esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 2 Numerical Approach
### Numerical Result and Validation  
The finite element results are compared and validated against experimental results [26] from previous research conducted at Edinburgh University, as well as the Janssen equation [15]. Pressure distribution readings throughout the silo wall are given in the experimental data at various sections.
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
###### Abstract
In order to achieve end-to-end learning, reference [22] proposed processing based on multi-layer wavelet networks, and further improved the denoising quality. [PERSON] et al. [23] first applied the principle of generative adversarial networks to SAR images. In order to improve denoising performance and image detail preservation ability, [PERSON] et al. [24] proposed a speckle noise suppression network based on adversarial generative networks. [PERSON] et al. [25] used an Autoencoder structure for SAR image denoising. The SAR image denoising method based on deep learning has stronger adaptability and flexibility, but in specific scenarios or tasks, the annotation and acquisition of SAR image data may be very difficult, and problems such as low feature matching and large computational complexity. Meanwhile, the denoising process may lead to the loss of image details, especially in processing important features such as target information, resulting in poor performance in dealing with complex noise distributions and SAR images. In response to the above issues, this paper proposes a convolutional neural network SAR image denoising algorithm based on self-learning strategy. Unlike traditional methods that rely on hand-crafted features or supervised training with noisy-clean pairs, our approach leverages a self-supervised training strategy that learns directly from the noisy SARdata. This allows the model to capture complex noise distributions inherent in SAR images, thereby significantly enhancing denoising performance and generalization. Considering that SAR images are affected by various types of noise, a noise recognition method based on residual statistical features was first designed. Digital image processing techniques were used to simulate the superposition state of SAR images with different noises, in order to determine the type of noise. In response to the problem of unknown noise, this paper further proposes a SAR image denoising algorithm based on Denoising Convolutional Neural Network (DnCNN). Based on the self-learning DnCNN denoising model, a twin convolutional network structure is adopted, which can specifically adapt to the characteristics of SAR images and enhance the model's generalization ability to different noise types and complex noise distributions. By constructing a noise original image dataset sample pair for training, image features and noise distribution are automatically learned, significantly improving the denoising effect of SAR images and having stronger generalization ability. Simulation experiments have demonstrated the effectiveness of the proposed method. The remainder of this paper is organized as follows. Section 2 introduces the SAR image denoising algorithm based on self-learning strategy DnCNN, including the algorithm design, data augmentation, and network structure. Section 3 presents simulation experiment results, including the evaluation of denoising effect and comparison with existing method. Section 4 concludes this paper.
## 3 Control Scheme Design
### Sliding Mode Control Scheme Design  
A common form of an ROVs dynamics model is shown in Equation (13), and is used, for example, in [24; 25]. In practice, by considering the complexity of subsequent computational modeling and simulations, the ROV dynamics model (13) can be rewritten in the form presented in [26] before designing an ROVs controller as follows:  
\[\dot{\eta}=\dot{J}(\eta)-J(\eta)\bar{M}^{-1}(\bar{C}(v)v+\bar{D}(v)v+\bar{G}( \eta))+J(\eta)\bar{M}^{-1}w_{\eta} \tag{14}\]  
Conventional SMC is famous for its great robustness to ROV parameter inaccuracies and environmental disturbances. The desired \(s\in\mathbb{R}^{6\times 1}\) is the sliding mode surface in sliding mode control theory, and \(e\) is denoted as the errors between the desired position and attitude \(\eta_{d}\) and the actual position and attitude \(\eta\), namely, the tracking errors \(e=\eta_{d}-\eta\). The traditional sliding mode surface usually is denoted as follows:  
\[s=\Gamma e+\dot{e} \tag{15}\]  
where \(\Gamma\in\mathbb{R}^{6\times 6}\) is a positive definite constant diagonal gain matrix.  
The standard SMC's control law [27] consists of an equivalent control law \(\tau_{eq}\) and a switching control law \(\tau_{sw}\) as follows:  
\[\tau=\tau_{eq}+\tau_{sw} \tag{16}\]  
Theoretically, the equivalent control law is continuous, mainly reflecting the model's information, and is capable of realizing the desired dynamic effect. When the state of the system reaches a specific sliding mode surface, then the equivalent control law can maintain this state on the sliding mode surface [28]. However, due to the existence of environmental disturbances and model uncertainty, switching control law is usually required to compensate the discrepancy between the estimated and actual dynamics. The control law of the switch is able to transfer the system state to the sliding mode surface.  
