esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 4 Incorporating RS as Covariates in DSM
### Remote Sensing Data as Proxies of Soil Properties Controlling Factors in the SCORPAN DSM Model  
#### 4.2.1 Climate  
On a global scale, climate is one of the major controlling factors for weathering and subsequent pedogenesis. Actually, the birth of pedology relates to [PERSON]'s pioneering  
Figure 5: Relative importance of the first 25 environmental covariates for SOC prediction by DSM in the Beauce region (France) using different learning soil data and incorporating RS data among the covariates. The color palette (light green to blue) indicates the degree of importance of the covariates; the least important covariates are in light green and the most influential covariates are in blue. The red squares are data derived from Sentinel 2 spectral bands; the pink squares are airborne gamma spectrometry maps; the yellow squares are other environmental covariates. Adapted from [PERSON] et al., Remote Sensing, **2003**, _15_, 2410, Figure 5.  
work on the zonal climatic distribution of soil types in Russia (e.g., [244; 245; 246; 247]). At this scale, climate is also the major controlling factor for SOC contents and stocks (e.g., [177; 248]), either through the effect of climate on vegetation-derived SOC inputs (e.g., in the hottest and driest regions) or through the effect of very low temperatures on lowering the rate of SOM mineralization (e.g., in the circumpolar and high-elevation regions).  
Figure 6 illustrates the global effect of climate on the world's carbon stock distribution downloaded from the website [[https://soilgrids.org/](https://soilgrids.org/)]([https://soilgrids.org/](https://soilgrids.org/)) accessed on 29 May 2023. The original work on DSM at the global scale is from [PERSON] et al. [249]. As explained in this article, climate is one of the most important driving factor of SOC stocks at the global scale. The map clearly highlights the effect of temperature in the Northern latitudes and in the mountains. There is also an effect of a very wet and hot climate, for example, in Indonesia.  
At the scale of mainland France, latitude and altitude are clearly the two main controlling factors for SOC, together with land cover (e.g., [250; 251; 252]). The effect of altitude was also demonstrated for climatic gradients related to elevation or rainfall in smaller regions (e.g., [238; 253; 254]).  
Rainfall intensity is another major controlling factor for soil weathering [255] and erosion [256]. Finally, climate change is one of the factors accelerating certain pedogenic processes and threats to soil [4].
# Three Rapid Methods for Averaging GPS Segments
## 2 Existing Works on Segment Averaging  
In [5], average segments (called \"centerline\") were used for k-means clustering of the GPS trajectories to detect road lanes. The averaging method was later defined in [6] based on piecewise linear regression. The data is treated as an unordered set of points for which piecewise polynomials are fit with continuity conditions at the knot points [7]. The drawback of using regression is that we lose the information in which order points were collected, which makes the problem harder to solve. In the Segment averaging challenge [4], only one proposal was based on the regression approach, and its performance was not among the best.  
Recent solutions consider the problem as sequence averaging and apply methods from a time series context using dynamic time warping as the distance function. For example, the CellNet segment averaging [8] takes the shortest path as the initial guess [9] and then applies the Majorize-minimize iterative algorithm [1] to estimate the mean. Another iterative algorithm uses kernelized time elastic averaging (iTEKA) for the averaging [10]. It considers all possible alignment paths and integrates a forward and backward alignment principle jointly.  
_Medoid_ is an alternative approach to mean. It is also defined as an optimization problem but with the difference that, instead of allowing any sequence, Medoid is selected as one of the input sequences. The problem can then be solved brute force by calculating all pairwise distances and selecting the sequence that minimizes the optimization function. Possible functions to measure the distance (or similarity) between GPS trajectories have been extensively studied in [11] and tested with the Medoid in [4]. In this paper, we use Hausdorff distance [12], as it is invariant to the travel order of the trajectories, and it provides competitive results among the other choices.  
