esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Regional Scale Analysis
### Hazard Indicators
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline z & Index & \multicolumn{2}{c}{Scores} & Description & Database & Bibliographic Reference \\ \hline \multirow{4}{*}{1} & & Very small slope, 0-3” = H1 & Slope is the & & \\  & Small slope, 3-10” = H2 & measurement of & & & \\  & Moderate slope, 10-20” = H3 & surface steepness, is & & & \\  & Steep slope, 20-3” = H4 & measured as a degree, & & & \\  & & and ranges between 0 & [82] & Celtek, 2020 [49] \\  & & Very steep slope, \textgreater{}30” = H5 & and 90”. & & \\ \hline \multirow{4}{*}{2} & & Good exposition (N, NE, NW: \(0^{+}\propto\zeta\,\zeta^{\prime}\,+292.5^{+}\times\varsigma\,\times\,360^{ \prime}\)) = H1 & Aspect depicts the & & \\  & Moderate exposition (Z, E, W: \(67.5^{+}\propto\zeta\,\leq 112.5^{+}\) + \(247.5^{+}\) + \(\varsigma\,\times\,292.5^{+}\)) = H3 & direction of the slope & & \\  & Low exposition (S, SE, SW: \(112.5^{+}\propto\zeta\,\geq 20.5^{+}\)) = H5 & & & \\ \hline \multirow{4}{*}{3} & & Slope stability in & & \\  & & & Alpine and & \\  & & & & \\  & & Deplicated areas between 1975 and 199 = H2 & high-latitude regions & ClaRiskAlp database & \\  & & Deplicated areas between 1995 and 199 = H2 & is significantly & \"Inventory of Current and & \\  & & Deplicated areas between 1999 and 2005 = H3 & influenced by glacier & Past Glacier Extents\" & \\  & & Deplicated areas between 2005 and 2019 = H4 & retract, leading to & [83] & \\  & & Potential deglated areas in the future (glaciers in 2019) = H5 & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{4} & & Global & & & \\  & Global & Global lakes = H3 & reconstructing & & \\  & features & & efficient glacier & & \\  & & Depositi and Hills = H5 & & & \\  & & & & \\ \hline \multirow{4}{*}{5} & & & Permanent in almost all conditions (values \(0<\kappa\leq 51\)) = H1 & The term ”Permafrost” & & \\  & & Permanent mostly in very cold conditions (values \(51<\kappa<102\)) = H2 & is defined as ”sol or & PermaNET project [84] & \\  & & Permanent mostly in cold conditions (values \(102<\kappa\leq 153\)) = H3 & rock having & and & \\  & degradation & Permanent only in favourable conditions (values \(153<\kappa\leq 204\)) = H4 & temperatures below 0 & PEEMOS & \\  & & Permanent only in very favourable conditions (values \(204<\kappa\leq 258\)) = H5 & \({}^{\zeta}\)C over at least two or & [85] & \\ \hline \end{tabular}
\end{table} 
Table 1: Indices used for the evaluation of the hazard indicator and defined scores for the subdivision into different hazard classes.
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 1 Introduction
[17] applied deep learning to fruit freshness grading and proposed a hierarchical approach in which multiple fruits are detected and classified through real-time object detection. The regions of interest are cropped from the source images and fed into convolutional neural network (CNN) models for regression, ultimately grading the freshness. [PERSON] et al. [18] used computer vision (CV) to study the ripening stages of Golden Delicious apples and graded their ripeness based on color. [PERSON] et al. [19] developed a more robust deep convolutional neural network model for detecting the ripeness levels and black spot disease in citrus fruits on trees. [PERSON] et al. [20] proposed a defect detection model, Yolo-FD, which integrates a three-dimensional coordinate attention (TDCA) mechanism into its backbone network, improving the precision of detecting minor defects on citrus peels. They also introduced a fruit morphology detection algorithm combined with the particle swarm optimized extreme learning machine (PSO-ELM) model, achieving a morphology detection accuracy of 91.42%. [PERSON] et al. [21] proposed a deep learning-based automatic citrus sorting algorithm. In the first stage, the algorithm classifies lemons and oranges. In the second stage, it uses a corresponding model to perform binary classification of fruit quality as either good or bad. [PERSON] et al. [22] designed a small-scale citrus washing and sorting machine suitable for farms, which is capable of weighing and sorting citrus fruits. Additionally, the custom lightweight CNN model \"SortNet\" classifies fruit quality into \"accept\" and \"reject\" categories based on appearance. The machine can sort up to 230 kg of citrus fruits per hour. Moreover, deep learning has been widely applied to grading other fruits and vegetables, including garlic, soybeans, dragon fruit, and corn [23; 24; 25]. These techniques demonstrated outstanding performance in the fruit grading industry. To improve the efficiency of Orah sorting, developing industrialized sorting equipment is an effective approach. This study is based on a self-developed sorting line prototype. An Orah grading algorithm was developed according to its operating principles. The aim is to achieve rapid, comprehensive, and accurate Orah grading through improvements in the neural network model and the application of data obtained from multiple sampling.
