esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Composite Separator Design  
### Basic Structural Design  
Since the de-bonding behavior of weakly cemented hydrate particles primarily occurs in the cylindrical section of a hydrocyclone separator, and the insufficient de-bonding performance of classic hydrocyclone separators is mainly due to the GHBS particles' inadequate residence time in the cylindrical section, a composite separator was designed to address this issue. To extend the residence time, the composite separator incorporated a helical flow channel within the cylindrical section of the classic hydrocyclone separator. The helical flow channel restricted the axial acceleration of the fluid and dispersed-phase particles, thereby achieving the goal of an extended residence time. In addition, the preset flow channel aided in pre-separation. As the mixed slurry entered the flow channel, its axial velocity was converted to tangential velocity, giving the slurry more time to stratify under the action of centrifugal force, thus enhancing the separation effect.  
The composite cyclone separator designed in this study was based on a hydrate solid-state fluidization development process, utilizing mechanical crushing and a water jet to extract solid hydrate. Since hydrate decomposition rarely occurs downhole, the influence of gaseous hydrate formation was ignored, and only the GHBS particles and drilling fluid were considered.  
The structure and working principle of the designed tangential inlet composite separator are shown in Figure 2. The mixed slurry containing hydrates, sand, and weakly cemented hydrate particles enters the composite separator through the tangential inlet. Initially, under the influence of the helical flow channel, the slurry undergoes both rotational motion around the separator main axis and axial movement, achieving pre-separation at this stage. The denser dispersed phase tends to adhere to the wall surface, while the lighter continuous phase remains closer to the center of the separator. During this phase, the weakly cemented hydrate sediment particles also experience rotational motion and de-bonding, decomposing into sand and hydrates. As the mixed slurry enters the conical section, the dispersed phase near the wall surface moves toward the underflow outlet due to axial velocity and the push from other dispersed phases, while the continuous phase is discharged from the overflow outlet.
## 2 Study Area and Data Acquisition  
### Site Description  
The study region of northern Shaanxi Province [36\({}^{\circ}\)9\({}^{\prime}\) N-39\({}^{\circ}\)35\({}^{\prime}\) N, 107\({}^{\circ}\)14\({}^{\prime}\) E-111\({}^{\circ}\)16\({}^{\prime}\) E] (Figure 1) has an area of 8.4 M ha, covering 25 county-level administrative units. This is a typical and highly dissected hilly region dominated by slope land with an altitude of 385-1911 m. The mean annual temperature is 8.2-13.1 \({}^{\circ}\)C and increases from north to south. The mean annual precipitation is around 600 mm in the southeast and gradually decreases to below 400 mm in the northwest. The precipitation varies seasonally, with more than 60% falling in the rainy season of July to September. Precipitation also varies significantly from year to year, with relative variation (CV) of 20%-30% [31]. In 2000, the land cover mainly featured cropland (52%), grassland (26%), forestland (11%), and shrubland (11%).
# Stereo Vision Based Sensory Substitution for the Visually Impaired
###### Abstract  
The development of computer vision based systems dedicated to help visually impaired people to perceive the environment, to orientate and navigate has been the main research subject of many works in the recent years. A significant ensemble of resources has been employed to support the development of sensory substitution devices (SSDs) and electronic travel aids for the rehabilitation of the visually impaired. The Sound of Vision (SoV) project used a comprehensive approach to develop such an SSD, tackling all the challenging aspects that so far restrained the large scale adoption of such systems by the intended audience: Wearability, real-time operation, pervasiveness, usability, cost. This article is set to present the artificial vision based component of the SoV SSD that performs the scene reconstruction and segmentation in outdoor environments. In contrast with the indoor use case, where the system acquires depth input from a structured light camera, in outdoors SoV relies on stereo vision to detect the elements of interest and provide an audio and/or haptic representation of the environment to the user. Our stereo-based method is designed to work with wearable acquisition devices and still provide a real-time, reliable description of the scene in the context of unreliable depth input from the stereo correspondence and of the complex 6 DOF motion of the head-worn camera. We quantitatively evaluate our approach on a custom benchmarking dataset acquired with SoV cameras and provide the highlights of the usability evaluation with visually impaired users.  
