esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
## 3 Numerical Simulation of the Conveying Process
### Wheat Flour Parameter Settings and Experimental Materials  
Given the small particle size and large quantity of powder particles, some scholars have employed the injection method based on scaling theory to conduct dynamic angle of repose experiments with wheat flour, achieving simulation parameter calibration while reducing computational costs. Through this method, the researchers scaled up the irregular wheat flour particles (originally 0.212 mm in size) to spherical particles with a diameter of 1 mm and validated them using the tilting method, thereby obtaining the corresponding contact parameters [21].  
In this study, these parameters were adopted as reference values with the JKR (Johnson-Kendall-Roberts) contact model, which better simulates particle adhesion during pneumatic conveying. The simulation was configured with a particle inlet flow rate of 30 g/s and a duration of 5 s. Other relevant physical parameters are presented in Table 2 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline Wheat flour density/(kg/m\({}^{3}\)) & 980 \\ Wheat flour particle size/(mm) & 1 \\ Wheat flour Poisson’s ratio & 0.3 \\ Wheat flour shear modulus/pa & \(1\times 10^{7}\) \\ Coefficient of restitution (wheat flour–wheat flour) & 0.1 \\ Static friction coefficient (wheat flour–wheat flour) & 0.8 \\ Rolling friction coefficient (wheat flour–wheat flour) & 0.22 \\ Coefficient of restitution (wheat flour–stainless steel) & 0.1 \\ Static friction coefficient (wheat flour–stainless steel) & 1 \\ Dynamic friction coefficient (wheat flour–stainless steel) & 0.7 \\ JKR surface energy/(mJ/m\({}^{3}\)) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for wheat flour in Rocky DEM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline  & velocity–inlet (m/s) \\  & 1.4 m/s \\  & 1.6 m/s \\ Boundary Conditions & 1.8 m/s \\  & 2.0 m/s \\  & Outlet–vent \\  & Stationary wall \\ \hline   Iterative Computation & Time step sizes (0.05 s) \\  & Maximum coupling iterations (10 times) \\ \hline  & Density/(kg/m\({}^{3}\)) \\ Fluid & 1.225 \\  & Viscosity/(10\({}^{-5}\) pa-s) \\  & 1.7894 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fluent fluid parameter settings.  
The experiment utilized three types of wheat flour with distinct particle size ranges: wheat flour A (0.21-0.212 mm), wheat flour B (0.125-0.149 mm), and wheat flour C (0.105-0.125 mm), all with a uniform density of 670 kg/m\({}^{3}\). Employing a single-variable control method, this study analyzed the effects of inlet air velocity and chute inclination angle on the conveying ratio (\(\delta\)), with the observed variation patterns serving to validate the simulation results.
# Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data
## 1 Introduction  
The human personality may be judged by different emotions which affect our daily lives, i.e., social networking, manners, behavioral attitudes, and strength of decision making. Human emotion patterns are critical to use in different applications, i.e., health [1], security [2], visual assessment of students [3], and emergency response [4]. Text is a useful source of information that may be used to detect the emotions of individuals in a given time. A huge bulk of textual data can be gathered from social media as users from different communities and cultures interact and discuss in various domains. Social media (e.g., Twitter, Facebook) offers timely information with a feedback facility to users and share of useful discussions in every second. Users post news and then different users around the globe may message to the corresponding post on social media platforms [5]. These messages are commonly known as tweets or microblogs that may enclose individual emotions such as tired, bored, happiness, sadness, anger, and depression. Thus, social media is a big corpus of the public's data that contains rich information of people's emotions. It is a better source of information for studying people's emotions that may be from diverse cultures. The fast-growing base of emotion-rich textual data builds a need to identify and evaluate people's sentiment conveyed in scripts.  
