esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Fresnel Diffraction Convolution Algorithm
## 3 Experiments and Results
Taking the image in the yellow box as an example, the existence of the oscillation phenomenon leads to an image background that is not smooth, and the lung cancer cell boundaries are presented as jagged-like boundaries. The oscillation phenomenon at the image background of the reconstructed image of D-FFT (Tukey) shown in Figure 9D is well suppressed, and the edges of the lung cancer cells are smoother. Figure 8: Different algorithms for lung cancer slice’s reconstruction results of the PSNR curves and SSIM curves: (**A**) PSNR curves; (**B**) SSIM curves. To visualize the oscillations of the reconstructed amplitudes of the different algorithms more intuitively, the profiles of amplitude at the dashed line in the yellow box and the solid line in the red box in Figure 9 are given in Figure 10. As shown in Figure 10A, the reconstructed amplitudes obtained by the D-FFT algorithm, T-FFT algorithm, and D-FFT (rect) algorithm have different degrees of oscillations at the edges of the field of view, with the T-FFT algorithm having the most serious oscillations. In contrast, the reconstructed amplitudes from the D-FFT (Tukey) algorithm at the edge of the field of view have very smooth profiles, and there is hardly any oscillation in the profiles. There are jumps not only at the edges but also inside the reconstructed image. As shown in Figure 10B, the amplitude profiles reconstructed by D-FFT (Tukey) have fewer jumps not only at the edges but also inside the reconstructed image. That is to say, D-FFT (Tukey) can effectively suppress the ringing effect. Figure 10: Profiles of reconstructed intensity obtained by different algorithms: (**A**) intensity profile plots (dashed line in yellow box); (**B**) intensity profile plots (solid line in red box). Figure 9: Intensity of reconstruction images obtained by different algorithms: (**A**) D-FFT; (**B**) D-FFT (rect); (**C**) T-FFT; (**D**) Tukey (\(\beta=0.81\)). In order to demonstrate the superiority of the D-FFT (Tukey) algorithm for reconstructing the phases in practical applications, the phases reconstructed by different algorithms are shown in Figure 11.  
When comparing the phases reconstructed by the four algorithms, as shown in Figure 11, it can be observed that lung cancer cell edges of the phases reconstructed by D-FFT, T-FFT, and D-FFT (rect) are often discontinuous, whereas the phases reconstructed by the D-FFT (Tukey) algorithm have smoother edges. In the background of Figure 11, the other three algorithms show noises similar to parallel pinstripes, while the D-FFT (Tukey) algorithm eliminates these noises while preserving the original phase of the lung cancer cells. That is, the D-FFT (Tukey) algorithm obtains a more accurate and higher-quality reconstructed phase, which is consistent with the simulation results.
# Enhancing QoS in Opportunistic Networks Through Direct Communication for Dynamic Routing Challenges
## 1 Introduction  
Delay-Tolerant Networks (DTNs) have garnered significant attention due to their exceptional adaptability in enabling communication, particularly in infrastructure-less environments. These networks employ opportunistic behaviors for data transmission and reception and are formally referred to as Opportunistic Networks (OppNets) [1, 2]. OppNets illustrate the remarkable capability to incorporate node mobility during data dissemination. OppNets offer an effective solution for data dissemination in scenarios lacking traditional communication infrastructure [3, 4]. Their applicability has been demonstrated in various challenging environments, such as mountainous regions and space communications. Typically, OppNets rely on identifying the next-hop node--usually the one closest to the destination--to forward data from the sender to the receiver. Intermediate nodes serve as relays, facilitating the delivery of data across the network [5, 6].  
This strategy limits the applicability of routing protocols typically designed for Mobile Ad Hoc Networks (MANETs). Furthermore, OppNets focus on forwarding data from the current node without considering the information from previous routing paths [7, 8]. Dueto challenges such as node mobility, frequent disconnections, and being out of network range, OppNets rely on additional node characteristics to ensure the successful delivery of data to the destination.  
Routing protocols in OppNets can broadly be classified into two categories: infrastructure-based and infrastructure-less. In an infrastructure-less OppNet environment, mobile devices carried by humans or vehicles function as nodes, often exhibiting regular movement patterns over a defined period [9; 10]. These nodes typically belong to specific communities, each comprising several members who frequently visit them. Such characteristics significantly influence the nodes' forwarding decisions [11; 12].  
