esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Participants and Sampling
The lower limit of detection (LLD) calculation of institute #1 used the EPA IO-3.3 method, which was developed and applied to XRF to analyze ambient and aerosol samples using an energy and wavelength dispersion spectrometer. It was able to sample inhalable atmospheric particulate matter (<10 um) with a dichotomous sampler, and analyzed specific metals by X-ray fluorescence [40]. Institute #2 XRF analysis was performed on a Thermo ARL Quant'X EDXRF instrument (Waltham, MA, USA) using 6 different excitation conditions under atmospheric vacuum to enhance the response of the light elements. It provides low-volume, high-volume, and trace element quantification for a wide range of samples including bulk solids, thin films, and liquids. It is also ideal for elemental analysis. The output of power ranging in the 6 excitation conditions is 4-50 kV to achieve maximum response for the 33 project-specific elements. A calibration curve was developed using 25 mm element thin film calibration standards. The certified standards were purchased from MicroMatter Inc. (Vancouver, BC, Canada), and are stored away from light in ambient temperatures. With each analytical run, a certified multi-element thin film standard is analyzed to verify the instrument functionality and stability across the 6 excitation conditions. The QC acceptance for the multi-element standard is less than 5% coefficient of variation and a recovery of 90-110%. Additionally, replicate samples were analyzed at a rate of 10% of the total number of samples. The elemental analysis for institute #3 was conducted using an Epsilon 5 EDXRF spectrometer (PANalytical, The Netherlands) which utilizes secondary excitation from 10 secondary selectable targets. Epsilon 5 ED-XRF spectrometer provides easy sample preparation, rapid sample throughput, and Epsilon 5 EDXRF spectrometer is capable of the required detection limits which is an important factor for the analysis. The spectrometer employs a 600 W dual (scandium/tungsten, Sc/W) anode X-ray tube, a 100 kV generator, and a solid state germanium (Ge) detector. A total of 48 MicroMatter XRF calibration standard polycarbonate films (MicroMatter Co., Vancouver, BC, Canada) were used for calibration of 48 elements ranging in atomic number from 11 (Na) to 82 (Pb). We also used the U.S. National Institute of Standards and Technology (NIST) standard reference material (SRM) 2783, which is to simulate ambient PM2.5 on filter media, for quality control of the analytical procedure.
# Computational Modeling of Ultrasound C-Scan Imaging Using Transmitted Signal Peak Density
## 2 Finite Element Analysis (FEA) Simulation
### Results and Discussion  
In the experimental work by [PERSON] et al., an intuitive threshold pressure magnitude was applied during the peak density calculation, which increased the image quality. The purpose of using the threshold value was to eliminate peaks with insignificant pressure magnitude. Similarly, in this study, a small threshold value of 0.02 Pa was applied during peak density calculation. The result shown in Figure 4 represents the peak density of the XY cross-section from the center of the \(Z\)-axis for all five sectors (Figure 2b). It was observed that peak density was higher in the sectors having a larger amount of pepper flake. Hence, Sector 1 had the lowest peak density, as there was no pepper flake in that sector. Sector 5 also showed a very low peak density instead of having a tiny portion of the pepper flake. During the image construction based on the peak density value, this kind of response (Sector 5) resulted in an image that did not visualize a portion of the structure (pepper flake) boundary. This issue was further discussed in the following sections.  
The received signal spectrum was also analyzed for the computational model described above. The purpose of this analysis was to explore the signal propagation in terms of the received signal pattern. In Figure 5, it was evident that in Sectors 1 and 5, the spectrums were similar to the Gaussian distribution of the incident pressure. This was because these sectors had very little or no amount of pepper flake, and the incident pressure field was almost uninterrupted by the pepper flake. In the case of Sectors 3 and 4, all the incident waves went through the pepper flake domain, and the incident pressure spectrum was very much affected by the solid domain.  
Figure 4: Peak density in all sectors at the center plane (Z = 0).  
Figure 3: Incident background pressure distribution.  
Figure 6 represents the C-scan image obtained from the computational model. From Figure 6b, it is evident that the pixel area containing the pepper flake had a higher value of peak density compared to the pixel areas having no pepper flake. The higher the peak density, the darker the pixel shade. The reconstructed image based on peak density values is shown in Figure 6c. Figure 6c delineates that almost all the pepper flake region was identified in the image except for the smaller region on the right side. This result was similar to the experimental results obtained by [PERSON] et al. (2020).  
