esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Unique Contributions of Ka-Band Information in AltiKa Data
### Complementarity between Ku and Ka Measurements over Ice Sheet Surfaces
The other geophysical estimates (significant wave height and backscattering coefficient) are impacted as well. Recent developments (in particular the \"Adaptive\" retracker) have shown very promising results to deal with such corrupted echoes [43]. The editing of altimeter data impacted by rain has been largely addressed in previous studies [44; 45; 46] and different methods exist to identify rain measurements. In the SARAL/AltiKa ground processing, the Matching Pursuit algorithm has been selected to flag rain data. This algorithm is based on the analysis of the short scale variations of the off-nadir angle estimate to identify rain cells in the SARAL/AltiKa measurements [41]. The performances of this algorithm have been assessed in [28] and show that the Matching Pursuit algorithm combined with the radiometer Integrated Liquid Water Content (ILWC) > 0.1 kg/m\({}^{2}\) provide a good rain flag. A map of 1 Hz flagged points is shown in Figure 21 illustrating the geographical distribution of the near 10% flagged measurements. As expected, most of the flagged points over ocean are located in the Intertropical Convergence Zone which reflects the global distribution of precipitations [47]. The comparison of the rain flag performances with WindSat and SSMIS-F16/F17 rainfall rates has shown acceptable performances but studies have been performed to improve and simplify rain flagging based on continuous wavelet transform. Results have been presented in [48]. Flagging SARAL/AltiKa measurements corrupted by rain presents a new opportunity to observe and study the rainfall climatology, seasonal and inter-annual variability. Moreover, the link between the signal attenuation and the rainfall rate can be exploited to extract valuable information on rain events. On-going studies seek to exploit Ka-band measurements to derive rain rate information, coupling them with the brightness temperature measurements provided by the radiometers for climatological monitoring. Figure 20: Variations of range std against rain rate. Please note that the first bin corresponding to dry measurements is truncated on the plot as it contains more than 70% of the measurement points. The plain blue line is the mean of the range standard deviation and the dashed blue lines represent the standard deviation for this mean. The green line is a linear regression of the mean (plain blue line).
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 4 Experimental Simulation  
### Simulation Scenario  
In this paper, a typical engineering scenario is taken into consideration, where the observation targets were generated randomly with different task numbers in the region between 40\({}^{\circ}\) and 45\({}^{\circ}\) (N) latitude and 117\({}^{\circ}\) and 130\({}^{\circ}\) (E) longitude, as shown in Figure 7. The simulation scenario was implemented by using the System Tool Kit (STK) and six typical orbital elements of an LEO observation satellite are selected, as listed in Table 2.  
In addition, 50, 100, 150 and 200 original tasks were generated to simulate practical users' requests. In addition, different task numbers represent the increasing complexity of the EOSSP. In the comparison experiment groups, the performance and scalability of the proposed DDPG algorithm were validated and discussed while the problem scale increased.  
The resource-related constraints, including the VTW, slewing angle maneuverability, total energy, total memory constraints, etc., were taken into account, and associated constant variables are summarized in Table 3.  
It is emphasized that tasks waiting to be scheduled were arranged in chronological order according to the start time of the VTW. Meanwhile, tasks were set up with different observation durations, ranging from 5 s to 15 s, and different priorities, ranging from 1 to 10.
# VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification
## 5 Conclusions  
This paper developed a 3D object classification system using VB-Net for point cloud data. The proposed system employed voxels to present point clouds, whereas a pretrained VoxNet was employed as a feature extractor, and the BLS was used for classification tasks. The proposed system was tested on the ModelNet40 dataset and ModelNet10 dataset. The average accuracies were as follows: 83.99% under voxel resolution of \(32\times 32\times 32\) and 90.08% under voxel resolution of \(20\times 20\times 20\) on ModelNet40; 82.41% under voxel resolution of \(32\times 32\times 32\) and 89.64% under voxel resolution of \(20\times 20\times 20\) on ModelNet10. An appropriate increment in voxel resolution improved the performance of the proposed system. Equally, an appropriate addition of enhancement nodes and/or feature nodes improved the classification accuracy of VB-Net. The proposed VB-Net also showed resistance in point cloud data with Gaussian noise. In future work, we will try to find compositions of enhancement nodes and feature nodes in VB-Net to achieve higher classification accuracy.  
