esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 5 Cloud Security  
The protection mechanism against unauthorized access, use, and modification of cloud resources is called security in cloud computing. We employ various technologies, including rules, processes, and controls, to safeguard the cloud-based system's infrastructure from potential threats [29, 30].  
Security risk is related to many types of attacks, threats, vulnerabilities, and other issues. Therefore, it is important to take care when selecting or building a system from a vendor or customer. Thus, the cloud provider uses unique security standards, methods, and models to satisfy the client's requirements.  
Cloud security protects data, applications, and infrastructure inside cloud computing environments. It encompasses an array of methodologies, instruments, and legislation aimed at safeguarding cloud-based systems from intrusions, data breaches, and various security risks [31, 32]. Snooping, data manipulation, and spoofing are three prevalent risks in cloud environments. Snooping attacks entail an unauthorized entity intercepting data transmission between two network hosts. An unauthorized entity can access all traffic data if data transmission lacks encryption [10, 17].  
Data tampering refers to the unauthorized modification (editing, deletion, or manipulation) of data. In a data transmission context lacking protection, an unauthorized individual may intercept the data packet, alter its contents, and redirect its destination. Malicious refers to altered data, including a script to compromise user personal information. Finally, spoofing is an assault executed by a nefarious individual who impersonates another to infiltrate a third system, appropriate data, misappropriate funds, or disseminate malware.  
Figure 6: The structure of a typical cloud-based scientific workflow system [27].  
Numerous spoofing attacks encompass email spoofing, caller ID spoofing, man-in-the-middle attacks, IP spoofing, and website spoofing [30; 31]. Various measures are employed to mitigate these assaults in the cloud, including encryption, intrusion detection systems, zero-trust architecture, antivirus software, and others.  
The Confidentiality, Integrity, and Availability (CIA) triad remains the most widely used framework for defining security vulnerabilities in traditional information systems. The primary goal of this section is to extend these security principles to the emerging cloud infrastructure. Figure 7 illustrates the key components of cloud computing data security, highlighting potential threats and corresponding countermeasures [32; 33]. By adapting the CIA model to cloud environments, we can better understand and address the unique challenges posed by distributed systems, multi-tenancy, and remote access inherent in cloud computing.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 3 System Models and Problem Formulation
### Task Model
#### 3.2.2 Application Completion Rate Model
\(B\) is defined as a set of bandwidth resource allocation policies \(B=\left\{B_{i,j}\right\}\) and \(B_{\max}\) is the bandwidth resources that can be allocated.
# Mechanisms of Sweep on the Performance of Transonic Centrifugal Compressor Impellers
## 5 Aerodynamic Effects of Sweep Feature
### Causal Link between Aerodynamics Effects and Compressor Efficiency  
The cause-effect relationship between the performance improvement and the aerodynamic effects of sweep is illustrated in Figure 23. The essential aerodynamic effect of sweep is the spanwise redistribution of the front loading, subsequently resulting in effects on the shock structure, the tip leakage vortex, and the flow separation. When the blade is forward-swept, the reduced tip loading leads to the suppressed shock strength and the suppressed tip leakage vortex, which improves the efficiency near the tip. When the blade is aft-swept, the decreased hub loading leads to suppressed flow separation, which improves the efficiency near the hub.  
Hence, it is the delicate balance between controlling the shock and the tip leakage vortex near the tip, as well as controlling the near-hub flow separation, that achieves the best efficiency. This is the underlying mechanism for the optimal sweep angle. Since the designed spanwise front loading distribution is usually different, it is deduced that forward sweep is efficient in cases whose front loading near the tip is heavy, while aft sweep raises the efficiency in cases that have high loading near the hub.  
For transonic centrifugal impellers, the tip section is usually heavily loaded due to the existence of shock. Thus, a forward swept main blade is expected to be beneficial. According to the literature survey in Table 2, an optimal sweep angle around +10\({}^{\circ}\) is found based on several transonic centrifugal impellers cases. With the increasing pressure ratio, the effect of sweep on compressor efficiency rises as well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\pi_{d}}\) & **Sweep Range (\({}^{\circ}\))** & \(\mathbf{\eta}\)**Range** & **Optimal Sweep (\({}^{\circ}\))** & **References** \\ \hline
3.0 & \(-25\) to +25 & 1.2\% & +11 & Current study \\
3.7 & \(-5\) to +10 & 1.5\% & +10 & [PERSON] and [PERSON] [25] \\
5.9 & 0 to +20 & 3.7\% & +10 & [PERSON] et al. [41] \\
5.7 & \(-10\) to +10 & 3.9\% & +8 & [PERSON] and [PERSON] [39] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimal sweep design in transonic impellers.  
