esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Results
#### 3.2.2 Deviation Correction Control Strategy Verification Experiment  
The purpose of this experiment is to verify the control accuracy of the proposed point position correction algorithm and the stability of the carrier in the correction process. The experiment uses the crawler robot to simulate the roadheader. The laser navigation system proposed in this paper is used to measure the X coordinate (lateral offset), Y coordinate (forward distance) and yaw angle of the robot in real-time. In the experiment, a ruler was placed to mark the road centerline and used as the Y axis. By taking the laser emitter position as the origin, as shown in Figure 14, a reference coordinate system is established with the right side of the vertical Y axis as the X axis. During the experiment, the pose parameters measured by the navigation system are transmitted to the controller. According to the current pose, the left and right trajectory speeds are controlled according to the deviation correction control strategy proposed in this paper.  
According to the road driving technology, the footage of each driving cycle is set to 1 m, and the maximum deviation correction amount of each driving cycle is 0.05 m. Three sets of initial conditions are established, as shown in Table 5. The three groups differ in thier initial conditions: the predicted deviation may be less than the initial deviation and the predicted deviation may be greater than the initial deviation. Thirdly the predicted deviation may be greater than the initial deviation and hence the initial deviation may be greater than the maximum correction amount. For the initial conditions of group 1 and group 3, according to the point position correction strategy, the deviation correction must be completed in two driving cycles, hence the Y coordinate of the endpoint is set to 2 m.  
Figure 17: Pose parameter monitoring data.  
Through comparison, two findings become apparent. Firstly, under different initial conditions, there is no difference in the position deviation and angle deviation at the endpoint by using the point position correction strategy or the conventional correction strategy. When applying the point position correction strategy, the maximum position deviation is 13 mm and the maximum angle deviation is 0.16\({}^{\circ}\). When applying the conventional deviation correction strategy, the maximum position deviation is 9 mm and the maximum angle deviation is 0.14\({}^{\circ}\). Secondly, under different initial conditions, the maximum angular velocity is less than 0.07 rad/s in the process of using the point correction strategy. The maximum angular velocity is 0.37, 0.46 and 0.82 rad/s, in the process of using the conventional correction strategy. This maximum angular velocity is a much greater angular velocity than when adopting a point correction strategy. The experimental results show that the point position correction strategy has good motion control accuracy, and the motion stability of the roadheader is better than the conventional correction strategy.
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
## 2 Methodology
### Statistics Analysis
#### 2.2.2 Multi-Collinearity Analysis  
When there is a high correlation between the independent variables in the multi-regression calculation, the problems of multi-collinearity may occur, which may decrease reliability of the regression coefficient estimates. Multi-collinearity can be evaluated through a variance inflation factor (VIF) such as that given in Equation (1).  
\[\text{Tolerance}=\left(1-\text{R}^{2}\right),\ \text{VIF}=\text{Tolerance}^{-1} \tag{1}\]  
The larger the VIF, the smaller the tolerance, and the more likely it is multicollinear. If the VIF is greater than 5, it is judged that the possibility of multi-collinearity is high. In general, if the VIF is greater than 10, it is determined that multi-collinearity is problematic [52].  
Multi-collinearity analysis is performed on six cases consisting of linear regression expressions of six TAs, which are classified into three types of representative rock (Table 2). Table 2 shows the results of the analysis by sequentially removing variables that cause multi-collinearity problems. As a result of the analysis, there is a multi-collinearity problem in Case No. 1 and 2, and Case No. 3-6 have a VIF of 4 or less. Thus, there is no multi-collinearity problem.
## 2 Single-Stage LGG Overview  
In this section, an overview of the single-stage LGG being developed for the MCAST Impacts Facility is presented; in addition, the preliminary layout of the facility is described. The design of the single-stage LGG takes into account the following objectives:  
* To establish a high-velocity impact test facility that can conduct testing for the aviation and space industries. The selected range of projectile velocities is suitable for testing impacts on aircraft parts as well as simulating low-speed impacts, like those observed in GEO orbit [41]. The LGG aims at replicating mainly impacts due to metal impactors, mainly aluminium alloys [42]. Its modular setup can also be adapted to other types of projectiles simulating space debris, such as plastics and silica materials.
