esa-sceva/satcom-chunk-collection huggingface dataset:
# The Properties and Durability of Self-Leveling and Thixotropic Mortars with Recycled Sand
## 1 Introduction
The chemical admixtures (water reducer, retarder, early strength agent, and stabilizer) represent another significant factor that can influence their performance and properties by prolonging the setting time and increasing the fluidity of SLMs. An essential additive component of mortar as a floor material is the redesipscribe powder (RP). This is prepared by spray-drying or other process of polymer emulsion. A very small amount of RP can deeply affect the properties and performance of SLMs. When the powder mixes with water, the polymer emulsion can significantly improve the fluidity, the wear resistance and flexural strength, the bond strength, the impact resistance, and the flexibility of self-leveling mortar [49; 50]. However, an excessive amount of RP can reduce the compressive strength of SLMs [50]  
Thixotropic mortars are commonly used for non-structural and structural restoring and smoothing of concrete. Their elastic modulus, drying shrinkage, and chemical permeability and compatibility with the damaged concrete must be taken into account. Cement-based or polymer-modified repair materials can be used [51]. The former includes grout, mortar, or concrete (containing Portland cement, aluminous cement, or silicate cement). They are characterized by properties similar to those of the original concrete, thus assuring a high compatibility. However, they cannot overcome the drawbacks of the original concrete, such as permeability, which can lead to cracking carbonization, rebar corrosion, and drying shrinkage, which can affect bonding strength and, thus, the overall efficacy of repairs. The latter consists of mortars that contain polymers, such as redispersible polymer powder. This improves the bonding strength of repairing mortar to the substrate, fills in capillary pores, enhances the freeze-thaw resistance, improve resistance to water and chemicals, and reduces surface cracking [51]. They also provide a protective film on the surfaces of aggregates, and form a link with the hydration products of cement. Furthermore, the addition of cellulose ethers provides high viscosity, air incorporation, and water retention [52]. These factors also prevent an excessive loss of water into the substrate,which could cause failures in the mechanism of adhesion and improve the sag resistance of the mortar. The viscosity of the cellulose ether solution is responsible of the thickening effect of cellulose ether on cement-based materials. In the present investigation, a self-leveling mortar and a thixotropic mortar containing 100% of recycled aggregate and specific chemical admixtures kits were produced. Their fresh and hardened state properties, such as workability, porosity, compressive and flexural strengths, and drying shrinkage, were evaluated. Freeze-thaw, sulphate, and thermal resistance of the prepared mortars were also investigated in order to assess their durability. Self-leveling and thixotropic mortar have the same mix design but that containing natural sand was used as a control group.
## 4 Proposed Distributed Trajectory Planner Based on PSO and Bresenham Algorithm
### Distributed Path Planning for Multi-UAVs  
The implementation of the proposed method is as follows: At the very beginning, it initializes the SAI values of each cell and establishes a connection among the monitoring drones. After establishing the connections, they share their positions and locations among themselves. We assumed that drone uses vision-based techniques and communication are done by mobile Ad-hoc manner [1].  
After establishing the connections, the monitoring drone starts checking the area by using the historical data of SAI values. If they are not all covered, they divide the whole operation area into different important levels accordingly. Then, the target points are set to surveillance priority according to a different important area. Then, individual trajectories are generated for each monitoring drone, and objective and constraints value will be checked for the feasibility of the paths. The monitoring drones keep communicating with each other continuously and make the trajectory for the whole coverage.  
[PERSON]'s line drawing algorithm was used in this work to check if all the targeted areas have been visited or not. When the monitoring drone flies from a one-way point to another waypoint, the SAI value of those cells becomes zero (0). Therefore, after visiting all the surveillance areas, it will check all the areas have been covered or not; if not, then it will make the new path planning to cover all the areas. Thus, we could ensure the surveillance of the whole operational area. The implementation step of distributive path planning, including Pseudocode and Flowchart (Figure 2) for the whole operational scenario, as shown in Algorithm 3, and Figure 1, respectively.
## 3 Observing System Simulation Experimental Framework  
### Framework of the GEO-MW OSSE  
The OSSE is an important means to develop and evaluate new observation systems. The basic principle of an OSSE is to assimilate the synthetic future spaceborne observation data derived from the Nature Run data, satellite orbit parameters, and instrument parameters and then evaluate the impact of new observations on analyses and forecasts [31]. The GEO-MW OSSE in this study was realized by the WRF model and WRFDA-4 DVar assimilation system. The flowchart of the GEO-MW OSSE is shown in Figure 1, including the following seven main steps:  
1. ERA-5 reanalysis data are used as the initial fields and boundary conditions to run the WRF model to forecast the Nature Run data.
2. The Radiative Transfer (RT) model takes in a set of input atmosphere physics parameters provided by the Nature Run and calculates upwelling brightness temperature (TB) emerging from the top of the atmosphere.
