esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### System Model  
The representative motion compensated embedded zero block coding (MC-EZBC) scalable video code [34] is a highly efficient member of the existing 3-D wavelet-based SVC schemes. However, the coding efficiency of MC-EZBC in the range of low bitrates is far from satisfactory. To improve this deficiency, [PERSON] et al. put forward the well-known enhanced motion-compensated embedded zero block coding (ENH-MC-EZBC), which retains its excellent rate-distortion performance at high bitrates and achieves significant improvement at low bitrates and/or low resolutions [35].  
In our work, the proposed algorithm is designed for the popular ENH-MC-EZBC considering quality scalability. Our choice of this codec system is motivated by the fact that ENH-MC-EZBC incorporates all the advanced encoding tools found in the state-of-the-art video coding schemes and obtains the excellent coding performance both at low and high bitrates. The ENH-MC-EZBC codec system model contains three parts: encoder, bitstream extractor, and decoder, which is shown in Figure 1. In the encoder, a motion compensated 3-D subband/wavelet transform naturally partitions the input video sequence into a range of spatiotemporal resolutions. We then use 3D-EZBC to encode the resulting spatio-temporal subbands. All the motion fields are coded as side information by using lossless spatial differential pulse code modulation (DPCM) and adaptive arithmetic coding. In the bitstream extractor, the fully embedded bitstream is truncated based on both user preferences and network conditions to generate a highly flexible scalable bitstream to meet specific applications. At the decoder, the respective reverse operations are carried out to reconstruct video sequences.
# Semantic Segmentation Based on Depth Background Blur
## 4 Experimental Results  
FCN [9], BiseNet [14], and HyperSeg [21] are selected as our segmentation networks, and relevant experiments are carried out on PASCAL VOC 2012 [19] and CamVid [20] benchmarks. A brief review of the corresponding datasets and metrics will be first presented. Following this, implementation and explanation of experiments will be given.  
PASCAL VOC 2012: As one of the rockstars used for semantic segmentation, PASCAL VOC 2012 covers not only indoor and outdoor scenes, but also night-time scenes with a total of 21 semantic categories (20 categories of interest and a class for the background). The whole dataset contains 4369 images, 1464 of which are used for training, 1449 for validation and 1456 for testing. The training set and validation set adopt full annotation, while the test set does not provide labels. The capacity has been later expanded in SBD [30] to reach 10,582 training samples.  
CamVid: As a small-scale urban street view dataset, CamVid includes a total of 701 fully annotated images, 367 of which are employed to train, 101 for validation and 233 for testing. The CamVid dataset consists of 11 semantic categories (e.g., cars, buildings, billboards, etc.) and an Unlabelled class. Each image has the same resolution: \(720\times 960\).  
Metrics: Let \(n_{ij}\) be the number of pixels that class \(i\) is predicted to be class \(j\), and \(C\) be the number of object classes (including the background class). We compute four indices: Pixel Acc, Mean Acc, Mean IOU and F.W IOU, as defined below. Naturally, the higher the values are, the better network performance is.  
* Pixel accuracy(Pixel Acc): \(\sum_{i=1}^{C}n_{ii}/\sum_{i=1}^{C}\sum_{j=1}^{C}n_{ij}\);
* Mean pixel accuracy(Mean Acc): \((1/C)\sum_{i=1}^{C}n_{ii}/\sum_{i=1}^{C}\sum_{j=1}^{C}n_{ij}\);
* Mean intersection over union(Mean IOU): \((1/C)\sum_{i=1}^{C}(n_{ii}/\sum_{j=1}^{C}n_{ij}+\sum_{j=1}^{C}n_{ij}-n_{ii})\));
* Frequency weight intersection over union (F.W IOU): \((1/\sum_{i=1}^{C}\sum_{j=1}^{C}n_{ij})\sum_{i=1}^{C}((\sum_{i=1}^{C}n_{ij}+ \sum_{j=1}^{C}n_{ji}-n_{ii}))\).
