esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Studies
### Effect of Strain-Rate Hardening Definition  
In the actual case, the dynamic collision responses are highly sensitive to strain rate because the material strength increases when the strain rate increases. Thus, it is necessary to consider the effect of strain rate on uncertainties in the numerical modelling for improving the accuracy and reliability of the numerical results. In this section, the comparisons of some well-known equations were discussed. Firstly, the Cowper-Symonds [33] plastic constitutive equations as Equation (11), these equations are widely applied for collision behavior of structures. In Equation (10), \(\sigma_{YD}\) and \(\sigma_{Y}\) are dynamic yield strength and static yield strength, respectively. \(\dot{\varepsilon}_{p}\) is equivalent strain rate while \(D\) and \(q\) are material constants. Generally, the coefficients \(D\) and \(q\) were estimated by \(D\) = 40.4 s\({}^{-1}\) and \(q\) = 5 for mild steel, and \(D\) = 3200 s\({}^{-1}\), \(q\) = 5 for high tensile steel. Secondly, [PERSON] and [PERSON] [34] provided the equation to improve the Cowper-Symonds equations by replacing the coefficient \(D\) as Equation (12). Finally, the formulations proposed by [PERSON] et al. [35] are also considered as Equations (6)-(9) in the previous section.  
\[\sigma_{YD}=\sigma_{Y}\bigg{[}1+\bigg{(}\frac{\dot{\varepsilon}_{p}}{D}\bigg{)} ^{1/q}\bigg{]} \tag{11}\]  
\[D=\left\{\begin{array}{cc}92000\ \exp\Big{(}\frac{\dot{\sigma}_{Y}}{364} \Big{)}-193779&\text{for $\sigma_{Y}>271$}\\ 40&\text{for $\sigma_{Y}\ \leq\ 271$}\end{array}\right. \tag{12}\]  
The numerical investigation results in terms of force-displacement relation curve are plotted in Figure 7. It is noticeable that the strain-rate hardening definitions are not influenced on the overall response tendencies of all curves. However, it is strongly affected by the maximum permanent deflection because the strain-rate hardening definitions slightly increased or decreased the stiffness of the struck model. When neglecting the strain rate effect (Case 4), the permanent defection is the largest, while that of Case 1 ([PERSON]'s equation) is the smallest. The permanent deflection value of Case 4 is larger than that of Case 1 with 12.5%. When the Cowper-Symonds formulation was applied with the coefficient D = 3200 s\({}^{-1}\) (Case 3), and D was calculated from equations provided by [PERSON] and [PERSON] (Case 2), the differences of permanent deflections are 10.9% and 9.1% compared with Case 4, respectively. Therefore, it is recommended that the strain-rate hardening definition should be considered in the numerical analyses for improving the accuracy and reliability of numerical results.
## 3 EDFA's Space Qualification and Test
### Radiation Tests  
The basic strategy to protect the EDFA against radiation was to shield the electronics module with an aluminum case instead of utilizing radiation-hardened components, with the exception of the fibers, which are partially exposed outside the case. To verify this approach, a preliminary test was carried out by using two dummy (non-functional) units which include only the electronic board inside the case (see Figure 6). In order to evaluate any variation after the TID test, the internal voltage and current were measured in 11 critical points of each of the 2 units, including the DC/DC converters, the low-dropout voltage regulators and the pump-current drivers. Non-significant variations, in the order of 0.1%, were found before and after these tests.  
The final radiation tests were carried out over the final two test EDFA modules, which are identical to the third flight model, with the goal of verifying their overall performance. During these tests, the output optical power as well as the noise figure was measured before and after the radiation exposure for stage 1 and stage 2 and for the whole EDFA module. The total degradation was smaller than 0.1 dB for the optical power (from 32.44 to 32.36 dB) and smaller than 0.5 dB for the noise figure (from 5.47 to 5.94 dB) after being exposed to a gamma-radiation TID of 20 krad.
