esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Result Analysis
### Energy Characteristics  
#### 3.3.1 Energy Calculation  
Under different strain rates, the essence of rock sample failure is the mutual transformation among various forms of strain energy [24]. The energy conservation relationship of its deformation process can be expressed as [25]. Therefore, the total input strain energy can be further expressed as  
\[U=U^{d}+U^{e} \tag{1}\]  
Under uniaxial stress loading, the total input strain energy, elastic strain energy, and dissipated strain energy are further expressed as [26]  
\[U=\int_{0}^{\epsilon}\sigma d\varepsilon \tag{2}\]  
\[U^{e}=\frac{\sigma^{2}}{2E} \tag{3}\]  
\[U^{d}=\int_{0}^{\epsilon}\sigma d\varepsilon-\frac{\sigma^{2}}{2E} \tag{4}\]  
where \(\sigma\) is the stress; \(\varepsilon\) is the strain; \(E\) is the elastic modulus.
## 3 Data and Methods
### Coulomb Stress Simulation  
Numerous studies have shown that Coulomb rupture stress can reveal the triggering relationships between earthquakes, the effects on subsequent aftershocks, and the seismic  
Figure 4: Three-dimensional data in the study area. Red and black lines represent Holocene and Pre-Holocene active faults, respectively (F\({}_{1}\): Huoshan Fault, F\({}_{2}\): Fushan Fault, F\({}_{3}\): Luoyunshan Fault, F\({}_{4}\): Hongtong-Subao Fault). A–A’ is the deep seismic reflection profile, whereas B–B’, C–C’, and D–D’ are velocity tomography imaging profiles. Green spheres indicate the 1695 Linfen M 7.8 earthquake, yellow spheres indicate the 1303 Hongtong M 8 earthquake, red spheres indicate \(M\geq 6.0\) earthquakes, and pink dots indicate the small earthquake relocations from 1981 to 2018.  
hazard of surrounding faults [52; 53; 54; 55; 56; 57; 58; 59; 60; 61]. This study calculated the influence of the 1303 Hongtong earthquake on the 1695 Linfen earthquake based on Coulomb stress theory.  
After an earthquake occurs, the accumulated stress on the fault does not disappear. Instead, part of the stress is transferred and concentrated on surrounding faults, influencing the occurrence of subsequent earthquakes and the Coulomb stress changes on these surrounding faults. The Coulomb failure stress change (\(\Delta\)CFS) can be defined as follows:  
\[\Delta\text{CFS}\;=\;\Delta\tau\;+\;\mu\Delta\sigma_{\text{n}} \tag{1}\]  
where \(\Delta\tau\) represents the shear stress change on the receiving fault caused by an earthquake; \(\Delta\)on is the normal stress change, with the tension being positive; and \(\mu\) is the coefficient of friction, which is typically set to 0.4 [62; 63]. When \(\Delta\)CFS \(>\) 0, the stress change may promote the occurrence of subsequent earthquakes or increase the load on surrounding faults; conversely, when \(\Delta\)CFS \(<\) 0, it may inhibit the occurrence of subsequent earthquakes or reduce the load on surrounding faults.
## 2 Materials and Methods
### WWTP Process and Model Description
#### 2.2.1 WWTP Process: BSM1  
The benchmark simulation model 1 [21] is used to represent the activated sludge process in a WWTP. The simulation platform (Benchmark simulation model, or BSM1) has been developed within COST Actions 624 and 682. The schematic representation of the WWTP layout and default control strategy considered in BSM1 is presented in Figure 1. The plant consists of five biological reactor tanks connected in a series, followed by a secondary setter. The first two tanks have a volume of 1000 m\({}^{3}\) each, and are anoxic and perfectly mixed. The remaining three tanks have a volume of 1333 m\({}^{3}\) each, and are aerated. The settler has a total volume of 6000 m\({}^{3}\), and is modeled in 10 layers, with the sixth layer when counting from bottom to top functioning as the feed layer. Two recycle flows, the first from the last tank, and the second from the underflow of the settler, complete the system. The sludge from the settling that is not recycled is led to be disposed, and is called wastage.  
Figure 1: Benchmark simulation model (BSM1) layout indicating the default control strategy. PI: Proportional Integral Controller; DO: Dissolved oxygen.  
The plant is designed for an average influent dry weather flow rate of 18,446 m\({}^{3}\)/day and an average biodegradable chemical oxygen demand (COD) in the influent of 300 g/m\({}^{3}\). Its hydraulic retention time, based on the average dry weather flow rate and the total tank and settler volume (12,000 m\({}^{3}\)), is 14.4 h. Q\({}_{\text{w}}\) is fixed to 385 m\({}^{3}\)/day, which determines, based on the total amount of biomass present in the system, a biomass sludge age of about nine days.  
