esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a New Supersonic Shock Wave Generator and Application in Power Generation
## 3 Supersonic Shock Wave Monitoring
### Experimental Analysis of Supersonic Shock Wave Monitoring
Input pressure and output pressure: We fixed the input pressure \(p_{in}\) at 5 kg/cm\({}^{2}\) in the shock wave monitoring. However, the maximum peak voltage of the pressure pulse measured appeared at the peak. Therefore, the output pressure \(p_{out}\) of the shock wave was calculated from the maximum peak voltage (\(V_{fpeak}\)) of the front-end pressure pulse or the maximum peak voltage (\(V_{\textit{peak}}\)) of the rear-end pressure pulse. The equation is as follows: \[V_{\textit{peak}}=\text{max}(V_{\textit{fpeak}},V_{\textit{peak}})\] (21) \[p_{out}=p_{in}+\frac{V_{\textit{peak}}-V_{\textit{min}}}{V_{max}-V_{\textit{ min}}}(P_{max}-P_{min})\] (22) Since \(V_{\textit{fpeak}}\) = 6.756 V and \(V_{\textit{peak}}\) = 6.728 V, \(V_{\textit{peak}}\) = 6.756 V. And, \(V_{\textit{min}}\) = 0 V, \(V_{\textit{max}}\) = 7.5 V, \(P_{min}\) = 0 kg/cm\({}^{2}\), and \(P_{max}\) = 1054 kg/cm\({}^{2}\); therefore, the calculated output pressure is \(p_{out}\) = (6.756 V/7.5 V) \(\times\) 1054 kg/cm\({}^{2}\) = 945.51 kg/cm\({}^{2}\). We can compare the output pressure of the shock wave measured by this monitoring system with the value measured by the magnetic output pressure gauge on the shock tube for verification. The data measured by the output pressure gauge on the shock tube were all about 945 kg/cm\({}^{2}\), verifying the pressure measured by this monitoring system. * Magnification ratio: The pressure magnification ratio \(r_{p}\) is the ratio of the output pressure \(p_{out}\) to the input pressure \(p_{in}\), namely \[r_{p}=p_{out}/p_{in}\] (23) Therefore, the pressure magnification ratio \(r_{p}=945.51/5=188.3\) (times). In this study, 20 shock wave monitoring experiments were conducted. We fixed the input pressure at 5 kg, and the system calculated the time difference (\(\Delta t\)) between the front and rear ends, velocity (\(v_{x}\)), Mach (\(M_{x}\)), output pressure (\(p_{out}\)), and magnification ratio (\(r_{p}\)) in each experiment. A list of the 20 experiment results is shown in Table 1. According to Table 1, for the 20 shock wave monitoring experiments, the average speed was 1480.33 m/s and equivalent to Mach 4.36, the average pressure was 943.31 kg/cm\({}^{2}\), and the average magnification ratio was 188.7 times. On the other hand, the table also shows that the shock wave speed was proportional to the pressure generated; the higher the speed, the higher the pressure. \begin{table}
\begin{tabular}{c c c c c c} \hline
**No. ** & \(\Delta t\) **(s)** & \(v_{x}\) **(m/s)** & \(M_{x}\) **(Mach)** & \(p_{out}\) **(kg/cm\({}^{2}\))** & \(r_{p}\) \\ \hline
1 & 0.00025 & 1400 & 4.12 & 935.7 & 187.14 \\ \hline
2 & 0.00024 & 1458.33 & 4.29 & 942.85 & 188.57 \\ \hline
3 & 0.00024 & 1458.33 & 4.29 & 943.27 & 188.65 \\ \hline
4 & 0.000
## 3 DTN Solutions for IoT  
It is known that IoT environments face many challenges such as availability, reliability, interoperability of heterogeneous systems, and power management [18]. One major challenge is the ability to cope with the high level of dynamism in the network. In recent years, DTN related research has been identified as potential enablers for the IoT [19], on that account a number of DTN solutions for IoT and similar domains especially Wireless Sensor Network (WSN) are found in the literature. In this section, we first motivate the importance of enabling delay tolerant schemes for IoT applications. We propose then a taxonomy of DTN solutions for IoT then we review various existing solutions.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 2 SEG Process Description and Model Development
### Chemical Reactions  
Table 1 lists all the chemical reactions considered in the model, including equations to calculate the reaction rates. The pyrolysis step (reaction 1) is modelled with a one-step reaction kinetic considering the products: Ash, char, H\({}_{2}\)O, gases (CO\({}_{2}\), CO, CH\({}_{4}\), and H\({}_{2}\)), non-condensable hydrocarbons (simplified as C\({}_{2}\)H\({}_{4}\)), and tars (simplified as Naphthalene: C\({}_{10}\)H\({}_{8}\)). For the mass fractions \(\omega_{j}\), experimental data from [PERSON] et al. [34] were used and interpolated to consider a temperature-dependent pyrolysis product composition. The values for the amounts of tar, however, originate from experiments with the 200 kW DFB system [19] and, hence, secondary pyrolysis reaction modelling was not required. With this assumption, catalytic effects of CaO on tar conversion are indirectly considered by measured concentrations. Residual char was handled as a mixture of C, H, and O and, according to the char analysis from [PERSON] et al. [34], the composition was also interpolated for different gasification temperatures. It is worth noting that the used elemental analysis of wood pellets (C: 48.99 wt-\(\%_{\text{waf}}\), H: 6.97 wt.-\(\%_{\text{waf}}\), O: 44.04 wt.