esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 3 Proposed Method
### NTN Simulator and Data Description
#### 3.1.2 User Equipment Data Collection  
To better understand the simulation of NTN communication between UE and Satellite, we collected important data about the user used to track all the UE in different cells.  
Figure 5 shows the first ten features of the user data after 120 s of simulation. The Dataset has 62,908 rows and 20 columns; Table 1 describes the details of each feature.  
Figure 4: LEO-Satellite Features: latitude, longitude, and altitude (LLA) data from the NTN Simulator.  
Figure 5: User Equipment Data Sample Description.  
Figure 3: The proposed intra-satellite handover architecture.  
Thus, one of the crucial features of the UE is the location provided during the simulation, and we also provided details of each feature about the information of the UE.
## 3 Results and Discussion
### Factors Investigation
#### 3.2.6 Liquid and Gas Velocity  
The liquid and gas velocities inside TBRs have a great effect on the liquid distribution by affecting the inertia force. The simulation results of different liquid and gas superficial velocities are shown in Table 9, and the liquid distribution inside the spherical packing is shown in Figures 9 and 10. The increase of liquid velocity lead to a rise in both liquid holdup and wetting efficiency, while the increasing of gas velocity had the opposite effect.  
In Figure 9 the accumulation of liquid inside the 4-hole cylinder packing is clearly shown. With increasing velocity, liquid tends to flow through more voids, then fills all possible space inside the TBR. It can be found that the quantity of liquid in \(v_{L}=0.002\) m/s was much less than \(v_{L}=0.005\) m/s and \(0.008\) m/s. Although the increase of liquid velocity gave rise to the liquid inertia force, it still cannot force liquid to leave the packing faster since the input quantity increased too. It is worth noticing that the increased liquid holdup from \(v_{L}=0.005\) m/s to \(0.008\) m/s was much less remarkable that that from \(0.002\) m/s to \(0.005\) m/s. It can be explained by the fact that most of the attainable free space was filled with liquid when \(v_{L}=0.005\) m/s and the increase of \(v_{L}\) from \(0.005\) m/s to \(0.008\) m/s may have happened  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Liquid Density (kg/m\({}^{3}\))** & **Liquid Holdup** & **Wetting Efficiency** \\ \hline
500 & 0.2694 & 0.6625 \\
1000 & 0.2633 & 0.6566 \\
1500 & 0.2543 & 0.6313 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Liquid holdup and wetting efficiency of three liquid densities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Liquid Superficial Velocity (m/s)** & **Gas Superficial Velocity (m/s)** & **Liquid Holdup** & **Wetting Efficiency** \\ \hline
0.002 & & 0.2096 & 0.5686 \\
0.005 & 0.245 & 0.2565 & 0.6339 \\
0.008 & & 0.2633 & 0.6566 \\ \hline  & 0.082 & 0.3086 & 0.7163 \\
0.008 & 0.163 & 0.2898 & 0.6881 \\  & 0.245 & 0.2633 & 0.6566 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Liquid holdup and wetting efficiency at different liquid and gas velocities.  
Figure 9: Liquid distributions with different liquid superficial velocities (\(v_{G}=0.245\) m/s).  
in the pulse flow regime, where the increase of liquid holdup with \(v_{L}\) was less significant than which in the trickle flow regime.  
The rise of the gas velocity brought a higher liquid-gas shear stress, making it easier for the liquid to leave the particle surface and the packing. The difference of liquid quantities shown in Figure 10 was remarkable, as the liquid quantity under \(v_{G}=0.245\) m/s was less than the other situations.
## 4 Discussion
### Limitations  
There are some limitations and prospects in this paper. First, as the bottom-TCC is mainly studied, the range of water blocking rate in this paper is 0.3-0.8. For the top-TCCs with the function of blocking floating objects on the water surface, its water blocking rate may be less than 0.3, which is not applicable to the analysis in this paper. Second, the results are obtained under the condition of uniform inflow velocity, and the effects of inhomogeneous velocity and large local velocity should be noted. Third, the simulation model in this paper ignores the mechanical deformation of TCCs caused by the fluid-structure interaction, which could be improved in future studies. Finally, this study only focuses on the case study reservoir, and the results should be further validated by expanding to other reservoirs.
