esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Method
### Fluid Domain Meshing  
In this article, to capture oil flow and distribution characteristics in bearing cavities with under-race lubrication, the fluid domain between the inner and outer rings is extracted, except the solid domain of spherical rollers and the cage. Besides, two circular fluid domains are added on both sides of the bearing to monitor the oil's splashing. On account of complex relative motion inside the bearing, the fluid domain is separated into four parts, as shown in Figure 2, including the hole region, the ball region, the main fluid region, and the external fluid region. The hole region has six radial cylindrical channels on the inner ring and rotates around the bearing center with the inner ring's rotating speed. The main fluid region is the cavity between the inner and outer ring, and it rotates with the cage speed to represent the rollers' revolution. The ball region consists of 20 hollow spherical domains with one homocentric roller in each one. The ball region revolves together with the main fluid region and rotates by its own rotating axis (the connection line of two roller-raceway  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Parameters** & **Value** \\ \hline Outer race diameter & 200 mm \\ Inner race diameter & 133.35 mm \\ Pitch diameter & 167 mm \\ Width & 40 mm \\ Ball diameter & 22 mm \\ Number of balls & 20 \\ Curvature coefficient of inner and outer & 0.52/0.515 \\ raceways & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure parameters of the ball bearing.  
Figure 1: Geometric model of ball bearing.  
contact points) at the same time. The extended external fluid region is located on both sides of the main fluid region to provide a reasonable boundary environment for the oil-air two-phase flow simulation.  
The software ANSYS2020R1 Mesh is employed to discretize the fluid domain in ball bearing with under-race lubrication, as shown in Figure 3. Due to the complex internal structure of the bearing, the entire fluid region is meshed using tetrahedral unstructured mesh, and the slit in the contact zone is locally refined to improve the mesh quality, such as the contract zones between balls and inner ring, balls and outer ring, balls and the cage, and the cage and guide face.  
In order to exclude the influence of the guiding model on simulation results, three different mesh models are compared for independence determination. The oil volume fraction inside the bearing is analyzed, and the result is listed in Table 2. The bearing rotating speed is 5000 rpm, with an oil flow rate of 3 L/min, oil viscosity of 0.0046 Pa-s, and oil density of 938.6 kg/m\({}^{3}\). It could be found that the calculated results are similar, and to ensure calculation accuracy with acceptable computational cost, a mesh with 3,143,919 cells and 598,702 nodes is selected.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 5 Results of the CIR-Based Methods  
This section will present the prediction performance of several CIR-based methods.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{3}{c}{**KWAUG14**} & \multicolumn{3}{c}{**SPACE08**} \\ \hline
**Method** & **MAE** & \begin{tabular}{c} **Pearson Corr.** \\ **Coeff.** \\ \end{tabular} & \
\begin{tabular}{c} **MAE** \\ **Coeff.** \\ \end{tabular} &
\begin{tabular}{c} **Pearson Corr.** \\ **Coeff.** \\ \end{tabular} \\ \hline Linear Regression & \(0.017\) & \(0.70\) & \(\mathbf{0.017}\) & \(\mathbf{0.63}\) \\  & \((3.0\times 10^{-4})\) & \((2.5\times 10^{-2})\) & \((\mathbf{1.3\times 10^{-4}})\) & \((\mathbf{3.1\times 10^{-3}})\) \\ \hline Two-Layer NN & \(0.019\) & \(0.65\) & \(0.018\) & \(0.60\) \\  & \((1.0\times 10^{-3})\) & \((3.7\times 10^{-2})\) & \((4.2\times 10^{-4})\) & \((2.0\times 10^{-2})\) \\ \hline KNN & \(0.0089\) & \(0.89\) & \(0.017\) & \(0.60\) \\  & \((1.5\times 10^{-4})\) & \((1.1\times 10^{-2})\) & \((1.0\times 10^{-4})\) & \((8.5\times 10^{-3})\) \\ \hline Dummy & \(0.024\) & NaN & \(0.021\) & NaN \\  & \((6.5\times 10^{-4})\) & & \((2.6\times 10^{-4})\) & \\ \hline Decision Tree & \(\mathbf{0.0087}\) & \(\mathbf{0.90}\) & \(0.021\) & \(0.52\) \\  & \((\mathbf{1.8\times 10^{-4}})\) & \((\mathbf{1.8\times 10^{-2}})\) & \((1.8\times 10^{-4})\) & \((4.9\times 10^{-3})\) \\ \hline \hline \end{tabular} Note: Each cell shows the relevant metric—mean (std-dev) based on five fold cross validation. Metrics marked in bold indicate best performing method on each data set based on a one-tailed \(t\)-test using a \(5\%\) significance level [29].  