Medoid has two main drawbacks: First, it requires O(\(k^{2}\)) distance calculations, where \(k\) is the number of trajectories. Standard implementations of distance functions like dynamic time warping usually require O(\(n^{2}\)), where \(n\) is the number of points in the longest trajectory. Thus, the total time complexity can be as high as O(\(k^{2}n^{2}\)). Another drawback is that Medoid is limited to be one of the input trajectories, which can be a poor approximation of the average segment, especially if \(k\) is small.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 3 Results
### Heat Production  
The coupling system can predict human heat production more accurately in both air exposure and water immersion environments, and all the predicted mean values are within SD of the measured values, according to a comparison of the predicted heat production results of the thermal manikin and the human thermal model coupling system with the experimental test results while wearing the two life preservers of LP1 and LP2 (see Figure 5). No significant difference between predicted and measured heat production was found using paired-sample t-tests (\(p>0.05\)). Additionally, while wearing LP1, the maximum deviation between measured and predicted values under conditions of air exposure and water immersion did not exceed 9.59 W and 27.51 W, respectively, and while wearing LP2, the maximum deviation between measured and predicted values under conditions of air exposure and water immersion did not exceed 6.79 W and 23.51 W, respectively. The results show that the RMSD during the test period was 18.91 W, which is smaller than the maximum value of the test result SD of 49.59 W. According to the statistical relationship between RMSD and SD, the accuracy of the core temperature prediction of the coupling system is high. For the majority of the water immersion processes, the predicted values were within the SD of the measured values, but the predicted values indicated with the residual analysis were still skewed. The model tended to predict LP2, and for LP1, the model tended to exceed the measured mean, suggesting that the predicted values underestimated the tested values. This phenomenon will be discussed in detail in the Discussion. In addition, the correlation coefficients \(R^{2}\) between the predicted and experimental values of heat production for wearing LP1 and LP2 were 0.952 and 0.946 (i.e., the closer the \(R^{2}\) is to 1, the stronger the correlation). According to the \(R^{2}\) test, it can be shown that the predicted and measured values are highly correlated.
## 3 Experimental Results and Discussion  
### Dataset  
The image dataset used for model training and detection in the experiments comes from the PKU-Market-PCB dataset released by the Open Laboratory for Intelligent Robotics at Peking University, which contains a total of 693 PCB images for six kinds of defects (missing holes, mouse bites, open circuits, short circuits, spur, and spurious copper). Considering the small number of sample images and defects available in the original dataset, direct use for training may not produce satisfactory results due to insufficient data volume. In addition, the PCB images in the dataset are well-shaped rectangles with a minimum tilt angle. In practice, if the PCBs are placed at an inclined angle or subjected to other disturbances, the detection results may be adversely affected. Therefore, to strengthen the generalization ability of the network [34] and considering the symmetric nature of PCB layout, image enhancement--including mosaic enhancement, flipping, random noise addition, and brightness adjustment--was used to randomly extend the dataset and improve the generalization ability of the detection model to 10,668 images. Figures 7 and 8 represent an example dataset.  
To visualize the defect information after data enhancement, we analyzed the overall distribution, size, and number of defects, as shown in Figure 9. As shown in Figure 9, the enhanced dataset generated by random data enhancement has different numbers of images for each of the six defect categories, but the differences are relatively small. Figure 9 shows the location of the centroid of all the defect detection bounding boxes in the enhanced images. It is apparent from Figure 9 that the centroids of all enhanced defects are widely dispersed, indicating that the defects do not follow a clear spatial pattern. Figure 9 shows the width and height of all bounding boxes in the enhanced dataset. As shown in Figure 9, most of the bounding boxes are centered near the origin, and their dimensions are mainly in the range of [0.025, 0.05]. This indicates that most of the detected bounding boxes are small, and most defects are small-sized targets.
## 4 Discussion  
### High Correlation of Geographic Variables to Electricity Consumption  
This study applied the socioeconomic survey data (TH and TP) and extracted geographic data from remote sensing images (SL, AL, and BU). The extracted data from satellite images including SL, AL, and BU presented a very high correlation to electricity consumption (0.77 \(\leq\) R \(\leq\) 0.83) compared to socioeconomic data (TH and TP), which provided R = 0.6. However, the GDP also provided a high correlation to total electricity consumption at 0.83. These results suggest that the variables extracted from remote sensing products could estimate the electricity consumption at the district level. Table 2 illustrates that SL was highly correlated with TEC. Moreover, the built-up area was a common predictor, chosen by all models through the feature selection method (see Table 3). Hence, we conclude that the remotely sensed extraction data could estimate electricity consumption at a district level.