-\(\%_{\text{waf}}\)) differed from the analysis of biomass in [PERSON] et al. [34]. Thus, yields from pyrolysis \(\omega_{j}\) needed to be adapted, in order to satisfy the elemental balance. Therefore, a linear equation system for the elements C, H, and O had to be solved. While coefficients from C\({}_{10}\)H\({}_{8}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), H\({}_{2}\)O, and char were fixed, the coefficients of CO, CO\({}_{2}\), and H\({}_{2}\) were fitted to close the elemental balance of wood pellets. Results for the mass fractions \(\omega_{j}\) are listed in Table 1. The reaction kinetics of ethene reformation (reaction 6) were adapted to fit the simulated H\({}_{2}\) and CO concentrations to experimental values from the 200 kW DFB pilot plant. For the carbonation reaction, sorbent deactivation, which is dependent on the number of calcination-carbonation cycles, was taken into account through the parameter \(X_{ave}\). Details of the calculation of \(X_{ave}\) are described in the subsequent section. For all gasification reactions, it is assumed that they occur only in the emulsion phase, due to the catalytic behavior of CaO and char, which enhance reaction rates compared to those in the gas phases [42]. \begin{table}
# Shear Mechanical Properties of Bolt-Grout Interface under Different Bolt Surface Profiles
## 3 Test Results
### Shear Dilation Behavior  
Since the normal displacement curves exhibited a similar trend for specimen under different normal stress conditions, we take the normal stress of 2 MPa as an example to illustrate the shear dilation behavior of Bolt-Grout interface. Figures 14 and 15 illustrate the normal displacement-shear displacement curves for different rib spacings and rib angles under a normal stress of 2 MPa. When the rib spacing is small, such as 12 mm and 20 mm, the normal displacement decreases because the gap at the Bolt-Grout interface is compacted in the early phases; afterward, the normal displacement progressively rises due to the shear dilation; finally, due to the existence of the \"climbing effect\", the normal displacement exhibits a cyclical pattern of initial decrease followed by a subsequent increase. For larger rib spacing, such as 28 mm, 36 mm and 44 mm conditions, the trend of normal displacement is approximately the same, and all show a tendency to decrease and then increase before leveling off. The rib spacings have different effects on the normal displacement in the final stage of shear, which is mainly due to the increase in rib spacings, the number of ribs in the interface decreases, so that the \"climbing effect\" is no longer obvious, and the number of cyclic changes in normal displacement decreases.  
The normal displacement-shear displacement curves under different rib angle conditions have roughly the same morphology, and the normal displacements show a tendency to decrease and then increase and then level off, as shown in Figure 12. In the early stage of shear, the difference in normal displacement under different rib angle conditions is small, which is because the change in normal displacement at this stage is caused by the compression density and elastic deformation, and the internal cleavage and elastic modulus of each specimen are the same again. As the shear displacement increases, the discrepancy in the  
Figure 14: Normal displacement-shear displacement curves of Bolt-Grout interface under different rib spacings (\(\sigma_{\text{n}}\) = 2 MPa).  
Figure 15: Normal displacement-shear displacement curves of Bolt-Grout interface under different rib angles (\(\sigma_{\text{n}}\) = 2 MPa).  
normal displacement among various rib angles becomes more pronounced, the normal displacement was the largest at 15\({}^{\circ}\) and the smallest at 30\({}^{\circ}\), and with the increase in rib angle, the normal displacement showed a tendency of decreasing first and then increasing. This is because shear expansion and slippage failure mainly occur at 15\({}^{\circ}\), and there is no shear fracture failure, and the normal displacement is larger, and the phenomenon of raised shear-break failure occurs at 30\({}^{\circ}\), and the normal displacement is smaller.
# Photogrammetry-Based 3D Textured Point Cloud Models Building and Rock Structure Estimation
## 6 Summary and Conclusions  
A low-cost method of obtaining photogrammetry-based, textured, 3D point cloud models was provided with two open-source software programs, Meshroom and Meshlab. A coordinate transformation means of converting model coordinates to physical coordinates was introduced, and the corresponding computer code was supplied. The length accuracy and angle accuracy of a textured 3D point cloud model obtained with the method were verified, taking the data of a total station as the reference. The accuracy of the total station was illustrated by an outdoor experiment. A section of a rock slope located in Dalian city was selected as the case study to represent the processes of textured 3D point cloud model building and the applicability of the method to rock structure estimation. The findings are summarized as follows.  
