esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, based on experiments using scanning electron microscopy, XRD, rock pyrolysis, and nitrogen adsorption, the shale pores of K\({}_{2}\)n\({}^{1+2}\) in the Songliao Basin were studied, and the following conclusions are addressed based on the advantages of the fractal dimensions in characterizing pores.  
1. The samples of K\({}_{2}\)n\({}^{1+2}\) mainly had mineral intergranular pores and small numbers of organic pores and mineral intragranular pores. The pores were mainly wedge-shaped. The inorganic pores were much larger than the organic pores. The clay mineral and quartz contents had no clear control on the pore development in the K\({}_{2}\)n\({}^{1+2}\) shales.
2. The negative correlation between the fractal dimensions (D1 and D2) and the TOC content may be due to expansion of hydrocarbon, which was generated more in high-TOC intervals. Therefore, when extracting oil resources in the low-maturity shales of K\({}_{2}\)n\({}^{1+2}\) in the Songliao Basin, it would be beneficial to find the area with higher TOC contents, smaller fractal dimensions, and larger pore size. This provides a reference for the shale oil evaluation of K\({}_{2}\)n\({}^{1+2}\) in the Songliao Basin and complements the lacustrine pore characteristics, which provides a reference value for oil and gas exploration and development in the Songliao Basin and a quantitative evaluation of continuous lacustrine pore characteristics.  
Conceptualization, L.K. and Y.G.; methodology, T.D.; software, L.K.; investigation, T.D. and L.K.; writing--original draft preparation, T.D., L.K. and Y.G.; writing--review and editing, T.D., L.K., Y.G. and Y.Z.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Geological Survey Program (DD20190502).  
Figure 11: Relationship between fractal dimensions and oil content of K\({}_{2}\)n\({}^{1+2}\) and K\({}_{2}\)q shale samples, (**a**) for S1; (**b**) for OSI. The K\({}_{2}\)q compared to [78].
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 4 Improved System Architecture Based on FMEA Recommendation
### Step 1: FMEA Generation from SysML Diagrams  
FMEA is a reliability tool widely used in safety analysis. Its target is to identify the critical Failure Modes (FM) that lead to the dysfunction of the system. During the system design process, it also evaluates the effect of possible failure modes and then reduces the potential risks. FMEA is presented as a table that contains components, functions, FM, S, O, D, and the Risk Priority Number (RPN). The risk priority number is computed by the multiplication of the three parameters (RPN = S \(\times\) O \(\times\) D). It is widely used in engineering analysis. Each FM will be allocated with its RPN and then analyzed in order to give corrective actions [30]. The objective of these corrective actions is the elimination of the critical failure modes characterized by the higher RPN.  
The first step of this methodology is the generation of FMEA from SysML diagram described in Figure 2. After defining the system requirements as indicated in the previous section, the system function is established. The stored functions are used to automatically generate a functional FMEA that contains a list of functions and a generic FM. After the establishment of the system function, the structure of the system is defined and each component is allocated to its functions. This logical architecture is used to generate automatically the Preliminary FMEA (PFMEA), which contains components, functions, and failure modes. This PFMEA ensures the allocation of components to their functions and failure modes. Then, a safety expert completes the PFMEA with necessary information (S, O, and D) to obtain FMEA. For each failure mode, the RPN and the risk class are defined and stored with other information in the data store. For more details about the generation of FMEA from SysML diagrams please refer to [16].  
Figure 2: Detailed process of the general methodology.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment
### Key Performance Indicators
#### 6.3.3 Communication Delays  
For the observer agents, the delay is defined as the time between consecutive updates of their control signals, which they broadcast periodically to the actuator agents. For the actuator agents, the delay is defined as the time between consecutive updates of the set-points which are sent to the Triphase power hardware.  
Figure 14 shows the delays between the iterations of the control algorithm, for each agent individually. The observer agents are implemented by dedicated single-board computers with few other processes running in the background. They manage to update the control signals every 1.5 s, with little deviation. The actuator agents however experience longer control delays, with large differences between both actuators. There are two main causes for these additional delays. Firstly, the actuator agents solve an optimization problem at each iteration. Secondly, the actuator agents are implemented by laptops. These laptops run additionally control software for the Triphase Rapid Prototyping Inverter System, which requires rather heavy processing. The laptop running actuator 2 is older, which shows in the performance. Adapting the implementation of the algorithm for the actuator agents can lower the delays. The lower limit for the delays is 1.5 s, which is the period with which the observer agents send updates of the control signal.  