To avoid the boundary detection issue and to image the object boundaries conclusively, the pixels could be selected in a way that the pepper flake sectors were completely covered by the flake. In that case, all the sectors containing pepper flake would return significantly higher peak density compared to the sectors that did not contain any pepper flake at all (Figure 4). Accordingly, pixel shades having pepper flakes would be more pronounced than the rest of the pixels.
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 3 The _CAVVPM_ Pof Protocol
### Parameter Selection
At the \(n\)th step, the required acceleration is \[\ddot{x}_{des}[n]=-\frac{1}{T}\big{(}\Delta\dot{x}[n]+\lambda\delta[n]\big{)}\] (1) \[\delta[n]=-d_{act}[n]+d,\Delta\dot{x}[n]=\dot{x}_{C}[n]-\dot{x}_{V}[n]\] (2) where \(\Delta\dot{x}[n]\) is the comparison between \(C\) and \(V\) in terms of velocity, \(d_{act}[n]\) is real swaying distance, \(\delta[n]\) represents the range uncertainty to \(d\), and \(\lambda>0\) regulates converging to \(d\). 2. Speed is applied using input from the preceding phase, rather than \(\ddot{x}_{des}\left[n\right]\). \[\ddot{x}[n]=\beta\cdot\ddot{x}_{des}[n]+(1-\beta)\cdot\ddot{x}[n-1],\beta=\frac {\Delta_{t}}{\tau+\Delta_{t}}.\] (3) The time interval between the (\(n\)\(-\)1)-st and \(n\)-th steps is denoted by \(\Delta t\), where \(\tau\) is commonly set to 0.5 s.
3. C's distance gain during \(\Delta t\) is calculated using this formula. \[l[n]=\dot{x}[n-1]\cdot\Delta_{t}+\frac{1}{2}\cdot\ddot{x}[n]\cdot\Delta_{t}^ {2}\] (4)
4. Step n's distance \([n]\) to checkpoint d is adjusted to this new value. \[M=\frac{(g_{max}-g_{min})\cdot U_{V}}{2\rho}+1\]0 (5)
5. Steps 1 to 4 are repeated until the desired result is achieved \(|\delta[n]|<\gamma\), In this example, \(\gamma\) is the checkpoint tolerance. Checkpoint d's \(t\) deadline is extended to \(t=\Delta t*n*\). This means that \(n*\) is the initial value of n, and \(\left.\left|[n\right]\right.<\gamma\).
## 3 Results
### Main Findings
#### 3.4.1 Instruments  
An element common to all the tests conducted is the use of a mechanical treadmill in the research protocol. Only [PERSON] et al. [29] conducted tests using a ramp. The kinematic analysis of running was usually carried out using optical systems [32; 33; 40; 43], vision  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Study** & **Sample (n)** & **Age (Years)** & **Height (cm)** & **Weight (kg)** \\ \hline [PERSON] et al. (2008) [29] & 8M, 5F & 22.3 \(\pm\) 2.8 & 174 \(\pm\) 10 & 69.9 \(\pm\) 13.2 \\ [PERSON] and [PERSON] (2005) [30] & 5M, 5F & 30.35 \(\pm\) 5.09 & 172 \(\pm\) 6 & 62.56 \(\pm\) 7.59 \\ [PERSON] et al. (2023) [31] & 19 ND & 34 \(\pm\) 10 & 175 \(\pm\) 10 & 68.5 \(\pm\) 12.2 \\ [PERSON] et al. (2015) [32] & 14M & 23.4 \(\pm\) 4.4 & 177.5 \(\pm\) 5.2 & 69.5 \(\pm\) 5.3 \\ [PERSON] et al. (2013) [33] & 14M & 23.4 \(\pm\) 4.4 & 177.5 \(\pm\) 5.2 & 69.5 \(\pm\) 5.3 \\ [PERSON] et al. (2012) [34] & 8 ND & 21.83 \(\pm\) 1.33 & 171 \(\pm\) 4.50 & 70.70 \(\pm\) 3.88 \\ [PERSON] et al. (2013) [35] & 18M & 33.0 \(\pm\) 8.5 & 171 \(\pm\) 4 & 62.6 \(\pm\) 5.2 \\ [PERSON] et al. (2023) [36] & 22M, 3F & 38.0 \(\pm\) 11 & 173 \(\pm\) 7 & 63 \(\pm\) 8 \\ [PERSON] et al. (2019) [37] & 15 ND & 25.6 \(\pm\) 4.27 & 177.0 \(\pm\) 5.0 & 75.38 \(\pm\) 5.02 \\ [PERSON] et al. (2011) [38] & 9M & 28.5 \(\pm\) 4.1 & leg length: & 69.1 \(\pm\) 5.3 \\  & & & 0.95 \(\pm\) 0.04 & & \\ [PERSON] et al. (2010) [39] & 9F, 10M & & 23.9 \(\pm\) 2.5 & F: 166 \(\pm\) 4.2 & F: 56.2 \(\pm\) 4.8 \\  & & & & 0.26.6 \(\pm\) 5.9 & M: 180.5 \(\pm\) 5.2 & M: 74.2 \(\pm\) 8.1 \\ [PERSON] et al. (2019) [40] & 12M & 34.40 \(\pm\) 6.93 & 174.08 \(\pm\) 6.59 & 72.67 \(\pm\) 5.30 \\ [PERSON] et al. (2020) [41] & 13M & ND & 181.10 \(\pm\) 6.59 & 71.0 \(\pm\) 9.0 \\ [PERSON] et al. (2021) [42] & 10M & 28 \(\pm\) 5 & 180.0 \(\pm\) 6.0 & 63.0 \(\pm\) 8.0 \\ [PERSON] et al. (2023) [43] & 11M, 9F & 25 \(\pm\) 6 & \multicolumn{2}{c}{BMI = \(20.76\pm 1.75\frac{kg}{m}\)} \\ \hline \hline \end{tabular}  
