esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Temperature Liquid-Liquid Phase Transition in Glass-Forming Liquid Pd43 Ni20 Cu27P10
## 3 Results and Discussion
### AIMD Simulations
#### 3.2.2 Atomic Structures
(**b**) Enlarged view of the first peak of PPDF for Ni-Cu atom pair at 1000 K and 900 K.  
face-centered cubic (fcc), is easy to form around Pd atoms. The population fraction of Cu- and Ni-centered <0 0 12 0> and <0 1 10 2> (Figure 6c,d), corresponding to icosahedral and twisted icosahedral clusters, respectively, has an obvious increase after LLPT, whereas the <0 3 6 4> fcc cluster is significantly reduced. In the P-centered cluster, the double-top square anti-prismatic clusters of <0 2 8 0> and <1 2 5 3> are significantly reduced, which also shows that LLPT is not conducive to <0 2 8 0> and < 1 2 5 3> cluster increase. The results of VP also show that LLPT promotes the formation of five-fold symmetry to a certain extent. Figure 6: (**a**) Total VP of Pd49 Ni20 Cu27P10 as a function of temperature. The model at the right figure is a typical <0 0 12 0> polygon. (**b–e**) Variation of VP centered on Pd, Cu, Ni, and P at different temperatures. The scattering points represent VP with little fractional change over the entire temperature range studied, while the scattering line represents VP with a significant change with temperature. The black dashed line represents the temperature at which LLPT occurs. To find the potential change in local structure, we analyzed the bond order orientation (BOO) of the system based on the spherical harmonic function [44]. The spherical harmonic function is a quantitative description of the symmetry of different bond orientations around the central atom. BOO analysis can sensitively reflect the changes in local structure. The average BOO parameters are used to judge the structural difference between high-temperature and low-temperature liquids. We first calculated the BOO parameters of \(q_{4}\), \(q_{6}\), \(w_{4}\), and \(w_{6}\) of each atom during the cooling process. Figure 7a-d shows the change of average \(q_{6}\), \(w_{6}\), \(q_{4}\), and \(w_{4}\) at different temperatures. It can be seen that as the temperature decreases, the \(q_{6}\) value of Ni, Cu, and P element increases. In the process of transforming high-temperature liquid into low-temperature liquid, only the increasing trend of \(w_{6}\) in P element is slowed down (Figure 7b). Immediately after LLPT, as indicated by the black dotted line, the decrease of \(w_{6}\) for P element is most prominent, and the other elements are not obvious. In \(w_{4}\) and \(q_{4}\), a similar phenomenon also exists. The BOO of P element has an inflection point near LLPT, and the trend of BOO for other elements does not change much. This indicates that the P element has an obvious change in the local environment and plays a key role in LLPT.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 3 Methodology
### Cluster Head (CH) Selection
#### 3.2.3 Evaluating the Objective Function  
The clustering process is viewed as a multi-objective model to evaluate the objective function. The heuristic value and objective function are expressed as:  
\[f_{t}=Weigh_{1}.\ f_{1}\ +Weigh_{2}.\ f_{2} \tag{10}\]  
where  
\[f_{1}(\Delta devia)=\sum_{i=1}^{N}Abs\ (\ D-|CM_{i}|) \tag{11}\]  
\[f_{2}\ (Dis-Sum)=\sum_{i=1}^{|N|}(\sum_{j=1}^{|CM_{i}|}PED\big{(}CH_{i},\ CM_{i,j} \big{)}\ ) \tag{12}\]  
In this context, \"\(weigh_{1}\)\" and \"\(weigh_{2}\)\" represent weight factors, both of which are set to 0.5. \"\(f_{1}\)\" corresponds to \(\Delta\)devia, which is the difference in cluster route length within N. \"\(f_{2}\)\" is defined as the sum of the distances between CMs (cluster members) and CHs (cluster heads) for all clusters. \"\(D\)\" is a constant value that quantifies the density degree for each cluster. First, the \(PED\) is computed for all \"\(i\)\" clusters, and it is the sum of the distances between inter-vehicular node \(CM_{ij}\) and \(CH_{i}\) for each cluster \"\(j\)\" within the total cluster set \"\(N\)\".
