esa-sceva/satcom-chunk-collection huggingface dataset:
# Holographic Mixed Reality System for Air Traffic Control and Management
## 7 Conclusions
Assistance for control judgments in emergency situations: It is true that the burden on control work increases under low visibility conditions due to severe weather, etc. Since this system actively provides possible additional information, quick judgments on, and coping with emergency situations can be expected. This system can also provide environments where the air traffic controller can logically recognize the inputs, requirements, or warnings easily. The following presents the characteristics of this study, which is differentiated from existing studies that have been conducted thus far. First, as the actual operating environment is complex, preliminary surveys of air traffic controllers were conducted before the system development to understand the work of air traffic controllers and derive problems in the control system. Second, this study applies real-time data of aircraft flight trajectories and provides 4D multi view for the effective presentation of clear flight information. By visually providing real-time flight trajectories, the user's understanding of information can be improved. Third, this study designed the system after identifying the equipment, control screen, and familiar interaction types used in the actual operation environment. It was a method to minimize the sense of difference in the work of the air traffic controller considering the fact that the system can be used in actual operating environments. This study can help with quick judgment and responses in emergency situations. Fourth, it is possible to improve the productivity of control work according to the interactive environment in actual use spaces instead of separate spaces. However, since stability and reliability should be supported because the system should be used in actual operating environments, the appropriate indicators are necessary for the quantitative evaluation of the quality level along with the measurement of user experience. In order to evaluate the quality level of this system and to measure user experience, the development of evaluation indicators suitable for MR environment software should precede. Therefore, the authors will develop measurement tools based on the studies of evaluation items suitable for this system hereafter. Conceptualization, J.W.L., K.H. and S.H.H.H.S. ; methodology, S.H.H.H.S. ; investigation, K.H. ; formal analysis, K.H. ; validation, K.H. ; writing--original draft preparation, K.H. and S.H.H.S. ; writing--review and editing, K.H., S.H.H.S. and J.W.L. ; and supervision, J.W.L. This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 19 CTAP-C142966-02-00000). The authors declare no conflict of interest.
# Non-Probabilistic Reliability Analysis of Slopes Based on Fuzzy Set Theory
## 2 Fuzzy Characterization Method for Geotechnical Parameters  
### Fuzzy Affiliation Function  
The fuzzy distribution characteristics of geotechnical parameters can be described by the fuzzy affiliation function. For an arbitrary element \(x\) in the region \(U\), its degree of belonging to the set \(A\) can be expressed by the affiliation degree \(\mu_{A}(x)\); \(\mu_{A}(x)\) takes a value ranging from 0 to 1, and the set \(A\) is called a fuzzy set [17] in 18 the region \(U\). The function composed of the affiliation degree of each element of the fuzzy set in the theoretical region \(U\) is called the affiliation function of the fuzzy set. The specific form of the affiliation function can be determined by various methods, such as the intuitive method, the fuzzy statistics method, the relative selection method and the fuzzy distribution fitting method. There are four types of fuzzy affiliation functions commonly used in slope engineering to describe the fuzziness of geotechnical parameters: the triangular distribution, the trapezoidal distribution, the normal distribution and the log-normal distribution, as shown in Figure 1.
## 3 Performance Evaluation
### Mutual Information  
The discrete channel (Figure 1) is described by input and output alphabets, as well as the set of conditional (transition or crossover) probabilities. The input and output alphabets are denoted by \(\{X\}=\{x_{1},x_{2},\cdots x_{M}\}\) and \(\{Y\}=\{y_{1},y_{2},\cdots y_{M}\}\), respectively. The probability of an input symbol \(x_{i},i=1,2,\cdots M\), is denoted by \(P(x_{i}),i=1,2,\cdots M\). The conditional probability, \(P\big{(}y_{j}\mid x_{i}\big{)}\), denotes the probability that symbol \(y_{j}\) will appear at the channel output if symbol \(x_{i}\) appears at the channel input. Since the conditional probability depends only on the current transmitted symbol, the channel is memoryless.  
