esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
###### Abstract  
In this study, an estimation method for human height is proposed using color and depth information. Color images are used for deep learning by mask R-CNN to detect a human body and a human head separately. If color images are not available for extracting the human body region due to low light environment, then the human body region is extracted by comparing between current frame in depth video and a pre-stored background depth image. The topmost point of the human head region is extracted as the top of the head and the bottommost point of the human body region as the bottom of the foot. The depth value of the head top-point is corrected to a pixel value that has high similarity to a neighboring pixel. The position of the body bottom-point is corrected by calculating a depth gradient between vertically adjacent pixels. Two head-top and foot-bottom points are converted into 3D real-world coordinates using depth information. Two real-world coordinates estimate human height by measuring a Euclidean distance. Estimation errors for human height are corrected as the average of accumulated heights. In experiment results, we achieve that the estimated errors of human height with a standing state are 0.7% and 2.2% when the human body region is extracted by mask R-CNN and the background depth image, respectively.  
human-height estimation; depth video; depth 3D conversion; artificial intelligence; convolutional neural networks +
Footnote †: journal: Applied Sciences
## 1 Introduction
[13] devised a pre-equalization network using the backpropagation (BP) algorithm to eliminate ISI caused by FTN shaping, resulting in a 5 dB improvement compared to the traditional equalization algorithms. Researching the existing literature revealed that conventional FTN systems' ISI cancellation methods often encounter high computational complexity, necessitating intricate algorithms and substantial computational resources, which limits their practical applications. Although the recent advancements in DL-based approaches have somewhat mitigated this complexity, they still fall short of completely eliminating ISI. Therefore, effectively eradicating ISI while minimizing computational complexity remains a critical challenge. To address this issue, we propose an algorithm that combines 1D CNN, Bi-LSTM, and MHSA to eliminate ISI. This algorithm aims to uncover the hidden features in interfering data and utilize its powerful computational capabilities to directly tackle the ISI introduced by FTN [14; 15; 16]. Simulation results indicate that our proposed algorithm achieves up to a 7 dB improvement in BER within the Mazo limit compared to a conventional system without this ISI cancellation algorithm. Furthermore, our algorithm not only maintains comparable BER performance but also reduces computational complexity by 31.15% compared to the point-by-point cancellation adaptive pre-equalization algorithm, nearly matching the performance of an orthogonal system. These results demonstrate an effective enhancement of system performance through our method and provide new insights for signal processing in complex OWC links. This manuscript is structured as follows: The first section provides a brief introduction to the study's background and significance; the second section explains the system model and the relevant theoretical foundations; the third section offers a detailed description of the experimental and simulation processes; the fourth section presents a thorough analysis of the results; and finally, the manuscript concludes with a summary.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 2 Dark Channel Prior Algorithm
### Disadvantages of Defogging Using Dark Channel Prior
Aiming at solving the above-mentioned problems, we first use a four-point weighting algorithm to accurately estimate the atmospheric light value. Then, under the premise that the atmospheric light value is accurately estimated, the dark channel prior is used to obtain an accurate transmittance value. At the same time, we use the gray-scale image of the input image as the guide image, which can better maintain the edge of the image. Thus, the edge effect of defogged images can be improved. Furthermore, the failure of the dark channel prior theory generally occurs in the areas where the intensity of the scene is close to the intensity of the atmospheric light, such as gray and white scenes, sky, reflective water surface, and other large areas with high brightness. This is because the true transmittance value of such areas is relatively high, and the visual effect is basically the same under fog-free and foggy weather. However, in the dark channel map, the gray value of the dark channel map is very high because there are no low pixel values in such areas. According to the transmittance formula, the estimated transmittance of this type of area is very small, which is different from the true transmittance of this area. As shown in Figure 2(a) (left side), the color of foggy images in large areas (e.g., the sky area) is similar to the visual effect of the atmospheric light intensity. As shown in Figure 2(b) (right side), after defogging based on the dark primary color principle, there is a phenomenon that the defogging is not obvious and the visual effect is poor in the sky area. In response to the above-mentioned problem that knowledge of dark channel prior for large areas of high brightness will fail, we first use a combination of edge detection and maximum inter-class variance to separate the sky and non-sky regions. Then, an enhancement algorithm based on sequential decomposition is used on the sky area; thus, the fog in large areas with high brightness can be removed thoroughly. Figure 1: The figure of local filtering for flat depth-of-field areas and depth-of-field changing edges.
