esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis
## 7 Conclusions  
This paper addresses the need for uncertainty quantification of multi-dimensional correlated flow field responses in CFD. Building upon the Gappy-POD framework, we develop a systematic experimental design methodology for constructing adaptive multi-fidelity surrogate models. This approach comprises two strategically integrated phases:  
* Initial sampling through \(k\)-means clustering of low-fidelity solution snapshots.
* Adaptive refinement driven by projection error quantification  
Based on the adaptive multi-fidelity method, the impact of uncertainty in the coefficients of the SA turbulence model on the distribution of wall friction coefficients for the NACA0012 airfoil and on the distribution of wall pressure coefficients for the M6 wing was analyzed. The results show that compared with the commonly used random sampling method, the high-fidelity sample experimental design method consistently achieves a smaller mean error. Additionally, the 0.995 quantile value of the error significantly decreases. This indicates that the experimental design method not only improves overall prediction accuracy, but also reduces the occurrence of larger errors. The entire model can accurately predict the distribution of wall friction or pressure, and is applicable to support uncertainty quantification in large-scale random sampling processes.  
However, it should be noted that the multi-fidelity model used in this study is based on the Gappy-POD framework. The fundamental principle of this method involves a linear POD on multi-dimensional responses. As such, it inherits the limitations associated with linear decomposition. As pointed out by many scholars, in processing highly non-linear datasets, the linear nature of POD decomposition often necessitates numerous POD modes for flow field reconstruction, and numerical stability cannot be guaranteed. To address the limitations of POD in handling non-linear data, kernel methods have been introduced, forming Kernel Proper Orthogonal Decomposition (Kernel POD or KPOD). In the context of a non-linear decomposition framework, how to construct a multi-fidelity model will be the focus of our future research.  
Conceptualization, W.X. and J.C.; methodology, W.X.; validation, Y.S. and J.Z.; investigation, W.X.; data curation, L.L.; writing--original draft preparation, W.X.; writing--review and editing, W.Z. and J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
Figure 19: The standard deviation distribution of the pressure coefficient on the wing surface.  
**Funding:** This research was funded by NSAF (Grant No. U2230208), National Natural Science Foundation of China (Grant No. 52175214), and the National Numerical Wind Tunnel Project. The APC was funded by the three aforementioned foundations.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Estimating Specific Patient Organ Dose for Chest CT Examinations with Monte Carlo Method
## 5 Conclusions  
This study investigated patient-specific organ doses from chest CT scans with ATCM, and further discussed the effect of patient size on organ dose. It can be concluded that patient organ doses are positively related to the _WED_, while, after _CTDI\({}_{\text{vol}}\)_ normalization, the quotient decreases with an increasing _WED_. Organ doses for chest CT examination in different populations under different CT models can be estimated by a specific exponential model. The results of our study could be used to assist physicists and doctors in evaluating the organ dose of patients after chest CT examination. This might help the operators to further optimize the parameter settings of CT protocol, so as to achieve reasonable patient dose reduction. In addition, a larger number of subjects need to be repeated to test for the validity of the dose-predictive relationships in the future.  
Conceptualization, H.L. and Y.Y.; methodology, H.L. and Y.Y.; software, T.X.; validation, W.Z., H.L. and Y.Y.; formal analysis, Y.Y.; investigation, Y.Y.; resources, H.L.; data curation, Y.Z. and C.W.; writing--original draft preparation, Y.Y.; writing--review and editing, W.Z., H.L., T.X. and Y.Y.; visualization, Y.Z.; supervision, W.Z. and H.L.; project administration, W.Z. and H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: 12075064; National key R&D Program of China: 2019 YFC0117304.  
Ethical review and approval were waived for this study, since this research is a retrospective study.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Supporting data includes CT scan parameters of each slice and raw data generated at the MC simulation. All of the data supporting the findings of the presented study are available from the corresponding authors on reasonable request.  
The authors declare no conflict of interest.
## 1 Introduction  
The present study is inspired by the tourist submarine with the transparent acrylic hull, designed to provide visitors a view of the surrounding ocean in a comfortable manner [1, 2]. The overall length of the studied submarine is about 25 m, while the external diameter of the acrylic hull is 2.64 m. The hull of the submarine has spherical heads on the ends, where one head is made of steel, while the other one has large acrylic dome to provide visibility to the pilot of the submarine. The specific feature of tourist submarines is their ability to move in different directions with low speed, while the power requirements are a central consideration in their design to maximize the cruising duration. Although power reduction can be achieved in different ways, e.g., by adopting energy-saving propulsion systems or controlling the boundary layer on the submarine's surface, the shaping of the submarine's hull is considered as the most efficient approach. So, the purpose of the present paper is twofold:  
1. To develop accurate and efficient numerical hydrodynamic model for the resistance coefficient of the submarine moving in forward and transverse directions.
