esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Verification of Developed 3D FDEM for Modeling of RC Structure Collapse
### Modeling Procedure of Three-Point Bending Test for RC Beam Fracturing  
The three-point bending test of a supported RC beam subjected to monotonically increasing three-point loads [34] was selected (Figure 6) to validate the rebar model. The following FDEM simulations use the calibrated values listed in Tables 2 and 3, because the mechanical properties of 35 MPa-Concrete used in [34] are similar to those of 35 MPa-concrete used in our previous research [33]. Physical and mechanical properties of the rebar such as diameter (d = 8 mm), elastic modulus (\(E_{steel}=200 GPa\)) and yield stress (\(\sigma_{y}=300 MPa\)) are used as the input parameters in the rebar model. Figure 7 depicts the 3D FDEM model, including concrete, and this model is used to simulate the three-point bending testing for the RC beam. Since FDEM is a mesh-dependent method in which crack paths are affected by element size, several average mesh sizes, \(h_{ave}\) (=100 mm, 50 mm, 25 mm and 12.5 mm), are chosen to investigate the effect of the element size on the fracture process of the RC structure. The load was applied with a critical damping scheme to achieve a quasi-static loading state [29] using a loading rod whose velocity is kept to 1 mm/s. The simulation is continued until the RC beam is collapsed. The FDEM simulation results analyses were finally compared to the experimental results [34].  
Figure 5: RJE constitutive model in (**a**) axial and (**b**) tangential directions (arrows indicate loading, unloading and reloading paths).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Unit** & **Value** \\ \hline Density, \(\rho\) & kg/m\({}^{3}\) & 2200 \\ Young’s modulus, \(E\) & GPa & 30.0 \\ Poisson’s ratio, \(\
u\) & - & 0.20 \\ Indirect tensile strength, \(S_{T}\) & MPa & 2.62 \\ Cohesion, \(c\) & MPa & 7.87 \\ Internal friction angle, \(\phi\) & Degrees & 35.0 \\ Uniaxial compressive strength, \(S_{c}\) & MPa & 35.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimentally investigated physical and mechanical parameters of 35 MPa concrete in previous research [33].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**FDEM Parameter** & **Unit** & **Unit** \\  & & **Concrete** \\ \hline Density (\(\rho\)) & kg/m\({}^{3}\) & 2200 \\ Young’s modulus (\(E\)) & GPa & 30.0 \\ Poisson’s ratio (\(\varphi\)) & - & 0.20 \\ Microscopic tensile strength (\(S_{T}\)) & MPa & 2.10 \\ Microscopic cohesion (\(c\)) & MPa & 6.70 \\ Microscopic internal friction angle (\(\phi\)) & Degrees & 35.0 \\ Microscopic mode I fracture energy (\(G_{lf}\)) & N/m & 30.0 \\ Microscopic mode II fracture energy (\(G_{lll}\)) & N/m & 90.0 \\ Artificial cohesive penalty (\(P_{open}\), \(P_{tan}\)) & GPa & 3000 \\ Artificial cohesive penalty (\(P_{overlap}\)) & GPa & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibrated input parameters for the FDEM simulation of fracture behaviors in the concrete structures [33].  
Figure 6: Schematic diagrams showing (**a**) the RC beam and (**b**) the center cross-sectional view of the beam for the three-point bending test.
## 3 Results
### IoT Framework: Hardware and Software Architecture  
Following the selection of sensor devices, a suitable system hardware and software architecture were designed for the end-to-end data collection system. This end-to-end architecture, shown in Figure 5, describes all elements of the monitoring system including network access technologies and required access points or gateway devices, internet backhaul elements, device-specific cloud storage or management platforms, the unified data management system, and potential end-user applications.  
