esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Fog Computing
### Fog Computing for Smart Cities  
Fog computing offers several benefits when applied to smart cities. By bringing computation, storage, and processing closer to users, fog computing enhances smart city applications' efficiency, responsiveness, and functionality. Table 7 summarizes some of the key benefits of deploying fog computing in smart cities [76; 77; 78]. Also, examples of smart city applications that can benefit are introduced. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Value Added** & \multicolumn{2}{c}{**Fog Computing for Smart City**} & \multicolumn{2}{c}{**Applications**} \\ \hline \multirow{4}{*}{Reduced delay} & - & Fog computing is a better way to reduce latency in & \(\bullet\) & Traffic management \\  & time-sensitive and location-sensitive smart city applications. & \(\bullet\) & Autonomous vehicles \\  & - & Fog computing reduces data transfer times. & \(\bullet\) & Emergency response systems \\  & - & This is crucial for low-latency smart city applications. & & \\ \hline \multirow{4}{*}{Improved spectral efficiency} & - & By processing data and filtering at the edge, fog computing & \multirow{4}{*}{
\begin{tabular}{c} Surveillance applications \\ Traffic management \\ Autonomous vehicles \\ \end{tabular} } \\  & - & This optimizes the network bandwidth and reduces & \(\bullet\) & Surveillance applications \\  & - & Congestion, ensuring efficient data utilization. & \(\bullet\) & Traffic management \\  & - & This is crucial for real-time and multimedia-based smart city applications. & & \\ \hline \multirow{4}{*}{Assisting scalability requirements} & - & Fog computing supports dynamic scalability by distributing & \(\bullet\) & Healthcare \\  & - & As smart city networks grow and demand increases, fog & \(\bullet\) & Traffic monitoring \\ \cline{1-1}  & - & nodes can seamlessly accommodate additional devices & \(\bullet\) & Energy monitoring \\ \cline{1-1}  & - & Smart homes & \(\bullet\) & Smart homes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Benefits of implementing fog computing for smart cities. Implementing fog computing for smart city networks involves many steps. In these steps, a combination of hardware, software, and network infrastructure works together to achieve distributed computing. Table 8 introduces the potential steps to implement fog computing for smart cities [78; 79].
## 1 Introduction
The vertical deformation rate of the Weihe Fault was derived from ascending Sentinel-1A images and descending ALOS-2 ScanSAR images, and the distribution of fault slip was subsequently inverted by using a multi-fault model [16]. The results confirmed the creep characteristics of the Weihe Fault. InSAR ground deformation maps were produced during 2015-2019 using Sentinel-1A/B and ALOS/PALSAR-2 ScanSAR images over the Weihe Basin, where the vertical and east-west deformation components decomposed from ascending and descending InSAR measurements showed that vertical movement dominated the deformation of the Weihe Basin [2]. A slip rate of \(\sim\)13.7 mm/yr on the Weihe Fault was inverted from the InSAR observations, which explained the phenomenon of uplift along the Weihe Fault. Although intensive research has been conducted in investigating the tectonic deformation of the Weihe Basin and Weihe Fault, most research is based on independent deformation observation technologies, such as GNSS-derived horizontal deformation, InSAR-derived line of slight (LOS) deformation, and leveling-derived vertical deformation. These independent observations may not reflect the accurate tectonic deformation of the Weihe Fault in a different temporal-spatial scale. In this context, this study combines the InSAR, GNSS, and leveling data to obtain high-spatial-resolution ground deformation along the Weihe Fault. Subsequently, long-wavelength deformation caused by tectonic movement and small-scale deformation caused by human activities are separated by the spherical wavelet-based multiscale approach. Finally, the fault slip rate and locking depth of the Weihe Fault are inverted from an improved dip-slip model.
# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 1 Introduction
For instance, Figure 1b shows the standard _'Stop'_ traffic sign used by various developed countries such as _USA_, _Canada_, etc. On the other hand, Figure 1c shows the alternative traffic sign _'Stop'_ that is used in _Vanuatu_. Here, we can see, in Figure 1b,c the shape of the symbol _'Stop'_ is different for different countries. Therefore, in this research, an intelligent and robust traffic sign detection and recognition system is developed that can identify the well-maintained, un-maintained, standard, and non-standard road traffic signs. The proposed system is developed using machine learning approaches. In the proposed system, the features were extracted using visual Bag-of-Words (BoW), and the discriminative features were chosen using \(k\)-means clustering approach. Finally, for the classification of the traffic sign,  
Figure 1: Traffic sign (Stop); (**a**) Faded stop sign used in Malaysia, (**b**) Stop sign used in USA, Canada, Australia; (**c**) Stop sign used in Vanuatu. three different classifiers were employed namely, ANN, SVM, and Ensemble subspace \(k\)NN (k-nearest neighbors) classifiers. Our experimental results showed 99.00% accuracy for traffic sign recognition using ANN classifier. This remainder of the paper is organized in the following way. Section 2 presents the related work. Research methodology is presented in Section 3. Section 4 shows experimental results. Section 5 presents the discussion based on obtained results. In Section 6, the significance of our proposed method is compared with one state-of-the-art baseline. Moreover, in addition to the collected dataset, the performance of the proposed method is also evaluated on one publicly available dataset of traffic sign images [25]. Finally, this research work is concluded in Section 7.
# Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber
###### Abstract  
Traditional radar calibration methods often use one standard sphere as the standard calibration object, but researchers seldom discuss the size and the material content of the calibration sphere. We propose using a pair of different-sized spheres to verify the standard method. In a microwave anechoic chamber, the simulated and measured results show that the radar cross-section (RCS) values of two spheres with radii of 0.15 m and 0.05 m in the Mie region differ from their physical cross-sections in the optic region, and only in the optic region, their difference of RCS value (i.e., 9.5424 dB) can be approximately equal to the theoretical one (i.e., 9.6803 dB). Thus, radar calibration should be conducted in the same scattering region for both the calibration object and the calibrated target. The use of two different-sized spheres can aid in three applications: (1) verifying the scattering regions, (2) searching the pure area in the microwave anechoic chamber, and (3) locating the positions of the targets in the range profiles.  
different-sized spheres; microwave anechoic chamber; radar calibration; scattering region 2022
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 3 Dataset Creation  
To establish a prediction model of the mechanical performance index of the steel bridge deck pavement system using machine learning algorithms, datasets are required to provide data support. Numerical simulation tests are conducted at the most unfavorable load location using a finite element model to obtain these datasets. Different combinations of design parameters were then considered under the most unfavorable load position, and the orthogonal design method was used to numerically simulate the deck system. Finally, numerical simulation results were used to establish a mechanical performance index dataset for steel deck pavement systems.
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 6 Conclusions
copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-preliminary-back-extension?tab=form, [[https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#)]([https://cds.climate.copernicus.eu/cdsapp#](https://cds.climate.copernicus.eu/cdsapp#))!/dataset/reanalysis-era5-single-levels-preliminary-back-extension?tab=form (accessed on 26 July 2022). The authors would like to thank the National Satellite Meteorological Center of Chinese Meteorology Administration for the FY-3E MWHS-2 data ([[https://pan.baidu.com/disk/main?from=homeFlow#/index?category=all&path=%2F%E5%8D%AB%E6%98%9F](https://pan.baidu.com/disk/main?from=homeFlow#/index?category=all&path=%2F%E5%8D%AB%E6%98%9F)]([https://pan.baidu.com/disk/main?from=homeFlow#/index?category=all&path=%2F%E5%8D%AB%E6%98%9F](https://pan.baidu.com/disk/main?from=homeFlow#/index?category=all&path=%2F%E5%8D%AB%E6%98%9F)) (accessed on 26 July 2022)) and the RTTOV coefficients. All data used in this paper are available from the authors upon request ([EMAIL_ADDRESS]). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
AMSR-E Advanced Microwave Scanning Radiometer for EOS ECMWF European Centre for Medium-Range Weather Forecasting EEMD Ensemble Empirical Mode Decomposition ERA-5 ECMWF 5 th-Generation Reanalysis FOV field of view FY-3E Fengyun-3E MWTS MicroWave Temperature Sounder MWHS MicroWave Humidity Sounder NCEP National Centers for Environmental Prediction
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 3 Results  
### Linear Model  
The model shown in Figure 1a shows the maximum ultrasound pressure and temperature rise on the ultrasound axis (z-axis) when the input sound power is 20 W, 40 W, 60 W, 80 W, and 100 W, and the irradiation time is 5 s, as shown in Figure 3.  