visual impairment; sensory substitution; stereo vision; 3D reconstruction; obstacle detection Article
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 3 Methods  
### CNN Model  
The convolutional neural network (CNN), as a kind of artificial neural network, first emerged from research on the cat visual system, wherein convolutional layers were constructed by introducing receptive field features into the neural network. The preliminary model of the CNN is LeNet, which delivers a high training speed with accuracy [29]. In the traffic field, the traffic flow of the target station is in connection with the adjacent station space, as well as the traffic flow and congestion of the upstream and downstream stations. Therefore, the CNN model could be adopted in this study to draw the spatial characteristics of the traffic monitoring points. Overall, CNN contains convolutional layers, activation layers, pooling layers, and fully connected layers.
###### Abstract  
Objectives: This study aimed to assess frequency and multiple-night variability of sleep bruxism (SB) as well as sleep-time masticatory muscle activities (sMMA) in the home environment in healthy young adults using a portable device that provides electrocardiographic (ECG) and surface electromyographic (EMG) recordings from the masticatory muscles. Methods: The study was performed on 27 subjects (11 males, 16 females; mean age 28.3 \(\pm\) 1.7 years) selected from a sample of healthy young students. Evaluation was carried out for four nights to record data on masticatory muscle activities using a compact portable device that previously showed an excellent agreement with polysomnography (PSG) for the detection of SB events. The number of SB episodes per sleep hour (bruxism index), and the number of tonic, phasic and mixed sMMA events per hour were assessed. A descriptive evaluation of the frequency of each condition was performed on all individuals, and gender comparison was investigated. Results: Mean sleep duration over the four recording nights was 7 \(\pm\) 1.3 h. The average SB index was 3.6 \(\pm\) 1.2. Most of the sMMA were tonic (49.9%) and phasic (44.1%). An ANOVA test showed the absence of significant differences between the four nights. No significant gender differences were detected for the SB index, phasic or tonic contractions; conversely, gender differences were detected for mixed sMMA events (\(p\) < 0.05). Conclusion: This investigation supports the concept that sMMA events are quite frequent in healthy adults. Differences over the four-night recording span were not significant. These data could be compared to subjects with underlying conditions that may lead to an additive bruxism activity and possible clinical consequences.  
sleep bruxism; masticatory muscle activity; compact portable device; surface electromyography
## 3 Methodology
### Machine Learning Methods
#### 3.2.2 Generalized Regression Neural Network (GRNN)  
The BPNN is a well-known neural network algorithm. However, it has the disadvantages of slow convergence and easily convergence to local minima. Another neural network, the GRNN, which is a special form of a radial basis function neural network, was proposed by [PERSON] [40]. The GRNN improves the local approximation ability and learning speed, because the hidden nodes of the GRNN are often connected by a Gaussian function, which is locally distributed and attenuated to the center of the radial symmetry [48]. Meanwhile, compared with the popular feedforward neural networks, the GRNN has the advantages of a relatively simple structure, rapid training, low computational cost, and global convergence. GRNN contains three layers, i.e., an input layer, a radial basis hidden layer, and a special linear output layer. The input variables are transferred to the radial basis hidden layer from the input layer through a transfer function, which is always a Gaussian function. The output of the radial basis hidden layer is then not directly connected with the linear output layer but is first transmitted by a dot function and then connected to the output layer by the linear transfer function to calculate the network output. The structure of the GRNN algorithm is shown in Figure 3. In our study, the input signals are date, latitude, longitude, NDVI, GPP, and Precipitation, and the output parameter is NMRI. GRNN model is implemented by using the neural network toolbox of MATLAB.  
Figure 3: Structure of Generalized Regression Neural Network (GRNN).
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Chemical Properties and Their Characterization of MNPs
#### 3.2.8 Small Angle X-ray Scattering (SAXS)  
SAXS is a crucial technique for nanoscale analysis. It provides valuable information about the arrangement and self-organization of dispersed nanoscale colloids in their natural solutions. It possesses a distinct capability to investigate the averaged structural information of the materials, including information about the crystals' structure, lattice spacing, and crystallites size [114].
