_{i}^{2}+y_{i}^{2}} \tag{1}\]  
\[\overline{R}=\frac{\sum_{i=1}^{n}R_{i}}{n} \tag{2}\]  
\[\Delta_{P}=\frac{n_{min}}{n} \tag{3}\]  
\[\overline{x}=\frac{\sum_{i=1}^{n}x_{i}}{n} \tag{4}\]  
\[\overline{y}=\frac{\sum_{i=1}^{n}y_{i}}{n} \tag{5}\]  
Applying the method on a set of points satisfying the properties discussed above results in replacing it with an ellipse defined by five parameters for the position of the center of the ellipse \(x_{\text{COG}}\) and \(y_{\text{COG}}\); the angle of inclination of the directional line \(\alpha\); the length of the semiaxis \(a\), parallel to the directional line; and the length of the semiaxis \(b\), perpendicular to the directional line.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G-MEC Testbeds
#### 3.1.4 EdgeX Foundry  
EdgeX Foundry [38] is an open source platform that is placed at the network's edge to build an MEC framework for IIoT applications. EdgeX was developed under the Linux Foundation Edge (LF Edge) project. At the code level, the golang version, EdgeX-Go, of EdgeX Foundry has seven subdirectories: cmd, Api, Docker, Internal, pkg, snap, and bin. The cmd folder contains the project program entry main.go and describes key information such as the IP and port of the called EdgeX microservice; the Api folder contains theApplication Programming Interface (API) in the form of HTTP for each microservice in the entire framework; the Docker folder contains instructions required to create the image; and the Internal folder is responsible for the initialization of the device microservice driver and the processing of northbound command requests. Snap contains all EdgeX-Go-based microservices and the bin contains the executable files. To sum up, EdgeX Foundry provides a dual conversion engine: one for uniformly converting data from different sensors and devices using various communication protocols and formats into common EdgeX data structures, and the other for specifying customer-specific data using a TCP/IP-based protocol format and providing these data to applications, enterprises, and cloud systems. EdgeX can run on small-scale equipment such as a Raspberry Pi [39].
# Bias Effects on g- and s-Factors in Westcott Convention
## 2 Formulas and Calculation Methods
The s factor in Equation (1), representing the ratio of resonance integral after subtraction of the 1/v component to \(\sigma_{0}\), is expressed as  
\[\text{s}(T_{n},\ T_{s})=\frac{1}{\sigma_{0}}\frac{2}{\sqrt{\pi}}\sqrt{\frac{T _{n}}{T_{0}}}\int_{0}^{\infty}\left(\sigma(E,\ T_{s})-\text{g}(T_{n},\ T_{s}) \cdot\sigma_{0}\cdot\sqrt{\frac{kT_{0}}{E}}\right)\cdot\frac{\Delta(E)}{E}dE \tag{7}\]  
Hereafter, the s-factor calculated using the joining functions \(\Delta_{2}^{\text{W}}\), \(\Delta_{4}^{\text{W}}\), \(\Delta_{4}^{H}\) as \(\Delta(E)\) in Equation (7) are expressed as \(\text{s}_{2}^{\text{W}}\), \(\text{s}_{4}^{\text{W}}\), \(\text{s}_{4}^{\text{H}}\). Fine-mesh numerical data of the cross section are available in a website of nuclear data center [14] for \(T_{s}\) = 0 and 300 K, corresponding to JENDL-4.0 nuclear data library [15]. The Doppler-broadened cross section at any sample temperature can be deduced from the unbroadened cross section \(\sigma(E,\ 0)\) at \(T_{s}\) = 0 K. The Doppler-broadened cross section for \(T_{s}\) is expressed by the next equation based on a free-gas model [16]:  
\[\sigma(E,\ T_{s})=\frac{1}{W_{D}\sqrt{\pi}}\int_{0}^{\infty}\left(e^{-\frac{4 (E-\sqrt{EEE})^{2}}{W_{D}{}^{2}}}-e^{-\frac{4(E+\sqrt{EEE})^{2}}{W_{D}{}^{2} }}\right)\sigma(E^{\prime},\ 0)\sqrt{\frac{E\prime}{E}}dE\prime \tag{8}\]  
the Doppler width \(\text{W}_{D}\) in Equation (8) is given by  
\[W_{D}(E,T_{S})=\sqrt{\frac{4\cdot E\cdot kT_{S}}{A}} \tag{9}\]  
where A is the ratio of the sample mass to the neutron mass. The capture cross sections of \({}^{241}\)Am deduced using Equation (8) for \(T_{s}\) = 300 and 1000 K are shown by a black solid line and a red solid line in Figure 2a, and those of \({}^{151}\)Eu in Figure 2b. The number of mesh points used for integration of Equation (8) is about 65 k for \({}^{241}\)Am and 22 k for \({}^{151}\)Eu. Figure 2: _Cont._
## 3 GPS Software-Defined Receiver  
Figure 1 shows an overview of an SDR architecture. The first aim of an SDR is to detect the visible satellites and roughly estimate the received signal's code phase and carrier frequency. Later, each satellite parameter will be tracked to refine these estimates and keep a lock on them. Then, the tracking loops' output is utilized to calculate the GPS measurements, which are pseudo-ranges and pseudo-range rates. In addition, the navigation data are decoded to extract the ephemeris information and, consequently, compute the position of all available satellites. Eventually, the SDR will calculate the Position, Navigation and Timing (PNT) solutions based on the GPS measurements along with the satellite position information. Software processing offers some level of customization and flexibility when compared to hardware implementation using an application-specific integrated circuit (ASIC) that has traditionally been used to realize the signal processor part of the receiver [13]. Moreover, software processing lessens the cost and time consumption accompanying hardware prototyping.
