esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 HHT-Based Signal Processing and Active Lamb Wave Based Monitoring Method  
### HHT-Based Signal Processing  
Following the demand of HT for the signal condition, HHT assumes that a signal is composed of several IMFs, each of which can be subjected to HT. However, in practice, most signals do not conform to the IMF definition. With HHT, a signal can be decomposed into a few IMFs by EMD, and then HT can be used to perform signal analysis in the time-frequency domain. The time-frequency processing procedure is given below.  
Assume that the \(k\)th-order IMF expression of a signal is \(\rm{c_{k}(t)}\), then, according to HHT theory, \(\rm{c_{k}(t)}\) can be defined as \(\rm{c_{k}(t)}=~{}A_{k}(t)\cos(\phi_{k}(t))\), where \(\rm{A_{k}(t)}>~{}0\). Let the HT of \(\rm{c_{k}(t)}\) be \(\rm{\dot{c_{k}(t)}}\), and then the analytical expression of the \(k\)th-order IMF can be written as [20]  
\[Z_{k}(t)=~{}c_{k}(t)+j\rm{\dot{c_{k}(t)}}=~{}A_{k}\cos(\phi_{k}(t))+jA_{k}(t) \sin(\phi_{k}(t))=~{}A_{k}(t)e^{j\phi_{k}(t)} \tag{1}\]  
Next, the instantaneous phase is given by  
\[\phi_{k}(t)=~{}\arctan\!\left(\frac{\rm{\dot{c_{k}(t)}}}{c_{k}(t)}\right) \tag{2}\]  
The instantaneous frequency (\(\rm{f_{i}(t)}\)) is defined as a derivative of the instantaneous phase (\(\rm{\phi(t)}\)), and the \(\rm{f_{i}(t)}\) of Equation (2) is given by  
\[\rm{f_{ik}(t)}=1/2\pi\times d(\phi_{k}(t))/dt \tag{3}\]  
Then, Equation (1) can be written as [20]  
\[\rm{Z_{k}(t)}=A_{k}(t)e^{j\int 2\pi f_{ik}(t)dt} \tag{4}\]  
where \(\left|Z_{k}(t)\right|\) represents the amplitude of a Hilbert spectrum, that is, \(\rm{A_{k}(t)}\). Thus, (\(\rm{t}\), \(\rm{f_{ik}}\), \(\rm{A_{k}}\)) is the Hilbert spectrum of the \(k^{\rm{th}}\)-order IMF, where each point (\(\rm{t}\), \(\rm{f_{ik}}\)) in Equation (4) corresponds to \(\rm{A_{k}}\). If this relationship is defined as \(\rm{H_{k}(t,\rm{f_{ik}})}\), then the Hilbert spectrum of all the IMFs of a signal can be defined as \(\rm{H(t,\rm{f_{i}})}\), and the marginal spectrum in the HHT can be expressed as [20]  
\[\rm{h(f)}=\int H(t,\rm{f_{ik}})dt \tag{5}\]  
The above processing method includes the instantaneous phase, instantaneous frequency, Hilbert spectrum, and marginal spectrum. More detailed descriptions of Equations (1)-(5) can be found in prior work [20]. The definition of instantaneous frequency can be regarded as a general extension of the frequency definition. In this paper, the instantaneous parameter characteristics processing technique of HHT is used to explore the active Lamb wave based monitoring signal, and changes in the instantaneous parameter characteristics before and after damage are analyzed.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 2 Materials and Methods  
### Eddy Covariance Flux Tower Data  
Data from the FLUXNET2015 dataset (Available online: [[http://www.fluxdata.org](http://www.fluxdata.org)]([http://www.fluxdata.org](http://www.fluxdata.org)), accessed on 11 March 2020) explain ecosystem-scale processes such as the exchange of carbon dioxide, water, and energy between the biosphere and the atmosphere. The data processing and site description details can be found on the FLUXNET2015 dataset website (Available online: [[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 9 June 2020). In this paper, a total of 70 eddy covariance (EC) sites were selected (Table S1), and each of these sites had more than five years of data that were able to capture interannual variations and long-term changes (Figure 1). According to the classification scheme developed by the International Geosphere-Biosphere Programme, the land cover classes are divided into 10 types (Table S1): Crop (CRO, 8 sites), Closed Shrub (CSH, 1 site), Deciduous Broadleaf Forest (DBF, 10 sites), Evergreen Broadleaf Forest (EBF, 5 sites), Evergreen Needleleaf Forest (ENF, 17 sites), Grassland (GRA, 14 sites), Mixed Forest (MF, 4 sites), Open Shrub (OSH, 2 sites), Wetlands (WET, 5 sites), and Woody Savanna (WSA, 4 sites).  
