esa-sceva/satcom-chunk-collection huggingface dataset:
# Changing Dynamics of SARS-CoV-2: A Global Challenge
## 4 Variants of SARS-CoV-2
### Variants of Concern
#### 4.2.1 The Alpha Variant (B.1.1.7)  
This variant was first reported in the United Kingdom in late September 2020 and has since spread to almost 180 countries, some of them being the USA, Germany, Denmark and Sweden (15 December, GISAID). The Alpha variant has a total of 15 mutations (Figure 6), out of which 7 mutations lie in the spike region. The N501Y is an RBD mutation (Figure 7) that causes a 10-fold increase in the binding affinity of spike glycoprotein to the hACE2 receptor, and this mutation, in combination with D614G, results in an increased number of open RBDs making the virus more contagious and deemed as a variant of concern (Table 1 and Figure 2b) [37,51]. The P681H mutation near the furin site slightly increases the S1/S2 cleavage [52] (Figures 2b and 3i). The spike also has 2 deletions, \(\Delta\) H69-V70, which increase the rate of S1/S2 cleavage and cell-cell membrane fusion. This deletion increases the infectivity of B.1.1.7 and it can also compensate for immune escape mutations that reduce infectivity [53]. Recent sequencing of some more strains of the Alpha variant shows new mutations in the spike region, E484K and L452R, as mentioned in Table 1 (WHO).
## 2 Force Field and Optimization Methods
### Force Field  
A force field, presented as parameters of mathematical functions in molecular mechanics, was used to describe the energies of the atoms. Force field parameters and functions are obtained from experimental tests and quantum mechanical calculations. Many force fields were developed and introduced by researchers, such as the Chemistry at HARvard Molecular Mechanics (CHARMM) Force Field [26], Assisted Model Building and Energy Refinement (AMBER) Force Field [27], Condensed-phase Optimized Molecular Potential for Atomistic Simulation Studies (COMPASS) Force Field [28], Optimized Potential for Liquid Simulation (OPLS) Force Field [22] and DREIDING Force Field [29]. In this study, the Amber Cornell Extension Force Field (ACEFF) was used to define the movement in the molecular system based on the Amber Cornell Force Field [30], and the experimental parameters in this force field were referenced from the General Amber Force Field (GAFF) [31]. The formula is shown in Equation (1).  
\[\begin{array}{l}E_{total}=\sum_{bonds}K_{r}\big{(}r-r_{eq}\big{)}^{2}+\sum_{ angles}K_{\theta}\big{(}\theta-\theta_{eq}\big{)}^{2}+\\ \sum_{dihedrals}\frac{V_{\theta}}{2}[1+\cos(n\varphi-\gamma)]+\sum_{i<l}[ \frac{A_{ij}}{R_{ij}^{2}}-\frac{B_{ij}}{R_{ij}^{6}}+\frac{q\cdot q_{ij}}{eK_{ ij}}]\end{array} \tag{1}\]  
where \(r_{eq}\) and \(\theta_{eq}\) are the equilibrium structural data from an X-ray test; \(K_{r}\) is the force coefficient determined by linear interpolation between the single and double bond values; \(K_{\theta}\) is the force coefficient from vibrational analysis of a simple sp2 atom; n is the multiplicity for dihedrals; \(\gamma\) is the phase angle for the torsional angle parameters; \(A\), \(B\) and \(q\) are the non-bonded potentials between atom pairs; \(R_{ij}\) is the distance between the atoms; and \(\epsilon\) is the well depth for van der Waals energy.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 2 Related Works
### Static Satellite Network Topology  
In general, typical static topologies of satellite networks cover star, ring, tree, and mesh topologies [6]. The study of satellite network topology analysis not only helps improve the constellation's overall performance but also plays a key role in performance evaluation. In recent years, scholars at home and abroad have been continuously studying and improving the structure of satellite network topology in depth [7]. Table 1 illustrates a comprehensive comparison of the four types of typical topologies commonly used and an analysis of their strengths and weaknesses.
