esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 2 Experimental Materials and Basic Parameters
### Basic Parameters of Suppressant Materials
#### 2.2.2. Wettability of Dust Suppressant Solutions  
The sedimentation time experiment is a robust method for assessing the wettability of dust suppressants on dust. The wettability of dust suppressants is directly proportional to the sedimentation rate at which the solution wets dust [26,27]. A schematic design of the sedimentation time experiment is shown in Figure 2(a). Specifically, the test solution (20.0 mL) was poured into a 25.0 mL test tube, dried test dust (1.0 g) was cautiously added into the test tube, and the duration it took for all the dust particles to settle to the bottom of the solution was measured, as shown in Figure 2(b).  
The sedimentation time in water, measured as 44.38 s, served as the benchmark for the maximum settling time expected. The average sedimentation time of SDS is 33.84 s, OP-10 is 26.63 s, and DA is 24.31 s. The results show that variations in mass concentration and surface tension significantly influence sedimentation behavior, as shown in Figures 1(a) and 2(b). Each suppressant exhibits distinct sedimentation dynamics due to differences in surface tension and solid-liquid interface interactions. The average sedimentation time of the three suppressant solutions at lower concentrations (1.0 \(\times\) 10\({}^{-5}\)%) is 38.21 s; this can be attributed to higher surface tension, which slows the settling rate. Conversely, the average sedimentation time of the three suppressant solutions at high concentrations (1.0%) is 19.75 s. This is due to the reduced surface tension, which facilitates faster sedimentation [28]. These findings suggest that concentration-dependent modifications in sedimentation behavior can be used to optimize suppressant performance for dust-mist coupling behavior.
## 3 Results and Discussion
### Impact of ECAP Processing Factors
#### 3.2.1 Microstructure Evolution
In fact, shearing pressures perpendicular to the basal planes [64] accounted for the twisting of the basal plane that occurred throughout ECAP processing. Furthermore, investigations of pure Mg [50; 72], AZ31B Mg alloy [73], ZK30 alloy [42; 69], and AZ31 alloy [74] all agreed precisely with the crystallographic texture observations of pure Mg. Linear regression (LR), Gaussian process regression (GPR), and support vector regression (SVR) were adopted as ML approaches to describe the ECAP associated variables of Mg alloy. The model evaluation results are depicted in Figure 6a,b along with Figure 1:1 The predicted grain size values in both the training and testing datasets closely matched the outcomes obtained from experiments, as depicted in Figure 6a,b. As depicted in Table 3, the effectiveness of the model was examined utilizing the RMSE and R\({}^{2}\) score after training and testing. The three ML approaches, including LR, GPR, and SVR, performed well in predicting the grain size with considerable accuracy, as reflected in R\({}^{2}\) scores ranging from 0.98 to 0.99 for the training dataset and from 0.85 to 0.88 for the testing dataset. Here,the GPR approach was selected to model the gain size properties as it provided the most optimal performance for both the training and testing phases.
# Changes in Pupil Size According to the Color of Cosmetic Packaging: Using Eye-Tracking Techniques
## 4 Discussion  
This study analyzed changes in pupil size in response to different cosmetic packaging colors to better understand consumer attention and its implications for marketing. Employing eye-tracking technology and statistical analysis, several key findings were derived,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Color**} & \multicolumn{6}{c}{**Saccade**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & \multirow{2}{*}{**Diff**} & \multirow{2}{*}{**T-Value**} \\ \cline{2-2} \cline{5-7}  & **N** & **Mean** & **N** & **Mean** & & **T-Value** \\ \hline Black & 155 & 2.67 & 144 & 2.69 & \(-\)0.02 & \(-\)1.05 \\ \hline White & 153 & 2.44 & 142 & 2.44 & 0.00 & \(-\)0.19 \\ \hline Blue & 140 & 2.57 & 121 & 2.59 & \(-\)0.02 & \(-\)1.14 \\ \hline Yellow & 145 & 2.43 & 137 & 2.44 & \(-\)0.01 & \(-\)0.54 \\ \hline Orange & 138 & 2.53 & 131 & 2.51 & 0.02 & 0.86 \\ \hline Blue-green & 133 & 2.50 & 123 & 2.51 & \(-\)0.01 & \(-\)0.45 \\ \hline Pink & 124 & 2.50 & 104 & 2.51 & \(-\)0.01 & \(-\)0.54 \\ \hline Sky blue & 132 & 2.51 & 114 & 2.52 & 0.00 & \(-\)0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 10: T-test results showing the average difference in relative pupil size by color during saccades when staring at the packaging (male vs. female).