esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Frequency-Domain Damping Extraction Methods
#### 3.1.2 Least-Square Rational Function Estimation  
The least-square rational function (LSRF), presented in [37], estimates the transfer function of a multi-input multi-output (MIMO) system by a rational function that consists of a numerator (\(\text{Num}(s)\)) divided by a denominator (\(\text{Den}(s)\)). Given the frequency response of a system, \(H(\omega)\), the LSRF tries to choose \(\text{Num}(i\omega)\) and \(\text{Den}(i\omega)\) such that minimizes the difference between the frequency response and the estimated one. Hence, the cost function \(J\) is introduced as  
\[J=\int\frac{|W(i\omega)|^{2}}{|\text{Den}(i\omega)|^{2}}||(\text{Num}(i\omega) -\text{Den}(i\omega)H(\omega))||^{2}d\omega, \tag{8}\]  
where \(W(i\omega)\) is a frequency-dependent weight. To solve this nonlinear least-square problem, the Sanathanan and Koerner (SK) iteration is utilized. This approach estimates the \(\text{Num}(i\omega)\) and \(\text{Den}(i\omega)\) iteratively by fixing the denominator [37; 38]. Then iterate:  
\[j^{k}=\int\frac{|W(i\omega)|^{2}}{|\text{Den}^{k-1}(i\omega)|^{2}}||\Big{|} \Big{(}\text{Num}^{k}(i\omega)-\text{Den}^{k}(i\omega)H(\omega)\Big{)}||^{2}d\omega, \tag{9}\]  
where \(k\) is the iteration step and \(\text{Den}^{k-1}(i\omega)\) is the denominator found from the previous iteration. By utilizing this method, calculating \(\text{Num}^{k}(i\omega)\) and \(\text{Den}^{k}(i\omega)\) is a linear least-square problem. It should be noted that the initial denominator is chosen as \(\text{Den}^{0}(s)=1\).
## 4 Conclusions  
Herein, AuNPs were successfully synthesized by reaction of the aqueous extract of _Zingiber officinal_ rhizome with chloroauric acid; this was confirmed by color change (from colorless to purple color). A UV-Vis spectrophotometer showed maximum SPR at 530 nm. TEM showed spherical nanoparticles with sizes of 5-53 nm (average nanoparticle size: 15.11 \(\pm\) 8.5 nm). XRD confirmed the crystalline nature of the material. FT-IR spectroscopy enabled identification of the main functional groups in the plant extract and synthesized AuNPs. The high stability of nanoparticles in the colloidal solution was confirmed by DLS and zeta potential characterizations. Biological activities, including antimicrobial activity, as well as antioxidant and in vitro cytotoxic efficacy, were investigated and found to be dose-dependent. The synthesized AuNPs displayed high activity against pathogenic microbes, high antioxidant activity, and target-oriented cytotoxicity toward cancer cell lines (compared to negligible cytotoxicity against normal cell lines). Overall, _Zingiber officinal_ extract shows high potential to produce highly active AuNPs, with potential for various biomedical applications.  
Conceptualization, A.F., A.M.E., E.G. and S.E.-D.H.; methodology, A.F., A.M.E., M.F.H. and S.E.-D.H.; software, A.F., A.M.E., E.G., M.F.H. and S.E.-D.H.; validation, A.F., E.G. and S.E.-D.H.; formal analysis, A.F., A.M.E., E.G., M.F.H. and S.E.-D.H.; investigation, A.F., A.M.E., D.H.M.A., D.E.-H. and S.E.-D.H.; resources, A.F., A.M.E., D.H.M.A., D.E.-H. and S.E.-D.H.; data curation, A.F., A.M.E. and E.G.; writing--original draft preparation, A.F., A.M.E. and S.E.-D.H.; writing--review and editing, A.F., E.G. and M.F.H.; visualization, A.F. and A.M.E.; supervision, A.F., E.G. and S.E.-D.H.; project administration, A.F. and E.G.; funding acquisition, A.F., M.F.H., D.H.M.A. and D.E.-H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding authors.  
