esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Tests and Results  
### Experiments  
On the basis of our theoretical analysis and simulation, an EMS inertial stabilizer was manufactured. The parameters of the device are shown in Table 1. The test device diagram, the schematic block diagram of the system, the test device and signal acquisition and the excitation voltage and displacement voltage signal in the stable suspension state are shown as Figures 13-16.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cross-sectional area of electromagnet core A/mm\({}^{2}\) & 290 \\ \hline Air permeability \(\mu_{0}/\left(\text{H}\cdot\text{m}^{-1}\right)\) & \(4\pi\times 10^{-7}\) \\ \hline Number of turns of electromagnetic winding N & 138 \\ \hline Diameter of enamel wire mm\({}^{2}\) & 1 \\ \hline Electromagnetic winding inductance \(L/\text{mH}\) & 2.49 \\ \hline Air gap \(g_{0}/\text{mm}\) & 1.5 \\ \hline Mass of inertia anti-rolling mass block \(m/\text{g}\) & 225 \\ \hline Base mass \(M/\text{g}\) & 1250 \\ \hline Acceleration sensor & JY61 \\ \hline Displacement sensor & Author’s own design \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of EMS inertial stabilizer.  
Figure 12: Displacement–time response curve under pulse excitation signal (pulse width: 100 μs).  
the base on the \(Z\)-axis are 1.05 g and 0.13 g, respectively, that is, 10.29 m/s\({}^{2}\) and 1.27 m/s\({}^{2}\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Hull Acceleration**} & \multicolumn{3}{c}{**Mass Block Acceleration**} \\ \hline
**X-Axis** & **Y-Axis** & **Z-Axis** & **X-Axis** & **Y-Axis** & **Z-Axis** \\ \hline \(-\)0.03 g & \(-\)0.05 g & \(-\)0.04 g & \(-\)0.11 g & 0.06 g & 0.56 g \\ \hline \(-\)0.04 g & \(-\)0.01 g & \(-\)0.05 g & \(-\)0.10 g & \(-\)0.19 g & 0.61 g \\ \hline \(-\)0.01 g & 0.00 g & \(-\)0.07 g & 0.09 g & 0.15 g & 0.25 g \\ \hline \(-\)0.01 g & 0.01 g & \(-\)0.02 g & 0.10 g & \(-\)0.04 g & 0.18 g \\ \hline \(0.00\) g & 0.02 g & \(-\)0.02 g & \(-\)0.10 g & 0.06 g & 0.08 g \\ \hline \(0.02\) g & 0.06 g & \(-\)0.03 g & 0.05 g & 0.09 g & 0.39 g \\ \hline \(-\)0.04 g & \(-\)0.03 g & \(-\)0.13 g & 0.04 g & \(-\)0.83 g & 0.98 g \\ \hline \(-\)0.02 g & 0.05 g & \(-\)0.04 g & 0.05 g & 0.03 g & 0.13 g \\ \hline \(-\)0.08 g & \(-\)0.01 g & \(-\)0.01 g & 0.09 g & 0.04 g & 0.16 g \\ \hline \(-\)0.08 g & 0.00 g & \(-\)0.01 g & 0.12 g & 0.14 g & 0.05 g \\ \hline \(-\)0.03 g & 0.05 g & \(-\)0.06 g & 0.19 g & \(-\)0.03 g & 0.53 g \\ \hline \(-\)0.05 g & 0.00 g & \(-\)0.02 g & \(-\)0.01 g & 0.16 g & 0.63 g \\ \hline \(-\)0.10 g & \(-\)0.04 g & \(-\)0.09 g & 0.05 g & \(-\)0.01 g & 1.05 g \\ \hline \(-\)0.04 g & 0.03 g & \(-\)0.01 g & \(-\)0.22 g & 0.02 g & 0.16 g \\ \hline \(-\)0.06 g & 0.03 g & \(-\)0.01 g & \(-\)0.23 g & 0.04 g & 0.30 g \\ \hline \(-\)0.01 g & 0.07 g & \(-\)0.03 g & 0.05 g & 0.07 g & 0.28 g \\ \hline \hline \end{tabular} Note: g in this table is the acceleration of gravity. The data werecollected at intervals of 0.01 s.  
\end{table}
Table 2: Part of the accelerometer data.  
Figure 16: Excitation voltage and displacement voltage signal in stable suspension state.