## 8 Current Challenges and Future Prospects
### Machine Learning and Artificial Intelligence  
Machine learning (ML) and artificial intelligence (AI) are utilized to mine data for trend analysis. For example, in agriculture, ML/AI is applied to explore which genes are very appropriate to produce high-yield crops. This gives farmers worldwide better varieties of seeds that are suitable for the location and climate. On the other hand, ML/AI algorithms have demonstrated which goods are in great demand and which goods are not accessible in the market. Thus, for growers, this gives valuable information for upcoming farming. The latest advances in ML/AI will make it possible for growers to correctly classify their crop products and eliminate fewer advantageous products before they start planting.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 4 Time Delay Optimization of Compressing SVSV Based on Deep Learning
### Modeling of CU Partition Structure  
In order to be able to describe the different ways of dividing different CUs in the ship vision sensor images, a hierarchical structure model of CU division based on the ship vision sensor images is proposed, as shown in Figure 10. Where \(\kappa e[0,1,2,3],i,\ je[1,2,3,4]\). The CU at different sizes and coordinates are uniformly represented by D(\(x,i,j)=0,1\), where x is the depth corresponding to the current CU size, \(i,j\) are the coordinates of the  
By using neural networks to predict all possible ways of dividing the current CTU, the structured output of a total of 21 CUs can be directly derived, saving the computational time consumed by predicting them one by one. Finally, based on the predicted CTU division structure, the step of calculating and comparing the CU rate distortion cost is skipped, and the computational redundancy caused by recursively traversing the CU rate distortion cost is avoided to a certain extent, which helps to reduce the complexity of intra-frame PC.
###### Abstract  
During the last 15-20 years, the experimental methods for autonomous navigation and inter-satellite links have been developing rapidly in order to ensure navigation control and data processing without commands from Earth stations. Inter-satellite links are related to relative ranging between the satellites from one constellation or different constellations and measuring the distances between them with the precision of at least 1 \(\upmu\)m micrometer (=10\({}^{-6}\) m), which should account for the bending of the light (radio or laser) signals due to the action of the Earth's gravitational field. Thus, the theoretical calculation of the propagation time of a signal should be described in the framework of general relativity theory and the s.c. null cone equation. This review paper summarizes the latest achievements in calculating the propagation time of a signal, emitted by a GPS satellite, moving along a plane elliptical orbit or a space-oriented orbit, described by the full set of six Kepler parameters. It has been proved that for the case of plane elliptical orbit, the propagation time is expressed by a sum of elliptic integrals of the first, the second and the third kind, while for the second case (assuming that only the true anomaly angle is the dynamical parameter), the propagation time is expressed by a sum of elliptic integrals of the second and of the fourth order. For both cases, it has been proved that the propagation time represents a real-valued expression and not an imaginary one, as it should be. For the typical parameters of a GPS orbit, numerical calculations for the first case give acceptable values of the propagation time and, especially, the Shapiro delay term of the order of nanoseconds, thus confirming that this is a propagation time for the signal and not for the time of motion of the satellite. Theoretical arguments, related to general relativity and differential geometry have also been presented in favor of this conclusion. A new analytical method has been developed for transforming an elliptic integral in the Legendre form into an integral in the Weierstrass form. Two different representations have been found, one of them based on the method of four-dimensional uniformization, exposed in the monograph of Whittaker and Watson. The result of this approach is a new formulae for the Weierstrass invariants, depending in a complicated manner on the modulus parameter q of the elliptic integral in the Legendre form.  
GPS inter-satellite communications; general relativity; elliptic functions and integrals +
Footnote †: journal: _Mathematics_**2025,** 13, 1286
# Deep-Learning Based Positron Range Correction of PET Images
## 2 Materials and Methods
### PET Acquisition Simulation and Reconstruction  
In order to generate images more similar to actual PET reconstructed images, the positron annihilation distributions obtained from PenEasy were used to simulate a realistic PET acquisition in a generic preclinical scanner similar to an Inveon PET/CT scanner [39] using the MC simulator MCGPU-PET [40]. MCGPU-PET is a version of the MC-GPU software adapted for PET, which was developed for X-ray imaging [41]. MCGPU-PET allows very fast and realistic simulation of PET acquisitions from voxelized activity, material, and density distribution. MCGPU-PET can generate data which can be histogrammed into 3D sinograms, in our case with 147 \(\times\) 168 \(\times\) 1293 bins considering a maximum ring difference of 79, an axial compression factor of 11 and a radial bin size of 0.795 mm. MCGPU-PET simulations in a computer with a GeForce GTX 1080 8 Gb GPU contain around 1.2 \(\times\) 10\({}^{9}\) coincidences in a minute (2 \(\times\) 10\({}^{7}\) coincidences/second), including scatter and non-scattered true coincidences.  
