esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 2 Methodology
### Generator  
The construction of the generator follows the automatic encoder structure, as shown in Figure 9. The generator is composed of an encoder and a decoder. The encoder has two blocks called downblocks. The downblocks will double the feature on the channel, halve the height and width of the feature and finally output an intermediate feature with a resolution of (256, 64, 64). At this point, we use the dense motion \(\mathcal{F}_{S\gets D}(z)\) distort the intermediate feature, so as to transfer the motion information from the source frame to the driving frame. In addition, in order to encode intermediate feature information at a deeper level, we use six modules called Resblock2d. In 2016, [PERSON] et al. (2019) proposed the use of Resblock2d in ResNet to solve the problem of network gradient disappearance and gradient explosion, so that deeper network models can be trained. The structure diagram of Resblock2d is shown in Figure 9.  
In the decoding stage, we first use the occlusion map of (64, 64) resolution to repair the intermediate feature. Specifically, the calculation is shown in Equation (13), where \(\xi_{in}\)  
Figure 8: Diagram of the multi-scale occlusion restoration module.  
Figure 9: Diagram of the generator.  
represents the input feature, \(\xi_{out}\) represents the feature after the occlusion map repair, and \(\mathcal{O}_{l}\) represents the occlusion map with resolution (\(i\), \(i\)) (\(i\) belongs to (64, 128, 256)). After that, the repaired feature will go through two blocks called upblocks. The upblocks will halve the channels of feature and double the height and width, which is the reverse operation of the downblocks. The output of each upblock will be repaired in detail through an occlusion map of the same resolution. Finally, convolution operation with the filter size of \(1\times 1\) is used to reduce the channels of feature to 3, and our reconstructed image is obtained after the sigmoid activation function.  
\[\xi_{out}=\mathcal{O}_{i}\odot\xi_{in} \tag{13}\]
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 3 Modeling the Entire Flux-Linkage Characteristics  
### Solving the Flux-Linkage Curve at the Middle Position  
Figure 5 shows the variation of flux-linkage with rotor position at specific current. In Figure 5, the flux-linkage curve can be divided into three regions, i.e., Region I \([\theta_{u},\theta_{1})\), Region II \([\theta_{1},\theta_{thr})\) and Region III \([\theta_{thr},\theta_{a}]\). \(\theta_{u}\) represents the unaligned position, which is defined as \(0^{\circ}\) in this paper; \(\theta_{1}\) is the corner point, where the approaching corners of the stator and rotor poles are aligned; \(\theta_{hr}\) is a dividing point, where the approaching corner of the stator and center line of the rotor are aligned; \(\theta_{a}\) represents the aligned position, which is \(22.5^{\circ}\) in this paper. It can be seen from Figure 5 that the flux-linkage exhibits an approximately linear relationship with the position in Region II. Therefore, as long as \([7.5^{\circ},15^{\circ}]\) is included in \([\theta_{1},\theta_{hr}]\), the flux-linkage curve at the middle position can be calculated.  
For SRM, the corner point \(\theta_{1}\) and the dividing point \(\theta_{hr}\) can be calculated as:  
\[\begin{cases}\theta_{1}=(\frac{360^{\circ}}{N_{r}}-\beta_{s}-\beta_{r})/2\\ \theta_{hr}=(\frac{360^{\circ}}{N_{r}}-\beta_{r})/2\end{cases} \tag{3}\]  
where \(\beta_{s}\) and \(\beta_{r}\) are pole arc widths of the stator and rotor, respectively, which satisfy the following equation:  
\[min(\beta_{s},\beta r)\geq\frac{360^{\circ}}{qN_{r}} \tag{4}\]  
where \(q\) is the number of phases of SRM and \(N_{r}\) is the number of rotor poles of SRM. For three-phase \(12/8\) pole SRM, \(q=3\) and \(N_{r}=8\). By substituting (4) into (3), we have:  
\[\begin{cases}\theta_{1}\leq 7.5^{\circ}\\ \theta_{hr}\geq 15^{\circ}\end{cases} \tag{5}\]  
Figure 5: The variation of the flux-linkage with rotor position.  