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 2 Methodology
### From Input Layer to Hidden Layer  
The input vector \(x\in\mathbb{R}^{64}\). The first fully connected layer has a weight matrix \(W_{1}\in\mathbb{R}^{20\times 64}\) and a bias vector \(b_{1}\in\mathbb{R}^{20}\). The operation performed by this layer can be expressed as:  
\[h=W_{1}\times x+b_{1} \tag{1}\]  
Then, the ReLU (Rectified Linear Unit) activation function is applied to \(h\). The ReLU function is defined as \(f(z)=\max(0,z)\), which introduces non-linearity, allowing the model to learn more complex functions:  
\[h^{\prime}=\max(0,h) \tag{2}\]  
In the hidden layer of the model, the ReLU (Rectified Linear Unit) [30] is used as the activation function. The ReLU function can enhance the non-linear modeling capabilities of the model and help capture the complex features of the input data.
## 4 Discussion
SS showed good results, being only after the NG system in terms of gloss and roughness, but it should be noted that a 4-step procedure with disks is time-consuming, and it is commonly used only for anterior restorations, as disks are difficult to use on occlusal surfaces. Whilst a chairside approach is time-saving, without the need for additional laboratory steps, there is still little indication about the ideal F&P procedure, as the advantages of an in-office procedure might be counterbalanced by a time-consuming manual procedure. In the present study, the 4-step disc system SS showed statistically significantly better results for both roughness and gloss than all the 1-step (O1) and the 2-step systems (SS, HL, LN, and LP). Another aspect to be considered is that most of the F&P procedures tested are subtractive, with the exception of OG and NG, which are additive. In these procedures, a resin layer is applied to the surface, possibly influencing the thickness. This does not affect the Roughness and Gloss measurements performed in the present study as they are surface measurements, but it should be taken into due consideration in clinical conditions, with the applied resin layer that could be worn by chewing and/or physical and chemical agents. A limitation of the present study, and a possible subject for future investigations, is that only one permanent material for 3 DP was tested and printed with only one 3D printing system, resulting in only one initial roughness (1.56 um, mean of all the specimens before treatment) and gloss (3.2 GU, mean of all the specimens before treatment), and it is known that the initial roughness affects the outcome [49]. Therefore, testing different materials and different printing systems could give more comprehensive results for F&P 3 DP composite resins for permanent restorations. The stability over time of the initially achieved results (e.g., after function and/or brushing) should also be the object of future studies, as there is scarce or absent information on this issue for this category of materials.
# Four-Dimensional Thermal Analysis of 888 nm Pumped Nd:YVO4 Dual-Rod Acousto-Optic Q-Switched Laser
## 2 Theoretical Model
### AO Q-Switched Laser Dynamics
Thus, we use the rate equation for the intracavity laser photon density to solve the output power, which is given by [22]:  
\[\frac{\mathrm{d}n_{\mathrm{up}}(\mathbf{r},t)}{\mathrm{d}t}=W_{\mathrm{up}}n_{4}^{2}( \mathbf{r},t)-\frac{n_{\mathrm{up}}(\mathbf{r},t)}{\tau_{\mathrm{up}}}, \tag{1}\]8where \(l_{\rm c}\) is the length of each laser crystal, the total length of gain medium in cavity should be \(2l_{\rm c}\) because of the dual-rod scheme studied in this work, \(l_{\rm eff}\) is the effective cavity length, \(\tau_{\rm r}=2l_{\rm eff}/l_{\rm c}\) is the round-trip transit time of light in the resonator, \(L\) is the intrinsic diffraction loss, \(T_{\rm oc}\) is the transmittance of laser output coupling mirror, and \(\delta_{\rm m}\) is the loss devoted by the acoustic optical modulator (AOM). \(\Delta n\) here is defined as the spatial average of the population inversion for both crystals. For the investigated dual-rod AO Q-switched laser resonator with two identical laser crystals, \(l_{\rm eff}=l+2l_{\rm c}(n_{\rm c}-1)+l_{\rm m}(n_{\rm m}-1)\), where \(l_{\rm m}\) and \(n_{\rm m}\) is the length and refractive index of AOM, and \(l\) is cavity length. The pump intensity can be seen as steady state, because the pump light is continuous-wave high-power in this work. According to Ref. [25], the loss function of AOM is  
\[\delta_{\rm m}(t)=\left\{ \begin{array}{ll}\delta_{0},&(n-1)T\leq t<(n-1)T+t_ {\rm on},\\ \delta_{0}{\rm exp}\left[-\left(\frac{t}{t_{\rm s}}\right)^{2}\right],&(n-1)T+ t_{\rm on}\leq t<nT,\end{array} \right. \tag{15}\]  
where \(T\) is the period of Q-switch, \(\delta_{0}\) is the running ultrasonic wave diffraction loss of AOM, \(t_{\rm on}\) is the running time of AOM in each period and \(t_{\rm s}\) is the intrinsic turnoff time of AOM. Actually, this function ignores the short process of loss variation at the beginning in each period, i.e., a step point at \(t=(n-1)T\), which barely affects the pulse producing process. The output laser power is given by:  
\[P_{\rm s}(t)=\frac{h\
u_{\rm s}\phi_{\rm s}(t)V_{\rm m}}{\tau_{\rm r}}\ln\left( \frac{1}{1-T_{\rm oc}}\right). \tag{16}\]
## 3 Results and Discussion
### tortuosity-Porosity Relationship  
Figures 9 and 10 illustrate the relationship between tortuosity and effective porosity for sandstone and carbonate rock samples, respectively. In Figure 9, which represents sandstone rocks, the tortuosity values estimated by the streamline method, Dijkstra, and empirical approach Equation (17) are generally consistent and fall within a similar range across varying porosity values.  
