esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 5 Other Transparent Nano-Ceramics
### Summary
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Year, Authors** & **Powder and Fabrication** & **Findings** & **Performance** & **Remarks** \\ \hline  & 99.9\% strontium nitrate hydrate, 99.9\% medium nitrate hydrate, and 99.9\% potassium fluoride hydrate, the chemical precipitation method. & SrF\({}_{2}\) nanoparticles with Nd\({}^{3+}\) doping fluoride structure. & The synthesized powder could prepare transparent ceramics with a transmittance of about 80\% at 1060 nm. & Nd\({}^{3+}\) was successfully introduced into the SrF\({}_{2}\) lattice, making it possible to use this material to make transparent ceramics. \\ \hline  & Nd:Ca\({}_{0.94}\)Gd\({}_{0.00}\))\({}_{2.06}\) nano powder; deionized water coreprication & As the Nd\({}^{3+}\) content increased from 0.5 to 5.0, the measured lifetime dropped sharply from 484.9 μs to 47.8 μs. & The transparent ceramic had high transparency and an almost non-porous microstructure. & The thermal conductivity of Nd:Ca\({}_{0.94}\)Gd\({}_{0.00}\)F\({}_{2.06}\) transparent ceramics was better than that of traditional laser glass, and transparent ceramic is a promising laser material. \\ \hline  & Industrial oxide powder, 0.3\% Ce: GGA\({}_{0.00}\)Gd\({}_{2.991}\)-Al\({}_{2}\)Ga\({}_{5}\)O\({}_{12}\) ceramic sample. & At 1250 \({}^{\circ}\)C, the formation of the GGA phase could be observed. This corresponds to the increase in shrinkage observed above 1200 \({}^{\circ}\)C, with the optimum at 1430 \({}^{\circ}\)C. & TEOS was determined to be the most suitable sintering aid in the tests, providing the highest sample density and clarity. & The optimum amount of sintering yield should be  further investigated. \\ \hline  & High-purity (99.99\%) starting material and 0.5\% tetraethyl orthosilicate (TEOS): the competition method. & The Ce\({}^{3+}\) comprehensive RT strength of single crystal increased 1.4 times from RT-450 \({}^{\circ}\)C, while polycrystalline ceramics exhibited high optical transparency up to about 2.5 eV. & Both single-crystal and polycrystalline ceramics could adapt to a wide temperature range and could be applied to many occasions. \\ \hline  & Mixure of 4 N-purity Lu\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), and La\({}_{2}\)O\({}_{3}\) oxides; the u-PD method using RF induction heating. & With the increase in La content, the main luminescence in the UV region gradually moved from 330 nm to 295 nm. & LuAG: La doping significantly reduced the scintillation afterglow of the LuAG: La crystals. \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of scintillation transparent nano-ceramics described in the text grouped by publication year.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 5 Simulation Results
### Channel Estimation Comparison with Number of Pilots  
The pilot is special data that is arranged on a specific subcarrier of a certain part of the OFDM symbol on the base station, and the receiving end estimates the channel according to the received pilot. The pilot data occupies a certain transmission resource. In order to improve the spectral efficiency of the system, it is inevitable to reduce the proportion of pilot data in each frame of OFDM symbols and reducing the number of pilots will inevitably affect the estimation performance. Therefore, it is necessary to carry out simulation experiments on the influence of the number of pilots on the performance of the channel estimation algorithm.  
Figure 6 shows that the NMSE of the SSA-CoSaMP algorithm, the S-CoSaMP algorithm, and the CoSaMP algorithm varies with the number of pilots when the SNR is 10 and 20 dB, respectively. It can be concluded from the experimental results that under different SNRs, the estimation performance of the three algorithms is improved as the number of pilots increases. When the number of pilots is lower than 750, the performance of the proposed SSA-CoSaMP is the best, followed by the S-CoSaMP algorithm, and the estimation performance of the CoSaMP algorithm is the worst. When the number of pilots is higher than 750, the estimated performance of the S-CoSaMP and the SSA-CoSaMP algorithms is basically the same.
