esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Branch Deep Residual Network for Single Image Super-Resolution
## 2 Related Work  
### Image Super-Resolution  
Thus far, many methods have been proposed to solve the super-resolution problem. They can be categorized into four types--image statistical methods, prediction based methods, edge based methods, and patch-based methods. Early algorithms apply interpolation techniques and statistical methods [25; 26] to SR, but work hardly with the lost details and realistic textures. Then, methods based on prediction, which are the first techniques for SISR, are proposed to reconstruct higher resolution images. While those filtering algorithms use bicubicing or linear filtering to oversimplify the SISR problem, methods based on edge preservation [27; 28] have been proposed. These approaches not only take advantage in speed, but can also achieve overly smooth texture reconstruction. In addition, many works based on patch [14; 26; 29; 30] are also designed for SR. With the patch redundancies across scales within the image, [PERSON] et al. [29] proposed an algorithm to drive the research and development of SR. Compared with the other three methods, these methods based on patch exhibit superior performance.  
In order to achieve a better reconstruction, a superior mapping function from low resolution to high resolution is necessary. Among the existing techniques, those works based on deep neural networks are considered to have a strong capability to achieve significant mapping in image super-resolution. [PERSON] et al. [10] first adopt convolutional neural network (CNN) architecture to solve the problem of SISR. They use a three-layer deep fully convolutional network to achieve state-of-the-art SR performance. This attempt shows that the CNN model can further improve reconstruction both in terms of speed and quality. Subsequently, various algorithms based on CNN model, including deeply-recursive convolutional network (DRCN) [8], are proposed to study for SR. With the residual network introduced by [PERSON] et al. [23], the CNN methods can be used to train much deeper network and have better performance. Such as the method of EDSR, which is proposed by [PERSON] et al. [24], uses enhanced deep residual networks for SISR and achieves improved performance. Moreover, [PERSON] et al. [9] first adopts the generative adversarial networks [31] (GAN) for SISR. In their model, powerful data generation capability of the GAN network and appropriate evaluation function based on the Nash equilibrium have been well applied.  
To train network models better, a perceptual loss function is needed. As the common loss function, mean squared error [32; 33] (MSE) has been widely used to calculate the pixel-wise error in general image restoration. Meanwhile, MSE is usually applied to compute the peak signal-to-noise ratio (PSNR), which is a major performance measure in reconstruction. Besides, the structural similarity index (SSIM) is another evaluation index for SR. The higher PSNR or SSIM is corresponding to better recovering quality.
###### Abstract  
In tunnel construction in western China, a vast amount of carbonaceous slate is encountered. High in situ stress and foliation structures cause the rock mass to exhibit pronounced anisotropic creep, readily inducing a series of engineering disasters like collapses and lining cracks. Investigating the anisotropic time-dependent characteristics of carbonaceous slate is beneficial to the long-term stability of tunnel construction and operation. In view of this, carbonaceous slate specimens with different angles, \(\beta\), between the foliation plane and loading direction were studied using a graded loading method through uniaxial compression creep tests. The results show that the instantaneous axial strain, \(\varepsilon_{t}\), the axial creep strain, \(\varepsilon_{\varepsilon}\), the duration time of decelerating creep stage, \(t_{d}\), and the steady creep strain rate, \(\dot{\varepsilon}_{s}\), increased with the rise in the loading ratio, \(k\). Their variations followed a power law relationship, with the \(R^{2}\) (Coefficient of Determination) values all exceeding 0.95. The value of \(\dot{\varepsilon}_{s}\) was observed to be less than \(1.5\times 10^{-4}/\mathrm{h}\) when \(\beta<45^{\circ}\), while it was found to exceed \(1.5\times 10^{-4}/\mathrm{h}\) in the cases of \(\beta\geq 45^{\circ}\). The long-term strength, \(\sigma_{L}\), of carbonaceous slate showed a U-shaped pattern with the variation in \(\beta\). The maximum \(\sigma_{L}\) occurred at \(\beta=90^{\circ}\) and the minimum was observed at \(\beta=15^{\circ}\). A fractional nonlinear creep model (FNC model) was developed. The sensitivity analysis reveals that the larger the fractional order \(n\) is, the \(t_{d}\) and \(\dot{\varepsilon}_{s}\) increase. \(\eta_{2}\) and \(E_{2}\) primarily affect the decelerated creep stage, while the \(\dot{\varepsilon}_{s}\) exhibits a rapid increase with the rise of \(\eta_{1}\). To further validate the FNC model, a comparison is made with the traditional Nishihara model. The \(R^{2}\) of the FNC model is larger than 0.965, which is higher than that of the Nishihara model (\(R^{2}\leq 0.911\)). The FNC model can effectively cope with the impact of the sudden increase in strain and well describe the characteristics of the decelerating, steady-state, and accelerating creep stages at any stress level and any angle. The results provide a reference for the study of the creep mechanism of layered rocks.  
