esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cluster Validation and Comparison Tools  
Cluster validation was done by employing the silhouette coefficient \((SC)\). The value of \(SC\) determines the optimum number of clusters. \(SC_{i}^{j}\) is formulated in Equation (17) [24]:  
\[SC_{i}^{j}=\frac{\xi_{i}^{j}-\zeta_{i}^{j}}{\text{max}\big{(}\zeta_{i}^{j},\xi_{ i}^{j}\big{)}} \tag{17}\]  
where \(SC_{i}^{j}\) is the \(SC\) value for all \(i\)th data at the \(j\)th cluster, \(\zeta_{i}^{j}\) is the average squared Euclidean distance that denotes how similar the \(i\)th data are to their cluster, and \(\xi_{i}^{j}\) indicates how similar the \(i\)th data are to the other clusters. The \(SC_{j}\) of a cluster in Equation (18) is the average value of all the data included in the \(j\)th cluster, \(SC_{i}^{j}\). The overall \(SC\) (\(OSC\)) of the image is the average of all \(SC_{j}\) of all clusters formulated in Equation (19):  
\[SC_{j}=\frac{1}{n_{j}}\sum\
olimits_{i=1}^{n_{j}}SC_{i}^{j}, \tag{18}\]  
\[OSC=\frac{1}{K}\sum\
olimits_{j=1}^{K}SC_{j}. \tag{19}\]  
The \(OSC\) has a range of values from \(-1\) to \(1\). The closer the \(OSC\) is to \(1\), the more likely the data should be in the cluster.  
The comparison measurement tool used for the segmentation result was the misclassification ratio \((MCR)\). The \(MCR\) calculates the differences between the segmentation results with the ground truth (GT) image. The smaller the value of \(MCR\), the better the segmentation results [17]. The formula of \(MCR\) is given by Equation (20):  
\[MCR=\frac{\sum\
olimits_{i=1}^{N}\big{|}\text{pixel cluster output}_{i}-\text{pixel ground truth}_{i}\big{|}}{N}. \tag{20}\]
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 2 Materials and Methods  
### Data  
We utilized electronic databases such as CAB Abstracts, Biological Abstract, Web of Science, and Google Scholar to search for relevant documents. Soil texture can influence SOC change by stabilization and protection of organic matter, water holding capacity, aeration, soil temperature, and so on. However, in this study, we focused on the impact of LUC, thus we included literature that reported SOC or soil organic matter (SOM) concentration before and after LUC as well as reserves per unit land area, with specific experimental designs such as paired sites, time series, or retrospective designs. From 173 publications, we collected data on the impact of converting primary forests to farmland on SOC storage across a wide range of regions. For each paper, we compiled information such as the source, first author, year of publication, location information (latitude and longitude), climatic data (mean annual temperature, MAT; precipitation and climatic zones), SOC or SOM concentration, storage per unit land area, soil sample depth, and soil bulk density. We calculated the mean value and standard error of soil organic carbon storage before and after LUC.  
As SOC takes years or decades to reach a new equilibrium after LUC, studies of the same land-use type were assumed to have similar soil conditions before LUC. Literature that lacked information on land-use types and soil types were excluded from the analysis. Additionally, studies that did not report time series data for LUC or had observation periods less than 5 years were excluded [7]. We also excluded studies that did not report standard deviations for changes in SOC or SOM before and after LUC. In this study, primary forest refers to natural vegetation that has not been reported or found to have significant human influence, which could be a mixture of trees, understory shrubs, and grasses. Farmland mainly includes annual crops (e.g., corn and beans) and perennial crops (e.g., sugar cane and coffee plantations). According to the above criteria after the filtering,a total of 411 observations from 41 publications were assembled, and 75% of them report the SOC or SOM storage (see Table S1 in Supplementary Materials). Three reviewers were involved in the screening and inclusion process of the literature, ensuring the reliability of the results.
