esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Damage Identification
#### 2.4.3 Ellipse Overlap  
Ellipse overlap is an indicator which measures the similarity in the dispersion and orientation of data between consecutive load steps by comparing the overlap of ellipses that represent the data distributions. A high degree of overlap suggests minimal changes in structural characteristics, indicating stability, while a low degree of overlap means significant variations, which might potentially indicate damage.  
To calculate the ellipse overlap, the data distributions are projected into a two-dimensional space, and ellipses are fitted to these projections. The overlap area between ellipses from consecutive load steps is then computed and normalized by dividing each overlap area by the maximum observed area. This normalization ensures a consistent scale for comparing the similarity in data distribution across different load conditions.  
An overlap matrix provides a detailed representation about how the ellipses overlap each other. Elements of this matrix measure the level of overlapping. By working with normalized values, these elements should be between 0 and 1. Values close to one indicate a high degree of overlapping, while values close to zero mean a low degree of overlapping. Logically, the terms of the principal diagonal of the matrix should be equal to one, since they measure the overlapping of an ellipse with itself.  
Ellipse overlap provides an additional perspective by focusing on the evolution of structural characteristics under different loading conditions. Unlike density and the convex hull volume, which address concentrations and spatial extents, ellipse overlap directly assesses the similarity in data distribution, thus capturing dynamic structural changes.  
The invariant in this case is the degree of overlap between ellipses representing consecutive load steps. In a healthy beam, the degree of overlap is high, indicating stability and consistency in structural behavior. As damage occurs, the degree of overlap decreases significantly, reflecting changes in the structural response. This reduction in the degree of overlap is invariant to the specific initial and final stages, making it a reliable indicator of structural changes.
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 3 Numerical Method  
For simplicity, a two-dimensional Cartesian mesh is applied. Mesh step sizes in \(x\) and \(y\) directions are uniform. The value of the vector of conservative flow variables in the center of control volume is \(\mathbf{U}_{i,j}\), \(i=1,2,\ldots,M\) and \(i=1,2,\ldots,N\). A number of control volumes in \(x\) and \(y\) directions are \(M\) and \(N\). The time step size is \(\Delta t\). The mesh function is constant over control volume.  
The numerical algorithm involves reconstruction of flow variables on the faces of control volumes using the averaged values of the vector of flow variables in the centers of control volumes. The reconstruction step is applied to physical variables. The Riemann problem is solved on each face of the control volume. Solutions of the Riemann problem on left and right faces of the control volume are \(\mathbf{U}_{i-1/2,j}\) and \(\mathbf{U}_{i+1/2,j}\). Application of the explicit time marching scheme and Godunov method leads to the numerical scheme  
\[\frac{\mathbf{U}_{i,j}^{n+1}-\mathbf{U}_{i,j}^{n}}{\Delta t}+\frac{\mathbf{F}_{i+1/2,j}^{ n}-\mathbf{F}_{i-1/2,j}^{n}}{\Delta y}+\frac{\mathbf{G}_{i,j+1/2}^{n}-\mathbf{G}_{i,j-1/2} ^{n}}{\Delta y}=0. \tag{2}\]  
The superscript \(n=0,1,\ldots\) is related to the time step.  
The flux on the faces of control volume is linearized and found as  
\[\mathbf{F}_{i+1/2,j}=\frac{1}{2}\Big{(}\mathbf{F}_{i,j}^{n}+\mathbf{F}_{i+1,j}^{n}\Big{)} +\frac{1}{2}|A|_{i+1/2,j}^{n}\Big{(}\mathbf{F}_{i,j}^{n}-\mathbf{F}_{i+1,j}^{n}\Big{)},\]  
where \(A=(\partial\mathbf{F}/\partial\mathbf{U})^{-1}\) is Jacobian.  
