esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
Hand gesture recognition is an emerging research topic, especially for interpreting the sign language communication of people with speech and hearing challenges. The related works on gesture recognition are broadly classified under two major categories: (i) device-based gesture recognition and (ii) device free gesture recognition. Device-based gesture recognition widely uses wearable sensors like data gloves to capture hand gestures in order to interpret sign language [30; 31]. However, device-free gesture recognition leverages signals of the commercial hardware devices for the gesture recognition task. These systems are categorized as: radar-based [32], RSSI [33; 34] and CSI-based [15; 35].  
CSI is of interest as it helps to produce better action recognition accuracy since it consists of both amplitude and phase information. It provides fine-grained signal information in subcarrier level, which has widespread application in the field of computer vision. WiGer [36] adopts DWT for recognizing hand gestures in 5 different scenarios and achieves an average accuracy of 97.28%, 91.8%, 95.5%, 94.4% and 91% respectively. WiCatch [37] rebuilds the trajectories of the of nine moving hand gestures and achieves a recognition accuracy of 96% using SVM. WiFinger [33] recognizes eight different finger gestures with an average accuracy of 95% using PCA and DTW. WiKey [15] recognizes the keystroke of 37 keys of the keyboard using CSI with an accuracy of 77.4% to 93.4% with 30 and 80 samples per key respectively using PCA and DWT based feature extraction. Mudra [38] recognizes seven different finger gestures using a modified DTW algorithm with 96% accuracy.  
State of the art CSI based sensing uses two major approaches: (i) using pre-processed CSI signals and (ii) using raw CSI signals. This section also discusses some of the works that adopt feature selection mechanisms.
## 3 Results and Discussion
### Morphologies and Structures
When there are one or few layers of graphite appearing on the catalyst surface, the concentration of carbon atoms near the top side will lack precipitation, leading to a bigger void part after the previous graphite layers are lifted up. In addition, the changes in surface energy and elastic energy of the carbon layers distort the surface particles into an irregular shape, as shown in Figure 4b. The curvature at the top of the distorted catalyst particle become smaller, turning Cu tips into spindle-like or even strip-like shapes. Consequently, the concentration of the carbon atoms at the top of Cu particle increases, so more cone-shaped graphite is formed at the surface of the distorted metal particle with a bigger void part. Meanwhile, nitrogen precipitates from the top side of the near-spindle-like metal to the graphite sheet, and some carbon atoms in the graphite sheet are replaced by nitrogen atoms, leading to the distortion of the graphite sheet. The above suggestion is supported by the results in the EDX (Energy Dispersive X-ray Spectroscopy) spectra of carbon nanostructures (as shown in Figure 3f), in which a lot of nitrogen elements have remained. To find further evidence, the decomposition of CNS on different Cu-Cr catalysts was investigated. Figure 5 shows the SEM images of carbon nanostructures on Cu-Cr films of different thicknesses in a C\({}_{2}\)H\({}_{2}\)/NH\({}_{3}\) atmosphere, where the inserts are the SEM images with low magnification. In Figure 5a, the CNTs that grow on small particles are short and curved, while the big Cu-Cr particles are wrapped over by translucent curved graphite. When the thickness of the Cu-Cr film increases to 500 nm, as shown in Figure 5b, CNLs grow into flowering shrubs on big catalyst particles (approximately 1 \(\upmu\)m), which is consistent with the root-growth pattern mentioned above. It is rather surprising to find that the special CNL can be catalytically deposited on the copper-containing catalyst during NH\({}_{3}\)-CVD. Figure 4: The proposed mechanism of the reed-leaf-like carbon nanostructure (CNL).
# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 2 Related Work
### Collision-Free Multi-Robot Problem  
The collision-free multi-robot problem is another major challenge for multi-robot systems. When a large number of robots work together in collaboration, due to their high mobility, the avoidance of collisions and conflicts is a problem that must be considered.  
One method involves installing various sensors, video surveillance devices, and radars on robots, thus enhancing their ability to perceive the environment and reduce collisions and conflicts [11; 24]. With the help of various sensors, the IoT, and remote communication technologies, multiple robots can be applied to carry out tasks in unknown environments and high-risk instances.  