The authors extend their appreciation to the Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Cairo, Egypt, for the great support in the achievement and publication of this research work. Furthermore, the authors acknowledge the support from Princess Nourah bint Abdulrahman University Researchers Supporting (project number PNURSP2022R15), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
## 2 Methodology of DLR Measurements
### Large-Aperture Telescope for DLR Measurements  
A larger telescope aperture can receive more laser photon echoes and then realize a better performance of DLR. In 1994, at the Ninth International Laser Ranging Conference in Canberra, FUGATE R announced that a telescope with an aperture of 3.5 m could range space debris at a distance of 1000 km. In 2004, Electro Optic Systems in Australia reconstructed a DLR telescope having an aperture of 1.8 m and ranged space debris of a size of 10 cm at a distance of 1000 km using an average laser power of 100 W [1]. The system could carry out the three-dimensional measurement of space debris, realizing the high-precision positioning and orbit determination of debris targets, and improve the accuracy of cataloging space debris. In 2012, the Grasse station in France employed a telescope with an aperture of 1.56 m to achieve DLR for an orbital height of 1700 km [23].  
SHAO developed a DLR system with a telescope having an aperture of 1.8 m and a laser system having a PRF of 200 Hz, pulse energy of 300 mJ and a wavelength of 532 nm. However, the noise from the background and debris targets was several times greater than that of the SHAO's DLR system with a telescope having an aperture of 60 cm, which affects the laser echo detection and then deteriorates the detection capability. Narrow spectral filters are commonly used to reduce the level of background noise in  
Figure 8: Experimental configuration of the efficient coupling of light to the fiber (**left**) and application of the SNSPD in laser ranging (**right**).  
Figure 9: Measurement results of DLR using the SNSPD detector at a PRF of 1 kHz with a high ratio of signal to noise.  
a laser measurement system by using the homochromy of the laser signal (e.g., a high-efficiency narrow-bandwidth spectral filter). Figure 10 shows the transmittance of a narrow-bandwidth spectral filter. The main characteristics of the filter are a central wavelength of 532 nm, a bandwidth of \(\pm\)1 nm, and a transmittance of greater than 90% at a wavelength of 532 nm.  
The technologies of the DLR measurements were implemented at the SHAO, which enable ranging measurements of small and far space debris at a distance of 1700 km (with an RCS of 0.05 m\({}^{2}\), catalog ID 1520) and at a distance exceeding 6700 km (with an RCS of 13.6 m\({}^{2}\), catalog ID 28118), as shown in Figures 11 and 12, respectively. These observations mark the first time that debris targets have been measured up to a distance of 6700 km.  
Figure 11: DLR measurements of a target with an RCS of 0.05 m\({}^{2}\) (catalog ID 1520).  
Figure 10: Transmittance of a narrow bandwidth spectral filter.
## 4 Data Transformation Impact on Gray Values Compression  
To increase the efficiency of compression, it is necessary to transform an input data stream, as shown in Figure 3. The methods for transforming input data streams described in this article are:  
* Delta coding.
* CTW--Context Tree Weighting.
* BWT--Burrows Wheeler Transformation.
* PPM--Prediction by Partial Matching.
* DMC--Dynamic Markov Compression.
# Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation
## 3 Proposed Method
### Storage Structures  
For point cloud data storage, in order to meet real-time performance, we give these requirements:  
* Fast read and write: For over 100,000 pieces of point cloud data, we need to quickly locate each point cloud and be able to modify its attributes.
* Data connection: For a point cloud scene, we need to express the connection between point clouds and find the neighbor points of any point cloud quickly.  
In response to the above requirements, we designed the following point cloud Two-Layer-Graph structure, the layer of range graph to save single points, and the layer of point cloud set graph to save points set.
# Experimental Study on Two PCM Macro-Encapsulation Designs in a Thermal Energy Storage Tank
## 2 Materials and Methods
### Methodology  
The experimental tests consisted of performing four different charging and discharging processes to evaluate the effect of the PCM macro-encapsulation design and the flow rate on the temperature distribution, heat transfer rate, and energy stored/released. At least three repetitions of each process were performed to ensure repeatability. A summary of the flow rates and temperatures used in the experimentation is shown in Table 3. Furthermore, the heat losses in the worst-case scenario analyzed represent 4% of the charging/discharging energy, therefore the analysis of heat losses was not included in the paper.  
