esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LMD-PNN Microseismic-Signal Recognition  
### Feature Extraction  
Taking into account the vector-redundancy problem of LMD decomposition components, this paper uses the correlation coefficient to judge the selection of an effective PF component and uses Equation (7) to obtain the correlation coefficient between the PF component and the original microseismic signal, and retains the PF component with a larger correlation coefficient.  
\[\rho_{PF_{k}}=\frac{C(PF_{k},x(t))}{\sqrt{D(PF_{k})}\cdot\sqrt{D(x(t))}} \tag{7}\]  
In the formula, \(PF_{k}\) represents the components decomposed by CLD. The \(x(t)\) represents the original microseismic signal. \(C(PF_{k},x(t))\) represents the covariance between the \(PF\) component and the original microseismic signal. \(D(PF_{k})\) represents the variance of the \(PF\) component. \(D(x(t))\) represents the variance of the original microseismic signal [16]. The boundary PF components are obtained by LMD, the correlation coefficient in information theory is introduced, the IMF components with low correlation coefficients are eliminated to form optimal groups of PF components, and then the optimal PF component of the LMD energy entropy is obtained by Equation (5). [PERSON] et al. [27] concluded from the experiment that the PF component whose correlation coefficient is less than 0.03 should be discarded. Therefore, this paper selects the correlation coefficient threshold of 0.3, that is, the energy entropy of the PF component with a correlation coefficient greater than 0.3 to construct the eigenvectors of the mine-microseismic-signal-classification model.  
Figure 1: PNN network structure.
## 3 Materials and Methods
### SPME-GC Analysis  
SPME-GC [34] was used to measure and identify the volatile compounds in the sample solution. The volatile compounds were collected using SPEM fiber (mixing fiber of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS)] and then analyzed using a GC-MS system (HP 6890 GC/HP5973 MSD/multi-purpose sampler MPS2; Agilent, Santa Clara, CA, USA) equipped with a Hewlett Packard 19091X-133 HP-Wax capillary column (30 m \(\times\) 25 mm, 25 \(\upmu\)m; Hewlett Packard Enterprise, Spring, TX, USA). The temperature of the oven was set at 40 \({}^{\circ}\)C for 5 min, subsequently increasing 40 \({}^{\circ}\)C\(\rightarrow\)180 \({}^{\circ}\)C (5 \({}^{\circ}\)C/min), and then held at 180 \({}^{\circ}\)C for 20 min. Ultra-high purity helium was used as the carrier gas at a flow rate of 1.0 mL/min. The injector and detector temperatures were set at 250 \({}^{\circ}\)C, and the temperature of the oven was set at 40 \({}^{\circ}\)C for 5 min, 40 \({}^{\circ}\)C\(\rightarrow\)180 \({}^{\circ}\)C (5 \({}^{\circ}\)C/min), and held at 180 \({}^{\circ}\)C for 20 min. One microliter of sample solution was injected in split mode (20:1). The Scan/SIM mode was set at \(m/z\) 10-500. The data-dependent MS\({}^{2}\) experiments were controlled using menu-driven in-house software, and the identification of volatile compounds was performed by comparing the MS\({}^{2}\) spectrum in the Wiley 8.0 database. Compounds with peak areas below 18.5% in the GC-MS chromatogram were considered as not detected (n.d.).
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 3 Materials and Methods
### Linear Embedding of Finger Vein Images  
Transformers were originally applied in the field of natural language processing, where the standard transformer receives a one-dimensional sequence of token embeddings as input [20]. Therefore, when we used a transformer to process 2D finger vein images, to fit the transformer architecture, we split the finger vein images into patches, linearly embedded each patch, and fed the resulting sequence of vectors into the transformer encoder [14; 30; 31].  
