esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study investigates the potential to use semi-active cable dampers controlled by clipped viscous damping with negative stiffness to compensate for the reduced cable motion at damper position due to cable flexural rigidity and fixed supported ends. For this a prototype hydraulic damper with real-time controlled bypass valve has been developed and experimentally tested. Based on the test results a model of the semi-active hydraulic damper is derived that includes the control force constraints resulting from the minimum and maximum viscous coefficients at fully open and fully closed bypass valve. This model is used to compute the cable damping ratios of the first four cable modes, typical damper positions and cable models without and with flexural rigidity and fixed supported ends. As benchmark, the passive damper being optimized to the first four cable modes is computed. The comparative study demonstrates that the presented semi-active cable damper leads to far greater cable damping ratios than the passive damper. This is explained by the facts that the viscous force is optimized in real time to the actual frequency of motion and the negative stiffness force increases the damper relative motion whereby the viscous force can dissipate more energy per cycle. Because of the dissipative nature of the semi-active damper force, stability issues as for active systems do not exist. In addition, power consumption is not critical because power is only needed to control the oil volume stream through the bypass valve but not to produce active forces. Therefore, the presented semi-active hydraulic damper represents an efficient, cost-effective and robust damping tool for stay cable damping.  
Conceptualization, F.W. and J.D.; methodology, F.W., S.S. and F.O.; software, F.W. and S.S.; formal analysis, F.W. and S.S.; writing--original draft preparation, F.W., S.S, F.O., J.D. and C.B.; writing--review and editing, F.W., S.S., F.O., J.D. and C.B.; project administration, F.W. and J.D. 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 on request from the corresponding author.  
The authors acknowledge the support of Maurer SE, Maurer Engineering GmbH and Maurer Switzerland GmbH.  
The authors declare no conflict of interest.
## 2 Numerical Model and Methods
### Grid Independence Validation  
When the meshing is completed, the value of \(Y\) + is in the range of 30-300, which meets the calculation requirements. Besides, the _cfl_ number for the selected computational grid is 0.7, which also meets the calculation requirements. A grid independence validation is conducted to analyze the numerical results of four cases possessing different amount of grid numbers (\(2\times 10^{5}\), \(2.3\times 10^{5}\), \(2.6\times 10^{5}\), \(3.3\times 10^{5}\), \(2\times 10^{6}\)). Hexahedral meshes (shown in Figure 4) are generated by using ANSYS ICEM to discretize the computational domain. To compare the calculated results produced by various grid numbers computational domains, the mass fraction of CH\({}_{4}\) on the combustor outlet is selected to be the metric quantifying parameter. Figure 5 shows the distributions of CH\({}_{4}\) mass fraction along the outlet for every grid type. When the total amount of mesh is \(2\times 10^{5}\) and \(2\times 10^{6}\), the distribution of CH\({}_{4}\) is already quite different from that of the other three kinds of grid total. There is less than 0.015% relative deviation between the medium grid sized domain (\(2.6\times 10^{5}\)) and fine grid sized domain (\(3.3\times 10^{5}\)), which means that the medium grid sized domain (\(2.6\times 10^{5}\)) is enough to produce analogous results compared to the fine sized domain.  
Figure 4: Grid of computational domain.  
Figure 5: CH\({}_{4}\) distribution on the outlet.
# Evaluation of Rice Straw Ash as a Pozzolanic Addition in Cementitious Mixtures
## 4 Conclusions  
The main conclusions drawn from this research are:  
* Chemical composition shows silica content to be about 52%, with high percentages of potassium oxide (12%) and chloride (3.5%). These chlorides can be reduced by washing the sample with hot water.
* The analysis of crystalline and amorphous silica indicates that RSA is mainly of an amorphous nature.
* A positive result is obtained in the Frattini test, and the pH and electrical conductivity behavior of the CH/ash suspensions demonstrated that RSA is considered a low-activity pozzolan. When RSA is washed, greater ash reactivity takes place, which is classified as a medium-reactivity pozzolan.
