esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real Case of Implementation of the Future 5G City
## 2 5G Network Architecture
### 5G Physical Infrastructure
#### 2.1.3 Roll-Out Phase--SA Architecture  
During the last part of the roll-out phase, the SA 5G NR architecture will be deployed. This implies full user and control plane capability for 5G NR, utilizing the new 5G CN architecture. In particular, _Option 2_ envisaged by 3 GPP [23], will be considered in the network deployment where 5G NR devices are directly connected to the new 5G CN. This choice implies simple implementation and independence on 4G network deployments that allow for higher freedom to the 5G operators. At the same time, this solution is challenging because it requires that the 5G end-to-end network is almost completely defined and developed before the end of the project, and substantial investments are needed to provide suitable territory coverage. In this phase, it is interesting to validate KPIs related to mobility and handover. In particular,  
* _mobility interruption time_: maximum no activity time during handover (the terminal cannot exchange data with the base station). Expected value: 1 s.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Partition of Unity Enrichment  
Partition of unity enrichment, in general, allows the incorporation of known features of the solution in the numerical approximation in the form of enrichment functions. If finite elements are used as the basis for the numerical approximation, then partition of unity enrichment can be realized as follows:  
\[\mathbf{u}\left(\mathbf{x}\right)=\underbrace{\sum_{\forall I}N_{I}\left( \mathbf{x}\right)\mathbf{u}_{I}}_{\text{FE approximation}}+\underbrace{\sum_{ \forall I}N_{I}^{*}\left(\mathbf{x}\right)\Phi\left(\mathbf{x}\right) \mathbf{b}_{I}}_{\text{enriched part}}, \tag{14}\]  
where \(N_{I}\left(\mathbf{x}\right)\) are the FE interpolation functions, \(\mathbf{u}_{I}\) are FE degrees of freedom (dofs), \(N_{I}^{*}\left(\mathbf{x}\right)\) is a basis of functions that form a partition of unity, \(\Phi\left(\mathbf{x}\right)\) is the enrichment function and \(\mathbf{b}_{I}\) are the enriched degrees of freedom.  
Most commonly, finite element shape functions are employed to form the partition of unity basis:  
\[N_{I}^{*}\left(\mathbf{x}\right)\equiv N_{I}\left(\mathbf{x}\right) \tag{15}\]  
Alternative PU bases have can also be found in the literature Zhang et al. [94], Griebel and Schweitzer [95], Hong and Lee [96], aiming mostly at improving specific aspects of the method, such as conditioning of the resulting system matrices.
## 2 Materials and Methods  
### Data Sources  
In this study, we used video data from CCTV cameras deployed over two unsignalized crosswalks for the recording of street crime incidents in Osan city, Republic of Korea; (1) Segyo complex #9 back gate #2 (spot A); and (2) Noriter daycare #2 (spot B). Figure 1 shows the deployed camera views at oblique angles from above the road. The widths of both crosswalks are 15 m, and speed limits on surrounding roads are 30 km/h. Spot A is near a high school but is not within a designated school zone, whereas spot B is located within a school zone. Thus, in spot B, road safety features are deployed to ensure the safe movement of children such as red urethane pavement to attract drivers' attention, and a fence separating the road and the sidewalk (see Figure 1b). Moreover, drivers who have accidents or break laws within these school zone areas receive heavy penalties, such as the doubling of fines.  
All videos frames were handled locally on a server we deployed in the Osan Smart City Integrated Operations Center. Since these areas are located near schools and residential complexes, the \"floating population\" passing through these areas is highest during commuting hours. Thus, we used video footage recorded between 8 am and 9 am on weekdays. We extracted only video clips containing scenes where at least one pedestrian and one car were simultaneously in camera view. As a result, we processed 429 and 359 video clips of potential vehicle-pedestrian interaction events in spots A and B, respectively. Frame sizes of the obtained video clips are \(1280\times 720\) pixels at both spots, and had been recorded at 15 and 11 fps (frames-per-second), respectively. Due to privacy issues, we viewed the processed trajectory data only after removing the original videos. Figure 2 represents spots A and B with the roads, sidewalks, and crosswalks from an overhead perspective, as well as illustrating a sample of object trajectories which resulted from our processing. Blue and green lines indicate pedestrians and vehicles, respectively.
