esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation Parameters
#### 3.2.2 Translation Error during Fine-Positioning Step  
The second parameter is the translational error of the IMN hole with respect to the axis of the guiding hole, or the normal axis of the guiding pattern. Because we assumed that the two planes became parallel during the first step, the error value was evaluated using X-ray images taken by the C-arm. As shown in Figure 9, the projected images of the IMN hole in green and the circle on the guiding pattern in red were both circular. Since the real outer diameter of the circle on the guiding pattern block \(d_{\text{Guiding\_Pattern\_Real}}\) was 20 mm, the real error value can be calculated as in Equation (2). When the X-ray image was acquired after the repeated fine-positioning process, the center of the IMN hole \(O_{1}\), and the center of the circle on the guiding pattern \(O_{2}\), were identified manually, and the distance (\(O_{1}O_{2}\)) on the X-ray image \(d_{\text{O1}\,O_{2\_Error\_Image}}\) was measured. Then the real translational error \(P_{\text{O1}\,O_{2\_Error\_Real}}\) was calculated as in Equation (2). Figure 10 shows the X-ray images before and after the fine-positioning step during the evaluation. Because the circle on the pattern block was larger than the IMN, it was easy to roughly align them. The grid pattern helps the surgeon to adjust the origin of the gird pattern toward the center of the IMN hole.  
\[P_{\text{O1}\,\text{O2}\,\text{Error\_Real}}=\frac{d_{\text{Guiding\_Pattern\_Real}}}{d_{ \text{Guiding\_Pattern\_Image}}}\,d_{\text{O1}\,\text{O2}\,\text{Error\_Image}} \tag{2}\]  
Figure 8: Measurement of angle error by OTS marker.  
Figure 9: Definition of translational error. (**a**) Conceptual diagram. (**b**) Real X-ray image overlaid by colored circles.
## 2 Simulation Model
### Fuel Cell Hybrid Tractor Model
#### 2.1.2 Fuel Cell Model  
The fuel system model includes the fuel cell stack and its associated components. When the fuel cell operates, energy losses inevitably occur due to chemical reactions. These irreversible losses are reflected in the polarization overpotential, and the polarization overpotential, along with the ideal electromotive force, determines the actual output voltage of the fuel cell. The output voltage of a single fuel cell is represented by the following equation:  
\[V_{cell}=E_{nerst}-V_{act}-V_{ohm}-V_{con} \tag{1}\]  
The ideal electromotive force (\(E_{nerst}\)) is related to pressure and temperature. It represents the electromotive force of the fuel cell at equilibrium when no current is flowing internally. The Nernst equation provides the formula for the ideal electromotive force:  
\[E_{nerst}=\frac{\Delta G}{2F}+\frac{\Delta S}{2F}\Big{(}T-T_{ref}\Big{)}+\frac {RT}{2F}\bigg{[}\text{ln}\big{(}P_{H_{2}}\big{)}+\frac{1}{2}\text{ln}\big{(}P _{O_{2}}\big{)}\bigg{]} \tag{2}\]  
\[V_{act}=\frac{R\cdot T}{\alpha\cdot n\cdot r}log\Big{(}\frac{(i_{stack}+i_{n} )}{i_{0}}\Big{)} \tag{3}\]  
\[V_{ohm}=R_{memb}\cdot i_{stack} \tag{4}\]  
\[V_{cons}=-B\cdot log\Big{(}1-\frac{i_{stack}}{i_{1}}\Big{)} \tag{5}\]  
To account for the actual output performance of a fuel cell, the fuel cell accessories are integrated into the fuel cell stack, forming a fuel cell system. These accessory components are collectively referred to as the Balance of Plant (BoP), which includes the anode, cathode, and cooling circuit. The anode's main function is to supply sufficient hydrogen to the fuel cell while also facilitating hydrogen recirculation and purging. The cathode includes a compressor that supplies air, which is humidified and then enters the fuel cell cathode flow channels. The exhausted air and generated water pass through a condenser before being released into the atmosphere. Changes in the gas excess ratio of the cathode and anode can result in variations in the performance output of the fuel cell. Reducing the gas excess ratio leads to a decrease in fuel cell performance and output voltage. The purpose of establishing  
Figure 2: The architecture of FCHEV.  
a fuel cell model is to simulate the actual working process of the fuel cell and investigate its degradation mechanisms. The schematic diagram of the fuel cell system's composition is shown in Figure 3.
