esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the case of the split Hopkinson pressure bar (SHPB), Equations (2) and (3) use the reflected pulse (\(\epsilon_{\text{R}}\)) and are generally employed to measure the nominal strain rate and strain of the specimen, respectively;  
\[\sigma(t)=\left(\frac{A}{A_{\text{o}}}\right)\text{E}_{\text{o}}\varepsilon_{ \text{T}}(t), \tag{1}\]  
\[\dot{\epsilon}(t)=-2\left(\frac{\text{C}_{\text{o}}}{L}\right)\text{E}_{\text {R}}(t), \tag{2}\]  
\[\varepsilon(t)=-2\left(\frac{\text{C}_{\text{o}}}{L}\right)\int_{0}^{t} \varepsilon_{\text{R}}(t)dt. \tag{3}\]  
In the above equations [1; 8; 9; 10; 11], \(A\) and \(L\) denote the initial cross-sectional area and initial length of the specimen, respectively; \(A_{\text{o}}\) and \(\text{C}_{\text{o}}\) denote the cross-sectional area and sound speeds of the bar, respectively; and \(t\) is the time. In an SHTB test, problems such as specimen design, gripping, and tensile pulse control [14; 15; 16; 17; 18] require resolution (see Figure 1b). This study focuses on the tensile pulse control. Control of the tensile pulse is necessary as a neat tensile pulse without any spurious pulses [18] should be used as an incident tensile wave (\(\varepsilon_{\text{I}}\)). Otherwise, the \(\varepsilon_{\text{R}}\) and \(\varepsilon_{\text{T}}\) waves originating from \(\varepsilon_{\text{I}}\) are hardly unspoiled, thus preventing the extraction of the neat stress-strain and strain rate-strain curves from the SHTB experiment. The magnitude and shape of the tensile pulse generated by the transfer flange are governed by many design parameters, such as flange height (\(H_{\text{f}}\)), flange length (\(L_{\text{f}}\)), striker thickness (\(T_{\text{s}}\)), and bar diameter (\(D_{\text{b}}\)). In the literature [18], a flange length comparable to the bar diameter has been found to be beneficial. However, the detailed mechanism of how the impact-induced compressive stress is transferred to the tensile stress through the flange structure has not been explored. If the stress transfer mechanism through the flange structure is uncovered, it may aid not only in understanding the stress wave interaction in the SHTB system for design purposes, but also in designing various impact protection structures. Considering the above, this study investigates the stress transfer mechanism of the flange. For this purpose, the pulse shapes at the strain gauge position of the incident bar are systematically monitored across a range of flange lengths (from \(D_{\text{b}}\) to \(50D_{\text{b}}\)), through an explicit finite element analysis. The monitored tensile stress profiles are interpreted using the three types of waves proposed in this paper: bar waves, flange waves, and a series of reverberation waves. Thereafter, the one-dimensional theory of impact is employed to verify the interpretation, which establishes the flange length-dependent stress transfer mechanism. A positive sign is assigned to the tensile property.
## 3 Numerical Analysis  
In this paper, firstly, based on the train-track-bridge interaction theory, the vehicle-track-bridge coupling dynamic models of three kinds of BGBs are established, which vibration characteristics by the external excitation are compared and analyzed. Secondly, taking the vibration response of three kinds of BGBs as acoustic boundary conditions, the acoustic boundary element method is adopted to predict the acoustic radiation characteristics of BGBs. Finally, the sound radiation mechanism of BGB is further analyzed by the vibration principle. The main research ideas are shown in Figure 2.  
