esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 5 Discussion
### Sound Localization Second Round: Horizontal and Vertical Planes  
The results of the Hodges-Ajne test revealed that participants could localize sound sources with significant alignment to actual horizontal positions at most spherical angles. This indicates effective horizontal localization, except at 180\({}^{\circ}\), where front-back confusion persisted, suggesting difficulties when trying to locate sounds in the rear position.  
The Mardia-Watson-Wheeler test further highlighted the influence of elevation on localization accuracy. Significant differences were noted at 90\({}^{\circ}\) and 270\({}^{\circ}\), indicating that participants were sensitive to changes in elevation at both sides, acceptably discerning vertical sound direction. Conversely, the lack of significant differences at 0\({}^{\circ}\) and 180\({}^{\circ}\) suggests poor auditory discrimination for vertical frontal and rear sources.  
Consistent with previous studies on elevation perception [38; 39], participants in the second round demonstrated limited ability to discriminate elevated sounds, reflecting the inherent challenges in vertical sound source localization. This difficulty might also be related to the spectral attributes of the sounds used, as sources that are elevated often have a concentrated frequency range in the high-mid spectrum (e.g., birds), which can lead to a bias in perceiving these sounds as coming from above [40].  
For the vertical plane assessment, [PERSON]'s data indicated more randomness in response distribution. While both studies only coincided in one position for elevation assessment, direct statistical comparisons were difficult to carry out due to differing methodologies and the specific assessed sound source positions. Nevertheless, the present experiment demonstrated notably accurate lateral elevation localization, which is a significant improvement over previous studies. Additionally, in line with other experiments [18], the order and arrangement of the Ambisonics setup may have influenced the results, suggesting that optimization of these factors could further enhance vertical localization accuracy. These findings further support the enhanced sound localization capabilities afforded by VR and gamification tools.  
Additionally, the VR test dynamics were smoother and more interactive, leading to shorter test times and allowing the possibility of considering more audio samples and repetitions.
## 5 Performance Evaluation
### Performance Evaluation of the Buffer-Based ABR Technique Using Predicted Network Bandwidth Values
The standard deviation of the proposed method is approximately 14, and that of BBA is about 27, which means that the QoE of the proposed method is more stable. Table 4 details the QoE measurement indicators for train scenario 1. The average video quality for the proposed method was 3762 KB, whereas the average video quality for BBA was 3668 KB, indicating that the proposed method selected higher image quality than the BBA. Both techniques had the same number of video quality changes, but 119 rebufferings occurred with BBA, whereas only 28 rebuffers occurred with the proposed technique. Figures 21 and 22 present the selected video quality and buffer occupancy in response to network bandwidth changes in the train scenario 1. After 200 s, as the network bandwidth decreased, the buffer occupancy also decreased. Consequently, both techniques began reducing image quality at approximately 500 s. As the network environment improved, the proposed technique increased video quality more rapidly than the BBA. Although the buffer occupancy of the proposed technique was lower than that of BBA, the image quality remained comparable. As the network bandwidth gradually decreased starting at 800 s and converged to zero at approximately 1000 s, rebuffering occurred in both techniques. However, while the BBA struggled to respond to instantaneous bandwidth changes, leading to prolonged rebuffering, the proposed technique quickly reduced video quality, preventing a significant drop in buffer occupancy and minimizing rebuffering. In our performance evaluations across 24 pedestrian, bus, and train scenarios, the proposed technique achieved a higher MOS than BBA in 11 scenarios, whereas BBA outperformed the proposed technique in six scenarios. Overall, the MOS of the proposed technique was approximately 40% greater than that of BBA. In the six scenarios where BBA had a higher MOS, the difference was approximately 7%. Therefore, the proposed technique not only delivers superior QoE compared with BBA in environments with significant network fluctuations, such as on trains, but also offers comparable QoE with BBA in more stable network environments, such as those experienced by pedestrians and bus passengers. In the performance evaluation results for the pedestrian, bus, and train scenarios, the proposed scheme yielded a higher overall QoE than did BBA. In the train scenario, where users moved rapidly, the average video quality improved, and fewer rebuffering events were measured. These results indicate that video rate selection based on the proposed accurate bandwidth prediction method is effective, even when the network environment varies dramatically, resulting in improved overall performance. However, the proposed scheme has limitations. When the network bandwidth fluctuates little, as in the pedestrian and bus scenarios, the average video quality improvement is insignificant. To improve the performance of the proposed scheme in practice, the network bandwidth prediction should be rapid even without a GPU.
