esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The existing benefit/cost analysis model among investment priorities determining measures in disaster prevention programs is inappropriate as a model for natural disaster hazard zones of different types such as collapse hazard zones which are not directly related with the flooded area or flooding frequency. This paper aims at filling this gap in the literature and presents a methodology for the benefit/cost analysis of collapse hazard zones. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Sources and Data Types** & **Actual or Potential Benefits** & **Benefit/Cost Results** \\ \hline \multirow{4}{*}{Ex-ante} & [PERSON] [1]: Preliminary feasibility & \multirow{2}{*}{Reduction of socioeconomic and indirect impacts} & \multirow{2}{*}{3.8} \\  & assessment of reclaimed land system in & & \\  & response to floods, Piura, Peru & & \\ \cline{1-1} \cline{2-4}  & [PERSON] [3]: Theoretical study of integrated & \multirow{2}{*}{Reduction of direct and indirect economic impact} & \multirow{2}{*}{2.5} \\  & water management and floods protection & & \\  & plan, Semarang, Indonesia & & \\ \cline{1-1} \cline{2-4}  & FEMA [4]: Disaster mitigation project effects & \multirow{2}{*}{Reduction of direct and indirect economic impact} & \multirow{2}{*}{4.0} \\ \cline{1-1} \cline{2-4}  & [PERSON] [5]: Four River Restoration Project & \multirow{2}{*}{Reduction of direct and indirect economic impact} & \multirow{2}{*}{More than 1.0} \\ \hline \multirow{4}{*}{Ex-post} & [PERSON] [6]: Post-evaluation methods of flood control has been executed in China for the past 40 years of the 20 th century & \multirow{2}{*}{Unclear, direct damage reduction} & \multirow{2}{*}{4.0} \\ \cline{1-1} \cline{2-4}  & IFRC [7]: Post-evaluation of “Red Cross & & \\ \cline{1-1} \cline{2-4}  & Manugree Planting Project” in order to protect coastal residents from typhoons and storms, Philippines & & \\ \cline{1-1} \cline{2-4}  & Venton and Venton [8]: Post-evaluation of combined disaster prevention and preparedness program, Bihar and Andhra Pradesh, India & & \\ \cline{1-1} \cline{2-4}  & NEMA [9]: Investment in the field of disaster prevention, Korea & & \\ \cline{1-1} \cline{2-4}  & NEMA [9]: Investment in the field of disaster prevention, Korea & & \\ \cline{1-1} \cline{2-4}  & reduction of direct and indirect economic impact & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benefit/cost analysis of disaster prevention projects.
## 5 Simulation
### Scenarios with Static Obstacles  
Two scenarios with static obstacles are conducted, focusing on evaluating the performance of the planning strategy. All the scenario data is collected by an autonomous vehicle  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Model/Value** \\ \hline Rapid Autopilot Controller & NI CompactRIO 9047 \\ Real-time Computer & NI PXle 8840 \\ Host Computer & HP Pavilion (I7-8550U, 16G RAM) \\ Scenario Generator & Lenovo Legion (I7-11800H, 16G RAM) \\ Vehicle length & 2.3 m \\ Vehicle width & 1.2 m \\ Vehicle wheelbase & 1.5 m \\ Planning resolution & 0.25 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 14: Hardware-in-loop simulation platform. (**a**) hardware-in-loop simulation system; (**b**) system architecture.  
Figure 13: Rule-based speed profile generation.  
platform and reproduced on our HIL simulation platform. The ego vehicle travels at a constant speed on city roads, with obstacles in front of and around it. The simulation results are shown in Figure 15, all the paths can successfully guide vehicles to avoid obstacles. In more detail. The average results of 50 simulations under the same scenario, including Plan A, Plan B, and a classic Lattice planning strategy [4], are shown in Table 2.  