Figure 1: Schematics of a BGB with a thickened top plate in a high-speed railway: (**a**) initial; (**b**) whole top plate thickened; (**c**) top plate locally thickened under the track. The train runs on an elevated line, which can be divided into the vehicle subsystem, the track subsystem, and the bridge subsystem for theoretical analysis. The dynamic equations of the three can be expressed as follows:  
\[\mathbf{M}_{\mathrm{v}}\ddot{u}_{\mathrm{v}}+\mathbf{C}_{\mathrm{v}}\dot{u}_{ \mathrm{v}}+\mathbf{K}_{\mathrm{v}}u_{\mathrm{v}}=\mathbf{F}_{\mathrm{v}}(t) \tag{1}\]  
\[\mathbf{M}_{\mathrm{t}}\ddot{u}_{\mathrm{t}}+\mathbf{C}_{\mathrm{t}}\dot{u}_{ \mathrm{t}}+\mathbf{K}_{\mathrm{t}}u_{\mathrm{t}}=\mathbf{F}_{\mathrm{t}}(t) \tag{2}\]  
\[\mathbf{M}_{\mathrm{b}}\ddot{u}_{\mathrm{b}}+\mathbf{C}_{\mathrm{b}}\dot{u}_{ \mathrm{b}}+\mathbf{K}_{\mathrm{b}}u_{\mathrm{b}}=\mathbf{F}_{\mathrm{b}}(t) \tag{3}\]  
The subscripts v, t, and b represent the vehicle, track, and bridge structures, respectively; \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are the mass, stiffness, and damping matrices, respectively; \(\ddot{u}\), \(\dot{u}\) and \(u\) are the displacement, velocity, and acceleration vectors, respectively; and F is the generalized load. The vehicle-track-bridge interaction model is shown in Figure 3. In the overall system, the vehicle submodel uses the classical 35-degree-of-freedom model. Each vehicle section includes a vehicle body, two steering racks, and four wheels sets, all considered to be rigid-body structures, connected by the first and second suspension systems; the suspension system exhibits linear and nonlinear characteristics. Each rigid body comprises lateral, vertical, side-roll, rocker, and nodding degrees of freedom; the vehicle model dynamic equations and related parameters are described in the literature [40]. The finite element model is used for determining the structure of the elevated infrastructure. The theoretical finite element model of the track and the BGB structure is shown in Figure 3. The relevant parameters and finite element type are shown in Table 1.  
Figure 2: Schematic representation of the vibration-acoustic radiation characteristics analysis of box-girder bridge. When accurately predicting the noise radiated by the bridge due to the movement of trains, the determination of wheel-rail dynamic forces is important. In this study, we employ a dynamic wheel-rail interaction model [41], whose reliability and low computational
## 2 Methodology  
The proposed method employs an improved SIFT algorithm using the Shi_Tomasi approach to detect feature points on enhanced images and a partition matching strategy considering geographic information to search for tie points. Seven steps are involved in the method (Figure 1), as follows:  
1. Constructing a pre-earthquake image database;
2. Image enhancement;
3. Image partitioning strategy based on geographic information constraints;
4. Image patch matching using the combination of Shi_Tomasi and SIFT;
5. Removing the outliers;
6. Homogenizing the even spatial distribution of matched tie points;
7. Image transformation and resampling.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 4 Discussion  
### Investigating the SIF-GPP Relationship  
In ISBA, the fluorescence is not simulated directly, but the photosynthesis activity is simulated through the calculation of the GPP, which is driven by plant growth and mortality in the model. [PERSON], [PERSON] et al. (2018) demonstrated that SIF and GPP were driven by the same environmental and biological factors and found that SIF observations from OCO-2 and GPP products from FLUXCOM were highly consistent in time and space. Their study was realized over various types of vegetation near Chicago, Illinois, U.S. At a much larger scale, the results of this study were consistent with ([PERSON] et al., 2018), as the observed SIF from GOME-2 and the simulated GPP also showed a high correspondence in both time and space. This indicates that the SIF observations could be used as a relevant and independent source of validation for GPP model simulations.  
The SCOPEmodel (S  
products into the model, a seasonal CDF matching is performed, which projects the observation values into the model space. This process reduces the impact of the SSM assimilation on the analyzed simulations. In order to improve LDAS-Monde analysis, building an observation operator for backscatter coefficients could be a way to overcome the use of the SWI product and directly assimilate the observed backscatter coefficients. Moreover, C-band observations also contain information on vegetation biomass. By building this forward operator, it would be thus possible to consider all the information contained in these observations. SSM products can also be derived from passive microwave satellite observations. In the same way as for the observed backscatter coefficients, building an operator for brightness temperatures (energy that is naturally emitted by the surface of the Earth and dependent on the soil moisture at L-band, or 1.4 GHz) is needed to take full advantage of the available observations.
