esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Prior Light-Scattering Calculations  
The forward inverse method described in Section 2.4 does not require any knowledge of boundary conditions or reference zone but several lidar-relevant quantities are still needed such as lidar extinction-to-backscatter ratio. These quantities are assumed constant within an aerosol layer with a specific composition, similar shape, and size distribution. These quantities are defined in Section 2.1 and can be computed from light-scattering models for spherical fog-oil particles using the Lorenz-Mie theory [54] and measurements provided by an optical particle counter ([PERSON], Fidas 200). Table 2 reports the fog-oil microphysical, optical, and lidar-relevant properties.  
The average lidar-relevant parameters \(\langle\mathrm{d}C^{\mathrm{bac}}\rangle\) and \(\langle\mathrm{LR}\rangle\) were calculated using the normalized log-normal function \(\mathrm{n_{p}}(\mathrm{R})\), i.e., normalized for a single particle per volume unit, with a modal radius \(R_{p}=0.180\) um and a geometric standard deviation \(\sigma_{g}=1.15\). It must be noted that these parameters were provided by the optical counter and that the minimum size measurable by the optical counter may slightly impact this value. The complex value \(m=1.508+i10^{-5}\) was used for the refractive index of the generated fog-oil plumes, which is the value reported in the literature [55; 56]. From these microphysical and optical properties, the two main lidar-relevant parameters of interest in this study, \(\langle\mathrm{dC}^{\mathrm{bac}}\rangle\) and \(\langle\mathrm{LR}\rangle\), were calculated using a double-precision Lorenz-Mie scattering code for spherical polydisperse homogeneous particles [23]. The average differential backscattering cross-section and lidar ratio were found to be \(\langle\mathrm{dC}^{\mathrm{bac}}\rangle=3.16\;10^{-3}\;\mathrm{\upmu m^{2} \;sr^{-1}}\) and \(\langle\mathrm{LR}\rangle=73.1\;\;\mathrm{sr}\), respectively.
## 7 Ground-Segment Optimization Aspects
### Optimization Metrics Related to the Ground Segment
\[FD_{\mathrm{s}}(\rho)=t(\rho)\sigma_{\mathrm{s}} \tag{20}\]  
\[t(\rho)=-0.061\left(\log_{10}\rho\right)^{3}+0.072\left(\log_{10}\
u\right)^{ 2}-1.71\log_{10}\rho \tag{21}\]  
For other optimization metrics from [150], the authors utilized the model by [PERSON] (a detailed explanation can be found in [152]) for the ionospheric delay and the International Telecommunication Union--Radio-communication unit (ITU-R) model for the rainfall attenuation. Another ground-segment optimization problem was addressed in [26] in the context of generic high-throughput satellites (with unspecified orbit altitudes) and using a machine-learning optimization via deep learning with Long Short-Term Memory (LSTM). The principle in [26] was to allow redundant ground stations (or gateways) in such a way that the effect of rain attenuation was mitigated during the ground-to-satellite and satellite-to-ground communications. The rain attenuation was modeled by ITU-R models in this study as well, and the optimization metric was the Carrier to Interference plus Noise Ratio (CINR), which is expressed in Equation (22) at a discrete sample (or time instant) \(n\) and for the \(i\)th gateway, where \(|h_{i}[n]|\) is the wireless channel amplitude at sample \(n\) for the \(i\)th gateway, \(\sigma_{s}\) is the noise variance for the ground-to-air channel, \(I_{i}\) is the interference level affecting each gateway site, and \(P_{i}\) is the average transmission power. \[\mathrm{CINR}_{i}[n]=\frac{P_{i}|h_{i}[n]|^{2}}{\sigma_{i}+I_{i}} \tag{22}\]  
Yet another ground-segment optimization in the context of optical ground-to-satellite links was investigated in [153], targeting the Link Outage Probability (LOP) as the optimization metric and considering three brute-force methods and a fixed maximum number of ground stations (namely 66) with fixed locations. The optimization output was a subset of ground station locations that offer the lowest LOP, which is defined as a function of \(M\)-out-of-\(N\)-availability given by Equation (23), where \(M\) refers to the smallest number of available GS required at a given time, \(N\) refers to the total number of GS in the network, \(A_{i}\) is the availability of \(i\)th GS, and \(q_{i}=1-A_{i}\). \[\mathrm{LOP}=\sum_{i=0}^{M-1}\binom{N}{i}A_{i}\times q_{N-i} \tag{23}\]
