esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
A 51-tap direct-detection least-mean-square (DD-LMS) technique was employed for equalization. After using traditional DSP algorithms, the resulting signal was further equalized with a 2D-CNN, a 1D-CNN, a 1D-DNN, and a Volterra nonlinear equalizer (VNE). The VNE used signal-processing methods to reduce distortion in the signal caused by device nonlinearity. The 1D-DNN and 1D-CNN used the learning abilities of neural networks to identify by stacking fully connected layers and using one-dimensional convolution, which helped reduce inter-symbol interference and minor nonlinear issues. The 2D-CNN changed the complex signal into a two-dimensional format that included both real and imaginary parts, allowing it to handle the amplitude and phase information of I/Q components at the same time.
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 4 Experimental Results and Analysis
### Real Scene Image Gaze Points Classification and Application
The experimental results show that the proposed method can efficiently classify the gaze points of the target and make the servo platform camera more accurately follow and aim achieving the servo platform visual aiming. In summary, the proposed gaze points classification method based on visual characteristics is experimentally tested by gazing at different 100 images, 6 real scene images and target. It can be seen that the distribution of gaze points in different gaze images reflects the different attention and gaze concentration of human eyes. For the gaze at a single background and a prominent target, the distribution of gaze points is highly concentrated. For the image having a complex background and no prominent target, the distribution of gaze points is scattered and widely distributed. The subjective and objective results obtained by the proposed method demonstrate that it achieves a high degree of gaze attention. In 100 gaze images average _Precision =_ **86.81%**, _Recall =_ **86.79%**, _We =_ **86.79%**, In 6 real scene the best _Ra =_ **91.34%**, _Co =_ **16.98**, _Precision =_ **98.15%**, _Recall =_ **98.17%**, _We =_ **98.16%**. These are also better and stable than those obtained by other methods. However, the evaluation values are not particularly improved. This is the presented study of visual characteristics and human physiological characteristics has not taken into consideration the different influencing factors, and there is no applicability for the establishment of various relationships. These still need to be further explored in future studies. Simultaneously, the experiment results of this study show the applicability of eye-tracking of gaze points classification, in order to achieve servo platform visual aiming. Figure 19: Gaze point distribution during visual aiming. (**a**). Distribution of the original gaze points; (**b**). Distribution of gaze points after classifying. Figure 20 present the gaze process 24 different time visual aiming state. Figure 20: 24 different time visual aiming state.
## 3 Results and Discussion
### Characterization of BNC/\(\kappa\)-Car Composites
#### 3.2.2 FTIR-ATR Analysis  
The FTIR spectra of BNC, \(\kappa\)-Car and BNC/\(\kappa\)-Car composites are presented in Figure 3. Samples were scanned over the range of 4000-650 cm\({}^{-1}\) to observe the effect of \(\kappa\)-Car presence in BNC membranes, caused by the in situ modification.  
In the case of pure BNC, two main peaks can be observed. A sharp characteristic peak of O-H stretching appears at 3347 cm\({}^{-1}\) and another important peak of 1645 cm\({}^{-1}\) is assigned to the glucose carbonyl stretching [38; 39]. A series of bands from 1200 to 1000 cm\({}^{-1}\) are related to the stretchingof C-O-C of sugar rings and C-O stretching vibrations of the primary (C\({}_{6}\)) and secondary hydroxyl (C\({}_{2}\), C\({}_{3}\)) groups. CH\({}_{2}\) deformation can be detected at 1428 cm\({}^{-1}\). The absorption band at 2896 cm\({}^{-1}\) corresponds to CH-stretching and CH\({}_{2}\) asymmetric stretching [40]. The peak at 1316 cm\({}^{-1}\) results from CH\({}_{2}\) wagging symmetric bending [41].  
