esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### EEG Acquisition and Preprocessing  
The resting state EEG was measured for 5 min using the Neuroprax(r) EEG system (NeuroConn GmbH, Ilmenau, Germany) before and after ten sessions of dual-mode stimulation in patients with subacute stroke. In healthy controls, EEG was measured at a resting state for only 5 min without stimulation. A 27-channel EEG cap with active Ag/AgCl electrodes (Easy Cap, Worthsee, Germany) was utilized using the international 10-10 system. These signals were also collected at a sampling rate frequency of 4000 Hz. EEG signals were referenced from the right-earlobe electrode, and the ground electrode was placed as a separate electrode between the CP1 and CP2 channels. All impedance of the electrodes remained below 15 k\(\Omega\).  
The continuous signals were processed using the EEGLAB toolbox [30] with MATLAB R2022a (MathWorks, Middlesex County, MA, USA). The EEG data were down-sampled at 500 Hz and were bandpass-filtered between 1 and 45 Hz using a basic finite impulse response filter. Re-referencing was performed using the common average reference [31]. Channel interpolation and independent component analysis were performed to remove or subtract artifacts. Finally, the continuous signals were segmented into 5 s epochs [32], and the output threshold was set from \(-\)100 to 100 \(\mu\)V to remove additional noise.  
EEG data were used after filtering into the mu band (8-13 Hz) and beta band (18-24 Hz), which are frequency bands related to motor function [33]. In terms of brain regions, the sensorimotor area, which is directly related to the motor area, was focused on. In particular, to investigate the connectivity between the two hemispheres, the left and right sensorimotor regions were grouped, as shown in Figure 2. Also, the left DLPFC was regarded as an F3 channel [23,34], and its connectivity with the M1 was analyzed. In this study, the purpose of dual-mode NBS was to promote the rehabilitation of motor function; therefore, the stimulation location was the M1. In this regard, we selected and limited the same test region and frequency bands for both ITS and STS groups. In the statistical analysis, P01, P03, and P05 data were rearranged based on the center to match the lesion side to the right.  
Figure 2: Location of EEG electrodes according to the international 10–10 system. The red square represents the left sensorimotor region, and the blue square represents the right sensorimotor region.
## 5 Conclusions  
According to the work performed above, the following conclusions could be drawn:  
1. A new WPCS core drilling tool was designed and applied for the first time in the deep sea during the hydrate survey voyage in the South China Sea of Guangzhou Marine Geological Survey, and good application results were obtained.
2. The core recovery rate was closely related to the structure of the core drilling tool and hydrate formation. In the process of core drilling in the sandy hydrate formation, because the rock breaking surface of the drill tool was too large, the concentration of cuttings near the core bit was too high, which facilitated the complex situation of core pipe plugging, water hole plugging of the drill bit, and the interaction of the inner and outer pipes.
3. The pressure-holding effect of the core drilling tool was related to the formation of hydrate, the sealing form of the ball valve, and the environmental pressure. Sandy hydrate formations often caused the ball valve to jam, while the muddy hydrate formation did not. The sealing form of the ball valve affected the effective driving force of the ball valve turning down, and the ambient pressure increased the friction resistance between the ball valve and the upper and lower ball valve seats.
4. In order to improve the application effect of WPCS, the coordination between WPCS and BHA should be further optimized. To maintain a clean and cool environment near the drill bit, the drilling fluid should be improved. In addition, the interaction between the drive spring and the environmental pressure should be quantified as much as possible.  
Gas hydrates remain stable in a low-temperature and high-pressure environment. According to the phase equilibrium curve of gas hydrate, the main safety and security of WPCS operation is to control the temperature rising, to avoid pressure rising rapidly in the pressure chamber system of WPCS due to the pressure core dissolving.
* The WPCS needs to develop abundant security measures to improve reliability, such as BHA being stuck and failing to retrieve.
## 6 Limitations and Future Work  
One limitation of this study is the lack of a comparable typically developed control group. Comparison data may have highlighted any potential differences in performance between participants with autism and neurologically healthy counterparts. Nonetheless, participants in this study still showed higher spatial attention towards binaurally rendered spatial audio. Demonstrating quantifiable evidence of audio spatial interactions.  