The nitrogen removal is achieved using a denitrification step performed in the anoxic tanks, and a nitrification step carried out in the aerated tanks. The internal recycle is used to supply the denitrification step with \(S_{\text{NO}}\). The biological phenomena of the reactors are simulated by using the Activated Sludge Process No1 (ASM1), which considers eight different biological processes. The double-exponential settling velocity model simulates the vertical transfers between layers in the settler [27]. No biological reaction is considered in the settler. The two models are internationally accepted and include 13 state variables [28]. The full mathematical model, the physical parameters and performance indices can be found in [22].  
The default control strategy proposed in the BSM1 (Figure 1) consists of two PI loops: (1) control of the dissolved oxygen concentration in the fifth reactor (\(S_{\text{O5}}\)) manipulating the oxygen transfer coefficient \(K_{La5}\), and (2) control of the nitrates concentration in the anoxic zone (\(S_{\text{NO2}}\)) manipulating the internal recycle flow rate \(Q_{a}\).  
The BSM1 also provides different influent profiles depending on the weather conditions (dry, rainy, stormy) to evaluate the plant performance. For example, dry weather influent flow is shown in Figure 2.
# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 3 Methodology
### Dataset Description  
The Hard Hat worker image dataset published by MakeML was used in this paper for the detection of hard helmets used in the construction industry [36]. It contains 5000 images with bounding box annotation files. Initially, it has three classes: Helmet, Person, and Head, but this research focuses on only two classes, Helmet and Head, which makes this a binary classification problem. Figure 3 shows some examples of Helmet and Head images.  
The data is further divided into three sets (training, testing, and validation) with the split of 60% for the training, 20% for the testing, and 20% for the validation. Therefore, the training set contains 3000 images, the testing set contains 1000 images, and the validation dataset contains 1000 images. The training dataset was used to train the deep learning model, while the validation dataset was used for early stopping criteria. Finally, the fine-tuned deep learning model was tested on an independent test dataset.
## 3 Results
### Water Vapor Transmission Rate (WVTR)  
The barrier capacity of the film expressed by the amount of water vapor that can permeate per one unit of material area for certain time is particularly important in packaging (and especially for dry products that can absorb moisture from the external environment). The following figure shows the tendency of the rate of the water permeating through the film which occurs when water vapor diffuses from a higher partial pressure to a lower partial pressure [54]. From Figure 5, it can be observed with red line the WVTR trend of the film samples. In the case of the film optimization process of \(\beta\)-glucan (BG), sodium alginate, and pomeranate juice, high values can be observed. Of course, the highest value of all three types of films developed from different juices was found in this case at Sample 3 with 32.1896 g/h\(\cdot\)m\({}^{2}\) having 0.5 g \(\beta\)-glucan content, 0.4 g sodium alginate (SA),  
Figure 4: Film thickness. (**a**) The thickness of the films related to the composition of the FFS; (**b**) 2-D SEM image of a cross-section film made from BG, SA and PJ. Values followed by different letters are significantly different at \(p<0.05\).  
and 30 mL (3.73 g dw.) pomergnate juice (PJ) [37]. From Sample 17, an abrupt decrease in the WVTR values was observed for the bilberry- and cranberry-based films. The lowest value of WVTR was found in the Sample 19 with 3.2562 g/h\(\cdot\)m\({}^{2}\) containing 1 g BG, 0.8 SA, 20 g bilberry juice (BJ) [38]. In the case of films made of \(\beta\)-glucan/cranberry juice, with the highest amount of dry weight and the highest sodium alginate content, a minimum WVTR value of 7.6918 g/h\(\cdot\)m\({}^{2}\) was identified. A drop in the WVTR values it could also be observed by the addition of soybean oil to the films based on bilberry and cranberry juices (Sample 18, 23, 25 and 27), compared to control samples (Sample 17, 22, 24 and 26). In all analyzed samples the amount of \(\beta\)-glucan no more than 1.5 g (dry weight) was added in the film-forming solution and varied at 0.5, 1 and 1.5 g. Sodium alginate did not exceed 1 g and the amount of plasticizer (glycerin) was 25% of total solids. However, such large differences between WVTR values (from 32.1896 to 3.2562 g/h\(\cdot\)m\({}^{2}\)) can only be explained by the change in juice in FFS.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 2 Materials and Mix Proportions  
### Raw Materials  
Type I Portland cement (PC), silica fume (SF), water, quartz sand, quartz powder, steel fibre (ST), and superplasticizer (SP) were used to produce the RPC samples. The Type I cement conforms to Chinese National Standard GB175-2007 [33], and has a specific gravity of 3.20. The silica fume is an extremely fine material with a particle size smaller than 0.1 \(\upmu\)m. The specific surface area and dry bulk density of the silica fume are 18.4 m\({}^{2}\)/g and 0.65 kg, respectively. The chemical components of the cement and silica fume are given in Table 1. Quartz sand with a particle size distribution in the range of 0.01-0.50 mm was used as an aggregate, and quartz powder with particle sizes from 10-45 \(\upmu\)m was used as a micro-filler. The superplasticizer is a polycarboxylic acid water reducer with a specific gravity of 1.20, allowing for water reduction of up to 25% to achieve the target workability. The mixing water is tap water from Harbin, China. The diameter and length of the steel fibre are 0.22 mm and 13 mm, respectively. The tensile strength of the steel fibre is greater than 2850 MPa. The steel fibre used in this study is shown in Figure 1.