# An Ultra-Wideband Microwave Photonic Channelized Receiver with Zero-IF Architecture
###### Abstract  
A scheme for realizing a zero-intermediate frequency (IF) channelized receiver using a dual-polarization quadrature phase-shift keying (DP-QPSK) modulator and a narrow-band optical filter is proposed. The channelized system only requires one optical frequency comb to achieve zero-IF multi-channel reception of wideband signals, and the spacing of the optical frequency comb only needs to be equal to the sub-channel width, which is very easy to implement. It is found that using photonic IQ demodulation and balanced detection and reception technology can not only eliminate many disadvantages of the traditional zero-IF receiver, including local oscillator (LO) leakage, direct current (DC) offset, even-order distortion, and in-phase/quadrature (I/Q) imbalance, but also reduce the bandwidth and sample rate of the analog-to-digital converter (ADC). It is theoretically proven that the radio frequency (RF) signal with a bandwidth of 3 GHz can be divided into five sub-channels with a bandwidth of 600 MHz and finally demodulated to I/Q basebands, which are also verified with simulation.  
microwave photonic channelization; balanced detection; zero-intermediate frequency (IF) receiver; IQ demodulation +
Footnote †: journal: Applied Sciences  
_Article_  
An Ultra-Wideband Microwave Photonic Channelized Receiver with Zero-IF Architecture  
[PERSON] _et al._ (2020), 10, 30; doi:10.3390/appl0010030
## 7 Sensor Data Fusion Based on TCPF
### Steps of Sensor Data Fusion Based on TCPF  
Step 1 generate an initial particle sample set \(\{xi(0),i=1,2,\ldots,n\}\) by sampling from the state transition function \(q(\mathbf{x}(k)|\ \mathbf{x}(k-1),\ \mathbf{z}(k))\) of the ZigBee-GPS/BDS multi-sensor.  
Step 2 perform basic UPF operations: \(\widetilde{x}_{i}(k)\gets T_{O}(\mathbf{x}_{i(k)}(k-1))\), \(k\in[1\),\(T]\), \(i\in[1\),\(n]\).  
Step 3 sample particles from the proposal distribution \(N(\widetilde{x}_{i}(k),\ \widetilde{p}_{i}(k))\):  
\[\widetilde{x}_{i}(k)\ \sim q(\mathbf{x}(k)|\ \mathbf{x}(k-1),\ \mathbf{z}(k))=N( \widetilde{x}_{i}(k),\ \widetilde{p}_{i}(k))\]  
Step 4 construct an adaptive threshold \(T_{c}\), and a Gaussian mixture continuous probability density function \(\hat{p}(\mathbf{x}(k)|\mathbf{z}(k))\) using cluster analysis.  
Step 5 the continuous probability density function is sampled hierarchically, and the sampling space is divided into \(l_{a}\), \(l_{b}\), and \(l_{c}\) layers.  
Step 6 set the proportional capacity, and divide the particles into \(l_{a}\), \(l_{b}\), and \(l_{c}\) layer groups according to the weight of the confidence factor \(\varsigma_{i}^{\text{ZGB}}\) of the sampled particles. At the same time, the weights of particles in the inferior layer \(l_{b}\) and \(l_{c}\) layer groups are combined and optimized, and they are added to the \(l_{a}\) layer to participate in sampling after the optimized particle weight \(\psi^{\prime}_{i}(k)\) is obtained.  
