esa-sceva/satcom-chunk-collection huggingface dataset:
# Attosecond Pulse Shaping by Multilayer Mirrors
## 1 Introduction
### What Is It Good for? The Applications  
Coherent EUV pulses from HHG and their optics components find a wide range of applications in experiments. Some experiments make use of the almost perfect coherence of the generated radiation, such as in experiments on EUV interferometry [30]; HHG microscopy, such as coherent diffractive imaging (CDI) [4]; or the generation of EUV frequency combs for advanced metrology [31].  
However, the majority of applications of HHG radiation utilize the attosecond temporal structure. For example, pump-probe experiments on electron streaking are providing insights into the electron wave packet dynamics in atoms [32] and molecules [33], as well as giving new insights into the photoemission process from solid surfaces [34] or nanostructures [35].  
While most of the experiments utilize single isolated attosecond pulses, as they are required for clean electron streaking experiments, the recent use of attosecond pulse trains has been also established in RABBITT (reconstruction of attosecond beating by two-photon-transitions) measurements. RABBITT might provide a path to attosecond angle-resolved photoemission spectroscopy (ARPES) characterization of solid surfaces in the near future [36].  
It is important to note that all those different experiments impose different requirements on the inevitable spectral filtering of the HHG spectrum by the EUV optical components. This can in many cases be best achieved by proper multilayer reflectors.
## 4 Result and Discussion  
The 3D electron densities at 00:00, 6:00 and 12:00 on 15 March 2022 are reconstructed by MART0 and MART1. The 3D background electron densities at different times are obtained by the IRI-2020 [20], and they are used as the background ionospheric electron density for the calculation of STEC in the simulation.  
The average deviation (AD) and root-mean-square error (RMSE) are used to evaluate the reconstruction accuracy, which are written as follows:  
\[\mathrm{AD} = \mathrm{mean}|\mathbf{n_{e\_CIT}}-\mathbf{n_{e\_BG}}| \tag{6}\] \[\mathrm{RMSE} = \sqrt{\frac{\sum\limits_{i=1}^{n}(\mathrm{n_{e\_CIT,i}}-\mathrm{ n_{e\_BG,i}})}{n}} \tag{7}\]  
here, \(\mathrm{n_{e\_CIT}}\) and \(\mathrm{n_{e\_BG}}\) are the electron density reconstructed by CIT and the background electron density, respectively. \(\mathrm{n_{e\_CIT,i}}\) and \(\mathrm{n_{e\_BG,i}}\) are the reconstructed and the background electron density in the \(i\)th voxel, respectively, and \(n\) is the total number of voxels.
## 2 Experimental Program  
### Materials  
Components of the PVA-FRCM in this study contain the PVA fiber, cement, fly ash, quartz sand, hydroxyl-propyl methyl cellulose (HPMC) and superplasticizer. In the PVA-FRCM mixture, the type of PVA fiber is KII-REC15. The properties of the PVA fiber are shown in Table 1. Portland cement with a model of P.O 42.5R was produced in Hohhot. The chemical composition of cement is presented in Table 2. Class I fly ash from the power station in Erdos was adopted and its chemical composition is listed in Table 3. Quartz sand originated from Tongliao was adopted. The sand size was from 0.075 mm to 0.106 mm. HPMC was produced in Jinan. 3301E superplasticizer was produced in Dalian.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Diameter** & **Length** & **Nominal Strength** & **Elongation** & **Young’s Modulus** & **Density** \\  & **(mm)** & **(mm)** & **(MPa)** & **(\%)** & **(GPa)** & **(g/cm\({}^{3}\))** \\ \hline KII-REC15 & 0.04 & 12 & 1600 & 6 & 40 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of polyvinyl alcohol (PVA) fiber.  
The mixture proportion of PVA-FRCM is shown in Table 4. The water-binder ratio of the mixture was fixed at 0.26 throughout the test. In the mixture proportion, PVA fiber volume contents (\(V_{\text{f}}\)) were 0, 0.5%, 1.0%, 1.5%, and 2.0%. The specimens were named in F0, F0.5, F1, F1.5, and F2 to represent the above PVA fiber volume contents respectively. HPMC and superplasticizer were added into the mixture at a cement weight of 0.05% and 2% respectively.
