esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Methodology
So, a selected ground motion parameter such as Peak Ground Acceleration (PGA) or Peak Ground Velocity (PGV) at the bedrock level is multiplied by an empirically-derived factor [25]. Indeed, the earthquake motion amplification is one of the most difficult site effects to be modeled. A commonly used approach to micro-zonation is to determine empirical site-amplification factors for a large set of sites by regression analysis of earthquake data, correlating them to different geotechnical parameters of the site [25]. Several multiplication factors have been identified for different regional areas based on statistical analysis of observed strong ground motion data. These factors are derived by input PGA or PGV values, by bedrock depth and average shear wave velocity of the soil deposit [26, 27]. The investigated geospatial methodology and tools are able to produce PGA maps, vulnerability maps and expected damage scenarios. In this way, it is possible to have a preliminary assessment of the expected damages after a seismic event. The seismic vulnerability is expressed in terms of macroseismic intensity (\(I_{\mathrm{MCS}}\)). In particular, PGA and \(I_{\mathrm{MCS}}\) values [28, 29] are correlated by using the law proposed by [PERSON] and [PERSON] [30]. In order to estimate the surface ground shaking in the interest area and calculate the PGA value, the following attenuation relationship [30] is used:  
\[\log_{10}(PGA)=a+bM+c\log_{10}(R^{2}+h^{2})^{1/2}+e_{1}S_{1}+e_{2}S_{s}\pm\sigma \tag{1}\]  
where \(M\) is the local magnitude, \(R\) the distance from the epicenter and \(\sigma\) the standard deviation of log PGA. The variables \(S_{1}\) and \(S_{2}\) refer to site classification and take the value of 1 for shallow and deep alluvium sites, and zero otherwise. The analyses do not take site effects into account and the PGA has been calculated considering bed rock condition. [PERSON] and [PERSON] [30] also provide an exhaustive description of the other parameters considered in the (1) equation. To convert PGA to \(I_{\text{MCS}}\), the following equation [28] is used:  
\[\log\,PGA=0.594+0,\text{197}\cdot I_{\text{MCS}} \tag{2}\]  
The PGA map of seismic attenuation is obtained by IDW (Inverse Distance Weighting) interpolation. This step provides numerical modeling of the various input data where each pixel in the digital map may assume values in range 0 and 1, that represent respectively the minimum and maximum ground shaking impact.
# Parameter Extraction of Solar Photovoltaic Model Based on Nutracker Optimization Algorithm
## 1 Introduction
proposed an improved queuing search optimization (QSO) algorithm based on DE technology and boundary constraint correction process [23], which identified the parameters of the SDM and DDM models. They used DE to increase the population diversity of IQSODE, thereby improving the exploration of the algorithm in the search space and solving the problem of the QSO algorithm being trapped in local optima. [PERSON] et al. proposed a dynamic reverse learning strategy for identifying photovoltaic cell model parameters using an adaptive differential evolution algorithm [24]. The strategy expanded the search range of particles in the global optimal solution search phase, thereby increasing the probability of particles approaching the optimal solution area. Previous research has mainly focused on one or two of the three models, yet this study presents a novel approach by employing a nature-inspired metaheuristic method known as the nutcracker optimization algorithm (NOA) [25] for the parameter extraction of single, double, and triple diode models. Aleo Solar S79Y300 monocrystalline silicon solar panels were tested under 1000 W/m\({}^{2}\) solar radiation and 25 \({}^{\circ}\)C temperature. These conditions are standard testing conditions and commonly encountered in many geographical regions globally where substantial investment in solar energy is being made, thereby ensuring our findings have practical and relevant applications. We evaluated the performance of NOA for PV parameter extraction by observing the RMSE error and running time. Subsequent non-parametric tests were performed to analyze the data further. Finally, a comparative analysis was conducted between the proposed NOA and three popular algorithms, PSO [26], FWA [27], and WOA [28], summarizing the results for comprehensive understanding. The outstanding contributions of the present article are the following:  
* Demonstrate the suitability and effectiveness of NOA in the field of the parameter extraction of the PV model. * Employ modern techniques to accurately identify the optimum parameters in the PV model. * Conduct a comparative study on four MAs to enhance the precision of a specific diode model optimization. * Analyze possible future trends in the area based on the test results.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 2 Network Layout
### Polarization Mode Dispersion  
This factor exists in SMF, when orthogonality between the two fundamental modes is disturbed, as shown in Figure 4. These modes are orthogonally polarized and coexist in SMF. A delay time difference occurs among these two orthogonally polarized modes, due to small perturbation inside the optical fiber. The impairments generated by this phenomenon are called PMD [26]. The impairments caused by the dispersion are more severe in the case of multi carrier OCN and, hence, affect the overall optical system efficiency. Therefore, PMD monitoring is needed by using network management or compensation techniques. In this paper, we implement DFE/FFE, Gaussian band pass filter, and Gaussian optical filter to minimize the effects of PMD and CD for multi carrier OCNs. In this section, we highlight the CD and PMD impairments, and the procedures on how to compensate these issues and propose OCN model. In the next section, we discuss technical background of the FFE/DFE techniques, which are used for CD and PMD minimization in multi carrier OCNs.