\end{table}
Table 1: Conventional regression results using traditionally available features.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 3 Results and Discussions
### Surface Plots
#### 3.2.3 Optimum Hardness  
The experimental results of the understudy CFRP hardness (Table 2) were transformed into regression Equation (4) and the surface plot (Figure 11). The equation and the surface plot were used to predict hardness within the upper and lower limit of curing parameters (\(T_{c}\), \(t_{c}\), and \(P_{au}\)) of experiments.  
The MRCC (120 \({}^{\circ}\)C, 120 min, 7 bar) produced a hardness of 51.2 HRB, whereas the maximum hardness of 57.6 HRB was observed at CC8 (140 \({}^{\circ}\)C, 120 min, 7 bar) (Figure 11b). A similar trend of increasing hardness with an increase in either \(T_{c}\) or \(t_{c}\) is found in the literature for CFRP [21]. Both tensile strength and hardness were maximum at the same  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Non-Optimal Curing** & **Non-Optimum Properties** & **Optimal Curing Parameters** & **Optimum Properties** & **Improvement (\%)** \\ \hline \multirow{4}{*}{**Hardness (HRB)**} & \multirow{4}{*}{51.2} & 140 \({}^{\circ}\)C & \multirow{4}{*}{57.6} & \multirow{4}{*}{12.5} \\  & & 120 min & & \\ \cline{1-1} \cline{4-5}  & & 7 bar & & \\ \cline{1-1} \cline{4-5}  & & (CC8) & & \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 10: Surface plots of impact toughness (\(IT\)) at a constant pressure of (**a**) 3 bar; (**b**) 7 bar. The optimum impact toughness was observed at CC4 (140 \({}^{\circ}\)C, 120 min, 3 bar), represented by a black dot.  
curing condition, i.e., CC8. Hardness is resistant to plastic deformation and is somewhat related to the highest point in the stress-strain curve. Since both tensile strength and hardness address the same physical phenomenon, maximum hardness was also observed under the same curing conditions as tensile strength. As we used a higher \(T_{c}\) of 140 \({}^{\circ}\)C, an increase of 12.5% in hardness was observed, as compared to hardness at MRCC. The result of optimal curing parameters along with optimum hardness is summarized in Table 6.
## 2 System Model
### Encounter Probability Analysis Model
Therefore, in integrated UAV-ICV networks, the packet loss rate of node \(j\) can be estimated as  
\[P_{\text{drop}}(j)=\frac{\sum\limits_{z(m)}L_{m}(j)}{\sum L_{\text{tot}}(j)}, \tag{8}\]  
where \(L_{\text{tot}}(j)\) is the total number of message copies in node \(j\). In summary, the probabilities that communication node \(i\) encounters the next-hop relay nodes of types I, II, and III can be represented as \(P_{\text{I}}\), \(P_{\text{II}}\), and \(P_{\text{III}}\), respectively. We have  
\[P_{\text{I}}=P_{(i_{1},\,i_{1})}=P_{(i_{2},\,i_{1})}=P_{(i_{3},\,i_{1})}P_{ \text{suc}}(i,\ j), \tag{9}\]  
\[P_{\text{II}}=P_{(i_{1},\,i_{2})}=P_{(i_{2},\,i_{2})}=P_{(i_{3},\,i_{2})}=\rho _{\text{II}}\lambda_{i,j}P_{\text{suc}}(i,\ j)\Big{[}1-P_{\text{drop}}(j)\Big{]}, \tag{10}\]\[P_{\text{III}}=P_{(i_{1},\,i_{3})}=P_{(i_{2},\,i_{3})}=P_{(i_{3},\,i_{3})}=\rho_{ \text{III}}\lambda_{i,j}P_{\text{suc}}(i,\,j)P_{\text{drop}}(j). \tag{11}\]  
In (9)-(11), \(\rho_{\text{I}}\), \(\rho_{\text{II}}\), and \(\rho_{\text{III}}\) are the probabilities that communication nodes of type I, II, and III forward message copies, respectively.