# Design and Evaluation of a Shaping and Pruning Machine for Dwarf and Densely Planted Jujube Trees
## 3 Results
### Results of Parameter Optimization and Field Validation Test  
#### 3.2.1 Results of Parameter Optimization  
To find the optimal combination of factors and parameters in the optimal state of the shaping and pruning machine and obtain better shaping and pruning performance for juibube trees, the missed pruning rate and stubble breakage rate in the interval [5%, 10%] were selected as the optimization targets to optimize the working parameters and structural parameters of the machine. The optimization module in Design-Expert 8.0.6.1 data analysis software was used to solve the two regression equation models (Equations (16) and (17)) with multi-factor optimization. Its objective function and constraint conditions are shown in Equation (18) [8].  
\[\left\{\begin{array}{c}P_{1}(X_{1},X_{2},X_{3})\in[5\%,10\%]\\ P_{2}(X_{1},X_{2},X_{3})\in[5\%,10\%]\end{array}\right. \tag{18}\]  
Figure 14: Response surface diagrams of influence of significant interaction factors on stubble breakage rate. (**a**) \(X_{3}=0.65\) m/s; (**b**) \(X_{2}=250\) mm.  
When the forward speed was set to 0.3 m/s, the response objective function had a coverage area, and the coverage diagram of the optimization for the response objective function was obtained, as shown in Figure 15. The combination with the highest degree of satisfaction was selected as the best parameter combination. The best parameter combination was the rotation speed of the cutter at 2300 r/min, the diameter of the cutter at 250 mm, and the forward speed at 0.3 m/s. In this case, the model predicted the missed pruning rate to be 5.07% and the stubble breakage rate to be 4.61%.
## 4 Numerical Verifications
### Model Validation  
The neutral axis locations were calculated using the strain extracted from the FEM under the same conditions as the experimental test of the beam during the post-tensioning process, i.e., the beam is subjected to a prestress force of 62 kN and 107 kN, respectively. The locations of the extracted strain are the same as those of the strain gauges attached to S2 and S3 of the beam in the experimental test. The neutral axis locations obtained from the strains for the second and third segments of the numerical beam model are denoted by MNAL2  
Figure 9: Developed finite element model in ANSYS.  
and MNAL3, respectively. Furthermore, the static responses, namely, vertical deflections under 15 kN and 20 kN loading levels, are also used for verifying the accuracy of the numerical model. The locations of the deflections extracted from the FEM are the same as those LVDTs installed in the experimental tests, as shown in Figure 8. Mid-span deflection is calculated as the average value of the displacement data measured from LVDT2 and LVDT3. The deflections obtained from the experimental test are represented as EL1, EL23, and EL4, and the deflections from the FEM are expressed as ML1, ML23, and ML4. The results of neutral axis locations and the deflections from the numerical model are compared with experimental results and presented in Tables 5 and 6, respectively.  
Table 5 indicates that the neutral axis locations from the experimental and numerical results of the segmental beams are close to each other for the cases of 62 kN and 107 kN prestress force. The relative errors are less than 3%, which implies that this model can basically represent the tested beam. The experimental deflection results of the segmented beam in general also agree with the numerical results, except at location L4, where a larger error of 11.81% is obtained. These errors might be introduced by the uncertainties in the FEM, such as the assumptions of the boundary conditions and material properties of the initial model. The differences in the neutral axis locations and the deflections between the experimental and numerical results show that it is necessary to refine the original FEM to ensure that the built numerical model can be used to represent the structural behavior more accurately. Therefore, in this study, the neutral axis locations and structural deflections under static loading tests are adopted to conduct the finite element model updating analysis. In particular, the neutral axis locations with 107 kN prestress force during the post-tension process and the deflections at four locations on the top of the beam under two specific static loading levels of 15 kN and 20 kN, are used.
## 1 Introduction
### Importance of Study Area  
The CPEC is a major project aimed at connecting western China to the Arabian Sea through the Port of Gwadar. [41]. This project is a key element of the Belt and Road Initiative (BRI), aimed at linking Asia with other continents through ports, highways, and railways [42]. The CPEC project will provide China with a shorter and more secure route to access the Arabian Sea, which will reduce the distance and time needed for shipping and trade [43]. Also, the project will be helpful in developing the industrial and agricultural sectors, which will create new business opportunities and increase Pakistan's export potential [44]. The total estimated cost of the CPEC project is around USD 62,000 million [45].  
