esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance under Light Conditions
#### 4.3.2 Effect of Photocatalysis on Fouling Performance  
In order to investigate the anti-fouling performance of the membrane modified by polydopamine-TiO\({}_{2}\) under light conditions, the membranes were tested in the transparent dead-end cell, and the results are shown in Figure 7.  
After 90 min of compaction by stirring the pure water, the composition (i.e., TiO\({}_{2}\)) of the membrane surfaces becomes stable. When these membranes make contact with the BSA solution, the fluxes drop immediately, and approach their equilibrium. The neat membrane, the polydopamine coated membrane, and the membrane modified by self-assembled TiO\({}_{2}\), retain around 85%, 88%, and 88% of their pure water fluxes, respectively, after 2 h of fouling experiment, and these membranes demonstrate similar flux trends to the ones in the dark conditions (Figures 5 and 7). This similar anti-fouling behaviour in the light and dark conditions indicates that the light has no significant influence on the anti-fouling performance of the aforementioned membranes.  
Unlike these membranes, the membrane modified by TiO\({}_{2}\) using polydopamine shows a different anti-fouling behaviour between the light and dark conditions. Other than reaching an equilibrium state in the dark conditions, the flux of the membrane modified by polydopamine-TiO\({}_{2}\) drops to 91%  
Figure 7: Relative flux of the unmodified ( neat) and modified membranes as a function of time during the filtration of BSA (0.5 g\(\cdot\)L\({}^{-1}\)) in light conditions.  
of the pure water flux first, and then slowly increases to around 95%. Since it is difficult to track the dynamic changes in the structures of the membrane surfaces, it can be assumed that this slight increase of the flux in the light conditions is probably either caused by degradation of the top layers as discussed in Section 4.3.1, or caused by the removal of the BSA layer from the surfaces.  
The holes on the surfaces of dopamine-modified TiO\({}_{2}\) are not capable of oxidizing water molecules to OH radicals, and the major part of the reactive oxygen species formed by visible light illumination in aqueous conditions, are superoxide anions [41]. As the illumination time increases, the superoxide anions accumulate on the membrane surface. These actions might have two consequences: (1) the superoxide anions degrade the top layer of the membrane; (2) these accumulated anions slowly increase the surface charge and the hydrophilicity of the membrane surface, which helps to loosen the attached BSA layer and also to avoid the BSA from attaching. The work suggests that the visible light illumination has an effect on the fouling performance of the membrane modified by this polydopamine-TiO\({}_{2}\). However, the real mechanisms still need to be further explored in the future.
# Influences of Different Factors and Sensitivity Analysis of Permeability of Gassy Coal
## 4 Influences of Gas Pressure on the Permeability of Gassy Coal
### Experimental Schemes
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Influencing Factor (A)** & **Influencing Factor (B)** & **Influencing Factor (C)** & **Permeability/md** \\  & **Axial Pressure/MPa** & **Confining Pressure/MPa** & **Gas Pressure/MPa** & **Permeability/md** \\ \hline
1 & 3 & 3 & 0.5 & 0.7388 \\
2 & 4 & 4 & 0.5 & 0.5873 \\
3 & 5 & 5 & 0.5 & 0.2674 \\
4 & 6 & 6 & 0.5 & 0.1917 \\
5 & 3 & 4 & 1 & 0.343 \\
6 & 4 & 3 & 1 & 0.4843 \\
7 & 5 & 6 & 1 & 0.2264 \\
8 & 6 & 5 & 1 & 0.255 \\
9 & 3 & 5 & 1.5 & 0.2701 \\
10 & 4 & 6 & 1.5 & 0.2204 \\
11 & 5 & 3 & 1.5 & 0.3126 \\
12 & 6 & 4 & 1.5 & 0.4803 \\
13 & 3 & 6 & 2 & 0.2272 \\
14 & 4 & 5 & 2 & 0.2696 \\
15 & 5 & 4 & 2 & 0.366 \\
