esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Table 2 shows that even the best models cannot perform best on all machine types and the performance of different machines of the same type varies greatly as shown in Figure 4. So we apply the model ensemble strategy. For the target machine, we choose the model with better performance on development data set. Our ensemble method achieves the highest average AUC of 95.82% and average pAUC of 92.32%, even outperforms all other methods on some machine types such as \"_fan_\". \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Fan** & **Pump** & **Slider** & **Valve** & **Toy-Car** & **Toy-Conveyor** & **Average** \\ \hline \hline  & **AUC(pAUC)** & **AUC(pAUC)** & **AUC(pAUC)** & **AUC(pAUC)** & **AUC(pAUC)** & **AUC(pAUC)** & **AUC(pAUC)** \\ \hline Baseline [6] & 82.80(65.80) & 82.37(64.11) & 79.41(58.87) & 57.37(50.79) & 80.14(66.17) & 85.36(66.95) & 77.91(62.12) \\ \hline Hayashi [9] & 92.72(80.52) & 90.63(73.61) & 95.68(81.48) & 97.43(89.69) & 91.75(83.97) & **92.10**(76.76) & 93.39(81.01) \\ \[PERSON] [10] & 93.75(80.68) & 93.19(81.10) & 95.71(79.45) & 94.87(83.58) & 94.06(86.80) & 84.22(69.12) & 92.63(80.12) \\ [PERSON] [11] & 90.74(83.38) & 88.70(75.97) & 96.18(87.49) & 97.48(92.46) & 94.32(89.01) & 64.38(53.79) & 88.63(80.35) \\ \[PERSON] [12] & 91.48(74.32) & 92.30(72.14) & 89.74(76.43) & 81.99(69.82) & 81.50(67.00) & 88.01(70.52) & 87.50(71.71) \\ \[PERSON] [13] & 94.54(84.30) & 93.65(81.73) & 97.63(89.73) & 96.13(90.89) & 94.34(89.73) & 91.19(73.34) & 94.58(84.95) \\ \[PERSON] [14] & 99.13(96.40) & 95.07(90.23) & 98.18(91.98) & 90.97(77.41) & 93.52(83.87) & 90.51(77.56) & 94.56(86.24) \\ \[PERSON] [15] & 96.84(95.24) & **97.76**(92.24) & 97.29(88.74) & 90.15(86.65) & 86.37(83.83) & 88.28(79.15) & 92.78(87.64) \\ [PERSON] [16] & 98.84(94.89) & 94.37(88.27) & 95.68(83.09) & **97.82**(**94.93) & 93.16(87.69) & 87.41(72.03) & 94.55(86.82) \\ [PERSON] [17] & 99.79(98.92) & 95.79(**92.60**) & 99.84(99.17) & 91.83(84.74) & **95.60**(**91.30**) & 73.61(64.06) & 92.74(88.47) \\ \hline Outlier classifier & 97.53(95.64) & 97.34(91.54) & 99.04(95.14) & 92.00(89.05) & 88.11(86.53) & 89.80(**80.61**) & 93.97(89.75) \\ ID classifier & 99.94(99.80) & 95.01(90.89) & 99.09(95.91) & 95.82(93.58) & 91.33(86.57) & 71.32(60.09) & 92.09(87.81) \\ ensemble & **99.96**(**99.84**) & 97.35(91.58) & **99.97**(**99.83**) & 95.82(93.58) & 92.02(88.50) & 89.80(**80.61**) & **95.82**(**92.32**) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC (%) and pAUC (%) for each machine
## 4 Performance Evaluation
### Unsupervised Evaluation  
This evaluation depends on the properties of the original image and its associated segmented outcome rather than relying on reference images. This approach is beneficial for segmenting images without established ground truths, e.g., Image Uniformity (_IU_) and Region Contrast (_RC_) [25; 31; 32]. An image uniformity measurement is used to assess the accuracy of the applied threshold, as stated in (12).  
\[IU(t)=1-\frac{{\sigma_{1}}^{2}(t)+{\sigma_{2}}^{2}(t)}{C} \tag{12}\]where \({\sigma_{1}}^{2}(t)\text{and}{\sigma_{2}}^{2}(t)\) are the variances of the first and the second regions, respectively, and \(C\) is computed in (13), and  
\[C=\frac{(L_{max}-L_{min})^{2}}{2} \tag{13}\]  
where \(L_{max}\) and \(L_{min}\) are the maximum and minimum intensity levels.  