The path shapes of Plan A and Lattice is similar. Due to the fact that Plan A is an approximate Lattice sampling process, in which the path candidate generation and the optimal path selection is similar to those of the control group [4]. To this end, the planning accuracy can be aligned with Lattice, and the computational time has been reduced by 8 ms (22% real-time performance improvement) thanks to the introduction of a planning area reduction process in Step 1.  
The path shape of Plan B is smoother and has less curvature variation than Plan A and Lattice. In particular, in Figure 15a, the path of Plan B is more in line with the human driver's driving habits, with a gentle yaw to avoid obstacles and then a gentle return to the map path [24]. In principle, Plan B has fewer nodes than Plan A, it is more suitable for planning comfortable smooth paths in scenarios with fewer obstacles [25]. For this reason, it has the lowest planning success rate (92.5%) of the three planning strategies, but has the best real-time performance, which takes 19 ms to run once.
_Appl. Sci._**2015**, \(5\), 320-343; doi:10.3390/app5030320  
[1]_OPENYCOESS_  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
[2]**Synthesis of Cascadable DDCC-Based Universal Filter Using NAM**  
[3]**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 3 and [PERSON] 1**  
[4] Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan; E-Mails: [EMAIL_ADDRESS] (H.-D.T.); [EMAIL_ADDRESS] (Q.-M.N.)  
[5] Department of Physics, University of Dalat, Dalat City, Vietnam  
[6] Department of Electronic Engineering, Kun Shan University, Tainan 710, Taiwan; E-Mail: [EMAIL_ADDRESS]  
[7] Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-7-381-4526 (ext. 5640); Fax: +886-7-381-1182.  
[8] Academic Editor: [PERSON]  
[9]_Received: 9 July 2015 / Accepted: 4 August 2015 / Published: 11 August 2015_  
[10]**Abstract:** A novel systematic approach for synthesizing DDCC-based voltage-mode biquadratic universal filters is proposed. The DDCCs are described by infinity-variables' models of nullor-mirror elements which can be used in the nodal admittance matrix expansion process. Applying the proposed method, the obtained 12 equivalent filters offer the following features: multi-input and two outputs, realization of all five standard filter functions, namely lowpass, bandpass, highpass, notch and allpass, high-input impedance, employing only grounded capacitors and resistors, orthogonal controllability between pole frequency and quality factor, and cascadable, low active and passive sensitivities. The workability of some synthesized filters is verified by HSPICE simulations to demonstrate the feasibility of the proposed method.  
**Keywords:** voltage-mode; universal filter; cascadable; NAM; DDCC
## 5 Results and Discussion
### Cubic Metric  
The conventional metric used to measure power fluctuations in an OFDM signal is Peak-to-Average Power Ratio (PAPR). However, the latter does not take into account the distortion induced by HPA. For this reason, the Third Generation Partnership Project (3 GPP) proposed the cubic metric [23; 24]. It is mathematically defined as follows:  
\[CM=\frac{RCM-RCM_{ref}}{K}, \tag{7}\]  
where \(RCM\) is the raw cubic metric, which is defined for a signal \(x\) as follows:  
\[RCM=20 log_{10}\bigg{(}\sqrt{E\bigg{\{}\bigg{(}\frac{x(t)}{\sqrt{x(t)}}\bigg{)} ^{3}\bigg{\}}}\bigg{)}\ (\mathrm{dB}). \tag{8}\]  
\(RCM_{ref}\) is the \(RCM\) reference that for OFDM takes the value 1.52 dB and \(K\) is 1.56 [24].