16 & 6 & 3 & 2 & 0.6602 \\ \(k_{1}\) & 0.394775 & 0.548975 & 0.4463 & \\ \(k_{2}\) & 0.3904 & 0.44415 & 0.327175 & \\ \(k_{3}\) & 0.2931 & 0.265525 & 0.32085 & \\ \(k_{4}\) & 0.3968 & 0.216425 & 0.38075 & \\ Extreme value (R) & 0.1037 & 0.33255 & 0.12545 & \\ Ranking of & & & & \\ influencing factors & & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Permeability test results and order of various influencing factors based on orthogonal experimental Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Influencing Factor (A)** & **Influencing Factor (B)** & **Influencing Factor (C)** & **Permeability/md** \\  & **Axial Pressure/MPa** & **Confining Pressure/MPa** & **Gas Pressure/MPa** & **Permeability/md** \\ \hline
1 & 6 & 6 & 3 & 0.2081 \\
2 & 7 & 7 & 3 & 0.1766 \\
3 & 8 & 8 & 3 & 0.1443 \\
4 & 9 & 9 & 3 & 0.1093 \\
5 & 6 & 7 & 3.5 & 0.1533 \\
6 & 7 & 6 & 3.5 & 0.195 \\
7 & 8 & 9 & 3.5 & 0.1135 \\
8 & 9 & 8 & 3.5 & 0.1252 \\
9 & 6 & 8 & 4 & 0.1346 \\
10 & 7 & 9 & 4 & 0.1171 \\
11 & 8 & 6 & 4 & 0.2013 \\
12 & 9 & 7 & 4 & 0.1587 \\
13 & 6 & 9 & 4.5 & 0.1206 \\
14 & 7 & 8 & 4.5 & 0.1376 \\
15 & 8 & 7 & 4.5 & 0.1651 \\
16 & 9 & 6 & 4.5 & 0.2212 \\ \(k_{1}\) & 0.6166 & 0.8256 & 0.159575 & \\ \(k_{2}\) & 0.6263 & 0.6537 & 0.14675 & \\ \(k_{3}\) & 0.6242 & 0.5417 & 0.152925 & \\ \(k_{4}\) & 0.6144 & 0.4605 & 0.161125 & \\ Extreme value (R) & 0.0119 & 0.3651 & 0.014375 & \\ Ranking of & & & & \\ Influencing factors & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Permeability test results and order of various influencing factors based on orthogonal experimental Table 2.  
permeability. That is to say, the permeability of gassy coal is most sensitive to the confining pressure, followed successively by gas pressure and axial pressure.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 5 Conclusions  
In this paper, fault detection, classification, and location techniques were applied on the simple radial distribution network. The knowledge-based technique was implemented using machine learning tools. Wavelet decomposition was used together with statistical measures to extract useful information from the measured current signal. Those features were used to train the neural networks. The proposed technique was applied on a simple radial distribution network with both balanced and unbalanced load conditions. The performance of the proposed fault locating method was evaluated in terms of the mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (\(R^{2}\)), Willmott's index of agreement (WIA), and Nash-Sutcliffe model efficiency coefficients (NSEC). The time it takes for training and testing were also considered. The proposed method shows that discrete wavelet transform with machine learning is a very accurate and reliable method to classify and locate faults in both a balanced and unbalanced radial system. In the two scenarios, it was found that detection was 100% accurate. Classification was also found to be 100% accurate, except for in 3 LG and 3L faults. The fault location was done using two options: ELM and MLP. Both methods performed very well. ELM was found to be faster to train the network. In other performance indexes, including testing time, slight differences were observed, but they were not sufficient enough to say that one is better than the other. It can be said that both ELM and MLP are very efficient for the classification and location of faults, and that ELM is faster to train. Although the proposed technique was tested only for a radial system, the methodology is general enough and can be applied to other types of networks. The study can be complemented by considering distribution networks with mesh topology and multiple generation units. Renewable generations in
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 1
#### 5.1.1 Impact of Method Sequence on Localization Times (Run 1)  
As shown in Figure 10 and summarized in Table 3, the traditional method was completed in less time across the entire population.  
* In Group A, where participants started with the traditional method (Figure 10b), significantly (\(p\) = 0.003) shorter localization times were observed, indicating an advantage when beginning with this method.