## Appendix A Derivation of the Hamiltonian Formalism
### Scale Analysis  
The expression of \(\mathcal{L}_{r}\) derived in the previous section includes the Legendre polynomials up to order \(k=2\), based on the hypothesis that \(\frac{\rho}{\bar{r}}<<1\). In fact, when examining the problem under the zonal harmonic perturbation, values of interest for \(\bar{r}\) are those corresponding to LEO altitudes \(h\in[400,700]\) km, while values of the relative distances are typically in the range \(\rho\in[1,1000]\) km. Consequently, for \(k=2\) the truncation error is always lower than \(2.2\cdot 10^{-2}\), an acceptable value which substantiates the approximation. Equivalent results can be obtained considering low lunar orbits.  
A similar analysis is extended to the zonal harmonic coefficients to identify the ones to include in the model for providing adequately accurate results. It shall be observed that \(\left(\frac{\rho}{\bar{r}}\right)^{2}\in\left[2.0\cdot 10^{-8},2.2\cdot 10^{-2}\right]\); thus, all the combinations of zonal harmonic coefficients in this range shall be taken into account in the analysis. According to the JGM-2 Earth gravity model [35], the following coefficients were considered for the analysis in Sections 4.1, 4.2, and 4.4: \(J_{2}-J_{14}\) and \(J_{23}\).  
Similarly for the Moon, \(\left(\frac{\rho}{\bar{r}}\right)^{2}\in\left[3.3\cdot 10^{-7},3.3\cdot 10^{-1}\right]\), so all the combinations of zonal harmonic coefficients in this range shall be considered and, according to the LP-165 Moon gravity model [36], the following coefficients were included in the analysis in Section 4.3: \(J_{2}-J_{4}\), \(J_{6}-J_{12}\), \(J_{14}\), \(J_{16}-J_{18}\), \(J_{20}\), \(J_{27}-J_{29}\), \(J_{31}-J_{32}\).
## 2 Results
### uEVs Isolation and Characterization  
Gel electrophoresis of uEVs from NC subjects and DN patients showed that the coprecipitation of albumin with uEVs by ultracentrifugation could be removed by sucrose gradient density centrifugation (Figure 1a). Transmission electron microscopy (TEM) illustrated the morphology of the uEVs (Figure 1b). Nanoparticle Tracking Analysis (NTA) revealed a varied size distribution of uEVs, with a peak diameter of less than 100 nm (Figure 1c). Western blot analysis confirmed uEVs enrichment in the final fraction, based on the expression of extracellular vesicle markers Alix, TSG101, and CD63 (Figure 1d). The NTA curve represents the concentrations of components of various particle sizes in the sample. According to the particle size distribution and concentration changes in NTA, the particle size distribution of uEVs purified by sucrose gradient density centrifugation was simpler, and the particle size was smaller than that of uEVs purified by ultracentrifugation. This observation aligns with the TEM findings. We used Alix, TSG101, and CD63 as uEVs markers based on current research on uEVs [6]. All these markers were detected in uEVs isolated by both methods, with high expression (Figure 1c). Only TSG101 was detected in the urine supernatant, indicating a degree of uEVs loss during centrifugation. This also suggests that there is no specific marker for uEVs, necessitating the use of various methods to characterize them. Our results demonstrate the successful isolation of uEVs from urine through sucrose gradient density centrifugation.