A region contrast measurement is used to assess the quality of the segmentation by determining whether the segmentation result includes contrast over neighboring, as stated in (14).  
\[RC(t)=\frac{|\mu_{1}(t)-\mu_{2}(t)|}{\mu_{1}(t)+\mu_{2}(t)} \tag{14}\]  
where \(\mu_{1}(t)\) and \(\mu_{2}(t)\) are the means of the first and second regions, respectively.
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Excess Pore-Pressure Generation and Stress Degradation  
Typical experimental results in terms of excess pore pressure (\(u_{e}\)) generation and effective stress paths (\(q-p^{\prime}\)) are presented in Figure 6 to show the liquefaction behavior of  
Figure 5: Relationship between the degree of saturation \(S_{r}\) and [PERSON]’s pore-pressure parameter \(B\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test** & **Test** & \(D_{r}\) & \(S_{r}\) & \(B\) & \(\sigma_{3}\) & \(u_{0}\) & \(\sigma_{e}^{\prime}\) & & \\
**No.** & **ID** & **(\%)** & **(\%)** & **(\%)** & **(kPa)** & **(kPa)** & **(kPa)** & **CSR** & \(N_{liq}\) \\ \hline
34 & PS34 & 40 & 91 & 0.22 & 250 & 150 & 100 & 0.25 & 24 \\
35 & PS35 & 39 & 89 & 0.17 & 250 & 150 & 100 & 0.30 & 11 \\
36 & PS36 & 40 & 89 & 0.16 & 250 & 150 & 100 & 0.40 & 1 \\
37 & PS37 & 39 & 90 & 0.17 & 300 & 200 & 100 & 0.20 & 53 \\
38 & PS38 & 42 & 91 & 0.21 & 300 & 200 & 100 & 0.25 & 18 \\
39 & PS39 & 39 & 89 & 0.16 & 300 & 200 & 100 & 0.30 & 6 \\
40 & PS40 & 41 & 90 & 0.17 & 300 & 200 & 100 & 0.40 & 1 \\
41 & PS41 & 84 & 89 & 0.16 & 200 & 100 & 100 & 0.25 & 289 \\
42 & PS42 & 85 & 88 & 0.14 & 200 & 100 & 100 & 0.30 & 65 \\
43 & PS43 & 83 & 91 & 0.20 & 200 & 100 & 100 & 0.35 & 20 \\
44 & PS44 & 84 & 90 & 0.17 & 200 & 100 & 100 & 0.40 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(Cont.\)the nearly / fully and partially saturated sand specimens. The plots on the left-hand side of the figure (Figure 6a) show the triaxial test data for loose, fully saturated sand (\(S_{r}\approx 100\%\)) tested under a back pressure of \(u_{0}=300\) kPa and an initial effective confining stress of \(\sigma_{c}^{\prime}=100\) kPa. The results on the right-hand side (Figure 6b) correspond to test data for loose, partially saturated sand (\(S_{r}\approx 95\%\)) examined under a back pressure of \(u_{0}=100\) kPa and an initial effective confining stress of \(\sigma_{c}^{\prime}=100\) kPa. In both cases, the specimens were subjected to a cyclic-stress ratio of \(CSR=0.2\).  
When both sand specimens were cyclically sheared in undrained conditions, the pore pressures accumulated and become almost equal to the initial total stress. The mean effective stresses (\(p^{\prime}\)) were reduced and approached zero as the excess pore pressures (\(u_{c}\)) increased and converged up to the initial effective confining stresses, which corresponded to liquefaction (\(r_{u}=1\)). Under similar loading conditions, the degradation of the mean effective stress was consistently slower, and the number of cycles required for liquefaction (\(N_{liq}\)) was consistently larger in the partially saturated sands than their fully saturated counterparts.
