esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 5 Conclusions and Outlook  
### Conclusions  
To accurately quantify the resilience of the bus network, this paper constructs a research framework for bus network resilience from the perspectives of spatio-temporal dynamics and structural statics of the bus network. The framework can be divided into three parts:  
1. A key indicator selection method based on CRITIC-entropy weighting is proposed. Based on 12 specific indicator items extracted from the monitoring indicators of bus network operations, the weights of each indicator are analyzed, calculated, and ranked from the aspects of entropy value, information redundancy, variability, and conflict. The key indicator that can best reflect the changes in bus route status is determined to be the card-swiping passenger flow.
2. Based on the resilience cycle theory, a resilience analysis model for route resilience is built by considering the resilience cycles of card-swiping passenger flows for each route and station under the impact of unexpected events. After verifying the scientificity of the model, resilience is calculated, analyzed, and compared using data from seven bus routes and 17 stations of Route 58.
3. Based on complex network theory, a resilience analysis model for the bus network is constructed. The resilience values of bus routes are used as the weights of network edges, and the bus routes are treated as edges while the bus stations are treated as nodes to construct an undirected weighted complex network of the bus network. By calculating the node closeness centrality and edge betweenness centrality indicators in the complex network, weighted by edge weights, a quantitative model for quantifying bus network resilience is established. The proposed model is applied to a case study involving 152 bus routes and 1547 bus stations, calculating the resilience of the bus network during a period of heavy rain from 10 to 16 August 2020.  
The research presented in this paper introduces a comprehensive framework for analyzing the resilience of bus networks in the context of unexpected events. This framework provides research methods and a foundation for the analysis and optimization of bus network resilience. Additionally, the research findings of this paper can be utilized by bus network operators and planning departments to evaluate the strengths and weaknesses of bus networks, as well as provide decision-making guidance for adjusting and optimizing bus routes and stations.
## 8 Future Directions
### Advanced Modulation Schemes and Error Correction Codes  
Using advanced modulation schemes like Quadrature Amplitude Modulation (QAM) can boost data rates and make THz-VLC systems more efficient. Employing Low-Density  
Figure 12: The outage probability performance comparison with varying \(\gamma_{th}\) for THz–VLC, RF–VLC, and FSO–VLC.  
Figure 13: Illustration of future directions in THz-VLC systems.  
Parity-Check (LDPC) codes or Turbo codes can significantly reduce the bit error rate (BER). For example, LDPC codes excel at correcting errors in high-data-rate communications, which is crucial for maintaining data integrity in THz-VLC links.
## 3 System Architecture  
We have designed a pixelated shutter based VLC receiver that automatically identifies and selects/isolates signals from multiple LEDs. The key components of the system, as illustrated in Figure 1 (left), include a photodiode (capable of high-speed sampling), a LCD shutter array, a shutter control unit, a computing unit, and a condenser lens for optical focusing. The key idea of proposed design is to allow signals from multiple LED transmitters to be correctly detected and decoded using a single photodetector VLC receiver. With the knowledge and assurance that only one LED signal impinges on the photodiode at each of its sampling instance, the receiver can be operated at the bandwidth matched to the LED's transmission, resulting in high SNR and thus potentially high data rate reception.  
The shutter uses LCDs which act as a digital aperture that allows (disallows) the impinging light beams, to reach the photodiode, based on the input voltage to the shutter. Using this digital aperture as a control the receiver is able to select which of the incoming light beams are to be decoded by the photodiode at each instance of time. The computing unit at the receiver hosts the decoding algorithms and mechanisms to efficiently decode the signal that has been selected. The digital control of the shutter is integrated with the decoding modules in the computing unit, such that there is active feedback on the quality of the received signal. The feedback information includes the received signal-to-noise-ratio (SNR) and a digital identification of the signal using packet header bits. This design enables a seamless  
Figure 1: (**left**) Conceptual diagram of _pixelated shutter_ visible light communication (VLC) receiver. (right) LED Positioning geometrical analysis to ensure focus of only 1 LED per shutter pixel.  
functioning of the selective control of the reception and the decoding in tandem. The selection of the desired signal(s) is a one-time process and needs to be repeated only when there is mobility or during link failures.