Another method involves performing path planning and re-planning for robots, ensuring that the running routes or topologies of different robots do not collide or conflict [19]. In a dynamic environment, the obstacles or robots cannot be known until they are detected. Then, a re-planned route is generated for the robot in order to adaptively avoid obstacles, with this process being carried out in real time. In [25], a model-free visual servo swarming method for manned USVs considering visibility maintenance and collision avoidance was proposed. Heuristic algorithms--such as the artificial bee colony (ABC) algorithm [18], model-reference reinforcement learning [24], the A*-based co-evolutionary approach [26], the whale optimization algorithm (WOA) [27], and PSO [4]--can efficiently generate new routes to prevent collision and conflicts in a swarm of robots. Due to the inherent flaws of various algorithms, scholars have proposed hybrid algorithms that integrate the characteristics of different algorithms and demonstrate significant advantages, such as the fuzzy adaptive WOA [27], an ACO-ABC hybrid algorithm [28], and the WOA-AGA algorithm [29].
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Linear Parameters**} & \multicolumn{3}{c}{**Nonlinear Parameters from z-Scan Measurements**\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ }}}}}}}}}}}}}}}\) & & \multicolumn{3}{c}{**Nonlinear Parameters**} \\ \cline{3-12}
**Sample** (**Thickness**) & \(n_{\omega}\) & \(n_{2\omega}\) & \(n_{\text{THz}}\) & \(\mathbf{n_{2},10^{-15}}\) & \(\mathbf{\beta,10^{-11}}\) & \(\mathbf{\text{Re}}_{X}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(2\omega)\) & \(\mathbf{\text{Im}}\)\({}^{(3)}(\omega)\) \\ \hline SiO\({}_{2}\) (0.9 mm or 2 mm) & 1.453 & 1.47 & 2 & 0.25 (\({}^{2}\)) & - & 0.13 & - & 1 & 1 & 1 & 1.5 \\ TWLA (2.3 mm) & 2.2 (\({}^{3}\)) & 2.35 (\({}^{3}\)) & \(\sim\)5 (\({}^{4}\)) & 5.1 & 6 & 6.3 & 0.47 & 47 & 65 & 9 \\ TWLB (2.3 mm) & 2.2 (\({}^{3}\)) & 2.35 (\({}^{3}\)) & \(\sim\)5 (\({}^{4}\)) & 6.1 & 8 & 7.5 & 0.63 & 56 & 80 & 11 \\ IKS23 & 2.45 (1.6 mm) & 2.9 (\({}^{3}\)—0.5) (\({}^{5}\)) & 2.9 & 145 (\({}^{6}\)) & 780 (\({}^{6}\)) & 220 & 75 & 1760 & 1200 & 148 \\ IKS25 & 2.9 (\({}^{6}\)) & (3.3—)(\({}^{5}\)) & 3.3 & - & - & - & - & - & 1900 & 130 \\ As\({}_{2}\)S\({}_{3}\) (6.1 mm) & 2.45 (\({}^{7}\)) & 3–0.51 (\({}^{7}\)) & 2.8 & 65 (\({}^{8}\)) & -250 (\({}^{9}\)) & 95 & -23 & 730 & 1200 & 152 \\ \hline \hline \end{tabular} \({}^{(3)}\) Estimated experimental errors of z-scan and TFISHG measurements are -20% and -40%, respectively. \({}^{(2)}\) Refs. [25; 38; 50]. \({}^{(9)}\) Refs. [51; 52]. \({}^{(4)}\) Refs. [53; 54]. \({}^{(5)}\) Refractive indices for IKS23 and IKS25 were taken close to As\({}_{2}\)S\({}_{3}\) and As\({}_{2}\)Se\({}_{3}\), Ref. [55], respectively, due to their close compositions. \({}^{(6)}\) Probe optical intensity is \(\sim\)0.8 GW/cm\({}^{2}\). \({}^{(7)}\) Ref. [55]. \({}^{(8)}\) Ref. [41]. \({}^{(9)}\) Ref. [42].  