*—Female, M—Male, ND—not defined, BMI—Body Mass Index.  
\end{table}
Table 2: Characteristics of the group.  
systems [29; 34; 35; 37; 39], and force platforms [30; 31; 38]. Pressure insoles for shoes were used by only one author [42] as was the measuring wheel attached to the treadmill belt [41]. All research procedures were well and meticulously planned. The goals and research concepts of the various authors resulted in wide variations in data recording and research methods. The length of trials varied and depended on many factors. The longest trial was conducted by [PERSON] et al. [32]. In their experiment, participants performed a treadmill run for seven series of 5 min each, at a speed of 10 km/h and with independent step length and frequency, for a total of 14 series over a period of one week. Each of these series was conducted at different inclinations, starting at \(-8\%\) and ending at 8%. The shortest trial, on the other hand, was completed before [PERSON] et al. [30]. Each of his trials lasted only 1 min, and data were collected for the last 10 s of each trial.
## 6 Conclusions  
In this article, we have presented a long-term deployment in a large real-world industrial environment using the SUN (Smart Utility Network) modulations, as introduced in the IEEE 802.15.4-2015 standard. The resulting dataset shows that despite the increased communication range (i.e., links up to 273.5 m), the use of the SUN modulations alone cannot guarantee the PDR (Packet Delivery Ratio) requirements of industrial and smart grid applications (i.e., PDR \(>\) 99%) due to multi-path propagation and external interference effects present in such a challenging scenario. Taking that into account, in the future, we aim to use the presented dataset as a tool to explore and validate the suitability of novel diversity mechanisms, such as combining packet replication with modulation diversity, to increase the PDR of low-power wireless communications while meeting its energy and regulatory constraints (i.e., duty cycling). This goal is aligned with the objectives defined by the RAW (Reliable and Available Wireless) WG (Working Group) at the IETF (Internet Engineering Task Force), in which protocols to manage diversity will be developed as a tool to ensure dependable wireless communications.  
[PERSON]-[PERSON] conducted the experiments, analyzed the data, and contributed to writing the manuscript. [PERSON] contributed to analyzing the data and writing the manuscript. [PERSON] contributed to writing the manuscript. [PERSON], [PERSON], and [PERSON] contributed to conceptualizing the experiments, supervising the project, and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the Generalitat de Catalunya (SGR-60-2017) and Spanish Ministry of Science, Innovation and Universities (SPOTS RTI2018-095438-A-I00) grants. This project is co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of total cost eligible (e4M). The author [PERSON] also has the support of the Brazilian National Council for Scientific and Technological Development (CNPq 421461/2018-7).  
The authors would like to express their gratitude for the technical and economic support from JG Ingenieros S.A. and Montepino Logistic S.L. for the development of this study.  
The authors declare no conflict of interest.
## 6 Results and Discussion
### Descriptive Statistics of the Experimental Dataset Variables
#### 6.1.5 Porosity vs. Density  
* Porosity and density are inversely related (r \(\approx\) -0.72), confirming that greater void space corresponds to lower bulk density (second-row, first-column scatter).