* The thermogravimetric analysis showed marked RSA reactivity, and high CH fixation values were obtained in the CH and cement pastes with RSA.
* In mechanical strength terms, good pozzolanic reactivity was found, along with major contribution to compressive strength for the 28-90 day period.
* RSA can be used as a pozzolanic material and is a good option for valorizing the resulting ash after biomass combustion. Only the high chloride content should be taken into account for use in applications in which the content of this chemical parameter is limited (e.g., steel-reinforced concrete or pre-stressed concrete). The use of RSA would not be considered in the EHE-08 standard.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mortar** & \(\alpha\) & \(\beta\) & \(\mathbf{R^{2}}\) \\ \hline
**Control** & 39.20 & 3.72 & 0.94 \\
**RSA 15\%** & 35.48 & 5.64 & 0.99 \\
**RSA 30\%** & 25.54 & 7.31 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters from the compressive strength fitting.  
Figure 13: Compressive strength of the control and PC/RSA mortars versus curing time.  
Using this type of cement containing RSA, precast concrete elements such as blocks, bricks, tubes, pavements and tiles, without steel reinforcement could be manufactured.  
[PERSON] performed much of the experimental section of this work. [PERSON] and [PERSON] contributed to the thermogravimetric, the mechanical and the FESEM tests. Furthermore, they were involved in the writing: the manuscript preparation, review and editing. [PERSON] and [PERSON] supported the research planning, and the manuscript writing and review. [PERSON] was the director of the investigation. She identified the DRX patterns and wrote the original draft, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Government and FEDER funds (MINECO/FEDER-Project RTI2018-09612-B-C21).  
Not applicable.  
The authors would like to thank the Electronic Microscopy Service at the UPV.  
The authors declares no conflict of interest.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 5 Experiments
### Validation of Effectiveness  
In this subsection, we use the OSCHINA-Gitee dataset to validate the effectiveness of UIL-HGAN. In UIL-HGAN, the multiple attention layers consist of the category-level attention layer, the expression-level attention layer, and the user-level attention layer. We used (Username), (URL-ID), (Username, URL-ID), and (Username, URL-ID, Follow) to carry out four groups of comparative experiments to validate the effectiveness of each layer in the multiple attention layers. Username and URL-ID are user profiles, and Follow is structural information. In the experiment, we set the dimension of features in the latent space as 32, set the ratio of positive and negative samples as 0.2, and set the training ratio to 20%, 40%, 60%, and 80%. The multi-layer perceptron has two hidden layers with the same dimension 32. The results of the experiment are shown in Table 1 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Training Ratio** & _hit@1_ & _hit@50_ & _hit@100_ & _hit@500_ & _hit@1000_ \\ \hline \multirow{4}{*}{(Username)} & 20\% & 0.5070 & 0.5108 & 0.5184 & 0.5623 & 0.6032 \\ \cline{2-7}  & 40\% & 0.5230 & 0.5258 & 0.5328 & 0.5773 & 0.6077 \\ \cline{2-7}  & 60\% & 0.5  
In Table 1 and Figure 5, the results of (Username) and (URL-ID) validate the effectiveness of the category-level attention layer, where features of usernames and URL-IDs are aggregated. The _hit@1_ precisions with an 80% training ratio of (Username) and (URL-ID) are 0.5620 and 0.5900, showing that the URL-ID feature has a better performance than the username feature in user identity linkage.  
Username and URL-ID are aggregated in the expression-level attention layer. Compared with the results of (Username), (URL-ID), and (Username, URL-ID) in Figure 5, the curve of (Username, URL-ID) is above the curves of (Username) and (URL-ID). It shows that (Username, URL-ID) has a better user identity linkage performance and validates the expression-level attention layer's effectiveness.  
User profiles and structural information are aggregated in the user-level attention layer. Compared with the results of (Username, URL-ID) and (Username, URL-ID, Follow) in Table 1, the _hit@k_ precisions of (Username, URL-ID, Follow) have larger values than the _hit@k_ precisions of (Username, URL-ID). It shows that (Username, URL-ID, Follow) has a better user identity linkage performance and validates the user-level attention layer's effectiveness.  