This paper addresses the issue of selecting the next hop in a network by focusing on scenarios where the neighboring node is either busy or unresponsive. In such cases, a high-speed channel, or direct communication link, is established with the destination node based on its proximity. All data packets are then transmitted directly to the destination node. This method effectively reduces the time typically required for identifying a neighboring node as the next hop.  
The remainder of this paper is organized as follows: Section 2 reviews the related work on OppNet routing protocols; Section 3 outlines the proposed methodology; Section 4 describes the simulation environment and presents the simulation results; and finally, Section 5 concludes this paper.
## 3 Results and Discussion
### Hg Species Localization and Redistribution
After rapid uptake through the gills, MeHg was transported in the heart, the liver, and the brain, and finally transferred to the muscle. According to the literature, dietary MeHg would be delivered to the liver through a portal system after uptake in the intestine [22; 25]. Then, MeHg could cross the hepatocyte plasma membrane and bind to thiol groups before being eliminated by the bile or redistributed by blood stream to other target tissues as muscles [26; 27]. When taken up by the gills, MeHg may first be transported to the heart and then to the liver before being redistributed to other organs or tissues. MeHg determination in the skeletal muscle is consistent with the results obtained for the endogenous \({}^{200}\)Hg, confirming that mercury mainly bioaccumulates under the methyl form. This is also in accordance with a previous analysis on numerous species collected in natural freshwater or marine systems [26]. The higher accumulation of MeHg than Hg(II) observed in the brain of glass eels has also been reported in other species [3; 8; 28]. The brain is protected by the blood-brain barriers, which strictly regulate the transport of molecules from the bloodstream. However, in contrast to Hg(II), thiol-bound MeHg (specifically MeHg cysteineate, the most dominant form of MeHg in vivo) can penetrate these barriers.
# GNSS-Based Monitoring Methods for Mining Headframes
## 4 Correlation Analysis and Risk Assessment
### Correlation Analysis of Deformation Factors
The Pearson correlation coefficients for the sheave wheel distance factor with \(\Delta\)X and \(\Delta\)Y are above 0.67, and its Pearson correlation coefficient for \(\Delta\)H is 0.09. Based on the definition of the Pearson correlation coefficient, the height exerts a minimal effect on the headframe deformation. The sheave wheel distance significantly affects the planar position (\(\Delta\)X, \(\Delta\)Y) of the headframe, while the vertical settlement \(\Delta\)H is not much affected by the sheave wheel distance. Considering that the headframe is a superheavy-duty damped composite pendulum structure and that its overall shape is smaller at the top and larger at the bottom, it is stable and sturdy in the vertical direction. Moreover, the height difference between the two sheave platforms is not significant, and the vertical pressure during the deformation of the headframe monitoring stations can be assumed to be roughly offset by the oblique support force of the headframe, resulting in minimal deformation in the elevation direction. The massive pendulum force generated during the operation of the sheave wheels imposes a horizontal pressure on nearby points because of the swing, and this pressure is greater than the stress of the headframe material itself. The closer one is to the sheave wheels, the greater the pressure, leading to a proportional relationship between the horizontal deformation and the distance from the sheave wheels.