* To set up a laboratory capable of working in conjunction with other European hypervelocity research centres, such as the one at the University of Padova [40]. The Centreof Studies and Activities for Space \"Giuseppe Colombo\" (CISAS) at the University of Padova hosts a world-class experimental facility for hypervelocity impacts (HVI), utilizing a two-stage LGG system. This two-stage LGG is capable of launching projectiles weighing up to approximately 100 mg at velocities of up to 5.5 km/s. Lower velocities of up to 2.5 km/s can be reached for impactors that are heavier (up to 1 g). The MCAST single-stage LGG facility, which has been developed to accommodate higher launch package weights ranging from 15 to 40 g and enable testing at lower speeds, complements this by expanding the operational range of test campaigns. The Malta facility also has a larger impact testing chamber than the one at CISAS, providing greater experimental versatility. This increased capability makes it easier to conduct comprehensive studies on novel materials that are suitable for use in aviation and space applications.
* Establishing an LGG facility is to achieve a cost-effective experimental setup that delivers high performance and high frequency while minimizing installation and maintenance expenses. In the following subsection, the main layout will be described.
## 2 Materials and Methods
### Data Sources and Preprocessing  
#### 2.3.1 Multi-Source Data  
Detailed information about the data used in this study is presented in Table 2. The cultivated land data for Guangzhou were obtained from China's National Land Use and Cover Change (CNLUCC) dataset. The dataset was generated using a uniform classification method with visual interpretation based on Landsat images, and it was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The overall accuracy of the dataset was >90% [59].  
The NDVI was calculated to evaluate the soil fertility of the cultivated land based on the Landsat Thematic Mapper/Operational Land Imager (TM/OLI) data in ENVI 5.3. However, due to the quality of Landsat remote sensing image being not good (cloud cover was greater than 10%) in 2018, we chose a high-quality Sentinel-2 image with higher spatial resolution (10 \(\times\) 10 m) as a substitute. To maintain the same resolution as the Landsat images, the Sentinel-2 image was resampled to 30 m.  
Using ArcGIS (version 10.6; Software for Spatial Analysis; Environmental Systems Research Institute, Redlands, CA, USA, 2019), the slope of the cultivated land was extracted based on the Digital Elevation Model (DEM) data, and the distance from each cultivated land pixel to the nearest road was calculated. In this study, the Fragstats (version 4.2; Software for Categorical Map Patterns; Oregon State University, Corvallis, OR, USA, 2017)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Indicator** & **Source** & **Year** & **Attribute** & **Resolution** \\ \hline Landsat/ & & USGS Earth Resources Observation and Science Center & 2009–2011; & Raster & 30 \(\times\) 30 m \\ Sentinel-2 images & & & ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)). & 2014–2019. & Raster & 10 \(\times\) 10 m \\  & & & accessed on 11 August 2020) & & & \\  & & & & & \\  & & & & & \\ DEM & Slope & ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)). & 2011 & Raster & 30 \(\times\) 30 m \\  & & & accessed on 12 October 2020) & & & \\ Harmonized & & & FAO Soils portal ([[http://www.fao.org/](http://www.fao.org/)]([http://www.fao.org/](http://www.fao.org/)) soils-portal, & & \\  & & & accessed on 10 October 2020) & & & \\  & & & & OpenStreetMap & & \\ Road vector data & Road accessibility & ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)), & & & \\  & & & accessed on 18 October 2020) & & & \\ China’s National & & Centralized & Resource and Environment Data Cloud Platform & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table}
Table 2: Data information.  
was used to calculate the Contig landscape index. The calculation of the mean and CV of the NPP was conducted in ENVI 5.3. To unify the evaluation unit, all data were resampled to 30 m.