## 4 Characterization Framework
### Data Management and Consistency
Two-phase commit (2 PC) pattern is the traditional and only synchronous solution recommended for the management of distributed transactions. This pattern consists of prepare and commit phases and ensures that the data in the entire service are consistent at any given time. According to its setup, in case of failure, writing operations are blocked and availability is compromised. _SAGA_ is another alternative for distributed transaction management. It is asynchronous and is used to ensure eventual data consistency without ACID operations when spanning multiple services [23]. When necessary, SAGA carries out compensatory actions at different stages to take it back when any business rule is violated. Each local transaction causes the start of the new local transaction by publishing a new domain event and, at the same time, compensatory functions are executed to undo local transactions as needed [24]. SAGA is difficult to implement due to the reasons of implementing these compensatory actions, the developers implementing these compensatory actions, and the difficulty of managing and debugging these processes. Furthermore, a microservice needs to update its business entity and transmit the message atomically to avoid data integrity and possible bugs. This situation is possible with some improvement of the solution brought by SAGA. With the event sourcing pattern, the atomicity problem is avoided. It stores all states of the business entity in order in the event store. State changes and message delivery are performed atomically on the business entity and a new state event store is added for each state change. In this way, a more reliable transaction infrastructure is provided. Moreover, the status of the business entity at any time can be determined by queries made over the event store [25]. Queries requiring different microservices have become difficult with the existence of distributed transactions. Because most queries are obtained by joining operations over data of more than one service, to overcome this situation, a structure that makes separate queries from each service and combines them can be considered. The API composer pattern recommends this. The results of the original query are calculated by firstly dividing the queries into the required services and then composing the results from each service. However, this situation often causes in-memory problems due to the excess of in-memory joins [23]. Another solution is the command query responsibility segregation (CORS) pattern. With the CQRS pattern, queries are made over a view database that is registered to domain events and shaped according to the type of queries, thus making handling of complex queries easier [26; 27].
## 4 Proposed Model
### Handling Imbalance Data  
SGCC dataset causes model bias towards the majority class. Figure 4 shows that normal users are greater in number as compared to abnormals, due to which the model misclassifies the minority class. To address the imbalanced data problem, two methods are used in the literature: cost function and sampling techniques. In this paper, we use the sampling technique. There are two types of sampling techniques: random undersampling and random oversampling. In random undersampling, some data points from the majority class are discarded and the majority class is made equal to the minority class. This sampling technique requires less execution time but leads to the loss of important information. In random oversampling, data points from the minority class are replicated randomly, so no information is lost and both majority and minority classes are balanced.  
In this paper, we propose SOSTLink, which is a combination of oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling technique Tomek Link. The SMOTE algorithm is used for oversampling and Tomek Link is used for undersampling. SMOTE generates data points by taking the means of two numbers from the minority class. For example, take an instance of the minority class as \((y_{1},y_{2})\), if its nearest neighbor is chosen as \((y^{\prime}_{1},y^{\prime}_{2})\), then the generated data points shown in Equation (2)  
\[(y_{1},y_{2})=(y^{\prime}_{1},y^{\prime}_{2})+random(0,1) \tag{2}\]  
Figure 3: Flow chart of proposed system model.  
where,  
\[X=(y_{1}^{\prime}-y_{1}),(y_{2}^{\prime}-y_{2}) \tag{3}\]  
Random function provides a random number between 0 and 1. By applying SMOTE, minority class data points are generated and balanced with the majority class. Figure 5 shows the data set after applying SMOTE. However, it does not consider neighboring examples from other class. This can increase overlapping of same class data, introduce additional noise, and keep its prediction away from actual residential customers. Moreover, it cannot be applied to high-dimensional data. Tomek Link is used for undersampling in case the observations near to the borderline of minority class are removed [37]. The undersampling steps are as follows.  