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 4 Results
### Task Completion Time  
The mean and standard deviation of each task completion time (TCT) based on two independent variables were measured as follows (Table 3). The average TCT of the large target was 2322 ms, and the small target was 2518 ms, indicating that the time required to select a small target was longer. For determining whether the difference in TCT was statistically significant depending on the size of the target, a two-way repeated measure of ANOVA (RM-ANOVA) analysis was performed. Thus, it was found that the size of the target could have a significant effect on TCT (\(p\) < 0.001; \(\alpha\) = 0.05).  
The manual-selection method took 2132 ms, and the gaze-selection method took 2708 ms. In the case of manual selection, the TCT was shorter than that of gaze selection. Likewise, RM-ANOVA analysis was conducted to find out whether the difference in target selection method was significant (\(p\) < 0.001). Additionally, the interaction effect between target size and input method was also significant (\(p\) < 0.001). Simple effect analysis showed that there was no effect on target size during gaze-selection, but TCT was significantly different according to target size in manual-selection (Figure 6).
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### 4.1.4 Monitoring Restoration Efforts and Success  
Monitoring the success of peatland restoration initiatives is crucial for assessing the effectiveness of these schemes. Hence, accurate above-ground biomass estimation is essential for understanding peatland carbon storage. [PERSON] et al. (2017) assessed the capabilities of multispectral satellite data, such as Landsat-7 ETM+, Spot-6 HRG, and Spot-5 HRG, in identifying the condition of peatlands impacted by peat extraction and abandoned deposits [74]. They demonstrated the potential of remote sensing for monitoring biomass changes and evaluating the effectiveness of restoration measures. [PERSON] et al. (2021) investigated remotely sensed vegetation phenological parameters in bogs, emphasising the potential of satellite data in monitoring long-term vegetation phenology and its implications for ecological processes and carbon dynamics [70]. In their respective investigations, [PERSON] et al. (2019) and [PERSON] et al. (2019) employed Moderate Resolution Imaging Spectroradiometer (MODIS) datasets to develop models for estimating gross primary productivity (GPP) in peatland ecosystems undergoing restoration [59], comparing the results with field measurements and introducing a Temperature and Greenness model for more accurate local-scale assessments [64].  
Peatlands store large amounts of carbon, the release of which depends strongly on water table depth (WTD) [75]. Hence, the peatland's water table must be monitored to ensure restoration success [75,76].  
Synthetic Aperture Radar stands out due to its unique ability to penetrate beneath the Earth's surface, reaching the uppermost soil layers. This unique characteristic offers invaluable insights that optical data alone cannot provide. In our pursuit to assess the feasibility of employing remote sensing-derived information for monitoring below-ground ecosystem resilience, we embark on an exploration. A noteworthy aspect is SAR backscatter's capability to estimate soil surface moisture, which has been shown to establish a correlation with the time series of WTD in peatlands and wetlands in general [76,77].  
The potential of the Sentinel-1 SAR sensor for tracking the dynamic shifts in soil moisture is substantial. This potential is attributed to its frequent return interval and high spatial resolution, capable of reaching as low as 10 metres. These attributes are precious in high-latitude regions where numerous peatlands are situated. Nonetheless, the capabilities of the Sentinel-1 SAR have not been fully explored due to the recent launch of the satellite pair in April 2014 and April 2016, resulting in a constrained time series of accessible satellite images [78].  
It is crucial to highlight that SAR cannot currently be directly utilised for comparing soil moisture across different sites. This limitation stems from the inconsistent relationship between SAR backscatter and soil moisture, which is influenced by factors including vegetation structure and surface topography [76,77].
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 3 Methods
### Tunnel Point Cloud Extraction  
Tunnel point cloud extraction refers to retaining only the point cloud data corresponding to the left and right walls, as well as the ceiling and floor of the tunnel, while filtering out noise and internal objects. In this paper, the left wall, right wall, ceiling, and floor of the tunnel are extracted based on the density distribution of the point cloud.  
