esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Antimicrobial Activity  
The antimicrobial activities of the non-sterilized and autoclave-sterilized extracts of dragon's blood rhizomes and xoconostle fruit were determined according to the methodology developed by [PERSON] et al. (2016). A mixture was prepared by combining 10 mL of Brain Heart Infusion (BHI) broth, 5 mg/mL non-sterilized or autoclave-sterilized extract, and 100 \(\upmu\)L of liquid medium containing _Streptococcus mutans_ (ATCC 25175, lot number: 62284317) at a concentration of 103 colony-forming units (CFU) per mL. Samples were incubated at 37 \({}^{\circ}\)C for 80 h. Changes in turbidity were measured every 4 h in the spectrophotometer at 600 nm, using a quartz cell (1000 \(\upmu\)L).  
Each extract was evaluated with the following: a blank containing BHI broth and the extract without _Streptococcus mutans_, a positive control containing 10 mL of BHI broth and 103 CFU/mL of _S. mutans_, and a negative control containing 10 mL of BHI broth, 103 CFU/mL of _S. mutans_, and 0.012 \(\upmu\)L of chlorhexidine/mL (GUM Paroex, Sunstar Suisse S.A., Etoy, Switzerland). The concentration of chlorhexidine was established according to research developed by [PERSON] et al. (2017). Results were expressed as log CFU/mL. The effect of each extract over the growth rate (\(\upmu\)) of _S. mutans_ was assessed.  
In addition, the minimum inhibitory concentration (MIC) was determined, which was defined as the lowest extract concentration at which the assayed bacteria did not exhibit visible growth. The BHI-broth macrodilution method and a slightly modified spectrophotometric method (S
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 3 Graph-Based App Usage Prediction  
App Prediction Problem is formally defined as follows: Given a list of installed Apps \(apps_{u}=\left\{a_{1}^{u},a_{2}^{u},...,a_{n}^{u}\right\}\) by a user \(u\) on his/her phone and the user's context \(C\) including spatiotemporal information and smartphone's state like battery level. The problem of App usage prediction is to find an App \(a_{i}^{u}\) that has the largest probability of being used under \(C\). Specifically, we aim to solve the problem:  
\[\underset{a_{i}^{u}}{max}\;P(a_{i}^{u}|C,u),\forall i,1\leq i\leq n. \tag{1}\]  
Based on the above prediction mechanism, our prediction task will resolve around predicting App usage of a typical user. To accurately predict App usage, it is crucial to be able to characterize the features before the App being used by utilizing relevant usage records to the observer. We propose AHNEAP, a framework based on network embedding which firstly transforms the historical App usage records into a large attributed heterogeneous network to obtain every node's embedding, and then trains a neural network to integrate various types of context. Figure 1 illustrates the proposed framework, which contains three major components: data pre-processing, representation learning, and link prediction.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 4 Analysis of Simulation and Measurement Results  
As an important frequency band for space satellite communications, Ku-band was used as the operating frequency band for the proposed CLA. Its central frequency point was 12.5 GHz. To demonstrate the feasibility of the design above, the proposed conformal antenna element and antenna array were simulated based on the optimized dimensions by CST, and its optimized dimensions are tabulated in Table 1. The equivalent medium model of the honeycomb core layer was applied to the simulation of the proposed antennas. Then, the prototypes were processed and measured by a vector network analyzer (PNA-X5244A) and microwave anechoic chamber in our laboratory. The proposed antenna element and antenna array were mounted on the mold through nylon screws, respectively, for conformability testing.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Mahalanobis Distance (MD) Formulation  
MD is defined as in Equation (1):  
\[MD_{j}=D_{j}^{2}=Z_{ij}^{T}C^{-1}Z_{ij}\text{ with }Z_{ij}=\frac{x_{ij}-m_{i}}{s_{i}} \tag{1}\]  
where;  
* \(k\) = the total number of features;
* \(i\) = the number of features (\(i\) = 1, 2,..., \(k\));
* \(j\) = the number of samples (\(j\) = 1, 2,..., \(n\));
* \(Z_{ij}\) = the standardized vector of normalized characteristics of \(x_{ij}\);
* \(x_{ij}\) = the value of the \(i\)th characteristic in the \(j\)th observation;
* \(m_{i}\) = the mean of the \(i\)th characteristic;
* \(s_{i}\) = the standard deviation of the \(i\)th characteristic;
* \(T\) = the transpose of the vector;
* \(C^{-1}\) = the inverse of the correlation coefficient matrix.  