The stability condition for Equation (1) is  
\[C=\max|\lambda_{\pm}|\frac{\Delta x}{\Delta y}\leqslant 1.\]  
Eigenvalues of the Jacobian are  
\[\lambda_{\pm}=\frac{uv\pm c(u^{2}+v^{2}-c^{2})^{1/2}}{u^{2}-c^{2}}.\]  
The speed of sound is  
\[c=\bigg{[}(\gamma-1)h-\frac{1}{2}(\gamma-1)(u^{2}+v^{2})\bigg{]}^{1/2},\]  
where \(h\) is the total enthalpy.  
A three-step Runge-Kutta scheme is applied to integration in time direction. Godunov method uses the analytical solution of the Riemann problem. The exact solution consists of two waves, a shock wave or a simple rarefaction, and a tangential discontinuity. These discontinuities are separated from each other by uniform flow zones. Numerical scheme (2) is the scheme of the first order of accuracy. Higher-order accuracy schemes are created using piece-wise distributions of flow quantities in the control volume and interpolation procedure to integrate Euler equations in marching direction.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 3 Optimization of Design Parameters for an LSTM-Based Predictive Maintenance Model
### Design of GA for Exploring Optimal Hyperparameters of LSTM
#### 3.3.4 Mutation Operator  
Similar to mutation occurring in nature, which means the emergence of a feature not observed in the parents, the mutation operator generates a solution containing new features different from those of the parental chromosomes. It prevents the algorithm from converging to a local optimal solution and guarantees the diversity of solutions. We use the  
Figure 6: Chromosome structure using integer-valued encoding.  
Makinen, Periaux, and Toivanen mutation (MPTM) operator that enables a robust search of the solution space specialized in real-valued encoding [35]. If \(hp_{r}\) is an original \(r\)-th hyperparameter, \(hp^{\prime}_{r}\) affected by the MPTM operator can be calculated by using the steps depicted in Figure 7, where \(UB_{r}\) and \(LB_{r}\) are upper and lower bounds that are the same as the ones used in generating the initial population. This operation is performed on the \(r\)-th gene with the probability of mutation \(p_{m}\).
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 5 Research Opportunities and Future Directions
### Beamforming  
Beamforming techniques created specifically for NLOS and LOS radio channels will be required for next-generation wireless communication systems. As the size of antenna elements is much reduced at mmWave bands and a large number of antenna elementsare involved in an array configuration, thus mmWave beamforming requires more RF components and RF chains, which increases the complexity of the antenna system [164]. In this view, the huge room is available to perform research to devise efficient, less complex, and low-cost analog/digital hybrid architectures for smart antenna systems. In a factory environment, multiple antenna systems with numerous radiating elements at the access point are particularly interesting options for obtaining very high data rates (mGbps) for users sharing the same spectrum at the same time [156].  
Furthermore, for an effective hardware implementation of a beamformer system operating in the mmWave band, a realistic understanding of hardware impairments, wave propagation, and antenna characteristics is required in industrial communication. Contemporary research challenges in mmWave beamforming include solving complicated challenges linked to analog/digital hybrid beamforming, polarization diversity, optimization of the beam search process, concurrent beamforming protocols, resilient adaptive beamforming, exploiting channel sparsity, and 3D beamforming. These systems must achieve the goals of lowering beamforming calculation costs, delays, and power consumption while maintaining acceptable QoS to provide mGbps throughput [62].
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 4 Experiments and Analysis of Results
### Sensitivity Analysis
#### 4.3.4 The Execution Time of DBTS  
Several experiments were conducted in order to compute the execution time of DBTS. The results are reported in Figure 5.  
Like other two-stage recommendation methods, the DBTS algorithm has an offline computing stage and an online recommendation stage. First, we experimented the offline stage of the DBTS algorithm. Ten independent experiments were conducted, which are shown in Figure 5. It can be seen that the offline execution time has a mean value of 360 s and a standard deviation of 37.7 s. This stage has a long computation time because it needs to calculate trust and interest similarities according to the trust propagation algorithm. Then we experimented the online stage of the DTBS algorithm. The number of recommendation list was set as 10, 15, 20, and 25. Four groups of experiments were carried out with 10 independent ones for each group. The experiment results are shown in Figure 5. When the number of recommendations is set as \(n=10\), the online stage's execution time is around 3 s. As the length of the recommendation list increases up to 20 and more, the algorithm's execution time increases to above 15 s.