carbonaceous slate; creep behavior; engineering sustainability; anisotropy +
Footnote †: journal: Applied sciences
# Biophotonics in Dentistry
## 2 Historical Events and Current Advancements
In regard to dental applications of QDs as luminophores, CdSe/ZnS core-shell semiconductor QDs were employed as resin dopant to tailor the fluorescence of dental resin composites [73]. Principally, the best colour matching between natural teeth and restoring composites is determined by their optical properties, such as light absorption, scattering and reflection. The incorporation of various sizes of QD cores into resin biomaterials results in similar fluorescence properties as those of natural human teeth, owing to the different band gap of QDs depending on their sizes [73]. Cancer diagnosis, treatment and photodynamic therapy are another biomedical application of QDs due to their ability to target and accumulate in tumours. Such applications are currently approved for various forms of cancers, including oral and lung cancers [74], owing to their high sensitivity to detect cancer cell biomarkers, as well as high permeability and retention effects. Figure 4: Applications of quantum dots in relation to dentistry. Graphene, one-atom-tick sheets of carbon atoms are attracting much interest in a wide range of applications, such as sensors, electronics, optoelectronics and biomedical applications [75, 76, 77, 78], due to their outstanding optical, electrical, physical and mechanical properties, such as large surface-to-volume ratio, robustness and high electrical/thermal conductivity. Their strong sensitivity to electrical perturbation makes them an excellent candidate for biosensors, in particular sensors based on field-effect transistors. Their large surface-to-volume ratio is another outstanding property that makes them a very strong candidate for drug delivery applications [79]. Graphene and its derivatives have a considerable impact in tissue engineering, particularly dental nanocomposites, owing to their ability to be functionalised with biomolecules and high mechanical strength--200 times stronger than steel. In terms of cytotoxicity, recent research confirmed that graphene oxide (GO) and graphene oxide composites, compared to other types of graphene derivations, is less cytotoxic; however its cytotoxicity depends on different parameters, including the functionalisation technique [80]. In addition, GO possesses excellent bacterial antiadhesive properties, which are a beneficial factor in the prevention of dental infection and implant surface infection [81, 82, 83].
# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 2 Materials and Methods
### Data Collection
#### 2.1.2 Stress Test  
In the table tennis training hall, the participants wear an electroencephalograph, which is Portable 40--the lead system of recording neural image electric potential--with the leads, onto which conductive glue is applied, attached to the scalp. The resistance at every electrode position should be under 5 \(\Omega\), the sampling frequency is 1024 Hz, and the intensity of brain waves is \(\mu\)V signals. The stress test is conducted to gather changes in electric potential on the scalp. For the electrode positions, the international 10-20 system is used [36; 37], as Figure 2 shows.  
Figure 1: Diagram for the research flow.  
The stress test is a test designed with reference made to Reference [21] and includes the SCWT and MA. Prior to the formal test, we would collect the brain waves in a resting state for 60 s and allow the subjects to practice once on the SCWT and MA. The formal test includes the following sequence: SCWT, MA, MA, black screen (participant at relaxed rest), and SCWT, as Figure 3 shows. The participants would not be informed of the sequence. The time for each test is 30 s. The time between two tests is 20 s. In this study, the EEG data were collected during the formal testing of the SCWT and MA.  