Various gateways and access points were required to facilitate the connectivity of the chosen devices to the wider internet and relevant cloud platforms for the hardware architecture system. All WiFi-based devices were connected via industrial grade Telktonika RUT-240 4G access points. These provided WiFi networks that each sensor could independently connect to and backhaul communication to the Internet via a 4G cellular connection. As LoRaWAN devices communicate with a cloud-based network server, we used a Dragino LPS-8 LoRaWAN gateway and connected it via wired ethernet to a Teltonika 4G access point to create a link from the LoRaWAN gateway to the internet. The flow of data largely moved from the raw sensed parameters over a range of different network technologies before entering what can be considered cloud or internet-based software systems.  
The software architecture of the implemented system consisted of three main components. These were the manufacturer/device-specific cloud platforms, the unified data management system, and any end-user applications that handle the manipulation or visualization of collected data.  
Figure 4: An example of an open plan office with sensor locations.  
In the IoT Cloud Platforms block of Figure 5, the manufacturer and/or device specific cloud platforms can be seen. _The Things Network_ can be used to access the Dragino gateway and the SenseCap Barotmetric Pressure sensors. _Netatmo Connect_ allows access to the Netatmo weather stations. _Cloud Garden Sensorhub_ is used to access the Cloud Garden indoor air quality sensors. Finally, _iMonnit_ is the platform to access the open/close door sensors.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 2 Study Area and Datasets  
### Study Area  
There are three polynyas in the Ross Sea: RISP, MSP, and TNBP, and their locations are shown in Figure 1. RISP is located along the Ross Ice Shelf, between Ross Island to the west and the 180\({}^{\circ}\) meridian to the east. MSP is located in the McMurdo Sound to the west of Ross Island. TNBP is located north of the Drygalski Glacier Tongue. In this study, we focus on the ice production of RISP and MSP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temporal** & **Mean Daily Area (km\({}^{2}\))** & **Mean Annual Cumulative Volume (km\({}^{3}\))** & **Data Sources** & **References** \\ \hline
2000–2008 & RISP + MSP = 30,000 & RISP + MSP = 600 & SSM/I & [PERSON] et al. (2011) [38] \\ \cline{2-4}
1992–1999 & RISP + MSP = 350 & SSM/I & \\ \hline
2003–2011 & RISP + MSP = 300 \(\pm\) 20 & AMSR–E & \\  & RISP + MSP = 316 \(\pm\) 34 & SSM/I & \\ \hline
2013–2015 & RISP + MSP = 317 \(\pm\) 18 & AMSR2 & \\  & RISP + MSP = 340 \(\pm\) 32 & SSM/S & \\ \hline
2003–2017 & RISP = 164–313 & AMSR–E & \\  & AMSR2 & [PERSON] et al. (2019) [39] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Rate of Penetration Estimation with Parameter Correction
## 1 Introduction
### ROP Estimation  
The ROP is a critical parameter in drilling operations, directly influencing the efficiency, cost, and safety of wellbore construction. The ROP represents the speed at which the drill bit advances through the formation, and it is a key indicator of drilling performance. Accurate ROP measurement is essential for optimizing drilling parameters, such as WOB, RPM, and mud properties to achieve the desired drilling outcomes while minimizingoperational costs, see works on ROP optimization [12; 13; 14]. However, calculating the ROP accurately is challenging due to the complex interplay of various factors, including well depth, formation properties, and dynamic drilling conditions. Traditionally, the ROP is calculated by dividing the depth increment drilled over a specific time interval by the time taken to drill that increment. This method, while straightforward, can lead to inaccuracies due to several factors that are often overlooked, such as drill string elongation, wellbore trajectory, and real-time variations in drilling parameters. Moreover, as drilling progresses to greater depths, the physical and dynamic conditions of the wellbore change, further complicating the accurate calculation of ROP.  