For reconstruction of the sinograms, we used GFIRST [42], which is the GPU-accelerated version of FIRST [30], a 3D-OSEM algorithm which allows a physical model to be incorporated into the SRM. In this case, the SRM used was the standard one created based on \({}^{18}\)F in water. We used one subset and 40 iterations. The final images consisted of 154 \(\times\)154 \(\times\) 80 voxels with a size of 0.28 \(\times\) 0.28 \(\times\) 0.795 mm, which is the typical size of  
Figure 3: Comparison of cross-sections of the mouse between the emission image (**a**), the \({}^{18}\)F positron annihilation image (**b**), and the \({}^{68}\)Ga positron annihilation image (**c**).  
the images reconstructed in the Inveon scanner [39]. The total reconstruction time was 50 s in a GTX 1080 8 Gb GPU. The values of the reconstructed images were converted into standardized uptake value units (SUV) to make it easier to evaluate the performance of the method. Using SUV units, a radiotracer with uniform distribution in the body would have a SUV of 1.  
At the end of the whole simulation workflow (see Figure 2), eight mice were simulated with \({}^{18}\)F and \({}^{68}\)Ga, for a total of 640 slices. In order to make the size of the slices more tractable to the neural network, each image of 154 \(\times\) 154 pixels was padded with zeros to reach a size of 160 \(\times\) 160 pixels.
## 2 System Description  
Three different IR-PCFC/GT systems are analyzed in this study. All systems consist of a PCFC, a combustion chamber (CC), a turbine (T), an air compressor (AC), two pumps (P1, P2), two heat exchangers (HX1, HX2), two mixers (MX1, MX2), and one gas splitter (SPLTR) as shown in Figures 1-3. In all the systems proposed in this study, there is no change in the inlet and outlet flow channel numbers of the main component. But, the arrangement of the IR-PCFC/GT exhaust gas is different to study the effect of heat recycling on the system performance. The main idea of the first system is to provide high-quality heat to anode preheater for the evaporation process. The idea of the second system is to provide high-quality heat to cathode preheater as the air flow rate is higher than the anode fuel flow rate. So, the temperature of the cathode is important for uniform heat distribution in the fuel cell, whereas in the last system, the high-quality heat from GT is proportionally split for preheating the anode and cathode reactants. The detailed operating principles of system operation for each IR-PCFC/GT system are as follows:Figure 2: Case 2, high-quality heat from turbine flows into the cathode preheater (HX2). Numbers 1, 1a, 1b, 1c and 2 on the red lines indicate cathode flow, numbers 3, 4, 5 and 6 on the green lines indicate anode flow, and numbers 7, 8, 9 and 10 on the blue lines indicate combustion flow.  
Figure 1: Case 1, high-quality heat from turbine flows into the anode preheater (HXI). A, AC, K, HX, MX, P, SPLTR, and T stand for the anode, air compressor, cathode, combustion chamber, heat exchanger, mixer, pump, splitter, and turbine, respectively. Numbers 1 and 2 on the red lines indicate cathode flow, numbers 3, 4, 5, 5a, 5b, 5c and 6 on the green lines indicate anode flow, and numbers 7, 8, 9 and 10 on the blue lines indicate combustion flow.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### Integration of the Vision-Based Technologies Together and with Other RTLS
#### 4.3.4 Point Cloud Data Fusion with UWB Data
However, the discrepancy between the accuracy of the UWB data and LiDAR mapping data need to be addressed [6,28].
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 5 Experimental Results
### Results Analysis  
First, Table 3 illustrates the comparison of our method with the baseline models in multi-domains, evaluated using the JGA. It can be observed that our method improves the overall performance of the model across multiple domains. Secondly, Table 4 presents the comparison of our method with the baseline models in single domains. The results indicate that the ER-DST outperforms the STAR baseline model in the attraction, hotel, and taxi domains. Moreover, it surpasses all baseline models in the hotel and taxi domains, achieving significant single-domain JGA. Furthermore, we conducted a detailed analysis of our method's performance on the 30 specific slots in the entire MultiWOZ 2.1, comparing it with the STAR and TripPy in Figure 4. As a result, 17 slots achieved better results compared to the baselines. For instance, slots like \"hotel-area\", \"restaurant-area\", \"taxic departure\", \"taxi-destination\", \"train-departure\", and \"train-destination\" contain values that are geographical locations, often exhibiting relationships among them. Since our  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CSFN-DST** & **SOM-DST** & **TripPy** & **STAR** &
\begin{tabular}{c} **ER-DST** \\ **(Ours)** \\ \end{tabular} \\ \hline Attraction & 64.78 & 69.83 & 73.37 & 70.95 & 71.16 \\ Hotel & 46.29 & 49.53 & 50.21 & 52.99 & 53.21 \\ Restaurant & 64.64 & 65.72 & 70.47 & 69.17 & 67.03 \\ Taxi & 47.35 & 59.96 & 37.64 & 66.67 & 66.95 \\ Train & 69.79 & 70.36 & 72.51 & 75.10 & 74.64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The joint goal accuracy (%) for specific domains in the MultiWOZ2.1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline No Slot Relation & 46.25\% \\ self-attention & 56.36\% \\ layer norm & 55.19\% \\ \hline Our Full & 56.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on the MultiWOZ 2.1.  