Mutual (transmitted) information ([15], (2.43)) is defined as a difference between a priori ([15], (2.1)) and a posteriori ([16], p. 98) entropies of the input list of symbols. After some mathematical manipulations, the mutual information can be presented in the form of  
\[I(X;Y)=\sum\limits_{i=1}^{M}\sum\limits_{j=1}^{M}P(x_{i})P(y_{j}\mid x_{i}) \log_{2}\frac{P(y_{j}\mid x_{i})}{\sum\limits_{k=1}^{M}P(x_{k})P(y_{j}\mid x_{ k})}\quad\bigg{[}\frac{\text{bit}}{\text{symb}}\bigg{]}. \tag{40}\]  
It is evident that the mutual information depends on the probabilities of input symbols and crossover probabilities. In our analysis, we assume that probabilities of input symbols are equal. Crossover probabilities depend on channel conditions and process of detection. These crossover probabilities are estimated by Monte Carlo simulations that are described in Section 3.1.2. For estimating a value of crossover probability, \(10^{5}\) symbols are transmitted over a channel.
## 4 Results and Discussion
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{4}\]  
where \(\mu_{x}\) is the pixel sample mean of \(x\), \(\mu_{y}\) is the pixel sample mean of \(y\), \(\sigma_{x}^{2}\) is the variance of \(x\), \(\sigma_{y}^{2}\) is the variance of \(y\), \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), \(C_{1}=\left(k_{1}L\right)^{2}\), \(C_{2}=\left(k_{2}L\right)^{2}\) are two variables to stabilize the division with a weak denominator, \(L\) is the dynamic range of the pixel values (typically, this is \(2^{\#\text{ }of\text{ }bits\text{ }per\text{ }pixel\text{ }}-1\)), \(k_{1}\) is \(0.01\), and \(k_{2}\) is \(0.03\) by default. SSIM values vary between 0 and 1. An SSIM value of 0 signifies that the two images are different. A higher SSIM value indicates a higher-quality image. The metric that evaluates the compression algorithm performance is defined by the CR given by Equation (5). \[multipliers and the shifters minimize the power. The PSNR is considered as a Figure of Merit (FOM) to evaluate the performance of the computational unit. The PSNR of the proposed design was better than the PSNR of the existing architectures in [46; 47; 48] by 5.89%, 9.88%, and 6.14%. Compared to the existing DCT architectures, the SSIM obtained was 0.967, which was quite good. The SSIM, PSNR, and CR values of the reconstructed and original CT scan images obtained by incorporating the computational unit CU4 in the Loeffler algorithm for the DCT and IDCT computations are illustrated in Figure 18. SSIM and PSNR are used in image processing as the figure of merit. An image with less noise and distortion is generally preferable as it gives a high signal-to-noise ratio. In image processing, high values of SSIM and PSNR indicate a good-quality image. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Technology: 45 nm**} \\ \hline
**Architecture** & **Power (mW)** & **Delay (nS)** & **Area (\(\upmu\)mm\({}^{2}\))** & **PDF (pJ)** & **SSIM** & **PSNR (dB)** \\ \hline High-Performance 2D Transform [43] & 11.89 & 32.86 & 23.78 & 393.10 & 0.898 & 37.766 \\ \hline Loeffler using AIE [44] & 11.77 & 26.45 & 10.12 & 311.32 & 0.901 & 31.23 \\ \hline Cost-Efficient Loeffler algorithm [45] & 8.17 & 21.5 & 9.5 & 175.65 & 0.886 & 31.45 \\ \hline EHA for DCT—Imp. Loeffler [46] & 13.64 & 23.76 & 18.98 & 320.32 & 0.928 & 38.33 \\ \hline DIC using Approx. Addition [47] & 15.31 & 19.89 & 21.34 & 304.51 & 0.915 & 36.94 \\ \hline DCT using [PERSON] [48] & 9.72 & 26.48 & 17.69 & 257.38 & 0.906 & 38.24 \\ \hline CU1 & 11.16 & 22.12 & 19.07 & 246.86 & 0.953 & 40.24 \\ \hline CU2 & 11.27 & 22.13 & 19.06 & 249.4 & 0.961 & 3  
The physical design was obtained using the place and route option of the Cadence Innovus tool. The physical design of the computational unit proposed and selected for the final implementation of the digital image compression process is depicted in Figure 19.