Methodology, Z.L.; project administration, W.S.; supervision, Y.T. and L.W.; funding acquisition, Y.S., L.S., and T.Z.; investigation, S.J. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was fund by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2020-0-01576) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation), the National Nature Science Foundation of China (No. 61503005), the Great Wall Scholar Program (CIT&TCD20190304, CIT&TCD20190305), the National Key R&D Program of China under Grant (2017 YFC0821102, 2917 YFC0822504), and the \"Yuyou\" and Education Reform Projects of North China University of Technology.  
The authors declare no conflict of interest.  
Figure 6: Accuracy under different scales of Gaussian noise.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 5 Future Scope  
Table 1 summarizes the comparisons of different ML algorithms (their pros and cons) when applied in various aspects of EM. Since the emergence of 5G technology, antenna arrays have received unprecedented attention and will continue to play a dominant role in next-generation wireless communications systems. Small cell clustering, MIMO channel estimation, bandwidth sharing automation, and different fault detections in 5G technology are gradually adopting ML to obtain automation and success [154]. The use of ML improves energy efficiency, allows for more signal-path diversity, and helps to mitigate multi-path fading. Electromagnetic radiation-based detection can be a prominent solution [155; 156] where deep learning can play an important role in analyzing the radiation pattern and facilitating a fast response. ML classifiers displayed significant accuracy in providing information beyond the line of sight. These have now been used in the Global Navigation Satellite System (GNSS) to eradicate the disputes caused by multipath and non-line-of-sight signal propagation between the earth and satellites [157]. Another study [158] has shown the feasibility of ML in the state recognition of satellite antennas by accumulating minimal data. In addition, published literature [159; 160] proved the viability of using ML algorithms in the detection of spoofing attacks in multiantenna snapshot receivers by tracking the antenna positions by satellite. ML can also automate the design procedure in different computational EM methods of intelligent CAD and CAE applications [161]. ML is becoming popular in the design of complex antennas for advanced and new applications involving 5G technology and IoT [162]. To represent the path loss in complex environments of future wireless sensors, ML can contribute significantly via advanced feature selection and modeling tools [163]. Again, determining the signal condition is an important aspect of modern wireless networks, in which ML algorithms can play a significant role [164]. AI and the subset ML approaches are better than conventional algorithms in the implementation of reconfigurable antenna arrays with greater robustness in the case of noisy and multipath environments [104], even for the protection of the devices [165].  
\begin{table}
\begin{tabular}{c c c c} \hline
## 2 Materials and Methods
### Data  
#### 2.2.1 Flux and Meteorological Data  
Flux and meteorological data were collected from the ChinaFLUX [51], Coordinated Observations and Integrated Research over Arid and Semi-arid China (COIRAS) [52], and the Heile Watershed Allied Telemetry Experimental Research (HiWATER) [53]. A total of 52 site-year observation data points collected from 18 flux sites during 2003-2014 were used in this study (Figure 1, Table 1). Among them, nine sites were distributed in the TP alpine grasslands and included 34 site-year observation data points. Another nine sites were distributed in the IM temperate grasslands and included 18 site-year observation data points. These flux sites represent the most extensive grassland ecosystem types, with a wide range of spatial, ecological, and climatic conditions.  