To perform a charging process, HTF was first circulated through the PCM tank until all sensors inside the tank reached a temperature of \(5\pm 1\)\({}^{\circ}\mathrm{C}\). Then, the HTF inlet temperature was set at \(25\pm 1\)\({}^{\circ}\mathrm{C}\) and the flow rate was set to the corresponding value of the experiment shown in Table 3. The charging process was considered complete when the HTF temperature at the outlet of the tank reached \(25\)\({}^{\circ}\mathrm{C}\). To perform a discharging process, HTF was first circulated through the PCM tank until all sensors inside the tank reached a temperature of \(25\pm 1\)\({}^{\circ}\mathrm{C}\). Then, the HTF inlet temperature was set at \(5\pm 1\)\({}^{\circ}\mathrm{C}\) and the flow rate was set to the corresponding value of the experiment in Table 3. The discharging process was considered complete when the HTF at the outlet of the tank reached \(7\)\({}^{\circ}\mathrm{C}\). This value was used instead of \(5\)\({}^{\circ}\mathrm{C}\) because a minimum temperature difference of \(2\)\({}^{\circ}\mathrm{C}\) was assumed between inlet and outlet of the storage tank as a constraint from the demand side.  
Figure 4: Temperature sensors location inside the storage tank.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Slab Type**} & \multirow{2}{*}{\
\begin{tabular}{c} **Flow Rate** \\ **[L/min]** \\ \end{tabular} } & \multicolumn{3}{c}{**HTF Inlet Temperature**} & \multicolumn{1}{c}{
\begin{tabular}{c} **PCM Tank** \\ **Average** \\ **Initial** \\ **Temperature** \\ **[\({}^{\circ}\mathrm{C}\)]** \\ \end{tabular} } & \multirow{2}{*}{**Code**} \\  & & & & [\({}^{\circ}\mathrm{C}\)] & \\ \hline Charge & ThinICE & 2 & 25 & \(5\pm 1\) & C\_ThinICE\_2L \\ Charge & ThinICE & 4 & 25 & \(5\pm 1\) & C\_ThinICE\_4L \\ Charge & FlatICE & 2 & 25 & \(5\pm 1\) & C\_FlatICE\_2L \\ Discharge & ThinICE & 4 & 25 & \(5\pm 1\) & C\_FlatICE\_4L \\ Discharge & ThinICE & 2 & 5 & \(25\pm 1\) & D\_ThinICE\_2L \\ Discharge & ThinICE & 4 & 5 & \(25\pm 1\) & D\_ThinICE\_4L \\ Discharge & FlatICE & 2 & 5 & \(25\pm 1\) & D\_FlatICE\_2L \\ Discharge & FlatICE & 4 & 5 & \(25\pm 1\) & D\_FlatICE\_4L \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the main parameters of the processes.
## Case Studies
### Data Collection and Privacy Protection  
In this study, data were collected from students from two academic institutions: 150 students from Kunsan National University in South Korea and 260 students from Shaanxi Institute of Technology in China. The data collected included quiz results, task completion rates, engagement in learning activities, and participation in online discussions. The data were automatically captured through the respective universities' learning management systems (LMS), ensuring consistency and accuracy in data recording.  
To ensure the protection of student privacy, all personal identifiers, such as names and student IDs, were removed during the data collection process. The data were anonymized before analysis and stored in a secure, encrypted database, accessible only to the research team. Strict access controls were implemented, ensuring that only authorized personnel could access the anonymized data.  
This study adhered to international data protection standards, including the GDPR (General Data Protection Regulation) and local regulations in South Korea and China. All participating students provided informed consent prior to data collection, ensuring transparency in how their data would be used for research purposes. These privacy protection measures were followed rigorously to guarantee the confidentiality of the students while preserving the integrity of the data.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Single-Robot Path Planning
#### 4.3.2 Path Instance Analysis  
The three algorithms were run 30 times, respectively, and the evaluation indicators were recorded. Three groups of different populations with _popsize_ = 100, _popsize_ = 150, and _popsize_ = 200 were set. The comparisons of the optimization results of the best individual fitness functions of the three algorithms are listed in Tables 1-3.  