# Improved Mandel System for Prefabricated Vertical Drain Installation: A Macro to Micro Analysis
## 4 Results and Discussion
### Correlations of Shear Strength and Pores of the Soil Structure  
The relationship between the shear strength and pore structure of a soil structure can be influenced by several factors, such as soil composition, particle size, soil structure, and moisture content [52]. In general, the shear strength of soil is related to its depth and distance [53], and it is influenced by the interlocking of soil particles and the bonding between them, which can be affected by the pore structure. Therefore, a reduction in pores can improve soil stability and increase the shear strength of soil [54]. To determine the shear strength of the soil in the three zones around the PVD installation, the number of pores in the soil structure was measured and correlated with the data in Figure 19.  
Figure 19: The relationship between shear strength and pores under vacuum preloading: (**a**) effect on all types of PVD in terms of size; (**b**) effect on all types of PVD in terms of percentage.  
The results in this study indicate that PVD type B in the inner smear zone had more influence on the shear strength of soil compared with the transition zone and undisturbed zone. Type C exhibited a large shear strength of soil in the inner smear zone, transition zone, and undisturbed zone, with the former two having a larger impact than the latter. The results of this experiment, similar to the findings in [55], revealed an increasing edge-to-face orientation of the microstructures after consolidation with increasing levels of PVD installation.  
Smaller pores can increase the interlocking between soil particles and improve the shear strength. On the other hand, the soil pore structure can also affect its hydraulic properties, such as permeability and water retention characteristics. Large pores can increase the soil's permeability, allowing water to flow more easily. However, this can also lead to reduced water retention, which can affect the stability of the soil structure. The presence of smaller pores can improve water retention, which can improve the stability of the soil structure.  
The results of this study showed that the relationship between the shear strength and pore structure of the soil is complex and can vary depending on the specific properties of the soil. The relationship between the pore structure and shear strength is a critical consideration in soil engineering and geotechnical analysis, as it can affect the stability of the soil structure and its suitability for various applications.
# Unfolded Algorithms for Deep Phase Retrieval
## 4 Numerical Results and Discussion  
In this section, we investigate the performance of the proposed UPR-SPARTA and UPR-IRWF frameworks for the task of phase retrieval through various numerical experiments and compare their performance relative to their baseline state-of-the-art SPARTA [20] and IRWF [18] algorithms. The numerical experiments are conducted for real-valued Gaussian signals per convention and are averaged over 100 Monte Carlo trials. The proposed deep architectures are implemented using the PyTorch library [55], and the optimization (training) of the networks is carried out using the Adam stochastic optimizer [56] with a learning rate of \(10^{-4}\) for 100 epochs. For a fair comparison, the parameters of all competing algorithms are initialized to their suggested values as reported in [18; 20]. As a figure of merit for evaluating the performance of the proposed methodologies, we make use of the empirical success rate (ESR) and the relative mean-square error metrics. If a signal (\(\mathbf{x}\)) is to be recovered, we define the relative MSE metric for the recovered signal (\(\mathbf{y}\)) as follows:  
\[\text{Relative MSE}\triangleq\frac{\mathbf{D}(\mathbf{y},\mathbf{x})}{\|\mathbf{x}\|_{2}}, \tag{32}\]  
where we declare success for the recovery trial if the estimated signal assumes a relative MSE of less than \(10^{-5}\). For both UPR frameworks, we consider a diagonal structure on the preconditioning matrices, i.e., we set \(\mathbf{G}_{i}=\mathbf{W}_{i}\mathbf{W}_{i}^{T}\), where \(\mathbf{W}_{i}=\text{Diag}(\alpha_{0},\alpha_{1},\cdots,\alpha_{n-1})\), and perform the training over \(\mathbf{W}_{i}\) to ensure the positive definiteness of the resulting \(\mathbf{G}_{i}\).
# Supporting Privacy of Computations in Mobile Big Data Systems +
## Complexity
### Cost of Evaluating the Garbled Circuit  
In order to perform the requested computation, the server \(p_{e}\) obtains the garbled circuit, \(GC\), from the server \(p_{c}\). Let \(N_{g}\) denote the total number of gates in the circuit \(B\). Each entry in the garbled table has a length of \((nk+1)\) bits. Therefore, the size of the garbled circuit equals \(4N_{g}\times(nk+1)\) bits.  
Figure 6 shows the size of the garbled circuit in Kbits for the 32-bit adder. This circuit has \(N_{g}=375\) gates. The security parameter \(k=128\).  
Figure 5: Total number of Mbits generated randomly while creating one garbled table entry for the 32-bit adder.  
