esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Regression Modeling for Coastal Urban Air Quality Estimates
## 3 Results
### Satellite Images of Yellow Dust Transport
When high PM concentrations due to the dust were detected in Beijing, its local wind speed also  
Figure 5: Hourly distribution of (**a**) PM\({}_{10}\) and PM\({}_{2.5}\) concentrations (\(\upmu\)g/m\({}^{3}\)) and (**b**) SO\({}_{2}\) (\(\times\)1000), (**c**) CO (\(\times\)100), (**d**) O\({}_{3}\) (\(\times\)1000) and NO\({}_{2}\) (\(\times\)1000) concentrations (ppm) at the Yongdingmen observation point in Beijing (China), corresponding to PM concentrations on 18 to 27 March at Kangnung, Korea. became higher, with a maximum of 5.8 m/s more than before; however, it was much lower due to the rough mountain topography surrounding the city than it was in the desert. As the relative humidity had very low values of 6% to 54%, it was very dry without precipitation in Kangnung. During the yellow sand period at Kangnung, the hourly air temperature and wind speed increased more than in other periods but the relative humidity tended to decrease. Thus, these trends for the meteorological parameters may affect the PM and gas concentrations in the city in the presence of absence of yellow sand events. Figure 5 shows that the SO\({}_{2}\) and CO concentrations during the second yellow sand period in Beijing from 00 LST, March 19 to 00 LST, March 21 decreased more markedly than before and after this period; however, as with the SO\({}_{2}\) and CO concentrations, the NO\({}_{2}\) concentration also slightly decreased but with smaller differences, whereas there was an opposite distribution of O\({}_{3}\) to NO\({}_{2}\) due to the NO\({}_{x}\) cycle. Therefore, the gases in Beijing may affect the PM and gas concentrations at Kangnung.
## 3 Simulation Results and Discussion
### Effects of Load Condition  
The real shear band directions of propagation are determined by local stress. Therefore, unlike uniaxial compress, different load cases coupling compression and shear loading have been tested. The ratio between two kinds of loads is defined as \(k=|\epsilon_{12}/\epsilon_{22}|\). Here, the MGMC models with 25 vol.% particles that were generated in the previous subsection are adopted to study the influence of loading condition on the mechanical properties of MGMCs. The equivalent stress and strain are respectively defined as:  
\[\overline{\sigma}=\sqrt{\frac{(\sigma_{11}-\sigma_{22})^{2}+\sigma_{11}^{2}+ \sigma_{22}^{2}+6\tau_{12}^{2}}{2}} \tag{12}\]  
\(\overline{\epsilon}=\frac{2}{3}\sqrt{\epsilon_{11}^{2}+\epsilon_{22}^{2}- \epsilon_{11}\epsilon_{22}+3\epsilon_{12}^{2}}\)  
Figure 10 illustrates the relationship between equivalent stress and strain under different load conditions. Similarly, the load condition almost plays no role on the stress-strain relationship in the elastic stage. Nevertheless, the yield strengths decease with the increase of the shear part in the applied load. In addition, the ductility of MGMCs decreases with the increase of the shear part in the applied load. Figure 11 shows the plastic distribution of BMG in MGMCs under different load conditions. It can be seen from Figure 11 that with the decrease of the compressive part in the load, the direction of the main shear band approaches the y-axis.  
Figure 8: The influence of particle volume fractions on the stress–strain curves of MGMCs.  
Figure 9: The influence of particle volume fractions on normalized free volume in BMG. (**a**) \(V_{f}=15\%\); (**b**) \(V_{f}=25\%\); (**c**) \(V_{f}=35\%\).
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 2 Materials and Methods
### Design of Fuzzy Controller
#### 2.3.2 Control Rules  
To increase the stability and reliability of the fuzzy controller in controlling the prototype vehicle to approach the target path, reasonable fuzzy control rules are desirable. According to the experience of experts and drivers, the following principles should be followed to generate control rules. When both lateral and heading deviations are significant, we need a smaller angle of steering (i.e., close to \(0^{\circ}\)) and a smaller steering radius to achieve a fast convergence to the desired path. However, when the deviation is slight, we need a larger steering angle (i.e., close to \(90^{\circ}\)) and a larger steering radius to fine-tune the prototype vehicle to ensure travel stability. Based on the principles mentioned above and through repeated optimization attempts, 25 control rules are designed (Table 1), and the query table is shown in Figure 7.
