esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
The proposed AAC-IoT system incorporates the HLF blockchain for the processing of an authentication and attribute-based access control scheme. The authentication process is constructed for the DO as well as the DU who uploads and accesses the data, respectively. The security credentials are secured using the lightweight PRESENT block cipher algorithm. The meta-data are uploaded to the HLF blockchain and the main data are encrypted and stored on a cloud server. The HLF is responsible for the authentication of the credentials that are in the form of hash values generated using a lightweight QUARK algorithm. The authentication credentials include certificate, signature and PUF. In this work, the number of attributes to be used in an access policy is determined using a fuzzy logic method. According to the importance of the data, the access policy is defined to minimize the matching access policy as it does not consider all the attributes for all types of data. On the other hand, the DOs and the DUs are authenticated on each request. To make it faster for multiple requests, an ANN is used for authentication of the DU. As an outcome, this proposed AAC-IoT achieves better in terms of latency, throughput and storage overhead than the previous method. In future, the designed AAC-IoT system model is planned to be evaluated using two different datasets from healthcare and industry, and the testing will be conducted in a large-scale environment.  
Conceptualization, S.A. and O.B.; funding acquisition, S.A.; investigation, S.A. and O.B.; methodology, S.A. and O.B.; project administration, S.A.; validation, S.A.; writing--original draft, S.A.; writing--review and editing, S.A. and O.B. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant No. J:71-612-1442. The authors, therefore, acknowledge with thanks DSR for technical and financial support.  
Not applicable.  
The authors declare no conflict of interest.
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 5 Results  
To evaluate the performance of the 3D distance measurement algorithm developed in this study, experiments were conducted on four body parts: waist circumference, chest circumference, buttock circumference, and hip circumference. For each body part, the total circumference was estimated based on front and side measurements of a total of four models, and the errors compared to actual measurements were analyzed. Tables 4-7 shows the results comparing the estimated circumferences with the actual measurements for each body part.  
Overall, the algorithm recorded relative errors within 8% for most body parts, and particularly showed low error rates below 4% for waist and hip circumferences. This indicates that the algorithm effectively applied keypoint correction and regression analysis methods to enhance the accuracy of body circumference measurements. In measuring the buttock circumference of actual human models, the error rates for Model 1 and Model 2 were relatively high at 7.8% and 7.4%, respectively. This can be attributed to the complex 3D structure of the buttock area and the difficulty in selecting keypoint positions. On the other hand, in measuring hip circumference, all subjects recorded low error rates within 2.6%, confirming the stability of the algorithm.  
Additionally, as shown in Figure 9(a),b, keypoint correction utilizing the depth map overcame the limitations of 2D image-based measurements and enabled more precise 3D measurements. Experimental results confirmed that the correction process through the depth map contributed to the accuracy improvement for each body part. Moreover, the real-time processing speed in actual experimental environments was sufficiently fast, suggesting practical application possibilities. The overall performance of the algorithm proposed in this study holds significant meaning for the transition from professional equipment to lightweight mobile devices. The conventional 2D length measurement methods were limited in measurement accuracy due to the inability to reflect depth information of the body, but this algorithm overcomes such limitations by effectively utilizing 3D information.