## 3 Structural Model
### Selected Near-Fault Ground Motions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **Seismic Name** & \begin{tabular}{c} **Ground** \\ **Motion Name** \\ \end{tabular} & \begin{tabular}{c} **Magnitude** \\ **(Mw)** \\ \end{tabular} & \begin{tabular}{c} **Distance** \\ **(km)** \\ \end{tabular} & \
\begin{tabular}{c} **PGA (g)** \\ **(cm/s)** \\ \end{tabular} &
\begin{tabular}{c} **PGV** \\ **PGD (cm)** \\ \end{tabular} \\ \hline
5 & Kobe 1995 & KJM090 & 6.9 & 0.96 & 0.599 & 74.3 & 19.95 \\ \hline
6 & Loma Prieta 1989 & LGP000 & 6.93 & 3.88 & 0.563 & 94.8 & 41.18 \\ \hline
7 & Loma Prieta 1989 & LGP090 & 6.93 & 3.88 & 0.605 & 51 & 11.5 \\ \hline
8 & Morgan Hill 1984 & HVR240 & 6.19 & 3.48 & 0.312 & 39.4 & 7.66 \\ \hline
9 & Imperial Valley-06 1979 & H-E04230 & 6.53 & 7.05 & 0.36 & 76.6 & 59.02 \\ \hline
10 & Imperial Valley-06 1982 & H-E07230 & 6.53 & 0.56 & 0.463 & 109.3 & 44.74 \\ \hline
11 & Cape Mendocino 1992 & CPM000 & 7.01 & 6.96 & 1.497 & 127.4 & 41.01 \\ \hline
12 & Cape Mendocino 1992 & CPM090 & 7.01 & 6.96 & 1.039 & 42 & 12.39 \\ \hline
13 & Cape Mendocino 1992 & PET000 & 7.01 & 8.18 & 0.59 & 48.4 & 21.74 \\ \hline
14 & Cape Mendocino 1992 & PET090 & 7.01 & 8.18 & 0.662 & 89.7 & 29.55 \\ \hline
15 & Duzce, Turkey 1999 & DZC270 & 7.14 & 6.58 & 0.535 & 83.5 & 51.59 \\ \hline
16 & Northridge 1994 & JEN022 & 6.69 & 5.43 & 0.424 & 106.2 & 43.06 \\ \hline
17 & Northridge 1994 & NWH360 & 6.69 & 5.92 & 0.59 & 97.2 & 38.05 \\ \hline
18 & Northridge 1994 & RRS228 & 6.69 & 6.5 & 0.838 & 166.1 & 28.7 \\ \hline
19 & Northridge 1994 & SCS142 & 6.7 & 5.35 & 0.897 & 102.8 & 46.99 \\ \hline
20 & Northridge 1994 & SPV270 & 6.7 & 8.44 & 0.753 & 84.8 & 18.68 \\ \hline
21 & Northridge 1994 & SY1360 & 6.7 & 5.3 & 0.843 & 129.6 & 32.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 9: The time-histories of near-fault earthquake waves KJM090 and H-E07230.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 5 Evaluation and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Discipline** & **Variable** & **Symbol** & **Variable** & **Range** & **Initial** & **CO** & **SysML-** & **SysML-** \\  & Expansion valve & & & **Type** & **Value** & **CO** & **CEA** & **RBMDO** \\ \hline \multirow{4}{*}{Air conditioning} & Expansion valve & \(x_{PR}\) & Discrete & [2,3,4] & 2 & 3 & 2 & 2 \\  & Condenser heat & & & & & & \\  & exchange & \(x_{HE}\) & Continuous & [5,10] & 8 & 7.53 & 6.86 & 6.97 \\  & Condensing fan air & \(x_{AirV}\) & Continuous & [50,80] & 80 & 70.32 & 65.22 & 63.81 \\  & Evaporator volume & \(x_{V}\) & Continuous & [60-100] & 80 & 65.75 & 62.88 & 61.47 \\  & Compressor cooling & \(x_{cc}\) & Aleatory & \(\mu=1.25\) & 1.5 & 1.42 & 1.23 & 1.18 \\  & capacity & & & & & & \\ \hline \multirow{4}{*}{Integrated air supply} & Ventilation fan air pressure & \(x_{WP}\) & Continuous & [120,500] & 300 & 153.46 & 138.25 & 142.35 \\  & Air volume of the & \(x_{Vicm}\) & Continuous & [25,60] & 50 & 38.10 & 35.24 & 34.17 \\  & internal circulator & & & & & & \\  & Integrated duct & \(x_{d}\) & Continuous & [50,100] & 100 & 67.24 & 65.78 & 63.58 \\  & diameter & & & & & & \\  & Dust collector & \(x_{Vdc}\) & Continuous & [30,55] & 50 & 40.54 & 40.20 & 38.94 \\  & volume & & & & & & \\ \hline Mass & Total mass & M & Continuous & / & / & 81.43 & 78.41 & 72.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimization results of MDO problem in-vehicle environmental control integrated system. manages and optimizes various aspects of real-world engineering systems. The case study not only highlights the potential of the SysML-based approach in addressing practical engineering challenges but also suggests its applicability to other domains. By bridging theoretical models with practical applications, our research provides valuable insights that advance the field and offer practical solutions for complex engineering systems.