## 3 Results
### Photoluminescence (PL) Spectra  
The PL excitation and emission spectra of sample A10 (pure Zn\({}_{2}\)TiO\({}_{4}\)) are shown in Figure 8a,b. The PL excitation spectrum contains one prominent band at 225 nm when monitored for an emission maximum at 346 nm (Figure 8a). Upon excitation at wavelengths 225, 254, and 275 nm, the emission maximum is obtained at 346 nm (Figure 8b). The excitation spectrum overlaps exactly with the UV-Vis diffuse reflectance spectrum, suggesting that the origin of the two is the same. The sample can be excited with any wavelength lower than 280 nm and needs UV-C light to produce efficient PL emission. The emission of the pure Zn\({}_{2}\)TiO\({}_{4}\) sample is in the UV-A region and extends between 300 and 390 nm. For this band-to-band excitation, the origin of PL emission is anticipated to be defects similar to the ones observed in undoped ZnGa\({}_{2}\)O\({}_{4}\) samples, where the prominent emission has been attributed to the self-activated centres of the octahedral Ga-O group in the spinel lattices [21]. While looking for information in the literature, we found a recent article that mentions the origin of PL emission in Zn\({}_{2}\)TiO\({}_{4}\) to be O\({}^{2-}\) and Zn\({}^{2+}\) vacancies related that are present in the crystal lattice [11]. The conduction band in Zn\({}_{2}\)TiO\({}_{4}\) lattice is made up of Ti-3s orbitals and the valance band is composed of Zn-3d and O-2s orbitals [22].
## 2 Prototype and Experimental Setup
### Shaking Table and Soil Container  
The experimental campaign was conducted at the multifunctional shaking table system of the State Key Laboratory of Disaster Reduction in Civil Engineering at Tongji University. The dimensions of the shaking table were 10.1 m \(\times\) 6.1 m in its plane, and it was capable of shaking up to 140 tons of payload with a maximum acceleration of 1.5 g. The controlled frequency range was from 0.1 Hz to 50 Hz. Real seismic records, as well as synthetic motions, could be simulated. Figure 2a presents an overview of the shaking table test.  
A newly designed laminar container (Figure 2b) was used in the experiments to host the model soil and structure. The laminar container consisted of 16 steel frames with effective internal dimensions of 9.5 m \(\times\) 5.5 m \(\times\) 2.16 m (length \(\times\) width \(\times\) height). The frames were stacked up through industrial ball bearings, which allowed the frames to slide smoothly in the direction of the shaking. A 5 mm thick rubber layer was covered at the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Soil** & **Depth (m)** & \(V_{s}\) **(m/s)** & \(\tau_{0}\) **(kN/m\({}^{3}\))** & \(c^{\prime}\) **(kPa)** & \(\mathbf{q^{\prime}}\) (\({}^{\circ}\))** \\ \hline
1 & Fill & 1.9 & 125 & 17.3 & - & - \\
2 & Silty clay & 3.5 & 128 & 18.5 & - & - \\
3 & Silty clay & 9.5 & 125 & 17.1 & 4 & 31 \\
4 & Mud clay & 17.6 & 137 & 16.6 & 3 & 28 \\
5 & Clay & 23.8 & 189 & 17.4 & 2 & 25 \\
6 & Silty clay & 30.9 & 235 & 19.4 & 5 & 26 \\
7 & Sandy silt & 42.8 & 255 & 18.9 & 7 & 33 \\
8 & Silt & 50 & 322 & 18.9 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prototype soil profile and key soil properties of the encountered layers.  
Figure 1: Prototype. (**a**) Location of the prototype station in the metro route map of Shanghai, China. (**b**) 3D model of the prototype station with the key dimensions (unit: m).  
inner surface of the container to reduce the vertical friction between the model soil and the box. The side of the box was connected with a restraining steel plate to prevent unfavorable torsion of the box. Based on the results of finite element (FE) pre-analysis of the laminar box, the thickness of the restraining plate was determined to be 3 mm so that the horizontal deformation of the model ground was acceptable and the dominant mode of the box (of 1.2 Hz) did not interfere with the fundamental vibration modes of the model ground. With such a configuration (Figure 2a), the shear box was only able to shear along the direction of its short side, along which the seismic motions were input in this experiment.