In this subsection, the OSCHINA-Gitee dataset's results validate each layer's effectiveness in the multiple attention layer and validate the effectiveness of UIL-HGAN. To a certain extent, UIL-HGAN solves the problems of weak data expression ability and ignored potential relationships.
## 2 Materials and Methods
### Data Collection
#### 2.2.3 Land Use and Land Cover and Change Dataset  
Land use and land cover (LULC) change is known to have a number of negative impacts on the environment [46] and vegetation dynamics are strongly influenced by anthropogenic land use and climate drivers [47]. Hence, LULC and land cover and change data were used to analyse trends in vegetation in response to different LULC classes and changes using LULC conversion data.  
Given that long-term monitoring is required, good quality (less than 10% cloud cover) wet season Landsat satellite images with 30 m spatial resolution were obtained from the United States Geological Survey (USGS) Global Visualization Viewer ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/)), accessed on 18 January 2020) dataset for years 1990, 1995, 1999, 2005, 2010, 2015 and 2019. However, due to data availability, some years close to the study period were downloaded and only one image had cloud cover of 14%.  
The South African national standard for Land Cover Classification System [48] was used to map the existing LULC in the study area (Table 1). The broad hierarchical level 1 was used on the Landsat images to identify the existing LULC. Since the study aims to investigate whether degradation in the district is induced by humans or rainfall, the level 2 and 3 classes were used to identify barren, cultivated and residential land for detailed mapping.  
The satellite images were pre-processed for radiometric and geometric anomalies and re-projected [49]. Landsat images were geo-referenced using the Georeferencing tool in ArcGIS 10.7 to correct false changes in the seasonal LULCC and images radiometrically corrected to improve image quality using histogram equalization. Colour balancing was applied using the colour corrector tool and balanced using the first order dodging method, while supervised classification was used to generate unique LULC classes. Classification accuracy was assessed based on a transition matrix.
# Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
## 3 Experimental Study
### Tool Wear Distribution Fitting and Parameters Calculation  
Linear, exponential, and Gaussian distribution as the alternatives are chosen to calculate the parameters of distribution depending on the distribution between the feature value and the tool wear. Table 2 is the parameter a, b, and c calculated of these distribution, and root mean square error (RMSE) is also shown in table below. RMSE of the table below illustrates that Gaussian distribution provides the best fit to describe the tendency of tool wear.  
As shown in Figure 7, linear distribution fitting curve, exponential distribution fitting curve, and Gaussian distribution fitting curve are compared with actual tool wear depending on these parameters calculated in Table 2. Figure 7 presents that the three distributions are similar in accuracy and all fit well, which indicates that the wear is related to the feature value. According to the Figure 7 and Table 2, Gaussian distribution is selected as the fitting distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distribution** & **Linear** & **Exponential** & **Gaussian** \\ \hline a & 0.02635 & 0.07289 & 0.192 \\ b & 0.06255 & 0.2148 & 6.261 \\ c & - & - & 5.985 \\ root mean square error (RMSE) & 0.0094 & 0.0099 & 0.0093 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated parameters of distribution.  
Figure 6: Time-dependent trend chart for Gn of vibration signals in the milling process.  
Figure 7: The tool wear fitting distribution figure.
