esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 5 System Architecture
### Architecture Design  
Under an uncontrolled environment, the secure sharing and circulation system of data assets needs to establish the basic ability of the dimensions of secure transaction circulation, secure outsourcing storage, secure cross-border transmission and so on, covering all aspects of the whole life cycle of data assets. In addition, factors such as complex and varied usage scenarios, large volume data asset protection, and interaction with business systems should also be taken into account [15]. The system proposed in this paper is based on the mechanism of micro-service and containerization of cryptography function, and adopts the cloud edge collaborative deployment mode to form an open system with continuous evolution ability and continuous improvement, which can efficiently connect the business system.  
As shown in Figure 6, the secure data asset sharing and circulation system in an uncontrolled environment is constructed based on micro-service and cloud edge framework. Through the integrated central management and control terminal, the whole process of data assets can be traced and is always in a monitored state. The data transmission channel security, data control security, data storage security and data operation security are realized by means of data security reinforcement. The automatic allocation of equipment is carried out by using the shared circulation task of data assets to realize the security of task control. Personnel-authorized control is used to isolate user rights and manage data, implementing data asset security management. The system platform adopts a hierarchical logic architecture, including an edge access layer, a service adaptation layer, a basic service layer and a system resource layer. The edge access layer is deployed in cluster mode to realize data cleaning, data encryption and decryption, secure channel access, application service upgrade, application service expansion and so on. Based on the construction technology of cryptography security service platform for uncontrolled environments, the service adaptation layer is mainly responsible for the arrangement and scheduling of all kinds of security services, including service registration, service discovery, service routing, service orchestration, service monitoring and so on. The basic service layer provides key data security services, including identity authentication, access control, secure storage, operation management, secure return, key management and other cryptography service modules. The system resource layer is mainly responsible for the centralized management of all kinds of security resource libraries on the platform, as well as unified access to the security algorithm library and security protocol library through the construction of resource management services.
## 4 DE Algorithm
### Selection
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Response**} & \multirow{2}{*}{**No**} & \multirow{2}{*}{**Min \(f_{1}\)**} & \multirow{2}{*}{**Min \(f_{2}\)**} & \multirow{2}{*}{**[PERSON] [29]**} & \multirow{2}{*}{**[PERSON] et al. [30]**} \\ \cline{3-3} \cline{5-10}  & & & & & & & & & & **Reduction** & **Value** & **Reduction** \\  & & \multirow{2}{*}{**Control**} & & & & & & & & **(\%)** & **Value** & **(\%)** \\ \hline \multirow{4}{*}{**Valuce**} & Peak displacement (m) & 0.042 & 0.01 & 76.190 & 0.0099 & 76.429 & 0.0093 & 77.857 & 0.0097 & 76.905 \\  & Peak rotation (rad) & 0.0096 & 0.0024 & 75.000 & 0.0023 & 76.042 & 0.0022 & 77.083 & 0.0023 & 76.042 \\  & Peak acceleration & (m/s\({}^{2}\)) & 36.99 & 9.18 & 75.182 & 8.86 & 76.048 & 7.7 & 79.184 & 8.5 & 77.021 \\ \hline \multirow{4}{*}{**Valuce**} & Peak displacement (m) & 0.0005 & 0.00031 & 38.000 & 0.00031 & 38.000 & 0.00041 & 18.000 & 0.00048 & 4.000 \\  & Peak rotation (rad) & 0.00046 & 0.00015 & 67.391 & 0.00016 & 65.217 & 0.00021 & 54.348 & 0.00029 & 36.957 \\  & Peak acceleration & (m/s\({}^{2}\)) & 17.72 & 4.6 & 74.041 & 5.04 & 71.558 & 6.46 & 63.544 & 9.6 & 45.824 \\ \hline \multirow{4}{*}{**Valuce**} & Peak displacement (m) & 0.000074 & 0.00007 & 5.405 & 0.000069 & 6.757 & 0.00011 & \(-\)48.649 & 0.00011 & \(-\)48.649 \\  & Peak rotation (rad) & 0.000053 & 0.000032 & 39.623 & 0.00003 & 43.396 & 0.000055 & \(-\)3.774 & 0.000078 & \(-\)47.170 \\  & Peak acceleration & (m/s\({}^{2}\)) & 8.93 & 2.66 & 70.213 & 2.4 & 73.124 & 5.45 & 38.970 & 9.61 & 7.615 \\ \hline \multirow{4}{*}{**Valuce**} & Peak displacement (m) & 0.02 & 0.011 & 45.00 & 0.01 & 50.000 & 0.011 & 45.000 & 0.01 & 50.000 \\  & Peak rotation (rad) & 0.0053 & 0.0034 & 35.849 & 0.0034 & 35.849 & 0.0037 & 30.189 & 0.0036 & 32.075 \\ \cline{1-1}  & Peak acceleration & (m/s\({}^{2}\)) & 296 & 282.97 & 4.402 & 285.1 & 3.682 & 288.39 & 2.571 & 284.82 & 3.777 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: In compression of 6 th peak node responses of the cantilever beam for the first mode control considering peak displacements, rotations, and peak accelerations. Figure 15: _Cont._  
Figure 16: Contour plot of the 6 th node vertical absolute displacement transfer function \(|H_{D6}(\omega)|\), with respect to frequency and (**a**) TMD mass coefficient, (**b**) TMD stiffness coefficient, and (**c**) TMD damping coefficient, considering \(f_{1}\) and \(f_{2}\) minimization for the first mode control. Figure 15: Contour plot of the 6 th node vertical absolute acceleration transfer function \(|H_{AA6}(\omega)|\) with respect to frequency and (**a**) TMD mass coefficient, (**b**) TMD stiffness coefficient, and (**c**) TMD damping coefficient, considering \(f_{1}\) and \(f_{2}\) minimization for the first mode control.