Let \(W\) and \(W_{o}\) denote the total number of wires and output wires, respectively, in the Boolean circuit \(B\). During evaluation, for each non-output wire of the circuit, the server \(p_{e}\) uses the PRNG \(G\)\(n\) times. Therefore, \(G\) is used for a total of \((W-W_{o})n\) times.
## 3 Results
### Salinity--Local Variety  
In the Treemap diagram in Figure 8, the mean values of all treatments are given for each agronomic trait, without salinity imposition and at salt concentrations of 80 mM NaCl and 160 mM NaCl. The results showed that at 80 mM NaCl salinity level, the biopriming/hydropriming treatment (T4) gave the highest mean values for almost all the traits with statistically significant differences from the no-priming treatment (T1) on the traits FWS (80.1%), FWR (124.9%), DWS (104.7%), DWR (260%), RL (47.4%), BIO (123.8%) and R/S (76.7%). The biopriming treatment (T3) gave the highest mean value for SL (24.9%) with statistically significant differences from the no-priming treatment (T1). The hydropriming (T2) treatment gave the lowest values across the treatments, like the no-priming (T1) treatment. At 160 mM NaCl salinity level, the biopriming/hydropriming (T4) treatment gave the highest mean values with statistically significant differences from the no-priming (T1) treatment for FWR (110.6%), DWR (145.7%) and R/S (49.7%). The biopriming treatment (T3) gave the highest mean values with statistically significant differences than the no-priming treatment (T1) for FWS (83.6%), DWS (91.8%), and BIO (90.5%). It also resulted in significantly higher values for FWR (67.5%) and DWR (82.9%). The hydropriming (T2) treatment gave the lowest values along with the no-priming (T1) treatment, except for the root length (RL).  
Figure 7: Treemap plot for the mean value of treatments for each trait separately for the commercial variety, in the salinity tests. Different Latin letters within the squares indicate significant differences according to the Least Significant Difference (LSD) test at \(p<0.05\), among the 12 treatments (4 for each variety). T1: no-priming, T2: hydropriming, T3: biopriming, T4: hydropriming/biopriming, FWR: Fresh Weight Root, DWR: Dry Weight Root, FWS: Fresh Weight Shoot, DWS: Dry Weight Shoot, RL: Root Length, SL: Shoot length, BIO: Biomass, R/S: Dry Weight Root/Dry Weight Shoot.  
Comparing the treatments between salinity levels, the biopriming treatment (T3) gave increased values at high salinity levels without statistically significant differences, while the biopriming/hydropriming treatment (T4) presented decreased values at high salinity levels with a statistically significant difference in FWR, DWS, and BIO traits. The hydropriming treatment (T2) presented significantly higher values under high salt stress regarding RL. Finally, the no-priming (T1) treatment maintained lower values at a high salinity regime compared to the other treatments except for the SL (Figures S6, S7 and S9).
## 3 Emergency Trajectory Planning Algorithm
### External Force  
As is common in the literature, the potential of the road prevents the vehicle from leaving the boundary and keeping its current lane on the road. When the vehicle drives out of the boundary, the value of the potential \(U_{boundary}^{t}(x)\) should become infinite. In the meantime, the potential will also give a relatively weak potential \(U_{lane}^{t}(x)\) to the vehicle when it drives on a certain lane (as in Figure 9). Thus, a repulsive force is employed according to Equation (5):  
\[F_{external,road}^{t}=-\
abla[U_{boundary}^{t}(x)+U_{lane}^{t}(x)]=\vec{u}_{road }\cdot(k_{lane}\cdot(x-sign(x)\cdot\frac{w}{2})+k_{boundary}\cdot(\frac{1}{p+ x}-\frac{1}{p-x})) \tag{5}\]  
where \(F_{external,road}^{t}\) is the external force provided by the road potential. \(\vec{u}_{road}\) is the direction vector (left side to the right side) of the \(x\)-axis in the relative coordinate system, and \(x\) is the current position in this coordinate system. The boundary force acting on the vehicle is in the area \(p-w\), where \(w\) is the width of the lane, the length of \(p\) is slightly bigger than \(w\). Coefficient \(k_{lane}\) and \(k_{boundary}\) are free parameters.  
Figure 8: Abrupt change of orientation.  
Figure 7: Abrupt change of velocity.  
