esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The color difference parameter \(\Delta\)E has values between 2.34 and 0.45, and in any case always lower than the JND (just noticeable difference = 2.37), the minimum value that the human eye can perceive (Figure 7b). It is important to underline how the color changes of the slates certainly depends on various factors. Figure 6: Slate damaging after aging: (**a,c**) S1 fractures due to volumetric expansion of the ice oriented parallel to the S0 slaty cleavage; (**b,d**) decay by spalling and exfoliation. Slates can be affected by the weathering of the iron sulphides (mainly pyrite FeS\({}_{2}\) and pyrrrhotic Fe\({}_{1-X}\)S, 0 \(<\) X \(<\) 0.2) that the stone may contain. This can lead to the formation of brown/yellow/red iron crust compounds (Figure 8a,d) bearing limonite (2 Fe\({}_{2}\)O\({}_{3}\)-3H\({}_{2}\)O) and goethite (FeO(OH)). The fading toward white could be linked to sulphation of the carbonates to gypsum or Na-sulphates. In this regard, we note that calcite (CaCO\({}_{3}\)) could be present in the slate. In some cases, white stains could derive from adhesives and/or cements used to install slate slabs in floors or claddings (Figure 8c). The presence of white depositions is also identified as corresponding to water runoff (Figure 8b,d). Figure 8: Color change of slate slabs: (**a**) oxidation; (**b**) deposition corresponding to water runoff; (**c**) dirty deposition of cement groups; (**d**) oxidation. Photo credits by [PERSON]. Figure 7: (**a**) Trends of CIEL*a*b* color parameters during 30 days of solar exposure test; (**b**) color difference according to the JND limit (just noticeable difference = 2.37). Furthermore, the type of finish and therefore the roughness of the stone is closely linked to the color, as shown in Figure 9. As detailed in these pictures, the greater the surface roughness (Ra), the whiter the color of the slate. In this regard, we highlight that in polished slate surfaces and in carbonate rocks exposed to rain and frost cycles, there may be a decrease in gloss linked to microcorrosion and therefore an increase in roughness [22; 61]. In Figure 9, in fact, a fading towards white is detected by Ra feature increases.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
###### Abstract  
With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient sample data. In this paper, we propose a ship detection and tracking model with high accuracy based on a few training samples with supervised information based on the few-shot learning framework. The transfer learning strategy is designed, innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships. The Shuffle Attention mechanism and smaller anchor boxes are introduced in the object detection network to improve the detection accuracy of different targets in different scenes. Compared with existing methods, the proposed method is characterized by fast training speed and high accuracy with small datasets, achieving 84.9% ([EMAIL_ADDRESS]) with only 585 training images.  
ship object detection; multi-object tracking (MOT); few-shot learning (FSL); transfer learning Article
## 2 The Proposed Non-Stationary Model for High-Speed Train mmWave Band Channel
### Viaducts and the Time-Varying Rician K-Parameters  
The main attributes of the viaduct structure that affect the received signal by the MRS are shown in Figure 1. The sectional view of the viaduct scenario shows that the MRS is installed on top of the train. The significance of the viaduct structure's attributes, particularly the viaduct's height, \(H_{viaduct}\), and the distance between BS and MRS have been used to study the impact of the time-varying Rician K-factor, and the equation is obtained from [18]. In this case, \(h=(H_{BS}-H_{viaduct}+H_{train}+H_{MRS})\), whereas the minimum distance can be calculated as \(D_{min}=L+W_{up}/2\). The Rician K-factor, \(K_{pq}(t)\), in the viaduct scenario can be expressed as  
\[K_{pq}(t)(dB)=\begin{cases}0.012D_{s}(t)+0.29,&D_{s}(t)\leq D_{BP}\\ 0.00037H_{viad}-\frac{0.18}{H_{viad}}+0.017D_{s}(t)\\ +0.148H_{viad}+\frac{72}{H_{viad}}-1.71,&D_{s}(t)>D_{BP}.\end{cases} \tag{10}\]
## 3 Extended Critical Damping Adjustment Scheme  
From the above analysis, it can be seen that the L\({}_{2}\)MF method belongs to the numerical methods with low-order and strong numerical stability. For this purpose, it is necessary to combine the implicit trapezoidal integration method, which is widely used in electromagnetic transient calculation, to calculate the implicit trapezoidal integration method under normal circumstances, and to switch to L\({}_{2}\)MF only when the system is mutated, such as switch operation and line fault. In practice, we can use the same method of auxiliary detection mutation as the CDA method and the algorithm switching strategy. Because the absolute value of the local truncation error of the four-step L\({}_{2}\)MF method is smaller than that of the backward Euler method (the absolute value of the main coefficient of local truncation error is 0.5), it was recommend that a four-step L\({}_{2}\)MF method and an implicit trapezoid integration method are used to form that combined integration algorithm for the electromagnetic transient simulation in power systems.  