2. To develop prediction tool for the resistance coefficients of the submarine in the initial design phase, without need to perform complex hydrodynamic analysis.  
Hydrodynamic calculations are performed by the numerical model in OpenFOAM, using Computational Fluid Dynamic (_CFD_). Practical engineering tool replacing hydrodynamic computations is developed using the Artificial Neural Network (_ANN_) with optimal number of neurons. As designers of marine structures may be disinclined for using _ANN_ as a \"black box\", relatively simple design equations are proposed using two _ANN_ models with small number of neurons, and by Nonlinear Least Squares Marquardt-Levenberg algorithm. Although the actual resistance coefficient of the tourist submarine is higher because of the appendices and additional structures, there is a great need to have preliminary estimate of the bare hull resistance, which is the main benefit of the present study [2].  
The paper is organized as follows. In the second part of Introduction, literature review regarding hydrodynamic analysis of submarines and combinations of physics with neural networks is provided. After the explanation of the governing equations and boundary conditions in the second section, the developed model was validated in the third section. Section 4 presents the results and comparative analysis of different prediction models for the resistance coefficients. Finally, the conclusions are presented in Section 5.
## 4 Results and Discussion
### Sensitivity Analysis  
The optimization of network architecture is necessary and should be considered over the use of standard parameters [28] because network architecture models are principally influenced by the analytical task and data type. Data could differ in size, relationships between independent and dependent variables, and complexity. Therefore, the neural architecture of the RNN and MLP-NN networks was enhanced using a grid search implemented in SciPy-python. The combinations of 10 parameters that can best identify landslide locations in densely vegetated areas were optimized.  
The Adam optimizer is the most suitable algorithm for the optimization of the two NN models. Using the Adam optimizer with default parameters (learning rate \(\mu\) = 0.001, beta \(\beta_{1}\) = 0.9, epsilon \(\epsilon\) = 1e-08 and weight decay = 0.0) yielded an accuracy of 0.77 and 0.825 for MLP-NN and RNN models, respectively, as shown in Figure 11. Rmsprop and Nadam optimizers also achieved excellent results for the two models. Overall, the Adam algorithm is more suitable for analyzing landslide data. However, better accuracy was obtained when Adadelta was used with the RNN model. Meanwhile, adding the weight decay in the neural network did not affect the results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Neural Network Model** & **Analysis Area** & **Test Area** \\ \hline RNN model & 83.33\% & 81.11\% \\ MLP-NN model & 78.38\% & 74.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross-validation accuracy results of the proposed models.  
Batch size, which refers to the number of training examples computed during optimization, has substantial effects on model accuracy. The results of the RNN and MLR-NN models are shown in Figure 12 and depict how increasing batch size from 2 to 128 (by \(\times\)2) affected model accuracy. The MLR-NN and RNN models exhibited the best accuracies with batch sizes of 64 and 128, respectively.  
Overfitting can be avoided when dropouts are controlled through the number of parameters in the RNN model. Figure 13 illustrates the sensitivity analysis of the effects of dropout rate with various keep probability parameters on the RNN model. The results showed that the appropriate dropout rate is 0.6 for the RNN model. The selected dropout rate considerably affects the performance of NN models. The keep probability was selected in each dataset and analysis was conducted via a systematic grid search.  
Figure 11: Impact of the optimization algorithm on the performance of MLP-NN and RNN models; SGD: Stochastic Gradient Descent.  
Figure 12: Impact of batch size on the performance of the MLP-NN and RNN models.
## 3 Results
### Life Cycle Analysis  
Within the limits of the system (Section 2.1.3), the environmental impact assessment of shelf egg production shows that the egg laying stage accounted for 79% of the environmental impact of the product (Figure 6). Of 18 middle-point impact categories (Table 2), 10 were identified as significant and represent at least 75% of the contribution in each category (Table 5). Of 10 identified categories in the egg-laying phase, seven are related to broiler compound feed. Therefore, this last one is the input with the largest number of impact categories. Other reports about egg production also identify the compound feed as the input with the most significant contribution of environmental impact in the entire system product [25].  
