esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### High-Speed Machine Tools  
The essential core in high-speed machine tool machining is the motorized spindle, as depicted in Figure 3. The motorized spindle is an integral part of machine tools. Machining accuracy is mainly dependent on the performance of the motorized spindle [21; 22; 23; 24]. Thermal deformation of a motorized spindle can seriously reduce the machining accuracy. Therefore, cooling and thermal elongation fluctuations of the motorized spindle attract the most attention. [PERSON] et al. [25] proposed a symmetric solution to the thermal error issue. Their real-time compensation system was developed for the spindle of a machining center, using the external machine zero shift function of the computer numerical control. The maximum axial thermal error was dramatically reduced from 55 m to 16 m, and the radial thermal error was similarly reduced from 15 m to 6 m.  
For thermal optimization of motors, achieving efficient heat dissipation is usually realized by reducing overall thermal resistance. The research focus of this thermal resistance in the existing published literature is shown in Figure 5. Most of the studies are focused on the optimization of the thermal resistance of the motor housing cooling. For lower thermal resistance, liquid cooling via the motor housing is the most important means of heat dissipation, which necessitates in-depth exploration. Moreover, thermal resistance optimization of spindle heat dissipation and gap convection heat dissipation are also extensively required. Cooling methods for high-temperature heat sources and optimization measures for key sources of thermal resistance are also major research directions.
# Decision Support Software for Forecasting Patient's Length of Stay
## 6 Conclusions  
In this work, we presented a user-friendly decision support system for the prediction of hospitalized patients LoS which incorporates a two-level machine learning classifier. Our numerical experiments revealed that the proposed classification technique exhibits better classification accuracy compared to some of the most popular and commonly used individual classification algorithms. Significant advantages of the presented software are the employment of a simple and user-friendly interface, its scalability due to its modular nature of design and implementation and its operating system neutrality. It is worth recalling that our expectation is that this work could be used as a reference for decision making in the admission process and strengthen the service system in hospitals by offering customized assistance according to patients' predicted hospitalization time.  
It is worth mentioning that the patients' attributes used in this work do not constitute a conclusive list. Currently, an updated version of the software is under design, providing the user with an even friendlier interface with new features and allowing him to introduce new attributes and other criteria. Therefore, the user would be able to import his/her own training data or even select specific classifiers used at each level. This extension could possibly introduce new attributes and other criteria such as vital signs or lab readings at the time of admission, which were not in the current database, but are collectable by medical staff and may potentially influence the performance and the quality of the prediction.  
Since our experimental results are quite encouraging, a next step could be to enlarge our database with data from more hospitals and more years and apply machine learning methods to predict LoS and extract the factors affecting it among various types of patients. Additionally, to address the problem of imbalanced dataset and further increase the accuracy of the proposed two-level classifier, we commit to incorporate techniques dedicated for imbalanced data such as feature selection, sampling, cost-sensitive learning, and instance weighting [35; 36; 37; 38; 39; 40]. Furthermore, an interesting aspect of future research is to update the presented software to provide on demand explanation of what are the underlying explanatory factors for reaching a certain prediction/decision [41]. The development of a contextual explanatory model could further assist medical staff in the decision-making process.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived of the idea, designed and performed the experiments, analyzed the results, drafted the initial manuscript and revised the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 3: Decision support system for forecasting patients’ length of stay.
# A Survey of Multi-Focus Image Fusion Methods
## 3 Evaluation Indicators
### Objective Evaluation  
Objective evaluation is to calculate the image quality through some algorithm, and the calculation results are used as evaluation criteria. [PERSON] et al. [62] divided 12 popular image fusion evaluation indexes into four categories: evaluation indexes based on information theory, evaluation indexes based on image features, evaluation indexes based on image structure similarity, and evaluation indexes based on human perception. Many researchers welcome this classification method.  
The commonly used objective evaluation indexes are shown in Table 2. When evaluating the quality of fused images, multiple evaluation indexes are often needed to be calculated. The better the evaluation effect, the better the corresponding fusion effect is.