The FTIR spectrum of \(\kappa\)-Car demonstrates peaks, which are characteristic of this polymer. The absorption bands at 1234, 929, and 844 cm\({}^{-1}\) are assigned to S=O of sulfate esters, C-O of anhydrogalactose and sulfation of C\({}_{4}\) on galactose [42], respectively. Sulfonic acid groups can be indicated based on the peak at 1368 cm\({}^{-1}\)[43]. The spectra of BNC/\(\kappa\)-Car composites provided evidence that the main component of these composites is cellulose. However, these spectra contain additional small peaks in the range of 1260-1240, 940-920 and 810-845 cm\({}^{-1}\), which are typical of \(\kappa\)-Car. The band's shape at 1030 cm\({}^{-1}\) suggests mixing of two peaks from both the polysaccharides. Shifting of absorption band of CO-SO\({}_{3}\) of the \(\beta\)-1,3-linked residue from 845 to 810 cm\({}^{-1}\) can be observed in the case of all the composites. The shift may be ascribed to the interactions of cellulose chains with \(\kappa\)-Car coils. The region of 3600-3000 cm\({}^{-1}\), which corresponds to -OH stretching is broader in the case of BNC/\(\kappa\)-Car, especially in case of BNC/\(\kappa\)-Car\({}_{2.0}\) and BNC/\(\kappa\)-Car\({}_{4.0}\), in comparison to the native BNC. This may suggest the interleaving of cellulose fibers and carrageenan matrix and occurrence of physical interactions between these biopolymers via additional hydrogen bonds. The latter effect was observed also for BNC composites with other compounds such as sodium alginate [44], agarose [45], chitosan [46], and collagen [47].
## 4 Results
### Patient Data Results  
The Pearson correlation of the dataset is presented in Figure 8. It can be noted that the risk level has a higher correlation with blood sugar and blood pressure than the other features. All features have a low-to-medium correlation with the RiskLevel class. The number of available features and the correlation plot show that there is no need for feature selection in this part of the work.  
Further analysis of the available features is presented in Figure 9. The plot shows the range of values for each feature and the existence of any outliers from the most common values. Since this is an XAI system, different data representations are required for medical personnel to monitor and understand the various factors affecting the patient's risk.  
Figure 6: Accuracy and loss training and validation charts for the state dataset.  
The influence of each feature on the different risk levels is represented in Figure 10. It can be noted from the figure that high values of BS and BodyTemp can help identify low-risk versus high-risk patients but are less powerful in the mid-risk class. To further explore the aspect of explainability, three patient samples were analyzed using the LIME model, as represented in Figure 11. The three subfigures show a patient at low risk (Figure 11a), another at a mid-level of risk (Figure 11b), and one at high risk (Figure 11c). The low-risk patient analysis shows that the BodyTemp and BS were the most influential factors, resulting in a probability of 0.57 for this patient as a low-risk patient versus 0.22 and 0.21 for mid- and high-risk, respectively. In Figure 11b, the patient represented with a BS value of 9 was classified as mid-risk mainly because of this feature, with a probability of 0.41. Finally, the patient record represented in Figure 11c was classified by the model as high risk with a probability of 0.58 because of the values of the features SystolicBP, BS, and BodyTemp.  
Figure 7: Feature influence on the low-risk class (**a**) and the high-risk class (**b**) in the region dataset.  
Figure 8: Pearson correlation of the dataset features.  
Figure 9: Box plot showing the range of values for each feature and whether there are any outliers.  
Figure 10: SHAP summary showing feature influence for low-risk (**a**), medium-risk (**b**), and high-risk (**c**) patients.  
The data were used in a 10-fold cross-validation manner to test multiple classifiers, as shown in Table 5. Similar to the region-level monitoring, different models were used to compare their performance versus the proposed model. The best results were achieved by the proposed CNN-GRU model in terms of accuracy, precision, recall, F1-score, and AUC. This suggests that the proposed CNN-GRU model is well suited for this task as well, effectively identifying dependencies in the patient data.  
Figure 11: LIME analysis for three different patient records: one at low risk (**a**), another at a mid-level of risk (**b**), and the final at high risk (**c**).
# Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning
###### Abstract  
Flow-induced vibration (FIV) is a phenomenon in which the flow passing through a structure exerts periodic forces on the structure. Most studies on FIVs focus on suppressing this phenomenon. However, the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan, USA, has developed a technology called the vortex-induced vibration for aquatic clean energy (VIVACE) converters that reinforces FIV and converse the energy in tidal currents to electrical energy. This study introduces the experimental data of the VIVACE converter and the associated method using deep neural networks (DNNs) to predict the dynamic responses of the converter. The DNN was trained and verified with experimental data from the MRELab, and the findings show that the amplitudes and frequencies of a single cylinder in the FIV predicted by the DNN under various test conditions were in good agreement with the experimental data. Finally, based on both the predicted and experimental data, the optimal power envelope of the VIVACE converter was generated as a function of the flow speed. The predictions using DNNs are expected to be more accurate as they can be trained with more experimental data in the future and will help to substantially reduce the number of experiments on FIVs.  
deep learning; flow-induced vibration (FIV); renewable energy; dynamic response +
Footnote †: journal: Applied sciences
# Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
## 2 Material and Methods
### Data  
In order to analyze the WUE pattern, data were initially obtained from the 6 th version of the Gross Primary Productivity (GPP, MOD17A2), Evapotranspiration (ET, MOD16A2), and Land Surface Temperature (LST, MOD11A2) products obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) obtained from the Earth Explorer website (available online: [[https://eartheplorer.usgs.gov](https://eartheplorer.usgs.gov)]([https://eartheplorer.usgs.gov](https://eartheplorer.usgs.gov)), accessed on 10 July 2020). In addition, annual accumulated precipitation data for 2001-2019 was obtained from Climate Hazard Group InfraRed Precipitation with Station (CHIRPS) data. This product has been verified by authors such as [27; 43] in northeastern Brazil, and [28] in the Brazilian Cerrado, with a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), available online: [[https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)]([https://data.chc.ucsb.edu/products/CHIRPS-2.0/](https://data.chc.ucsb.edu/products/CHIRPS-2.0/)), accessed on 10 July 2020. The extraction and manipulation of data and all calculations were performed using R version 3.6.3 [44] and Quantum GIS (QGIS) version 3.4.6 [45].  
Figure 1: Geographic location of the study area—westernmost region of Bahia (**a**), and Land Cover (**b**), respectively. Land Cover data source: ESA [40].
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 2 Fracture Behaviors of MGs
### Typical Mode I Fracture of MGs
#### 2.1.1 Multiple Shear Bands Formation and Sliding  
With the increasing notch-tip stress intensity during loading, shear bands are formed. Different from the plane shear bands formed in uniaxial compression/tension tests, the shear bands formed during mode I fracture are curved. This is because the propagation of shear bands in MGs follows the direction of the local maximum shear stress [24]. Therefore, the shear bands will reproduce the shear stress direction in the notch-tip stress field. After formation, the shear bands can slide for a distance, which ranges from less than one micron to tens of microns [25; 26]. Figure 1 shows the evolution of shear-offsets and load-displacement curve of a typical ductile Pd77.5 Cu6 Si16.5 metallic glass during 3-point bending test, with a sample size of \(3\times 6\times 30\) mm\({}^{3}\)[27]. Curved shear offsets could be observed on the sample's side surface (Figure 1a-e, taken by an optical camera, Nikon D810, Japan), and some small stress-drops/serrations could be noticed on the loading curve (Figure 1g), representing the activation and sliding of these shear bands. Enlarged SEM (taken by a LEO-1530 field emission scanning electron microscope, German) image shows that the shear direction of the shear offsets points into the sample (Figure 1f). Due to the randomness in the shear band motions of MGs, it is almost impossible to predict the actual formation moment, sequence, position and sliding distance for each shear band in the real case.
# A Comprehensive Review of Micro UAV Charging Techniques
## 4 Wireless Power Transfer (WPT)
### Laser Power Transfer (LPT)  
Laser power transfer is another method used to charge UAVs, mostly in space and military applications [85]. With this charging technique, laser beams of specific wavelength and frequency feed PV cells mounted on the UAV. These PV cells are used to harvest  
Figure 9: Solar-powered UAVs [75; 76; 77].  
