esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 5 Results
### Gottwald-Melbourne Test  
To verify that the trajectories generated by the proposed algorithm are indeed chaotic, we use the 0-1 Gottwald-Melbourne test [18]. This test determines if a discrete deterministic dynamic system is chaotic or not. Contrary to the usual method of calculating the maximum Lyapunov exponent, this method is applied directly to the time series and does not require space-phase reconstruction. In addition, the dimension of the dynamic system and the form of its equations are irrelevant. To perform the test, 300 iterations of the chaotic trajectory generator algorithm are executed to the mobile robot with different initial conditions. Taking into consideration the data of the time series of the \(x\) and \(y\) states of the model of the differential drive mobile robot, obtained from the results of the performed numerical simulations, we can perform the 0-1 test to verify evidence of chaos in the trajectories of the mobile robot generated by the algorithm. The input is the data of the time series and the output is the amount of chaos between 0 and 1; this value indicates whether the input is chaotic or not, 0 denoting not chaotic and 1 totally chaotic. The test is applied to any dynamic deterministic system; see details of the test in [18]. Figure 11 shows the time series of the \(x\) and \(y\) coordinates for the initial experimental conditions \((x_{0},y_{0},\theta_{0})=(0,0,0)\).  
Figure 11: \(x\) and \(y\) time series for initial conditions \((x_{0},y_{0},\theta_{0})=(0,0,0)\): (**a**) \(x\) time series; (**b**) \(y\) time series.  
Figure 10: Mobile robot’s trajectories with initial conditions \((x_{0},y_{0},\theta_{0})=(0,0,0)\): (**a**) 100 iterations; (**b**) 300 iterations.  
The resulting value of the 0-1 test for our proposed algorithm in the \(x\) axis is 0.9854 and for the \(y\) axis is 0.9745. The number close to 1 is a clear indication that the test concludes that such time series are indeed chaotic.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 4 Verification of Passenger Car AEB Control Algorithm
### Test Results of AEB Control Algorithm
#### 4.3.2 CCRm Condition  
The parameters of the CCRm condition are set as follows: the relative distance is 120 m, the initial speed of VUT is 80 km/h, and the VT runs at a constant speed of 32 km/h. The HIL testing and SIL testing results in the CCRm condition are shown in Figure 11.  
Figure 11: HIL testing and SIL testing results of CCRm condition: **(a)** speed of VUT and acceleration of VUT; **(b)** relative speed and relative distance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & **Speed of VUT/(km/h)** & **Test Distance/m** & **Speed of VUT/(km/h)** & **Target Acceleration/(m/s\({}^{2}\))** \\ \hline CCRs & 80 & 120 & 0 & 0 \\ CCRm & 80 & 120 & 32 & 0 \\ CCRb & 80 & 30 & 80 & \(-3\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Passenger car AEB test conditions.  
Figure 10: HIL testing and SIL testing results of CCRs condition: **(a)** speed of VUT and acceleration of VUT; **(b)** relative speed and relative distance.  
Take the HIL testing curve in Figure 11 as an example, at 6.1 s, the VUT enters the L I AEB braking status. The VUT enters the L II AEB braking status at 7.1 s. At 8.8 s, the VUT returns to the L I AEB status. At the end of braking at 9.8 s, the speed of VUT is consistent with that of VT. At the end of AEB braking, the relative distance is 11.19 m, and there is no collision between the VUT and the VT.
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 2 Classification of Transplanters  
Dryland transplanting machines face significant heterogeneity in working environments and diversity in crop varieties during operation. The differential coupling effect of key parameters--such as terrain and geomorphology, soil mechanical properties, crop biological attributes, and agronomic technical specifications--directly leads to significant technical differentiation in the mechanism configuration, operating parameters, and functional modules of transplanting equipment [37].  