\end{table} 
Table 1: Linear and nonlinear parameters of the studied samples. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data underlying the results presented in this article may be obtained from the authors upon reasonable request. **Acknowledgments:** We are grateful to [PERSON] from the Institute of Chemistry of High-Purity Substances of RAS, Nizhny Novgorod, Russia, for providing the samples of tellurite glasses. **Conflicts of Interest:** The authors declare no conflict of interest.
# Fast Implementation of NIST P-256 Elliptic Curve Cryptography on 8-Bit AVR Processor
###### Abstract  
In this paper, we present a highly optimized implementation of elliptic curve cryptography (ECC) over NIST P-256 curve for an 8-bit AVR microcontroller. For improving the performance of ECC implementation, we focus on optimizing field arithmetics. In particular, we optimize the modular multiplication and squaring method exploiting the state-of-the-art optimization technique, namely range shifted representation (RSR). With optimized field arithmetics, we significantly improve the performance of scalar multiplication and set the speed record for execution time of variable base scalar multiplication over NIST P-256 curve. When compared with previous works, we achieve a performance gain of 17.3% over the best previous result on the same platform. Moreover, the execution time of our result is even faster than that over the NIST P-192 curve of the well-known TinyECC library. Our result shows that RSR can be applied to all field arithmetics and evaluate the impact of the adoption of RSR over the performance of scalar multiplication. Additionally, our implementation provides a high degree of regularity to withstand side-channel attacks.  
elliptic curve cryptography; NIST curves; software implementation; 8-bit AVR microcontroller 26 October 2020; Published: 9 December 2020
###### Abstract  
There is significant interaction between the new supporting structure and the existing adjacent retaining wall in the dredging and excavation project of urban rivers. In addition, the three factors, the spatial location, the stiffness of the structures, and the soil conditions of the two sides of the interaction will exert effects on the bearing properties of the two structures. Combined with an actual dredging project, FLAC\({}^{\text{3D}}\) software was applied to analyze the influencing rule of U-shaped concrete sheet pile (USCSP) section size, pile length, retaining wall height, and pile-wall spacing on the supporting structure and the bearing properties of the existing gravity retaining wall during dredging excavation. The results are that when the length of the sheet pile increases, the horizontal displacement of the pile gradually decreases, the horizontal displacement of the existing retaining wall declines, and the earth pressure at the wall's back rises. With the increase in the section size of the sheet pile, its bending resistance enhances gradually, and the horizontal displacement of the existing retaining wall reduces, while the earth pressure slightly increases. When the pile-wall spacing grows, the interaction between the supporting structure and the retaining wall is gradually weakened under the process of excavation, the horizontal displacements of the sheet pile and the retaining wall decrease continuously, and the earth pressure at the retaining wall's bottom continues to strengthen. Moreover, with the retaining wall growing, the passive resistance from the soil in front of the wall is greater for keeping the stability of the retaining wall, and the horizontal displacement and the stress of the sheet pile increase significantly after excavation. The above results indicate that the characteristics of the pile-wall interaction should be deeply considered in designing and constructing such projects in order to determine the overall stability of the retaining pile and the existing retaining wall. In this study, FLAC\({}^{\text{3D}}\) software was used to analyze the influence of various factors on the structure in order to provide reference for ensuring the safety of the whole structure.  