Step 7 output the fusion sampling results.  
Step 8 \(k=k+1\), and return to step 2.
# Vehicle-Following Control Based on Deep Reinforcement Learning
## 4 Methodology
### Subsection-PPO  
In urban congested roads, due to the high density of vehicles and the slow speed, frequent starts and stops are often caused, and accidents also occur. Therefore, this paper proposes to divide vehicle-following into two stages: start-stop and steady driving. Therefore, the collected trajectories \(\tau=(s_{1},a_{1},r_{1},s_{2},a_{2},r_{2},\cdots,s_{n},a_{n},r_{n})\) data need to be divided into two categories and processed separately. The original data are divided into two categories, the algorithm also needs to make corresponding changes. Hence this paper proposes the Subsection-PPO algorithm on the basis of the proximal policy optimization algorithm (PPO), using two Actor networks to learn different stages policy, as shown in Figure 2. It is worth noting that in order to ensure consistent state value estimation, we only use one Critic network to estimate the state value of the two stages. Afterwards, simulation experiments show that this method is effective. The following will introduce the division method of different stages:* start-stop stage \(\left\{\begin{array}{l}\text{start, when }acc_{-}p>0\text{ \&\&\&}\\ dist\
otin[speedX_{-}r*T_{mth},speedX_{-}r*T_{mth}+2\text{ }]\\ \text{stop, when }acc_{-}p\leq 0\text{ \&\&\&}\\ dist\
otin[speedX_{-}r*T_{mth},speedX_{-}r*T_{mth}+2\text{ }]\\ \end{array}\right.\) The trajectories generated in this stage are start-stop data.
* steady stage: when \(dist\in[\text{ }speedX_{-}r*T_{mth},speedX_{-}r*T_{mth}+2\text{ }]\) The trajectories generated in this stage are steady driving data.  
In the PPO algorithm, the importance sampling method changes the algorithm from on-policy to off-policy, which improves the utilization of data, and controls the update region of the policy network by clipping the importance weight to ensure that the update process is monotonous unabated. Although the importance sampling method is unbiased in estimating the distribution of the data, using a new distribution to estimate the old distribution will lead to large variance, so the model training efficiency is not as good as the sampling method that is both unbiased and consistent. To solve this problem, this paper proposes to use the weighted importance sampling method instead of the importance sampling method to estimate the actual objective function. Then, the final objective function is transformed from Equation (5) to:  
\[L^{CPI}(\theta)=\mathbb{E}_{t}\left[\sum_{n=1}^{N}\frac{\omega^{n}}{\sum_{m=1} ^{N}\omega^{n}}\text{ A}_{t}\right]=\mathbb{E}_{t}[r_{t}(\theta)\text{ A}_{t}] \tag{6}\]  
where \(\omega^{n}=\frac{\pi_{\theta_{old}}(\alpha|s_{t})}{\pi^{\theta}\theta_{(2t|s_ {t})}}\). The weighted importance sampling is both unbiased and consistent. Hence as the sampling volume increases, the method can render the estimated value increasingly close to the true distribution of the objective function. Figure 3 shows the overall framework of the subsection-PPO algorithm.  
Figure 2: Actor-network at different stages.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
After completing the complete concealing of the message, we applied the third stage, which is the encryption of the new Arabic text after concealment within it, using the encryption algorithm (using the same agreed key). The output will be full ciphertext (as shown in Table 4). Finally, we concealed the large ciphertext within the RGB image according to Algorithm 3 within the proposed approach. For the purposes of checking the quality of any steganography approach, usually we use the correlation between the result image (after insertion) and the cover image (before insertion) by calculating the similarity (a good method is one that gives higher similarity and a lower error rate). Additionally, the mean squared error (MSE) and bit error rate (BER) are common metrics to find the rate of changes after hiding and rate of errors [11]. The equations of the selected metrics are presented in (1), (2), and (  
\[Correlation=\frac{Number\ of\ marching\ pixels}{Total\ number\ of\ pixels} \tag{1}\]  
\[MSE=\frac{1}{mn}\sum_{i=1}^{n}\sum_{j=1}^{m}[C(ij)-N(ij)]^{2} \tag{2}\]  
where \(n\) is the number of rows, \(m\) is the number of columns, and \(C\) and \(N\) represent the cover and new images. \[BER=\frac{Number\ of\ errors}{Total\ number\ of\ bits} \tag{3}\]  
Figure 15 shows the \(MSE\) (Equation (2)) in the resulting image compared to the original image before embedding. When the data size changes (number of bits), it is noticed that with the gradual increase in the size of the data, the error rate increases, but it continues to have a small margin, which is an indicator that confirms the efficacy of the method used and explains the small value of the error due to the modification of the least important bit in the color matrix, only one for each pixel. Figure 16 shows the percentage of correlation between the original image and the resulting image after pasting with the change in the size of the data that is pasted. The percentage of similarity or correlation is calculated through equation number (1), and the higher it is, the better indicator. However, embedding in more data means an increase in the number of variable pixels and thus a decrease in the similarity value from 1. Figure 14: Comparison between the histogram of the original and new image after hiding. Figure 15: MSE value vs. Message length.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 2 Literature Review  
This section provides an overview of recent research related to radio wave source localization, particularly focusing on fingerprinting-based methods, the application of UAVs, and trajectory optimization techniques. Emphasis is placed on studies published in the last five years to highlight the current state of the art. The goal is to clarify the positioning of this study in relation to existing works and to identify research gaps that motivate our proposed approach.