## 2 Materials and Methods
### Methods
#### 2.2.7 Evaluation of the Explanation Framework  
There is no standard procedure to quantify the quality of the saliency maps. The grad-CAM algorithm is usually used to visually assess the correctness of the classification. This means that it is used as an observer-dependent measure. Since the breast density classification is an intensity-based classification, it is possible to directly study the correlation between the mammograms and the saliency maps in order to quantify at least whether there is a monotonic dependence between the images and their explanation. For this reason, we computed the Spearman's rank correlation between the pre-processed images, which actually contain the information strictly related to the breast density provided in input to the CNN, and their relative saliency maps. Since mammograms are gray-scaled we computed the Spearman's rank correlation between the pixel intensities and the gray-scaled map intensity values to test whether they are in an increasing monotonic relationship, as we would expect. The value for the perfect increasingly monotonic relationship between two variables is 1.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System
### Phase Shift Keying (PSK) Modulation Technique  
Phase shift keying (PSK) is a digital modulation scheme that conveys data by changing, or modulating, the phase of a reference signal (the carrier wave). Although Binary-PSK (BPSK) is the simplest form of PSK, which uses two phases separated by 180\({}^{\circ}\), it can only modulate at 1 bit/symbol. The motivation behind M-ary PSK (MPSK) is to increase the bandwidth efficiency of the PSK modulation scheme. Figure 10 illustrated the constellation diagrams for BPSK, QPSK and 8 PSK. Gray encoding was also used to map the sets of bits to the appropriate symbol. Where \(erfc(.)\) is the error function, the BER and SNR relation of BPSK and QPSK can be determined by Equations (14) and (15) [118], respectively:  
\[BER_{BPSK}=\frac{1}{2}erfc\bigg{(}\sqrt{SNR}\bigg{)} \tag{14}\]  
\[BER_{BPSK}=erfc\bigg{(}\sqrt{SNR}\bigg{)}=2 BER_{BPSK} \tag{15}\]  
Figure 9: The (**a**) V-t diagram and (**b**) eye diagram of a pseudo-random binary sequence (PRBS) OOK signal.
# Flavor and Functional Analysis of _Lactobacillus plantarum_
## 3 Results and Discussion
### Composition and Content of Organic Acids in Fermented Apricot Juice  
The distribution of organic acids affects the sensory property of fruit juices [29]. The acidity of fermented apricot juice was determined by estimating the composition and concentration of organic acids [30]. The composition of the five organic acids (including malic, citric, lactic, quinic and oxalic acids) in fermented apricot juice was detected at different stages of fermentation (Figure S1A).  
Malolactic fermentation is an important process improving the flavor of fruit and vegetable juices, in which malic acid is converted to lactic acid and CO\({}_{2}\) by malic lactase of LAB [31; 32]. In this study, the content of organic acids in fermented apricot juice varied at different stages (Figure S1B). In the fermentation process, malic acid showed a downward trend and lactic acid showed an upward trend. The malic acid content was the highest at 2 h (1.93 mg/L), and the lactic acid content was the highest (2.36 mg/L) at the end of fermentation. The participation of malic acid in the production of lactic acid may lead to a decrease in malic acid [26]. Malic acid provides a pungent taste to apricot juice [33] and can synergize with other flavors to create a more natural flavor [34]. In the tricarboxylic acid cycle, lactic acid is the final product of LAB fermentation, which is synthesized by pyruvate reduction or malic acid conversion [17]. Lactic acid provides a full and soft flavor to apricot juice [35]. In addition to malic acid, residual sugar and citric acid, other components are also decomposed by LAB [36].  
Citric acid has an effect on the acidity of fermented apricot juice [34]. Citric acid in fermented apricot juice showed a downward trend. The initial content was 1.63 mg/L, which was 1.52 mg/L after 12 h of fermentation, with a decrease of 0.11 mg/L. In LAB fermentation, citric acid can be degraded to produce acetic acid, diacetyl, etc. [37]. Quinic acid decreased from 0.67 to 0.51 mg/L. Oxalic acid content did not change significantly during the fermentation.
# Time-Frequency Attribute Analysis of Channel 1 Data of Lunar Penetrating Radar
## 1 Introduction
The EEMD method can solve the pattern blending, but it creates a new pattern. In the use of complete ensemble empirical mode decomposition (CEEMD) [23], all the stages after decomposition will add some specific noise. Next, a unique residue is calculated to obtain each pattern. The final decomposition is finished, and the error is negligible. Compared to EMD and EEMD, CEEMD provides a better mode spectral separation and fewer screening iterations while also reducing computational costs. As mentioned by [PERSON] et al. [20], the signal-to-noise ratio (SNR) of the CH-1 data is extremely low and there are many false reflectors in the CH-1 data. It is important to be able to extract useful information from the CH-1 data. In order to analyze and decompose the CH-1 data, an analysis method, which combines CEEMD and time-frequency analysis, is developed. This method can not only analyze the LPR CH-1 data of CE-3, but also provide an analysis method for the subsequent Chang'E-4 (CE-4) radar data. In this paper, time-frequency analysis is applied to the troubled data. Then, CEEMD is conducted to decompose the channel 1 data, and the Hilbert transform gives us the chance for further data analysis. Furthermore, combined with geological information, the previous summary, and the channel 2 data, usability analysis of the LPR channel 1 data has been conducted, which can provide a reference for the usability of the CE-4 LPR data.