Figure 23: Illustration of sweep mechanism.  
Figure 22: 3D hub streamlines and static pressure isolines on main blade and splitter I.
# A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer
## 4 Experimental Results
### Performance Analysis  
The results of the models only using the feature data are shown in Table 1. As can be seen in the experimental results, the AUC and KS values of the XGBoost model are higher than those of LR, indicating that the performance of the ensemble learning algorithm is superior to that of the single linear model.  
The results of the models only using the behavioral data are shown in Table 2 and Figure 5. For the models only using the behavioral data, the performance of the deep learning models (LSTM and transformer) exceed that of the traditional machine learning algorithms (LR and XGBoost). This is because the deep learning algorithm can extract higher-level feature information. At the same time, consistent with the results of existing research, the effect of the AM-LSTM model is better than that of the basic LSTM model. The transformer model used in this study performs better than LSTM, AM-LSTM, and traditional machine learning models, and it achieves the highest AUC and KS values.  
The input data of the models using all data include the behavioral data and feature data. The results of the models using all data are shown in Table 3 and Figure 6. From the experimental results, it can be seen that the performance of the LR and XGBoost models is better when using all data than when only using feature data. This indicates that user behavioral data can improve the prediction effect of the credit scoring model. The performance of the deep learning models (LSTM and the transformer) exceeds that of the traditional machine learning models (LR and XGBoost). The performance of the FE-Transformer model proposed in this study is better than that of the other machine  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \hline  & **KS** & **AUC** & **KS** & **AUC** \\ \hline LR & 0.23 & 0.622 & 0.23 & 0.621 \\ XGBoost & 0.248 & 0.634 & 0.241 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of models only using credit data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \hline  & **KS** & **AUC** & **KS** & **AUC** \\ \hline LR & 0.092 & 0.57 & 0.09 & 0.54 \\ XGBoost & 0.1 & 0.58 & 0.095 & 0.553 \\ LSTM & 0.203 & 0.631 & 0.198 & 0.62 \\ AM-LSTM & 0.243 & 0.66 & 0.238 & 0.661 \\ Transformer & 0.26 & 0.679 & 0.25 & 0.672 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of models only using behavioral data.  
learning models, and it also achieved the highest AUC (0.72) and the highest KS values (0.32) on the test dataset.
## 4 Lagrangian Relaxation-Based Solution Procedures
### Procedure of Step 6: Obtaining the Primal Feasible Solutions
#### 4.4.1 Urgent and Penalty (_Uip_) Involved  
Task assignment and scheduling are considered in combination in the first step for obtaining primal feasible solutions. A heuristic is proposed to determine the order of tasks sorted by the level of significance. An index in Equation (10), urgent and penalty (_Uip_), is created by analyzing the coefficients of SUB4 with the penalty value. A brief analysis of the coefficient of SUB4 implies the significance of tasks. However, the multipliers, \(\mu_{i}^{5}\) and \(\mu_{i}^{6}\), are represented as the requested processing time's satisfaction variables and assigned time slot. \(N_{i}\) is the penalty of a task that occurs if the requested processing time is not satisfied. The orchestrator is allowed to determine the crucial decisions of rejection and each task's assigned processing time.  
According to constraints (C. 6) and (C. 7), the decision variable \(b_{i}\) is set to zero if the processing time required by task \(i\) is satisfied. Otherwise, it is set to one if the requirement is not fulfilled. Thus, combine corresponding multipliers \(\mu_{i}^{5}\) and \(\mu_{i}^{6}\) with the penalty of rejection \(N_{i}\) as an index of processing priority.  
