esa-sceva/satcom-chunk-collection huggingface dataset:
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
## 1 Introduction
An oil spill at sea can adversely affect organisms, ecosystems, and coastal activities (e.g., beach bathing, diving, fishing) and can lead to concerns from the population, local businesses (e.g., hotels, restaurants, and tourism), the local government, industries that rely on marine resources, and other sectors of society that utilize the affected environment [18; 19; 20; 21]. Since the 1940s, the oceans have experienced 25 major oil spill events that have destroyed wildlife, affected various regions, and impacted tourism and recreational activities [22; 23]. A powerful tool available to the scientific community to reconcile the balance between exploration and preservation is oil spill modeling, which is already in use by the scientific community. These models range from simple trajectory determinations or particle monitoring to three-dimensional oil trajectory models that include response actions and biological processes, among others [24]. Oil spill modeling is a fundamental tool in the study and management of E&P activities. This technique can define the indirect influence area of oil activities, which forms the basis for the holistic environmental diagnosis. Modeling can also be used to define scenarios via simulations, thereby enabling the development of necessary strategies for emergency responses to maritime oil spill accidents within the context of individual emergency plans [25]. In this context, the objective of this study was to perform dispersion modeling of oil that was hypothetically released into the sea by tanker ships along the east and northeast coasts of Brazil with the goal of increasing our understanding of the processes involved in the arrival of oil on the country's coastline during the second half of 2019. To the best of our knowledge, this work is the first to perform oil spill modeling along a ship route in the Northeast Brazil 2019 oil spill event. We hope that this approach will be embraced and implemented with greater frequency in the future. This oil spill event occurred in late August 2019 when the northeastern coast of Brazil was hit by approximately 5000 tons of oil residue, which, by the end of December, had contaminated approximately 3000 km of coastline, including protected areas and coral reefs [20; 26; 27; 28; 29]. During the oil spill period or prior to that period, no accidents were reported by oil companies and tankers or platforms in the region. After additional subsequent analysis, the oil was found to have Venezuelan biomarkers [29; 30; 31; 32; 33].
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 3 Fractal Reflectarray Antennas
### Review of Existing Fractal Reflectarray Elements
#### 3.1.2 Fixed-Size Fractal Patches
Furthermore, as demonstrated in [47], they  
Figure 9: Minkowski patch with slotted ground plane: (**a**) unit cell, (**b**) fractal construction, (**c**) simulated reflection phase for square, first-, and second-order Minkowski patches (Adapted with permission from [45], IEEE, 2012). exhibit uniform mutual coupling levels vs cell geometry variations, which is useful for wide-angle beam-steering applications. In [48], a multilayer unit cell was proposed, consisting of a Minkowski fractal reflector with aperture-coupled phase stubs. The aim of the work was to extend the tuning phase range of Minkowski fractal reflectarrays. The authors achieved a linear phase curve with a range of more than two cycles and a return loss of less than 0.25 dB. However, this result was obtained at the cost of using a multilevel structure with a higher overall thickness.
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 2 Computational Method  
### Aerodynamic Model  
The prerequisite for predicting BVI noise is accurate calculation of the unsteady impulsive airload on the blade surface. As Figure 1 shows, the flow field is divided into two zones in the coupled method: a Navier-Stokes code is used to model the tip vortex generation region, and a viscous wake model is used to account for the effect of the far wake.
# View Synthesis with Scene Recognition for Cross-View Image Localization
## 4 Performance Evaluation for View Synthesis  
### Dataset and Experimental Setup  
The framework was developed with Python running on Ubuntu 20.04, and the deep learning model for style transfer and attribute extractor were implemented in PyTorch with Nvidia GPU 1080 Ti. To evaluate the performance of the proposed image patching framework, we selected Google Street View as the reference image data source, which is also used [12; 22] for various cross-view geo-localization tasks. Google Street View images are sampled from the GPS locations reported BDD100K [32], one of the largest self-driving datasets, with 100,000 vehicle driving trajectories from diverse locations under different weather conditions and different times of the day. We sampled one GPS location from each vehicle trajectory to represent the area of interest, then acquired the patch images from the same area by using the Flickr API accordingly.