## 3 Results and Discussion  
### The Modeled SMLM Image and Nanoclusters  
In recent photo activated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM) experiments, the membrane proteins outside cluster were measured and had a density of \(\sim\)1000 molecules/\(\upmu\)m\({}^{2}\), whereas the density of protein inside cluster proved to be about 10\(\times\) higher [14]. We thus first generated a random distribution of spatial point patterns, at a density of \(\mathrm{D_{out}}~{}=~{}1000\) molecules/\(\upmu\)m\({}^{2}\). Peak analysis method was applied for analyzing domain size. The results indicated a nice agreement with the expectations [16]; the measured average radius of clusters was \(\mathrm{R_{cluster}^{mes}~{}=~{}0}\), which meant that no clustering occurred (Supplementary, Figure S1). We next simulated the clusters with an average radius of \(\mathrm{R_{cluster}^{set}~{}=~{}50 nm}\) and a protein density inside cluster of \(\mathrm{D_{in}}~{}=~{}10,000\) molecules/\(\upmu\)m\({}^{2}\) (as seen in the background of Figure S1, in Figure 1a). The disk-shaped domains were simulated as clusters, as shown in Figure 1b. The red simulated image in Figure 1c was generated by Gaussian convolution of the points in Figure 1a with a spatial resolution of 30 nm. The [PERSON]'s H plot was presented in Figure 1d.  
It is clear that the spatial point pattern displays a high degree of clustering. The value of [PERSON]mathrm{L(r)}~{}-~{}\mathrm{r}\) above zero indicated that more points were being encircled by concentric circle radius between \(0~{}\mathrm{nm}\) and \(180~{}\mathrm{nm}\) for each point than the expectation of random distribution. The average radius of clusters was given by the r value corresponding to the maximum of \(\mathrm{L(r)}~{}-~{}\mathrm{r}\), \(\mathrm{R}^{\mathrm{mes}}_{\mathrm{cluster}}~{}=~{}63\mathrm{nm}\), which has \(+26\%\) error of the actual radius \(\mathrm{R}^{\mathrm{set}}_{\mathrm{cluster}}~{}=~{}50\mathrm{nm}\). For comparison, we used the well-known software ImageJ to process the modeled SMLM image of Figure 1b (Supplementary, Figure S2). By measuring the intensity distributions of the background (the simulated image except the clusters), we found less than \(10\%\) of the points had an intensity higher than \(2\times\) the average intensity. The threshold was thus chosen as \(2\times\) the average intensity of the background. The generated binary map of clusters is in Figure S2b. By counting the total number of white pixels, we obtained radius \(\mathrm{R}^{\mathrm{mes}}_{\mathrm{cluster}}~{}=~{}65\mathrm{nm}\). The peak analysis method gave comparable results as ImageJ analysis, while it has the advantage of direct prediction of clusters. To further evaluate the accuracy of quantitative cluster analysis, we applied different parameters to the simulated images. The effects of cluster separation, cluster diameter and protein density inside/outside cluster on the accuracy of peak analysis will be discussed.
# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 4 Experimental Results
### Datasets
#### 4.1.2 PubLayNet  
The PubLayNet [44] dataset is the largest public and open source dataset for document layout analysis. The automatic matching of the XML representations of PDF articles with their content helps create such a large dataset. These articles are from the medical domain. The dataset has over 360,000 document scanned images [44]. The dataset annotates the most popular and typical graphical objects, such as text, titles, lists, tables, and figures. The dataset goes a long way in the training of real-world object detectors. The PubLayNet dataset is frequently used as a corpus to train detectors to detect document layouts, especially in scientific articles. Table 4 shows the statistics of the dataset's training, validation, and test split.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Sets** & **Text** & **Title** & **Lists** & **Tables** & **Figures** \\ \hline Training & 2,376,702 & 633,359 & 81,850 & 103,057 & 116,692 \\ Validation & 93,528 & 19,908 & 4561 & 4905 & 4913 \\ Test & 95,780 & 20,340 & 5156 & 5166 & 5333 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of training, validation, and testing sets in PubLayNet [44] dataset.  
Figure 7: The detection pipeline for the Sparse R-CNN detection head. Sparse R-CNN proposes a paradigm shift by utilizing sparse and learnable proposal boxes and features instead of region proposal networks (RPNs).
## 2 MSFGWO Algorithm Model  
The MSFGWO method integrates the GWO algorithm with a hybrid approach that incorporates the perturbation mechanism of the Variable Neighborhood Search (VNS) algorithm, the GA mutation strategy, and the Entropy Weight Method (EWM). This integration addresses the issues of low population diversity and susceptibility to local optima in the GWO algorithm, as well as mitigating the frequent satellite switching problem commonly encountered with LEO satellites. The MSFGWO method employs the Doppler Geometric Dilution of Precision (DGDOP) value as the fitness function for optimization.
## 1 Introduction  
Certain specialized organic materials are very much of interest because of their many promising applications in various devices. Ultra-thin organic films such as self-assembled monolayers (SAMs) have been studied extensively owing to their potential to modify the surface electronic properties of metallic and semiconductor materials [1; 2; 3]. Recently, organic molecules or ligands have been used for Si surface passivation, improving minority carrier lifetime (\(\tau_{\text{eff}}\)), and reducing surface recombination velocity (SRV) [4; 5].  