The carbon flux data were processed through triple coordinate rotation, the Webb-Pearman-Leuning (WPL) correction, and abnormal data rejection [51]. To match the eight-day compositing interval of the MODIS products, the half-hour observed RE, Ta and photosynthetically active radiation (PAR) data were averaged and the PRE was summed over eight days. At the site scale, these meteorological data were synchronously observed using the eddy covariance system, and were gap-filled following the method proposed by [PERSON] et al. [54]. Finally, we obtained a total of 1923 items of site observation data with an eight-day temporal resolution. At the wider spatial scale, Ta, PRE, and PAR data from 2001 to 2015 with 1 km spatial resolution were produced using ANUSPLIN software to spatialize the observation data from Chinese meteorological stations. The regional-scale Ta, PRE, and PAR data were then cross-validated based on the site observation data. The validation results showed that the interpolation products were consistent with the site observation data [55; 56]. In line with the RE data, the regional Ta and PAR data were averaged and the PRE was summed for eight days.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 5 Conclusions
Furthermore, while our study only utilized host process data, a more comprehensive detection model for various types of malicious behavior can be developed by leveraging other types of data such as network traffic, system logs, and user behavior data, addressing the limitations of relying solely on host process data. Moreover, potential challenges and limitations of using machine learning algorithms for detecting malicious behavior in real-world settings should be considered, such as issues with data privacy, algorithm bias, and real-time detection requirements. Strategies to mitigate these concerns, including bias-correction techniques, compliance with privacy regulations, and optimization of processing times and computational costs for real-time deployment, should be explored. While our study focused on analyzing static malicious code, there is a need to conduct study on analyzing dynamic malicious code. To achieve this, dynamic analysis techniques can be used to monitor the execution process of malicious code and develop models for detecting malicious behavior based on this. Lastly, while our study focused on analyzing host process data, there is a need for study on detecting malicious behavior in cloud environments. In cloud environments, multiple users share resources simultaneously, and virtual resources such as virtual machines are used. Therefore, it is important to develop a detection model that considers these factors. In conclusion, further study in these areas can provide valuable insights for the development of effective and efficient models for detecting malicious behavior in various settings. By incorporating the latest technologies and analysis techniques, we can develop more accurate and comprehensive detection models that can be applied to real-world situations, including real-time systems where rapid response and minimal computational overhead are critical. Conceptualization, R.H., K.K. and B.C. ; Funding acquisition, R.H.; Methodology, R.H., K.K. and B.C. ; Software, K.K. ; Supervision, Y.J. ; Validation, Y.J. ; Writing-original draft, R.H. and K.K. ; Writing-review & editing, B.C. and Y.J. ; All authors have read and agreed to the published version of the manuscript. This research was supported by the Future Challenge Defense Technology Research and Development Project (9129156) hosted by the Agency for Defense Development Institute in 2020. Data Availability Statement: Not applicable. Data used in this study was sponsored by SOMMA, Inc.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
## 4 Simulations and Analysis
### Optimization Consistency Analysis between Optimization Results and Experiments  
In order to verify the aforementioned analysis results, we set up the experiment prototype illustrated in Figure 14, and pick a set of coil design parameters under traditional design, \(M\)/\(R\) single-objective optimization, and multi-objective optimization for experiments when \(R_{L}\) = 53.2 \(\Omega\).  
When the power on the load resistor \(R_{L}\) is about 200 W, the power transmission efficiency \(\eta\) for traditional design, \(M\)/\(R\) single-objective optimization, and multi-objective  
Figure 14: The experiment setup.  
Figure 13: Pareto front curves under different load resistance.  
optimization are 93.2%, 93.5%, and 86.3%, respectively, and the input voltage of the power supply is 131 V, 112 V, and 35 V, which means the voltage modulus gain \(K\) are 0.82, 0.94, and 3.45, respectively. In the experiment, the equivalent resistance of the capacitor and the resistance of the connecting wire have an effect on the power transmission efficiency \(\eta\) and the voltage modulus gain \(K\). The multi-objective optimization's coil resistance is smallest, meaning it is the most affected. In addition, coils in the experiment are not totally symmetrical, and the load resistance has a little inductance; it's hard to make the power transmission coils in resonance condition at the same time, which will reduce efficiency. Further, some iron clips that will increase the magnetic field are around coils, and there are measurement errors. Due to these factors, the phenomenon whereby experimental results are lower than the theoretical experimental results are reasonable and acceptable.  
In total, multi-objective optimization of coil parameters can achieve higher voltage gain at the small cost of efficiency, which can guide the design process when the range of input power supply is restricted. The theoretical analysis is confirmed by experimental results.