## 3 Results and Discussion
### Hybrid Neural Network Models' Performance Overview  
The three neural network models (ANN, CNN-ANN, and CNN-LSTM-ANN) developed in this are capable of giving a good solar irradiance prediction. It is noteworthy that the performance of all the models developed in this study is very good, the r values range from 0.9662 to 0.9930. This is superior to the model developed in literature [49] for solar radiation prediction in which their r-value range between 0.8426 and 0.9356. Additionally, the performance of the models is better for countries with a well-distributed global solar radiation resource. According to the statistical summary of the data used in training and testing the models (Table 2), countries with a good mean value (Chad, Egypt, and Somalia) that corresponds to the maximum solar radiation have a better prediction performance. The RMSE and MAE of the models indicate that the model can accurately predict solar radiation for all the countries across Africa. It is noteworthy that the units of the MAE and RMSE are \(\mathrm{W}/\mathrm{m}^{2}\) which makes the errors above 10 in most cases. Typically, the errors (MAE and RMSE) are presented in \(\mathrm{M}\mathrm{J}/\mathrm{m}^{2}\) or \(\mathrm{k}\mathrm{W}/\mathrm{m}^{2}\)[45; 49] and this will reduce these values significantly.  
Out of all the case studies, the CNN-ANN hybrid model had the best performance for seven countries (Ghana, Nigeria, Central African Republic, Chad, Egypt, South Africa, and Namibia) while CNN-LSTM-ANN had the best performance for three countries (Ethiopia, Somalia, and Algeria) considering the r-value and other metrics (Table 4). The time taken to train and test the hybrid (CNN-ANN and CNN-LSTM-ANN) models is smaller in comparison to the ANN model and this is an outstanding attribute for these models. CNN-ANN model for solar radiation prediction in Chad had the best accuracy of all the countries considered in this study. Although the r-value of the ANN model (0.9939) is higher than that of the CNN-ANN (0.9930), the performance of the ANN model considering the MAE, RMSE, MAPE, and other evaluation metrics shows that the prediction accuracy is not as good as the CNN-ANN model. The \(\Delta\)RMSE for the ANN model is 5.843 and this reflects that the model has not trained very well in comparison to the CNN-ANN model. Furthermore, the ANN model performance for the Central African Republic had the least performance of all the models in this study (Table 4). To further highlight the strength and accuracy of all the models, out of the entire testing dataset, the performance of the models within 24 h period is plotted (Figures 6-10). This plot shows the fitting characteristics of each model.  
Figure 6: CNN-ANN predictive performance in comparison to other models for West Africa (Ghana).  
Figure 8: CNN-ANN predictive performance in comparison to other models for Central Africa (Chad).  
Figure 7: CNN-LSTM-ANN predictive performance in comparison to other models for East Africa (Ethiopia).
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 2 Ultra-Tight GPS/INS Integration and RISS
### Attitude Equations  
First step is the azimuth angle calculation, which is obtained by removing the rotational components due to the transport rate and Earth rotation rate and then the integration of the gyroscope measurement. An assumption is set in land vehicle applications that vehicles are most of the time travelling in the horizontal plane. Hence, we can assume relatively small pitch and roll angles. Thus, the rate of change of the azimuth angle can be written as:  
\[\dot{A}\approx-\left[(\omega_{2}-b_{2})-\omega^{r}\sin\varphi-\frac{v_{e}\tan \varphi}{R_{N}+h}\right] \tag{2}\]  
where \(\dot{A}\) is the rate of change of the azimuth angle, \(\omega_{2}\) is gyroscope's measurement, \(b_{2}\) is the gyroscope bias, \(\varphi\) is latitude, \(v_{e}\) is east velocity, \(\omega^{r}\) is the Earth rotation rate, \(h\) is the altitude of the vehicle and \(R_{N}\) is the normal radius of the Earth's ellipsoid.  
