esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
There are various methods for measuring \(k_{L}\) in general [8; 20; 21] allowing mass transfer to be measured as a complex phenomenon; however, the measurement of individual components, namely the interfacial area \(a\), is of particular interest, since it allows determination of the factors that specifically affect the interfacial surface known as the most important component of the mass transfer process when aiming to achieve high efficiency of technological processes based on the mass transfer. Meanwhile the direct measurement of the interfacial surface is a well-known challenge [22]. Various bubble size measurement tools have been developed and are in use both in research and industry to provide the determination of the gas bubble sizes, e.g., _the Mettler Toledo ParticleViewTM Measurement probe (PVM)_ or _The Analyzer EasyViewer 100 System_, as well as _The Anglo Platinum Bubble Sizer (APBS)_ probe manufactured by Stone Three, comparative analysis for which was performed by [23]. However, these methodsand tools have limitations that significantly narrow the possibility of using them for project tasks. Thus, PVM has a microfield of view of \(1300\times 890\)\(\mu\) and known to be ineffective at high flow rates such as at 1 m/s and over, while APBS requires sampling which significantly complicates the measurement procedure and might lead to data loss. We also note that, in addition to pure fundamental research interest, the task of assessing the mass transfer coefficient in bioreactors using the latest modern methods is also of high practical importance, since the cost of the final products directly depends on exactly the efficiency of bioreactor operation [24; 25; 26]: the cost of electricity can reach up to 25% of bioprotein total cost, while the mass transfer coefficient \(k_{L}\) determines the maximum achievable concentration of microorganisms, set by the limit of dissolved gases, and therefore determines the productivity of the process.
## 4 Dynamic Modeling of the Vibration Isolator
### Analysis of Transmissibility
The damping is a highly-sensitive parameter that consumes much energy in the resonance region, so we can find in the figure that displacement transmissibility changes drastically with a slight change of the frequency ratio, and the dynamic characteristics exhibit good properties. Therefore, the performance of the nonlinear vibration isolation system is not only related to the damping ratio, but also to the input excitation amplitude. Figure 8 demonstrates the responses under different input disturbance amplitudes at a set \(k\) value of 200 N/m and nearly identical maximum transmissibility. Figure 7: Transmissibility of vibration isolation. Figure 8 demonstrates that transmissibility is affected by the input harmonic disturbance amplitude, such that a higher input amplitude is associated with a higher down-jump transmissibility frequency. At \(\Theta=0.26\), the down-jump frequency exceeds the linear natural frequency of the system (\(\Omega=1\)), and the isolator performs relatively poorly. However, if the input disturbance amplitude is small (e.g., \(\Theta=0.13\) or \(0.05\)), the down-jump frequency is less than the linear natural frequency. In other words, the capacity of this vibration isolator exceeds that of a linear vibration isolator at a low-amplitude vibration. However, if the input disturbance frequency exceeds \(\sqrt{2}\) at an amplitude of \(\Theta=0.26\), the transmissibility is approximately \(-11\) dB, and the linear isolator would yield a better performance. Regarding the entire range of vibration isolation, the capacity of this isolator is superior at low input disturbance amplitudes.
## 3 Method
### Folding-Based Decoder  
We use the \"codeword\" output from the DGCNN-based encoder and a 2D grid as input to our decoder. A folding-based decoder is then utilized to reconstruct input \"codeword\" with a 2D grid to 3D point clouds by two successive folding operations.  