For the continuous optimization problem, in BQGA, the two parameters \(\theta\) and \(\varphi\) of the quantum bit can be adjusted simultaneously by using the current quantum bit to rotate around a certain rotation axis toward the target quantum bit, and the corresponding Bloch coordinates can be directly obtained by the projection measurement of the quantum bit, which achieves the best matching of the amount of adjustment of the two parameters in the quantum bit adjustment. Obviously, this best-matched rotation has higher optimization efficiency. Therefore, the optimization efficiency of BQGA is higher than that of ACA and GA, i.e., the shorter the time, the larger the average result value, which indicates  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best Result** & **Worst Result** & **Average Result** & **Time/s** \\ \hline ACA & 126 & 110 & 118 & 1.2914 \\ GA & 127 & 113 & 120 & 1.2784 \\ BQGA & 128 & 118 & 123 & 1.2476 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of ACA, GA and BQGA under evacuation conditions of _Maxgen_ = 100, _Popsize_ = 100.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best Result** & **Worst Result** & **Average Result** & **Time/s** \\ \hline ACA & 127 & 110 & 118.5 & 1.8490 \\ GA & 126 & 112 & 119 & 1.8043 \\ BQGA & 128 & 118 & 123 & 1.7658 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ACA, GA and BQGA under evacuation conditions of _Maxgen_ = 100, _Popsize_ = 150.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best Result** & **Worst Result** & **Average Result** & **Time/s** \\ \hline ACA & 126 & 112 & 119 & 2.3565 \\ GA & 125 & 111 & 118 & 2.3367 \\ BQGA & 128 & 118 & 123 & 2.3102 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ACA, GA and BQGA under evacuation conditions of _Maxgen_ = 100, _Popsize_ = 200.  
its effectiveness and efficiency. The optimization efficiency of BQGA is improved by approximately 3.39% and 2.41% compared to the other two algorithms, respectively.
## 2 Materials and Methods
### Analysis Methodology  
#### 2.4.1 Merging the ARERA Technical Datasheets and the EU Ecolabel Criteria  
As mentioned in the introduction section, the aim of the present work is to verify whether the simplified ARERA technical datasheets can be applied to estimate the increase in energy  
Figure 1: Official EU Ecolabel logo [62].  
efficiency consequent to the adoption of some actions planned for the issuance of a EU Ecolabel for Tourist Accommodation Services, without necessarily going through the building simulation. Obviously, an increase in the energy efficiency implies a corresponding decrease in the release of polluting substances.  
Therefore, since this work is mainly focused on the energy criteria, starting from the assumption that the considered agritourism meets all the mandatory criteria, it was decided to analyze possible \"environmental action packages\", consisting of different combinations of the actions established by the optional energy criteria, which are better suited to a scenario such as agritourism, and which allow the obtaining of the weight of the energy category on the twenty points minimum limit set by the regulation, which corresponds to 7.34 points.  
To this purpose, only the ARERA datasheets regarding the actions related to the improvement of the structure energy efficiency that could be transferred and applied to agritourism structures, according to the EU Ecolabel for Tourist Accommodation energy criteria, have been considered, as reported in Table 1, where the correspondent energy consumption categories have also been reported.  
As can be seen in Table 1, under the dotted line, the two ARERA datasheets 6T and 20T, additional to those that can be associated to the EU Ecolabel, have also been taken into account. In fact, even though these two intervention typologies are not foreseen by the current Ecolabel scheme, they represent actions that can actually be applied to an agritourism structure in the perspective of a possible \"nearly Zero Energy Agritourism (nZEA)\" as a parallel with the well-known nZEB concept; and also, in view of a possible future improvement of the Ecolabel scheme.  
The selected datasheets (Table 1), have been then put into the form of appropriate excel spreadsheets.  
The equations relative to the ARERA calculation procedures, for each considered technical datasheet, are given in Appendix B.  
One aspect that must be highlighted here regards the fact that while datasheets 5, 6, 15T, 19T, 20T, and 27T enable obtaining savings of consumed energy (energy saving measures, ESM), datasheets 7 and 8T allow, instead, the production of energy from renewable sources (renewable energy sources, RES).
## 3 Methodology
### Dynamic Feature Point Removal Method
The algorithm's workflow is summarized in Table 1.  