# Advanced First-Principle Modeling of Relativistic Ruddlesden--Popper Strontium Iridates
## 4 Results and Discussions
### Optical Spectra
Second, it appears that there are experimental complications (difficulties in synthesizing stoichiometric  
Figure 10: The experimental and calculated optical conductivity spectra \(\sigma(\omega)\) of (**a**) Sr\({}_{2}\)IrO\({}_{4}\), (**b**) Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\), and (**c**) SrIrO\({}_{3}\). The experimental data at 10 K are adapted from Reference [18] for Sr\({}_{2}\)IrO\({}_{4}\) single crystals, Reference [31] for Sr\({}_{3}\)Ir\({}_{2}\)O\({}_{7}\) single crystals, and Reference [36] for SrIrO\({}_{3}\) polycrystalline samples. Note that the sharp peaks below 0.1 eV in the experimental spectra arise from optical phonon modes. The DMFT simulated spectra are taken from Reference [154]. The grey vertical lines represent the oscillator strength (divided by \(10^{4}\) here) for which the contributions to \(\alpha\) and \(\beta\) peaks are highlighted in red and blue, respectively. (**d–f**) \(GW\) band structure. (**g–i**) \(GW\) total density of states. The red and blue arrows in (**d–f**) represent the dominant interband transitions for the \(\alpha\) and \(\beta\) peaks, respectively. The width of the arrows denotes the normalized amplitude of BSE eigenvectors \(|X^{\rm A}_{\rm<\bf k|}\). The arrows in (**g–i**) show the involved optical transitions schematically. Reproduced with permission from Reference [100]. Copyright 2018 by the American Physical Society. crystals, degradation in ambient conditions, sensitivity to lithographic processing, and presence of oxygen vacancies) that make it difficult to perform systematic and reproducible measurements of transport properties [161; 162; 163]. This clearly hinders a direct comparison with theory. In fact, depending on the specific type of sample (polycrystalline [36] or SrIrO\({}_{3}\) films grown on MgO [11] or SrTiO\({}_{3}\)[164]), different optical conductivity spectra have been reported in the literature which differ even in fundamental aspects such as the presence or absence of an \(\alpha\) peak. Finally, we found that, by going from \(n=1\) to \(n=\infty\), the \(\alpha\) and \(\beta\) peaks are progressively shifted towards lower energies, in agreement with experimental observations [11]. This trend is correlated with the progressive decrease in interaction \(U\) (see Table 2) and with the gradual closing of the \(GW\) gap [100].
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 3 Results
### Comparison of Objective Evaluation Indicators
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Set5**} & \multicolumn{2}{c}{**Set14**} & \multicolumn{2}{c}{**B100**} & \multicolumn{2}{c}{**Urban100**} & \multicolumn{2}{c}{**Manga109**} \\ \cline{3-11}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \hline \multirow{2}{*}{**Bicubic**} & 33.66 & 0.930 & 30.23 & 0.879 & 29.55 & 0.826 & 26.75 & 0.826 & 30.73 & 0.931 \\ \cline{2-11} SRCNN [3,4] & 36.50 & 0.954 & 32.42 & 0.910 & 31.36 & 0.863 & 29.34 & 0.893 & 35.60 & 0.957 \\ \hline \multirow{2}{*}{ VDSR [5]} & 37.54 & 0.956 & 33.03 & 0.912 & 31.53 & 0.895 & 30.48 & 0.917 & 37.06 & 0.968 \\ \cline{2-11} Lap-SRN [16,17] & 37.52 & 0.959 & 33.08 & 0.913 & 31.90 & 0.897 & 30.41 & 0.919 & 37.22 & 0.969 \\ \hline \multirow{2}{*}{ Meta-SR [21]} & 37.10 & 0.957 & 34.18 & 0.911 & 31.88 & 0.910 & 30.52 & 0.932 & 37.35 & **0.985** \\ \cline{2-11} RCAN [19] & **38.34** & **0.967** & 34.37 & 0.927 & 32.53 & 0.934 & **33.02** & **0.939** & 39.24 & 0.977 \\ \hline \multirow{2}{*}{ ILSRCNN [20]} & 37.65 & 0.9586 & 33.32 & 0.915 & 31.95 & 0.896 & 31.45 & **0.921** & 38.73 & 0.984 \\ \cline{2-11} IDMF-SR & 38.20 & **0.967** & **34.38** & **0.930** & **32.57** & **0.938** & 32.96 & 0.920 & **40.15** & 0.980 \\ \hline \multirow{2}{*}{ Bicubic} & 28.30 & 0.810 & 25.98 & 0.639 & 25.79 & 0.668 & 23.04 & 0.658 & 24.86 & 0.787 \\ \cline{2-11} SRCNN [3,4] & 30.12 & 0.862 & 26.89 & 0.745 & 26.87 & 0.710 & 24.48 & 0.722 & 27.54 & 0.856 \\ \hline \multirow{2}{*}{ VDSR [5]} & 31.34 & 0.866 & 27.68 & 0.752 & 27.25 & 0.723 & 25.16 & 0.754 & 28.82 & 0.889 \\ \cline{2-11} Lap-SRN [16,17] & 31.45 & 0.885 & 28.17 & 0.769 & 27.32 & 0.736 & 25.21 & 0.756 & 29.17 & 0.890 \\ \hline \multirow{2}{*}{ Meta-SR [21]} & 31.85 & 0.906 & 28.32 & 0.778 & 27.52 & **0.790** & 25.82 & 0.760 & 29.89 & 0.917 \\ \cline{2-11} RCAN [19] & **32.62** & **0.912** & 28.89 & 0.790 & **27.99** & 0.751 & 26.88 & 0.812 & 30.97 & **0.921** \\ \hline \multirow{2}{*}{ ILSRCNN [20]} & 31.88 & 0.890 & 28.44 & 0.778 & 27.45 & 0.731 & 25.77 & 0.773 & **30.99** & 0.919 \\ \cline{2-11} IDMF-SR & **32.62** & 0.910 & **28.90** & **0.792** & **27.99** & **0.790** & **27.10** & **0.818** & 30.98 & **0.921** \\ \hline \multirow{2}{*}{ Bicubic} & 24.40 & 0.656 & 23.06 & 0.567 & 23.67 & 0.545 & 20.74 & 0.516 & 21.48 & 0.650 \\ \cline{2-11} SRCNN [3,4] & 25.24 & 0.691 & 23.74 & 0.593 & 24.23 & 0.566 & 21.29 & 0.548 & 22.45 & 0.695 \\ \hline \multirow{2}{*}{ VDSR [5]} & 25.59 & 0.710 & 24.02 & 0.603 & 24.50 & 0.583 & 21.52 & 0.573 & 23.17 & 0.732 \\ \cline{2-11} Lap-SRN [16,17] & 25.92 & 0.728 & 24.28 & 0.614 & 24.54 & 0.590 & 21.67 & 0.582 & 23.40 & 0.759 \\ \hline \multirow{2}{*}{ Meta-SR [21]} & 26.91 & 0.750 & 24.32 &
## 2 Materials and Methods
### Data Source and Pre-Processing  
The 8-day composite MOD09A1 dataset of MODIS was downloaded from the USGS website (www.usgs.gov/). The MOD09A1 is a level 3 product from the Terra platform with 500 m spatial resolution, where geometrical corrections and cloud contamination removal were done. The quality assessment band-flagged pixels of high quality were used for the analysis. A total of 380 images from 1 May 2000 to 1 October 2018 were applied for GPP estimation. The HDF format was transformed into GeoTIFF format and the sinusoidal projection was converted to the WGS84 projection with the MODIS Reprojection Tool (MRT), which can be acquired from the NASA Land Processes Distributed Active Archive Center. The cultivated land grids with 500 m spatial resolution were collected from the agricultural land classification database. The images were masked by the cultivated land grids using the Google Earth Engine (GEE) platform. The meteorological datasets consisting of 8-day mean temperature data were collected from the National Meteorological Center of China. The data were from 51 meteorological stations between 2000 and 2018. The observations were interpolated over 8 days with the same resolution as the MOD09A1 for obtaining spatiotemporal distributions of temperature to estimate the GPP. The flowchart of this paper is shown in Figure 2.  
Figure 1: Description of study areas in this study.  
Figure 2: The flowchart for the temporal analysis of CLP.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.4 Multirobot ROS-(Deep) RL Platform  
In [72], the authors present a simulation platform called \"_Multi Robot Deep Reinforcement Learning with ROS_\" (MRDRL-ROS), which is designed to simplify development and test DRL algorithms for multiagents. The platform addresses the complexity of robot platforms and the need for customization in different robotic applications, with the aim of simplifying the process of building and modifying environments for various types of robot tasks and simulators. The MRDRL-ROS platform helps in developing and testing applications that use DRL with an ROS and can be integrated with the OpenAI Gym interface. The platform acts as a benchmark to compare the DRL algorithms' performance and enables researchers to train robots for various tasks.  
The authors in [72] used the PPO RL algorithm to verify the effectiveness of the method through experiments. An ROS served as the backbone of the platform, connecting all components and enabling interaction with various robot simulators and hardware drivers. As shown in Figure 10, the platform has three components: \"_simulation, robots, and task modules_\". The upper section uses OpenAI Gym APIs to support RL algorithms based on gym environments. The lower section integrates advanced robot simulations or hardware drivers through ROS communication. The authors have contributed significantly by creating MRDRL-ROS, a modular platform that successfully tackles the challenges of robotic system development.  