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Color**} & \multicolumn{6}{c}{**Saccade**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Non-Body**} & \multicolumn{2}{c}{**Body**} & \multirow{2}{*}{**Diff**} & \multirow{2}{*}{**T-Value**} \\ \cline{2-2} \cline{5-7}  & **N** & **Mean** & **N** & **Mean** & & **T-Value** \\ \hline Black & 68 & 2.64 & 299 & 2.68 & \(-\)0.03 & \(-\)0.47 \\ \hline White & 46 & 2.44 & 295 & 2.44 & 0.00 & \(-\)0.11 \\ \hline Blue & 52 & 2.56 & 261 & 2.58 & \(-\)0.02 & \(-\)0.24 \\ \hline Yellow & 49 & 2.43 & 282 & 2.43 & \(-\)0.01 & \(-\)0.09 \\ \hline Orange & 52 & 2.39 & 269 & 2.52 & \(-\)0.13 & \(-\)1.18 \\ \hline Blue-green & 62 & 2.46 & 256 & 2.50 & \(-\)0.04 & 0.79 \\ \hline Pink & 42 & 2.40 & 228 & 2.50 & \(-\)0.11 & \(-\)1.11 \\ \hline Sky blue & 42 & 2.52 & 246 & 2.52 & 0.00 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 9: T-test results showing the average difference in relative pupil size by color during saccades when staring at the packaging.  
shedding light on how packaging colors influence visual attention and cognitive responses. The discussion below explores these findings and their implications in greater depth.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Adsorption Kinetics  
Figure 5 illustrates the fluoride adsorption capacities of Al-S957 over different contact times. A rapid adsorption phase occurred within the first 120 min, after which the rate gradually slowed, reaching equilibrium at approximately 240 min. To further elucidate the adsorption kinetics, both pseudo-first-order kinetic and pseudo-second-order kinetic models were applied to the experimental data. The corresponding equations are presented in Equations (1) and (2):  
\[Q_{\mathrm{t}}=Q_{\mathrm{e}}\Big{(}1-\mathrm{e}^{-k_{1}t}\Big{)} \tag{1}\]  
\[Q_{\mathrm{t}}=\left(\frac{1}{Q_{\mathrm{e}}}+\frac{1}{k_{2}Q_{\mathrm{e}}^{2 }t}\right)^{-1} \tag{2}\]  
where \(Q_{\mathrm{t}}\) (mg\(\cdot\)g\({}^{-1}\)) represents the fluoride adsorption capacity at time = \(t\), \(Q_{\mathrm{e}}\) (mg\(\cdot\)g\({}^{-1}\)) represents the equilibrium adsorption capacity, and \(k_{1}\) (min\({}^{-1}\)) and \(k_{2}\) (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) represent the adsorption rate constants for the pseudo-first-order kinetic and pseudo-second-order kinetic models, respectively.  
The fitting results and corresponding parameters are displayed in Figure 5 and Table S2. The data reveal that the pseudo-second-order kinetic model offers a superior fit, with a strong correlation coefficient greater than 0.99, indicating that chemisorption likely governs the fluoride adsorption process on Al-S957 [36].  
Additionally, to investigate the influence of coexisting anions on adsorption kinetics, experiments were conducted at varying Cl\({}^{-}\) concentrations. As shown in Figure 5 and Table S2, the rate constants derived from the pseudo-second-order model gradually decreased with increasing Cl\({}^{-}\) concentration. This trend is attributed to the enhanced ionic strength, which increases the solution's viscosity and impedes the diffusion of fluoride toward the active sites [37].
## 3 Theoretical Analysis of Undersea DC Power System Stability  
### Steady-State Equilibrium Point  
In order to analyze the steady-state equilibrium point of a multi-node CSO undersea DC power system, it is necessary to establish a simplified system model, that is, one long-distance submarine cable, one shore station PFE and one undersea station, as shown in Figure 5. The shore station PFE is modeled by an ideal voltage source. The submarine cable is modeled by cascaded lumped parameter circuits. The undersea station is modeled by a CPL.  
