esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Open Research Issues, Challenges and Opportunities
### WBAN Design Challenges for Life and Safety Critical Applications
In order to better understand the specific data requirements of the sensors in worker safety scenarios a brief analysis is presented as follows. First for the health monitoring, most of the collected vital sign information such as temperature, oxygen saturation, heart rate, pulse and respiratory count can be transmitted to the WBAN coordinator just in one packet/s (for example, since data packet at the physical layer in IEEE 802.15.6 can be up to 256 Bytes and as the information in these sensors are not more than few bytes). Therefore, information can also be aggregated and finally transmitted after one second. However, the data requirements for ECG, EMG, and EEG signals are much more in comparison with the above basic data, therefore, these signals are often compressed to lower rates. For example, ECG can be compressed up to 4 Kb/s/electrodes (with a sampling rate of 250 Hz and 16 bits resolutions [125]), which means 12 data packets per second for three electrodes (which is a minimum requirement for a physician to understand complete cardio activity) are required. However, the effective number of packets will be more after adding the lower layers overheads). For the case of EEG, data can be compressed up to 40 Kb/s/electrodes [126], which will result in 40 data packets per second for each electrode. Similarly more than 50 packets for EMG could be required [127]. Second, for the movement activity and protection cases, accelerometers', gyroscopes' and gas sensors' data requirements can be met in one-to-two packets/s. Overall, the MAC super frame has to manage more than 100 packets/s of 256 Bytes which is more than 200 Kb/s (only for the payload). From the above analysis, it can be seen that the low-power and high data rate requirements of the safety and critical applications can only be properly realized and meet by using the WBAN-specific IEEE 802.15.6 standard. It can also be seen that the packet delay has to be managed properly (for example 125 ms is the maximum latency for the case of IEEE 802.15.6). Due to critical nature of the applications as well as due to limited memory size of the sensor devices as it is not possible to keep buffering the packets more than few seconds, therefore, the time constraint could be very crucial for these new applications.
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
## 2 Methods
### Color Space Transformations Based on Radar Signal Mapping Rules  
Image brightness adjustment and color space conversion on color channels are effective methods for data augmentation. The two-dimensional profile of GPR generally adopts a waveform image and greyscale image, in which the waveform image is formed by multiple A-scan traces (see example in Figure 3a). The greyscale image is obtained by mapping the amplitude values of the multiple A-scan traces into the greyscale information (see example in Figure 3b). The GPR device recorded signal amplitude values with a range of [\(-\)20,000, 20,000] [36]. The traditional signal imaging method directly maps the amplitude values into the greyscale image. Black corresponds to the maximum value of the positive signal phase, while white corresponds to the minimum value of the negative signal phase. Other values of the amplitude are mapped into gray through proportional interpolation. However, the greyscale image is a single channel image with a pixel value range of [0, 255], which will cause the loss of reflected signal information. This method can only display the difference of the amplitude greater than 156 in the greyscale image and cannot preserve the phase information, which is very important to identify the target type.  
Compared with the single-channel grayscale image, the color image can obtain various colors through the changes in the three channels of red, yellow, and blue. Additionally, three channels all have the pixel value range of [0, 255] for each color, so the color image can retain more reflected signal information. A suitable mapping method can reduce the information loss of the GPR reflected signal and strengthen the features of the target. Therefore, we changed the radar signal mapping method for data augmentation. The mapping rules include adjusting different color scales and color space, and 8 colormaps (gray, afnhot, bwr, cool, jet, ocean, rainbow, and RdGy) are chosen in the matplotlib module for signal imaging. Figure 5 shows the GPR images under different mapping rules.  
Figure 4: (**a**) The original GPR image; (**b**–**h**) are the interval 1, 2, 3, 4, 5, 6, 7 traces are selected to extract one single signal, respectively.
# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 2 Related Work
### CNN-Based Super-Resolution Method  
With the rise of deep learning, especially convolutional neural networks, the SISR algorithm based on CNN has made brilliant achievements. The SRCNN [7] proposed by [PERSON] et al. is the pioneering work of CNN applied to SR. With the help of sparse coding, they introduced CNN to SR tasks, creating a precedent for the study of SISR based on deep learning. Later, in response to the slower speed of SRCNN [7], they proposed FSRCNN [26], which uses postsampling and deconvolution layers to reduce network parameters, which greatly improves the speed of the algorithm, but the super-resolution effect is not improved compared to that of SRCNN [7]. The VDSR [12] of [PERSON] et al. enhances the super-resolution effect by deepening the network structure, but the problem that it creates is that the network parameters are greatly increased. DRCN [13] uses the idea of iteration to deepen the network, and residual learning and recursive supervision strategies are used to stabilize the network training process. Although the network parameters are reduced, the calculation amount is not, and there is also the problem that the network is difficult to train. In order to overcome the training difficulties caused by network deepening, SRResNet introduces the idea of local residuals. DRRN [27] combines the ideas of local residuals, global residuals, and convolutional layer recursion to reduce the computational cost and improve the effect of the algorithm. In EDSR, the BN layer in the residual block is not needed, and it also stacks deeper networks by reducing the computational cost. SRDenseNet draws on the idea of DenseNet [28] and uses the complementary fusion of features of different depths for super-segmentation tasks. RDN [15] is a further improvement on DRRN. It applies a dense residual block and introduces local residual learning and global residual learning to improve the effect of the model. RCAN [29] introduces channel attention into the residual block and uses the RCAB structure to improve network expression ability. MSRN [30] extracts rich feature information from a multiscale perspective. Although the CNN-based methods have made many achievements, the characteristics of the convolution kernel always limit the global feature extraction ability of such networks, which cannot fundamentally achieve the effective fusion of global and local features.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 1 Introduction  
For more than 20 years, wind power use is expanding in Germany and other countries in order to increase the regenerative and, therefore, sustainable production of electricity. In Germany, the goal is to produce 80 % of the total electrical power by means of regenerative sources, with a focus on wind power [1]. In addition to large off-shore locations, further allotments of on-shore locations as well as \"repowering\" activities (i.e., replacing older wind turbines by newer ones with increased performance and efficiency) will be necessary in order to fulfil the goals of reducing the carbon footprint as prescribed by the German Climate Protection Plan [2].  
Increasing density of wind parks, especially on-shore, however, is an issue for radar systems, as the large wind turbine rotors cause \"radar clutter\", i.e., unwanted signal return, most notably for aviation safety radars and weather radars. The \"Deutsche Wetterdienst\" (DWD, German Meteorological Service) generally requires a clearance of at least 5 km around its radar installations as well as a height limit for surrounding wind turbines in order to limit wind-turbine influences. An additional field of conflict is the fact that radar systems and wind turbines have overlapping requirements for their location: places that are good for radar installations usually are also well-suited for wind turbines [3; 4]. This is also true for radars for aviation safety, where air traffic surveillance radar is also negatively influenced by wind-turbine clutter [5; 6].  
The aim of the study presented in the following is to qualify and quantify the influence of different constructive parameters of idealized and typical wind turbines with respect to their radar cross section (RCS). This number represents a compounded virtual \"area\" of the radar target. It is directly linked to detectability: larger RCS means better detectability. As weather radars also include polarimetric measurements [7], dependence on polarization is also investigated.  
The phenomenon of radar disturbance has been studied in many measurement campaigns for wind parks [8; 9; 10; 11] as well as for single turbines [12; 13; 14]. The aspect of modifications of the aerodynamicparameters, to the author's best knowledge, has not yet been investigated separately. Therefore, special focus is put on the aerodynamic parameters of the rotor blades.
## 3 Results  
### Diversity and Richness of Fungal Microorganism Communities  
To characterize the diversity of fungal microorganism community distributed on sea buckthorn at different ripening stages, we harvested berries from the three distinct shrubs at private organic plantation and generated a combined sample as one biological replicate. For metagenomics analysis, three biological replicate samples of unripe berries (UB) obtained from different locations of the sea buckthorn plantation and three corresponding to ripe berries (RB) were used. Sequencing of the ITS2 rRNA partial gene region of DNA samples generated a total of 726,634 reads with an average of 121,105 sequences per sample (range 897,20-164,922) (Table 1). Following quality filtering, 454,538 sequences were recovered. The clustering of the sequences generated a total of 1615 unique amplicon sequence variants (ASVs) (1014 [338\(\pm\)70, hereafter median for 3 samples \(\pm\) standard deviation] for fungal ITS2 from unripe berries and 601 [200\(\pm\)35.6] ASV for ripe berries). The observed number of ASV reveals a significant difference in fungal community richness between UB and RB sample groups (ANOVA p=0.038).  
