esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Further Developments
### Limitations and Further Development  
Despite the contributions of this study, some limitations remain to be addressed. Firstly, since the data collected in this questionnaire were not balanced from different regions, i.e., there were relatively small sample sizes from central and western China, this study did not explore the impact of regions on teachers' readiness for and intentions to implement STEM education. Previous studies have proven that data from different regions could predict behavior more accurately and meaningfully [81]. Therefore, more studies investigating whether the educational development of regions has impacts on teachers' readiness and teaching intentions to implement STEM education should be carried out. Secondly, the significant impacts of subjective norms on teachers' intentions to implement STEM education have yet to be tested, but this does not indicate that there is no significant impact between them. Research on non-STEM education has proven that subjective norms are among the impact factors of behavioral intention [67; 82]. Further studies can focus on the correlation between subjective norms and teachers' intentions to implement STEM education. Thirdly, most of the previous studies focused on explaining and predicting behavioral intention and actions. Since the DTPB model can intervene in behavior, further studies can explore how to intervene in STEM teaching behavior based on the DTPB.  
Conceptualization, P.W. and L.Y.; methodology, P.W. and L.Y.; software, B.L. and Y.W.; validation, L.Y.; formal analysis, B.L. and Y.W.; investigation, P.W. and X.H.; project administration, P.W. and X.H.; writing--original draft preparation, B.L., Q.L., Y.W., and J.H.; writing--review and editing, P.W., L.Y., and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Development Planning Project of Philosophy and Social Science in Guangzhou (2020 GZYB27 and 2022 GZYB50).  
Not applicable.  
The informed Consent Statement: The informed consent was obtained in the survey.  
Not applicable.  
The authors declare no conflict of interest.
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## 2 Building a Realistic UAV Simulator
### Modeling  
#### 2.2.1 Quadrotor Kinematics  
Kinematics studies the movement of bodies without considering the forces that generate it. In this way, quadrotor kinematics describes the quadrotor movements concerning the generalized coordinates.  
As detailed before, the position of the quadrotor is given by its mass center location, i.e., \(\xi=[x,y,z]^{T}\), which is used to describe the translation movement. The rotation movement is related to the vector \(\mathbf{\eta}=[\phi,\theta,\psi]^{T}\), and the convention of Euler angles \(ZXY\). With the angles of the vector \(\mathbf{\eta}\), it can be established the orientation relationships between the frames \(\{I\}\) and \(\{B\}\) employing the elementary rotation matrices for each coordinate axis:  
\[R_{x}(\phi)=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi&\cos\phi\end{array}\right] \tag{1}\]  
\[R_{y}(\theta)=\left[\begin{array}{ccc}\cos\theta&0&\sin\theta\\ 0&1&0\\ -\sin\theta&0&\cos\theta\end{array}\right] \tag{2}\]  
\[R_{z}(\psi)=\left[\begin{array}{ccc}\cos\psi&-\sin\psi&0\\ \sin\psi&\cos\psi&0\\ 0&0&1\end{array}\right] \tag{3}\]  
Then, any vector \(P^{B}\) in the coordinate system \(\{B\}\) (e.g., the vector of a rotor position) can be written in the coordinate system \(\{I\}\) using:  
\[P^{I}=\xi+R_{zxy}P^{B} \tag{4}\]  
where \(R_{zxy}(\psi,\phi,\theta)=R_{z}(\psi)R_{x}(\phi)R_{y}(\theta)\).