## 2 Materials and Methods
### The Basic Structure and Principle of ED Net
#### 2.2.2. GELU Activation Function  
In the modeling process of neural networks, an important property of the model is nonlinearity, and for the model's generalization ability, it is necessary to include a stochastic regular. The input to the model is determined by both the stochastic regularity and the nonlinear activation. In the field of activation functions, these common activation functions have their drawbacks. Sigmoid is easily saturated. Exponential Linear Unit (EIU) and Rectified Linear Unit (ReLU) lack the stochastic factor. However, Gaussian Error Linear Unit (GELU) introduces the idea of stochastic regularity in activation, which is intuitively more natural. Literature [25] confirms that GELU is a high-performance neural network activation function that incorporates stochastic regularity in activation, a probabilistic description of neuron input. It is expressed as:  
\[\text{GELU}(x)=x\phi(x) \tag{1}\]  
\(x\) represents the activated input value of the current neuron, whereas \(\phi(x)\) represents the cumulative distribution of the Gaussian normal distribution, or the definite integral of the Gaussian distribution in the interval (\(-\infty\), \(x\)]. This is more intuitively consistent with natural understanding and helps to increase the model's nonlinear capability. The conventional ReLU activation function is designed to be replaced by GELU in the ED Net model proposed in this paper to improve recognition accuracy.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 7 Discussion  
The stochastic feature of the renewable energy items, i.e., wind and solar energies, introduces oscillations in the MG frequency. Under such circumstances, LFC plays a great role in the MG system because of its duty to preserve frequency in its scheduled value, in normal conditions, and in case of a very slight deviation of the load. However, the control of the hybrid power system operations in uncertain environments is a more complex task, without using a sufficient analytical model. This makes it necessary to apply advanced control methodologies for the realization of the system stability requirements.  
Having knowledge of all the aforesaid, a new model-based FOSMC controller is developed to ameliorate the LFC performance of an MG with high penetration of REs. To ascertain the superiority of the suggested model-based controller, two scenarios (Case 1 and Case 2) corresponding to the fluctuation of REs and their severe parametric variations (robustness analysis) are applied. From the experimental results of Case 1 and Case 2, it is reflected that in spite of having the high system complexity with the fluctuation nature of the RESs, all the designed LFC controllers can stabilize the grid frequency effectively. In comparison, the FOSMC outperforms the MPC and conventional SMC in terms of the settling time and overshoot. The peak overshoot and undershoot of frequency deviation, using the different controllers, for the two concerned scenarios are compared, as illustrated in Figure 9. Besides, the percentage of improvement of the suggested model-based controller over the MPC and conventional SMC is depicted in Figure 10. The bar graphs of Figures 9 and 10 prove the dominance of the FOSMC controller over both of the other controllers. By comparing the results of Case 1 and Case 2, it is shown that the performance of the designed LFC controllers is deteriorated when the parametric  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Variation Range** \\ \hline \(R\) & \(-20\%\) \\ \hline \(D\) & \(+35\%\) \\ \hline \(H\) & \(-10\%\) \\ \hline \(T_{inv}\) & \(-25\%\) \\ \hline \(T_{g}\) & \(+25\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uncertain parameters of the test MG.  
Figure 8: Curves of the MG response according to Case 2.  
variation is applied to the test MG. However, a higher level of stability is achieved by the proposed method than the other compared controllers.
# Real-Time Kinematics-Based Self-Collision Avoidance Algorithm for Dual-Arm Robots
## 4 Simulation and Application of the Algorithm
### Simulations
(**a.2**) Left arm collides with right arm; the repulsive velocity acts on link 5 on the right arm and on the end-effector on the right arm. (**b.1**) Left arm collides with right arm; the repulsive velocity acts on link 6 on the left arm and on the end-effector on the right arm. (**b.2**) Left arm collides with right arm; the repulsive velocity acts on link 6 on the right arm and on the end-effector on the left arm. (**b.3**) Left arm collides with right arm; the repulsive velocity acts on link 6 on the right arm and on link 6 on the left arm. The repulsive vector ensures that the trajectory of the robot remains collision free. Compared with the traditional artificial potential field method, the method of this paper improved the problems of the traditional method due as we considered the action of the end-effector. Meanwhile, the simulation results of the different tasks described above prove the effectiveness of this real-time self-collision algorithm based on the kinematic model of a robot. The algorithm uses a simple collision model and the distance calculation method to decrease the computational costs of the algorithm. Figure 9: Process of avoiding self-collision in the dual-arm system using traditional artificial potential field method. Two arms of the dual-arm had a common target. (**a**) The initial state and final state of the robot; (**b**) Trajectory of the end-effector of each arm.