In the new combination integration scheme, the implicit trapezoidal integration method had the ability of self-starting computation, and the L\({}_{2}\)MF method did not have the self-starting calculation ability, which needed a startup algorithm for independent calculation. The starting numerical method used in this paper was explicit Euler method (forward Euler method). Figure 3 is a flow chart of electromagnetic transient simulation in power systems on a basis of extended CDA method (ECDA).  
Figure 3: Flow chart of electromagnetic transient simulation in power systems on a basis of extended critical damping adjustment method (ECDA).  
The combined integration algorithm proposed in this paper was a class of algorithms, and the combination algorithm was the CDA method when \(s\) takes one. Therefore, the new combination algorithm proposed in this paper was also named as the extended CDA method.
## 4 Results and Discussion
### Discussion
#### 4.2.1 Performance Analysis  
In two study areas, classification using time-series feature representation performed better than classification using raw time-series features. This indicated that the proposed contrastive learning framework could learn the inherent time-series characteristics of crop growth. In the meantime, it was observed that this proposed method performed better on the Zhaosu dataset than on the Dijon dataset. There are two reasons for this difference. One is that the crop category system in Zhaosu was more straightforward than its counterpart in Dijon. In addition, the sample category was more evenly distributed in the Zhaosu study area.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 3 Approach  
We propose a fast Transformer-based global matching framework (FTGM) that replaces the previously widely used iterative refinement strategies in PIV estimation with a simple and effective Transformer and global matching structure. FTGM comprises feature extraction, feature enhancement, global matching, and flow propagation. Figure 1 provides an overview of FTGM, and each module is detailed in the following sections.
# Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
## 4 Simulation Analysis and Verification
### Simulation Analysis of Signal-to-Noise Ratio When m Is a Fixed Value  
First, we analyze the influence of the ratio \(r_{B}\) on the SNR when \(m\) is a fixed value. Without loss of generality, in Figures 4 and 5, we arbitrarily select \(m=20\) and simulate the average SNR of the interferogram and the reconstructed spectrum within \(0<r_{B}\leq 1\) at this time. It can be seen that when \(m\) is a fixed value, the average SNR of the interferogram and the reconstructed spectrum both increase with the increase in \(r_{B}\), which is consistent with our previous analysis.  
In order to compare the difference between FTS and BPS-FTS in the average SNR of the interferogram and the average SNR of the reconstructed spectrum, without loss of generality, \(r_{B}\) is taken as 0.5 for both, and in BPS-FTS, we arbitrarily take m = 20. Figure 6 shows the interferogram (expressed in the number of electrons) output by FTS when Nyquist sampling (m = 1), and Figure 7 shows the interferogram (expressed in the number  
Figure 4: Average SNR of interferogram when \(m=20\) and \(0<r_{B}\leq 1\). The curve shows the average SNR of the interferogram changing with \(r_{B}\) when \(m=20\).  
Figure 5: Average SNR of reconstructed spectrum when \(m=20\) and \(0<r_{B}\leq 1\). The curve shows the average SNR of the reconstructed spectrum changing with \(r_{B}\) when \(m=20\).  
of electrons) output by BPS-FTS. By comparing the two, it can be seen that the number of electrons corresponding to the interferogram under bandpass sampling is much higher than that under Nyquist sampling. This is because at this time, the sampling interval of BPS-FTS is significantly larger than that of FTS, resulting in a significant increase in its integration time, thereby increasing the energy of its interferogram. Through simulation calculation, it can be obtained that the average SNR of the interferogram of FTS is 176.75, while 791.61 in BPS-FTS, which is consistent with the theoretical analysis. At this time, the simulation calculation shows that the average SNR of the reconstructed spectrum of FTS is 1337.60 and that of BPS-FTS is 1340.41, which is also consistent with the analysis conclusion of the average SNR of the reconstructed spectrum.