\[UP_{i}=N_{i}-\mu_{i}^{5}+\mu_{i}^{6}\qquad\forall i\in I. \tag{10}\]
## 2 Materials and Methods
### Preparation of FeOx/g-C\({}_{3}\)N\({}_{4}\) Composite Thin Films  
The FeOx/g-C\({}_{3}\)N\({}_{4}\) composite materials were prepared by employing the iron oxide urchin film described in Section 2.2 as a support for the g-C\({}_{3}\)N\({}_{4}\), following two distinct procedures as described below and schematically presented in Figure 1:  
1. The g-C\({}_{3}\)N\({}_{4}\) powder was synthesized ex situ by thermally heating melamine for 4 h (protocol from Section 2.3). The synthesized g-C\({}_{3}\)N\({}_{4}\) was dispersed in ethanol with concentration of 40 mg/mL. The prepared suspension was spin-coated on top of the iron urchins with a rotational speed of 800 rpm for 60 s. The composite material was dried at 60 \({}^{\circ}\)C for 4 h to evaporate the extra solvent. The final composite sample is referred to from here onwards as (FeOx/g-C\({}_{3}\)N\({}_{4}\))_ex_.
2. Starting from melamine, g-C\({}_{3}\)N\({}_{4}\) was synthesized in situ on the surface of iron oxide urchin. Specifically, the melamine powder was dispersed in ethanol with a concentration of 100 mg/mL. The prepared suspension was used to spread a controlled amount of melamine precursor on top of the iron urchin film via the drop-casting method. Subsequently, films were heated in a muffle furnace at 550 \({}^{\circ}\)C for 1 h to obtain the g-C\({}_{3}\)N\({}_{4}\) on top of the iron oxide urchins. The prepared composite is referred to from here onwards as (FeOx/g-C\({}_{3}\)N\({}_{4}\))_in_.  
Here, we selected 4 h and 1 h durations of annealing for ex situ and in situ methods, respectively, based on the optimisation described in the Supplementary Materials (Section S1).
# Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications
## 3 Data Analysis
### S-1A Radiometric Stability
Thus, the mean \(\sigma^{\circ}\)-values of the first and third sub-time series are almost equal (with a difference of less than 0.3 dB) (Table 1). Moreover, the mean \(\sigma^{\circ}\)-values of the second sub-time series are higher than those of the first and third by roughly 0.9 dB on average (Table 1). Figure 2: Temporal variations of S-1A \(\sigma^{\circ}\) in both VH and VV polarizations at medium (**a,b**) and high (**c-e**) incidence angles. Dashed vertical lines to the left and right refer to trends for 19 March 2015 and 25 November 2015, respectively. Continuous horizontal black lines in (**a**) and (**c**) denote RS-2 \(\sigma^{\circ}\) for VV polarization. A: Ascending mode; D: Descending mode. Circles denote the S-1A \(\sigma^{\circ}\) affected by rainfall.
## 4 Discussion
### Formation of the Two-Phase Region  
#### 4.8.1 Thickness and Hydrostatic Pressure  
As a result, of the displacement of liquid from the porous medium to the tray, its saturation progressively decreases, causing the formation of a two-phase region in which thickness increases over time. This thickness, \(z_{bf}\), is calculated as a function of time by means of the equation:  
\[z_{bf}(t)=\frac{V_{dt}(t)}{A\varepsilon} \tag{13}\]  
and Equation (6). The thickness of the saturated layer of the porous medium, \(h_{sat}\), is calculated as:  
\[h_{sat}(t)=h_{mp}-z_{bf}(t) \tag{14}\]  
where \(h_{mp}\) is the initial thickness. Figure 13 shows the values of the thickness of the saturated layer of the porous media.  
The hydrostatic pressure \(P_{hs}\) in this region can be evaluated as:  
\[p_{hs}=\int_{z_{pl}}^{h_{mp}}\rho_{(T_{mp})}gdz+\int_{0}^{h_{cb}}\rho_{(T_{cb} )}gdz \tag{15}\]  
where \(z_{pl}\) is the distance to the plate of the interface between the two-phase region and the saturated region of the porous medium. By knowing the density values at the temperature of the liquid in the saturated region \(\rho_{(T_{mp})}\) and in the tray \(\rho_{(T_{cb})}\), Equation (15) is applied to the porous media studied, giving the results shown in Figure 14.  
Figure 13: Thickness of the saturated layer.  
