esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Design of Simulation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**QTN**} & \multirow{2}{*}{**Chr.**} & \multirow{2}{*}{**Position(bp)**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**Effect**} & \multicolumn{6}{c}{**Power (\%)**} & \multicolumn{6}{c}{**Mean Squared Errors (MSE)**} \\ \cline{5-16}  & & & & & **HRePML** & **MLMM** & **FarmCPU** & **GEMMA** & **HRePML** & **MLMM** & **FarmCPU** & **GEMMA** \\ \hline
1 & 1 & 404108 & 0.01 & 0.4328 & 9.9 & 2.4 & 1.6 & 0.0 & 0.0509 & 0.1334 & 0.1224 & na * \\ \hline
2 & 1 & 636788 & 0.03 & 0.7497 & 45.1 & 39.9 & 53.7 & 0.2 & 0.0193 & 0.0440 & 0.0241 & 0.3112 \\ \hline
3 & 3 & 507976 & 0.03 & 0.7497 & 66.7 & 40.1 & 13.9 & 8.1 & 0.1443 & 0.0992 & 0.2756 & 0.1597 \\ \hline
4 & 3 & 931437 & 0.05 & 0.9679 & 89.5 & 69.5 & 58.8 & 55.3 & 0.0321 & 0.0276 & 0.0434 & 0.0770 \\ \hline
5 & 4 & 75898 & 0.08 & 1.2243 & 100.0 & 99.8 & 100.0 & 97.5 & 0.0407 & 0.0375 & 0.0527 & 0.0283 \\ \hline
6 & 4 & 461978 & 0.01 & 0.4328 & 12.7 & 5.0 & 8.9 & 0.7 & 0.2488 & 0.3808 & 0.3429 & 0.5502 \\ \hline
7 & 4 & 607026 & 0.05 & 0.9679 & 69.6 & 80.9 & 98.5 & 73.8 & 0.0421 & 0.0988 & 0.0367 & 0.1544 \\ \hline
8 & 5 & 282008 & 0.05 & 0.9679 & 89.6 & 87.6 & 90.3 & 55.1 & 0.0397 & 0.0334 & 0.0345 & 0.0725 \\ \hline \hline \end{tabular}  
* In the first simulation study, the dataset consists of 500 individuals and 10,000 single nucleotide polymorphism (SNP) markers with 1000 replicates. Eight true QTNs are set in each replicate. Then, this dataset can be regarded as having 10,000,000 SNPs and 800 true QTNs in total. *  
\end{table} 
Table 1: Comparison of the statistical power and mean squared errors (MSE) for each quantitative trait nucleotide (QTN) among the hybrid of restricted and penalized maximum likelihood (HRePML), multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA) methods in the first simulation study *. In the third simulation study, our goal was to investigate the influence of the sample size on the running time and statistical power. The sample size was set to 500, 1000, 2000, and 4000, respectively. Meanwhile, the number of markers was fixed at 10,000. In the fourth simulation study, our aim was to investigate the impact on running time as the number of markers increased. The number of markers was set to 10,000, 50,000, 100,000, and 200,000, respectively. At the same time, the sample size was fixed at 500. In these two simulation studies, the repeat times were set to 100. The position and heritability of each QTN were set to the same as those of first simulation study. Their parameters are listed in Table S3.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 6 Results and Analysis  
First, the recordings were analyzed with a standard GNSS software-defined radio (SDR) to estimate the power level of the recorded signals. In this analysis, each antenna channel was analyzed separately (i.e., no beamforming is applied). The roof recording received the signals between 39 and 45 dBHz, and the laboratory spoofing setup received the signals between 46 and 48 dBHz. This showsthat the spoofing scenario has 3 to 7 dB signal power, which is representative of many spoofing attacks; however, this also directly impacts the detection capability of any method. Secondly, the number of acquired satellites by the snapshot receiver was also analyzed. For the roof recording, 5 to 8 satellites were acquired, whereas 10 to 12 were acquired by the spoofing scenario. This also proved to have an effect on the performance of the detectors.  
The sky-plot with visible GPS satellites is shown in Figure 6.  
The detectors were evaluated at different values for the steering vector angular threshold \(\xi_{\text{Th}}\). This allowed for the performance of the detector to be determined at different thresholds. Open-sky real-world recordings using the array were used.
