esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
An analytical approach to investigate the immunity levels to commutation failure of the multi-infeed LCC-HVDC system was presented [8]. However, the dynamic responses during transient conditions and the effects of different reactive power compensators were not considered. A fast calculation approach to evaluate the risk of commutation failure in the multi-infeed LCC-HVDC system was proposed [9]. However, the transient responses of different control parameters were not undertaken. The commutation failure in the LCC-HVDC system using the spatial-temporal discreteness of AC disturbances was investigated [10]. However, this approach was based on a steady-state model, which meant that results obtained under different fault situations could have a lack in accuracy. Two indices, referred to as the commutation failure immunity index (CFII) and commutation failure probability index (CFPI), were proposed in [11]. This approach was based on transient simulations, however could only evaluate the commutation failure under certain faulty conditions. Thus, it is meaningful to propose a more comprehensive index to evaluate the commutation failure under a wider fault range from the transient performance. This paper proposes a novel index termed as area ratio of commutation failure probability, to comprehensively and quantitatively evaluate the commutation failure of the LCC-HVDC system with a SC under certain fault ranges. Considering the CIGRE benchmark model, the LCC-HVDC system with a SC was built in the PSCAD/EMTDC. Different scenarios assuming the number of synchronous condenser units were carried out in order to examine its effect on the area ratio of the commutation failure probability of the LCC-HVDC system. The effectiveness of the proposed index was validated through simulation results under different fault types and fault severity conditions. The paper is arranged as follows. In Section 2, a detailed configuration of the LCC-HVDC system with a SC is presented. In Section 3, the overall control mechanism for the LCC-HVDC systems and a SC is discussed. In Section 4, a new index termed as area ratio of commutation failure probability is proposed and verified through the PSCAD/EMTDC under different fault conditions. In Section 5, the conclusion is provided.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 7 Performance Evaluation
### Communication Overhead  
The communication overhead is estimated by calculating the number of bytes transmitted during the message exchange process. In order to compute the size of each packet, the size of each message is calculated using the information in Table 1, as shown in Table 2, excluding the size of symmetric and asymmetric encryption and the messages exchanged offline between the CSC, the PO and the CS.  
As observed in Table 2, the size of \(m_{6}\) depends on the number of segments, \(N\), that need to be activated to satisfy the charging requirements of the EV, which means that the communication overhead increases as \(N\) increases. Nevertheless, although the EV needs to allocate sufficient memory to store \(N\) hash keys, the computational complexity of its authentication module is reduced as it does not need to run the hashing algorithm or generate the hash chain, as this is performed by the CSC. On the other hand, the hash key verification is performed on the CS side using a single hashing operation per CS. This offers lightweight authentication, particularly due to the robustness and low computational complexity of the SHA-256 algorithm used for hashing. This is further clarified in the computational cost calculation that follows. Furthermore, the inherent sequentiality of the hash keys formed in the chain structure allows the EV to authenticate with the CSs in order, which improves the security of the proposed model.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 1 Introduction
### Related Work
Although there have been studies that have made significant progress in UAV-assisted D2D communication, NOMA in UAV communication, and the application of reinforcement learning in UAV-assisted wireless communication, most of these studies have only considered UAV motion in a 2D plane, failing to take full advantage of UAVs' 3D mobility and failing to incorporate D2D communication, NOMA, and NOMA in 3D scenarios where multiple UAVs collaborate. Therefore, the question of how to jointly optimize channel allocation, UAV trajectory design, and power control based on NOMA's multi-UAV-assisted D2D communication model in a multi-UAV three-dimensional dynamic environment remains a challenging and under-studied problem. Based on the above research status and its shortcomings, this paper is devoted to the study of a NOMA-based multi-UAV-assisted D2D communication model, focusing on the joint optimization of channel allocation, trajectory design, and power control. By combining advanced dynamic hypergraphs and reinforcement learning algorithms, this paper proposes a multi-intelligent deep Q network algorithm for joint dynamic hypergraphs in multi-UAV 3D scenarios, aiming to cope with complex and dynamic network environments and maximize system throughput. This study not only fills the research gap of joint optimization of D2D-NOMA communication models in multi-UAV 3D scenarios but also provides new ideas for UAV communication in future three-layer heterogeneous air-to-ground networks.
