esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 2 Related Works  
### Methods of Image Restoration Based on Priori Knowledge  
Methods for image restoration based on a priori knowledge can be categorized into two types. The first method is based on partial differential equations that propagate the morphological information of the intact region to the defective region, generating pixels in the defective region. The second method is based on texture synthesis, which selects the best-matching block of the defective region according to the principle of similarity matching and completes the restoration after filling in the defective area.  
[PERSON] et al. [8] proposed a computerized image restoration algorithm based on a partial differential equation. The algorithm attempts to replicate the basic techniques of professional restoreers. Later, [PERSON] et al. [9] proposed a total variation-based image restoration algorithm for the partial restoration of non-textured images. [PERSON] and [PERSON] [10] proposed a nonlinear partial differential model based on curvature-driven diffusion guided by the principles of human visual perception. These methods have either focused on the spread of semantic information [11] or relied on statistics with visual features [12]. Only small area defects and simple textures could be restored.  
Some studies have proposed image restoration algorithms based on texture synthesis. [PERSON] et al. [13] proposed a block-based image restoration algorithm that obtains defective information in an iterative manner. [PERSON] et al. [14] proposed a prioritized texture restoration algorithm that searches for best-matching blocks. Robustness with respect to the shape of the manually selected target region is also demonstrated. Subsequently, [PERSON] et al. [15] optimized the search method to improve the [PERSON]'s exemplar-based inpainting based on Search Strategy and Color Transfer. [PERSON] et al. [16] attempted to improve the algorithm performance using multiple reference blocks for image restoration. A novel approach to the problem of image restoration through the use of nonlocal means is introduced. Then, [PERSON] et al. [17] proposed determining the optimal image restoration for defective areas from an image database. They employed patch-based frameworks in the optimization process to reduce the difference between the restored and original textures [18; 19; 20; 21; 22].  
Patch-based methods rely on matching local pixels or texture features with the restored content associated with a given template image. If matching does not occur or unsuitable pairwise matching occurs, the restoration results may be unrelated to the defect content. Consequently, these methods have low restoration accuracy in terms of texture and structure. They are less effective in restoring images that are morphologically informative with high specificity and a low repetition rate of elements.
## 1 Introduction
### Our Contributions
* In addition, for \(k>2\) partitions, we show that our algorithm beats METIS and FENNEL (on Twitter) by a reasonable factor. For \(k=2\), the results that we obtain beats the output of METIS but it is slightly inferior to the result reported by FENNEL. * For both graphs, our results are consistently superior to that of [PERSON] [19] with a wide margin. * We also note that our algorithms admit scalable distributed implementation for small or large number of partitions. More specifically, changing \(k\) from 2 to tens of thousands does not change the running time significantly. * As for comparing various initialization methods, we observe that while geographic Hilbert-curve techniques outperform the random ordering, the methods based on the hierarchical clustering using the number of common neighbors as the similarity measure between nodes outperform the geography-based initial embeddings, even for map graphs. Note that the computation for the number of common neighbors is needed only once to obtain the initial graph, and the new edge weights in the subsequent rounds are computed via aggregation. Further note that approximate triangle counting can be done efficiently in MapReduce; see, for example, Reference [18]. * As for comparing other techniques, we observe the random swap techniques are effective on Twitter, and the minimum-cut-based or contract and dynamic program techniques are very effective for the map graphs. Overall, we realize that these techniques complement each other, and combining them in a Combination algorithm is the most effective method. 5. As mentioned earlier, we apply our results to the Google Maps Driving Directions application, and deploy two linear-embedding-based algorithms on the world's map graph. We first examine the best imbalance factor for our cut-optimization technique, and observe that we can reduce 21% of cross-shard queries by increasing the imbalance factor from 0% to 10%. The two methods that we examined via live experiments were (i) a baseline approach based on the Hilbert-curve embedding, and (ii) one method based on applying our cut-optimization post-processing techniques. In live experiments on the real traffic, we observe the number of multi-shard queries from our cut-optimization techniques is 40% less compared to the baseline Hilbert embedding technique. This, in turn, results in less CPU usage in response to queries. (The exact decrease in CPU usage depends on the underlying serving infrastructure which is not our focus and is not revealed due to company policies. We note that there are several other algorithmic techniques and system tricks that are involved in setting up the distributed serving infrastructure for Google Maps driving directions. This system is handled by the Maps engineering team, and is not discussed here as it is not the focus of this paper.)