Figure 3: Ultra-Tightly-Coupled GPS/INS Integration.  
Second, the pitch and roll angles are derived from accelerometer readings after compensating for accelerations due to forward speed and transversal acceleration component, respectively. The pitch angle is calculated as:  
\[p=\sin^{-1}\left(\frac{f_{y}-a_{0}}{g}\right) \tag{3}\]  
where \(f_{y}\) is the forward accelerometer measurement, \(a_{0}\) is the vehicle acceleration derived from the vehicle odometer, and \(g\) is the Earth's gravity. The roll angle is calculated as:  
\[r=-\sin^{-1}\left(\frac{f_{x}+v_{o}(\omega_{z}-b_{z})}{g\cos p}\right) \tag{4}\]  
where \(f_{x}\) is the transversal accelerometer measurement, \(v_{o}\) is the vehicle speed obtained from the vehicle odometer.
# A Nighttime and Daytime Single-Image Dehazing Method
## 4 Experimental Results and Discussion
### Quantitative Comparison  
Quantitatively, Tables 1 and 2 consider three well-known metrics: PSNR [29], SSIM and CIEDE2000 [30; 31]. PSNR is the ratio of the maximum signal amount to the noise intensity, which measures the degree of image distortion. SSIM evaluates image quality from three aspects: brightness, contrast, and structure, which indicates the similarity of image structure information. Higher values indicate better quality for PSNR and SSIM. CIEDE2000 evaluates image quality from five aspects: brightness, chroma, hue, chroma difference, and hue difference, which indicates the chromatic aberration of the image. A lower value indicates better quality for CIEDE2000.  
Figure 6: Comparison of daytime dehazing [9; 12; 14; 15; 21].  
As shown in Tables 1 and 2, whether it is nighttime or daytime, MF Dehazer is excellent in all metrics, especially in that the PSNR and CIEDE are much higher than in the other methods. It shows that the proposed method has less distortion after dehazing, can effectively reduce noise, and well retains the color of the original image. At the same time, the proposed MF Dehazer has high stability, and the various metrics do not fluctuate. It can adapt to hazy images of different environments and complexity and restore high-definition, high-contrast and high-structure dehazing images.
## 3 Results
### Atmospheric Correction for Sentinel-2 Imagery  
Comparison of Sentinel-2 reflectance values of the water and water hyacinth at different locations in Lake Tana using three distinct image levels and products showed clear differences. The water reflectance values derived from the Sen2 Cor product at the BOA level (BOAsen2 cor) at points P2, P3, and P4 were higher compared to the BOA reflectance values derived from C2 RCC (BOAC2 RCC) for the full range of wavelengths (Figure 3b). The lower values of reflectance determined after atmospheric corrections compared to  
Figure 3: (**a**) Control points (in red) of water hyacinth (P1) and water (P2-P4) from a Sentinel-2 image on 18 September 2020; comparison of values between (**b**) BOA reflectance derived from C2 RCC and Sen2 Cor, (**c**) level-1C (TOA) reflectance and BOA C2 RCC, and (**d**) level-1C (TOA) reflectance and BOA Sen2 Cor. There was no water hyacinth reflectance value at P1 from the C2 RCC processor, due to masked data considered as land.  
TOA were due to the removal of atmospheric effects (e.g., Rayleigh scattering, water vapor, and aerosols). Moreover, both C2 RCC and Sen2 Cor products at the BOA level showed a lower reflectance value for water (P4) compared with Sentinel-2 images at TOA Level-1C (Figure 3c,d). The C2 RCC processor did not produce a value for P1, a water hyacinth zone, as it was considered as land (masked data) (Figure 3b,c). In contrast, the Sen2 Cor processor did return a value for water hyacinth reflectance at P1. This value was lower (<0.1) than the values from other processors all the way up to the 704 nm wavelength. However, above that wavelength, over the red-edge and NIR spectrum, the reflectance of water hyacinth was much higher (~0.6). Similar results were obtained after evaluation of other, different control points over the lake (not included in this study).