With the SCWT, new words appear on the screen at three-second intervals; these testing words come in random combinations of colors among red, blue, green, and yellow and a font type. The participants are required to choose correctly the color they see in the words on placards. The brain wave data collected at this test are of high stress compared with the rest state but are of a medium-stress state when compared with the brain waves of a low-stress state in the MA test.  
With MA, questions of arithmetic (addition, subtraction, multiplication, and division) with numbers in the range of 1 to 1000 appear on the screen at five-second intervals, where  
Figure 3: Diagram for the stress test.  
Figure 2: International 10–20 system, with the designator Fp corresponding to the frontal polar area of the brain, F to the frontal area, C to the central area, P to the parietal area, O to the occipital, T to the temporal area, and A to the auricular area. In this study, a total of 10 electrode positions on the head are recorded; they are Fp1 (left frontal lobe electrode), Fp2 (right frontal lobe electrode), Fz (frontal midline), C3 (left central), C4 (right central), Pz (middle line of vertex), O1 (left occipital lobe), O2 (right occipital lobe), A1 (left auricular), and A2 (right auricular), of which A1 and A2 are the reference electrodes.  
the participants should complete responses by MA. The brain waves collected at this test are those of a high-stress state if compared with the SCWT.
## 1 Introduction
[21] investigated the anchoring mechanism and engineering application of tension-compression-dispersed anchor cables, establishing stable and reliable load transfer in engineering applications with positive outcomes. [PERSON] et al. [22] conducted a comparative test between tension-compression-dispersed anchor cables and tension anchor cables, revealing a significantly higher ultimate bearing capacity for tension-compression-dispersed anchor cables, enabling better control of anchor cable displacement and improved adaptability for projects with higher anchorage deformation requirements. [PERSON] et al. [23] conducted theoretical and experimental research on the anchorage performance of tension-compression composite anchors, demonstrating that the ultimate bearing capacity of composite anchors exceeds twice that of tension anchors, effectively mitigating local stress concentration and exhibiting good deformation resistance. There are still few studies on load-distributing composite anchors, and the load-bearing performance of this new anchor needs to be further explored. The aforementioned studies on load-dispersed anchors have demonstrated their favorable anchoring performance. However, existing research predominantly focuses on load-dispersed anchors with a single stress form. There are a limited number of experimental and theoretical investigations concerning tension-compression composite anchors with a simple structure, which can effectively mitigate local stress concentration and enhance ultimate bearing capacity. Therefore, this paper presents an innovative approach in the field of geotechnical engineering. The innovative tension-compression decentralized composite anchor introduced in this study overcomes the constraints of current anchor designs and provides a practical solution for deep foundation pit projects with shallow groundwater levels. Extensive field testing and comparative analysis with traditional tension anchors demonstrate a substantial improvement in anchoring performance, emphasizing the potential for a new era in anchor systems. This research provides valuable insights for practical applications. Additionally, it provides robust groundwork for further exploration and development in the field of geotechnical engineering. The contributions of this study have the potential to reshape how we approach anchoring in complex and challenging environments, providing new possibilities and enhanced stability for future construction projects.
## 5 Simulation Verification
### Performance Comparison and Analysis  
In this section, the effect of the number of source nodes on energy consumption and delay is examined. The number of source nodes was set to vary from 10 to 40, with a step size of 5. The effect of the number of source nodes on energy (the sum of all the energy transmitted, received, crosstalk and collision in the process of data acquisition by source nodes) is shown in Figure 12. It can be seen that the energy consumption of the three algorithms increased with the increase of the number of source nodes. When the number of source nodes increases from 10 to 40, the energy of E-MIP changes from 0.2 J/task to 0.54 J/task. By contrast, Dijkstra is about 0.07 J less than E-MIP, while D-MIP is about 0.02 J less than Dijkstra.  
