esa-sceva/satcom-chunk-collection huggingface dataset:
divided into two methods: semantic segmentation [11] and object detection [12]. In the area of object detection, there are a variety of object detectors, which are constantly innovative. With the use of attention mechanisms, the accuracy of the object detector is improved. In particular, from 2018 to 2020, the IEEE (Institute of Electrical and Electronics Engineers) held the GRDDC (Global Road Damage Detection Challenge) [13]. The vision-based detection method can detect the number of potholes and the size and shape of potholes, but this method is easily affected by light, ponding in potholes, and other environments, as shown in Figure 1. In terms of the pothole detection model, the current model algorithm [14; 15; 16; 17] has high redundancy, many parameters, and a poor real-time performance. Now, no matter whether in a road assessment and maintenance scenarios or in the field of automatic driving, there are high requirements for the convenience of detection model deployment, in real time, and the accuracy of pothole detection. Compared with manual detection, automatic detection methods have improved in all aspects, but they can not achieve the balance of cost, efficiency, and accuracy and still can not meet the requirements of detection efficiency and accuracy.  
In order to solve the above problems, we propose a one-stage object detector (AAL-Net) for pothole detection. In the model, through the LF module and the NAM attention module, we achieve the goals of lightweight and high accuracy of the model and meet the real-time and accuracy requirements of pothole detection. At the same time, we make our own dataset to test the effectiveness of our model. Finally, we propose a data augmentation method to improve the accuracy and robustness of pothole detection.  
To sum up, our contributions are as follows:  
1. We make a new pothole detection dataset for pothole detection.  
Figure 1: Potholes with different environments, shapes, and sizes in the self-made dataset. The upper left is a small round pothole in the normal environment. On the upper right is a long strip of the small pothole in the shadow environment. At the bottom left is a small round pothole in rainy weather. At the lower right is a large irregular pothole in rainy weather.  
2. We propose a one-stage object detector, the AAL-Net. By designing the LF module and using the NAM attention module, we achieve the goal of a lightweight network model and high detection accuracy.
3. We design a data augmentation method to improve the accuracy and robustness of pothole detection.  
The content of this article is organized as follows: The second part discusses the related work, the third part details the AAL-Net we designed and the data augmentation methods we used, the fourth part introduces the dataset and the effectiveness of the comparative experiments and data augmentation methods, and the last section summarizes the results.
# Practical I-Voting on Stellar Blockchain
## 3 System Proposition
### Architecture of I-Voting on Stellar Platform
Ideally, the system should consist only of a client-app that allows users to interact with the Stellar network, getting rid of the centralized IdP and TDS. The proposed solution requires a central authority for voter authentication, thus becoming a single point of failure. This is a potential weak point of the proposed solution, where results can be manipulated. However, possible options for preventing this vulnerability have been addressed in Section 5. We assume that the number of eligible voters is public information. Hence, becoming the value of a maximum number of tokens that can be issued in elections. After token creation, the token's Issuer account has to lockout from creating new tokens, so everyone can be sure that no more tokens are ever created. Assets (tokens) in Stellar blockchain are divisible to seven decimal points. This is an unwanted feature since we do not want users to vote by a fraction of their votes. To prevent it, we distribute one vote as the smallest indivisible amount possible in Stellar blockchain, which is \(10^{-7}\) of particular vote token.
## 2 Materials and Methods
### Case Design and Boundary Conditions  
To investigate the impacts of the elevator car movement on the driven airflow and pollutant transmission across floors, a total of five cases considering different car movement scenarios were designed, as shown in Figure 2. Case 0 was a reference case in which the elevator car was omitted. The pollutant marked in purple was assumed to be emitted from the lobby on the first floor. The pollutant transmission in Case 0 was merely due to molecular diffusion.  