MD has been well deployed in a broad array of applications [20; 21] mainly because it is very effective in tracking intervariable correlations in data.
## 2 Geometric Model, Mesh Division, and Boundary Conditions
### Mesh Generation and Independence Verification  
Mesh generation was conducted using _ANSYS Mesh 2022 R1_, resulting in a final mesh count of 2,215,496. The minimum element size was 0.00127 m, the maximum area size was 0.146 m, and the average size was 0.003 m. ANSYS Mesh evaluates mesh quality based on criteria such as Element Quality, Skewness, and Orthogonal Quality. Both Element Quality and Orthogonal Quality range from 0 (worst) to 1 (best), with values above 0.75 considered good. Skewness ranges from 0 (best) to 1 (worst) [15].  
The global mesh of the model is shown in Figure 1b. Figure 3a focuses on the grid situation of the T/R component. In addition, in order to improve the mesh quality of the key area, the model is also locally refined, as shown in Figure 3b. The average mesh size used in the meshing process is 0.005 m, and the corners of the T/R component and the cold plate model are encrypted so that the accurate results can be calculated better in the calculation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Boundary Conditions** \\ \hline Inlet & Mass flow inlet with a mass flow rate of 6.946 kg/s \\ \hline Outlet & Pressure outlet \\ \hline \multirow{4}{*}{Wall} & Static non-slip wall \\  & Adiabatic \\  & Roughness: Ra3.2 \\ \hline \multirow{2}{*}{Fluid properties} & Viscosity 0.0067 kg/m\(\cdot\)s \\  & Density 1089 kg/m\({}^{3}\) \\ \hline \multirow{4}{*}{Ambient temperature} & Material: aluminum \\  & 297 K \\ \cline{1-1}  & Ambient pressure \\ \cline{1-1}  & 101,325 Pa \\ \cline{1-1}  & Runner material \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary condition settings.  
Figure 3: Details of grid division.  
In the Element Quality and Orthogonal Quality checks, the average Element Quality for all mesh elements was 0.83005, and the average Orthogonal Quality was 0.79, with the minimum values for both metrics exceeding 0.2. The average skewness was 0.239, indicating that the mesh quality satisfies the computational requirements [16].  
To investigate the relationship between numerical simulation and grid size, grids with 1,032,541, 2,215,496, and 3,186,437 nodes were selected to simulate the branched pipe flow under the same boundary conditions. Ethylene glycol was used as the fluid, with a mass flow rate of 6.946 kg/s. The flow distributions in the branch pipes are analyzed in Figure 4 for mesh sizes of 1 million, 2 million, and 3 million nodes, respectively. The simulation results indicate that the flow distribution in each branch pipe remains essentially the same at different grid sizes, with the most stable flow observed at 2 million elements. Therefore, the data calculated with a grid of 2 million elements was selected for this analysis.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
## 6 Conclusions and Future Work  
In this paper, we have proposed a WuR system based on the transmission of legacy frames by an IEEE 802.11 standard-compliant transmitter during a TXOP period. We have performed system validation through the Matlab software tool and shown the feasibility of the solution proposed, considering legacy IEEE 802.11 transmitters and different propagation models from TGn/ac. The proposed solution includes the design of a simple receiver. It provides operational ranges comprised between 18 m and 50 m using one bit per symbol (17.26 kbps, 16.14 kbps, and 13.58 kbps for IEEE 802.11n, 11 ac, and 11 ax, respectively), and between 12 m and 30 m for two bits per symbol (32.81 kbps and 27.17 kbps for IEEE 802.11 ac and 11 ax, respectively), depending on the propagation conditions. The bit rate achieved is higher than for other WuR solutions based on the transmission of legacy IEEE 802.11 frames. Although similar to other solutions, the operational range is short if we assume the requirement of having a range comparable to the primary radio. Both ranges converge when the fastest MCSs are being used in communications through the primary radio. However, further research is necessary to be conducted. In this sense, the next step in our future work is to study more efficient WuRx designs. Additionally, the WuR system presented includes intrinsic protection in front of other transmissions (other WuCs and IEEE 802.11 legacy transmissions) due to the usage of the standard-compliant TXOP mechanism. Besides, we have verified the feasibility of this solution using off-the-shelf Wi-Fi devices by looking at their capability to change the transmission bandwidth on a per-packet basis. However, experiments showed an additional delay in the inter-frame time, thus slightly reducing the effective bit rate of the generated WuC. As part of our future work, we plan to carry out further research in a practical implementation of the proposed and validated WuR solution presented in this paper.  