The proposed algorithm effectively leverages both spatial information from dynamic and potential dynamic objects and the LK optical flow method to analyze feature point motion. This approach enables efficient estimation of motion changes between frames. Compared to more complex dynamic feature point detection methods, the proposed algorithm offers a lower computational cost, making it suitable for real-time SLAM systems in dynamic environments. The algorithm's workflow using LK optical flow is summarized in Table 2.
## 3 Numerical Modelling Setup
### Algorithm Computational Cost  
Each design evaluation cycle required an average of 11 min and 42 s, with specific times presented in Table 3. The relatively fast design evaluation was achievable through a simplification of the fluid-structure interface (FSI) modules on the Workbench project by directly importing the steady state CFD results. The average times resulted in an expected algorithm runtime of \(\sim\)65 h and 31 min, or 2.73 days. The calculated runtime was highly theoretical and was subject to computer facility specific technicality problems.  
Figure 7: Overview of CFD setup using ANSYS CFX turbo mode.  
In order to further improve run time of the algorithm, a cluster network was implemented, this solution cut the total running time down to little over 4 h. The solution consisted of a \"master\" computer, which would manage the data structure and all specific operations required to complete a genetic algorithm, and 21 \"slave\" computers, one for each population member, that were only tasked to perform simulations. The master PC would generate data sets and save them in a folder, shared across all computers.
## 2 System Modeling of Road Disturbance and Vehicle Suspension
### Two-DOF Quarter Semi-Active Vehicle Suspension  
The suspension system is affected by many factors, and correspondingly, there are models of suspension systems with complex and diverse degrees of freedom. The two-degree-of-freedom suspension is the most refined structure in the field of automotive suspensions, which can meet the basic requirements of vibration control studies for different levels of roads by intuitively responding to the vibration of a single tire. In this paper, by studying the control strategy of the 2-DOF suspension model, the advantages and disadvantages of the control method can be roughly judged, and the system structure and calculation are relatively simple, which is the basis for the study of the control system of the full vehicle [26]. The 2-DOF quarter semi-active suspension system model is shown in Figure 1.  
\(m_{s}\) is the sprung mass; \(m_{u}\) is the unsprung mass; \(k_{s}\) is the sprung stiffness; \(k_{t}\) is the tire stiffness; \(c_{s}\) is the damping coefficient of the suspension; \(U\) is the damping force of the semi-active suspension; \(z_{s}\) is the sprung mass displacement; \(z_{u}\) is the unsprung mass displacement; \(z_{r}\) is the road input displacement.  
According to the system model in Figure 1 and [PERSON]'s second law, the following mechanical model equations of motion can be obtained:  
\[\left\{\begin{array}{l}\dot{z}_{s}=-\frac{k_{s}}{m_{s}}(z_{s}-z_{u})-\frac{ c_{s}}{m_{s}}(z_{s}-\dot{z}_{u})+\frac{U}{m_{s}},\\ \dot{z}_{u}=-\frac{k_{s}}{m_{u}}(z_{u}-z_{s})+\frac{c_{s}}{m_{u}}(z_{s}-\dot{z }_{u})-\frac{k_{t}}{m_{u}}(z_{u}-z_{r})-\frac{U}{m_{u}},\end{array}\right. \tag{3}\]  
where \(\dot{z}_{s}\) is the body acceleration as one of the suspension performance parameter, and the other two choose the suspension deflection \(z_{s}-z_{u}\) and tire dynamic load \(k_{t}(z_{u}-z_{r})\), through dynamic adjustment of the three scale coefficients for multi-control objective optimization, so that the three control objectives can have a certain optimization effect at the same time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Road Level**} & \multicolumn{2}{c}{**Road Unevenness Coefficient \(G_{g}(n_{0})/10^{-6}\) m\({}^{3}\)**} & \multicolumn{1}{c}{\(n_{0}\) = 0.1 m\({}^{-1}\)**} \\ \cline{2-4}  & **Lower Limit** & **Geometric Mean** & **Upper Limit** \\ \hline A & 8 & 16 & 32 \\ B & 32 & 64 & 128 \\ C & 128 & 256 & 512 \\ D & 512 & 1024 & 2048 \\ E & 2048 & 4096 & 8192 \\ F & 8192 & 16,384 & 32,768 \\ G & 32,768 & 65,536 & 131,072 \\ H & 131,072 & 262,144 & 524,288 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Eight-level classification of road disturbance roughness.