river dredging; U-shaped concrete sheet pile; gravity retaining wall; bearing characteristics; structures interaction; structural safety +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Properties of Ring-Spun Complex Yarn  
#### 3.1.1 Tensile Tenacity and Twist Contraction  
Figure 4 shows the effect of twist number on the tensile tenacity and twist contraction of complex yarns. Twisting gives yarn mechanical strength. Figure 4a shows that with increase of the twist number, tensile tenacity increased to a certain value and then decreased. BC and PC complex yarn twisted at 12 TPI had the highest tensile tenacity. For BPC yarn, tensile tenacity slightly decreased from 9 to 12 TPI and then continued to decrease largely with twist number. With the increase in twists per inch, the outer layer of fiber gradually pushed into the interspace of inner yarn, and the cohesive force between fibers evidently increased and led to yarn compactness and higher tenacity [21]. However, when the twist number exceeded 12 TPI, the inner yarn did not have sufficient space to hold more fibers, and the outer yarn appeared to fracture, which reduced tensile tenacity [21]. The tensile tendency of BPC yarns to twist number differed from those of the BC and PC complex yarns. This finding was due to the fact that BC and PC shell yarn generated higher compression to the core yarn, and the cohesive force between outer and inner yarn was non-uniformly distributed and easily produced stress concentration, which resulted in decreased tensile tenacity at a lower twist number.  
Figure 4b shows that the twist contraction increases continuously with the twist number. The relation between the twist contraction and the twist number was consistent with Equation (3) as derived by [PERSON] [22]. When the twist number increased, the angle (\(\theta\)) formed by the spirals with yarn axis increased and the twist contraction ratio (\(C\)) also increased.  
\[C=100(1-\frac{1}{\sqrt{1+tan^{2}\theta}}) \tag{3}\]  
Figure 4: Tensile tenacity (**a**) and twist contraction ratio (**b**) of ring-spun yarns with different twist number (9, 12, 15, 18, and 21 per inch (TPI)).  
Moreover, at the same twist number, the BPC complex yarn had much higher twist contraction ratio than others (BC, and PC yarn). This finding can be explained by Equation (4) based on the correlations of angle (\(\theta\)) between the diameter (\(d_{t}\)) and twist number (\(T\)) of yarns [22].  
\[tan\theta=\pi d_{t}T \tag{4}\]  
Table 1 shows that BPC yarn has a higher fineness than BC and PC yarns. Even with the same twist number, the spiral angles with the yarn axis was higher; thus, the twist contraction ratio of BPC yarn was the largest. Twisting angle is one of the concepts to characterize the degree of twisting. Because of inconvenient to measure it, twist coefficient is also used to compare the degree of twisting between yarns. Twist coefficient \(\alpha_{tex}\) is expressed as Equation (5).  
\[\alpha_{tex}=\frac{10T}{2.54}\sqrt{N_{tex}} \tag{5}\]  
where \(N_{tex}\) shows the yarn count in tex-system, T shows twists per inch.  
Comparing the contraction ratio with the same twist coefficient, no difference was found among BC, PC, and BPC yarns. Therefore, twist contraction ratio depended on the twist number and the yarn count.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 3 Results
### Tarm Basin
Thus, the nominal GWS rates in these two regions are caused by changes in other TWS compartments rather than GWS. If the entire TBR is included to obtain GWS changes using GRACE and models, significant non-groundwater mass change signals outside the basin will leak into the basin and result in the biased GWS change estimates in the TBR. To reduce the leakage effect, we only calculated the time series of GWS changes in the plain area of TBR, following the definition of the Tarim Basin aquifer from WHYMAP. For temporal variability in GWS, the magnitudes of GWS changes from WGHM and PCR-GLOBWB are far smaller than that from GRACE (Figure 10). An obvious GWS decline from 2006 to 2009 is detected by GRACE (Figure 10a), consistent with less annual precipitation in the same period (Figure 10b). Unfortunately, there are few long-term monitoring well observations available over the TRB. Only three monitoring wells are located in Korla at the northeast of TRB, with groundwater level data available from 2007 to 2013. For the other three wells in Kashgar at the west of TRB, there are many gaps in groundwater level data. As shown in Figure 11, the groundwater tables are generally deeper in Korla than those in Kashgar. Steady groundwater level declines are observed in Korla at a rate of -0.6 m/yr during 2007-2013 (Figure 11). Although there is no information on specific yields in the TRB to translate level changes into storage changes, the negative level rate from ground wells is consistent with the GWS decline from GRACE during the same period. Additionally, the well station #3 in Kashgar shows an obvious recovery of water level in 2010, which is also consistent with GRACE-based GWS results and positive annual precipitation in the same year. Figure 10: (**a**) Time series of GWS changes in the Tarim Basin during 2002-2014 from GRACE, WGHM, and PCR-GLOBWB. Shaded areas are estimated uncertainties in GRACE-derived GWS changes. (**b**) Annual precipitation anomalies and PDSI values in the Tarim Basin from 2002 to 2014.