## 2 Antenna Design Evolution and Analysis  
The proposed mm-wave MIMO antenna optimization is successfully achieved in four different steps (Figure 1) and is evidenced by the corresponding \(\ket{S_{11}}\) 1-parameters in Figure 2. In Figure 1a, a triangular patch measuring 10 \(\times\) 8.60 mm\({}^{2}\) (top view) is connected to the tapered microstrip feed line. A full rectangular ground plane is used on the bottom of the substrate. Initially, this antenna's first resonance was at 11.8 GHz; other resonances were in mm-wave (24-40 GHz). This antenna was designed using the equation given in [21]. The theoretical resonating frequency at the 10 mm side length is 11.8 GHz, which is close to the simulated value of 12.7 GHz. This discrepancy might be due to tapered feed, which improved impedance matching in the mm-wave frequency bands because our primary objective was to design an antenna for mm-wave applications.  
In Step 1, the \(\ket{S_{11}}\) 1-parameters reveal multiple resonances, indicating the presence of desired frequency responses. However, the impedance matching is suboptimal. In step 2, inserting two cross-shaped slots into the ground plane, as depicted in Figure 1b,  
Figure 1: Antenna design evolution steps.  
Figure 2: \(\ket{S_{11}}\) curves for various antenna designs.  
causes the resonance frequency to shift lower. This shift is due to the increased current path length. Additionally, two resonant dips, each below \(-\)10 dB, are observed. The cross-shaped slot has its horizontal and vertical dimensions 2.0 \(\times\) 0.5 mm\({}^{2}\) and 3.15 \(\times\) 0.4 mm\({}^{2}\), respectively. Furthermore, in step 3, introducing a circular notch with a radius of 2.58 mm into the top of the radiating triangular patch (Figure 1c) shifts the resonance points to the right. This shift is due to the reduction in patch dimensions, which also disrupts impedance matching. In the final step, two vertical slots measuring 3.3 \(\times\) 0.9 mm\({}^{2}\) and a horizontal slot measuring 1.7 \(\times\) 0.5 mm\({}^{2}\) are integrated into the radiating patch (as shown in Figure 1d). This adjustment enables the antenna to operate within the desired frequency bands. Specifically, the antenna's \(\mid\)S\({}_{11}\mid\) -parameters are determined to fall within the operating frequency ranges of 26.68-27.12 GHz, 27.85-28.33 GHz, 32.28-32.71 GHz, and 37.71-38.10 GHz, as depicted in Figure 2.  
This frequency bandwidth is a result of extending the current path within the patch. The single-element antenna's overall dimensions measure 11 \(\times\) 11 \(\times\) 0.25 mm\({}^{3}\), with a flexible substrate Rogers 3003 (thickness 0.25 mm) material having relative permittivity (\(\epsilon_{r}\)) of 3.0 and a loss tangent (tan8) of 0.001, as depicted in Figure 3. The optimized parameters are given in Table 1.  
Figure 3: Single antenna design.
## 3 Results and Discussion
### Tensile Tests at Elevated Temperatures  
The stress-strain curves as a function of temperature are shown in Figure 6. The martensite specimens were deformed up to fracture (Figure 5(a)), whereas for the austenite samples the elongation at fracture could not be determined since it is beyond the limit of admissible elongations of the displacement sensor of the dilatometer. Testing austenite at 700 \({}^{\circ}\)C did not result in reliable data, since it was not possible to avoid its transformation into ferrite and pearlite. A higher strain rate of 0.05 s\({}^{-1}\) during the tensile test was also tried to shorten the testing time, but it had no effect as the recorded stress-strain curve was very similar to the one shown in Figure 5(b); therefore, the decomposition of austenite could not be avoided since it occurs within 5 s. The yield stress \(R_{\text{p0.2}}\) and the ultimate tensile strength \(R_{\text{m}}\) from each curve is listed in Table 3. The temperature-dependent Young's moduli determined using the resonance frequency measurements, shown in Figure 7, were utilized in order to deduce the \(R_{\text{p0.2}}\) from the stress-strain curves.  
The yield stress of austenite at temperatures above A\({}_{\text{c3}}\) ranges from 37 to 77 MPa. This is very low compared to the shrinkage stresses that are generated during quenching. Referring back to the discussion in Section 1.1 and Figure 10(a), it can be seen that the austenite in the surface region and in the core would undergo a large amount of plastic deformation during quenching.  