# Dynamic Behavior Analysis of I-Shaped RC Beams under Combined Blast and Impact Loads
## 2 Finite Element Analysis of the I-Shaped RC Beam under the Blast Load
### Result Analysis  
Figure 2 compares the finite element numerical results and experimental results of the I-shaped RC beam under blast loading. The results show that when the weight of RDX is 100 g, the blast surface of the beam is subjected to instantaneous high-intensity explosion load. Shear failure occurred on the top plate, and large-scale concrete spalling occurred locally. The length of the damaged region is 425.3 mm. The shock wave propagates downward through the top plate, and the shear failure extends to the vertical rib area. The damage depth was 133.4 mm, which is 44.4% of the beam height, and the concrete at the edge of the bottom slab was cracked. Under instantaneous impact and high temperature, the longitudinal reinforcement of the top plate expanded, and the transverse reinforcement in the damaged area reached the ultimate strength and breaks. During the explosion  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{Concrete} & Mass density (kg/m\({}^{3}\)) & 2350 \\  & Compressive strength (MPa) & 49.6 \\  & Rate effects & Turn on \\ \hline \multirow{3}{*}{Rebar (longitudinal and transverse)} & Failure strain & 0.14 \\  & Poisson’s ratio & 0.3 \\ \cline{1-1}  & Strain rate parameter C & 40 \\ \cline{1-1}  & Strain rate parameter p & 5.0 \\ \hline \multirow{3}{*}{Transverse rebar} & Young’s modulus (GPa) & 195.6 \\ \cline{1-1}  & Yield stress (MPa) & 320.3 \\ \hline \multirow{3}{*}{Longitudinal rebar} & Young’s modulus (GPa) & 205.4 \\ \cline{1-1}  & Yield stress (MPa) & 410.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used for I-shaped RC beams under blast loads.  
process of the component, the kinetic energy generated by the explosion shock caused the reinforcement to vibrate at high frequency, and the crack on the top plate of the component extended longitudinally. According to the finite element numerical simulation results, the failure behavior and damage state of the components were basically consistent with the experimental results, and the error value was about 10%. Therefore, it can be considered that the method of using the numerical model and algorithm to simulate the dynamic response of I-shaped RC beams under blast load is reliable.
###### Abstract  
With the continuous expansion of air traffic flow, the increasingly serious aviation network security threats have a significant and far-reaching impact on the safe operation of the aviation industry. The networked air traffic management (ATM) system is an integrated space-air-ground network integrating communication, network, satellite, and data chain technology, which adopts a network-centric structure to provide network-enabled services and applications for the air transportation system. In view of the serious network threats faced by networked ATM, this paper studies the basic theories and key technologies of ATM information security assurance and designs a credible security architecture to provide comprehensive and systematic security assurance for networked ATM. Starting from the problems and development trend of ATM security assurance, this paper investigates the dynamic and complex data processing process of ATM system, analyzes the complex interaction between its cyber and physical system, and then constructs the networked ATM cyber-physical fusion system model and threat model. Furthermore, the causal relationship between ATM security threat alert information is mapped into a Bayesian network, and the game model of networked ATM is proposed using Bayesian Nash equilibrium strategy. At last, the information security assurance architecture of networked ATM system based on blockchain technology is formed by establishing a distributed co-trust mechanism and multi-chain storage structure. We expect this paper to bring some inspiration to the related research in academia and aviation so as to provide useful reference for the construction of ATM safety and security system and the development of technology.  
air traffic management (ATM); cyber-physical system (CPS); blockchain; cyber security; information security assurance +
Footnote †: journal: Electronics Electronics
## 3 Results and Discussion
### Simulated Foot Traffic Experiments
#### 3.2.2 Percent Transfer to Uncontaminated Surfaces for Straight-Line Walking  
Figure 14 shows the average %T of Sr-85 contaminated particles from the NSCs and from the surfaces stabilized with CaCl\({}_{2}\), Phos-Chek MVP-Fx, and Soil\({}_{2}\)O treated pavers to pavers that were previously uncontaminated. Table 9 shows the \(p\)-values from the \(t\)-tests performed on the data. The \(p\)-values indicate the probability that the groupings of data were from the same population, i.e., statistically similar.  
Figure 13: Percent Removal of Sr-85 Contaminated Particles for Straight-line Walking Experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Surfaces** & **Stab. Tech.** & **CaCl\({}_{2}\)** & **MVP-Fx** & **Soil\({}_{2}\)O** \\ \hline \multirow{3}{*}{Asphalt} & Control & 0.0024 & \textless{}0.0001 & 0.0009 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.0366 & 0.5490 \\ \cline{2-5}  & MVP-Fx & & & 0.0027 \\ \hline \multirow{3}{*}{Concrete} & Control & 0.0429 & 0.5701 & 0.2801 \\ \cline{2-5}  & CaCl\({}_{2}\) & & 0.0230 & 0.3127 \\ \cline{1-1} \cline{2-5}  & MVP-Fx & & & 0.1435 \\ \hline \end{tabular}
\end{table}
Table 8: Statistical results (\(p\)-values) for foot traffic experiment percent removal. Gray shading indicates a significant difference (\(p<0.05\)).  