Another important consideration is the use of a non-individual HRTF's database within this study. This database incorporates anechoic recordings using the Neumann KU-100 dummy head simulator which is-based upon average dimensions of the human head and ears [28]. HRTF's differ greatly between individuals, due to the unique shape of the pinnae, head, and torso. Therefore, the use of a generalized HRTF can sometimes result in localization confusion and reduced externalization of target auditory events within a virtual environment [72; 73]. In addition, recent research which compared individualized to non-individual HRTF's did observe higher localization errors when using a generic database [74]. This may provide some explanation for the varying accuracy and localization times between participants within the 3D audio experiment group as well as the lower effect scores yielded in the statistical analysis. However, there are significant challenges involved in obtaining individualized recordings which warrants the use of generic dummy head recordings in the development of virtual reality soundscapes [75].  
Future investigations could also build upon the work carried out by [PERSON] et al. [55] and measure self-reported presence within virtual environments that use spatial audio rendering techniques. Changes in behavior, spatial focus, and interactions when altering the audio reproduction could be compared. Furthermore, studies could be conducted to measure if removing aspects of the background audio has any negative effects on the feelings of presence felt by the participants.
## 4 Results and Discussions  
### Boiling Water Test  
The peeling condition of the asphalt film on the RCA surface before and after the boiling water test is shown in Figure 9. The results show that there is a significant difference in asphalt film stripping on the surface of RCA before and after SCA modification. Specifically, when the RCA is not modified by SCA, it can be observed that a large area of the asphalt film on the RCA surface peels off after the boiling water test, and the adhesion level is below grade 3 as shown in Table 4. This is because the surface of the aggregate absorbs a considerable amount of cement mortar, and there are massive pores on the surface. When a large number of pores exists on the surface of RCA, it will cause structural defects at the interface between the RCA and asphalt. During the boiling water experiment, the high temperature causes the gas inside the pores of the RCA to expand. This expansion leads to the destruction of the interface between the RCA and the asphalt, resulting in further shedding of the asphalt on the aggregate surface. This results in poor adhesion between asphalt and RCA and a weak interface transition zone. When RCA was modified by SCA, the asphalt film on the surface of RCA remained relatively intact after the boiling water experiment, and no apparent bubbles were observed in the boiling water experiment. According to Table 4, it can be concluded that the adhesion grade of the RCA asphalt mixture reached grade 5. This indicates that the SCA modification improves the interface properties of the RCA asphalt mixture.
## 4 Discussion
### Microbial-Mediated Flavor Formation  
The differences in flavor substances between the two workshops may be related to the brewing processes and raw materials used [44]. For instance, the higher furfural content in workshop L could be associated with the amount or variety of rice husk added, which further influences the accumulation of aldehyde compounds [44]. Additionally, the significant accumulation of phenylethyl alcohol in workshop H and its absence in workshop L indicate distinct microbial metabolic activities between the two workshops [45]. These differences may stem from variations in fermentation conditions, microbial community composition, or process parameters, ultimately leading to significant disparities in the types and contents of flavor substances. These findings provide important insights for further optimizing brewing processes and regulating flavor profiles. It is noteworthy that the contents of ethyl acetate and phenylethyl acetate in the esters of workshop H are significantly higher than those in workshop L. This may be due to the high content of acetic acid in workshop H, which accelerates the esterification reaction [46]. In workshop L, the contents of ethyl phenylacetate, ethyl lactate, and ethyl oleate suddenly increased on the fourth and fifth days of the stacking fermentation process, indicating an increase in the rate of esterification toward the end of the process [47]. The accumulation of these flavor substances laid the foundation for the subsequent fermentation of sauce-flavor Baijiu, ultimately enhancing the aroma of the finished product.  