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
## 3 Results and Discussion
### Establishing the Optimal Conditions for Multiplex PCR  
Although the detection of only one event using an eight-primer mixture was confirmed by PCR analysis, most LM products were imported with stacked genes or traits that contained more than one gene. To detect multiple genes in one event at once, multiplex PCR analysis with varying conditions such as primer concentration and PCR conditions (extension time, primer melting temperature, annealing temperature, etc.) was performed. Consequentially, the primer concentration and PCR conditions played an important role. It was difficult to determine the appropriate concentration of GHB119 and GHB614 primers. Typically, the final concentration of each primer was ~0.05-1 uM in the PCR reaction mixture. Several tests showed that the reaction concentrations of six primer combination sets were 0.08 uM, 0.74 uM for GHB119, and 0.41 uM for GHB614, yielding eight consecutive multiplexes in a single PCR reaction (Figure 4A). The touchdown PCR method was adopted to increase the specificity of PCR amplification (Figure 4B).
## 3 Experimental Setup
### Implementation
#### 3.2.1 Method A: No Image Rotation and 2D Gradient Evaluation  
For the gradient evaluation method A, the _Canny Edge_ algorithm of the _openCV_ library is used. The algorithm first reduces noise with a 5 \(\times\) 5 Gaussian filter with \(\sigma=1\) px before obtaining the first derivative \(G_{x}\) in the horizontal and \(G_{y}\) in the vertical direction with a 3 \(\times\) 3 _Sobel_ kernel. Afterwards, the gradients' magnitude \(G\) and direction \(\beta\) are calculated by  
\[G=\sqrt{{G_{x}}^{2}+{G_{y}}^{2}} \tag{3}\]  
and  
\[\beta=\tan^{-1}\left(\frac{G_{y}}{G_{x}}\right). \tag{4}\]  
In order to thin out the detected edges in the image, a _non-maximum suppression_ is conducted: The gradients of the neighboring pixel before and after the currently observed pixel in the direction of its gradient are investigated. If the current pixel represents the local maxima of these gradients, it is considered further, otherwise, it is suppressed. The last step applies a threshold test. All local gradients with a magnitude below a threshold are suppressed. Afterwards, a selection of the gradients on the basis of their direction corresponding to the a priori knowledge about the direction of the transition line segments is made.  
In order to be sensitive with respect to weak, low-contrast turbulence wedges (case C of Section 1.2), a low threshold \(\delta t_{\text{pre}}\) is chosen for the evaluation of the local gradient maxima \(\Delta_{\text{pre}}\) that have a direction corresponding to the orientation of the premature flow transitions segments. For the evaluation of the gradients in direction of the flow, a higher threshold \(\delta t_{\text{nat}}\) is used to robustly detected the local gradient maxima \(\Delta_{\text{nat}}\) representing the natural flow transition as their magnitude is higher compared to premature flow transitions' ones.
## 6 Experiment  
### Description of Simulation Environment  
To validate the efficacy of our proposed model, we executed simulation experiments within a cloud environment. We employed Kubernetes, a renowned container orchestration tool, given its proficiency in streamlining the deployment, scheduling, and elastic scaling of applications. As illustrated in Figure 6, our Kubernetes cluster comprises a master node equipped with a 16-core processor, 16 GB of memory, and 1 TB of storage, alongside three-node nodes, each featuring an 8-core processor, 8 GB of memory, and 1 TB of storage.  
The master node's primary responsibility is orchestrating the creation and termination of pods, with the actual pod runtime environment being situated on the node nodes. These node nodes are designed to communicate seamlessly amongst each other. Utilizing the master node, we deployed a variety of applications across distinct pods, including web services, FTP services, and databases. The interconnectivity between these pods is depicted in Figure 6. For the purposes of our experiment, the pod running the MySQL database service was designated as the target node, with all other application pods serving as intermediate nodes. One particular web service pod was chosen as the attacker's entry node, and a pod is created as a defense node through the master node and defense tools are deployed. The attacker initiates lateral movement from the pod of the entry node to the rest of the pods to find the MySQL service pod. Concurrently, the defender embarks from the defense node, aiming to pinpoint the attacker's exact location. It is worth noting that the gray nodes depicted in Figure 6 represent offline pods. These are inactive, unable to offer services or engage in communication with their counterparts. Comprehensive details regarding the software and hardware configurations used in our simulation experiment can be found in Table 1.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.3 Anomaly Detection  
Anomaly detection focuses on identifying irregular or unexpected events in video streams, particularly in security-critical domains such as industrial monitoring, smart surveillance, and emergency response [5]. Techniques range from unsupervised models like autoencoders [71] and isolation forests [72] to more recent methods leveraging graph neural networks (GNNs) [73] and Transformers [74]. Benchmark datasets including UCSD [75], Avenue, and ShanghaiTech [76], are commonly used for evaluation.