## 5 Conclusions  
In conclusion, a novel A-DBP-LTM implemented by RMSProp is proposed in order to achieve low-complexity and high-robustness nonlinear compensation. By adaptively adjusting the learning rate \(\mu\) based on all of the gradients of the previous iterations, the A-DBP-LTM based on RMSProp delivers the best compensation performance. Not only in a 69 Gbaud DP-16 QAM simulation system over 2000 km but also in a 26-\(\lambda\), 63 Gbaud, DP-16 QAM experimental system over 1200 km, the proposed method demonstrates low complexity and high robustness. Compared to conventional DBP, A-DBP based on the GDA, and A-DBP based on the GDAM, it reduces the complexity with the same effectiveness and improves the OSNR tolerance. It is a highly effective approach to obtain the optimal value of the nonlinear scaling factor \(k\) and adaptively tune the learning rate \(\mu\), enabling effective nonlinear compensation in large-capacity systems for long-haul transmission.  
Conceptualization, M.Z. and H.Y.; methodology, Y.L.; software, H.Y. and Y.L.; validation, M.Z. and H.Y.; writing--original draft preparation, M.Z. and H.Y.; writing--review and editing, Y.L., Z.X., D.W., S.C., Z.Z. and H.L.; funding acquisition, D.W. and H.L. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China (No. 2022 YFB2903301).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
This article is a revised and expanded version of a paper entitled Fast and High-Robustness Adaptive Digital Back-Propagation for Fiber Nonlinearity Compensation, which was presented at ACP2023.  
The authors declare that there are no conflicts of interest related to this article.
## 3 Results  
Four specific example problems applied to a rod are now generated and solved to illustrate this method. All of the problems analyzed are fixed-free rods with a harmonic point load applied to the free end. These problems have a linear variation, a triangular variation, a Bessel function variation and a Gamma function variation of the cross-sectional area. These problems are calculated using a set of parameters that result in a volume for each rod equal to 0.00324 m\({}^{3}\) so that a comparison can be made between the different geometrical shapes. The parameter set that is constant is [PERSON]'s modulus \(E=1\times 10^{7}\) N m\({}^{-2}\), density \(\rho=1200\) kg m\({}^{-3}\), rod length \(L=3.0\) m, rod width \(w=0.03\) m, applied spatial load location \(x_{f}=3.0\) m, magnitude of applied spatial load \(F_{0}=1\) N and location of the response \(x_{r}=2.25\) m. The constants \(m\) and \(b\) are used to define the variation of the rod's height. These values and units vary for each example problem and are listed in Table 1. The solution to these problems is displacement versus frequency, and it is displayed graphically for the first five natural frequencies of the rod. The first problem analyzed is a rod that has a linear variation in cross-sectional area, as shown in Figure 1. For this problem, the area is equal to  
\[A(x)=2(mx+b)w\quad>0\,\forall\,x\in[0,L], \tag{15}\]  
and the derivative of the area with respect to the longitudinal variable is  
\[\frac{\mathrm{d}A(x)}{\mathrm{d}x}=2 mw. \tag{16}\]  
The integrals from Equations (10), (11) and (13) are as follows  
\[H_{p,n}=2w\Bigg{\{}\rho\omega^{2}-E\bigg{[}\frac{(2n-1)\pi}{2L}\bigg{]}^{2} \Bigg{\}}\Bigg{[}\frac{bL}{2}+\frac{mL^{2}(4\pi^{2}n^{2}-4\pi^{2}n+\pi^{2}+4)} {4\pi^{2}(2n-1)^{2}}\Bigg{]}\qquad p=n, \tag{17}\]  