3. The upwelling TB is input into the GEO-MW observation models of GMR, GeoSTAR, and GIMS with the geostationary orbit parameters and the three instrument parameters to simulate the observed brightness temperature (TA).
4. NCEP/FNL reanalysis data are used as the initial fields to run the WRF model, and the 6 th-hour prediction field is used as the background field of WRFDA-4 DVar assimilation.
5. The simulated GEO-MW observation TA data of GMR, GeoSTAR, and GIMS are assimilated by the WRFDA-4 DVar assimilation system to obtain the analysis fields.
6. The 72-h predictions are obtained from the WRF model initialized with the analysis fields (GEO-MW assimilation experiments) and the background field (control experiment).
7. The impacts of the GEO-MW observation data assimilation are evaluated by comparing the predicted typhoon tracks with the best typhoon tracks provided by the CMA.  
Figure 1: Geostationary orbit microwave observation system simulation experimental framework.
## 4 Experimental Results and Analysis
### Hysteresis Curves  
Figure 9 shows the specimen behavior regarding the joint-bending moment versus drift response in terms of the hysteresis loop for two specimens. It can be seen that in both specimens, there was no pinching effect when the loading was inversed. It can also be observed that specimen SC2 was stiffer and stronger than specimen SC1. Furthermore, the specimen behaved more like steel joint behavior than RC joint behavior; this is to say that the energy dissipation capacity and the ductility of the studied hybrid joint are suitable for RCS frames as part of a seismic design.  
Figure 8: Crack pattern of specimen SC2.  
The cyclic moment-drift envelope curves for each specimen is presented in Figure 10 such that they can be compared. Table 2 presents the values of the joint-bending moment corresponding to the maximum drift. As can be seen, the moment-bearing capacity of specimen SC1 was 779.2 kNm (at 4% drift) while that of specimen SC2 was 927.3 kNm (at 5% drift). This is to say that the two plates used in the joint region increased the moment-bearing capacity by about 19% on average for pull and push. For instance, for the 5.5% drift, specimen SC2 had a moment-bearing capacity that was 21% greater than specimen SC1. Furthermore, Table 2 also indicates that the strength degradation was more apparent in specimen SC1 than SC2.  
By observing the moment-drift envelope curves in Figure 10, one can say that the global cyclic behavior of the tested specimens was similar. Indeed, when the drift was smaller than 1%, the two curves were superimposed. The moment-drift response of the joint could be subdivided into three steps. Each step was characterized by several variables, such as drift, stiffness degradation, and yielding of the joints' components. The first step was from zero to 2% drift, where the behavior was almost linear and the cracking was not clear. The second step was from 2% to 4% drift, where the behavior was nonlinear and the yielding took place at different points of the steel component in the joint region. This was correlated with the important crack propagation and opening observed during this step. The last step was from 4% drift to the end of the test, where the specimen reached the load-bearing capacity and the resistance decreased due to concrete splitting and spalling. In conclusion, the use of the steel plates at the joint region significantly improved the behavior of the studied hybrid joint as seen by the increase in the moment-bearing capacity and the decrease in the residual deformations. As such, this kind of joint detail is suitable for restoring, repairing, and strengthening structures after experiencing damage.  
Figure 10: Comparison between the moment–drift envelope curves.  
Figure 9: Hysteresis moment–drift response of specimens.
# Edge Computing Approach for Vessel Monitoring System
## 3 Proposed Approach
### Temporal Analysis  
Temporal patterns allow identifying working periods based on date and time. They are used at the beginning of the trip and do not have a great effect on this local process for the identification if the data was to be transmitted. After testing with data from a year period (2018), it was decided not to implement this analysis due to the fact that unclear patterns were identified during the test year.
## 3 Results and Discussion
### SEM Analysis
#### 3.2.4 TiO\({}_{2}\)/Zeolite Photocatalytic Material  
The SEM micrographs in Figure 5a,b exhibit the morphology of the TiO\({}_{2}\)/zeolite photocatalyst synthesized via the sol-gel method. The images confirm the presence of zeolite and TiO\({}_{2}\) nanoparticles, aligning with the EDS analysis and consistent with findings from prior studies [13; 50; 51]. The diameter of the TiO\({}_{2}\) nanoparticles on the zeolite surface was determined to be in the range from 15 nm to 47 nm. Furthermore, the TiO\({}_{2}\)/zeolite composite demonstrates enhanced material dispersion within its structure compared to that in the pure TiO\({}_{2}\) sample. The EDS spectrum (Figure 5c) and Table 1 reveal that SiO\({}_{2}\) and TiO\({}_{2}\) are the predominant components, constituting the majority, while the remaining 0.90% comprises various other oxides.