# Impact of Nitric Oxide on the Surface Properties of Selected Polymers
## 2 Experimental Section
### FTIR-ATR Measurements  
FTIR-ATR measurements were conducted using an Agilent Varian 670 IR spectroscope (Agilent Technologies Inc., Santa Clara, CA, USA), and a Pike Technologies GladiATR probe head (Pike Technologies, Fitchburg, WI, USA) was used to conduct the measurements. The dried polymer samples were pinned onto the diamond of the probe head using the integrated clamp to ensure complete and consistent coverage of the diamond surface. The sampling depth of this method ranges from 2 to 15 \(\upmu\)m depending on both sample material and radiation wavelength [25]. The spectra were recorded with 16 scans at a resolution of 2 cm\({}^{-1}\). The spectral range was 4000 to 500 cm\({}^{-1}\). For both PMP and MABS, the signals caused by the NO treatment were also investigated in terms of peak height. For this purpose, the spectra were baseline-corrected and normalized using the signal located at 1466 cm\({}^{-1}\) in the case of PMP and 1720 cm\({}^{-1}\) for MABS. These signals were chosen based on their high intensity and consistency across different measurements. The highest data point determined the height of the signals.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 5 Case Study
### ESCR Is High and 20% Changes in Load (ESCR = 2.5)  
In case 5, the 20% load decreased in the system when the strength of the AC system increased to 2.5. Figure 9 shows the result of system response to load decrease when the onshore system becomes stronger. In Figure 9, the results show that the onshore power system becomes stronger, the system's own frequency stability enhanced, and it can cope with more complex changing conditions. In Figure 9a, with the enhancement of the onshore system, the inertia constant provided by the improved inertia control still makes the DC variation. In addition, as the ESCR value of the onshore system increases, it can be found through the proposed method that the minimum inertia constant required by the system also increases. The active power of the SG changed under the stronger onshore system in Figure 9b. In Figure 9c, with the enhancement of the onshore system, the improved inertia control is still effective to damp the rotor speed of the SG. With the effective support of improved inertia control, the system frequency variation decreases in Figure 9d. It also has a good control effect for MMC output active power as shown in Figure 9e. Similarly, when \(H_{MMC}\) is increased to 0.8, the response result of the system is better than before.  
Figure 8: ESCR is 1.7 for 20% load decrease: (**a**) DC voltage; (**b**) SG active power; (**c**) SG rotor speed; (**d**) AC system frequency; (**e**) MMC active power; (**f**) MMC2 active power with HMMC = 0.361 s; (**g**) MMC active power with HMMC = 0.4 s.
## 3 Implementation of The Proposed System
### Data Storage
#### (1) File Metadata  
The Hadoop Distributed File System (HDFS) includes a single NameNode and Secondary NameNode, and a number of DataNodes. NameNode manages the namespace of the entire file system, and provides metadata information for the user to access files, the byte size of the metadata of a file is \(224+2\times\) length of filename, the byte size of the directory is \(264+2\times\) length of filename, and its each piece (includes all copy blocks) of metadata requires \(152+72\times\) the number of copies. DataNode is used to store the files in the form of data block. In Hadoop1.x, the default data block sizeis 64 MB, which is the smallest storage unit for NameNode management. Files are divided into the data blocks according to the setting, and the data blocks and backups will be distributed evenly in the cluster. Too many small files can overburden the metadata of NameNode. HDFS is more suitable for large files storage. HBase can more efficiently manage and store the small files. HBase, a distributed column-oriented database, runs on the top of HDFS. Therefore, it could be used as a direct input/output source of MapReduce, combining with Hadoop seamlessly [48]. Regions are the basic elements of availability and distribution for a table. Each table in HBase can be split into multiple regions by row. As a region reaches the size threshold, it will split again. These different regions will be stored in multiple Region Servers by load balancing. In each region, data is organized into multiple Stores. A Store corresponds to a Column Family for a given region in a table. For small files, HBase can merge them into Stores. Hence, HBase has a more efficient way to store large amount of small files. Metadata is important for reproducible experiments and the reliability and applicability of experimental data. The experimental data without metadata is almost meaningless for the researchers. Therefore, different metadata information is required to describe different plant electrical signals recording methods and experimental protocols. Considering the characteristics of plant electrophysiological experiments, we established a two-level metadata description. The first level is the dataset metadata, and the second level is the experimental metadata of the respective record file. The information about the dataset and three kinds of plant electrophysiological methods are shown in Table 3.
