esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Results and Analysis
### Characteristics of Fluid Velocity and Pressure Changes Under Different Circumferential Sections  
Figure 6 shows the fluid velocity distribution in the vortex pump channel at different circumferential sections when the flow rate of the vortex pump is 0.6\(\mathrm{Q_{d}}\) and the rotational speed is 2700 r/min. In which, the section starts from the positive direction of the \(Y\)-axis and rotates counterclockwise around the \(Z\)-axis, taking sections at positions 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), and 315\({}^{\circ}\) in sequence. It can be seen from the figure that under the rotation of the impeller, there is a symmetrical fluid velocity distribution wake in the area outside the radius of the impeller at each section, and the fluid velocity gradually increases along the flow velocity. The fluid velocity is higher near the impeller and along the outer edge of the flow channel. At the section from 45\({}^{\circ}\) to 315\({}^{\circ}\), the outermost velocity of the channel is 0 m/s, which is also due to the fact that the wall condition is set to no slip during the numerical calculation. At the 180\({}^{\circ}\) section, the fluid velocity and pressure are both relatively high due to the rotation effect of the impeller and the gravity of the fluid itself. At the 315\({}^{\circ}\) section, the fluid velocity is maximum at the outlet due to the fluid accumulating energy and flowing towards the outlet during the continuous rotation of the impeller.  
Figure 7 shows the pressure distribution in the circumferential section of the vortex pump, and the section position corresponds to Figure 6. As shown in the figure, the wake flow is formed on the circumferential section due to the rotational force of the impeller on the fluid, resulting in the pressure distribution of the fluid internal to the vortex pump being basically similar in different sections, and the pressure distribution of the outer flow channel showing symmetry. The pressure on the fluid along the flow direction gradually increases under the rotation of the impeller, with the minimum pressure at the 45\({}^{\circ}\) section and the maximum pressure at the 315\({}^{\circ}\) section. This is because the fluid enters the flow channel from the inlet and flows along the channel to the outlet under the rotation of the impeller. The fluid in the blade is under the influence of the periodic rotation of the continuous accumulation of energy gained at the outlet to reach the maximum pressure so that the vortex pump can obtain the high head.  
Figure 6: Distribution of circumferential sectional velocity of the vortex pump.
## 4 Experimental Validation
### Trajectory Planning  
A trajectory planning strategy for the SG flexible tool was developed in Powermill, a commercial CAM software, based on the processing properties of the SG flexible tool in this study. This strategy included linear projection finish machining, reference line embedding, and embedded reference line finish machining, as shown in Figure 11. According to the optimum trajectory space for surfaces of different curvatures in Figure 11, the linear projection finish machining strategy was used to allow the tool tip to traverse  
Figure 11: Theoretical and experimental material removal profiles for surfaces of different curvature.  
the 3D model surface. The generated trajectory was then inserted into a newly created reference line and the reference line was embedded into the 3D model surface using the nearest-point method.  
The embedded reference line finish machining strategy was applied to plan and design the grinding and polishing trajectory for the SG flexible tool. A five-axis machining strategy was used, with the workpiece rotating. The tool axis and normal surface contact had a front angle of \(-15^{\circ}\), as shown in Figure 12b, and a side angle of \(0^{\circ}\), indicating that the tool axis is parallel to the contact normal, as shown in Figure 12c. The required polishing trajectory was then generated for subsequent grinding and polishing.
# Atomic Layer Deposition of Inorganic Thin Films on 3D Polymer Nanonetworks
## 4 Application of 3D Inorganic Nanonetworks
### Highly Porous Hollow Inorganics
The lowering of the thermal conductivity comes from the enhancement of the phonon boundary scattering, while maintaining the electrical conductivity through the continuous nanonetwork structure. Although bulk pure ZnO cannot be used as a thermoelectric device, owing to its negligible ZT at room temperature, [PERSON] et al. succeeded in fabricating the nanostructured ZnO-based thermoelectric device, producing 60 mV at a temperature difference of about 40 K.  