The microbial community analysis according to the nonparametric Kruskal-Wallis test showed a statistically significant decrease (\(p\)-value 0.046) in fungal microorganism diversity amongst RB samples in comparison to UB samples. In agreement with ASV data, [PERSON]'s diversity and Simpson indexes estimate that unripe sea buckthorn berries have a higher fungal microorganism diversity than ripe berries. Rarefaction curve approached plateaus indicating that the sequencing depth was sufficient to capture the microbial diversity of each sample (Figure S1). Principal coordinate analysis (PCoA) accomplished with the representative ASVs demonstrated a clear separation of UB and RB  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Reads** & **High Quality** & **ASV** & **Shanon** & **Pielou** & **Simpson** \\  & **Obtained** & **Reads** & **Density** & **Eveness** & **Index** \\ \hline
**UB1** & 103,024 & 49,891 & 300 & 5.26 & 0.64 & 0.94 \\
**UB2** & 164,922 & 110,782 & 419 & 5.00 & 0.58 & 0.91 \\
**UB3** & 89,720 & 43,458 & 295 & 5.37 & 0.66 & 0.94 \\
**RB1** & 114,814 & 76,641 & 190 & 3.71 & 0.49 & 0.86 \\
**RB2** & 135,098 & 92,822 & 240 & 3.795 & 0.48 & 0.85 \\
**RB3** & 119,056 & 80,944 & 171 & 3.21 & 0.44 & 0.79 \\ \hline
**Total** & 726,634 & 454,538 & 1615 & & & \\ \hline \end{tabular}
\end{table}
Table 1: Total sequences obtained for eukaryotic microbial community for unripe (UB) and ripe (RB) sea buckthorn samples.  
samples, indicating a difference in the composition of the fungal microbiota (Figure 1). Clustering of triplicate sample sequence libraries using the Bray-Curtis dissimilarity index revealed consistency of microbial community structure among replicates.
## 2 Theoretical Formula Derivation
### Poisson Distribution  
We utilize the Poisson distribution to describe the probability distribution of receiving photons in UV light communication. The Poisson distribution expression is given by the following:  
\[P(X=n)=e^{-\lambda}\frac{\lambda^{n}}{n!},\lambda>0, \tag{4}\]  
Here, \(X\) represents the count of events (number of photons detected), which is a non-negative integer. The parameter \(\lambda\) represents the average number of UV photons arriving at the receiver per unit time (Poisson parameter). The probability mass function \(P(X=n)\) represents the probability of detecting exactly \(n\) photons at the receiver within a unit time interval.  
For a single symbol, under OOK modulation, the mean number of photons for symbol 1 at the transmitter is \(\lambda_{s}\). Therefore, the probability density function of receiving photon counts for any symbol 1 can be expressed as follows:  
\[P_{one}(n)=\frac{e^{-\lambda_{s}}\cdot\lambda_{s}^{n}}{n!}, \tag{5}\]  
Here, \(n\) represents the number of photons, and \(P_{one}(n)\) denotes the probability of receiving \(n\) photons when transmitting symbol 1. Similarly, assuming the mean number of photons for symbol 0 at the transmitter under OOK modulation is \(\lambda_{b}\), the probability density function of receiving photon counts for symbol 0 can be expressed as follows:  
\[P_{zero}(n)=\frac{e^{-\lambda_{b}}\cdot\lambda_{b}^{n}}{n!}, \tag{6}\]  
Here, \(n\) represents the number of photons, \(P_{zero}(n)\) represents the probability of receiving \(n\) photons when transmitting symbol 0. We can use the above two Poisson formulas to characterize the probability of receiving photon signals by the system.