# Shear Lag Effect on Box Steel Beams with Wide Curved Flanges
## 3 Review of Previous Research on Shear Lag Effect
Since flanged flexural members are likely to have plastic deformation at their ultimate limit state and the study of inelastic shear lag is limited, [PERSON] and [PERSON] [22] put extensive effort into the analytical modeling of inelastic shear lag, followed by the laboratory tests of two steel box beams. A comparison of the results indicated that the proposed analytical method can predict normal stress and strain distribution and therefore, beam deflection with decent accuracy. The authors in [23] marked that the beam axial equilibrium condition is not strictly satisfied according to conventional beam methods that imply that the neutral axis of the section coincides with the centroid. It is proposed that three separate functions have to define shear lag in the top slab, the bottom slab, and the cantilever slab in order to calculate the axial stresses and deflections on simply supported box girders more accurately. Recent research have shown the possibility of combining 1D and 2D FEM analysis in the shear lag of beams of arbitrary cross-sections [24]; the authors of a parametric numerical study in [25] have shown that in the case of a three-span continuous curved steel box girder bridge, the curvature radius and the width-span ratio have a significant influence on the shear lag effect apart from the height-span ratio. In [26], the authors have pointed out that (compression) plate buckling effects, which result in an effective area of the flange, may occur in addition to the shear lag effects. That was observed in the case of steel box girders of a crane runway, but it may be applied to all thin-walled steel sections, including bridges. In [27; 28; 29], special effort has been dedicated to the analysis of the shear lag effect on composite box girder bridges with corrugated steel webs, including the computation of deflections. Also, significant effort has been put in research regarding the shear lag effect of steel-Ultra-High-Performance Concrete (UHPC) ribbed slab composite structure (SU-RSCS) in the elastic range [30].
## 3 Results and Discussion
### Construction of Initial Dataset and Verification Set  
The results of uniform design experiments are analyzed by regression. According to the regression equation, the effects of the various contents of each filler on the mass ablation rate are shown in Figure 5a. There is a complex nonlinear relationship between the ablation rate and the amount of filler. Algorithms such as RF, GBDT, and AdaBoost were used to train the samples in the initial dataset. The fitting of the real samples is shown in Figure 5b; GBDT interprets the training set slightly better than AdaBoost. RF has the worst fit, but its R2 is close to 0.9. Therefore, GBDT was chosen as the first algorithm to train the initial dataset, and active learning of the pure exploitation strategies was further performed based on this model. Through four rounds of sampling in the sample pool, a total of eight groups of samples were selected and labeled in the exploitation strategy. Finally, experimental results of uniform design, verification experiment, and exploitation can be obtained, as shown in Figure 5c. It can be noted that the mass ablation rate of the verification samples selected from the regression analysis based on the results of uniform experimentation is significantly lower than that of the samples of uniform experimental designs. It indicates that the sample points chosen through uniform design are indeed representative, and the regression result effectively captures the relationship between the filler contents and the ablation rate.  
In comparison, the overall mass ablation rate for the samples selected by the pure exploitation strategy is significantly lower. It can be argued that the exploitation strategy can indeed find advantageous samples relatively quickly. However, the essence of such a strategy is to find the sample with the minimum prediction value of the MAR, so the collected samples may not improve the performance of prediction greatly, and it is often easy to fall into the local optimal situation. Therefore, it can be seen that although the training samples increase, the MAR of the new samples selected by the GBDT model gradually increases as well, implying a decline in the predictive capacity of the GBDT model. Finally, the samples selected by pure exploitation were combined with the two samples selected by regression analysis to form a dataset with a sample size of ten, which served as a test set for subsequent exploration and EI sampling methods.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 3 Finite Element-Based Analytical Model of Phase-to-Ground Line Spacing for Ice-Covered Lines
### Calculation of Ice and Wind Loads on Conductors  
#### 3.3.1 Ice Cover Load and De-Icing  
Ice cover increases the pressure of overhead lines and alters their mechanical properties. The cross-sectional shape and characteristics of the ice are influenced by factors such as climate, ice purity, and structural properties. For practical engineering estimates, a simplified approach assumes uniform ice distribution along the conductor. Figure 5 illustrates a schematic of the ice-covered conductor cross-section.  