## 4 Results
### Trace Element Distribution in Fe-Ti-Oxides and Sulphides Mineralization Determined by EPMA
Magnetic commonly contained an admixture of Ti (range from 0.02 to 2.7 wt%, mean = 0.60 wt% for \(n\) = 81) which was substituted for Fe \({}^{2+}\)(Figure 14B). Figure 14C,D shows the vanadium to titanium and vanadium to chromium ratios observed in magnetite, revealing a weak correlation with the Ti and Cr distribution. Figure 14: Bivariate diagrams of iron vs. vanadium (**A**), iron vs. titanium (**B**), vanadium vs. titanium (**C**), and chromium (**D**) in magnetite from the Udryn and Krzemianka Fe-Ti-V deposits. Trend lines represent correlations between elements. All plots represent the same set of samples; 0 values and samples with values below the level of precision of a single measurement are not presented on the axis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Mineral** & **Element** & **S** & **Cu** & **Ni** & **Co** & **Fe** & **Mineral** \\ \hline \multirow{4}{*}{**V.csv**} & \multirow{4}{*}{C.csv} & S & 1.00 & 0.08 & \(-\)0.34 & 0.08 & \(-\)0.01 & \multirow{4}{*}{**V.csv**} \\  & & Cu & \(-\)0.03 & 1.00 & \(-\)0.12 & \(-\)0.04 & 0.13 & \\  & & Ni & \(-\)0.62 & \(-\)0.13 & 1.00 & \(-\)0.31 & \(-\)0.76 & \\  & & Co & \(-\)0.02 & \(-\)0.09 & \(-\)0.54 & 1.00 & 0.10 & \\  & & Fe & 0.30 & \(-\)0.02 & \(-\)0.29 & \(-\)0.50 & 1.00 & \\ \hline \multirow{4}{*}{**V.csv**} & \multirow{4}{*}{C.csv} & S & 1.00 & 0.11 & \(-\)0.22 & \(-\)0.19 & 0.22 & \multirow{4}{*}{**V.csv**} \\  & & Cu & \(-\)0.08 & 1.00 & 0.18 & \(-\)0.20 & 0.01 & \\  & & Ni & \(-\)0.14 & \(-\)0.08 & 1.00 & \(-\)0.27 & \(-\)0.46 & \\  & & Co & 0.14 & \(-\)0.18 & 0.73 & 1.00 & \(-\)0.71 & \\  & & Fe & \(-\)0.31 & \(-\)0.28 & \(-\)0.31 & \(-\)0.41 & 1.00 & \\ \hline \multirow{4}{*}{**V.csv**} & \multirow{4}{*}{C.csv} & S & 1.00 & \(-\)0.39 & \(-\)0.78 & 0.16 & 0.73 & \multirow{4}{*}{**V.csv**} \\  & & Cu & 0.26 & 1.00 & 0.38 & \(-\)0.36 & \(-\)0.31 & \\ \cline{1-1}  & & Ni & \(-\)0.15 & \(-\)0.33 & 1.00 & \(-\)0.42 & \(-\)0.62 & \\ \cline{1-1}  & & Co & \(-\)0.08 & \(-\)0.13 & 0.62 & 1.00 & \(-\)0.34 & \\ \cline{1-1}  & & Fe & \(-\)0.09 & \(-\)0.83 & 0.12 & 0.25 & 1.00 & \\ \hline \hline \end{tabular} Note: [PERSON]’s correlation was based on EPMA in pentlandite (_n_ = 45 measurements), pyrrhotic (_n_ = 61), chalcopyrite (_n_ = 50), pyrite (_n_ = 37), cubanite (_n_ = 10), and siegenite (_n_ = 16). Coefficients with absolute values greater than 0.28 are statistically significant at the 95% level.  