## 4 Results
### Comparing Models
#### 4.4.3. Applicability of Models Trained on DS2 (COVID-19 Dataset) to DS1 (Climate Change Dataset)  
Table 11 shows the accuracies of all models trained on DS2 and tested on the DS2 and DS1 datasets for each of the selected data processing methods (also Figure 8). For each model, the highest value obtained with the LIWC analysis is indicated in green, the maximum value obtained with the n-gram analysis in blue, and the maximum obtained using the combined approach analysis is indicated in red. At the same time, for each model, the largest of the three listed values is bold. By testing the performance of the models trained on DS2 and tested on the DS1 data set, the best results were achieved with the multinomial Naive Bayes models (81.44%), SVM (81.05%) and logistic regression (79.90%) by analyzing (1,3)-grams, while the analysis using combined approach achieved the maximum only using random forest (76.88%). By comparing the accuracy of models trained on DS2 and tested on DS1 dataset compared to DS2, an improvement in model performance was observed using n-gram analysis across all selected sets of n-grams. Also, the analysis with the combined approach achieved more favorable results on almost all sets of n-grams, while LIWC achieved slightly less favorable results. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**DS1 Models Tested on Dataset DS1**} & \multicolumn{6}{c}{**DS1 Models Tested on Dataset DS2**} \\ \cline{2-10}  & & **LR** & **SVM** & **NB** & **RF** & **LR** & **SVM** & **NB** & **RF** \\ \cline{2-10}  & LIWC & 78.02 & 77.24 & 76.47 & 78.05 & 67.34 & 64.37 & 64.38 & 63.08 \\ \hline \multirow{3}{*}{**1,1**} & n-grams & 75.00 & 72.74 & 76.15 & 72.71 & 67.92 & 69.57 & 67.95 & 65.67 \\  & n-grams + & & & & & & & & \\  & LIWC & 78.02 & 76.14 & 76.91 & 73.43 & 69.89 & 67.79 & 71.72 & 70.74 \\ \hline \multirow{3}{*}{**1,2**} & n-grams & 79.16 & 78.80 & 81.47 & 74.62 & 69.66 & 70.00 & 72.21 & 65.40 \\ \cline{2-10}  & n-grams + & 78.80 & 75.77 & **81.47** & 74.96 & **70.29** & 67.79 & 73.44 & 67.84 \\ \hline \multirow{3}{*}{**2,2**} & n-grams & 78.79 & 76.52 & 79.56 & 72.38 & 69.58 & 68.28 & 71.70 & 71.30 \\ \cline{2-10}  & n-grams + & 78.40 & 77.21 & 77.26 & 74.22 & 67.34 & 68.61 & 67.88 & 65.25 \\ \hline \multirow{3}{*}{**1,3**} & n-grams & 80.68 & 76.13 & 79.94 & 75.04 & 69.66 & 70.49 & 73.04 & 65.67 \\ \cline{2-10}  & n-grams + & 79.56 & 76.52 & 78.77 & 75.34 & 69.86 & 68.62 & **73.82** & 69.93 \\ \hline \multirow{3}{*}{**2,3**} & n-grams & 76.14 & 76.14 & 78.42 & 71.25 & 70.02 & 68.73 & 70.04 & 64.47 \\ \cline{2-10}  & n-grams + & 79.16 & **79.52** & 79.57 & 74.97 & 68.59 & 68.99 & 68.30 & 66.12 \\ \hline \multirow{3}{*}{**3,3**} & n-grams & 68.99 & 68.59 & 70.11 & 67.08 & 59.75 & 60.18 & 61.92 & 57.59 \\ \cline{2-10}  & n-grams + & 78.80 & 74.96 & 74.92 & 74.20 & 67.74 & 67.32 & 64.80 & 66.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of models trained on DS1 (climate change dataset) and tested on DS1 and DS2 (COVID-19 dataset).