## 2 Finite Element Model of FSW  
### Model Description  
The friction stir welding process can be roughly divided into the following stages: the plunging stage, the dwelling stage, and the welding stage. The process is shown in Figure 1. In the first two stages of welding, the tool and the workpiece to be welded generate heat by friction, the thin layer of material around the FSW tool is softened into a viscous flow state, the viscous fluid material is plastically deformed under the stirring of the FSW tool, the plastic deformation process is accompanied by deformation and heat generation, and the temperature around the FSW tool is continuously increased. When the frictional heat generation and the plastic deformation heat generation enter a stable state, the thin layer of the viscous fluid material around the FSW tool reaches a stable thickness and the welding enters a stable state. In the stable welding stage, due to the rotation and movement of the FSW tool, the front edge viscous fluid material fills the cavity created in the rear of the welding direction, forming a tight weld under the action of forging force [23; 24].  
The FSW finite element simulation is the same as the real welding process and is divided into three stages: plunging, dwelling, and welding. During the pressing process, the FSW tool is inclined to the trailing edge by 2.5\({}^{\circ}\); the tool travels at a defined rotational speed with a feed velocity of 6 mm/min, and the shoulder maintains a plunge depth of 0.25 mm. After the pressing reaches the specified depth, it stays for 10 s. Finally, the FSW tool is welded at a speed of 80 mm/min.
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
###### Abstract  
The detection and classification of threats in computer systems has been one of the main problems researched in Cybersecurity. As technology evolves, the tactics employed by adversaries have also become more sophisticated to evade detection systems. In consequence, systems that previously detected and classified those threats are now outdated. This paper proposes a detection system based on the analysis of events and matching the risk level with the MITRE ATT&CK matrix and Cyber Kill Chain. Extensive testing of attacks, using nine malware codes and applying three different obfuscation techniques, was performed. Each malicious code was analyzed using the proposed event management system and also executed in a controlled environment to examine if commercial malware detection systems (antivirus) were successful. The results show that evading techniques such as obfuscation and in-memory extraction of malicious payloads, impose unexpected difficulties to standard antivirus software.  
SIEM; antivirus; event-based threat detection; MITRE; Cyber Kill Chain +
Footnote †: journal: Medical Security  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-  
For a better understanding of the contents of this paper, it was organized as follows: Section 2 describes all the elements that must be known to understand the methodology proposed in this paper; therefore, the classification of malware families, the current functioning of antivirus systems, the most common evasion techniques, and the threat classification model used to analyze events are explained. Section 3 describes the proposed methodology and its implementation. Section 4 describes the experiments performed and the results obtained and includes the comparison with several known antivirus systems. Section 5 analyzes the results obtained with the proposed methodology. Section 6 concludes this paper and presents future works.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
###### Abstract  
This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model--with conventional convolutional layers replacing the separable convolutional layers--can be adopted as a more precise alternative.  