## 6 Discussion--Challenges and Future Work  
### Blockchain Implementation Challenges  
As can be seen from the above, blockchain technology is gaining more and more ground in the supply chains sector. Blockchains and DLT may solve the vast majority of existing issues, although they are not a panacea, especially considering the fact that the agriculture supply chain is very sensitive to criticism and, in many cases, can operate under overwhelming pressure. In parallel, the use of blockchain technologies must also take into consideration various societal, technical and financial factors. Regarding the societal impact of blockchain adoption, [PERSON] in [12] notices that the agriculture sector has its own unwritten rules and fragile relationships that should not be disturbed. In a more subtle wording, the agri-food supply chain is very complex and reluctant to a blockchain adoption for the following reasons:  
1. Technological knowledge of many stakeholders is of low level;
2. Products throughout the chain undergo many transformations;
3. The roles and business of the large number of stakeholders involved are extremely heterogeneous;
4. The food supply chain is distributed to large geographical areas, even in different continents across the globe, a fact that poses significant interoperability and deployment obstacles. What is more, issues related to data management, particularly data ownership and data retention within the blockchain, are also important and have to be carefully considered [93]. From a technical perspective, blockchains may be immutable ledgers, but the accuracy of data that are inserted by the sensors or by persons cannot be guaranteed. Thus, for example, if a sensor malfunctions, then the information across the blockchain is not accurate [86]. In general, there are difficulties in the monitoring, integration and evaluation of certain categories of data within the agricultural supply chain. For instance, environmental data are hard to retrieve and assess using objective methods [88]. Another important technical issue is the cooperation and seamless integration of blockchains with legacy traceability systems, preserving in parallel blockchains' distributed nature as a DLT. Most of the works surveyed and presented in Sections 3 and 4 are research efforts that have been tested in laboratory environments, with a limited number of users and transactions. The scalability of such traceability systems remains a challenge in the context of larger implementations and deployments [93]. The selection of the proper type (public, private, permissioned) and implementation of blockchain is also an associated issue. As observed from Table 2 in Section 3.4, a large number of research efforts do
## 5 Conclusions  
In summary, this study addressed the LSM dynamic update problem using PS/DS-InSAR data. The model tested the performance of LR, SVM, and RF on earthquake-induced landslides and the landslide inventory, and then the optimal model was corrected using SAR deformation data. The results of the model assessment show that all three models had AUC values greater than 0.80, indicating good landslide susceptibility prediction for the three typical models. Furthermore, the results of the four statistical indicators of F1 score, recall, precision, and accuracy indicate that the RF models produced good and reasonable results and outperformed the other two models. Landslide susceptibility in the area was dynamically refined using the proposed correction method, and the change in susceptibility was evaluated four years after the earthquake. This dynamic landslide susceptibility effectively helps to reduce landslide damage and protect lives and property. In addition, the results also illustrate that landslide susceptibility in the Jiuzhaigou scenic area has greatly been reduced after some ecological restoration.  
Thanks to the full time series and global coverage of Sentinel data, refined LSMs enable dynamic initial LSMs and increase the reliability and usability of landslide susceptibility information. In particular, InSAR technology demonstrated good performance in measuring slow-moving landslides. In addition, the freely available Sentinel-1 data can effectively reduce the cost of landslide susceptibility studies, making this method a good option for a large number of developing countries to carry out such studies.  
Conceptualization, Y.S. and C.X.; data curation, H.F.; funding acquisition, Y.S. and C.X.; investigation, Y.Z., Q.Y. and Y.Y.; methodology, H.F. and C.X.; project administration, Y.G.; software, H.F. and B.T.; supervision, Y.S. and B.T.; writing--original draft, H.F.; writing--review and editing, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Remote Sensing Identification and Monitoring of Geological Disaster Hazards in Sichuan Province (2020), Sichuan Research Institute of Ecological Restoration of Land Space and Geological Disaster Prevention and Control, grant number E1H2110702, and Multi-Source Remote Sensing Regional Landslide Hazard Risk Mapping and Key Landslide Fine Survey, Science and Technology Bureau of Fuzhou City, Fujian Province, grant number E1D711010.  