## 1 Introduction  
Schottky junction solar cells have always been one of the most potential candidates for light energy conversion technology. Due to an uncomplicated and economical fabrication process, such as the direct deposition of a thin metal layer on a polycrystalline semiconductor wafer, the Schottky barrier has received high consideration from science society [1, 2, 3]. Unfortunately, this benefit concurrently turns into a drawback since the thickness of the thin metal layer will negatively impact the light absorption of the semiconductor [4]. Thanks to intense research efforts, several methods are issued to improve this matter [5, 6, 7]. Among them, plasmonic nano-antenna, such as metal nanostructures (MNS), are proposed as light harvesting for enhancing the overall performance of photonic devices [8, 9, 10]. The optical field strengthening from the localized surface plasmon resonance (LSPR) existing on the MNS could increase the photon absorbance into nearby materials. Besides, the solar spectra region of the surface plasmon resonance wavelength (\(\lambda_{\text{SPR}}\)) is strongly dependent on the size, shape, and concentration of MNS. Particularly, metal nanoparticles (MNPs) become very attractive due to their flexibility, straightforward synthetic, and high-uniform contribution [11, 12]. Small sizes of MNPs (below 50 nm) are supposed to induce the intensive near-field localization; then, generate the light scattering in the board area of solar spectra [13; 14]. However, plasmon assistance of MNPs on the semiconductor wafer leads to the external quantum efficiency (EQE) loss in the UV and near UV region that has already been testified [15; 16; 17; 18]. Additionally, quantum dots (QDs) luminescent layers are applied as the other approach to enhance solar cell efficiency. These layers absorb photons in the UV and near UV spectrum, then convert them into photons in the visible region to accumulate more incident photons [19; 20; 21]. Moreover, the properties of QDs, such as downshifting and anti-reflective, which facilitate the EQE enhancement of solar cells in the UV region, are demonstrated by research articles [22; 23; 24].  
In this study, both plasmonic and luminescent materials are employed for enhancing the performance of silicon-based Schottky solar cells. CdSe/ZnS core/shell QDs and controlled sizes of gold nanoparticles (Au NPs) have been deployed to investigate the effect of hybrid structure on optoelectrical properties of the solar cell. This integration results in elevated EQE for the entire solar spectrum, averagely of 10%. The increase in the solar cell parameters, including the short circuit current (JSC), the fill factor (FF), and the efficiency enhancement (\(\eta\)), are achieved of 20%, 8%, and 28%, respectively.
# Brake Light Detection Algorithm for Predictive Braking
## 4 Results
### Algorithm Output  
Additional video material was captured for analysis from the Helsinki metropolitan area, mostly driving on two specific main roads during intensive traffic or congestion. Based on this experimental test with video material, the proposed method shows promising results on detecting brake lights in video material. It is shown in Figure 9 in Pictures (a)-(d) that brake lights were detected well. Interestingly, even on a rainy day the algorithm performed well as shown in Picture (b). Examples of false predictions are shown in the second row in Pictures (e)-(h). The most notable cause of false predictions was capturing vehicle bounding boxes that did not actually contain vehicle rears.