Optical and mechanical metamaterials are receiving great attention in academia today. Metamaterials exhibit superior or new properties over conventional materials through artificially ordered structures of various scales. Several hollow 3D nanonetwork structures are optimized to implement these metamaterials. [PERSON] et al. reported a hollow 3D ceramic nanonetwork designed to stretch a beam rather than bend it [16]. The key parameter of nanonetwork design is the ratio of wall thickness to beam radius, and if this ratio is small enough, the beam does not break by compression. Based on this structural design principle, they succeeded in producing a resilient Al\({}_{2}\)O\({}_{3}\)-based metamaterial with a hollow 3D nanonetwork structure through the combination of DLW and ALD. While common ceramics are very brittle and fragile, the developed mechanical metamaterial surprisingly recovered its original shape after more than 50% compression. Similarly, a mechanical metamaterial made of TiN converted from a polymer template, designed and produced through DLW, was also reported (Figure 7g) [15]. Most recently, to produce more practical-sized mechanical metamaterials, PnP optimized for large-area 3D nanofabrication was combined with this strategy to develop an inch-scale Al\({}_{2}\)O\({}_{3}\)-based mechanical metamaterial [25]. In addition to these unnatural mechanical properties, hollow 3D nanonetwork structures can also be applied to realize exceptional optical properties. For example, when forming a hollow 3D nanonetwork with an ultrathin (~5 nm) Al\({}_{2}\)O\({}_{3}\) film through polymer template-assisted ALD and subsequent template removal, the refractive index of the material is theoretically lowered to 1.025, owing to the extremely small volume ratio (Figure 7h,i) [28; 29]. This means that the resulting ceramic film is unnaturally transparent. By combining the above-mentioned novel mechanical and optical properties resulting from hollow 3D nanonetwork materials, a new level of multifunctional optical films can be achieved.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 2 Related Work
### Introduction of Indicator Matrix  
The indicator matrix \(S=[S_{1},S_{2},\ldots,S_{k}]\in R^{d\times k}\) is a binary matrix, where \(S_{i}\) denotes a column vector with only one element value equal to 1. To understand the indicator matrix \(S\) for feature selection more clearly, we establish an example. First, we assume that the data matrix contains four samples, and that each sample has five features; the data matrix can be described as follows:  
\[X=\begin{array}{ccccc}&f_{1}&f_{2}&f_{3}&f_{4}&f_{5}\\ x_{1}&\left(\begin{array}{ccccc}0.9&0.1&0.7&1.0&0.2\\ 0.7&0.4&0.5&0.3&0.4\\ 0.3&0.8&0.4&0.6&0.1\\ x_{4}&\left(\begin{array}{ccccc}1.0&0.6&0.6&0.8&1.0\\ 0.6&0.6&0.8&1.0\\ \end{array}\right)\end{array}\right)\end{array}\right.. \tag{4}\]  
Then, supposing that three features are selected (i.e.,\(f_{1},f_{4}\) and \(f_{5}\)), we can obtain the selected feature subset as follows:  
\[X^{\prime}=\begin{array}{ccccc}&f_{1}&f_{4}&f_{5}\\ x_{1}&\left(\begin{array}{ccccc}0.9&1.0&0.2\\ 0.7&0.3&0.4\\ 0.3&0.6&0.1\\ 1.0&0.8&1.0\\ \end{array}\right)\end{array}\right.. \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Symbol** & **Description** \\ \hline \(Z\in R^{n\times d}\) & Sample matrix & \(d\) & Dimension of sample \\ \(S\in R^{d\times k}\) & Indicator matrix & \(n\) & Number of samples \\ \(A\in R^{k\times d}\) & Low-dimensional & \(k\) & Number of selected \\ \(W\in R^{n\times n}\) & Weight matrix & \(||\cdot||_{1}\) & \(L_{1}\)-norm \\ \(I\in R^{k\times k}\) & Identity matrix & \(||\cdot||_{2}\) & \(L_{2}\)-norm \\ \(U\in R^{n\times n}\) & Diagonal matrix & \(||\cdot||_{2,1}\) & \(L_{21}\)-norm \\ \(C\in R^{d\times d}\) & Diagonal matrix & \(tr(\cdot)\) & Matrix trace \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of commonly used symbols.  