## Data and Methods
### Data
#### 3.1.3 CHIRPS  
The Climate Hazard Group Infrared Precipitation with Stations Data (CHIRPS) is a combined product of remotely sensed and in-situ observations developed for monitoring droughts and extremes. The main input data sources used in CHIRPS are monthly precipitation climatology from Climate Hazard group (CHPClim), quasi global geostationary thermal infrared observations from National Oceanic and Atmospheric Administration, Climate Prediction Center (NOAA-CPC), the National Climatic Data Center (NCDC), TRMM 3B42, NOAA Climate Forecast System version 2 (CFSv2) and gauge observations from various sources. The algorithm is based on the cold cloud duration (CCD) notion, that is, the amount of time a given pixel is covered by high cold clouds infrared brightness temperature (IR BT \(<\) 235 K). Precipitation estimates are obtained through a CCD's calibration procedure exploiting the TMPA 3B42 data set from 2000 to 2013. These IR-based precipitation estimates are expressed as a percentage of the 1981-2013 mean precipitation estimates and then multiplied by the corresponding CHPClim values, so obtaining the CHIRP product. This last step is carried out to reduce the systematic bias in the IR based precipitation estimates. Finally, IR estimates are merged to the rain gauge measurements using the inverse distance weighting algorithm to produce the CHIRPS product. For the current study we used daily observations with a grid size resolution of 0.05 degrees. Further details can be seen at [4].
## 1 Introduction
The best horizontal and vertical positioning errors achievable for the Xiaomi 8 smartphone with GPS, BDS and Galileo systems were, on average, 1.65 and 8.1 m in [PERSON] et al. [14]. [PERSON] et al., based on the observations received from three smartphones (Xiaomi 8, Google Pixel 4 and Pixel 4 XL), the multi-GNSS PPP was conducted using ultra-rapid and IGS Real-Time Service (IGS-RTS) products, respectively. The PPP accuracy using IGS-RTS can be improved by about 0.11%, 2.8% and 4.4% compared to ultra-rapid products for Xiaomi 5, Pixel 4 and Pixel 11 XL smartphones, respectively [15]. [PERSON] et al. proposed a real-time PPP method for smart devices for in-vehicle motion localization. The RMS error of the positioning results obtained based on real-time PPP was about 1.0-1.5 m in the horizontal direction and 1-2 m in the vertical direction when the smartphone was placed in the vehicle [16]. It can be clearly seen that most of the above studies are based on post-processing precise orbit and clock offset for post-processing PPP and RTK. Fewer studies have been conducted on the comparative evaluation of the PPP performance based on real-time streaming data of recent mobile phones. Because there is a time delay for post-processing precise orbit and clock offset products, which cannot meet the real-time application. In addition, previous studies have used older smart devices, such as Xiaomi 8, while little research has reported on the real-time PPP performance based on single- and dual-frequency and multi-GNSS of relatively new smartphones, such as the Huawei Mate40 and P40; the positioning performance of these newer smartphones is still unrevealed [17; 18; 19; 20]. Therefore, the data quality and real-time positioning performance using Huawei Mate40 and P40 should be further investigated. This contribution investigates the multi-GNSS observation data quality evaluation and PPP performance based on the real-time data stream of the smartphone. Based on the observation data from Huawei Mate40 and P40 and geodetic receiver and the real-time precise orbit and clock offset products from GFZ on 16 November 2022, the observation data quality of the Huawei Mate40 and P40 smartphones and the real-time satellite orbit and clock offset accuracy were assessed, and the real-time PPP performance using smartphones was evaluated. After this introduction, the real-time smartphone PPP model was introduced. Then the data quality of the smartphone was evaluated in terms of C/N0, the number of satellites and PDOP. Furthermore, we analyzed the performance of real-time orbit and clock offset. Thereafter, we investigated the real-time static and kinematic PPP performance of smartphones. Finally, the conclusions are given.