The daily GPP variable (GPP_NT_VUT_REF) was estimated using net ecosystem exchange (NEE) methods [38]. The air temperature (Ta), sensible heat (H), and latent heat (LE) variables were quantified for each site. Using marginal distribution sampling (MDS), the micrometeorological variables have been gap-filled with reanalysis datasets, while the carbon flux measurement data were gap-filled with LE and H. The daily average values were aggregated to the 8-day time step to match the time step of other data.
## 4 Discussion
### Application of the QPAML Model in Other Metabolic Pathways  
Examining the results against the confusion matrix (Figure 6) revealed specific classification patterns. Despite the model's primary training in the tryptophan pathway, it demonstrated effective categorization of reactions from the metabolic pathways of 31 metabolites, achieving a notable F1-score of 92.34. Notably, there were no misclassifications of candidate reactions for knockdown as low flux, highlighting the essential nature of knockdown reactions for cell growth and the good performance of the iML1515a model. Furthermore, the model consistently differentiated between silence and attenuation reactions based on their essentiality, with no interchanges in classifications between these two classes. A reaction for overexpression involving the conversion of acetate to acetyl-CoA (ACS) was misclassified as low flux [83]. As an alternative strategy, the model advised silencing the ACK and PTAr reactions to prevent acetyl-CoA conversion to acetate, thereby avoiding a futile cycle.  
The specific conditions of the culture influence reaction assignment for overexpression instead of knockout. For instance, the model identified organic acid fermentation reactions as overexpression targets, potentially driven by an imbalance in the NADH/NAD ratio under microaerobic conditions. In scenarios where these reactions regenerate NAD without adequate oxygen, they become essential for growth under anaerobic conditions [53].
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method
### Loss Function  
The MGMM is a statistical model describing the data distribution. It is well suited to dealing with multivariate and multimodal data sets. It uses a linear combination of multiple Gaussian distributions to model the data distribution. Each Gaussian distribution has a mean and covariance matrix. The different distributions are combined using weights to capture the data characteristics. The samples S are modeled in groups to determine the time-dependent features of the TLE sample data. Let \(x\) be a random variable for sample \(S\). The MGMM is defined as follows:  
\[P(x|\theta)=\sum_{k=1}^{K}\alpha_{k}f(x,\mu_{k},\mathbf{\Sigma}_{k})=\sum_{i=1}^{N }\sum_{k=1}^{K}\alpha_{k}\frac{1}{\sqrt{2\pi}|\mathbf{\Sigma}_{k}|^{\frac{1}{2}}} \exp[-\frac{(x-\mu_{k})^{T}\mathbf{\Sigma}_{k}^{-1}(x-\mu_{k})}{2}] \tag{2}\]  
where \(\theta\) includes the weight \(\alpha_{k}\), the expected value \(\mu_{k}\), and the variance \(\mathbf{\Sigma}_{k}\) of each component; \(K\) is the number of Gaussian components. Since the establishment of the samples is the same for each group, we do not show, in the subsequent equations, the sample to which the random variable belongs to simplify the labeling.  
We propose a MGMM that separates the Gaussian components to capture TLE outliers:  
\[P(x|\theta)=\sum_{k=1}^{K-1}\alpha_{k}f(x,\mu_{k},\mathbf{\Sigma}_{k})+wf(x,\mu_{ k},\mathbf{\Sigma}_{k}) \tag{3}\]  
\[\sum_{k=1}^{K-1}\alpha_{k}+w=1 \tag{4}\]  
where \(w\) is the anomaly level and the scale factor of the \(K\)-th Gaussian component. Reference [26] used a GMM to model the probability distribution of the TLE prediction error. The results showed that three to four Gaussian distributions were sufficient to obtain a satisfactory fit.  
