esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Test Material and Method  
### Material Characterization  
Three rock types commonly encountered in rock engineering applications were chosen for experiments, i.e., one igneous rock (granite), one sedimentary rock (sandstone) and one metamorphic rock (marble). The sampling locations for the three rocks are shown in Figure 2. We conducted X-ray diffraction (XRD) and thin section analysis to obtain the accurate petrologic compositions of the tested materials. Table 1 shows the constituent mineralogical components and their proportions.  
The sandstone was collected from Chuxiong, Yunnan province, China. It consisted of 57.20% quartz, 13.48% feldspar, 5.26% calcite, 3.84% mica and 13.77% clay minerals (9.72% of the sample being smectite and 4.05% being chlorite). The main cement was calcite. The granite was collected from Yueyang, Hunan province, China. It comprises 38.77% feldspar, 32.82% quartz, 22.85% muscovite and 5.56% chlorite. The marble was collected from Hengyang, Hunan province, China. Within the marble, dolomite (96.98%) grains are dominant, in which a little calcite (3.02%) is dispersedly embedded.  
Figure 1: Percentage distribution of uniaxial compressive strength (UCS) reduction induced by water saturation for different rock types (red numbers denote the total percentage falling under a specific range of UCS reduction for various rock types).  
Mean grain sizes were measured using optical polarized micrographs of thin sections. The test results exhibit mean grain sizes of 0.055, 1.970 and 0.083 mm for the sandstone, the granite and the marble, respectively. Nuclear magnetic resonance (NMR) tests were also carried out to determine rock porosities. The porosities of the sandstone, the granite and the marble were 8.50%, 1.07% and 0.63%, respectively. More essential physical parameters are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Type** & **Mineral Composition** & **Percentage 2/\%** & **Grain Size 3/mm** & **Average Grain Size/mm** \\ \hline \multirow{5}{*}{Sandstone} & Quartz & 57.20 & 0.02–0.25 & \multirow{5}{*}{0.055} \\  & Feldspar & 13.48 & 0.05–0.24 & \multirow{5}{*}{1.970} \\  & Calcite & 5.26 & 0.01–0.15 & \\  & Mica & 3.84 & 0.04–0.14 & \\  & Smectite & 9.72 & 0.01–0.03 & \\  & Chlorite & 4.05 & 0.01–0.05 & \\ \hline \multirow{5}{*}{Granite} & Feldspar & 38.77 & 0.1–2.5 & \multirow{5}{*}{1.970} \\  & Quartz & 32.82 & 0.2–2.2 & \\ \cline{1-1}  & Muscovite & 22.85 & 0.05–2.2 & \\ \cline{1-1}  & Chlorite & 5.56 & 0.01–0.2 & \\ \hline \multirow{2}{*}{Marble} & Dolomite & 96.98 & 0.1–0.6 & \multirow{2}{*}{0.083} \\  & Calcite & 3.02 & 0.1–0.4 & \\ \hline \hline \end{tabular}  
* The percentage of mineral compositions was determined by X-ray diffraction technique. 2 The grain size was measured by optical micrographs.  
\end{table}
Table 1: Percentage and grain size of mineralogical compositions for three rock types.  
Figure 2: Sampling locations of the tested rocks and optical polarized micrographs for each rock.
## 2 Data Sources  
In our study, we used data from the SABER radiometer aboard the TIMED satellite ([[http://saber.gats-inc.com/](http://saber.gats-inc.com/)]([http://saber.gats-inc.com/](http://saber.gats-inc.com/)) accessed on 18 April 2023). SABER conducts global measurements of atmospheric parameters with a 10-channel broadband infrared radiometer using the method of Earth's limb scanning within the spectral range from 1.27 to 17 \(\mu\)m [15]. These measurements are employed to plot vertical profiles of kinetic temperature, pressure, geopotential height, \(O_{3}\)(ozone) mixing ratio, OH (hydroxyl) emission intensity and \(O_{2}\)(molecular oxygen) concentration. To compute the height profile of the upper atmosphere I 557.7 nm, we used the height profile of atomic oxygen concentration and filtered data over the area of a 700 km radius with the center at GPO Tory (51\({}^{\circ}\)48\({}^{\prime}\) N 103\({}^{\circ}\)04\({}^{\prime}\) E). More details of SABER data collection and the spatial filtration and calculation of 557.7 nm intensity are given in [16].  