Twitter is a microblogging social network website in which users post their opinions on different topics, discuss different issues, and express their views on daily products. It provides useful information that can be used to investigate crowd emotions. This analysis is further useful to study the characteristics of people's mood and behavior [6]. The company may evaluate the quality of the corresponding product based on the users' tweets about the product. It is very hard to gain useful patterns from an extensive collection of textual data. The intelligent text classification method is required to automate the analysis of twitter's data. Text classification methods used a combination of information from different domains, including data mining, information retrieval, artificial intelligence, Natural Language Processing (NLP), and machine learning. This is a supervised machine learning problem where a model is trained with different examples and then use to predict the unseen piece of text. It may be used to classify the tweets emotions. Recently, countless innovations are accomplished in the NLP and machine learning domains. It dramatically improves the effectiveness of text processing techniques such as vector space model in word embedding [7], softmax based classification [8], tree booting technique [9], and neural networks [10]. However, tuning the designed algorithm for the optimal solution is still quite hard to sustain.
## 3 Results and Discussion
### Prediction of the Erosion Surface Geometry
#### 3.3.2 Prediction of Maximum Soil-Breaking Depth  
To predict the geometric shape of the erosion surface, it is necessary to first determine one of the following: the maximum lateral distance, the maximum soil-breaking depth, and the maximum jet distance. Considering that the maximum soil-breaking depth is widely used in engineering, we selected this parameter for prediction analysis.  
The points in Figure 12 are the soil-breaking depths corresponding to the jet pressures and nozzle moving speeds measured in the experiment. The influences of jet pressure and nozzle speed on soil-breaking depth can be analysed by connecting each point into a three-dimensional surface. Figure 7 shows that, as the jet pressure becomes greater, the nozzle speed becomes smaller, and the soil-breaking depth becomes greater. At a constant nozzle speed, increases in jet pressure always have a significant effect on the soil-breaking depth, and the curve can be approximated as a parabola. When the jet pressure is constant, the soil-breaking depth decreases gradually with increases in nozzle speed, and the curve can be approximated to a power function curve of 0.5.  
Combined with the above analysis, the functional relationship between the soil-breaking depth and the two factors of jet pressure and nozzle moving speed can be fitted.  
\[h_{\text{max}}=0.0393p_{j}^{2}+57.8/\left(u_{nz}^{0.5}+1.65\right)-5.76 \tag{2}\]  
where \(p_{j}\) is the jet pressure (MPa), \(u_{nz}\) is the nozzle moving speed (cm/s), and \(h_{\text{max}}\) is the soil-breaking depth (cm).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{\begin{tabular}{c} **Jet** \\ **Pressure** \\ **MPa** \\ \end{tabular} } & \begin{tabular}{c} **Nozzle** \\ **Traversal** \\ **Speed** \\ **cm/s** \\ \end{tabular} & \begin{tabular}{c} **Horizontal** \\ **Ratio** \\ **(\(\alpha_{x}\))** \\ **(\(\alpha_{h}\))** \\ \end{tabular} & \begin{tabular}{c} **Depth** \\ **Ratio** \\ **(\(\alpha_{x}\))** \\ **(\(\alpha_{s}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Distance** \\ **Ratio** \\ **(\(\alpha_{x}\))** \\ **(\(\alpha_{y}\))** \\ \end{tabular} &
\begin{tabular}{c} **Initial** \\ **Slope** \\ **\(\alpha_{h}\)** \\ \end{tabular} \\ \hline T1-1 & 5 & 1 & 27.5 & 60.6 & 50.2 & 4.92 \\ T1-2 & 5 & 2 & 30.0 & 66.9 & 48.0 & 3.19 \\ T1-3 & 5 & 3 & 34.5 & 65.4 & 47.6 & 2.28 \\ T1-4 & 5 & 4 & 23.0 & 56.3 & 40.9 & 3.56 \\ T1-5 & 5 & 10 & 30.0 & 66.9 & 46.9 & 2.97 \\ T1-6 & 5 & 20 & 2 The analysis of the variance of Equation (2) shows a correlation coefficient of \(R^{2}\) = 0.94, indicating that the fitting effect of Equation (2) is good. Therefore, Equation (2) can be used as an empirical formula for predicting the depth of soil breaking by a moving high-pressure water jet. The applicable ranges are as follows: jet pressure = 5-30 MPa, nozzle speed = 1-20 cm/s.  