## 3 Method
### Heterogeneous Enhanced Graph Neural Network Feature Embedding Framework
#### 3.3.3 Attention Feature Enhancement  
To better simulate the competitive process between different operations for machines in a real production environment, a multi-head attention mechanism is introduced to update the features of machine nodes. This mechanism dynamically adjusts the weights of feature aggregation to capture the competitive relationships between operation nodes [42]. The core of the attention mechanism lies in calculating the importance weights of each operation node concerning the machine node \(M_{k}\). The feature update formula for the machine node is as follows:  
\[\beta_{k}^{(l+1)}=\text{ReLU}\Bigg{(}\sum_{\{i,j\in N(M_{k})}}\alpha_{ijk}^{(h )}\cdot W^{(l,h)}\cdot\alpha_{ij}^{(l)}\Bigg{)} \tag{13}\]  
Here, \(N(M_{k})\) denotes the set of operation nodes connected to the machine node \(M_{k}\), \(\alpha_{ij}^{(l)}\) is the feature vector of operation node \(O_{ij}\) at layer \(l\), \(\alpha_{ijk}^{(h)}\) is the attention weight calculated by the \(h\)-th attention head, which measures the importance of operation node \(l\) to machine node \(l\), and \(W^{(l,h)}\) is the weight matrix for the \(h\)-th attention head at layer \(l\).  
The calculation formula for the attention weight \(\alpha_{ijk}^{(h)}\) is given by  
\[\alpha_{ijk}^{(h)}=\frac{\exp\Bigl{(}e_{ijk}^{(h)}\Bigr{)}}{\sum_{i^{\prime}j^{ \prime}\in N(M_{k})}\exp\Bigl{(}e_{ijk}^{(h)}\Bigr{)}} \tag{14}\]  
where \(e_{ijk}^{(h)}\) is the raw attention score computed based on the features of operation and machine nodes:  
\[e_{ijk}^{(h)}=\text{LeakyReLU}\Bigl{(}a^{(h)\top}\cdot\Bigl{[}W^{(l,h)}\cdot \beta_{k}^{(l)}\parallel W^{(l,h)}\cdot\alpha_{ij}^{(l)}\Bigr{]}\Bigr{)} \tag{15}\]  
Here, \(a^{(h)}\) is the learnable parameter vector for the \(h\)-th attention head, and \(\parallel\) denotes the vector concatenation operation. Through the multi-head attention mechanism, machine nodes can more effectively aggregate information from important operation nodes, thereby better reflecting decision preferences in actual production. The outputs of multiple heads are finally concatenated or weighted summed to obtain a richer feature representation:  
\[\beta_{k}^{(l+1)}=\text{Concat}\Bigl{(}\beta_{k}^{(l+1,1)},\beta_{k}^{(l+1,2)},\dots,\beta_{k}^{(l+1,H)}\Bigr{)}W^{\text{out}} \tag{16}\]  
where \(H\) represents the number of attention heads, and \(W^{\text{out}}\) is the weight matrix for the output layer.
## 2 Materials and Methods
### Measurement System  
Data on the running performance of the players were collected by GPS technology (GPSports SPI Pro-X, Camberra, Australia; triaxial accelerometer sampling 6 g at 100 Hz integrated; size = 48 \(\times\) 20 \(\times\) 87 mm; mass = 76 g) with a sampling frequency of 15 Hz. It has been previously demonstrated that this type of system provides valid and reliable estimates. However, to avoid inter-unit errors, players used the same GPS unit throughout the experimental period, thereby excluding variability between units [53; 54].
# Improved Block Element Method for Simulating Rock Failure
## 6 Conclusions  
This study proposed an improved block element method, which can simulate the entire process from damage initiation to macroscopic fracture failure in rocks and capture rock damage paths more realistically. Several conclusions can be obtained as follows:  
1. In terms of mesh discretization, a random Voronoi polygon grid discretization technique is adopted, combined with an iterative algorithm and the area-perimeter fractal dimension as a control index, achieving mesh optimization that balances geometric rationality and the necessary randomness;
2. This study proposed the construction of a novel damage constitutive model that incorporates normal-tangential coupling effects. Through the geometric mapping relationship between the two damage surfaces, it accurately describes the entire rock damage evolution process. The experimental validation tests show that the model achieves a high accuracy in both macro-mechanical response and failure mode prediction.
3. In the sensitivity analysis of uniaxial compression, the macroscopic rock strength is mainly controlled by the mesoscopic shear strength parameter, and the mesoscopic fracture energy dominates the behavior in the softening stage. The effect of mesoscopic tensile strength is negligible in compression tests, while it becomes the primary controlling factor in the uniaxial tension test.  