## 4 Experimental Results
### Results of Key Frame Detection  
In order to measure the accuracy of the key frame detection, the key frames of all samples observed and labeled, manually, were regarded as the ground truths. The distance \(\mathrm{D}^{\mathrm{(f)}}\) of the key detected frame \(\mathrm{f_{p}}\) and the manual ground truths \(\mathrm{f_{g}}\), was calculated using the equation:  
\[\mathrm{D}^{\mathrm{(f)}}=\left|\mathrm{f_{p}}-\mathrm{f_{g}}\right| \tag{4}\]  
The accuracy of the key frame detection could be measured by calculating the percentage of the key frames for which the distance \(\mathrm{D}^{\mathrm{(f)}}\) was within a threshold in the frames. The accuracy of the key frame detection for each cover test was given, as shown in Table 2.  
Taking the unilateral cover test as an example, the detection accuracy was 93.1%, 97.9%, and 97.9%, at a distance of within 2, 4, and 6 frames, separately. As we can see, the detection rate in the alternating cover test was lower than that in others within the 2 and 4 frames intervals. This could be attributed to the phantom effects which might occur with the rapid motion of the occluder. It might interfere with the detection in the related frames, as the residual color of the trace left by the occluder merges with the color of the eyes. The movement of the occluder between two eyes brings more perturbation than that on one side. The detection rate appears good results for each cover test when the interval is set within 6 frames. As the deviation calculation method (Section 3.5) relaxes the reliance on key frame detection, our method could get a promising result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Unilateral Cover Test** & **Alternating Cover Test** & **Cover-Uncover Test** \\ \hline \(\mathrm{D}^{\mathbf{(b)}}\leq\mathbf{2}\) & 93.1\% & 62.5\% & 85.4\% \\ \(\mathrm{D}^{\mathbf{(b)}}\leq\mathbf{4}\) & 97.9\% & 88.2\% & 89.6\% \\ \(\mathrm{D}^{\mathbf{(b)}}\leq\mathbf{6}\) & 97.9\% & 97.2\% & 91.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy of the key frame detection for cover tests.  
Figure 8: An example of the iris measure in 10 consecutive frames. The iris boundary located by the methodology is marked by the red circle. The radius of the iris was determined to be 33 pixels, according to this strategy.
## 2 Materials and Methods
### Experimental Setting  
Each subject was asked to exercise on a cycle ergometer, starting from an initial power output of 20 W, followed by a continuous ramped increase in power output of 30 W\(\cdot\)min\({}^{-1}\) up to 380 W. Each subject was equipped with a wearable device (BioHarmess 3.0 by Zephyr, Annapolis, MD, USA) [19; 20] used to record the heart rate (HR\({}_{\text{WD}}\)) and the breathing rate (BR\({}_{\text{WD}}\)) with sampling frequency of 1 Hz. Moreover, each subject was also equipped with a metabolic cart (Quark CPET by Cosmed, Rome, Italy), a type of spirometer which is able to measure the oxygen consumption and carbon dioxide production.  
This system was used to record breath-by-breath tidal volume (TV), heart rate (HR\({}_{\text{REF}}\)), and breathing rate (BR\({}_{\text{REF}}\)), which served as reference instrumentation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Age** & **Height** & **Weight** & **BMI** & **BSA** \\  & **(years)** & **(cm)** & **(kg)** & **(kg/m\({}^{2}\))** & **(m\({}^{2}\))** \\ \hline
1 & 42 & 170 & 69.9 & 24.2 & 1.81 \\
2 & 51 & 180 & 81.0 & 25.0 & 2.01 \\
3 & 52 & 184 & 75.0 & 22.0 & 1.97 \\
4 & 45 & 176 & 72.8 & 23.5 & 1.86 \\
5 & 53 & 180 & 73.5 & 22.7 & 1.93 \\
6 & 46 & 171 & 65.2 & 22.3 & 1.76 \\
7 & 27 & 175 & 64.2 & 21.0 & 1.78 \\
8 & 26 & 169 & 68.8 & 24.1 & 1.79 \\
9 & 40 & 179 & 77.2 & 24.1 & 1.96 \\ \hline All & 42 \(\pm\) 10 & 176 \(\pm\) 5 & 78.0 \(\pm\) 4.2 & 22.7 \(\pm\) 2.1 & 1.87 \(\pm\) 0.09 \\ \hline \end{tabular} BMI: body mass index (kg/m\({}^{2}\)); BSA: body surface area (m\({}^{2}\)). Data of all subjects are reported as mean \(\pm\) standard deviation.  
\end{table}
Table 1: Characteristics of the volunteers.