# Limitations on the Use of Recycled Asphalt Pavement in Structural Concrete
## 4 Mechanical Behaviour of Concrete Containing RAP
### Flexural Strength  
Similarly to compressive strength, the flexural strength decreases as the content of RAP increases. According to [PERSON] et al. [23], the percentage of reduction in flexural strength of the concrete containing RAP is lower than the corresponding percentage of reduction in compressive strength: a mix containing 40% RAP and w/c equal to 0.53 exhibits a reduction of 37% if compared to a mix containing virgin aggregates, while in terms of compressive strength, the same mix showed a reduction of 52% (values at 28 days). According to [PERSON] [4], flexural strength values of a concrete mix containing RAP is lower than that of a reference concrete (28% lower on average at 28 days). Moreover, it was noticed that the increase in w/c did not result in any significant effects on flexural strength, due to the fact that the bond strength of the asphalt-mortar attached to the aggregate, which is directly related to the flexural strength, and remained unchanged [4]. This was also confirmed by the studies of [23].  
In another type of application where RAP was used as a coarse aggregate for a pavement quality concrete, [PERSON] et al. [27] noticed that a concrete with up to 30% of RAP aggregate had a flexural strength higher than 4.5 MPa, which is the minimum required for flexural strength of pavement concrete according to the IS Standards [100] to avoid both rheological and mechanical underperformance.  
According to [PERSON] [3], with the 20% RAP aggregate, although the concrete flexural strength was 14% lower than the mix without RAP, the flexural toughness was 48% higher compared to that of standard concrete.
# Eco-Efficient Cement-Based Materials Using Biomass Bottom Ash: A Review
## 4 Biomass Bottom Ash in Concrete
### Dosages and Mix Proportions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multirow{2}{*}{**Cement**} & \multirow{2}{*}{**BBA**} & \multirow{2}{*}{**RA**} & \multicolumn{2}{c}{**Natural Sand**} & \multicolumn{2}{c}{**Coarse Aggregate**} & \multicolumn{2}{c}{**Lightweight**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Additive**} \\  & & & & & & & & & \\ \hline \multicolumn{8}{c}{**kg/m\({}^{3}\)**} \\ \hline
**[PERSON] et al. [19]** & & & & & & & & \\ \hline Control-1 & 300 & 0 & 0 & 819 & 1109 & - & 180 & 3 \\ BBA-2-NG & 300 & 24.6 & 0 & 794 & 1109 & - & 183 & 3.9 \\ BBA-3-NG & 300 & 49.2 & 0 & 770 & 1109 & - & 186 & 4.5 \\ BBA-4-RCA & 300 & 0 & 985 & 868 & 0 & - & 228 & 3 \\ BBA-35-RCA & 300 & 26 & 985 & 842 & 0 & - & 231 & 3.9 \\ BBA-6-RCA & 300 & 52 & 985 & 816 & 0 & - & 234 & 4.5 \\ BBA-7-MRA & 300 & 0 & 942 & 846 & 0 & - & 226 & 3 \\ BBA-8-MRA & 300 & 26 & 942 & 820.5 & 0 & - & 229 & 3.9 \\ BBA-9-MRA & 300 & 52 & 942 & 795 & 0 & - & 232 & 4.5 \\
**[PERSON] et al. [25]** & & & & & & & & \\ \hline Control-10-RCC & 300 & 0 & - & 931 & 1171 & - & 96 & 2.1 \\ BBA-11-RCC & 296 & 373 & - & 375 & 1314 & - & 95 & 2.1 \\ BBA-12-RCC & 296 & 615 & - & 0 & 1423 & - & 95 & 2.1 \\ Control-13-PCC & 300 & 0 & - & 924 & 1186 & - & 111 & 2.1 \\ BBA-14-PCC & 300 & 368 & - & 368 & 1275 & - & 111 & 2.1 \\ BBA-15-PCC & 300 & 606 & - & 0 & 1381 & - & 111 & 2.1 \\
**[PERSON] et al. [27]** & & & & & & & & \\ \hline Control-16 & 393 & 0 & - & 668.1 & 1139.6 & - & 192.6 & 0 \\ BBA-17 & 372.9 & 19.6 & - & 667.3 & 1138.3 & - & 192.3 & 0 \\ BBA-18 & 352.8 & 39.2 & - & 666.5 & 1136.9 & - & 190.4 & 2.4 \\ BBA-19 & 372.9 & 19.6 & - & 667.3 & 1138.3 & - & 191.7 & 0.9 \\ BBA-20 & 352.8 & 39.2 & - & 666.5 & 1136.9 & - & 190.3 & 2.5 \\
**[PERSON] et al. [23]** & & & & & & & & \\ \hline Control-21 & 300 & 0 & 0 & 710.3 & 494.2 & 108.7 & 180 & 2.8 \\ BBA-22 & 300 & 163.3 & 0 & 497.2 & 494.2 & 109.7 & 180 & 3.3 \\ BBA-23-MRA & 300 & 163.3 & 217.8 & 498.2 & 247.1 & 110.7 & 180 & 3.3 \\ BBA-24-MRA & 300 & 163.3 & 435.5 & 499.2 & 0 & 111.7 & 180 & 4 \\ BBA-25-MRA & 300 & 86.1 & 321.9 & 609.5 & 247.1 & 72.5 & 180 & 3.4 \\ BBA-26-MRA & 300 & 292.3 & 321.9 & 497.2 & 247.1 & 54.4 & 180 & 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Concrete mix proportions by authors. two, natural aggregates and lightweight aggregates was replaced by BBA in two percentages (15% and 55%) and MRA, according to the particle size. The amount of cement (300 kg/m\({}^{3}\)), the water-cement ratio (w/c ratio 0.6), and the slump (85 \(\pm\) 5 mm) were constant in all the mixes.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 3 Performance Analysis
### System Throughput Analysis  
In order to further evaluate the performance of the TWR C-NOMA system, the system throughput is obtained for IQI and ipSIC conditions, as well as for that of the ideal condition, respectively, and given by:  
\[R^{IQI,ipSIC}=\left(1-\mathrm{P}_{out,D_{1}}^{IQI,ipSIC}\right)\bar{R}_{1}+ \left(1-\mathrm{P}_{out,D_{2}}^{IQI,ipSIC}\right)\bar{R}_{2}, \tag{31}\]  
\[R^{ideal}=\left(1-\mathrm{P}_{out,D_{1}}^{ideal}\right)\bar{R}_{1}+\left(1- \mathrm{P}_{out,D_{2}}^{ideal}\right)\bar{R}_{2}, \tag{32}\]  
where \(\bar{R}_{1}\) and \(\bar{R}_{2}\) represent the target rates at the receiver to decode the desired signals \(s_{1}\) and \(s_{2}\), respectively.
## 2 Materials and Methods  
In order to investigate the influence of both low and high values of current, pulsed GMAW was used. The power source used was a Librudi Dimetrics GoldTrack VI (Librudi Engineering Limited, Dundas, ON, Canada), which provides a current pulse profile with nearly ideal square pulse shape [27].  
For the metal transfer and arc phenomenon imaging, a high-speed camera (FASTCAM Mini UX50, Photron, San Diego, CA, USA) was used. The lens used was a C-mount close focus zoom lens (6X, 18-108 mm FL, Edmund Optics Inc., Barrington, NJ, USA) equipped with a 25/25.4 mm diameter C-mount lens mount (Thick Lens Mount from Edmund Optics Inc., Edmund Optics Inc., Barrington, NJ, USA) with a holder to incorporate band-pass filters, which were used to limit the amount of light from the arc reaching the camera sensor. This would reveal different features of the arc and/or molten metal at both wire electrode and weld pool. The lens aperture was fixed at f/22, and the high-speed camera recording the arc at a frequency of 5000 frames per second, synchronized with a data acquisition system (DAQ) which was recording the welding current and voltage at a frequency of 20 kHz for a period of 1 s.  
Bandpass interference filters (Edmund Optics Inc., Barrington, NJ, USA) of two wavelengths -- 515 \(\pm\) 10 and 900 \(\pm\) 10 nm -- were used. The 515 \(\pm\) 10 nm wavelength allowed the imaging of the features in the arc core, which is composed mostly of iron vapor. The imaging of iron vapor is possible owing to the fact that iron has a strong peak emission line at a 516.74 nm wavelength [28; 29; 30]. To capture the outer cone of the electric arc, droplet formation and transfer, and weld pool dynamics, a narrow band pass filter of 900 \(\pm\) 10 nm wavelength was used. The lens aperture was fixed at f/22 and different exposure times were used.  