Therefore, the flux-linkage-position characteristic (\(\psi-\theta\)) at \([7.5^{\circ},15^{\circ}]\) is approximately a linear relationship. Further, the flux-linkage curve of the middle position \(11.25^{\circ}\) can be calculated as:  
\[\psi_{11.25^{\circ}}(i,\theta)=\frac{\psi_{7.5^{\circ}}(i,\theta)+\psi_{15^{ \circ}}(i,\theta)}{2} \tag{6}\]  
To evaluate the accuracy of the estimate result at middle position \(11.25^{\circ}\), the comparison between the conventional rotor clamping method and the proposed method is carried out and shown as in Figure 6. It can be noticed that there are still small differences between the estimated and the measured results, but the accuracy is good enough. Therefore, in the next subsection, the entire electromagnetic characteristics of the SRM will be modeled based on the four measured and one estimated flux-linkage curves.
###### Abstract  
Ultra-high-pressure hydrogen solenoid valves face a fundamental design challenge of operating across a wide pressure range from 2 MPa to 87.5 MPa. To address the conflicting requirements of effective sealing at low pressures and structural integrity at high pressures, this study proposes a novel concave contact geometry based on Hertzian contact theory. Finite element analysis examines the mechanical relationships between plunger curvature radius (\(R_{p}\)), seat curvature radius (\(R_{s}\)), and eccentricity (\(e\)). Optimization utilizing Latin hypercube sampling and kriging metamodeling yields an optimal design (\(R_{p}\) = 5.73 mm, \(R_{s}\) = 4.68 mm, \(e\) = 0.95 mm) with an \(R_{p}\)/\(R_{s}\) ratio of 1.22. The optimized concave contact geometry achieves 23.7% higher contact pressure at 2.0 MPa and 42.7% lower maximum equivalent stress at 87.5 MPa compared to conventional rectangular geometry. Experimental validation confirms the concave contact geometry seals at 1.7 \(\pm\) 0.2 MPa, below the AIS-195 standard requirement of 2.0 MPa and 69.6% lower than the rectangular design (5.6 \(\pm\) 0.7 MPa). Structural analysis after 87.5 MPa high-pressure exposure reveals no measurable deformation in the concave design, while the rectangular design exhibits permanent deformation of 0.0580 \(\pm\) 0.007 mm. This integrated methodology provides a framework for optimizing contact geometries in fluid control components operating under extreme pressure conditions, successfully reconciling contradictory requirements across the entire pressure range.  
ultra-high-pressure hydrogen valves; concave contact geometry; Hertzian contact theory; sealing performance; structural integrity; contact mechanics; design optimization; finite element analysis; kriging metamodeling; hydrogen fuel cell +
Footnote †: journal: Applied Sciences
# Demosaicing by Differentiable Deep Restoration
## 4 Experiments
### Reconstruction from Noise-Free Data  
We first compare the proposed method with state-of-the-art methods on noise-free data. Our method is trained on the training set of the MIT dataset [9] consisting of the _vdp_ and _moire_ datasets. We train our network with a fixed Bayer pattern and a jointly learned CFA separately for evaluation. We evaluate different demosaicing techniques on the test set of the _vdp_ and _moire_ datasets as well as the commonly used Kodak [10] and McMaster [11] datasets. For our network, as the resolution of input image is required to be a multiply of the downsampling factor of U-Net, we employ replicated padding on the input image to satisfy this requirement and crop the prediction to the original resolution in the end for evaluation. Table 1 lists the average PSNR and average SSIM of different methods on these four datasets. Techniques based on Bayer pattern CFA are compared in the top part, while in the bottom are techniques with non-Bayer CFAs. For fair comparison, we evaluate these methods with the source code and the trained models provided by authors if they are available. All result images are saved to disk to have the same quantization (i.e., 0-255) and then calculated PSNR and SSIM. Otherwise, we directly quote the evaluation result  
from the published paper [20; 21; 22; 49]. For methods in [35; 50], whose reconstruction is not the full image, we only evaluate within the reconstructed area. Our method produces the best PSNR in all the four datasets with both settings of Bayer pattern and non-Bayer CFA. Our strong performance in these four datasets demonstrates the advantages of our design of combining machine learning and physically-based optimization. Meanwhile, our method with jointly learned CFA performs much better than the setting of Bayer pattern. This verifies the importance of CFA design and the advantages of jointly optimizing the CFA pattern and the demosaicing algorithm. While our method is only trained on the training set of the MIT dataset [9] where the image resolution is fixed at \(128\times 128\), it has strong generalization capability to deal with the Kodak [10] and McMaster [11] datasets with high resolution images. Instead of only measuring pixel-intensity differences as PSNR, SSIM considers the fidelity of image structure. We can see from Table 1 that our method also achieves comparable and even higher SSIM than other demosaicing techniques. In addition, we compare the result quality of our method with other demosaicing techniques visually in Figure 4. From left to right, the different columns show the ground truth reference image, the results by the methods of [PERSON] et al. [52], [PERSON] et al. [9], [PERSON] et al. [40], [PERSON] et al. [3], and our method with jointly learned CFA pattern and demosaicing algorithm. It is clear that our method presents better results with more details, especially in the high frequency regions. While the other methods often suffer from color artifacts on these challenging examples.