As shown in Figure 2, first, we needed to chunk the input finger vein image, assuming that the image information parameter of the input image \(x\) is \((H,W,C)\), where \((H,W)\) is the resolution of the finger vein image and \(C\) is the number of channels. Assuming the patch size was \((P,P)\), then \(N=HW/P^{2}\) image blocks were divided for the vein image with input parameters \((H,W)\), and we chose the patch size \(P\), which was \(16\times 16\) or \(28\times 28\). If the patch size was too large, the parameter scale of the model exponentially increased, and conversely, if the size of the patch was too small, the final performance of the model was affected. Therefore, in the process of finger vein images in our proposed model, the patch size was selected as \(28\times 28\). Then, each block was regarded as a vector, and all the vectors were combined into a sequence, resulting in a dimension of \(\left(N,P^{2}C\right)\) for the data.  
Transformers use a constant vector size \(D\) in all layers, so we flattened all the patches and mapped them to \(D\) dimensions through a trainable linear projection. The final embedded sequence of the patch, with the token \(Z_{0}\), is given in Equation (1), where \(x_{class}\) is a learnable classification token that is needed to perform the classification task. The patch embeddings are the output of this projection.  
\[Z_{0}=\left[x_{class};x_{p}^{1}E;x_{p}^{2}E;\cdots;x_{p}^{N}E\right]+E_{pos}, \qquad E\in R^{(P^{2}\cdot C)\times D},E_{pos}\in R^{(N+1)\times D} \tag{1}\]
## 3 Results
### Applications in Teaching Dental Morphology  
The study of anatomy is commonly performed by using static two-dimensional (2D) images such as lecture slides, textbooks and flashcards [12]. Since anatomical structures are in three-dimensions (3D), the comprehension of spatial relationships using such static 2D images may be difficult.  
Through 3D virtual models, different anatomical structures may be visualized, making the learning experience more immersive than the traditional 2D and 3D methods [13]. The exploration of a virtual environment may help to develop spatial knowledge, reducing the gap between students who struggle to visualize spatial structures and those confident in their spatial ability [14]. In addition, [PERSON] et al. highlighted that dental students appear more interested in learning anatomical structures through a 3D model than through traditional 2D methods [15].  
AR and VR may also be used to learn dental morphology and tooth features such as exterior shape, position, size and internal structure. Basing on software programs with interactive data set, the tooth can be selected, moved and visualized from different directions and angulations. Moreover, specific tooth structures can be made transparent such that the users can see beyond them or even navigate through the root canal. [PERSON] et al. tested the use of VR in teaching root canal anatomy and found that VR appears to be a valuable instrument suitable for training a student [16]. Thus, both VR and AR may be considered valuable platforms for teaching anatomy and promoting benefits such as increased learner immersion, even if there are no statistically significant differences regarding the amount of knowledge acquired by VR/AR and the traditional method [16].
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 4 Coupling Simulation Analysis
### Effect of Slotted-Tip in Brownout  
By slotting the blade tip and connecting the leading edge and side of the blade tip with a socket, the blade vortex interaction (BVI) noise of the blade can be effectively reduced. The principle is that the leading-edge slot guides the airflow to the side flow around the blade tip and flows in the core area of the blade tip vortex to form part of the turbulent region, which destroys the original layout of vortex's high-speed rotating flow to inhibit the formation of turbulence in the blade tip region, also speeds up the dissipation of the blade tip vortex, and finally weakens the strength of the blade tip vortex.  
The blade of the UH60 is improved by using this method. Four circular slots are added at the blade tip. The model is shown in Figure 20.  
Figure 19: Distribution of sand particles.  
Figure 20: Slotted-tip rotor model. (**a**) The reference blade, (**b**) The slotted-Tip blade, (**c**) Local sketch of four circular slots, (**d**) The right view of blade tip.  
Based on this model, other simulation parameters are consistent with the hovering state (hovering height is 0.52R, blade tip Mach number is 0.63, increased particle number is 5000 per second). The simulation results of the two types are compared and analyzed.  
It can be seen from the Figure 21 that after the time of t = 1.75 s, the average height of particles in the flow field affected by the blade slotting is lower than that of the normal blade, and gradually lags behind that of the normal blade. This situation can show that after the blade slotting, the sand particles in the sand-dust environment will reduce the suspension height because of the weakening of the blade tip vortex, reduce the impact of suspended dust on flight safety, and achieve the effect of restraining brownout.  