# Finite Element Simulation of the Flat Crush Behavior of Corrugated Packages
## 2 Experiment Design
### Material Properties  
The required material properties for FEA were the Young's modulus, Poisson's ratio, shear modulus, yield strength, and frictional coefficient among the corrugated paperboard components. These components, such as liners and corrugated medium, were assumed to be orthotropic materials [22], implying that their material properties are symmetrical for x(MD), y(CD), and the z-plane (Figure 2). Therefore, each paperboard possessed nine elastic material properties, such as E\({}_{\text{x}}\), E\({}_{\text{y}}\), E\({}_{\text{z}}\), G\({}_{\text{xy}}\), G\({}_{\text{xz}}\), G\({}_{\text{yz}}\), \(\mu_{\text{xy}}\), \(\mu_{\text{xz}}\), and \(\mu_{\text{yz}}\), and two strength values, \(\sigma_{\text{x}}\)(MD) and \(\sigma_{\text{y}}\)(CD) [3; 23]. The values of these properties in the FEA were set based on those reported by [PERSON] et al. [3], as listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Paperboards**} & \multicolumn{3}{c}{**Young’s Modulus (GPa)**} & \multicolumn{3}{c}{**Poisson’s Ratio**} & \multicolumn{3}{c}{**Shear Modulus (GPa)**} & \multicolumn{3}{c}{**Yield Strength (MPa)**} \\ \cline{2-11}  & **E\({}_{\text{x}}\) (MD)** & **E\({}_{\text{y}}\) (CD)** & **E\({}_{\text{z}}\)** & **\(\mu_{\text{xy}}\)** & **\(\mu_{\text{xz}}\)** & **\(\mu_{\text{yz}}\)** & **G\({}_{\text{xy}}\)** & **G\({}_{\text{xz}}\)** & **G\({}_{\text{yz}}\)** & **\(\sigma_{\text{x}}\) (MD)** & **\(\sigma_{\text{y}}\) (CD)** \\ \hline \multirow{2}{*}{K180} & 2.20 & 0.37 & 0.011 & 0.34 & 0.01 & 0.01 & 0.349 & 0.040 & 0.010 & 29.09 & 12.12 \\  & (\(\pm\)0.02) & (\(\pm\)0.01) & & & & & & & & (\(\pm\)0.8) & (\(\pm\)0.1) \\ \hline \multirow{2}{*}{SK180} & 3.16 & 0.40 & 0.016 & 0.34 & 0.01 & 0.01 & 0.435 & 0.057 & 0.011 & 42.50 & 19.50 \\  & (\(\pm\)0.07) & (\(\pm\)0.01) & & & & & & & & (\(\pm\)0.8) & (\(\pm\)0.5) \\ \hline \hline \end{tabular} Note: ( ) standard deviation.  
\end{table}
Table 2: Orthotropic material properties of corrugated paperboards used for the FEA [3].  
Figure 3: DW-BB/F of different phase angle (\(\alpha\)) applied to the FEA. (**a**) the difference between the phase angle of the upper and lower flute is 180\({}^{\circ}\), (**b**) the difference between the phase angle of the upper and lower flute is 90\({}^{\circ}\) and (**c**) the difference between the phase angle of the upper and lower flute is 0\({}^{\circ}\).  
Figure 2: Meshed 3D models for FE simulation.  
As the modeled test specimen was compressed, the contact area between the flute and liner on the left and right portions of the shared nodes increased gradually. Accordingly, the frictional contact conditions at the portion where contact was expected were implemented to accurately analyze the flat crush behavior of the corrugated paperboard. In this study, the static-frictional coefficients, listed in Table 3, were set based on those reported by [PERSON] et al. [3].
# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 3 Method
### Algorithm Design
#### 3.2.3 Multi-Class Domain Discriminator  
The multi-class domain discriminator (\(G_{l}\)) is responsible for aligning the conditional distributions between the source and target domains. It consists of \(C\) domain discriminators (where \(C\) is the number of data categories), each with a structure similar to that of the domain discriminator, including a GRL and a fully connected domain classifier. Each domain classifier is responsible for aligning the features of one specific category, ensuring the alignment of the conditional distributions between the source and target domains. The loss function for the multi-class domain discriminator (\(G_{l}\)) is as follows:  
\[L_{l}=\frac{1}{n_{s}+n_{t}}\sum_{c=1}^{C}\sum_{x_{m}\in D_{s}\cup D_{t}}\hat{g }_{m}^{c}L_{d}^{c}\left(d_{c},d_{c}\right) \tag{12}\]  
\(\hat{d}_{k}=G_{d}^{c}\left(R\left(G_{f}(x_{m})\right)\right)n_{s}\) and \(n_{t}\) are the numbers of datasets in the source domain and the target domain, respectively. If \(x_{m}\) is from the target domain data, it is the \(\hat{y}_{m}^{c}\) item of the C-dimensional vector output by the label classifier. If \(x_{m}\) comes from the source domain data, it is the \(\hat{y}_{m}^{c}\) item of the label corresponding to the data \(x_{m}\).  