# High-Temperature Liquid-Liquid Phase Transition in Glass-Forming Liquid Pd43 Ni20 Cu27P10
## 3 Results and Discussion  
### Experimental Results  
DSC analysis for monitoring changes in thermodynamic properties of the Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) spin ribbon was based on the changes of \(T_{g}\), \(T_{x}\), \(T_{m}\), and \(T_{l}\). As shown in Figure 1, glass transition (endothermic phenomenon) occurs at \(T_{g}\) = 580 K, exothermic crystallization occurs at \(T_{x}\) = 621 K, the melting point is at \(T_{m}\) = 874 K, and the liquidus temperature is at \(T_{l}\) = 1020 K. Consequently, the width (\(\Delta T_{x}=T_{x}-T_{g}\)) of the supercooled liquid region is 41 K. The measured \(T_{g}\) and \(T_{x}\) are close to those of Pd\({}_{43}\)Ni\({}_{10}\)Cu\({}_{27}\)P\({}_{20}\)[41] and Pd\({}_{42}\)Cu\({}_{30}\)Ni\({}_{7.5}\)P\({}_{20}\)[42] (both of them have \(T_{g}\) of 568 K and \(T_{x}\) of 640 K). However, the liquidus temperature of the studied Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) is much higher than the 850 K of Pd\({}_{42.5}\)Cu\({}_{30}\)Ni\({}_{7.5}\)P\({}_{20}\).  
Although conventional DSC is a very effective tool for exploring the glass transition, the low scan rate limits its application in investigating thermodynamic and dynamic  
Figure 1: DSC trace of the Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) metallic glass measured at a heating rate of 0.67 K/s.  
properties. The heat flow versus temperature behavior of Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) has been obtained as a function of different cooling rates with the help of FDSC, as illustrated in Figure 2. Intriguingly, an anomalous endothermic peak presents at 893 K for the cooling rate of 400 K/s. The peak temperature shifts to a higher value as the cooling rate increases, and it attains 902 K at the cooling rate of 2100 K/s. The endothermic peak occurring in glass-forming liquid regime is a signature of thermodynamic phase transition, namely, LLPT. The anomalous endothermic LLPT in the Pd-Ni-Cu-P system was also observed in our previous work for Pd\({}_{40}\)Ni\({}_{40}\)P\({}_{20}\)[23]. The atomistic insights into the structural evolution of the transition are discussed below.
## 4 Design of the Enhanced Fault Current Limiting Circuit
### Determination of Boundary Condition  
Reference [33] mentioned that the hybrid DCCB has been tested to interrupt the maximum fault current of 9 kA within 2 ms. Thus, \(I_{B\_max}\) is set to 9 kA in this study. The DCCB operation time \(t_{B\_op}\) is set to 2 ms. Therefore, \((di/dt)_{B\_max}\) is set to 9 kA/2 ms = 4.5 kA/ms. The fault detection time under the DC fault in the HVDC system has been reduced to within 1 ms via a wavelet-based algorithm without using communication [40]. Consequently, the fault detection time \(t_{fd}\) can only be set to 1 ms, and the capacitor discharging time \(t_{bl}\) must be more than 1 ms + 2 ms = 3 ms. The worst fault scenario has been adopted in this part as the PPF at the end of the DC overhead line. Furthermore, the threshold value of the arm current (\(I_{th}\)) is normally set to twice as that of the rated current (\(I_{N}\)) to block the IGBT. Therefore, \(I_{\mathrm{th}}=2\times I_{\mathrm{N}}=2\times 1000\) A = 2000 A with \(I_{N}\) is set to 1000 A in this study.  
The capacitor discharging current is defined in Equation (9) on the basis of the fault current analysis discussed in Section 2. The contributions from the AC grid and distributed line capacitance are omitted here. According to Constraint (38), the time during fault detection and main breaker operation delay is 3 ms.  
\[\max(i_{dc})=i_{\mathrm{dis}}(t)\big{|}_{t=3\ \mathrm{ms}}<I_{\mathrm{B\_max}}=9 \ \mathrm{kA} \tag{41}\]  
The change rate in the DC fault current can be obtained using Equation (42); the change rate must be less than 4.5 kA/ms.  
\[\max(di_{dc}/dt)=\max(i_{\mathrm{dis}}(t)^{\prime})<4.5\mathrm{kA/ms} \tag{42}\]  
In reference to Equations (35) and (36), Constraint (40) must be considered to decrease the arm current at 3 ms less than the threshold value of blocking the IGBT after fault occurrence.  