As shown in Figure 3, there is a linear relationship between the sound power and peak ultrasound pressure. As the power linearly increases, the peak ultrasound pressure and maximum temperature increase. The larger the power difference, the more pronounced this trend becomes.  
The simulated sound and thermal field distributions that occur when the sound power is 100 W are shown in Figure 4. Figure 4a depicts the ultrasound field distribution of the ultrasound, showcasing an elliptical region within the \(-6\) dB focal zone. Furthermore, Figure 4c illustrates the ultrasound pressure distribution along the sound axis, offering a deeper understanding of the intensity and shape of the ultrasound waves at various positions. In theory, the focus should be on the origin of the coordinate, and the maximum ultrasound pressure should coincide with it. However, the simulation results showed that the actual peak ultrasound pressure occurred at the coordinate \(-5.17\) mm, indicating a 5.17 mm offset, which is independent of the power. When the HIFU penetrates multiple layers of tissue, the propagation path of the ultrasound waves is bent due to factors such as absorption and scattering, which affect the focusing effect. Figure 4b shows the temperature distribution, with the highest temperature point deviating from the focal point by 5.5 mm. This phenomenon can be attributed to the influence of thermal effects and the thermal conductivity properties within the tissue. These factors work together to cause non-uniformity in the temperature distribution. Figure 4d shows the highest temperature rise after 5 s of ultrasound application. The highest temperature within the tissue rapidly increased from 20 \({}^{\circ}\)C to 71 \({}^{\circ}\)C. At the end of the ultrasound application, the temperature  
Figure 3: Peak acoustic pressure, maximum temperature, and coordinate shift corresponding to different input powers under linear conditions.  
begins to decrease slowly; this is related to tissue heat conduction, heat dissipation, and action time.
## 2 Materials and Methods
### Validation of Variables: Height and Ethnicity  
We implemented a sequence of manual data entry using a validation process. In the first stage, researchers entered data on weight, date of birth, and date of death in a sample of 1620 cases.  
Two trained coders (Coder 1 and Coder 2) then recorded the remaining cases (1178 and 1187, respectively), after which a third coder (Coder 3) validated the codification. The number of mismatches (disagreements) identified for these three variables was 72, 72, and 49, respectively. We reviewed and resolved all mismatches. Ethnicity was recorded by two independent coders and classified in three categories: African-American, White/Asian, and Mixed. Coders were trained to identify players who were born from parents with different ethnicity, such as [PERSON], [PERSON], [PERSON] or [PERSON]. Once this process was finished, all researchers reviewed the codification and discussed dubious cases. The final categorization was reached by consensus. Coding of handedness was also done manually by two of the authors without any recorded mismatches.  
Registration of height required particular attention because differences were found from one source to another. While we initially collected the data from www.basketball-reference.com in April 2015, the height of 43 players had changed when the data were checked again in June 2017. To fix this divergence, we consulted www.NBA.com, where the heights recorded were similar to those obtained in June 2017 from www.basketball-reference.com (39 of 43 coincidences). We decided to use heights from NBA.com for all of these 43 players. The International Basketball Federation (FIBA) also reports the height of participating players; we identified 238 players from the FIBA website who were also in our database and found that the heights reported for FIBA tournaments varied for almost all cases. As there was no way to decide which of the sources was most reliable, we regressed FIBA heights on NBA heights for the 238 players identified and observed a statistically significant constant (7.040) and regression coefficient (0.964) (standard errors were bootstrapped), with an R-square of 0.953. Using these parameters, we estimated the heights of the remaining 3757 NBA players as a proxy of their FIBA height. Consequently, we had two different versions of the height variable.  