First, the point cloud is sliced along the tunnel direction (Y-axis) at intervals of distance \(d\). For each sliced point cloud, the following calculations are performed:  
(1) Density distribution histograms are generated for the sliced point cloud along both the X-axis and Z-axis. Taking the distribution histogram along the X-axis as an example, Figure 6 illustrates the point cloud density distribution along the X-axis. To generate this distribution, we first determined the range of the point cloud along the X-axis, \([X_{min},X_{max}]\). This range was then divided into smaller intervals of length \(l\) (1 cm in this work). For each interval \([X_{min}+l\cdot i,X_{min}+l\cdot(i+1)]\), where \(0\leq i\leq\left\lfloor\frac{X_{max}-X_{min}}{l}\right\rfloor\), we counted the number of points whose X-coordinate fell within that interval. This produced a density distribution histogram along the X-axis. The distance on the horizontal axis represents the distance of each point from the slice's starting point \(X_{min}\), measured in centimeters (cm).  
(2) Boundary range determination is then performed. The tunnel point cloud is represented by the first and last density distribution extremes in the density distribution histogram. Taking the left wall point cloud as an example, the first local density maximum \(X_{\lambda_{max}}^{left}\) in the X-axis density distribution histogram corresponds to the left wall point cloud (as shown by the red arrow in Figure 6), and the local minimum values \(X_{\lambda_{0}}^{left}\) and \(X_{\lambda_{1}}^{left}\) on both sides of this maximum are selected as the boundary range of the left wall point cloud (as shown by the green shade in Figure 6). Similarly, for the right wall point cloud, the last local maximum \(X_{\lambda_{max}}^{right}\) in the X-axis density distribution histogram corresponds to the right wall point cloud, and its spatial range is \(\left(X_{\lambda_{0}}^{right},X_{\lambda_{1}}^{right})\). Likewise, the ceiling and floor  
Figure 5: Y-axis view of tunnel. (**a**) Roll angle of tunnel. (**b**) Minimum bounding rectangle of tunnel. (**c**) Tunnel after rotation.  
point clouds correspond to the last and first local maximum values in the Z-axis density distribution histogram, and their boundary ranges are determined in a similar manner.  
To minimize noise interference, local maximum values greater than the mean of the density distribution are selected, while local minimum values are chosen if they are less than the mean. Once the final interval is selected, the local maximum values within the interval are reconfirmed, as shown in Figure 6.
## 2 Constellation Design
### Constellation Composition  
The HSC design comprises ten satellites, including six optical satellites (HN-1 01/02/03/04 and WC-1 01/02), two hyperspectral satellites (SY-1 01/02), and two SAR satellites (SS-1 01/02). The project is implemented in two stages, the first one involving the development and launch of four optical satellites. However, due to commercial satellite project constraints, the lack of opportunities for launching small-inclination satellites resulted in the successful launch of four optical satellites (HN-1 01/02 and WC-1 01/02) into a Sun Synchronous Orbit (SSO) at an inclination of 97\({}^{\circ}\) and orbit height of 535 km in 2022. The remaining six satellites (HN-1 03/04, SY-1 01/02, and SS-1 01/02) will be developed in the second stage and will operate at a 30\({}^{\circ}\) inclination and an orbit height of \(\sim\)500 km, as illustrated in Figure 1.
## 3 Theory and Methodology
### The Proposed Tsallis-Entropy-Based Method
#### 3.2.3 The Multilevel Thresholding Method  
The histograms of real medical images often exhibit complex distributions with more than two distinct peaks, reflecting the diverse nature of tissues and structures in the images. To achieve accurate segmentation in such cases, it is essential to employ advanced methods that can handle multilevel thresholding.  
Cauchy-TB can accurately identify one of the valleys in the histogram, segmenting the image into category 1, representing one type of tissue or structure, and a region to be further segmented, containing categories 2 and 3, which represent other types of tissues or structures. Within this region, the method can be applied recursively for binary segmentation to separate categories 2 and 3 as shown in Figure 7.  
By applying this recursive method, it is possible to extend the segmentation to four or more levels, enabling the identification and extraction of multiple regions of interest within the image. This approach enhances the segmentation effectiveness, particularly in complex medical images with diverse tissue types and structures. The pseudocode Algorithm 1 of our algorithm is as follows.  