The maximum likelihood method is commonly used to estimate parameter values. It assumes that each data point is independent. The probability density function was used. The loss function of the MGMM is defined as follows:  
\[L(\theta)=\prod_{i=1}^{n}P(x_{i}|\theta)=\prod_{i=1}^{n}\left[\sum_{k=1}^{K-1 }\log\alpha_{k}f(x_{i},\mu_{k},\mathbf{\Sigma}_{k})+\log wf(x_{i},\mu_{K},\mathbf{ \Sigma}_{K})\right] \tag{5}\]  
Since the logarithmic function is monotonic, the location of the extreme value does not change. Even a small change in the 0-1 range can cause relatively large fluctuations in the output value. Thus, we used the logarithmic likelihood function to calculate the final loss:  
\[\log L(\theta)=\sum_{i=1}^{n}\log\left[\sum_{k=1}^{K-1}\log\alpha_{k}f(x_{i}, \mu_{k},\mathbf{\Sigma}_{k})+\log wf(x_{i},\mu_{K},\mathbf{\Sigma}_{K})\right] \tag{6}\]  
Many parameter estimation methods for GMM have been used, such as the expectation-maximization (EM) algorithm, fuzzy clustering, and Monte Carlo sampling [27]. The EM algorithm is the most common approach; it is sensitive to the initial value. We propose the use of a multi-strategy genetic algorithm (MGA) for the MGMM parameters.
## 2 Satellite Sensor Data
### Terra/aqua-modis  
The Moderate Resolution Imaging Spectrometer (MODIS) is a satellite based visible/infrared radiometer for the sensing of terrestrial and oceanic phenomena. It is operating on both the Terra and Aqua spacecraft with a viewing swath width of 2330 km. Its detectors measure 36 spectral bands  
Figure 2: Processing levels hierarchy. **(a)** NOAA-AVHRR data and, **(b)** SeaStar-SeaWiFS data.  
between 0.405 and 14.385 \(\upmu\)m, and it acquires data at three spatial resolutions (250 m, 500 m, and 1000 m).  
MODIS bands 20, 22 and 23 in mid-infrared (3.7-4.1 \(\upmu\)m) and bands 31 and 32 in thermal-infrared (11.0-12.0 \(\upmu\)m) were placed to optimize their use for SST determination [17]. The mid-infrared bands, whilst situated where the influence of column water vapour is minimal, suffer from a decrease in the availability of the Earth radiance and solar radiance reflection during daylight. The thermal-infrared bands are near the maximum of the Earth's emission and have larger bandwidth, but they are more affected by atmospheric water vapour absorption.  
Through the Distributed Active Archive Center run by the Earth Sciences Department of NASA's Goddard Space Flight Center (GSFC), sea surface temperature products are available at 1-km resolution (Level 2) and at 4.6 km, 36 km, and 1\({}^{\circ}\) resolutions (Level 3). In our work we have used the Level 2 SST product which is produced daily using two different algorithms, from the thermal or the mid-infrared bands. The corresponding coefficients of the non-linear algorithms are tuned on a monthly basis to avoid a seasonal bias [18].  
Finally, to properly select the reliable areas in our registration algorithm we have used the MODIS Cloud Mask product and the Quality-Assessment parameter included for each SST pixel.
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 4 Experiments
### Dataset  
The data in this study were primarily built upon the publicly available dataset CMU-MOSI. The CMU-MOSI dataset was collected by researchers at Carnegie Mellon University and aims to provide benchmark data for tasks such as emotion recognition, sentiment analysis, and multimodal emotion analysis. It consists of video, audio, and text data, with over 93 h of video data, 39 speakers, 23,453 sentences, and approximately ten million words. The emotional labels in the dataset are based on the VA affective model dimensions, namely valence, arousal, and dominance. Additionally, the dataset includes audio and text features to support multimodal emotion-analysis tasks. This dataset has been widely used in research on emotion recognition, sentiment analysis, and multimodal emotion analysis, making it a valuable resource.
## 6 Example Analysis
### Comparison and Analysis of Prediction Results
#### 6.4.2 Comparison of Training and Test Results  
The training effect of each model on the training set is given in Figure 10, which shows 150 data points of the training set as well as the training results of each model. And as far as is observed in the figure, it is known that the training effect of the VMD-NRBO-Transformer-BiLSTM model significantly outperforms that of the Transformer, Transformer-BiLSTM, and VMD-Transformer-BiLSTM models, especially at the peak PV output, which is closer to the real curve.  