The folding-based decoder in our AE network is adopted from FoldingNet's decoder that contains two successive folding operations. The first one folds the 2D manifold into 3D space, and the second one operates inside the 3D space directly. As shown in Figure 1, we modified the decoder of FoldingNet to make it usable with different sizes of input \"codeword\" \(\theta\) (512-dimensional and 1024-dimensional) instead of a fixed-size 512-dimensional representation in FoldingNet. Before feeding the \"codeword\" into the folding-based decoder, we replicate the \"codeword\" \(\theta\ m\) times and concatenate the replicated \"codeword\" \((m,\ C_{out})\) matrix with an \((m,\ 2)\) matrix, which contains the \(m\) grid points (\(U\)) on a square centered at the origin. As each row of \(U\) is a 2D grid point, we define the \(i\)-th row of \(U\) as \(u_{i}\). Thus, the \(i\)-th row of the input matrix to the folding operation is \([u_{i},C]\) after above concatenation, where \(C=C_{out}+2\). The following two folding operations essentially form a universal 2D-to-3D mapping by two successive MLPs. The MLPs are applied in parallel to each row of the input matrix. We denote the \(i\)-th row of the output matrix as \(f([u_{i},C])\), where \(f\) is approximated by the MLPs, which can be tuned by the input \"codeword\" and learn a \"force\" to reconstruct the input into arbitrary point-cloud surfaces. During the training process, as shown in Equation (4), we use Chamfer distance [57] as our reconstruction loss \(\mathcal{L}_{rec}\), which measures the similarity of the reconstructed point cloud and the input point cloud:  
\[\mathcal{L}_{rec}(x,y)=\sum_{x_{i}\in x}\min_{y_{i}\in y}\lVert x_{i}-y_{i} \rVert_{2}^{2}+\sum_{y_{i}\in y}\min_{x_{i}\in x}\lVert x_{i}-y_{i}\rVert_{2}^ {2} \tag{4}\]  
With the graph-based encoder and folding-based decoder, we learn a set of powerful and separable features and continue to the downstream semantic segmentation task (see next Section 3.3).
## 5 Experimental Results and Discussion
### Evaluation Criteria  
The pixel accuracy, relative limit measurement accuracy, and mean intersection over the union were utilized as objective criteria to evaluate the quality of image extraction [40].  
The pixel accuracy rate \(PA\) was used to calculate the ratio of the number of correctly segmented pixels to the number of pixels in the image:  
\[PA=\frac{\sum_{i=1}^{n}n_{ii}}{\sum_{i=1}^{n}t_{i}}\times 100\% \tag{15}\]  
where \(t_{i}\) is the number of pixels belonging to the division category \(i\) in the original picture, \(n_{ii}\) represents the number of pixels whose actual category is \(i\), and the prediction category is also \(i\).  
The relative limit measurement accuracy \(RLMA\) indicates the deviation value between the actual value of the segmented image and the true value of the foreground region:  
\[RLMA=\frac{\left|\alpha-\beta\right|}{\alpha}\times 100\% \tag{16}\]  
where \(\alpha\) is the actual number of pixels in the image to be segmented, and \(\beta\) is the number of pixels in the foreground region obtained by segmentation. The smaller the \(RLMA\), the better the segmentation effect.  
Mean intersection over union \(M\)Io\(U\) is used as the intersection ratio calculation of the segmentation result and the true value, which can reflect the accuracy and completeness of the segmentation result, and is the most commonly used evaluation index:  
\[M\)Io\(U=\frac{1}{n+1}\sum\
olimits_{i=0}^{n}\frac{n_{ii}}{t_{i}+\sum_{j=0}^{n}n_{ji}-n _{ii}}\times 100\% \tag{17}\]  
where \(n_{ji}\) denotes the number of pixels whose actual category is \(j\), the prediction category is \(i\), and \(t_{i}\) is the number of pixels belonging to category \(i\).
# Strength Characteristics of Iron Tailings Blended Soil as a Road Base Material
## 3 Results and Discussion
### Microstructures Affected by Iron Tailing Sand Content  
In order to explore the microstructural change of ITBS with different sand and soil ratio, based on the optimal mix proportion of ITBS, adding 5% cement and 2% curing agent, ITBS with different proportions of 30% ITS + 70% soil, 50% ITS + 50% soil, 70% ITS + 30% soil were prepared respectively. After standard curing for 28 days, samples were taken for SEM analysis, and the test results are shown in Figure 11. It can be seen from the figure when the ratio of ITS to soil ratio is 30%:70%, the proportion of soil is larger than that of ITS, which represents the wholesomeness of soil particles, so the basic unit is mainly formed by  
Figure 10: SEM images of ITBS with (**a**) 50% pure ITS; (**b**) 50% ITS and 2% curing agent; (**c**) ITS and 5% cement; and (**d**) ITS and 5% cement and 2% curing agent.  