energy from the laser transmitter to power the UAV and charge its batteries. Laser beaming technique is used for rotary wing and fixed-ing UAVs. It has become a viable solution to unlimited endurance. It has the capability to deliver high energy to the receiver using narrow beam divergence [23]. Laser power transfer is envisaged to empower several energy-hungry missions of UAVs over long distances. Some studies [86; 87] have reported on the feasibility of laser power transfer for UAVs. In [88], the authors presented results pertaining to current, voltage and efficiency based on laser wavelength in a laser PV cell and material type. In [24], the authors discussed results pertaining to wavelength and temperature output of PV cells used to charge the system through laser beams. In [87], the authors proposed a controller design strategy on the basis of laser PV module output characteristics to control the power converter of a laser power transfer system. Some critical concerns are blockage, mobility and performance in long-range flights. This method is restricted in certain areas, such as military areas and airports and other scenarios where laser beams are hazardous to human health and living environments. Figure 10 illustrates an overview of a multi-UAV WPT system using laser beams.
# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI  
### Product Design Based on Structured Data  
Big data in the product lifecycle can be categorized into structured, semi-structured, and unstructured [30]. Structured data separate the structure and content, whereas semi-structured data mix them. Unstructured data, such as audio, video, images, text, and location, have no fixed structure.  
Structured data refer to information that has been formatted and transformed into a predefined data model, which provides regularity and strict formatting. However, structured data can suffer from poor scalability and limited flexibility. Semi-structured data can be considered as an extension of structured data, that offers greater flexibility and extensibility. For this reason, in this paper, we will treat structured data and semi-structured as the same and introduce their applications in design together.  
To address the limitations of traditional questionnaire surveys, including small survey scope, difficulty in updating, time-consuming, and labor-intensive, [PERSON] et al. [83] proposed a machine learning-based affective design dynamic mapping approach (MLADM). The approach involves collecting Kansei words from literature, manually clustering them, obtaining product features and images from shopping websites, and generating online questionnaires. Four machine learning algorithms are used to construct the relationship model. However, despite MLADM being able to predict product feeling, it still heavily relies on the expert, and the online questionnaire data are also highly subjective. To overcome these limitations, some studies explore objective data for design knowledge. For instance, [PERSON] et al. [84] established a database to record affective needs, design elements, and Kansei words from past sales records and previous product specifications. They applied association rule mining to construct the relationship model and used it as the product design inference pattern.  
Limited by data sources' openness, it is not easy for designers to obtain structured data, which can impede their research in product design. In contrast, unstructured data holds advantages in terms of volume and accessibility, accounting for nearly 95% of big data [85]. Despite its amorphous and complex nature, unstructured data are still of great interest to scholars because of their implicit information and value. In Sections 4.3-4.6, we will, respectively, introduce the application of textual, image, audio, and video data in product design, as well as the AI algorithms involved.
## 2 System Update and Optimization
The product is transferred to flash distillation tower S22 to obtain 87.75% H\({}_{2}\)SO\({}_{4}\). It is then mixed with the remaining H\({}_{2}\)SO\({}_{4}\) stream 27 and heated to 1123.15 K in H21. Subsequently, it enters reactor R21 for decomposition into SO\({}_{2}\), O\({}_{2}\), and H\({}_{2}\)O, achieving a conversion rate of 70%. The liquid-phase stream 27 is mixed in M21 with the H\({}_{2}\)SO\({}_{4}\) from the flash distillation, while the gas-phase stream 28 is recycled to the Bunsen reaction. In the HI module, stream 19 is purified in R1 at 390.15 K to remove H\({}_{2}\)SO\({}_{4}\). Afterward, the purified flow is transferred to B1 for distillation. At the top of the tower, HI vapor with a concentration of 91.44% is obtained. Stream 25 from the reboiler is condensed in condenser H3 before being transferred to the Bunsen reaction module. Stream 3 is heated to 773.15 K in H1 and undergoes decomposition in reactor R11, achieving a 15% conversion rate to produce H\({}_{2}\) and I\({}_{2}\). After condensing to 393.15 K in H2, it is mixed with stream 2 and transferred to S1 for gas-liquid separation. Liquid-phase stream 12 enters the distillation column for the small cycle, while gas-phase stream 15 is collected as the final product. Table 2 shows the external thermal and electrical energy demands before and after the system optimization. In the absence of heat recovery utilization, the thermal efficiency is defined as  