Figure 11 illustrates the prediction performance of each model on the test set, based on the results from 70 sampling points. The solution times of the four models for the test set are 3.64 s, 4.21 s, 4.52 s, and 4.46 s, respectively. As shown in the figure, the overall prediction curve of the VMD-NRBO-Transformer-BiLSTM model closely aligns with the actual curve. This model effectively captures the fluctuations in the output, demonstrating superior accuracy in simulating the volatility of photovoltaic output.  
Figure 11: Comparison of testing performance of four prediction models.  
Figure 10: Comparison of training performance of four prediction models.  
Figure 12 illustrates the error distribution between the predicted and actual values for the four models on the test set.
## 3 Materials and Methods
### Methodology
#### 3.3.4 Accuracy Measures  
To verify the performance of crop type classification, it was necessary to perform an accuracy assessment. For this purpose, we computed the overall accuracy (\(OA\)) and the Kappa coefficient. \(OA\) is the total number of correctly classified samples (diagonals of the matrix). The precision of the whole image was measured without indicating the precision of individual categories [46]. \(OA\) can be described with the following equation:  
\[OA=\frac{\text{TP}+\text{TN}}{\text{N}} \tag{2}\]Producer accuracy (_PA_) is the probability that a value in a given class was classified correctly. Having considered class number 1, _PA_ is determined as follow:  
\[PA=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
User accuracy (_LIA_) is the probability that a value predicted to be in a certain class really is that class. The probability is based on the fraction of correctly predicted values to the total number of values predicted to be in a class. Having considered positive class, _PA_ is determined as follow  
\[UA=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
where:  
* true positive is an outcome where the model correctly predicts the positive class (TP).
* true negative is an outcome where the model correctly predicts the negative class (TN).
* false negative is an outcome where the model assigned observation to the negative class, which in reality belong to the positive class (FN),
* false positive is an outcome where the model assign observation to positive class, which in reality belong to the negative class (FP),
* N total number of pixels.  
Kappa is a coefficient which was introduced by [PERSON] in 1960. In remote sensing, this is an index used to express the accuracy of the classification of an image [47]. In calculations of the Kappa coefficient, all elements of the error matrix are taken into consideration instead of just the diagonals of the matrix. The Kappa index provides a measure of how much better the classification performance is compared with the probability of randomly assigning pixels to their correct categories [48]. A kappa value of 1 represents perfect agreement, while a value of 0 represents no agreement [49]. The kappa coefficient is computed as follows:  
\[\kappa=\frac{N\sum_{i=1}^{n}m_{i,i}-\sum_{i=1}^{n}(G_{i}C_{i})}{N^{2}\sum_{i=1 }^{n}-(G_{i}C_{i})}\]  
where:  
\(i\) is the number of classes,  
\(N\) is the total number of classified values compared to truth values  
\(m_{i,i}\) is the number of values belonging to the truth class \(i\) that have also been classified as class \(i\) (i.e., values found along the diagonal of the confusion matrix)  
\(C_{i}\) is the total number of predicted values belonging to class \(i\)  
\(G_{i}\) is the total number of truth values belonging to class \(i\)
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 4 Network Generalization Experiments
### Other Simulation Scene
#### 4.1.1 Different Layered Structure  
In the first group of experiments, we change the layered structure. We firstly add another layer at different depths to generate two new models, as shown in Figure 10. The network trained above with the original model in Figure 4 does not need to be trained again. We select three channels of A-scan data, corresponding to positions A, B, and C, to test the generalization ability of the LSTM network trained above. Position A is far away from the target, so the corresponding A-scan echo, shown as the black line in Figure 10, is used as the ground truth of the wavelet for the new scene. The yellow line and the green dotted line are the predicted wavelet with the A-scan echo at position B and position C, respectively. It can be seen that the wavelet of the new scene contains a new signal peak at  
Figure 9: Comparison of deconvolution processing.  
the tail of the echo, which is consistent to the new layer position. Therefore, the proposed network can predict the response of the new layer structure.  
As shown in Figure 11, model 3 only changes the thickness \(d_{1}\). According to the layered geometry, the position of the second layer signal will change from the 387 th sampling point to the 465 th sampling point. Model 4 further changes the dielectric constants of the two layers, and the position of the second layer signal is moved to the 417 th sampling point. The LSTM network trained above with the original model is used to predict the wavelet for the new model directly. The prediction results correctly indicate these layered geometries. Therefore, the proposed network can capture the layer thickness and background medium to make accurate predictions quickly.  