Not applicable.  
The authors are thankful to the Sichuan Institute of Ecological Restoration of Land Space and Geological Disaster Prevention, a Unicom (Fuijan) Industrial Internet Co., Ltd.  
The authors declare no conflict of interest.
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
###### Abstract  
Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics' ability to work with 2.5D prints under limited conditions.  
image quality metrics; quality assessment; 2.5D printing +
Footnote †: journal: Applied  
10 July 2021 12 August 2021 12 August 2021 14 August 2021  
**Publicisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Adticle Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics**  
**[PERSON]**  
Department of Computer Science, Norwegian University of Science and Technology, 2802 Gjevik, Norway; vlado.  
as decoration (e.g., interior design), signage, and maps (e.g., for visually impaired people), to name a few. Therefore, 2.5D prints quality should be assessed carefully before releasing them into the market for sale. Many studies have explored the quality of images [7; 8; 9; 10; 11] and prints [12; 13; 14]. The quality of 2.5D prints in this work can be related to the definitions given by [PERSON] [15] and ISO [16], where image quality is an observer's perception of image excellence where the observer was not involved with anything related to the given image. In addition, 2.5D prints tend to have surface elevation. This can impact on the overall quality perception by customers. The importance of 2.5D prints is that the variations in surface elevation of 2.5D prints can create many possible options for shapes and texture patterns such as diversification of what printer can print as well as enhancement of appearance of prints [17]. This is useful in many applications such as signage, decorations, maps or reproduction of art works (e.g., to reproduce brush strokes). First, brief QA aspects and then capture aspects will be described for 2D and 2.5D prints.
# Influences of High-Speed Train Speed on Tunnel Aerodynamic Pressures
## 4 Results
### Aerodynamic Pressures in Stage I  
When the train nose arrives at the entrance at a speed of 350 km/h, the aerodynamic pressure time histories at some specific monitoring points are shown in Figure 12. It takes 0.18 s (60 m / 340 m/s = 0.18 s) for the initial pressure wave produced by running of train to reach the monitoring point 1-V. When the train nose arrives at the entrance, the propagation distance of the initial pressure wave is 174.86 m (340 m/s \(\times\) (50 m/97.22 m/s) = 174.86 m). The airflows from the tunnel entrance to the initial wave surface are disturbed by the initial pressure wave. As a result, the aerodynamic pressures are recorded from monitoring section 1 to monitoring section 6. Meanwhile, the airflows from the initial wave surface to the tunnel exit are undisturbed.  
The distributions of the maximum peak pressures on the tunnel vault along the tunnel at different train speeds are shown in Figure 13. When the train nose arrives at the entrance, the propagation distances of the initial pressure waves at different train speeds are shown in Table 1. The peak pressure values of the monitoring points located at 200 m from tunnel entrance are not available when the train speed is faster than 300 km/h. The characteristics of maximum peak pressures on the tunnel vault along the tunnel's longitudinal axis at different train speeds are similar. The maximum peak pressure decreases with increasing distance from the entrance. The maximum peak pressure increases with increasing train speed.  
Figure 12: Time histories of aerodynamic pressure of typical monitoring points in Stage I (\(\
u\) = 350 km/h).  
Figure 13: Peak pressure acting on tunnel vault along tunnel’s longitudinal axis in Stage I.  
The maximum peak pressures at different train speeds of monitoring point 1-V and the related fitting curve are shown in Figure 14. The '\(x\)' and '\(y\)' show the train speed and the maximum peak pressure, respectively. The maximum peak pressure on tunnel vault increases linearly with increasing train speed, and the value of the adjusted coefficient of determination \(R^{2}\) is 0.99.