Most data points are densely clustered within an effective porosity interval of 0.001 to 0.4, indicating a stable agreement among the three methods. This consistency suggests that in relatively homogeneous porous structures like sandstones, Dijkstra and empirical models can approximate the streamline tortuosity with reasonable accuracy.  
In general, Equation (17) yielded the highest average coefficient of determination (R\({}^{2}\)) among the three approaches for fitting the relationship between tortuosity and porosity in sandstone samples, although the value was relatively low at approximately 0.023. For the Dijkstra method, the highest R\({}^{2}\) was observed in the Bandera Brown rock, while the lowest values were found in Bandera Gray and Kirby. The streamline method obtained the highest R\({}^{2}\) in Bandera Gray at 0.049, whereas the lowest was in Kirby (0.001). The best fit achieved by Equation (17) was in the Bentheimer rock (R\({}^{2}\) = 0.096), while the poorest fit occurred in the Berea rock (R\({}^{2}\) = 0.001). In contrast, there were significant discrepancies in carbonate  
Figure 9: tortuosity-porosity relationship of in sandstone samples (**a**) Bandera Brown, (**b**) Bandera Gray, (**c**) Buff Berea, (**d**) Bentheimer, (**e**) Berea, (**f**) Berea Sister Gray, (**g**) Berea Upper Gray, (**h**) Castlegate, (**i**) Kirby, and (**j**) Leopard.  
rock samples between streamline-based tortuosity and those estimated using Dijkstra and the empirical equation (Figure 10). Overall, the streamline method provided the best fit (R\({}^{2}\) = 0.348), while the geometric method showed the poorest agreement. The lowest R\({}^{2}\) for Dijkstra was observed in sample SW04 (0.001), and the highest in SW08 (0.230). For the streamlined approach, R\({}^{2}\) ranged from 0.000 (SW03) to 0.794 (SW05), while the empirical method had a range from 0.003 (SW02) to 0.281 (SW10).  
The pronounced deviation of streamline tortuosity reflects the complex and heterogeneous pore structure of carbonate rocks, where features such as vugs, irregular throats, and disconnected pore clusters significantly influence fluid flow. In such systems, geometric and empirical models often fail to capture the true transport pathways; therefore, streamline approaches, such as the lattice Boltzmann method, are critical to accurately quantifying tortuosity in rocks with substantial pore-scale variability. This highlights the limitations of applying generalized models across lithologically diverse media.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
###### Abstract  
The evolution of 5G technology has led to significant advancements in high-accuracy positioning. However, the actual performance of 5G signals for user equipment (UE) positioning has not been thoroughly examined, especially under varying propagation conditions. This research presents a comprehensive evaluation of 5G positioning using both uplink sounding reference signals (UL-SRS) and downlink positioning reference signals (DL-PRS) under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. In the uplink scenario, the UE transmits SRS signals to the gNBs, enabling precise localization. In the downlink scenario, the gNBs transmit PRS signals to the UE for accurate position estimation. Expanding beyond LOS environments, this study explores the challenges posed by NLOS conditions and analyzes their impact on positioning accuracy. Through a comparative analysis of UL-SRS and DL-PRS signals, this study enhances the current understanding of 5G positioning performance, offering empirical insights and quantitative benchmarks that serve as a guide for the development of more precise localization methods. The simulation results show that DL-PRS achieves high accuracy in LOS conditions, while UL-SRS performs well for UE positioning under NLOS conditions in urban environments.  