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 2 Materials and Methods
### Mix Design and Test Methods  
A total of 10 concrete mixtures (two natural aggregate concretes and eight RACs) with a target compressive strength of 30 MPa at 28 days were designed. The 0% RA replacement rate represents natural aggregate concrete (NAC) designed according to the ACI standard, representing a control mix. RACs were designed according to the EMV mix design [28], with replacement rates of 25% and 50%. The water-cement ratio was 0.45 for all mixtures. The superplasticizer was added as 1% by mass of cement. The EMV mix design was proposed by [PERSON] [43] to improve the properties of RAC. Figure 3 shows the mix proportions of NAC and RAC designed by the conventional mix design and the EMV mix design. In the conventional mix design, RCA itself is considered as a coarse aggregate. For this reason, the author noted that the mortar volume of RAC produced by the conventional mixed design method was larger than that of NAC. The main feature of the EMV method is to consider the residual mortar of RCA as part of the mortar of concrete. Therefore, RAC made with the EMV mix design has the same mortar volume as NAC (see Table 2). A detailed description of the EMV mix design can be found in [28; 43]. The procedure of the EMV mix design involved a series of calculations, an example of which is given in Appendix A. Table 2 shows volumetric mix proportions of concrete mixtures used in the study according to the EMV mix design. The same volume applies to the RAC-OA series. Figure 3: Illustration of the EMV concept for the mix design of concretes with recycled aggregates. Natural coarse and fine aggregate were in the saturated surface-dry (SSD) condition before mixing. RCA requires pre-wetting due to its high water absorption, but using RCA in the SSD state for concrete is unfavorable to the mechanical strength of RAC [44; 45; 46]. Therefore, the water-saturated RCA was dried in air for 24 h before mixing to contain sufficient moisture but not completely saturated. The detailed mix proportions of concretes are shown in Table 3: Control mixtures with 100% of NA having the particle size distribution of RCA1-OA and RCA2-OA (NAC1 and NAC2); four RAC mixtures in which the apparent size and the apparent particle size distribution of the partially replaced RCA was identical to the particle size distribution of the OA (RAC1-AP-25, RAC2-AP-25, RAC1-AP-50, and RAC2-AP-50); four RAC mixtures in which the apparent size of partially replaced RCA was 25 mm while having the OA particle size distribution inside (RAC1-OA-25, RAC2-OA-25, RAC1-OA-50, and RAC2-OA-50). All the concrete mixtures were prepared in a pan mixer with a capacity of 60 L. The coarse and fine aggregates were put in the mixer and mixed for 30 s, then cement was
## 5 Discussion
### Analysis of Path Planning Effectiveness
In contrast, the path planned by the IGWO-IAPF algorithm maintains a straight flight after the waypoint at (41, 28, 65), resulting in better obstacle avoidance and higher path smoothness compared to the other four algorithms. Furthermore, the path length generated by the IGWO-IAPF algorithm is relatively shorter with less redundancy, resulting in better path quality.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 5 Quantitative Analyses on Effect of Topographic Truncation
### Metrics on Spectra Shifts  
To quantify the shift of energy for spectra in frequency, three normalized frequencies [37]: \(f_{l},f_{m}\), and \(f_{lu}\), which are the cut-off frequencies of the different energy intervals, can be calculated from Equations (6) and (7):  
\[\int_{0}^{n_{c}}ns_{u}(n)/\sigma^{2}d[\ln(n)]-a_{c}=0 \tag{6}\]  
\[f_{c}=n_{c}Z/U(Z) \tag{7}\]  
where \(n\) is the frequency, \(nS_{u}(n)/\sigma^{2}\) is nonnegative and integrates to one in (\(-\infty\), \(+\infty\)), and \(c=l\), \(m\), \(u\). The value of \(a_{c}\) varies with \(c\), and \(a_{l}=0.05\), \(a_{m}=0.5\), and \(a_{u}=0.95\). Hence, the bandwidth between \(f_{l}\) and \(f_{lu}\), which means 90% energy interval bandwidth, is calculated from Equation (8):  
\[\Delta f=\log_{10}(f_{u}/f_{l}) \tag{8}\]As illustrated in Figure 10, \(f_{m,OT}\) and \(\Delta f_{OT}\) mean median of normalized frequency and bandwidth of normalized frequency in the case of OT, respectively. As seen in Figure 10a, the normalized median of all cases and measurement locations shows values lower than 1. This indicates that the spectra obtained from the truncation terrain model show a higher value than that from OT, which also supports our analysis in Sect. 4.2. In addition, it can be concluded that energy shifts to a low-frequency range when using the truncation terrain model to obtain spectra.  