The predicted wavelets of the new models are used for background elimination. Figure 12 indicates that the predicted wavelets removal can highlight the target information, while the B-scan obtained with other methods still has some layered signal residues. Therefore, the LSTM wavelet prediction network model is suitable for the layered medium model of different layers and thicknesses.  
Figure 10: Wavelet prediction results of derived models 1–2.
# Hybrid Multimodal Medical Image Fusion Method Based on LatLRR and ED-D\({}^{2}\)GAN
## 4 Discussion  
The method based on the hybrid model is an important research direction in the field of multimodal medical image fusion. The main work of this paper is an attempt in this research direction. By combining the multi-scale decomposition method with the method based on GAN, a significant lesion area of the fused images can be obtained. It has an important clinical application value in the early diagnosis and evaluation of the curative effect of tumors.  
The fusion method based on a hybrid model can make up for the shortcomings of the single fusion method and improve the quality of the fused images effectively. This paper proposed a hybrid multimodal medical image fusion method based on LatLRR and ED-D\({}^{2}\)GAN, as shown in Figure 1. Due to LatLRR being able to obtain the noise component contained in the source images, this paper adopted LatLRR to decompose the source images and discard the noise component in the whole fusion process. Then, in order to improve the details of the lesion areas in the fused images, the low-rank CT images decomposed by LatLRR were enhanced in this paper, and the dual-discriminator GAN model was proposed as the fusion rule of low-rank region images. It is appropriate to use the GAN model as the fusion rule of low-rank region images, because it has a strong feature extraction ability and can constantly adjust the quality of fused images through the adversarial game process between the generator and discriminator. In addition, the use of two discriminators enables the final fused images to retain more details from the two source images at the same time, thus obtaining a better fused image. Finally, a threshold adaptive weighted algorithm based on a regional energy ratio was proposed as a fusion rule for salient region images. On the one hand, the correlation between multiple pixels in a local area was fully considered in this rule. On the other hand, when the energy of two local regions is very similar, this method can avoid the information loss caused by directly selecting the pixel value with the largest amount of energy of the region to a certain extent.  
In summary, it can be seen from Figures 3-10 that the visual effect of the fused images obtained by the method LatLRR-GAN proposed in this paper is superior to other fusion methods, which proves the superiority of LatLRR-GAN. Similarly, it can be seen from the objective evaluation metric values in Tables 1-4, which can be quantitatively compared, that the LatLRR-GAN has certain advantages in AG, EI, IE, SF and SD.  
Although LatLRR-GAN has some advantages in both qualitative and quantitative comparisons, this work still has limitations. As can be seen from Tables 1-4, due to the method in this paper lacking further research on edge information retention, the performance of LatLRR-GAN on the Q\({}^{\text{AB/F}}\) value is a little poor. At present, researchers have proposed a variety of methods for edge information extraction, and in future work, the authors will pay more attention to this problem.
## 3 Results and Discussion
### In Vitro Disintegration Test
#### 3.8.3 Total Soluble Solids (TSSs)  
The sugar content of the samples was determined under the same conditions as the in vitro dissolution tests. Figure 10 shows dissolved sugars (\({}^{\circ}\)Brix) results for the TPS-CH-M and TPS-CH-M-A samples. In general, an increase in values is observed as the dissolution time progresses [61]. This effect is more pronounced in the first 5 min for the TPS-CH-M-A sample compared to TPS-CH-M. The increase in values is indicative of the release of the oligosaccharide maltodextrin into the solution and partial disintegration of the starch [62]. This phenomenon is attributed to the free volume generated by acetaminophen particles within the polymer chains. The presence of these particles creates spaces that facilitate water molecule penetration during dissolution. This behavior is similar to that observed in microencapsulation systems composed of polysaccharide mixtures, as reported by [PERSON] et al. (2023) [63]. Additionally, these findings corroborate the results discussed in Section 3.7, which explain how dissolved sugars in the formulations contribute to moisture absorption, thereby limiting the availability of free water in the medium. After 10 min, the sugar content values of both samples became comparable, and at this stage, their impact on ODF applications is considered negligible.  
Figure 10: Quantification of sugar at different dissolution times of the polymeric film loaded with the drug.  
Figure 9: Percent of drug released at certain dissolution times.