\end{table} 
Table 5: [PERSON]’s correlation coefficients of selected elements for the sulphides from magnetite-illemite ones in the Fe-Ti-V deposits in Poland. Ilmenite contained vanadium from 0.05 to 0.24 wt% V (mean = 0.14 wt% V, \(n\) = 59), (Figure 15A,B). Massive ilmenites had the highest V contents, in the range of 0.18-0.24 wt%m with an increase in iron content ranging from 36 to 40 wt%. There was a slight enrichment of massive ilmenites with vanadium (>0.18 wt%) when compared to ilmenite lamellae exsolutions in magnetite or to ilmenite pseudomorphs after magnetite (Figure 15A).
## 3 Feature Extraction from Dermoscopic Images
### Extraction of Color Feature Information from Color Space Histogram  
To extract the color information, which is another visual information of skin lesions, the image was converted to RGB, YCbCr, and HSV color models, which are representative color spaces generally used in object segmentation and classification. Additionally, histogram analysis was performed on each channel of the respective converted color models for feature extraction. The YCbCr color model represents chromaticity components in terms of luminance (Y), blue-difference (Cb), and red-difference (Cr). The HSV model comprises hue (H), saturation (S), and value (V) components, which clearly distinguishes luminance and chrominance, and is typically used in color image segmentation because its color space components are the most intuitive to human recognition. Figure 7 shows an example of a dermoscopic image converted to the three color models.  
When a dermoscopic image was converted to three color spaces, the chrominance of the lesion was clearly generated in contrast to normal, as shown in Figure 7, and the color information vector \(CFM[\cdot]\) was generated as shown in Equation (6) using histogram information.  
\[\begin{array}{c}CFA[\cdot]=[C_{1}(H_{R},H_{G},H_{B}),C_{1}(H_{Y},H_{Cr},H_{ Cb}),C_{1}(H_{H},H_{S},H_{V}),]\\ \mathrm{where},H_{m}=[h_{0},\cdots,h_{k},\cdots,h_{K}],\\ m=R,G,B,Y,Cr,Cb,H,S,V,(K=255)\\ h_{k}=Histogram(k).\end{array} \tag{6}\]  
In Equation (6), \(CFA[\cdot]\) represents the color information corresponding to three channels for each of the three color models, and it is represented as a vector with a size of 3 (color model) \(\times\) 3 (number of channels) \(\times\) 256 (histogram), thus resulting in nine vectors with 256 elements. \(h_{k}\) is an element of \(H_{m}[\cdot]\), which is a histogram information vector of one channel and indicates the value of level k in the channel's histogram.  
Figure 7: Image example of 3-color space; (**a**) RGB image, (**b**) YCrb image, (**c**) HSV image.
## 2 Materials and Methods
### Analysis Phase
#### 2.1.1 Scheduling Algorithms
Usually, state-of-the-art algorithms rely on handcrafted heuristics to make decisions that are otherwise too expensive to compute or mathematically not well defined [41]. This is where machine learning comes into play because it has the ability to make these decisions in a more principled and optimized way.
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 2 Methods
### An Efficient Visual Modeling Approach Based on Mamba  
All current state-of-the-art base models use transformers with self-attention mechanisms. Self-attention mechanisms can achieve more accurate image understanding and more efficient task execution when processing images through the advantages of global information capture, dynamic feature adaptation, parallel computation, and long-range dependency modeling. However, the computational complexity of the self-attention mechanism is about the square level of the length of the input sequence n, which means that the training process requires more computational resources and longer training time.  
Recently, Mamba [7], a sequence model based on State Space Models (SSM), maintains computational efficiency in long sequence modeling through a selective scanning mechanism. The Mamba architecture approximates transformer in terms of global modeling capability while maintaining the computational overhead of linear growth rate and has been applied to vision tasks by scholars. VMamba [28] achieves linear complexity without sacrificing the global sensory field by introducing a state model and a cross-scanning module, while also improving the computational efficiency; U-Mamba [29] addresses the limitations of traditional networks in dealing with long-range dependencies in medical image segmentation tasks, and achieves significant results in a series of complex biomedical image segmentation tasks; Vision Mamba [30] designed a bi-directional SSM and introduced positional coding to specialize in visual signals. In various classification, detection, and segmentation tasks, Vision Mamba has significantly improved accuracy, computation, and memory efficiency compared to existing vision transformers.  
