esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of The Compliant Buoy-Ballast Sensing System
### Drag Forces  
In steady state, the velocities of the buoys and ballast are considered constant, and a static analysis is conducted to estimate the relationship of the distance between the buoys as a function of the drive speed. The forces exerted by the fluid on the buoys and the ballast depend on the type of flow, which can be laminar, turbulent, or in a transitional state between both.  
Usually, the moving object's related Reynolds number is used to identify the type of water flow around the object, regardless of whether it creates turbulence:  
\[Re = \frac{\rho_{w}.v.L}{\mu} \tag{4}\]  
where \(v\) is the relative speed between the object and the fluid, \(\mu\) is the water viscosity, which is approx. \(10^{-3}\) kg/m/s at \(20\)\({}^{\circ}\)C, and \(L\) is the characteristic length of the object, perpendicular to the direction of motion, such as for the diameter of a sphere. For a Reynolds number below 10, the flow can be considered as laminar, which is the case for very small objects. Above \(10^{6}\), the flow is turbulent at the surface and behind the object. Between 10 and \(10^{6}\), the flow can be either laminar or turbulent, or in a transitional state.  
Figure 1: The compliant buoy–ballast system mounted on a neutrally buoyant cable.  
The order of magnitude of a 4 cm long compact buoy or ballast, moving at the average speed of 0.5 m/s is  
\[Re = \frac{10^{3}\;0.5\;4.10^{-2}}{10^{-3}}\approx 2.10^{4} \tag{5}\]  
When the water flow is laminar, the drag force \(F_{d}^{lam}\) is proportional to the velocity, where the drag force in turbulent flow is quadratic, namely \(F_{d}^{turb}\). The formulae for these two forces are given below.  
\[F_{d}^{lam} = k.\mu.v \tag{6}\] \[F_{d}^{turb} = \frac{1}{2}.C_{D}.A.\rho_{w}.v^{2} \tag{7}\]  
where  
* \(k\) is a coefficient that depends on the shape of the object. For a sphere of radius \(R\), \(k=6\pi.R\).
* \(C_{D}\) is the dimensionless quadratic drag coefficient, which is a property of the object. For a spherical shape, \(C_{D}^{ballast}\approx 0.47\), and for a cuboid shape, \(C_{D}^{buoy}\approx 1.05\).
* \(A\) is the cross-sectional area of the object, perpendicularly to the motion, which can be approximated by \(\pi.R^{2}/2\), with \(R=L/2\).
## 2 Relief Hole Image Generation Based on SinGAN
### Basic Principle of GAN  
GAN adopts an unsupervised learning mode, the core aim of which is to make two neural networks confront each other to achieve the purpose of learning. This is mainly composed of generator G and discriminator D. The purpose of the generator is to generate an image that is as similar to the original image as possible from zero after the input noise \(z\) is continuously trained by the model, and to try to confuse the discriminator. Through mutual improvement in the process of the continuous game, the discriminator has strong discrimination ability, but there is no way to distinguish whether the input data are true or false. In this case, it can be determined that the generator has mastered the feature distribution of the original image through training.  
In 2019, [PERSON] et al. proposed a single-image generative adversarial network model. The basic structure is shown in Figure 1[24], where \(z_{N}\) represents random noise; \(G_{N}\) represents the generator; \(\widetilde{x}_{N}\) represents the image generated by generator; \(x_{N}\) represents the real image; and \(D_{N}\) represents discriminator. In general, by training SinGAN to generate images, at least thousands of training samples are required. Its input is a single natural image, which, like the GAN model, is unsupervised. SinGAN can generate a new image similar to the original image, and the general features of the image will essentially be the same, but some details and structures will have changed.  
Although SinGAN can generate any number of images with high authenticity and diversity, the premise is that the input image is a natural scene image with an obvious  
Figure 1: Basic structure of the SinGAN model.  
structure. Due to the insufficient layout of the relief hole image structure, the diversity of the generated images will be poor, and the performance of the SinGAN model will have a large gap with the actual demand.