Transplanters can be classified into handheld, semi-automatic, and fully automatic types based on their operating principles. Figure 1 illustrates the overall classification of dryland transplanters according to planting principles, and Table 1 illustrates the differences among various transplanting machines. Semi-automatic and fully automatic transplanters share the same planting mechanism. Planting mechanisms are categorized into duckbill-type, chain-clip type, seedling guide-tube type, and flexible-disc type by operation mode. Among them, the duckbill-type planting mechanism exhibits strong adaptability to plug seedlings and wide applicability due to its structural features. It enables transplanting on mulch films and is currently the most widely used planting mechanism in semi-automatic transplanters for practical production [39]. During the operation of fully automatic transplanters, the seedling picking mechanism extracts seedlings from seedling trays and delivers them to the planting mechanism for transplantation. Scholars have borrowed the principle of rice carpet-seedling cutting and transplanting to design vegetable carpet-seedling transplanters [40]. This carpet-style seedling transplanting method can effectively reduce the manufacturing, maintenance, seedling-raising, and time costs of fully automatic transplanters. However, due to the constraints of crop biological characteristics, few vegetable varieties can be grown as carpet seedlings. Currently, only rapeseed [41], tobacco [42], chili peppers [43], and broccoli [44] are suitable for carpet-seedling cultivation.
## 2 Space Segment
### Satellite Description and Classification
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Orbit** & **Altitude** &
\begin{tabular}{c} **Onboard** \\ **Angular Range** \\ \end{tabular} & **Visibility Time** & **Latency** \\ \hline VLEO & \textless{}500 km & Beyond \(\pm\) 60\({}^{\circ}\) & \textless{}20 min. & \textless{}20 ms \\ LEO & \textless{}1000 km & \(\pm\)60\({}^{\circ}\) & 20 min. & \textless{}20 ms \\ MEO & \textless{}10,000 km & \(\pm\)20\({}^{\circ}\) & 45 min. & \textless{}100 ms \\ GEO & 35,786 km & \(\pm\)8.7\({}^{\circ}\) & Permanent & \textless{}250 ms \\ HEO & Up to 40,000 km at apogee & \(\pm\)10\({}^{\circ}\) & A few hours & \textless{}250 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key parameters of typical satellite Earth orbits. for GEO platforms, this may not be sufficient in the case of antenna systems producing highly directive beams. As a rule of thumb, the pointing accuracy is generally specified to be a tenth of the antenna beamwidth to avoid oversizing the performance based on the edge of coverage, including instability. This requires implementing specific tracking systems using beacons on the ground to further improve the pointing accuracy of the antenna, as often used in reflector antenna systems. For smaller satellites, attitude control is not always available. When not present, antennas with quasi-isotropic patterns are generally implemented to ensure a communication link. A solution, also implemented in telemetry and telecommand (TMTC) systems to guarantee a link in case the control of the satellite is lost, consists of using two antennas on opposite faces of the platform with quasi-hemispherical patterns. The following section provides a more detailed discussion of satellite constellations.
# Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach
## 5 Numerical Result  
The task studied in this paper is multi-UAV path planning based on ocean monitoring. In this scenario, the UAVs need to detect and cover targets within a rectangular area in the ocean. There exist unknown obstacles in the area (e.g., sudden emergence of a ship or other UAVs used for detection). Therefore, the UAVs need to autonomously avoid obstacles while detecting the target points. As the target points are all in the ocean area, the scenario can be simplified to a two-dimensional plane.
## 4 Results
### Structural Characteristics of the Causal Feature Network
#### 4.2.2 Nodes' Centrality of the Causal Feature Network  
We use centrality indicators to describe the micro situation of the network and calculate the degree, closeness, betweenness and PageRank centrality of CFN's nodes. The results, as in Table 2, show that the degree centrality ranges from 0.105 to 0.895; closeness centrality, from 0.514 to 0.905; betweenness centrality, from 0.059 to 59.730; and PageRank centrality, from 0.023 to 0.157.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mean Degree** & **Network Diameter** & **Graph Density** & **Mean Agglomeration Coefficient** & **Mean Path Length** \\ \hline
5.1 & 3 & 0.268 & 0.356 & 1.784 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the network’s basic characteristics.  