the aggregation and stacking of soil particles. The microstructure pores are bigger and have lesser interconnection. The cohesive force between aggregates is also minimal. Therefore, the strength of ITBS with ITS to soil ratio of 30%:70% is lower. When the ITS to soil ratio is 50%:50%, the basic unit is formed by the stacking of soil particles and ITS particles, with lesser number of pores on the surface and unconnected micro cracks between them. The cohesive force between aggregates is strong. Therefore, the strength of ITBS with ITS to soil ratio of 50%:50% is higher. When the ITS to soil ratio is 70%:30%, the proportion of ITS is larger than that of soil and the whole is characterized by \"sanding\", so the basic unit is mainly composed of ITS particles. Because the pores between sand particles are not filled with a sufficient amount of soil particles, the cracks between the particles are wider and the compactness is reduced, which reduces the strength.  
It can be summarized that due to the difference of particle diameter between ITS and soil, different sand to soil ratio has a significant influence on the pore structure of mixed soil. When one of these two substances has a higher content than the other it will be difficult to ensure the close combination between soil particles and ITS particles. When the contents of the two components are equal, the full connection between sand and soil particles will produce higher strength.
## 4 Comparison with Previous Works  
Table 2 shows a comparison between the presented MIMO antenna and previous studies. The presented MIMO antenna offers a wider bandwidth, lower mutual coupling, and lower ECC values. Despite the smaller size of the antenna presented in [10], it exhibits lower gain, a narrower bandwidth, and lower isolation compared to the proposed antenna. Moreover, the antenna designs provided in [11; 14] demonstrate wide bandwidths, but with lower gain and isolation. Therefore, as mentioned earlier, this antenna provides superior bandwidth and gain, demonstrating better performance and making it the perfect choice for deployment in WiMAX and 5G networks.
## 3 Results and Discussion  
### The Influence of Temperature  
The critical simulation parameters are shown in Table 1.  
When the temperature of the structure changes by small amounts, COMSOL can calculate the thermoelastic deformation of the piston and the bushing. As the bushing thickness is slight, assume that the bushing temperature is the same as the film. Energy dissipation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Piston outer diameter (mm) [2] & \(D_{p}\) & 20.700 \\ Bushing inner diameter (mm) [2] & \(D_{b}\) & 20.724 \\ Piston height (mm) & \(L_{p}\) & 54 \\ Swash plate inclination (\({}^{\circ}\)) & \(\beta\) & 17 \\ Hydraulic fluid dynamic viscosity at & \(\mu\) & 32 \\
40 \({}^{\circ}\)C (cst) & \(\mu_{ext}\) & 0.02 \\ Friction coefficient & \(\mu_{ext}\) & 0.02 \\ Minimum film height (\(\mu\)m) & \(h_{min}\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 10: Calculation process.  
occurs due to friction of the viscous fluid in the gap, making the film temperature higher than the hydraulic oil in the high/low-pressure chamber. The temperature distribution can be obtained by solving the energy equation, and the average temperature can be calculated.  
The next step is to calculate the thermoelastic deformation of the piston and the cylinder block based on the temperature boundary conditions, as shown in Table 2. The pressure of the condition 1 is 8.1 MPa, the pressure of the condition 2 is 12.1 MPa, the pressure of condition 3 is 15.1 MPa, and the angular speed of the three conditions is 1000 rpm [2]. It indicates that, as the temperature increases, the gap between the piston and the bushing does not change significantly, as the piston and the bushing expand simultaneously. However, the dynamic viscosity of the oil changes significantly compared with the viscosity at 40 \({}^{\circ}\)C.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.16 TMVOC  
TMVOC is a FORTRAN model developed for simulating and analysing VOC flows [731]. This tool can simulate the flow from one to three dimensions in porous or fissured media with different saturation. It can therefore be used to study the fate of NAPLs in the vadose zone and in groundwater. In addition, the model can simulate the formation of oil lenses at the groundwater surface, the dissolution and transport of VOCs into the groundwater, the evaporation and migration of VOCs in the interstitial air of the unsaturated zone, and the reversible sorption of VOCs on the rock matrix. The main application of TMVOC could be NAPL outfall sites and remediation alternatives in the va-dose zone and below the water table. It has been tested and the results have been published by Batistelli [732], Erning et al. [733], MacKenzie [734], and Guo et al. [55]. The latter study was related to the assessment and prediction of the effects of bioremediation on chlorinated hydrocarbons in a very large fractured karst aquifer in Zibo City, China. In the modelling study, the effects of hydrocarbon compounds on the chemical status of groundwater were reproduced using ions: NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), HCO\({}_{3}\)\({}^{-}\), Cl\({}^{-}\), and an isotope \(\delta^{13}\)C(DIC).