The comparison of task duration of the three algorithms is shown in Figure 13. The task duration of E-MIP algorithm is from the time MA, leaving the Sink node to the time agent returning Sink. This is the same as the average end-to-end delay. In the MIP algorithm, multi-agent tasks are executed in parallel, and the duration of tasks is the delay of agents, so that the number of source nodes has a greater influence on the delays than on energy consumption. In the E-MIP algorithm, as the number of source nodes increases, the delay increases rapidly. As the number of source nodes (n) increases, the agent carries more and more data. Compared with E-MIP, in the Dijkstra algorithm, when n = 40, the task duration is less than 0.5 s. The reason is that all source node data dist  
Figure 11: Corresponding Relationship between Energy Consumption, Task Delay, EDP and Control Parameter \(\alpha\).  
need to be collected by a single agent, which causes a large delay. Multi-agent concurrent execution speeds up the task process. The delay of D-MIP is the lowest among all algorithms. The delay of D-MIP algorithm is 0.1 s lower than Dijkstra, when n = 40.  
For time-sensitive WSN applications, energy delay product (EDP) = energy \(\times\) Delay is defined to evaluate the overall energy consumption and delay performance of the algorithm. The smaller the EDP, the better the performance of the algorithm. The EDP performance of the three algorithms is compared in Figure 14. Because of the worst performance of E-MIP, the EDP is the largest. D-MIP algorithm is 25% lower than Dijkstra algorithm, when n = 40.  
Figure 14: The effect of the number of source nodes on EDP.  
Figure 12: The effect of the number of source nodes on energy.  
Figure 13: The effect of the number of source nodes on task duration.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 2 Multiple-Parallel Morphological Anti-Aliasing Algorithm
### Classification of the Aliasing Patterns  
After completing the edge detection of the image, we need to classify the edge of the image into different aliasing patterns to facilitate the subsequent calculation of the blend weight coefficient. In our algorithm, since the edge detection of the image needs to be carried out in the R, G, and B channels of the image, we need to classify the aliasing patterns of the edges of the image in the R, G, and B channels. Next, we will take Figure 3, which only has an effective value in the R channel, as an example to demonstrate the process of the classification of the aliasing patterns.  
Figure 3: Process of the classification of the aliasing pattern.  
Figure 2: Process of the edge detection of the image.  
In the process of the classification of aliasing patterns, we classify the edge of the image into \"L-shaped\", \"Z-shaped\", and \"U-shaped\" aliasing patterns according to the shape of the image edge. Each aliasing pattern is composed of one or two short edges and one long edge, and each aliasing pattern is divided into horizontal or vertical directions according to the orientation of its long edge. After classifying the aliasing patterns of the image edges from the horizontal and vertical directions, we can obtain the horizontal and vertical aliasing patterns of the image. Through this method, after completing the classification of the horizontal aliasing patterns, we can obtain the horizontal \"L-shaped\" aliasing pattern marked by the yellow line and the \"Z-shaped\" aliasing pattern marked by the blue line in Figure 3. After completing the classification of vertical aliasing patterns, we can obtain the vertical \"U-shaped\" aliasing pattern marked by the green line in Figure 3.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Feature-Based (Signal Processing) Approach and Wavelet and Model-Based Approaches
A tool condition monitoring approach is demonstrated in an end-milling operation based on the vibration signal collected through a low-cost, microcontroller-based data acquisition system [63]. Marinescu et al. used a time-frequency acoustic emission (AE)-based monitoring technique to identify work piece surface malfunctions in milling with multiple teeth cutting simultaneously [64]. New methods for supervising cutting processes were proposed with multiple teeth cutting simultaneously by use of AE signals backed-up by force data. Sometimes, hybrid approaches are used since single approach might not represent all uncertainties/non-stationary processes of the system or product. These wavelet-based models are very powerful in terms of modelling failure behavior if the proper signal could be collected with necessary accuracy.