Gwadar, located on the southwestern coast of Pakistan, in the eastern part of the MSZ, has become an important economic hub in the country due to its strategic location and the development of the Gwadar Port as part of the CPEC [46]. It is situated at the entrance of the Persian Gulf, close to major shipping lanes, making it a strategic location for trade and commerce. The development of the Gwadar Port has transformed the city into an important hub for maritime trade. The port has the potential to become a major transshipment hub in the region, connecting South Asia, Central Asia, and the Middle East, which will have a positive impact on Pakistan's economy.  
In essence, the investment in Gwadar under the CPEC is anticipated to elevate the city into a prominent economic hub in the region. The development of the port is expected to boost trade and commerce, significantly impacting the economic development of Pakistan [47; 48]. The major completed and ongoing projects are tabulated in Table 1.  
Figure 2: Seismic map of MSZ, adapted from Ref. [40].
## 4 Technological Innovations
### Voice Recognition  
Voice recognition has also become a cornerstone of hands-free interaction. It was introduced in 2005, in Honda Acura and Odyssey models, where navigation systems allowed drivers to select street names using voice commands [34]. Currently used systems, such as Amazon Alexa Auto [35] and Google Assistant [36], leverage advanced natural language processing to interpret commands in conversational language, providing drivers with a safer means of controlling navigation, climate, and infotainment systems. Tesla's voice control system [37] allows drivers to configure specific vehicle settings using spoken commands, thus minimizing the need for manual input. Studies show that voice systems not only enhance safety but also improve overall user satisfaction, particularly among demographics less comfortable with touch interfaces. Voice recognition systems enable a more intuitive and natural interaction with the vehicle, which is crucial for enhancing driver comfort and safety [38; 39].  
Voice-based HMIs, shown in Figure 4, are a key enabler for multimodal interaction and also strengthen connectivity when integrated with cloud-based services such as Amazon Alexa. They significantly support safety goals by allowing hands-free operations.
## 4 Numerical Calculations
### The Constitutive Model and Parameter Calibration  
The Mohr-Coulomb model is typically used to simulate the deformation and failure of engineering rock masses. However, in actual engineering, after the excavation of the roadway, the shallow surrounding rock yields and fails first, and the cracks expand deeper, i.e., strain softening occurs. In the linear phase, the strength parameters of the surrounding rock are significantly reduced. When the stress reaches peak strength with continued deformation, deterioration becomes more prominent. The material parameters in the Mohr-Coulomb model are always pre-set as constants, but the softening effect due to changes in the surrounding rock parameters cannot be reflected. In the strain-softening model, change in the material cohesion, friction angle, and dilatancy angle with plastic strain is considered, and their relationship can be defined by a piecewise-linear function. Furthermore, the yield criterion, potential function, plastic flow law, and stress correction in this model are identical to those of the Mohr-Coulomb model.  
Based on the observations from the drill holes of the surrounding rock within 3 m of the roadway, a strain-softening model was used to describe the nonlinear mechanical characteristics of the shallow surrounding rock. The Mohr-Coulomb model and the empty model were applied to model the roadway excavation for the deep surrounding rock; simultaneously, the beam element was used to simulate the shed support. The rock mass parameters obtained from laboratory tests could not be directly applied to the numerical calculations. To select the appropriate parameters for the strain softening model, multiple excavation calculations were performed based on the laboratory data to ensure that the deformation of the surrounding rock is consistent with the monitored results. In this study, before the optimization of the roadway support scheme, the roof and floor moved 1000 mm from each other, and the movement amount used in this model was 1029 mm. The parameters of the Mohr-Coulomb model are listed in Table 2, and the parameters of the strain-softening Mohr-Coulomb model are listed in Table 3. Table 3 shows the variation of cohesion, tensile strength and friction angle with plastic shear strain.  
Figure 4: Numerical calculation model.