orthogonal time frequency space; signal detection; convolutional neural network +
Footnote †: journal:  
0000-0001-6507-7889  
1
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 4 Performance Evaluation
Thus, the computational complexity for J-MT is  
\[\mathcal{O}\Big{(}N_{\mathbf{\Lambda}}^{3}N_{\mathbf{\Lambda}}^{3}+N_{\mathbf{ \Lambda}}^{2}N_{\mathbf{\Sigma}}^{2}N_{\text{SP}}+N_{\mathbf{\Lambda}}N_{\mathbf{\mathrm{K}} }(N_{\mathbf{\Lambda}}N_{\mathbf{\mathrm{K}}}-N_{\mathbf{\mathrm{L}}})G_{\theta}G_{\phi}G_{ \tau}\] \[\qquad+(N_{\mathbf{\Lambda}}N_{\mathbf{\mathrm{K}}}-N_{\mathbf{\mathrm{L}}})G_ {\theta}G_{\phi}G_{\tau}+(9N_{\mathbf{\Lambda}}N_{\mathbf{\mathrm{K}}}(N_{\mathbf{\mathrm{ A}}}N_{\mathbf{\mathrm{K}}}-N_{\mathbf{\mathrm{L}}})\] \[\qquad\qquad+9(N_{\mathbf{\Lambda}}N_{\mathbf{\mathrm{K}}}-N_{\mathbf{\mathrm{ L}}})+4N_{\mathbf{\mathrm{A}}}N_{\mathbf{\mathrm{K}}})N_{\mathbf{\mathrm{T}}}\Big{)}\]  
and for J-2M,  
\[\mathcal{O}\Big{(}N_{\mathbf{\mathrm{A}}}^{3}N_{\mathbf{\mathrm{K}}}^{3}+ N_{\mathbf{\mathrm{A}}}^{2}N_{\mathbf{\mathrm{K}}}^{2}N_{\text{SP}}+\big{(}N_{\mathbf{ \mathrm{A}}}N_{\mathbf{\mathrm{K}}}(N_{\mathbf{\mathrm{A}}}N_{\mathbf{\mathrm{K}}}-N_\mathbf{{\mathrm{L}}})+\] \[\qquad\qquad+6(G_{\theta 1}G_{\phi 1}G_{\tau 1}+G_{\theta 2}G_{\phi 2 }G_{\tau 2})\Big{)}\]  
Adopting the settings for Figure 5, and assuming the maximum iterations \(N_{\mathbf{\mathrm{T}}}=500\) for J-MT (the worst case in our simulations), for one Monte Carlo realization, the computational complexity of J-MT (\(2.80\times 10^{10}\)) takes up only 0.97% of MM-2D complexity (\(2.89\times 10^{12}\)).
# Skin Barrier Function: The Interplay of Physical, Chemical, and Immunologic Properties
## 2 The Roles of Acid, Sphingolipids, and Mitochondria in the Skin Barrier
As such, upregulation of kallikreins through changes in the skin surface pH could lead to multiple clinical symptoms. Additionally, regulatory enzymes including \(\beta\)-glucocerebrosidase and acid sphingomyelinase, which have an optimal pH of 5.6 and 4.5, respectively, require an acidic pH for proper function [13; 36]. The ceramide levels in the SC are regulated by the balance of \(\beta\)-glucocerebrosidase, acid sphingomyelinase, and acid ceramidase, so any disturbance in these enzymes will alter the ceramide levels in the SC [13; 37]. SC lipids such as ceramides also contribute to the barrier function of the epidermis, while also facilitating maintenance of an acid mantle. Stratum corneum lipids form a hydrophobic layer which, along with the tight junctions and desmosomes underlying the SC, prevent dehydration and retain water inside the skin [1]. Ceramides, a derivative of sphingolipids, are a marker of SC lipids. Ceramides are composed of an acyl chain and a sphingoid base. The barrier function of the SC is determined by the different subclasses of ceramides, which can differ in the acyl chain, sphingoid base structure, length of sidechains, and other structural variations [37; 38; 39]. As lipids, ceramides constitute part of the physical skin barrier, contributing to the intercellular lamellar sheets in the SC [40; 41; 42; 43]. In this structure, ceramides help skin barrier health by repelling water, thus maintaining skin moisture by preventing water loss while also preventing skin irritation from outside substances [44]. Additionally, ceramides promote mitophagy, eliminating abnormal mitochondria [45]. Maintaining a healthy skin barrier requires intact mitochondria in all layers of the skin that produce the energy needed to pump out acid and maintain the acid mantle of the skin. Ceramide deficiency may also be attributed to elevated activity of certain enzymes that exhibit increased activity in an alkaline environment, such as alkaline ceramidase, which is involved in barrier lipid degradation [13; 36]. Studies have shown that pH elevation in normal skin alters the barrier function due to increased serine proteases activity and a reduction in ceramide precursor enzyme activity (5,6). Ceramides may also play a role in the immune regulation of the skin, as ceramides contribute to IL-12 induction, which then activates the Th1 immune program [46]. Overall, the interplay between acid, mitochondria, and ceramides is responsible for multiple factors related to skin health. (Figure 1). The roles of these three factors will also be discussed more in depth in the following sections in the context of skin barrier restoration.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 5 Proposed Algorithm