Substituting (14) into (16) gives the following:  
\[\dot{s} =\Gamma\dot{e}+\ddot{e}=\Gamma\dot{e}+\dot{\eta}_{d}-\dot{\eta} \tag{17}\] \[=\Gamma\dot{e}+\ddot{\eta}_{d}-(\dot{J}(\eta)v-J(\eta)\bar{M}^{-1} (\bar{C}(v)v+\bar{D}(v)v+\bar{G}(\eta))\] \[+J(\eta)\bar{M}^{-1}\tau+J(\eta)\bar{M}^{-1}w_{\eta})\]  
Assuming that \(\dot{s}\) equals zero, the equivalent control law can be expressed as follows:  
\[\tau_{eq}=\bar{C}(v)v+\bar{D}(v)v+\bar{G}(\eta)+\bar{M}J^{-1}(\eta)\big{(} \Gamma\dot{e}+\ddot{\eta}_{d}-\dot{J}(\eta)v\big{)}-w_{\eta} \tag{18}\]
## 3 Parametric Model Definition
### Surface Meshing Method
#### 3.1.4 Adapatative Compass Method Proposal  
The ACM is conceived to find a balance between accuracy and processing speed. As described above, accuracy problems arise on surfaces with high local curvatures, so this method allows subdividing the surface into \(n\) domains with a similar curvature range and adapting the meshing radius to the curvature of each domain.  
For each domain, a resolution factor \(S_{n}\) is set with a value from \(n\) to 1, where \(n\) corresponds to the highest curvature and 1 to the lowest. The mesh radius used in each domain is \(r/S_{n}\), with \(r\) the predefined distance between nodes of the quadrangular mesh. Thus, the larger the \(S_{n}\), the smaller the mesh radius and, consequently, the denser and more accurate the meshing.  
Figure 12 shows the process on the test surface divided into three curvature domains. This starts with the subdivision of the master curves. In this case, for the domain of higher curvature, the master curves are subdivided with \(r/3\); for the area of medium curvature with \(r/2\); and for the area of lower curvature, the standard \(r\) is kept (Figure 12a). Once the variable subdivision of the master curves is done, the algorithm runs a CM on the whole surface, adapting the radius of the spheres to the corresponding domain (Figure 12b).  
To analyse the improvement in accuracy of the ACM and the ICM over the CM, the distance of the resulting mesh points is measured at two positions on the test surface calculated with the three methods. As already mentioned, the accuracy depends on the curvature of the surface and the extent of the surface covered in each direction, since the meshing error is cumulative in the CM and in its derivative methods (ICM and ACM). The runtime difference between the methods is also compared because of its key importance to the optimisation processes.  
Figure 13 shows the test surface and the test points \(O_{1}\) and \(O_{3}\). \(O_{1}\) corresponds to the last subdivision of one of the master curves starting at \(O_{0}\), which is the longest curve over the surface. \(O_{3}\) is one of the points on the grid furthest away from the starting point \(O_{0}\) and from the two master curves. It is obtained after the subdivision process of the master curves (\(O_{0}\)-\(O_{2}\) and \(O_{0}\)-\(O_{2^{\prime}}\)) and the meshing over the surface.  
The results of the comparison are presented in Table 1. With CM being the reference method, both ICM and ACM perform a similar accuracy improvement whatever the mean curvature radius along the path (\(r_{\rho,\text{mean}}\)). It is observed that the improvement is greater when computing the subdivision in a \(\mathbb{R}^{2}\) space (test point \(O_{3}\)) than in a \(\mathbb{R}^{1}\) space (test point \(O_{1}\)). In terms of runtime, the ACM performs 2.54 times faster than the ICM.  
Figure 12: Adapatative Compass Method: (**a**) Surface curvature domains and subdivision of master curves; (**b**) Resulting net of points.