## 3 Conventional Geolocation Techniques
### Power Difference of Arrival (PDOA)  
The PDOA technique involves the measurement of the RSS of the received signal. It depends on the difference in received signal strength instead of the difference in time of arrival [51; 52; 53]. Mathematically, the PDOA measurement model is the received power difference between two sensors formulated as  
\[p_{r,i1}=p_{r,i}-p_{r,1}=10\,\alpha log_{10}\bigg{(}\frac{d_{i}}{d_{1}}\bigg{)} \tag{6}\]
## 4 Experimental Results  
### Experimental Setup  
Figure 3 shows the experimental setup of the prototype MFLT, ECT and PSECT systems [11, 12, 13]. The sensor probes are placed inside the MSR finned tube and scanned over the tube by the support of the motor scanner. The signal processing units, including amplifiers, high/low pass filters and RMS circuits, are integrated into a single board while the ADC is the multifunctional device from National Instrument (NI-USB 6255, National Instruments, Austin, TX, USA). The MFLT probe has the Hall sensor array and a magnet; the ECT probe has Hall sensor array and a bobbin coil; and the PSECT probe has Hall sensor array, a magnet and a bobbin coil. The PSECT probe could be the MFLT probe if the bobbin coil is disconnected (open circuit). The bobbin coil has 130 turns in 10 mm length and using 0.2 mm diameter copper wire. The diameter of the sensor probes is 13.5 mm, and the inner/outer diameters of the MSR finned tube are 14.1/19.1 mm. The height and step of the fin are 1.3 mm and 1.1 fins/mm (28 fins/inch), respectively. The flat-bottom holes were produced on the outer surface of the MSR tube as a simulation for outer surface corrosion. The simulated corrosion has depths of 10-100% wall thickness and diameters of 1.3 mm and 2.5 mm. More detailed information could be found in our previous works [11, 12, 13].
## 4 Results and Discussion
### Experimental Validation of Current Injection Models  
Current injection models indicate the functions of \(f(y)\) and \(g(y)\) in Equation (25), which are derived from the boundary conditions on the current-injected surface. Therefore, they describe how current inflow and outflow occur.  
\[\sigma\frac{\partial u}{\partial n}=I\text{ on }\partial\Omega_{I}\Rightarrow u_{x}(0,y )=g(y)\text{ and }u_{x}(a,y)=f(y) \tag{25}\]  
Three models were investigated: Dirac delta function, Heaviside step function, and Gaussian function (also known as the normal distribution function). The Dirac delta function was used in a previous study [13]. However, since the electrodes where the current goes in and out have a certain width, the Heaviside step function might better represent the current inflow and outflow than the Dirac delta function. Nevertheless, the Gaussian function might better describe the current density than the Heaviside step function. This is because it can better describe common physical phenomena, such as stress density and heat density, even though the ideal density for calculations is the Heaviside step function.
# Light-Weighting of Planetary Gearbox Based on Multi-Strategy Optimization Sparrow Search Algorithm
## 3 Methodology
### Sparrow Search Algorithm  
The SSA [23], as an emerging bio-inspired optimization algorithm, draws its design inspiration from the behavior of sparrows foraging for food in the natural environment. Sparrows exhibit group cooperation, information sharing, and adaptive behaviors during foraging. These characteristics have been abstracted by the algorithm designers and applied to solve optimization problems.  
