esa-sceva/satcom-chunk-collection huggingface dataset:
# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 1 Introduction
In [9], the authors used VGG19 as their choice for a pre-trained CNN along with multi-view features computed from vertical and horizontal gradients. The best features were then used in a Naive Bayes model for recognition. A unique training procedure was introduced by the authors of [10], who used a pre-trained CNN for the spatial stream on single frames of a video, and for the temporal stream, they extracted three different RGB frames at different times in the video, converted them to grayscale, and assigned each to an RGB plane. Afterwards, the three images were merged into a Stacked Grayscale 3 channel Image (SG3I) which then could be used with a conventional pre-trained CNN. In [11], the work looked into improving motion representations by tuning the spatial stream to predict the outputs of the temporal stream, eliminating the need for a two-stream architecture, and combining them into a single stream. In [12], the authors opted to use dilated CNNs to extract salient discriminative features and then feed them into a bidirectional LSTM to learn dependencies followed by an attention mechanism to enhance the recognition performance. Researchers in [13] combined spatial and temporal features into one set of spatiotemporal features and used the OFF CNN for feature extraction. In a recent work [14], Optical Flow was used to represent the motion between video frames on a pixel basis. This result was arranged as an Optical Flow image and then converted into a feature vector using either VGG-Net or GoogleNet. Since such algorithms result in high-dimensionality feature vectors, the dimensionality of the results was reduced, and a multi-channel 1D-CNN was used to classify the resultant time series. In this work, on the other hand, we utilized the High-Efficiency Video Coder (HEVC) [15] to compute feature variables for each video frame. During the HEVC coding process, many variables were computed to optimize the processes of intra- and inter-frame coding, taking into account the spatiotemporal activities for the video frames. We then eliminated redundancies in the temporal domain of the feature vector sequences by means of computing the measures of central tendencies and depression of each variable and in terms of PCA projections. Random forests (RFs) [16] were then used for model generation and classification. In short, HAR is a major field of study that has advanced over time. Traditional methods depended on manually created features and classifiers, but recent developments in deep learning have resulted in considerable performance gains. The creation of more precise and reliable models has also been aided by the availability of carefully curated large-scale datasets. The rest of the paper is organized as follows. Section 2 introduces the system overview of the proposed solution including feature extraction and temporal domain elimination. Section 3 describes the used dataset, Section 4 presents the experimental setup and results, and Section 5 concludes the paper.
## 4 Numerical Analysis of Active Bearing of the FSP  
### Numerical Model Building and Material Parameters  
The plane numerical calculation model is established by ABAQUS software, and the model size is 80 m \(\times\) 45 m. Since the influence of the base reaction force is generally ignored in the weak surrounding rock, the upper bench arch is suspended in the calculation part. The x-direction constraints are applied to the model on both sides. The x and y direction constraints are applied at the bottom. A vertical load is applied to the upper part to simulate the actual burial depth, and the numerical calculation model is shown in Figure 11.  
The FSP is simplified in the calculation process, and the FSP considers only the vertical support force of the arch frame. Instead of establishing the beam element model separately, the FSP's bearing capacity is extracted separately and directly applied to the arch foot. Considering the nonlinear characteristics of the load-bearing deformation curve of the FSP, the built-in nonlinear spring element in ABAQUS is adopted to simulate the vertical bearing capacity of the FSP, as shown in Figure 12. When a nonlinear spring is used to replace the FSP, only the restraint force in the direction is required. By moving the displacement of the fixed end of the spring, the active bearing effect of the steel pipe can be quickly simulated. The active bearing effect of the FSP was analyzed by controlling the preload of the FSP.  
Figure 10: Schematic diagram of nonactive and active bearing process of FSP.  
According to the basic requirements of the geotechnical test, physical and mechanical tests were carried out on the Q2 loess around Yulinzi Tunnel, including measuring the soil moisture content and natural density and measuring its cohesion and internal friction angle through the direct shear test. Partial material parameters can be determined as shown in Table 1. The factors considered in the calculation of the working conditions mainly include two aspects: On the one hand, the change of the parameters of the FSP, considering that the diameter of the FSP is 42 mm, 50 mm, 89 mm, and the number of pipes is 2 and 4. On the other hand, different load release coefficients are selected.