The composition and metabolic activities of microbial communities play a crucial role in the formation of flavor substances. In workshop H, the positive correlations between fungi such as _Saccharomyces_ and _Thermomyces_ and various flavor substances, particularly the significant associations with ethyl acetate and phenylethyl acetate, are likely the main reasons for the higher content of these ester compounds in workshop H. In contrast, the weaker correlations between _Saccharomyces_ and flavor substances in workshop L may be attributed to the lower content of precursors such as acetic acid, which inhibits the esterification process [48]. Additionally, the positive correlations between microorganisms like _Ascomycota_ and _Kroppenstedtia_ and multiple flavor substances in workshop L indicate that these microorganisms play an important role in flavor formation in workshop L. Notably, _Ralstonia_ showed positive correlations with various flavor substances in both workshops, which may be related to its ability to produce amylase and promote the conversion of glucose into acids, thereby indirectly influencing the synthesis of flavor substances [49]. These findings provide important insights for further elucidating the relationship between microorganisms and flavor substances, as well as theoretical support for optimizing brewing processes to regulate the formation of specific flavor compounds.
## 2 Moment-Based Estimators
From (3) we can immediately get the second and fourth moments  
\[\mu_{2} =\frac{A^{2}}{2}+\sigma^{2} \tag{4}\] \[\mu_{4} =\frac{3}{8}A^{4}+3A^{2}\sigma^{2}+3\sigma^{4}. \tag{5}\]  
Let \(S=A^{2}/2\) and \(N=\sigma^{2}\); then, from (4) and (5) we obtain the estimators for \(S\) and \(N\) as  
\[\hat{S} =\sqrt{\left|2\hat{\mu}_{2}^{2}-\frac{2}{3}\hat{\mu}_{4}\right|} \tag{6}\] \[\hat{N} =\hat{\mu}_{2}-\hat{S}, \tag{7}\]  
where, as required by the method of moments, instead of the true moments we use their corresponding natural estimators  
\[\hat{\mu}_{2} =\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{2} \tag{8}\] \[\hat{\mu}_{4} =\frac{1}{K}\sum_{k=0}^{K-1}y_{k}^{4}. \tag{9}\]  
The SNR estimator is immediately built as the ratio of the two estimators derived above; i.e.,  
\[\hat{\rho}=\frac{\hat{S}}{\hat{N}}. \tag{10}\]In the following sections performances of the proposed estimators are studied both theoretically and numerically in terms of squared bias and variance. For a generic estimator \(\hat{\theta}\) the squared bias is defined as [7; 13]  
\[b^{2}\left(\hat{\theta}\right)\triangleq\left[E[\hat{\theta}]-\theta\right]^{2}, \tag{11}\]  
while the variance is defined as  
\[\text{var}(\hat{\theta})=E[|\hat{\theta}-E[\hat{\theta}]|^{2}]. \tag{12}\]
## 3 Materials and Methods
### Performance Metrics  
Three error evaluation metrics, namely root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)), are chosen to assess the accuracy of MARS model predictions. The R\({}^{2}\) metric represents the linear relationship between the predicted and actual values, with its value ranging from zero to one. A value of R\({}^{2}\) nearing one signifies a more accurate model prediction. RMSE provides insight into the dispersion between predicted and real data and is sensitive due to its relation to the  
Figure 3: Tuning of hyperparameters using five-fold cross-validation.  
Euclidean distance. Meanwhile, MAE is a measure of the average magnitude of errors between predicted and observed values. Lower values of RMSE and MAE suggest superior prediction performance by the algorithm. The three metrics are outlined as follows:  
Root mean squared error (RMSE):  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(G_{pi}-G_{Ti}\right)^{2}}{n}} \tag{6}\]  
Correlation of determination (R\({}^{2}\)):  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(G_{pi}-G_{Ti}\right)^{2}}{\sum_{i=1}^ {n}\left(G_{Ti}-G_{Ti}\right)^{2}} \tag{7}\]  
Mean absolute error (MAE):  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|G_{pi}-G_{Ti}\right| \tag{8}\]  
where \(G_{Ti}\) and \(G_{pi}\) are the true and predicted values for the observation, respectively. \(G_{Ti}\) is the mean of all the true values. \(n\) is the number of samples.