Meanwhile, the indicator matrix \(S\) can be assumed as follows:  
\[S=\begin{array}{c}1\\ 2\\ 3\\ 4\\ 5\end{array}\qquad\left(\begin{array}{ccc}1&4&5\\ 1&0&0\\ 0&0&0\\ 0&0&0\\ 0&1&0\\ 0&0&1\end{array}\right). \tag{6}\]  
Thus, we can obtain  
\[\left(\begin{array}{cccc}0.9&0.1&0.7&1.0&0.2\\ 0.7&0.4&0.5&0.3&0.4\\ 0.3&0.8&0.4&0.6&0.1\\ 1.0&0.6&0.6&0.8&1.0\end{array}\right)\left(\begin{array}{ccc}1&0&0\\ 0&0&0\\ 0&0&0\\ 0&1&0\\ 0&0&1\end{array}\right)=\left(\begin{array}{ccc}0.9&1.0&0.2\\ 0.7&0.3&0.4\\ 0.3&0.6&0.1\\ 1.0&0.8&1.0\end{array}\right). \tag{7}\]  
Equation (7) can describe the process of feature selection using the indicator matrix \(S\). Finally, we can also obtain \(S^{T}S=I_{3\times 3}\) as follows:  
\[S^{T}\quad S = I\] \[\left(\begin{array}{cccc}1&0&0&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right)\left(\begin{array}{ccc}1&0&0\\ 0&0&0\\ 0&0&0\\ 0&1&0\\ 0&0&1\end{array}\right)=\left(\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right). \tag{8}\]
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 2 Preliminary and Security Requirements
### Security Requirements
#### 2.2.5 Forward and Backward Secrecy  
If a malicious attacker compromises the session key which is established between the personal mobile device and the hospital medical device, he/she may use the compromised session key for future malicious communications or to obtain previously transmitted messages. A blockchain-based secure inter-hospital EMR sharing system thus ought to achieve forward and backward secrecy.
## 4 Results and Interpretation
### Pore-Scale Wettability Characteristics  
It has been demonstrated that the wetting condition of sandstone samples can be determined using ESEM observation/imaging of the state of water condensation on pore walls: film-shaped water indicates strong water-wet conditions, low-dome-shaped water indicates weak water-wet conditions, and the absence of condensed water or dome  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sample** & **Quartz** & **Feldspar** & **Illite** & **Chlorite** & **Kaolinite** & **Calcite** & **Dolomite** & **Muscovite** & **Biotite** & **Hydrocarbon** & **Others \({}^{*}\)** \\ \hline C1 & 33.86 & 31.78 & 11.05 & 1.64 & / & 3.19 & / & 0.78 & 4.08 & 5.56 & 8.06 \\ C2 & 30.55 & 25.56 & 4.93 & 2.39 & 0.43 & 3.87 & 0.21 & 0.26 & 0.04 & 22.85 & 8.9 \\ C3 & 39.37 & 40.22 & 0.29 & 2.26 & / & 1.35 & / & 0.75 & 0.04 & 13.51 & 2.2 \\ C4 & 34.48 & 28.77 & 0.12 & 7.59 & 0.03 & 2 & / & 0.83 & 8.13 & 10.76 & 7.29 \\ \hline \hline \end{tabular} \({}^{*}\)Some components are not listed here due to their low content, including Apatite, Rutle, Barite, Fe-oxide, Axinite, Halite, Titanite, Pyrite, and pore.  
\end{table}
Table 2: Estimated mineral and hydrocarbon contents in the tight sandstone samples investigated.  