## 3 Improvement via Clusterization  
In this section, we consider the clusterization method to improve the secret key rate of the satellite-mediated protocol by suppressing the fading noise. We first analyze the additional fading noise caused by transmittance fluctuation in both downlink and uplink scenarios. Then, a clusterization method is suggested to suppress the fading noise for improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Parameter** & **Notation** & **Value** \\ \hline Wavelength & \(\lambda\) & 800 nm \\ Beam curvature & \(R_{0}\) & \(\infty\) \\ Beam spot size & \(\omega_{0}\) & 0.4 m \\ Receiver aperture & \(a_{R}\) & 1 m \\ Detector efficiency & \(\eta_{\rm eff}\) & 0.7 \\ Atmospheric parameter 1  & \(A_{0}\) & \(1.7\times 10^{-14}\) m\({}^{-2/3}\) \\ Windspeed & \(v\) & 21 m/s \\ Mean number of background thermal photons & \(\bar{n}_{\rm B}\) & 0.02 \\ Pointing error & \(\sigma_{\rm P}^{2}\) & 1 \(\mu\)rad \\ Sea-level value (\(\lambda\) = 800 nm) & \(\alpha_{0}\) & \(5\times 10^{-6}\) m\({}^{-1}\) \\ Modulation variance & \(\sigma_{x}^{2}\) & 6 \\ Reconciliation efficiency & \(\beta\) & 0.98 \\ Satellite altitude & \(h\) & 100–500 km \\ Zenith angle & \(\theta\) & 0–60 deg \\ Detector shot-noise (homodyne) & \(\upsilon_{\rm det}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters.  
Figure 2: The asymptotic key rate of the GMCS protocol over the satellite-mediated links versus satellite altitude and zenith angle: (**a**) downlink scenario, (**b**) uplink scenario.
## 6 Results and Discussion  
Table 3 shows our obtained results for 12 Akca's instances. Five main indicators are computed: depots opening costs (OC), which is formed by both fixed and variable costs; routing costs (RC); failure costs (FC), which is incurred whenever the vehicle must perform either a detour or a round-trip to the depot; total costs (TC); and the estimated safety stock (SS) level. Four types of solutions are compared. All of them are flexible, i.e., they consider facility sizing decisions. Firstly, our best deterministic solutions are shown, i.e, there is no uncertainty in the customers' demand and its realization is exactly as expected. In this case, a safety stock is not necessary and there are no failure costs. Secondly, we show the best stochastic solutions reported by [PERSON] et al. (2017), in which the exact customers' demand is not known. Instead, all of them follow a log-normal distribution with known mean and standard deviation. Thirdly, our best hybrid fuzzy-stochastic solutions are displayed, in which half of the customers' demand follows a log-normal distribution, and half of the customers' demand is considered to be fuzzy. Finally, our best fuzzy solutions are shown, in which all customers' demand is considered to be fuzzy, due to a high level of uncertainty. Additionally, results for three levels of variability (\(\lambda\)) are shown. Clearly, our best deterministic solutions are the same regardless of the variability level, given the total absence of uncertainty.