## 2 Results and Discussion
### Correlation of Genes and C6 Volatiles  
To elucidate the relationship between these LOX-HPL pathway key genes and the C6 volatiles accumulated in these cultivars, a [PERSON]'s correlation analysis was carried out (Table 2). It was found that the _VvLOXA_ expression was strongly positively correlated with the total C6 volatiles, total aldehydes, and total alcohols in these cultivars during berry development. This indicated that the differential expression of the _VvLOXA_ played a primary role in diversifying the C6 volatile profiles in these cultivars. For example, a high level of the C6 volatiles in the \"Xiangfei\" and \"Tamina\" resulted mainly from the high expression of _VvLOXA_ during the maturation period. The differential expression of _VvLOXA_ in these cultivars might be attributed to the nucleotide sequence diversity in either the non-coding regulation region of the gene encoding a key enzyme or the promotor region of an unknown transcriptional factor that can activate the key gene expression [41; 42; 43]. However, a further study should be carried out to elucidate the regulation mechanisms. In addition, the accumulation of the C6 aldehydes and C6 acid was related to the expression of _VvADH2_ and _VvHPL1_ since a correlation was found among _VvADH2_, _VvHPL1_, hexanal, (_E_)-2-hexenal, and hexanoic acid. The content of hexyl acetate was closely correlated with the _VvAAT_ expression.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & _AAT_ & _ADH1_ & _ADH2_ & _HPL1_ & _LOXA_ & _LOXO_ \\ \hline Hexanal & 0.478 * & 0.525 * & 0.533 * & 0.701 ** & 0.529 * \\ (E)-2-Hexenal & 0.748 ** & 0.766 ** & 0.496 * & 0.748 ** & \\ Aldehydes & 0.685 ** & 0.720 ** & 0.570 ** & 0.804 ** & \\
1-Hexanol & 0.564 ** & & & 0.692 ** & \\ (E)-2-Hexen-1-ol & 0.668 ** & & & 0.569 ** & \\ (Z)-2-Hexen-1-ol & 0.805 ** & & & 0.636 ** & \\ (E)-3-Hexen-1-ol & 0.583 ** & & & & 0.638 ** & \\ (Z)-3-Hexen-1-ol & & & & \(-\)0.496 * & \\ Alcohols & 0.655 ** & & & & 0.568 ** & \\ Ethly hexanoate & & & & & 0.452 * & \\ Hexyl acetate & 0.757 ** & & & 0.483 * & \\ (Z)-3-Hexenyl acetate & & & & & \\ Esters & & & & & \\ Hexanoic acid & 0.772 ** & 0.564 ** & 0.509 * & 0.664 ** & 0.613 ** \\ Total amount & 0.785 ** & 0.630 ** & 0.455 * & 0.792 ** & \\ \hline \end{tabular}  
* * and ** indicate a significant effect at \(p\) < 0.05 and 0.01.  
\end{table}
Table 2: [PERSON]’s correlation between C6 volatile compounds and expression of LOX-HPL genes in seven table grape cultivars.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 4 Proposed Framework
### Multi-Fidelity Model Composition and Target Model Substitution  
After the development of lower-fidelity models for the target model, we needed to composite a multi-fidelity model (MFM). For composition, the formal structure of the MFM is illustrated in Figure 9. The MFM consists of two types of models: internal target models and a selection model. The original target model and its derived models with lower-fidelity correspond to the internal target models. A selection model determines an appropriate internal model for each predefined region, which implies that, among the several internal models, only one model is activated for each simulation time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluation Index** & **High-Fidelity Model** & **Low-Fidelity Model** \\ \hline Execution time (s) 1  & 2.79 & 0.41 \\ \(\epsilon\) 2  & 0 & 0.21 \\ \hline \end{tabular}
\end{table}
Table 2: Execution times and errors of high- and low-fidelity models for the urban transportation vehicle (UTV) routing simulation.  
Figure 8: High- and low-fidelity model design for the discrete event system: Example of an urban transportation vehicle (UTV) routing simulation.  