###### Abstract  
The magnetohydrodynamic angular rate sensor (MHD ARS) is a high-bandwidth, high-accuracy sensor that is increasingly used to measure spacecraft harmonic vibration. However, the amplitude of harmonic vibration is usually on the order of microradian to milliradian, and the induced electric potential signal of MHD ARS is only on the order of nanovolt to microvolt, which is easily disturbed by noise. In this paper, an improved method based on autocorrelation with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Threshold Denoising (WTD) is proposed to denoise the signal of MHD ARS. Firstly, CEEMDAN is used to decompose noisy signals and obtain intrinsic mode functions (IMFs), and autocorrelation is used to determine the relevant modes where the effective signals are located. Then, the improved threshold and thresholding function are used to denoise the relevant modes. Finally, the denoised signal is obtained by combining the denoised relevant modes. In the experiment, noisy MHD ARS signals were recorded in static and dynamic conditions, and the effects of the proposed method and conventional methods were compared. The results of the Allan variance in the static condition and root-mean-square error in the dynamic condition show that the proposed method can effectively overcome the shortcomings of conventional methods and obtain a better denoising effect.  
harmonic vibration; MHD ARS; autocorrelation; CEEMDAN; WTD
## 3 Efficiency and Accuracy of the Proposed Simulation Method
### Wind Tunnel Test Performed by [PERSON] and [PERSON]
#### 3.1.1 FDS Model to Simulate the Experiment  
The FDS model used to simulate the experiment can be easily constructed through the method introduced in Section 2.1.1 based on the FDSGenerator. The wind speed profile of the FDS model is made consistent with that used in the wind tunnel test (see Figure 8). The size of the computational domain in the FDS model was the same as that of the wind tunnel. A multi-grid technique, which refined the computational domain under the height of 0.1 m, was employed in the FDS model to  
Figure 6: Visualization of the dispersive harmful air and concentration isosurface.  
improve the simulation's accuracy. Specifically, the grid size of the domain under the height of 0.1 m was set to 0.0025 m \(\times\) 0.0025 m \(\times\) 0.0025 m. The grid size of the domain between the heights of 0.1 m and 0.3 m was set to 0.005 m, whereas that of the remaining domain was set to 0.02 m. The total number of cells was \(3.255\times 10^{6}\). The constant pollutant release rate of the vent at the center of the cube rooftop was set to \(1.25\times 10^{-5}\) m\({}^{3}\)/s according to the experiment setup, and the mass fraction of pollutant on the vent surface was taken as 100%. According to the FDS user guide [33], the \"PERIODIC\" boundary condition was adopted for the inlet, outlet, and lateral boundaries, while the \"GROUND\" and \"OPEN\" boundary conditions were used for the ground and top boundary, respectively. The total simulation time was set to 30 s, of which the first half was used to achieve a stable flow field while the remaining half was used to record the pollutant concentration data at various monitoring points. The time step size during the simulation would be adjusted automatically by the FDS based on the model attributes.