## 2 Methods and Materials
### Statistical Machine Learning Models and SHAP Method
#### 2.4.7 SHapley Additive exPlanations  
SHAP (SHapley Additive exPlanations) is a cutting-edge method for understanding how features influence a model's predictions. It adheres to key principles of accuracy, completeness, and consistency, ensuring reliable and interpretable explanations for a wide array of models, from simple Linear Regression to complex deep learning models like transformers used in natural language processing. Although alternative explainability methods such as LIME and DeepLIFT exist [46], SHAP was selected due to its strong theoretical foundation and broad applicability. SHAP unifies these approaches through a game-theoretic framework based on Shapley values, offering consistent and locally accurate feature attributions. This makes it particularly well suited for comparing multiple model types in a unified, interpretable manner. SHAP excels in handling the complexities of correlated features, making it a versatile and powerful tool for gaining insights into both simple and complex machine learning models. The SHAP method, which belongs to additive feature attribution methods, has the following properties: (a) Local accuracy: This property indicates that SHAP values offer a precise and localised interpretation of the model's prediction for a particular input. (b) Missingness: This ensures robustness to missing data and prevents irrelevant features from skewing the interpretation. (c) Consistency: The values remain constant unless there is a change in the contribution of a feature. Consequently, they offer a consistent interpretation of the model's behaviour, even amidst alterations in the model architecture or parameters.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 2 Preliminaries  
### Wavenet-Based Denoising  
Wavenet is an internal speech DNN model within a voice synthesis model, Tacotron [18], to create raw sound waveforms. Since Wavenet enables the effective understanding of speech data features, it has been proposed as a denoising tool [13]. Figure 3 describes its overall architecture [13], which allows to identify the comprehensive features of speech data effectively using a dilated convolution layer, which enables to extend the reception field with a small number of layers. It also tries to avoid overfitting and to reduce computational costs by skipping several layers randomly using the concept of skip connection. For these reasons, Wavenet-based denoising performs better compared to Wiener filter [4]. Specifically, in a speech quality assessment involving 33 participants, Wavenet-based denoising is scored 3.6 while Wiener filter 2.92 [13].
## 2 Mathematical Model of Energy and Quality of Service at the Local and Remote Cluster  
The local cluster (LC) is assumed to incorporate a rack of \(L\) processors and related peripheral devices, with a power profile:  
\[\Pi_{L}=A_{L}+L.B_{L}\rho_{L} \tag{4}\]  
where:  
* \(A_{L}\) is the local power consumption related to the internal networking and shared memory systems (main a secondary) plus their induced cooling and ventilation costs;
* \(B_{L}\) is the workload proportional power consumption, including cooling, per processor in the LC rack, and
* \(\rho_{L}\) is the individual utilisation (percentage of time it is busy) of each of the \(L\) processors in the local rack.  
The local computational workload is represented by a flow of \(\lambda_{L}\) jobs per second, each of which on average takes \(S_{L}\) of processing time, and jobs are equally distributed to the \(L\) processors, so that:  
\[\rho_{L}=\frac{\lambda_{L}S_{L}}{L} \tag{5}\]  
As a result, the total expenditure of energy per job in the LC is the ratio of power consumption to total job arrival rate, or:  
\[E_{L}=\frac{A_{L}}{\lambda_{L}}+B_{L}S_{L} \tag{6}\]  
If the average response time \(W(F,\lambda)\) is a function of job arrival rate \(\lambda\) and job service time distribution \(F\), we will have:  
\[W_{L}=W(F_{L},\frac{\lambda_{L}}{L}) \tag{7}\]
## 2 System and Channel Models
### Gamma-Gamma Distribution Model for Moderate-to-Strong Turbulence  
Apart from the weak turbulence, the communication link will also encounter moderate-to-strong turbulence. Therefore, the moderate-to-strong turbulence, which can be characterized by the Gamma-Gamma distribution model, is also introduced to compare the system performance under various noises and the results of the comparison are presented in Section 4. In the Gamma-Gamma distribution model, the influence of both large-scale and small-scale turbulence on the optical signal is considered, and is assumed to obey a Gamma distribution. The PDF of the optical signal scattered by the atmospheric turbulence under moderate-to-strong conditions can therefore be expressed as [3],  