In Figure 5, \(U_{S}\) represents the output voltage of the shore station PFE. \(R_{0}\), \(L_{0}\) and \(C_{0}\) represent the resistance, inductance and capacitance of a cable segment, respectively. \(P_{L}\), \(U_{L}\) and \(I_{L}\) represent the power consumption, input voltage and input current of the undersea station, respectively, and n represents the number of submarine cable segments.  
The steady-state equation of the simplified system model is as follows:  
\[\left\{\begin{array}{l}U_{L}=U_{S}-nR_{0}I_{L}\\ I_{L}=\frac{P_{L}}{U_{L}}\end{array}\right. \tag{9}\]  
The equilibrium point of the simplified system model is:  
\[\left\{\begin{array}{l}U_{L}=\frac{U_{S}\pm\sqrt{U_{S}^{2}-4 nR_{0}P_{L}}}{2} \\ I_{L}=\frac{P_{L}}{U_{L}}\end{array}\right. \tag{10}\]  
In order to avoid complex roots, it must satisfy the following inequality:  
\[R_{0}<\frac{{U_{S}}^{2}}{4 nP_{L}}, \tag{11}\]  
Figure 4: The simulation results of different submarine cable models.  
Figure 5: A simplified model of a single-node CSO undersea DC power system.  
which is the steady-state equilibrium point criterion for the simplified model of a single-node undersea DC power system.  
A medium-voltage experimental prototype of a single-node CSO undersea DC power system was built in the laboratory, as shown in Figure 6. The parameters of each component in the medium-voltage experimental prototype are shown in Table 3.  
Substituting the values of Table 3 into the inequality Equation (11), it can be obtained:  
\[\left\{\begin{array}{l}R_{0}=10\\ \frac{U_{\text{c}}{}^{2}}{4 nP_{L}}=13.23\end{array}\right.. \tag{12}\]  
So, the steady-state equilibrium point criterion is satisfied, which indicates that the medium-voltage experimental prototype has steady-state equilibrium points and can operate normally without disturbances.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 4 Simulation Results
### Evaluation of DRCC for Different Threshold Values  
As discussed in Section 3, the upper and lower threshold values are used to control the performance of the DRCC algorithm. These two values determine the upper and lower limits of the \"acceptable\" CBR levels experienced by the vehicles. In this section, we compare the performance of the DRCC approach using five different combinations of \(upper_{th}\) and \(lower_{th}\). The different variations of DRCC are numbered as DRCC1-DRCC5, based on the selected threshold values, as shown in Table 2.  
Figure 1 shows a comparison of the different DRCC versions in terms of the average channel load. We can see that DRCC1 consistently maintains the lowest CBR values and DRCC5 produces the highest, as expected. For low channel loads, all versions are able to keep the CBR below their target \(upper_{th}\) values. As the load increases, DRCC1 and DRCC2 exceed their \(upper_{th}\) but still maintain a lower CBR compared to the other versions.  
Figure 2 compares the total number of received packets for the five DRCC variations. It is clear that as the thresholds increase, the total number of received BSMs also increases and higher values of \(upper_{th}\) are produced. Also, as the packet size is increased from 256 bytes to 1024 bytes, the number of received packets decreases significantly for the same number of sent packets and the same beacon intervals. This is due to the higher channel loads and consequent packet collisions and dropped packets. It is  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Maximum beacon interval & 100 ms \\ Minimum beacon interval & 20 ms \\ Fixed transmission power & 20 mW \\ BSM size & 256 B, 1024 B \\ Minimum power level & \(-\)110 dBm \\ Noise floor & \(-\)98 dBm \\ Vehicle number & 80 \\ Highway length & 1 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DRCC1** & **DRCC2** & **DRCC3** & **DRCC4** & **DRCC5** \\ \hline \(lower_{th}\) & 0.2 & 0.3 & 0.3 & 0.5 & 0.6 \\ \(upper_{th}\) & 0.4 & 0.5 & 0.7 & 0.7 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Threshold values for DRCC.  
that a higher number of received packets for DRCC4 and DRCC5 may not mean they are always the best choice, as many of these may be from vehicles farther away rather than from nearby vehicles.  