## 5 Conclusions and Outlook  
In this manuscript the performances of two acoustic detection modules for the detection of ethane in the near IR range (1684 nm) have been compared. The two ADMs are composed of a spectrophone and a current-to-voltage transducer preamplifier having a gain-bandwidth optimized for the resonance frequency of the employed QTF. The two spectrophotones employ a standard 32.7 kHz quartz tuning fork (SP1) and a custom 12.4 kHz quartz tuning fork (SP2), respectively, acoustically coupled with a pair of micro-resonator tubes. For each ADM a preliminary test allowed identifying the best operating conditions. As already demonstrated in the mid-IR range as well as in the near-IR range operating with custom QTFs is preferable. Indeed, SP2 demonstrated a better QEPAS sensor long-term stability with respect to SP1. Furthermore, the SNR measured using SP2 was 3.3 times greater than the value measured using SP1, thus leading to a lower minimum detection limit. The linearity of photoacoustic detection was also proven for SP2 spectrophone over a wide C\({}_{2}\)H\({}_{6}\) concentration range, from 100 ppm up to 1%, with a conversion factor of 0.00627 mV/ppm. The sensor presented in this work represents the first step for the development of a prototype lightweight, low-power consumption, high-sensitivity, robust sensor system, allowing installation of the sensor on UAVs and real-time in situ ethane gas leak detection.  
Conceptualization, all (F.S., G.M., S.D.R., A.S., P.P., M.G., E.R., V.M.N.P., F.K.T. and V.S.); methodology, All; investigation, F.S., G.M., A.S., P.P. and M.G.; data curation, All; writing--original draft preparation, F.S. and G.M.; writing--review and editing, All; supervision, V.S. All authors have read and agreed to the published version of the manuscript.  
The authors from Dipartimento Interateneo di Fisica di Bari acknowledge the financial support from the European Union's Horizon 2020 research and innovation program under the [PERSON] project OPTAPHI, grant No. 860808 and from THORLABS GmbH within the joint-research laboratory. [PERSON] acknowledges support by the Robert Welch Foundation (Grant No. C0586).  
Authors acknowledge Eblana Photonics for the design and realization of a custom pigtailed laser diode having center emission wavelength of 1684 nm.  
The authors declare no conflict of interest.  
Figure 7: Spectral scan of the ethane absorption line for eight different concentrations of C\({}_{2}\)H\({}_{6}\):N\({}_{2}\), in the range 100 ppm—1%, plotted as a function of injected current. In the inset, a detail of the spectra acquired for 100 ppm and 500 ppm C\({}_{2}\)H\({}_{6}\):N\({}_{2}\) concentrations are shown.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### Hybrid Algorithms  
[PERSON] et al. [18] proposed an hybrid heuristic framework incorporating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. It is worth noting that the PSO algorithm is recognized for its quick search speed, whereas the ACO algorithm is particularly known for its ability to achieve greater precision. The authors considered factors such as completion time, energy consumption, and reliability.  
[PERSON] et al. [69] introduces a hybrid multi-criteria decision-making (Hybrid-MCD) model which minimize application response time by integrating iterative critical path search and layer-based priority rationing (LPR). Moreover, their application placement strategy utilizes a Deadline-aware stepwise Reliability Optimization (DARO) algorithm  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Task Scheduling Algorithms** & **Use in Literature** \\ \hline Integer Linear Programming & [31; 32] \\ Heuristic and Meta-heuristic & [26; 34; 38; 52; 68] \\ Hybrid & [18; 28; 58; 69] \\ Reinforcement Learning & [8; 72; 54; 64; 72; 74] \\ Deep Reinforcement Learning & [3; 5; 15; 16; 19; 35; 36; 41; 43] \\ Fuzzy Logic & [30; 51; 71; 75] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Techniques to solve the task scheduling problem.  
to reassign tasks along the non-critical path. This process serves to elevate the overall reliability of the application.  
[PERSON] et al. [58] introduced a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm to addresses energy-aware task scheduling. The authors consider leveraging the Dynamic Voltage and Frequency Scaling (DVFS) technique to regulate the energy consumption of fog nodes by adjusting their voltage and frequency levels based on workload demand.  
The study conducted by [PERSON] et al. [28] aimed to decrease task completion time and energy consumption. Their proposed approach utilizes a population-based meta-heuristic technique crafted to choose the most optimal fog nodes. This novel approach, called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA), combines Whale optimization algorithm (WOA), Opposition-based learning, and Chaos theory.