## 3 Controller Design and Stability Analysis
### Simulation Results for HSTs without Anti-Actuator Saturation  
In addition, to further verify the necessity of considering anti-actuator saturation in the design of a controller, simulation was carried out for the case without anti-actuator saturation. Without considering the anti-actuator saturation, and in the case of 30% loss of actuator effectiveness, i.e., \(k_{c}=0.7\), the control performance for HSTs with the actuator output limits \(F_{\max}=60000N\) and \(F_{\min}=-60000N\) is shown in Figure 7. It can be seen that the tracking errors of the system increases substantially, and the system becomes unstable. Moreover, the chattering problem of the actuator's actual output becomes very serious, which is not allowed in practical train operation. Figure 7 demonstrates the importance and necessity of considering anti-actuator saturation in train fault-tolerant tracking controllers.  
Figure 7: \(Cont.\)
# A Clinical-Based Diagnostic Approach to Cerebellar Atrophy in Children
## 2 Materials and Methods  
Eighty pediatric patients with radiological evidence of CA were collected from 2010 to 2020 in a single referral center in Milan, Italy.  
Patients underwent a diagnostic workup, including general and neurological assessment, brain MRI, EEG, metabolic investigations, and muscle biopsy with respiratory chain complex study. Metabolic tests included serum amino acids (AA), urinary organic acids (OA), serum lactate and pyruvate, cholesterol, albumin, alpha-fetoprotein (AFP), and transferrin isoelectric focusing (T-IEF). Further exams were performed in most patients, following clinical indications: electromyography (EMG) and/or nerve conduction studies (NCS); electroretinography (ERG) and visual, auditory, sensory evoked potentials (VEP, BAEP, SSEP); additional metabolic studies (e.g., vitamin E, TSH). Genetic studies were performed in all patients and included single-gene Sanger sequencing, next-generation sequencing (NGS) techniques for custom gene panels or whole exome sequencing (WES), specific testing to search for nucleotides expansions, and Array-CGH analyses. All the genetic tests were conducted with parental consent and according to laboratory and ethical standards. Array-CGH analyses were performed using Cytoscape oligo ISCA180K platform with a medium resolution of 40 Kb. The variants detected by molecular analyses were classified according to ACMG guidelines [10]; all the variants identified by NGS techniques were validated by Sanger sequencing.  
Of the original 80-patient cohort, we discuss data of the 52 patients who had a definite diagnosis based on genetic confirmation or, in cases still lacking a definite molecular diagnosis, histopathological and/or biochemical confirmation of a genetic determined condition.