Overall, the delays are as expected. Only the delays for actuator 2 could be shorter to be in line with the other devices. Upgrading actuator 2 to hardware similar to actuator 1, should resolve these additional delays.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(V_{i}\)} & \multicolumn{3}{c}{\(E_{i}\)} \\ \cline{2-5}  & **Node 0** & **Node 1** & **Node 2** & **E** \\ \hline without control & 0 & 19.719 & 39.005 & 58.724 \\ \hline with control & 0 & 0.081 & 2.552 & 2.633 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Voltage quality metrics of the regulated and unregulated voltage profiles.  
Figure 13: Voltage quality metric: sum of the surfaces above and below the voltage limits.
## 3 Numerical Solution  
The nonlinear and coupled boundary value problem (BVP) given by Equations (1)-(4) is not amenable to analytic solution techniques. Direct numerical treatment of the equations as given is also very challenging. We deploy an innovative, robust, and efficient numerical approach that recasts the boundary value problem as a time -dependent initial-boundary value problem (IBVP). We then extract the steady solutions of the IBVP. Indeed, the steady-state solutions of the IBVP are the same as the solutions of the original BVP. The main advantage here is that the IBVP solutions are obtained efficiently, accurately, and with relative ease. In this work, we employ numerical solutions based on the Finite Difference Methods (FDM). Similar time-dependent numerical solutions for non-isothermal non-Newtonian (viscoelastic) fluid flow have been recently developed via Finite Volume Methods, see for example [24; 25].  
Equations (1)-(3) are, therefore, recast as the equivalent time-dependent IBVP,  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0, \tag{6}\]  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial u }{\partial y}=\frac{1}{\text{Re}}\,\frac{\partial}{\partial y}\left(\eta( \dot{\gamma},\Theta)\frac{\partial u}{\partial y}\right)+\frac{\text{Gr}}{ \text{Re}^{2}}\,\Theta, \tag{7}\]  
\[\frac{\partial\Theta}{\partial t}+u\frac{\partial\Theta}{\partial x}+v\frac{ \partial\Theta}{\partial y}=\frac{1}{\text{Re}\,\text{Pr}}\left(\frac{ \partial^{2}\Theta}{\partial x^{2}}+\frac{\partial^{2}\Theta}{\partial y^{2} }\right)+\frac{\eta(\dot{\gamma},\Theta)}{\text{Re}\,\text{Pr}}\left(\frac{ \partial u}{\partial y}\right)^{2}. \tag{8}\]  
In addition to the boundary conditions in Equation (4), the IBVP as represented by Equations (6)-(8) would need to be supplemented by appropriate initial conditions. We employ zero initial conditions,  
\[u(t,x,y)=0,\,v(t,x,y)=0,\,\Theta(t,x,y)=0,\quad\text{for}\quad t=0\quad\text{ and}\quad\forall(x,y)\in\Omega\backslash\delta\Omega, \tag{9}\]  
where,  
\[\delta\Omega=\left\{x=0\right\}\cup\left\{x=L_{x}\right\}\cup\left\{y=0 \right\}\cup\left\{y=L_{y}\right\},\]  
is the boundary to the domain \(\Omega\). The boundary conditions are also recast to include the time as,  
\[u(t,0,y)=0,\,u(t,L_{x},y)=0,\,u(t,x,0)=0,\,u(t,x,L_{y})=0,\,v(t,0,y)=0, \tag{10}\]  
\[\Theta(t,x,0)=0,\,\Theta(t,x,L_{y})=1,\,\Theta(t,0,y)=1,\,\Theta(t,L_{x},y)=0,\quad\text{for $t\geq 0$ and $(x,y)\in\delta\Omega$.}\]
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.3 Technological Infrastructure  
Developing DTs requires significant computational power to process and analyze extensive biological data and simulate real-time interactions. High-performance computing capabilities are essential to maintain the accuracy and predictive capabilities of DT models [69]. Furthermore, upgrading outdated IT infrastructure and ensuring robust connectivity are necessary to support the demands of DT implementation. The complex architecture of DT systems, including sensors, software platforms, and data processing tools, can pose substantial technical hurdles and necessitate significant investments in infrastructure [71].