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 5 Discussion  
### Achievements  
In the presented use case, we demonstrate the feasibility of the proposed pipeline to go from a centralized training setup to a federated environment to train collaborative models. Specifically, the Notebook Federator proposes to considerably reduce the com  
\begin{table}
\begin{tabular}{l c c c c} \hline  & \(I_{A}\) & \(I_{B}\) & \(I_{C}\) & Federation \\ \hline Sensitivity & 0.7921 & 0.5896 & 0.7887 & **0.8174** \\ Specificity & 0.7842 & **0.9874** & 0.8337 & 0.9247 \\ Positive predictive value & 0.7813 & **0.9961** & 0.8457 & 0.9355 \\ Negative predictive value & **0.7949** & 0.3066 & 0.7734 & 0.7910 \\ F1-Score & 0.7866 & 0.7407 & 0.8162 & **0.8725** \\ Accuracy & 0.7881 & 0.6514 & 0.8096 & **0.8633** \\ \hline \end{tabular}
\end{table}
Table 2: Classification results reached on the test set for patch-level ROI selection on the spitzoid melanocytic lesions dataset; \(I_{A}\), \(I_{B}\), and \(I_{C}\): centralized setups at each local institution; Federation: decentralized federated setup, leveraging all institutions’ data.  
Figure 5: Implementation of the proposed solution, Notebook Federator, in a federation composed of institutions located in different countries and relying on different hardware resources.  
plexity of FL systems' setup for AI Jupyter users by leveraging the latest technologies and available assets.  
Starting from a Jupyter environment on the end-user side (i.e., the AI expert or data scientist), we put an emphasis on the reproducibility and scalability of the method for researchers. The Jupyter user only has to complete the indicated lines of the template provided as a code base, and that code is then included in the client code. Building and running the resulting Docker is fast and automatic, thus simplifying the quick implementation of the code on the client machines.  
In contrast to other FL solutions and implementations, in this work, we wish to provide a real-world use case, with the server and clients located in different cities and countries, which all have access to different computing resources. Additionally, as our solution aims to simplify the complexity of FL setups and seeks to be easily reproducible, the provided use case leverages a commonly used baseline framework with a strong community and documentation, FLOWER [18]. Although the example we provide is applied to a domain-specific task in computational pathology, it is important to note that the Notebook Federator, as a technology, is generic and can easily be adapted to other tasks and baseline frameworks.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 5 Conclusions
In Proceedings of the 2 nd International Workshop on Intelligent Information Technologies and Systems of Information Security, Khmelnytskyi, Ukraine, 24-26 March 2021; pp. 330-339. * [PERSON] (1970) [PERSON] _On the Invertibility of Finite State Machines_; Tech. Rep. TR-EE-703; Faculdade De Ciencias Universidade Do Porto: Porto, Portugal, 1970. * [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON] On statistical tests for randomness included in the NIST SP800-22 test suite and based on the binomial distribution. _IEEE Trans. Inf. Forensics Secur._**2012**, \(7\), 491-505. [CrossRef]
* [PERSON] et al. (2014) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A simple, sensitive and secure image encryption algorithm based on hyper-chaotic system with only one round diffusion process. _Multimed. Tools Appl._**2014**, _71_, 1469-1497. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] ECC based novel color image encryption methodology using primitive polynomial. _Multimed. Tools Appl._**2024**, 1-40. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON] Chaotic and Hyperchaotic Map based Image Encryption Protocol for High-End Colour density Images using enhanced S-box pixel permutator. In Proceedings of the 2021 2 nd International Conference on Computational Methods in Science & Technology (ICCMST), Mohali, India, 17-18 December 2021; pp. 174-180. * [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] Improvement of an image encryption algorithm based on hyper-chaos. _Telecommun. Syst._**2013**, _52_, 539-549. [CrossRef]
* [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] An efficient chaotic image encryption algorithm based on a generalized Arnold map. _Nonlinear Dyn._**2012**, _69_, 2079-2087. [CrossRef]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] An image encryption scheme based on new spatiotemporal chaos. _Opt.-Int. J. Light Electron Opt._**2013**, _124_, 3329-3334. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] A novel hyperchaotic image encryption system based on particle swarm optimization algorithm and cellular automata. _Scur. Commun. Netw._**2021**, _2021_, 6675-565. [CrossRef]