Figure 9: General structure of the multi-fidelity model.  
To be specific, the selection model has two important roles. When the selection model receives an input (\(X_{in}\) in Figure 9) from outside of the MFM, it decides whether the current internal model needs to be changed based on the FCC. First, if the model requires no change, the selection model sends the input to the model. The internal model carries out its own actions and generates two outputs: an actual model result for the input (\(Y_{out}\)) and its current state. The current state is transferred to the selection model. These are the cases of the input bypassing and the state copy, as shown in Figure 9. Otherwise, if a model change is required, the selection model activates the other model by sending the input \(X_{in}\) with the copied state for continued simulation. This is relevant to the model activation in Figure 9. In this way, the selection model will manage the trade-off between the computational resources and the accuracy of the simulation results. Specifically, it will decrease the computational burden in noncritical regions while increasing the simulation accuracy in critical regions. The specification for the MFM is described as follows.  
\[MFM=\langle X,Y,\{M_{i}\},SM,MCS\rangle\]  
\(X=\{X_{in}\}\) is the set of inputs;  
\(Y=\{Y_{out}\}\) is the set of outputs;  
\(\{M_{i}\}=\{M_{1},\cdots,\ M_{n}\}\) is the set of models with variable fidelities;  
\(SM\) is the selection model;  
\(MCS\subseteq EIC\cup EOC\cup IC\) is the model coupling scheme;  
\(EIC\subseteq\ X\times SM.X\) is the external input coupling;  
\(EOC\subseteq\ U_{i=1}^{n}M_{i}.Y\times Y\) is the external output coupling;  
\(IC\subseteq(SM.Y\times U_{i=1}^{n}M_{i}.X)\cup(U_{i=1}^{n}M_{i}.Y\times SM.X)\) is the internal coupling.
## 3 Results and Discussion  
### Characterization of the MBG Scaffold  
The crystalline phases of the mesoporous scaffolds are shown on the XRD patterns in Figure 2A. The figure indicates that the MBG scaffolds could be successfully fabricatedby the sponge replication method. The standard amorphous silica peak was observed at 20 = 22\({}^{\circ}\) (ICPDS No. 29-0085). Figure 2A(a,b) shows the morphology of the scaffolds before and after being sintered at 700\({}^{\circ}\)C. The PU foam possesses a microporous structure between the neighboring struts of approximately 300-480 \(\upmu\)m. After being impregnated with the biogas slurry and sintered procedure, the scaffolds had an interconnected network of pores; these were preserved and no cracks appeared on the surface. These pores should be adequate to allow cell penetration and control the rate of drug release [24]. The nitrogen adsorption measurement is used to characterize porous material (as shown in Figure 2B). According to the classical approach, the capillary condensation was observed from the corresponding Type IV isotherm at relative pressure. It was assumed that the mesoporous behavior that always accompanies the hysteresis model occurred. The hysteresis loop from the desorption cycle of the MBG scaffold contributed to the disk-shaped (H3) silts. The BJH analysis can be employed to determine the pore size distribution of the MBG scaffold. The small image in Figure 2B has shown the mesoporous material of the MBG scaffold sample containing pores in the range 10-20 nm. This pore size has proven advantageous for the uptake and release of drugs, growth factors, and improved cell attachment [25].
## 3 Methodology  
This section presents the analytical framework and detailed model architectures used for road damage detection based on pre- and post-disaster satellite imagery. Focusing on change detection techniques applied to temporally aligned image pairs, this study systematically designs, implements, and compares three distinct approaches: single-timeframe overlay segmentation, difference image-based segmentation, and a Siamese network for feature-level fusion.