* A reverse pattern can be noticed in Group B, where participants started with the HoloLens(tm) method (Figure 10c) and achieved slightly shorter times (median) when using the HoloLens(tm) method. However, these differences were not statistically significant, suggesting a minimal impact of method sequence on task completion times for this group.
* Interpretation: Across the entire population (Figure 10a), there is a weak effect, suggesting that the traditional method is more efficient than the HoloLens(tm)-based method. For the subgroup starting with the traditional method, this effect is statistically significant. Subjects performed, on average, better in the first attempt, regardless of which method they used. This indicates the subjects' loss of attention or possible fatigue during the experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Method** & \begin{tabular}{c} **Mean** \\ **(Seconds)** \\ \end{tabular} & \
\begin{tabular}{c} **Median** \\ **(Seconds)** \\ \end{tabular} &
\begin{tabular}{c} **Wilcoxon Test** \\ **(Statistic,** \\ _p-Value)** \\ \end{tabular} \\ \hline Entire Population (\(n\) = 10) & Traditional & 17.20 & 17.00 \\ \hline \multirow{2}{*}{Group A (\(n\) = 5)} & HoloLens(tm) & 25.75 & 20.50 \\ \hline Group A (\(n\) = 5) & Traditional & 13.70 & 13.00 \\ \hline \multirow{2}{*}{Group B (\(n\) = 5)} & HoloLens(tm) & 31.50 & 30.50 \\ \hline Group B (\(n\) = 5) & HoloLens(tm) & 20.00 & 15.50 \\ \hline \multirow{2}{*}{Individual} & Traditional & 20.70 & 22.00 \\ \cline{2-2} \cline{2
# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and the Gastrointestinal System  
Intestinal perforation (IP) in preterm infants is a life-threatening condition. It can be spontaneous or secondary to necrotizing enterocolitis and requires urgent surgical intervention. Even if risk factors for IP have been elucidated, preventive measures and prediction models are not easily developed because the pathogenesis of this condition in preterm babies is still not fully understood. ML can be helpful, allowing a meticulous assessment of complex conditions starting from large datasets. Given the deep clinical impact of such conditions on outcomes such as death and quality of life, it is undeniable that having an AI tool helping and supporting clinicians in management could be extremely helpful. A recent study developed an AI algorithm trained on a large national dataset relating to the clinical features of infants [79]. The proposed algorithm predicted and characterized IP preterm infants with excellent accuracy, outperforming all other classic ML algorithms. The authors speculated that the assessment of different clinical data, such as vital signs, radiologic findings, biomarkers, and laboratory results, might enable the development of a more accurate ML model, eventually making it possible to achieve early prediction of IP and improved outcomes for preterm babies. In the nutrition field, a recent study by [PERSON] et al. focused on the possibility of using AI to predict postnatal growth failure and thus plan timely interventions. ML models were developed employing different techniques with a large dataset (approximately 8000 very low birth weight infants from various Chinese NICUs), and their feasibility and good predictive performance were demonstrated [80]. In any case, despite the vast size of the dataset, it did not include important information regarding enteral and parenteral nutrition, limiting the study results.