Conceptualization, E.L.-A. and E.G.-V.; methodology, E.L.-A. and E.G.-V.; validation, E.L.-A. and E.G.-V.; investigation, E.L.-A. and E.G.-V.; writing--original draft preparation, E.L.-A. and E.G.-V.; writing--review and editing, E.L.-A. and E.G.-V.; visualization, E.L.-A. and E.G.-V.; supervision, E.L.-A. and E.G.-V. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Spanish MCIN/AEI/10.13039/501100011033 through project PID2019-106808 RA-I00.  
Not applicable.  
The authors declare no conflict of interest.
# Rotation Accuracy Analysis of Aerostatic Spindle Considering Shaft's Roundness and Cylindricity
## 5 Conclusions  
In this paper, the roundness and cylinder error data of the shaft are collected by the Taylor Hobson cylinder meter as the data source of FDM simulation. The influence of shaft shape errors on spindle rotation accuracy is verified by comparing the simulation results with experimental results. The following conclusions have been drawn.  
1. Because of the errors of roundness and cylindricity in the shaft, the film thickness inside the spindle will be different at different places, resulting in an uneven distribution of film pressure. With the rotation of the shaft, the pressure of the gas film will keep changing, resulting in an unbalanced film force, which will affect the stability of the spindle;
2. The errors of roundness and cylindricity of the shaft can not adequately reflect the distribution of film thickness inside the spindle. Shafts with similar errors may have large differences in unbalanced film force and rotation errors;
3. The dispersion coefficient reflects the fluctuation of the shaft radius. Shafts with similar discrete coefficients will not demonstrate much difference in their roundness error values, and the unbalanced film forces acting on the shaft during rotation are close to each other. Compared with roundness and cylindricity errors, the discrete coefficient is a better index to predict the spindle rotation accuracy. Therefore, during the design and manufacturing process of the spindle, the shaft radius dispersion coefficient should be controlled and measured for better spindle rotation accuracy.  
Figure 16: Unbalanced gas film force of ideal shaft: (**a**) ideal shaft with similar roundness; (**b**) ideal shaft with similar discrete coefficients.  
**Author Contributions:** Conceptualization, G.Z.; methodology, J.Z.; software, G.Z. and H.Y.; validation, R.Z. and W.S.; formal analysis, J.Z. and H.Y.; investigation, J.Z. and H.Y.; data curation, G.Z. and J.W.; writing--original draft preparation, G.Z.; writing--review and editing, J.Z. and J.W.; visualization, R.Z. and W.S.; funding acquisition, J.Z. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51875586; training plan for young backbone teachers in colleges and universities of Henan province, grant number 2018 CGJS105.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on reasonable request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Absorption Properties
\begin{table}
\begin{tabular}{c c c c} \hline
**Dye Molecule** & **Absorption Wavelength (nm)** & **Excitation Energy (eV)** & **Oscillator Strength** \\ \hline Cyanidin & 1382.88 & 0.8966 & 0.0005 \\ Delphinidin & 564.34 & 2.1970 & 0.0025 \\ Pelargonidin & 879.92 & 1.4090 & 0.0014 \\ Peonidin & 1401.18 & 0.8849 & 0.0005 \\ Petunidin & 563.29 & 2.2011 & 0.0025 \\ \hline \end{tabular}
\end{table} 
Table 1: Absorption parameters of the selected anthocyanin dyes in the gas phase. \begin{table}
\begin{tabular}{c c c c} \hline
**Dye Molecule** & **Absorption Wavelength (nm)** & **Excitation Energy (eV)** & **Oscillator Strength** \\ \hline Cyanidin & 1430.93 & 0.8665 & 0.0005 \\ Delphinidin & 565.12 & 2.1940 & 0.0028 \\ Pelargonidin & 857.00 & 1.4467 & 0.0023 \\ Peonidin & 1447.05 & 0.8568 & 0.0005 \\ Petunidin & 565.83 & 2.1912 & 0.0028 \\ \hline \end{tabular}
\end{table} 
Table 2: Absorption parameters of the selected anthocyanin dyes in the ethanol solvent phase.