The direct application of Mamba to visual detection tasks leads to a loss of information in the global receptive field. To address this challenge, VMamba uses the VSS block module. By introducing a two-dimensional selective scanning mechanism (SS2D), linear complexity can be preserved while maintaining the global sensory field. In view of the different scales and irregularities of packaging defects, the model needs to have long-distance dependent modeling capabilities, so the long-distance modeling ability of selective state space is transferred to the spatial dimension to process the image information, which is very suitable for detecting packaging defects. Therefore, we design Mamba Block, the core module of this paper, based on the VSS Block. The architecture of the VSS Block and two-dimensional selective scanning mechanism (SS2D) modules is shown in Figure 1.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## SEIRS-D Agent-Based Model  
The SEIRS-D agent-based model proposed in this work is an individual, discrete and stochastic model. This novel model has allowed analysing the malware behaviour from a new perspective, through the integration of new elements that allow the adjustment of the characteristics of the model to more realistic values according to the environment. Therefore, the environment, human action and the devices that interact with the network have been defined as new agents. Coefficients have been group the characteristics of the different agents and the behaviour of the WSN in the environment. Also, transition rules have been adjusted with the coefficients.  
Additionally, the behaviour and characteristics of an agent can be evaluated individually in a step of time \(t\). Finally, this model adds the advantages of agent-based models as a new paradigm for the propagation of malware in wireless sensor networks.  
In the proposed SEIRS-D model, the sensor nodes can adopt, in each instant of time \(t\), one of the following states (see Figure 1):  
* Susceptible: the sensor has not been infected by malware, but they have the computational characteristics to be infected.
* Exposed: the sensor reached by malware but, they are not able to transmit malware to neighbour sensor due to the characteristics of those sensors and malware.
* Infected: the sensor that has been infected by malware. This infected sensor may have the ability to make infection attempts to its neighbours.  
* Recovered: the sensor that acquires temporal immunity when malware has been successfully removed or security fixes have been installed.
* Dead: the sensor that dies because their power has quickly depleted when they have been infected by malware, physical damage or battery life out  
It is supposed that the population of nodes remains constant, consequently: \(S(t)+E(t)+I(t)+R(t)+D(t)=N\) at each step of time \(t\). For a given time \(t\), \(N\) is the total number of agents, \(S(t)\) stands for the number of susceptible agents, \(E(t)\) represents the number of agents in exposed state, \(I(t)\) are the infectious agents, \(R(t)\) denotes the agents in the recovered state and, finally, \(D(t)\) is the total number of agents in dead state.  
Agents are autonomous and heterogeneous entities that can interact with both each other and the environment, according to the transition rules. In the model proposed in this study, these agents are the actors that will be defined in Section 3.1. The environment of agents represents the simulated virtual world in which agents exist. Moreover, the coefficients determine the characteristics associated with each agent; these coefficients will be described in Section 3.2. Finally, the behaviour of agents is established by rules that define the response of the agents to environment changes and relations with other agents. In Section 3.3 these transition rules will be detailed.
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 5 Conclusions and Future Work  
In this paper, we have proposed two SR-based lightweight neural networks (SR-ILLNN and SR-SLNN) for single image super-resolution. We investigated the trade-offs between the accuracy of SR (PSNR and SSIM) and the network complexity, such as the number of parameters, memory capacity, and inference speed. Firstly, SR-ILLNN was trained on both low-resolution and high-resolution images. Secondly, SR-SLNN was designed to reduce the network complexity of SR-ILLNN. For training the proposed SR-LNNs, we used the DIV2K image dataset and evaluated both the accuracy of SR and the network complexity on Set5, Set14, BSD100, and Urban100 test image datasets. Our experimental results show that the SR-ILLNN and SR-SLNN can significantly reduce the number of parameters by 8.1% and 4.8%, respectively, while maintaining similar image quality compared to the previous methods. As future work, we plan to extend the proposed SR-LNNs to other color components as well as a luminance component for improving SR performance on color images.  
