esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of Cold Spray Process for Creation of Pin Fin Geometries
## 2 Numerical Modelling
### Discrete Phase  
The discrete phase (i.e., particles) was solved using the transient Lagrangian Discrete Phase Model (DPM) in FLUENT. As mentioned, the continuous phase was initially fully resolved, at which point, the transient particles were injected. The particle injection time step was set at \(10^{-5}\) s. Two-way turbulence coupling was enabled to account for changes in turbulent quantities. Depending on the size of the particle, particles can dampen or produce turbulent eddies, which is accounted for using turbulence coupling [45]. O'Rourke's stochastic collision [45; 46] was also enabled, as it results in a greater dispersed particulate flow, which is closer to experimental observation [31]. Stochastic tracking using the Discrete Random Walk (DRW) model was also enabled, which considers the effects of turbulent velocity fluctuations on the particle trajectories [45].  
Nickel powder was used as the particle feedstock. The nickel powder was injected normally to the injector surface at 10 m/s and 300 K, at a mass flow rate of 1 g/s. The injector surface was treated as a pressure inlet, with an inlet pressure 10% higher than the nozzle inlet pressure [31]. The inlet temperature was kept the same as the nozzle inlet temperature. The powder diameter ranged from 17 m to 77 m, with a mean diameter of 48 m. The particle diameter distribution was based on the Rosin-Rammler distribution from the study performed by [PERSON] et al. [31].  
To better capture the drag behavior of the particles at the high flow speeds, the drag correlation proposed by [PERSON] was implemented. The [PERSON]'s drag coefficient is valid for a wide range of particle Mach and Reynolds numbers and considers compressibility and rarefaction effects [31; 43; 47; 48]. To model the particle heat transfer, the Ranz-Marshall correlation was used for the calculation of the Nusselt number. The lumped capacity method was used, neglecting the temperature gradient inside the particle. Similar to previous studies [31; 32; 33], the trap boundary condition was used at the substrate and mask.  
Figure 5: Zoomed-in view of the mesh, showing structured and unstructured regions.  
When a particle landed on the substrate surface or the mask, it was assumed to adhere to the surface and was removed from the simulation.
## 1 Introduction
Based on a literature review a new model called the methodology for searching representative elements (MIRE) was designed which assists us in processing huge quantities of data. The result of the method is an approximation of the real situation, while deviations are checked by feedback. All five steps of the MIRE are repeated as necessary, until a satisfactory ratio between the approximation accuracy and time savings is obtained in the processing of data.
## 1 Introduction  
Approximately 17,000 new cases of brain metastasis (BM) are diagnosed each year, and the incidence has risen steadily over the past 2 decades [1]. BM is common in patients with advanced renal cell carcinoma (RCC) and is known to progress to intracranial hemorrhage [2, 3]. Metastasises are found in 30-40% of RCC patients, and BM occurs in more than 10% of RCC patients [4].  
BM usually has a poor prognosis despite specific localized treatment with neurosurgery or radiation therapy [5; 6]. A study of the overall survival (OS) rate of BM patients showed that OS was poor when metastases occurred in multiple locations rather than only in the brain, and the smaller the lesion size, the better the OS [7; 8]. As a result, early identification of BM in patients would be important from the OS perspective because the location and size of the lesion are limited at the initial stage.  
However, the current European Association of Urology and American Urologic Association guidelines recommend brain imaging only for patients with RCC who have central nervous system symptoms or abnormal experimental values [9; 10]. As a result, in the case of asymptomatic patients, brain images are not taken, and metastases are discovered only later. If brain metastases are not detected and treated early, the prognosis is poor; therefore, methods for early prediction are required.  
With the development of computer technology, studies using machine learning (ML) have been conducted. Breast [11] and prostate cancer [12] were predicted with high accuracy in studies that used medical data. However, no studies have been conducted to predict BM using ML in patients with RCC, and previous studies attempting to identify risk factors affecting BM have examined only statistical techniques such as survival analysis or logistic analysis [7; 13; 14]. In addition, although the risk of each variable can be identified through the odds ratio or hazard ratio of the risk factors obtained in previous studies, the risk probability cannot be calculated from the measured value of each variable. By contrast, ML can be used as a diagnostic aid tool because it is possible to predict the probability of occurrence of BM from the given variable values.  