The potential for %T appeared to be highest for the sand surface, as evidenced by the control %T being more than four times larger than the other %T values. However, the sand results were determined from the 5 min continuous walking experiment, so the results are not exactly comparable. Interpreting the efficacy values in the figure in combination with the \(p\)-values from the table reveals the following for specific surfaces.  
_Asphalt._ CaCl2 (ND %T), MVP-Fx (ND %T), and Soil2O (0.2 \(\pm\) 0.2 %T) resulted in significantly decreased %T compared to the NSC (1 \(\pm\) 1 %T). CaCl2 (ND %T) and MVP-Fx (ND %T) generated %T values that were not significantly different from one another, and both produced %T values that were significantly less than Soil2O (0.2 \(\pm\) 0.2 %T).  
_Concrete._ CaCl2 (ND %T) resulted in significantly decreased %T compared to the NSC (1 \(\pm\) 0.1 %T), while MVP-Fx (0.9 \(\pm\) 0.2 %T) and Soil2O (1 \(\pm\) 0.2 %T) did not. Soil2O (1 \(\pm\) 0.2 %T) and MVP-Fx (0.9 \(\pm\) 0.2 %T) generated %T values that were not significantly different from one another, and both produced %T values that were significantly higher than CaCl2 (ND %T).  
_Sand._ Note: There was a single experiment for each stabilization technology, and the SD was based on the right and left sides of the path [N = 2], so no \(p\)-values were calculated. CaCl2 (0.6 \(\pm\) 0.6 %T), MVP-Fx (2 \(\pm\) 0.2 %T), and Soil2O (0.8 \(\pm\) 0.2 %T) resulted in decreased %T compared to the NSC (8 \(\pm\) 2%T).
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 4 Big Stream Fog-Based e-Learning Architecture
### Data Sources  
In this subsection, we discuss the main software sources of the data that can be produced and managed by the VLE architecture we envisioned.  
The sources described in [4] can be subdivided into the cloud and the fog layers according to Figure 4. The possible categories are the following:  
* _Only-Cloud_ sources: These include the subcategory of application-general subsystems and modules such as communication services, shared documents, content packaging (SCORM),  
Figure 3: Details of the fog layer where different big data streams (dashed lines), as well as Storm’s spouts and bolts are visible.  
as well as administrative and business object managers. In this subcategory, one can consider also the recent Learning Record Store ([[http://tincanapi.com/learning-record-store/](http://tincanapi.com/learning-record-store/)]([http://tincanapi.com/learning-record-store/](http://tincanapi.com/learning-record-store/))) (LRS) modules, able to gather many sources of learning data. Other possible subcategories that contain only-cloud sources are middleware with centralized web servers and administration toolkits.
* _Cloud and Fog_ sources: These may be subdivided into the following three subcategories: middleware reusable building blocks, system software and application-specific subsystems:
* The first include services such as APIs and DBMSsthat could be distributed and differently characterized across the cloud and the fog;
* The second encompasses operating systems (present also in smart devices) and network protocols (be they the traditional TCP/IP stack for cloud servers or the lightweight 6 LowPAN and DTLS for IoT gadgets and gateways);
* The third revolves around user profile managers (portfolios, working groups, etc.), course management modules, educational resource delivery tools (tracking of results and of time of studying) and student support systems (recommendations, bookmarks, statistics, shared annotation spaces, etc.)
* _Only-Fog_ sources: These include physical sources such as laptops, tablets, smartphones, smartbands and smartwatches, as well as their embedded sensors (GPS, gyroscopes, accelerometers, etc.) and the other sensors not embedded and positioned in the IaaS layer of Figure 3.