method enhances the capability to identify relationships between slots, the performance of these slots has been improved.  
The above results indicate ER-DST enhances the perception of slot relationships. However, although our model reduces the error rates for several slots related to \"names\", such as \"restaurant-name\" and \"hotel-name\", they still exhibit relatively high error rates. For some slots with numerical values, such as \"time\", there is still room for improvement, especially for non-enumerable slots of this type.
## Materials
### Nano Hydrophobic Silane Silica
The SEM images of the matrix asphalt with 3% nanosilica (NS-MA) and the matrix asphalt with 3% NHSS (NHSS-MA) are demonstrated in Figure 3a,b and the light particles observed in Figure 3 are NHSS and nanosilica. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Technical Parameters**} & **BET** & **Average** & **Loss on Drying** & **Loss on Ignition** & **Carbon** & **SiO\({}_{2}\)** \\  & **Particle** & **(105 °C, 2 h, wt\%)** & **(1000 °C, 2 h, wt\%)** & **PHI** & **Cantent** & **Content** \\  & **(m\({}^{2}\)/g)** & **Size (nm)** & **wt\%)** & **wt\%)** & **Value** & **(\%)** & **(\%)** \\ \hline
**Test results** & \(125\pm 20\) & 12 & \(\leq\)0.5 & 1.5–2.5 & 5.0–8.0 & 2.0–3.5 & \(\geq\)99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Technical parameters of nano hydrophobic silane silica (NHSS). Figure 1: Grafting process of silane coupling agent on nanosilica. Figure 3: Scanning electron micrographs of nanosilica modified asphalt and NHSS modified asphalt. Figure 2: SEM images of nanosilica and nano hydrophobic silane silica (NHSS) at magnifications of \(\times\)10000. It can be seen from Figure 2 that the surface of aggregates formed by the NHSS particles is rough compared with nanosilica particles, and the NHSS particles combine to form microchains and micro-mesh structures in three-dimensional reticular distribution. The three-dimensional distribution of NHSS particles is due to the formation of an organic film on the NHSS particles by the silane coupling agent, and the organic film reduces the tension on the surface of the particles, exerts a good steric hindrance effect, and effectively inhibits the agglomeration between the particles. Moreover, the multilayer structure and micro-pores of NHSS particles aggregate contributed greatly to the adhesive property of modified asphalt. From Figure 3, it can be observed that the size of NHSS particles aggregates is smaller than that of nanosilica particles aggregates in the same random region, which indicates that NHSS has better dispersibility in asphalt. The main reason for the difference of dispersion between nanosilica and NHSS in asphalt is the surface of NHSS contains a large number of atoms, which makes it easier to form a short-range annular diffusion channel in asphalt compared with the nanosilica. Thus, the NHSS has higher diffusivity in the preparation process of the modified asphalt.
# Robust LQR Control for PWM Converters with Parameter-Dependent Lyapunov Functions
## 3 New Formulation of the LQR Problem for Linear Parameter-Varying (LPV) Polytopic Systems  
### Proposed Representation of Uncertainty and Its Rate of Variation  
Let us consider the continuous linear parameter time-dependent system, given by the state representation:  
\[\dot{x}(t)=A(\theta(t))x(t)+B_{u}(\theta(t))u(t) \tag{7}\]  
where \(x(t)\in\Re^{n}\) is the state and \(u(t)\in\Re^{m}\) is the input.  