In this study, we determine whether the risk factors of previous studies have similar characteristics in Korean RCC patients and propose an ML model that predicts the probability of BM using a combination of factors. Because our algorithm was trained using data from a multicenter cohort collected from a tertiary hospital in Korea, the characteristics of domestic RCC patients are well reflected without bias. Moreover, to the best of our knowledge, this study is the first study that uses ML to predict BM in RCC patients. If the algorithm we developed is used, patients with BM could be identified early and treated appropriately, improving their prognosis. Additionally, using it for brain imaging diagnosis in high-risk patients would address the problem of delayed diagnosis due to missing brain images in asymptomatic patients.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)  
In this section, we present the two main parts of our work. The first is \(MCCM\); a distributed algorithm which runs independently on each device \(D_{i}\) for coverage maximization. Then, the bridging part that connects the isolated islands using alpha-shape method. The approach consists of three steps as shown in Figure 1: Neighborhood discovery, coverage maximization and bridging process.
## 3 Results and Discussion
### Verification of Numerical Simulation Method
This resulted in significant errors between the experimental and simulated results for each layer at this stage. Additionally, due to the high air velocity in the pores of the wheat particles on the third and fourth layers, the experimental values may first receive the external input of cold air, leading to significant fluctuations. Although two porosity distribution models can reflect the temperature variation trend in the experimental results, the non-uniform porosity distribution model has a better fit in terms of predicting the temperature change results for the entire ventilation process.
## 4 Conclusions  
In this paper, based on the electronic theory, the conservation equations of mass, momentum, energy, and the lumped capacitance method, the multichannel discharge model, and plasma synthesis jet actuator model are put forward. Coupled with the two models, an analytical model to simulate the characteristics of the PSJA array is developed. Based on this model, the influence of actuator number in the PSJA array is investigated. The main conclusions are as follows.  
With the increase in the actuator number, the discharge energy in a single actuator decreases. As a result, the jet strength induced by a single actuator decreases. When the actuator number increases from 1 to 20, the weakening extent of mass ejected, peak jet velocity, and jet duration time is 62%, 54%, and 33%, respectively. Therefore, the influence of the actuator number on the jet duration time is the weakest.  
As the actuator increases, the discharge efficiency and thermodynamic efficiency show different tendencies. The discharge efficiency increases, while the thermodynamic efficiency decreases. As a result, the total energy efficiency doesn't always increase with the increase in the actuator number. However, in practical application, the extra resistance in the discharge circuit is large (to the order of kilo Ohms), and the discharge efficiency of one channel discharge is low. Therefore, it can be concluded that the PSJA array is more suitable to the practical application.  
Formal analysis, H.S.; Investigation, S.Z.; Supervision, Z.Z. and S.H.; Validation, W.Y. and L.Y.  
This research was funded by the National Natural Science Foundation of China (91541120, 91641204, and 51790511) and Royal Society (IE150612).  
The authors declare no conflicts of interest.