The external force also aims to keep the current vehicle a safe distance d0 away from other dynamic or static obstacles by building an obstacle potential \(U^{t}_{obstacle}(x)\) that rises to infinite strength when any other vehicles approach any part of them. In some emergency situations, steering to avoid is encouraged instead of stop, which means a shaped obstacle potential is desired. Thus, we choose an obstacle force as shown in Equation (6):  
\[F^{t}_{external,obs}=-\
abla[U^{t}_{obstacle}(x)]=\vec{u}_{obs}\cdot k_{obs} \cdot(\frac{1}{d}-\frac{1}{d0})\cdot(\frac{1}{d^{3}}) \tag{6}\]  
where \(F^{t}_{external,obs}\) is the external force provided by the obstacle potential, \(\vec{u}_{obs}\) is the direction vector from the center of the obstacle to the current point. \(d\) is the distance to the boundary of the obstacle, and \(k_{obs}\) is a positive constant. In order to take into account moving obstacles, each node at time step \(t\) has its own environment and therefore its own potentials \(U^{t}\), and the external force acting on this trajectory node is the sum of the forces provided by these potentials.
## 2 System Model  
This section details the system model for Time-Sensitive Networking (TSN) scheduling, outlining the foundational assumptions, key definitions, mathematical constraints, and the proposed algorithms. While the model is primarily motivated and validated by the demanding requirements of in-vehicle networks, its principles are broadly applicable to various real-time systems within the Future Internet and IoT paradigms. The primary objective is to achieve deterministic data transmission for time-critical applications.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 3 Modified Nonlinear Fatigue Damage Accumulation Model Considering the Vibration Load Effect
### Total Cycle Determination  
Assuming that the S-N curve of the materials is stated by the Basquin's equation [5; 32] as is in Equation (25),  
\[N_{i}*\sigma_{i}^{b}=a^{b} \tag{25}\]  
where \(N_{i}\) is the total number of cycles that the element can sustain at a given stress level \(\sigma_{i}\) and the constant material parameters a and b represent the intercept and the slope of the S-N curve, respectively. They are determined as  
\[a=\frac{\left(fS_{ut}\right)^{2}}{S_{e}} \tag{26}\]  
\[b=-\frac{1}{3}log\left[\frac{fS_{ut}}{S_{e}}\right] \tag{27}\]  
where \(f\) is the fatigue resistance fraction, \(S_{ut}\) is the ultimate resistance stress and \(S_{e}\) is the endurance limit. Then, from Equation (25), the total cycles \(N_{i}\) are determined as  
\[N_{i}=\left(\frac{\sigma_{i}}{a}\right)^{\frac{1}{b}} \tag{28}\]  
In mechanical design, the stress \(\sigma_{i}\) given in Equation (28) is the equivalent stress or static stress. However, because here we are focused on the field of dynamic random vibration, then, in vibration analysis, the \(\sigma_{i}\) is replaced by the vibration bending stress \(\sigma i_{vb}\) determined in Section 3.1. Therefore, for vibration analysis, the total cycles are determined as  
\[N_{i}=\left(\frac{\sigma i_{vb}}{a}\right)^{\frac{1}{b}} \tag{29}\]  
Now that the cycles of vibration applied \(n_{i}\) at the stress \(\sigma i_{vb}\) and the total cycles \(N_{i}\) that withstand at that stress \(\sigma i_{vb}\) are known, we proceed to determine the accumulated fatigue damage that is caused by the random vibration loading, as follows.
## 3 Results and Discussion
### pH of Anthocyanin Extracts  
During the experiments, the pH of the solvents and final extracts was closely monitored to study the effect of extraction parameters on this parameter. The pH value of the water-ethanol extracts ranged from 1.5 to 2.6, while for water-citric acid extracts, it ranged from 1.5 to 1.9 (Table 3). Statistical analysis showed a significant (\(p<0.05\)) effect of material/solvent ratio on the pH value of the extracts, where for the water-ethanol solvent, the increase in pH was most pronounced. This is due to an increase in the concentration of dissolved compounds in the solution and, thus, a decrease in the buffering capacity of the solvent. However, it should be noted that the water-citric acid solvent had a better buffering capacity. Extraction temperature was significant (\(p<0.05\)) only for the material/solvent ratio of 1:7 (the highest pH values were observed at 25 \({}^{\circ}\)C and 45 \({}^{\circ}\)C). As for extraction time, it was only significant (\(p<0.05\)) in combination with the 1:7 material/solvent ratio, water-ethanol solvent and extraction temperatures of 35 \({}^{\circ}\)C and 45 \({}^{\circ}\)C. The 30 and 60 min of the UAE process, combined with the increased temperature, allowed more substances to be extracted [36].  
An increase in the pH value of water acidified with acetic acid at the end of the extraction of anthocyanins from blackcurrant pomace was also observed by [PERSON] et al. [20]. However, the pH of the extracts increased from 1.5 to 1.92, a value similar to that found by us only for the water-citric acid solvent with a material/solvent ratio of 1:7.  