# Gold Nanofilm-Coated Porous Silicon as Surface-Enhanced Raman Scattering Substrate
## 1 Introduction
In addition, the thickness of Au film over etched PSi substrates was controlled by changing the deposition time from 10 to 30 s [16]. On the other hand, [PERSON] et al. prepared Au-capped PSi substrate by the same procedure of galvanostatic anodic etch on the smooth surface of the crystalline Si wafer to achieve a specific thickness PSi with appropriate porous nanostructure, followed by PVD-based Au sputtering to deposit Au film of variable thickness (10, 30, 50, 100, 200, and 300 nm) [17]. However, in another study, [PERSON] et al. theoretically and experimentally investigated the PSi SERS substrate consisting of sputtered Ag nanofilm with desired surface roughness and thickness by changing the deposited template [18]. A PSi-Ag substrate using nanovoid template exhibited the highest SERS signal at the thickness of 20 nm, and the signal was decreased with increasing Ag film thickness. SERS intensity of 10 nm Ag film using a flat substrate as template was less sensitive compared to the same thickness of Ag film coated on nanovoid structure. Their theoretical studies thus provided useful understanding about the role of template with different surface morphology and metallic film thickness to fabricate more accurate, stable, and cost-effective SERS substrates [18]. Herein this study, the Au nanofilm-coated PSi was investigated as a simple, robust, and cost-effective SERS substrate. Unlike the previously reported nanostructured template fabricated by the conventional lithographic process, in this study, a more cost-effective and less time consuming single step electrochemical etching process was followed to obtain PSi with nano-sized surface roughness. Moreover, the metallic film deposition over the PSi template was done by the sputtering technique,which aided in obtaining the homogeneous nanofilm rapidly with large scale. In addition, Au films of different thicknesses (5, 7, and 10 nm) were sputtered on PSi and flat Si surface to observe the contribution of Au nanocrystallites in the variable layer, as well as surface roughness in the SERS enhancement. SERS study of Si- or PSi-coated Au thin films was conducted by using R6G dye. PSi-Au thin film was proved as a better SERS substrate than Si-Au thin film, while PSi-Au thin film of 7 nm thickness exhibited the highest SERS activity of all the fabricated composites. Therefore, the facile and straightforward fabrication method led to achieve a robust and reproducible PSi-Au thin film SERS substrate, which would be beneficial to a wide range of sophisticated applications.
## 4 Experiments  
### Dataset  
#### 4.1.1 DAGM2007  
The German Association for Pattern Recognition (DAGM) and the German Chapter of the European Neural Network Society (GNNS) launched a competition for industrial defect detection. They provided a dataset called \"Weakly Supervised Learning for Industrial Optical Inspection\", including 10 classes' images. The first six classes of images are composed of 150 defective images and 1000 non-defective images, while the last four kinds of images are composed of 300 defective images and 2000 non-defective images. The image samples of 10 classes with size 512 \(\times\) 512 pixel are shown in Figure 11, where each of them is generated by different texture models and defect models, and the defect areas have been circled with ellipse labels. At present, many algorithms have been tested on this dataset, but there is still room for improvement. The main reason is that: (1) The defect area is only weakly labelled by an ellipse, which means there are some non-defective images in the ellipse; (2) compared with the whole image, the defect area is very small, and most of them are not obvious; and (3) the background texture of some images varies a lot.
## 2 Materials and Methods
### Site Effect  
Studies of site effects are elaborate and well-known, starting with the study by [PERSON] [20]. The resonance frequency of the near-surface layer and the corresponding amplification coefficient are the two main parameters of site effects. In the case of two-layered geology represented by loose sediment and rigid basement, the resonance frequency is related to the former layer (Figure 3c). However, geology is usually complex; the resonating layer can combine many layers of different geology and elastic properties. The resonance frequency largely depends on the thickness of this resonating layer, which acts as a bandpass filter that enhances selected frequencies. The amplification coefficient shows how many times amplitudes are amplified; thus, it can affect the registered signals by increasing the registered amplitudes for specific frequencies. The amplification increases as the impedance between stiff and loose rocks increases (Figure 3c). The empirical method allowing to find the site effect parameters is the horizontal-to-vertical spectral ratio (HVSR) technique, based on a spectral ratio of translation amplitude recorded for two main directions (Figure 3a): horizontal, H, and vertical, V [36; 37; 38; 39; 40].