## 2 Materials and Methods
### Electroless Plating of MWCNT
#### 2.1.2 Electroless Plating of Sn on Cu-MWCNT Composite Powders  
The tin solution bath composition was adopted from [PERSON] et al. [15]. Stirred in 80 mL of stilled water were 0.2 M thiourea (CH\({}_{4}\)N\({}_{2}\)S), 0.0014 M of ethylene diamine tetra acetic acid (EDTA), 0.0038 M of hydroquinone (C\({}_{6}\)H\({}_{6}\)O\({}_{2}\)), 0.2 M of sodium hypophosphite (Na\({}_{2}\)PO\({}_{2}\)H\({}_{2}\)), 0.0042 M of methane sulphonic acid (CH\({}_{4}\)O\({}_{3}\)S), and 15 mL/L glycol (CH\({}_{2}\)OH)\({}_{2}\). Separately, 2 g of stannous chloride was mixed in 1 mL of HCl. The latter was added to the former solution drop-wise and stirred to obtain a clear solution. The Cu-MWCNT composite powders were added to the Sn electroless plating bath and stirred for 2 h at room temperature. The Sn coated Cu-MWCNT composite powders were filtered, washed subsequently with distilled water, and dried in the oven. The weight of Sn coating was controlled by varying the ratio of Cu-MWCNT powders to stannous chloride in grams. The weight gain percentage after coating was calculated using the formula as described above. The mass percent of Cu and Sn in the coating were maintained at approximately 62% and 38%, respectively. The powders were then characterized using XRD using Cu K-\(\alpha\) spectrum with wavelength of 1.5406 A (Bruker AXS GmbH-D8 DISCOVER, Karlsruhe, Germany) and analytical scanning SEM (JEOL JSM-6010 PLUS, Tokyo, Japan) attached with energy dispersive spectrometer (EDS).
## 5 Conclusions  
Low-cost LiDARs have lower point cloud density compared to high-cost LiDARs, which limits their perception effectiveness. To achieve better environmental perception results on low-cost LiDARs, we propose the use of TSE-UNet for sparse point cloud super-resolution. First, we project the 3D point cloud onto a 2D plane. Then, we process consecutive frames using dilated convolutional layers. We use a temporal feature aggregation convolution and a spatial attention module to extract point cloud features from consecutive frames. Using the proposed TSE-UNet model, we generate super-resolution point feature maps. Finally, by reprojecting the feature maps back into 3D space, we achieve point cloud super-resolution. Through comparative experiments, ablation experiments, visualizations, and performance testing, we demonstrate that the proposed method effectively completes the task of point cloud super-resolution. Remarkably, our approach not only achieves a PSNR of 27.52 on CARLA32-128 and 24.82 on Ruby32-128, surpassing the performance of prior methods, but also is capable of real-time operation. In the future, we plan to combine TSE-UNet with point cloud segmentation and point cloud detection tasks to promote lower-cost environmental perception for autonomous vehicles and intelligent robots.  
Conceptualization, L.R.; methodology, L.R. and Z.O.; software, L.R.; validation, L.R.; formal analysis, L.R. and Z.O.; investigation, L.R.; resources, Z.Z.; data curation, L.R. and D.L.; writing--original draft preparation, L.R.; writing--review and editing, D.L.; visualization, L.R. and Z.O.; supervision, L.R.; project administration, Z.Z.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was fully supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY23F020026, NSFC No. 62303036, and partly supported by Tanmushan Laboratory Research Project TK-2023-B-010 and TK-2023-C-020.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.1.2 Steering and Course Variance Data  
Data were collected from April 2022 to January 2023 using a voyage data recorder. Throughout 14 voyages, the saved records were backed up periodically by connecting to the equipment's storage. The data were kept in text files 15 s long with all the connected sensor data. A total of 233,759 files and 3,506,399 s of data were extracted and arranged in tabular format. In the extraction, basic text analytics for text patterns was used to select the specific values of the sensors. The collected data include the \"ship's course\", \"GPS position\", \"rudder angle order\", \"ship's speed\", and \"steering system operation mode\".  
Unlike the AIS data, the data from the voyage data recorder were just about noticed. Hence, the missing value was interpolated linearly because the length of the missing parts was in a few seconds, thus not requiring additional data smoothing techniques. As this research focuses on manual steering, the collected data were sorted for only \"manual steering mode\". Then, the timelines with ship speeds less than ten or over fifteen knots were eliminated to restrict the speed variable. Afterward, the course alteration angle per second was calculated by separating consecutive timelines into groups and merging the data into the whole dataset.