# Current Context and Research Trends in Linear DC-DC Converters
## 8 Future Prospects for LDO Regulators in Power Supplies
### Advantages of Using SCALDO in Portable Devices  
For portable applications, SCALDO technique has much greater advantages compared to the currently used switch mode + LDO combination, mainly because the SCALDO prototype IC (Figure 12b) design provides a huge reduction of PCB space without the bulky inductors required in the case of SMPS. Another prominent characteristic of SCALDO is very low EMI and noise level, which is due to the very-low-frequency operation in the range of Hz to MHz. This will be used to a good advantage in portable devices that commonly consist of microcontrollers and processors. This will eliminate the need for additional filters.  
The SCALDO technique has been further developed with two new versions, DO-SCALDO and RS-SCALDO, which can be used in many specific applications in portable devices too [63; 75; 76].
# Post-Quantum Migration of the Tor Application
## 6 Conclusions
### Limitations  
The drawbacks of this work can be represented by three main points below.  
* While trying to satisfy the low-bandwidth requirement, the selected schemes only cover the two first NIST security categories. Furthermore, they lack diversity in their mathematical assumptions, relying solely on lattice problems.
* The benchmarking is heavily subjective. Firstly, it is influenced by the technology used, such as the optimisation of cryptosystems based on a CPU architecture. In this context, this theoretical bare-metal Tor network simulation could be improved by using hardware generally used in the real Tor network. This would require more intrusive relay metrics and may go against security and privacy principles. Secondly, the timings are greatly influenced by the configuration. The torrc files were adapted to obtain a functional local network with the given number of nodes. Yet, another blend of settings could result in a more realistic implementation. Adding more nodes should improve the stability of the setup. Going further, the nodes could be distanced geographically. Planet Lab [58] may be an appropriate test bed. Additionally, the methods and tools employed in the measurement process can significantly alter the results; for instance, time is consistently taken as a reference point, and other measuring units, such as CPU cycles, are neglected.  
* The estimation of the PQC overhead is only theoretical and misses a multitude of parameters, such as different traffic types or volumes. In a virtual environment, further testing with Shadow could provide valuable insights based on the real Tor traffic, while on physical machines, adapting or creating tools like OnionPerf to target local instantiations could also produce more relevant measures. In the future, Tor's software optimisations and restructuring of the underlying protocols will also impact network performance.
## 7 Conclusions  
In this paper, the FDTD simulation method for wave propagation in the media described by the TF constitutive relations is presented. TF Maxwell's equations are derived based on these constitutive relations and the Grunwald-Letnikov definition of a fractional derivative. Then the FDTD algorithm, which includes memory effects and energy dissipation of the considered media, is introduced. The stability limit of the time-step size is derived for the proposed FDTD method which, to the best of our knowledge, has not been presented in the literature before. The obtained FDTD results are compared with those obtained with the use of the reference frequency-domain method already presented in the literature, and the compatibility between both methods is hereby demonstrated. Therefore, FDTD is proved to be an appropriate method for simulation of the FO media as well. High spatial resolution is required, however, to obtain accurate results. FO FDTD is, unfortunately, computation and memory demanding compared to the ordinary (i.e., IO) FDTD algorithm and, therefore, requires larger computational resources than in the IO case.  
Our future research focus will include investigation of implicit FDTD schemes applied to the FO media, as well as consideration of their acceleration techniques.  
Conceptualization, P.P. and T.P.S.; methodology, P.P., T.P.S. and J.G.; validation, P.P. and J.G.; writing--original draft preparation, P.P., T.P.S. and J.G.; writing--review and editing, P.P., T.P.S. and J.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 7: Dependence of execution time on domain size \(N_{z}\) for \(\alpha=0.98\) and varying iteration number \(N_{t}\).
## 6 Conclusions  
We proposed a power control algorithm to maximize the energy efficiency in the H-CRAN architecture. We formulated the maximization problem of the energy efficiency using the outage probability. We designed the RRH switching operation based on mobility prediction using the Markov method. We considered the service of UEs and use of resource blocks to maximize the number of UEs serviced by optimal RRHs while performing the RRH switching operation. The algorithm finds the optimal transmission power with the gradient method to maximize the energy efficiency. The simulation results demonstrated that the proposed algorithm provided the best energy efficiency and SINR among the compared algorithms.  