# Enhancing Meteor Observations with Photodiode Detectors
## 3 Concept
### Noise Analysis
We also selected the best possible night, without moonlight and with very good visual transparency. Therefore, we can treat this sample series as good example of recording with minimal ambient noise. The RMS values were compared for the diode observing the sky (one of three diodes--all showed very similar signals) and for the diode with the light access blocked. The time series and the corresponding spectra are presented in Figure 7. The mean value of both signals was removed before plotting. The real noise appeared to be approximately 10 times larger than the expected 46 \(\upmu\)V and reached \(\sigma_{detector}=430\)\(\upmu\)V for module protected from light and \(\sigma_{sky+detector}=660\)\(\upmu\)V for the diode observing night sky. Assuming that noise from module and sky noise are uncorrelated (i.e., \(\sigma_{sky+detector}^{2}=\sigma_{detector}^{2}+\sigma_{sky}^{2}\)), we can derive that the noise from the sky background equals \(\sigma_{sky}=510\)\(\upmu\)V. The noise associated with the varying nighttime conditions shows mostly low frequencies, well below 5 Hz. The spectra for the two diodes, starting from 10 Hz, look very similar and flat. The strongest component is present at 50 Hz, which is the effect of residual interference from the mains, which remained in the system. The experimentally determined real noise values suggest that the level of the smallest ambient noises (i.e., originating from various light sources) registered in our experiments exceeds the inherent noise level of the system. The observed order-of-magnitude increase in noise level might be attributed to various electromagnetic effects inducing additional currents in the sensitive analog circuit. Even picoamperes can lead to distortions and the emergence of extra noise in such finely tuned systems. Additionally, environmental conditions like humidity can significantly affect the performance. Moisture in the air can lead to condensation on the circuit components, potentially causing minute short-circuits or changes in the electrical properties of the circuit, thus contributing to the noise. Furthermore, the proximity to electronic devices or power lines might result in electromagnetic  
Figure 7: Sample 10-s signal from two diodes: observing sky and protected from external light. interference, amplifying the noise levels. Given the highly sensitive nature of the system, such external factors, though seemingly minor, can have a substantial impact on the overall noise profile. Summarizing, although the measured intrinsic noise is higher than the theoretical predictions, our detector can still be considered as a device introducing a minimal amount of additional noise, which is smaller than the fluctuations of the signals from the clear-sky dark night.
## 3 Proposed Optimization Method
### Overview of the FLA
#### 3.1.3 DO Operator (Discovery Phase)  
If there is a high concentration difference between two regions, molecules change their position according to the region concentration. \(T_{DO}^{t}\) is defined as follows [29].  
\[T_{DO}^{t}=C_{5}\times TF^{t}-r,\ \ \ C_{5}=2 \tag{44}\]  
The flow direction is determined according to the value of \(T_{DO}^{t}\) as follows [29]:  
\[X_{p,i}^{t}=\left\{\begin{array}{cc}from\ i\ \ to\ j\ region\ \ \ \ T_{DO}^{t}<\ rand\\ from\ j\ to\ i\ region\ \ \ otherwise\end{array}\right. \tag{45}\]  
If the concentration of region \(i\) is higher than that of region \(j\), some molecules will migrate from region \(i\) to region \(j\), and other remaining molecules in region \(i\) are influenced by this movement. The number of molecules transferred (\(NT_{ij}\)) and the number of remaining molecules within molecular group \(i\) (\(NR_{ij}\)) can be defined as follows [31]:  
\[NT_{ij}\approx N_{i}\times r_{1}\times(C_{4}-C_{3})+N_{i}\times C_{3},\ \ \ \ \ \ \ \ NR_{ij}\approx N_{i}-NT_{ij} \tag{46}\]  
\(NT_{ij}\) means transferred molecules then move to another region and their position is mainly updated concerning the most favorable equilibrium molecule in area \(j\) as follows [29]:  
\[X_{p,i}^{t+1}=X_{EO,j}^{t}+DF_{p,j}^{t}\times DOF\times r_{2}\times(I_{i,j}^{ t}\times X_{EO,j}^{t}-X_{p,j}^{t+1}) \tag{47}\]  
Furthermore, \(DOF\) indicates the time-varying flow direction, which is defined as Equation (48) [29].  