###### Abstract  
In this paper, we focused on the advancement of Dam Monitoring Software that incorporates the Finite Element Method (FEM), as these large infrastructure constructions are crucial for ensuring a dependable water supply, irrigation, flood control, renewable electric energy generation, and safe operation, which is of utmost importance to any country. However, the material properties and geotechnical environments of dams can change (deteriorate) over time, while the standards and legal norms that govern them become more and more rigorous, so in order to accurately assess the state of a dam and detect any concerning behavior, the software must be updated as well. The custom-developed FEM solver, unlike many commercial alternatives, is adaptable and can be reconfigured to function within a Dam Monitoring System. In this paper, we present the procedure for interpolating numerical values at measurement points, when the position of the measurement point does not align with the node of the element, allowing for additional instrument locations to be added to the monitored system without the need for remeshing the numerical model. This procedure is used to compare the actual pore pressures and temperature values of the concrete dam structure with the prediction of the numerical model, and the agreement is much greater with the new interpolation algorithm in comparison to the nearest nodal values, with the average relative difference for pore pressure reduced from 8.89% to 8.10%, justifying this implementation.  
finite element method; finite element interpolation; piezometer measurements; dam monitoring system; structural health monitoring; hydrological monitoring
## 5 Simulation Results
### Simulations Results for the FHS
With the goal to analyze the behavior of each algorithm, Figure 19 shows a comparison of each fuzzy algorithm with noise in the model for the two benchmark problems. Figure 19 shows that DE algorithm is better when noise is added in the model compared to BCO and HS in both benchmark problems. Tables 12 and 13 show the times obtained when using the three original methods with noise and without noise (BCO, DE, HS) and the three proposed methods with noise and without noise (FBCO, FDE, FHS) for each FLC (water tank and inverted pendulum). It should be noted that the time shown is in seconds that lasted for one experiment. Times vary depending on the complexity of the FLC but the fuzzy method takes a little longer but in most cases, it is more efficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Wuter Tank Controller**} \\ \hline \multirow{2}{*}{**Method**} & **Original FLC** & **Original FLC** & **Proposed** & **Proposed** \\  & **without Noise** & **with Noise** & **FLC without Noise** & **FLC with Noise** \\ \hline BCO & 381.603 & 515.283 & 408.816 & 495.371 \\ DE & 205.357 & 237.064 & 228.372 & 268.867 \\ HS & 890.95 & 65.63 & 885.70 & 77.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the time obtained from the water tank controller for the original and proposed algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Inverted Pendulum on a Cart**} \\ \hline \multirow{2}{*}{**Method**} & **Original** & **Original with** & **Proposed** & **Proposed** \\  & **without Noise** & **Noise** & **without Noise** & **with Noise** \\ \hline BCO & 323.84 & 298.92 & 492.03 & 682.54 \\ DE & 310.66 & 380.76 & 474.44 & 682.14 \\ HS & 316.08 & 2326.63 & 508.74 & 1173.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of the time obtained from the inverted pendulum controller for the original and proposed algorithms. Figure 19: Comparison of the RMSE for each method for both benchmark problems with noise: (**a**) water tank controller, and (**b**) inverted pendulum on a cart controller.
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 5 Simulation and Analysis  
### Simulation Settings  
To verify the proposed methods, simulations of wind field identification and prediction are carried out using PC with Inter(R) Core(TM) i7-6820 HQ CPU @2.70 GHz and 16.0 GB RAM. The parameters of the parafoil system are listed as Table 1. Assume that the inertial coordinate coincides with the body coordinate at the initial state, and the initial states of the parafoil system are set as follows: velocity \([v_{x},v_{y},v_{z}]^{T}=[16,0,2]^{T}\) (m/s), Eular angles \([\zeta,\theta,\psi]^{T}=[0,0,0]^{T}\) (rad), and angular velocity \([w_{x},w_{y},w_{z}]^{T}=[0,0,0]^{T}\) (rad/s). The total flight time of the parafoil system is set to 125 s, the crosswind is added at 25 s, the steering rope is pulled down at 37.5 s. The sample interval of parafoil system's position information is set to 1 s, and the position data in 5 s is used to identify the wind field.  
In this paper, the relative error \(e_{r}\) is applied to measure the identification precision, which is defined as  
\[e_{r}=\frac{v_{\textit{user}}-v_{\textit{wind}}}{v_{\textit{wset}}}\times 100\% \tag{26}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value (Unit)** \\ \hline Aspect ratio & 1.73 \\ Area of canopy & 22 m\({}^{2}\) \\ Length of lines & 3.7 m \\ Rigging angle & 7 \({}^{\circ}\) \\ Length of riser & 0.5 m \\ Mass of payload & 80 kg \\ Characteristic area of drag of payload & 0.5 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the parafoil and the payload.  