The KEO Scientific \"Arinae\" Fabry-Perot interferometer (FPI) is in the ISTP SB RAS GPO (Institute of Solar-Terrestrial Physics Siberian Branch of Russian Academy of SciencesGeophysical Observatory) near the Tory settlement in the Republic of Buryatia, Russia. The interferometer detects temperature and wind velocity from Doppler broadening and a shift of natural nightglow spectral lines. We use data obtained using the 557.7 nm line emitted by atomic oxygen in a \(\sim\)10 km thick layer at the altitude of 90-100 km above the Earth's surface. Unlike satellite instruments, the ground-based interferometer detects the airglow of the entire layer, without separation according to altitudes. Therefore, the obtained parameters (airglow intensity, temperature, wind) represent some integral values averaged over the entire airglow layer. The technique of detecting atmospheric parameters using the FPI is presented in [17].  
The SATI spectrometer is made on the basis of the ISP-51 serial spectrometer and VIDEOSKAN-285 camera. ISP-51 is a three-prism spectrometer with glass optics, which comprises two identical 60\({}^{\circ}\) prisms and one constant-deviation prism. The prisms are made of AR-coated flint glass and allow the use of the 360-1000 nm spectral band. Just like an interferometer, a spectrometer observes integral airglow intensity. In this paper, we use data on I 557.7 nm as received from this device.  
In our study, we use model NRLMSIS 2.0 developed by a team of authors in [18] on the basis of model NRLMSISE-00. NRLMSIS 2.0 employs rocket and satellite measurements and data from incoherent scatter radars. The model calculates concentrations of He, O, N\({}_{2}\), O\({}_{2}\), Ar, H, N, total mass density and neutral temperature at heights up to 1000 km from the Earth's surface.  
The calculation of nightly averaged values within the area where the SABER data were taken was performed for tangent-point solar-zenith angles over 94 degrees. For NRLMSIS and FPI data, it was performed for nightly data between civilian twilight (solar angles over 99 from zenith).
# Modeling of Tunneling Total Loads Based on Symbolic Regression Algorithm
## 3 Constructing \(\pi\) Quantities Based on Dimensional Analysis
### Constructing \(\pi\) Quantities  
Based on the selected fundamental quantities, the \(\pi\) quantities are constructed through matrix operations. The independent variables \(x_{i}\) and the target quantity \(y\) are expressed by the basic dimensions \(M,L\), and \(T\) in the form shown in Equation (2), where \(\alpha_{i},\beta_{i},\gamma_{i}\) are the basic dimensional indices of the parameter \(x_{i}\), and \(\alpha,\beta,\gamma\) are the basic dimensional indices of the target quantity \(y\).  
\[\dim(x_{i})=M^{\alpha_{i}}L^{\beta_{i}}T^{\gamma_{i}},\dim(y)=M^{\alpha}L^{ \beta}T^{\gamma} \tag{3}\]  
The selected basic quantities are denoted as \(x_{r},x_{s},x_{t}\), and the exponents to be solved are \(s_{ir},s_{is},s_{it}\), respectively. The following operations are performed for each parameter:  
\[\left[\begin{array}{ccc}\alpha_{r}&\alpha_{s}&\alpha_{t}\\ \beta_{r}&\beta_{s}&\beta_{t}\\ \gamma_{r}&\gamma_{s}&\gamma_{t}\end{array}\right]\left[\begin{array}{c}s_{ ir}\\ s_{is}\\ s_{it}\end{array}\right]=\left[\begin{array}{c}\alpha_{i}\\ \beta_{i}\\ \gamma_{i}\end{array}\right]\Rightarrow\pi_{i}=\frac{x_{i}}{x_{r}s^{s_{ir}}x_ {s}s_{ir}x_{t}},\pi_{y}=\frac{y}{x_{r}s^{s_{ir}}x_{s}s_{in}x_{t}s_{it}} \tag{4}\]  
The \(\pi\) quantities corresponding to the parameters \(v,\mu,c,\rho,\sigma_{c},\sigma_{t},F,T\) are as follows:  
\[\pi_{1}=\frac{v}{D\omega},\pi_{2}=\mu,\pi_{3}=\frac{c}{G},\pi_{4}=\frac{\rho \omega^{2}D^{2}}{G},\pi_{5}=\frac{\sigma_{c}}{G},\pi_{6}=\frac{\sigma_{t}}{G},\pi_{F}=\frac{F}{GD^{2}},\pi_{T}=\frac{T}{GD^{3}} \tag{5}\]