## 5 Results and Discussion  
### Performance Results of Comparison Simulation Experiment  
In the training set data, the corresponding TopN list of each learner was calculated by the multi-disciplinary semantic tensor decomposition algorithm HO-SVD-OI in this section. The accuracy recall curve was used to reflect the changing trend of the accuracy of the method in this paper and the mainstream classical algorithm in matching STEAM learning topics with course standards, and the performance gap of different algorithms was reflected by the harmonic mean F1 index [29]. In this section, we selected the Kmeans IDF algorithm [30] based on semantic tags and K-means clustering of user access resources, the classical tensor decomposition Co SClu algorithm [31] based on semantic tag co-occurrence spectrum clustering, and the classical TD algorithm [32] based on original tensor decomposition as the reference comparison algorithm.  
The P-R curve and F1 index curve simulation results of the above four algorithms are shown in Figure 4. In the P-R curve comparison chart, as the training scale of the data set increased, the accuracy and recall rate of the algorithm showed a reverse proportion change trend. The more the values gathered to the upper right side of the image area, the better the matching effect of the digital learning resources organized by the algorithm on the learner's knowledge tag. In the F1 index curve comparison chart, the more values gathered above the image area, the higher the accuracy of the learning topics generated by the algorithm for the coverage of multi-disciplinary curriculum standards. In addition, whether it was the HO-SVD-OI algorithm applied in this chapter or the high-dimensional semantic tensor decomposition resource organization matching algorithm based on Kmeans clustering or spectral clustering proposed by our predecessors, its performance was significantly better than that of the simple tensor decomposition algorithm. By observing the F1 index curve, we found that when the scale of STEAM learning topics approached about 20, the four algorithms all achieved the best performance in the coverage accuracy of the STEAM discipline curriculum standard.  
Figure 3: Interdisciplinary knowledge graph automatically generated based on keyword “peas”.
## 3 Methodology
### Region-Level Distance  
Since the image-level distance only measures the compatibility globally, it may fail to capture the small details in the scene that may have a deeper impact on item matching than others. To address this problem, we divide the scene into \(n*n\) regions. Similar to \(g(\Theta,\cdot)\), an embedding function \(f(\Phi,\cdot)\) is learned to downscale the image features extracted from each region of scene \(s\):  
\[r_{s,k}=f(\Phi,\varphi_{s,k}) \tag{4}\]  
where \(\varphi_{s,k}\in\mathbb{R}^{L}\) denotes the extracted features of the \(k\)-\(th\) region of scene \(s\); \(r_{s,k}\) denotes the embedding vector for the \(k\)-\(th\) region. \(f(\Phi,\cdot)\) is a two-layer feed-forward network with parameter set \(\Phi\). The architecture is _Linear-sigmoid-Linear-sigmoid_, which can be described as follows:  
\[h=sigmoid(W_{1}\varphi_{s,k}+b_{1}) \tag{5}\]  
\[r_{s,k}=sigmoid(W_{2}h+b_{2}) \tag{6}\]  
where \(W_{1}\in\mathbb{R}^{K^{\prime}\times L}\) and \(W_{2}\in\mathbb{R}^{K\times K^{\prime}}\) are embedding matrixes, while \(b_{1}\in\mathbb{R}^{k^{\prime}}\) and \(b_{2}\in\mathbb{R}^{K}\) are bias vectors. Here, \(sigmoid(\cdot)\) is used as the activation function for each linear layer.  