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 4 Results
### Sigmoidal Models
#### 4.2.2 Logistics Model  
Figure 9b shows the logistic model defined by Equation (13), to model the evolution of cost as a function of time, using the modified formula of the traditional sigmoid model. This model utilizes the modified formula of the traditional sigmoid model. The model's general behavior adequately represented the increasing trend observed in the experimental data in the intermediate sections of the construction process. The iteration curves were found to be consistently grouped around the main curve, thereby demonstrating the model's stability and consistency under various simulation scenarios.  
Figure 9: (**a**) Proposed sigmoidal model, (**b**) Logistic model, (**c**) Von Bertalanffy model, and (**d**) Gompertz model, where each color represents the estimated S-curve of each project.  
The upper and lower limits delineated the dispersion of the iteration curves, thereby providing a useful frame of reference for the technical planning of rigid pavement projects. The lower limit of the range indicates a scenario characterized by contained costs in the initial stages, followed by a progressive increase associated with an acceleration in the execution to meet the established deadlines.  
This alternative logistic model was distinguished by its presentation of a reduced transition in the slope changes, a feature that proved advantageous in its representation of construction processes characterized by gradual variations in execution rhythms.
## 3 Results and Discussion
### Method Validation
#### 3.3.1 Specificity, Linearity, LLOD, and LLOQ  
The satisfactory specificity of an analytical method shows that the method may distinguish between the compound and other interfering substances. As demonstrated by the following chromatograms (Figure 2), the blank biological samples did not show interfering signals near the retention times of Hu7691 andloratadine, and the two analytes were well separated, indicating a satisfactory specificity of the method.  
The linearity of the standard curve (Figure 3) was assessed by the squared coefficient of correlation (\(R^{2}\)). The \(R^{2}\) values were all above 0.99, and the typical calibration curve equation for plasma Hu7691 was \(y\) = 5.65\(\times 10^{4}x\) \(-\) 7.64\(\times 10^{3}\) with \(r\) = 0.995178, indicating a good linearity.  
The LLOQ of Hu7691 was 5 ng/mL [RSD = 6.9%, n = 5, signal-to-noise ratio (S/N) > 10], and the LLOD was 2 ng/mL (n = 5, S/N > 3).
# Fractal Contact Mechanics Model for the Rough Surface of a Beveloid Gear with Elliptical Asperities
## 2 A New Fractal Characterization Approach for a Rough Surface Texture
### Modified Model of the Island Area Distribution Function for a Point Contact
#### 3.3.2 Elliptical Contact Area Distribution Function  
As illustrated in Figure 6, the contact area of an elliptical asperity on the rough tooth surface is an ellipse with a semiminor radius \(r_{x}\) and a semimajor radius \(r_{y}\).  
Based on Equation (40), the expression for the total number of islands between the contact area \(a\) and the maximum contact area \(a_{l}\) of any elliptical asperities can be deduced as:  
\[N(A>a)=\Big{(}\frac{a_{l}}{a}\Big{)}^{\zeta(D_{1}+D_{2})/2} \tag{42}\]  
where \(D_{1}\), \(D_{2}\) are the fractal dimensions of the rough surface in the \(x\)-axis and \(y\)-axis directions, respectively. \(\zeta\) is the contact coefficient of the elliptical asperity, and the contact coefficient \(\zeta\) can be expressed as:  
\[\zeta=\frac{1}{2}\Big{(}1-e^{2}\Big{)}^{1/2} \tag{43}\]  
Figure 6: Comparison of the contact area between spherical asperity and elliptical asperity.  