5G NR; uplink SRS; downlink PRS; time of arrival Article
## 3 Results
### Structure Prediction, Refinement and Validation
#### 3.5.3 Structure Validation, Refinement, Quality Assessment, and Model Selection
Last, we utilized the Z-score, as determined by the ProSA tool, to assess the overall quality of the AlphaFold and RoseTTAFold structure models, which assesses model accuracy based on statistical comparisons with experimentally validated structures. The RoseTTAFold model exhibited a more favorable Z-score of \(-\)4.79, indicating a higher-quality structure closely resembling native proteins solved by X-ray crystallography. In contrast, the AlphaFold model yielded a Z-score of \(-\)3.01, suggesting comparatively lower structural quality, as shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & **Metric** & **Refined RoseTTAFold** & **Refined AlphaFold** \\ \hline \multirow{4}{*}{All-Atom Contacts} & Clashscore (number of & & \\  & serious steric overlaps & & & \\  & (\(>\)0.4 Å) per 1000 & 1.93 (99 th percentile) & 18.88 (36 th percentile) & \\  & atoms), all atoms & & & \\ Protein Geometry & Poor rotamers & 1 (0.27\%) & 2 (0.55\%) & Goal: \(<\)0.3\% \\  & Favored rotamers & 362 (98.91\%) & 357 (97.54\%) & Goal: \(>\)98\% \\  & Ramachandran outliers & 1 (0.24\%) & 3 (0.72\%) & Goal: \(<\)0.05\% \\  & Ramachandran favored & 406 (97.83\%) & 400 (96.39\%) & Goal: \(>\)98\% \\  & Rama distribution & \(-\)0.25 \(\pm\) 0.38 & \(-\)2.65 \(\pm\) 0.27 & Goal: abs (Z score) \textless{} 2 \\  & MolProbity score & 1.00 (100 th percentile) & 2.01 (75 th percentile) & \\  & C\(\beta\) deviations \textgreater{}0.25Å & 2 (0.51\%) & 0 (0.00\%) & Goal: 0 \\  & Bad bonds & 17/3218 (0.53\%) & 21/3218 (0.65\%) & Goal: 0\% \\  & Bad angles & 25/4324 (0.58\%) & 22/4324 (0.51\%) & Goal: \(<\)0.1\% \\ \multirow{2}{*}{Peptide Omegas} & Cis Prolines & 0/14 (0.00\%) & 0/14 (0.00\%) & Expected: \(\leq\)1 per chain, or \(\leq\)5\% \\  & CaBLAM outliers & 8 (1.9\%) & 10 (2.4\%) & Goal: \(<\)1.0\% \\  & CA Geometry outliers & 2 (0.48\%) & 3 (0.73\%) & Goal: \(<\)0.5\% \\ \hline \end{tabular}
\end{table} 
Table 2: Structural Evaluation Metrics Using MolProbity for Plant Defense VLP of N. tabacum Modeled by RoseTTAFold and AlphaFold. Overall, the RoseTTAFold refined model outperformed the refined AlphaFold model in most structural evaluation metrics, indicating a higher quality and greater accuracy in the structure of VLP in _N. tabucum_. The RoseTTAFold refined 3D structural model of Pathogen Defense VLP in _N. tabucum_ has been deposited in the ModelArchive repository, an open-access platform for computational structure models. The model can be accessed via [[https://www.modelarchive.org/doi/10.5452/ma-wajr2](https://www.modelarchive.org/doi/10.5452/ma-wajr2)]([https://www.modelarchive.org/doi/10.5452/ma-wajr2](https://www.modelarchive.org/doi/10.5452/ma-wajr2)) (accessed on 13 April 2025).
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 8 Hardware Limitations  
Machine learning solutions eminently depend on hardware resources for the computational handling of the task. From this experiment, we observe that training the model for 1 km of link length and utilized RF input power is not a tedious task. The training efficiency is converged in good proportions with one hidden layer and 30 neurons per layer. However, with optimized training, selection of the number of hidden layers and size of neurons per layer, linearization of longer link distances and higher bandwidths can be implied. Higher bandwidth signals such as multiple LTE carriers or 5G new radio (NR) waveforms would lead to elevated complexity of DPD performance due to vigorous PAPR. Eventually, this will lead to an overall bandwidth elevation in the baseband memory of the model.