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### Feature Extraction
The results show that the classifier with the combined features perform better than with only geometric features or only texture features [12]. Mel-Frequency Cepstral Coefficients (MFCCs) as low-dimensional, fixed dimension feature vectors are very effective in speech processing [42]. They are already successfully tested as s damage sensitive features for mechanical systems [43] and applied in damage detection for structural health monitoring (SHM) [44]. [PERSON] used them with the polynomial features for weld defect identification. These features extracted from the 1D lexicographically ordered signals or their power density spectra are suitable for defect detection in the existence of noise. [11; 45]. These features are defined on the gray-lever curve of line image or 2D images. In general, the appropriate feature is decided by the inspector or researcher who judges according to his experience and knowledge. Thus, different inspectors are inclined to diverse types of features. It is difficult to say what kind of feature is best.
## 3 Results
### cMD: EbPA/Ellagic Acid Complex (RMSD and Total Interaction Energy)  
The target/compound interaction was dissected by analyzing structural and energy features performing cMDs for each complex and free state EbpA. To define the stability of the binding pose of compounds within the target binding pocket, the distance between the center of mass of the ligand and the center of mass of the binding pocket was evaluated. Ellagic acid showed a stable distance trend, suggesting high binding pose stability; surprisingly, brevifolia left the target binding pocket in the first frames of the MD run (Supplementary Materials Figure S1), likely due to the different interactions of the starting docking pose; thus, the EbpA/brevifolia complex was not considered for further MD analysis.  
The target backbone structural integrity was assessed by evaluating the RMSD values for the EbpA/ellagic acid complex and free state EbpA. RMSD analyses suggested good structural stability, showing a stable RMSD trend of 0.15 A and 0.2 A for the complex and the free state, respectively. However, the EbpA/ellagic acid complex RMSD trend showed a higher RMSD value (moreover in the last frames of the MD run) than free state EbpA, exhibiting a significant increase of RMSD value until 0.25 A. The binding pose of the compound within the target binding pocket did not significantly show fluctuations during the MD run, providing a very stable RMSD range between 0.03 A and 0.05 A, confirming the high stability and reliability of the selected starting docking pose (Figure 2). To strengthen the impact of the docking and cMD simulation, the target/compound interaction energy was evaluated. From energy analyses, we noticed that our compound was able to spontaneously bind on the target with a total interaction energy of \(-\)38.1 \(\pm\) 3.8 kcal/mol (Figure S2).  
Figure 1: Overview of the docked pose of ellagic acid and brevifolia acid in complex with EbpA. EbpA3D structure is depicted in transparency orange cartoon/surface, while the magnesium ion was reported as a green sphere. Interaction network of (**A**) brevifolia (in gray) acid and (**B**) ellagic acid (in purple) in complex with EbpA after the docking simulation. The binding residues forming hydrophobic interactions (grey dashed lines), hydrogen bonds (blue dashed lines), and salt bridges (yellow dashed lines) are shown as cyan, green, and pink sticks, respectively.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 2 Materials and Methods
### Literature Search
#### 2.1.2 Eligibility Criteria  
Studies were included if they met the following criteria: (1) focused on medical image quality enhancement--defined as increased spatial resolution, contrast enhancement, denoising, or enhancement of structure boundaries; (2) used post-image processing techniques on B-mode ultrasound images, i.e., image-to-image methods; (3) proposed a deep learning algorithm; (4) proposed algorithm was specifically developed for ultrasound images; (5) full-text original article was written in English.  