Finally, having completed the codification and validation process, we investigated the cause of death of those 734 players using the Google search engine and specialist sources such as basketball networks (APBR), digital newspapers (New York Times, LA Times), sports websites (ESPN), and other media resources. Only 443 of the 734 deaths (60.35%) could be clearly assigned to a cause. We codifiedevery cause of death using the International Statistical Classification of Diseases and Related Health Problems 10 th Revision (ICD-10)-WHO Version for 2016.
## 3 Experimental Setup and Evaluation Indicators
### Evaluation Indicators  
This paper employs the following four commonly used semantic segmentation metrics to comprehensively evaluate the segmentation performance of the model: mean intersection over union (mIoU), mean pixel accuracy (mPA), frames per second (FPS), and number of model parameters. True positive (TP) represents the number of correctly predicted positive samples, true negative (TN) denotes the number of correctly predicted negative samples, false positive (FP) indicates the number of negative samples incorrectly predicted as positive, and false negative (FN) represents the number of positive samples incorrectly predicted as negative.  
Figure 4: The RGSM structure.  
Figure 5: Schematic of soldering defects. The first row is the images without defects; the second row is the images with defects. (**a**) MS. (**b**) MP. (**c**) SS. (**d**) OD. (**e**) SB.  
The mIoU is a commonly used evaluation metric for semantic image segmentation. It calculates the intersection ratio to the union between the predicted set and the ground truth for each class, and then computes the average across classes. The definition of the mIoU is as follows:  
\[\text{mIoU}=\frac{1}{N}\sum_{i=1}^{N}\frac{\text{TP}_{i}}{\text{TP}_{i}+\text{ FP}_{i}+\text{FN}_{i}} \tag{6}\]  
The mPA aims to measure the accuracy of pixel-level classification tasks. It represents the average proportion of correctly predicted pixels for each class out of the total number of pixels. The calculation formula is as follows:  
\[\text{mPA}=\frac{1}{N}\sum_{i=1}^{N}\frac{\text{TP}_{i}+\text{TN}_{i}}{\text{ TP}_{i}+\text{TN}_{i}+\text{FP}_{i}+\text{FN}_{i}} \tag{7}\]
## 2 Material Characteristics
### Superelastic Shape Memory Alloys  
Recent developments in the construction industry have shown a growing interest in smart construction materials capable of actively responding to various loads and autonomously restoring their original form through intelligent capabilities. Superelastic shape memory alloys, among these smart materials, are increasingly recognized for their exceptional behavior characteristics and are the subject of extensive research in engineering. These alloys, proposed as alternative materials for rubber expansion joints in expansion joint devices, demonstrate flag-shaped behavior characteristics, as depicted in Figure 4. Unlike conventional steel, superelastic shape memory alloys can revert to their original form after undergoing plastic deformation beyond the elastic limit [5; 6; 7]. A critical feature of these alloys is the presence of austenite, which exhibits a superior performance under stress and temperature conditions, while martensite, in comparison, shows a relatively poorer performance. At sub-room temperatures, the martensite in superelastic shape memory alloys is primarily in the form of twinned martensite, which transforms into detwinned martensite under external loads. Consequently, these alloys experience lower stress at temperatures below room temperature and tend to exhibit significant residual displacement. Addressing this issue involves the heat treatment of the superelastic shape memory alloys, transforming martensite into the austenite phase, thus facilitating the restoration of the original shape. Furthermore, in the austenite phase, superelastic behavior is exhibited, allowing the material to return to its original shape without additional heat treatment, even after plastic deformation, at temperatures above room temperature.  
Figure 4: Material behavior of superelastic shape memory alloys.  
Figure 3: Stress–strain curve for rubber expansion joints.  