Figure 3: High-resolution, large-field photo mosaic of backscattered electron images and mineral composition maps of tight sandstone. Images **(a,b)** from Sample C1; images **(c,d)** from Sample C2; images **(e,f)** from Sample C3; images **(g,h)** from Sample C4.  
or spherical-shaped water indicates oil-wet conditions [34]. The condensation behavior of water on mineral surface/pore walls can be significantly influenced by temperature and pressure. After multiple experimental adjustments, optimal ESEM chamber conditions for determining the wetting conditions of the Es4x tight sandstone are 2 \({}^{\circ}\)C and 6.6-6.9 Torr. The wettability measurement of the four samples was conducted under this P-T condition. Sample C1 is predominantly covered with hemispherical-shaped water droplets of different sizes, along with some spherical-shaped water droplets (Figure 4a-c), indicating weak oil-wet conditions. A significant amount of spherical-shaped water droplets are present in Sample C2 (Figure 4d-f), indicating a strong oil-wet condition. Sample C3 contains primarily low-dome-shaped water droplets, with some hemispherical-shaped water droplets (Figure 4g-i), indicating weak water-wet conditions. Sample C4 is covered completely by film-shaped water (Figure 4j-l), indicating a strong water-wet condition. The morphologies of the condensed water were found sometimes to vary within different regions of the same sample, suggesting fractional wettability characteristics at the micro-scale [35].
###### Abstract  
This research proposes a unified guidance and control framework for Autonomous Underwater Vehicles (AUVs) based on the task priority control approach, incorporating various behaviors such as path following, terrain following, obstacle avoidance, as well as homing and docking to stationary and moving stations. The integration of homing and docking maneuvers into the task priority framework is thus a novel contribution of this paper. This integration allows, for example, to execute homing maneuvers close to uneven seafloor or obstacles, ensuring the safety of the AUV, as safety tasks can be given the highest priority. Furthermore, another contribution shown in the paper is that the proposed approach tackles a wide range of scenarios without ad hoc solutions. Indeed, the proposed approach is well suited for both the emerging trend of resident AUVs, which stay underwater for a long period inside garage stations, exiting to perform inspection and maintenance missions and homing back to them, and for AUVs that are required to dock to moving stations such as surface vehicles, or towed docking stations. The proposed techniques are studied in a simulation setting, taking into account the rich number of aforementioned scenarios.  
autonomous underwater vehicles; homing; docking; task priority control
## 4 Experiments and Results
### Candidate Region Extraction Performance of RT-SPeeDet  
First, Table 2 shows the performance of the proposed multiple feature map-based candidate region extractor. The candidate region extractor extracted 4339 candidate regions from all film images in the test set, and approximately 96% of all pit defects were included. Recall is a performance index that finds defective samples, and is used as an important index to reduce the number of defective products during the manufacturing processes. In terms of recall, it can be seen that the proposed candidate region extraction algorithm RT-SPeeDet achieves an excellent performance. For the processing speed of each module, it took 60 ms to extract positive regions and 30 ms to extract candidate regions from positive regions. As a result, candidate region image patches can be extracted from high-resolution input images at a processing speed of 0.1 s or less.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.6. Variation of Active and Reactive Power Method  
By varying the injected inverter power and monitoring changes in voltage amplitude and frequency, islanding can be detected using this method. When the main grid is cut off, the generated active power by DG is absorbed by the load, and the voltage change must satisfy the following equation to achieve a complete balance between the DG active power and load:  
\[\mathrm{P}_{\mathrm{DG}}=\mathrm{P}_{\mathrm{Load}}=\mathrm{V}^{2}/\mathrm{R}, \tag{8}\]  
Therefore, islanding will be identified if the voltage change exceeds the threshold value. Similar to how the reactive power disturbance affects the frequency change, islanding is seen when the frequency surpasses the threshold value [22].  
This method's implementation is simple, and the NDZ is little. However, employing this approach can have an impact on the system's power quality and transient stability. In addition, the effectiveness of this method declines when the system consists of multiple inverters connected in parallel.