# Radiation Damage of Polydimethylsiloxane and Polyimide by X-ray Free-Electron Laser
## 2 Materials and Methods
### Radiation Damage of PDMS and PI by XFEL  
Radiation damage of PDMS and PI by the XFEL was performed at the nano-crystallography and coherent imaging (NCI) hutch [52] at the Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL) [53]. The XFEL energy and photon flux were 9.5 keV and ~5 \(\times\) 10\({}^{11}\)phs/pulse, respectively. The beam had a size of approximately 3 \(\upmu\)m \(\times\) 2 \(\upmu\)m (vertical \(\times\) horizontal: FWHM) and was focused by the Kirkpatrick-Baez mirror [54]. The repetition rate of the XFEL used in this experiment was 30 Hz. In the study of radiation damage using PDMS or PI film, the film was mounted on a two-dimensional (2D) manipulator inside a fixed target chamber, and raster-scanned in the vertical and horizontal directions. The XFEL exposure interval during raster scanning was 50 \(\upmu\)m in both vertical and horizontal directions. In an experiment to monitor radiation damage in real time, the XFEL was continuously exposed at one location, and this was recorded using a high-speed camera (IDP-Express R2000, Photron, Japan). When exposing PDMS and PI based microfluidics to the XFEL, one position on the microfluidic channel was continuously exposed. All the XFEL experiments were performed at room temperature (24-26 \({}^{\circ}\)C) in ambient air.
# Simulation Study of a New Magnetorheological Polishing Fluid Collector Based on Air Seal
## 3 Simulation Parameters of MR Polishing Liquid Collector  
### Fluid Control Equation  
The commercial software FLUENT is used to simulate and analyze the airflow seal collection of MR polishing fluid. MR polishing fluid contains a large number of magnetically sensitive particles and abrasive polishing particles, which can be regarded as a solid-liquid multiphase flow. The particle group is regarded as a quasi-fluid, and the fluid is regarded as a continuous medium. It is assumed that the solid particles are uniformly distributed in the fluid, with continuous velocity field and temperature field [30].  
A mixture multiphase flow model is used to simulate the movement of each phase fluid. The continuous equation of MR polishing fluid is [31]:  
\[\frac{\partial}{\partial_{t}}(\rho_{m})+\
abla(\rho_{m}\overset{\rightharpoonup}{ \
u}_{m})=0 \tag{1}\]  
\[\begin{split}\frac{\partial}{\partial_{t}}(\rho_{m}\overset{ \rightharpoonup}{\
u}_{m})+\
abla\cdot(\rho_{m}\overset{\rightharpoonup}{\
u}_ {m}\overset{\rightharpoonup}{\
u}_{m})=-\
abla p+\
abla\bigg{[}\mu_{m}\bigg{(} \
abla\overset{\rightharpoonup}{\
u}_{m}+\overset{\rightharpoonup}{\
u}_{m} \bigg{)}\bigg{]}+\rho_{m}\overset{\rightharpoonup}{\delta}_{m}+\overset{ \rightharpoonup}{F}\\ +\
abla\bigg{(}\underset{k=1}{\overset{n}{\sum}}\alpha_{k}p_{k} \overset{\rightharpoonup}{\
u}_{dr,k}\overset{\rightharpoonup}{\
u}_{dr,k} \bigg{)}\end{split} \tag{2}\]  
where \(\rho\) is mixed density, \(\overset{\rightharpoonup}{\
u}_{m}\) is the average mass velocity, \(\mu_{m}\) is the mixed viscosity coefficient, \(\overset{\rightharpoonup}{F}\) is volume force, \(\alpha_{k}\) is the volume fraction of phase \(k\), \(\rho_{k}\) is the density of phase \(k\), \(\overset{\rightharpoonup}{\
u}_{dr,k}\) is the phase \(k\) drift velocity, and \(\overset{\rightharpoonup}{F}\) is the source term of magnetic force after applying a magnetic field to the MR polishing fluid.  