All the welds were performed using bead on plate welds performed using AWS A5.18 ER70S-6 wire of 1.2 mm (0.045\") nominal diameter. The base metal was 1020 steel plates of 9.5 mm thickness, and contact tip to workpiece distance of 20 mm. The shielding gas used was a mixture of 85% Ar-15% CO\({}_{2}\) at at flow rate of 18.9 L/min (40 cfh).
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 6 Discussions  
Due to the difficulty in launching a dedicated satellite and allocating a specified spectrum for IoT communications, we propose one framework, which uses the legacy satellite and its authorized spectrum to support the IoT transmissions. Due to the interference between the legacy satellite users and the IoT users, we propose one joint resource allocation scheme to balance the two types of transmissions, where the legacy satellite users have higher priority. Simulation results show the advantages and the effectiveness of the spectrum sharing and joint resource allocation scheme. In a nutshell, the main contributions of this paper are summarized as follows.  
* The cognitive LEO SatCom system is proposed to support IoT transmissions, which can effectively enhance the coverage and spectrum efficiency.
* Due to the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic SINR and obtain the achievable rate for IoT and legacy systems.
* The receive power of the legacy and IoT transmissions are jointly optimized and the closed-form expressions of the optimal receive powers for these two systems are derived.
* Extensive simulation results are provided to validate the effectiveness of the designed power allocation scheme, which shows that IoT devices can achieve information transmission on the basis of ensuring the performance of the primary system.
# Advanced 2D Computational Fluid Dynamics Model of an External Gear Pump Considering Relief Grooves
## 3 CFD Model
### Finite Element Mesh  
The used finite element mesh is shown in Figure 7. A basic mesh size of 0.14 mm was used for the initial meshing. The mesh is refined in the areas with gaps, with sizes between 0.013 mm and 0.025 mm for the involutes and contacting walls of the tangential grooves; 0.034 mm--for rounding; 0.05 mm--for the teeth tips; and 0.08 mm--for the walls. The static zones (inlet zone and outlet zone in Figure 6) have a structured mesh of rectangles with a size of 0.25 mm. During the solution, the mesh is dynamically changed. The remeshing parameters are set to minimum length scale 0.0007 mm; maximum length scale 0.32 mm; and maximum mesh skewness 0.67. Thus, after the first steps of the solution, some of the mesh cells become larger, and the general number of cells decreases from 79,821 to 65,401; however, the cells remain smaller near the walls that come into contact.  
The chosen mesh settings have a significant impact on the overall solution. To a large extent, they are the result of a mesh-independent study carried out in our previous research [3]. The described mesh is fine enough to provide a detailed and accurate solution, while being economical in terms of computational resources. Besides the number of cells being relatively small for the modeled geometry, the mesh allows the time step to be set in the interval from 1 \(\times\) 10\({}^{-6}\) s to over 4 \(\times\) 10\({}^{-6}\) s (at a nominal rotation frequency of 1450 min\({}^{-1}\)). An average of 1024 steps were used to turn the wheels by 30\({}^{\circ}\).  
Due to the large lengths and small gaps of the contact areas, as well as due to the presence of sharp transitions between the narrow and wide sections that constantly change their position, the solution is significantly more unstable than the classical 2D model. It often crashes due to Floating Point Exception. To deal with the problem, the following measures were used: varying the time step (from 2 to 4.2 \(\times\) 10\({}^{-6}\) s), activating the High Order Term Relaxation option with Relaxation Factor 0.75, and increasing the number of iterations from 20 to 35 for every step. The latter increased the computation time by about 25%. The mesh layering method is also enabled with the default settings (height-based, split factor 0.4, and collapse factor 0.2).  
The computing time for the described model is of the order of 40 min/30\({}^{\circ}\) on a workstation with an Intel(r) Core(tm) i9-14900K (Intel Corporation, Santa Clara, CA, USA) processor and 64 GB DDR5 RAM.  