## 4 Discussion and Conclusions
### Design Recommendations for Robot Developers  
Based on the findings of this study, several explicit design recommendations can be made for robot developers. First, robots should incorporate synchronized gestures and facial expressions to create more natural and engaging interactions, as these features were particularly effective in fostering trust and emotional engagement, especially with the Pepper robot. Additionally, robot behaviors should be tailored to specific tasks and user needs. For instance, the Double 3 robot's telepresence capabilities could be optimized by improving its navigation interface to enable smoother transitions in confined spaces.  
Developers should also carefully consider the physical appearance of robots in relation to their intended roles, as humanoid features were found to increase perceived usability in social contexts, as demonstrated by the Pepper robot. Furthermore, incorporating real-time feedback mechanisms during interactions can significantly enhance usability and reduce confusion. This was evident in the positive reception to both robots' verbal guidance during the simulated warehouse tasks. Finally, developers should design robot systems for scalability, ensuring that they can be adapted for broader applications without extensive reprogramming. This is particularly important for versatile robots like the Double 3, which can serve multiple functions across domains. By implementing these recommendations, developers can create robots that not only meet functional requirements but also enhance user satisfaction and trust, ultimately contributing to the broader acceptance and integration of robots in diverse settings.  
Looking ahead, this study offers a foundation for future research to explore long-term interactions, diverse user groups, and the ethical implications of robotics. By addressing these dimensions, the field can move closer to developing robots that are not only effective tools but also empathetic companions in human environments.
## 4 Test Results and Analysis
### Hysteretic Curve of Isolation Layer  
As all of the concrete rods' blasting structure is released, it attenuates the vibration freely, allowing for the determination of the structure's force-displacement relationship curve throughout horizontal movement and during the blasting to be obtained [31,32], as illustrated in Figure 21.  
As illustrated in Figure 21, the falling slope of the curve is sharp at the point of blasting release, as the structure rapidly loses the horizontal support given by the concrete rods, in contrast to the leisurely unloading form of the hydraulic jack described in [12]. This technique's unloading is more realistic, as the upper structure has already lost its horizontal support prior to being reset, and the reset time is unaffected by the hydraulic jack's unloading time. Additionally, the study demonstrates that the isolation layer's force-displacement relationship curve is a plump hysteretic loop, demonstrating its superior energy dissipation capacity. The hysteretic curve agrees with theoretical analytical [11].  
Figure 20: Acceleration attenuation curve and the related spectrum: (**a**) acceleration attenuation curve (roof floor); (**b**) spectrum (roof floor); (**c**) acceleration attenuation curve (first floor); (**d**) spectrum (first floor).  
It should be noted that since the time-history curve for structural reset is not gathered, the figure's reset energy consumption curve is replaced by a straight line. Additionally, the isolation layer's entire hysteretic curve reflects the lead bearing's peculiar viscous elastic-plastic characteristics. The isolation layer's capacity to absorb and consume seismic energy is mostly determined by the lead bearing.
## 1 Introduction
The methodology presented in this paper allows the estimation of interpolation-error magnitude and its trend across one period. The performed experimental investigation of the particular standard linear encoder interpolation error under different scanning speeds showed that the magnitude of this low-frequency error highly depends on the traversing speed of the encoder's reading head. Moreover, a detailed harmonic analysis of the SDE could help to understand the physical nature of the error, and improve the performance of the encoder and the whole application.