1. The accuracy of the total station is enough as a reference for verifying the accuracy of the 3D point cloud model. The accuracy of the point coordinates of the 3D point cloud model could achieve 3.96 mm. The accuracy of the dip, dip direction, and length are 0.315 degrees, 0.69 degrees, and 0.51 mm, respectively, at a distance of 10 m.  
Figure 13: Distribution of the lengths of intact rock core pieces of the joint disk model.  
2. The accuracy of the 3D point cloud model is high enough for engineering applications, taking the coordinates obtained by the total station as a reference. The accuracies of the length, dip, and dip direction are 1.57 mm, 0.38 degrees, and 0.42 degrees, respectively.
3. The method of building a photogrammetry-based 3D point cloud model is applicable. A total of 2993 joint traces of the rock slope were successfully extracted, and the rock structure was estimated with a joint disk model built according to the traces.
4. The rock structure of the slope is good, according to the value of the rock quality designation. The joints of the slope can be grouped into three sets. The joints in set 1 are generally subhorizontal, with a mean dip value of 14.7\({}^{\circ}\), while the joints in sets 2 and 3 are nearly vertical, with mean dip values of 76.6\({}^{\circ}\) and 81.5\({}^{\circ}\), respectively.  
Methodology, T.L.; Writing--review & editing, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number No. U19A2098. The APC was funded by No. U19A2098.  
Not applicable.  
The authors thank [PERSON] and [PERSON] for their contributions to the data collection and revision processes.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.3 Delegatecall to Insecure Contracts  
Delegatecall is a special function in Solidity. It is the same as the call function with one major difference. The delegatecall re-uses codes of the called contract and executes them in the caller's context. For example, in the following code excerpt in Listing 2 (modified based on [77]), msg.sender, msg.data in the contract VulneroContract will be the caller's address and data, and the state variables will use the caller contract's. Once the contract InsecureContract is called via delegatecall, the function dsSomething is executed in the caller's context in which the caller's state variable owner in the contract ValernableContract will have an unexpected modification. The VulnerContract block does not provide any way to change its ownership once it is constructed. However, when the attacker calls this contract at a certain point of execution, it will delegatedcall InsecureContract in which the state variable owner is modified to the new value of msg.sender, which is the address of the attacker. In other words, delegatecall gives other contracts, such as InsecureContract, permission to alter its own state variables, such as address public owner. This is because the delegatecall function preservers context.  
```
contractVulernableContract{ addresspublicowner; InsecureContractpublic; constructor(InsecureContract_c)public{ owner=msg.sender; c=InsecureContract(_c); }/*Othercodens...*/ c.delegatecall(abi.encodeWithSignature(''doSomething()'')); /*Othercodens...*/ } contractInsecureContract{ addresspublicowner; functiondsSomething()public{ /*Othercodens...*/ owner=msg.sender; /*Othercodens...*/ } }
```  
_Countermeasures._ Since delegatecall exploits the context change of smart contracts, the first thing to do is checking the possible context of contracts and their calling libraries (or other contracts). Solidity also provides keyword Library to restrict aligning state variable slots with a caller's state variables [78]. The Solidity library cannot have its state variable, so there will be no context switching and unauthorized change of caller's state variables.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 5 Conclusions  
This work studied the MTTP for space robotics, including the free-flying space robot (FFSR1) and the free-floating space robot (FFSR2). The trajectory-planning problem was converted into an optimization problem by depicting the joint movements with piecewise- and continuous-sine functions. An IGA was proposed to solve the optimization problem. The main contributions are expressed as follows:  
1. Cubic coefficients of arguments of piecewise-sine functions share the same absolute value, making the third part of each chromosome only consider one value after decoding. Therefore, computational cost is reduced.
2. In contrast to the setting on joint angular velocities in most recent studies about the MTTP for industrial robotics, each joint angular velocity varies based on Equation (10), contributing to the high-precision results.  
For future research, the MTTP for space robotics will be further studied in the case that one or more obstacles appear, especially the case when obstacles move. Furthermore, the corresponding trajectory-tracking control problem will be studied, which is of great significance for practical applications.  
**Author Contributions:** conceptualization, S.Z. and Z.Z.; methodology, S.Z.; writing--original-draft preparation, S.Z.; writing--review and editing, S.Z. and Z.Z.; supervision, Z.Z. and J.L.; funding acquisition, Z.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 11472213).  