## 2 Results and Discussion
### Comparison of results with other Cartosat-1 investigations on the same test sites  
The outcomes of this study are in large parts comparable with those obtained by other investigators using the same dataset. Regarding the DSM's generation for the test field of Salon de Provence, [PERSON] and [PERSON] [17] described for 30 m resolution Cartosat-1 DSMs a RMSE between 6.5 m and 9.5 m when comparing results to MNT DBTOPO(r) DTM. [PERSON] _et al._[39] assessed for 10 m resolution Cartosat-1 DSMs a mean value of residuals of -3.3 m and a standard deviation of 7.5 m. Similarly, the same authors [39] reported for 10 m resolution Cartosat-1 DSMs a mean value of residuals of -0.4 m and a standard deviation of 7.0 m when checking results with the SRTM DSM. [PERSON] _et al._[49], using only excellent points from hierarchical matching in the generation process, reported a mean of height differences to the reference DSM of -1.5 m and standard deviation of 2.2 m for 10-meters Cartosat-1 DSMs. The same authors [49] assessed a mean of height differences to the reference DSM of -1.1 m and standard deviation 3.5 m when using all the points from obtained with region growing.  
Regarding the DSM's generation for the test field of Mausanne les Alpilles, [PERSON] [48] produced 10-meters grid cell size Cartosat-1 DSMs with the following statistics: RMSE = 7.2 m, LE90 = 14.1 m, mean error = 0.8 m and mean absolute error = 4.4 m. Similarly to Salon de Provence, [PERSON] _et al._[49], using only excellent points from hierarchical matching in the generation process, reported a mean of height differences to the reference DSM of -1.9 m and standard deviation of 2.2 m for 10-meters DSMs. Moreover, the same authors [49] assessed a mean of height differences to the reference DSM of -1.4 m and standard deviation 3.8 m when using all the points from obtained with region growing. [PERSON] _et al._[46] in their investigation concluded that Cartosat-1 data have a good potential for the generation of DSMs with a grid spacing of about 10 m and reported a RMSE of 3 m. However, it is important to note that the authors found this result investigating only a reduced area of the Mausanneles Alpilles test site and not the whole test site, so results may be better than those of other investigators.  
Finally, for both Salon de Provence and Mausanne les Alpilles test sites, [PERSON] _et al._[49] also described an improved technique which, using a multi-ray (4-ray) forward intersection for the overlapping area of the test fields, was able to give Cartosat-1 DSMs a mean value of residuals of -1.3 m and a standard deviation of 3.4 m.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges  
The usage of NOMA in the context of 5G and B5G is in its early stages. To realise its influence on end users, a number of outstanding concerns and research challenges must be addressed. This section uses NOMA variations to explore the present and future communication problems given in Figure 12.  
Figure 12: Some of the open issues and challenges in cooperative PD-NOMA.
## 2 Impact Energy Recognition Algorithm Based on Fiber Bragg Grating Strain Sensing Technology
### Low Velocity Impact Energy Recognition Steps  
In the low velocity impact energy monitoring and recognition process, the low velocity impact energy is evaluated by the impact energy feature of the sample signals. First of all, all the sample impact signals and the signal to be measured need to be whitening EMD decomposed. Secondly, calculate the eigenvalues of the impact energy of the sample signals and the signal to be measured, i.e., the spectral peak of a certain order component of the signals in the sensor network, and establish the energy feature set of the sample impact signals. Finally, because the smaller thickness area of the specimen plate is more sensitive to the impact energy, the integrated thickness coefficient is determined according to a certain order component of the sensor network signals, which is used to correct the energy feature set and achieve the actual impact energy assessment. In summary, the specific steps of low velocity impact energy recognition algorithm for variable thickness composite laminates are as follows:  
(1) At the key impact position, the low velocity impact sample signals with multiple impact energies are collected and decomposed by whitening EMD, and then the cross-correlation coefficients between each component and the original signal are obtained.  
(2) The components with large correlation coefficients are selected for spectrum analysis to get the spectrum peak set; the first or second order components (judged according to the actual aliasing of decomposition) are used for spectrum analysis to get the peak number set, and the comprehensive thickness coefficient set is obtained after normalization.  
(3) The spectrum peak set of the variable thickness plate is obtained by modifying the spectrum peak set with the comprehensive thickness coefficient set, and the maximum value of the set is obtained as the energy eigenvalue of the impact sample signal. Based on this, feature sets of variable thickness plates with multiple energies are established.  
(4) The collected energy impact signal \(Z\) whose energy is to be measured is decomposed by whitening EMD, and the eigenvalue \(f\) and range values of variable thickness plate feature set \(F\) are compared to evaluate the impact energy level. If the eigenvalue \(f\) is between the feature sets \(F_{1}\) and \(F_{2}\) of two levels of energy \(E_{1}\) and \(E_{2}\), the evaluation is based on \(f\) and the energy feature set boundaries \(F_{1 max}\) and \(F_{2 min}\):  
\[E_{z}=E_{2}-\frac{(F_{2 min}-f)\cdot(E_{2}-E_{1})}{F_{2 min}-F_{1 max}} \tag{8}\]  
Shown in Figure 1 is the low velocity impact energy recognition flow chart of variable thickness composite laminates. The thickness correction step is to compensate for the distortion of the variable thickness structure signal, and to improve the recognition effect of impact energy by using the thickness feature in the spectrum.  