# Copernicus Global Land Cover Layers--Collection 2
## 2 Methodological Overview
### Classification/Regression and Product Generation  
The optimized training data set, together with the quality indicator of the input data (DDI dataset, see Section 2.2) were inputs for the supervised classification algorithms. As the core classifier, Random Forest (RF) techniques were used. All the generated models were optimized via 5-folded cross-validation in order to estimate the optimal classifier/regressor parameters.  
RF classification was used to produce a hard classification, showing the discrete class for each pixel, as well as the predicted class probability. This class probability is provided as a quality indicator layer in the final product. In a second step, the vegetation coverage per pixel was generated via RF regression, based on the cover fractions collected for all training points, for each class separately. Moreover, the standard deviation of the applied RF model for each pixel was outputted and added in the product as additional quality layers. Note that in the final product, the cover fraction layers for built-up (urban), permanent inland water bodies and seasonal inland water bodies were derived from the processed ancillary data products.  
As a last step, the different classification outputs (discrete map, probability maps as well as the regression maps), were combined with ancillary data sets to produce the final CGLS-LC100 product through a set of expert rules. For example, the classes for permanent snow, built-up, and permanent water were added via rules on the corresponding cover fraction layers. The produced LC map uses a hierarchical legend based on the UN LCCS. Compatibility with existing global LC products was hereby taken into account.
## 4 Systematic Experimental Process and Results Analysis
### Experimental Results
#### 4.4.2 Comparison of the Experimental Results of the Emotion Analysis Model  
The data source can strengthen the model's adaptation performance, and the quality of the model also determines the quality of the data analysis. Therefore, to verify the effectiveness of the models proposed in this paper, the following comparison is made with commonly used models:  
* GRU [56]: This model has one less internal \"gating\" mechanism than LSTM, but the experimental results are comparable to LSTM.  
\begin{table}
\begin{tabular}{c c c} \hline
**Network Model** & **Image Text Detection Effect** & **Detection Confidence** \\ \hline \multirow{4}{*}{Model before improvement} & 40.257 POSSIBLE FORMULA & 0.815 \\  & UNIQUE SKINCARE FORMULA FOR & 0.936 \\  & SALE & 0.913 \\  & omo & 0.738 \\ \hline \multirow{4}{*}{Improved model} & 40.257 POSSIBLEFORMULA & 0.837 \\  & INOLOCICALDIACNOSIS & 0.859 \\ \cline{1-1}  & UNIQUESKINCAREFORMULAFOR & 0.949 \\ \cline{1-1}  & SALE & 0.927 \\ \cline{1-1}  & lomd & 0.695 \\ \cline{1-1}  & MACREME & 0.924 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of image text detection effect before and after improvement.  
Figure 10: Comparison of the effect of models P, R and F before and after improvement; **(a)** ICDAR 2015 dataset test results; **(b)** Test results on the video data collected by the crawler.  
BiLSTM [57]: The model was proposed by [PERSON] et al. to better capture the semantic dependencies in both directions.
* TextCNN [58]: [PERSON]. [PERSON] et al. did not introduce an attention mechanism in this model and did not consider contextual relationships.
* BERT-BiGRU [59]: A BERT-BiGRU-CRF entity recognition model is proposed for the text data of electronic cases, which can obtain the best sequence information, but the potential information of electronic medical records needs to be further explored.
* BERT-BiLSTM [60]: In the process of information extraction, the model is capable of F1 values up to 75%, but not by considering the important parts of the many extracted pieces of information.
* BERT-BiLSTM + Attention [61]: The model uses BERT as a word embedding and combines BiLSTM with attention to achieve the desired inference.  