## 3 Results and Discussions
### Shear Strength  
#### 3.3.1 Saturated Sample (CU Test)  
The variations in deviator stresses and corresponding strains and shearing-induced pore water pressures for the pure soil and XG-treated specimens under 50 kPa confining pressure are shown in Figure 5.  
As seen in Figure 5, the addition of biopolymer reduces the brittleness of soil in all XG-treated specimens. While there is no well-defined peak of shear stress for biopolymer-treated specimens, the pure soil experienced brittle behavior, and peak deviator stress is clearly determined.  
The soil strength was significantly reduced by adding XG biopolymer (Figure 5a). Soil treated with 1% and 2% XG experienced a considerable 40% and 60% reduction in maximum deviatoric stress at 200 kPa confining pressure. The reason arises from the hydrophilic feature of gel-type biopolymers such as XG, which leads to absorbing water during the initial phase of the CU test. This water fills the pores between soil particles, causing a considerable decrease in soil strength. Corresponding behavior from generated  
Figure 4: Changes in UCS with curing time.  
Figure 3: Relationship between the secant stiffness and curing time for the different content of XG.  
pore water pressure during shearing was also observed (Figure 5b). The absolute values of induced pore water pressures in pure soil were much higher than in the treated soil with XG.  
The soil shear parameters for the untreated and XG-treated soil were obtained through the plotted failure envelope curves (Figure 6).  
The XG biopolymer caused a reduction in both soil cohesion and friction angle (Figure 6). A higher concentration of XG significantly reduced soil cohesion and friction angle of soil. As mentioned, this is ascribed to the higher absorbed water due to the gel-type feature of XG, which resulted in occupying the voids with water.  
Typically soil friction angle depends on the angularity of soil particles, soil gradation, normal stress, etc. Adding biopolymer provides a coating over soil particles leading to a reduction in soil grain angularity and surface roughness. This causes a reduction in internal friction angle after adding biopolymer. Table 2 provides the calculated shear parameters from the CU triaxial tests.  
Figure 5: Results of CU triaxial tests, (**a**) deviatoric stress-axial strain curves; (**b**) pore water pressures.  
Figure 6: Effective shear stress vs. effective normal stress curves.
## 4 Parameter Study and Design Buckling Curves
The numerical model applied was similar to the model used for the recalculation of the experiments (see Figure 3), but without modeling the load introduction (universal joint). The load was applied directly at the nodes of the top of the cross-section instead. As previously stated, the investigations have been carried out especially for cantilever beams, used, for example, as a canopy. Therefore, a constant bending moment proved unrealistic for practical use and was not considered. The design buckling curves were fitted to the numerical results by using two different assumptions:  
1. The first assumption used a threshold value \(\overline{\lambda}_{0}\), which was obtained as near as possible to the results. 2. For comparison then, a fixed threshold value with \(\overline{\lambda}_{0}=0.20\) was used. This value is in accordance with the recommended design buckling curve given in [8]. For both assumptions, the imperfection factor \(\alpha\) was then increased until the design buckling curve reached the first numerical obtained result. A distinction was also made between the loading situation (single load F and distributed load q) and the glass type (FT and HS). The determined curves are shown in Figure 9. For comparison, the recommended design buckling curve from [8] with \(\overline{\lambda}_{0}=0.20\) and \(\alpha=0.45\) was also plotted. When using a threshold value of \(\overline{\lambda}_{0}=0.20\) (green lines), the developed curves appear to agree with the proposed curve given in [8]. In one case, even identical coefficients were obtained (see Figure 9b). When using threshold values \(\overline{\lambda}_{0}>0.20\) (red lines), the developed curves appear in being slightly more economical, especially when using FT-glass. Especially in the slenderness range of 0.2 to 0.5, other authors reported lower ultimate loads. Due to the chosen parameters (see Table 7), only a few values were obtained in this range in the presented parameter study. However, as mentioned above, other parameters seemed to be unrealistic for practical usage. Other reasons for the different coefficients  
Figure 9: Proposed design buckling curves, based on \(\mathrm{v}_{0}=\mathrm{L}/400\) and \(\phi_{0}=0.01\) rad, for different situations:  
**(a,b)** Single load F, **(c,d)** distributed load q, **(a,c)** fully tempered glass, **(b,d)** heat strengthened glass. also arise maybe from the different loading situations and the somewhat different consideration of the imperfections. For a better comparison, the authors recommend [8] for further reading.