\[i_{\mathrm{arm}}(t)\big{|}_{t=3\mathrm{ms}}<I_{\mathrm{th}}=2\mathrm{kA} \tag{43}\]  
Equations (35)-(37) express that the equivalent capacitance and inductance determine the fault current development and capacitor discharging time. Moreover, the fault current curve must beseparated into two stages using the proposed EFCLC (Figure 12b). Specifically, the fault current rising speed is higher in the first 1 ms stage than in the rest time. Thus, the fault current change rate within the first 1 ms must be limited by Constraint (42). Furthermore, the fault current value at 3 ms after fault occurrence must be obtained by superposing the fault current calculation in two discharging stages. Therefore, varying the fault circuit parameters, which are similarly considered in determining the capacitor discharging time, must be addressed.
## 4 Numerical Analysis Results and Data Analysis
### Interaction Analysis
#### 4.3.2 Multiple Regression Analysis  
Relative Importance of Explanatory Variables  
To explore the relative importance of explanatory variables, multiple regression analysis was performed, and the results are shown in Table 11. Interactions can be included in the regression model, but the variance inflation factor exceeds ten when the interaction is included. Therefore, it was not included as evidence that multicollinearity exists.  
The regression model satisfied all residual assumptions (normality, independently, homoskedasticity, linearity), and no outliers were found. The regression coefficient of the aspect ratio and cover depth was calculated as a negative number, which has the effect of suppressing vibration.  
When there are multiple explanatory variables in a regression model, the method for evaluating the relative importance of each variable can be found by creating all possible submodels and finding how much the R-square value increases on average when one explanatory variable is added. This shows the relative importance of the explanatory variables in the regression model.  
Based on the selected multiple linear regression model, the relative importance of the explanatory variables was confirmed using the degree of influence on the coefficient of determination (Figure 7). Among the explanatory variables, the vehicle speed had the greatest effect on the coefficient of determination (46.73%). Among the geometrical characteristics of the lava tube, the joint orientation (39.78%), cover depth (10.61%) and the shape ratio (2.88%) affected the coefficient of determination in that order. This is the same as the multi-way ANOVA result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Coefficient** & **Std. Error** & **\(t\)-Value** & **\(p\)-Value** \\ \hline Intercept & \(-5.5245\) & \(0.142\) & \(-38.906\) & \(0.00\) \\ Joint orientation & \(0.2828\) & \(0.2319\) & \(12.197\) & \(1.5\times 10^{-15}\) \\ Aspect ratio & \(-0.2083\) & \(0.0635\) & \(-3.280\) & \(0.206\times 10^{-2}\) \\ Cover depth & \(-0.1633\) & \(0.0259\) & \(-6.299\) & \(1.34\times 10^{-7}\) \\ Vehicle speed & \(0.0171\) & \(0.0013\) & \(13.220\) & \(0.00\) \\ \hline \hline \end{tabular} \(R^{2}=0.8969\), \(Adj-R^{2}=0.8873\)  
\end{table}
Table 11: Multiple regression analysis result.  
Figure 7: Relative importance of explanatory variables.
## 3 Results and Discussion
### Analysis of Water Retention of Non-Ionic Fluorinated Dust Suppressants
#### 3.2.2 Analysis of Segmental Weight Loss Rate Variation of Non-Ionic Fluorinated Solutions
Among them, when the concentration of the solution is 0.03%, the total mass lost is the least, 10.0909 g, followed by the total mass lost at the concentration of 0.08%, 10.3259 g, and at the concentration of 0.3%, the solution loses the most total mass, 15.4086 g. The least mass was lost and the best water retention was achieved at 0.03% and 0.08% solution concentrations, while the most mass was lost and the worst water retention was achieved at 0.3% solution concentration. As can be seen in Figure 7c, the total mass lost from the solution in the water retention experiment fluctuates up and down with increasing solution concentration, reaching a trough at solution concentrations of 0.03%, 0.08%, and 0.1%. Among them, when the concentration of the solution was 0.03%, the total mass lost was the least, at 10.0909 g,  
Figure 7: Total water loss of FS-30, FS-31, FS-34, FS-50 solution at different concentrations. followed by the total mass lost at a concentration of 0.08%, which was 10.3259 g, and at a concentration of 0.3%, the solution lost the most total mass, at 15.4086 g.  