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 4 Experiment Results and Analysis
### Experimental Environment and Parameter Setting  
The experimental environment based on VMware featured the following: Intel Xeon E5-2658, dominant frequency 2 \(\times\) 2.10 GHz with 8G RAM, Windows 2008 server, and the algorithm was coded in MATLAB 2016b. MATLAB is a commercial mathematical software produced by American MathWorks company(USA) which includes row matrix operation, drawing functions and data, implementing algorithms, creating user interfaces, connecting programs of other programming languages, and so on. It is used in data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, robotics, control systems, and other fields. In our experiment, the alternative values were tested and modified for some functions to obtain the most reasonable initial values of these parameters. These selected values of the parameters take on the optimal solution and the most reasonable running time to efficiently complete the solving problem. The parameter settings of the FCM, K-means, K-means plus, and noise K-means are shown in Table 2. The number of clustering iterations was 200, and each algorithm ran 20 times independently. Generally, the evaluation results will be directly affected when the smaller clustering number of the comparison algorithm is set. FCM selected the clustering center according to the fuzzy parameters. K-means and K-means plus selected clustering center randomly. Noise-based K-means could obtain the clustering number and the initialization of the clustering center automatically. At the same time, the clustering numbers of noise-based K-means and FCM, K-means, and K-means plus were the same, and the clustering evaluation results of the corresponding clustering algorithm were also similar.  
\begin{table}
\begin{tabular}{c c c} \hline
**Taxi GPS Dataset** & **Latitude and Longitude Region** & **Number of GPS Data Points** \\ \hline Aracaju (Brazil) & 0.14 \(\times\) 0.16 & 16,513 \\ San Francisco (USA) [39] & 0.10 \(\times\) 0.10 & 21,826 \\ Rome (Italy) [39] & 0.35 \(\times\) 0.50 & 20,254 \\ Chongqing (China) [1] & 0.60 \(\times\) 0.36 & 19,149 \\ Beijing (China) [40] & 0.90 \(\times\) 0.90 & 17,387 \\ \hline \end{tabular}
\end{table}
Table 1: Taxi GPS datasets.
## Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems  
**Shabnam Jabari * and Yun Zhang**  
Department of Geodesy and Geomatics Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada; E-Mail: yunzhang@unb.ca  
* Author to whom correspondence should be addressed; E-Mail: sh.jabari@unb.ca; Tel.: +1-506-453-5140; Fax: +1-506-453-4943.  
_Received: 23 August 2013; in revised form: 22 October 2013 / Accepted: 6 November 2013 / Published: 12 November 2013_  
**Abstract:** The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, _etc._, using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process. In this study, an inclusive semi-automatic method for image classification is offered, which presents the configuration of the related fuzzy functions as well as fuzzy rules. The produced results are compared to the results of a normal classification using the same parameters, but with crisp rules. The overall accuracies and kappa coefficients of the presented method stand higher than the check projects.  
**Keywords:** fuzzy rule based systems; object-based image classification; very high resolution satellite imagery; urban land cover
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from HH Polarization
#### 3.2.3 Compared with HY-2B SCA Wind Speed  
Figure 7 shows a comparison of the wind speeds obtained by applying the above model to the GF3-02 SAR HH polarization data and the HY-2B SCA wind speed. Table 5 shows the retrieval results of the wind speed of the GF3-02 HH Polarization data based on each GMF model. The RMSE of the retrieved wind speeds are 1.91 m/s, 1.70 m/s, 2.58 m/s, 1.67 m/s, and 1.52 m/s, respectively. Consistent with the ERA5 wind speed comparison results, the RMSE of CMODH_HH is also the smallest, the deviation is \(-\)1.12 m/s, and the correlation coefficient is 0.80.  