Exclusion criteria included: (1) other quality enhancement methods like restoration and inpainting or different study aims like domain conversion and 3D reconstruction; (2) hardware changes were required; (3) RF ultrasound data were used as input; (4) research on (microbubble) contrast-enhanced ultrasound, ultrasound computed tomography, elastography, color Doppler ultrasound, quantitative ultrasound, and high-intensity focused ultrasound; (5) non-journal publications (e.g., reviews, comments, dissertations, newspapers, and books); (6) non-accessible full-text publications.
# Design Study of Steel Fibre Reinforced Concrete Shaft Lining for Swelling Ground in Toronto, Canada
## 4 Structural Design of Lining
### Theoretical Approaches for the Alternative Design Using SFRC
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Properties**} & **RC** & **SFRC** \\ \cline{2-3}  & **Steel Bar** & **FF3** \\  & **(20** & 0.75 \(\times\) 50) \\  & **mm@300)** & 0.75 \(\times\) 50) \\ \hline Base of the cross-section (mm) & 1000 & 1000 \\ Thickness of cross-section (mm) & 400 & 400 \\ Tensile strength of reinforcement bar (MPa) & 414 & - \\ Area of reinforcement bar (mm\({}^{2}\)) & 1047.20 & - \\ Depth of flexural reinforcement bar position (mm) & 325 & - \\ \hline Ductility of SFRC (\%) & - & 102 \\ Concrete first crack strength for 35 MPa-grade (MPa) & - & 4.70 \\ Equivalent resistance of SFRC by flexural test ASTM C1609 (MPa) & - & 4.79 \\ \hline Moment resistance for cross-section (MNmm) & 126.8 & 127.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Flexural comparison of conventional reinforced concrete (RC) and SFRC. Based on the theoretical and experimental approaches, the final proposed concrete solution using steel fibres is the 35 MPa-grade concrete, reinforced with 40 kg/m\({}^{3}\) of Wirand(r) FF3 steel fibres, conforming to ASTM A820 and ASTM C1116, as depicted in Figure 5.
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 5 Performance Evaluation of Underwater Network
In Figure 9, we report the energy for a given data traffic for the single hop acoustic modem. Clearly, the required power is much larger with respect to the optical modem but does not depend on the water quality. To compare the optical and acoustical modems, we report in Table 4 the average bit for Joule required for different optical communication links and for the acoustic communication. For each of the two technologies, we considered the typical value of the data rates and we calculated the total energy consumed normalized with respect to the data transmitted over a second. We considered different network topologies according to the values of \(K\); for each topology, we evaluated the average number of hops required for a communication. Acoustic communications emerge to be far more energy hungry than the optical one, even considering that for optical communication the shorter communication range implies the needs of multi-hop transmissions also in scenarios where a single acoustic data transmission suffices to cover the required communication range (up to \(\approx\)100 m). **Energy for broadcast transmissions**: Figure 10 shows the performance in terms of total consumed energy for a network where all the nodes need to communicate periodically with all the remaining nodes using optical communication link. In particular, we considered a linear topology of 100 m \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Type** & **Max Distance** & **Data Rate** & **Avg Bit/Joule** & **Avg n Hops** \\ \hline Acoustic & 100 m & 100 kbit/s & \(0.50\times 10^{4}\) & 1 \\ Optical (K = 0.623) & 5.9 m & 2 Mbit/s & \(0.38\times 10^{6}\) & 9.0 \\ Optical (K = 0.419) & 8.3 m & 2 Mbit/s & \(0.71\times 10^{6}\) & 4.8 \\ Optical (K = 0.212) & 14.6 m & 2 Mbit/s & \(1.20\times 10^{6}\) & 3.0 \\ Optical (K = 0.037) & 58.7 m & 2 Mbit/s & \(0.05\times 10^{5}\) & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average bit/Joule efficiency. Figure 8: Energy consumption in the case of communication among randomly selected peers on a linear topology. with the same parameters as in the previous case. Broadcast was implemented with multiple optical point-to-point communication where the range of the optical communication varies according to the quality of the water expressed by the coefficient \(K\). Broadcast communications are indeed used in many different underwater applications such as cooperative SLAM algorithm [18; 19] and cooperative discovery algorithm [20], where the nodes must share data (e.g., covariance matrix) to improve their own estimation and thus the achieved performance.