## 4 Motif Sampling and Retrieving
### Sampling Motifs over \(G\)  
Figure 4 shows an overview of Mosar. [PERSON] first generates a subgraph \(G^{*}=(V^{*},E^{*},L^{*})\) of \(G=(V,E,L)\) by iterating each edge and sampling it with the same probability \(p\). We assume that \(G^{*}\) can be fitted into memory, which can be easily achieved using a small \(p\). Then, [PERSON] uses existing CIS enumeration methods such as [16] to enumerate all \(k\)-node CIS of \(G^{*}\). For a graph \(s\), let \(V(s)\) and \(E(s)\) denote the set of nodes and edges contained in \(s\). For a \(k\)-node CIS \(s^{*}\) of \(G^{*}\), let \(s\) be its original \(k\)-node CIS, which is defined as the \(k\)-node CIS of \(G\) with the same nodes in \(s^{*}\), i.e., \(V(s)=V(s^{*})\). We can easily find that \(s^{*}\) can be quite different from \(s\). To eliminate the estimation error introduced by this uncertainty, when traversing \(s^{*}\), we combine the edge information of the original graph \(G\) to retrieve the \(s\) of the original graph. Formally, we let \(C^{(k,*)}\) denote all \(k\)-node CIS of \(G^{*}\). Finally, we obtain all pairs of \(\text{CIS}\,s^{*}\in C^{(k,*)}\) and their original CIS, \(s\in C^{(k)}\), i.e.,  
\[S^{(k)}_{G^{*},G}=\{(s^{*},s):s^{*}\in C^{(k,*)},s\in C^{(k)},V(s^{*})=V(s)\}. \tag{1}\]  
The pseudocode of Mosar is shown in Algorithm 1.  
```
input :\(G=(V,E,L)\) and \(p\) output :\(S^{(k)}_{G^{*},G}\) \(V^{*}\leftarrow\emptyset\), \(E^{*}\leftarrow\emptyset\), \(L^{*}\leftarrow\emptyset\), \(S^{(k)}_{G^{*},G}\leftarrow\emptyset\); /* motif sampling. */ for\((u,v)\in E\)do /* \(U(0,1)\) is a uniform (0,1) random sample. */  Generate \(p^{*}\gets U(0,1)\); if\(p^{*}\geq p\)then \(V^{*}\gets V^{*}\cup\{u,v\}\); \(E^{*}\gets E^{*}\cup\{(u,v)\}\); \(L^{*}\gets L^{*}\cup\{l_{u,v}\}\);  end if  end for /* motif retrieving. */ for\(s^{*}\in C^{(k,*)}\) combine \(G\)do /* \(\texttt{originalCIS}(s^{*},G)\) returns the original CIS of \(s^{*}\). */ \(s\leftarrow\texttt{originalCIS}(s^{*},G)\); \(S^{(k)}_{G^{*},G}\gets S^{(k)}_{G^{*},G}\cup\{(s^{*},s)\}\);  end for
```  
**Algorithm 1**The pseudocode of Mosar.
# Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG
## 4 Methodology
### EEG Headset-Based Command
#### 4.2.3 Algorithm of Classification  
Figure 7 represents the architecture of our system. In this work, we processed EEG signals for the estimation of facial expressions using a CNN and LSTM combination [34]. The ability to extract robust features from CNNs and go beyond the drawbacks of conventional techniques is a true advantage of CNN networks. Three layers make up the network: LSTM, max-pooling, and convolutional. The convolutional layer filters the input signals. The max-pooling layer reduces the size of the data while preserving their features, which helps to shorten the training period. Using the EEG database, the LSTM layer trains the model and performs classification [34].  
Table 3 provides the specifics of our neural network's construction. The EEG modalities' 1D-CNN-LSTM architecture is displayed. Sequence data include EEG recordings that last one second. Two one-dimensional convolutional layers, Conv Layers 1 and 2, were then applied to this sequence as is indicated in Figure 7. Max-pooling and ReLU activation layers came next for each, allowing for the direct extraction of temporal properties from the time series data. The collected features were then flattened for the LSTM layer. This LSTM layer determines the order link between the gathered temporal features to categorize time series data. Next, by connecting the layer to a fully linked class, the relationship between the locations of features in the EEG data and a class was established. In essence, the dimensions of the tensor are altered by the dense layer.  
The combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) offers several advantages in the processing of signals. CNNs excel at extracting local features from inputs by utilizing convolution operations and pooling layers. They are particularly adept at capturing patterns in spatial dimensions. On the other hand, LSTMs possess exceptional capabilities when it comes to processing sequential data, such as time series or EEG signals, due to their ability to handle long dependencies through gated recurrent units.