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 3 Proposed Algorithm  
### Overall Framework  
The proposed algorithm is shown in Figure 4. Plenty of text candidate bounding boxes were obtained from the multibox processing. Meanwhile, the text semantic segmentation result was obtained from semantic segmentation processing. A softmax layer was added to the output layer of the semantic segmentation process to obtain the classification probability of each pixel. The text candidate bounding boxes and semantic segmentation results are merged in the bounding box enhancement module (BEM) to eliminate the false results of multibox processing. The semantic segmentation result enter the semantic bounding box module (SBM). After the CRF algorithm optimizes the text boundaries, the text semantic bounding box is computed. Finally, the outputs of the BEM and SBM are sent to the non-maximum suppression (NMS) to remove duplicate bounding boxes.
## 3 Results and Discussion
### Antioxidant Activity In Vitro  
#### 3.4.1 Cell Viability Assay  
Oxidative stress produces free radicals and reactive oxygen species (ROS) that have been implicated in numerous disease states, especially in neurodegenerative diseases, physiological senescence, and cardiovascular disease [30; 31]. Thus, the use of antioxidants for scavenging ROS is one of the most effective means of decreasing oxidative stress.  
The effects of the extracted polysaccharides on the cell survival rate in the presence of oxidative stress were evaluated. H\({}_{2}\)O\({}_{2}\) is a membrane-permeable redox-active agent that is used to model cellular oxidative damage [32]. The exposure of RAW264.7 cells to exogenous H\({}_{2}\)O\({}_{2}\) induced a significant decrease in cell viability. The cell viability increased significantly when the polysaccharides were added. Interestingly, this effect was time- and dosage-dependent at polysaccharide concentrations of 0.1 and 1 mg/mL. In addition, the polysaccharides generated from ultrasound treatment allowed for greater cell survival in the presence of H\({}_{2}\)O\({}_{2}\) compared to the hot-water-extracted polysaccharides (Figure 7). These results indicated that the polysaccharides were not toxic to RAW264.7 cells and that the polysaccharides extracted using ultrasonication had a stronger protective ability against H\({}_{2}\)O\({}_{2}\)-induced injury than those extracted using the hot water method.  
Figure 6: TGA curves of PAP extracted by different methods.  
4.2 Effect of PAPs on MDA Production and Both GSH-Px and SOD Activities in H\({}_{2}\)O\({}_{2}\)-Treated RAW264.7 Cells  
The antioxidative effects of polysaccharides from _Ophiopogon japonicus_, _Phellinus luteus_, and _Cordyceps cicadae_ have been reported to decrease MDA content and increase SOD and GSH-Px activities [33; 34; 35]. MDA is a product of lipid peroxidation and is a biomarker of oxidative stress. It was found that MDA levels in the negative control group were significantly higher compared to the control group (\(p<0.05\)). Both polysaccharide groups showed similar abilities to reduce MDA levels and this effect was dose-dependent. The ultrasonicated product had a slightly greater ability and reduced the MDA levels to 53.32% at 1.0 mg/mL after 12 h compared to hot-water-extracted polysaccharides (50.13%) (Figure 8A).  
SOD and GSH-Px are endogenous cellular antioxidant enzymes that protect against oxidative damage. It was found that RAW264.7 cells treated with H\({}_{2}\)O\({}_{2}\) exhibited lower SOD and GSH-Px activities compared to the controls (\(p<0.05\)). However, incubation with the polysaccharide preparations markedly attenuated these increases in a dose-dependent manner. In addition, the ultrasonicated product generated higher levels of these activities than the hot-water-extracted product in the concentration range of 0.1-1.0 mg/mL (Figure 8B,C).  
Figure 7: Effects of PAPs on cell viability. The data are presented as mean \(\pm\) SD. \(\triangle\Delta\): \(p<0.01\) compared with the control group, *: \(p<0.05\) compared with the negative group, **: \(p<0.01\) compared with the negative group.
## 3 Experiment Results
### The Performance Study of the Reservoir Size (\(U\))  
To verify the theoretical conclusions drawn in Section 2.3, the UWOC system performance with different DeepESN reservoir configurations is studied here. To simplify the study, the PAM signal is chosen as the objective in this section, and the receiving optical power range is from \(-20.24\) dBm to \(-21.36\) dBm. According to the UWOC system configuration stated in Section 2.1, \(\mathbf{N_{sps}}\) is equal to 5, 4, and 3 for the data rates of 100, 125, and 167 Mbps, respectively. Consequently, in the study of DeepESN configuration, the range of \(U\) is from 1 to 20, including at least four PAM symbols in each reservoir.  