The hardness of the martensitic specimens after the tensile test was around 200 HV30, which corresponds to tempered martensite. The same values were also observed in the near-surface tempered martensite region of the rebar in Figure 4. Thus, it was not possible to avoid the tempering of the martensite during the tensile test, although the duration of the test was kept as short as possible. At high temperatures, the migration of carbon atoms started within seconds, followed by precipitation of transient iron carbides in the first few minutes [28]. Consequently, the martensitic specimen gained substantial ductility at the higher temperatures, as seen in Figure 5(a).  
Figure 5: Microstructures observed in the cross-section of the rebar ranging from slowly cooled ferrite to tempered martensite.  
The tensile test at room temperature was conducted in the universal tensile testing machine on a standard cylindrical specimen. The extensometer was removed after reaching \(R_{\text{p0.2}}\), and the strain until fracture was evaluated on the basis of the crosshead displacement. The specimen tested at 300 \({}^{\circ}\)C in the dilatometer also had a slightly smaller cross-section and shorter length than the other flat specimens. Due to these different specimen geometries, the elongation values from these tests are not directly comparable to the other tests when strain localizes beyond uniform elongation.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 4 Conclusions  
To provide useful engineering insight into the aerodynamics of a small vertical-axis wind turbine with PGF and SGF, a numerical simulation was conducted on an H-type Darrieus wind turbine with an NACA0021 airfoil. The conclusions are summarized as follows:  
(1) In comparison to the baseline airfoil, both PGF and SGF airfoils demonstrate significant improvements in the lift coefficient. At a height of 6%, the maximum lift coefficients of PGF and SGF are increased by 89.19% and 65.6%, respectively. As the flap height increases, the lift-to-drag ratio of the SGF gradually exceeds that of the PGF.  
(2) The flap is beneficial to delay surface flow separation and promote the transition occurrence. The serration generates a secondary pair of vortices in the normal plane which breaks up the columnar vortex structure of the PGF airfoil. This is the reason why the SGF has a lower drag coefficient than the PGF.  
(3) Flap effectiveness depends on its height and operation condition. A higher SGF flap is preferred over a PGF flap for a higher tip speed. Compared with the baseline turbine, when the flap height reaches 6%, the PGF rotor's performance deteriorates obviously, and the performance of the SGF rotor surpasses the PGF one by 13.9% at TSR = 2.62.  
The results from the current investigation illustrate that compared to the PGF, SGF can improve the performance of VAWT due to its reduced instability phenomenon, especially at high TSRs. The higher the flap height, the more obvious the advantages of the SGF. However, limited to the calculation method used in the current study of VAWT, a more detailed physical mechanism for the effect of the SGF on VAWT is still under further study. In addition, the influence of structural parameters such as the serrated shape and thickness will be further considered.  
Conceptualization, L.C. ([PERSON]) and P.Y.; methodology, P.Y. and B.Z.; validation, B.Z.; formal analysis, L.C. ([PERSON]), P.Y., B.Z., and L.C. ([PERSON]); investigation, L.C. ([PERSON]) and P.Y.; resources, L.C. ([PERSON]); writing--original draft preparation, L.C. ([PERSON]); writing--review and editing, L.C. ([PERSON]), P.Y., B.Z., and L.C. ([PERSON]); supervision, L.C. ([PERSON]); project administration, L.C. ([PERSON]); funding acquisition, L.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Project No. 52006147, 52276034).  
No applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 2 Related Work
In contrast to the mobile sink, various data aggregation techniques are proposed using a static sink where the network aggregation at intermediate nodes usually results in a reduction of data packets. In [27], the author discusses the problem of scheduling virtual data aggregation trees to maximize the network lifetime where the size of the data packet is fixed for the aggregation, named as the Maximum Lifetime Data Aggregation Tree Scheduling (MLDATS) problem. In this paper, the authors proposed a Local Tree Reconstruction-Based Scheduling Algorithm (LTRBSA) for the MLDATS problem. In addition, the authors showed that the MLDATS problem is NP-complete. In [28], the author proposed a distributed algorithm to find a backbone in the dual-radio network whenever a new backbone was needed; and examined the difficulty of constructing virtual backbones to increase the network lifetime. As several energy-efficient algorithms are proposed with different cluster formations and mobility patterns, these research works provide the proper functional mathematical analysis [29]. These analyses provide the determination of the clustering range in diverse scenarios. Based on the literature review, it is demonstrated that network performance can be enhanced with the proper mobility pattern of the sink. Moreover, energy consumption can be reduced with frequent broadcasting of the sink location to CHs. The purpose of this research is to optimize the network, reduce the overall energy consumption and enhance the network lifetime with more stable data delivery in large-scale networks. Therefore, we propose the controlled spiral mobility pattern, offering a better solution in real-time network operations. This protocol neither requires complex algorithms for operations, nor imposes too many constraints on the sensor nodes. These characteristics of this controlled mobility pattern, in energy-efficient manner, make this protocol preferable in a huge range of WSN applications as compared to existing routing protocols.
###### Abstract  
In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.  
deep learning; deep neural network; wireless powered communication network; SPCA 2020, 10, 4622; doi:10.3390/app10134622
## 3 Results and Discussion
### Adsorption and Desorption Experiments
#### 3.2.3 Effect of Fluent  
To find a suitable eluent, different concentrations of hydrochloric acid, sulfuric acid, and nitric acid were provided through the microcolumn at a certain flow rate to elute Cd(II) adsorbed on Cd-I-MA-_co_-AN. The Cd(II) concentration was determined by FAAS. As shown in Figure 6, the desorption efficiency of nitric acid was better than that of sulfuric acid and hydrochloric acid under the same concentration. The desorption efficiency of 1 mol L\({}^{-1}\) nitric acid was 94%. At low acid concentrations, cadmium ions are not completely eluted. When the acid concentration is higher than a certain value, the imprinted sites are completely protonated. The structure of Cd-I-MA-_co_-AN may be destroyed, decreasing the interaction with Cd(II) [35]. Therefore, 1 mol L\({}^{-1}\) nitric acid was chosen for eluting.  
Figure 4: Effect of pH on the adsorption efficiency.  
Figure 5: Effect of flow rate on the adsorption efficiency.
## 4 Discussion
### Study Limitations and Future Prospects  
This study increases the knowledge about this research topic; however, a few limitations could be identified with a view to further research. Firstly, only the TD and TD > 21 km\(\cdot\)h\({}^{-1}\) were considered, so deeper analysis based on more physical variables should be interesting to increase knowledge about the influence of opponent quality on physical performance. Secondly, more research is required considering a few factors, such as the playing style or match status, since the physical performance could depend on different contextual-related variables. Finally, only data from the Spanish soccer matches were involved in this study, so this could limit the application of these findings to other leagues. For that reason, future studies could replicate the protocol reported in this study to find differences in the match movement profiles between different opponent quality in other European leagues.
## 4 Conclusions  
In this paper the simplified mathematical models are discussed for the two important agents of the marine robotic information system, namely, for underwater gliders (UG) and wave gliders (WG). The former model is based on the equations of forces and moments equilibrium of a UG, moving (descending or ascending) along its trajectory in a vertical plane on the assumptions of depth-wise constant water density and zero compression of the pressure hull. The latter model reduces the problem of a WG as a two-module marine robot, performing motions under the action of regular waves, to finding oscillations of mass-spring type simple mechanical system excited by a harmonic force, and modeling the conversion of these wave-driven motion into the thrust of the lower (wing) module, propelling the vehicle forward.  
Both models provide straightforward insights into the influence of the geometry and kinematics of the corresponding vehicle upon its performance and suggest guidelines for efficient design and more elaborate investigations based on computational methods and experimentation. For example, the UG model readily yields dependencies of speed on the trajectory and range upon the pitch angle, the geometry of the pressure hull and lifting system, the volume of the buoyancy engine, the relative volume of the battery compartment and the specific power of the batteries. The WG model enables one to evaluate the influence  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda[\mathbf{m}]\) & \(\boldsymbol{U}_{0}[\mathbf{m/s}]\) & \(\langle R_{T}\rangle[\mathbf{N}]\) & \(\boldsymbol{\eta}_{i}\) \\ \hline
3 & 0.43 & 60 & 0.53 \\ \hline
4 & 0.40 & 45 & 0.53 \\ \hline
5 & 0.37 & 33 & 0.53 \\ \hline
10 & 0.38 & 38 & 0.53 \\ \hline
20 & 0.51 & 72 & 0.54 \\ \hline
30 & 0.62 & 117 & 0.56 \\ \hline
40 & 0.73 & 172 & 0.57 \\ \hline
50 & 0.90 & 241 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wavelength, Speed, Thrust, Ideal Efficiency.  