## 5 Results and Discussions of the PNMCC and CIM-PNMCC Algorithms
Finally, a practical speech is considered as the input signal to test the performance of the devised CIM-PNMCC. In this experiment, the used speech signal is a 2 s real speech, which has a sampling ratio of 8 kHz. The parameter used herein is the same as the last experiment. The speech signal, as well as the results, are shown in Figures 11 and 12, respectively. The proposed CIM-PNMCC can still get the best convergence speed and lower estimation error for echo cancellation applications. According to the aforementioned discussion of the proposed PNMCC and CIM-PNMCC algorithms, the CIM-PNMCC achieves the smallest estimation misalignment and quickest convergence compared with the mentioned adaptive system identification algorithms. For the proposed PNMCC algorithm, it has a similar complexity to the PNLMS, while it can provide a little better gain than the PNLMS in impulsive noise environments. Moreover, the CIM-PNMCC performs better than the PNMCC since it introduces a CIM zero-attractor to use the sparsity characteristics in the used systems. This is attributed to the CIM measurement which can account for the dominant non-zero coefficients. In addition, the CIM zero attractor forces the inactive coefficients to zero rapidly. Additionally, the developed CIM-PNMCC algorithm has an additional regularization parameter \(\rho_{\text{CIM}}\) that is used for controlling the zero attraction ability. Figure 2.3 Speech signal used in the experiment. Figure 2.4 Tracking behaviors of the PNMCC and CIM-PNMCC algorithms for estimating an echo channel (An example of typical echo channel is also included on the top of this figure. ).
# Developing a Building Fire Information Management System Based on 3D Object Visualization
## 5 Prototype Development and Scenario Analysis
### Expert Feedback on the BFIMS Prototype  
The system validation was conducted with experts in building fire disaster. The group of experts included two facility managers (senior manager and practitioner) and two firefighting crew members (crew commander and leading firefighter) with more than 10 years of experience. This focus group interview was conducted to determine how they can effectively identify the location data and relevant information at the case study application project, using the BFIMS. They were also asked about how the accuracy level of the fire-related information can be improved and how they can save time taken to acquire information about the fire site.  
In terms of the locations of the building structures and internal facilities, all interviewees replied that 3D models are more powerful than 2D drawings, as the former can easily provide detailed information based on each location. They also responded that the information management of the proposed system can reduce the time and errors in documenting and reporting the building status,compared to the conventional (manual) approach. From the perspective of fire fighters, they responded that an effective and efficient fire response strategy can be obtained with the BFIMS applications.  
However, the facility managers who maintain and update the system database should input the raw data in a manual basis, thus generating additional work. The feedback indicates that the system should be sophisticated enough to allow automation of data input. Furthermore, they revealed that public sector buildings are less benefited compared with private sector buildings because the facility managers, who usually reside in the building, are already familiar with the building information, given that they regularly and systematically conduct facility inspections with their own respective systems. However, the firefighting crew asserted that BFIMS would be advantageous in reducing the time required for information collection, and prioritizing the rescue activities under the assumption that the system can be shared among the emergency rescuers. In particular, the firefighting crew responded that they would appreciate if the internal location data could be instantly gathered by the proposed system. Additional feedback indicated that the system effectiveness would be improved if the spreading of flames and smoke during a building fire could be indicated in real time. In summary, it is concluded that the new system demonstrates a potential benefit to emergency responders who are currently managing information conventionally (using paper-based documents alone).
## 2 Materials and Methods
### Experimental Setup  
The schematic of the experimental setup is shown in Figure 1. Six carbon sources (sodium carbonate, sodium bicarbonate, sodium acetate, glucose, sucrose, and glycerol) were supplemented to ADSW at 10 g/L (based on preliminary experimental results) to prepare test media. A control (CK) group with no carbon addition was included. After 14-day microalgae cultivation, biomass (dry cell weight), pigment concentration (chlorophyll \(a\) and carotenoids), and nutrient removal rates (TN and TP) were assessed to identify the optimal carbon source.  