## 3 Materials and Method
### Proposed Method
#### 3.2.3 Skip Attention Mechanism
Mathematically, the design of the skip attention mechanism ensures that information can still be effectively transmitted even as the number of network layers increases, thus maintaining the model's learning and generalization capabilities. This design shows significant advantages in tasks with complex structural dependencies, such as long-text analysis in natural language processing and the real-time prediction of high-frequency trading data. In summary, the introduction of the skip attention mechanism not only optimizes the computational process and enhances efficiency but also strengthens the model's ability to capture key features in data, bringing a new, efficient solution to the field of secure computing.
## 5 Case Study: Magnetic Levitation System  
Inteco magnetic levitation system (MLS) [29] is an open-loop unstable and nonlinear laboratory system for demonstrating phenomena of magnetic levitation (Figure 4). Levitation techniques are used in practical applications such as transportation, contactless melting, magnetic bearings, etc. One successful approach of providing optimal control of a multivariable magnetic levitation system is presented in [30]. In general, the levitation principles are based on digital and analogue solutions to maintain a steel ball in an electromagnetic field. MLS elements are the electromagnet, the hollow steel sphere, the position sensor, and the computer interface board with appropriate connecting cables. It is a single degree of freedom control system with time-varying dynamical characteristics.  
For levitation purposes of the laboratory model, a real-time controller is required. In order to keep a levitating sphere in a desirable position, the equilibrium stage of the gravitational and electromagnetic force is required. MLS operates the voltage to the electromagnet to enable the object to levitate where the levitating object's position is determined in every time step (0.01 s) through the sensor. The measured voltage corresponds to the sphere distance. Additionally, the coil current, as another measurable variable, can help explore control strategies. All control experiments of MLS can be executed in real-time in the Matlab software package, specifically Simulink Toolbox. The software control logic can be used for online identification of processes, modelling of control systems, and all design and simulation phases. Finally, the implementation of new control algorithms can also be performed in real-time.  
Figure 4: Laboratory system of MLS.  
MLS can be mathematically described in the following format [29]:  
\[\begin{split}\dot{x}_{1}&=x_{2}\\ \dot{x}_{2}&=-\frac{F_{em}}{m}+g\\ \dot{x}_{3}&=\frac{f_{1}(x_{1})}{f_{1}(x_{1})}(k_{ i}u+c_{i}-x_{3})\\ F_{em}&=x_{3}^{2}\frac{F_{em}p_{1}}{F_{em}p_{2}} \exp\Bigl{(}-\frac{x_{1}}{F_{em}p_{2}}\Bigr{)}\\ f_{i}(x_{1})&=\frac{f_{i}p_{1}}{f_{i}p_{2}}\exp \Bigl{(}-\frac{x_{1}}{f_{i}p_{2}}\Bigr{)}\end{split} \tag{22}\]  
where, \(x_{1}\in[0,0.016]\), \(x_{2}\in\Re\), \(x_{3}\in[x_{3 MIN},2.38]\), \(u\in[u_{MIN},1]\). The electromagnetic force (\(F_{em}\)) depends on the distance of ball from the electromagnet and the electromagnetic coil current.
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 4 The Proposed Data Publishing Method  
### Description of Problem  
The general data method based on differential privacy protection is the original data by differential privacy protection, releasing a private dataset that users can use to perform any query operation of general data, but this method of the original data for privacy protection adds a lot of noise and greatly reduces the release data utility. By reducing the sensitivity of differential privacy and allocating the privacy budget reasonably, the amount of noise added to satisfy differential privacy can be effectively reduced, and the availability of published data can be improved.  
Most existing methods do not consider the complex correlation between attributes in the data. When adding noise to sensitive attributes in the data, the correlation between sensitive attributes and non-sensitive attributes in the data should be considered, and then the non-sensitive attributes with a strong correlation with sensitive attributes should be protected.  
Based on the above problems, this paper proposes a differential privacy data publishing method SSKM_DP based on the SFLA-Kohonen network. This method conducts a clustering operation on the original data, reduces the query sensitivity, and reduces the intake of noise while reducing the data dimension, and then it determines the correlation between attributes. The noise required by differential privacy is added to protect the privacy. When the same differential privacy protection effect is achieved for the published  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Scope of** & **Standardized** & **Computational** & **Robustness** \\  & **Application** & & **Complexity** & **Robustness** \\ \hline Pearson coefficient & Linear data & Yes & Low & Low \\  & Linear data & & & \\ Spearman coefficient & simple monotone & Yes & Low & Medium \\  & nonlinear data & & & \\ KNN & Linear data & No & High & High \\  & Linear data & Yes & Low & High \\  & nonlinear data & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of common coefficients.  
data generated by the SSKM_DP method, less noise is added and the availability of the data is better.