The above-mentioned work shows the flexibility and ability of the platform to expedite research in multirobot learning. Adding complex settings to MRDRL-ROS, such as adaptive learning standards, dynamic environment, and real-time transfer learning capabilities, will further improve it. These advances will facilitate more effective training and deployment of robotic models in diverse and rapidly changing environments.
# Digital Content Management Using Non-Fungible Tokens and the Interplanetary File System
## 2 Related Work
### NFTs  
Unlike conventional virtual assets authenticated with digital certificates, NFTs assign unique values to digital assets, making them irreplaceable and unforgeable. Furthermore, as they are stored on the blockchain, deleting or counterfeiting them is impossible [10; 11]. Consequently, they can be used to authenticate the ownership of digital assets [12]. Recently, South Korean universities have awarded diplomas and prizes using NFTs. Additionally, they are being used for various purposes on campuses, and their uses are continuously expanding. NFTs offer the advantage of authenticating qualifications through their proof function, thereby enhancing transparency. Moreover, they can save time and costs because they can be easily authenticated using an electronic wallet. Furthermore, certificates can be easily issued on the Internet, which increases administrative accessibility and convenience [13].  
If we define the existing problems with NFTs, we can highlight issues related to copyright and ownership, security and privacy, and technical complexity. Here are the explanations for each:  
* Issues with copyright and ownership: NFTs are used to prove ownership of digital content but do not fundamentally alter the ownership of the actual content [9]. For instance, purchasing an NFT of digital art does not transfer the copyright or intellectual property rights of the art itself. Additionally, the link between the original artwork and its associated NFT can be unclear, leading to potential ownership disputes. Therefore, the new ownership model brought by NFTs raises debates regarding how it aligns with existing copyright laws [14].
* Security and privacy: Certain NFT platforms may possess security vulnerabilities, potentially allowing hackers to expose user information or attempt fraudulent activities, like forging transactions [15]. Moreover, due to the immutable nature of blockchain, there are concerns about user data privacy since transaction records are publicly available, risking the exposure of personal information on the blockchain [16].
* Technical complexity: For users unfamiliar with blockchain and cryptocurrency technology, purchasing and trading NFTs can be complex and challenging. Technical aspects such as wallet creation, transaction fees, gas costs, etc., can pose difficulties, making entry into the NFT market challenging for the general public [17].
## 4 Methodology
### ANN Architecture  
Various network topologies are accessible, including competitive neural networks, Jordan neural networks, Fully Recurrent Neural Networks (FRNNs), and Multi-Layer Perceptron neural networks (MLP). In this study, we employed the most popular type of neural network model, the MLP network, trained using feed-forward (FF-ANN) learning [29; 44]. The FF-ANN distinguishes itself through its rapid processing capabilities compared to alternative models, evident through its use of supervised learning and nonlinear classification techniques. Nonetheless, it is essential to acknowledge the inherent limitations of this network, which encompass challenges in providing comprehensive explanations for specific outcomes, as the inner workings of the hidden layers remain unobservable. Additionally, this network necessitates substantial volumes of data for effective operation and is susceptible to overfitting processes.  
Each FF-ANN ANN model comprises at least one hidden layer with multiple nodes, one output layer with one or more output nodes, and one input layer with multiple input nodes (Figure 4). Each node in a network can manipulate data using a unique activation function, such as the sigmoidal function [21; 44]. FF-ANNs, on the other hand, have no connection between nodes located on the same layer. Regularization is often utilized in learning methods to increase generalization capacity when working with small sample sets or when data samples are meaningfully contaminated by noise [45]. In this research, we used the Bayesian Regularization (B.R.) method to improve the ANN's generalization ability by modifying the objective function of the ANN to avoid overfitting and to smooth down ANN's mapping by enforcing certain prior distributions on the model parameters and penalizing large weights [46].  
The network identifies the deviation by comparing its output to the desired outcome. The network then modifies its weights (\(w\)) and biases (\(b\)) (Tables S1 and S2) so that its output (\(y\)) is more in line with the target value (\(o\)). The mean sum of squares of network errors, a measure of the gap between the target and actual outputs (\(t\) and \(y\), respectively) (Equation (2)) [47], is a common performance function.  
\[o=\frac{1}{m}{\sum\limits_{i=1}^{m}{(t_{i}-y_{i})}^{2}} \tag{2}\]