\[p(I)=\frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma(\beta )}I^{\frac{\alpha+\beta}{2}-1}K_{\alpha-\beta}\left(2\sqrt{\alpha\beta I} \right), \tag{5}\]  
where \(\Gamma(\cdot)\) and \(K_{n}(\cdot)\) are the Gamma function and a Bessel function of the second kind and in the \(n\)-th order, respectively. \(\alpha\) and \(\beta\) are the effective turbulence numbers at small and large scales, and are given as  
\[\alpha=\left[\exp\left(\frac{0.49\sigma_{x}^{2}}{\left(1+1.11\sigma_{x}^{\frac {12}{6}}\right)^{\frac{12}{6}}}\right)-1\right]^{-1},\beta=\left[\exp\left( \frac{0.51\sigma_{x}^{2}}{\left(1+1.11\sigma_{x}^{\frac{12}{6}}\right)^{\frac {12}{6}}}\right)-1\right]^{-1}. \tag{6}\]
###### Abstract
Moreover, the consideration of permutation on the bit-level instead of the perspectives of pixel-level has also grown rapidly. To reduce the redundancies implied by the Fridrich's structure, a novel circular inter- and intrapixel bit-level permutation strategy was mentioned in [20]. [PERSON] et al. [21] proposed a 2-bit-level pixel substitution by using DNA and chaos. However, there are vital flaws for the methods mentioned above: (1) Although the use of block surely reduced the time redundancy, all these referred block sizes are not determined by the property of the image. (2) All the bits of each pixel were treated equally without the consideration of the discrepancy of the quantity of information they respectively contained. Nevertheless, though these proposed schemes proved to be of high security and efficiency in the process of image encryption, there is one thing they certainly have in common: all these schemes are symmetric. This significant attribute results in one inevitable flaw: the important and sensitive key must be transmitted through a dedicated secure channel to prevent eavesdropping. That means if the key is disclosed, security for further communication is impossible [22]. In view of the shortcomings above, we propose a novel asymmetric hyperchaotic image encryption scheme based on elliptic curve cryptography (ECC). The author of [23] illustrated the good time efficiency and security of ECC theoretically, and [PERSON] et al. [24] made an analysis of various encryption systems, in which ECC shows its excellent characteristics. The advantages of this scheme are as follows: (1) Based on the high security of ECC, sensitive data related to the initial parameters can be transmitted with the cipher image through the public channels. (2) With the application of block-level bit permutation, after the permutation process, because the permutated planes are bit-level planes, the place of the pixels in the image and the values of each pixel are both therefore changed, which means better time efficiency of the encryption with the even better effects. (3) During the diffusion process, the same hyperchaotic sequences used in permutation significantly reduces the complexity of the algorithm and provides better storage efficiency. The remainder of the paper is arranged as follows. In Section 2, elliptic curve integrate encrypt scheme (ECIES) for the encryption of the sensitive data is applied and the hyperchaotic system is briefly introduced. In Section 3, we described this novel asymmetric hyperchaotic image encryption scheme based on ECC in detail, followed by the experimental simulation in Section 4. Finally, the conclusions are presented in Section 5.
###### Abstract  
As space exploration activities are developing rapidly, spacecraft with large antennas have gained wide acceptance in providing reliable telecommunications and astrophysical observations. In this paper, the dynamic responses and control strategy for a spacecraft with a large hoop-truss antenna under solar flux shock are studied. According to the momentum and angular momentum principle, the rigid-flexible coupled rotational dynamic equation and the translational dynamic equation of the system are established, which include the attitude motion of the rigid main body and the vibration of the antenna. Then, a finite element model of the antenna is established to analytically obtain the corresponding vibration modal shape matrix and natural frequencies. Last, the coupled responses for the attitude motion and vibration are investigated. The corresponding control strategy is designed based on a double-loop structure sliding mode control method. The Lyapunov method is used to demonstrate the global asymptotic stability of the system. Simulations verify the effectiveness of the proposed rigid-flexible coupled model and control strategy.  