Soil-breaking depth has a quadratic relationship with jet pressure and a 0.5 power function relationship with nozzle speed.
## 4 Numerical Examples and Discussions
### Case of SV Wave Incidence  
Figure 6 shows the reflection coefficients for different wavelengths. Compared with the classical scenario, the results reveal a decrease in the reflection coefficients as the wavelength increases. Notably, surface waves exhibit a higher sensitivity to changes in the wavelength than body waves, and the impact of the microstructural effect on elastic waves gradually diminishes.  
Figure 7 displays the reflection coefficients for different applied magnetic fields. The reflected SV wave exhibited a decreasing trend in the reflection amplitude ratio with an increase in H0. In contrast, the reflected P, SP, and SS waves all demonstrate an increasing trend in the reflection amplitude ratio with an increase in H0. Compared with the microstructure, the applied magnetic field only causes numerical variations without altering the dispersion characteristics of the waves.  
Figure 8 illustrates the influence of different microstructural parameters on the critical angle. In classical elastic solids, the interface produces a critical angle when the incident angle reaches 30 degrees. The results indicate that the microstructural parameters can cause a decrease in the numerical value of the critical angle. As the microstructural parameters increase, the critical angle also increases.  
Figure 4: The influence of microstructural parameters on reflection angle when P wave is incident (\(\overline{\lambda}\)\(=8\)).  
Figure 5: The influence of applied magnetic field on the reflected wave when P wave is incident (\(\epsilon\)= 0.05 and \(\overline{\lambda}\)= 8).  
Figure 6: The influence of the wavelength on the reflected wave when the SV wave is incident (\(\epsilon\)= 0.05).
## 3 Results
### Quantitative Synthesis
#### 3.5.1 Effect of VRBR on Knee Pain  
Eight studies [58; 63; 64; 65; 66; 67; 68; 69] provided data from 819 participants to assess the effect of VRBR in reducing knee pain just after therapy (immediate assessment). Our findings reported, with data from eight studies, a medium effect (SMD = \(-\)0.36; 95% CI \(-\)0.56 to \(-\)0.17; \(p<\) 0.001) favors VRBR in comparison to conventional approaches (Table 4, Figure 2). No risk of publication bias (Egger \(p\) = 0.02; adj SMD = \(-\)0.36; Figure S1, Supplementary) and heterogeneity were found (I\({}^{2}\) = 0%; Q = 5.9; df = 7; \(p\) = 0.55). Sensitivity analysis did not report substantial variations with respect to the original pooled effect.  
At 3 months of follow-up (with data from 2 studies [67; 68]), the effect of VRBR was more than the conventional approaches (SMD = \(-\)0.44; 95% CI \(-\)0.72 to \(-\)0.16; \(p\) = 0.002) (Table 4). Subgroup analysis according to specific comparisons showed that at immediate assessment, the use of VRBR in combination with CT [63; 66; 67] (SMD = \(-\)0.6; 95% CI \(-\)0.9 to \(-\)0.3; \(p\) < 0.001) was major that the isolated use of VRBR [58; 64; 65; 68; 69] (SMD = \(-\)0.22; 95% CI \(-\)0.4 to \(-\)0.04; \(p\) = 0.014), both in comparisons with CT (Table 4 and Figure S2). Finally, we observed that in reducing knee pain, the effect of VRBR using immersive VR devices [63; 66; 67] (SMD = \(-\)0.6; 95% CI \(-\)0.9 to \(-\)0.3; \(p\) < 0.001) might be better than non-immersive VR devices [58; 64; 65; 68; 69] (SMD = \(-\)0.22; 95% CI \(-\)0.4 to \(-\)0.04; \(p\) = 0.014).