Thus, the contact area distribution of elliptical asperities can be obtained as a function of the fractal dimension:  
\[n(a)=\frac{\zeta(D_{1}+D_{2})}{2}\frac{a_{l}^{\zeta(D_{1}+D_{2})/2}}{a^{(\zeta(D_ {1}+D_{2})/2+1)}} \tag{44}\]  
The total contact area \(A_{r}\) can be obtained by integrating the contact area distribution function described in Equation (44) as:  
\[A_{r}=\int_{0}^{a_{l}}n(a)ada=\frac{\zeta(D_{1}+D_{2})}{2-\zeta(D_{1}+D_{2})}a _{l} \tag{45}\]
# Preparation of Nanoparticle-Loaded Microbubbles via an Electrohydrodynamic Atomization Process
## 3 Results and Discussion
### Effect of Gas to Liquid Flow Rate Ratio on the Microbubbles  
It was found that when nanoparticle-loaded microbubbles were prepared by electro-hydrodynamic atomization, gas injection flow had a significant effect on the successful preparation of microbubbles. Therefore, the effect of gas to liquid flow rate ratio on the stability of microbubbles was studied and analyzed under the conditions a voltage of 12 kV and a nanoparticle concentration \(C_{NP}=1\) mg/mL. Figure 9 shows the variations of normalized microbubble number with time at different gas/liquid flow rates. With the increase of gas/liquid flow rate from 40/10 to 50/10 (mL/h/mL/h), the stability of microbubbles increased. Further increasing the gas flow rate to 60 mL/h did not enhance stability. Note that beyond these gas/liquid flow rates, the production process of microbubbles was unstable.  
Figure 10 depicts the size distributions of microbubbles at different gas/liquid flow rates. It can be seen that with the increase of gas/liquid flow rate ratio, the average size of microbubbles became larger, while the size distributions became broader. About 68% of microbubbles obtained at a gas flow rate ratio of 50/10 (mL/h/mL/h) were distributed in the 120\(\sim\)200 \(\upmu\)m size range. As with the analysis and discussion in previous sections, microbubbles under this condition showed relatively ideal stability. To sum up, the results show that with a voltage at of 12 kV and a nanoparticle concentration \(C_{NP}=1\) mg/mL, the best gas/liquid flow rate ratio for the preparation of microbubbles with good stability is 50/10 (mL/h/mL/h).  
Figure 8: Size distributions of microbubbles loaded with different nanoparticle concentrations. (**a**) \(C_{NP}=0\) mg/mL; (**b**) \(C_{NP}=0.5\) mg/mL; (**c**) \(C_{NP}=1\) mg/mL; (**d**) \(C_{NP}=2\) mg/mL.
## 3 Results
### Spatio-Temporal Characteristics of DE and GD
#### 3.1.2 Dynamic Distribution Evolution of DE and GD Level  
After employing the unconditional kernel density estimation, the dynamic distribution evolution and convergence tendencies of the DE and GD were analyzed. Figure 2 illustrates the unconditional kernel density and contours for the DE level during the periods 2013-2017 and 2017-2021. The relatively sparse contour lines clustered above the 45\({}^{\circ}\) diagonal line, indicating a singular peak, which suggests that the DE level experienced an upward trend from 2013 to 2017, accompanied by regional fluctuations (Figure 2a). Contour lines converged toward the 45\({}^{\circ}\) diagonal, yielding two crests during 2017-2021, pointing to a continued upward trend in the level that approached stability in this period (Figure 2b). Similarly, Figure 3 presents the unconditional kernel density and contours accordingly for the GD level during 2013-2017 and 2017-2021. Contour lines, concentrated along the 45\({}^{\circ}\) diagonal and a total of two peaks, signified that the GD level maintained relative stability from 2013 to 2017 (Figure 3a). During 2017-2021, in comparison, the contours diverged from the 45\({}^{\circ}\) diagonal with a consistent crest count, indicating a gradual upward trend in the level (Figure 3b).  
Figure 1: Spatial distribution of the DE and GD levels in 2013, 2017, and 2021. (**a**) Spatial distribution of the DE level in 2013, 2017, and 2021. (**b**) Spatial distribution of the GD level in 2013, 2017, and 2021.  
Figure 3: Spatial kernel density and density contours of the GD level in different periods. **(a)** Spatial kernel density and density contours of the GD level in 2013–2017. **(b)** Spatial kernel density and density contours of the GD level in 2017–2021.  
Figure 2: Spatial kernel density and density contours of the DE level in different periods. **(a)** Spatial kernel density and density contours of the DE level in 2013–2017. **(b)** Spatial kernel density and density contours of the DE level in 2017–2021.