In conclusion, the comprehensive analysis of the wind speed retrieved from the GF3-02 SAR HH polarization data with the NDBC, ERA5, and the HY-2B SCA wind speeds shows that CMOD_HH exhibits the overall best performance of the GF3-02 SAR HH polarization. Compared with the three data sets, the RMSE is 1.45 m/s, 1.19 m/s, and 1.52 m/s, which can also meet the operational requirements of GF3-02 SAR.  
\begin{table}
\begin{tabular}{c c c c} \hline
**GMFs** & **Bias (m/s)** & **RMSE (m/s)** & **Corr** \\ \hline PR\_[PERSON] + CMOD5.N & \(-\)0.81 & 1.51 & 0.80 \\ PR\_[PERSON] + CMOD5.N & \(-\)0.43 & 1.41 & 0.79 \\ PR\_[PERSON] + CMOD5.N & \(-\)1.11 & 1.90 & 0.73 \\ PR\_[PERSON] + CMOD5.N & \(-\)0.76 & 1.38 & 0.84 \\ CMODH\_HH & \(-\)0.40 & 1.19 & 0.85 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of wind speed retrieval results of the GF3-02 HH polarization data based on each GMF model and ERA5 wind data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**GMFs** & **Bias (m/s)** & **RMSE (m/s)** & **Corr** \\ \hline PR\_[PERSON] + CMOD5.N & \(-\)1.62 & 1.91 & 0.80 \\ PR\_[PERSON] + CMOD5.N & \(-\)1.32 & 1.70 & 0.79 \\ PR\_[PERSON] + CMOD5.N & \(-\)2.37 & 2.58 & 0.78 \\ PR\_[PERSON] + CMOD5.N & \(-\)1.31 & 1.67 & 0.81 \\ CMODH\_HH & \(-\)1.12 & 1.52 & 0.80 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of wind speed retrieval results of the GF3-02 HH polarization data based on each GMF model and HY-2B SCA wind data.  
Figure 6: Comparison of wind speed retrieval results from GF3-02 HH polarization data based on (**a**) CMOD5.N+PR_[PERSON], (**b**) CMOD5.N+PR_[PERSON], (**c**) CMOD5.N+PR_[PERSON], (**d**) CMOD5.N+PR_[PERSON], and (**e**) CMODH_HH and ERA5 wind data.
## 2 Problem Statement  
Privacy in WSN is typically categorized into two categories: _data privacy_ and _context privacy_[7, 8, 9, 10]. The data privacy includes _data aggregation_ and _data query_. The context privacy includes _routing privacy_, _identity privacy_, _location privacy_ and _timing privacy_. In this work, we shall focus on using anonymity to provide _location privacy_, which includes two subcategories, source location privacy and base-station location privacy. One of the first works to classify context privacy was done by [PERSON] _et al._[11, 12], where they addressed the panda hunter game. They claim that the routing scheme is responsible for hiding source location of a subject. They have used two metrics to measure SLP: _Safety period_ and _capture likelihood_. Safety period is the number of messages a source sends before it is captured. The capture likelihood is the probability that an adversary can capture the source within a certain period. There are generally two ways to locate a source using passive attacks: _Traffic analysis_[7, 13] and _packet tracing_[7, 14, 15]. The traffic analysis can determine the source or sink locations by analyzing the traffic. Packet tracing can also be used to find the source location since adversaries may use radio-frequency localization techniques to perform a hop-by-hop trace. The adversary can move quickly during packet trace. It could be used to trace mobile nodes due to its fast response compared to traffic analysis [7, 14]. We provide a framework that can be tested against other solutions using the following metrics: (i) _Security_: the probability that the adversary successfully identifies the source, the intermediary SNs or the sink; (ii) _energy cost_; (iii) _storage and memory cost_; (iv) _delivery time_; (v) _safety period_: how long it takes the adversary to capture the first sensor node in the network. Our proposed framework provides a modular system that could be configured for a varietyof network models and for a variety of threat models. The rest of this paper is organized as follows: in Section 3, we give some background and literature survey. In Section 4, we will explain the suggested system model, network model, threat model and the traffic model. In Section 5, we will introduce the anonymity module. In Section 6, we will discuss the module of data authentication and integrity. In Section 7, we will discuss temporal privacy. In Section 8, we will have a thorough security analysis. In Section 9, we will have performance analysis and evaluation. In Section 10, we will summarize our work and suggest some additional development to the framework in the future work.