In order to further analyze the comparison of particle distribution in the air, the results of particle numbers in the height range of 600 mm-4000 mm at different times are compared.  
It can be seen from the Figure 22 that the number of particles in the normal blade group will be less than that in the slotted blade group when the height above the ground is low, and the number of suspended particles in the normal blade group will be more than that in the slotted blade group when the distance is more than 3000 mm. From the perspective of helicopter flight safety, the slotted blade can reduce the average height of particle suspension and the reduction of the number of suspended particles near the blade area (the blade height is 4250 mm) is more significant, which can effectively reduce the impact of brownout.  
Figure 21: Comparison of average height of particles in different blades by time.  
Figure 22: Comparison of particles numbers by height in different blades at 2 s and 2.25 s.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Motion Equations
#### 4.5.3 Aerodynamics  
The force and torque vector \(\Lambda\) can include different components depending on the aerodynamics effects considered in each work. Based on many research papers, these aerodynamics components can be summarized as follows:  
* Gravitational force: it affects only the linear terms and is determined by the acceleration of the quadcopter due to gravity [117; 120; 126; 127; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142];
* Gyroscopic torque: it affects only the angular terms and can be generated when the roll or pitch values are non-zero. Additionally, it is produced when the propellers rotate, with one pair rotating clockwise and the other pair rotating counterclockwise. Consequently, if the total speed of propellers is non-zero, an imbalance will occur [120; 126; 127; 129; 131; 132; 133; 135; 138; 143; 142; 143];
* Forces and torques: they are generated by the quadcopter's movements, as mentioned in Section 4.3, where drag and thrust factors are also included in their corresponding equations. Additionally, quadcopters may face different external disturbances, such as wind, which can cause a blade-flapping effect that affects the motor force equations. This flapping effect can be categorized into two types: low-amplitude, known as Vortex Ring State (VRS), and high-amplitude, known as Turbulence Wake State (TWS). These fluctuations can be attenuated by increasing the quadcopter's horizontal speed [138; 139; 142; 129; 130; 131; 132; 133; 136; 138; 139; 143].  
After discussing quadcopters' modelling in all its aspects, attention now shifts to the challenges faced in their deployment and the future directions that could enhance their impact in agricultural practices.
## 2 Literature Review
### Count Data Model  
Crash-frequency data are non-negative, integer, and random event counts [16]. With the satisfaction of underlying distribution assumptions, traffic crash data are widely explored through the count-data modeling approach, such as the Poisson model [16]. As an extension of the Poisson regression model, the negative binomial model is employed to overcome the possible over-dispersion [24], because of the excess zero in the crash data. Since the traffic crash data are usually collected in a certain time unit as a sequence, a time series model, such as Autoregressive, integrating the moving average (ARIMA) model and Seasonal-ARIMA, is commonly used to model time series count data and has enjoyed many applications for the past years as a time sequential analysis technique with serial correlation [16]. However, the ARIMA model cannot be applied for non-negative integer-valued data such as traffic accidents, since the underlying assumption of errors in the ARIMA model is normal distribution. Even if the information does not comprise any lower numbers, the ARIMA framework can theoretically still develop negative assumptions [16]. To model traffic accident distribution and take into account time series temporal structure, this paper proposes a time series count data model with fusion of multi-sources data as covariates to forecast the occurrences of crashes.
## 9 Performance Validation and Analysis
### Open Sky Scenario  
In the open sky scenario, the only errors are Gaussian white noise from tracking loops, atmospheric and receiver/satellite clock corrections. In Table 3, GNSS signal information for open sky scenario is represented.  
Horizontal radial error for open sky environment is shown in the Figure 4 in east and north error terms. There is a very little difference between RCEP of three models. However, PMD of the proposed scheme is larger than the other two schemes (except the difference is substantively small). The primary cause of better performance of LSE/WLSE is the presence of Gaussian white noise. As RCEP provides the 90% position error radius, results indicate that WLSE and the proposed schemes are more robust although the mean position error distance of LSE is smaller. Two additional simulations are also created by modifying the noise characteristics of observations.  
Figure 4: Horizontal radial error of open sky static scenario.