In Figure 11, the performance of machine learning-based sentiment analysis methods is slightly inferior to that of deep learning in the experimental effect of sentiment analysis due to the complexity of manually produced features and the human subjective factors that can influence prediction results. Existing sentiment analysis models based on BERT that have been enhanced on the basis of BERT achieve great outcomes, but there is a significant disadvantage in that the improved method considers all words in a sentence and cannot center in on the most significant parts of the text. This paper's proposed method of combining BERT with a block attention mechanism takes into account the benefits of both local and global contextual features, and the P value, R value, and F value of the experimental effect are all greater than those of other baseline models.
## 4 Discussion
### FTIR  
Dental materials used in dentistry for various vital procedures must be biocompatible, inhibit bacterial growth, prevent microleakage and form a bond between the tooth and the material. Knowing the material's composition and structural and chemical features is desirable, as they are placed directly or closely to vital tissues [20]. Fourier transform infrared spectroscopy is a vibrational technique popularly used to know the chemical and structural properties of a material. It absorbs the energy in the IR region, resulting in the transfer of photons from lower to higher energy, causing molecular bond vibrational movement [21].  
Morphological and chemical analyses of biodentin have found that the FTIR spectrum shows characteristic absorption bands of phosphate and hydroxyl groups. In addition, [PERSON] et al. [4] reported that Biodentine contains calcium hydroxide in its composition.  
The FTIR spectra of biodentin and different groups of nano-zirconia-modified biodentin are shown in Figure 1. Table 2 shows the main infrared absorption of the biodentin and modified biodentin. The OH stretching vibrations of biodentin gave a broad band at 3438.33 cm\({}^{-1}\). As seen in the spectra of biodentin and its nano-zirconia-modified groups, the stretching vibration shifted from 3643.26 cm\({}^{-1}\) for group 2 to 3438.33 cm\({}^{-1}\) for group 3 and 3643.26 cm\({}^{-1}\) for group 4. There was an interaction between the biodentin components and the nano zirconia in all groups, and there were some free functional groups for bonding  
Figure 3: Solubility between different groups. * significant difference.  
in group 2 and group 4, as the absorption band was 3643.26 cm\({}^{-1}\). The absorption bands for group 1, the control group, and group 3 were the same: 3438.33 cm\({}^{-1}\). In accordance with this, the results for all objectives of our research also show that group 3 had better and equal properties compared to biodentin. Aliphatic C-H stretching and C-H stretching were also seen in the 3000-2800 cm\({}^{-1}\) range. Not much variation was seen in this region of absorption. In the absorption range of IR 2000-1650 cm\({}^{-1}\), the formation of C=C of aromatic ring stretching indicated a medium interactive bond between nano zirconia and components of biodentin. At absorptions of 709.59 cm\({}^{-1}\) and 813.81 cm\({}^{-1}\), there were trisubstituted and monosubstituted molecules (Table 2). All these sharper peaks were assigned to a broader new peak with an idealized formula (Ca\({}_{8}\)(Si\({}_{6}\)O\({}_{18}\)H\({}_{2}\))(OH)\({}_{8}\)(Ca.6H\({}_{2}\)O) with C/S ratio \(>\) 1.5).
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 4 The Proposed Watermarking Method
### Watermark Embedding Algorithm  
Suppose that \(H\) and \(W\) are the host image and the original binary watermark image, respectively. And their sizes are \(M\times N\) and \(P\times Q\). Steps of embedding watermark into the original image are described as follows:  
(1) Using the method described in Section 3, generate the encrypted binary hologram \(CW\) of the original watermark \(W\) first. Then map \(CW\) into a 1-D array \(WM\) by use of the zigzag scan process.  
(2) Decompose \(H\) with 1-level SFLCT to achieve the low frequency sub-band \(SL\).  
(3) \(SL\) is divided into non-overlapping blocks of \(z\times z\) pixels.  
(4) In term of the Section 4.1, select \(PQ\) blocks which are suitable for embedding watermark signal.  
(5) Apply Schur decomposition to all selected blocks.  