In this design, we drew inspiration from multi-adversarial domain adaptation (MADA) [38], wherein different domain discriminators are used for different categories to align the conditional distributions. However, our implementation differs from MADA in several ways. First, instead of using separate GRL and fully connected classifiers for each of the C domain discriminators, which can lead to a significant increase in the network parameters when the number of classes is large, we use a single fully connected network with C output nodes. Each node represents the domain classification for a specific category. Additionally, MADA requires the availability of target domain labels, but in our case, target domain labels are not available and are instead part of the model's learning objective. In MADA, pseudo-labels generated during training are used to guide the domain discriminator, but these pseudo-labels can be inaccurate, leading to error amplification. In our approach, we impose constraints on the pseudo-label acquisition. Before performing conditional distribution alignment, we first align the marginal distributions, ensuring the accuracy of the pseudo-labels. Furthermore, we introduce a confidence threshold (\(\delta\)), where only target domain data with predicted class confidence values higher than the \(\delta\) are included in the conditional distribution alignment.
###### Abstract  
Purpose: Knowledge of the hemodynamics in the vascular system is important to understand and treat vascular pathology. The present study aimed to evaluate the hemodynamics in the human carotid artery bifurcation measured by four-dimensional (4D) flow magnetic resonance imaging (MRI) as compared to computational fluid dynamics (CFD). Methods: This protocol used MRI data of 12 healthy volunteers for the 3D vascular models and 4D flow MRI measurements for the boundary conditions in CFD simulation. We compared the velocities measured at the carotid bifurcation and the 3D velocity streamlines of the carotid arteries obtained by these two methods. Results: There was a good agreement for both maximum and minimum velocity values between the 2 methods for velocity magnitude at the bifurcation plane. However, on the 3D blood flow visualization, secondary flows, and recirculation regions are of poorer quality when visualized through the 4D flow MRI. Conclusion: 4D flow MRI and CFD show reasonable agreement in demonstrated velocity magnitudes at the carotid artery bifurcation. However, the visualization of blood flow at the recirculation regions and the assessment of secondary flow characteristics should be enhanced for the use of 4D flow MRI in clinical situations.  
caroid artery; carotid bifurcation; flow patterns; computational fluid dynamics (CFD); four-dimension flow magnetic resonance imaging (4D flow MRI); velocity magnitudes +
Footnote †: journal: Physics Letters B
## 3 Results
### Binary Systems
In Figure 7, the VLE of R744 + R1234 ze(E) binary system is reported together with the results from molecular Gibbs Ensemble Monte Carlo simulations [35]. From the figure, it is possible to notice that the system is zeotropic and shows an ideal behavior in terms of Raoult's law. The same results were achieved for the R744 + R1234 yf binary system. Furthermore, for R744 + R1234 ze(E), the literature shows [35] that they are in good agreement with the values calculated from the isochoric apparatus. From the _T-x_ data (Figures 5 and 6), it is evident that for the R744 + R1234 yf binary system, it was not possible to detect a eutectic point. This was probably due to the very far from each other triple point temperatures of the two system constituents, which probably caused a eutectic point at a very low carbon dioxide concentration region. For the R744 + R1234 ze(E) binary system, the eutectic point was estimated at \(x_{1}=0.30\pm 0.05\) and \(T=164\pm 1\) K.  
The SLE data obtained confirm that the binary systems under analysis can be potentially adopted in plants at very low temperatures without any solidification problem. Figure 7: VLE for the R744 (1) + R1234 ze(E) (2) binary pair. In general, all the performed measurements confirm that the studied pure fluids and binary systems are valid options for the replacement of non-environmentally friendly working fluids.
## 3 Identity Authentication and Secure Transmission Based on Blockchain
### UAV Distributed Identity Authentication Scheme Based on Blockchain  
In this paper, the distributed identity authentication based on DID information is established, and the DID document is used to store the identity information of the drone and its owner. The distributed identity authentication of UAVs is uniformly completed by blockchain. The owner of the UAV only generates the identity information for the UAV when it is registered. The identity information received by the blockchain is securely stored on the block after consensus of other nodes in the network. The drone to be authenticated communicates securely with the blockchain to complete distributed identity authentication.  
In B-UIM-M, the blockchain stores the DID and DID documents of the drone owner and the drone. For different application scenarios, the content of the DID document may be different. In this paper, the content design of the DID document is shown in Table 2.  
The DID and DID document are stored on the blockchain in the form of key-value pairs, where DID is the key and DID document is the value.The detailed content is shown in Figure 4.  
When a drone requests identity authentication, it must provide the blockchain with the DID of the drone to be authenticated, the digital signature, and the hash of the message sent by the drone. The blockchain first finds the DID document from the chain, and returns the identity authentication result after decrypting and verifying the signature. The detailed processing of the identity authentication is shown in Algorithm 3.  