\[G_{p,n}=2w\Bigg{\{}\rho\omega^{2}-E\bigg{[}\frac{(2n-1)\pi}{2L}\bigg{]}^{2} \Bigg{\}}\times \tag{18}\]  
\[H_{p,n}=2 wE\bigg{[}\frac{(2n-1)\pi}{2L}\bigg{]}\bigg{[}\frac{mL}{\pi(2n-1)} \bigg{]}\qquad p=n, \tag{19}\] \begin{table}
\begin{tabular}{c c c c c} \hline
**Rod Shape** & \(m\) & **Units of \(m\)** & \(b\) & **Units of \(b\)** \\ \hline Linear & \(-0.0080\) & dimensionless & 0.0300 & M \\ Triangular & 0.0040 & m & 0.0280 & M \\ Bessel & 0.7500 & m\({}^{-1}\) & 0.0277 & M \\ Gamma & 0.0800 & m\({}^{-1}\) & 0.0120 & M \\ \hline \end{tabular}
\end{table} 
Table 1: Values of the geometric parameters for specific rod shapes. \[H_{p,n}=2 wE\bigg{[}\frac{(2n-1)\pi}{2L}\bigg{]}\bigg{\{}\frac{mL[\cos(n\pi)\cos(p \pi)-2n\cos(n\pi)\cos(p\pi)+2p-1]}{2\pi(-n^{2}+n+p^{2}-p)}\bigg{\}}\qquad p\
eq n \tag{20}\]  
and  
\[f_{p,1}=F_{0}\sin\bigg{[}\frac{(2p-1)\pi}{2}\bigg{]}. \tag{21}\]  
Figure 2 is a comparison of the series (modal) solution (solid line) to a previously derived analytical solution for a rod (circular markers) and a rod with uniform area (dashed line) called the baseline model. The series solution was calculated using 25 terms.
## 5 Experiment Verifications
### Electric Field Measurement  
The electric field measurement experiment schematic is shown in Figure 15, and the experiment setup is shown in Figure 16.  
The electric field measurement is composed by the electric field probe, optical fiber, and measuring system. The ball screw structure is used as the carrier of the electric field probe to adjust the distance between the probe and the CDPS mechanism. The electric field components in three axes are separately measured and integrated to compare with the simulation results.  
Figure 14: The cable forces when the mechanism maintains the attitude in Figure 11c.  
Figure 13: The cable forces when the mechanism maintains the attitude in Figure 11b.  
The measured and calculated electric field distributions are compared in Figure 17. The average error is 9.57%, which proves that the simulation results are consistent with the experiment results. The error can be further decreased by performing the experiment in the shield room to eliminate the environment interference.
## 2 Background and Theory
### Preprocessing Methods
#### 2.1.1 Notch Filtering  
The preprocessing of EEG data begins with the application of a notch filter [58], a widely used technique for removing specific unwanted frequencies from EEG signals. This filter creates a sharp, narrow attenuation zone--commonly referred to as a \"notch\"--at a predefined frequency. By adjusting parameters such as the sampling rate (_Fs_), powerline frequency (_f_0), and quality factor (_Q_), this filtering method effectively reduces signal interference. The digital representation of the notch filter is mathematically defined according to the transfer function \(H(z)\), as outlined in Equation (1):  
\[H(z)=\frac{Y(z)}{X(z)}=\frac{\sum_{m=0}^{M}b_{m}z^{-m}}{1+\sum_{n=1}^{N}a_{n}z ^{-n}} \tag{1}\]  
In this context, \(b_{m}\) signifies the feedforward coefficients (numerator), whereas \(a_{n}\) represents the feedback coefficients (denominator). The symbol \(M\) indicates the numerator's order, while \(N\) specifies the denominator's order. Furthermore, \(X(z)\) and \(Y(z)\) denote the \(Z\)-transforms of the input and output signals, respectively.