As displayed in Figure 10b, a decrease in this bandwidth is found at the measurement locations of the leeward region (A2 and A3) where the normalized bandwidth is lower than 1. However, the trend of the measurement locations at the valley region (A4 and A5) is markedly different with normalized bandwidth higher than 1. In addition, the situation for the measurement location at the windward region (A1) can be divided into two parts according to whether the transition section is applied. The value of the normalized frequency bandwidth in the case with transition section is higher than 1, while the value is lower than 1 in the case of WTS. This shows that topographic truncation brought inaccuracy into spectral bandwidth, but its effect is not certain and seems to be related to the character of the terrain where the measurement is located. Additionally, implementing transition sections at the edge of the terrain model greatly influenced the normalized frequency bandwidth, which may be attributed to the change in topography ahead leading to different flow behaviors.
## 3 Results and Discussion
### Chemical Composition by HPLC  
Figure 3 reports the HPLC profiles representative of 'Aglianico' and 'Greco di Tufo' leaves collected in October and November extracted with MeOH:H\({}_{2}\)O (70:30), at the wavelengths of 280, and 360 nm.  
The different absorption characteristics of the present compounds have been exploited to identify phenolic acids at 280 nm and flavonoids at 360 nm. Peak identification is based on standard analysis and literature data [34; 41; 42; 43]. In all samples, the most abundant components are the pair quercetin-3-O-glucose plus quercetin-3-O-galactoside as reported by [PERSON] et al., 2013 [44]. Our results indicate that other than quercetin derivatives, also Kaempfer derivatives are the most abundant phenolic compounds, in agreement with studies by [PERSON] et al. (2013) and [PERSON] et al. (2015) [29; 45] showing that quercetin and Kaempfer were the major phenolic compounds in the grape plant. _V.vinifera_ is also characterized by the presence of quercetin glycosides [5]. Recently, quercetin glycosidic derivatives have been identified as the most abundant polyphenols in Greco di Tufo leaves [34]. The profiles of 'Aglianico' and 'Greco di Tufo' of October and 'Aglianico' and 'Greco di Tufo' of November are widely overlapping, except for a peak of Gallic acid, absent in 'Aglianico' leaves of November. As reported by [PERSON] (2015) [41], Gallic acid was found to give a maximum response at 280 nm rather than 360 nm. Overlapping profiles have been obtained for the Greco di Tufo of October and November.  