Rubber expansion joints in public bridge infrastructure are engineered to withstand continuous, repeated loads, reflecting the behavioral characteristics of the bridge girders. Consequently, not only durability, but also the shape memory effect, are critical aspects for these rubber expansion joints. Various types of shape memory alloys can serve as replacements for rubber expansion joints, with compositions including alloys mixed with copper or iron and nitinol-based shape memory alloys consisting of nickel (Ni) and titanium (Ti). In this study, nitinol-based shape memory alloys were selected, due to their superior shape memory effects and deformation range compared to copper or iron-mixed alloys, as detailed in Table 5. The material properties of these nitinol-based shape memory alloys, such as their yield strength and tensile strength, are superior to those of general structural steel, as shown in Table 6, and their elongation rate exhibits a significant difference, being approximately 40-fold higher [8; 9; 10].
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 4 Discussion
### Implications for Future  
Our study found that the LULC change was beneficial to the improvement in regional greening and plant productivity in southwest China. Karst landforms are widelydistributed in the study area. Due to its unique binary structure, precipitation is rapidly lost, which leads to low utilization of precipitation by vegetation [81]. Moreover, the karst regions have thin soil layers and poor water storage capacities, and the climate warming trend may have an inhibitory effect on the growth of forests [14]. Studies have shown that ecological restoration and LULC pattern change not only ameliorate land degradation [40] but also affect local and regional climate [82]. For example, land surface temperature decreases significantly when forest is converted to cropland [83]. The global warming trend has a greater impact on ecosystems [84; 85], which has led to dramatic changes in land cover types and plant biomass in the high northern latitudes and promoted the expansion of woody shrubs and forest areas [86]. Meanwhile, the rise in temperature has removed local environmental boundaries, allowing alpine plant species to move to higher altitudes [87]. These results indicate that in future implementations of ecological projects, it is necessary to consider the structural composition and environmental carrying capacity of LULC types under the background of climate warming so that they can more effectively serve the maintenance of local, sustainable ecosystem balance.
## 5 Reliability-Driven Optimization Design for Key Structural Elements
### Bi-Objective Optimization  
The NSGA-II genetic algorithm, which can effectively solve nonlinear optimization problems, breaks through the bottleneck of traditional multi-objective optimization methods such as the weighted summation method, linear programming method, and \(\epsilon-\) constraint method, which are ineffective and even fail under the lack of experience. The specific optimization process is shown in Figure 8 [32].  
The NSGA-II genetic algorithm is set to have the crossover probability as 0.9, the crossover distribution index is 20, the variance probability is 0.1, the variance distribution index is 20, the population size is 200, and the number of iterations is 200. The Pareto set is derived using MATLAB programming as shown in Figure 9.  
The variation pattern of the ring gear mass and fatigue reliability indexes obtained from the Pareto set results under the influence of the rim thickness is shown in Figure 10a. The elastic deformation results of the ring gear and the planet carrier, and the reliability results of the planetary gear system is shown in Figure 10b. With the increase in the ring gear rim thickness, its elastic deformation gradually decreases, and it can be found from Figure 10b that the maximum node resultant displacement almost stops decreasing when the rim thickness reaches 22.5 mm, indicating that the rigidity reserve of the ring gear raised by increasing the rim size can no longer be effectively utilized at this limit value.  
Figure 8: NSGA-II algorithm flow chart.  
Figure 9: Result of Pareto optimal solution set.  
As the ring gear rim thickness increases, the reliability of the system will continue to grow, meanwhile the elastic deformation keeps decreasing. It indicates that the scale growth of the ring gear has a mutual promotion effect in optimizing the system reliability index. However, as the performance requirements of the large aviation planetary system continue to improve, the mass becomes an important factor limiting the improvement in planetary institution indicators. In the pursuit of reliability and the elastic deformation of the best at the same time, continuously increasing the rim thickness will not achieve the lightweight requirement.  
Comprehensive analysis of Figure 8 shows that the value of the rim thickness reaches 22.5 mm, the reliability growth is weak, and the same is true for the elastic deformation. At the same time, considering the static strength requirements of the ring gear, the optimal matching value of the ring gear rim thickness for the large aviation planetary system is further determined in the range of 22.5 to 30 mm.  
The set of Pareto solutions with rim thicknesses in the range of 22.5 to 30 mm is shown in Table 5.