## 3 Experimental Results and Analysis
### CNN Parameter Settings  
The CNN network is trained by using the random gradient descent method. The initial learning rate is 0.0001, the weight decay is set to 0.005, the batch size is 64, and 75 epochs are trained. The dataset is divided into a training set and test set according to a proportion of 80% and 20%, respectively. Through experiments, we found that the initial learning rate has a significant impact on the training process of the proposed model, which is manifested as the \"gradient jitter\", as shown in Figure 4a,b. It can be seen that when the initial learning rate is 0.001 in Figure 4a and 0.0001 in Figure 4b, there is a significant jitter.  
At present, most of the literature adopts mean Accuracy (mA) as the evaluation metric of attributes recognition algorithms [1; 5; 6]. Therefore, we also adopted mA as the metric to measure the recognition performance in this paper. The calculation process of mA is as follows.  
Figure 4: The impact of learning rate on performance during training with (**a**) a 0.001 initial learning rate, and (**b**) a 0.0001 initial learning rate.  
Figure 3: Examples of pedestrian images in the PETA and RAP datasets: (**a**) Sample images in the PETA dataset; (**b**) sample images in the RAP dataset.  
For each pedestrian attribute, the recognition accuracy of positive and negative samples is calculated separately, and then the average value is taken as the final recognition accuracy of the attributes. After that, the average value of the total pedestrian attributes recognition accuracy will be used as the final recognition rate, that is, mA is calculated as follows:  
\[mA=\frac{1}{2L}\sum\
olimits_{i=1}^{L}{(\frac{TP_{i}}{P_{i}}+\frac{TN_{i}}{N_{ i}})} \tag{5}\]  
where \(L\) represents the number of attributes, \(P_{i}\) represents the number of positive samples of the \(i\)th attribute in test samples, \(TP_{i}\) represents the number of correctly predicted positive labels of the \(i\)th attribute in test samples, \(N_{i}\) represents the number of negative samples of the \(i\)th attribute in test samples, and \(TN_{i}\) represents the number of correctly predicted negative labels of the \(i\)th attribute in test samples.
## 4 Results and Discussion
### Influence of Coefficient of Variation (COV) in the Random Field
It can be seen from Figure 9a,b that both the R\({}^{2}\) values in the prediction datasets of the uniaxial horizontal and moment bearing capacity factor are larger than 0.98, while the RMSE values are smaller than 0.12, indicating that \(N_{CH}\) and \(N_{CM}\) predicted by the surrogate 2D-CNN model are highly consistent with the results calculated by the RFEM. The unified model performs better than the model trained by the dataset with single value of COV, especially when COV is larger than 0.1, as shown in Figure 8. This is mainly caused by the average effect that the samples with small COV in the dataset are beneficial to the prediction of the unified model. The cumulative  
Figure 8: Effect of COV on RMSE and R\({}^{2}\) value for **(a)**\(N_{CH}\) and **(b)**\(N_{CM}\). density function (CDF) of the predicted and calculated bearing capacity factors under uniaxial horizontal and moment loading are shown in Figure 9c,d, respectively. The satisfactory agreement between the CNN-derived and RFEM-derived CDFs of \(N_{CH}\) and \(N_{cM}\) demonstrates that the global distribution of the uniaxial bearing capacity factor of the skirted foundation under the soil random field with different COV is well captured by the proposed unified 2D-CNN model. In addition, the mean, minimum and maximum values of the results obtained by the two methods are compared for the quantitative analysis of the prediction performance. As to the uniaxial horizontal bearing capacity factor, the relative errors of the mean, minimum, and maximum values between the predicted and calculated results are 0.15%, 3.16%, and 0.49%, respectively. Meanwhile, the three relative errors of the uniaxial moment bearing capacity factors are 0.23%, 0.42%, and 0.13%, respectively. The relative errors of the three evaluating indicators are all restricted within 4.00%, indicating the distribution of the uniaxial bearing capacity factors calculated by the time-consuming RFEM can be replaced by the proposed 2D-CNN model. The probability density functions (PDFs) of the relative errors between the CNN-predicted and RFEM-calculated bearing capacity factors are given in Figure 10. The relative errors of the 1200 predicted uniaxial horizonal and moment bearing capacity factors are less than 8%. In addition, the confidence intervals (CIs) of the relative errors are 97.7% and 96.3%, with the margin of error at 5%, for the predicted bearing capacity factors under pure horizontal and moment loading, respectively. The unified 2D-CNN model is accurate enough to predict the uniaxial horizontal and moment bearing capacity factors for the probabilistic analysis of the skirted foundation located in spatially variable soil with different COV. Figure 9: Comparison of the predicted and actual uniaxial bearing capacity factors under mixed COV: (**a**) Scatter of \(N_{CH}\); (**b**) Scatter of \(N_{CM}\); (**c**) CDFs of \(N_{CH}\); (**d**) CDFs of \(N_{CM}\).