# Scattering of Matter Wave Solitons on Localized Potentials
## 3 Solitons
### Dark Solitons  
Dark solitons are formed in repulsive BECs, governed by the homogeneous GPE, Equation (4), and fulfil the boundary condition \(\lim_{x\rightarrow\pm\infty}\left|\psi(x)\right|^{2}=n_{0}\), where \(n_{0}\) is the bulk value of the condensate density and also the dimensionless chemical potential. A dark soliton moving with velocity \(v\) is described by  
\[\psi_{\mathrm{D}}(x,t)=\sqrt{n_{0}}\left[i\frac{v}{c}+\sqrt{1-\frac{v^{2}}{c^{ 2}}}\tanh\left(\frac{x-vt}{\xi_{v}}\right)\right]\mathrm{e}^{-ipt/\hbar}, \tag{9}\]  
where \(\xi_{v}=\xi/\sqrt{1-\frac{v^{2}}{c^{2}}}\), with \(\xi\) the healing length and \(c=\sqrt{\frac{n_{0}\xi}{m}}\) the speed of sound, both unity in our dimensionless units [4]. The dark soliton has a density dip around \(x=vt\), and we will characterize the size of the soliton by the number of atoms, \(N_{\mathrm{D}}\), missing compared to the homogeneous case,  
\[N_{\mathrm{D}}=\int\mathrm{d}x\,\left(n_{0}-\left|\psi_{\mathrm{D}}\right|^{2 }\right)=2n_{0}\xi\sqrt{1-\frac{v^{2}}{c^{2}}}. \tag{10}\]  
The density depression moves in the opposite direction of the atoms in the condensate, whose motion is given by the gradient of the phase of the condensate wave function. The energy of the dark soliton is given by the difference between the energy of the condensate containing a dark soliton and the energy of the homogeneous condensate,  
\[\begin{split} E_{\mathrm{D}}&=\int\mathrm{d}x\, \left(\frac{\hbar^{2}}{2m}\left|\frac{\partial\psi_{\mathrm{D}}}{\partial x} \right|^{2}+\frac{\xi}{2}\left(\left|\psi_{\mathrm{D}}\right|^{2}-n_{0}\right) ^{2}\right)\\ &=\frac{4}{3}n_{0}\hbar c\left(1-\frac{v^{2}}{c^{2}}\right)^{3/2}.\end{split} \tag{11}\]The soliton energy decreases with increasing soliton velocity, and for \(\frac{v}{c}\ll 1\) one finds  
\[E_{\rm D}\simeq\frac{4}{3}n_{0}\hbar c-\frac{2n_{0}\hbar}{c}v^{2}, \tag{12}\]  
corresponding to an effective negative mass of \(-4n_{0}\frac{\hbar}{c}\).
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 1 Introduction
[13] proposed a distributed model predictive control method for the special situation of fully autonomous vehicles in which multiple agents can pass through intersections at the same time, while maintaining a sufficient safe distance from conflicting agents. After the simulation, the study conclusively demonstrated the effectiveness of the method. [PERSON] [14] constructed a new lateral control algorithm for driverless cars that utilizes the linear complementarity problem (LCP) approach as an MPC optimization solution. The simulation shows that this leads to improvements in driving stability and real-time performance. [PERSON] et al. [15] investigated and studied the current planning and control algorithms and provided side-by-side comparisons that contribute to an in-depth understanding of the advantages and limitations of the reviewed methods and help researchers make system-level design choices. In order to avoid the frequent switching between the acceleration and braking modes of the driverless ferry vehicles and the unknown obstacles encountered by the driverless ferry vehicles on the road, using the fusion of PID control and fuzzy control, the fuzzy PID longitudinal motion controller was built, and the switching logic of acceleration tolerance zone was added to realize the switch between acceleration and deceleration, so as to realize the driving and braking control of the driverless ferry vehicle and finally realize the stable tracking of the expected speed of the driverless ferry vehicle. The vehicle's three-degrees-of-freedom dynamic model was established, the tire cornering stiffness was estimated based on the recursive least squares method, and the MPC controller with obstacle avoidance module was designed and co-simulated with CarSim 2019.0, using MATLAB/Simulink 2022b. When the longitudinal control is separately controlled, the tracking control effect of the vehicle at uniform speed and variable speed is analyzed, and when the vehicle is controlled separately in the lateral direction, the tracking effect of the vehicle at different speeds in different predictive time domains is analyzed. Finally, the transverse motion and longitudinal motion are correlated in the predictive time domain; thus, the horizontal and longitudinal coordinated control is realized, and the position of unknown obstacles in the first half of the road and the second half of the road are simulated and analyzed under the condition of double moving lines.