## 3 Results and Discussion
### TDS-1 Data Analysis at Regional Scale  
In this section, an analysis similar to the one conducted in the previous section is performed, but over three different regions where 1 km resolution L4 downscaled SMOS surface soil moisture data has been computed. These regions belong to soil moisture networks, and for one of them the LISFLOOD and MUSA hydrological models have been run to estimate the 50 cm and 100 cm SM values, respectively. In practice, TDS-1 GNSS-R ground tracks are so far away from the network stations that few matchups are available, and only when the maximum distance is increased up to 30 km results become statistically significant. Unfortunately, this distance is similar to the SMOS L3 resolution, so no conclusive results can be derived. A description of the different data sets is given below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{\(\Gamma\) = (a \(\pm\)\(\Delta\)a)-SM + (b \(\pm\)\(\Delta\)b)} & \multicolumn{3}{c}{\(\Delta\)b} \\ \hline  & **a** & \multicolumn{3}{c}{**Aa**} & \multicolumn{3}{c}{**b**} & \multicolumn{3}{c}{**\(\Delta\)b**} \\ \hline  & NO & R & V + R & NO & R & V + R & NO & R & V + R & NO & R & V + R \\ \hline
**0\({}^{\circ}\) to 30\({}^{\circ}\)** & 6.76 & 6.47 & 8.75 & 0.20 & 0.20 & 0.20 & \(-19.52\) & \(-18.92\) & \(-16.26\) & 0.17 & 0.17 & 0.21 \\
**30\({}^{\circ}\) to 60\({}^{\circ}\)** & 2.47 & 2.20 & 5.66 & 0.10 & 0.10 & 0.20 & \(-19.92\) & \(-19.32\) & \(-16.13\) & 0.12 & 0.12 & 0.20 \\
**60\({}^{\circ}\) to 90\({}^{\circ}\)** & 0.35 & 0.04 & 2.78 & 0.20 & 0.20 & 0.50 & \(-14.70\) & \(-14.10\) & \(-8.22\) & 0.24 & 0.24 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reflectivity fit with respect to range of incidence angles for different corrections: NO: no vegetation correction, no surface roughness correction; R: no vegetation (VOD, \(\omega\)) correction, surface roughness correction applied; and V + R: vegetation (VOD, \(\omega\) = 0) and surface roughness corrections applied.  
Figure 12: Calibrated TDS-1 GNSS-R reflectivity \(\Gamma\) [dB] but not corrected for vegetation and surface roughness effects vs. SMOS SM data for different ranges of incidence angle: (**a**) from 0\({}^{\circ}\) to 30\({}^{\circ}\); (**b**) from 30\({}^{\circ}\) to 60\({}^{\circ}\); and (**c**) from 60\({}^{\circ}\) to 90\({}^{\circ}\).
## 4 Performance Analysis
### Simulations Results Corresponding to Network Radius  
Through the system model in Section 2, it is intuitively known that the network radius of the BS plays an important role in defining the width of the overlapping region. By considering the insights gained from examining the effects of BS motion in Section 4.3, this analysis aims to understand the influence of the BS network radius on handover decision errors, particularly in scenarios where the direction of motion for BS1 and BS2 is the same as the direction of AUV movement.  
Figure 7: \(D_{BA12}\) with respect to time and sea state in the overlapping area when \(r\) is 100 m, and BS1 and BS2 move in the same direction as the AUV.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 4 Lateral Stability  
The real safe tilt of the tractor is that this tractor is able to prevent overturning and skidding when it travels on terrains.  
The tilt value mainly depends on the following factors. Exterior factors are, such as the ground conditions (slope angle and obstacle shape and height). Interior factors can be changed to adjust tractor stability, such as the moment of inertia, wheelbase, COG position, track width, ballast weight, implement position, and tire properties. Human behavioral factors of tractor drivers include speed, being in adverse environmental and weather conditions, drinking, or taking drugs [35].  
Lateral overturns account for the majority of tractor accidents. In addition, posture adjustment in a tractor is a possible way to enhance locomotion performance. This designed tractor can actively adjust its roll angle to enhance its lateral stability. There  
Figure 5: Leveled attitude.  
fore, it is essential to model a tractor on a lateral slope while taking the tractor's attitude into consideration.  