## 2 Materials and Methods
### Optimization Experimental Design  
Mixed regression analysis was applied to the data to determine the estimated coefficients and significance of the model terms, the F-test, and the coefficient of determination (R\({}^{2}\)). The results were first fitted to all available mixture regression models of increasing complexity, from linear to special cubic models. Model significance, lack of fit significance, and adjusted R\({}^{2}\) value were used to assess the adequacy of model fit. The adjusted R\({}^{2}\) value describes the proportion of variation in responses explained by the model, and the value was adjusted for the number of terms in the model. The models were then reduced (if any), leaving only significant terms. Finally, it was found that the responses for the analysis were best fitted using a quadratic model or the special cubic model [26].  
\[\text{Y}=\beta 1\text{X}1+\beta 2\text{X}2+\beta 3\text{X}3+\beta 12\text{X}1 \text{X}2+\beta 13\text{X}1\text{X}2+\beta 23\text{X}2\text{X}3\text{ (Quadratic)}\]  
OR  
\[\text{Y}=\beta 1\text{X}1+\beta 2\text{X}2+\beta 3\text{X}3+\beta 12\text{X}1 \text{X}2+\beta 13\text{X}1\text{X}2+\beta 23\text{X}2\text{X}3+\beta 123\text{X}1 \text{X}2\text{X}3\text{ (Special Cubic)}\]  
where Y is the predicted response of the analysis; \(\beta 1\), \(\beta 2\), and \(\beta 3\) are the estimated coefficients of the individual linear effect terms; \(\beta 12\), \(\beta 13\), and \(\beta 23\) are the binary interaction effect terms; and \(\beta\)123 is the ternary interaction effect term. Once the estimated model equation for each response was obtained, contour and surface plots were generated.  
The simultaneous optimization of the response variables was performed using the desirability function approach, which is the most popular method for solving optimization problems with multiple quality features [33]. This technique consists of converting each response variable into an individual desirability function that varies in the range of 0-1. A value of 0.0 was assigned to the predicted values of the response variable outside the region of interest, and a desirability value of 1.0 corresponded to the response variable when it reached its target. The most influential response variables for emulsion stability were selected, and thus the desirability function was applied. After applying the desirability function, a new ternary mixture was used to produce new emulsions (three repetitions were performed. n = 3). Finally, the model was validated by comparing the average responses of these emulsions with the respective values predicted by the model equations.
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 1 Introduction
By successfully integrating point cloud data with robot actions, an automated robot coating laser cleaning system is achieved, significantly improving the cleaning efficiency in the absence of CAD models. The distance between the cleaning actuator and the surface is determined by estimating the normal vector of trajectory points and utilizing the focal length of the laser. This distance is maintained, allowing the cleaning actuator to move along the complex surface to be cleaned. Simultaneously, the laser beam remains perpendicular to the workpiece surface, achieving the desired cleaning posture planning. This approach ensures both curved surface cleaning and reduces energy loss of the laser, thereby enhancing the cleaning quality.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 3 A Practical Non-Stationary of 3-D Massive MIMO Channel Model  
### Generation of the Cluster/Channel in System Level  
In this section, some important characteristics of the outdoor massive MIMO channel model for the far-field effects needs to be investigated, such as the distance between TX and RX, cluster generation and the appearance on the antenna array, movement direction of clusters and movement direction of antenna arrays. In Figure 2, all the details and dimensions of the non-stationary communication scenario for LOS component and NLOS components are shown. In addition, we implemented different effective scatters around the TX and RX. We also used a spherical model for dipole antenna elements at the TX and a spherical model for omnidirectional antenna elements at the RX to minimize cluster-dispersing under the beam pattern assumption. Regarding the non-stationary channel modeling, we need to distinguish the moving user in a cell, stationary of TX and random roadside environment (building, trees, cars, etc.). Therefore, to generate the channel coefficient, two parts must be modeled from the TX to the RX such as (a) toward the outdoor cluster at the TX, and (b) toward the receive antenna elements.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 2 Basic System Structure of CU-MTDC  
### System Structure of CU-MTDC  
A general VSC-MTDC system used for the coastal urban power system is adopted in this paper, as shown in Figure 1.  
Figure 1: System structure of coastal urban multi-terminal DC (CU-MTDC).  