## 3 Model Structure
### Overall Model Architecture  
This study initially segments time-series data into numerous sub-series using the Variational Mode Decomposition (VMD) technique, designed to uncover the data's intrinsic structure and dynamic properties. Subsequently, the system calculates autocorrelation and partial correlation coefficients for each subseries, representing a critical step in identifying temporal dependencies within the data. The system employs distinct models for training, depending on the presence or absence of seasonal trends in each subsequence: the CT-Transformer model for subsequences with seasonal trends to capture cyclical variations and the TCN-LSTM model for those without, focusing on long-term dependency extraction and learning. This differential approach allows for the most suitable analysis and prediction of each subsequence.  
The VMD technique is ultimately applied to reconstruct the subsequences trained by the models, synthesizing the final predicted sequences. This integration process culminates in a composite prediction framework that leverages the multidimensional nature of time-series data to enhance prediction accuracy. Figure 1 illustrates the detailed model architecture design, delineating the complete process from data decomposition through model training to sequence reconstruction.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Comparison with Other Methods
#### 4.3.2 Results on CFD  
Figure 8 shows the segmentation results of the six methods on some images in the CFD dataset, and Table 2 shows the quantitative comparison of the IoU, precision, recall, and F1 score metrics. As can be seen in Figure 8, DeepCrack and DeepLab v3+ perform poorly for predicting the crack boundary, making the crack region in the segmentation results wider than the ground truth. Unet 3+ and our MCIE-Net show a stronger ability to capture details and ensure the continuity in the crack area. Meanwhile, compared with other methods, our MCIE-Net can distinguish cracks and interference factors in background more effectively. As shown in Table 2, U-Net and Unet 3+ achieve the highest precision of 98.65% but a lower recall, while DeepCrack achieves higher recall of 88.62% but a lower precision. The F1 score takes into account both precision and recall, and the best result in this metric is achieved by our MCIE-Net, which is 89.47%. In addition, MCIE-Net also achieves the best result in the IoU metric, which is 81.32%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **IoU (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline DeepCrack & 66.49 & 66.67 & 85.28 & 70.01 \\ DeepLab v3+ & 75.95 & 86.18 & 85.04 & 84.52 \\ U-Net & 85.97 & 94.45 & 89.26 & 90.31 \\ UNet++ & 87.68 & 94.82 & 91.89 & 92.36 \\ Unet 3+ & 88.05 & 94.64 & 92.07 & 93.27 \\ MCIE-Net & 91.28 & 95.67 & 94.59 & 94.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different methods on the DeepCrack-DB dataset.  
Figure 7: Sample results of using different methods on the DeepCrack-DB dataset. (**a**) Images, (**b**) ground truth, (**c**) DeepCrack, (**d**) DeepLab v3+, (**e**) U-Net, (**f**) UNet++, (**g**) Unet 3+, (**h**) MCIE-Net.
## 4 Results and Discussion
### Energy Analysis  
Figure 11 represents the net power comparison of the azimuth-based system and sensor-based and system during day hours.  
The net power production of the sensor-based tracking system and the azimuth-based tracking system was calculated by subtracting the power generation from the power consumption. The results show that the sensor-based PV system's module has a higher net power generation than the azimuth-based PV system. The maximum net power generation of the sensor-based system was 56.9 W at 12:20 p.m. In contrast, the minimum net power generation was 43.8 W at 9:04 a.m. As for the azimuth-based system, the maximum net power generation was 48.6 W at 8:52 a.m. On the other hand, the minimum net power generation was 39.2 W at 12:47 p.m. The maximum difference between the systems' net power generation was 13.2 W, which was recorded at 9:55 a.m. The mean net power of the sensor-based system and the azimuth-based systems were 51.3 W and 44.7 W, respectively, where the sensor-based system had a higher power generation of 14.8%.  