## 2 Materials and Methods
### Permeability Test  
Figure 3 shows the schematic diagram of the permeability test device. The size of the acrylic container was 26 cm in height, 6 cm in inner diameter, and 0.5 cm in thickness. The steps of the permeability test were as follows. The test sample was 15 cm in height and was placed in the container  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Foaming Agents**} & \multirow{2}{*}{**Concentration, _wt\%_**} & \multicolumn{2}{c}{\(T_{\rm{hl}}\), min**} \\ \cline{3-4}  & & **FER = 10, H/ = 20 cm** & **Code** \\ \hline ZDF-01 & & 9.5 & ZD10 \\ \hline ZDF-02 & & 21 & ZD20 \\ \hline ZDF-01P & & 27.4 & ZD30 \\ \hline ZDF-03 & & 39.5 & ZD40 \\ \hline ZDF-03P & & 47.4 & ZD50 \\ \hline ZDF-03 AB & & 58.2 & ZD60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Liquid half-life for the different foaming agents.  
Figure 2: Device for testing the foam liquid half-life.  
before 200 mL of water was poured on top. The air pressure was set to 0.3 bar. After opening the air valve switch, the mass of the total exudate water was recorded every 2-5 min (converted to volume Q according to the density of water (lg/cm\({}^{3}\)) until the sample was penetrated or the penetration time was greater than 60 min. Similar experimental device has also been used by many scholars [19; 20; 21] to find reasonable formulas for calculating permeability coefficients in slurry TBM. To compare the conditioned sand permeability with different foam parameters, [PERSON]'s seepage formula Equation (1) was selected to calculate permeability coefficient (\(k\)).  
\[k=\frac{\Delta Q\times L}{S\times P} \tag{1}\]  
where \(\Delta Q\) is the average flow volume of the unit per time (cm\({}^{3}\)/s). \(L\) is the penetration length of the sample, which is a fixed value of 15 cm. \(S\) is the area of the cross section of water, \(S=\pi\times\frac{d^{2}}{4}\), where d = 6 cm. P is the water pressure, which is 0.3 bar in this study.  
Standard sand (ISO 679:2009), provided by Xiamen ISO Sand Co., Ltd., was selected in this study for the permeability test. The measured average particle size gradation curve is shown in Figure 4. The average density of standard sand was 1.74 g/cm\({}^{3}\), as determined by experiments. Table 2 shows the foam injection weight of different FIRs when the foam expansion ratio was 10. According to the design of the permeability experiment (D = 6 cm, filling height = 15 cm), the mass of the standard sand required for each group of permeability experiments was 750 g (the equivalent volume was 420 mL).  
Figure 4: Standard sand average particle size gradation curve.  
Figure 3: Permeability test device.
## 2 Experimental Investigation
### Experimental Methods  
#### 2.5.1 Heating Performance Test  
There are neither separate domestic nor foreign regulations on test standards to evaluate the heating performance of cement-based composites. The voltage applied when conducting heating performance tests varies greatly among researchers. The dimensions of the specimens used in this study were fabricated to be 40 \(\times\) 40 \(\times\) 160 mm in accordance with KS L ISO 679 [36] and ASTM C348 [37], which are domestic and foreign standards for flexural strength tests. Clamps were connected to the (+) and (-) ends of the electrodes of the copper mesh embedded in the specimen, and then voltages of 10 V, 20 V and 30 V were applied for 3600 s using a DC power supply (AK 3005). Then, the surface temperature was measured at intervals of 10 s. The maximum output voltage and current of the power supply were 30 V and 3 A (90 VA), respectively. At the same time, the surface temperature was measured in real time through an infrared thermal camera (FLIR T630 sc, Wilsonville, OR, USA). Figure 4 shows the overall schematic diagram and test setup scene for measuring the surface temperature of the specimens. The initial temperature of all specimens before applying voltage was about 20 \({}^{\circ}\)C, and the distance between the infrared thermal camera and the specimen was fixed at about 45 cm. In addition, the laboratory room temperature was kept constant at about 20 \(\pm\) 1 \({}^{\circ}\)C and the humidity was set in the range of 50 \(\pm\) 5% in order to prevent heat loss due to the temperature difference between the specimen and the surroundings of the laboratory. For checking the strength of the applied voltage, a multitester was used to confirm the application of constant voltage, and all specimens were placed on an insulating rubber plate and a constant voltage was applied to ensure safety by preventing electrical shock and short circuits while testing.
###### Abstract  
The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.  
land cover; Google Earth Engine; ensemble-learning; random forest; extreme gradient boosting; Nepal 2020, 10, 8083; doi:10.3390/app10228083