The relationship between well depth and the ROP is fundamental to drilling operations. In [5], it is highlighted that well depth directly influences ROP, as the depth measurement is integral to calculating penetration rates. Their research emphasized the need for accurate depth correction to avoid significant errors in ROP estimation, particularly in deep wells where elongation effects become more pronounced. In [6], correction models are presented to account for many factors, like drill string elongation, dynamic conditions, and thermal expansion to enhance accuracy. In [15], the authors provided a comprehensive survey of filtering techniques, including their applications to depth measurement in drilling operations. Their work highlights the importance of these techniques in improving the accuracy of depth measurements in complex drilling environments.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 1 Introduction
The experimental validation for SNFSs was carried out in [29] by [PERSON] et al. later on. [PERSON] studied different variants of the jerk equation using simple nonlinear terms [11; 30] that could be easily practically realizable compared to the nonlinear component of the Chua's circuit [31; 32]. The jerk equation has the third derivative of '\(x\)' (position), where the 1 st derivative is the velocity, the 2 nd derivative is the acceleration, and the 3 rd derivative is the Jerk. A detailed analysis for the family of jerky equations to exhibit chaotic behavior was done in [33]. In the last decade, many researchers have proposed their chaotic circuit based on a jerk equation. The simplest of all was reported in [22], which utilized a single Op-Amp circuit with a FET operating in the triade mode as a source of non-linearity. Other notable contributions were from [PERSON] et al., who used hyperbolic and exponential nonlinear terms with the jerk equation [34; 35]. Recently [PERSON] et al. introduced a more generalized exponential non-linearity \(\phi k(x)=0.5(exp(kx)-exp(-x))\), which reduces to hyperbolic sine function for \(k=1\), with jerk equation for producing chaos. Although in most of the literature reported, authors have used nonlinear function with the jerk equation. Still, only a single effort has been reported where a discrete chaotic map is used with the jerk equation as a nonlinear function [9]. In this paper, a discrete sine map is added as a nonlinear function in the jerk equation to get the single scroll chaotic attractor. The proposed system is vibrant in terms of chaotic behavior and can be easily implemented using any generic Op-Amp with very little hassle. The rest of the paper is organized as follows: Section 2 outlines the problem description; Section 3 briefly discusses the proposed methodology. In Section 4, eigenvalue analysis for the proposed chaotic system is carried out; Section 5 summarizes the detailed numerical simulations for the proposed system, Experimental validation is mentioned in Section 6, discussion and conclusion are summed up in Sections 7 and 8.