## 6 Security Analysis and Performance Evaluation
### Performance of the Multi-Level ACE-Based IoT Knowledge Sharing Algorithm
With each additional client, the sanitizer processes one more message, and the server decrypts an additional message. Both the total sanitization time in the edge sanitizer and the decryption time of a single cloud server exhibit a linear relationship with the number of clients. As the number of users increases, the decryption and sanitization times also increase proportionally. The total sanitization time in the edge is the cumulative time taken by all edge servers to run ACE\({}^{n}.San\) algorithm. In practical scenarios, many edge sanitizers are deployed, with each being responsible for sanitizing messages from a few clients in its vicinity. The decryption burden on a single sanitizer is relatively low. Both sanitization and decryption operations are performed by servers with robust computing capabilities, making milliseconds of computational overhead negligible. Finally, we observe the impact of the number of security levels in the system on the time and storage overhead of each partial algorithm for a certain amount of clients. there are a total of 30 clients in the system, and the number of security levels is 2, 3, 4, 5, respectively. Each group of experiments follows the principle of having a higher safety level and thus a lower number of clients. As shown in Figure 3c, the time of total sanitization in edge increases with the number of security levels; however, the time of decryption of a single cloud server is not affected. The length of the local ciphertext and sanitized ciphertext increases with the number of security levels in Figure 3f. Because there are more security levels, the more there are ciphertext \(C\) and \(C^{\prime}\) components, the more parts need to be sanitized for each edge server. But the decrypted part is certain, which is only related to the total number of clients.
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters
### Datum of the In Situ SSH Measurements
Moreover, the sea level measured by the hydrological station placed on Dan'gan island was collected from January 2017 to December 2019 when the S3A flew through this area (accessed from Pearl River Hydrology and Water Resources Survey Center on 30 March 2020). The distance between the ATG and the hydrological station was less than 5 m and worked at the same time from August to December 2019. They were compared with each other in a standard bias of 13.2 mm. The datum of the data was transformed to be the same with the ATG. Such data were also used to accomplish the Cal/Val of S3A altimeter in this research.
## 3 Results and Discussions
As well known, the strong phase dispersion within the transparency window can be used to slow down the speed of light. Generally, the slow light effect can be precisely depicted by the group delay [38], \(t_{g}=d\varphi/d\omega\), where \(\varphi\) is the transmission phase shift. Figure 6 shows the group delay of the hybrid PIT metamaterial for the different Fermi energies. It can be observed, from no graphene to \(E_{F}=0.8\) eV, the group delay of the transparency peak gradually decreases from 1.27 ps to 0.23 ps, corresponding PIT resonance decay process. Hence, the designed PIT device shows the ability of active modulation of slow light, which has potential applications in terahertz communication.
## 5 Challenges and Open Issues
### Challenges/Open Issues for MAC protocols  
In MAC protocols, the energy waste is due to idle listening, collisions, packet overheads and overhearing useless traffic. As a WBASN may consist of multiple physiological sensors that require different data rates, the selection of the appropriate radio frequency (RF) is a crucial part of deploying patient monitoring systems. The heterogeneous nature of biomedical sensors in terms of sensing and transmitting data make the required QoS more complex for the MAC layer as it may need to send some data with high priority, such as ECG data in emergency scenarios. Hence, a priority mechanism should be adopted according to the nodes' requirements rather than providing services based on predefined values. A balanced MAC protocol is required for WBASNs that can provide QoS (bounded delay, required throughput, minimum energy consumption and minimum collision) parameters simultaneously.
## 3 Application to SFRC Post-Tensioned Dry Joints
### Structural Characterization of the Dry Joint  
Several models of a dry joint, with three and five keys, were analyzed. The models are based on the experimental shear test carried out by [PERSON] for one-keyed joint and three-keyed joint, and including a five-keyed joint FE model analyzed by [PERSON] et al. [17; 21]. The overall dimensions of the three keys specimen are \(900\times 950\) mm\({}^{2}\), and \(1600\times 1700\) mm\({}^{2}\) in the case of the five keys specimen, with 250 mm thickness in both cases, as depicted in Figure 3. The keys are displayed at a distance of 100 mm between them, and have a penetration length of 50 mm.  
For the FE discretization, four-node bilinear plane stress elements with reduced integration and with hourglass control (CPS4R) have been used, with an average element size of 10 mm. The mesh is refined in the area where cracking is expected, using an element size of 4.5 mm.  