1. Read input from the dataset.
2. Minority samples are generated from input dataset.
3. Majority samples, which are nearest neighbors of minority observation, are also generated.
4. Combine both observation samples from majority and minority.
5. Delete all majority samples that are the nearest neighbor of minority samples.  
Figure 4: Normal data.  
Figure 5: Data after applying Synthetic Minority Oversampling TEchnique (SMOTE) algorithm.  
Now dataset is undersampled as observations from majority class are removed.  
In our proposed SOSTLink, samples are generated in the minority class and samples are removed from the majority class as shown in Figure 6. It corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class.
## 4 Results
### Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**AUC (Numerical Datasets)**} & \multicolumn{8}{c}{**AUC (Discretized Datasets)**} \\ \hline
**Course** & **HCI** & **IS** & **ICS2** & **IP** & **PM** & **IFPs** & **avg** & **HCI** & **IS** & **ICS2** & **IP** & **PM** & **IFPs** & **avg** \\ \hline
**HCI** & 0.943 & 0.510 & 0.522 & 0.538 & 0.524 & 0.457 & 0.58 & 0.769 & 0.621 & 0.569 & 0.417 & 0.570 & 0.550 & 0.58 \\
**IS** & 0.485 & 0.927 & 0.494 & 0.470 & 0.606 & 0.520 & 0.58 & 0.479 & 0.816 & 0.577 & 0.555 & **0.656** & 0.596 & 0.61 \\
**ICS2** & 0.514 & 0.590 & 0.783 & 0.500 & 0.569 & 0.513 & 0.58 & 0.503 & 0.558 & 0.619 & 0.485 & 0.516 & 0.552 & 0.54 \\
**IP** & 0.484 & 0.420 & 0.472 & 0.862 & 0.490 & **0.627** & 0.56 & 0.519 & 0.576 & 0.535 & 0.761 & 0.491 & 0.409 & 0.55 \\
**PM** & 0.514 & 0.489 & 0.530 & 0.618 & 0.899 & 0.610 & 0.61 & 0.574 & 0.488 & 0.522 & 0.592 & 0.793 & 0.480 & 0.57 \\
**IPs** & 0.516 & 0.529 & 0.514 & 0.427 & 0.597 & 0.921 & 0.58 & 0.507 & 0.638 & 0.485 & 0.514 & 0.460 & 0.884 & 0.58 \\  & \multicolumn{8}{c}{avg mcm} & & & 0.58 & & & & & & & & & & \\ \hline \multicolumn{8}{c}{**AUC LOSS (Numerical Datasets)**} & \multicolumn{8}{c}{**AUC LOSS (Discretized Datasets)**} \\ \hline
**Course** & **HCI** & **IS** & **ICS2** & **IP** & **PM** & **IFPs** & **avg** & **HCI** & **IS** & **ICS2** & **IP** & **PM** & **IFPs** & **avg** \\ \hline
**HCI** & - & 0.432 & 0.421 & 0.404 & 0.418 & 0.486 & 0.43 & - & 0.148 & 0.201 & 0.352 & 0.200 & 0.220 & 0.22 \\
**IS** & 0.442 & - & 0.433 & 0.457 & 0.321 & 0.407 & 0.41 & 0.337 & - & 0.238 & 0.260 & 0.160 & 0.219 & 0.24 \\
**ICS2** & 0.270 & **0.193** & - & 0.283 & 0.215 & 0.271 & **0.25** & 0.116 & **0.061** & - & 0.134 & 0.103 & 0.067 & **0.10** \\
**IP** & 0.378 & 0.441 & 0.390 & - & 0.371 & 0.235 & 0.36 & 0.242 & 0.184 & 0.225 & - & 0.269 & 0.352 & 0.25 \\
**PM** & 0.385 & 0.410 & 0.369 & 0.281 & - & 0.290 & 0.35 & 0.219 & 0.305 & 0.271 & 0.200 & - & 0.313 & 0.26 \\
**IPs** & 0.405 & 0.392 & 0.407 & 0.495 & 0.324 & - & 0.40 & 0.377 & 0.246 & 0.400 & 0.370 & 0.424 & - & 0.36 \\  & \multicolumn{8}{c}{avg mean} & & & **0.37** & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: AUC Results and Loss in Portability in high level of usage of Moodle group.