As shown in Figure 1, the coastal urban power system is divided into N center load area and M non-center load area. The characteristic of the center load area is having heavy load and fewer sources, which causes the critical AC line in this area to have a larger possibility of power supply security problem. The characteristic of the non-center load area has a light load, and the inner source in the non-center load area is sufficient. Therefore, in order to realize better power flow distribution, increasing the offshore wind power integrating and enhancing the power supply security, a VSC-MTDC system is proposed and embedded into the coastal urban power system.  
The proposed VSC-MTDC system has N inverter converters, which are named the center of coastal urban power grid (CCUP) converters and are directly connected with the center load area. In addition, there are M rectifier converters connected with non-center load areas or other power systems with higher voltage. The Wind-farm converter (WFVSC) aims at realizing integrating with wind power.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 3 Performance Analysis
### Ts-D2d Scheme
#### 3.2.2 Outage Probability of \(\text{U}_{2}\)
\begin{split}\varphi_{4}&=\mathrm{Pr}\Big{[}\Lambda _{\mathrm{suz}2,ts}<\gamma_{p,ts}-\Lambda_{\mathrm{suz}2,ts},{\gamma^{\prime}}_{p }\geq\gamma_{p,ts}-\Lambda_{\mathrm{suz}2,ts}\Big{]}\\ &=\mathrm{Pr}\Big{[}\Lambda_{\mathrm{suz}2,ts}<\gamma_{p,ts}- \Lambda_{\mathrm{suz}2,ts}\,\Lambda_{\mathrm{suz}2,ts}\geq\gamma_{p,ts}-{ \gamma^{\prime}}_{p}\Big{]}\\ &=\int_{0}^{\gamma_{p,ts}}\int_{\gamma_{p,ts}-{\gamma^{\prime}}_{p,ts}}^{\gamma_{p,ts}-y}f_{\Lambda_{\mathrm{suz}2,ts}}(x)f_{\Lambda_{\mathrm{suz }2,ts}}(y)\mathrm{d}x\mathrm{d}y\\ &=\int_{0}^{\gamma_{p,ts}}F_{\Lambda_{\mathrm{suz}2,ts}}(\gamma_{p, ts}-y)f_{\Lambda_{\mathrm{suz}2,ts}}(y)\mathrm{d}y-\int_{0}^{\gamma_{p,ts}}F_{\Lambda_{\mathrm{suz}2,ts}}(\gamma_{p,ts}-{\gamma^{\prime}}_{p})f_{ \Lambda_{\mathrm{suz}2,ts}}(y)\mathrm{d}y.\end{split}  \tag{54}\]Adopting the \(L\)-step staircase approximation approach, we can rewrite the probability of case 4 as  
\[ \begin{split}\varphi_{4}&\approx\sum\limits_{i=0}^{L-1} \left\{F_{\Lambda_{\text{sa}2s}}\left(\frac{i+1}{L}\gamma_{p,ts}\right)-F_{ \Lambda_{\text{sa}2s}}\left(\frac{i}{L}\gamma_{p,ts}\right)\right\}\\ &\quad\times F_{\Lambda_{\text{sa}12s},n}\left(\frac{L-i}{L} \gamma_{p,ts}\right)-F_{\Lambda_{\text{sa}12s},n}\left(\gamma_{p,ts}-\gamma^{ \prime}{}_{p}\right)F_{\Lambda_{\text{sa}2s}}(\gamma_{p,ts}).\end{split}  \tag{55}\]  
Substituting Equations (49) and (51) into Equation (55), we can obtain \(\varphi_{4}\). Then, substituting Equations (53) and (55) into Equation (52), we can obtain the OP of \(U_{2}\) for the TS-D2D scheme.
# Low-Profile Dual-Band Reflector Antenna for High-Frequency Applications
## 2 Antenna Configuration and Design
### Composite Feed
Additionally, the shade loss results from the shielding of the lower-band SR at \(f_{h}\). Furthermore, the simulated gain closely matches the calculated gain. Figure 5: Simulated reflection coefficients of composite feed. Figure 6: Simulated (**a**) gain and aperture efficiency and (**b**) normalized far-field radiation patterns at \(f_{h}\).