In the UE mobility prediction, there is an issue in terms of the number of employed history trajectories. By increasing the number of trajectories, the prediction accuracy could increase; however, storage problems emerge owing to the large number of UEs. In addition, predictions using the existing Markov method are less accurate in movements with irregular patterns. Therefore, it is required to develop a technology to adapt the use of trajectories and accordingly compensate irregular patterns. In environments with a variety of mobility, traditional network architectures often generate unnecessary handovers. In addition, as the need for location-based services increases, traffic is being frequently generated to provide data to satisfy demands for location-based services. Therefore, it is necessary to perform further studies combined with new future architecture like F-RAN (Fog Radio Access Networks) [23] that solves such problems by increasing the use of endpoint devices. It can relieve the burden of fronthaul and BBU pool and manage resources efficiently from changing mobility.  
The authors contributed collectively to the theoretical analysis, modeling, simulation, and manuscript preparation.  
Figure 6: Comparison of the energy efficiencies in environments with 15 RRHs. (**a**) San Francisco and (**c**) Beijing datasets with Gaussian distributions; (**b**) San Francisco and (**d**) Beijing datasets with uniform distributions.  
Funding: This study was supported by the National Research Foundation of Korea (NRF), grant funded by the Korean government (MSIT) [No. NRF-2015R1D1A1A09057141 and NRF-2018R1A2B6002505].
## 1 Introduction
The primary objectives of the research are:  
* To explore the potential of remote sensing in detecting and classifying forests, with a particular focus on binary classification;
* To define optimal parameters of the SVM algorithm, specifically the C and gamma parameters, for effective forest classification;
* To evaluate the advantages of binary classification in forest detection and discuss its implications for environmental management and conservation;
* To contribute to the existing body of knowledge by introducing an original approach to forest detection using remote sensing technologies. The study's findings are expected to provide valuable insights into the application of remote sensing and SVM in forest detection, potentially informing future research and practices in the field.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 2 Materials and Methods
### Ex Vivo Analysis
#### 2.2.1. _Biaxial Testing and Data Analysis_
The equi-biaxial mechanical response for a representative dataset is shown in Figure 1 in both 3D (Figure 1c) and 2D (Figure 1d) versions. Repeating the procedure for all the specimens allows comparison of the tissues' mechanical properties at an equivalent deformation state. This approach represents an attractive alternative to constitutive modelling when finite element simulations are not the end goal. The curves obtained from testing were then used to calculate the mechanical properties using a custom software pipeline developed in Python (version 3.7.6; Python Software Foundation). All mechanical parameters are shown in Figure 1d for an example of unprocessed equi-biaxial stress-stretch response in the circumferential direction (11). Data was first processed using a Savitsky-Golay digital filter, which relies on a local least-squares polynomial approximation to reduce noise without distorting the signal [26]. The linear regions of the curves can be described with a low-strain tangential modulus (LTM) and high-strain tangential modulus (HTM) which characterize the elastin-based and collagen-dominated response of the tissue, respectively [27; 28]. These two linear regions are separated by a non-linear transition zone where the load is shared by varying proportions of collagen and elastin (Figure 1d). To determine LTM, HTM, and the transition zone, a polygonal approximation of the curve was generated using a Ramer-Douglas-Peucker (RDP) algorithm [29] with a fixed deviation value of 0.025 to produce a high-precision fit. LTM and HTM were evaluated as the slopes of the first and last segment, respectively, while the transition zone onset (TZo) and transition zone end (TZe) were found as the start and end points of the transition zone between the LTM and HTM segments (Figure 1d). For curves with two segments or less, there was deemed to be no transition zone. A visual representation of RDP and original curves were overlaid to confirm fit. Given the marked difference in AAA mechanical behavior with respect to direction, the level of mechanical anisotropy was also investigated for each mechanical parameter (MP) extracted from the biaxial response of the tissue samples as the ratio of the response in the circumferential direction to the response in the axial direction (MP\({}_{11}\)/MP\({}_{22}\)).