\[\eta\ =\ \frac{\Delta H_{H_{2},\,HHV}\times q}{H_{heat}+G_{ele}/\eta_{e}} \times 100\% \tag{6}\]  
where \(\Delta H_{H_{2},\,HHV}\) denotes the higher heating value of hydrogen, which is 286 kJ/mol-H\({}_{2}\); \(q\) represents the hydrogen production rate; \(H_{heat}\) is the total external heat required; \(G_{ele}\) is the electrical energy needed for the system; and \(\eta_{e}\) is the conversion efficiency of electrical energy, which is 40% here. The calculated thermal efficiency is 53%, representing a 12% increase compared to the efficiency before the update [23]. Based on the Aspen simulation results, the hydrogen production pilot platform was updated. The system utilizes industrial materials such as HC276, TC4, carbon steel lined with enamel, and SiC. All the thermal energy for the system is provided by electric heating. Figure 2 is a schematic diagram of the modified hydrogen production pilot platform. \begin{table}
\begin{tabular}{c c c c c} \hline
**Item** & **Heat—Original (kJ) [23]** & **Heat—Update (kJ)** & **Electic—Original (kJ) [23]** & **Electic—Update (kJ)** \\ \hline HI distillation & 37,780 & 41,775,35 & & \\ HI decomposition & 3175 & 2797.8 & & \\ H\({}_{2}\)SO\({}_{4}\) distillation & 21,810 & 13,374 & & \\ HeSO\({}_{4}\) decomposition & 74,610 & 44,226.7 & & \\ Pump & & & 6695 & 6695 \\ \hline \end{tabular}
\end{table} 
Table 2: Heat and electric energy demands of the system. Figure 2: A schematic diagram of the pilot test platform after renovation.
## 3 Results and Discussion
### The Discussion of Polarization-Angle-Insensitive and Angle-Insensitive  
Based on the perfect symmetrical pattern of graphene nanorings, we discuss the polarization-angle influence of the proposed triple hybridization absorber. We set up structural parameters like the initial value in Figure 1 and fix \(E_{f}\) = 0.9 eV to all of the three rings. Then we vary the polarization from 0 degrees to 45 degrees at 15-degree intervals. Figure 6a shows a schematic diagram of the angle of incidence relative to the absorption structure, specifically indicating the position of the polarization angle \(\varphi\). We can clearly find that the data graph of the absorption effect is strictly insensitive to the change of the angle in Figure 6b. The absorption graphs of 0 degrees, 15 degrees, 30 degrees and 45 degrees are completely coincident. The slight fluctuations of the results obtained in FDTD can be ignored. Therefore, we can consider that our perfect absorption structure is an absorption structure that is not sensitive to the polarization angle, which is crucial for the application of the absorption structure in practical devices.  
Figure 5: Simulated electric field patterns (arrow for the direction and color for the contrast of the field strength) at the top graphene layers of the MPAs in Figure 3. Red line at the absorption peak. (**a**) is the peak at the 1.07 THz and (**b**) is the peak at the 1.54 THz.  
We also tried to simulate the incident angle \(k\) from \(0^{\circ}\) to \(30^{\circ}\) every \(10^{\circ}\) to get a graph of the absorption data for the incident angle change (We define the angle of \(k\) as the angle between the propagation direction of the incident light and the normal to the plane of the absorption structure). In FDTD, we set the \(E_{f}=0.8\) eV and the structure keeps original parameters which we give in Figure 1. From Figure 7, we can see that the response frequency has a certain change with the angle. Fluctuations occur, but generally does not affect the absorption effect of the entire absorption structure. Since the proposed absorber is based on the perfectly symmetrical pattern design of graphene, our absorbent structure is insensitive to the incident angle.  
Figure 6: Schematic diagram and absorption of the polarization angle of the incident electromagnetic wave propagating to the absorbing structure. (**a**) is the scheme which describes the polarization angle \(\varphi\) to the proposed absorber, (**b**) is the absorption of the different polarization angle in the range of 0.5 THz to 2.5 THz. The red line indicates the absorption rate at \(\varphi=0^{\circ}\), the yellow line indicates the absorption rate at \(\varphi=15^{\circ}\), the blue line indicates \(\varphi=30^{\circ}\), and the brown line indicates \(\varphi=45^{\circ}\).