* [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] Color image encryption using spatial bit-level permutation and high-dimension chaotic system. _Opt. Commun._**2011**, _284_, 3895-3903. [CrossRef]  
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# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 3 Experimental Results and Analysis
### Comparisons with Traditional Watermarking Methods  
We have proposed an image watermarking technique that considers frequency domain imperceptibility, aimed at protecting the copyright of images generated by generative networks. In this section, we wish to highlight the distinctions between our proposed method and traditional image watermarking techniques, summarized as follows:  
**Novelty of the research problem:** focusing on the high-frequency artifacts problem in generative model watermarking, an image watermarking algorithm that can reduce high-frequency artifacts and improve the effect of copyright protection is proposed.  
**Innovativeness of the solution:** adopting an end-to-end deep learning approach, the watermark is directly embedded and extracted using neural networks to avoid high-frequency artifacts and improve concealment. In contrast, traditional methods require in-depth understanding of various mathematical techniques and careful design of embedding/extraction methods.  
**Superior Performance:** We have compared our method with traditional image watermarking techniques based on DWT-SVD-DCT [36]. Due to computational constraints, we only compare the case of embedding \"MDPI\" in the \"de-raining\" task. As shown in Table 7, traditional image watermarking techniques tend to be limited in watermark capacity due to the need for carefully designed embedding methods. In contrast, our deep learning-based image watermarking algorithm leverages the capabilities of neural networks to embed a large capacity of watermarks easily. Moreover, our proposed method outperforms in terms of spatial image quality, frequency domain invisibility, and watermark extraction quality.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 1 Introduction  
Recently, the scientific interest in extremophilic microorganisms has increased largely because of their potential use in industrial biotechnological processes, where there is a need for enzymes capable of catalyzing reactions under various harsh conditions [1; 2; 3]. Those extremophiles could survive and thrive in stressful environments such as high or cold temperatures, acid or alkaline conditions, lack of nutrients, high salinity, and many others that were considered non-habitable. To overcome unfavorable conditions, microorganisms found special mechanisms, not found in non-extremophiles, allowing them to adapt. These could be highly flexible metabolisms, changes in the conformation of enzymes, production of secondary metabolites, and unique structural properties of their biomacromolecules [4; 5; 6]. While working with extremophiles and extremozymes, especially those obtained from saline environments, scientists mostly focus on bacteria, archaea, and even algae rather than on filamentous fungi that are still mostly undiscovered [7], even though extremophilic fungi show great potential for isolating new unusual compounds, especially since over 40% of active compounds obtained from microorganisms are produced by fungi [3; 8].  
Hypersline environments are one of such extreme habitats where extremophilic microorganisms have been found. Those include not only seas, salt lakes, saline soil, salt deserts, salters, and brines but also food products with a high salt content [9; 10; 11]. Therefore, halophiles are organisms that can survive in environments with high salinity, and occasionally, they even require salt to grow [12]. Halophilic microorganisms could be found in every domain, and they are mostly represented by bacteria, archaea, algae, and fungi [13]. All of them produce several interesting biomolecules with unique features that can be used in industrial applications.  
Halophiles can have an industrial advantage as processes involving them in seawater can solve the problem of shortage of fresh water, avoid microbial contamination due to the presence of salt, and in the aftermath enable continuous processing without the needof sterilization [14; 15]. For these reasons, industrial biotechnology using halophiles can compete with chemical processes as a technology allowing low consumption of fresh water and energy [15].
# Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring
## 4 Results and Discussion  
### Experimental Setup  
To test the proposed REMR, we conducted several trials in NS3 using a clustered approach. For this, we defined two clusters in a wireless scenario and tested our proposed approach with ad hoc on-demand distance vector (AODV) [23] using different parameters. Table 1 outlines the simulation parameters used for the experimental setup. AODV is selected for comparative analysis, as the comparison with a well-established routing protocol helps establish the novelty of proposed region-based multipath routing to conserve energy following a better overlay approach.
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Scale**} & \multicolumn{6}{c}{**Datasets**} & \multirow{2}{*}{**Average**} \\ \cline{3-3} \cline{5-8}  & & **EPFL** & & **HCInew** & & **HCIold** & **INRIA** & **STFgantry** \\ \hline PMFN-FE + ASPP & & 29.15/0.910 & 30.76/0.911 & 36.82/0.968 & 31.47/0.947 & 30.71/0.942 & \(-0.18/-0.005\) \\ PMFN-FE + ResBlock & & 28.95/0.908 & 30.69/0.910 & 36.66/0.967 & 31.35/0.947 & 30.61/0.941 & \(-0.32/-0.006\) \\ PMFN-FF + FFB1 & & 29.07/0.902 & 30.47/0.905 & 36.51/0.966 & 31.08/0.945 & 30.32/0.931 & \(-0.48/-0.011\) \\ PMFN-FF + FFB2 & & 29.17/0.910 & 30.79/0.911 & 36.75/0.968 & 31.51/0.947 & 30.83/0.942 & \(-0.16/-0.005\) \\ PMFN-FF + FFB3 & & 28.92/0.908 & 30.62/0.909 & 36.57/0.967 & 31.21/0.946 & 30.44/0.938 & \(-0.42/-0.007\) \\ PMFN-FF - FFB & & 28.71/0.904 & 30.32/0.903 & 36.28/0.965 & 30.88/0.943 & 29.70/0.928 & \(-0.79/-0.012\) \\ PMFN-FF - FEB & & 29.28/0.912 & 30.88/0.913 & 36.91/0.969 & 31.62/0.948 & 30.88/0.944 & \(-0.05/-0.003\) \\ PMFN & & **29.30/0.917** & **30.93/0.917** & **36.94/0.969** & **31.70/0.950** & **30.97/0.949** & **31.97/0.940** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR/SSIM values achieved by PMFN and its variants for 4\(\times\) SR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ang** & **Num** & **Scale** & **Params.(m)** & **FLOPs(G)** & **PSNR** \\ \hline
1 & & 1.37 & 19.35 & 29.07 \\
2 & & 2.23 & 34.46 & 29.15 \\
5 \(\times\) 5 & 3 & 4\(\times\) & 3.11 & 49.61 & 29.30 \\
4 & & 4.00 & 64.81 & 29.31 \\
5 & & 4.90 & 80.03 & 29.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparative results of different number of DMFMs on the EPFL for 4\(\times\) LFSR. Structure of FEB: The FEB is used in our PMFN for high-frequency details reconstruction. To demonstrate its effectiveness, we remove the middle two cascaded \(3\times 3\) convolutions with a ReLU and only preserve the last convolution. The result (PMFN-FF - FEB) is lower than PMFN (0.05 dB). That is because the high-frequency features of HR images have been further fine-tuned. Number of angular resolution: We also analyse the performance of our PMFN in different SAIs. We used \(3\times 3\), \(5\times 5\) and \(7\times 7\) SAIs in both synthetic (STFgantry) and real-world (HCInew) datasets. As shown in Table 6, the values of PSNR and SSIM can be improved as the number of angular resolutions increases. That is because more complementary information is fused into the reference view with angular resolution increases. In this paper, all results compared with state-of-the-art are obtained with \(5\times 5\) SAIs, because these SAIs are enough to investigate the efficiency of our method.