Figure 12: Calculation of the thrust and speed of WG (short waves).  
of wave parameters, dimensions and shape of the float and wing module on the efficiency of the wave-induced thrust and speed of the vehicle. It also allows for the investigation of options for _tuning up_ the WG-plus-waves system for a chosen ocean basin.  
_The insufficiencies of the proposed approaches_ are due to excessive simplifications aimed at obtaining a better understanding of the functioning of the technology under study.  
_Further research_ envisages:  
* extending the proposed UG model to the case of depth-wise density variation and pressure hull compression with a more detailed description of its geometry and the use of computational mechanics;
* extending the proposed WG model to the simulation of a multi-wing lower module connected to the float by means of a flexible and elastic umbilical.
## 2 Risk Modelling
### Obstacle Modelling With Current  
The traditional A-Star algorithm can generate an optimal path with keeping off the obstacle nodes, which shown in Figure 1. From Figure 1, it can be seen that the generated path could be very close to obstacles to guarantee the path shortest. However, if this path is adopted by a sailing vessel as the planning route, the vessel will have a high risk to collide the obstacles. Meanwhile, the current will also increase the collision risk if the current pushes the vessel to approach one of obstacles. Therefore, it is indispensable to consider both keeping an appropriate distance from obstacles and current influence during path planning.  
The obstacles for vessel can be divided into 5 types, namely, bridge pier, shore, vessel, port, and others. Different types of obstacles have different risks for vessel navigation. The risk degree \(r_{o}\left[m,n\right]\) of navigable node \(N\left[m\right]\) nearby obstacle node \(O\left[n\right]\) is defined as follows:  
\[r_{o}\left[m,n\right]=\frac{\mathrm{e}^{-d}}{a}+\frac{c_{m}v_{m}}{d}, \tag{1}\]  
where \(d\) stands for the Euclidean distance from an navigable node \(N\left[m\right]\) to the obstacle node \(O\left[n\right]\). \(a\) stands for the risk coefficient for different types of obstacles. In this article, \(a\) is assigned as \(5,5,3,4,3\) for bridge pier, shore, vessel, port, and others. \(v_{m}\) stands for the current velocity. \(c_{m}\) stands for the direction coefficient, which is as follows:  
\[c_{m}=\begin{cases}-\cos\left(\phi_{c}-\phi_{g}\right),\cos\left(\phi_{c}-\phi _{g}\right)<0\\ 0,\cos\left(\phi_{c}-\phi_{g}\right)\geq 0\end{cases}, \tag{2}\]  
where, \(\phi_{c}\in\left[0,2\pi\right)\) is the current direction toward the true North; \(\phi_{g}\in\left[0,2\pi\right)\) is the node direction relative to obstacle node \(O\left[n\right]\) toward the true North, i.e., the direction of vector \(\overrightarrow{N\left[m\right]O\left[n\right]}\) toward the true North.  
Figure 1: The grid map and path generated by the traditional A-Star.  
A grid map considering the obstacle with current is represented in Figure 2. In Figure 2, the number in a navigable node denotes the risk degree, and the direction and length of an arrow denote the current direction and velocity, respectively. A navigable node \(N\left[m\right]\) could have \(k\) risk degree values if there exist \(k\) obstacle nodes nearby \(N\left[m\right]\) according to Equation (1). In this case, the maximum one will be selected as the final risk degree. Therefore, the final risk degree of node \(N\left[m\right]\), \(r_{o}\left[m\right]\), is defined as follows:  
\[r_{o}\left[m\right]=\max\left\{r_{o}\left[m,1\right],r_{o}\left[m,2\right],...,r_{o}\left[m,k\right]\right\}, \tag{3}\]  
In Figure 2, the path is generated by the traditional A-Star algorithm, which chooses an optimal path node without considering the risk degree of the path node.