```
1:Input: Brain MRI grayscale image, number of thresholds \(n\)
2:Compute the grayscale histogram of the input image
3:for\(i=1\)to\(n\)do,
4: Initialize the maximum Tsallis entropy (denoted as \(TE\)), the corresponding optimal threshold \(t_{i}\), and the current histogram grayscale range \([L_{\text{min}},L_{\text{max}}]\)
5:for\(t=L_{\text{min}}\)to\(L_{\text{max}}\)do
6: Compute the Cauchy distribution parameters \(x\) and \(\mu\) for the regions on both sides of the threshold \(t\)
7: Calculate the posterior probabilities for each grayscale level between the left and right classes
8: Compute the Tsallis entropy \(E\) based on the posterior probabilities
9:if\(E>TE\)then
10: Update \(TE=E\) and set the optimal threshold \(t_{i}=t\)
11:endif
12:endfor
13: The threshold \(t_{i}\) found at this step is the \(i\)-th optimal threshold, and the histogram is divided for further processing in the subsequent iterations
14:endfor
15:Output: A set of optimal thresholds
```  
**Algorithm 1** Cauchy-TB for Multi-Threshold Image Segmentation
## 3 Design of Beam Steering and Adjustment Structure  
### Structure Design Scheme  
Based on the three-aperture layout, a 45\({}^{\circ}\) inclined planar mirror and a 45\({}^{\circ}\) triangular cone mirror are used to realize the steering of the light path, as shown in Figure 9. The triangular cone mirror is mounted on a fine-adjusting precision platform adjustable for 3 degrees of freedom (\(XY\theta\)) to ensure that the triangular cone is at the center of the three subapertures. The planar mirror is mounted on a piezoelectric ceramic (\(PZT\)) microdisplacement actuator and fixed with a bracket, which together constitutes a fast steering mirror (\(FSM\)) structure to adjust the piston error and tilt error of the light path.  
The key issue considered in the design is the adjustment accuracy of the structure, which is also the guarantee of the system imaging quality. The entire adjustment structure is divided into rough adjustment and precision adjustment; rough adjustment meets the adjustment range of the light path, and precision adjustment meets the cophasing error control requirement. The mechanical adjustment structure of \"three screws for pull and three screws for push\" is adopted for the \(FSM\) rough adjustment, as shown in Figure 10.  
The \(FSM\) precision adjustment is driven by a \(PZT\) (PI S316) with a \(Z\)-direction translation stroke of 12 \(\upmu\)m, a closed-loop resolution of 0.4 nm, a tilt angle stroke of 1200 \(\upmu\)rad, and a resolution of 0.1 \(\upmu\)rad.  
Figure 8: Simulated imaging with different tilt errors.  
Figure 9: Three-dimensional model of the beam steering and adjustment structure.  
In order to ensure that the _FSM_ does not jitter or vibrate during the motion of the piezoelectric ceramics, we analyzed the first- and tenth-order vibration modes of the _FSM_ structure by using ANSYS Workbench software, as shown in Figure 11a,b. In addition, the harmonic response analysis results of the _FSM_ structure are supplemented, as shown in Figure 11c,d. The vibration frequency of piezoelectric ceramics is known to be 3400 Hz from the product presentation. Therefore, the _FSM_ structure will not resonate during the motion of the piezoelectric ceramics, and the dynamic stiffness of the system meets the operating requirements [19].
# Current-Sensing Topology with Multi Resistors in Parallel and Its Protection Circuit
## 2 Materials and Methods
### Rectified Mean Magnitude Detection  
The crest factor \(C_{F}\) is defined as the ratio of the waveform peak value \(X_{PK}\) to the effective value \(X_{RMS}\):  
\[C_{F}=X_{PK}/X_{RMS} \tag{8}\]  
Peak factor \(P_{F}\) is defined as the ratio of measurement peak value to effective value for a specified range:  
\[P_{F}=I_{PK}/I_{RMS} \tag{9}\]  
\(I_{PK}\) is the maximum instantaneous current covered by this range, indicating the measurement span of \(-I_{PK}\)\(\sim\)\(I_{PK}\). The waveform exceeding the span would be distorted. \(I_{RMS}\) is the maximum heat equivalent current covered by this range. It depends on the heat  
Figure 5: Work flow of double threshold-detection protection circuit.  
dissipation property of the sensing resistors and conduction switches. If \(I_{RMS}\) is significantly exceeded for a long time, the components in the sensing path may be damaged.  