Electricity is an input that can be modified by reducing its use in the process without compromising the bird's biology, unlike diet. Table 6 shows the environmental contribution derived from the use of electricity and fuel in the stages of the egg production process (kWh/kg egg).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LCA Inventory** & **Impact Category** & **Symbol** & **Input** & **Egg Posture** & **Total, Process** \\ \hline \multirow{8}{*}{**Compound feed broilers/NL Mass**} & Climate change & CC & & 4.4 kg CO\({}_{2}\)-eq & 5.6 kg CO\({}_{2}\)-eq \\  & Water depletion & AA & & \(6.3\times 10^{-2}\) m\({}^{3}\) & \(7.8\times 10^{-2}\) m\({}^{3}\) \\ \cline{1-1}  & Human toxicity & TH & & \(1.4\times 10^{-1}\) kg & \(1.6\times 10^{-1}\) kg \\ \cline{1-1}  & Fresh water & & & 1.4-DB-eq & 1,4-DB-eq \\ \cline{1-1}  & **MX Lq** & eutrophication & EAF & Food & \(5.3\times 10^{-4}\) kg & \(6.5\times 10^{-4}\) kg \\ \cline{1-1}  & Terrestrial ecotoxicity & ECT & & \(6.6\times 10^{-2}\) kg & \(8.5\times 10^{-2}\) kg \\ \cline{1-1}  & Fresh water ecotoxicity & ECAF & & \(1.4\)-DB-eq & 1,4-DB-eq \\ \cline{1-1}  & Agriculture land use & USA & & \(4.2\) m\({}^{2}\)a & \(5.2\) m\({}^{2}\)a \\ \hline \multirow{2}{*}{**Laying hens \textgreater{} 17 weeks, breeding,** at farm/NL Mass MX Lq} & Particulate material & \multirow{2}{*}{FMP} & \multirow{2}{*}{Birds} & \(7.5\times 10^{-3}\) kg & \(1.1\times 10^{-2}\) kg \\ \cline{1-1}  & & formation & & PM\({}_{10}\)-eq & PM\({}_{10}\)-eq \\ \hline \multirow{4}{*}{**Electricity, medium voltage [MX]** electricity voltage transformation** from high to medium voltage | **Alloc**} & Fossil resources depletion & AF & Electricity & \(7.4\times 10^{-1}\) kg & \(9.4\times 10^{-1}\) kg \\ \cline{1-1}  & **depletion** & & crude oil-eq & crude oil-eq \\ \cline{1-1}  & **depletion** & & & \(2.7\times 10^{-2}\) kg & \(3.6\times 10^{-7}\) kg \\ \cline{1-1}  & **depletion** & & & CFC-11-eq & CFC-11-eq \\ \hline \hline \end{tabular}
\end{table}
Table 5: Significant impact categories for egg posture phase and total shelf egg production process (per kilogram).  
Figure 6: Potential environmental impact in the egg production phases.
# Half-Spectrum Suppression in Dynamic Resonant Tunneling
## 2 The Model and Its Dynamics  
The system is presented in Figure 1. A particle with energy \(\Omega\) penetrates an opaque barrier. In the center of the barrier there is an oscillating well. For simplicity, the well is presented by a delta function; however, this is a very good approximation for the case where the well is considerably narrower than the particle's de Broglie wavelength [32].  
The Schrodinger equation of this system reads  
\[i\frac{\partial\psi}{\partial t}=-\frac{\partial^{2}}{\partial x^{2}}\psi+V(x) \psi+(\alpha+\beta\cos\omega t)\delta(x)\psi \tag{12}\]  
where the units were taken for convenience considering that [PERSON]'s constant is \(\hbar=1\) and the particle'  
\[\varphi_{n}^{-}\rightarrow\tau_{\Omega_{n}}\exp[-ik_{n}x-i\Omega_{n}t]\ (for\ x\rightarrow-\infty) \tag{22}\]  
where \(k_{n}=\sqrt{\Omega_{n}}\) and \(\Omega_{n}=\Omega+n\omega\). \(\left|\tau_{\Omega}\right|^{2}\) is the probability to penetrate the stationary barrier with energy \(\Omega\).  