# Performance-Enhancing Sulfur-Doped TiO2 Photoanodes for Perovskite Solar Cells
## 3 Results
### Field Emission Scanning Electron Microscope (FESEM) Analysis  
The effect of sulfur doping on the microstructure of the TiO\({}_{2}\) thin film photo-anode was assessed using the FESEM micrograph. Figure 5a,b indicate the FESEM morphological images of the un-doped and S-doped TiO\({}_{2}\) photo-anode thin films sintered at 450 \({}^{\circ}\)C, respectively. High-magnification images of the TiO\({}_{2}\) thin film revealed the nano-crystallisation of the deposited thin film, as suggested in the XRD diffractograms in Figure 2. Additionally, the thin film showed a dense and uniform morphology of homogenous granular and spherical grain. As mentioned in a previous study, the incorporation of S in the TiO\({}_{2}\) lattice would not contribute to a major change in the morphology of the thin film [22]. The EDX elemental spectra were recorded for the un-doped and S-doped TiO\({}_{2}\) thin films to observe the elements, as shown in Figure 5c,d, respectively. The EDX spectra for the S-doped TiO\({}_{2}\) thin film featured a minor peak for S at approximately 2.3 keV [24]. Additionally, the Ti peaks were observed at approximately 0.2 keV and a strong peak at 4.5 keV, which were attributed to the surface and bulk TiO\({}_{2}\), respectively [29]. Figure 5e,f show the EDX elemental mapping of Ti, O and S in the un-doped and S-doped TiO\({}_{2}\) photo-anode thin films, respectively. The mapping images suggested the highly distributed behaviour of S ions throughout the S-doped TiO\({}_{2}\) thin film and the absence of S ions in the un-doped TiO\({}_{2}\) sample. Additionally, the presence of carbon in the S-doped TiO\({}_{2}\) photo-anode thin film most likely due to the usage of thiourea in the preparation of the thin film, as also described in previous study [30].
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
## 3 Results and Discussion
### Late Stage
Physical measurements showed that the lower part of the frost flowers were brine-wetted at this time. At 35\({}^{\circ}\) incidence, the co-polarized backscatter remained stable (steady within \(\pm\)1 dB) from 14:40 on 13 January to 02:40 on 14 January while the cross-polarized backscatter had an increase and some fluctuations (\(\sim\)5 dB). The co-polarized backscatter (HH) reached a local minimum at 06:40 on 14 January, which was followed by an increase toward the end of the experiment. The co-polarized VV backscatter remained steady from about 13:40 on January 13 to the end of the experiment. For 45\({}^{\circ}\) incidence, the co-polarized backscatter remained stable (steady within \(\pm\)0.5 dB) from 14:40 on 13 January to 01:40 on 14 January while the cross-polarized backscatter had a gradual decrease and some fluctuations (\(\sim\)2 dB). Overnight, the co-polarized backscatter (both VV and HH) decreased slightly, which reached a local minimum at 06:40 on 14 January followed by an increase toward the end of the experiment. Major fluctuations were not observed in the C-band backscatter results in Stage 3, which is consistent with results from our previous experiment [19]. The sea ice surface frost flower spatial coverage and vertical heights did not change measurably during Stage 3, which indicates the C-band scatterometer measurements were responding primarily to thermodynamic changes. In evaluating the surface coverage from LiDAR data and photographs, the surface was not uniformly covered with frost flowers. Therefore, direct comparison of results at different incidence angles is complex. Based on our results (Figure 6), the lowest incidence angles (i.e., 25\({}^{\circ}\)) are the most sensitive to the changes in frost flower coverage, physical changes, and thermodynamic effects. This agrees with our previous assessment in Reference [19]. Moreover, we have shown that the co-polarized ratio at the 25\({}^{\circ}\) incidence angle provides additional information that is not readily apparent from the individual co-polarized channels.
## 4 Results and Discussion
The obtained values are in good agreement with the research results that were obtained experimentally and numerically by the authors in their earlier studies related to ORC systems equipped with a volumetric expander [49; 50]. In the Carnot battery technologies, the performance can be expressed using Equation (13); therefore, in this discussion, the obtained results of this parameter (\(\zeta\)) are also presented here, as illustrated in Figure 5. The obtained results show the comparison between \(\zeta\) with an isentropic efficiency of the expander in ORC and heat pump modes. The results show that \(\zeta\) can be expressed as a function of HE1 temperature, and the conclusion can be drawn that increasing HE1 temperature decreases \(\zeta\). Figure 4: The obtained results for the RRTC in _COP_–\(\eta\) diagrams with the basis efficiency of the expander: (a) 0.8 and (b) 0.7. Figure 5 shows that the expander's isentropic efficiency significantly influences the Carnot battery's performance. The obtained result shows that, when the isentropic efficiency of the expander is equal to 0.8, the \(\zeta\) parameter of the RRTC ranges between 0.6 and 0.98; and when the isentropic efficiency of the expander is equal to 0.7, the \(\zeta\) parameter is in the range of 0.53-0.84 (which is about 14% lower). It appears that, if the isentropic efficiency of the expander operating in ORC mode is equal to 0.8, and assuming that the isentropic efficiency of the expander operating in heat pump mode is 0.5, it can be concluded that the \(\zeta\) parameter is still relatively higher than the isentropic efficiency of the expander for both ORC and heat pump modes. In this case, the difference is about 9.79%. The above-mentioned novel RRTC design using one expander for ORC and heat pump modes may be an alternate solution for a more cost-effective Carnot battery design. In this case, further study and analysis considering some important aspects (such as the thermo-economics, technical issues relating to the type and design of suitable expanders, structural analysis, wear, lubricants, bubble growth, and working fluids) would be interesting to discuss in the future.