Assuming that the magnetically sensitive particles are ideally spherical, the magnetic dipole moment under the magnetic field condition is:  
\[\overset{\rightharpoonup}{m}=\
u\overset{\rightharpoonup}{M}=\
u\frac{3(\mu -\mu_{0})}{(\mu+2\mu_{0})}\overset{\rightharpoonup}{H} \tag{3}\]  
\[\overset{\rightharpoonup}{M}=\chi\overset{\rightharpoonup}{H}=\frac{\chi}{1+ \chi\mu_{0}}\overset{\rightharpoonup}{B} \tag{4}\]  
The magnetic force is:  
\[\overset{\rightharpoonup}{F}=(\overset{\rightharpoonup}{m}\cdot\
abla) \overset{\rightharpoonup}{B} \tag{5}\]  
where \(\
u\) is the volume of carbonyl iron powder, \(\overset{\rightharpoonup}{M}\) is the magnetization of carbonyl iron powder, \(\chi\) is magnetic susceptibility, \(\mu\) is the relative permeability of carbonyl iron powder, \(\mu_{0}\) is vacuum permeability, and \(\overset{\rightharpoonup}{B}\) is the magnetic induction intensity of carbonyl iron powder.  
Figure 4: Airflow seal collection device construction.
## 6 Experimental Research
### Test of Single Actuator  
A single actuator test takes the boom as an example to monitor the pilot pressure coefficient, boom estimated flow, electric motor judgment speed, motor target speed, and electric motor actual speed. The obtained parameters curves are shown in Figure 17.  
The boom single actuator action test completes three lifting and lowering actions by operating the boom pilot hydraulic handle. It can be seen from Figure 17 that the estimated flow of a single actuator changes with the pilot pressure, and the motor judges the speed to follow the estimated flow of the actuator in real time, which ensures the function of predicting the flow and judging the speed to follow the displacement of the hydraulic handle. The whole machine controller determines the speed and speed segmented control strategy by combining the obtained electric motor and giving the target segmented speed. The actual speed of the electric motor actively follows the target speed. It can be seen that the effect of the speed classification strategy is remarkable. The actual speed of the motor strictly follows the speed segmented strategy and has a fast response speed.  
In Figure 17b, the green circle part shows the obvious embodiment of the speed segmented control strategy. The electric motor judges that the speed fluctuates around 1700 rpm. According to the speed segmented control strategy, the target speed of the  
Figure 16: Physical drawing and three-dimensional drawing of part of the system.  
Figure 17: Relevant parameter curves of electric motor speed segmented control. (**a**) Pilot signal and estimated flow curve of the system; (**b**) electric motor judgment speed, motor target speed, and motor actual speed curve.  
motor is set at 2000 rpm, and the actual speed of the motor is also well stable around 2000 rpm, maintaining a small fluctuation range. In this range, the hydraulic pump reduces the displacement through LS pressure to realize the self-adaptive function of pump displacement.
## 4 Results
### Performance of RGBSformer  
To prove that RGB modality and skeleton modality are supplementary for human action recognition. We compared RGBSformer and the single pathway only to three kinds of models: skeleton-based models, RGB video-based models, and multi-modality models. The detail of results are shown in Table 1. On the fine-grained dataset, FineGym99, our fast-pathway outperforms all methods except RSANet-R50 [38]. It proves that significantly distinguishable features of human actions can be captured by pseudo heatmaps. After fusing with the RGB modality, our results are highly improved and outperform all methods, showing that RGB is indeed complementary to the skeleton modality. The two-stream framework also performs well on the Kinetics400 dataset. However, the fast-pathway performs badly on Kinetics400. The reason may be that many videos in Kinetics400 are too difficult to extract skeleton data. Figure 6 presents two examples of these videos. There are four examples of the three models on FineGym99 in Figure 7. From the result of the top five categories, it can be seen that the fusion result obtained by RGBSformer is the best.  
For the fourth example, the RGB stream gave the wrong prediction, but after the fusion with the skeleton stream, the RGSformer obtained the right answer.  
Figure 6: The examples of Kinetics400 in which it is difficult for skeleton-based methods to handle human action recognition tasks. The people are in small regions, or there are no people in frames.  
Figure 7: The examples of four videos from FineGym99 (**left**). The top five classes predicted by the skeleton stream, the RGB stream, and RGSformer (**right**).
# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Analysis of Existing DT Frameworks for Road Infrastructure Management
#### 4.1.1 Road Infrastructure Network
Frameworks that have been implemented and tested are labeled as applied/validated, while those that remain theoretical are identified as generic/conceptual (not validated). In Section 4.1.1, we conducted a thorough review of eight papers focused on road infrastructure, primarily during the O&M phase. These papers addressed various topics, including road inspection and management, road pavement, road network resilience, road transportation planning, smart civil infrastructure management, and a DT framework for roads, bridges, and tunnels. The DT framework or architectural model proposed in these papers consisted of multiple layers, each with different names. For instance, the layer responsible for physical asset data acquisition was referred to as the physical layer, data acquisition and preprocessing layer, structural monitoring layer, monitoring and reality capture layer, and sensor and carrier layer, among others. While some DT layers had similar semantics, others had distinct roles and definitions. These inconsistencies need to be addressed, and it is essential to define a common layer that integrates these diverse characteristics into a comprehensive and cohesive DT framework. Furthermore, each discussed layer included various components and functionalities, which will contribute to establishing a robust DT framework for road management.
## 1 Overview  
Many of the peculiar characteristic properties of Aurivillius materials are determined by their crystalline structure. These compounds have the general formula [Bi\({}_{2}\)O\({}_{2}\)]\({}^{2+}\)[A\({}_{n-1}\)B\({}_{n}\)O\({}_{3n+1}\)]\({}^{2-}\) and are built from \(n\) pseudoperovskite layers alternating with [Bi\({}_{2}\)O\({}_{2}\)]\({}^{2+}\) layers [1]. Figure 1 shows the prototype of the Aurivillius structure as a function of the number of pseudo-perovskite layers [2]. The \"\(n\)\" values can range from 1 to \(\infty\). In this case (\(n=\infty\)), the repeated cell is the pure perovskite. The number of perovskites can be odd, even or a mixture. Examples of each case are Bi\({}_{2}\)WO\({}_{6}\) (\(n=1\)) and Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\) (\(n=3\)); Bi\({}_{3}\)TiNbO\({}_{9}\), SrBi\({}_{2}\)Nb\({}_{2}\)O\({}_{9}\) (\(n=2\)) and CaBi\({}_{4}\)Ti\({}_{4}\)O\({}_{12}\) (\(n=4\)); an example for a mixture Aurivillius is Bi\({}_{7}\)Ti\({}_{4}\)NbO\({}_{21}\). In this case, layers with 2 or 3 pseudo-perovskites are alternated with the [Bi\({}_{2}\)O\({}_{2}\)]\({}^{2+}\) layers.  
At difference of the ferroelectrics with perovskite structure, the polarization is not produced by the displacement of the B cation within the oxygen octahedral but by relative shifts of the oxygen octahedral through the a-axis with respect to the [001] bismuth chain. They can be divided in 3 main movements, as illustrated in references [3; 4; 5]:  
Oxygen octahedral in the a-axis.  
Relative displacements in the opposite direction to B cations of the perovskite with respect to the octahedral.  
Displacement through the a-axis of the oxygen ions of the Bi-O layer.  
Besides, this particular crystalline structure determines the growth habit for these materials. They grow with anisotropic shapes, as platelets with the minor dimension in parallel to the c-axis. This is a disadvantage for the sintering process to obtain dense bulk ceramics but it favors the grain alignment that allows the texture and the crystal orientation. It can be carried out by different means, as hot pressing, tape-casting, templated grain growth or others, as will be explained in Section 2.1.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Walker Constellation  
The Walker constellation comprises a certain number of satellites with the same orbital inclination, semimajor axis, eccentricity, and argument of perigee \((i,a,e,\omega)\). Three integer parameters of the total number of satellites \(N\), the number of orbital planes \(P\), and the configuration number F determine the configuration of the Walker constellation. Using the three parameters \((N,P,F)\), the mean anomaly \(M\) and the right ascension of the ascending node \(\Omega\) of the \(j\)-th satellite on the \(i\)-th orbital plane can be obtained [38].  