Subsequently, the optimal carbon source was added to ADSW at seven concentrations (0, 2, 5, 8, 10, 20, and 30 g/L). After 14 days of cultivation, biomass, pigment concentration, and nutrient removal of algal cells were also measured to determine an optimal supplementation concentration for subsequent experiments.  
On this basis, the nutrient removal efficiencies were thus studied in more detail under such optimized wastewater conditions. The biochemical composition (lipid, carbohydrate, and protein content), lipid productivity, and fatty acid profiles were also analyzed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Non-Autoclaved ADSW** & **Autoclaved ADSW** \\ \hline TN (mg/L) & 370.67 \(\pm\) 26.42 & 332.47 \(\pm\) 41.37 \\ TP (mg/L) & 16.68 \(\pm\) 0.81 & 12.400 \(\pm\) 0.91 \\ NH\({}_{4}\)\({}^{+}\)N (mg/L) & 221.60 \(\pm\) 3.60 & 100.73 \(\pm\) 12.59 \\ COD (mg/L) & 1050.19 \(\pm\) 18.83 & 702.25 \(\pm\) 12.18 \\ TOC (mg/L) & 356.45 \(\pm\) 10.72 & 168.01 \(\pm\) 5.74 \\ IC (mg/L) & 301.32 \(\pm\) 10.67 & 157.98 \(\pm\) 6.65 \\ pH & 7.45 \(\pm\) 0.62 & 8.47 \(\pm\) 0.53 \\ \hline \end{tabular}
\end{table}
Table 1: Compositions of ADSW.  
Figure 1: The schematic of the experimental setup.
## Materials and Methods  
### Module Design and Fabrication  
The Raschig ring modules were designed on the basis of the Raschig ring structure. They were cylindrical with a hollow canal to promote mass transfer by sufficient contact with the culture medium. To achieve further mass transportation, transverse-pore modules with an additional canal on the side wall were also designed. The concept of a transverse-pore module was sketched using a computer-assisted-design (CAD) software program (Solidworks 2018, Dassault, Paris, France). The dimensions of the module were set as follows: 1000 \(\upmu\)m in height, 500 \(\upmu\)m in hollow diameter, and 1100 \(\upmu\)m in outer diameter (Figure 1a). The immobilized cells in the above-mentioned modules simultaneously had access to oxygen and nutrient supply from both the outer surface of the module and the canal. The additional canal on the side wall allowed unimpeded flow between different modules, thereby reducing the force on individual modules, especially under a dynamic-flow condition as illustrated in Figure 1b. Cylindrical modules without hollow canals served as controls.  
PGA (1.5 \(\times\) 10\({}^{5}\) M.W., Daicel, Japan) powder with a mean diameter of 50 \(\upmu\)m was selected as the raw polymer material. The density of raw polymer material was 1.5 g/cm\({}^{3}\), and energy density 55.6 kJ/mm\({}^{2}\). Modified SLS technology was used to fabricate the modules. In conventional SLS, the preheating process requires a high-energy electrical source and a long processing period. Due to the preheating process, it was difficult to restart the process if it was interrupted by problems such as computer crashes and recycle materials. Therefore, this study modified the SLS technology by canceling the preheating process to fabricate modules with the advantage of energy reduction, time reduction and a high percentage of material recycling. Before module fabrication, a series of rectangular layers of varying thicknesses were designed to optimize fabrication parameters (Figure 2a). The sintering process was accomplished by a CO\({}_{2}\) laser with a scan pe  
Figure 1: (**a**) Design and dimension of polyglycolic acid (PGA) modules. (**b**) Transverse-pore, Raschig ring, and cylindrical modules under dynamic-flow conditions.  
scanning space was 20 \(\upmu\)m, and maximal powder bed temperature was 200 \({}^{\circ}\)C. Laser power was carefully selected to achieve the highest porosity of modules.