## 2 Materials and Methods
### Determination of Dynamic Water-Vapour Sorption Behaviour  
The dynamic water-vapour sorption behaviour of the composite samples was analysed using a dynamic vapour sorption (DVS) apparatus (DVS Intrinsic; Surface Measurement Systems Ltd., London, UK). Measurements were conducted at three relative humidity (RH) levels: 65%, 75%, and 85%, under room-temperature conditions. Each test began with an initial drying step at 0% RH to remove residual moisture, followed by exposure to the target RH level until the sample mass stabilised (defined as a change of less than 0.002% over a 10 min period). Time vs. mass change curves were generated for the PBAT composites containing 5 wt.%, 10 wt.%, and 20 wt.% alum sludge and compared with the neat PBAT.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 1 Introduction  
### Research Motivation  
Human endeavors prioritize economic growth, but expanding urban areas frequently exacerbate air pollution and associated health issues [1]. The timely delivery of air quality data is critical for maximizing its impact on decision making, public health assessments, and scientific studies. On-time access to accurate information enables authorities and stakeholders to respond promptly to air quality issues, assess trends, and design effective interventions. Studies show that delivering real-time air quality data can directly reduce hospital admissions, as observed when local and national authorities receive live data on pollution levels [2]. To fully support stakeholders, air quality data and analytical tools must be accessible and user-friendly, allowing public health professionals, urban planners, and policymakers to act based on solid, interpretable data.
## 3 Materials and Methods  
### Chemicals and Reagents  
Folin-Ciocalteu reagent, sodium carbonate, gallic acid, FRAP reagent, acetate buffer, acetic acid, 2,4,6-tripyrid-1,3,5-triazine (TPTZ), 2,2'-azinobis(3-ethylbenzothazoline-6-sulfonic acid) (ABTS), potassium persulfate, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), \(p\)-methoxyphenol, chloroform/(folsch), BF\({}_{3}\)/MeOH, potassium hydroxide, hexane, sodium chloride, methanol, hydrochloric acid, magnesium sulfate, pyridine, and \(N\),O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) were purchased from Sigma-Aldrich (Steinheim, Germany).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Drying Conditions**} & \multicolumn{3}{c}{**Color Parameters**} \\ \cline{2-3}  & \(L^{\star}\) & \(a^{\star}\) & \(b^{\star}\) \\ \hline Fresh & 35.54 \(\pm\) 0.504 a & \(-\)2.98 \(\pm\) 0.309 a,d & 6.42 \(\pm\) 0.414 a \\ Freeze drying & 43.93 \(\pm\) 0.380 b & \(-\)3.39 \(\pm\) 0.064 b & 10.67 \(\pm\) 0.172 b \\ CD 40 \({}^{\circ}\)C & 43.20 \(\pm\) 0.131 c & \(-\)3.32 \(\pm\) 0.055 b,e & 9.68 \(\pm\) 0.163 c \\ CD 50 \({}^{\circ}\)C & 41.20 \(\pm\) 0.365 d & \(-\)2.69 \(\pm\) 0.068 c & 8.35 \(\pm\) 0.053 d \\ CD 60 \({}^{\circ}\)C & 41.70 \(\pm\) 0.078 d,e & \(-\)2.75 \(\pm\) 0.058 c,d & 8.84 \(\pm\) 0.126 e \\ VMD 6 W/g & 43.24 \(\pm\) 0.128 c & \(-\)3.37 \(\pm\) 0.110 b & 10.13 \(\pm\) 0.074 f,g \\ VMD 9 W/g & 43.30 \(\pm\) 0.196 c & \(-\)3.39 \(\pm\) 0.088 b & 9.84 \(\pm\) 0.133 c,f \\ VMD 12 W/g & 42.96 \(\pm\) 0.092 c & \(-\)3.18 \(\pm\) 0.055 a,b,e & 10.25 \(\pm\) 0.090 g \\ CPD-VMFD & 42.01 \(\pm\) 0.190 e & \(-\)3.10 \(\pm\) 0.080 a,e & 9.24 \(\pm\) 0.093 h \\ \hline \hline \end{tabular} CD–convective drying: VMD–vacuum microwave driving: CPD–convective pre-drying: VMFD–vacuum microwave finish drying: values with the same letter within a column were not significantly different (\(p<0.05\)), according to [PERSON]’s test.  
\end{table}
Table 6: Color parameters \(L^{\star}\), \(a^{\star}\) and \(b^{\star}\) of _S. crispus_ affected by drying methods.  
Figure 10: Image of ground _S. crispus_ dried using FD, CD at 40, 50, and 60 \({}^{\circ}\)C, VMD at 6, 9, and 12 W/g, and CPD-VMFD at 50 \({}^{\circ}\)C and 9 W/g.