esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This research aimed to analyze the possibility of installing an ultrasonic emitter in an already manufactured car and to prove the possibility of cleaning the exhaust gases of an internal combustion engine through the action of an ultrasonic wave due to coagulation and examining the optimal regimes of its work. The existing theoretical solution to describe the proposed process was analyzed. A Mercedes-Benz M-Class ML 270 CDI MT car with the OM 612 DE 27 LA Diesel engine was used for the experiment. An ultrasound generator and an ultrasound emitter were connected to the muffler. The stand was connected to the car via the inlet with a rubber hose that directs the exhaust gases out of the car. The crankshaft speed of the engine was changed in the range of 750 to 1250 rpm, which corresponds to urban conditions when cars are moving in heavy traffic jams. The content of CH, CO, CO\({}_{2}\), and O\({}_{2}\) in the exhaust gas of the vehicle was determined as a function of the crankshaft speed without ultrasonic exposure and with ultrasonic exposure at an ultrasound frequency of 25, 28, and 40 kHz. The results of the experiment showed that the introduction of an ultrasonic emitter into the muffler reduced the smoke content of the gas, increased the oxygen content, and reduced the amount of carbon dioxide in the exhaust gases. With an increase in the ratio between the ultrasonic frequency and the angular velocity of the engine crankshaft (\(f/\omega\)), the smoke content of the gas also decreased. At the maximum values of ultrasonic frequency and angular velocity of the engine crankshaft selected in the experimental studies, the minimum value of the ratio of gas smoke indicators was achieved, and the degree of purification was 10-13%. Such results correspond to the condition of optimal operation of the ultrasonic muffler, where the ratio of gas to smoke values should tend to a minimum. These results confirm the potential of using ultrasound as a method for cleaning exhaust gases and underline the need for further research in this area.  
car; exhaust gas; muffler; ultrasound; frequency; coagulation; experiment +
Footnote †: journal: Physics Letters A
# Rod-Airfoil Interaction Noise Reduction Using Gradient Distributed Porous Leading Edges
## 4 Conclusions  
In this study, the acoustic radiation characteristics of piecewise gradient distributed porous leading edges were experimentally investigated and compared with the baseline nonporous leading edge. The experimental results showed that the proposed modifications can substantially reduce rod-airfoil interaction noise around and above the peak frequency of the rod-airfoil configuration. The findings of systematically parametric studies showed that the noise reduction effects are sensitive to the properties of the porous materials. In general, higher noise reduction is achieved with higher vortex-porous interactions through larger pores, porous coverage, and higher downstream airfoil attack angle. Moreover, we found that the rod-airfoil interaction noise reduction effects are related to the characteristicsof the incoming shedding vortex to a certain extent: pores located in downstream positions or gradually more sparsely distributed pores can achieve greater noise reduction. The underlying mechanisms of these findings will revealed through numerical simulations and flow-field measurements in our future work. Moreover, we did not consider the effect of gradient distributed porous leading edges on aerodynamics with changes in lift and drag in this experimental study, due to the lack of suitable measuring equipment (such as balance and its support), which is another future research direction.  
All authors substantially contributed to this research. [PERSON] and [PERSON] conceived the topic and basic framework; [PERSON] and [PERSON] performed the experimental tests; [PERSON] and [PERSON] wrote the original draft; [PERSON] and [PERSON] provided professional advice and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by the National Natural Science Foundation of China, under grant numbers 12102451, 11802114, and 11602290.  
Not applicable.  
Note of the data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors want to thank the editor and anonymous reviewers for their helpful comments and suggestions.  
The authors declare no conflict of interest.