## 4 Experimental Results
### Experimental Procedures
#### 4.2.1 Building the Brush Trajectory Model
As each image is produced via the adjustment of various parameters, the experimental parameters of each image were written in a two-out-of-five code on the sheet itself to  
Figure 16: Relationship between the horizontal handle displacement and footprint width/lag. (**a**) Width for Brush Pen #1. (**b**) Lag for Brush Pen #1. (**c**) Width for Brush Pen #2. (**d**) Lag for Brush Pen #2.  
facilitate manual perusal. These data were also stored as ASCII codes in the blank parts of the image to facilitate batch extraction by a computer program. In this experiment, the parameters were \(z1=z2\in\{100,200,300\}\), \(\theta\in\{60^{\circ},90^{\circ}\}\), and \(L_{2}\in\{100,200,400,700\}\). Therefore, there were a total of \(3\times 2\times 4=24\) combinations of parameters, and 18 samples were obtained for each combination. \(L_{1}\) is a random value. The length parameters are in units of 0.01 mm, and \(\theta\) is the angle between the handle's trajectory and the x-axis. It is positive (negative) in the counterclockwise (clockwise) direction from the handle's trajectory. \(\theta\) is \(90^{\circ}\) during most of this stroke, and \(60^{\circ}\) during the last part of the stroke. In Figure 17, \(m_{0}\) to \(m_{10}\) show the positions where the brush pen was moved, turned and raised for \(\theta=60^{\circ}\), and the two-out-of-five code for \(L_{1}\), \(L_{2}\), and \(\theta\) is 7-4-2-1-0. In Figure 17b, \(z_{1}=z_{2}=3\) mm, and the intersection between \(\overline{m_{7}m_{8}}\) and \(\overline{m_{9}m_{1}0}\) is the center of a circle, \(C\), while the intersection between \(\overline{m_{1}m_{2}}\) and \(\overline{m_{5}m_{6}}\) is the turning point of the handle trajectory, \(P\). The footprint that is closest to \(C\) is \(F\) (the front of the footprint), and \(\alpha=\angle DCF\). Figure 18 illustrates the change in \(\alpha\) with \(L\). Suppose that a straight line can be drawn through the handle, front end of the footprint, and brush tip. It is then possible to (a) obtain the angle of the footprint's front end, \(\alpha\), from Figure 18 and (b) obtain the footprint lag and outer frame of the footprint from Figure 16. After \(\alpha\), the footprint lag and the outer frame of the footprint have been obtained, the trajectory of the footprint's movements may then be computationally modeled using the brush trajectory model. Figure 13:0 Measurement of footprints for a bent line where \(m\) represents the coordinate of the projection of the handle. (**a**) Raw image (\(580\times 587\)). (**b**) Corrected image (\(2100\times 2500\)). Figure 13:1 Dependence of the angle between the trajectories of the handle and front end of the footprint, \(\alpha\), on the handle displacement distance. (**a**) Brush Pen #1, \(z_{0}=z_{1}=1\) mm. (**b**) Brush Pen #2, \(z_{0}=z_{1}=1\) mm.
## 1 Introduction
[18] used Landsat 8 as a data source, based on the GEE cloud platform and used neural networks, support vector machines, decision trees, and random forest classification methods for the large-scale classification and mapping of crops in Ukraine, comparing support vector machines and random forests for higher classification accuracy. [PERSON] et al. [19] used the random forest algorithm to classify maize and non-maize areas in Tanzania and Kenya, based on the GEE cloud platform combined with Sentinel-1 and Sentinel-2 image data, with good accuracies of 79% and 63%, respectively. In terms of crop area extraction and growth monitoring, previous studies have focused on food crops such as wheat and rice [20; 21; 22; 23], and relatively few studies have been conducted on orchards such as walnut trees. Moreover, most studies chose MODIS image data for monitoring and analysis. The low spatial resolution of the MODIS image data can lead to a large number of mixed pixels. This study selected 10 m resolution Sentinel-2 image data as its data source, constructed a training feature set on the GEE cloud platform, and extracted the walnut planting area of Ganquan Town from 2017 to 2021 using the random forest algorithm. This study also combined these methods with MODIS image data to monitor and analyze the growth of walnuts in Ganquan Town from mid-April to September 2021, providing references and objective data support for realizing dynamic crop monitoring, which was further supported by high-frequency and medium- and high-resolution images, building a crop information extraction system based on the cloud platform, machine learning, and agricultural production.