**Acknowledgments:** The authors sincerely acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No. 11472213). The authors would also thank the members of the National Key Laboratory of Aerospace Flight Dynamics for their useful and valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 16: Case 2: FFSR2 movements. (**a**,**b**) Time histories of position and attitude of the end effector, respectively; (**c**) time histories of attitude of the free-floating base; (**d**,**e**) time histories of joint angles; (**f**,**g**) time histories of joint angular velocities.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 3 Results  
### WUE Spatial Variation  
The spatial distribution of multiyear averages of GPP, ET and WUE for seven ecoregions in the North China Plain from 2001 to 2017 is shown in Figure 3. The spatiotemporal distributions of GPP in the North China Plain had significant differences, and the high value areas of GPP (\(>\)1000 g C m\({}^{-2}\) y\({}^{-1}\)) are concentrated in the QLMDF, CJPEF and HHPMF ecological functional areas, while the low value areas of GPP (\(<\)400 g C m\({}^{-2}\) y\({}^{-1}\)) are concentrated in the MGMG, BHSSM and HHPMF ecological functional areas (Figure 3a). The spatiotemporal distribution of ET in the North China Plain has a clear distribution trend, gradually increasing from northwest to southeast, and the high ET values (\(>\)800 g C m\({}^{-2}\) y\({}^{-1}\)) are concentrated in the QLMDF, CJPEF and HHPMF ecological functional areas, while the low ET values are mainly distributed in the MGMG, CCLPMF and HHPMF ecological functional areas (Figure 3b). The spatiotemporal distributions of WUE and GPP in the North China Plain are similar, indicating that the spatial variation of WUE may be caused by GPP. The mean value of WUE in the North China Plain was 0.74 g C m\({}^{-2}\) y\({}^{-1}\) (Figure 3c).  
Figure 4 shows that the regions with increasing trends of GPP are concentrated in the northwestern and central-eastern parts of the North China Plain, while the regions with increasing trends of ET are concentrated in the western, southern, and eastern parts of the North China Plain, and the differences in the distribution of change trends of GPP and ET are relatively large. Compared with the spatial trend distribution of ET, the spatial trend distributions of WUE and GPP were more similar. In the study area, the WUE change trend was mainly influenced by the GPP change trend, which accounts for approximately 44.38% of the total study area. This is followed by the fact that the WUE variation trend was influenced by both GPP and ET variation trends, whose area accounts for approximately 19.75% of the area of the North China Plain. The influence of ET on the trend of WUE accounted for approximately 0.87% of the area of the North China Plain, indicating that GPP dominated the trend of WUE in the study area.
###### Abstract  
The purpose of this study is to establish a method of hazard assessment for the river terraces along the Chenyulan River and use 40 of them as protected objects. Using a geographic information system, the researchers extracted nine parameters for such terraces. These are length to attack shore, distance away from fault, distance from river channel, number of creeks and streams with possibility of debris flows, height above stream level, average slope degree, geology, number of erosion ditches, and distance from landslide area behind. Next, the weightings identified by analytic hierarchy process analysis were used as the basis for grading the various factors affecting river terraces. Hazard assessment for the river terraces then proceeded via totaling of the potential trends of the various factors and the protected objects, as well as comparison of historical disaster conditions and satellite images. The results showed that there were 8 high-risk river terraces, 14 medium-high-risk river terraces, 14 medium-low-risk river terraces and 4 low-risk river terraces. The evaluation of the current conditions of the settlement environment through parameter weighting has a certain accuracy and reference value in reducing the disaster impact of the riverine terrace settlement.  
geographic information system; hazard assessment; river terraces; risk assessment Article
###### Abstract
tried to analyze signal quality using deep learning technology in the mmWave spectrum [13; 14]. Also, [PERSON], [PERSON] attempted to optimize channel capacity through MCS control in a satellite communications link [15; 16; 17]. Moreover, there is prominent research focused on enhancing channel capacity through the use of emerging technologies, including reconfigurable intelligent surfaces (RISs) technology and the optimization of orthogonal time-frequency space (OTFS) modulation for RISs [18]. However, there is still a lack of link adaptation techniques that can be used in the VDES, a maritime communication standard. In particular, despite the spread of the VDES through IMO regulations and the expansion of applications requiring high throughput using the VDES, there is a lack of research on link adaptation that optimizes VDES performance for the maritime communication environment. In this study, we propose a new LA technique for optimizing throughput in a maritime communication environment using the VDES, referred to as the maritime auto-rate fall-back (mARF) technique. The mARF technique adapts MCS techniques based on communication success history, considering the characteristics of maritime communication environments. The proposed mARF supports fast recovery and a fast drop-out scheme, significantly enhances the channel utility, and achieves nearly 18% better performance than the existing LA technique. The remainder of this paper is organized as follows: Section 2 provides an explanation of the VDES and the maritime communication environment; Section 3 presents the proposed mARF technique; Section 4 offers performance evaluations; and Section 5 concludes the paper.