## 4 Experiments
### Results
#### 4.3.3 Results of Behavior Recognition
Figure 6 demonstrates that the improved model has solved this problem, while simultaneously increasing the feature map diversity. To evaluate the real inference effect, comparisons are made between the TSM-ResNet50 model and the improved TSM model. For an intuitive comparison, the behaviors are sorted in descending order of likelihood and displayed within the corresponding detection boxes, as shown in Figure 7. The probability of the driving behavior is represented by the action probability, which uses a sigmoid function to map behavior classification scores in the range (0,1). When the action probability is less than 0.5, the model predicts that the target does not exhibit the behavior, indicating a negative confidence score. As the action's probability increases, the model interprets this as a higher likelihood of the target exhibiting the behavior. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone Network** & **Parameters/M** & \
\begin{tabular}{c} **UCF101** \\ **\%** \\ \end{tabular} &
\begin{tabular}{c} **HMDB51** \\ **\%** \\ \end{tabular} \\ \hline TSN [19] & InceptionV2 & 10.7 & 84.4 & 53.3 \\ I3D [15] & InceptionV2 & 25 & 95.6 & 74.8 \\ \hline TSM [20] & ResNet50 & 23.52 & 96.0 & 73.2 \\ VidTr [36] & VidTr-L & 86 & 96.7 & 74.4 \\ AMD [37] & ViT-B & 87 & 97.1 & 79.6 \\ Improved TSM & Improved EfficientNet-B3 & 10.93 & 96.7 & 75.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative results based on public datasets. From Figure 7, it is evident that the TSM-ResNet50 model inaccurately recognizes the left driver's sitting behavior (indicated by the red box), suggesting instead that the driver is more likely standing. In contrast, the improved TSM model demonstrates a greater likelihood of correctly identifying the sitting behavior compared to standing. Although the standing confidence level remains relatively high, this suggests that the improved model is moving in the right direction, towards more accurate behavior recognition. The experiments also involved a class-wise accuracy comparison across different behavior categories, as shown in Table 9.  
The improved TSM model achieves performance gains in all the categories, with the most notable improvement in the driving category, where the accuracy reaches 91.35%, representing an increase of 9.21 percentage points. This improvement is primarily attributed to the collaborative modeling of the bounding box expansion and the CA attention mechanism, which effectively captures contextual information from the dashboard background. In contrast, the model performs relatively less well in regard to abnormal behaviors, such  
Figure 6: Comparison of various networks’ shallow feature maps: (**a**) original samples; (**b**) TSM-ResNet50; (**c**) TSM–ResNet101; and (**d**) the improved TSM. as playing on a phone and smoking, suggesting that further optimization is needed to better address the unique characteristics of these behaviors.
## 2 Related Work  
### Line Feature Analysis  
In the VIO study, line features had higher stability in harsh environments. The PL-SLAM [23] system proposed by [PERSON] et al. adds a line segment detector (LSD) [24] on top of ORB-SLAM [25], which implements point feature and line feature extraction in SLAM. [PERSON] et al. achieved real-time extraction by modifying the hidden parameters of the LSD algorithm [19]. [PERSON] et al. fused corner point and line features to construct point-line residuals by utilizing the similarity of their positions, and the two features complemented each other to enrich the structural information of the carried environment [20]. In the above study, LSD was widely used as a line feature extractor, but it still failed to be accurate in scenarios with noise. [PERSON] et al. proposed an EDLines linear timeline segment detector [26], which utilizes the edge drawing (ED) algorithm to detect edge pixels continuously, ultimately improving the detection speed while controlling the number of false detections. [PERSON] et al. proposed a line segment extraction and merging algorithm to achieve feature tracking between points and lines with geometric constraints based on the removal of short and useless line segments [27]. [PERSON] et al. proposed ELSED, which utilizes a local line segment growth algorithm to align pixels by connecting gradients at breakpoints, greatly improving the efficiency of line segment detection [28]. The PLI-VINS proposed by [PERSON] et al. designs adaptive thresholds for line segment extraction algorithm and constructs an adjacency matrix to obtain the orientation of the line segments, which greatly improves the efficiency of the line feature processing in indoor point and line vision SLAM systems [29]. [PERSON] et al. proposed probabilistic clustering methods using descriptors based on grid computation, which reduces the number of samples within the grid through a special pixel selection process to design new image representations for hyperspectral images [30]. The analysis of line features is summarized in Table 1.
# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation
### Improved Knowledge Sharing across Domains
#### 5.1.1 Meta Model for Stochastic Discrete-Event Simulation  
Stochastic DES is applied for modeling systems where the variables change at discrete time points, and the time of the next event is determined stochastically [51; 52]. In cell biology, e.g., modeling and simulating stochastic effects is of significant interest, especially for processes that involve low copy numbers [53]. Stochastic DESs are also increasingly applied in demography [54] or epidemiology [55] as an alternative to the traditional discrete-time step simulation approach.  