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 3 Analytical Solutions of a Wall-Free Stagnation-Point Flow and Their Singularities
### Case \(k_{2}\
\begin{split}\text{(i)}&\text{If }k_{1}^{2}-k_{2}^{2}>0 \text{ and }k_{1}^{2}-k_{2}^{2}\
eq 1:\\ &\quad T_{11}=k_{2}\bigg{(}F_{1}\cdot y^{\frac{1+\omega}{W_{1}}}+F_ {2}\cdot y^{\frac{1-\omega}{W_{1}}}\bigg{)}+\frac{k_{2}(k_{3}+2)-(k_{1}+1)(k_{ 3}-2)}{1-k_{1}^{2}+k_{2}^{2}},\\ &\quad T_{22}=(k_{1}+\omega)F_{1}\cdot y^{\frac{1+\omega}{W_{1}}} +(k_{1}-\omega)F_{2}\cdot y^{\frac{1-\omega}{W_{1}}}+\frac{(k_{1}-1)(k_{3}+2)- k_{2}(k_{3}-2)}{1-k_{1}^{2}+k_{2}^{2}},\\ &\quad T_{12}=F_{3}\cdot y^{\frac{1}{W_{1}}}.\end{split}  \tag{68}\]  
\( \begin{split}\text{(ii)}&\text{If }k_{1}^{2}-k_{2}^{2}=1, \text{ the solutions take the form:}\\ &\quad T_{11}= k_{2}\bigg{(}F_{1}\cdot y^{\frac{2}{W_{1}}}+F_{2}\bigg{)}-\frac{1}{2W_ {1}}[k_{2}(k_{3}+2)-(k_{1}+1)(k_{3}-2)]\ln y,\\ &\quad T_{22}= (k_{1}+1)F_{1}\cdot y^{\frac{2}{W_{1}}}+(k_{1}-1)F_{2}-\frac{k_{1}- 1}{2k_{2}W_{1}}[k_{2}(k_{3}+2)-(k_{1}+1)(k_{3}-2)]\ln y\\ &\quad-\frac{1}{2k_{2}}[k_{2}(k_{3}+2)-(k_{1}-1)(k_{3}-2)],\\ &\quad T_{12}= F_{3}\cdot y^{\frac{1}{W_{1}}}\end{split} \] (69)  
\( \begin{split}\text{(iii)}&\text{If }k_{1}^{2}-k_{2}^{2}=0:\\ &\quad T_{11}=k_{2}F_{1}\cdot y^{\frac{1}{W_{1}}}+\frac{k_{2}}{W_ {1}}F_{2}\cdot\ln y\cdot y^{\frac{1}{W_{1}}}+\frac{k_{2}(k_{3}+2)-(k_{1}+1)(k_{ 3}-2)}{1-k_{1}^{2}+k_{2}^{2}},\\ &\quad T_{22}=k_{1}F_{1}\cdot y^{\frac{1}{W_{1}}}+F_{2}\cdot y^{ \frac{1}{W_{1}}}+\frac{k_{1}}{W_{1}}F_{2}\cdot\ln y\cdot y^{\frac{1}{W_{1}}}+ \frac{(k_{1}-1)(k_{3}+2)-k_{2}(k_{3}-2)}{1-k_{1}^{2}+k_{2}^{2}},\\ &\quad T_{12}=F_{3}\cdot y^{\frac{1}{W_{1}}}.\end{split} \] (70)  
\( \begin{split}\text{(iv)}&\text{If }k_{1}^{2}-k_{2}^{2}<0:\\ &\quad T_{11}= k_{2}\Big{[}F_{1}\cdot\cos\Big{(}\ln y^{\frac{\omega}{W_{1}}} \Big{)}+F_{2}\cdot\sin\Big{(}\ln y^{\frac{\omega}{W_{1}}}\Big{)}\Big{]}\cdot y ^{\frac{1}{W_{1}}}\\ &\quad+\frac{k_{2}(k_{3}+2)-(k_{1}+1)(k_{3}-2)}{1-k_{1}^{2}+k_{2 }^{2}},\\ &\quad T_{22}= \Big{[}(k_{1}F_{1}+\omega F_{2})\cdot\cos\Big{(}\ln y^{\frac{ \omega}{W_{1}}}\Big{)}+(k_{1}F_{2}-\omega F_{1})\cdot\sin\Big{(}\ln y^{\frac{ \omega}{W_{1}}}\Big{)}\Big{]}\cdot y^{\frac{1}{W_{1}}}\\ &\quad+\frac{(k_{1}-1)(k_{3}+2)-k_{2}(k_{3}-2)}{1-k_{1}^{2}+k_{2 }^{2}},\\ &\quad T_{12}= F_{3}\cdot y^{\frac{1}{W_{1}}}.\end{split} \] (71)  
Here, \(\omega=\sqrt{k_{1}^{2}-k_{2}^{2}}\), \(F_{1}\), \(F_{2}\) and \(F_{3}\) are arbitrary functions of \(xy\). The logarithmic singularity caused by the term \(\ln y\) in the solution (69) can only be avoided under the special circumstance \(k_{2}(k_{3}+2)-(k_{1}+1)(k_{3}-2)=0\). Similar to the cases in the last subsection, satisfying the compatibility condition yields three to four restriction equations on the functions \(F_{1}\), \(F_{2}\), and \(F_{3}\) according to the relationship between \(\omega\) and \(k_{1}\). Furthermore, to avoid the singularity near the stagnation point \((x,y)=(0,0)\), the obtained final stress distribution either depends on only one variable or is again meaninglessly uniform. These tedious derivations are not given.
# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 2 The Proposed System Overview  
The main objectives of this work are summarized as follows:  
* Development of a novel approach/method to detect hidden suspicious abnormalities in ROIs that are partially or completely masked by surrounding tissue by taking into account the local breast density as recognized by a CNN classifier that marks the tissue ACR into four levels (A, B, C, D).
* Elimination of the masking effect due to the surrounding tissue on the examined ROI by transforming the ROI's ACR level into the A category. This procedure is achieved using a GAN/CycleGAN topology that cycles through the ACR levels.  
* Improvement of the overall abnormal region detection performance using a CNN network architecture based on a fine-tuned VGG16 network and extended tests on five of the most well-known datasets in the field. The proposed CAD system used in this work is illustrated in Figure 2. It consists of three main modules: (a) the data preparation module where image preprocessing and data augmentation take place, (b) the deep learning module where breast tissue segmentation, breast density transformation, and suspicious region detection are performed, and finally, (c) the evaluation module where annotation of abnormal regions on the given mammogram under examination is performed. To make our system more robust and to consider the different types of mammograms acquired during examination (fully digital, as well as scanned X-ray film), we thoroughly tested our system using five different datasets containing digital and film-scanned mammograms.
## 5 Materials and Methods
### Performance Comparison Methodology
#### 5.6.3 Computational Environment  
The experiments were conducted on a system with the following configuration:  
* Processor: Intel(r) Core(tm) i7 CPU at 3.4 GHz.
* Memory: 16 GB RAM.
* Software: Python 3.9, TensorFlow/Keras for LSTM training, NumPy and SciPy for numerical computations.  
This evaluation framework ensures a fair comparison between the proposed approach and baseline optimization methods.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
## 4 Architecture of the Proposal
Low data rates, an asymmetric performance of the uplink and downlink channels, and significant delays are the main reasons why datagram-based protocols are preferable. It is important to note that our network interface is designed to be adapted to different LPWAN technologies. For this, the MI interface should be implemented to communicate with the modem used, as well as the CI (if this is not implemented by the same modem). As said above, our reference implementation uses LoRaWAN, and MI uses AT commands to talk with the modem. CI is implemented with SPI communication within the LoRa transceiver. Although the LPWAN network interface is mainly described from a sender point of view, it is important to note that the inverse process is needed when an LPWAN packet is received from the wireless medium. The RI interface prevents the OBU from receiving packets from unauthorized senders. Moreover, when an entering packet is received, a server socket is supposed to be listening in a proper port.
## 4 Experimental Results and Discussion
### Performance Evaluation of 3D Object Generation and Processing Speed  
To assess the efficiency of the proposed vector-based 3D object generation method, a comprehensive performance evaluation was conducted based on total processing time, with a dataset size of up to 1 GB.  
The evaluation criteria included the following:  
* Vector data loading.
* Mesh generation.
* DEM mapping.  
The proposed method was compared against Method A [18] and Method B [21] to verify its superiority.  
The total processing time was measured from the start of 3D object generation to the final output, including vector data conversion, texture mapping, and mesh generation.  
The experimental results demonstrate that the proposed method achieved an average processing time of 15 s, which is 300% faster than Method A Table 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Metric** & **Bing Maps** & **V-World** & **WIT** \\ \hline \multirow{2}{*}{1} & Edge Sharpness & \multirow{2}{*}{898.63} & \multirow{2}{*}{1471.99} & \multirow{2}{*}{3008.24} \\  & (Laplacian Variance) & & & \\ \multirow{2}{*}{2} & Contrast (Pixel & \multirow{2}{*}{71.32} & \multirow{2}{*}{75.35} & \multirow{2}{*}{78.87} \\  & Intensity STD) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 19: Objective Texture Quality Comparison.  
The breakdown of execution time per processing stage is summarized in the table below.  