## 2 Theoretical Background and Hypothesis Development
### Competence-Related Traits
#### 2.2.1 Inner Traits  
The inner traits used in this study include four traits extracted from prior studies: skill, expression efficiency, intelligence, and capability. (a) Skill. Skills refer to particular ways to perform tasks well. Skill is a comprehensive concept that includes professional skills, learning skills, communication skills, and comprehension skills [16; 19; 20]. People tend to associate skill with competence since it is an indicator of expertise and high performance [41]. For instance, salespersons with analytical skills earn higher sales [42], adaptive selling skills enable salespersons to better position a firm's products over those of its competitors [43], and interpersonal mentalizing skills help salespersons reduce burnout, thus improving their performance [44]. Thus, skill is anticipated to be positively correlated with competence. (b) Expression efficiency. Efficiency refers to carrying out work processes efficiently [19; 20]. Here, efficiency is mainly manifested by expression efficiency, such as organizing ideas into points clearly and using rhetoric (e.g., metaphors and exemplification) to convey information vividly [45]. Researchers have proven that the use of metaphors enhances the idea quality, as determined by listeners [46]; the use of exemplification helps listeners understand complex topics since it increases vividness and authenticity [47; 48]. Expression efficiency is a critical component of high service quality [49; 50] and client trust [51]. Thus, expression efficiency is anticipated to be positively correlated with competence. (c) Intelligence. Intelligence is a measure of the extent to which an individual masters knowledge about a product. As individual-level intellectual capital, intelligence increases salespersons' information-based behavior and provides a basis for identifying competence cues [12; 52]. Higher performance in terms of selling activities is obtained by salespersons who are able to display product knowledge intelligently [53; 54]. Therefore, intelligence is anticipated to be positively correlated with competence. (d) Capability. Capability means that individuals possess the ability to perform a certain task [55], for instance, being capable of satisfying the needs of customers or having decision-making control over selling issues such as offering discounts and free shipping. Prior research has indicated that, in addition to skills, staff members also need empowerment to complete work tasks [56]. Salespersons who are capable of determining their sales tasks and courses of action have higher self-efficiency and sales performance [57]. Researchers have also found that by introducing capability into the competence framework, the robustness of the framework improves [24]. Given the above, capability is anticipated to be positively correlated with competence.
## 3 Experiments  
### Data and Implementation Details  
One hundred and twenty point clouds provided by PU-GAN [13] plus three blade surface point clouds are used for training. Then, 200 patches are cropped from each 3D model as the input of the network for a total of 24,600 patches, and the number of points \(N\) per patch is 256. Each patch is composed of low-resolution point clouds and ground truth point clouds sampled by Poisson disks. The batch size is set to 32. The upsampling rate \(r\) is set to 4. The [PERSON] algorithm [21] with a two time-scale update rule (TTUR) [22] is adopted to train the network. The learning rate of the generator is 0.001, and the learning rate of the discriminator is 0.0001. After 30,000 iterations, the learning rate is gradually reduced by a decay rate of 0.8 per 30,000 iterations until \(10^{-6}\). The network is implemented on NVIDIA GeForce RTX 2080 Ti GPU and Intel Xeon Gold 5218 CPU using TensorFlow [23] on the Ubuntu 16.04 system.  
In order to reduce the interference of noise and reduce the amount of data, we use the pass-through filter to prune parts of the point cloud that are not relevant to the experiment and use FCSOR to remove noise values from point clouds. For FCSOR, we set the noise denoising coefficient \(\mu\) to 1 and the number of points \(k\) in the neighborhood to 50. The software framework is developed using the C++ programming language based on the Point Cloud Library (PCL) [24].  
The 3D camera, Sizector HD40, which is designed with phase-shifting structured light technology, produced by Shanghai ShengXiang Industrial Detection Technology, is selected to obtain point cloud data for evaluating the algorithm's performance. The point cloud data used in the X-ray diffraction experiment are acquired by Sizector 3D R600 and also produced by Shanghai ShengXiang Industrial Detection Technology. The experimental hardware setup is shown in Figure 5. The robot holds the 3D camera through an adaptor. The blade sample is positioned on the goniometer platform of the diffractometer.  