We assume the system matrices \(A(\theta(t))\) and \(B_{u}(\theta(t))\) are dependent on the parameter \(\theta_{i}(t)\), i.e.,  
\[A(\theta(t))=\sum_{i=1}^{N}\theta_{i}(t)A_{i} \tag{8}\]  
\[B_{u}(\theta(t))=\sum_{i=1}^{N}\theta_{i}(t)B_{ui} \tag{9}\]  
where \(A_{i}\) and \(B_{i}\) are now constant matrices (\(i=1..N\)).  
Figure 2: Plot of nonlinear uncertainty function (f(D’)) (solid line) and three-dimensional projection of the polytope \(P_{2009}\) (dashed line), as in [7].
# State of the Art in Metal Matrix Composites Research: A Bibliometric Analysis
## 1 Introduction
### Metal Matrix Composites
In order to account for the various complex mechanisms active during MMC machining, the researchers categorised MMC machining forces into shearing, ploughing and particle fracture/debonding for improved force modeling and prediction [51; 52; 53; 54]. These modeling theories are primarily based on the seminal works of [PERSON] (orthogonal cutting model) [55], [PERSON] (slip line field model) [56] and [PERSON] (equivalent cutting edge concept) [57]. The Johnson-Cook's constitutive equation [58] is also widely used by researchers to model shear stress generation during MMC machining. This model incorporates strain hardening, strain rate as well as thermal softening phenomena to predict material flow stress. The foregoing discussion gives a brief insight into the complex nature of some aspects of metal matrix composites. Similarly, researchers have extensively worked upon other MMC aspects such as preparation methods, characterisation techniques, varied applications and more. In the present study, a detailed bibliometric analysis has been carried out to understand the important contributing authors, institutions, countries as well as the current and upcoming trends of this rapidly emerging field.
## 4 Experiments and Results
### Validation Experiments
#### 4.2.2 Validation of the Anti-Overfitting Mechanism  
In order to demonstrate the effectiveness of the anti-overfitting mechanism on improving the strategy's generalization ability, the performance of the strategies learned from the templates with and without the anti-overfitting mechanism is compared on the test set. The evaluation results are shown in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Strategy** & **VR** & **DR** & **GIE** & **TS** & **SR** \\ \hline APF strategy proposed in this paper & \(-\)52.878 & \(-\)72.027 & **1.891** & **7.681** & **1.0** \\ APF strategy proposed in [46] & \(-\)**29.041** & \(-\)**35.881** & 1.713 & 7.881 & 0.975 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results under the control of the two expert strategies. The bold data represent the optimal values of different strategies on the same evaluation metric. Same as the tables below.  
Figure 4: Variation of the single-step consistency evaluation metrics under the control of the two expert strategies in a specific episode. (**a**) Variation of the single-step VR. (**b**) Variation of the single-step DR. (**c**) Variation of the single-step GIE.  
It can be seen from Table 2 that the introduction of the anti-overfitting mechanism results in better performance of both the effectiveness and speed of achieving consistency, especially in terms of the SR. This indicates that the introduction of the anti-overfitting mechanism has a positive impact on improving the strategy's generalization ability.
## 3 Simulation Results and Analyses
### ASIC Synthesis  
To evaluate the hardware consumption of the proposed calibration structure, the application-specific integrated circuit (ASIC) design flow was adopted in this framework. The proposed calibration structure is implemented using ASIC design flow, targeting a 14-bit 3 GS/s eight-channel TIADC. Our proposed calibration technique is optimized with fixed-point coding and implemented in MATLAB. The simulation results obtained from Register Transfer Level (RTL) and MATLAB simulations are consistent. Subsequently, the register transfer level (RTL) design is synthesized to a gate-level netlist with Synopsys design compiler (DC) tool, targeting the 28 nm technology. Logic simulations are carried out by means of the gate-level netlist and Verilog testbench. The obtained Verilog Value-Change Dump (VCD) file provides data on the switching activity of specific signals. By analyzing the VCD file, the power consumption of the proposed calibration structure can be accurately estimated.  
The power and area synthesized results for the proposed calibration algorithm are presented in Table 2. The proposed calibration technique exhibits a power consumption of 21.3 mW and occupies a compact area of 0.035 mm\({}^{2}\), as demonstrated in the results. Specifically, the coarse-fine correction module utilizing three FIR filters contributes to a substantial 84% of the total power dissipation.  
Figure 15: **(a)** SNDR and **(b)** SFDR versus different input frequencies for an eight-channel TIADC with adoption of proposed calibration technique.