The rectified mean circuit could be adopted as the magnitude-detection circuit in Figures 2 and 4. It is a rough method for effective value measurement, as shown in Figure 6.  
The circuit in Figure 6 is divided into two stages, each located in a dotted box. The first stage is a precision rectifier circuit constructed with two operational amps, where \(R_{1}\) and \(R_{2}\) have equal resistance value. It is also called the absolute value circuit. The second stage is a first-order low-pass filter composed of a resistor \(R\) and a capacitor \(C\) and is called the average value circuit.
## 3 Results
### Genome Annotation and Bioinformatics Analysis  
The data of genomic annotations were summarized in Table S1. During the analysis of GO classification, 9044 genes were annotated with the three main categories: cellular component, molecular function, and biological process, respectively (Figure 3A). The identified coding proteins associated with molecular function are more than other two groups. In the KEGG pathway annotation, the 8300 predicated genes could be divided into 46 categories based on their functions (Figure 3B). Among these categories, 94 genes are associated with the metabolism of polyketides and terpenoids, which indicates that _D. tabescens_ was a potential natural resource for polyketides and terpenoids biosynthesis. In the carbohydrate-active enzymes (CAZymes) annotation, 509 candidate CAZymes coding genes were identified in the genome of the strain CPCC 401429. Significantly, 199 glycoside hydrolases (GH) dominated in the classes of CAZymes, followed by 145 auxiliary activities proteins (AA), carbohydrate esterases (CE), glycosyltransferases (GT), polysaccharide lyases (PL), and carbohydrate-binding modules (CBM). Compared with the genome of other close _Armillaria_ species, the strain CPCC 401429 had less CAZymes in which the GHs, GTs, and CBMs represented the smallest sets among six _Armillaria_ strains (Table S2, supporting information I). In addition, as saprotrophic wood-rotting fungi, _Armillaria_ species exhibit higher quantities of CEs and AAs than other Agarales exemplified as _Agaricus bisporus_, _Pleurotus ostreatus_, _Coprimosis cinerea_, _Schiophyllum commune_, _Lentiunda edodes_, and _Lacaria bicolor_. Interestingly, most CEs and AAs could be functioning as lignin-, cellulose-, pectin- and hemicellulose-degrading enzymes, indicating the strong potential to degrade plant cell wall (PCW) components. Cytochrome P450s, as multifunctional oxygenases, play a key role in adaptation to nutritional niche or secondary metabolites biosynthesis. Based on domain and pfam prediction, a total of 404 cytochrome P450s coding genes were screened in the genome. The number exceeded those of _A. gallica_ 012m (_n_ = 271), _A. cepistipes_ B5 (_n_ = 324) and _A. gallica_ Ar21-2 (_n_ = 330). These P450s might be closely related to the formation of basic metabolism and secondary metabolites biosynthesis in _D. tabescens_[4]. There are three major bioactive compounds in _D. tabescens_, exemplified as melleolides, armillamide, and ergosterol [4]. The synthesis of onsellinate-derived melleolides and armillamide are catalyzed by a cascade of redox reactions through the combined action of cytochrome P450s and oxidoreductase, respectively [4].  
Figure 2: MLST-based phylogeny of the strain _D. tabescens_ CPCC 401429 and twenty-four sequenced basidiomycetes encompassing white and brown rot fungi. The species of _Heterobasition irregular_ TC 32-1 served as outgroup [23].  
Figure 3: Gene function annotation of _D. tabescens_ CPCC 401429 genome. (**A**) GO enrichment analysis of annotated genes from the strain CPCC 401429. (**B**) KEGG pathway annotation of the genome.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Realization Details  
To ensure unbiased results, we randomly initialize the model parameters in our experimental setup. The hyperparameters are carefully tuned on the validation set. Specifically, we set the threshold to 0.07, the dropout rate to 0.45, and consistently set the batch size to 32 across all experiments. Our model training process consists of 100 epochs, employing the Adam optimizer with an Initial learning rate of \(1\times 10^{-3}\). We incorporate learning rate decay using a multi-step scheduler, learning rates are adjusted to \(1\times 10^{-3}\) and \(1\times 10^{-5}\) at epochs 5 and 15. We implemented the entire experiment using Python 3.7.0 and PyTorch 1.10.0, with CUDA 11.4 utilized on a device equipped with 64 GB of memory and an NVIDIA-SMI 472.39 GPU. To ensure the reliability of our findings, we repeated the experiment five times using distinct random seeds.