## 4 Discussion
### Reliability of Applied Methods in Aeromonas salmonica subsp. salmonicida Biofilm Testing  
A few authors proposed the formula to calculate the strength of biofilm production based on the obtained ODc values from the crystal violet method, and it is proposed that, based on the results of the calculation, strains should be categorized into strong, moderate, weak biofilm producers, and those that do not produce biofilm [35]. The vast majority of scientific works still apply the crystal violet method, but without this proposed categorization, just simply showing obtained ODc values [19]. In our research, we did not apply the mentioned formulas because the break point values between the biofilm categories still do not exist, so proposed calculations, which are basically arbitrary and give only approximate, descriptive results, could have led us to the wrong conclusion that _Aeromonas salmonicida_ subsp. _salmonicida_ does not produce any biofilm, because of the very low ODc values obtained in M9 and ABTG media. Also, in our research, ODc values oscillated irregularly, especially at low temperatures and in samples with no detected viable cells. From the obtained results, it can be concluded that crystal violet is not a reliable method for testing _Aeromonas salmonicida_ subsp. _salmonicida_ biofilms in nutritionally scarce media, at low temperatures. [PERSON] [25] used crystal violet in PhD research, investigating only one _Aeromonas salmonicida_ subsp. _salmonicida_ strain in CM9 (M9 with added casamino acids), at elevated temperatures (20 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 28 \({}^{\circ}\)C), incubated for only 12-24 h, and obtained relatively low ODc values at approx. 0.05 at 20 \({}^{\circ}\)C.  
However, from the results obtained in our investigation, the direct enumeration of viable cells in the biofilm with the determination of log CFU proved to be a more reliable method, which successfully detected even weak, hardly detectable biofilms in scarce media.
## 4 Influence of Ageing on Charge Transport Properties and Discussion
### Influence of Ageing on Trap Distributions
On the one hand, the internal molecular chain of silicone rubber may be curled, entangled, and crosslinked, and some branch chains and tail groups are combined to generate a large number of new shallow traps. So, the density of shallow traps is increased [19]. On the other hand, the molecular chain may also be broken, causing shallow traps inside the silicone rubber [6]. In addition, crosslinking reactions were observed in silicone rubber by the nuclear magnetic resonance technique [20]. Additionally, density functional theory calculations found that the vinyl groups and cross-linking points of silicone rubber can generate traps [13]. The results of shallower trap levels obtained through surface potential decay experiments in Figure 7 are consistent with those obtained by density functional theory calculations. These shallow traps can assist the carrier hopping on the material and elevate the conductivity [21], as shown in Figure 5.  
Figure 7: Surface trap distributions of pristine and aged silicone rubber samples after (**a**) positive corona charging and (**b**) negative corona charging.
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 3 Finite Element Model  
The seismic analysis of the prototype bridge was performed using three dimensional (3D) finite element software OpenSees [23]. A total of 691 nodes and 532 elements were used in modeling the bridge. Figure 2 presents the 3D finite element modeling approaches of the components of the bridge and their property values. The pylons were modeled with fiber sections comprised of unconfined concrete, steel, and confined concrete. The deck was assumed to remain elastic during the whole stage, thus, linear-elastic beam-column elements were adopted according to [PERSON] and [PERSON] [24]. In particular, the vertical, transversal, axial, and torsional stiffness properties (in Table 1) of the deck section are concentrated in a single central longitudinal spine, made up of 246 two-node beam elements. Additionally, 68 couples of transversal offset rigid links connect the spine nodes with each of the 136 cable anchorage points. The mass of each spine node is equally arranged at its anchorage points, so  
Figure 1: General view of the prototype bridge: (**a**) longitudinal geometry of the bridge; (**b**) geometry of the bridge pylon.  