## 3 Model and Analysis
### Construction and Analysis of Stochastic Evolutionary Game Model  
#### 3.3.1 Game Model Establishment  
The extended supply chain and long PB operation time introduce multiple interferences in decision-making: the government's allocation of carbon subsidies and quotas [59], carbon taxes settings [60; 61] subsidies and tax incentives [61; 62], low-interest loans from financial institutions to PC manufacturers [62], labor quantity and quality [62; 63], advanced construction technology applications [62], and market fluctuations [12]. In addition, participants in PBSC have subjective uncertainties, such as developers' environmental awareness [60], additional costs of seeking partners [63], and suppliers' strategic preference [64], which are random disturbance factors. For PBSC, these random disturbance factors are long-term and unavoidable and cannot be ignored in practice. Gaussian white noise is a zero-mean, constant-variance random noise with independent, identical distribution at each time and a flat spectrum [65]. Gaussian white noise's simplicity and various features make it suitable for modeling random perturbation factors [66]. The randomness and uncertainty of white Gaussian noise correspond to the unpredictability of disturbance factors; its stable statistical properties reflect the long-term existence of random factors; and spectral flatness allows adjustment of noise intensity to simulate the game system under different random disturbances. Therefore, Gaussian white noise is applied to simulate random perturbation factors to construct a stochastic evolutionary game model [45; 46; 47; 48]. Referring to [PERSON] et al. [67], since \(x,y,z,u\in[0,1]\), terms like 1\(-\)x in replicator dynamic equations do not affect the evolutionary process, and are omitted. The replicator dynamic equations for X, Y, Z, and U, after being affected by random perturbations, are shown in Equations (8)-(11):  
\[dx(t)=x(t)[(1+y\beta_{XY}+z\beta_{XZ}+u\beta_{XU})E_{X}-C_{X}+\Pi_{X}+(1-y)N_{ Y}+(1-z)N_{Z}+(1-u)N_{U}+G_{X}]dt+\sigma x(t)dw(t) \tag{8}\]  
\[dy(t)=y(t)[(1+x\beta_{YX}+z\beta_{YZ}+u\beta_{YU}-x\alpha_{YX})E_{Y}-C_{Y}+\Pi _{Y}+G_{Y}+xN_{Y}]dt+\sigma y(t)dw(t) \tag{9}\]  
\[dz(t)=z(t)[(1+x\beta_{ZX}+y\beta_{ZY}+u\beta_{ZU}-x\alpha_{ZX}-y\alpha_{ZY})E _{Z}-C_{Z}+\Pi_{Z}+G_{Z}+xN_{Z}]dt+\sigma z(t)dw(t) \tag{10}\]  
\[du(t)=u(t)[(1+x\beta_{UX}+y\beta_{UY}+z\beta_{UZ}-xa_{UX}-y\alpha_{UY}-z\alpha _{UZ})E_{U}-C_{U}+\Pi_{U}+G_{U}+xN_{U}]dt+\sigma u(t)dw(t) \tag{11}\]  
\(\sigma\) in the above equation denotes the random perturbation intensity. \(w(t)\) follows a one-dimensional Brownian motion and reflects the effect of the random disturbance factor. \(dw(t)\) denotes Gaussian white noise, and the increment \(\Delta w(t)=w(t+h)-w(t)\) follows a normal distribution when \(t>0\) and the step size \(h>0\).
## 4 Analysis Results  
### Flexural Deformation Behavior  
The load-deflection curves of the beams tested in this study have been compared in Figure 11 with the analysis results that are predicted using homogeneous or inhomogeneous material properties. Unlike the analysis result using a homogeneous condition, the proposed model can produce many varying results depending on the material parameters selected for the simulation. An example of one of these results is shown in Figure 11.  
As shown in the figure, the effect of localization influences the flexural ductility significantly. When the material properties were assumed to be homogeneous, the analysis overestimated the flexural ductility of the beam, since the effect of the localized crack, that is loading and unloading elements, is not included in the analysis. On the other hand, the proposed model considering inhomogeneous material properties showed better agreement with the test results.  
For realistic simulation of flexural behavior of HPFRCC, the localized and non-localized behaviors need to be considered, because HPFRCCs have multiple cracking and deformation hardening as characteristic behaviors; in addition, these multiple cracks do not behave the same when crack bridging [9,32]. Non-uniform distribution of fibers can also be another cause for variation in the crack-bridging behavior [33,34].  
The proposed model better predicts the flexural behavior of HPFRCC beams when the crack widths are compared with each other. The crack widths measured with CMOD gauges in two specimens are compared with the analysis results, as shown in Figure 12. When homogeneous material properties were used in the analysis, every segment and every crack showed identical behavior, so that only one crack width-deflection relationship was obtained. On the other hand, when the inhomogeneous material properties were considered, each segment and each crack showed different histories of the structural behavior. While the crack in the weakest segment opened gradually due to its localization, the crack widths in the other segments increased as the applied load increased, and then decreased due to unloading. Consequently, the analysis with homogeneous material property gives only one crack  
Figure 11: Moment–deflection curve of specimen.  
width-deflection response, shown by the dotted line in Figure 12, while the analysis with inhomogeneous material properties gives several crack width-deflection responses; one response is for a localized element, while the other response is for non-localized elements. Through the comparisons presented in Figure 12, which give the crack widths predicted by each model, it can be proved that the proposed model can describe the maximum crack width of HPFRCC member under flexural loading reasonably well. Therefore, it can be concluded that the multiple cracking behavior of HPFRCC members under flexural loading can be reasonably described by the proposed model.
# Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification
## 5 Experimental Results
### Omniglot Character Recognition  
Figure 3 shows some examples of Omniglot data [34] which are a handwritten dataset for various characters. It consists of 1623 characters collected from 50 alphabets, and each character has 20 samples drawn by different individuals. We follow the procedure of [PERSON] et al. [8] for data preparation and augmentation. The original \(105\times 105\) data are resized to \(28\times 28\) and rotated by multiples of 90 degrees. By rotating the existing image, we obtained four times as many classes as the original one. The embedding network with four convolutional blocks transforms an image into a 64-dimensional feature.  
Since the embedding vector is 64 dimensions, the covariance matrix is 64 \(\times\) 64, and thus we have 4096 parameters to be estimated, which is much larger than the number of data given in the few-shot task. This is prone to cause a singularity in the inversion of the covariance matrix during distance calculation. To avoid this, we added an identity matrix as a regularization term for estimating covariance, as shown in Equation (11). In the test phase, we also added the covariance \(\sum_{\text{tm}}\) obtained from training data as shown in Equation (12).  
Table 1 compares the classification accuracy of the proposed method with the conventional methods, under the 5-shot settings. The Omniglot data are one of the representative benchmark sets for few-shot classification, but it is relatively simple. Thus, as shown in Table 1, all the methods show good results while the proposed one achieves the best results. In Figure 4, we compared the performance changes according to the number of test classes. In order to see the effectiveness of the proposed method, we compare the performance with  
Figure 3: Character variation samples of Omniglot data.  
the original prototypical network [9] as well as the Gaussian prototypical network [22] that uses class-wise covariance information. As shown in the graph, the performance degradation of the proposed method is gentler than the original prototypical network. Though the Gaussian prototypical network shows better performance than the original one, it can be seen that the proposed intra-class covariance gives a more effective distance measure than the class-wise covariance used in [19]. Since the proposed probabilistic similarity is estimated by using intra-class difference vectors which increase in proportion to the number of classes, it is possible to estimate the covariance matrix \(\sum_{\text{st}}\) more accurately as \(N\) increases. This may act as a strength of the proposed method, showing better performance in the case of large \(N\). In particular, when compared with the gaussian prototypical network using statistical characteristics, it can be seen that the performance gap increases as N increases.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Ground Control Data  
In situ investigations were performed for the five field sites from 17 to 22 October 2019. Unmanned aerial vehicle (UAV) photos were taken at each site as a combination between the satellite imagery and the ground data. The photos were taken at an oblique angle and hence cannot be accurately projected to a geographic coordinate system. Consequently, visual interpretation was required to determine typical land (or surface) cover types in the satellite image on 15 October 2019. Although most of the cover types in the satellite image can be clearly determined by referring to the UAV photos, it is still hard to identify whether the discoloration of a tree is caused by PWD or related to phenology. To solve this problem, field crews were dispatched to validate the species and the reason for discoloration in these trees. The wilted pine trees and the discolored deciduous trees were then recorded separately (e.g., at Site A, Figure 2).  
We found that the mean canopy width of the wilted pine trees in the five field sites was 5.6 m (SD = 1.7), which was used to evaluate the appropriate spatial resolution for tree-scale PWD mapping. For each wilted pine tree at each site, the canopy width was recorded and classified based on the spatial resolution of the PlanetScope image used in this study (Table 1). All of the wilted pine trees newly added in 2019 were collected and saved as point features. As the spectral signal is significantly distinct from the neighborhood in the case that the wilted pine tree mostly covers at least one pixel [34; 35], the wilted pine trees with a canopy width less than 3 m were not considered for the subsequent analysis. Consequently, a total of 203 wilted trees with canopy width greater than 3 m were selected. Afterwards, a pixel-wise sample set was simultaneously built based on the  
Figure 2: The locations of the wilted pine trees (marked with Arabic numerals) and discolored deciduous trees (marked with Roman numerals) on (**a**) the UAV photo and (**b**) the satellite image at Site A. The canopy widths of the Nos. 2, 9, and 15 trees were less than 3 m, and the No. 14 pine tree was mostly blocked by the adjacent trees.  
selected wilted pine trees and visual interpretation. There were nine cover types in the sample set, including healthy and wilted pine trees, deciduous trees, grass, crops, water, buildings, roads and barren. A total of 1231 sample pixels (15-40 pixels for each of the cover types in each site) were labeled for temporal behavior analysis and classifier training.