Figure 1: Low velocity impact energy recognition flow chart of variable thickness composite laminates.
## 3 Test Results and Analysis
### Vertical Compression Performance of Bearings
#### 3.1.1 Loading Frequency  
Test data from cases 1-12 were extracted, and correlation tests of loading frequencies were carried out on three bearings, which aimed to study the influence on hysteresis behavior of high-damping isolation bearings. Due to space limitations, Figure 5 only gives the hysteresis curves of the three bearings at different loading frequencies when the vertical pressure is 3400 kN. It can be seen that the shape of the vertical hysteresis curves for all the bearings obtained by cyclic loading with different loading frequencies are basically the same. Loading frequency has a diverse effect degree on the vertical mechanical properties of different bearings; TLRB is most sensitive to it. With the augment of loading frequency, the vertical stiffness of each bearing presents an increasing trend. When the loading frequency was rather low, it had a slight influence on the vertical performance of the bearings, while the vertical compression displacements observably changed as the loading frequency was relatively high (0.05 Hz and 0.1 Hz).
## 4 Results and Discussions
### Trend Test and Serial Correlation Test  
The graph is plotted for all five subsystems of the engine between cumulative time between successive failures and the cumulative number of failures using Grapher software. The linearity of the graph will validate that collected data has no trend and they are independent and identically distributed. Next, with TBF data, a plot between (i \(-\) 1) th TBF and i th TBF is drawn for all five subsystems. The scattered plot will reveal whether that the data have no trend and no serial correlation exists [7]. The grouped TBF data of Table 6 is considered for plotting the graph. The trend test plots of five subsystems. They are shown in Figure 4a-e.  
\begin{table}
\begin{tabular}{c c c} \hline
**SI. No.** & **Subsystem** & **TBF (hours)** \\ \hline
1 & Air supply & 2655, 633, 4112, 422, 600, 2036, 1479, 77, 3585, 1673, 646. \\ \hline
2 & Self-starting & 1246, 44, 856, 2328, 3913, 759, 423, 185, 761, 1197, 1116, 3450, 1920, 797, 550, 191, 917, 1595 \\ \hline
3 & Fuel supply & 423, 240, 525, 934, 3856, 96, 316, 914, 2036, 112, 1449, 290, 225, 2828 \\ \hline
4 & Lubrication & 2566, 2278, 426, 1584, 757, 238, 991, 916, 855, 1115, 1503, 1367, 990, 2926. \\ \hline
5 & Cooling & 3827, 2356, 577, 1823, 1177, 680, 1424, 3236, 170, 108, 219, 934, 329, 2149. \\ \hline \end{tabular}
\end{table}
Table 6: Grouped TBF data for engine subsystems.  
Figure 4: _Cont_.  
The plots in Figure 5a-e show the serial correlation tests of all five subsystems. From the plots above, no trend is observed in air supply and lubrication subsystems as the plotted points are in a straight line. The trend is seen for self-starting, fuel supply and cooling subsystems. No serial correlation is found for all five subsystems due to the scattered nature of the graphs (Figure 5a-e). Hence, self-starting, fuel supply and cooling subsystems do not follow _iid_ characteristics whereas the air supply and lubrication subsystem follows it.