Figure 3: The wind field prediction of parafoil systems.  
where \(v_{wset}=(v_{wset,x},v_{wset,y})\) is the set wind velocity, and \(v_{wid}=(v_{wid,x},v_{wid,y})\) is the identified wind velocity.  
In terms of the special structure of the parafoil canopy, the parafoil system usually flies at a low speed, thus it is vulnerable to wind fields. For simplicity, we only consider the horizontal constant winds in the simulation environment. Different wind fields and deflections are applied, as shown in Table 2.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 3 Simulation and Analysis
### Result Simulation
#### 3.2.2 Coefficient Quantification Performance Analysis  
Under different fiber transmission distances, we consider multiple quantization levels M, compare the CDE performance of the filter in Equation (4) and the filter in weight-optimized coefficient quantization time-domain chromatic dispersion equalization (CQWO-TD-CDE), such as Equation (20), and study the dependence of BER on the transmission distance of several filters and the influence of the quantization level M on the performance of the filter. In a real link, the signal OSNR is dependent on the link length, and the OSNR required to reach a certain BER level is analyzed. However, in the simulation experiment in this article, we consider the difference of BER under the same OSNR. Therefore, we added the setup OSNR component after the fiber link transmission in the simulation system to add a defined noise floor level to the signal. Figure 5(a,b) depict the four filters'  
Figure 5: The BER performance of the DP-QPSK and DP-16 QAM data. We compare the TD-CDE FIR filter, FD-CDE FIR filter, and WO-TD-CDE FIR filter under different OSNR. (**a**) Under the DP-QPSK modulation format, the fiber transmission distance is 1000 km and 4000 km, respectively. (**b**) Under the DP-16 QAM, the fiber transmission distance is 500 km and 700 km, respectively.  
performance under DP-QPSK and DP-16 QAM modulation, with the OSNR of 15 dB and 21 dB, respectively. From the Figure, we can find that as the number of quantization stages M increases, the filter's performance in Equation (20) is close to the filter in Equation (4). To quantitatively analyze the performance loss caused by quantization, we introduce the quality factor Q, which is defined as the ratio of the difference between the BER before and after quantization to the BER before quantization. Its expression is  
\[Q=(BER_{\text{CQWO-TD-CDE}}-BER_{\text{TD-CDE}})/BER_{\text{TD-CDE}} \tag{24}\]  
The larger the \(Q\), the greater the performance loss caused by quantization. Figure 6c,d show the relationship between quality factor \(Q\) and transmission distance under different quantization stages M.  
Figure 6: The BER and Q-penalty performance of the DP-QPSK and DP-16 QAM data. We compare the filters of the TD-CDE FIR filter and the CQWO-TD-CDE FIR filter under different transmission distances. **(a)** BER curve under the DP-QPSK; the fiber transmission distance is from 500 km to 4000 km, respectively. **(b)** BER curve under the DP-QPSK; the fiber transmission distance is from 100 km to 800 km. **(c)** Q-penalty curve under the DP-QPSK; the fiber transmission distance is from 500 km to 4000 km. **(d)** Q-penalty curve under the DP-16 QAM; the fiber transmission distance is from 100 km to 800 km.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Hybrid Epoxy Nanocomposite
#### 6.3.3 Thermoplastic Resin/Epoxy Resin Nanocomposite  
[PERSON] et al. [109] systematically investigated the synergistic toughening mechanisms of poly(sulfone) (PSF) and graphene oxide (GO) in a diglycidyl ether of bisphenol A (DGEBA) epoxy system. Experimental results demonstrated that the ternary composite containing 5 phr PSF and 0.2 phr GO exhibited a fracture toughness (KIC) of 1.88 MPa\(\cdot\)m\({}^{0.5}\), representing an 89.90% increase compared to the neat epoxy (0.99 MPa\(\cdot\)m\({}^{0.5}\)), significantly surpassing the individual enhancements achieved by PSF (60.6%) or GO (79.8%) alone. Furthermore, the tensile strength increased by 8.80% (from 83.28 MPa to 90.61 MPa), the elongation at break improved by 53.10% (from 4.67% to 7.15%), and the Young's modulus showed a slight enhancement. Thermal analysis revealed that the glass transition temperature (Tg) rose from 157.9 \({}^{\circ}\)C to 161.0 \({}^{\circ}\)C, while the thermal decomposition temperature remained stable at 360 \({}^{\circ}\)C. Microscopic characterization confirmed that PSF induced matrix plastic deformation through uniform dispersion, while GO enhanced interfacial bonding via bridging effects and crack pinning. The synergistic optimization of mechanical properties was attributed to the multi-mechanistic contributions of cavitation-bridging-pull-out interactions between PSF and GO.  