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 3 Results
### Performances of RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm  
This section shows the performance of RTK with the assistance of an IMU-based pedestrian navigation algorithm. We walked through a narrow path with tall teaching buildings on both sides, as Figure 14 shows. We walked following the red arrows and turned left at the green point. We turned right at the blue point with a five-meter distance between the two points. We kept going straight at other moments.  
Tall buildings could not only block GNSS signals, but also aggravate the multipath effect, which hindered RTK from providing positioning results, as Figure 15a shows. There were only two points between the two blue points in Figure 15a, which meant RTK could hardly provide the positioning results because of the GNSS outage. Although the RTK could provide two positioning results as the green point and the black point shown in Figure 15a, the positioning results were not precise enough. The black point was to the southwest of the green point, which was incompatible with the fact that we kept walking toward roughly north. Severe multipath effects in the urban canyon limited the accuracy of RTK. As a result, it was unreliable to provide positioning results with RTK based on the Xiaomi Mi 8 in the urban canyon. We tied the Xiaomi Mi 8 to our foot to utilize the Z  
Figure 14: A narrow path with tall teaching buildings on both sides.  
Figure 13: The performance of the ZUPT aiding INS. (**a**) Place where we walked; (**b**) the estimated trajectory.  
continuous positioning results during GNSS outage, as mentioned in the Materials and Methods. The positioning results are shown in Figure 15b. The outputs of the ZUPT aiding INS were able to fill in the position when the positioning results of the RTK were lost, as Figure 15b shows. Positioning results in Figure 15b could reflect our trajectory as there were two changes in the direction of motion. The last output of the ZUPT aiding INS was consistent with the first positioning result provided by the RTK after we passed through the urban canyon. Figures 15 and 16 show that the combination of RTK, the Madgwick algorithm, and the ZUPT aiding INS could be more robust and more reliable than RTK without the assistance of an IMU-based pedestrian navigation algorithm. RTK with the assistance of an IMU-based pedestrian navigation algorithm could be more adaptable to urban areas because of the capacity of providing positioning results during GNSS outage. Our experiment demonstrates the feasibility of providing continuous positioning results by using RTK with the assistance of an IMU-based pedestrian navigation algorithm.
## 3 Results
### Use Case of Parametric Circuits Synthesis  
This use case is mainly based on a reinforcement learning type (Figure 2). In such a case, the ML system tries to prepare a symbolic transfer function and receives a corresponding \"reward\" according to the level of how this transfer function fits the desired output.  
The \"new step\" in Figure 2 could involve adding or removing an existing electric component (e.g., \(R\), \(L\), \(C\)) to specific nodes of the electrical circuit. The steps are as follows:  
* AI tries to put electronic components in a semi-random place in the schematic considering its previous experience.
* Calculate the symbolic expression of the transfer function.
* Try to optimize component values with some constraints.
* Check the best result with the target frequency characteristic and provide the corresponding \"reward\".  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target ID** & **Description** & **Data Type** & **Valid Range** \\
**and Short Name** & & Integral deviation of the obtained transfer & \\ \hline T1.1 & function from desired. It is obtained after & & \\ Integral deviation of obtained & substitution of the numerical values (may be & \\ transfer function from desired & also optimization applied) into the symbolic & & \\ transfer function and integral comparison with & & & \\  & the target transfer function. & & \\ \hline T1.2 & & & \\ Local deviation of obtained & Like above but evaluated in a specified local & & \\ transfer function from desired & range of time or frequency. & & \\ \hline T1.3 & Is it possible that module of transfer function & & \\ Amplification possibility & causes signal amplification? & Categorical & [”Yes”, “No”]. \\ \hline T1.4 & Is the non-stability of the system/circuit & & \\ Possible non-stability & possible? & Categorical & [”Yes”, “No”] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example target definitions of the ML system applied to FSM.  
Figure 2: Use case of circuit synthesis using FSM and AI.  
At each iteration, AI adjusts its coefficients and remembers the change in the schematic and corresponding impact. The Q-learning ML method could be used for remembering previous experiences.  