## 1 Introduction
A detailed testing framework is designed to evaluate the performance of SGX-based TEE in edge environments, covering implementations based on SGX Software Development Kit (SDK) and Library Operating System (LibOS), as well as their usage in a virtual environment. 3. Based on the survey and experimental results, performance bottlenecks in SGX are identified, offering insights and optimization directions for the application of TEE technologies in edge intelligence scenarios. The rest of the paper is organized as follows. In Section 2, we provide a brief introduction to the fundamental concepts of SGX-based TEE, focusing on two key features: isolated execution and attestation. Section 3 discusses the application of TEE in IoT and the challenges it faces in edge intelligence scenarios, such as I/O security and performance degradation. Section 4 presents a detailed performance evaluation of SGX in edge scenarios and proposes performance improvement directions based on the test results. Section 5 covers a review of related research and Section 6 concludes the paper.
# A Fast Simulation Model of Pantograph-Stitched-Catenary Interaction in Long-Distance Travel
## 2 Reduced Pantograph-Stitched-Catenary Interaction Model
### Modeling of the Pantograph  
The pantograph is modeled as a lumped mass system model, which consists of a contact strip, panhead, frame, and spring-damper elements. Based on [31], the contact strip, panhead, and frame are simplified as lumped masses, and the contact strip and panhead are combined and connected with the frame through spring-damper elements, as shown in Figure 2b. The frame is connected to the car body through spring-damper elements as well. The contact strip, panhead, and frame mass of the pantograph are \(m_{3}\), \(m_{2}\), and \(m_{1}\), respectively. The stiffness and damping of the spring-damper elements between the panhead and frame are \(K_{2}\) and \(C_{2}\), respectively, and those of the spring-damper elements between the frame and car body are \(K_{1}\) and \(C_{1}\), respectively. The parameters of the pantograph can be found in [31], which result in the same frequency responses from the model as those from a real pantograph. The dynamic equation of the pantograph is expressed as  
\[\mathbf{M}_{P}\ddot{\mathbf{q}}_{p}+\mathbf{C}_{P}\dot{\mathbf{q}}_{p}+ \mathbf{K}_{P}\mathbf{q}_{p}=\mathbf{Q}_{cp}+\mathbf{Q}_{p} \tag{6}\]where \(\mathbf{M}_{P}\), \(\mathbf{C}_{P}\), and \(\mathbf{K}_{P}\) denote the spring, damping, and stiffness matrices of the pantograph, respectively. \(\mathbf{q}_{P}\) is the generalized coordinate vector, \(\mathbf{Q}_{CP}\) is the generalized force vector caused by the pantograph-catenary interaction, and \(\mathbf{Q}_{P}\) is the generalized force vector generated by the pantograph itself, which includes gravity, uplift forces, and aerodynamics. The uplift force of the present pantograph consists of a constant uplift force and an aerodynamic force. The aerodynamic force is applied on \(m_{2}\) (panhead), and the constant uplift force is applied on \(m_{1}\) (frame). Details of these matrices are shown in [1]. Note that the present model is developed based on Sweden's high-speed pantograph, and it only has one contact rail (contact strip). In addition, the main target of the present work is developing a fast simulation model of pantograph-stitched-catenary interaction. Thus, the influence of contact rails' distance on the stability of the pantograph-catenary interaction is not considered in the present investigation.