large hoop-truss antenna; rigid-flexible coupled; double-loop structure sliding mode control method; thermal induced vibration
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 4 The Case Study Phases and Results
### The Deep Learning Approach Implementation
\"The average TU number\" was calculated for each error-specific dataset and the results are reported in Table 6. Henceforth, each dataset was independently treated, dividing each into 80% for training and 20% for testing; an additional 20% of the training dataset was left for validation during the training process. Before each training session, the data chunks were shuffled and the training lasted for a maximum of 500 epochs, with an early stop mechanism implemented to stop the training if the validation error exhibited an increasing trend across epochs (\(patience=10\) and \(min\_delta=0.1\)). The Learning Rate (LR) was set at \(\alpha=0.001\), and the Adam optimiser was used to calculate the error, while the Mean Square Error was used as a loss function. To find the most suitable network architecture, the Pytorch Ray Tune was used to write a test matrix with grid search on LSTM layers (\(1-5-10\)), attention head number (\(2-10-50-100\)), and dropout rate (\(0.1-0.2-0.3\)) [63]. The total size of the network and the performance obtained by each prototype were compared. To manage the high computational complexity of the models and data, mini-batch training with a \(batch\_size=5000\) was applied. The results of the prototypes were contrasting, as increasing the complexity of the network did not bring any relevant improvements to the test set. Therefore, considering that the final networks would be deployed on edge machines, with limited computational resources, we opted for the smallest architecture with the highest performance. These share five stacked LSTM layers with 100 hidden units to extract sequential dependencies. Furthermore, a single layer with 10 attention heads concentrates on the most important parts of the data. Then, a fully connected layer processes the attention output, followed by a linear layer and a ReLU activation layer. The architecture is finalised with another linear layer and the dropout rate is set to 0.2. The general approach for LSTM applications suggests converting the dataset into sliding windows of size \(n\times p\), where \(n\) represents the total number of TUs in the window and \(p\) denotes the TUs to predict. When discussing with the company's domain experts, a suggestion was given to look for the error a few minutes before the event occurrence and considering a few instants after its rising. Hence, we decided to design a window size of \(n=100\) and \(p=10\), to allow a small warning gap.
## 2 Related Work
### Cross-Domain Authentication Based on PKI and Blockchain
They proposed a split-chain mechanism to accelerate write operations, though the trade-off between consistency and performance remains a challenge. [PERSON] et al. [16] employed decentralized oracles and zero-knowledge proofs to enable anonymous identity verification across heterogeneous domains, effectively preventing identity tracking. Likewise, [PERSON] et al. [10; 11] utilized pseudonym-based mechanisms distributed by edge servers to allow for mutual authentication. [PERSON] et al. [9] proposed a complete cross-domain authentication based on consortium blockchain with a privacy protection scheme for the Internet of Things to preserve device privacy. [PERSON] et al. [17] proposed integrating a blacklist Merkletree to support revocation while maintaining data integrity. However, this introduces additional computational and storage costs. Although existing research has made notable progress in integrating blockchain with PKI, the hierarchical structure of PKI still poses a challenge. It increases interaction overhead during authentication and indirectly raises latency. Furthermore, few studies have explored optimizations of the computational and latency overhead from the perspective of cryptographic primitives employed in the authentication workflow. For example, related work [7; 10; 11; 12; 14; 15; 17] still relies heavily on traditional PKI mechanisms. In particular, the certificate-based signature verification in their scheme typically employs elliptic curve cryptography (ECC), which is computationally intensive for resource-constrained IIoT devices. [PERSON] et al. [8] claim lightweight key agreement, but the use of modular arithmetic over elliptic curves and multiple temporary key exchanges still results in resource-intensive cryptographic computations on the device side. [PERSON] et al. [9] and [PERSON] et al. [16] rely on relatively complex cryptographic operations such as threshold cryptography, bilinear pairings, and non-interactive zero-knowledge proofs, which can impose significant computational overhead on the system. We eliminate the complexity of traditional PKI hierarchical structures by leveraging blockchain as a decentralized and trusted authority for public key verification, thereby improving both authentication efficiency and system scalability. Moreover, most existing blockchain-based PKI schemes rely on computationally intensive operations such as RSA exponentiation, bilinear pairings, and elliptic curve point multiplications. To reduce this overhead, we propose an authentication scheme for IIoT based on Chebyshev chaotic maps. Chaotic-map-based authentication mechanisms have been proven to offer superior computational efficiency compared to conventional cryptographic techniques. According to [40; 41; 42], the Chebyshev polynomial computation operation takes one-third of the computational time of the ECC point multiplication operation.