# Development of Fingerprint Identification Based on Device Flow in Industrial Control System
### Problem Description  
The current research work on device fingerprinting is mainly based on machine learning [11; 12; 13] or neural network [14; 15]. However, the device addition and deletion operations are frequent in the industrial control system, and the device view is often changed, and the model trained for a device view will soon become invalid. Even though the model proposed by the literature [16] avoids repeated training, it sacrifices a certain accuracy. Some studies [17; 18] build device fingerprints based on traffic characteristics, such as extracting features through special protocol packets such as Domain Name System (DNS) and Address Resolution Protocol (ARP), but many industrial control networks do not have such protocol packets, so corresponding features cannot be extracted. Some systems automatically configure the device network module, so the features extracted from the transport layer and the network layer are almost the same and not unique. Moreover, as the number of devices increases, the accuracy of the feature-based recognition model decreases, and even device confusion occurs. The industrial control system is usually a cyclic production system with a fixed process. The tasks that the equipment is responsible for will not change in a short time, and the data traffic packets sent and received are periodic, so the fingerprint database can be constructed and identified based on the periodicity.
## 5 Simulation Results
### Rates for Different Cell Radius Lengths  
The last analyzed CDFs are generated with different cell sizes, i.e., different radius lengths. By taking into account the path loss term, it is easy to expect lower per-users SINRs, on average, as the cell radius increases; the greater the distance between BS and UT, the greater the path loss. This behavior is clearly represented in Figure 13. We considered a fixed average number of UTs inside the cell for all different cell sizes, therefore the users' density \(\mu\) is changed accordingly such that \(\mu\frac{3\sqrt{3}}{2}R^{2}\approx 65\). Both outage and rate show better results for smaller cell radius.
## 1 Introduction
To overcome this limitation, contrastive learning has recently achieved optimal progress [11; 12] in the field of self-supervised representation learning. Subsequently, contrastive unpaired translation (CUT) [13] introduced contrastive learning to maximize the learning of mutual information between the two image domains for unpaired image translation. In this paper, we propose a self-supervised generative adversarial network (GAN) based on contrastive learning for the virtual staining of single-channel images of FPM. While CUT has demonstrated the efficiency of contrastive learning, it may not efficiently capture domain gaps, color, and the high-frequency information of the images in our application. To further take advantage of contrastive learning while avoiding the disadvantages of cyclic consistency, our method trains a GAN with efficient channel residual (ECR) block and content-consistency loss to learn the cross-channel and high-frequency information of images more effectively, as well as to reduce coherent artifacts, and improve the image quality. The network training and testing processes are illustrated in Figure 2. This method provides a better foundation for subsequent research on medical cell recognition, segmentation, classification, and other related fields. The main contributions of this paper are listed below:  
Figure 1: Different H&E color styles on the two datasets. (a) GlaS dataset, (b) MoNuSeg dataset. We adopt the unpaired learning architecture of CUT to treat the virtual staining of the FPM pathology images as a transformation from a single-channel image to a color image. Since CUT cannot accurately identify the light-colored part of the image and will produce unnecessary artifacts, we design the ECR block in the generator to extract cross-channel interaction information and improve the network performance. * CUT cannot effectively capture the edges and details of the image. We introduce the content-consistency loss based on multi-scale structural similarity (MS-SSIM) to avoid feature distortion between input and output images and enhance the high-frequency information of images. We conducted comparative experiments to demonstrate the qualitative and quantitative advantages of our method in single-channel FPM virtual staining tasks compared to other methods. In addition, our method theoretically reduces the collection time of 2/3 of color FPM, standardizes the staining process, and improves the objectivity of disease diagnosis. Moreover, we do not need to obtain paired data, as simply using unpaired input domain X and output domain Y images can achieve good staining results, which effectively solves the problem of difficult matching data acquisition, especially in digital pathology.