(6) During the embedding process, an element of \(WM\) is superposed on one block. The (1,1)\({}^{\text{th}}\) element in \(S\) matrix of the chosen block is altered to insert the watermark. To embed \(WM\), the first element of \(WM\) is inserted into the first selected block, and then the embedding procedure is repeated until the rest elements of \(WM\) are inserted into the other chosen blocks in sequence. Let \(S_{i}\) be the \(S\) matrix of the \(i^{\text{th}}\) selected block after Schur decomposition, \(WM_{i}\) be the \(i^{\text{th}}\) element of \(WM\). Here, \(i=1,2\),..., \(PQ\). The watermark is embedded via Equation (14).  
\[S_{i}^{\prime}(1,1)=\left\{\begin{array}{ll}S_{i}(1,1)-\Delta+0.25q,&WM_{i} =0\quad\text{and}\quad\Delta\in[0,0.75q)\\ S_{i}(1,1)-\Delta+1.25q,&WM_{i}=0\quad\text{and}\quad\Delta\in[0.75q,q)\\ S_{i}(1,1)-\Delta-0.25q,&WM_{i}=1\quad\text{and}\quad\Delta\in[0,0.25q)\\ S_{i}(1,1)-\Delta+0.75q,&WM_{i}=1\quad\text{and}\quad\Delta\in[0.25q,q)\\ \end{array}\right. \tag{14}\]  
where \(\Delta=\text{mod}(S_{i}(1,1),q)\), \(S_{i}(1,1)\) is the (1,1)\({}^{\text{th}}\) element in \(S_{i}\) matrix, \(q\) is the quantization step. mod() is the modulo operation. Experiments show that the proposed method has high imperceptibility and good robustness against attacks when \(q\in\)[40; 60].  
(7) Apply inverse Schur decomposition to every embedded block. Then the watermarked image can be obtained by use of inverse SFLCT. The \(x\)-coordinates and the \(y\)-coordinates of the first pixels of the selected blocks are saved in a \(2\times PQ\) matrix. They will be used for watermark extraction.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experimental Results Overview  
A comprehensive evaluation and detailed analysis were conducted to assess the overall performance of the hybrid genetic algorithm. Figures 6 and 7 depict the visualization results of synchronous task scheduling. Different symbols in the figures represent various task types, with the numbers following the symbols indicating task generation checkpoints and the numbers after the colons denoting task execution times. The x-axis shows the start and completion times of task execution, while the y-axis represents the servers and clients to which the tasks are assigned. The figures illustrate that tasks are executed synchronously across all servers and clients, with consistent start and completion times.  
Figures 6 and 7 also compare the results of the Dynamic Synchronous Scheduling algorithm with those of the hybrid genetic algorithm. The depicted scenario includes a central laboratory, five distributed servers, and two detection points, with three clients at point 1 and four clients at point 2. In this setup, the hybrid genetic algorithm achieves a minimum completion time of 612, compared to 810 for the Dynamic Synchronous Scheduling (DSS) algorithm, clearly demonstrating the superior performance of the hybrid genetic algorithm. Further analysis reveals that the hybrid genetic algorithm allows tasks to be executed seamlessly on the server with the longest occupancy time, leading to a near-optimal solution for this allocation method.  
The previous experiment demonstrated the outcomes of synchronous task scheduling and highlighted the optimization capabilities of the hybrid genetic algorithm. To further validate its performance, we extended our tests across different scenarios. These scenarios included a small-scale setting with a random distribution of 4 servers and 4 clients, a medium-scale setting with 8 servers and 15 clients, and a large-scale setting with 12 servers and 18 clients, handling 100, 180, and 300 tasks, respectively. Each scenario was tested nine times, with the results summarized in Figure 8. The initial data points in the figure represent randomly generated feasible solutions.  
By comparing the results across these three scenarios, it is evident that the hybrid genetic algorithm consistently produces stable solutions in various settings. Moreover, the optimization process is noticeably quicker in the small-scale scenario, suggesting that the algorithm scales effectively and performs efficiently even as the complexity of the task distribution increases.
###### Abstract  
This paper considers a novel fault-tolerant control (FTC) scheme for a category of cascade nonlinear systems with mismatched uncertainties and unknown actuator faults. The robust adaptive dynamic programming (RADP) is used to design a novel optimal sliding surface (SS) off-line, which renders the corresponding sliding-mode dynamics able to obtain robustness of stability to mismatched uncertainties. Subsequently, a simple sliding-mode control (SMC) with the adaptive fault compensation is developed to guarantee reachability of the sliding mode. Then, it is proven that the weight errors of neural networks (NN) of RADP and the closed-loop system are stable based on Lyapunov stability theory. In the simulation section, this proposed scheme is used to deal with the attitude FTC of a spacecraft, and simulation results verify the effectiveness of the proposed novel control scheme.  
nonlinear spacecraft systems; mismatched uncertainties; fault-tolerant control; sliding surface design; adaptive dynamic programming (ADP)