In the remaining sections, we will use DRCC1 and DRCC5 for comparison with existing approaches.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 2 Positioning Architecture
### Slave EKF-Based Doppler Positioning Model
#### 2.2.3 EKF Update Process  
After defining the EKF measurement and the state-space model, the prediction and update processes were required to estimate the receiver's states. First, the attitude, position, and velocity of the receiver were predicted using the kinematic INS equations, which were discussed previously. Additionally, the clock states of the receiver were predicted using the receiver's clock bias state-space model. The predicted covariance matrix was obtained by Equation (20).  
\[P_{k+1|k}=F_{k}P_{k|k}F_{k}^{T}+Q_{k}, \tag{20}\]  
where \(F_{k}\) was obtained using the Jacobian of the INS equations, and \(Q_{k}\) is the covariance of the process noise. The matrix \(Q_{k}\) is defined as \(diag\big{[}Q_{lms},c^{2}Q_{clk}\big{]}\) in which \(Q_{clk}\) is described in Equation (12); \(Q_{lms}\) is implemented as the covariance of dynamic disturbance noise in a standard INS kinematics (refer to [30]). Second, the receiver's states were updated using the Kalman gain, \(K_{k}\), and the pseudorange-rate measurements of LEO satellites, \(z\). Finally, the covariance matrix and the Kalman gain were updated. Equations (21)-(23) showed the updated EKF process.  
\[x_{k+1|k+1}=x_{k+1|k}+K_{k+1}\Big{[}z_{k+1}-H_{k+1}x_{k+1|k}\Big{]}, \tag{21}\]  
\[P_{k+1|k+1}=P_{k+1|k}-K_{k+1}H_{k+1}P_{k+1|k}, \tag{22}\]  
\[K_{k+1}=P_{k+1|k}H_{k+1}^{T}\Big{[}H_{k+1}P_{k+1|k}H_{k+1}^{T}+R_{k+1}\Big{]}^ {-1}, \tag{23}\]  
where \(R\) is the covariance of the measurement noise. As mentioned before, the EKF-based slave system estimates the receiver's states using the above measurement model and the known orbital data of LEO satellites. These estimated states and the estimated states of the master INS will be the main source of the alignment system. The INS alignment system is totally investigated in the following part.  
Figure 2: PSD analysis of the Iridium-Next bursts in different time slots.
## 3 Motivation  
This section mainly discusses MMR's impact on program performance. When evaluating program performance, we choose the execution time as a metric. Due to the variability in the results of a single test, we adopt the average execution time after five independent tests as the final result. Our research reveals that MMR has the potential to improve or degrade the performance, with the specific effects varying depending on the project. We choose Kevin, a Java project on GitHub 2.0 as our research project. Kevin is a Kotlin-based event system, and our research focuses on ad9790 submission in version 1.0 of the project. In this commit, the developer moved the _prepare()_ method from the OrbitBenchmark class to the Benchmarks class. The _prepare()_ method is responsible for creating and configuring a map of multiple listeners that is critical to the functional implementation of numerous test classes in the project. With this refactoring, the modularity of the code has been improved, significantly enhancing its maintainability and readability.  
To comprehensively evaluate the impact of MMR on program performance, we need to consider moving multiple methods, thereby generating various refactoring results. Relying on the _prepare()_ method to move alone is not enough, and it is not possible to move just a single method during actual code maintenance. Through an analysis of the code structure, we find that the _benchmark()_ method in the OrbitBenchmark class and the method with the same name in the NorbitBenchmark class are potential candidates for MMR. The _benchmark()_ method publishes an OrbitEvent event containing a Blackhole object to the event bus for performance testing. In this section, we select the _prepare()_ and _benchmark()_ methods in the OrbitBenchmark class as examples to study the impact of MMR on program performance.  
After conducting tests on the execution time of the original project before refactoring in version 1.0, we obtained a result of 101,758.47 ms. Table 1 shows that we performed two refactoring operations and tested their execution times. When methods _prepare()_ and _benchmark()_ are moved to class Benchmarks, the execution time is 51,456.93 ms, which amounts to a decrease of 50,301.54 ms. This refactoring effectively optimizes the program's execution efficiency. However, only the _prepare()_ method is being moved to the NorbitBenchmark class, while the _benchmark()_ method remains in place. The execution time for this change is 102,263.37 ms, demonstrating an increase of 504.9 ms. In summary, the impact of MMR on program performance is complex and variable. It can lead to both performance improvements and degradation, with the specific effects strongly influenced by the details of the refactoring operation and its context. Therefore, there is an urgent need to study the performance of programs after MMR.