## 3 Calibration
### Radiometric Calibration  
The absolute spectral response (ASR) of G-LiHT's imaging spectrometer was calculated using measurements made with a portable version of the US National Institute of Standards and Technology's (NIST) Spectral Irradiance and Radiance Responsivity Calibrations Using Uniform Sources (SIRCUS) [18], which is currently housed at NASA Goddard Space Flight Center (GSFC). SIRCUS uses continuously tunable lasers coupled to an integrating sphere as a radiance source for the calibration of detectors operating in the solar reflective spectrum. SIRCUS and appropriately characterized transfer radiometers allow absolute radiometric calibration with uncertainties of 0.5% and traceability to national standards [19]. The availability of lasers determines the spectral coverage on SIRCUS, and the uncertainties achievable are determined by the quality of the transfer radiometers and measurement technique. The high power and wavelength stability of the laser-based sources enable large aperture instruments to be characterized and calibrated. All irradiance and radiance responsivity calibrations are traceable to NIST's Primary Optical Watt Radiometer (POWR) through regularly characterized transfer radiometers. The short calibration chain from NIST's Primary Optical Watt Radiometer (POWR) to the transfer radiometer and imaging spectrometer minimizes the uncertainty of the Spectral Radiometer Facility (SRF). Spectral radiance calibrations at power levels typical of solar irradiance values allowed for quantitative measurements of the spectral response functions and under flight-like conditions over the focal plane from 415 to 1,020 nm. In addition to providing spectral and radiometric calibration, the combination of full-field, full-aperture and near-monochromatic method of radiometric calibration used here allows the unique capability of assessing and correcting for the total system stray light of the imaging spectrometer, not possible with traditional broadband calibration sources.
## 3 Results and Discussion
### Underpinned Foundation Behavior  
A loading test on a raft with four PCP without underpinning foundation was conducted first. The test result used as a reference loading for comparison with the results for loading test with underpinning foundation. Subsequent tests considered underpinned foundations with initial and additional loading. A \(Q_{i}\) of 730 N (the summation of the four PCP's bearing capacities) was applied to the existing foundations. After the addition of the micropile, \(Q_{a}\) was gradually applied to the underpinned foundation, up to 150% of \(Q_{i}\) (1825 N).
# Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service
## 2 Literature Review
As of 2017, there are 1049 rotary-wing air ambulances operating from 908 bases across the U.S. serving 84.3% of Americans within a 20 min response time [5]. While historical data indicates that the number of aircraft and bases increased between 2005 and 2015, a number of business and legislative factors have contributed to the consolidation of service providers. These developments have caused a stabilization in the number of aircraft while the number of bases continue to increase. Both ground and air ambulances have nine service levels, as defined by the U.S. Centers for Medicare and Medicaid Services (CMS) [25; 26]. Each service level has different medical equipment, crew, and vehicle requirements and can be served either by a ground ambulance or an air ambulance (either helicopter or fixed-wing aircraft) depending on local emergency service dispatch protocol. A description of these nine service levels and crew requirements are summarized in Tables 1 and 2, respectively. A single emergency eVTOL will require four full time pilots, four full time flight nurses, and four full time paramedics with Commission on Accreditation of Medical Transport Systems (CAMTS) accreditation and annual training according to Federal Aviation Administration (FAA) duty hour requirements. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**CMS Service Level**} & \multicolumn{1}{c}{**Description**} \\ \hline \hline \multirow{6}{*}{
\begin{tabular}{c} Ground \\ Ambulance \\ \end{tabular} } & Basic Life Support (BLS) Non- & \multicolumn{1}{c}{Provision of medical supplies and services} \\  & Emergent & \multicolumn{1}{c}{} & \\ \cline{2-3}  & BLS Emergency & \multicolumn{1}{c}{} & \\ \cline{2-3}  & BLS Emergency & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Advanced Life Support (ALS) Non-Emergent & \multicolumn{1}{c}{} & \\ \cline{2-3}  & ALS1 Emergent & \multicolumn{1}{c}{} & \\ \cline{2-3}  & ALS2 (3 Separate & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Medications by IV) & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Specialty Care Transport (SCT) & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Specialty Care Transport (SCT) & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Specialty Care Transport (SCT) & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Rotorcraft (Helicopters) & BLS or ALS type service for short distances that require rapid air transport \\ \cline{2-3} Air & \multicolumn{1}{c}{} & \\ \cline{2-3} Ambulance & Fixed Wing Aircraft & \multicolumn{1}{c}{} & \\ \cline{2-3}  & Summarized from [23]. & \multicolumn{1}{c}{} & \\ \cline{2-3}  & & & \\ \end{tabular}
\end{table} 
Table 1: Nine ambulance service levels.