# A 5G NR FR2 Beamforming System with Integrated Transceiver Module
## 2 A 28 GHz RF Beamforming System and Transceiver Module
### Development of 28 GHz RF Beamforming System
\[\text{Antenna Gain}=\frac{\text{Radiation intensity in desired direction}}{\text{Radiation intensity of}}=10\text{LogN}+\text{Ge} \tag{1}\]  
\[\text{isotropic antenna (all angles)}\]  
\[\text{EIRP}=\text{Pt}\times\text{Gt} \tag{2}\]  
where N is the number of elements, Ge is the element gain, Gt is the antenna gain, and Pt is the total transmitted power. Figure 4: (a) Proposed RF beamforming antenna system. (b) Cross-section of RF PCB with the bonding. Figure 5: Block diagram of 28 GHz eight-channel multimode BFIC. The eight channels of the fully differential BFIC are connected to the respective antenna through the feeding line. All the RF feeding lines are designed with grounded CPW lines to prevent unwanted coupling by adjacent channels. The layout of the feeding lines connecting each channel of BFIC and the respective series fed antenna is shown in Figure 3:0 The whole feedline length is equal, and the feedline CPW ground is isolated from the overall ground to improve the performance. The EM simulation of all the feedlines is performed to ensure the loss and the same phase of the feedline. The return losses of the feedline are over \(-10\) dB at a frequency of 27.5 GHz to 29 GHz, as shown in Figure 8a. The phase difference in all the feedlines is around \(1^{\circ}\) at 28 GHz, as shown in Figure 8b. The eight-channel BFIC integrates an SPI. This SPI facilitates both phase and amplitude controls directly on the chip. The SPI controls are wire bonded from the chip to connect the connector in the PCB. Hence, the beamforming module includes SPI interfaces for BFIC control and DC power connectors. A non-soldered K-type connector with a small form factor was used to measure the antenna beam pattern. Figure 3:1 Microphotograph of the 28 GHz eight-channel multimode BFIC [26]. Figure 3:2 Feedline layout of RF line connecting the BFIC and the series-fed antenna.
# Structural Optimization of Slotting Nozzle Used to Improve Coal-Seam Permeability
## 2 Structural Optimization of Slotting Nozzle Design
### Design of Slotting Nozzle Structural Parameters Orthogonal Optimization  
Orthogonal design is a scientific method used to select a suitable number of representative experimental points from a large number of experimental points according to the principle of mathematical statistics, and to rationally arrange multi-factor experiments using a standardized \"orthogonal table\". The above analysis showed that the performance of a slotting nozzle is mainly related to stable segment length \(L\), convergent angle \(\theta\), and straight segment length \(l\). Each factor was considered at four levels in the appropriate ranges for these three factors. According to the selected model of three factors and four levels, an orthogonal table was selected to include sixteen numerical experiments, as shown in Table 1.  
A slotting nozzle suitable for coal-seam slotting conditions requires not only good convergence of the water jet, but also higher velocity and striking force of the jet for the same target distance. The striking force of a water jet on a target is directly related to its dynamic pressure (\(p_{k}\)). The dynamic pressure of a water jet is proportional to the jet velocity, so we used dynamic pressure as the evaluation  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment** & **Stable Segment Length** & **Convergent Angle** & **Straight Segment** \\
**Number** & \(L\) **(mm)** & \(\theta\) **(\({}^{\circ}\))** & **Length \(l\) (mm)** \\ \hline
1 & 0 & 10 & 3 \\
2 & 0 & 20 & 6 \\
3 & 0 & 30 & 9 \\
4 & 0 & 40 & 12 \\
5 & 3 & 10 & 6 \\
6 & 3 & 20 & 3 \\
7 & 3 & 30 & 12 \\
8 & 3 & 40 & 9 \\
9 & 6 & 10 & 9 \\
10 & 6 & 20 & 12 \\
11 & 6 & 30 & 3 \\
12 & 6 & 40 & 6 \\
13 & 9 & 10 & 12 \\
14 & 9 & 20 & 9 \\
15 & 9 & 30 & 6 \\
16 & 9 & 40 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Slotting nozzle structural parameters orthogonal optimization table \(L_{16}\) (4\({}^{3}\)).  
Figure 1: Conventional slotting nozzle structure.  
index. The performances of 16 different types of slotting nozzles were compared by investigating the water jet dynamic pressure under the same working pressure: specifically, the axial dynamic pressure (\(p_{k1}\)) and longitudinal profile dynamic pressure (\(p_{k2}\)) of the water jet were evaluated at a distance of 150 mm from the slotting nozzle outlet.