## 7 Technical Considerations for Implementing 6G Technology  
The implementation of 6G technology requires addressing several key technical challenges, including electromagnetic wave propagation at THz frequencies, cell architecture, and advanced technologies like intelligent reflecting surfaces (IRSs).
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## System Design of SRSS/ILMO
### Relay Transmission Buoy Controller Design  
The relay transmission buoy controller adopts the ARM processor as the main processor to schedule system tasks. The underwater acoustic communication module is used to interact with the seabed observation system. The Beidou satellite is used to communicate with the remote monitoring system. The buoy system is equipped with azimuth attitude sensor TCM XB and GPS for system safety monitoring. In terms of buoy power supply, the combination scheme of solar battery plus lithium battery is adopted. Under the condition of sufficient sunlight for five hours, the energy storage of the lithium battery can meet the power supply demand of the relay transmission buoy system without sunlight for one month. The design of the relay transmission buoy controller is shown in Figure 6.
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 2 System Model and Problem Formulation
### Modeling Line of Sight Probability
The probability \(P_{\text{LoS}}\) of having LoS rays at position \(r\ \leq\ r_{\text{max}}\) for GUs is given by  
\[P_{\text{LoS}}=\prod\
olimits_{0}^{N_{n}-1}P_{\text{LoS},n}, \tag{4}\]  
Equation (4) can be considered a continuous function of the elevation angle and the three environment parameters and can be used for any \(h_{\text{TX}}\) and \(h_{\text{RX}}\) heights. In the case of the UAV air base station platform, we can disregard \(h_{\text{RX}}\) since it is much lower than the altitude of the UAV platform. However, the series in Equation (4) cannot be further reduced; therefore, it is necessary to simplify the calculation of LoS probability. Note that the trend of LoS probability can be closely approximated to a modified Sigmoid (S-shape) function with the following form:  
\[P_{\text{LoS}}(\theta)=\frac{1}{1+a\text{exp}(-b[\theta-a])}, \tag{5}\]  
where \(a=\sum_{j=0}^{3}\sum_{i=0}^{3-j}\mathcal{A}_{ij}(\alpha\beta)^{i}\gamma^{j}\) and \(b=\sum_{j=0}^{3}\sum_{i=0}^{3-j}\mathcal{B}_{ij}(\alpha\beta)^{i}\gamma^{j}\) are the fitting parameters, while \(\mathcal{A}_{ij}\) and \(\mathcal{B}_{ij}\) are the polynomial coefficients. In order to link the fitting parameters with environmental parameters, surface fitting can be carried out using two variables (\(\alpha\times\beta\)) and (\(\gamma\)), as shown in Figure 3.  
Substituting Equation (5) into Equation (2), we can obtain the following:  
\[PL=\frac{\eta_{\text{LoS}}-\eta_{\text{NLoS}}}{1+a\text{exp}(-b[\theta-a])}+2 0\text{log}\left(\frac{4\pi fd}{c}\right)+\eta_{\text{NLoS}}, \tag{6}\]  
Figure 3: Three-dimensional fitting of the S-curve parameters and the environmental parameters. where \(f\) is the carrier frequency and \(c\) is the speed of light.
# Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
## 3 Results
### Effect of Exercise Conditions on the Ball Speed  
Results from the ANOVA revealed a significant main effect of exercise condition on ball speed (\(p\) < 0.05) with a very large effect size \(\eta^{2}\) = 0.82. The post hoc analysis found a significant difference in ball speed between all exercise conditions (\(p\) < 0.05), except between exercise 1 (forehand topspin on backspin ball) and exercise 4 (backhand topspin on topspin ball) (\(p\) = 1) (see Figure 9).  
The box plot presented in Figure 9 highlights that the data distributions for backhand exercises (exercises 2 and 4) exhibit significantly narrower ranges compared to those for forehand exercises (exercises 1 and 3). This is supported by a variance test of Levene, which indicated a significant difference in ball linear speed variance (\(p\) < 0.001). This disparity suggests a greater consistency in performance outcomes for the backhand exercises.  
Figure 8: Ball linear speed and spin relationship. Exercises 1 and 2 refer to a topspin forehand/backhand on a backspinning ball. Exercises 3 and 4 refer to a topspin forehand/backhand on a topspinning ball.