The reward is the criterion for how well the obtained FSM output matches the desired output. For example, the targets defined in Table 5 could be used.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 2 Introduction to Performance Assessment and Fault Warning Methods
### Performance Evaluation Metrics--Mahalanobis Distance  
Marine diesel main engine performance parameters exhibit strong interdependencies with heterogeneous dimensionality and variation ranges. To address this multivariate challenge, the Mahalanobis distance (MD)--a covariance-based metric proposed by [PERSON]--was adopted for its efficacy in quantifying similarity between multidimensional datasets. Consider a sample\({}_{x}\). The Mahalanobis distance from X to another sample set can be mathematically expressed as  
\[\rho_{Mah}(x)=\sqrt{\left(x-\mu\right)^{T}\!S^{-1}(x-\mu)} \tag{5}\]  
In the equation, \(\mu\) denotes the sample mean of the dataset, and \(S\) represents the covariance matrix. When \(S\) reduces to an identity matrix, the Mahalanobis distance becomes equivalent to the Euclidean distance. Two critical properties emerge from this formulation: First, the Mahalanobis distance calculation is scale-invariant [24], meaning it remains unaffected by measurement unit variations across parameters in different datasets. Second, through explicit incorporation of the covariance matrix, this metric inherently eliminates parameter correlations and incorporates the overall distribution characteristics of the dataset.
## 4 Empirical Propagation Models
### Empirical Models for Vegetation
#### 4.3.2 ITU-R Model  
This model became known as the Early ITU model, proposed by the International Telecommunication Union (ITU) based on measurement campaigns in 1988. It is valid for frequencies between 200 MHz and 95 GHz and for distances between the transmitting and receiving antennas of less than 400 m [45]. Its equation for calculating excess loss, added to the free space loss, is given by (9):  
\[Att_{ITU-R}=0.2\times f^{0.3}\times d^{0.6},\hskip 28.452756 ptd<400\ \text{m} \tag{9}\]
* \(Att_{ITU-R}\) is the excess attenuation according to the ITU-R model (dB);
* \(d\) is the distance between Tx and Rx (m);
* \(f\) is the system's operating frequency (MHz).
## 4 GNSS-Based Location Context Profiling
### Feature Engineering Pipeline
#### 4.1.1 GNSS Data from Android 7.0  
Previously, GPS modules handled all tasks, from receiving and processing signals to calculating pseudoranges and, thus, estimating locations; they only returned estimated locations and their corresponding accuracy levels to front-end applications. Starting from Android 7.0, raw GNSS measurements became available to users on a number of devices [17]. Each sample of a GNSS measurement includes three general types of fields: (i) state indicators that describe clock synchronization, multipath effects, and hardware clock continuity; (ii) raw measurements, such as carrier frequency, cycles, and phases; and (iii) computed measurements, such as the uncertainty of some raw measurements. A detailed list of fields can be found in [33]. By invoking the GNSS raw measurement API, a sample including all these fields is returned from every visible satellite. In order to facilitate our learning purpose, we regroup them into two categories of features (part of them shown in Table 1) and also derive a few new features. We briefly explain them in the following:  
* **Binary features** take only boolean values and they are related to the clock status, carrier phase validity, and multipath effect. Some of these features may not be directly applicable to the profiling, yet they indicate the validity of other features.
* **Scalar features** take float scalar values and primarily include measurements concerning information quality. For example, there are measurements concerning SNR and Doppler shift, along with their respective uncertainty fields providing 1-\(\sigma\) values. Although these features are often used to infer localization accuracy, given a model-based approach, we intend to use them in a non-parametric manner to avoid losing information.
* **Derived features** are not directly obtained from Android API; we compute them, mainly to facilitate reshaping the input to deep learning modules. In particular, _SvPosition_ is the position (under a spherical coordinate system) of a satellite when a GNSS sample is received, derived using the GPS ephemeris [34] and the receiving timestamps.