```
1:identity identifier DID, signature \(M\), and hash value \(H\) of sending information
2:identity authentication result \(AuResult\)
3:for i = block \"1\" to block = \"n\" do
4: find DID according to DID document
5:endfor
6:read public key form DID document
7:\(H\leftarrow\) decrypt(\(M\))
8:compare \(H\) and \(M\)
9:return\(AuResult\)
```  
\begin{table}
\begin{tabular}{c c} \hline \hline
Integrity proof of the DID document, i.e., the signature of its distribution agency  
\end{table}
Table 2: DID document content.  
Figure 4: DID document stored on the blockchain.
## 5 Sensitivity Analysis
### PV/BESS Capital Cost  
The sensitivity analysis of PV/BESS capital cost is divided into four parts: total PV/BESS capital cost, PV system capital cost, BESS capital cost, and PCS capital cost. The evaluation analyzes the impact on economic benefits under different cost conditions with a change ratio of 1-0.5 times and each interval of 10%. Figure 15 shows the economic benefits obtained are significantly higher than reducing the BESS or PCS capital cost by reducing the expenditure of the PV system capital cost. Because the capital cost of the PV system is higher than that of BESS and PCS in terms of the degree of impact on economic benefits. According to the analyzed result of Table 7, the project benefit can be increased by 30.09% if the expenditure of PV system capital cost is reduced by 10%. However, the project benefits will restrictively increase by 4.07 and 0.68% related to capital cost expenditure of BESS or PCS reduced by 10%, respectively. If the expenditure of total PV/BESS capital cost is reduced by 10%, the results at the ratio of 0.9 reveal that the project benefit increased to 34.84%, the IRR increased to 4.30%, and the discounted payback shortened to 13.83 years.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PV/BESS Cost** & **1** & **0.9** & **0.8** & **0.7** & **0.6** & **0.5** \\
**Reduction Ratio** & & & & & & \\ \hline NPC-Without PV/BESS (\$) & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 \\ \hline NPC-With PV/BESS (\$) & \(-\)551,222,008 & \(-\)549,970,765 & \(-\)548,719,523 & \(-\)547,468,281 & \(-\)546,217,039 & \(-\)544,965,797 \\ \hline Project benefit (\$) & \(\$\)3,591,197 & \(\$\)4,842,440 & \(\$\)6,093,682 & \(\$\)7,344,924 & \(\$\)8,596,166 & \(\$\)9,847,408 \\ \hline ROI (\%) & 1.77\% & 2.55\% & 3.53\% & 4.79\% & 6.47\% & 8.82\% \\ \hline IRR(\%) & 3.08\% & 4.30\% & 5.75\% & 7.49\% & 9.68\% & 12.56\% \\ \hline Discounted payback (year) & 15.41 & 13.83 & 12.25 & 10.69 & 8.67 & 7.21 \\ \hline LOCE-Without PV/BESS (\$/kWh) & 0.09401 & 0.09401 & 0.09401 & 0.09401 & 0.09401 & 0.09401 \\ \hline LOCE-With PV/BESS (\$/kWh) & 0.08785 & 0.08570 & 0.08355 & 0.08141 & 0.07926 & 0.07712 \\ \hline PV cost reduction ratio & 1 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 \\ \hline NPC-Without PV/BESS (\$) & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 & \(-\)554,813,205 \\ \hline NPC-With PV/BESS (\$) & \(-\)551,222,008 & \(-\)550,141,258 & \(-\)549,060,508 & \(-\)547,979,758 & \(-\)546,899,008 & \(-\)545,818,258 \\ \hline Project benefit (\$) & \(\$\)3,591,197 & \(\$\)4,671,947 & \(\$\)5,752,697 & \(\$\)6,833,447 & \(\$\)57,914,197 & \(\$\)58,994,947 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analyzed results of PV/BESS combinations corresponding to different contract capacities.  
Figure 15: Effect of varying key capital costs on the project benefit.
## 4 Performance Analysis of Islanding Detection Methods  
The accuracy of operation and time response of IDM methods are the main factors on which the performance of IDM depends. There are many criteria that can be used to determine the suitability and capability of islanding detection methods, as presented in Figure 3. The most appropriate method can operate successfully under any circumstances, based on which certain criteria are taken into account. Those criteria are fully explained below.