## 5 Conclusions  
From the results obtained in this work, the following conclusions can be derived:  
* The residual normal stresses \(\sigma_{xx}\) and \(\sigma_{yy}\) were tensile, while the residual shear stresses \(\tau_{xy}\) were negative with the value changes in a range of \(-\)265\(\sim\)\(-\)211.5 MPa which demonstrated that the residual shear stress was not negligible.
* No clear and orderly tendency was found to conclude that the residual normal and shear stresses influenced the fatigue life. It was demonstrated that the residual stress in only one direction was not a suitable indicator for evaluating fatigue life. It is suggested that the fatigue life of a specimen was not directly dependent upon the surface shear and normal stresses.
* A linear relationship between the equivalent stress and the fatigue life was observed. The fatigue life increased by 39.4% as the equivalent stress decreased by 5.2% in this research. It was indicated  
Figure 12: Fatigue crack growth rate at a stress ratio of 0.1 for Inconel 718.  
that the equivalent stress which considered residual shear and normal stress simultaneously was the dominate factor affecting fatigue life.
* The mean values of \(\Delta K_{th}\) calculated by the proposed model had good accordance with the experimental data that were demonstrated by [PERSON] [29] and [PERSON] [30].
* The effect of residual shear stress on the threshold stress intensity factor was significant and ignoring it could cause a considerable deviation from the experimental results.  
[PERSON] conceived and designed the study; [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the paper; [PERSON] reviewed this project and proposed constructive guidance to make the article more complete.  
This work was support from the National Natural Science Foundation of China (No. 51425503 and 91860207). This work was also supported by grants from Taishan Scholar Foundation, the Major projects of National Science and Technology (No. 2019 ZX04001031), the National Key Research and Development Program of China (2018 YFB2002201), and Shandong Provincial Natural Science Foundation of China (ZR2019 MEE073).  
The authors declare no conflicts of interest.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 4 System Implementation
### Signal Conditioning  
The signal for training and categorization should encode the finger gesture and the performance should be relatively unaffected by the level of ambient light. Any reflected light from an object near to the PD should not result in a high amplitude signal that cannot be recognized from the same motion without reflection. Therefore, the signal should be normalized such that all signals have the same amplitude regardless of the ambient light intensity. The normalization scales the signal according to the minimum and peak signal level recorded over the measurement period. As the ambient light changes more slowly than the direct LED light across a measurement frame, this is a simple and efficient step. The recorded gesture features may vary slightly between each motion and also due to environment. Each user also presents their hands at a slightly different angle and moves them at a variable speed, and there will be temporal variations and potentially irregular random reflex movements. The natural light present in the morning will be different to  
Figure 6: Received signal captured on VLC photodiode corresponding to (from top): (**a**) 1 finger; (**b**) 2 fingers; (**c**) 3 fingers; and (**d**) 4 fingers gestures.  
the artificial light in the evening and can vary if it is cloudy or sunny. All PDs exhibit a noise floor, and the TIA has a noise figure which contributes to a lowering of the signal integrity. Signal conditioning is required to manage these effects and to provide a clean representative signal which contains the essential features of each gesture to the ML algorithm. After conditioning, the Rx signal has range \(-1/+1\) and is processed by the LSTM algorithm.
## 5 Experimental Study
### Selected Domain: Webinar System  
Webinar is a combination of the words \"web\" and \"seminar\". It is used for systems that allow users to organize and participate in a video workshop, lecture, or presentation in an online environment. With the current COVID-19, such systems have become a part of our lives. Due to its popularity, we chose webinar systems for this experiment's domain.  
Our chosen feature set for the webinar system contains the following features: live and pre-recorded video streaming, mobile accessibility, chat messaging, file sharing capabilities, virtual whiteboards, shared screens, virtual waiting rooms, breakout rooms, pass-presenter tools, polling tools, app integration, audio and video recording, in-app conference registration, automated reminders, Q and A tools, engagement analysis for attendance and lead generation, in-app offers, integration with live-streaming social media platforms, active speaker view, AI and gamification features, and backgrounds to create a branded virtual environment [30].
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 5 Performance Evaluation
### Effect of the Number of Mobile Nodes  
Figures 6 and 7 show the effect of the number of mobile nodes on the average sum data rate and the average number of links, where each simulation corresponds to the average value of 10,000 iterations. Figure 6 shows the average sum data rate for the mobile node.  
Figure 4: Average number of links over iterations.  
Figure 5: Convergence time over iterations.  
Figure 3: Average sum data rate over iterations.  
It can be demonstrated that as the number of mobile nodes increases, the average sum data rate increases. However, the average sum data rate of the random and distance models increases slowly and is lower than that of the proposed model. This is because not all the mobile nodes are connected to the relay node, as shown in Figure 7. On the other side, the proposed model connects to as many of the relays as possible to maximize the data rate, as shown in Figure 7. Thus, the proposed model not only maximizes the data rate but also guarantees the quality of the link.