[PERSON] et al. [110] investigated the dispersion of nanoclay particles in poly(methyl methacrylate) (PMMA) using epoxy resin as a dispersant. Through melt-mixing techniques, the dispersion state of nanoclay particles in PMMA at different ratios and their effects on the mechanical properties of the material were studied. Experimental results showed that, when the mass ratio of epoxy resin to clay was 10, a fully exfoliated state of clay particles with a nanoscale dispersion structure was achieved, significantly improving the tensile strength and impact toughness of the material.  
[PERSON] et al. [111] investigated the mechanical properties of ternary composites fabricated by incorporating multi-walled carbon nanotubes (MWCNTs) and high-impactpolystyrene (HIPS) into epoxy resin, aiming to enhance their comprehensive mechanical performance. The study demonstrated that the addition of MWCNTs and HIPS significantly improved the tensile strength, compressive strength, and impact strength of the composites by 52%, 43%, and 334%, respectively, while the flexural strength showed no notable enhancement. Additionally, the elongation at break of the composites also increased.
## 3 Methodology
### Preprocessing  
Machine learning algorithms may require numerical data in specific formats for optimal performance. Initially, our dataset contains textual labels for activity and unformatted numerical data for features. To facilitate data processing, the binary classes \"HIGH\" and \"LOW\" are converted to numerical values using Label Encoding. Label Encoding is a technique that transforms categorical text data into numerical values. In this case, the labels \"HIGH\" and \"LOW\" are encoded as 1 and 0, respectively.  
Furthermore, machine learning algorithms generally perform better when the numerical values are normalized within a specific range, typically between 0 and 1. However, the values in our datasets ranged from 0 to 100, representing percentage compositions. To address this issue, the Min-Max Scaling technique is employed. Min-Max Scaling transforms the data by rescaling each feature to a specified range, usually 0 to 1. This is achieved by subtracting the minimum value of the feature from each data point and then dividing by the range of the feature (the difference between the maximum and minimum values), as per Equation (1).  
\[X^{\prime}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{1}\]  
In Equation (1), \(X\) is the original value, \(X_{min}\) is the minimum value of the feature, \(X_{max}\) is the maximum value of the feature, and \(X^{\prime}\) is the scaled value. This normalization process ensures that the selected machine learning models are effectively trained, as the dataset now contains standardized feature values to a common scale.  
Further preprocessing beyond these steps was not necessary, as the dataset does not contain any outliers. All numerical values are between 0 and 100. Moreover, the dataset does not contain any missing values as all chemical compounds were accounted for during the process of collecting their data from the literature.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset Number** & **Composition Sum Threshold Criteria** & **Number of Chemical Compounds** & **Dataset Name** \\ \hline
1 & Sum greater than 0 & 428 & all\_compounds.csv \\ \hline
2 & Sum greater than 0.1 & 416 & more\_than\_0.1\_compounds.csv \\ \hline
3 & Sum greater than 0.2 & 389 & more\_than\_0.2\_compounds.csv \\ \hline
4 & Sum greater than 0.5 & 301 & more\_than\_0.5\_compounds.csv \\ \hline
5 & Sum greater than 1 & 226 & more\_than\_01\_compounds.csv \\ \hline
6 & Sum greater than 2 & 139 & more\_than\_02\_compounds.csv \\ \hline
7 & Sum greater than 5 & 77 & more\_than\_05\_compounds.csv \\ \hline
8 & Sum greater than 10 & 46 & more\_than\_10\_compounds.csv \\ \hline
9 & Sum greater than 20 & 25 & more\_than\_20\_compounds.csv \\ \hline
10 & Sum greater than 30 & 16 & more\_than\_30\_compounds.csv \\ \hline
11 & Sum greater than 40 & 9 & more\_than\_40\_compounds.csv \\ \hline
12 & Sum greater than 50 & 8 & more\_than\_50\_compounds.csv \\ \hline \end{tabular}
\end{table}
Table 2: Composition distribution of the twelve datasets.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Encoding Parameters  
In this subsection, we will investigate the impacts of two encoding parameters of resolution and QP on the three QoE aspects of perceptual quality, presence, and acceptability. Figures 4-6 respectively show the presence scores, the perceptual quality scores, and the acceptability rates at different QP values and resolutions of three video contents. We can see that lower QP and/or