# E-Sail Three-Dimensional Interplanetary Transfer with Fixed Pitch Angle
###### Abstract  
The electric solar wind sail (E-sail) is a propellantless propulsion system concept based on the use of a system of very long and thin conducting tethers, which create an artificial electric field that is able to deflect the solar-wind-charged particles in order to generate a net propulsive acceleration outside the planetary magnetospheres. The radial rig of conducting tethers is deployed and stretched by rotating the spacecraft about an axis perpendicular to the nominal plane of the sail. This rapid rotation complicates the thrust vectoring of the E-sail-based spacecraft, which is achieved by changing the orientation of the sail nominal plane with respect to an orbital reference frame. For this reason, some interesting steering techniques have recently been proposed which are based, for example, on maintaining the inertial direction of the spacecraft spin axis or on limiting the excursion of the so-called pitch angle, which is defined as the angle formed by the unit vector perpendicular to the sail nominal plane with the (radial) direction of propagation of the solar wind. In this paper, a different control strategy based on maintaining the pitch angle value constant during a typical interplanetary flight is investigated. In this highly constrained configuration, the spacecraft spin axis can rotate freely around the radial direction, performing a sort of conical motion around the Sun-vehicle line. Considering an interplanetary Earth-Venus or Earth-Mars mission scenario, the flight performance is here compared with a typical unconstrained optimal transfer, aiming to quantify the flight time variation due to the pitch angle value constraint. In this regard, simulation results indicate that the proposed control law provides a rather limited (percentage) performance variation in the case where the reference propulsive acceleration of the E-sail-based spacecraft is compatible with a medium- or low-performance propellantless propulsion system.  
e lectric solar wind sail; spacecraft propellantless thruster; continuous-thrust propulsion system; spacecraft trajectory optimization; interplanetary transfer; E-sail constrained guidance law +
Footnote †: journal: Applied Sciences
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 1 Introduction
Yet, there is still no accurate model that has been extensively validated against experimental data. Based on the abovementioned remarks, the objective of the study was to investigate the structural behavior under cyclic loading of RC columns subjected both to uniform and pitting corrosion. The presented remarks highlight the intense effect of pitting corrosion on the structural performance and failure mode of steel reinforcement and the significance of conducting cyclic loadings to structures subjected to deterioration.
## 4 Comparative Analysis with the Existing Online Detection Approaches
### Metric-Based Detector  
Recently, [PERSON] et al. in [4] defined a set of four metrics to detect a kidnapping event. Their work is mainly derived under a deterministic scan matching-based localization called Normal Distribution Transform (NDT) introduced by [PERSON] and [PERSON] in [10]. Then, [PERSON] et al. extended NDT in three-dimensional space [28] and [29].  
Given two sequential point clouds extracted from environment scan \(\mathcal{P}\) and \(\mathcal{Q}\), matrix \(\mathcal{P}_{\mathcal{Q}}\boldsymbol{T}\) estimated by NDT, which transforms coordinate frame \(\mathcal{Q}\) into coordinate frame of \(\mathcal{P}\), and the same transformation estimated by the odometry \(\mathcal{P}_{\mathcal{Q}}\boldsymbol{U}\), the four metrics are defined as follow:  
1. Mean squared error between the two NDT-aligned point clouds, which is written as \[Q_{e}\Big{(}\mathcal{P},\mathcal{Q},\mathcal{P}_{\mathcal{Q}}\boldsymbol{T }\Big{)}=\frac{1}{n}\sum_{i=1}^{n}\|\overrightarrow{p}_{i}-\overrightarrow{ q}_{i}\|^{2}\] (13) where \(n\) is the number of overlapping points, \(\overrightarrow{p}_{i}\) is a point in \(\mathcal{P}\), and \(\overrightarrow{q}_{i}\) the nearest-neighbor point of \(\overrightarrow{p}_{i}\).
2. The likelihood that the transformed point cloud \(\mathcal{Q}\) lies on the surface of NDT of \(\mathcal{P}\), that is \[Q_{s}\Big{(}\mathcal{P},\mathcal{Q},\mathcal{P}_{\mathcal{Q}}\boldsymbol{T }\Big{)}=\frac{1}{n}\sum_{i=1}^{n}\tilde{p}\bigg{(}\mathcal{Q}\boldsymbol{T},\overrightarrow{b}_{i}\bigg{)}\] (14) where \(n\) is the number of points of \(\overrightarrow{b}_{i}\) in \(\mathcal{Q}\).
3. Standard deviation from the least known pose degree of freedom, calculated from maximum eigenvalue \(\lambda\) of NDT optimization function. \[Q_{h}\Big{(}\mathcal{P},\mathcal{Q},\mathcal{P}_{\mathcal{Q}}\boldsymbol{T }\Big{)}=\sqrt{\underset{i=1:v}{max}\lambda_{i}}\] (15) where \(v\) is degree of freedom of a robot's pose.