Figure 5: Visualization of the adjacency matrix of feature causality. The output values are 0 and 1, where 1 represents causal relationships, and 0 represents no such relationships.  
Among all features, fea13 and fea20 have the highest degree, closeness, betweenness and PageRank centrality, which shows that these two features share the most and closest causality with other features, and they, as the bridge, are most passed through by the shortest paths between random features. Other nodes, such as fea1, fea4, fea6 and fea7, rank lower in all centrality indicators. The lower ranking of the nodes in degree, closeness and PageRank centrality shows that they share the least and least close causality with other features, and the lower ranking in betweenness centrality shows that they are marginal in the CFN network and serve less as the bridge.  
Previous studies selected features based on the feature correlations and fault types. Incorporating centrality indicators, we find that features with high centrality indicators are weakly correlated with fault types. The finding shows that high-centrality features will spread their influence among other nodes through the transmission of causality. Therefore, low-centrality features play a critical role in fault type recognition. As low-centrality features are marginal, causalities related to them are easily neglected. However, such causalities are unique. In this case, we establish the feature selection criteria where we select features in ascending order of nodes' centrality. We further verify the criteria's effectiveness.
## 2 Materials and Methods
### Data
#### 2.2.3 CMA's Land Data Assimilation System (CLDAS-V2.0)  
To verify that the TVDI can effectively reflect SM to some extent, this study conducts a correlation analysis between the TVDI and CLDAS-V2.0. The CMA's Land Data Assimilation System (CLDAS) is a land surface observation data assimilation system developed by the China Meteorological Administration (CMA) [42]. It aims to improve the accuracy and reliability of land surface water and energy balance models, providing reliable land surface data for scientific research and practical applications in meteorology, hydrology, and ecology. CLDAS-V2.0 is the second-generation product of the CLDAS system and primarily includes six variables: soil temperature, soil moisture, vegetation coverage, surface temperature, surface energy balance, and precipitation. The technical level and accuracy of the CLDAS dataset meet the standards of similar international products, such as GLDAS and NLDAS. Additionally, the quality of the CLDAS dataset surpasses that of similar products in the China region and offers higher spatial and temporal resolution [43].
## 2 Materials and Methods
### Characterization  
Various characterization tests were performed to compare the features among the zeolite, TiO\({}_{2}\), and TiO\({}_{2}\)/zeolite samples. In the case of pure TiO\({}_{2}\), two types were used: those that were commercially obtained from Sigma-Aldrich and a sample synthesized according to what was described above. The crystal structure and identification of the phases in the obtained samples were analyzed using an X-ray diffractometer (XRD, Aeris Research, Malvern PANalytical, Malvern, UK) equipped with a CuK\(\alpha\) radiation source (\(\lambda\) = 1.5406 A). Morphological analyses were conducted through scanning electron microscopy (Scios 2, ThermoFisher Scientific, Waltham, MA, USA), and energy-dispersive X-ray spectroscopy (EDS, Thermo Scientific, UltraDry) was utilized for elemental chemical analysis with a working range of 20-25 keV.  
To estimate the energy of the band gap of semiconductors, it is crucial to highlight the effectiveness of contemporary methodologies, such as the photoacoustic methodology, in meticulously deciphering the distinct contributions of scattering and absorption. These advanced techniques, which are grounded in the Kubelka-Munk theory, provide a sophisticated perspective on the intricacies of semiconductors, facilitating a more detailed exploration of their energy gap characteristics [30; 31; 32]. In this study, the optical properties of the samples were tested using a UV-vis DRS (diffuse reflectance spectroscopy) spectrophotometer (Perkin Elmer, Waltham, MA, USA) equipped with an integration sphere in the wavelength range of 200-700 nm. The energy band gaps (\(E_{g}\)) were determinedusing the Kubelka-Munk theory and Tauc plot analysis, as per Equations (1) and (2), respectively [33].  