## 4 Experiments and Results
### Results Using Classification Models  
In this section, different heart sound classification models published in the state of the art are tested [9; 10; 11; 12]. These models focus on discrimination between normal and abnormal heart sounds. They were trained with a total of 805 heart sounds (415 normal and 390 abnormal), obtained from the following databases: the PhysioNet/Computing in Cardiology Challenge 2016 [30], Pascal challenge database [31], Database of the University of Michigan [48], Database of the University of Washington [49], Thinklabs database (digital stethoscope) [50], and 3M database (digital stethoscope) [51].  
Table 4 presents the different characteristics extracted in the proposed classification methods [9; 10; 11; 12]. These characteristics belong to the domains of time, frequency, time-frequency, and perception.  
Each feature set was used as input to the following machine learning (ML) models: support vector machine (SVM), k-nearest neighbors (KNNs), random forest (RF), and multilayer perceptron (MLP).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Features** \\ \hline
[9] & Six power values (three in systole and three in diastole) \\ \hline
[10] & Nineteen mel–frequency cepstral coefficients (MFCCs) and 24 discrete wavelet transform features \\ \hline
[11] & Statistical domain: mean value, median value, standard deviation, mean absolute deviation, quartile 25, quartile 75, inter-quartile range (IQR), skewness, kurtosis, and coefficient of variation. Frequency domain: entropy, dominant frequency value, dominant frequency magnitude, and dominant frequency ratio. Perceptual domain: 13 MFCCs. \\ \hline
[12] & Six linear prediction coefficient (LPCs) + 14 MFCCs per segment (S1, S2, systole, and diastole) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Features extracted in models [9; 10; 11; 12].  
Figure 12: Result of MCD distortion using one synthetic signal with training dataset.  
In Table 5, the accuracy results of each one of the combinations of characteristics with the ML models are presented, applying a 10-fold cross-validation. The analysis of these results is described in more detail in [9].  
In this work, these classification models were used to test the synthetic signals generated with the proposed method (GAN). Therefore, 50 synthetic signals were used as the test dataset, and the accuracy results are presented in Table 6. The same procedure was done with the synthetic signals without applying the EWT algorithm, with the accuracy results presented in Table 7.  
The best results were obtained with the power characteristics proposed in [9]. However, in several combinations of characteristics and ML models, precision results greater than 90% were obtained, as was the case of the combination of LPC and MFCC proposed in [12]. From these results, it can be argued that the synthetic signals generated with the proposed method have similar characteristics to the natural signals, since the classification results on both type of signals are similar.
## 3 Results and Discussion
Tables 1 and 2 show the physical properties of PbTiO\({}_{3}\) system ceramics with sintering temperature and the variation of the side length of the piezoelectric device, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sintering** & **Density** & **Vibration** & **Fa** & **Fa** & **Zr (\(\Omega\))** & **Za (\(\Omega\))** & **k\({}_{\text{t}}\)** & **D\({}_{\text{mt}}\)** & **D.R** \\
**Tem.(\({}^{\circ}\)C)** & **[g/cm\({}^{3}\)]** & **Mode** & **fr (MHz)** & **(MHz)** & **Zr (\(\Omega\))** & **Za (\(\Omega\))** & **k\({}_{\text{t}}\)** & **Constant** & **Q\({}_{\text{mt}}\)** & **(dB)** \\ \hline \multirow{2}{*}{1200} & \multirow{2}{*}{7.8} & First & 3.035 & 3.315 & 2.18 & 11,060 & 0.5486 & \multirow{2}{*}{202} & 345 & 74.1 \\ \cline{2-2} \cline{5-8}  & & Third & 9.830 & 9.955 & 5.16 & 419 & 0.219 & & 292 & 38.19 \\ \hline \multirow{2}{*}{1230} & \multirow{2}{*}{7.72} & First & 3.090 & 3.370 & 7.749 & 2943 & 0.544 & \multirow{2}{*}{229} & 94.3 & 51.59 \\ \cline{2-2} \cline{5-8}  & & Third & 9.850 & 10.090 & 11.452 & 99.3 & 0.299 & & 67.8 & 18.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The physical properties of PbTiO\({}_{3}\) system ceramics with sintering temperature. Figure 10: Dynamic ratio (D.R) with varying piezoelectric device side lengths.