Case 1 involved 2 stages, as shown in Figure 2b. In the 1 st stage of Case 1, the elevator car moved upward from the 1 st floor to the 15 th floor at a speed of 1.5 m/s, and the total movement time was 25.2 s. The first stage was designed to study the impact of upward car motion on the driven airflow and pollutant transmission. In the 2 nd stage, the car was parked on the 15 th floor for 8 s, which was the typical time duration when the car stopped at the destination. The second stage was designed to explore the influence of car parking on both the flow and pollutant transmission.  
In Case 2, the elevator car moved downward from the 15 th floor to the 1 st floor with the same movement speed of 1.5 m/s and parked for 8 s on the 1 st floor. The total time duration in Case 2 was 33.2 s. This case was designed to study the resulting impacts of the downward car movement. Apparently, Case 2 addressed the aftermath of Case 1 and concerned the impacts of the downward movement of the elevator car.  
In Case 3, the elevator car was assumed to start from the 1 st floor from a stationary status and then moved to the 3 rd floor and parking there. Case 4 resembled Case 3 but would park on the 8 th floor. The two cases were designed to explore the impacts of the elevator parking on different floors on both the flow and pollutant transmission.  
Table 2 summarizes the boundary conditions used in the CFD modeling. Air could flow in or out of the building through the windows and the vent hole, which were set as  
Figure 2: Schematics of the 5 studied cases (purple virus: pollutant source; orange rectangle: elevator car; P: car parking floor; green arrow: car movement direction): (**a**) Case 0; (**b**) Case 1; (**c**) Case 2; (**d**) Case 3; (**e**) Case 4.  
zero-pressure openings. The clearance openings on the protecting doors and the room doors were treated as internal openings that would allow for air passage. The isothermal airflow was solved so that the temperature-driven buoyancy flow was neglected. This investigation employed a tracer gas SF\({}_{6}\) as a surrogate for aerosol particles, and the mass fraction of SF\({}_{6}\) released to the lobby of the first floor was 0.5%.
## 3 Dynamic Response Analysis of Ground Buildings Under Blasting
### Analysis of the Influence of Buildings Under Residual Footage Blasting
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Field**} & \multicolumn{2}{c}{**Measured Peak Vibration**} & \multicolumn{4}{c}{**Simulated Vibration Velocity**} & \multicolumn{4}{c}{**Relative Error**} \\  & \multicolumn{2}{c}{**Velocity (cm/s)**} & \multicolumn{4}{c}{**Peak (cm/s)**} & & \\  & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline Point 1-1 & 0.151 & 0.154 & 0.181 & 0.133 & 0.136 & 0.164 & 11.9\% & 9.4\% & 10.5\% \\ Point 2-1 & 0.156 & 0.172 & 0.164 & 0.136 & 0.154 & 0.148 & 13.2\% & 10.5\% & 9.8\% \\ Point 1-3 & 0.152 & 0.165 & 0.176 & 0.138 & 0.142 & 0.158 & 9.2\% & 13.9\% & 10.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of peak vibration velocity field data and simulation data. Figure 5: The vibration velocity cloud map of the science and technology building. Figure 6: The vibration velocity cloud map of the district office. In addition, the blasting numerical analysis is carried out at underground horizontal distances of 50 m, 100 m, 150 m, and 200 m from the buildings. Table 4 presents the influence of vibrations on the buildings under the blasting effect for different remaining distances. The M(kg) explosive blasting under the remaining footage N(m) of the working face is named M-N in this table. It can be seen that the maximum vibration velocity in the buildings' three directions is 0.72 cm/s, and the maximum effective stress is 0.537 MPa. Figure 8: The maximum effective stress cloud map of buildings. Figure 7: The vibration velocity cloud map of the chimney. (**a**) X-direction vibration velocity cloud map, (**b**) Y-direction vibration velocity cloud map, (**c**) Z-direction vibration velocity cloud map, (**d**) Combined velocity cloud map. Therefore, the buildings can be considered safe. Comparing the three different buildings, the ground vibration velocity is the largest for the district office, second-largest for the chimney, and smallest for the science and technology building. Based on the structure and stress response of buildings, the chimney, as a high-rise masonry structure, is greatly affected by the blasting effect. According to Table 5, the Sadovsky formula is derived from four sets of numerical simulation results. The relationship between the remaining footage of the working face and the maximum safe charge of one blast is obtained. It is compared with the maximum safe charge obtained using the Sadovsky formula for the measured field, as shown in Figure 9.  