## 2 Results and Discussion
### The Ailanstigols A and B Lead to Intracellular Protein Leakage  
Treatment of bacteria with certain compounds could disrupt the bacterial cell membrane, leading to the outflow of intracellular macromolecules such as nucleic acids and proteins, and ultimately resulting in cell death. In this experiment, the ES4326, PXO 71A, or PXO 86A strain was cultured in the presence of compound **1** or **2**. Cell supernatants were collected by centrifugation after culture, and the protein content was measured. The results showed that compound **1** significantly increased the leakage of intracellular proteins in the ES4326 and PXO 86 strains. Following 6 h of culture of ES4326 with compound **1** at 4 times the IC\({}_{50}\) concentration, compared with the solvent control group, the protein leakage increased by 41.41 \(\pm\) 11.27% (Figure 5A). Furthermore, compound **1** at 4 times the IC\({}_{50}\) concentration increased the leakage of the PXO 86 strain protein by 44.43 \(\pm\) 2.54% compared with the solvent control group, and there was a significant dose-dependent relationship between the compound treatment concentration and the amount of protein leakage in bacterial cells (Figure 5C). In contrast, compound **1** caused only very little intracellular protein leakage in the PXO 71A strain (Figure 5B).  
Compound **2** also increased the leakage of intracellular proteins in the PXO 71A and PXO 86A strains, but the effect was weaker than that of compound **1**. Following 6 h of culture of the PXO 71A strain with compound **2** at 4 times the IC\({}_{50}\) concentration, the  
Figure 5: Determination of intracellular protein leakage after culture of _Xanthomonas oryzae_ pv. _orgaze_ PXO 71A, PXO 86A, and _Pseudomonas syringae_ pv. _maculicola_ ES4326 in the presence of compounds **1** or **2**. **(A–C)** The results of the measurement of intracellular protein leakage after treatment of the ES4326, PXO 71A, or PXO 86A strain with compound **1** for 6 h; **(D–F)** the results of the measurement of intracellular protein leakage after treatment of the ES4326, PXO 71A, or PXO 86A strain with compound **2** for 6 h. IC\({}_{50}\): half maximal inhibitory concentration. The IC\({}_{50}\) values of compound **1** against the PXO 71A, PXO 86A, and ES4326 strain were determined to be 485.85 \(\mu\)g/mL, 314.16 \(\mu\)g/mL, and 289.23 \(\mu\)g/mL, respectively. The IC\({}_{50}\) values of compound **2** against the PXO 71A, PXO 86A, and ES4326 strains were determined to be 354.09 \(\mu\)g/mL, 274.12 \(\mu\)g/mL, and 476.18 \(\mu\)g/mL, respectively. One-way ANOVA and Tukey’s tests were used to compare mean differences, and different lowercase letters (a, b, c, and d) represent significant differences (\(p<0.05\)).  
protein leakage increased by 35.41 \(\pm\) 3.82% compared with the solvent control group (Figure 5E). Indeed, at 2 or 4 times the IC\({}_{50}\) concentration did compound **2** even moderately increase the leakage of intracellular proteins in the PXO 86A strain (Figure 5F). Additionally, compound **2** had a slight influence on the leakage of intracellular proteins in the ES4326 strain (Figure 5D).