The pH of the pigment solutions, such as anthocyanins obtained in this study, is important for their use in food applications. The pH primarily determines the color of anthocyanins. In a strongly acidic environment, at pH \(<3\), only the flavylium cation is present, which gives a red color [37]. The intense red color of the pigments is most desired by food manufacturers, so it is necessary to ensure that at the end of pomace extraction, the pH is below 3.  
Figure 5: Coloring strength (E1%—values of absorptivity at \(\lambda\) = 520 nm). Blue bars—material/solvent ratio 1:20, red bars—material/solvent ratio 1:7; **(a)** extracts obtained with water–ethanol (50/50 \(v/v\)) solvent; **(b)** extracts obtained with water–citric acid solvent. The values with different letters within the same color are significantly different (\(p<0.05\)).
## 3 Results and Discussion
### Electrode Size Effect on sEMG  
The amplitude of the recorded EMG signal is an important parameter to achieve a high SNR electrode. Since the microelectrode has a relatively small electrode size compared to a conventional electrode, the recorded sEMG signal is sensitive to the electrode size. To observe the effect of the electrode size on the measurements, we compared our two microelectrode types: Electrode 1 of size 17,671 \(\upmu\)m\({}^{2}\) and Electrode 2 of size 59,325 \(\upmu\)m\({}^{2}\). The size of Electrode 2 is 3.357 times greater than that of Electrode 1 (Figure 4A). Assuming that the electrode size affects the amplitude of the sEMG signal, the sEMG was recorded in the same environment to minimize any errors attributable to the body condition. Figure 4A shows the root-mean-square amplitudes (V\({}_{\text{rms}}\)) from the two microelectrode arrays (eight channels for each array). The V\({}_{\text{rms}}\) of the larger electrode (Electrode 2) was 2.64 times greater than that of the smaller electrode (Electrode 1). The V\({}_{\text{rms}}\) difference corresponds to the size difference between Electrodes 1 and 2 to some extent. Furthermore, a comparison between the data from the best channel of each microelectrode array shows the 2.85 times gap of the V\({}_{\text{rms}}\), as shown in Figure 4B, indicating consistent results with the earlier comparison. Further, the SNRs of Electrodes 1 and 2 were 16.9 and 21.4 dB, respectively, showing a higher SNR from the larger electrode (Electrode 2). This is attributed to the increased recording ability due to the enlarged electrode size. From the FFT  
Figure 4: Comparison of the recorded sEMG for different electrode sizes. (**A**) Averaged V\({}_{\text{rms}}\) (root-mean-square voltage) of the recorded sEMG with Electrode 1 and Electrode 2. (**B**) Comparison between the best channel of each microelectrode array. (**C**) Averaged fast Fourier transform (FFT) data of Electrodes 1 and 2.  
signal processing shown in Figure 4C, we observe that both the electrodes have similar peaks in the frequency domain, indicating that both microelectrode arrays could reliably record sEMGs with different signal amplitudes. Although more studies are required to further investigate the effects of electrode size, the present results are expected to be of help for recording sEMGs using microscale flexible electrode arrays.
## 2 Results and Discussion
### Nephroprotective Effect of APTM
#### 2.4.6 Effect of APTM on Serum Gamma-GT and Alkaline Phosphatase (ALP) Level  
The impact of APTM extract on the serum concentrations of gamma-GT and ALP in GM-intoxicated rats was tested for its hepatotoxicity efficacy against GM hepatotoxicity, as shown in Figure 6. These results support the findings of [PERSON] et al. (2017), who also reported an increase in serum ALP and gamma-GT after eight days of GM treatment [25]. GM-injection generates cell membrane damage and permeability and increases serum ALP, and gamma-GT levels, generally deemed sensitive markers of liver function [37]. Hepatocyte membranes have localized gamma-GT activity, and ALP, an indicator for the endoplasmic reticulum, is routinely used to examine the integrity of the plasma membrane. [8]. The high concentration of these enzymes in the serum highlighted that the GM's oxidative stress had damaged the rat's liver. The rat treated with GM had higher levels of these enzymes in its serum [8; 38; 39]. The findings of our study show that the pretreatments with APTM at doses of 200 and 400 mg/kg significantly reduced increases in blood gamma-GT and ALP in GM-poisoned rats. The study's findings were in line with prior investigations, in which chemical or plant extracts improved the steady-state concentration of these enzymes and lessened hepatocyte damage [38; 39].