The consistency between the numerical simulation fitting curve and field monitoring data can be seen in the figure. Both show that the maximum safe charge of one blast decreases with the increase of the advancing distance from the working face. However, the maximum charge of one blast obtained using field monitoring data is lower than that of the numerical calculation fitting analysis. Therefore
## 2 Theoretical Analysis and Experimental Details
### Theoretical Model Analysis of Digging
#### 2.1.2 Passive Material Pressure and Its Failure Surface  
As observed through comparative analysis of the active pressure of the material, the material in front of the grab generates passive pressure, and the material is destroyed, resulting in a failure surface, as shown in Figure 5. The analysis of the interaction force between the grab and the material shows that the material's weights \(W_{ABD}\), \(W_{BED}\) and passive pressure \(F_{E}\) are as calculated in Formula (6):  
\[W_{ABD}=\rho bS_{ABD},\ W_{BED}=\rho bS_{BED},\ F_{E}=\int_{0}^{H}p_{E}(z)dz \tag{6}\]  
where \(W\) is the weight of body ABE (N), \(\rho\) is the material density (N/m\({}^{3}\)), \(b\) is the grab width (m), \(S_{ABD}\) is the area of body ABD (\(m^{2}\)), \(S_{BED}\) is the area of body BED (m\({}^{2}\)), \(F_{E}\) is the passive resultant value (N), \(H\) is the digging depth (m), \(z\) represents the integral formal parameters (m), and \(p_{E}\) is the pressure value at depth \(z\) (N/m).  
Figure 4: Force diagram of the material body under active pressure.  
Figure 5: Force diagram of material body under passive pressure.  
The force balance condition of the material body ABE is:  
\[\overset{\rightarrow}{F}_{E}+\overset{\rightarrow}{W}_{ABD}+\overset{ \rightarrow}{W}_{BDE}+\overset{\rightarrow}{R}=0 \tag{7}\]
## 1 Introduction
### Statistical Characterization of DEMs  
One of the assumptions of this study is the low variance of the data represented in DEMs, and as a consequence, there is a need to develop a new methodology that considers this aspect to detect the highlighted points to be used in accuracy assessment procedures. To contrast this assumption, we have developed a preliminary analysis comparing the statistics of several images representing DEMs with other real images, such as those commonly used with the SIFT algorithm. In this regard, we used 12 official DEMs related to 3 cartographic sheets obtained from an official institution in Spain, which are more specifically described in Section 3 of this document, and 904 images obtained from the Image Matching Challenge PhotoTourism (IMC-PT) 2020 dataset [21]. These images are shared to both train and evaluate new strategies for image matching and, more specifically, to obtain dense and accurate 3D reconstructions from large collections of images based on the SIFT algorithm. Considering these datasets, we developed a statistical description based on the normalized values of the mean and the standard deviation of the digital levels of each image. The results are shown in Figure 1, where the graph displayed in Figure 1a shows the statistics of the mean values, and Figure 1b shows those related to the standard deviations. In the case of IMC-PT, the median value of all the means is close to 0.5 (Figure 1a), while the average standard deviation is higher than 0.3 (Figure 1b). On the other hand, the DEMs show a similar median value of the means of all the images with respect to the other dataset (Figure 1a), although this value is highly conditioned by the relief of the zones analyzed. However, the average standard deviation is lower than 0.2 and shows less variability (Figure 1b). This reduced data dispersion confirms that DEMs have a lower variance than the IMC-PT images analyzed and a lack of outlier values. Consequently, this aspect should be considered in point detection procedures because of its possible influence on feature identification. Interestingly, this lack of outliers indicates that an algorithm based on variance should have poor results and low highlighted point identification.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 4 Experiments
### Ablation Study
#### 4.3.3 Ablation of MGA Module  
The MGA module, proposed in Section 3.2.2, is a hidden state aggregation module that introduces long-range dependencies into the hidden state for enhancing the propagation of matching information. Unlike GMA [48], the MGA performs global aggregation directly on the adaptively masked hidden state. The MGA is evaluated in rows 7 and 11 of Table 1. Compared to the models without hidden state aggregation (rows 5 and 10), the disparity EPE is reduced by about 7.2%. In contrast, the EPE of the model with GMA (row 6) is reduced by only 4.6%. This indicates that the adaptive masked aggregation of MGA is more effective. In addition, according to rows 4 and 7 of Table 2, compared to the models without MGA (rows 3 and 6), the average EPE reduction ratio is improved by about 33.7%, which shows the improvement of disparity refinement efficiency.