We proceed now to compare the vapor pressure corresponding to the surface temperature of the plate in contact with the porous medium, \(P_{\rm{\it{p}}}(T_{pl})\), with pressure at the temperature at the interface, \((P_{sat})_{d}\), and the hydrostatic pressure, \(P_{\rm{\it{hs}}}\). The saturation vapor pressure in the thin layer of capillary condensation, assuming negligible solute effects, is calculated by the Kelvin equation (Carey [37]):  
\[\frac{\left(P_{sat}\right)_{\delta}}{P_{sat}(T_{\delta})}=e^{\frac{2\sigma}{ \pi\rho_{\rm{\it{H}}}T_{\delta}}} \tag{16}\]  
where \(P_{sat}(T_{\delta})\) is the saturated water vapor pressure at the vapor absolute temperature \(T_{\delta}\); \(\sigma\) is the surface tension of liquid water in contact with its own vapor, \(\rho_{l}\) is the liquid density, and \(r_{m}\) is the mean meniscus radius. Figure 15 shows the results of these magnitudes.  
By observing Figure 15, it is verified that the vapor pressure in the interface is higher than the hydrostatic pressure. The pressure difference, or capillary pressure, promotes its displacement and, consequently, the displacement of liquid to the trap, meanwhile the vapor expands to reach a new state of equilibrium.  
Figure 14: Hydrostatic pressure in the porous media.  
Figure 15: Comparison of vapor pressure corresponding to the surface temperature of the plate in contact with the porous medium, \(P_{\rm{\it{p}}}(T_{pl})\), with the pressure at temperature at the interface, \((P_{sat})\), and the hydrostatic pressure, \(P_{\rm{\it{hs}}}\), in the S+DS porous medium.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 2 Related Work
### Existing Research  
[PERSON] et al. [11] proposed a secure lightweight D2D communication system with multiple sensors. Their proposed communication system is designed based on lightweight key generation and a distribution scheme by leveraging an acceleration sensor and secure near field authentication by using a device's microphone and speaker as sensors and for data transmission, which includes encryption/decryption by audio and RF channels. [PERSON] et al. [12] proposed a lightweight authentication scheme based on elliptic ElGamal encryption, which is public key algorithm based on elliptic curve discrete logarithm problem (ECDLP). This paper presents an authentication scheme based on public key infrastructure (PKI) and uses a combination of ECC to select key pair and ElGamal encryption to exchange the secret key. [PERSON] et al. [13] also proposed a lightweight security scheme based on ECC and ElGamal encryption over public key infrastructure. This paper uses ECC to create keys and ElGamal for encryption and decryption. [PERSON] et al. [14] proposed an incentive-aware lightweight secure data sharing scheme for D2D communication in 5G networks. In their proposed scheme, users obtain digital signatures to prove successful data sharing and, in the sharing process, the symmetric encryption algorithm and MAC are used. [PERSON] et al. [15] proposed a D2D authenticating mechanism employing smartphone sensor behaviour analysis. Their authentication scheme is designed based on certificateless cryptography for group authentication and user's behavior analysis extracted from smartphone sensors is employed for continuous authentication. [PERSON], [PERSON] et al. [16] proposed a lightweight key exchange mechanism for LTE-A assisted D2D communication that can be applied in 5G networks. Their mechanism is designed by using ECC-based symmetric keys. [PERSON] et al. [17] proposed privacy protection device discovery and an authentication mechanism for D2D using the identity-based prefix encryption and ECDH key agreement protocol. All of these studies can provide authentication and data confidentiality/integrity and most of them use ECC based cryptographic algorithms. However, they have some limitations in that some of the results cannot provide anonymity or the researches did not deeply consider the data transmission step of D2D communication. Moreover, most of the existing schemes use only lightweight public key algorithms not lightweight symmetric encryption algorithms. Table 1 shows a taxonomy of strategies of existing research in terms of the security functions provided (confidentiality/integrity, authentication, anonymity) and the steps considered (device discovery, link setup, data transmission). Since our proposed system uses ECC and lightweight AEAD cipher for covering our security considerations and all of the steps in D2D communication, it can improve the efficiency and security of D2D communication.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 4 Simulation Results and Discussion