## 3 Methods  
In Section 3.1, the functional model of the network and user is presented. This entails the observation equations of the code and phase measurements, and the handling of rank deficiencies in the resulting design matrix compatible with sequential processing. Then, in Section 3.2, the sequential processing scheme is presented. This includes specific properties of the processing, including the narrow-lane and wide-lane implementations, the process noise of the parameters, and details of the method of ambiguity resolution.  
\begin{table}
\begin{tabular}{c c c} \hline
**GNSS** & **Receiver Type A** & **Receiver Type B** \\ \hline GPS & *1C, *2W, *5Q & *1C, *2W, *5X \\ Galileo & *1Q, *5Q, *6C, *7Q, *8Q & *1X, *5X, *6X, *7X, *8X \\ BDS & *2I, *6I, *1P, *5P & *2I, *6I, *1X, *5X \\ \hline \end{tabular}
\end{table}
Table 1: GNSS and signals considered. Note that the signal name follows the RINEX 3 conventions. The * is a wildcard for C (= code) or L (= phase).  
Figure 1: Map of reference stations used. Station identifiers of the network: AJAC, BOR1, BRST, BRUX, BUCU, CEBR, DLF1, DYNG, EGLT, EUSK, GANP, GOPE, HOFN, INVR, KIRU; and the rover: AUBG, KIRU, LEK6, MAR7, OBE4, ONSA, ORID, OST6, PTBB, RABT, REDU, SALA, SBG2, SPT0, VFCH, VILL, WSRT.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 3 Results
### Experiment 1: Algorithm Comparison
#### 3.1.1 Difference Analysis for Direct and Exp  
First, we compared the difference (E \(-\) T), in which E is the BA integrated by the exp and direct Abel integral algorithms, together with two interpolation methods. The altitude starts from 5 km because the signal-to-noise ratio below it is low, and some RO observations are unavailable. As shown in Figure 3a,b, differences of both algorithms are in the same order of magnitude; 1 \(\times\) 10\({}^{-5}\) is within 5-30 km, and 1 \(\times\) 10\({}^{-6}\) is within 30-60 km. However, the exp is more accurate than the direct because it is closer to the true value except at heights of 12.5-17.5 km, which reflects the errors passed by refractivity. Figure 3c,d shows the difference between the two algorithms. The result of the direct is larger than that of the exp until at a height of 50 km. Figure 3e,f shows the difference due to interpolation. In the height range of 5-30 km, the error induced by log-linear is less than \(\pm\)1 \(\times\) 10\({}^{-6}\), and above 30 km, it is less than \(\pm\)0.5 \(\times\) 10\({}^{-7}\). Interestingly, log-linear interpolation causes more undulation compared with log-cubic interpolation. The undulation due to interpolation is larger for the exp algorithm. To verify this result, we repeated this experiment using MetOp as the true value, and the same conclusion was drawn (see Figure A4 in Appendix B).
## 3 Model Calibration  
Corrections for the gas environment include intake flow, intake temperature, intake pressure, and exhaust temperature.  
Figure 4 shows the error curve of the comparison between the simulation results of the intake and exhaust parameters and the test value of 600 kW. It can be seen from the figure that the errors are controlled within 5%.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **X (mm)** & **Y (mm)** & **Z (mm)** & \\ \hline Spark location & 0.0 & 0.0 & \(-\)3.0 & \\ \hline  & **X (mm)** & **Y (mm)** & **Diameter (mm)** & **Height/Depth (mm)** \\ \hline Head dome & 0.0 & 0.0 & 190.0 & 0.0 \\ \hline Piston cup & 0.0 & 0.0 & 136.0 & 18.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Some flame object parameters.  
Figure 5 shows the simulated and measured cylinder pressure at the condition of 600 kW and 400 kW, 1000 r/min. The excess air/fuel ratio is fixed at 1.36. The spark ignition timing is set at -22 \({}^{\circ}\)CA.  
Figure 4: Comparison between simulation and test of intake and exhaust parameters. (**a**) Intake flow (**b**) Intake Pressure(**c**) Intake temperature (**d**) Exhaust temperature.  
Figure 5: Comparison of the simulated and measured cylinder pressure.  
In the simulation, the initial spark size accounts for the energy to initiate the flame kernel and was adjusted to a value of 1 mm in diameter to fit the measured cylinder pressure trace. Although the simulated \(p_{max}\) is a little lower than the test data under 600 kW case, there is no doubt that the error (<5%) between simulation and actual test data is acceptable.  