The extraction was carried out with MeOH:H\({}_{2}\)O (70:30) and H\({}_{2}\)O gave similar HPLC profiles (Figure 4A). On the contrary, EtOH extraction gave a profile different from MeOH:H\({}_{2}\)O (70:30) and H\({}_{2}\)O, and there were differences in the ratio between the quercetin derivatives(Figure 4B). In conclusion, the extractions with MeOH:H\({}_{2}\)O (70:30) and H\({}_{2}\)O lead to overlapping profiles also in the intensity of the peaks, both in the 'Aglianico' and 'Greco di Tufo' leaves. In both varieties, the extraction with EtOH leads to different profiles compared to MetOH:H\({}_{2}\)O (70:30) and H\({}_{2}\)O.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{_Vitis vinifera Varieties_} & \multicolumn{4}{c}{**Spectrum Wavelength cm\({}^{-1}\) 4000–650**} \\ \cline{2-6}  & **Groups** & **Recognition (\%) a** & **Rejection (\%) b** & **Interclass Distance c [FOOTNOTE:c]Footnote c: \(\bullet\)\(\bullet\  
Figure 3: HPLC profile of leaf extract of Aglianico of October (black line), Aglianico of November (red line), Greco di Tufo of October (blue line), and Greco di Tufo of November (green line) at 280 NM and 360 NM. 1 = gallic acid, 2 = vanillic acid, 3–4 = quercetin derivatives, 5–6 = Kaempfer derivatives.
## 5 Decision Engine for Relay  
This section extends the framework described in Section 4 to the two-hop scenario illustrated in Figure 1. On top of optimizing the type and number of IR bits to be transmitted, the intermediate station also has to decide between using an amplify and forward (AF) or decode and forward (DF) relay strategy. In order to compare both strategies, we propose a parametric cost model for each of them and a decision engine to minimize the average cost per successfully delivered information bit. Specifically, we model the cost of AF and DF (\(c_{AF}\) and \(c_{DF}\)) as functions of the SNR on both links (\(\text{SNR}_{1}\) and \(\text{SNR}_{2}\)) and the code rate in the first link (\(R_{1}\)). As in the single link decision engine, the decoding cost \(c_{D}\) and the overhead cost \(c_{R}\) are normalized by the cost of transmitting 1 bit of information over one link.
# Design of a Computed Tomography Automation Architecture
## 4 Post-Imaging Techniques
### Artifact Reduction  
[PERSON] et al. (2019) focused on reducing artifacts due to high absorbing metal parts in X-ray CT scans on multi-material objects with high photon attenuating metal parts. Their solution suggested using projection data from multiple scans with differently positioned object orientations and fusing the data. They used the example of just two different scans with different axes of rotation. They took both scans and independently produced two separate 3D reconstructions. From this, they estimated the local quality of the resulting volumes and then fused these volumes into an optimized volume. This approach was based on the idea of creating more reliable information and ignoring unreliable information. The voxels that were more attenuated in one scan were determined to be the less reliable ones, and hence the other was chosen to optimize the volume. This method did not determine the optimal object positioning, and instead purely relied on increasing the number of CT scanning positions. An additional approach proposed in this research was to introduce shrinking merged algebraic reconstruction technique (smART) and merge sinograms of different scans, estimate the reliability of each projection pixel and then reconstruct the merged sinogram with an iterative reconstruction algorithm. Figure 5 is an example of a partial architecture that has been modified after the imaging step has been performed by changing the way the 3D volume is generated. This can be compared to the generic CT procedure in Figure 2, and it represents the method used by [PERSON] et al. (2019).  
Figure 5: Partial architecture based on “Artifact reduction in X-ray computed tomography by multi-positional data fusion using local image quality measures” by [PERSON] et al. (2019).
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Registration Phase  
When a client desires to access any service provided by a remote server, the client must first register on that server. The steps of registration phase are illustrated in Figure 3 and described below:  
1. The client selects an identity \(ID_{i}\), a password \(PW_{i}\), and a random number \(b\). Then, the client computes \(HPW_{i}=h(PW_{i}||b)\) and sends the registration message \(\{ID_{i},HPW_{i}\}\) to the server.
2. On receiving the registration message, the server generates a random integer \(m\) and then computes \(A_{i}=h(ID_{i}||k_{s})\), \(B_{i}=h(h(ID_{i}||HPW_{i})mod\ m)\), and \(C_{i}=A_{i}\oplus HPW_{i}\oplus B_{i}\). After that, the server issues the data \(\{B_{i},C_{i},m,K_{s},E_{s}(\cdot),D_{s}(\cdot),h(\cdot)\}\) into a memory device and sends it to the client.
3. When the memory device is received from the server, the client stores \(b\) into it. Finally, the memory device contains \(\{B_{i},C_{i},m,b,K_{s},E_{s}(\cdot),D_{s}(\cdot),h(\cdot)\}\).