4. Mean squared error of point clouds \(\mathcal{Q}\) transformed by \(\mathcal{P}_{\mathcal{Q}}\boldsymbol{T}\) and \(\mathcal{P}_{\mathcal{Q}}\boldsymbol{U}\), \[Q_{h}\Big{(}\mathcal{Q},\mathcal{P}_{\mathcal{Q}}\boldsymbol{T},\mathcal{P} _{\mathcal{Q}}\boldsymbol{U}\Big{)}=\frac{1}{n}\sum_{i=1}^{n}\| \overrightarrow{q}_{i}-\overrightarrow{r}_{i}\|^{2}\] (16)
## 6 Simulation Results
Under the RSSC control algorithm, it can be seen that the convergence time of the base attitude stabilization and the tracking control of the joints is within 10 s. However, the convergence time under the RSC-NV algorithm is more than 30 s. The RSSC algorithm has a faster convergence speed than the RSC-NV algorithm. Therefore, the system has better control performance under the RSSC algorithm than the RSC-NV control method. Figure 4: Trajectory tracking curve of the system joint \(O_{2}\). Figure 3: Trajectory tracking curve of the system joint \(O_{1}\). Figure 2: Trajectory tracking curve of the system base attitude. The first mode coordinates of the flexible link \(B_{1}\) are 2 mm under the RSC-NV algorithm, and they are 0.05 mm under the RSSC algorithm, as depicted in Figure 8. Figure 9 shows  
Figure 8: The first mode coordinates of the flexible link \(B_{1}\). Figure 10: The first mode coordinates of the flexible link \(B_{2}\). Figure 9: The second mode coordinates of the flexible link \(B_{1}\). that the second mode coordinates of the flexible link \(B_{1}\) are 0.5 mm under the RSC-NV algorithm, and they are 0.05 mm under the RSSC algorithm. Meanwhile, from Figure 10, one can see that the first mode coordinates of the flexible link \(B_{2}\) is 1 mm under the RSC-NV algorithm, and it is 0.2 mm under the RSSC algorithm. Figure 11 shows that the second mode coordinates of the flexible link \(B_{2}\) is 0.2 mm under the RSC-NV algorithm, and it is 0.02 mm under the RSSC algorithm. All of the mode coordinates are obviously suppressed under the RSSC, which illustrates the effectiveness of the control schemes in the vibration suppression of system links.
## 4 Discussion
### Effects of Environmental Factors on the IWUE in Different Ecosystems  
There was a significant correlation between temperature/Rg and IWUE across all forest ecosystems, consistent with the study in a tropical monsoon forest in western Thailand [31; 32; 33]. However, we found no significant relationship between IWUE and annual precipitation, except in EBF. One explanation is that the evapotranspiration of forest ecosystems was not limited by precipitation. The WUE of forests has a certain threshold effect on precipitation in areas with water scarcity [34], which might explain the poor relationship between precipitation and IWUE. [PERSON] et al. found that the IWUE of young trees was more sensitive to precipitation than old ones, indicating that the EBF stands were younger than the other forests [35]. In a previous study, the sensitivity of broad-leaved forests to environmental changes was higher than that of needle-leaved forests [8]. This can also explain that only the IWUE of EBF had a significant impact on precipitation. Although temperature and solar radiation explained 57.3% and 25.0% of the changes in IWUE, respectively, the correlation between \(\Delta\)IWUE and meteorological factors showed that precipitation controls the interannual variation of the IWUE forest ecosystems. This leads us to infer that precipitation was the main factor limiting carbon fixation and plant growth in forest ecosystems. The adjustment of photosynthesis and transpiration may also be an adaptive strategy to changes in precipitation.  
The factors affecting the WUE of forest ecosystems are highly complex and not only affected by external environmental conditions but also by the plants themselves [18]. For example, the LAI values were significantly negatively correlated with the IWUE values of broad-leaved forests but not with those of needle-leaved forests. Possibly, the increase in transpiration was higher than that in carbon acquisition since radiation was blocked by overlapping leaves of broad-leaved forests. Needle-leaved tree species were not affectedby LAI because of the elongated leaves and the large specific surface area, increasing the amount of radiation received [26]. In addition, several factors also affected the IWUE, such as CO\({}_{2}\) concentration, nitrogen deposition, stomatal conductance, genotype, and age, indirectly affecting the WUE. Further studies on the above factors are therefore needed to obtain a deeper insight into these issues.