High quality passivation of Si is especially important for solar cells, which require minimization of the SRV of photo-generated carriers. Conventionally, dielectric thin films such as silicon dioxide (SiO\({}_{2}\)) and silicon nitride (SiN\({}_{\text{X}}\)) are commonly used to provide effective passivation. However, the deposition of these dielectric passivation layers typically requires high vacuum and/or high temperature processes, which add cost to the devices. The use of organic coatings will reduce cost; in addition, the rising interest in nanostructured devices that require ultra-thin and highly conformal passivation layers further motivates the organic molecular passivation approach regardless of its relatively low stability compared with conventional dielectric passivation approaches [6].  
_Appl. Sci._**2019**, \(9\), 3645
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control
### Baseline  
As the baseline for comparison, we set up a system similar to that developed in a previous study [13], using a commercial universal PI controller (LB1005). Figure 2 presents the block diagram of the system. The output of a digital-to-analog converter (DAC) (Texas Instruments DAC8734, Dallas, TX, USA) was utilized as the set-point voltage. In addition, the helical resonator in the baseline method was not connected to a vacuum chamber. These conditions are different from those of the previous work. The heat capacity of a general vacuum chamber is significantly greater than that of the helical resonator. A substance with a large heat capacity is less sensitive to changes in the ambient temperature compared to a substance with a small heat capacity. Moreover, the substance with large heat capacity is also less sensitive to physical impacts than that with a small heat capacity. Therefore, the helical resonator mounted on a vacuum chamber is less sensitive to the changes in the ambient temperature of the helical resonator and the minute vibration in the optical table than the unmounted one. In this system, the fluctuation in the amplitude of the RF signal  
Figure 1: An experimental structure amplifying the RF signal applied to ion traps using a helical resonator.  
was 1.806%, which is larger than that in [13]. We will demonstrate in the following sections how a more advanced control architecture can reduce the amplitude fluctuation.
# A Large Area Pixelated Silicon Array Detector for Independent Transit In Vivo Dosimetry
Despite this, one of the main motives for recommending an EPID dosimetry system is that it is often already installed on modern linacs [4], making it appear to be a convenient option. The complex commissioning and data correction process could be significantly reduced if vendors offered a water equivalent EPID, but no such system is commercially available. A major limitation to the production of a water equivalent EPID is that attempting to reduce the use of high \(Z\) material results in a reduced sensitivity of the imager [12]. Two previous studies have proposed dual detector systems [13; 14] where an additional detector, optimised for dosimetry, would be mechanically attached on top of the EPID. Both identified that accurate dosimetry could be performed in these conditions without the EPID negatively impacting the response of the detector, while the additional detector had minimal impact on the quality of EPID images. The first study adopted an array of ionization chambers [13], while the second used a pixelated silicon detector [14]. The use of the pixelated silicon was shown to be capable of performing accurate dosimetry, that could be directly compared with the treatment planning system (TPS) for a plan delivered to a heterogeneous phantom. It was also capable of correctly identifying a 5 mm shift in target positioning and a 5.2% overdose. While this system required no field size or scatter corrections, the detector used did have a high dose rate dependence, a limited sensitive area with a limited sensitivity from each diode and a poor spatial resolution. To make this dual detector system advantageous over the EPID, these issues needed to be addressed. The development of a large area, high sensitivity and spatial resolution, pixelated silicon detector array, with minimal dose rate dependence would allow advancement of transit in vivo dosimetry systems, that avoids the large commissioning work required for EPID dosimetry and allow for co-registration of patient imaging and dose distribution.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
###### Abstract  
Multi-access Edge Computing (MEC) will be a technology pillar of forthcoming 5G networks. Nonetheless, there is a great interest in also deploying MEC solutions in current 4G infrastructures. MEC enables data processing in proximity to end users. Thus, latency can be minimized, high data rates locally achieved, and real-time information about radio link status or consumer geographical position exploited to develop high-value services. To consolidate network elements and edge applications on the same virtualization infrastructure, network operators aim to combine MEC with Network Function Virtualization (NFV). However, MEC in NFV integration is not fully established yet: in fact, various architectural issues are currently open, even at standardization level. This paper describes a novel MEC in an NFV system which successfully combines, at management level, MEC functional blocks with an NFV Orchestrator, and can neutrally support any \"over the top\" Mobile Edge application with minimal integration effort. A specific ME app combined with an end-user app for the provision of immersive video services is presented. To provide low latency, CPU-intensive services to end users, the proposed architecture exploits High-Performance Computing resources embedded in the edge infrastructure. Experimental results showing the effectiveness of the proposed architecture are reported and discussed.  