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### Deep-Learning-Based IDS
#### 2.4.1 Recurrent Neural Networks  
Recurrent neural networks (RNNs) [22] are a type of artificial intelligence neural network that considers the instance of the current input and what they have perceived as input in the past, implying that they have a second memory input. An RNN's decision at time \(t-1\) influences the decision made at time \(t\). Therefore, the RNN receives input from two different sources (the present and recent past) that work together to determine the RNN's response to new data. The main distinction between RNNs and feed-forward neural networks is the feedback loop. Unfortunately, one of the RNN's flaws is the vanishing gradient problem that occurs when the gradient is minimal, preventing the weights from changing and stopping the neural network from further training.
Appl. Sci._**2012**, \(2\), 682-708; doi:10.3390/app2040682  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
Fluorinated and Non-Fluorinated Electro-Optic Copolymers: Determination of the Time and Temperature Stability of the Induced Electro-Optic Coefficient  
**[PERSON]**  
Dipartimento di Scienze di Base ed Applicate per l'Ingegneria, Sapienza Universita di Roma and  
CNISM/Via A. Scarpa 14, I-00161 Roma, Italy; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-06-4996-800; Fax: +39-06-4424-0183  
_Received: 12 September 2012; in revised form: 28 September 2012 / Accepted: 28 September 2012 / Published: 9 October 2012_  
**Abstract:** Organic fluorinated materials demonstrate their excellent electro-optic properties and versatility for technological applications. The partial substitution of hydrogen with fluorine in carbon-halides bounds allows the reduction of absorption losses at the telecommunication wavelengths. In these interesting compounds, the electro-optic coefficient was typically induced by a poling procedure. The magnitude and the time stability of the coefficient is an important issue to be investigated in order to compare copolymer species. Here, a review of different measurement techniques (such as nonlinear ellipsometry, second harmonic generation, temperature scanning and isothermal relaxation) was shown and applied to a variety of fluorinated and non-fluorinated electro-optic compounds.  
**Keywords:** electro-optic polymers; fluorinated polymers; electro-optic time stability
## 3 Results and Discussion  
### Fractional Composition and Moisture Content of BA  
The fractional composition of the MSWI BA is presented in Figure 1.  
More than half (52.94 \(\pm\) 0.1%) of the BA consisted of particles smaller than 5.6 mm. Particles of 5.6 to 11.2 and 11.2 to 24.4 mm represented 23.26 \(\pm\) 0.1% and 16.58 \(\pm\) 0.1% of the content, respectively. The largest (>40.0 mm) bottom ash particles comprised the smallest portion of the sample (1.19 \(\pm\) 0.1%). Thus, BA is similar to the optimum distribution with a sand-like fraction and can be classified as a 0-25 mm material with good graduation [24].  
The results of the determination of the moisture content of the different fractions are shown in Figure 3.  
Moisture analysis showed that the water content in the untreated BA ranged from 8.50% to 17.16%. The mean value was 12.60 \(\pm\) 0.39%. The amount of water in BA is not a stable value as the BA is cooled by water in an incinerator. Some of the water immediately evaporates from the hot bottom ash, while some is absorbed.
# Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images
## 3 Problem Formulation  
### System Model  
In the initial stage, we outline the processing chain of our method, illustrated in Figure 2. We consider a batch of omnidirectional images under ERP, with sizes \(M\times N\) where \(N=2M\), as depicted in Figure 2a. We partition the images into blocks of size \(L\times L\) to facilitate subsequent transform, similar to traditional image processing techniques. The signal in a block is represented as an \(L\times L\) matrix denoted as \(\mathbf{X}\). After performing block-wise transform, the coefficient matrices can be written as \(\mathbf{C}=\mathbf{T}\mathbf{X}^{T}\), where \(\mathbf{T}\) is an \(L\times L\) transform matrix, e.g., the DCT matrix. Subsequently, we vectorize the matrix \(\mathbf{C}\) and denote the coefficients by \(\mathbf{c}\in\mathbb{R}^{L^{2}}\). The omnidirectional image is then divided into \(K=M/L\) rows and \(2K\) columns, with latitudes denoted by \(k\) for \(k=1,\dots,K\), as depicted in Figure 2b. We denote \(\mathbf{c}\) at latitude \(k\) by \(\mathbf{c}_{k}\) and the \(L^{2}\) elements in \(\mathbf{c}\) by \(\mathbf{c}_{k,l}\) where \(l=1,2,\dots,L^{2}\). Throughout this process, we quantize the coefficients with \(KL^{2}\) scalar quantizers produced by the bit allocation process, which will be elaborated in detail subsequently. Figure 2c presents the recovered images after quantization and inverse transform. At last, we evaluate the quality of quantized images by WS-PSNR.