Additionally, there are still many things worth studying based on this research. First, as only the failure process of rocks under uniaxial loading was considered, it is necessary to study the failure behavior of rocks under multiaxial loading. Second, due to the advantages of the block element method in calculating the stiffness matrix of the elements, heterogeneity related to rock failure can be considered. Third, in order to apply the improved block element method to more complex models, parallel computing also can be considered. These are meaningful for the wide application of the block element method in geotechnical engineering and worth investigating in further studies.  
Methodology, Y.H. and Q.R.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H., Q.R. and L.S.; visualization, Y.H. and Y.Y.; supervision, Q.R.; funding acquisition, Q.R. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51739006, 51908195), the Fundamental Research Funds for the Central Universities (No. 2019B65014), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJKY19_0429).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to express their appreciation to [PERSON] and [PERSON] from Hohai University.  
The authors declare no conflicts of interest.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 2 Proposed Method
### Receiver Acquisition Algorithm Design
#### 2.2.1 Receiver Hardware Architecture Design
The digital IF signal after ADC sampling enters baseband signal processing, and different parameter configurations are used according to the respective signal characteristics. The final output observations of the baseband processing are the pseudocode phase, carrier phase, carrier Doppler, receiver local time, and message information, which are transferred to the processor through the local bus for subsequent information processing, such as the solution of the observation equation and the conversion of the coordinate system, and finally the receiver position, receiver clock difference, receiver speed, receiver clock drift, signal carrier-to-noise ratio, and other information. The frequency allocation of the signal processing is shown in Table 8.
## 1 Introduction
### System Hardware
In industrial applications, high-efficiency switching mode converters are preferred instead of linear regulators for LED drivers [35]. With the MOSFET switching in the driver circuit, the 5 V DC PWM signal is converted to the 12 V DC PWM signal required for the LED strip lamp. In a lighting system where the PWM signal is used, the desired light intensity is adjusted by quickly switching the power provided for the LED lamp on and off. The basis of PWM is based on the principle of applying a square wave digital signal--whose pulse width is adjusted at a certain frequency--to the load. In this respect, signal frequency and  
Figure 4: Raspberry-Pi-based LED strip lamp and electromagnet driver circuit. Figure 3: Closed-loop lighting control system. duty cycle are important parameters in the use of PWM. The duty cycle (\(D\)) is expressed as in Equation (1) by considering the high time of the pulse (\(T_{on}\)) and the low time of the pulse (\(T_{off}\)) in a digital signal [36; 37]. By changing the current between 0 and I\({}_{\mathrm{max}}\) in proportion to the value, which \(D\) will take between 0 and 100 in the system, the electrical power applied to the LED strip lamp, and thus the lighting intensity, can be adjusted. Thus, the electrical power (\(P_{LED}\)) of the LED strip lamp depending on the \(D\) value of the PWM signal can be expressed in W as in Equation (2). \[D=\frac{T_{on}}{T_{on}+T_{off}}100\% \tag{1}\]  
\[P_{LED}=V_{LED}.I_{max}\frac{T_{on}}{T_{on}+T_{off}} \tag{2}\]
# Triangular Mesh Surface Subdivision Based on Graph Neural Network
###### Abstract  
Mesh subdivision is a common mesh-processing algorithm used to improve model accuracy and surface smoothness. Its classical scheme adopts a fixed linear vertex update strategy and is implemented iteratively, which often results in excessive mesh smoothness. In recent years, a nonlinear subdivision method that uses neural network methods, called neural subdivision (NS), has been proposed. However, as a new scheme, its application scope and the effect of its algorithm need to be improved. To solve the above problems, a graph neural network method based on neural subdivision was used to realize mesh subdivision. Unlike fixed half-flap structures, the non-fixed mesh patches used in this paper naturally expressed the interior and boundary of a mesh and learned its spatial and topological features. The tensor voting strategy was used to replace the half-flap spatial transformation method of neural subdivision to ensure the translation, rotation, and scaling invariance of the algorithm. Dynamic graph convolution was introduced to learn the global features of the mesh in the way of stacking, so as to improve the subdivision effect of the network on the extreme input mesh. In addition, vertex neighborhood information was added to the training data to improve the robustness of the subdivision network. The experimental results show that the proposed algorithm achieved a good subdivision of both the general input mesh and extreme input mesh. In addition, it effectively subdivided mesh boundaries. In particular, using the general input mesh, the algorithm in this paper was compared to neural subdivision through quantitative experiments. The proposed method reduced the Hausdorff distance and the mean surface distance by 27.53% and 43.01%, respectively.  