# Repair of Voids in Multi-Labeled Triangular Mesh
## 3 Overview of the Method  
The previous model repairing methods mainly focus on the mesh quality of a single mesh, and there are few studies on the topological adjacent relationship between models composed of different materials. In this paper, we define a multi-labeled mesh data structure to represent the topological adjacent relationship between the input meshes and repair the unexpected voids by modifying the labels of triangular facets in the meshes.  
As an example, Figure 1 shows the repairing process and modeling effect of interior voids between two simple meshes. Among them, Figure 1a,b are two original input meshes that meet the characteristics of closed and manifold, and the black box in Figure 1c indicates an interior void between the two meshes. By defining the judgment rules of voids among multiple meshes, we can find the set of connected triangular facets that make up the void, and use the method based on model priorities to repair the void. When the priority of the red mesh is higher than the yellow mesh, the void will be filled by the yellow mesh with lower priority. In addition, the corresponding model repairing effect is shown in Figure 1d. When the priority of the yellow mesh is higher than the red mesh, the void will be filled by the red mesh with lower priority. In addition, the corresponding model repair effect is shown in Figure 1e.  
The method proposed in this paper is mainly composed of the following steps. Figure 2 shows the overall process of the method.  
1. Convert the input unlabeled meshes into a multi-labeled mesh that is convenient to determine the adjacent relationship between meshes. Based on the idea of graph theory, the multi-labeled mesh is regarded as an undirected graph with different label attributes and the topology adjacent relationship can be obtained by traversing the triangular facets directly.
2. The original multi-labeled mesh can be separated into connected sub-graphs according to the connectivity of undirected graphs, and each connected sub-graph corresponds to a connected multi-labeled sub-mesh. Traversing each separated sub-graph, search for the quasi-voids using the breadth-first searching (BFS) algorithm and record the labels associated with corresponding quasi-voids.
3. Distinguish the true voids of the input meshes according to the judgment rules of voids and fill the voids by modifying the labels of triangular facets according to the specified model priorities. Remove the invalid triangular facets and obtain the repaired meshes.
4. Merge the repaired multi-labeled sub-meshes into a single multi-labeled mesh and convert the multi-labeled mesh into the unlabeled meshes.  
Figure 1: Repairing examples of the multi-labeled triangular mesh: (**a,b**) the input meshes; (**c**) an interior void combined by the input meshes; (**d,e**) the repaired results.
###### Abstract  
In this study, we conducted experimental and numerical investigations of a Darrieus rotor with asymmetrical blades, which has two structural configurations--with and without horizontal parallel plates. Experimental tests were conducted in a wind tunnel at various air flow velocities (ranging from 3 m/s to 15 m/s), measuring rotor rotation frequency, torque, and thrust force. The computational simulation used the ANSYS 2022 R2 Fluent software package, where CFD simulations of air flow around both rotor configurations were performed. The calculations employed the Realizable k-\(\epsilon\) turbulence model, while an unstructured mesh with local refinement in the blade-flow interaction zones was used for grid generation. The study results showed that the rotor with horizontal parallel plates exhibits higher aerodynamic efficiency at low wind velocities compared to the no-plates rotor. The experimental findings indicated that at wind speeds of 3-6 m/s, the rotor with plates demonstrates 18-22% higher torque, which facilitates the self-start process and stabilizes turbine operation. The numerical simulations confirmed that horizontal plates contribute to stabilizing the air flow by reducing the intensity of vortex structures behind the blades, thereby decreasing aerodynamic drag and minimizing energy losses. It was also found that the presence of plates creates a directed flow effect, increasing the lift force on the blades and improving the power coefficient (Cp). In the case of the rotor without plates, the CFD simulations identified significant low-pressure zones and high turbulence regions behind the blades, leading to increased aerodynamic losses and reduced efficiency. Thus, the experimental and numerical modeling results confirm that the Darrieus rotor with horizontal parallel plates is a more efficient solution for operation under low and variable wind conditions. The optimized design with plates ensures more stable flow, reduces energy losses, and increases the turbine's power coefficient. These findings may be useful for designing small-scale wind energy systems intended for areas with low wind speeds.  