The additional force simulation method models ice loading by applying equally spaced concentrated forces, where the mass \(M\) of each concentrated load is determined as follows:  
\[\left\{\begin{array}{l}m=\rho\tau b(D+b)\\ M=mgL/n\end{array}\right. \tag{6}\]  
where \(m\) denotes the mass per unit length of the ice cover on the conductor (kg/m); \(\rho\) and \(b\) represent the ice cover's density (900 kg/m\({}^{3}\)) and thickness (mm), respectively; \(D\) and \(L\) refer to the wire's outer diameter (mm) and length (m), respectively; \(g\) is the acceleration of gravity (9.8 N/kg); \(n\) represents the number of dividing units. In order to simplify the problem, this paper set the de-icing time to 0.02 s in the simulation and used the method of sudden load withdrawal to simulate the shedding process of the overlying ice.
###### Abstract
To further the understanding of the causes and environmental effects of haze episodes in central China, we conducted a comprehensive study in Wuhan during 6-14 June 2012. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Moderate-resolution Imaging Spectroradiometer (MODIS) active fire products were used to trace the source of the haze. In addition, the aerosol optical properties in Wuhan were measured using ground-based equipment that included an integrated nephelometer, aethalometer, meteorological station, and particle detector. The vertical distribution of the aerosols was obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite.
## 2 Related Work
### Spin Images  
Many papers describe the characteristics of local surfaces of 3D objects [27; 28; 29; 30; 31; 32]. Spin image representation, one of the local surface descriptors, has been widely used for face recognition and object classification. Originally, the technique was introduced to address the problems of surface matching in computer vision [25]. The generation of SIs on the surface of a 3D object can reveal the local structural properties around each surface point. Assume that oriented points are distributed in the 3D space, each point is represented as a pair of its position \(\mathbf{v}\) and normal vector \(\mathbf{n}\), as shown in Figure 0(a).  
An imaginary plane \(P\) that contains \(\mathbf{v}_{i}\) and has the same normal \(\mathbf{n}\) divides the 3D space around \(\mathbf{v}_{i}\) into two non-overlapping subspaces: the upper subspace above the plane and the lower subspace below it. Similarly, several imaginary planes that are parallel to \(P\) and equally spaced apart from each other also divide the space into smaller non-overlapping subspaces. Each subspace can be further partitioned radially from \(\mathbf{n}\) based on the distance to \(\mathbf{n}\), as shown in Figure 0(b). The SI for a certain oriented point (\(\mathbf{v}_{i}\), \(\mathbf{n}\)), denoted by \(S(\mathbf{v}_{i})\), is computed by encoding the coordinates of neighboring points, \(\mathbf{v}_{j}\), around \(\mathbf{v}_{i}\) into a 2D histogram with two indices, \(\rho\) and \(\gamma\), as shown in Figure 0(c). Specifically, the coordinates of the SI, \(\rho\) and \(\gamma\), indicate the indices of discrete intervals defined along the radial direction from \(\mathbf{n}\) and the direction of \(\mathbf{n}\), respectively. As a result, a bin of the SI represents a cylindrical space with a hole whose axis coincides with \(\mathbf{n}\), as shown in the left image in Figure 0(c), and  
Figure 1: SI representation for a reference oriented point (\(\mathbf{v}_{i}\), \(\mathbf{n}\)): (a) 3D point cloud and an imaginary plane \(P\) containing \(\mathbf{v}_{i}\) and having the normal, \(\mathbf{n}\); (b) the 3D space around \(\mathbf{v}_{i}\) is partitioned into non-overlapping subspaces; and (c) the 2D histogram of the spin image with axes \(\rho\) and \(\gamma\).  
the bin value indicates the number of points belonging to the space. The mathematical representation of the spin image coordinate of any 3D surface point, \(\mathbf{v}_{j}\), is mathematically given as  
\[\mathbf{v}_{j}\rightarrow(\rho,\gamma)=\left(\left\lfloor\frac{\sqrt{\|\mathbf{ v}_{i}-\mathbf{v}_{j}\|^{2}-(\mathbf{n}\cdot(\mathbf{v}_{i}-\mathbf{v}_{j}))^{2}}}{ \Delta\rho}\right\rfloor,\ \left\lfloor\frac{\mathbf{n}\cdot(\mathbf{v}_{i}- \mathbf{v}_{j})}{\Delta\gamma}\right\rfloor\right). \tag{3}\]  
The generation of SI is dependent on the bin sizes and image dimension. While the bin sizes \(\Delta\rho\) and \(\Delta\gamma\) determine the volume of each cylindrical subspace, the image dimension is the number of rows, \(N_{\gamma}\), and columns, \(N_{\rho}\), of the SI.