Conceptualization, S.K. and D.J.; methodology, S.K. and D.J.; software, S.K.; validation, D.J., B.-G.K., H.L., and E.R.; formal analysis, S.K. and D.J.; investigation, S.K. and D.J.; resources, D.J.; data curation, S.K.; writing--original draft preparation, S.K.; Writing--Review and  
Figure 8: Inference speed on BSD100.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SR-CNN [(9)]** & **AR-CNN [(14)]** & **SR-DenseNet [(18)]** & **SR-ILLNN** & **SR-SLNN** \\ \hline Num. of parameters & 57,281 & 106,564 & 5,452,449 & 439,393 & 262,977 \\ Parameter size (MB) & 0.22 & 0.41 & 20.80 & 1.68 & 1.00 \\ Total memory size (MB) & 14.98 & 17.61 & 224.81 & 80.83 & 36.21 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Analysis of the number of parameters and memory size.  
editing, D.J.; visualization, S.K.; supervision, D.J.; project administration, D.J.; funding acquisition, H.L. and E.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and ICT(MSIT) of the Korea government, grant number20 PQWO-B153369-02.  
Not applicable.  
This research was supported by a grant (20 PQWO-B153369-02) from Smart road lighting platform development and empirical study on testbed Program funded by Ministry of Science and ICT(MSIT) of the Korea government. This research was results of a study on the \"HPC Support\" Project, supported by the 'Ministry of Science and ICT' and NIPA.  
The authors declare no conflict of interest.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Short-Range Static Communications
#### 7.3.2 Wi-Fi (802.11a/b/g/n/ac)  
Standards such as 802.11a/b/g/n/ac aid in fast local wireless communication within a limited area of a car or other nearby devices. Since Wi-Fi standards have great versatility, they have become indispensable for IoV applications. Wi-Fi opens up the possibility of building networks to support vehicles to communicate and make in-vehicle connectivity really intelligent vehicles [102]. Furthermore, Wi-Fi makes it possible to stream data, audio, and video with ease to enjoy a more enriched in-car entertainment experience. In addition to providing an entertainment platform for users, these Wi-Fi standards allow for wireless D2D communication, allowing users to easily connect and securely share information between their devices within the vehicle. The use of Wi-Fi technologies for the IoV framework indicates their great role in the creation of a vehicle with a connected, collaborative, and technologically advanced environment.
## 5 Conclusions  
In summary, this study demonstrated that HR2302-30E and C3G exert anti-allergic effects by inhibiting the activation of mast cells stimulated by IgE. HR2302-30E exhibited strong antioxidant activity based on its high phenolic and flavonoid contents. In addition, the content of cyanidin-3-glucoside (C3G), a representative anthocyanin compound of HR2302-30E, was analyzed to be 7.02 \(\pm\) 0.17 mg/g. HR2302-30E and C3G were shown to inhibit the release of \(\beta\)-hexosaminidase and histamine in RBL-2H3 cells. The anti-allergic mechanisms of HR2302-30E and C3G are associated with the suppression of Fc\(\epsilon\)RI and Fyn/Syk pathway activation in activated mast cells, as well as the inhibition of downstream signaling molecules, such as PLC\(\gamma\), PKC8, and MAPK. This ultimately regulates mast cell degranulation and allergic inflammation. In this study, the antiallergic effects of HR2302-30E and C3G were evaluated in vitro using mast cells. However, future research should validate these findings by using primary human mast cells or basophils and assess reproducibility across various cell lines. Additionally, further in vivo studies utilizing animal models of allergic diseases, such as atopic dermatitis or allergic rhinitis, are essential to confirm the observed effects. Moreover, exploring the potential impact of HR2302-30E and C3G on epigenetic regulation in allergic inflammation could provide valuable insights into their therapeutic potential. Taken together, our results indicate that HR2302-30E and C3G have the potential as natural functional foods for modulating mast cell-mediated allergic diseases.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON], [PERSON]-[PERSON] and [PERSON]-[PERSON]; software, [PERSON]-W.J., [PERSON]-[PERSON] and [PERSON]-H.P.; validation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, [PERSON]-M.Y., [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON]-[PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]-H.P.; supervision, [PERSON] and [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE), grant number 2021 RIS-001.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.