\[DOF=\exp(-C_{2}(TF^{t}-r_{1})),\ \ \ C_{2}=2 \tag{48}\]  
\(f_{i,j}^{t}\) refers to the diffusion flux considering effective diffusion constant (\(D\)) and concentration gradient (concentration gradient), which is presented as follows [29]:  
\[J_{i,j}^{t}=-D\frac{dc_{i,j}^{t}}{dx_{i,j}^{t}} \tag{49}\]  
\(dc_{i,j}^{t}\) and \(dx_{i,j}^{t}\) are obtained considering the average position of the molecule in regions \(i\) and \(j\) (\(X_{m,j}^{t}\) and \(X_{m,i}^{t}\)) as follows [29]:  
\[dc_{i,j}^{t}=X_{m,j}^{t}-X_{m,i}^{t} \tag{50}\]  
\[dx_{i,j}^{t}=\sqrt{\left(x_{EO,j}^{t}\right)^{2}-\left(x_{p,i}^{t}\right)^{2} +eps} \tag{51}\]  
The position of remaining molecules in the \(i\)th group in region \(i\) is updated based on the movement between distinct stages (equilibrium positions in regions \(i\) and \(j\) and problem boundary and no change in the positions). This strategy can be described by Equation (52) [29] considering the equilibrium point in area \(i\) (\(X_{EO,i}^{t}\)) and upper (\(U\)) and lower (\(L\)) limits of the problem:  
\[X_{p,j}^{t+1}=\left\{\begin{array}{ll}X_{EO,i}^{t}&rand<0.8\\ X_{EO,i}^{t}+DOF\times(r_{3}\times(U-L)+L&rand<0.9\\ X_{p,i}^{t+1}&otherwise\end{array}\right. \tag{52}\]Molecules in region \(j\) modify their position in the same region due to their higher concentration without traveling; that is, their position is updated relative to the equilibrium point. The problem boundary is defined by Equation (53) [29]:  
\[X_{p,j}^{t+1}=X_{EO,j}^{t}+DOF\times(r_{4}\times(U-L)+L \tag{53}\]
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Cluster Properties
EEDCF: When it comes to consuming less energy during clustering-based routing operations and cluster-head identification, the EEDCF [16] protocol, also known as \"Energy-Efficient Distributed Clustering Based on Fuzzy\" [16], is a differential algorithm in WSN.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 5 Practical Test on USV
### USV Communication Architecture  
The communication architecture of the USV is shown in Figure 21. To create real-time communication connection between the USV and the remote-controlled app, a communication application is developed and deployed on an app server within a data center. All control commands can be transferred from the Android app to the USV in real time via the 4G/5G network and the communication application. All water quality and USV status data, such as GPS position, velocity and heading, are also transferred from the USV to the data center and the remote app via the communication application. In addition, users also can access the water quality data through a web platform deployed in the data center.
## 3 Propagation and Update of the ICEKF  
The propagation and update of the ICEKF are described in detail in this section. The key matrices in the propagation step determine the internal transition process of the filter. The update step rectifies the filtered outcomes in reference to the measurement results.
# Cement Pastes with Hygyroscopic Polymeric Additions for Potential Building Applications
## 3 Results and Discussion
### SEM Observations  
SEM observations (Figures 2 and 3) on the samples containing nylon fibers (NY and NY/SA-PA) after 3 and 28 days of aging showed the presence of large empty cavities, especially at the fiber-matrix interface: fibers did not adhere well to the matrix in the early stages of aging, and the cavities were too large to be completely closed by CSH crystal growth. The chemical interactions between the cement paste and polyamide fibers were poor [40]. The great amount of water (up to 12% wt) absorbed on the fibers' surface, due to the highly polar hydroxyl and amide functional groups [41], influenced the interconnection with porosity much more than polymeric fibers typically used for reinforcement, such as polyethylene (PE) or polypropylene (PP). Moreover, observing Figures 2 and 3, it can be noted that crystals are everywhere, but are small and not interconnected; the presence of highly hygroscopic addition seemed to have stopped crystallization after the contact between the water and powder mix. The observed crystals were those nuclei formed by the crystallization admixture during mixing. Water, absorbed by fibers, was subjected to crystallization and then evaporated due to its associated porosity. The addition of both fibers and polyacrylate, albeit in half the proportions, increased the removal of water from  
Figure 1: Porosity distribution during the first 28 days of aging and after thermal treatment.  
the cement hydration. Accordingly, the stiffness of NY/SA-PA subjected to the same aging was hypothesized to be lower than that of NY.  
Figure 3: SEM micrographs of 3-day-aged (**a,b**) and 28-day-aged (**c,d**) NY/SA-PA samples.  
Figure 2: SEM micrographs of 3-day-aged (**a,b**) and 28-day-aged (**c,d**) NY samples.  
The samples without SA-PA and CS fibers (Figures 4 and 5, respectively) appeared well crystallized. SA-PA absorbed water during mixing, but released it slowly during the hydration of the cement phase, which was continuous and homogeneous, without the formation of large empty cavities.