Each solar tracking system's energy generated and consumed was calculated by taking the area under the power curves vs. time to obtain the total energy generated and consumed for both systems, as shown in Table 3. The higher energy generation was from the sensor-based system with a total of 9.06 MJ compared to the azimuth-based where the total energy generation was 8.41 MJ; therefore, the total energy generation of the sensor-based system was higher in comparison to the azimuth-based system by 7.7%. On the other hand, the total energy consumed by both systems was calculated by the same method to extract the energy consumed by the sensor-based and the azimuth-based systems' actuators. The system with the higher energy consumption was the azimuth-based actuator with a total of 0.670 MJ, and as for the sensor-based system, the total energy consumption was 0.234 MJ. The azimuth-based system had a higher consumption of 65% than the sensor-based system's energy. After that, the total net energy was calculated by subtracting the total energy consumption from the total energy generation of each system, as shown in Table 2. The total net energy generated by the sensor-based system was 12.68% higher than the azimuth-based SAST.  
Figure 11: Net power comparison of the azimuth-based system and the sensor-based system.  
The results of the net power generation and the total energy of the different systems show that the sensor-based solar tracking is more effective than the azimuth-based solar tracking system and has more potential in the region where the test was carried out.
# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
###### Abstract  
Background: Identifying the diagnostic value of bitewing radiographs (BW) is highly dependent on the operator's knowledge and experience. The aim of this study is to assess the effectiveness of machine learning (ML) to classify the BW according to their diagnostic quality. Methods: 864 BW radiographs from records of 100 patients presented at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia were assessed. The radiographic errors in representing proximal contact areas (\(n=1951\)) were categorized into diagnostic and non-diagnostic. Labeling and training of the BW were done using Roboflow. Data were divided into validation, training, and testing sets to train the pre-trained model Efficentdet-d0 using TensorFlow. The model's performance was assessed by calculating recall, precision, F1 score, and log loss value. Results: The model excelled at detecting \"overlap within enamel\" and \"overlap within restoration (clear margins) with F1 score of 0.89 and 0.76, respectively. The overall system errors made by the built model showed a log loss value of 0.15 indicating high accuracy of the model. Conclusions: The model is a \"proof of concept\" for the effectiveness of ML in diagnosing the quality of the BW radiographs based on the contact areas. More dataset specification and optimization are needed to overcome the class imbalance.  
artificial intelligence; bitewing radiographs; contact areas; machine learning; radiographic errors
## 5 Experiments and Results
### Stellar
#### 5.2.3 Results  
Table 2 details a comparison of query times with and without aggregation. Reading the logs in plaintext takes approximately 0.723 \(\upmu\)s, while reading the same logs in encrypted form takes approximately 60.235 \(\upmu\)s, meaning that encryption introduces significant overhead in the form of an approximately 83 times longer reading time.
###### Abstract  
Recently, the metalens has been investigated for its application in many fields due to its advantages of being much smaller than a conventional lens and is compatible with nano-devices. Although metalenses have extraordinary optical performance, it is still not enough in some occasions such as wavefront detection for adaptive optics and display for large area applications. Using a metalens array is an ideal solution to solve these problems. Unfortunately, the common metalens array cannot be adjusted once it is fabricated, which limits its range of application. In this article, we designed an actively tunable metalens array for the first time by arranging the patterned phase change material Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\) (GST) appropriately. For the metalens array designed at the wavelength of 4.6 \(\upmu\)m, it had excellent broadband performance in the range from 4.5 \(\upmu\)m to 5.2 \(\upmu\)m. On the other hand, by tuning the phase state of GST, the focus and display of the metalens array can be controlled, acting as switching on or off. Furthermore, any graphics constructed with patterned focal spots can be achieved when the metalens array has sufficient secondary unit cells. The proposed metalens may have potential application value in the adaptive optics and dynamic display field.  