## 5 Conclusions  
This paper investigates a new problem about optimal UC and DR caused by electricity tariffs in a factory power system. The uncertain amounts of generated power from renewable sources, which may reduce the total cost, are considered in the factory power system. The contributions of this paper can be summarized as follows:  
1. The problem concerning optimal DR and UC, considering uncertain PV power generation, in a factory power system, rather than the UC in the bulk power system or DR at home, is formulated and studied.
2. The method based on novel genetic algorithms that are associated with the point estimation and interior point methods is proposed to determine the UC and DR in the factory power system.
3. The proposed string encoding in genetic algorithms efficiently performs both crossover and mutation operations for the UC together with DR. This proposed method ensures that feasible chromosomes can evolve to the fittest solution.
4. Impacts of different parameters (such as PV generations, electricity tariffs, minimum on/down times, ramp rates and must-run hours) were completely investigated on the optimal solutions.  
The results of the simulation verify the applicability of the proposed method using a 30-bus factory power system.  
Future studies will include modeling different tariffs for electricity and renewable energy as well as different tariffs for purchase and selling energy. Power flow studies in the factory power system will be investigated to ensure that both the voltages and line flows meet the security constraints.  
The authors acknowledge the financial supported from the Ministry of Science and Technology (Taiwan) under Grant MOST 104-2221-E-033-029.  
[PERSON] proposed the whole methodology and wrote this article. [PERSON] developed the program code to realize the method and analyzed the simulation results.  
The authors declare no conflict of interest.
# On the Use of Deep Learning for Video Classification
## 2 Convolutional Neural Networks (CNN) for Image Analysis  
Deep learning models, specifically convolutional neural networks (CNNs), are well known for understanding images. A number of CNN architectures are proposed and developed in the scientific literature for image analysis. Among these, the most popular architectures are LeNet-5 [12], AlexNet [13], VGGNet [14], GoogleNet [15], ResNet [16], and DenseNet [17]. The trend that follows from the formerly proposed architectures towards the recently proposed architectures is to deepen the network. A summary of these popular CNN architectures along with trend of deepening the network is shown in Figure 1, where the depth of network increases from left-most (LeNet-5) to right-most (DenseNet). Deep networks are believed to better approximate the target function and to generate better feature representation with more powerful discriminatory powers [18]. Although deeper networks are better in terms of having more discriminatory powers, the deeper networks require more data for training and more parameters to tune [19]. Finding a professionally labeled, huge dataset is still a big challenge faced by the research community, and therefore, it limits the development of deeper neural networks.  
Figure 1: State-of-art image recognition CNN networks. The trend is that the depth and discriminatory powers of network architectures increases from formerly proposed architectures towards the recently proposed architectures.
## 5 Experimental Results and Discussion
### Estimated State Transition Probabilities
#### 5.3.3 The Error-Free Run Distribution Graph  
The probability of transitioning to \(m\)-consecutive error-free transmissions after an error transmission is depicted by the error-free run distribution (EFRD) graph. The distribution of error-free runs gives a rough idea of how likely a burst or cluster of bit errors is.  