# Improvement of Aerodynamic Performance of Savonius Wind Rotor Using Straight-Arc Curtain
## 5 Conclusions  
In this study, the aerodynamic performance of the Savonius wind rotor without and with the straight-arc curtain is calculated by numerical simulations. The aim of these numerical simulations is to investigate the enhancement in the performance of the Savonius wind rotor by the arrangement of the straight-arc curtain placed in front of the wind rotor. The following conclusions can be drawn from the present study.  
1. When the wind rotor is static, the straight-arc curtain can provide better static performance than without the curtain. The static torque values of the rotor with the curtain are always positive, which is not the case with the conventional wind rotor. In all cases, Curtain-c has the best enhancement effect.
2. In terms of the power coefficient, the wind rotor with Curtain-c is superior to the wind rotor without the curtain. The \(C_{pmax}\) value of the wind rotor is increased by about 30.3% by arranging Curtain-c in front of the rotor.
3. The curtain is a wind-energy harvester device, which can increase the wind flow and wind velocity around the wind rotor, thus improving the aerodynamic performance of the wind rotor.
4. The curtain can prevent wind flow to the convex blade and increase the positive contribution of the concave and convex blades to the overall torque.  
Conceptualization, H.Z. and D.X.; data curation, H.Z. and D.X.; formal analysis, H.Z.; methodology, H.Z.; software, H.Z. and J.Z.; validation, H.Z. and Z.L.; writing--original draft, H.Z.; visualization, Z.L.; investigation, Z.L. and J.Z.; funding acquisition, D.X.; project administration, D.X.; resources, D.X.; supervision, D.X.; writing--review & editing, D.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51908107 and Grant No. 51878131) and the Fundamental Research Funds for the Central Universities (Grant No. 2572018 BJ07).  
The authors declare no conflict of interest.
## 4 Conclusions  
In this work, SCD was applied in order to maximize the reaction of free lime and periclase that resides in converter slag. Although the amount of reaction with SCD was not sufficient to make periclase completely react, this work has found a potential applicability of SCD for stabilization of converter slag to be used either as land reclamation material or as fine aggregate. According to the results obtained from this work, the following conclusions can be drawn.  
(1) After reaction with SCD, density and absorption capacity of converter slag increased. Increase in density was mainly associated with the conversion from portlandite to calcite or aragonite, whereas the increase in absorption capacity was associated with cracks caused by the reaction of free lime and periclase.  
(2) CO\({}_{2}\) uptake was found to be 7.80 g per 100 g converter slag. Reaction of converter slag with SCD can be an effective source of CO\({}_{2}\) mineralization that can reduce greenhouse gas emission.  
(3) Mortar bar made of unreacted converter slag was completely destroyed at 19 days showing 0.13% expansion. One of the mortar bars made of reacted converted slag was also broken at 26 days showing 0.04% expansion. It was recommended not to use converter slag as concrete aggregate if there is any lime or periclase present, regardless of its amount. Complete removal of free lime and periclase is necessary in order for its successful use as concrete aggregate.  