t 11 October 2019; Accepted: 14 November 2019; Published: 16 November 2019
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods  
This section describes an overview of the proposed approach. The details of data collection, fingerprint database creation, training CNN, and localization process are explained.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Heart Rate Extraction
#### 3.1.1 Doppler-Based System  
This is one of the most famous signal extraction techniques from an object using radar signal. This technology has been used to extract the signals from the human body to monitor physiological parameters. [PERSON] [10] evaluated the relationship between breath-by-breath oxygen uptake (VO2), cardia index (CI), stock volume (SU) and HR determined by a non-invasive doppler-based system in athletes during exercise. Authors co-related VO2 with CI, SU, and HR in various stages of exercise such as peak, 1 min recovery, and 3 min recovery. A similar work was presented by [PERSON] [11] ([PERSON] et al., 2017) to monitor breathing rate and heart rate using the RADAR based Doppler system during standing and walking positions. For this purpose, five tests had been performed with different distances of the human body from antenna, different angles or positions of antenna and body, and standing and walking positions. The maximum distance covered was 150 cm. The signal acquired from the antenna is followed by various signal processing steps including low pass filter, band pass filter, etc., and finally that signal was input to oscilloscope.
## 3 Results  
### Accuracy of the Original Satellite Precipitation Dataset  
The reliability of the raw satellite precipitation dataset is verified with observations from 29 rain gauges. According to the time when the daily precipitation data are obtained from the rain gauge results (8:00 p.m. the previous day to 8:00 p.m. on the current day), the obtained satellite precipitation data with original time resolution (30 min and 1 h) are converted according to Coordinated Universal Time (UTC) and Beijing time, and accumulated to the corresponding time period to obtain daily satellite precipitation data.  
Figure 3 describes the relationship between original satellite precipitation and the rain gauge measurement value according to the daily time scale at one selected station location in the period of 2010-2014. The CC, BIAS and RMSE of these three satellite precipitation data and rain gauge data in the period of 2010-2014 are shown in Table 2. The CCs are as follows: 0.542 (ERA5), 0.615 (CMORPH), and 0.644 (IMERG), all of which pass the significance test at the 0.01 level, indicating that there is an obvious linear correlation and consistency between the three initial satellite rainfall data and rain gauge data. The  
BIAS results for CMORPH, IMERG, and ERA5 are 0.050, \(-\)0.013, and 0.885, respectively, implying that all three datasets overestimate or underestimate the precipitation to some extent compared with the RGS observations. Among them, the IMERG data are of the best quality. The lowest-scoring data quality was found for ERA5, which considerably overestimates precipitation.
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations on the Effect of Coolant Boiling Caused by Falling Debris
#### 3.5.2 Experimental Results and Discussion  
Figure 20 shows the DBF flow regime characteristics with boiling liquid surrounding the falling particles. It was found that the mechanisms and effects of bubbling flow resulting from the falling particles worked differently than those generated by accumulated debris [72]. As shown in Figure 20, it was anticipated that the bubbling flows surrounding the falling debris would contribute to the centralization of the particle accumulation by means of two vortices and generate a counterforce against the central falling particle flow. This would mitigate the impact of pool convection resulting from discharged particles, which pushes the particles away to the peripherical areas resulting in lower-numbered regimes. However, in the case of boiling caused by falling particles, the dispersal of particles was averted from the central region of the water pool, leading to greater-numbered regimes. Further, parametric analyses confirmed that the flow regimes tended to transit to larger-numbered ones when higher particle and water temperatures were employed due to the enhanced bubbling flows surrounding the falling debris and restricting their dispersion [72]. In addition, particles with higher thermal conductivity were likely helpful in the effective heat transfer between particles and liquid, resulting in more intense boiling and the formation of greater-numbered regimes. Further, enlarging the nozzle diameter may cause non-monotonous flow-regime transitions. This is because the larger nozzle diameter would serve to accelerate the particle-releasing rate, which is expected to not only improve the intensities of particle-triggered pool convection but also facilitate bubble generation surrounding the falling particles.  
Figure 19: Effect of bubbling method on vertex angle (\(H_{\rm w}\) = 60 cm, \(d_{\rm n}\) = 30 mm, Regime IV).