Figures 16 and 17 depict the EFRD plots for Scenario 1 (Cases 1 and 2), whereas Figures 18 and 19 depict the EFRD plots for Scenario 2 (Cases 3 and 4). Calculations were performed to obtain the EFRD probabilities for the measured error sequences. Next, using the estimated model parameters, an error sequence of the same length as the measured error sequences obtained from the experimental measurements was generated. After this, the EFRD was calculated for the measured error sequence and the model error sequence that matched it. The EFRD derived from the experimental measurement was then compared
# Optimal Design and Hybrid Control for the Electro-Hydraulic Dual-Shaking Table System
## 5 Conclusions  
In this paper, an electro-hydraulic dual-shaking table system is developed and the system parameters are designed and optimized in detail. To improve the attitude synchronization and tracking control precision, a novel hybrid control scheme is proposed, which is a combination of the novel ASMC cross-coupled controller and the observer-based feed-forward inverse compensation controller. The ASMC is introduced to the cross-coupled controller to reduce synchronization error, and the observer-based feed-forward inverse controller is proposed to improve tracking precision.  
The dual-shaking table experiment system is built based on the xPC target technique and various comparison experiments are performed. The servo valve experimental results indicate that the developed servo valve with a parameter-tunable valve controller can improve the consistency and extend the servo valve frequency width from 60 Hz to 100 Hz. The IFS and GBC experimental results show that the control strategy can reduce the hydraulic cylinders' maximum inner forces from 2.7 \(\times\) 10\({}^{4}\) N to 2000 N and maximum output forces from 4.8 \(\times\) 10\({}^{4}\) N to 1200 N, respectively. The synchronization and tracking experimental results demonstrate that the proposed ASMC can reduce the RMS synchronization error from 0.056 mm to 0.0089 mm, which is almost 15.8% lower compared with the traditional controller. The proposed tracking controller can reduce the RMS tracking errors from 0.735 mm/0.801 mm to 0.181 mm/0.233 mm, which is about 24.6%/29% of the former. Experimental results demonstrate that the developed dual-shaking tables system with the proposed hybrid control scheme can allow synchronization and tracking control of a dual-shaking table with high precision.  
This research is supported by the National Natural Science Foundation of China under Grant No. 51205077, 51475116 and 51305095. The authors would like to thank the editor, associate editors and anonymous reviewers for their constructive comments.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 27: The acceleration results with proposed compound control. (**a**) Time domain; (**b**) Spectral acceleration; (**c**) Spectral acceleration errors (absolute); (**d**) Spectral acceleration errors (relative).
## 3 System Model and Problem Formulation
### GDRL-SFCR Intelligent Routing Framework
#### 4.2.2 Obtaining State Space and Action Distribution  
The above embedding process can be thought of as converting the original feature \(\mu\) of each network node into an embedding \(\mu^{\prime}\) using the GNN. When making a routing decision, we only need to consider selecting an optimal next-hop node based on the states of the current network node and its neighbor nodes. In order to reduce the dimensionality of the state space, we consider using the degree of similarity between the current node and its neighbor nodes as the current effective state space. After applying the GNN, the embedding of the current node \(H_{c}\) and the embedding of its neighboring nodes \(H_{n}\) are transformed in their dimensions using \(MLP_{f}\) and \(MLP_{g}\), respectively, after which the inner product of these node embeddings is computed and expanded to be consistent with the dimensions of the states defined earlier in the paper to obtain the current desired effective state, as follows:  
\[H_{c}^{\prime}=MLP_{f}[H_{c}]=W_{f}\cdot H_{c}+b_{f} \tag{21}\] \[H_{n}^{\prime}=MLP_{g}[H_{n}]=W_{g}\cdot H_{n}+b_{g}\] (22) \[S_{t}^{\prime}=\sum_{j=1}^{N_{f}}\sum_{i=1}^{\dim(G)}H_{c}^{{}^ {\prime}}(j)\cdot H_{n,i}^{{}^{\prime}}(j)\] (23) \[S_{t}=[S_{t}^{\prime},0,\cdots,0] \tag{24}\]  
The above process allows states with different numbers of neighboring nodes to be transformed into valid states with uniform dimensions.