# The Role of HNO2 in the Generation of Plasma-Activated Water by Air Transient Spark Discharge
## 2 Materials and Methods  
Several types of experiments were performed in a one-stage system (ISS) with a single reactor (Figure 1), or in two-stage system (2 SS) with two reactors in series (Figure 2). In ISS, the single reactor enables a simultaneous generation of TS with a direct ES of water. This reactor can be also used for generation of TS without ES, or vice-versa, for generation of ES without TS. In 2 SS, the gas is treated by TS inside the first reactor, and subsequently passed into the second reactor with water electrospray on the microdroplets. In 2 SS, the second reactor is the same as the single reactor used in 1 SS (Figure 2).  
The treated air was either dry synthetic air (99.999% purity, Messer Tatragas, Bratislava, Slovakia), or humidified synthetic air. In addition, dry and humidified O\({}_{2}\) (99.95% purity, Linde Gas, Bratislava, Slovakia) were used in two comparative experiments. In order to moisturize the gas, the dry air was bubbled through a bubbler filled with deionised (DI) water (with conductivity \(<3\) uS/cm). Typically, the relative humidity around 94-96% was achieved, verified by humidity sensor. The gas flow rate 1.1 L/min was constant in all experiments, controlled by rotameters Aalborg. We also performed additional comparative experiments in 1 SS focused on NO and NO\({}_{2}\) solvation to ES water microdroplets without discharge. For this purpose, as well as for calibration of spectrometers, two special pressure tanks with calibration gases were used: one with 2000 ppm of NO in N\({}_{2}\), the second one with 1000 ppm of NO\({}_{2}\) in synthetic air. Lower concentration of NO and NO\({}_{2}\) was achieved by mixing of gases from these bottles with N\({}_{2}\) or synthetic air, respectively. More specific description of the used discharge and individual components of our experimental setups follow.  
Figure 1: Simplified schematic of the experimental setup of one-stage system (ISS), with a single reactor enabling simultaneous TS and ES generation; \(R\)—resistor, \(C\)—capacitor, HV—high voltage.
## 3 Security Issues in the Cloud
### Cloud Data Storage  
Several issues are identified in the studies conducted in the literature related to the incoming and outgoing data in the cloud environment. These include operations on plaintext data, incomplete data deletion, the way cloud data backup is handled, loss of control, collocation of data with others, and data breaches in the cloud DB. In the cloud environment, the common operations performed on the data are uploading the data, processing them, and the transfer of the data among different users and servers. In the cloud, the data in transit and at rest need to be protected. If data transfer takes place as plaintext, the confidentiality of the data will be at risk [40]. Proper encryption mechanisms are needed to protect the data at rest, in transit, and also during processing. Ensuring data confidentiality at the lowest possible cost is crucial. Conventional cryptographic algorithms require data to be decrypted before performing any computations, which can be inefficient [45]. Homomorphic encryption offers a more cost-effective solution, as it allows computations to be performed directly on encrypted data without the need for decryption [56]. One of the characteristics of the cloud is to have a good backup facility, which may help the users to obtain the data and service available to them as and when needed. At the same time, every user has the right to request data deletion. However, while deleting the user's data it should be ensured that the same data will be deleted from every source [40]. Data backup plays a very important role: if in the future any kind of mishap happens with user data, then it can rescue the user data from data loss. The service provider maintains the backup of user data on a regular basis. This is how data availability is ensured; however, if appropriate encryption techniques are not used, it will result in privacy leakage because the service provider (who is storing the backup data) is treated as an untrusted third party [44]. Users store their data on the cloud, where the data and access policies are entirely under the authority of the CSP. Based on the access constraints, the cloud will determine which request it will respond to by transmitting the data. Thus, incompetencies in identity and access management (IAM) may result in major problems [41]. Any malevolent insider or outsider can alter the stored data when IAM is compromised in the cloud [53; 55]. Data collision is possible in the cloud if sufficient separation is not maintained. Data privacy will be compromised as a result [50]. If both internal as well external malicious users obtain access to data stored in the cloud DB, then they can tamper with the original data. Consequently, the data integrity will be compromised [52].
# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 4 Experimental Setup and Analysis of Results
### Experimental Datasets  
The data used in the experiment were bulk amorphous materials data. There were 5935 pieces of data in the dataset, each of which included 94 atomic features and a phase formation label including bulk metallic glass (BMG), crystalline alloy (CRA), and ribbon metallic glass (RMG). The purpose of the experiment was to identify the phase formation of a certain material. This dataset contained 5935 pieces of data, including 675 pieces of BMG, 1552 pieces of CRA, and 3708 pieces of RMG. Thus, it was an imbalanced dataset with a class-imbalanced ratio of 1:2.29:5.49. The dataset was prepared through the following pre-processing steps. The label attribute was changed to the corresponding one-hot encoding, and each feature of the data was normalized to the interval [-1, 1].
## 3 The Multi-Agent Framework
### Framework Design  
The aim of the multi-agent framework is to improve the overall routing performance of existing routing approaches. In this framework, each agent can autonomously create a cooperative neighbour set and each agent can dynamically add or remove cooperative neighbours into or from its cooperative neighbour set. Afterwards, when a sensor has a packet to send, it sends the packet to one of its cooperative neighbours, and the cooperative neighbour then relays the packet to one of the cooperative neighbour's cooperative neighbours until the packet reaches the sink node. For the packet transmission between cooperative neighbours, any existing routing approach can be used.
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
## 4 Experimental Results  
In this section, we evaluate the proposed schemes by simulation, and the experimental results are shown in Figures 4-7 for \(n=3,4,5\) and 6, respectively. The secret image we used in the experiment is a halftone image with \(300\times 300\) pixels. Because there is no pixel expansion, the size of two shares is also \(300\times 300\) pixels after encryption. Information on the restored image can be identified clearly after stacking these two generated shares perfectly or with a 1-, 2- or 3-pixel shift. It shows that the proposed schemes are effective and that our analysis is valid.  
then, the CI is calculated as the average of all CI(B) and CI(W). For two shares \(G_{1}\) and \(G_{2}\), using the parameter transmittance \(T_{B}(G_{1}\oplus G_{2})\) and \(T_{W}(G_{1}\oplus G_{2})\) for the black and white pixels of the secret image, we have:  
\[\text{CI}(\text{B})\ =\ 1-2T_{B}(G_{1}\oplus G_{2}),\ \text{CI}(W)\ =\ 2T_{W}(G_{1}\oplus G_{2})\ \text{and}\ \text{CI}\ =\ (\text{CI}(\text{B})\ +\ \text{CI}(\text{W}))/2\]  
Figure 4: The experimental results for \(n=3\).  
Figure 5: The experimental results for \(n=4\).  
Figure 6: The experimental results for \(n=5\).  
Besides, in their paper, the parameter \(\alpha^{2}\) is used as the ratio of pixel expansion, that is \(\alpha\) can be seen as the ratio of pixel expansion in one dimension, and PB is the percentages of the big subpixels in a share.  
Figure 8: Comparison of our schemes with the misalignment-tolerant visual secret sharing (MTVSS) scheme for shifting the same pixels. CI, correctness index.  
Figure 7: The experimental results for \(n=6\).  
In the following, we compare the value CI of our schemes to the MTVSS scheme. In Figure 8, we use the result of the MTVSS scheme when \(\alpha=2\), which means that the pixel expansion of the shares is four-times the secret image. It is not difficult to see that our results are similar to that of PB = 1 in the MTVSS scheme when \(n\) is four and five and better when \(n\) is six. Figure 9 compares our schemes to the MTVSS scheme for shifting the same percentage of the shares (\(d_{x}/\alpha\)). We use the result of the MTVSS scheme when PB = 1 (the best performance among different PBs) for different values of \(\alpha\). One can see that our results are better than the MTVSS scheme when \(n\) is bigger than three. In conclusion, both the theoretical analysis and simulation results demonstrate the effectiveness and practicality of our proposed schemes. In Table 10, we list some capabilities of our scheme compared to some previous works.