## Nomenclature  
\begin{tabular}{l l l l}
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \(r\) & Composite robustness criterion & \(\pi(\textit{VP})\) & Weight function in prospect theory \\ \(r_{s}\) & Robustness criterion, start time deviation & \(\rho_{\text{subjective}=4}\) & Subjective weight of robustness criterion \\ \(r_{p}\) & Robustness criterion, structural deviation & \(\rho_{\text{objective}=4}\) & Objective weight of robustness criterion \\ \(r_{c}\) & Robustness criterion, completion probability & \(C_{a}\) & Intermediate variable containing information of \\ \(\rho_{a}\) & Weight of robustness criterion, \(a\)=\(s\), \(p\), \(c\) & \(\sigma_{a}\) & Robustness criterion in Copula-CRITIC method \\ \(i\) & Activity number, \(I\)\(\in\)\(N+\) & \(l_{i=4}\) & Correlation coefficient among robustness criteria \\ \(\omega_{i}\) & Weight of activity \(i\) & \(\overset{\wedge}{\text{C}}\big{(}r_{s},r_{p},r_{c}\big{)}\) & Empirical Copula function \\ \(S_{i}\) & Actual start time of activity \(i\) & \(\overset{\wedge}{\text{C}}\big{(}r_{s},r_{p},r_{c}\big{)}\) & Fitting Copula function \\ \(s_{i}\) & Planned start time of activity \(i\) & \(d^{2}\) & Square Euclidean distance between \(C\big{(}r_{s},r_{p},r_{c}\big{)}\) \\ \(P_{i}\) & Actual construction order activity \(i\) & \(H\) & Acceptation degree of \(\rho_{\text{subjective}}\) and \(\rho_{\text{objective}}\) \\ \(p_{i}\) & Planned construction order activity \(i\) & \(U(\cdot)\) & Utility function of H \\ \(T\) & Actual construction duration & \(S(\rho_{a}(r_{a}))\) & Belief distribution of \(\rho_{a}\) to \(r_{a}\) \\ \(T^{\prime}\) & Planned construction duration & \(e\) & Basic probability assignment (BPA) \\ \(En\) & Expert & \(e_{x,b}\) & BPA of total weight to \(\rho_{b}\) with the acceptation \\ \(\rho_{am}^{\text{sub}}\) & Subjective weights given by experts & \(e_{H,b}\) & BPA of total weight to \(\rho_{b}\) with all acceptation \\ \(\upsilon_{am}^{\text{sub}}\) & Reference interval & \(\widetilde{e}_{H,b}\) & \(e_{H,b}\)'s result of other weights \\ \(\upsilon p\) & Matrix of the reference interval & \(\widetilde{e}_{H,b}\) & \(e_{H,b}\)'s result of the unascertained acceptation \\ \(M\) & Gain/loss matrix & \(K\) & Orthogonal constant, represent the \\ \(m\)\(\big{(}\upsilon p_{a},p_{am}^{\text{sub}}\big{)}\) & Entries in matrix \(M\), the distance between \(\upsilon p_{a}\) & \(Ent(\cdot)\) & Entropy, represent the effectiveness of weight \\ \(\upsilon(m)\) & Value function & \(\theta_{x}\) & Probability that \(\rho_{b}\) is accepted with \(H_{x}\) \\ \(\alpha\) & Positive risk attitude coefficient, \(\alpha=0.89\) & \(\theta_{H}\) & Probability that \(\rho_{b}\) is accepted with \(H\) \\ \(\beta\) & Negative risk attitude coefficient, \(\beta=0.92\) & \(U_{\text{min}}\) & Minimum utility \\ \(\lambda\) & Loss avoidance coefficient, \(\lambda=2.25\) & \(U_{\text{max}}\) & Maximum utility \\ \(V\) & Comprehensive prospect value & \(U_{\text{avg}}\) & Average utility \\ \end{tabular}
## 3 Results
### Iris-Exosome Reduces Intracellular ROS Levels in nHEKs  
Our previous results led us to investigate the antioxidant potential of _Iris_-exosomes. The DPPH and ABTS assays were initially conducted to determine whether _Iris_-exosomes possess a radical scavenging effect. The _Iris_-exosomes showed concentration-dependent scavenging activity against DPPH and ABTS radicals but revealed a less potent overall effect than L-ascorbic acid (Figure 3a,b). To determine whether exosomes can scavenge intracellular ROS in nHEKs, a DCF-DA assay was conducted in which nHEKs were pretreated using _Iris_-exosomes for 4 h and then exposed to 500 uM H2O2 for 30 min. As shown in Figure 3c,d, the increased DCF fluorescence intensity caused by the H2O2 treatment decreased in the _Iris_-exosome-treated cells in a concentration-dependent manner. To confirm whether these results could be explained by the expression of antioxidant-related genes in the cells, several antioxidant-related genes were measured after _Iris_-exosome treatment. _HO_-_I_, _CAT_, _GSS_, _GPx1_, _SOD_, and _Nrf2_ were transcriptionally upregulated in nHEKs following _Iris_-exosome treatment (Figure 3e). According to these results, it can be said that _Iris_-exosomes appear to exert a protective effect against cellular oxidative stress caused by H2O2.  