###### Abstract  
Evapotranspiration (ET) is an essential part of the hydrological cycle and accurately estimating it plays a crucial role in water resource management. Surface energy balance (SEB) models are widely used to estimate regional ET with remote sensing. The presence of horizontal advection, however, perturbs the surface energy balance system and contributes to the uncertainty of energy influxes. Thus, it is vital to consider horizontal advection when applying SEB models to estimate ET. This study proposes an innovative and simplified approach, the surface energy balance-advection (SEB-A) method, which is based on the energy balance theory and also takes into account the horizontal advection to determine ET by remote sensing. The SEB-A method considers that the actual ET consists of two parts: the local ET that is regulated by the energy balance system and the exotic ET that arises from horizontal advection. To evaluate the SEB-A method, it was applied to the middle region of the Heihe River in China. Instantaneous ET for three days were acquired and assessed with ET measurements from eddy covariance (EC) systems. The results demonstrated that the ET estimates had a high accuracy, with a correlation coefficient (R\({}^{2}\)) of 0.713, a mean average error (MAE) of 39.3 W/m\({}^{2}\) and a root mean square error (RMSE) of 54.6 W/m\({}^{2}\) between the estimates and corresponding measurements. Percent error was calculated to more rigorously assess the accuracy of these estimates, and it ranged from 0% to 35%, with over 80% of the locations within a 20% error. To better understand the SEB-A method, the relationship between the ET estimates and land use types was analyzed, and the results indicated that the ET estimates had spatial distributions that correlated with vegetation patterns and could well demonstrate the ET differences caused by different land use types. The sensitivity analysis suggested that the SEB-A method requested accurate estimation of the available energy, \(R_{n}-G\), but was less constrained with the difference between ground and air temperature, \(T_{0}-T_{n-loc}\).  
evapotranspiration; remote sensing; energy balance; horizontal advection 20168644; doi:10.3390/rs080644
# Laser-Accelerated Plasma-Propulsion System
## 6 LAPPS Analytical Model
### LAPPS Thrust
For this reason, it is convenient to investigate more in detail the effects of the target and laser properties on the thrust. In Figure 7a, the effect of the target thickness \(d\) and pulse duration \(\tau_{L}\) is considered. Assuming a conservative value of \(\chi=0.001\), we find that better results can be obtained for thicker targets and for shorter pulses. However, in the limit of high intensity, the target thickness becomes irrelevant while the effect of the pulse duration is preserved as it is proportional to \(\tau_{acc}/\tau_{L}\). In some specific configurations (always characterized by very low values of \(\chi\)), it may occur that specific impulse and thrust can be simultaneously maximised, though without producingany significant thrust. In Figure 7b, the same examples of Figure 7a are reported but for the higher values of \(\chi_{max}=0.05\). As is shown, the diagram of the thrust substantially changes. Considerations about \(d\) and \(\tau_{L}\) remain valid, but, in this case, it is always convenient to operate at low intensity to optimize the thrust as there are no local minima or maxima in the considered intensity range. In general, at fixed values of \(l_{0}\), \(\chi\), and \(\varepsilon_{L}\), the thrust can be easily adjusted by a factor 10 by tuning the duration of ultra-short laser pulses and the thickness of the targets. However, the major role in the thrust maximization is played by the parameter \(\chi\), and thus on \(V\sim\langle v_{r}\rangle^{All}\). These properties are evident from Figure 8, where the performance of the LAPPS engine is reported. Indeed, considering that \(\dot{M}\propto l_{0}^{-1}\), the thrust is very sensitive to any increase of the specific impulse in the low intensity range, which is linear in \(\chi\) (see Equation (52)). In Ref. [39], a maximum thrust of \(F=3.1\times 10^{-2}\) N was found for a 1 kHz pulse of \(\varepsilon_{L}=1\) kJ energy focalized onto a thick gold-foil target. This result can be easily improved, in our model, by using a thin (10 \(\upmu\)m) carbon foil with an optimized laser pulse with the same energy. Considering the upper limit of \(\chi=0.05\), which is not unrealistic, the thrust can reach and exceed the remarkable values of 4 N. In this example, for a laser efficiency of \(\eta_{L}=5\%\), the averaged power generated by the nuclear reactor is given by \(P_{R}=20\) MW (thermal).