The solution methodology involves several key steps: initially, a population of \(N\) sparrows is instantiated and their fitness is evaluated. Subsequently, the behavior of sparrows searching for food is simulated, with the algorithm iteratively guiding the population towards regions of higher fitness. Through multiple iterations, the sparrow population gradually converges towards the optimal solution.  
In the algorithm, sparrows are classified into three types, including producers, scroungers, and alerters.  
Producers are responsible for extensive exploration within the search space to discover new potential solutions. The location update formula is given in Equation (31).  
\[X_{i,j}^{t+1}=\left\{\begin{array}{ll}X_{i,j}^{t}.exp\Big{(}\frac{-i}{x \cdot\mathrm{i}t\ell_{max}}\Big{)}&\mbox{if }R_{2}<ST\\ X_{i,j}^{t}+Q\cdot L&\mbox{if }R_{2}\geq ST\end{array}\right. \tag{31}\]  
\(X_{i,j}^{t+1}\) represents the \(j\)th dimension of the \(i\)th sparrow of generation \(t\), \(iter_{max}\) is the maximum number of iterations, and \(L\) is a d-dimensional row vector with each element being one.  
Scroungers focus on further improvements to well-behaved solutions. The location update formula is given in Equation (32).  
\[X_{i,j}^{t+1}=\left\{\begin{array}{ll}Q\cdot exp\Bigg{(}\frac{X_{i,j}^{t}-X_{ i,j}^{t}}{\mathrm{i}^{2}}\Bigg{)}&\mbox{if }i>N/2\\ X_{P}^{t+1}+\Big{|}X_{i,j}^{t}-X_{P}^{t+1}\Big{|}\cdot A^{+}\cdot L&\mbox{ otherwise}\end{array}\right. \tag{32}\]  
\(X_{P}\) represents the best position held by producers, \(X_{worst}\) represents the worst position found so far, and \(A\) is a \(1\times D\) matrix with elements randomly \(1\) or \(-1\), \(A^{+}=A^{T}\Big{(}AA^{T}\Big{)}^{-1}\).  
Alerters act as a monitor and balance in the search process. It ensures that the search process does not get stuck in a local optimal solution too early. The location update formula is given in Equation (33).  
\[X_{i,j}^{t+1}=\left\{\begin{array}{ll}X_{best}^{t}+\beta\cdot\left|X_{i,j}^{t }-X_{best}^{t}\right|&if\ f_{i}>f_{g}\\ X_{i,j}^{t}+K\cdot\left(\frac{\left|X_{i,j}^{t}-X_{best}^{t}\right|}{(f_{i}-f_{ n})+\varepsilon}\right)&if\ f_{i}=f_{g}\end{array}\right. \tag{33}\]  
\(X_{best}^{t}\) represents the current optimal position, \(\beta\) is a normal distribution of random numbers, and \(K\) is a random number, \(K\in[-1,1]\). \(f_{i}\) is the fitness value of the current sparrow. \(f_{g}\) and \(f_{w}\) are the current global best and worst fitness values, respectively. \(\varepsilon\) is the smallest constant.  
The specific process is shown in Figure 2.
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
## 2 Test Design  
### Test Apparatus and Sample Preparation  
The sand-gravel material, which is composed of limestone and was used in the test, originates from a valley in Xinjiang. These materials are rounded and exhibit some degree of cementation, as shown in Figure 1. This study tested a series of sand-gravel materials under undrained conditions through cyclic triaxial tests conducted with the GCTS Instruments large-scale dynamic triaxial apparatus, as illustrated in Figure 2. The test sample diameter \(D\) is 300 mm and the height \(H\) is 700 mm. For this experiment, the sand-gravel material was prepared using the equivalent substitution method to achieve the desired gradation, with a maximum allowable particle size of 60 mm. The grain size distribution is shown in Figure 3 and the basic physical properties of the prepared material are shown in Table 2. The sample preparation was divided into five layers and is based on the surface vibration method.  