As can be seen in Figure 7d, the total mass lost from the solution in the water retention experiment fluctuates up and down as the solution concentration increases, reaching troughs at solution concentrations of 0.08% and 0.3% and peaking at a concentration of 0.06%. The total mass lost is the least at a concentration of 0.3%, with a value of 12.3719 g, followed by 13.9915 g at a concentration of 0.08%, and 16.4639 g at a concentration of 0.06%. The best water retention and the least mass lost were achieved at solution concentrations of 0.3% and 0.08%, respectively, while the worst water retention and the most mass lost were observed at a solution concentration of 0.06%. Non-ionic fluorocarbon solutions contain hydrophobic functional groups, typically composed of carbon and fluorine atoms. These functional groups have a low affinity for water, leading to the formation of a hydrophobic environment within the solution. This hydrophobicity affects the ability of the solution to retain water. When the solution concentration is low, such as at 0.01% and 0.03%, the hydrophobic functional groups are relatively dilute. As a result, there is limited interaction between the solution molecules and water molecules. This leads to a relatively high loss of water from the solution, resulting in higher total mass lost. At concentrations of 0.05% and 0.2%, the hydrophobic functional groups become more concentrated, creating a denser hydrophobic environment. This increased concentration enhances the interaction between the solution molecules and water molecules, reducing the overall loss of water from the solution. Consequently, these concentrations exhibit better water retention, with the least mass lost compared to other concentrations tested.
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### GNSS Error Analysis and Modeling
#### 3.2.3 Tropospheric Delay Error  
The troposphere will greatly change the refraction coefficient of satellite navigation signals transmitted in the troposphere, causing signal transmission delays and large errors in the final measured distance [30]. The approximate model is as follows:  
\[T\propto\left[1-(\frac{\cos\theta_{nt}^{as}}{1.001})^{2}\right]^{\frac{1}{2}}. \tag{31}\]
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 4 Results and Evaluation  
LAES was assessed and compared against the well-known lightweight crypto algorithms Celfia and Present. The evaluation was conducted using several measures designed based on the guidance of the NIST statistical test suite for cryptographic applications [27]; frequency test, block test, runs test, effective (total) encryption time, average encryption time for a single block, and average encryption time for a single round. Facing identical cases, the three algorithms were challenged to encrypt various images with sizes from 1 KB up to 4 MB. The experiments included the encryption of around 15,000 image files. The randomness level was measured using the first three measures, while the rest were used to indicate the encryption latency for LAES and its counterparts.
## 2 Materials and Methods  
### Unmanned Underwater Vehicle Model  
#### 2.1.1 Kinematic Model  
As described by [PERSON] (1986), two reference frames are needed to build the mathematical model that represents the underwater vehicle: one referenced to the earth (labeled as Earth-fixed frame) and the other referenced to the vehicle (labeled as body-fixed frame). The orthonormal axes are denoted by \(x,y,z\) for the Earth-fixed frame, and by \(x_{b},y_{b},z_{b}\) for the body-fixed frame. These reference frames are shown in Figure 1.  
The Society of Naval Architects and Marine Engineers (SNAME) has a convention to express the position and orientation of vehicles, and the forces and moments applied to them. The SNAME nomenclature for position, velocity, and forces in underwater vehicles (1986) is presented in Table 1.  
The position of the vehicle with respect to the Earth-fixed frame \(\eta\) and its velocity with respect the body-fixed frame \(\
u\) can be represented as  
\[\eta =(x,y,z,\varphi,\theta,\psi)^{\mathrm{T}}, \tag{1}\] \[\
u =(u,v,w,p,q,r)^{\mathrm{T}}, \tag{2}\]  
The forces and moments of the vehicle with respect to the body-fixed frame are described as  
\[\tau=(X,Y,Z,K,M,N)^{\mathrm{T}}, \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movement** & **Name** & **Position** & **Velocity** & **Force/Moment** \\ \hline \(X\) translation & Surge & \(x\) & \(u\) & \(X\) \\ \hline \(Y\) translation & Sway & \(y\) & \(v\) & \(Y\) \\ \hline \(Z\) translation & Heave & \(z\) & \(w\) & \(Z\) \\ \hline \(X\) rotation & Roll & \(\varphi\) & \(p\) & \(K\) \\ \hline \(Y\) rotation & Pitch & \(\theta\) & \(q\) & \(M\) \\ \hline \(Z\) rotation & Yaw & \(\psi\) & \(r\) & \(N\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation for underwater vehicle’s movements, positions, velocities, and forces.  
Figure 1: Reference frames needed to describe the BlueROV2.