### Algorithm Design  
The transmitted signal by the BS can be written as:  
\[x(t)=\sum_{k=1}^{K}x_{k}(t)\sqrt{\alpha_{k}\,P_{T}} \tag{11}\]  
where \(P_{T}\) denotes the base station transmit power and \(\alpha_{k}\) is the power allocation factor for the \(UE_{K}\) power allocation factor. The power allocated to each \(UE_{k}\) can be written as:  
\[P_{k}=\alpha_{k}\,P_{T} \tag{12}\]  
and the received signal at the \(UE_{k}\) can be expressed as:  
\[y_{k}(t)=h_{bu}\sum_{k=1}^{K}x_{k}(t)\sqrt{\alpha_{k}\,P_{T}}+n_{uk} \tag{13}\]  
where \(n_{uk}\) represents the received Gaussian white noise, and \(h_{bu}\) indicates the channel from the base station to \(UE_{K}\). \(UE_{1}\) first detects \(x_{k}(t)\) and eliminates the interference of the signal to \(y_{k}(t)\), and then detects \(x_{k-1}(t)\) and eliminates the interference of the signal to \(y_{k}(t)\), until \(x_{1}\) is checked. Therefore, the SINR for \(UE_{K}\) can be written as:  
\[SINR_{k}=\frac{P_{k}\left|H_{bu}\right|^{2}}{\sum_{i=1}^{K-1}P_{i}\left|H_{bu} \right|^{2}+1} \tag{14}\]  
Using NOMA, the throughput of each UE in bps can be expressed as:  
\[R_{k}=Wlog_{2}\left[1+\frac{P_{k}\left|H_{bu}\right|^{2}}{\sum_{i=1}^{k-1}P_{ i}\left|H_{bu}\right|^{2}+n}\right] \tag{15}\]  
where \(W\) denotes the transmission bandwidth. Furthermore, let \(J\) denote the fairness index which can be written as:  
\[J=\frac{(\sum R_{k})^{2}}{K\sum R_{k}^{2}}. \tag{16}\]  
In Equation (16) represents the fairness of sharing system capacity among UEs. When \(J\) is close to 1, the capacity of each UE is close to each other. We can set the goal of the power allocation mechanism to maximize the total capacity of the NOMA system under fairness constraints. The nonlinear optimization problem can be expressed as:  
\[\begin{split}\max_{\alpha_{k}}& W\log_{2}\left[1+\frac{P_{k} \left|h_{bu}\right|^{2}}{\sum_{i=1}^{k-1}P_{i}\left|h_{bu}\right|^{2}+n}\right] \\ \text{s.t.}& J\geq J^{\prime}\\ & P_{k}\geq 0,\forall k\\ \sum_{k=1}^{k}\alpha_{k}\leq 1\\ \sum_{k=1}^{k}P_{k}\leq P_{T}\end{split} \tag{17}\]  
where \(J^{\prime}\) is the target fairness index in the network.
## 1 Introduction
The proposed PBFT method, with its innovative clustering and dynamic grouping, holds promise for diverse applications, particularly in sectors where rapid and scalable consensus is important. By minimizing communication latency and efficiently managing communication overhead, this method can revolutionize blockchain-based systems in finance, supply chain management, and beyond, offering enhanced security and reliability. The structure of this paper is as follows. Section 2 provides an overview of relevant background knowledge and related research. Section 3 details the methodology of the proposed approach. Section 4 is dedicated to this proposed method's performance evaluation and analysis. Section 5 describes the discussion on the results of the paper. Finally, Section 6 concludes the paper with a summary of the findings and potential directions for future research.
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods  
### Design Considerations  
An application suite refers to a set of interrelated, seamlessly integrated software programs that provide a variety of functions to meet the needs of specific users and improve productivity and convenience [9]. For example, the Office suite integrates various application software. Users can choose one software or use different software together to share data and information to meet diverse needs.  
With a consideration of the insufficiency of digital agricultural GIS application systems; the characteristics and industry needs of the digital agriculture field; and based on the ideas of Office Suite integration, collaboration, and functional diversity; this paper constructs an application suite seamlessly integrated by multiple applications from the dual perspectives of GIS technology and digital agricultural applications and puts forward the design concept of a digital agricultural GIS application suite, as shown in Figure 1.