surface subdivision; graph neural network; triangular mesh; 3D deep learning; dynamic graph convolution +
Footnote †: journal: Applied Sciences
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 2 VoIP Application and Scenarios
### VoIP Application in WMNs  
VoIP is commonly used as a critical service in modern communication networks. Today, people often prefer making VoIP calls instead of using PSTN services, GSM phones [22], texting or sending emails due to their low cost, wide range of supported features, and ease of use. Studies also show that VoIP usage is increasing in countries where mobile operator call tariffs are higher [23]. On the other hand, there are specific scenarios where only voice calls are feasible and/or the preferred means of communication. VoIP services offering with acceptable or improved quality with the consideration of the MOS rating scale is very much important in cases of emergency networks, which has been studied in, e.g., [24]. For example, in the case of natural or man-made disasters, people who are affected and people who approach the affected area are turning to voice calls to reach each other. In scenarios where the main communication mediums through wired or infrastructure-based wireless network are affected by natural or man-made disasters, building a temporary or long-lasting network using the available mobile devices on the scene can save lives and makes it possible to communicate, coordinate, assist, locate, send help, and provide the affected people with the necessary support services and medical care.
# Feasible Energy Density Pushes of Li-Metal vs. Li-Ion Cells
## 2 Understanding the Need for a Li-Metal Anode
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cathode**} & \multirow{2}{*}{**Abbreviation**} & **Specific Capacity** & \multicolumn{1}{c}{* **Potential vs.**} & \multirow{2}{*}{**Ref.**} \\  & & **(mAh/g)** & & **Li/Li\({}^{*}\) (V)** & \\ \hline LiMn\({}_{2}\)O\({}_{4}\) & LMO & 110 & 4.1 & [16] \\ LiFePO\({}_{4}\) & LFP & 160 & 3.45 & [16; 17] \\ LiNi\({}_{0.5}\)Mn\({}_{0.3}\)Co\({}_{0.2}\)O\({}_{2}\) & NMC 532 & 175 & 3.8 & [16; 17] \\ LiNi\({}_{0.8}\)Mn\({}_{0.1}\)Co\({}_{0.1}\)O\({}_{2}\) & NMC 811 & 220 & 3.8 & [16; 17] \\ \hline \multicolumn{4}{c}{**Anode**} & & & \\ \hline Li\({}_{4}\)Ti\({}_{5}\)O\({}_{12}\) & LTO & 175 & 1.5 & [16] \\ Graphite & C & 372 & 0.1 & [16; 17] \\ Silicon & Si/C (1:3) & 1100 & 0.4 & [18] \\ Li-metal & Li & 3860 & 0.0 & [16; 19] \\ \hline \hline \end{tabular}  
* Cell potential is equal to difference of chosen electrode pair potentials.  
\end{table} 
Table 1: Specific properties of the most common anode and cathode materials in LIBs. discharging process causes stress on the graphite anode [31]. This stress does not result only in volume changes and cracks in the anode structure but also in a slow diffusion of Li ions during the intercalation process. Undoubtedly, this second phenomenon tends to be more aggressive if the charging rate is increased. Fast charging may also cause overheating of the liquid electrolyte, and these electrolytes can catch fire. Since all solid-state batteries (ASSBs) contains no liquid in the cell, this risk is eliminated and they can be charged at higher C-rates. Last but not least, the cost of ASSBs is expected to be lower due to (i) use of less anode active material for higher capacity and ED, (ii) no need for sophisticated safety components (saving more space), and (iii) less production steps compared to LIBs (no electrolyte filling). All points discussed above favor the utilization of Li-metal in the batteries instead of conventional graphite anodes. The crucial question of \"how much Li-metal in the cell?\" still remains not fully answered. The following sections will discuss this question, taking into account CE and cycle life of the battery through different scenarios.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 6 Results and Discussion  
This section discusses the results of the developed CAR-MOSOA for multicast routing protocol in WSN-based IoT. The results and discussion of this proposed SOA method are clearly described in this section. The Network Simulator-2.34 (NS-2.34) was used for theimplementation of the CAR-MOSOA, in that the system used a 4-GB RAM and Intel Core processor. The implemented SOA method was used to accomplish secure data transmission between the source to the BS. The performance of the proposed algorithm was studied based on the following parameters:  
* Energy consumption: It is defined as the overall energy consumed by the system for the period of data packet transmission.