For the paper, we use tables to represent the meta models in a compact and readable way. The implementation as JSON Schema documents can be viewed in our source code. Table 1 shows the developed DES meta model, which comprises three essential components, i.e., model configuration, simulation initialization, and observation (represented by sections in the table). In the meta model, each component of a simulation experiment requires specific inputs (table rows). For instance, typical ingredients for a stochastic simulation are now made explicit, such as the number of replications, the random seed, and the number of parallel threads (see the simulation component). Each input is characterized by a unique name, a description, a data type, a set of choices, a default value, and information about whether this input is required or optional (table columns). Properties of type Map (e.g., configuration) assemble related inputs as key-value pairs. The assembled sub-properties are indicated by \"\(\bullet\)\". Other properties (e.g., model, stopCondition, or observationTime) are of type _Alternative_ and provide different ways of specifying a property. The different options are indicated by \"\(\rightarrow\)\". For instance, the simulation model can be provided by either a folder and file name (local files) or a reference to an online resource.  
The above meta model generalizes the structure and ingredients of the simulation experiments at the level of the modeling and simulation approach (i.e., DES). It can therefore be used for supporting the specification and execution of basic simulation experiments in various modeling domains, such as cell biology or digital circuits.
## 4 System Testing and Performance  
Specifications regarding performance are offered for the three different components that, from a technical point of view, have been developed for the whole system: (a) the Unmanned Aerial Vehicle (UAV) used to set a fight plan or fly the drone under given coordinates, (b) the Mobile Data Acquisition System (MDAS) used to collect information and (c) the Graphical User Interface (GUI), useful for data representation and user visualization. Each of these parts benefits from Galileo as the GNSS in their own way, either for data gathering or vehicle navigation. Overall, the proposed systems was built using the elements that have been previously presented. For them to work together and offer outputs that truly represent a combination of all of the features of the system, rather than isolated results, a series of experiments were carried out. These experiments were strongly related to the system features that have been described in the previous section and involved flying the drone and taking mobile network coverage measurements. Such measurements were taken by having the UAV execute flights with the MDAS subsystem already mounted on it, and being active in collecting information. As the UAV flew, data from the LTE communications were gathered by the application running on the smartphone and stored in a log file so they could be processed afterwards by the GUI used for data display. In any case, logs were also shown, so that the most relevant parameters could be visualized in this paper.
## 3 Methodology  
### Ensemble Empirical Mode Decomposition  
Empirical mode decomposition (EMD) is an approach to decompose nonlinear signals into a finite number of simple components called intrinsic mode functions (IMFs). These components form a complete and nearly orthogonal basis for the original signal. The main idea of EMD is repeatedly subtracting the local mean from the original signal. EEMD was improved from EMD to overcome modal aliasing problems by adding white noise [32], and it has been widely used for the decomposition of nonlinear and nonstationary signals [33]. EEMD has the advantages of robust self-adaptability and local variation. As shown in Figure 4, the EEMD decomposition process can be briefly described as the following steps:  
Figure 4: Schematic diagram of the EEMD decomposition process.  
Add a random noise signal \(n_{j}(t)\) to the original raw data \(x(t)\) to obtain the noise-added data signal \(x_{j}(t)\)  
\[x_{j}(t)=x(t)+n_{j}(t),j=1,2,\cdots,M \tag{1}\]  
Use EMD to decompose the noise data \(x_{j}(t)\) into some IMFs:  
\[x_{j}(t)=\sum\limits_{i=1}^{L}c_{i,j}(t)+r_{L,j}(t)),j=1,2,\cdots,M \tag{2}\]  
where \(c_{i,j}(t)\) is the \(i\)th IMF of noise-added data \(x_{j}(t)\) in the \(j\)th decomposition and \(r_{L,j}(t)\) is the corresponding residue.  
Perform M trials by repeating steps (1) and (2) with diverse white noise.  