\[F(R)=\left[\frac{K}{S}\right] \tag{1}\]  
\[\left[F(R_{\infty})\ x\ hv\right]^{1/p}=A\big{(}hv-E_{g}\big{)} \tag{2}\]  
where \(K\) and \(S\) are the absorption and scattering coefficients, \(hv\) is the photon energy, \(E_{g}\) is the optical band gap energy, and \(A\) is a proportionality constant independent of the photon energy [34]. To determine the band gap energy (\(E_{g}\)), a linear fit to a plot of \(\left[F(R_{\infty})x\ hv\right]^{1/p}\) versus \(hv\) must be obtained through extrapolation to zero. In the case of an indirect band gap, such as that of TiO\({}_{2}\), a value of \(p=2\) will be used [33; 34].  
The textural properties of the samples were assessed through nitrogen isotherms at 77 K using a Micromeritics Gemini VII (Micromeritics, Norcross, GA, USA), where each sample (approximately 0.2 g) underwent a preliminary degassing at 200 \({}^{\circ}\)C for 2 h. The specific surface area was determined using the multipoint BET calculation method by following the BET expression in its linear form as outlined by [PERSON] [35].
# New Trends in 4D Printing: A Critical Review
## 4 Shape Memory Materials
### Shape Memory Alloys (SMAs)  
SMAs are programmable alloys able to recover their initial shape through external stimulus [98]. Typically, SMAs go through a programming procedure in between two metal alloy transformation steps via a temperature or magnetic field. The transformation phenomenon is known as SME. There are various publications [94; 95; 96] providing an in-depth study of SMAs characteristics; which is not the aim of this section, we will just refer to two important characteristics of these materials for better understanding. The shape shifting capability of SMAs is due to the thermoelastic phase transformation between austenite and martensite. Through heating at a certain temperature, martensite shifts to austenite and shape recovery takes place, returning to the original shape. This reversible transformation can also be provided by using special training processes based on magnetic fields, but they show limitations by means of process repeatability. These alloys are characterized by excellent super-elastic properties [97] and can be used in a wide range of applications: aerospace, biomedical, civil, automotive, and aeronautics [63]. SMAs are typically of two types: \"copper-based\", such as Cu-Al (Zn, Ni, Be, etc.), and nickel-titanium, with a small number of elements such as NiTi (Fe, Cu, Co, etc.). NiTi is the most popular among SMAs, since it allows exceptional applications in orthopedics, cardiology, and neurology [67]. A critical drawback in the NiTi SMA fabrication of complex shapes is the high sensitivity to composition change. Fe-Mn-Si-based SMAs are less sensitive to compositional change and have attracted interest as a cost-effective alternative to NiTi alloys [98].  
One of the issues limiting the application of SMAs is the functional fatigue life due to repeated actuations through mechanical or thermal loading. The increasing accumulation of irreversible strain dictates the SMAs' performance, toughness, and service life. In order to reduce or eliminate this problem, a pathway is added to the typical martensitic phase transformation: it is known as the symmetry-dictated non-phase-transformation pathway. This additional pathway may play a crucial role in functional fatigue.  
SMAs for printing or manufacturing are frequently related to Selective Laser Melting (SLM) technology. Due to their slow rate of degradation, the use of these kinds of SMs is constrained. For example, it is crucial that manufactured implants (like bone scaffolds) that play a significant role in tissue regeneration are absorbed by native tissues at the proper time, atomically, over the course of time. However, because alloys have a low biodegradability rate, the implant may stay in the body for years, leading to the need for additional surgery. So, it appears that further research is needed before SMA can be used in 4 DP to meet current limits and concerns [99].