To analyze the effects of posture change on tractor stability on side slopes, a model is combined with a model of active mechanism and a complete tractor model. The former has been established in Section 3, describing the relationship between the input, configuration, output, and height of the COG of the adjustment mechanism. The latter is proposed in this section, introducing wheel-soil contact forces as a function of overturn parameters. By combining these two models, the instability indicator of tractor overturn is obtained for an arbitrary configuration of the tractor.  
In this section, we make several assumptions to simplify the problem:  
1. The tractor is equipped with four rigid wheels.
2. The steering angle of the tractor is fixed at zero.
3. The tractor travels along a flat lateral slope.
4. The traversing speed is low, and the motion of the tractor remains in a steady state.
## 4 Materials and Methods
### Pigment Profiling  
Leaves from the different treatments were weighted before (FW) and after (DW) freeze-drying and this was used to calculate the leaf relative water content as RWC (%) = (FW \(-\) DW)/FW. Ground freeze-dried leaf samples were extracted with 100% acetone added and subjected to an ultra-sound bath for 1 min to ensure complete disaggregation of the leaf material. Extraction occurred in the dark for 24 h at \(-\)20 \({}^{\circ}\)C, after which the samples were centrifuged at 4000\(\times\) g, 15 min at 4 \({}^{\circ}\)C. Absorbance of supernatants were scanned in the range of 350 nm to 750 nm in 1 nm steps, using a dual-beam spectrophotometer (Shimadzu UV/VIS UV160I Spectrophotometer, Kyoto, Japan). The absorption spectra were analysed and pigment concentrations were quantified, employing the Gauss-Peak Spectra (GPS) method [79]. The sample spectrum was analysed, through the GPS fitting library, using SigmaPlot Software. This method is based on the sample spectrum fitting, by a linear combination, to the Gauss-peak spectra, which describes each pigment in the detected spectrum, identifying the samples pigment profile, chlorophyll \(a\), chlorophyll \(b\), aurovanthin, anthreaxanthin, \(\beta\)-carotene, lutein, violaxanthin, and zexaxanthin. For a better evaluation of the light-harvesting and photoproduction mechanisms, the De-Eposidation State (DES) was calculated as:  
\[\text{DES}=\frac{([\text{Antheaxanthin}]+[\text{Zeaxanthin}])}{([\text{Violaxanthin}]+[\text{Antheaxanthin}]+[\text{Zeaxanthin}])}\]
## 4 Architecture
The OpenDaylight controller automatically collects the port statistics  
Figure 1: The proposed SDN-based network architecture. of switches every three seconds by OpenFlow and provides the port statistics to the SDN applications via the northbound REST API. Initially, a list of possible traffic patterns is given as Bayesian attractors to our application. The BAM assigns stochastic variables to these attractors that indicate the confidence level of each attractor. Together with the traffic patterns, pre-calculated network slice topologies and routing tables optimized for each traffic pattern are given to our application by its API. Our SDN application periodically receives the utilization statistics of a set of edge links from the OpenDaylight controller. The BAM updates the confidence level of attractors each time when there is a new observation of these fluctuating and noisy statistics. By comparing these confidence levels, the application tries to identify the current traffic pattern from a list of stored patterns. In the case of a change in the identified traffic pattern, the application sends the network slice VNT configuration and the routing tables in each network slice, which were pre-optimized for the identified traffic, to the VTN coordinator application via the REST API. The VTN coordinator application translates the requested configuration and sends it to the VTN manager module in the corresponding OpenDaylight controller, which controls the SDN network that is to be modified. The VTN manager module updates the configuration of the OpenDaylight controller for setting up the new logical network. Finally, the OpenDaylight controller pushes down the changes to the switches by the southbound OpenFlow API. In case of a change in the traffic patterns to be identified, the new list of traffic patterns can be given to the application, which restarts the BAM. As BAM is an online algorithm and it does not require training, BAM can identify the new patterns after getting a few samples of utilization statistics.