However, due to the variable nature of solar radiation, which can be affected by clouds and shadows, PV systems require a controller to operate at maximum power point (MPP). Therefore, numerous studies have been conducted on maximum power point tracking (MPPT) algorithms.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Medium/Large Satellite Constellations  
SAR missions in Table 1 have satellite launch mass greater than 500 kg and do not technically fall into the small satellite category. Either medium (500 kg to 1 ton) or large (>1 ton), these satellites were designed following rather conventional principles, less affected by design constraints in mass and volume than small satellites. Nonetheless, deployment and operation details of missions in Table 1 still provide useful guidelines. For example, SAR-Lupe is a 5-satellite system and Cosmo-SkyMed is a 4-satellite system, being the largest and the second-largest (homogeneous) constellations launched so far. Future missions with larger numbers of small satellites would require efficient strategies for constellation design and deployment, for which SAR-Lupe and Cosmo-SkyMed could be good references. TanDEM-X demonstrates various formation flight technologies with two satellites flying close to each other in space, and HJ-1C is an interesting example of a heterogeneous constellation of SAR platforms and optical platforms.
## 3 Preliminaries
### Spectrum Allocation in Optical WDM Networks and EONs  
The spectrum allocations in optical Dense WDM (DWDM) networks and EONs with effective utilization are shown in Figure 1a,b with a wavelength grid of 50 GHz and an FS of 12.5 GHz, respectively. We assume that each wavelength with binary phase shift key (BPSK) coding can transport 40 Gbps like STM-256, and here STM denotes synchronous transfer module in the synchronous digital hierarchy (SDH) used widely. Figure 1a shows the effective spectrum usage of the DWDM network if traffic \(1\) is 10 Gbps and traffic \(2\) requests 100 Gbps which can be carried with three wavelengths in two-40 Gbps and one 20 Gbps. Note that the first 10 Gbps and the second 20 Gbps could not be transported within the same single wavelength since their destinations are not the same and only one optical switch is allowed without grooming in the electronic field.  
In contrast, EONs use exactly one FS to transmit traffic \(1\). It employs OFDM technology to gather only eight continuous FSs into a super-wavelength for transferring traffic \(2\) with 100 Gbps, covering only 56.25 GHz. Here, GS denotes the guard FS that separates two different carriers for demodulation. Compared with WDM networks, EONs provide variable granularity units from one FS to serval FSs that are referred to as a superchannel and transmit requests by overlapping multiple FSs through OFDM technology, saving the network spectrum by as much as 59.4% ((4 \(\times\) 50-6.5 \(\times\) 12.5)/4 \(\times\) 50).
# Analysis of AMI Communication Methods in Various Field Environments
## 6 Discussions
### Discussions on the Power Line Communication
Using the regression models of Table 2 for each type of areas, the received power and SNR values are graphically simulated on the NDIS map. This visualization for the received powers and SNR values on NDIS maps is useful in planning PLC networks of AMI for a given site. When running the simulation software, we can select the starting point, which is usually a utility pole mounted with a DCU, on the NDIS map. We can then select a point where we hope to know the SNR values. The received power, minimum SNR, and average SNR obtained with the regression model can be shown. In addition, along the path, the received power can be displayed in color on the map. Figure 31 shows the visualization result on Site 1-2 as an example. The rightmost point on the map has the transmitter as a starting point and the other points on the left represent MPs. Along the path, the signal power is visualized by different colors. The SNR value corresponding to the color is displayed on the outside right of the map. For the final MP, the distance from the starting point, the received power, minimum SNR, and average SNR are displayed below the map with its corresponding zone, i.e., the Good, Gray, or Poor Zone. Figure 31a shows the simulation results at a point of 33.7 m from the starting point. The received power is \(-35.9\) dBm, the minimum SNR is \(3.57\) dB, and the average SNR is 11.3 dB. Below the map, it is also displayed that the MP is in the Good Zone in terms of PER. Next, another point is tried along the line. Figure 31b shows the simulation result at a point of 102 m from the same starting point. The received power is reduced to \(-\)50.3 dBm, the minimum SNR is \(-\)10.8 dB, and the average SNR is \(-\)3.15 dB. Below the map, it is displayed that the MP is in the Poor Zone. Figure 30: Signal-to-noise ratio (SNR) and PER with respect to the distance. (**a**) Overhead Residential Area. (**b**) Overhead Commercial Area. (**c**) Overhead Rural Area. (**d**) Overhead Factory Area. (**e**) Underground Area.