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Fusion Module
#### 3.6.1 Weighted Design Based on Pixel Intensity  
The pixel intensity weighting design computes pixel intensity weights derived from pixel intensity values, which are utilized to emphasize the brightness information across images captured with varying exposures.  
The pixel intensity-based weighting design regulates the weights to correspond with the overall brightness of the image, considering the presence of more well-exposed pixels when there is a significant difference between the input image and its neighboring exposed images. Consequently, the first weight is denoted as  
\[w_{1,n}(x)=\exp\left(-\frac{(L_{n}(x)-(1-m_{n}))^{2}}{2\sigma_{n}^{2}}\right) \tag{24}\]  
In Equation (24),  
\[\sigma_{n}=\left\{\begin{array}{ll}2\kappa(m_{n+1}-m_{n})&n=1\\ \alpha(m_{n+1}-m_{n-1})&1<n<N\\ 2\kappa(m_{n}-m_{n-1})&n=N\end{array}\right. \tag{25}\]  
\(N\) is the number of images in a set of multiple exposure images. \(L_{n}(x)\) is the pixel intensity of the nth image. \(m_{n}\) denotes the average of the pixel intensities of the nth image. When \(L_{n}(x)\) is close to \(1-m_{n}\), the weight is significant, denoted by \(\exp\left(-(L_{n}(x)-(1-m_{n}))^{2}\right)\). The assignment takes into account the exposure levels of the input image and its neighboring images. \(\sigma_{n}\) varies with different levels of brightness differences. The authors of the original article proposed setting \(\alpha\) to 0.75, a value that was also validated by [PERSON] et al. [26] in underwater images. Consequently, we have also set the value of \(\alpha\) to 0.75. \(w_{1,n}(x)\) represents the first weight that encodes the relative luminance.
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 4 Data Collection
### Augmentation and Preprocessing  
The size of the dataset is further multiplied through augmentation. Augmentation generates new data from existing data by applying label-preserving transformations to the image. While the convolution operations in the network are invariant to translations, they are not invariant to flipping or rotations. These additional data samples acts regularizing during training to achieve lower generalization error.  
For the presented system, every existing window was quadrupled. Its new copies were rotated by 180\({}^{\circ}\) and/or horizontally flipped. Further augmentation by 90\({}^{\circ}\) rotations was waived, since this would alter the fine, vertical structures in the background textures apparent in Figure 2. This results in a final dataset size of 32.000 samples.  
Figure 3: **(top)** Simulated defects on otherwise defect-free images. **(bottom)** Corresponding segmentation images used as labels during neural network training.  
Before the data samples are passed to the neural network for training, further augmentation is applied online. These augmentations include slight cropping, rotations and brightness variations. Importantly, every transformation applied to the input image must also be applied to the targeted segmentation mask. With every epoch of training the neural network, these additional augmentations differ. The network thus never sees the exact same image twice.