breakaway; aerodynamic coefficients; thrust forces; aerodynamics of asymmetrical blades; wind rotor +
Footnote †: journal:  
Article
## 3 Results
### Accuracy of Scatter Correction on Simulated CXRs
(f) Difference pixel by pixel between the scatter-corrected ground-truth image (represented in Figure 1) and the scatter-corrected image estimated by the 2-output dual-energy model. **  
Comparing the results of single- and dual-energy models, Figure 8 shows that the 2-output dual-energy algorithm yields higher accuracy in scatter correction considering the metrics of MAPE, SSIM, and relative error for high-energy CXRs, while in low-energy images, the best accuracy is obtained with the 1-output dual-energy algorithm. Taking as reference the case of SDD = 180 cm, in high-energy estimations, the average value of MAPE yielded by the 2-output dual-energy NN is 13% smaller than the one of the 1-output dual-energy model and 47% smaller with respect to the single-energy algorithm; the relative error is 33% and 11% better, respectively, while the SSIM is only 0.15% bigger in the 2-output dual-energy model than in the single-energy model, and 0.04% bigger with respect to the 1-output dual-energy NN, although a visual difference can be appreciated in the corresponding graphics. In the low-energy case, the 1-output dual-energy model outperforms the single-energy and the 2-output dual-energy algorithms in 22% and 19% in MAPE and a 9% and 4% in the relative error, respectively. The difference in the SSIM is minimal. Moreover, it is observed in Figure 8 that the model with the best accuracy in each case also has a lower standard deviation, represented by a smaller size of the box in the plot (green boxes in the metrics for low-energy estimations, corresponding to the 1-output dual-energy model, and violet boxes for high-energy estimations, corresponding to the 2-output dual-energy model). The relevance of the differences between the three algorithms will be further discussed in Section 4. Figure 8: Box plot of the MSE, MAPE, SSIM, and relative error for the 22 test cases at different source-to-detector distances. A blue dashed line represents the median value of the metric for each SDD. The box extends from the lower to the upper quartile values of the data, while the range (also referred to as whiskers) shows the rest of the distribution.
# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 4 Experiments and Results
### Model Complexity  
We used the number of floating point operations (FLOPs) and frames per second (FPS) to measure the time complexity of the different models; the smaller the FLOPS, the smaller the computational demand of the model, and the FPS indicates the speed of detecting cracked images per second. Params are used to measure the spatial complexity of different models, and the parameter size indicates the memory size occupied by the model, and the results are shown in Table 7.  
Table 7 presents the FLOPS, FPS, and PARAMS of all methods based on the TMCracks dataset. The detection speed of the pure convolutional model was better than that of the model with a transformer, and DeepLab V3+ had the fastest detection speed among all methods, at 50 FPS. The detection speed of this model was 25 FPS, meaning it can process 25 crack images per second, and it has a smaller computational volume with less memory usage than the model with a pure transformer. TMcrack-Net has more parameters than most of these methods, so the model proposed in this paper also has some limitations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **P\%** & **R\%** & **F\%** & **MIou\%** & **Dice\%** & **Jaccard\%** \\ \hline U-Net & 85.40 & 88.90 & 87.11 & 78.86 & 75.82 & 61.06 \\ UNet++ & 88.04 & 85.28 & 86.38 & 78.28 & 74.73 & 59.65 \\ PSPnet & 87.37 & 77.78 & 82.30 & 72.43 & 65.43 & 48.62 \\ DeepLabV3+ & 88.03 & 84.43 & 86.20 & 77.70 & 73.86 & 58.55 \\ Swin-Unet & 86.56 & 82.59 & 84.52 & 75.57 & 70.66 & 54.63 \\ UCTransNet & 88.42 & 83.49 & 85.88 & 77.23 & 73.13 & 57.64 \\ TMcrack-Net & 88.09 & 86.85 & 87.47 & 79.44 & 76.41 & 61.82 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of different methods on Crack500.  
Figure 10: Comparison of the results of different methods for selected crack images from the test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Params (M)** & **Flops (G)** & **FPS** \\ \hline U-Net & 43.93 & 17.62 & 46 \\ PSPnet & 49.07 & 11.82 & 47 \\ DeepLab V3+ & 5.81 & 5.05 & 50 \\ UNet++ & 47.2 & 153.25 & 20 \\ Swin-Unet & 154.24 & 119.37 & 17 \\ UCTransNet & 66.43 & 32.94 & 18 \\ TMcrack-Net & 89.27 & 19.15 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The FLOPs, Params, and FPS of all methods on the TMcrack dataset.