Keywords:Multi-access Edge Computing; Network Function Virtualization; Orchestration; video processing; immersive video; virtualization +
Footnote †: journal: Computer Science
## 5 Simulation Results and Analysis
### Deformation and Stress Analysis
#### 5.2.2 Equivalent Stress  
The equivalent stress of the overall structure of the regulator with 50% opening is compared in Figure 5b. It shows that the stress in the regulator sleeve, valve bore, and export valve seat wall is relatively large. The gas regulator under different degrees of opening and the equivalent stress on each part of the statistics are drawn into the relevant curve shown in Figure 7 to intuitively understand the regulator of the main stress parts of the stress trends.  
Figure 4: The temperature and heat flux curves of the valve spool for the gas pressure regulator with different openings.  
Figure 5: Total deformation and stress of the gas pressure regulator at the opening 50%. (**a**) Total deformation, (**b**) Equivalent stress.  
Figure 6: Variation of deformation of the gas pressure regulator under different openings.  
Figure 7 shows that when the valve opening is small, the regulator is subjected to large stresses at the sleeve, valve opening, and outlet seat wall. At the same time, as the valve opening increases, the stress on each part decreases significantly. When the opening is 20%, the working conditions of the regulator are more complex, and the sleeve and the outlet valve seat wall stress have a certain rise. The stress at the valve bore has not shown a significant decline. When the opening is greater than 50%, the stress on the regulator parts decreases significantly and is maintained at 150 MPa. Meanwhile, the regulator's operating conditions have improved.  
Considering the above factors, the thermo-hydro-mechanical coupling model of the gas pressure regulator with a 20% opening is selected as the optimization object in this paper.
## 4 Results
### Soils Stabilized with Bottom Ash and Lime
#### 4.1.2 Variation of the Optimum Humidity and Maximum Dry Density  
In the case of soil S1, the addition of ash decreases the optimum humidity. This is because the particle size of the bottom ash has a maximum size of 2 mm, and soil S1 (100% clay) has a clearly fine Particle size distribution.  
In the case of soil S2, the variation in the particle size is much smaller. On increasing the ash content, the percentage of fine particles less than 2 mm also increases, so the optimum humidity increases. Soil S3 has a similar behavior to soil S2 in terms of the variation in the humidity with the addition of ash.  
Soils S2 and S3 with added lime undergo an increase in optimum humidity due to the great fineness of lime. Soil S1 undergoes a different change when no ash is added. Given that it is made up of clay with a particle size equal to or less than the lime, the optimal humidity decreases as the specific surface decreases. However, if ash and lime are added, what happens is that the specific surface increases, as does the optimum humidity. A binder material with a very small particle size has a lot of specific surface area, and therefore, the optimal humidity increases. Even so, due to their characteristics, bottom-ash and lime soils increase the load-bearing capacity and decrease the maximum density of the Proctor test of the original soil.  
As for the density, the same occurs in the case of all three soils and with the addition of ash and/or lime; the density decreases in all the cases, given that the density of the ash and of the lime is less than that of the starting soils (Table 3).  
Figure 4: Particle size analysis for soils.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Maximum Density (KN/m3)**} & \multicolumn{4}{c}{**Optimum Wet**} \\ \cline{2-10}  & & \multicolumn{4}{c}{**Lime Content**} & & \multicolumn{4}{c}{**Lime Content**} \\ \hline
**Soil** & **Bottom Ash** & **0\%** & **1\%** & **2\%** & **3\%** & **Bottom Ash** & **0\%** & **1\%** & **2\%** & **3\%** \\ \hline  & 0\% & 21.12 & 20.70 & 20.48 & 20.10 & 0\% & 11.80\% & 9.60\% & 10.30\% & 10.90\% \\ S1 (100\% & 15\% & 19.60 & 19.30 & 19.22 & - & 15\% & 9.25\% & 10.50\% & 10.25\% & - \\ clay) & 20\% & 19.20 & 19.20 & 18.75 & 18.60 & 20\% & 9.20\% & 9.30\% & 10.60\% & 10.70\% \\  & 25\% & 19.20 & 18.62 & 18.43 & 18.37 & 25\% & 9.80\% & 10.50\% & 11.10\% & 10.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum densities and optimum wet. Soils Stabilized with Bottom Ash and lime.  
The results of the present Proctor test are similar to [PERSON]'s results ([PERSON], 2017) on how the addition of lime increases the optimal water content and reduces the maximum density obtained.
# Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse
###### Abstract  
An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator.  
airborne lidar simulator; radiative transfer; total propagated uncertainty +
Footnote †: journal: Applied Physics Letters