## 3 Results  
Tables 2-4 show the IoU, Precision, and Recall values of all implemented experiments for the two scenarios, as shown in Figure 7. To simplify the text, we use _Kule_ to replace the Kule refugee camp and _Kule Dry_ (or _Wet_) to replace the Kule refugee camp during the dry (or wet) season. _Nguenygiel_ and _Nguenygiel Dry_ (or _Wet_) follow the same nomenclature. In addition, we use _OBIA-OCOS models_, and _Manual-OCOS models_ to represent models trained with the OBIA labels and the Manual labels by the OCOS strategy, respectively. The other three strategies (OCTS, TCOS, and TCTS) follow the same nomenclature. The highest values of metrics for each camp and season by each training strategy are highlighted in blue. The highest values for each camp and season among all training strategies are highlighted in red.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Refuge** & \multirow{2}{*}{**Season**} & **Training** & **Baseline** & **Scenario I** & \multicolumn{5}{c}{**Scenario II**} \\ \cline{3-10}
**Camp** & & **Strategy** & **Manual** & **OBIA** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{3}{*}{Kule} & \multirow{3}{*}{Dry} & OCOS & 0.5857 & 0.5113 & 0.5829 & 0.6033 & 0.6019 & 0.5969 & 0.5999 \\  & & OCTS & 0.5828 & 0.5202 & 0.5836 & 0.5927 & 0.5936 & 0.5958 & 0.5917 \\  & & TCOS & 0.5887 & 0.5277 & 0.5916 & 0.5927 & 0.5927 & 0.5934 & 0.5927 \\  & & TCTS & 0.5884 & 0.5276 & 0.5696 & 0.5847 & 0.5836 & 0.5866 & 0.5830 \\ \hline \multirow{3}{*}{Kule} & \multirow{3}{*}{Wet} & OCOS & 0.6400 & 0.5539 & 0.6386 & 0.6541 & 0.6569 & 0.6412 & 0.6425 \\  & & OCTS & 0.6459 & 0.5434 & 0.6188 & 0.6389 & 0.6446 & 0.6325 & 0.6413 \\  & & TCOS & 0.6387 & 0.5273 & 0.6415 & 0.6470 & 0.6480 & 0.6493 & 0.6451 \\  & & TCTS & 0.6339 & 0.5573 & 0.6296 & 0.6327 & 0.6480 & 0.6404 & 0.6364 \\ \hline \multirow{3}{*}{Nguenygiel} & \multirow{3}{*}{Dry} & OCOS & 0.6377 & 0.6051 & 0.6359 & 0.6393 & 0.6394 & 0.6389 & 0.6353 \\  & & OCTS & 0.6388 & 0.6074 & 0.6129 & 0.6302 & 0.6375 & 0.6369 & 0.6366 \\ \cline{1-1}  & & TCOS & 0.6379 & 0.6051 & 0.6333 & 0.6371 & 0.6380 & 0.6385 & 0.6375 \\ \cline{1-1}  & & TCTS & 0.6391 & 0.6107 & 0.6399 & 0.6493 & 0.6471 & 0.6481 & 0.6440 \\ \hline \multirow{3}{*}{Nguenygiel} & \multirow{3}{*}{Wet} & OCOS & 0.6472 & 0.6196 & 0.6491 & 0.6538 & 0.6522 & 0.6493 & 0.6477 \\ \cline{1-1}  & & OCTS & 0.6465 & 0.6168 & 0.6486 & 0.6517 & 0.6518 & 0.6480 & 0.6452 \\ \cline{1-1}  & & TCOS & 0.6507 & 0.6167 & 0.6608 & 0.6636 & 0.6630 & 0.6611 & 0.6653 \\ \cline{1-1}  & & TCTS & 0.6634 & 0.6302 & 0.6330 & 0.6152 & 0.6624 & 0.6353 & 0.6604 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The IoU values of built-up structures of all implemented experiments (OCOS: One Camp One Season; OCTS: One Camp Two Seasons; TCOS: Two Camps One Season; TCTS: Two Camps Two Seasons). The mean IoU values can be found in Table A2 in Appendix A.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 7 Conclusions  
We collected location data with a stationary GPS sensor at four locations: Dunedin (New Zealand) and North Stradbroke Island, Port Douglas and Thursday Island (Australia). The durations of these time-series range from a day to 25 days. We found appreciable autocorrelation within the noise present in the signal; thus novel, noise-models were constructed and compared for performance. In order to rank the performance of different noise models, we used the log-score. We found that an enhanced Kalman filter augmented with the Ornstein-Uhlenbeck noise model performed the best, and its pseudocode is provided. The computational cost of the corresponding algorithm is modest, and it generalises to sensor fusion application easily; thus, it is promising for embedded computing.  
Conceptualization: A.W.R.S., P. B., D.S. and T.C.A.M.; methodology: A.W.R.S., A.M., M.P., D.S. and T.C.A.M.; software and validation: A.W.R.S. and A.M.; formal analysis: A.W.R.S., A.M. and D.S.; resources: B.J.P., P.B. and T.C.A.M.; writing: A.W.R.S., A.M. and D.S.; review and editing: A.M. and D.S.; visualization: A.M., D.S.; supervision: D.S. and T.C.A.M. All authors read and agreed to the published version of the manuscript.  
This research was funded by the New Zealand Ministry of Business, Innovation and Employment Grant Number UOOX-1208. [PERSON] is grateful for the financial support from the Dodd-Walls Centre for Photonic and Quantum Technologies.  