The boundary conditions at \(x=0\) are  
\[\psi_{l}(0)=\psi_{r}(0)\ \text{and}\ \psi_{l}^{\prime}(0)-\psi_{r}^{\prime}(0)=-( \alpha+\beta\cos(\omega t))\psi_{r}(0) \tag{23}\]  
where the subscripts \"\(r\)\" and \"\(l\)\" represent the wavefunction to the right and to the left of the well, respectively. Using this terminology, the dynamics can be presented with the following difference equation:  
\[-\chi_{n}\delta(n)=(\alpha-\chi_{n})s_{n}+\frac{\beta}{2}(s_{n+1}+s_{n-1}) \tag{24}\]  
where \(s_{n}\equiv t_{n}\big{(}\varphi_{n}^{+}/\varphi_{0}^{+}\big{)}\) and \(\chi_{n}\equiv\frac{\partial_{n}^{+^{\prime}}}{\partial_{n}^{+}}-\frac{ \partial_{n}^{-^{\prime}}}{\partial_{n}^{-}}=\frac{1}{G_{n}^{+}(0)}\equiv-2 \rho_{n}\text{tanh}[\rho_{n}L-\text{iarctan}(k_{n}/\rho_{n})]\)[8; 33]. \(G_{n}^{+}(0)\) is the outgoing Green function of Equation (20) at \(x=0\)  
\[-\frac{\partial^{2}}{\partial x^{2}}G_{n}^{+}(x)+[V(x)-\Omega_{n}]G_{n}^{+}(x) =-\delta(x) \tag{25}\]  
and \(\rho_{n}=\sqrt{U-\Omega_{n}}\). Finally, the current through the barrier is  
\[j=2\Re\sum_{n=-\infty}^{\infty}|t_{n}|^{2}|\tau_{n}|^{2}k_{n} \tag{26}\]  
where the abbreviation \(\tau_{n}\equiv\tau_{\Omega_{n}}\) is used.  
Equation (24) can be solved numerically using matrix formalism:  
\[\mathbf{s}=M^{-1}\mathbf{v} \tag{27}\]  
where  
\[\mathbf{s}\equiv\begin{pmatrix}\vdots\\ s_{-2}\\ s_{-1}\\ s_{0}\\ s_{1}\\ s_{2}\\ \vdots\end{pmatrix},\ M=\begin{pmatrix}\ddots&\ddots&&&&\\ \ddots&\alpha-\chi_{-2}&\beta/2&&&&\\ &\beta/2&\alpha-\chi_{-1}&\beta/2&&&\\ &&\beta/2&\alpha-\chi_{0}&\beta/2&&&\\ &&&\beta/2&\alpha-\chi_{1}&\beta/2&&\\ &&&&\beta/2&\alpha-\chi_{2}&\ddots\\ &&&&\ddots&\ddots\end{pmatrix}\ \text{and}\ \ \mathbf{v}=\begin{pmatrix}\vdots\\ 0\\ 0\\ -\chi_{0}\\ 0\\ \vdots\end{pmatrix} \tag{28}\]  
A zero current occurs when there are no propagating modes. This occurs when the higher half of the spectrum vanishes, i.e., \(s_{n}=0\) for \(n\geq 0\), and the incoming particle's energy is lower than the oscillating frequency \(\Omega<\omega\).
## 4 Experimental Results and Discussion
### Results
#### 4.4.2 Robustness for Various Attacks  
With the trained weight set, robustness experiments were conducted on various types and strengths of attacks on the evaluation dataset. Figure 4 shows some examples of the attacked images. The purpose of the attack is to use the image without ownership by malicious or non-malicious weakening or removing WM. However, as you can see from the figures, some attacks damage the image too much to reuse that those attacks are entirely meaningless. However, we included them to compare with previous works that included them. The experimental robustness results are shown in Table 4 (right now, the first column of the three BER columns), in which the BER values are the average values for all the images in the evaluation dataset. Note that the values in Table 4 are when \(s=1\). As you can see in Table 4, the BER values tend to increase as the attack strength increases for each kind of attack. Note that the rotation attack disturbs the image information most at the 45 degrees. So the BER increases as the rotation angle increases, but after 45 degrees, it decreases as the angle increases more. It means that the proposed network was trained well without overfitting to a specific kind of strength. As values in the table, most pixel-value change attacks showed high robustness as less BERs than 10% except Gaussian filtering attacks with 7 \(\times\) 7 and higher filters, Gaussian noise attacks with \(\sigma\) larger than 0.08, and JPEG attack with higher compression than quality 40. Especially, it showed strong robustness for the salt-and-pepper noise addition attacks. For the geometric attacks, it is quite vital for the rotation attacks but shows high BERs against more than 50% of crop and dropout attacks and higher dropout attacks of 30%. However, those attacks for which the proposed scheme shows  
Figure 3: Examples showing invisibility: (**a**) host images, (**b**) watermarked images, and (**c**) magnified difference image, peak-signal-to-noise-ratios (PSNRs) of the first, second, and third row are 37.46 dB, 40.87 dB, and 42.13 dB, respectively. higher than 10% of BER are potent attacks that damage the host image a lot. Therefore, we believe the proposed scheme would be robust enough for meaningful attacks. For reference, Figure 5 shows some examples of the extracted watermarks according to their BERs. From the figures, it is quite clear that the extracted WM with a higher BER value than 10% cannot guarantee to protect the host image's intellectual property rights. Figure 4: Examples of attacked images: (**a**) host image, (**b**) Gaussian filtering (9 \(\times\) 9), (**c**) average filtering (5 \(\times\) 5), (**d**) median filtering (5 \(\times\) 5), (**e**) salt and pepper noise addition (_p_ = 0.1), (**f**) Gaussian noise addition (sigma = 0.1), (**g**) Laplacian sharpening (5-point stencil), (**h**) JPEG compression (quality factor = 50), (**i**) rotation (30\({}^{\circ}\)), (**j**) crop (_p_ = 0.5), (**k**) dropout (_p_ = 0.5).