(4) The compressive strength of the mortar was slightly decreased after reaction of converter slag with SCD. However, completely reacted converter slag can be successfully used as fine aggregate as the strength of mortar was 30% higher than mortar made of standard sand.  
It should be noted that experimental condition in this work was fixed at 80 \({}^{\circ}\)C 100 bar. Since parameters such as reaction time, temperature, and pressure can significantly affect the reaction with SCD, the relationship between such parameters and reactivity of converter slag should be investigated in the future. The effect of particle size also needs to be quantitatively investigated, although it is certain that a finer size will have a larger amount of reaction with SCD.  
Data curation, H.M. and J.-H.K.; formal analysis, C.-W.C. and M.L.; funding acquisition, C.-W.C.; investigation, H.M. and K.K.; methodology, H.M., K.K., J.-H.K., and M.L.; project administration, J.-H.K.; resources, M.L.; supervision, C.-W.C. and M.L.; writing--original draft, H.M.; writing--review and editing, J.-H.K. and C.-W.C. All authors have read and agreed to the published version of the manuscript.  
Figure 10: The 28 days compressive strength of mortar using supercritical carbon dioxide-reacted converter slag as fine aggregate.  
Korea Agency for Infrastructure Technology Advancement (Grant 20 CTAP-C151965-02).  
**Informed Consent Statement:** Not applicable.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C151965-02).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Proposed Method Validation  
To verify the effectiveness of the 3D convolutional neural network and triplet constraint, we compare the proposed method with Net_3 Dcnn and Net_cnn, which are shown in Figures 8 and 9, respectively. Net_3 Dcnn is constructed by removing the triplet constraint of the proposed method. And Net_cnn is the method proposed in [4], which sends the histogram representation to the CNN to predict the crop yield directly.  
Table 2 shows the empirical results of different methods in terms of RMSE. Smaller RMSE implies better performance. Compared with Net_cnn, the RMSE of Net_3 Dcnn with the best-performing baseline 3D convolutional neural network is lower for all years, and a 1.7% improvement in terms of average RMSE is observed. The Net_cnn only uses spatial information from the data, while Net_3 Dcnn uses both spatial and temporal information to estimate crop yield. The experimental results fully demonstrate the effectiveness of the 3D convolutional neural network replacing the 2D convolutional neural network. Moreover, it can be seen that the proposed method consistently outperforms the other two methods for all years. The proposed method outperforms Net_3 Dcnn, with 2.7% improvement in average RMSE, and outperforms Net_cnn with 4.2% improvement, which indicates that it is more effective to utilize the proposed feature constraint and the 3D convolutional neural network.  
In addition, the scatter plots of the predicted yield and the observed yield per county for the different methods are shown in Figure 10. In this figure, the horizontal axis represents the true crop yield value, and the vertical axis represents the prediction crop yield value learned from the network. Each point represents a yield prediction for a county,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Year**} & \multirow{2}{*}{**Avg**} \\ \cline{2-2} \cline{5-8}  & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Net\_cnn & 6.918 & 4.620 & 6.200 & 4.883 & 5.454 & 5.321 & 5.566 \\ \hline Net\_3 Dcnn & 6.736 & 4.524 & 6.176 & 4.758 & 5.315 & 5.298 & 5.468 \\ \hline The proposed method & 6.428 & 4.406 & 6.058 & 4.664 & 5.198 & 5.243 & 5.333 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE comparison of different methods.  
Figure 8: The structure of the Net_3 Dcnn.  
Figure 9: The structure of the Net_cnn.  
and the plots indicate the positive linear relationship between the predicted yield and the observed yield. We can see the points of the proposed method are more clustered. Moreover, the points of the proposed method lie closer to the \(y=x\) line, which indicates that the predicted yield of the proposed method is closer to the observed yield. This result also demonstrates the effectiveness of the feature constraint and the 3D convolutional neural network of the proposed method from the perspective of data analysis.