Figure 3: _Iris_-exosome can reduce oxidative stress in nHEKs. **(a)** DPPH and **(b)** ABTS radical scavenging activity of _Iris_-exosome. Ascorbic acid (5 ug/mL) as a positive control. Cells were pretreated with _Iris_-exosomes for 4 h, followed by treatment with 200 uM H2O2 for 30 min. After being incubated at 37 \({}^{\circ}\)C in the dark for 30 min in culture medium containing 10 uM DCF-DA, intracellular ROS generation was measured using **(c)** fluorescence microscopy (scale bar, 250 um) and **(d)** a fluorescence microplate reader. **(e)** nHEKs were treated using _Iris_-exosomes for 4 h and examined to assess the mRNA level of antioxidant-related genes (_NQO-1_, _HO_-1_, _CAT_, _GSS_, _GPx1_, _SOD_, and _Nrf2_) using RT-PCR, normalized with _GAPDH_ and _\(\beta\)-actin_. Data are presented as mean \(\pm\) SD of three independent experiments. ***\(p<0.001\) compared with untreated cells; ***\(p<0.001\) compared with H2O2-treated cells.
# The Stability of New Single-Layer Combined Lattice Shell Based on Aluminum Alloy Honeycomb Panels
## 3 Analysis of the Nonlinear Stability of NSCLS to Defects
### Experimental Verification of Random Defect Mode Method  
Before analyzing the nonlinear stability of the single-layer aluminum alloy lattice shells using the stochastic defect method, it is necessary to know the probability characteristics of each random variable, including its distribution and statistical parameters. This means that we need to determine the distribution of the initial geometric imperfections, and its mean, mean square deviation, and defect radius R (the absolute value of the maximum defect size), as well as other statistical parameters.  
Figure 6: Load-displacement curves at the loading points of the test models: (**a**) Curves for the lattice shell models without reinforcing plates; (**b**) Curves for the combined lattice shell models; and, (**c**) Curves for the six test models.  
In this paper, we assume that the initial geometric imperfections are normally distributed. Due to the limitation of the nodal position deviation, we use a truncated Gaussian distribution. This is essentially a normal distribution, but only two truncation limits are set based on the normal distribution. Therefore, the input parameters for the distribution of the random variables that need to be determined are as follows: the mean \(\mu\), the standard deviation \(\sigma\), the correlation coefficient \(\rho\), and the truncation limits Xmin and Xmax. Because of the limited length of this paper, only two typical models of the six models are discussed (i.e., model A\({}_{1}\), the lattice shell with no aluminum plates or defects, and model B\({}_{2}\), the combined lattice shell model with defects).
# Novel Molecular Non-Volatile Memory: Application of Redox-Active Molecules
## 1 Introduction  
The continuous complementary metal-oxide-semiconductor (CMOS) scaling is reaching the fundamental limits imposed by heat dissipation and short-channel effects. This will finally stop the increase of integration density and the ability of metal-oxide-semiconductor field-effect transistors (MOSFET) predicted by [PERSON]'s law [1; 2; 3; 4]. Huge demand has been thus created for high-performance semiconductor devices for future applications in memory and emerging portable/wearable electronic devices. Molecular technology has been aggressively pursued for its potential impact in nano-scale electronics since the early 1970s due to the inherent scalability and intrinsic properties [5; 6; 7]. Molecular memory, among all of the new emerging candidates in recent years, is considered promising, particularly for the aim of reducing the size per cell and enhancing the memory speed, density and reliability [8; 9; 10]. Molecular electronic devices are typically fabricated by forming a self-assembled monolayer (SAM) or multi-layer on different surfaces with very inexpensive and simple processing methods. Moreover, molecular memory works by the controlling of fewer electrons at the molecule scale and, therefore, has potential for low-power and ultra-high-density memory applications with a lower fabrication cost. Redox-active molecules have become attractive to replace the floating gate in conventional flash memory, due to their inherent and reliable redox behaviors. Typically, applying an oxidation voltage will cause electron loss in redox molecules; reversely, under a reduction voltage, electrons will be driven back to the molecules. Due to the oxidation and reduction of the redox centers, these redox molecules can exhibit distinct charged or discharged states, which can be deemed as logic on and off states, at different voltages with very fast write and erase speeds. It has been demonstrated that redox-active molecules attached to Si structures are stable and can endure more than 10\({}^{12}\) program/erase (P/E) cycles [11]. This excellent performance is naturally derived from the intrinsicproperties of the redox behavior in molecules. These advantageous properties of redox molecules make them very promising candidates for future applications in on-chip non-volatile memory, such as SRAM and dynamic random-access memory (DRAM). In addition, due to the scalability of molecules and the naturally-derived multiple redox states for robust charge storage, the molecular memory density can be much higher than the conventional memory devices.  
Great efforts have been made to integrate molecules as the active component for future electronic devices. In this paper, we review the development of molecular electronics for non-volatile memory applications with a focus on redox-active molecules, meanwhile presenting further strategies to extend their applications in future low-power and ultra-high-density non-volatile memory applications.