Then, we calculate the distance between each region and item \(i\), which is given by:  
\[d_{s,k,i}=\left\|r_{s,k}-v_{i}\right\|_{2} \tag{7}\]  
As different regions influence the result differently, we compute the weighted sum of all the regional distances. The region-level distance \(L_{R}(s,i)\) is then defined as the weighted sum of \(n*n\) regional distances:  
\[L_{R}(s,i)=\sum_{k=1}^{n*n}\omega_{k}*d_{s,k,i} \tag{8}\]where the weight parameter \(\omega_{k}\) is computed by SoftMax function:  
\[\omega_{k}=\frac{\left\|r_{s,k}-c_{i}\right\|_{2}}{\sum_{t=1}^{t=t}\left\|r_{s,t} -c_{i}\right\|_{2}} \tag{9}\]  
where \(c_{i}\in\mathbb{R}^{K}\) is the embedding vector for the category of item \(i\), which is learned during training. Please note that \(\omega_{k}\) is a category-aware attention term. The category of the masked item may affect compatibility computation between scene and item. For example, when the masked item is a pair of shoes, the bottom item may have more influence on visual matching while the top item may have less influence. However, when the masked item is a hat, the top item may have more influence than the bottom item. Hence, the category-aware attention term \(\omega_{k}\) improves the accuracy of region-level distance calculation.
## 3 Application of Uncertainty Quantification Methods and Results
### Results and Discussion of Uncertainty Quantification
#### 3.3.2 Discussion of the Uncertainty Quantification  
As mentioned above, it was confirmed that the accuracy and uncertainty of radar-based precipitation estimates could be changed depending on the method applied at each stage. In this section, the results provided in the previous section are reanalyzed to determine the superior method for each stage. The fuzzy algorithm in the quality control stage, WPMM in the precipitation estimation stage, and the LGC method in the post-processing stage were selected, and the results of applying these three methods are shown in Figure 6.  
For the MEM in each stage, the uncertainty gradually decreased, i.e., to 4.15, 3.90, and 3.77 in association with the fuzzy algorithm, WPMM, and LGC method, respectively. Assuming that the final uncertainty in the MEM was 100%, the uncertainty of the fuzzy algorithm was 110%, and that of the WPMM was 103%. However, the uncertainty of the LGC method was 100%, i.e., the same as the final uncertainty. The tendency of uncertainty propagation showed that the major contributor was the quality of the radar measurements, and that the uncertainty was reduced by applying the superior method at each subsequent stage. In contrast, for the UDM and M-FUM, even if the best method was applied at each stage, the uncertainty was propagated in the same way as the quantitative uncertainty when applying the UDM and M-FUM, mainly because all methods in the precipitation estimation stage were major contributors to increasing the uncertainty. This indicates that there might be stages that could increase the uncertainty even if a method that performs well at each stage is selected. In summary, in the entire radar-based precipitation estimation process, selecting a method with superior accuracy in each stage will result in the lowest uncertainty.
## 4 Results  
### Using the Synthetic Database  
Given that the best results were obtained using the VV alone (NN1, NN1_dry, and NN1_wet), the results from NN1, NN1_dry, and NN1_wet will be thoroughly commented upon (Section 4.1.1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**NNs Name**} & \multicolumn{2}{c}{**Noisy Training**} & \multicolumn{2}{c}{**Noisy**} & \multirow{2}{*}{**Input Vector**} & \multirow{2}{*}{**Output**} \\  & & **Database** & & & \\ \hline \multirow{3}{*}{No a priori} & NN1 & \(2\leq mv\leq 40\) & \(2\leq mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VV, NDVI & \(mv\) \\  & NN2 & \(2\leq mv\leq 40\) & \(2\leq mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\  & NN3 & \(2\leq mv\leq 40\) & \(2\leq mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\ \hline \multirow{3}{*}{A priori: dry to slightly wet} & NN1\_dry & \(2\leq mv\leq 30\) & \(2\leq mv\leq 25\) & \(\theta\), \(\sigma^{\circ}\)VV, NDVI & \(mv\) \\  & NN2\_dry & \(2\leq mv\leq 30\) & \(2\leq mv\leq 25\) & \(\theta\), \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\  & NN3\_dry & \(2\leq mv\leq 30\) & \(2\leq mv\leq 25\) & \(\theta\), \(\sigma^{\circ}\)VV, \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\ \hline \multirow{3}{*}{A priori: very wet} & NN1\_wet & \(20\leq mv\leq 40\) & \(25<mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VV, NDVI & \(mv\) \\  & NN2\_wet & \(20\leq mv\leq 40\) & \(25<mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\ \cline{1-1}  & NN3\_wet & \(20\leq mv\leq 40\) & \(25<mv\leq 40\) & \(\theta\), \(\sigma^{\circ}\)VV, \(\sigma^{\circ}\)VH, NDVI & \(mv\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input and output parameters for the nine neural networks developed in this study.  