## 2 Results and Discussion
### Factors Influencing the T/O Compound Variation in the Eastern Drinking Water Source of Chaohu Lake  
Correlation coefficients were employed to assess the impact of biological, physicochemical, and organic indices on the temporal variability of T/O compounds in the eastern drinking water source of Chaohu Lake (Table 1). Notably, all four T/O compounds showed no significant correlation with cyanobacterial densities and Chl-\(a\) concentrations, suggesting independence from overall cyanobacterial abundance. However, specific associations emerged; MIB correlated positively with _Microcytsis_ spp., and ION showed a positive relationship with _Dolichospermum_ spp. Both GSM and MIB displayed significant inverse correlations with _Dolichospermum_ spp., indicating a nuanced interaction. In terms of physicochemical indices, MIB inversely correlated with dissolved oxygen and positively correlated with temperature, pH, and conductivity, suggesting sensitivity to these parameters. GSM exhibited a positive association with conductivity. Nutrient-wise, GSM and MIB correlated positively with nitrate and ammonia nitrogen, respectively, while ION had a negative correlation with total phosphorus, indicating nutrient influence. Regarding organic indices, MIB correlated positively with the potassium permanganate index and dissolved organic carbon, linking its concentration to water's organic content. Figure 1: Seasonal variations of dissolved and particulate geosmin (**a**), 2-methyl isoborneol (**b**), \(\beta\)-cyclodextrin (**c**), and \(\beta\)-ionone (**d**) in the eastern drinking water source of Chaohu Lake. Dotted lines indicate the OTCs of 4, 15, 19.3, and 7 ng\(\cdot\)L\({}^{-1}\) for geosmin, MIB, \(\beta\)-cyclodextrin, and \(\beta\)-ionone, respectively, [PERSON] (2004); Error bars indicate the standard deviation of three sampling sites. The utilization of redundancy analysis systematically identified key environmental factors influencing the seasonal fluctuations of both dissolved and particulate T/O compounds. Among the fifteen examined variables, five, namely the densities of _Microcytsis_ spp. and _Dolichospermum_ spp., water temperature, pH, and nitrate, significantly contributed to the variance of T/O compounds (\(p<0.05\)) (Figure 2). The outcomes indicated a positive correlation between dissolved MIB and ION, both of which exhibited high associations with the cell density of _Microcytsis_ spp. and water temperature. Additionally, a positive correlation emerged between dissolved GSM and CYC, with the former positively and the latter negatively correlated with nitrate and pH, respectively. Moreover, particulate GSM and ION, along with particulate MIB and CYC, exhibited positive interrelations. All these particulate compounds were positively correlated with pH and the concentrations of _Dolichosperm_ spp.
# Crystallinity and Molecular Packing of Small Molecules in Bulk-Heterojunction Organic Solar Cells
## 5 Conclusions  
The advent of the DA-type donors, particularly DTS, oligothiophene, BDT SM donors, marked the start of a new era in SM-BHJ in a way that the crystallinity and, more importantly, the packing of molecules shifted to a more disordered liquid-crystalline-like type. This trend is likely the result of incorporating a large number of alkyl chains in the chemical structure of the donors with respect to early SM donors. Indeed, it is remarkable that SM donors that led to efficiencies above 7% have a ratio of aliphatic to aromatic carbons often superior to one. Therefore, it is consistent with the fact that the added disorder incurred by the alkyl chains likely directs intermolecular interactions towards exclusive \(\pi\)-\(\pi\) stacking and hydrophobic interactions. The disordered liquid crystalline-like packing type is consequently greatly favoured. Although the link between this increase in disorder brought about by the numerous alkyl chains and the higher PCEs is counterintuitive, it can be readily rationalised by the fact that an ordered packing type is rather difficult to achieve in such a complex medium as that of OSCs' active layers. As demonstrated in our previous study [64], the subtle thermodynamics and kinetics parameters underpinning the nucleation and growth processes are extremely difficult to control. As a consequence, the crystallites formed in such active layers often display extreme features, e.g., either too small or too large crystallites with sub-optimal anisotropic features. Liquid-crystalline-like packing SMs arise from limited types of interactions, namely p-p stacking and alkyl chain intermolecular interactions, forming much more predictable stacks of molecules. Despite the fact that disorder is more predominant in the crystalline domains, the gain in crystallisation control outcompetes the former setback to a large extent. The system is more forgiving towards the formation of homogeneously distributed (size and orientation) crystalline domains, hence the consistent increase in PCE over the years.  
Figure 9: (a) X-ray diffraction single crystal structure of BTR, with the alkyl side chains omitted for clarity; (b) 2D-WAXS of BTR filament measured at 30 \({}^{\circ}\)C; (c) GIWAXS of spin-cast BTR thin film on silicon (p-stacking reflection is indicated by an arrow). Adapted with permission from the Nature Publishing Group, copyright 2015 [82].  