### Statistical Analysis for R.T.C. France Solar Cell
#### 4.2.3 Three Diode Model  
For this model, Table 7 provides the optimal values of the control variables related to the best run of the proposed TFWO and the compared algorithms, while Figure 13 describes their convergence rates. From both, it can be observed that the best _RMSE_ value (0.000983646) is achieved by the TFWO algorithm, while the second-best _RMSE_ (0.000984242) is achieved by CSO, followed by MRFO, EO, BSA, MPA, BSDE, and GWO. Figure 14 describes the I-V and P-V characteristic curves in comparison with the experimental data, while Figure 15 displays the related errors. From both figures, the coincidence of the simulated data based on TFWO with the experimental data is very high.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{_RMSE_} \\ \cline{2-7}  & **Min.** & **Max.** & **Mean** & **SD** \\ \hline TFWO & 0.000982723 & 0.0012 & 0.00099392 & 3.9352 \(\times\) 10\({}^{-5}\) \\ MRFO & 0.000983378 & 0.001353061 & 0.001077661 & 8.45223 \(\times\) 10\({}^{-5}\) \\ BSDE & 0.000989247 & 0.001492072 & 0.001113348 & 0.000112212 \\ MPA & 0.00101626823 & 0.002869201 & 0.001779704 & 0.000616954 \\ EO & 0.000986861 & 0.001256857 & 0.001033158 & 6.33746 \(\times\) 10\({}^{-5}\) \\ CSO & 0.000983888 & 0.001428127 & 0.00113901 & 0.000155378 \\ GWO & 0.001003603 & 0.038150899 & 0.00640054 & 0.011254562 \\ BSA & 0.000993668 & 0.001214824 & 0.001080621 & 5.45125 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical analysis of _RMSE_ for R.T.C. France DDM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **BSA** & **GWO** & **CSO** & **EO** & **MPA** & **BSD** & **MRFO** \\ \cline{2-7} \(I_{\text{A}}\) (A) & 0.76088788 & 0.761840018 & 0.760767839 & 0.760665312 & 0.76060128 & 0.760721516 & 0.7608 \\ \(I_{\text{A}}\) (A) & 6.11525 \(\times\) 10\({}^{-8}\) & 6.26386 \(\times\) 10\({}^{-7}\) & 8.65078  
For the 30 runs, the minimum, maximum, mean, and standard deviation of the _RMSE_ are tabulated in Table 8. As shown, the proposed TFWO gives the lowest values of the minimum, maximum, mean, and standard deviation as 0.000983646, 0.00102314, 0.000987683, and 7.32713 \(\times\) 10\({}^{-6}\), respectively. Figure 16 displays the _RMSE_ values of the 30 runs for the R.T.C. France TDM, which demonstrate the efficacy of the proposed TFWO in finding the minimum _RMSE_ values compared to the others.  
Figure 16: The RMS values of the 30 runs for R.T.C. France TDM.  
Figure 15: Error values for R.T.C. France TDM based on TFWO: (**a**) Current error values and (**b**) power error values.
## 4 Technologies to Support Contextualization
### Comparing the Present Situation with Historical Data  
We have discussed how data can be interpreted based on formal models of the system. However, especially when systems are too complex to model sufficiently and datasets are too large and noisy to be interpretable for humans, an alternative to such model-based integration is to rely on comparisons with the past. Historical data can be used to find patterns and similarities. Such similarities can either be determined based on large sets of process data or based on situation descriptions generated by humans.
## 2 Materials and Methods
### Properties of the Soil and HPMC  
The gradation curve of the tested soil is shown in Figure 1, wherein D\({}_{10}\), D\({}_{30}\), and D\({}_{60}\) were 0.02 mm, 0.18 mm, and 0.62 mm, respectively. In addition, the coefficient of uniformity (C\({}_{\text{u}}\)) and the coefficient of gradation (C\({}_{\text{c}}\)) of the soil sample were 3.10 and 2.61, respectively. According to ASTM D2487, the tested soil used in this study was silty sand, which was sterilized at 160 \({}^{\circ}\)C for 24 h.  
In this study, HPMC was used as the gelling material, which was purchased from Tianjin Kemeiou Chemical Reagent Co., Ltd. (Tianjin, China). HPMC is a fibrous white powder at room temperature. HPMC has relatively stable properties, superb thickening, cohesiveness, and good compatibility with water. Moreover, HPMC has good film-forming properties and is safe, non-toxic, and non-polluting. The basic physical parameters of HPMC are shown in Table 1. The viscosity of HPMC used in this study was 25 mpa.s.