# Banana Peels: A Genuine Waste or a Wonderful Opportunity?
## 3 Banana Composition and Waste
### Banana Peel
#### 3.2.1 Nutritional Composition of Banana Peel  
Cultivar, variety and ripening stage of the fruit define the nutrient composition of banana peel. Banana peel composition is variable based on these above factors and contains crude protein (2-8%), crude fat (3-11%), carbohydrate (3%) and polyunsaturated fatty acids such as linoleic acid [54]. Table 1 represents the comparison of the proximate composition of peels between the two categories (dessert and cooking) and different cultivars. Banana peels from various cultivars and groups were analysed to compare their nutrient composition. As shown in Table 1, the ripening stage and banana type significantly influence carbohydrate content, including starch and sugar. In the unripe stage, carbohydrates primarily exist as starch. However, as ripening progresses, starch molecules undergo hydrolysis, converting into soluble sugars and leading to an increased total soluble sugar content in ripe banana peels [55]. Moreover, plantain peel has an amylose percentage of starch ranging from 22% to 38%, which makes it less sweet and more resistant to starch hydrolysis [56]. Banana peel is enriched with essential macronutrients and micronutrients. It comprises Ca, K, Mg, P, Zn, Fe, Cu, Mn and amino acids such as leucine, valine, phenylalanine and threonine. Banana peel is also enriched with pectin (10-21%), lignin (6-12%), cellulose (-6-15%) and hemicellulose (6.4-20%) [60]. The most abundant mineral is P, followed by Na, Ca, Fe and Mg in the peel of _Musa sapientum_[61]. Their concentrations (mg/100 g) in the peel were found to be 211.3, 115.1, 59.1, 47.0 and 45.5, respectively [30; 61]. In another study, K content was highest in the peel of _M. sinensis_, followed by Ca and P (1708.6, 40.9, 28.62 and 27.84 mg/100 g, respectively) [58]. Thus, the abundant minerals found in the peel are P and K. However, the results vary across the studies depending on the type of banana, its maturation stage and the method used for respective analysis [44; 58; 61]. The major minerals found in banana pulp are K, Mg, P and Na, while peel has higher concentrations of P, Ca, Na, Mg and K, as shown in Table 2.  
However, the concentrations of these elements were observed to change in different studies depending on the species, cultivar and maturation stage. A study revealed a lower Fe concentration in three banana varieties: Pachabale (10 mg/100 g), Nendranbale (4 mg/100 g) and Yelakkibale (3.33 mg/100 g). However, they observed a Ca concentration of 44.68 mg/100 g in Yelakkiale banana peels. Vitamin C (17.83 mg/100 g), tannins (1073 mg/100 g), phytic acid and oxalates were also higher in the Yelakkiale variety [62]. Table 2 describes the difference in mineral composition between banana pulp and banana peel.
## 2 Numerical Methods and Experimental Procedure
### Sensor Effect  
The AE numerical signals from a single node provide the out-of-plane top surface displacement or velocity corresponding to a perfect point-contact sensor. We assume that the sensors are only sensitive to displacement normal to the surface of the sample. Reception wave sensitivity was measured on a steel block [46; 47; 48]. The sensor is taken into account with respect to its transfer function, which is experimentally determined by the reciprocity method. The post-processing phase accounts for this transfer function in the Fourier domain, following Equation (8):  
\[S_{sensor}(f)=M(f)\times S_{surf}(f) \tag{8}\]  
where \(S_{sensor}(f)\) is the Fourier Transform of the signal, \(M(f)\) is the reception sensitivity function, and \(S_{surf}(f)\) is the Fourier Transform of the simulated signal recorded on a single node at the sensor position. If we assume that the sensor is a point receiver, this implies that the sensor acts on regions that are small compared to the wavelengths of interest. In this study, we do not take into account the aperture effect of the sensor and the coupling medium. The aperture effect is certainly most evident for the sensor R15, for which the diameter of the piezoelectric element is large [46; 49]. Figure 5 shows the sensitivity of reception obtained for the two sensors used in this study (R15 and \(\upmu\)80) with the reciprocity method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & \(f_{max}\) & \(R\) & \(\lambda_{min}\) & \(l_{e}\) & \(\Delta t_{lim}\) \\ \hline Value & 1 MHz & 8 & 3 mm & 400 \(\upmu\)m & \(1.35\times 10^{-7}\)s \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the finite element calculation.  
Figure 5: Response in reception of the sensor \(\upmu\)80 (**a**) and R15 (**b**) experimentally obtained using the reciprocity method.
## 5 Discussion
One of the areas of further research will be the development of a method to ensure the survivability of the network based on the assessment of survivability, calculated using the presented approach. As mentioned above, the topography is not accounted for and coverage is limited to a circular communication path around each sensor. In further research, topography will beaccounted for. Taking into account the terrain will affect the geometry of the coverage area of the sensor, which will increase the accuracy of the coverage. Screenshots of the developed software are presented in Figures 6 and 12. The software is not currently available to the reader and is developed in the form of a desktop application. One of the stages in the further development of the study is the improvement and presentation of software in a free access web application.