## 3 Results
### Image Quality Optimization  
The main simulations to optimize the image quality for the selenium detector were carried out for six phantom thicknesses (32 mm, 45 mm, 53 mm, 60 mm, 75 mm, and 90 mm) and three additional filter options (30 \(\upmu\)m Mo, 25 \(\upmu\)m Rh, and 50 \(\upmu\)m Rh) (see Table 1). The simulations were performed for voltages ranging from 20 kV to 40 kV every 1 kV. The number of simulated primary X-rays emitted for a single simulation ranged from \(10^{9}\) for small phantom thicknesses to \(10^{10}\) for large phantom thicknesses. To reduce statistical uncertainty, some low-voltage simulations were repeated with a different value of the starting parameter of the pseudorandom number generator. Figure 6 illustrates the \(Q\) parameter as a function of the tube voltage for the 75 mm-thick breast and various anode-filter combinations.  
Table 5 presents the determined optimal voltages corresponding to the maximum values of the \(Q\) parameter (\(Q_{\text{max}}\)) for breasts of various structures and thicknesses after the breast compression. The obtained results show that the optimal configuration for a digital mammography machine with a selenium image detector is a tungsten X-ray tube with rhodium filtration (the highest values parameter \(Q_{\text{max}}\)). The least preferred system is the Mo-Mo combination, which is routinely used in analog mammography.  
The reason for this is that the spectrum of the tungsten X-ray tube exhibited higher energy than that of the X-ray tubes made of the other considered materials. Although the object's contrast decreased with increasing energy, as the cross-section area for the photoelectric effect decreased, the decrease in object contrast was compensated by the improvement in SNR that occurred for the selenium detector. In the case of analog mammography, this effect cannot be used due to the limited dynamics of the X-ray film. At the same time, amorphous selenium is characterized by a greater cross-section for the absorption of photons with higher energy than the Gd\({}_{2}\)O\({}_{2}\)S, which is the material used for reinforcement screens [35].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Anode-Filter** & \multicolumn{4}{c}{**Mo-Rh**} & \multicolumn{4}{c}{**Mo-Mo**} & \multicolumn{4}{c}{**W-Rh**} \\ \hline
**Voltage (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}\)\) & & & & & & & \\ \hline measurements & & & & & & & & \\ \(HVL\) (mm) & 0.315 & 0.372 & 0.405 & 0.268 & 0.311 & 0.351 & 0.456 & 0.515 & 0.549 \\ \(\Delta\) (mm) & 0.022 & 0.021 & 0.027 & 0.019 & 0.023 & 0.019 & 0.022 & 0.031 & 0.026 \\ simulations & & & & & & & & \\ \(HVL\) (mm) & 0.324 & 0.340 & 0.372 & 0.257 & 0.311 & 0.337 & 0.433 & 0.494 & 0.572 \\ \(\Delta\) (mm) & 0.015 & 0.014 & 0.018 & 0.010 & 0.010 & 0.015 & 0.013 & 0.040 & 0.009 \\ \hline \end{tabular}
\end{table}
Table 4: Determined thicknesses of the Al half-value layer; \(\Delta\), the expanded uncertainty of measurement/simulation for the 95% confidence level.
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 2 Antenna Array Configuration
Therefore, the slot element spacing is set as \(d_{x}=d_{y}=0.5\lambda_{g}\)= \(0.5\lambda_{g\_VP}=0.5\lambda_{g\_HP}\), where \(\lambda_{g\_VP}\) and \(\lambda_{g\_HP}\) are the guide wavelengths in the radiating waveguide of the VP and HP array, respectively. As shown in Figure 1d, the two ends of the radiated waveguide are terminated by two metal plates representing the short circuit, with the distance to the center of the last slot being \(d_{y}/2\). Finally, the aperture size of the proposed dual-polarized RWSAA is \(23.2\times 179.6\) mm.
# Consistency Models of NoSQL Databases
## 3 Consistency Models
### Strong Consistency  
Strong Consistency or Linearization is the strongest consistency model. Each operation must appear committed immediately, and all clients will operate over the same data state. A read operation in an object must wait until the write commits before being able to read the new version. There is also a single global order of events accepted by all storage systems' instances [15].  
Strong Consistency leads to a high consistency system, but it compromises scaling by decreasing availability and network partition tolerance.