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 2 Experimental Section
### Catalyst Synthesis and Characterization  
The \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst was prepared and characterized as reported in our previous work [22]. The synthetic route maybe summarized as follows (Scheme 1), Fe\({}^{2+}\) and Fe\({}^{3+}\) ions in alkaline solution were co-precipitated under nitrogen to produce Fe\({}_{3}\)O\({}_{4}\)nanoparticles. These were then coated with HAP, formed fromCa\({}^{2+}\) and PO\({}_{4}\)\({}^{3-}\) at pH 11, and the product calcined at 300 \({}^{\circ}\)C for 3 h to produce \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP. The brown powdery-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd\({}^{2+}\) was prepared by cation exchange of Ca\({}^{2+}\) in\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP with Pd\({}^{2+}\), and the Pd\({}^{2+}\) reduced by NaBH\({}_{4}\)inethanol to generate the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd(0) catalyst. After addition of NaBH\({}_{4}\), the color of the catalyst changed immediately from brown to dark grey, confirming that the Pd\({}^{2+}\) in\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd\({}^{2+}\) had been successfully reduced to Pd(0) nanoparticles. The content of Pd was determined as 2 wt% by ICP- OES analysis, while the particle size distribution of Pd nanoparticles was determined as 2.8 nm by TEM [22].
## 4 Fluid Simulation Test
### Fluid Simulation of the Water-Isolated Core Protection Drill Bit  
#### 4.2.1 Establishment of a Water-Isolated Core Protection Drill Bit Model  
The structure of the water circuit system directly impacts the fluid flow field at the bit's cutting face, which, in turn, affects the quality of core drilling and the cleaning of cuttings from the bottom of the hole by the flushing fluid. This simulation focuses on the flow field simulation of the water-isolated core protection drill bit designed in Section 2.2, evaluating the effectiveness of the designed water circuit structure in cleaning cuttings from the bottom of the hole, cooling and lubricating the drill bit, and the effectiveness of preventing water from penetrating the core. According to the design, the model of the water-isolated core protection drill bit is established as shown in Figure 12. As seen in Figure 13, during the drilling process, the outer diameter of the core catcher is identical to that of the inner step surface, serving as a water barrier. The flushing fluid can only enter the small holes on the inner surface of the drill bit and exits through the water ports at the bit's bottom, where the high-velocity flushing fluid scrubs the fractured surface of the hole bottom, quickly cleaning the cuttings while also assisting in rock fragmentation. Therefore, no flushing fluid will flow out through the gap between the core catcher and the drill bit. The drill bit working model is imported into Ansys Space Claim for model optimization, extracting the fluid region inside the drill bit and meshing it as shown in Figure 13. The maximum aspect ratio of the grid is 17.29; the minimum grid skewness is 9.279 \(\times\) 10\({}^{-7}\), and the maximum is 0.9423; the maximum orthogonal quality of the grid is 0.99948, with an average of 0.85647. These values meet the grid generation standards, allowing for simulation calculations.  
Figure 11: Optimized Model Fluid Simulation Results.  
Figure 12: Fluid Region Mesh Division.