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 2 Data Description
### GNSS Observations  
The original code and phase observations from the GPS and BDS satellites are collected by the antenna and receiver, which are parts of the monitoring system, as shown in Figure 1. To be specific, the Leica AR20 multi-GNSS antenna (Leica Geosystems AG, Heerbrugg, Switzerland) is placed on the roof without being blocked by the surroundings, and the receiver is the Hexagon FangZhou receiver (Hexagon Geosystems (Qingdao) Co., Ltd., Qingdao, China), which can decode signals from both GPS and BDS satellites. The GNSS observation will be transferred to the IPC, in which the raw data will be stored and processed through the NRT processing mode for water vapor monitoring.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 3 Methodology
### Hypotheses  
Building on the exploration of AR technology's impact on participatory exhibition viewing, the following hypotheses are proposed:  
**Hypotheses 1 (H1).**_The distances and durations covered in the exhibition-viewing and message modes showed significant differences._  
**Hypotheses 2 (H2).**_The movement paths of visitors will show significant differences between the exhibition-viewing mode and message mode._  
These hypotheses are designed to explore both the quantitative and qualitative impacts of AR technology on visitor engagement and navigation within the exhibition environment, providing a comprehensive framework for assessing the technology's effectiveness in facilitating a more interactive and immersive exhibition experience.
# Analysis of Grinding Flow Field under Minimum Quantity Lubrication Condition
## 2 Flow Field Analysis of the Grinding Wheel
### Two-Phase Flow Model  
The Eulerian and Lagrangian methods are commonly used in the field of fluid dynamics research to describe the motion of the flow field. Based on the Eulerian and Lagrangian methods, the Eulerian-Eulerian and Eulerian-Lagrangian methods were developed in the two-phase flow simulation. The Eulerian-Eulerian method is studied in the Eulerian coordinate system. In the Eulerian-Eulerian simulation, both the fluid and the particles are considered as continuous phases, and both of them can interpenetrate and interact with each other. In the Eulerian-Lagrangian method, fluid phases are considered as continuous phases, such as gas and liquid phases, and particles in the phases are treated as discrete phases, such as liquid droplets in a spray. In this study, the main Eulerian-Lagrangian model in simulation is the DPM (Discrete Phase Model) [21].  
MQL grinding technology is a lubrication method that mixes grinding fluid with compressed air, atomizes it into micron-sized droplets, and then enters the grinding area for cooling and lubrication purposes. The amount of grinding fluid in micro lubrication is much less than the amount of air. In the fluid calculation simulation, when the volume rate of liquid and gas is less than 10%, the discrete phase model should be used. Therefore, the discrete phase model is more consistent with the simulation of the coupled field of MQL jet and grinding wheel flow field.