At the same time, the discharging flow was calibrated too. For 600 kW operations, the simulation shows that the air flow rate is 1640 kg/h with a pressure ratio of 1.69, and the exhaust temperature is 860 K. Their errors are 3.96%, 0.05%, and 0.77%.
## 1 Introduction and Motivation
### Contributions of the Proposed Method  
Several approaches have been presented for solving a motion-planning problem for autonomous vehicles. All presented approaches have their own advantages and disadvantages. For example the neural-network-based approach can be computed faster than the graph-based methods, but guaranteeing the performances of this algorithm is challenging. However, the neural-network-based algorithm through the learning process can take into consideration more factors, such as comfort or energy requirements. The goal of this paper is to present a low-complexity motion-planning for overtaking scenarios in parallel traffic. The proposed graph-based motion-planning algorithm takes into consideration the predicted motion of the surrounding vehicles.  
The motion prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. Using density functions, the probabilities of collision with another vehicle can be computed for the given prediction horizon. During motion-planning, some constraints must be guaranteed. The first one is to guarantee the safety of the planned trajectory and the second one is to guarantee the comfort requirements. The safety requirements can be guaranteed through the limitation of the maximum lateral displacement of the vehicle from the reference trajectory and the comfort requirements can be guaranteed through the minimization of the lateral acceleration values.  
Briefly, the algorithm consists of the following parts:  
1. The core of the motion-planning algorithm is a graph-based method. The generation of the graph is mainly based on the lateral dynamics of the vehicle, which is presented in Section 2. Furthermore, the generated graph is extended with the result of the motion prediction in order to get a collision-free trajectory.
2. In the next step, the motion prediction of the surrounding vehicles must be performed. The motion of the vehicles is predicted using density functions. These functions are determined using the Next Generation Simulation (NGSIM) dataset. The computation of these function is detailed in Section 3.
3. Finally, the computation of the feasible trajectory is made using the neural-network-based approach.
## 2 Antenna Geometry and Design  
The proposed simple and compact CP DRA antenna design consists of a Dielectric Resonator (DR), substrate, coaxial probe feeding and a copper strip on the top of the DR. The initial dimensions of the DR are finalized with the help of the Dielectric Wave Guide model (DWM) [3; 23]. A slot of rectangular shape is also carved in the ground plane. Figure 1a,b and c suggests the detailed geometry of the proposed design. The DR has dimensions of \(L_{\text{DR}}\), \(W_{\text{DR}}\) and h, which are the length, width and height of the DR, respectively, while its permittivity is 10.2. The DR is positioned on the top surface of the substrate which consists of Roger material with 2.33 relative permittivity (\(\varepsilon\)r) and loss tangent tan\(\delta\) = 0.0012. \(L_{\text{sub}}\) and \(W_{\text{sub}}\) are the length and width of the substrate. \(L_{\text{slot}}\) and \(W_{\text{slot}}\) are the dimensions of the ground slot. A stacked metallic strip on the top of the DR has the same width as that of the DR while having 1 mm length. The details of the dimensions are given in Table 1.
## 3 Experiments and Analysis
### Model Evaluation Criteria
This suggests that the model is essentially making random predictions akin to a coin flip, unable to correctly differentiate between positive and negative samples. Figure 3 portrays the extreme case where the _TPRate_ is lower than the _FPRate_. Here, the AUC value is less than 0.5, indicating that the model is more likely to classify sample categories incorrectly. Interestingly, reversing the predicted categories in this scenario could potentially yield a capability evaluation model with an AUC greater than 0.5. The evaluation metrics of Accuracy, F1 score, and AUC are typically employed to gauge a model's proficiency in distinguishing positive instances in binary classification problems. However, given the multiclass nature of the ability evaluation issue addressed in this paper, relying solely on Accuracy, F1 score, and AUC for evaluation is not feasible. Consequently, we adopt a \"one-vs.-one balanced\" strategy (Macro Average), wherein each label serves as the positive class once, with the remaining acting as the negative class. The Accuracy, F1 score, and AUC are calculated at each iteration, followed by averaging the totals. A higher average value across Accuracy, F1 score, and AUC indicates a stronger classification performance of the ability evaluation model. Figure 1: Scenario where _TPRate_ is greater than _FPRate_. Figure 2: Scenario where _TPRate_ equals _FPRate_.