## 4 Simulation Study
### Case Study 5: Comparison with the Conventional Method  
In this case study, we focus on analyzing the differences between the proposed method and some conventional methods. It has been previously mentioned that the main vulnerability analysis methods of power systems can be classified as the methods based on system stability and the ones based on complex network theory. The methods based on system stability (e.g., modal analysis based on the Jacobian matrix of the power flow equations, which aims at defining the sensitivity coefficient to assess the stability of the power system and uses the values of sensitivity indexes to identify the critical nodes or bus [8,9]) contain a mass of complex operations which make their analysis a very time-consuming and computation-intensive task. Therefore, it is not very suitable for the vulnerability analysis of large-scale power systems. As an alternative, the methods based on complex network theory have been proposed to assess the vulnerability of power systems in recent decades, which take both the electrical parameters and the topological characteristics of power systems into account in their analysis [18]. This method originated from graph theory and studies the subject systems from the point of view of structure and dynamic function of an array of nodes and lines without heavily relying on the system dimensionality [3], which makes it very suitable for the analysis of large-scale systems. However, as discussed above, most of the existing literature on this issue [3] and [11, 12, 13, 14, 15, 16, 17, 18] rigorously requires that the weight of each link must be known and does not consider the system's controllability, which seriously limits its applications in real systems. Compared with the conventional methods  
Figure 8: Identification results of the IEEE 300 bus system. (**a**) Initial controllability index distribution; (**b**) Increase in \(N_{D}\) of the network after arracks; (**c**) Controllability index distribution after ten targeted attacks. (**d**) Controllability index distribution after ten random attacks.  
mentioned above, the method in this paper presented a new way to analyze the vulnerability of power systems by considering the system structure controllability, which is suitable to assessing the vulnerability of large-scale power systems as it does not contain complex operations. Moreover, it only requires knowing if there is a direct connection between any two nodes but does not require knowing its accurate connection weight, which can overcome inherently incomplete weight information in real systems.
# Photovoltaic Cell and Module I-V Characteristic Approximation Using Bezier Curves
###### Abstract  
The aim of this work was to introduce new ways to model the I-V characteristic of a photovoltaic (PV) cell or PV module using straight lines and Bezier curves. This is a complete novel approach, Bezier curves being previously used mainly for computer graphics. The I-V characteristic is divided into three sections, modeled with lines and a quadratic Bezier curve in the first case and with three cubic Bezier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: \(V_{oc}\) (the open circuit voltage), \(I_{sc}\) (the short circuit current), \(V_{mp}\) (the maximum power corresponding voltage) and \(I_{mp}\) (the maximum power corresponding current), and the parasitic resistances \(R_{s\!s\!s\!0}\) (shunt resistance at \(I_{sc}\)) and \(R_{s\!s\!0}\) (series resistance at \(V_{oc}\)). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bezier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.  
PV cell; I-V characteristic; model; simulation; interpolation; Bezier curve; control points; least squares fitting method +
Footnote †: journal: Applied Physics Letters
# A Novel Interactive Fusion Method with Images and Point Clouds for 3D Object Detection
## 3 Experiments
### Kernel Design  
Our proposed single level fusion algorithm, which is depicted by Figure 1, uses uniform convolution layers that are designed with kernel size 1*1, stride size 1, the output feature map size is 16, and its input feature map comes from the last feature extraction layer whose feature map resolution is the same as the original network input. The multilevel fusion algorithm, which is depicted by Figure 2, uses uniform convolution layers that are designed with kernel size 1*1, stride size 1, output feature  
Figure 4: Visualization of multilevel fusion algorithm results on KITTI val set, including 2D localization, category classification, 3D localization, orientation estimation, and category classification.  
map size 8, and its input feature maps come from the last feature extraction layer in which the feature map resolution is the same as the original network input and the last but one feature extraction layer of which the feature map resolution is only half of the original network input.  
In AVOD [16], the feature map size cropped for the proposal region is 3*3 and the feature map size cropped for the final prediction is 7*7. During training, we investigated how the kernel with different sizes affects the performance and how the kernel group inspired by GoogLeNet [18] performs. The baseline 1 diverse kernel size multilevel fusion algorithm has 1*1, 3*3 kernel in the first fusion part and 1*1, 3*3, 5*5, and 7*7 kernel in the second fusion part. The baseline 2 diverse kernel size multilevel fusion algorithm has 1*1, 3*3 kernel in the first fusion part and 1*1, 7*7 kernel in the second fusion part. The outputs of the proposal are the same as the outputs of AVOD. Our regression targets are therefore (\(\Delta\)x 1... \(\Delta\)x 4, \(\Delta\)y 1... \(\Delta\)y 4, \(\Delta\)h 1, \(\Delta\)h 2), the corner and the height offsets from the ground plane between the proposals and the ground truth boxes. The results are show in Table 3. It can be seen that our approach obtained the best result in the ratio of feature maps size and zero padding. The higher ratio, means that the number of zero by zero padding is equal or greater than the number of elements in the feature maps, and more disturbance to the feature maps caused by zero is predominant so that the original distribution of feature maps is deviated to a large extent.