The water head method was used to saturate the specimen. In this method, the water slowly infiltrates from the bottom of the specimen until the water at the top of the specimen seeps out. First, the inlet valve was opened, and 35 kPa lateral pressure was applied to the sample. Thereafter, lateral pressure and axial pressure were applied in a stepwise fashion until the lateral pressure and axial pressure reached the predetermined consolidation stress ratio of each test. The drain valve was then opened to drain and consolidate the sample, after which the drain valve was closed when the sample was consolidated. When the seeped water is stable and no bubbles are generated, the saturation of the specimen is deemed to exceed 95%, which meets the test requirements.  
Figure 1: Sand-gravel materials in Xinjiang.
## 4 Conclusions  
In this work, a comparative study on the effects of different diesel-ethanol blended fuels on combustion, engine performance, and emission characteristics were investigated in a four-cylinder common rail direct injection (CRDI) diesel engine. The main conclusions are as follows:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Fuels**} & \multicolumn{3}{c}{**NOx**} & \multicolumn{3}{c}{**Smoke**} \\ \cline{2-7}  & **30 Nm** & **60 Nm** & **90 Nm** & **30 Nm** & **60 Nm** & **90 Nm** \\ \hline D100 & 0 & 0 & 0 & 0 & 0 & 0 \\ D95E5 & \(-\)0.22 & \(-\)3.37 & \(-\)1.60 & \(-\)12.73 & \(-\)32.43 & \(-\)21.74 \\ D90E10 & \(-\)3.43 & \(-\)6.63 & \(+\)3.20 & \(-\)21.82 & \(-\)40.54 & \(-\)36.23 \\ D85E15 & \(-\)18.46 & \(-\)12.05 & \(-\)6.40 & \(-\)54.55 & \(-\)66.22 & \(-\)55.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Addition of bioethanol improved improve the NOx-smoke trade-offs (\({}^{\prime}-^{\prime}\) indicates decrease, \({}^{\prime\prime}+^{\prime}\) indicates increase, compared with diesel).  
Figure 10: Smoke emissions for all test fuels according to various engine loads.  
1. For the combustion characteristics, the increase of engine load significantly increases the pressure and heat release rate (HRR) in the cylinder, and the start of combustion is also advanced. The addition of ethanol has no significant effect on in-cylinder pressure and HRR. Under most experimental conditions, the in-cylinder pressure is only reduced by 1%. In addition, only 15 vol.% ethanol with high mixture ratio causes a slight increase in HRR.
2. For the engine performance, the increase of engine load significantly decreases the brake specific fuel consumption (BSFC). The BSFC is reduced by 32.30% at most when 15 vol.% ethanol is added to diesel fuel at 90 Nm. On the other hand, the addition of ethanol leads to the increase of BSFC in varying degrees. The BSFC is increased by 12.88% at most when 15 vol.% ethanol is added to diesel fuel at 30 Nm.
3. For the emission characteristics, although the increase of engine load is beneficial to the reduction of carbon monoxide (CO) and unburned hydrocarbon (HC), it increases nitrogen oxides (NOx) and smoke. Surprisingly, the addition of ethanol significantly reduced CO, NOx, and smoke. The CO is reduced by up to 30.09% when 10 vol.% ethanol is added to diesel fuel at 90 Nm. The NOx is reduced by up to 18.46% when 15 vol.% ethanol is added to diesel fuel at 30 Nm. The smoke is reduced by up to 66.22% when 15 vol.% ethanol is added to diesel fuel at 60 Nm.  
In the future work, a wider range of operating conditions such as injection timings, injection pressures, and idle speeds will be compared to study the combustion and emissions of diesel engines direct fueled with bioethanol with different concentrations. In addition, whether the addition of biodiesel will also be studied as an important base-line data.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.