Figure 7: Finite elements initial and final mesh view. Zoomed areas are marked with rectangles.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Analytical Model
The final result of the matrices product in Equation (3) is:  
\[E_{Out\_Norm}^{Opt}(t)=\left( \begin{array}{c}e^{-i(\frac{\Gamma_{b}+\Gamma_ {s}}{2})}-e^{i(\frac{\Gamma_{b}+\Gamma_{s}}{2})}\\ e^{i(\frac{\Gamma_{b}+\Gamma_{s}}{2})}-e^{-i(\frac{\Gamma_{b}+\Gamma_{s}}{2} )}\end{array} \right) \tag{6}\]  
which simplifies, using [PERSON]'s formula, to:  
\[E_{Out\_Norm}^{Opt}(t)=\frac{i}{\sqrt{2}}\left( \begin{array}{c}-\sin(\frac{ \Gamma_{b}+\Gamma_{s}}{2})\\ \sin(\frac{\Gamma_{b}+\Gamma_{s}}{2})\end{array} \right) \tag{7}\]  
Finally, since for complex field holds \(I=|E|^{2}\), the normalised intensity collected by the photodetector is:  
\[I(t)=E_{Out\_Norm}^{Opt,*}(t)\cdot E_{Out\_Norm}^{Opt}(t)=\sin^{2}(\frac{ \Gamma_{b}+\Gamma_{s}}{2}) \tag{8}\]  
where \(E_{Out\_Norm}^{Opt,*}(t)\) is the conjugate value of the normalized light wave on the output of the optical chain. Substituting the values of \(\Gamma_{b}\) and \(\Gamma_{s}\) from Equation (5), assuming constant \(E_{dc\_bias}\) and collecting all the time invariant terms under a factor \(\Gamma_{0}\), Equation (8) is transformed into:  
\[I(t)=\sin^{2}(\Gamma_{0}-\alpha(A\sin(2\pi ft))-\beta(E_{ext})) \tag{9}\]where \(\alpha=\frac{\pi}{\lambda}(n_{e}^{3}r_{33}-n_{o}^{3}r_{13})L_{yb}\) and \(\beta=\alpha\frac{L_{\pi n}}{L_{yb}}\). The output intensity is then proportional to the function \(\sin^{2}(\sin(x)+\phi)\), where the phase term is linearly proportional to the intentionally applied DC bias voltage and to the external electric field under measurement:  
\[I(t)\propto\sin^{2}(\sin(2\pi ft)+\phi(E_{dc\_bias}E_{ext})) \tag{10}\]  
The analytical results shown in this paper are obtained by numerical computation of Equation (10).
## 2 Previous Work
The working of the proposed algorithm is measured with twenty benchmark functions and compared with the standard DE and opposition-based DE (ODE) algorithm. Numerical results illustrate that the proposed technique enhances the convergence rate. To tackle the thresholding problem of the image, an enhanced variant of the standard DE algorithm with a local search (termed as LED) and low discrepancy sequences is introduced [53]. Experimental results conclude that the performance of the introduced algorithm is superior for finding the optimum threshold. For the steelmaking continuous (SCC) problem, in [54], the authors presented a novel enhanced technique of DE based on the two-step procedure for producing an initial population, as well as, a novel mutation approach. Furthermore, an incremental methodology for generating the initial population was also incorporated in DE to handle dynamic events. Computational experiments conducted with the presented approach show the effectiveness of the presented approach than others. Additionally, as per concern, in the application area, the authors utilized BA for the antenna optimization problem in [55], moreover, pan evaporation was estimated by using BA [56]. Beside this, in [57], the authors applied a new variant of DE for path-planning of mobile robots. According to the above-mentioned studies, we conclude that the efficiency of meta-heuristic algorithms is affected by using the random number for the initialization of the population. Due to this reason, various articles used the quasi-random number sequences for the population's initialization in meta-heuristic algorithms. However, the majority of the researchers used limited quasi-random sequences for initializing the population and did not perform any comparative analysis for their effect on the initialization of population algorithms. Similarly, Knuth, Well, and Torus sequences from quasi-random sequences are still not proposed in DE and BA for the initialization of the population. After analyzing all the literature, we found the above-mentioned gaps and try to fill it.