The system performance against different receiving powers and varying numbers of reservoir units is shown in Figure 4. In line with the trend exhibited in Section 3.2, Figure 4a supports the conclusion that higher-data-rate signals receive a better improvement on transmission performance from DeepESN processing. In the floor range (\(U>8\)), the BER decreases by approximately 0.2 dB and 0.4 dB between 100 and 125 Mbps cases and 125 and 167 Mbps cases, respectively.  
Based on Equation (4), to achieve good training performance, the size of the reservoir must cover the samples that form one or more symbol(s). BERs reach the floor when \(U\) is no less than a certain value, which is 5 for 100 Mbps, 4 for 125 Mbps, and 3 for 167 Mbps, corresponding to \(\mathbf{N_{sps}}\) (\(m=1\)) in the received signal at each data rate. Before BERs reach the floor (\(m<1\)), the considerably high value of BERs indicates the poor prediction performance of DeepESN due to insufficient training.  
In the floor range, BERs experience a further decline when \(2\leq m\leq 3\), such as \(U=8\) and 12 for 125-Mbps signal (\(\mathbf{N_{sps}}=4\)). This decline is slighter than that in the range of \(m<1\), but DeepESN achieves better training. If \(U\) keep increasing, fluctuation is observed in all cases (where \(m\geq 4\)), indicating that overfitting happens in this range.  
For the sake of comparison, a conventional ESN was also applied at the receiving power of \(-20.05\) dBm and a data rate of 100 Mbps, as shown in Figure 4a. This follows the trend stated for DeepESN previously. The curve reaches the error floor when \(U=5\), but there is a 1.8 dB BER gap between the DeepESN and ESN results.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework
### Low-Frequency Components Fusion  
#### 3.3.1 L2-Norm Based Fusion  
The weighted average is used to fuse low-frequency components. It assumes that \(k\) input images can be split to \(k\) low-frequency components \(\mathbf{I}_{ii},i\in(1,2...k)\) and \(k\) high-frequency components \(\mathbf{I}_{hi},i\in(1,2...k)\). It calculates the \"L2-norm\" \(W_{i}=\|\mathbf{I}_{hi}\|_{2},i\in(1,2...k)\) of high-frequency components to estimate the percentage of corresponding low-frequency components.  
L2-norm as a Euclidean norm is used to measure a vector difference as a standard quantity.  
\[D=\parallel x_{1}-x_{2}\parallel_{2}=\sqrt{\sum_{i}(x_{1i}-x_{2i})^{2}} \tag{6}\]  
It tries to fine the sparsest solution of the underdetermined linear system that has fewest non-zero entries. The minimization of L2-norm was used to calculate the weights of high-frequency components for the fusion of low-frequency components by [PERSON] [23] and [PERSON] [66]. The L2-norm minimization is formulated by  
\[min\parallel x\parallel_{2},subject\textit{ to }Ax=b \tag{7}\]  
It assumes the constraint matrix A has full rank. So this underdetermined system has infinite solutions. The lowest L2-norm is one of the best solutions from infinite solutions. For reducing computation costs, Lagrange multipliers are introduced to L2-norm.  
\[\mathcal{L}(x)=\parallel x\parallel_{2}^{2}+\lambda^{T}(Ax-b) \tag{8}\]  
where \(\lambda\) is the introduced Lagrange multipliers. It takes derivative of this equation equal to zero to find a optimal solution and get  
\[\hat{x}_{opt}=-\frac{1}{2}A^{T}\lambda \tag{9}\]Then it substitutes this solution into the constraint to get  
\[A\hat{x}_{opt} =-\frac{1}{2}AA^{T}\lambda=b \tag{10}\] \[\lambda =-2(AA^{T})^{-1}b \tag{11}\]  
Finally, it gets  
\[\hat{x}_{opt}=A^{T}(AA^{T})^{-1}b=A^{+}b \tag{12}\]  
So it can instantly calculate an optimal solution of L2-norm problem by using the above Equations (7)-(12). Equation (7) is the basic optimization of L2-norm. According to Equation (7), the calculation processes of weighted average of high-frequency components are deduced as Equation (8)-(12).  