On the other hand, the results obtained with the VH alone (NN_2, NN_2_dry, and NN_2_wet), as well as the VV and VH together (NN_3, NN_3_dry, and NN_3_wet), will be briefly discussed (Sections 4.1.2 and 4.1.3).
# Crack Location and Degree Detection Method Based on YOLOX Model
## 4 Experimental Results and Analysis
### Training Results and Analysis  
There are four standard versions of YOLOX, namely YOLOXs, YOLOXm, YOLOXI, and YOLOXx. Each version holds the same network structure, but the depth and width are different. The model depth and width can be set by the pre-weights required for training.  
The initial learning rate (lr) is an important hyperparameter of the YOLOX model, which affects the accuracy and convergence speed by controlling the step size of the weight update. If the learning rate is too large, it is likely to exceed the optimal value and make the loss function unable to converge. If the learning rate is too small, the network optimization efficiency is too low, the loss function cannot converge for a long time, and it is easy to make the network fall into local optimization. Therefore, an appropriate learning rate needs to be found through continuous attempts, which does not only ensure model convergence as soon as possible but also makes the model recognition effect the best.  
This study discusses the impact of different initial learning rate settings on the performance of four YOLOX models and selects the optimal result file as the crack location and degree detector. The initial learning rates are set to 0.01, 0.001, and 0.0001, respectively, and the validation results of the model after training with the same parameters are shown in Figure 10. YOLOX models obtain the best results on the validation set when the initial learning rate is 0.001, with mAP values of 88.57% (YOLOXs), 89.39% (YOLOXm), 90.05% (YOLOXl), and 91.17% (YOLOXx), respectively. Therefore, the proposed detector is trained with an initial learning rate of 0.001.
## 4 MLO-Based Spacecraft Attitude Fault-Tolerant Stabilization Control
### Design of a Mixed Learning Sliding-Mode Controller  
To design a mixed learning sliding-mode controller, a linear sliding-mode manifold and the reaching law [31] are suggested as follows:  
\[S=\omega+\delta q \tag{27}\]  
and  
\[\dot{S}=-\kappa_{1}\text{sgn}(S)-\kappa_{2}S, \tag{28}\]  
where \(\delta>0\) and \(\kappa_{i}>0,i=1,2\).  
For the attitude dynamics (26), a novel MLSM-based spacecraft attitude fault-tolerant stabilization controller is designed as follows:  
\[u_{c}=u_{smc}+u_{rec}, \tag{29}\]  
where \(u_{smc}\) denotes sliding-mode control law and \(u_{rec}\) denotes the mixed learning algorithm-based jointed disturbance compensation part. In the MLSM controller, \(u_{smc}\) and \(u_{rec}\) are respectively designed as follows:  
\[u_{smc}=\omega^{\times}J_{s}\omega-\frac{1}{2}\delta J_{s}(q^{\times}+q_{0}I_{ 3})\omega-\kappa_{1}S-\kappa_{2}\text{sgn}(S) \tag{30}\]  
and  
\[u_{rec}= -\hat{u}_{df}-\hat{u}_{f}(t) \tag{31a}\] \[\hat{u}_{df}(t)= \theta_{3}\hat{u}_{df}(t-\tau)+K_{3}(\sigma_{3}S(t-\tau)+\sigma_{ 4}S(t)), \tag{31b}\]  
where \(K_{3}\in\mathbb{R}^{3\times 3}\) represents controller gain matrix and \(\theta_{3}\), \(\phi\), \(\sigma_{3}\), and \(\sigma_{4}\) are positive scalars that will be determined later.  
According to (31a) and (31b), the estimation error dynamics of \(\hat{u}_{df}\) can be obtained as:  
\[\hat{u}_{df}(t)=\theta_{3}\hat{u}_{df}(t-\tau)-K_{3}(\sigma_{3}S(t-\tau)+ \sigma_{4}S(t))+\Delta\hat{u}_{df}(t), \tag{32}\]  
where \(\Delta\hat{u}_{df}(t)=u_{df}(t)-\theta_{3}u_{df}(t-\tau)\).