The authors declare no conflict of interest. The funding agencies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Advanced Multidimensional Techniques for Aerosol WSOM Analysis
### Application of Off-Line Multidimensional NMR Spectroscopy for Water-Soluble OA Analysis
Furthermore, due to the inherent complexity of the aerosol WSOM fraction, the application of a chromatographic separation procedure prior to NMR analysis can directly provide major advances in the knowledge gained from the sample. Very few studies have applied LC combined with 1D and 2D NMR detection for the identification of individual unknown molecular components in aerosol WSOM samples. The studies published thus far using this analytical approach share a common feature: their main purpose is the identification of SOA tracers produced in laboratory chambers/reactors  
Figure 6: Synchronous map of the 2-D heterospectral correlation analysis of the \({}^{13}\)C cross-polarization with magic-angle spinning (CP-MAS) nuclear magnetic resonance (NMR) and Fourier-transform infrared (FTIR)-attenuated total reflectance (ATR) (region between 2000 and 750 cm\({}^{-1}\)) spectra of five aerosol WSOM samples collected during different seasonal periods (including summer, winter, autumn, spring). The top and the right sides are the average \({}^{13}\)C CP-MAS NMR and FTIR-ATR spectra. Red and blue represent positive and negative correlations, respectively. Reprinted from _Atmospheric Environment_, 116, [PERSON]. [PERSON], [PERSON], [PERSON], Investigating the water-soluble organic functionality of urban aerosols using two- dimensional correlation of solid-state \({}^{13}\)C NMR and FTIR spectral data, 245-252, Copyright (2015), with permission from Elsevier. to simulate atmospheric chemistry [74; 75; 76]. To the best of the authors' knowledge, the use of LC coupled to NMR for the analysis of field water-soluble OAs samples has yet to be carried out, a situation that is likely explained by the amount of sample needed to obtain a meaningful NMR signal. Nonetheless, taking into account the results of the recently developed LC \(\times\) LC method for resolving the chemical heterogeneity of aerosol WSOM samples [27; 58], the development of protocols based on both LC \(\times\) LC and NMR techniques may provide valuable clues for unfolding the complexity of water-soluble OA.
# Mechanism of Stratum Instability and Dynamic Deformation under Discontinuous Boundary Conditions
## 7 Conclusions  
1. The expression of the rock movement line and the distance \(u(x)\) from different horizontal heights of the rock movement line to the fault plane were obtained using the dip angle of the fault and the angle of rock movement.  
Figure 15: Vertical stress variation curves of each monitoring point during the advancement of the working face. (**a**) Vertical stress at point A. (**b**) Vertical stress at point B. (**c**) Vertical stress at point C. (**d**) Vertical stress at point D. (**e**) Vertical stress at point M. (**f**) Vertical stress at point N.  
2. The criterion for the activity instability of rock layers in the fault zone was obtained through material mechanics analysis, and the mechanical criterion at the end of the rock layer can determine the range of bending and sinking zones.
3. Data validation was conducted on the key layer slip mechanics model and the key layer displacement instability model through on-site cases.
4. Through numerical simulation and zoning monitoring of vertical stress on the upper, middle, and lower parts of the fault, combined with the vertical stress distribution map and plastic zone distribution map of the mining area, it was found that the upper rock layer of the fault has been activated, and the middle rock layer of the fault zone is affected by the joint action of the upper rock layer's unloading zone and the protective coal pillar's stress-concentration zone. The surrounding rock of the lower part of the fault zone is always in the stress-concentration zone.
5. Through the double-criterion mechanical model, it can be seen that increasing the width of the fault-protection coal pillar is the most effective means for the influence of smaller faults on overlying strata. At the same time, the stability of the surrounding rock near the goaf can be increased by grouting. It is also necessary to drill anchor cables in the vertical fault direction of the rock strata above the open-off cut to prevent the collapse of the boundary's surrounding rock.  
Conceptualization, H.G.; Methodology, H.G. and W.Y.; Software, H.G.; Validation, Z.L.; Formal analysis, H.G.; Investigation, K.L.; Resources, W.Y.; Data curation, G.W., K.L. and Z.L.; Writing--original draft, H.G.; Writing--review & editing, H.G.; Supervision, W.Y. and G.W.; Project administration, W.Y.; Funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (Grant Nos. 52174076 and 51974117), the Hunan Provincial Natural Science Foundation of China (Grant No. 2023 JJ030261), the Postgraduate Scientific Research Innovation Project of Hunan Province (Grant No. CX20210984), and the Open Fund for Hunan Province Key Laboratory of Coal Resources Clean-utilization and Mine Environment Protection (No. E22235). The financial support is greatly appreciated.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Data Collection
#### 4.3.2 Biomechanical Measurements and Equipment  
Motion capture technology (Vicon Motion Systems (Oxford, UK), 200 Hz) was used to collect kinematic data. Key metrics included:  
* Body posture: Joint angles of the shoulder, elbow, wrist, hip, knee, and ankle.