The performance improvements achieved by the proposed method are summarized as follows:  
* The proposed vector-based approach significantly reduces total processing time, demonstrating a threefold improvement over Method A.
* Unlike previous methods that lack a structured mesh generation and DEM integration step, the proposed approach optimally processes spatial data, enabling efficient 3D object creation.
* The results indicate that the proposed vector-based method is highly suitable for a large-scale 3D modeling technique, ensuring faster data transformation, texture application, and terrain mapping.  
These findings confirm that the proposed techniques outperform conventional approaches in terms of processing speed and computational efficiency, making them ideal for real-time and high-resolution 3D geospatial applications.
## 6 A Feasibility Analysis of the Optimization Method for the Finished State
### Calculation of the Influence Matrices  
Following the procedure outlined in Figure 2, the influence matrix for the cable forces on the structural displacements, internal forces, and cable forces was first calculated, as shown in Figure 4. This allowed for the determination of the most sensitive cable locations for various states of the main girder and pylons. Due to the symmetrical nature of the bridge in the longitudinal direction, only one half of the structure was considered in the influence matrix calculation. A unit cable force of 1000 kN was employed.  
Figure 4: Influence matrix of cable force on values of selected structural responses. (**a**) Vertical displacement of main girder; (**b**) horizontal displacement of pylon; (**c**) bending moment of main girder; (**d**) bending moment of pylon; (**e**) cable force.  
Figure 4a indicates that cable stays J22 and A19 had the greatest influence on the vertical displacement of the main beam. Figure 5 exhibits the maximum influence on the vertical displacement of the main girder. Under a unit cable force of J22 on the main-span side, the maximum upward deflection of the main girder was 101 mm, occurring mid-span. Under a unit cable force of A19 on the side-span side, the maximum downward deflection of the main girder was 12 mm, occurring near \(2L/3\) of the adjacent side span. The cable forces of J22 and A22 exhibited the maximum influence on the horizontal displacement of the pylon. Under a unit cable force of J22 on the main-span side, the horizontal displacement of the pylon top was 0.16 mm towards the mid-span side. Under a unit cable force of A22 on the side-span side, the horizontal displacement of the pylon top was 15 mm towards the side-span side.  
Figure 4c shows that cable stays J22 and J2 had the greatest influence on the bending moment of the main girder. Figure 6 demonstrates that the cable forces of J22 and J2 had the greatest influence on the bending moment of the main girder. The unit force of cable J22 resulted in a maximum positive bending moment of 7766 kN-m in the main beam, occurring near \(2L/5\) of the main span. Under the unit cable force of J2 near the pylon, the maximum negative bending moment of the main girder was \(-9584\) kN-m, occurring at the anchor section of cable J2. The cable forces of A15 and A22 had the greatest influence on the bending moment of the pylon. Under the unit cable force of A15 on the side-span side, the maximum positive bending moment of the pylon was \(4042\) kN-m, occurring at the upper-middle part of the upper pylon. Under the unit cable force of A22 on the side-span side, the maximum negative bending moment of the pylon was \(-26\),\(855\) kN-m, occurring at the bottom of the pylon.  
Figure 5: Displacement evoked by the unit cable force: (**a**) vertical (main girder); (**b**) horizontal ( pylon).  
Figure 6: Bending moment evoked by the unit cable force in (**a**) the main girder and (**b**) the pylon.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 3 Approach  
The overall framework of the MSIQA is shown in Figure 1. In the first stage of training, the multi-scale contour prediction network is trained to predict the contour features of diverse scale pictures in scale-spaces. In the second stage of training, the MSIQA model combines the central attention peripheral inhibition module to learn to predict the quality score of the image.  
Figure 1: Illustration of the MSIQA model. The model consists of a multi-scale Contour Prediction Network and a Central Attention Peripheral Inhibition Module. The solid line is the first step of training, and the dashed line is the second step of training.