## 4 Numerical Results
### Effect of Parameters  
It has been observed that the two parameters \(A\) and \(Q\) noticeably impact performance of the algorithm. Therefore, the investigations their effects have been carried out. Firstly, in order to investigate effect of \(A\), which denotes the number of ants in the colony, the use case that has 60 operations was experimented with different values of \(A\), while all other parameters remained the same. The results of this experiment are show in Figure 6. It can be seen that when the number of ants in the colony is increased, less iterations are required for the solution to converge. This can be explained as the more ants mean the more possibilities for the solution are explored in each iteration. Therefore, the best solution can be discovered in less iterations. Nonetheless, it should be noted that more computation is required for more ants in each iteration and this might slow down the overall running time of the algorithm.  
Figure 5: Gantt chart from Preactor AS.  
In order to investigate effect of the parameter \(Q\), the same use case that has 60 operations was also used. Three test cases were experimented with the value of \(Q\) varied, while other parameters were kept the same. The results of these test cases are shown in Figure 7. It can be observed from Figure 7a-c that the higher value of \(Q\) is defined, the lesser iterations are required for the solution to converge. This is because based on Equation (4), the effect of \(Q\) is to amplify the enhancement of pheromone when the global updating rule is applied for the best solution. Therefore, the convergence rate towards the best solution is increased. However, it should be noted that an excessively high value of \(Q\) might lead to a local optimal solution. For example, in Figure 7d, the value of \(Q\) is increased to 50,000 and the best solution found is 240 min, while in the other cases, the best solution found is 230 min.
## 5 Results and Discussion
### General Characteristics
#### 5.1.1 Information Systems (ISs) (RQ1)  
The majority of the respondents 83 out of 96 stated that the companies they work for, they use IS while 13 of them do not. Of the 83 companies the 82 specified which systems they actually use (Table 2). The majority of the companies use ERP (85.4%). Quite enough of them--32, 31, and 29--use WMS, OMS, and CRM, respectively. The rest of the systems have a percentage of less than 26% each of them. It would be interesting to notice that the SCM which is an area that is in development and will continue to be in the years to come is only used by 13.4% of the companies. As we referred previously in these days the competition has moved or moves from company-company to supply chain-supply chain. The percentages sum up to much more than 100 percent because most companies use multiple ISs.  
Comparing the current research with the one that was conducted by [PERSON] et al. [10] one can observe differences or similar results regarding the percentage of the companies regarding the adoption of an IS. Even if the percentages of current and future users of the ERP system are added [10] the percentage (49%) is much lower of that in the current research (85.4%). On the other hand the percentage of the companies in the current research which use systems such as WMS, MRP, SCM, SRM, and APS are lesser than the companies they were already using those systems in the [10] research. The percentage of the companies which use CRM in the current research is between the percentages of those they were already using the system but not greater if we add those were intended to implement it in the future.  
\begin{table}
\begin{tabular}{c c c} \hline
**District** & **Number of Companies** & **Percentage** \\ \hline Thessaloniki & 21 & 21.9\% \\ Kilkis & 11 & 11.5\% \\ Drama & 9 & 9.4\% \\ Serres & 8 & 8.3\% \\ Imathia & 8 & 8.3\% \\ Pieria & 7 & 7.3\% \\ Rodopi & 6 & 6.3\% \\ Xanthi & 6 & 6.3\% \\ Other & 20 & 20.9\% \\ \hline \end{tabular}
\end{table}
Table 1: Profile of the companies: District they facilitate.  
Figure 1: Profile of the companies: Industry sector.  
More than half companies, 47 out of 83, have stated that they invested in IS the last couple of years which means that they care about their systems being up to date (Figure 2). On the other hand, only eight of them have not invested in IS in the last 10 years besides the financial crisis that hit the country.