The calculated corresponding weighted average \(W_{i}\) of each high frequency component is used to fuse low frequency components of source images. The fused low-frequency components can be obtained by  
\[\mathbf{I}_{l}^{f}=(\sum_{i=1,2\_k}W_{i}\cdot\mathbf{I}_{li})/(\sum_{i=1,2\_k}W _{i}) \tag{13}\]
# Advances in the Noninvasive Diagnosis of Dry Eye Disease
## 5 Anterior Segment Optical Coherence Tomography  
Optical coherence tomography (OCT) is a noninvasive technique developed to obtain tomographic image reconstruction of a biological tissue with a longitudinal and lateral spatial resolution of a few micrometers by using low-coherence interferometry [29; 30]. Anterior segment OCT (AS-OCT) can obtain images of important structures of the ocular surface [31] and is a useful tool in the diagnosis and follow-up of several diseases of the anterior segment of the eye. In DED, the measurement of parameters of the tear film by means of AS-OCT, such as pre-corneal tear film thickness (TFT), tear meniscus height (TMH), curvature (TMR), cross-sectional area (TMA) and tear meniscus depth (TMD), are widely used in routine clinical practice thanks to the noncontact nature of these examination and the rapid image acquisition by the instrument [32; 33]. The average value of TFT measured by AS-OCT in healthy subjects is approximately 5 \(\upmu\)m, while it is reduced in DED patients. This reduction is correlated with objective and subjective assessments, such as BUT and OSDI [34]. The value of TFT increases up to 24 h with the topical administration of chitosan-N-acetylcysteine, perfluorohexyl octane or low-dose hydrocortisone [35; 36], but is less influenced by low-viscosity agents. TMH and TMA are two useful biomarkers of DED, as suggested by the DEWS II Report. Spectral Domain AS-OCT demonstrated a sensitivity of 80.5% and 86.1% and a specificity of 89.3% and 85.3% for TMH and TMA, respectively; however, the diagnostic ability of evaporative DED was low (<50%) [37]. In clinical practice, some confounding factors related to the environment or the ocular surface's anatomy (e.g., conjunctivochalasis, lid-parallel conjunctival folds, disorders of lid margin congruity) should be taken into account. AS-OCT is also useful for the 3D high-resolution of Meibomian glands (MG) [38]. In patients with Meibomian gland dysfunction (MGD), AS-OCT demonstrated a decreased MG length and width, which correlated with ocular discomfort symptoms and signs [39].
## 3 Results and Discussion
### Analysis of Polymer
#### 3.2.2 Influencing Factors of Polymer
The viscosity of the polymer solution increased at first and then decreased slowly. The increase in the solution viscosity was related to the formation of intramolecular hydrogen bonds in HPAM and the arrangement of amides and carboxyl groups in HPAM molecular chains. When the molecular amides and carboxyl groups were arranged in blocks, the probability of intramolecular hydrogen bond formation was small, and the expansion of the molecular chain was mainly caused by the electrostatic repulsion between the carboxyl groups on the chain. With the increase in the number of carboxyl groups, the electrostatic repulsion increased, and the polymer chain tended to stretch, so the solution viscosity increased. At the same time, polymers rely on intermolecular association to form supramolecular aggregates, and the supramolecular aggregates can connect to each other to form a uniform three-dimensional network structure covering the whole system, so the polymer had excellent long-term stability. Under the conditions of high temperature and high salinity, the viscosity was still 31.4 mPa-s after aging for 60 days.