## 2 Related Works
### Other Related Works
#### 2.3.4 Post-Deployment Tuning Process  
The post-deployment tuning process is the mechanism for adjusting the machines to be deployed. After massive systems are deployed, some settings of each individual machine in the HPC clusters or computer classrooms should be unique. For example, the computer names and security identifiers (SIDs), etc., should be different for each PC with Microsoft Windows. For the computers with the Linux system, the hardware records for the media access control (MAC) address of the NIC and the disk identification (ID) must be modified to fit the machine to be deployed. To fulfill the needs of the post-deployment tuning process, Microsoft provides Sysprep [74]. The open source solution DRBL-Winroll [75] was also developed for the purposes. All of the post-deployment tuning programs are used to help the massive deployment programs finish the last parts, which cannot be done during a deployment. In the future, the turning functions can be added in the massive deployment program so that the massive deployment can be more efficient.  
Table 1 summarizes the existing solutions for system deployment mentioned in this section. Based on the review results, the BT-based solution has the best scalability and can be used for data transmission between many-to-many nodes. Although the BT-based solution consumes the most bandwidth, from the aspect of performance enhancement, such a solution outperformed others. Therefore, in this work BT was chosen as the solution for the massive bare-metal provisioning.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Solution** & **Network Protocol** & **Transmission** & **Bandwidth Consumption** & **Scalability** \\ \hline Unicast & TCP & One to one & Average & Worst \\ Broadcast & UDP & One to many, many & Least & Average \\ Multicast & UDP & One to all & Least & Average \\ BT & TCP, uTP, etc. & Many to many & Most & Best \\ \hline \end{tabular}
\end{table}
Table 1: Comparisons of existing solutions for system deployment. BT: BitTorrent, TCP: Transmission Control Protocol, UDP: User Datagram Protocol, uTP: uTP Micro Transport Protocol.  
The review of past works magnifies the scalability problem existing in current massive deployment solutions based on the broadcast or multicast protocols. Although the BT protocol-based solution can scale up the deployment, the problem of insufficient temporary storage still exists. Traditionally, adding more hardware resources to the deploying machine (e.g., putting enough RAM or adding secondary storage) are the sole solutions. Therefore, a novel scheme that can apply the BT protocol on massive deployment without adding hardware resources is very important to improve the massive deployment performance.
## 2 Full-Scale Converter-Based Wind Turbines  
The full-scale converter configuration, which is shown in Figure 1, is one of the popular configurations for variable speed wind turbines. In this configuration, the grid side converter usually controls the dc-link voltage, and the machine side converter controls the active power [26]. If the small oscillations of the dc-link voltage are neglected, the dynamics of the wind turbine around and above the fundamental frequency can be predicted by modelling of the grid side converter [27]. Therefore, the dynamics of the machine side converter can be neglected in this paper.  
Figure 2 shows the grid side converter of a wind turbine with its control in the dq frame. The grid side converter is a voltage source converter, and its control consists of four main parts: grid synchronization, dc-link voltage controller, current controller and pulse width modulation (PWM) [28]. The grid synchronization loop estimates the phase angle of the point-of-connection (PoC) voltage, and this angle is used to transform the signals between the abc frame and the dq frame [29]. The current references (\(I_{d}\)_ref_ and \(I_{q}\)_ref_) are generated from the dc-link voltage and reactive power controller. The error of these references is compared to the measured grid currents (\(I_{d}\) and \(I_{q}\)) and fed to proportional integral (PI) controllers (\(G_{\rm cc}\)).  
The outputs of the current controllers are fed to the PWM modulator to generate the final switching signals of the insulated-gate bipolar transistors (IGBTs). The time-delay of the PWM and the discrete control can be modelled by using the Pade approximation [30].  
Figure 1: Simplified representation of full-scale converter-based wind turbine connected to the network.