## 1 Introduction
As a result, a shift in the spectral line or increase or decrease in spectral intensity can be expected, compared to the LIBS signal in a static state with no gas flow. In general, the LIBS analysis method does not require a vacuum environment, so it has the advantage of being suitable for remote measurement in extreme environments outside the laboratory, such as deep sea or outer space [18; 19]. Under conditions of external gas flow, there is a significantly greater influence of the surrounding atmospheric flow compared to the plasma signal of laser ablation under the same conditions. Therefore, it is necessary to adjust the results for the plasma signal induced by laser irradiation according to the information of the surrounding gas flow, which affects the flow characteristics. Examples of existing methods for obtaining gas flow characteristics include in-pipe flow measurement methods, using a Pitot tube, gas sensors [20; 21], ultrasonic flow meters [22], optical fiber cantilevers [23; 24], and surface plasmon resonance spectroscopy [25]. However, these methods may be difficult to apply in the absence of controlled environmental conditions, and depending on specific objects, the scale, and interference from other analysis methods. Flow rate measurement using LIBS represents another new method. In this study, with a gas flow around the plasma, the pattern of signal change in the LIBS spectrum was monitored to confirm the gas flow rate. Based on plasma sensitively reacting to the type of surrounding gas and the flow, the correlation between changes in flow rate and the signal of plasma observed by LIBS was explored. Theoretically, the effect of plasma deformation was predicted, and the signal of the plasma was measured and analyzed. Based on the results, the effects of the gas flow rate on the LIBS signal were confirmed both qualitatively and quantitatively.
## 2 Materials and Methods
### Partial Least Squares  
_Selection of the Wavenumber Interval_  
The PLS algorithm relies on the entire NIR spectrum to estimate the sample composition, being based on latent variables (LVs) [11]. The iPLS and siPLS algorithms allow an improvement in PLS performance and the elimination of inappropriate spectral variables.  
The iPLS models were constructed in 20 spectral intervals of a similar width, generating a graphical representation indicating the optimum number of LV and RMSECV values in each interval. The selected sub-intervals presented the lowest RMSECV values. The siPLS models were developed based on the spectral set divided into 20 intervals and combinations of 3 intervals. The combined sub-intervals defined by the lowest RMSECV values were selected [13]. The performance of the final PLS model was evaluated based on the RMSECV and the correlation coefficient (R), defined by  
\[\text{RMSECV}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n-1}} \tag{5}\]  
where \(n\) is the number of samples in the test set validation, \(y_{i}\) corresponds to the reference measurement for the test set of sample \(i\), and \(\hat{y}_{i}\) represents the estimated values for test sample \(i\). The performance of the final iPLS and siPLS models was evaluated using the root-mean-square error of prediction (RMSEP) and the coefficient of determination (R\({}^{2}\)). RMSEP is defined as  
\[\text{RMSEP}=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}} \tag{6}\]  
The correlation coefficient (R) for calibration and test set evaluation is related to the predicted and measured data (Equation (7)). The parameter \(\tilde{y}\) is the average of the reference data for all samples.  
\[R=\sqrt{1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}}} \tag{7}\]
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 5 Experiment
### Case Study 2: Effect under Noisy Environment
When the task was complex and could not be accurately described by the model-driven method, the data-driven deep learning method could make up for the difficulties in the model, learn deep essential features from a large number of samples, and have strong fitting capabilities to meet the needs of tasks such as signal separation. Therefore, the separation  
Figure 7: Loss under different SNR. effect of Stacked-LSTM, Wave-U-Net, TasNet, and ConvTasNet was better than that of ICA and NMF. Among them, LSTM was used as a block in the TasNet network, and a large number of parameters increased the calculation cost. In order to ensure a reasonable network model and calculation speed, the number of blocks needed to be controlled, and the modeling accuracy of the timing signal was not enough, so the effect was poor. The Wave-U-Net network processes the time series context by repeatedly performing downsampling and convolution of the feature map, which had good results, but there were fluctuations. In the ConvTasNet method, hole convolution was used in the separation module to reduce the number of parameters. The same network model size could increase the number of repetitions of the block, but the sequence receiving window was small, and sequence-level modeling could not be performed. In Stacked-LSTM, organizing LSTM in a deep structure was a simple and effective method for modeling extremely long sequences. The attention mechanism with SE module was used to capture global information, and Union-LSTM was used instead of 1D-CNN, which is separated in time domain. The mid-sequence-level modeling network had the best accuracy and stability among the three deep learning methods.