Calculate the mean values of the corresponding IMFs \(\overline{c}_{i}(t)\) and residue \(\overline{r}_{L}(t)\) as follows:  
\[\overline{c}_{i}(t)=\sum\limits_{j=1}^{M}c_{i,j}(t)/M \tag{3}\]  
\[\overline{r}_{L}(t)=\sum\limits_{j=1}^{M}r_{L,j}(t)/M \tag{4}\]
## 6 The Analysis of Direction Finding Error
\[\Delta\alpha{=}\frac{1}{\frac{2\pi r}{\lambda}\cos\beta\sqrt{N\times SNR}} \cdot\frac{\cos(\phi_{\rm C}-\alpha)-\cos(\phi_{\rm B}-\alpha)}{\left[\sin(\phi _{\rm B}-\phi_{\rm C})+\sin(\phi_{\rm C}-\phi_{\rm A})+\sin(\phi_{\rm A}-\phi _{\rm B})\right]} \tag{37}\]  
\[\Delta\beta{=}\frac{1}{\frac{2\pi r}{\lambda}\sin\beta\sqrt{N\times SNR}} \cdot\frac{\sin(\phi_{\rm C}-\alpha)-\sin(\phi_{\rm B}-\alpha)}{\left[\sin( \phi_{\rm B}-\phi_{\rm C})+\sin(\phi_{\rm C}-\phi_{\rm A})+\sin(\phi_{\rm A}- \phi_{\rm B})\right]} \tag{38}\]  
It can be seen obviously from Equations (31) and (34) that the error of 3D is always bigger than that of 2D. For (25) and (28) of 2D array, it is known that \(\Delta\alpha\) and \(\Delta\beta\) become smaller as the SNR increases. _i.e._, Increasing SNR helps to improve the accuracy of direction finding. Also, \(\Delta\alpha\) and \(\Delta\beta\) become smaller as the frequency increases. As shown in Figure 3, the coverage of azimuth \(\alpha\) belongs to \([0,2\pi]\) and \(\beta\) belongs to \([0,\pi/2]\). Because the sinusoidal trigonometric function is not monotonous from \(0\) to \(2\pi\), it is not obvious to see how the changing of \(\alpha\) affects the direction finding error in Equations (37) and (38). The sinusoidal trigonometric function is monotonous from \(0\) to \(\pi/2\). It can be seen that \(\Delta\alpha\) becomes larger with \(\beta\) increases. However, \(\Delta\beta\) becomes smaller with \(\beta\) increases.
## 2 Data and Methods  
By using the COSMIC-2 RO temperature data from December 2019 to November 2021, we obtain the GW Ep profiles in the Indian region, based on which the LS GWs activity in this region is analyzed, and the influences of the main factors, including the wind, the wind shear, and the convection, on the LS GWs are systematically investigated. Note that considering the impact of TEJ on the LS GWs in the studied region, besides the mean wind and wind shear between 20 and 30 km, the mean wind and wind shear in the height range of the core of TEJ that the maximum zonal wind speed appears, i.e., 15-17 km, are also considered as the main influencing factors. We use the CCs to represent the correlation between each factor and the LS GW Ep, and for the first time, by using the DA method, the relative importance of each factor on the LS GWs is quantified, and the individual contributions of different factors are presented.
## 3 Methodology
### Multi-Source Remote Sensing Data Fusion  
The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), initially developed by [PERSON] et al. [82] for downscaling reflectance using MODIS and Landsat reflectance band data, has been used effectively to prepare surface biophysical parameters with high spatial and temporal resolution [83; 84; 85; 86]. ESTARFM applies a linear endmember and mixture pixel spectral mode at date \(t_{0}\) and \(t_{p}\):  
\[\begin{array}{l}M_{\text{o}}=\sum\limits_{i=1}^{N}f_{i}\Big{(}\frac{1}{a}L _{\text{io}}-\frac{b}{a}\Big{)}+\varepsilon\\ M_{p}=\sum\limits_{i=1}^{N}f_{i}\Big{(}\frac{1}{a}L_{ip}-\frac{b}{a}\Big{)}+\varepsilon \end{array} \tag{11}\]  
where \(M_{\text{o}}\) and \(M_{p}\) are the reflectance of mixed MODIS pixels at \(t_{o}\) and \(t_{p}\), respectively;\(f_{i}\) is the fraction of the \(i\)th endmember; \(L_{\text{io}}\) and \(L_{ip}\) are the reflectance of the \(i\)th endmember obtained from Landsat images at \(t_{o}\) and \(t_{p}\), respectively; \(N\) is the total number of endmembers; \(\varepsilon\) is the residual error; and \(a\) and \(b\) are the coefficients.  