Meshlab [25] is used for point cloud visualization, which is an open-source, portable, and extensible 3D geometry processing system, mainly used for interactive processing and unstructured editing of 3D triangular meshes.
## 3 Experimental Results
### Analysis of Radiation Noise Characteristic
#### 3.2.2 Acoustic Directivity at Different Flow Rates  
The radiation noise intensity of the high-speed centrifugal pump under different flow conditions was measured with a rotation speed of 6750 r/min. The test was conducted in a semi-anechoic chamber,  
Figure 8: Pump performance curves at different rotational speed: (**a**) dimensionless head curve; (**b**) pump efficiency curves.  
Figure 9: Variation of radiated noise at different speeds.  
and the pump was fixed to a heavy foundation. Details such as the location of microphone sensors and pump position can be seen in Figure 3. In order to make the directivity feature more compatible with natural intuition, the directivity curves were plotted using linear interpolation, as shown in Figure 10. In the vertical direction, the directivity of the radiation noise shows quadrupole characteristics at a small flow rate and the polarity appears in 45 \({}^{\circ}\)and 135 \({}^{\circ}\)directions, respectively. The acoustic radiation expands outward, and the intensity increases gradually with the increase of flow rate. When the flow rate increases to 0.85 \(Q_{t}\), the quadrupole shape of the characteristics becomes not so obvious, and the distribution of sound pressure level tends to be uniform at each measuring point. When the flow rate reaches \(Q_{t}\), the polarity of 45\({}^{\circ}\)and 135\({}^{\circ}\) measuring points disappears, and the directivity curve shows the characteristics of dipoles. In view of the flow-induced noise mechanism, the backflow at the inlet and outlet of impeller and flow separation from the turbulence inside the flow-path can be regarded as a quadrupole sound source under a small flow rate. The increase of flow rate makes the flow field more uniform, which leads to the decrease of the contribution of the quadrupole sound source to the whole sound field. When the flow rate reaches the design operation point, the interaction between the moving fluid and the solid boundary is enhanced and the contribution of dipole sound source to the radiation noise increases, which becomes the main noise source. At 45\({}^{\circ}\) oblique direction, the directivity characteristics of radiation noise are not obvious, which indicates that the directivity characteristics of radiation noise are closely related to the spatial distribution.
## 4 Simulation Setup
### Inertial Parameters Uncertainty  
In order to test the robustness of the kinematic controller to parameters uncertainty, we consider the possibility of using inertial parameters that differ from the reference values used in the dynamic simulation. The parameters taken into consideration are the UAV and link masses (\(m_{B},\ m_{i}\)) and moments of inertia (\(I_{B},\ I_{G_{i}}\)). We assign to each parameter a realistic possible variation (expressed as a percentage of the reference value) because the real parameters can differ from the nominal ones. For example, a \(+/-15\%\) possible variation is chosen for the mass, \(m_{B}\), and the moment of inertia, \(I_{B}\), of the UAV, considering that these can vary greatly depending on the effective on-board instrumentation and how it is positioned on the UAV frame. The assumed variations in the inertial parameters between the ones used in the kinematic controller and their reference values are listed in Table 5.
# Application of Laser-Ultrasonics for Evaluating Textures and Anisotropy
## Materials and Methods
### Quenched Carbon Steel  
A hot-rolled and quenched martensitic carbon steel was also investigated, both in the as-quenched condition and after tempering for 1 h at various temperatures. The purpose of this was to determine how the anisotropy of the steel varied as its strength level was changed. These samples were cut out from a 12 mm thick sheet and were then turned into cylinders coaxial with the normal direction so that measurements of P-wave velocity were made in the plane containing rolling and transverse directions. The post-annealing and oxidised samples are shown in Figure 7 below. The samples were measured in a laser-ultrasonics setup, both generating the ultrasound and detecting it at the same position.  
Textures were determined for the steel using the same EBSD procedure, as described above, on both the as-quenched specimen and after tempering. However, as expected, these were virtually identical, so any variations in ultrasonic parameters arose from the condition of the martensite and not from differences in texture. All these measurements were restricted to the central third of the plate thickness, where the texture is uniform, corresponding also to the positions where the LUS measurements were made.