## 3 Results and Discussion
### Coffee Powder Discriminant Volatiles That Predict Brews' Profile  
Although the heat map representation of all volatile compounds gave a profile for each blend, both in the powders and brews, it is possible that the analysis of some of these compounds in the coffee powder reflects the volatile potential in the corresponding brew. Thus, considering the Pearson correlation of coffee powders and respective brews (Figure 54), it was found that 15 compounds (hexyl acetate, 2-hexyl-5-methyltetrahydrofuran, methyl hexanoate, butyl 2-methyl-2-propenoate, allo-ocimene, linalool, linalool oxide (isomer), \(\beta\)-ocimene (isomer), 2,6-dimethyl-2,6-octadiene (isomer), hexyl formate, 3-octen-2-one, isoamyl acetate, methyl dodecanoate, \(\gamma\)-terpinene, and \(\alpha\)-terpinolene--Figure 5b) exhibited a correlation coefficient (\(r\)) higher than 0.7, suggesting a strong relationship between these variables. Figure 5 shows a PCA representation of the data considering these 15 compounds (7 terpenic compounds, 6 esters, 1 furan derivative, and 1 ketone (Figure 5a), as well as the coefficients associated (Figure 5b) and the graphical representation of the data for each compound (Figure 5c). The samples grouped together regardless the matrix, powder, and brew (Figure 5a). Thus, it seems that there was a relationship between the amount of these compounds in the powder and in the respective brew. The samples were also grouped in three major clusters: Blend Dec, Sup and intensity blends (1-3), and origin blends (4-6). The distribution in Figure 5a seems to follow the distribution observed in PC2 for all bre compounds (Figure 3b). From the 390 compounds organized in 17 chemical families, terpenic compounds and esters stand out, which are compounds associated with the coffee variety, explaining the association between these compounds and origin blends. Figure 5c highlights that the pattern for all terpenic compounds was similar, contrasting with the pattern for the remaining compounds. On the other hand, intensity blends (as well as Sup) were poorly separated, suggesting they were derived from similar coffee blends, although with different roasting degrees. Thus, these seven terpenic compounds, although usually in low abundance in coffee matrices, can be used to predict the aroma potential of the brew when analyzing the coffee powder.  
Figure 5: Biplot principal component analysis (PCA) of the 15 volatile compounds (**a**) with Pearson correlation > 0.7 and \(p\)-value < 0.0001 (Figure S4) (**b**), considering the values of GC \(\times\) GC peak areas for the 8 commercial coffee powders and the respective coffee brews. The correlations are displayed in graph representations (**c**), with all values represented by black dots (above) and showing the average \(\pm\) standard deviation of each sample (below). Letters “P” and “B” after the identification of each blend sample correspond to the powder and brew, respectively.
## 4 _Article_ Modeling Snow Depth and Snow Model
[36] used a stable isotope technique to analyze the influence of snow melt water on regional hydrological processes in the upper reaches of the Irtysh River Basin. [PERSON] et al. [37] relied on the Geomorphology-Based Ecohydrological Model (GBEHM) to simulate snowmelt processes of a river basin in the Altai Mountains of northwestern China. However, these studies mainly focused on small parts of the Irtysh River Basin, and there is a lack of research on the snow cover process in the whole basin. Based on the above background, this study used two sets of high-resolution meteorological forcing data sets as drivers to simulate the spatial-temporal change in snow cover in the Irtysh River Basin from 2000 to 2018 by using the Noah-MP model. The main objective of this study is to obtain the dynamic change process of snow cover in the Irtysh River Basin in recent decades. The rest of the paper is arranged as follows: The overview of the study area and the models, data and statistical methods used in this study are introduced in Section 2. In Section 3, the simulation results of the Noah-MP model were verified at a single site, and the parameterization scheme suitable for the study area was selected and the long time series snow cover process in the whole study area was simulated. In Section 4, we discuss the possible reasons for the simulation errors and the shortcomings of this study. The conclusions are presented in Section 5.