It is assumed that the proportion and change rate of endmember reflectance are steady. A simple linear regression model was used to obtain the conversion coefficient. The geographic and spectral distance weights were considered by using the neighboring pixel information within a moving window. Thus, the predicted reflectance (\(R\)) of high-spatial-resolution pixel at \(t_{p}\) can be described as:  
\[L_{p}(x_{w/2},y_{w/2},R)=L_{o}(x_{w/2},y_{w/2},R)+\sum_{i=1}^{S}W_{i}V_{i}(M_{p }(x_{i},y_{i},R)-M_{o}(x_{i},y_{i},R)), \tag{12}\]  
where \(S\) is the number of similar pixels; \(W_{i}\) is the weight, determined by the distances of similar pixels and the spectral similarity from Landsat and MODIS data; and \(V_{i}\) is the conversion coefficient, which is computed with a simple linear regression model based on similar pixels.  
Finally, the temporal weighting method, based on changes of the coarse spatial resolution reflectance between time \(t_{o}\) (o = m or n) and prediction time \(t_{p}\), was used to further improve the prediction accuracy of the algorithm. A more detailed description of ESTARFM is in [PERSON] et al. (2018). Here, ESTARFM was applied to produce land surface variables, such as AWLST (daily), NDVI (16 day), and albedo (daily), at a spatial resolution of 100 m using multi-platform remote sensing data described in Section 2.1. Previous studies have reported that direct fusion of surface physical parameters (e.g., NDVI) is very effective ([PERSON] et al., 2018; [PERSON] et al., 2018).
## 4 Simulation and Results
### Reference Path  
In order to verify the performance of the proposed controller, we designed the reference path, which is parameterized as a curvature profile and position profile, as shown in Figure 6. The path generation methods were proposed in [22; 29]. The longitudinal speed tracking was achieved using a PID controller, the longitudinal speed fluctuated between 55-65 (km/h) in order to verify the proposed controller.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Symbol** & **Value** & **Units** \\ \hline Vehicle mass & \(m\) & 1230 & kg \\ Yaw inertia & \(l_{\text{Z}}\) & 1343.1 & kg\(\cdot\)m\({}^{2}\) \\ Front axle-O distance & \(a\) & 1.04 & m \\ Rear axle-O distance & \(b\) & 1.56 & m \\ Front cornering stiffness & Ca\({}^{\prime}\) & 48,840 & N/rad \\ Rear cornering stiffness & Ca\({}^{\prime}\) & 32,887 & N/rad \\ Friction coefficient & \(\mu\) & 1 & n/a \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the vehicle and path.  
Figure 6: The reference path for the MPC controller to track: curvature (**a**), path position (**b**).
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 1 Introduction
Despite the higher cost of the antenna, this combination is still a cost-effective alternative to high-end geodetic equipment in situations where high positioning accuracy is required. A complementary objective of our work was to provide further research results on the accuracy of low-cost dual-frequency GNSS receivers to the relatively small base available in the scientific literature. The large number of tests performed in different conditions and locations worldwide will contribute to a statistically reliable evaluation of equipment with high potential applications.
## 6 Discussion on Best Results Achieved
Further investigations could be carried out by employing multi-mission satellite data (e.g., using nowadays imagery acquired by Cosmo-SkyMed, TerraSAR-X, Pulsar and Sentinel 1). Over vegetated plots in particular, the more suitable band pairs to assess \(m_{\rm v}\) were L\({}_{\rm HH}\)-L\({}_{\rm VV}\) and L\({}_{\rm HH}\)-L\({}_{\rm HV}\) (\(r_{\rm t}>1.0971\)); however, the inter-channel approach opens the possibility to assess \(m_{\rm v}\) for all pairs except C\({}_{\rm HH}\)-C\({}_{\rm VV}\), X\({}_{\rm HH}\)-C\({}_{\rm HH}\) and C\({}_{\rm HH}\)-L\({}_{\rm HH}\). As for the \(m_{\rm v}\), several channel pairs are suitable to assess \(F_{\rm v}\) (strong to very strong significant correlations) with the exception of the C\({}_{\rm HH}\)-L\({}_{\rm HH}\) pair. Channel pairs more suitable to assess _LAI_ are: \(X_{\rm HH}\)-X\({}_{\rm VV}\), C\({}_{\rm HH}\)-L\({}_{\rm HH}\), C\({}_{\rm HV}\)-L\({}_{\rm HV}\).