\[\left\{\begin{array}{c}\Omega_{ij}=\Omega_{0}+\frac{2\pi}{P}(i-1)\\ M_{ij}=M_{0}+2\pi\cdot\left[\frac{F}{N}(i-1)+\frac{P}{N}(j-1)\right]\end{array}\right. \tag{1}\]  
where \(i=1,2,\ldots,P,j=1,2,\ldots,N/P\), and the data range of \(F\) is \(Fe[0,P-1]\). \(\Omega_{0}\) and \(M_{0}\) represent the datum values of the RAAN and the mean anomaly, respectively.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Previous Work Comparison and Evaluation
#### 6.3.1 Prediction Accuracy  
The similarity between the proposed real-time predictor and the results of [PERSON] et al. is that the main regression models are all ARIMAs. However, for the optimization of prediction results, the two works choose totally different methods. The proposed predictor employs BIC to constitute a timely prediction error feedback mechanism and finally improves the prediction accuracy [5]. This improvement only aims at the linear regression part and does not explain the deficiency of the nonlinear part. By comparison, the results of [PERSON] et al. introduced ANN to overcome the shortage of ARIMA in nonlinear regression, which provides a more complex structure of the prediction model. However, in terms of prediction accuracy, both \(R^{2}\) results and the RMSD values indicate that the proposed predictor outperforms the results of [PERSON] et al.  
The prediction model of the result of [PERSON] et al. is more complex and advanced. The LSTM provides higher prediction results than the hybrid model [PERSON] et al. used. However, in terms of residential power consumption data, the traditional prediction model training method does not consider the influence of random events on model training, which may inevitably lead to an overfitting phenomenon. By comparison, the design of the moving window and the BIC provide an effective combination that minimizes the discrepancy between the observed values and the values obtained with the model.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 2 Proposed Asymmetrical Bidirectional Rendering Approach
### Color Correction-Based Merging
It can be seen that the proposed asymmetric bidirectional rendering can significantly reduce the holes in the virtual image. Note that even in the bidirectional rendering, there are still holes in the virtual image because some occluded regions are not visible in both reference views. In addition, the holes caused by depth errors are also waiting to be filled.
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 6 Analysis of the Accuracy of ELMs
### Response to Seismic Excitations  
Considering the analysis in Sections 6.1 and 6.2, it can be concluded that ELM1 and ELM2 perform better in the comparison of displacement standard deviation. Since ELM1 and ELM2 are based on the adaption of FRF and the decay of free vibration, the comparison seems unfair for ELM3. In this section, three natural seismic waves and an artificial wave of firm soil are selected for time-history analysis of the CID system and ELS. The peak ground acceleration (PGA) of the seismic waves are all adjusted by 0.2g, with g being the value of gravity acceleration. Figure 9 shows the displacement time-history curves of the CID system and ELS with different ELMs when the inertance-mass ration is 0.5. The results show that ELM3 seems to be the most accurate method, although sometimes the response of ELM3 is slightly smaller than the CID system. The responses of ELM1 and ELM2 are commonly slightly larger than the CID system. The error analysis in Figure 10 shows that the error of ELM1 and ELM2 are quite the same. The response of the CID system shows a high demand for damping, since the equivalent damping obtained with ELM3 is larger than with ELM1 and ELM2.  
Figure 8: Displacement response to harmonic excitations and its error analysis for all ELMs. (**a**) Displacement response when \(\mu\) is 0.5; (**b**) Error of displacement standard deviation varying with \(\mu\).  
Figure 9: Displacement response of the CID system and equivalent linear system (ELS) to seismic excitations when \(\mu\) is 0.5. (**a**) Electron wave; (**b**) Kobe wave; (**c**) Northridge wave; (**d**) Artificial wave.