* End-to-end delay: It is defined as the period between the transmission of the data packet from the source node and its arrival at the destination node.
* Packet delivery ratio (PDR): It is demarcated as the proportion of the number of packets delivered to the endpoint to the number of packets transmitted by a base station.
* Life time: It is calculated at each node while passing through the route request. On the other hand, each node guesses the route lifespan between the actual and aforementioned nodes.  
The design and simulation of the CAR-MOSOA method in NS-2.34 with various constraints such as network size, traffic type, packet size, and so on are listed in Table 1.  
To analyze the effect of CBR traffic type, the network size was considered as 500 \(\times\) 500. The number of nodes was fixed as 400. The proposed CAR-MOSOA was effectively executed and compared with customary cluster-based methodologies, for example, the proposed calculations and their prevalent execution such as energy consumption, end-to-end delay, PDR, the throughput of the above-mentioned node counts. Here, the existing approaches such as FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33] were implemented using the same specifications mentioned in Table 1.
## 3 System Design and Platform Subsystems
### Atmospheric/Surface State Retrieval Algorithm  
Observations from the Argus 2000 spectrometer constitute a single sounding covering a ground sampling distance of approximately \(1.5\times 1.5\) km from a 550 km altitude. Each sounding will be analyzed with a retrieval algorithm, which will fit the measured spectra by adjusting the unknown atmospheric, surface, and instrumental parameters. The retrieval consists of two parts: a forward model and a statistical comparison method [20]. The forward model is an approximate scheme to describe radiative transfer in the atmosphere, reflection by the surface, and the effects of the instrument on the incident radiation to generate detailed lookup tables [20]. Forward modeling will be performed using GENSPECT [21] software. GENSPECT is a radiative transfer modeling tool developed for CANX-2 mission.  
The principal characteristics and flow of the retrieval algorithm are presented schematically in Figure 13. The initial step of the retrieval process begins by inputting an assumed environmental state that is defined by the surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, and other trace gases, in addition to cloud and aerosol optical depth distributions [20]. These parameters can be initialized from known climatology and additional satellite data products e.g., MODIS for surface reflectance, cloud, and aerosol optical distribution. This information is combined with pre-tabulated, wavelength-dependent gas, aerosol, and cloud optical properties. The gas absorption cross-sections for the spectral range are derived and tabulated using GENSPECT and spectral line databases such as HITRAN [22]. The synthetic spectra are then processed with a solar model and a model that simulates the instrument's spectral response to the incident radiation and produces results that can be compared directly to the calibrated spectra [20].  
The comparison model compares the synthetic radiance spectra to the observations and uses an optimal estimation technique and produces a match to the measured spectra in the spectral range [20]. Finally, the atmospheric composition is derived from the best-match atmospheric state validated statistically.  
To validate the results, data provided by the Total Carbon Column Observing Network (TCCON) [23] will be used. The satellite will acquire the data over the stations and the corresponding ground data acquired within \(\pm 30\) min of satellite overpass time will be considered for comparison as in [24]. A portable SWIR spectrometer will also be used to accurately measure spectral characteristics of  
Figure 13: Atmospheric/surface state retrieval algorithm.  
surface and downwelling radiation at a spectrally homogenous desert site in the UAE using systematic sampling to match the GSD. This data will be used to validate the instrument performance and for recalibration if needed.