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Multi-Source Data Fusion  
To integrate AIS and video data, this paper proposes a matching method that combines camera view partitioning with pixel coordinate mapping. The method divides the video frame into multiple smaller regions, and by linking the vessel's pixel coordinates with its actual geographical location, it identifies the region to which the vessel belongs and performs the matching process. The specific implementation is outlined as follows.
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## Abbreviations  
### The Definition of the Acronyms or Symbols Used in the Text  
\begin{tabular}{l l}
**Acronyms or Symbols** & **The Definition of the Acronyms or Symbols Used in the Text** \\ ANN & Artificial neural network \\ _aod\({}_{k}\)_ & The AOD at the location k \\ AOD & Aerosol optical depth \\ BP & Back propagation \\ CCN & Cloud condensation nuclei \\ C(\(x_{i},y_{i}\)) & The corrected visibility at the grid point \((x_{i},y_{i})\) \\ \(D_{k}\) & The distance between the location k and the position of ICOADS data located \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ \(E_{k}\) & The difference between measured visibility and the referred visibility on the nearest grid \\  & point to the measured visibility \\ e(\(x_{i},y_{i}\)) & The calculated difference between referred visibility and measured visibility at the grid \\  & point \((x_{i},y_{i})\) \\ ICOADS & International Comprehensive Ocean-Atmosphere Data Set \\ JTWC & Joint Typhoon Warning Centre \\ LWC & Liquid water content \\ MODIS & Moderate Resolution Imaging Spectrometer \\ Nd & Droplet number concentration \\ NWP & Numerical weather prediction \\ NMPF & Microphysical parameterization for fog \\ NASA & National Aeronautics and Space Administration \\ NCDC & National Climate Data Centre of the United States \\ PCR & Principal component regression \\ P(\(x_{i},y_{i}\)) & Referred visibility at the grid point \((x_{i},y_{i})\) \\ \(P_{k}\) & The referred visibility on the nearest grid point to \(S_{k}\) \\ \end{tabular}
## 3 Results and Discussion
### Improvement of Thermal Conductivity in Carbon Blocks According to CB Addition  
The thermal conductivity of the prepared carbon blocks was analyzed by the hot-disk method and the results are shown in Figure 9. To prepare a high thermal conductivity carbon block, large particle size NGF should be used, but in this study, NGF with small particle size (44 \(\upmu\)m) was used as a filler to confirm the effect of carbon black addition. The CB addition increased the thermal conductivity of the carbon block, but the thermal conductivity did not show a significant change even when the amount of CB added increased. In general, as the volume expansion and porosity of the carbon block decreased, the thermal conductivity increased [36; 37; 38; 39]. Therefore, the thermal conductivity of C_CB_10 (the lowest volume expansion and porosity) should be the highest, but the thermal conductivity of C_CB_10 was not significantly different from C_CB_1 and C_CB_5. CB had a low thermal conductivity of less than 2 W/mK [40]. As the CB amount with low thermal conductivity increased, the thermal conductivity of the carbon block decreased. As a result, as the CB amount increased, the thermal conductivity of the carbon block was increased by reducing the volume expansion and porosity of the carbon block. But due to the low thermal conductivity of CB, the thermal conductivity of the carbon block did not change significantly even when the content of CB increased.
## 6 Simulation Results
### The Major Role Player in a Grid-Tied MG  
The MG described in the simulation has a compact collection of electricity generators (PV, utility grid, and battery storage). Figure 10 depicts a graphical representation of the distinct electrical generators, which tend to meet the DC load demand. The electrochemical energy storage devices (Li-ion and LA) are a major contributor at 45%, the PV array at 43%, and the utility grid at 12%. Consequently, this illustrates that the small generators (energy storage and PV system) are eligible to minimize the footprint of the utility grid and partially create an autonomous network.
# Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique
###### Abstract  
A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques.  
Keywords:railway; condition monitoring; fault detection; preventive maintenance; machine learning +
Footnote †: journal: Applied Sciences