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 5 Estimation and Analysis of Inter-system Biases
### Inter-day Variation of ISB  
After the analysis of the intra-day variation of ISB parameters, we concluded that the ISB parameters show high intra-day stability. To further analyze inter-day variation, the average values of the ISB parameters of all stations for 28 days (150-177 th days of 2018) are shown in Figure 10 and the inter-day time series of the three sets of ISB parameters was obtained, as shown in Figure 11. Figures 10 and 11 clearly show the distribution sequence of three groups of mean ISB parameters of these 8 stations in 28 days, which clearly reflects the following rules:  
(a) It can be seen from the figures that the ISB between BDS and GPS jumps on the 152 th-153 th day of the year of 2018, and the average value of the ISB in the following day is consistent with that in the 153 th day. Then on the 154 th-155 th day of the year, it recovers around the average value of the ISB of before the 153 th day. For more than 20 days after the 155 th day, the ISB sequence fluctuated slightly.  
(b) The ISB between GLONASS and GPS and the ISB between Galileo and GPS had obvious jump on the152 th-153 th, 153 th-154 th, 168 th-169 th, 170 th-171 th, 171 th-172 nd and 176 th-177 th days. Among them, the number of consecutive days of jump is at least 2 days, and the ISB value will not fluctuate significantly after the jump until the next jump.  
(c) The jumping period of the ISB sequence between BDS and GPS is not identical with that of the other two groups of ISB sequences. Except for the two-day jumping, there is no obvious fluctuation in other days. This may be related to the BDS receiver, and the specific reasons need to be further studied.  
(d) In general, the average value of the ISB of single-day solution of each station is not stable in some time period, and the amplitude of jump is irregular (up to 60 ns).  
Figure 9: Fluctuation of inter-system bias (ISB) parameters of G-R, G-E and G-C on a single day (based on the respective mean values).  
(e) The ISB parameters of all stations show similar variation trends and amplitude on the same day. This situation is independent of receiver, which may be ascribed to a satellite reference clock of a different system ([PERSON] et al. 2018).  
Figure 10: Time series of mean variation of ISB parameters of G-R, G-E and G-C.
## 5 Conclusions  
This paper focuses on achieving better swarm consistency and improving the swarm robustness with restricted communications. Based on theoretical analysis and experimental verification, the following conclusions can be drawn: First, graph importance entropy can effectively portray the swarm robustness and can be used as a quantitative evaluation metric for swarm robustness. Second, achieving a balanced distribution of graph  
Figure 9: Motion trajectories and intermediate forms of the swarm in the communication topology mutation experiment. (**a**) Trajectories and intermediate forms of the IDGCNs-based strategy. (**b**) Trajectories and intermediate forms of the DAGNNs-based strategy. (**c**) Trajectories and intermediate forms of the distributed expert strategy.  
importance, i.e., graph importance consistency, effectively improves swarm robustness. Third, compared with the distributed APF strategy and the similar distributed learning strategy based on graph neural networks, the distributed IDGCNs-based strategy has better performance on the effectiveness and speed of achieving swarm consistency. At the same time, the IDGCNs-based strategy has better performance in avoiding swarm splitting, scaling performance, tracking performance, and coping with communication topology mutations. In summary, we propose the experimental results demonstrate that the distributed IDGCNs-based strategy does help improve the swarm robustness and achieve better swarm consistency in restricted communication environments.  
In future work, firstly, we would like to apply the proposed approach to physical robots. For practical use, we hope the flocking strategy can be directly applied to large-scale physical robot swarms by training with only a small number of agents. Secondly, we will consider heterogeneous node types and hierarchical communication network structures. Thirdly, we will focus on the flocking control in the presence of static and dynamic obstacles, addressing the rapid propagation of environmental threats in swarms. Fourthly, we will also consider various observations and their limits in reality, such as raw vision information input with limited field-of-view.  
Conceptualization, C.G. and Z.Z. ([PERSON]); data curation, P.Z. and H.L.; formal analysis, C.G.; investigation, P.Z.; methodology, C.G., P.Z. and Z.Z. ([PERSON]); project administration, L.L.; resources, C.G. and H.L.; software, C.G.; supervision, Z.Z. ([PERSON]), L.L., Z.Z. ([PERSON]) and H.L.; validation, Z.Z. ([PERSON]), Z.Z. ([PERSON]) and H.L.; writing--original draft, C.G.; writing--review and editing, P.Z., Z.Z. ([PERSON]), Z.Z. ([PERSON]) and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61773393, U1913202).  
Not applicable.  
All data generated or analyzed during this study are included in this paper or are available from the corresponding authors on reasonable request.  
The authors declare no conflict of interest.