###### Abstract  
As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions.  
attention module; dual mutual feedback; lightweight; medical image super-resolution; unity feedback Article
## 2 Kinematics Analysis
### Forward Kinematic Problem  
In practical terms, it is hard to directly measure the motion posture of the moving platform in six degrees of freedom. Therefore, an efficient and high-accuracy forward solution algorithm is of great significance for the real-time estimation and detection of motion platform posture, which is critical for high precision positioning.  
Contrary to the inverse kinematic problem, the forward kinematics focuses on finding the unknown posture \((x,y,z,\alpha,\beta,\gamma)\) from the given six leg length \((l_{1},l_{2},l_{3},l_{4},l_{5},l_{6})\). The multivariate nonlinear equations which describe the forward kinematics are as follows.  
\[f_{i}(x,y,z,\alpha,\beta,\gamma)=l_{i}-\sqrt{\left(t_{i}+RA_{i}-OB_{i}\right) ^{T}(t_{i}+RA_{i}-OB_{i})}=0 \tag{4}\]  
\[i=1,2,3\ldots 6\]  
These six multivariate nonlinear equations describe the forward kinematics of the SGP and the posture can be gotten by solving them which is also a complex problem.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline
**Varying Unsaturated Parameter** & **Soil Layer** & **Lower Layer** & **Upper Layer** \\ \hline \(\mid\)q/kc\(\mid\) & q = 1.15 \(\times\) 10\({}^{8}\) m/s & 0.7101 & 0.8194 \\  & q = \(-\)3.14 \(\times\) 10\({}^{8}\) m/s & 0.6934 & 0.7219 \\
1/\(\alpha\) & q = 1.15 \(\times\) 10\({}^{8}\) m/s & 0.7084 & 0.7781 \\  & q = \(-\)3.14 \(\times\) 10\({}^{8}\) m/s & 0.6418 & 0.6499 \\ n & q = 1.15 \(\times\) 10\({}^{8}\) m/s & 0.7491 & 0.8669 \\  & q = \(-\)3.14 \(\times\) 10\({}^{8}\) m/s & 0.4134 & 0.4375 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of stability coefficient calculations under varying unsaturated parameters. with opposite trends observed for the upper and lower soil layers under different seepage conditions. Specifically, under evaporation conditions, increasing \(\mid\)q/k\(\mid\) in the upper layer decreases FS, while increasing it in the lower layer increases FS. In contrast, under infiltration conditions, higher suction coefficients generally lead to higher FS. Further analysis of the influence of different unsaturated parameters, such as the air entry suction (\(1/\alpha\)) and the pore size distribution n, reveals more complex relationships with the safety factor. For example, changes in the pore size distribution n have a stronger correlation with the support force results compared to other parameters. The grey relational analysis (GRA) shows that the correlation between unsaturated parameters and tunnel stability is stronger under evaporation conditions than under infiltration, particularly for the upper soil layers. This indicates that the unsaturated effects in the upper soil layer have a more significant impact on tunnel stability, with the correlation being larger for evaporation than for infiltration. The findings emphasize the importance of accounting for unsaturated parameters, particularly the pore size distribution n, when designing tunnel support systems in inclined layered soils. The sensitivity of the system to changes in these parameters, especially under different seepage conditions, highlights the need for precise modeling and careful consideration of hydrological factors to ensure the safety and stability of tunnels in such environments. Validation, formal analysis, writing--original draft, methodology, H.W. ; investigation, supervision, Y.W. ; resources, project administration, J.Z. ; visualization, project administration, date curation, supervision, H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data analyzed are available from the corresponding author on reasonable request. The data are not publicly available due to reasons of privacy. The financial support provided by Changzhou Science and Technology Support Programme \"Study on KeyTechnologies for Prevention and Control of Surface Deformation Induced by Construction and Operation of Changzhou Metro Tunnel (CE20235048)\" is greatly acknowledged. The authors declare no conflicts of interest.