Funding: The authors thank E2S UPPA for awarding the INTERMAT international chair to E.P. Conflicts of Interest: The authors declare no conflict of interest.
# Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints
## 6 Mission Planning Simulation
### Simulation of Mobile Mission Midpoint Planning  
According to the planner prosed in Section 5, we can add an intermediate point in the task and construct its planning modeling to achieve the task motion planning in Figure 7. After searching the path, the planned trajectory results are shown in Figure 8. We added one intermediate point with coordinates of (2.77 m, 3.11 m, 0.50 m, \(-85.37^{\circ}\), 29.79\({}^{\circ}\), 76.78\({}^{\circ}\)) and corresponding joint angles of (93.74\({}^{\circ}\), \(-92.40^{\circ}\), 31.21\({}^{\circ}\), 67.04\({}^{\circ}\), 54.97\({}^{\circ}\), \(-86.49^{\circ}\), 70.01\({}^{\circ}\)). By adding intermediate points, the collision and interference during direct movement could be avoided, and the task path was automatically optimized.  
When there was no task planning, the direct path planning between the starting point and the target point led to a collision between the manipulator and the cabin, as shown in Figure 9. The joint and end motion curves of the manipulator are shown in Figure 10 and met the constraints.  
Figure 8: Movement trajectory with task planning. (**a**) Starting point. (**b**) Intermediate point. (**c**) Target point.  
Figure 7: Initial state of task planning.  
Figure 9: Movement trajectory without task planning.  
Figure 10: _Cont._  
Other simulation cases: the task planning module followed the optimal path constraints and could add intermediate points to achieve the purpose of planning the optimal path that meets the constraints.  
The initial joint angle of the manipulator was (\(-90^{\circ}\), \(103^{\circ}\), \(114^{\circ}\), \(126^{\circ}\), \(169^{\circ}\), \(-3^{\circ}\), \(0^{\circ}\)), the pose of the target point was (8.2 m, 3.47 m, 1.5 m, \(-167.88^{\circ}\), \(24.207^{\circ}\), \(43.487^{\circ}\)). The position search step was 0.5 m. As shown in Figure 11, collision-free intermediate points were automatically added to the task planning results.  
It can be seen from the simulation results that the motion goal of the manipulator was to achieve the accessibility of the given position and attitude. When considering the constraints, such as cabin interference, manipulator configuration and operation flexibility, the middle point of the motion process was added. On the basis of hierarchical task planning, the optimization of motion strategy was realized.
## 1 Introduction
It can manipulate the band structure effectively [36] and can be used as a bandgap switch for the metamaterial structure [37; 38]. [PERSON] et al. [39] found that the initial compressive stress will cause the bandgap to move towards low frequencies, while the initial tensile stress will cause the bandgap to move towards high frequencies. The influence of configuration changes in the bandgap of CCPM in article [11] has been discussed in detail. However, the numerical calculation results of the bandgap are not very consistent with the experimental results, and there is a significant difference between them. In fact, initial stress occurs while the configuration changes, and the initial stress can have a certain influence on the bandgap of the structure [36; 37; 38; 39]. And the initial stress has a more significant impact on CCPM. In this paper, initial stress refers to the average axial stress when axial loading displacement is applied to the structure in our manuscript. We use the same specimen of paper [11] to analyze the initial stress distribution and investigate the influence of the initial stress on the bandgap structure of the CCPM. The experimental platform was built to study the distribution of the initial stress of the CCPM under axial displacement, and the infinite element model of the CCPM was founded. Numerical results have a good agreement with the experimental data. In addition, the influence of the bandgap characteristics on the CCPM was studied, and the results were compared with that of paper [11]. Through the comparison, it is found that the bandgap structure considering the influence of the initial stress is more consistent with the experiment results of the transmittance spectra. Moreover, the specific effect of initial stress on the CCPM bandgap will be explored and discussed in detail.
# Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
## 2 Materials and Methods
### Modified Hyperbolic Model

Table 3: Direct shear specimen trials for various percentages of gypsum and RHA as a primary and complementary additive, respectively, in expansive soil. The introduction of volumetric strain parameters in the modified hyperbolic model begins with the relationship of the volumetric strain (\(\varepsilon_{\
u}\)) against the shear strain (\(\gamma\)). The hyperbolic function in Equation (4) determines this behavior. \[\varepsilon_{\
u}=\frac{\gamma}{\alpha+\beta\gamma} \tag{4}\]  
Parameters \(\alpha\) and \(\beta\) are constants derived from test data; \(\alpha\) is the intercept of the linearly fit line in a \(\gamma/\varepsilon_{\
u}\) versus \(\gamma\) graph, while \(\beta\) is the slope of the same line. These values can also be expressed in a power equation as a function of the normal effective stress, as presented in Equations (5) and (6). \[\alpha=K_{a}P_{a}\bigg{(}\frac{\sigma^{\prime}}{P_{a}}\bigg{)}^{m} \tag{5}\]  
\[\beta=K_{b}P_{a}\bigg{(}\frac{\sigma^{\prime}}{P_{a}}\bigg{)}^{r} \tag{6}\]  
In these equations, \(K_{a}\) and \(K_{b}\) are volumetric strain numbers, while \(m\) and \(r\) are volumetric strain exponents. These constants are determined in normalized logarithmic plots of \(\alpha/P_{a}\) and \(\beta/P_{a}\) against \(\sigma^{\prime}/P_{a}\). The volumetric strain numbers are the equivalent normalized \(a\) and \(\beta\) for a normalized confining stress of 1 atmosphere. On the other hand, the volumetric strain exponents are the slope of the resulting straight-line function in the normalized logarithmic plot. Meanwhile, the failure shear stress is identified through a function of normal effective stress, as expressed with Equation (7). \[\tau_{f}=\sigma^{\prime}tan\phi^{\prime} \tag{7}\]  
The angle of internal friction in Equation (8) varies logarithmically with the effective stress given the non-linearity of the failure envelope. This expression is shown as:  
\[\phi^{\prime}=\phi_{o}-\Delta\phi log\bigg{(}\frac{\sigma^{\prime}}{P_{a}} \bigg{)} \tag{8}\]  
Summing all modified hyperbolic parameters in a predictive function, the model can estimate the stress-strain response of the test specimen. The shear stress and the volumetric strain can be approximated through the following equations:  
\[\tau=\frac{\gamma}{\frac{1}{KP_{a}\bigg{(}\frac{\sigma^{\prime}}{P_{a}}\bigg{)} ^{n}}+K_{f}\bigg{(}\frac{\gamma}{\tau_{f}}\bigg{)}} \tag{9}\]  
\[\varepsilon_{\
u}=\frac{\gamma}{\frac{1}{K_{a}P_{a}\bigg{(}\frac{\sigma^{ \prime}}{P_{a}}\bigg{)}^{m}}+\gamma K_{b}P_{a}\bigg{(}\frac{\sigma^{\prime}}{ P_{a}}\bigg{)}^{r}} \tag{10}\]
## 3 Results and Discussion
### Xrd  
Figure 3 illustrates the XRD patterns of cement-based materials subjected to sulfate attack. As shown in Figure 3a, the primary phases at day 0 include hydrated calcium silicate, quartz, ettringite, calcite, gypsum, and portlandite gel. In the absence of erosion, neither gypsum nor ettringite is detected in the XRD diffraction patterns. As depicted in Figure 3b, with prolonged erosion exposure, diffraction peaks corresponding to gypsum and ettringite become evident, confirming that these compounds are byproducts of sulfate-induced degradation in cement-based materials. Initially, ettringite and gypsum fill the internal pores within the specimen; however, as their concentrations exceed a critical threshold, internal expansion occurs within the cement matrix. When this expansion force surpasses the tensile strength of the specimen, cracking is initiates. The flexural and compressive strengths of the cement-based material specimens initially increase but subsequently decrease.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 2 Threat Intelligence Background
### CTI Standards
#### 2.3.3. Stix  
Structured Threat Information eXpression [19] is a language developed by MITRE to share TI information within stakeholders of an ecosystem. It is used to acquire, classify and share any data related to an attack campaign, providing tools and patterns to represent information in such a manner in which CTI data can be efficiently used by security professionals inside cyber threat intelligence Tools.  
It allows users to standardize unstructured data and individual IOCs inside a machine-readable language in which automation and interoperability can be applied. The first version of STIX (STIXv1) was XML-based and had eight cores. The second and current version of STIX (STIXv2) is a JSON-based language [20] with various architectural improvements to become close to a CTI environment.