that the longitudinal rotational inertia of the deck can be considered in the model [25]. Several methods have been proposed to model the stay cables, such as the single truss model (one element cable system-OECS) [26] and multi-element cable system (MECS) [25; 27; 28; 29]. The OECS model does not account for the local cable transversal motion, and fails to describe its interaction with the global dynamics. On the contrary, the MECS model can capture the local vibration of stay cables, thus, it is considered a more refined cable modeling method. However, the less refined OECS model is adopted in this paper due to its simplicity and less computational time. The stiffness characteristics of an inclined cable can exhibit nonlinear behavior due to cable tension and sag, and this effect was considered by linearization of the cable stiffness using an equivalent modulus of elasticity. The large-displacement truss element in OpenSees was used to simulate stay cables. The cable force can be applied to the bridge by defining a tension-only material with initial strain, and this initial strain can be calculated based on the force, equivalent modulus of elasticity, and the area of a cable. Rigid links were used to connect the cables to the pylons and deck. The passive pressure and the pile of the abutments were modeled with multilinear backbone curves recommended by Caltrans criteria [30]. Elasto-plastic bearings are used in the bridge, and the yield displacement and force are specified in Figure 2. Pounding between deck and abutments in the bridge is molded by zero-length elements using the bilinear model recommended by [PERSON] et al. [31].  
Figure 2: Analytical model of the prototype bridge.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 2 Models and Setup
### Numerical Setup  
In this section, the cruise altitude of the transport aircraft is around 10 km and the HEMP generates at a height of about 30 km [1]. In the scope of the horizon, the transport aircraft suffers from the HEMP field with a direction of oblique incidence. At the same time, the field strength solution point is set as shown in Figure 3. In Figure 3, the gray surface represents the layers for solving the electric field strength distribution. The three layers include: the top layer, inner layer and bottom layer.  
Figure 2: Typical transport aircraft electromagnetic model.  
For the frequency sweep method with full-size dimensions, the main structures of the aircraft within the HEMP field are considered. Additionally, the main structures are composed of relatively complete large-size structures (such as the fuselage, the wing, and the tail). These structures are relatively simple without obvious sudden changes and electrical discontinuity. Meanwhile, the smaller and finer structures are mainly located in the engines, wing edges, and junctions of different structures. As these positions occupy a minor proportion of the aircraft geometry model, they are of less concern. Before the calculation, three mesh sizes (2941, 5882 and 11,764) are used to obtain the optimal size. For the electric field intensity \(E(t)\), the relative errors of \(E(t)\) at \(t=5\) ns at a certain point are 3.9% and 0.2% respectively. To ensure accuracy and time efficiency, an appropriate mesh size of 5882 has been selected as shown in Figure 4.
###### Abstract
The American Association for the Study of Liver Disease (AASLD) recommends the use of two algorithms, FIB-4 and NFS, for the assessment of liver fibrosis based on routine clinical and laboratory parameters, which seem to be useful in predicting advanced hepatic cirrhosis or fibrosis [14]. Thus, FIB-4 takes into account the age, platelets count, aspartate aminotransferase (AST), and alanine aminotransferase, and it was proved to be useful for the stratification of NAFLD patients differentiating between those with advanced fibrosis versus none [15; 16; 17]. NFS is an even more complex algorithm including not only the age, the platelets, and AST/ALT ration, but also BMI, albumin levels, and the assessment of glucose tolerance/diabetes, representing a reliable alternative for liver biopsy [8]. Studies performed on children and adolescents with obesity pointed out higher levels of AST/platelets ratio index (APRI) in these patients, but failed in identifying a correlation with liver stiffness [18; 19]. In addition to liver transaminases, bilirubin, lipid profile parameters, fasting glucose, and insulin are also useful for diagnosing fatty liver disease in patients with metabolic syndrome [13]. A clear bidirectional interrelation has been stated between NAFLD and metabolic syndrome, whose components (e.g., type 2 diabetes mellitus, obesity, hypertension, dyslipidemia) additionally increase the risk of developing NAFLD [20; 21; 22; 23]. Furthermore, NASH carries higher rates of both morbidity and mortality increasing the risk of cirrhosis and hepatocellular carcinoma [24], with a higher mortality as compared to isolated or simple steatosis [25]. Taking into account the current era of pediatric obesity, the early detection and staging of NAFLD based on a more complex non-invasive approach including clinical, laboratory, and elastography parameters might contribute to the effective prevention of further irreversible hepatic lesions and decrease mortality during adulthood. The aim of this review was to assess the role of elastography in pediatric NAFLD staging, as well as identifying its potential limitations and challenges in this age group.