## 5 Experimental Setups and Procedures
### Setup I Configuration  
The experimental setup imitates the system model using a testbed based on a real scenario, which enables dual connectivity by transmitting data packets over two wireless networks of different technologies. The wireless nodes employed in the lab experiments are single-board computers (SBCs), which can simultaneously connect to an LTE network via attached LTE universal serial bus (USB) modems and connect to the WiFi network through attached USB WiFi adapters. Due to budget and space limitations, the experiment engages only 10 SBCs: one acts as a sender and the rest are receivers. All SBCs run the Ubuntu Linux server with the Linux kernel implementation of MPTCP, supporting five congestion control algorithms, namely, OLIA, LIA, Cubic, BALIA, and wVegas. The receiving nodes are connected to a WiFi router, which is directly connected to another WiFi router serving the sending node. The two routers run on two different WiFi frequency channels. Moreover, the receiving nodes are connected to a real (not simulated) single-cell LTE network, which consists of a real eNodeB implemented using software-defined radio (SDR) connected to  
Figure 1: System model.  
a real evolved packet core (EPC) system implementing the mobility management entity (MME), serving gateway SGW, packet data network gateway (PGW), and home subscriber server (HSS). The LTE network uses the 2.6 GHz band. Therefore, all the LTE modems connect to the network through an isolated RF enclosure, which constrains the locations of the nodes. The sending node has two interfaces. One is connected to a WiFi router, whereas the other is connected to the EPC via an Ethernet switch, as shown in Figure 2.
## 1 Introduction
### Technical Background
[40] performed an experimental campaign mainly aiming to compare the hydraulic performance of equally sized OBREC and traditional rubble mound breakwaters. According to the physical model tests, the average overtopping discharge rate in OBREC was slightly larger than in the traditional device. The safety against the overtopping OBREC was shown to be even smaller by adopting a curved, instead of a flat, ramp, as reported by [21; 41]. In the aforesaid studies, some preliminary results demonstrated that the installation of the nose allowed to reduce the wave overtopping by 50-60%, whereas the production of electricity was unaltered being related to the wave overtopping discharge in the front reservoir. Nevertheless, the influence of the geometry of the parquet on the overtopping phenomenon was not evaluated in detail. A comprehensive understanding of the nose behavior would be a desirable outcome, given that its installation should lead to the following:  
1. An enhancement of the hydraulic safety level as compared to a traditional breakwater;
2. The lowering of the OBREC crest height to provide lower visual impact on the city skyline;
3. An increase of the potential energy to be converted because the up-rushing waves are redirected into the reservoir;
4. An effective and economically feasible solution to counter the effects derived by the SLR. Moving from these motivations, the present work intends to assess the effectiveness of the nose to oppose the wave overtopping potentially generated by an increase of the sea water level. The influence of the nose geometrical layout on the wave overtopping at the rear side of the OBREC is analyzed in the following, and some empirical recommendations to be used for optimally designing the nose structure are presented. The paper is organized as follows: Section 2 provides detailed information on the experimental facility and tests program. In Section 3, an analysis of the physical model data on the hydraulic performance of the nose is provided. Here, a new formula to be applied for predicting the dimensionless wave overtopping discharge is suggested. Section 4 is finally devoted to an overall discussion with some concluding remarks.