* Racket trajectory: 3D path of the racket head during the shot.
* Shuttlecock trajectory: 3D flight path of the shuttlecock post-impact.
###### Abstract  
The utilization of \"green\" hydrogen in transportation areas gives rise to production- and supply infrastructure-related challenges; therefore, its wider application in automotive transport would lead to higher demand with cost reduction and a faster expansion of the hydrogen refuelling network. This study presents energy and environmental performance indicators analyses of a Nissan Qashqai J10 engine during the Worldwide Harmonised Light Vehicles Test Cycle (WLTC), replacing conventional fossil gasoline with dual-fuel (D-F) gasoline and hydrogen. Numerical modelling was conducted using AVL Cruise(tm) (Version R2022.2) software, utilizing the torque, fuel consumption, and environmental performance data of the HR16 DE engine obtained through experimental testing across a wide range of loads and speeds on an engine test bench. The experimental investigation was carried out in two stages: using pure gasoline (G100); injecting a hydrogen additive into the intake air, constituting 5% of the gasoline mass (G95H5). Following similar stages, numerical modelling was conducted using the vehicle's technical specifications to calculate engine load and speed throughout the WLTC range. Instant fuel consumption and pollutant emissions (CO, CH, NOx) were determined for various driving modes using experimental data maps. CO2 emissions were calculated considering fuel composition and consumption. By integrating the instant values, the total and specific fuel consumption and emissions were calculated. As a result, this study identified the effect of a 5% hydrogen additive in improving engine energy efficiency, reducing incomplete combustion products and lowering greenhouse gas (CO2) emissions under various driving modes. Finally, the results were compared with the requirements of EU standards.  
spark ignition engine; hydrogen; dual fuel; WLTC; AVL Cruise(tm) specific emissions
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## 5 Performance Evaluation and Results
### Impact of Design Parameters \(\alpha\) and \(\beta\)  
In this section, we analyze the effect of the design parameters \(\alpha\) and \(\beta\) in COOF. The main purpose of this investigation is to determine how these parameters contribute to improving COOF performance. To this end, each design parameter is selectively included in the COOF, and compares the performance of each factor with different performance metrics.  
Figure 16a shows the impact of smoothing factor in COOF. During parent selection process, it reduces the influence of the parent node's remaining energy. With the increasing value of the smoothing parameter, the average parent change frequency also increases accordingly. In particular, when the smoothing factor was set to 0.35, COOF achieved the best performance regarding the parent change frequency of the node. Similarly, with this parameter, COOF also achieves the best performance in terms of the network lifetime, which is shown in Figure 16b. It is because the REI considers the residual energy of the nodes recursively.  
The effect of weighting factor in COOF is depicted in Figure 16c-d. Particularly, the weighting factor \(\alpha\) is used to prioritize the current measurement of the queue. The queue loss ratio is defined as the ratio between the dropped packets and the injected packets into the queue of the network layer. As the weighting factor is used to prioritize between current queue occupancy than previous, the queue loss ratio is lower when the weighting factor is set below 0.5. Figure 16c shows the queue loss ratio with varying weight factors. Similarly, with this weighting factor, the packet receiving ratio is  
Figure 16: The effect of the design parameters on performance; (**a**) average parent change frequency with varying smoothing factor \(\beta\), (**b**) lifetime in seconds with varying \(\beta\), (**c**) queue loss ratio with varying weight factor \(\alpha\), and (**d**) packet receiving ratio with varying \(\alpha\).  
high. It can be noticed that the packet receiving ratio is almost similar when the weighting factor is set to below 0.5.  
From this analysis, we come up with the conclusion that these parameters affect the performance of COOF. In addition, by observing the network performance, these parameters can be empirically optimized. From the results, it is clear that COOF enhances the performance when the parameters are set to \(\alpha=0.4\) and \(\beta=0.35\). Therefore, we have used these values throughout the evaluation.