###### Abstract  
Acidification and heavy metal stress pose challenging threats to the terrestrial environment. This investigation endeavors to scrutinize the combined effects of vermicompost and steel slag, either singularly or in concert with Ryegrass (_Lolium perenne_ L.), on the remediation of acidic soil resulting from sulfide copper mining. The findings illuminate substantial ameliorations in soil attributes. The application of these amendments precipitates an elevation in soil pH of 1.39-3.08, an augmentation in organic matter of 4.05-8.65, a concomitant reduction in total Cu content of 43.2-44.7%, and a marked mitigation in Cu bioavailability of 64.2-80.3%. The pronounced reduction in soil Cu bioavailability within the steel slag treatment group (L2) is noteworthy. Characterization analyses of vermicompost and steel slag further elucidate their propensity for sequestering Cu2+ ions in the soil matrix. Concerning botanical analysis, the vermicompost treatment group (L1) significantly enhances soil fertility, culminating in the accumulation of 208.35 mg kg\({}^{-1}\) of Cu in \(L\)_perenne_ stems and 1412.05 mg kg\({}^{-1}\) in the roots. Additionally, the introduction of vermicompost and steel slag enriches soil OTU (Operational Taxonomic Units) quantity, thereby augmenting soil bacterial community diversity. Particularly noteworthy is the substantial augmentation observed in OTU quantities for the vermicompost treatment group (L1) and the combined vermicompost with steel slag treatment group (L3), exhibiting increments of 126.04% and 119.53% in comparison to the control (CK). In summation, the application of vermicompost and steel slag efficaciously diminishes the bioavailability of Cu in the soil, augments Cu accumulation in \(L\)_perenne_, induces shifts in the soil microbial community structure, and amplifies soil bacterial diversity. Crucially, the concomitant application of vermicompost and steel slag emerges as a holistic and promising strategy for the remediation of sulfide copper mining acidic soil.  
vermicompost; steel slag; copper; soil remediation; bacterial diversity +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effects of \(n\)  
The effects of variations in \(n\) on the viscosity distribution \(\mu(y)\) across the porous channel are illustrated in Figure 2 for the values of the parameters identified in Table 1 (Reference Case). Figure 2 shows that at the upper channel wall, \(y=1\), and viscosity is independent of \(n\). This can be seen from Equation (29), where at the upper wall, \(p=p_{0}\); hence, \(\mu(1)=f(p_{0})=\mu_{0}=1\) for all values of \(n\).  
At the lower channel bounding wall, \(y=0\); Equation (22) gives the pressure \(p(0)=p_{0}+cos45^{\circ}=1+\frac{1}{\sqrt{2}}\). The quantity \(\frac{p(0)}{p_{0}}=1+\frac{1}{\sqrt{2}}\) results in increasing \(\mu(y)\) with increasing \(n\), as can be seen from Equation (29). This translates into increasing \(\mu(0)\) and an associated nonlinear increase in the viscosity profiles across the channel with increasing \(n\).  
The results from the variation of \(n\) can be found in Figure 7 for \(n=-1,\ 0,\ 1,\ 2,\ \text{and}\ 3\), while all other parameters take on the values identified in the Reference Case given in Table 1. For the case of \(n=0\), \(\left(\frac{d\mu}{dy}\right)=0\), so the resulting flow is simply that of a constant viscosity fluid and corresponding symmetric velocity, Darcy friction, and \(\mu\left(\frac{d^{2}u}{dy^{2}}\right)\) profiles.  
For the cases of \(n>0\), \(\mu\) increases with depth, thereby producing a reduction in the velocity in the lower part of the channel. In the extreme case of \(n=3\), a significant region of inflection is present in the velocity profile with a corresponding sign reversal of the viscous shear term \(\mu\Big{(}\frac{d\mu}{dy^{2}}\Big{)}\). Furthermore, the dramatic variation of the viscosity is also seen to produce significant variation in \(\Big{(}\frac{d\mu}{dy}\Big{)}\Big{(}\frac{d\mu}{dy}\Big{)}\) across the domain.  
For the case of \(n=-1\), \(\left(\frac{du}{dy}\right)>0\), meaning that, unlike all of the other flows considered in this work, the viscosity decreases with depth. The reduction in viscosity with depth results in less resistance to flow in the lower portion of the channel; therefore, higher velocities can be observed there.