The boundary conditions applied to the bottom surface do not allow vertical displacements. Additionally, the horizontal displacement of a central node was restrained. In a first step, the confining prestress was applied via two external loads on both edges of the model, compressing the joint. In a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**V\({}_{\rm f}\) (\%)** & **E (GPa)** & **f\({}_{\rm t}\) (MPa)** & **f\({}_{\rm c}\) (MPa)** & **G\({}_{\rm F}\) (N/mm)** \\ \hline
0.0 & 30.18 & 3.67 & 40.74 & 0.04 \\
0.5 & 30.36 & 4.01 & 41.35 & 0.45 \\
1.0 & 30.55 & 4.34 & 41.96 & 1.05 \\
1.5 & 30.74 & 4.68 & 42.58 & 1.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated elastic and inelastic properties for SFRC at different fiber volume fractions.  
subsequent step, a vertical load was applied in the central point of the upper surface by imposing a vertical displacement, rising from zero to the value that causes the failure of the joint.  
Hard contact model has been used in the surface normal direction to model the contact. It allows the separation between the surfaces and does not permit penetration. The tangential behavior is based on the Coulomb friction model. A value of \(\mu\) = 0.72 was assigned to the friction coefficient, according to the experimental results by [PERSON] [20].  
A total number of 40 simulations were carried out in Abaqus/Explicit [26], accounting for three different design parameters: (i) fiber volume fraction (V\({}_{\text{f}}\) = 0, 0.5, 1.0, and 1.5%); (ii) number of keys (N\({}_{\text{k}}\) = 3 and 5); and (iii) prestress (\(\sigma_{\text{n}}\) = 0.5, 1, 1.5, 2, and 3 MPa). From these simulations, we can obtain, among other variables, the shear capacity (i.e., the vertical force at which the joint starts to fail), tensile damage distribution or equivalent plastic strains, as well as the crack patterns within the joint. The effect of the aforementioned design parameters (V\({}_{\text{f}}\), N\({}_{\text{k}}\), and \(\sigma_{\text{n}}\)) on these output variables at the structure level are presented and discussed in the next section.
## 2 Materials and Methods
### Bio-Membrane Treatment and Activation  
In order to promote ion mobility, the bio-membrane must be previously activated. This step is critical, and the treatment employed depends upon the composition of the organic phase and the type of electrochemical cell used [31; 32]. Therefore, this section describes the evaluation of several treatments for the proposed bio-membrane which were considered.  
For this process, a 9 cm\({}^{2}\) (3 \(\times\) 3 cm) _Agave salmiana_ leaf was taken with the adaxial side facing down, and the membrane was carefully cut off with a scalpel. The external part was exfoliated with sandpaper (Grit designation of 500) until a 0.08 mm film was obtained [33].  
Different treatments are proposed in this work, and an unmodified membrane serves as a control. The obtained bio-membrane was incorporated into an electrolyzer, while the bio-membrane with the best behavior was incorporated into a fuel cell. Five treatments were evaluated:  
1. Individual immersion (24 h) of the bio-membrane in 50 ml of: An acid solution (HCl 0.1 M), an alkaline solution (NaOH 0.1 M) and a neutral medium (deionized H\({}_{2}\)O).
2. Clean-up treatment of the abaxial face with a cotton ball soaked in organic solvents (chloroform, ethyl ether, hexane and methanol).
3. Polymeric coating of the bio-membrane by immersion in a Nafion (5 wt%) ethanol:water (1:2 v:v) solution during 60 minutes with agitation.
4. Scraping of the cuticle abaxial face to remove any wax from its surface.
5. Thermal treatment by immersing the bio-membrane into boiling water for 4 h.  
In all cases, the bio-membrane was washed twice with deionized water (20 mL) after its treatment. The bio-membrane was maintained at 4 \({}^{\circ}\)C in deionized water until its incorporation into the electrolyzer or fuel cell.  
Before its use in different cells, the bio-membrane was placed between two stainless steel electrodes and immersed in NaOH 0.1 M solution. To stimulate ion exchange in the cuticle stomata, a potential of 15.0 V was applied for 3.0 min.