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 4 Analysis of Simulation and Experiment Results  
In this section, we evaluate the performance of the proposed NM-SLE algorithm through simulation and field experiment results. The effectiveness of the proposed method for NLOS observation identification and suppression was validated in real environments. The proposed algorithm can adequately exploit the features of NLOS signals and effectively suppress the residual NLOS measurement errors in the position estimation process. In both simulation and field experiments, we compared the performance of the proposed localization algorithm with those of four existing, advanced NLOS error elimination algorithms: SVM-REM [25], Vector Tracking Loop (VTL) [26], Virtual Base Station (VBS) [28], and Improved Robust Unscented Kalman Filter (IRUKF) [35]. The dataset used in the simulation was composed of 12,000 LOS samples and 5200 NLOS samples.
## 1 Introduction
#### Contributions of the Work  
The main research contributions are listed here as follows:  
* Introducing the novel SLP technique (SLP-E), which offers a comprehensive solution by simultaneously enhancing both the safety period and network lifetime. The solution represents a significant enhancement in safeguarding the privacy of source nodes when compared to recent random walk-based SLP solutions. Notably, privacy is enhanced with the improvement in the network lifetime. This is important because sensor nodes are often deployed in vast, challenging environments, making the replacement or tracking of dead nodes a difficult task. Therefore, in addition to ensuring the privacy of the information's source, there is a crucial focus on extending the network's lifetime. Moreover, the proposed enhancements are not constrained by the event's location within the network. Regardless of where an event (animal under observation) appears in the network, the improvements in both the safety period and the network lifetime persist, offering a robust and adaptable solution.  
* Developing an analytical model that estimates the average delay of the proposed solution. The work analytically estimates the average packet transmission latency from the source node to the base station in different network settings. We also provide rigorous analyses on privacy through a probabilistic approach.
* Investigating the impact of the radio range of sensor nodes on the network privacy and NLT. This serves to reveal that the transmission range (sensing range) of sensor nodes can either have an impact on or remain independent of both privacy and the network lifetime.  
This article's remaining sections are organized as follows: We present the associated studies in Section 2. We go over the adversary and network models that were taken into consideration in our work in Section 3. Section 4 provides a description of the suggested routing protocol and Section 5 presents the privacy analysis, while the performance measures employed in this work and complexity analysis are presented in Sections 6 and 7, respectively. The results and comments, as well as the work's conclusions, are provided in Sections 8 and 9, respectively.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 8 Conclusions  
In this paper, an efficient, lightweight and privacy-preserving authentication and billing scheme is proposed to be utilized for dynamic wireless charging of vehicles in an IoEV. The model preserves the user's information by using pseudonyms and supports a single aggregated bill for efficient and fair payment. Due to the limited computation capabilities of the charging segments, a lightweight EV-to-segment authentication model is employed, while major EV registration and authentication operations are performed with the charging service provider entities before the EV approaches the charging lane. In particular, hash chain generation is assigned to the entities with higher computational capabilities, namely the charging service company (CSC), while only simple hash key verification is required by the charging segments. Furthermore, the computationally extensive public key encryption and digital signature algorithms are employed by the entities with higher computational resources, namely the CSC and the PO, while the charging segments are only required to perform symmetric key encryption and decryption with lower computational  
Figure 6: Time required for EV registration and authentication with the CSC and the PO before reaching the first charging segment.  
requirements. This guarantees faster authentication as the EV approaches the charging lane. The proposed approach is also analyzed against different possible attacks and is considered robust against double-spending attacks, MITM attacks and impersonation attacks. Future research shall study the utilization of advanced asymmetric cryptography techniques with lower computational costs to reduce the overall execution time of the proposed scheme. In addition, integrating the proposed scheme with an EV charging coordination system shall leverage on its advantages to ensure reliable and effective energy management in an IoEV.  
Conceptualization, E.E., I.A., M.H. and A.O.; methodology, I.A. and E.E.; software, I.A.; validation, M.H. and A.O.; formal analysis, E.E. and I.A.; investigation, I.A. and E.E.; resources, M.H. and A.O.; data curation, I.A.; writing--original draft preparation, E.E.; writing--review and editing, M.H. and A.O.; visualization, E.E., I.A., M.H. and A.O.; supervision, M.H. and A.O.; project administration, M.H. and A.O.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.  
This work is jointly supported by the American University of Sharjah through SCRI grant number SCRI 18-CEN-10, and by Sharjah Research Academy (SRA), Sharjah, United Arab Emirates. This work is also supported by the American University of Sharjah OAP Program # OAPGEN-1410-E00002.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.