esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation
### Validation by OCT  
TCFA plaque is characterised by a necrotic core with an overlying thin fibrous cap (\(<\)65 \(\upmu\)m). The principal difficulty with diagnosis of the TCFA lesions with IVUS modality is the limited axial resolution (approximately 150 \(\upmu\)m). Therefore, IVUS-based technology is unable to visualise the thin fibrous caps [30]. This modality is presented as the in-vivo \"gold standard\" for measuring the fibrous cap thickness for diagnosis of TCFAs [106]. By means of the validated criteria for plaque characterisation, the minimum fibrous cap thickness is measured at the minimum luminal area site in OCT images [107]. To validate TCFA plaque, VH-IVUS images can be compared to their corresponding OCT image. Plaques are classified into TCFAs and non-TCFAs based on the fibrous cap thickness in OCT.  
In this experiment, 18 geometric features were extracted from 14 VH-IVUS images, which were provided by [PERSON] et al. [108]. The SVM classifier with a RBF kernel was then applied to classify each feature vector into TCFA and non-TCFA plaques. These images were then labelled based on their corresponding OCT images using the proposed method by [PERSON], [PERSON] and [PERSON] [108]. In this manner, TCFA was extracted from the corresponding OCT frames. If TCFA exists in those OCT frames, '1' is assigned; otherwise, '0' is assigned, to represent non-TCFA plaque. Based on the results, SVM classifies the VH-IVUS images into 2 TCFA and 11 non-TCFA plaques; meanwhile, the OCT-based method classifies the data into 3 TCFA and 10 non-TCFA plaques. The confusion matrix was then calculated, and the performance measures were presented in terms of true positive (TP), true negative (TN), false positive (FP), false negative (FN), accuracy, sensitivity, specificity, precision, and F-score. Table 7 depicts the diagnostic accuracy of VH-IVUS for detecting TCFA as determined by OCT.  
Based on the above table, TP and TN demonstrate that VH-IVUS correctly identified 11 non-TCFA and 2 TCFA plaques. In addition, FP represents that all non-TCFA plaque was correctly classified, whereas FN indicates that VH-IVUS misclassified 1 TCFA plaque, as determined by OCT. Furthermore, VH-IVUS demonstrates an acceptable result in terms of accuracy, sensitivity, specificity, precision, and F-score to identify TCFA as determined by OCT.
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 4 C-PBFT Consensus Algorithm
### Double-Layer Consensus Structure  
In the Internet of Vehicles environment, due to the wide range of vehicle activities, a large number of RSU nodes, and high communication complexity, it is necessary to design a network structure with scalability and flexibility to cope with the rapid change in network topology when vehicles are running.  
In this paper, we propose a double-layer PBFT consensus structure, as shown in Figure 2, with \(n\) nodes in the first layer and \(m\) nodes in each region in the second layer, i.e., a total of \(n+m\) nodes. Where the replica of the committee in the first layer is noted as \(r_{i}^{1}\) (superscript 1 indicates the number of layers, and subscript \(i\) indicates the region), \(r_{i}^{1}\) serves as the leader of the corresponding replica of each regional committee in the second layer \(r_{i}^{2}\). An upper layer \(r_{i}^{1}\) together with the \(r_{i}^{2}\) in its region forms a regional consensus committee.  
As shown in Figure 3, the C\(-\)PBFT consensus process is divided into two parts: first, regional local consensus is carried out, and then global consensus is carried out.  
Figure 2: Topology of C\(-\)PBFT system.
## 3 Results
### TSS Model
#### 3.1.3 Accuracy Assessment Based on Synchronous Remote Sensing Images  
Besides, we also used 19 in situ data to validate the three-band model on the synchronized Landsat OLI imagery. The validation result shows that the _RMSE_ and _MRE_ between 19 field TSS concentrations are 3.55 mg/L and 27.55%, respectively. Besides, all the sample points can be evenly distributed around the 1:1 line and the error is within 30%. Figure 3 shows the retrieval and validation results of the TSS concentration in Hedi Reservoir on 16 October 2015, where (a) depicts the spatiotemporal distribution pattern of the TSS concentration in Hedi Reservoir with a large variation ranging from 0.67 to 94.98 mg/L and (b) shows the validation results between in situ measured TSS and OLI-retrieved TSS.
## 3 Analysis of Results for Optimal Model
### Inductance Analysis  
D- and Q-axis inductance are important design factors that affect the magnitude of torque and voltage. As output torque in Equation (1) shows, the difference between the two inductances determines the reluctance torque magnitude, indicating that the optimized motor-shape design has a close relationship with output torque. Inductance was calculated using FEM analysis at 1800 r/min and 1 to 10 Arms, the same as the optimal LPM design condition, and the results are shown in Figure 8. The horizontal axis of the graph indicates the magnitude of D- and Q-axis currents, and the vertical axis indicates the inductance magnitude for each current. Inductance distribution shows that the Q-axis inductance appeared to be almost constant after 3A, while D-axis inductance, calculated through nonlinear analysis, had a relatively large difference in magnitude between LPM and FEM. Both Ld and Lq showed the largest error in the low-current section of 1-3 A, and the magnitude of error gradually increased after 6 A in Lq. The error in nonlinear D-axis inductance calculated at 10 A current was 0.5 mH, showing an error rate of approximately  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Harmonics** & **Phase Voltage [Vrms]** & **Line to Line Voltage [Vrms]** \\ \hline
1 & 12.9 & 22.4 \\
5 & 0.9 & 1.5 \\
7 & 0.4 & 0.7 \\
11 & 0.5 & 0.9 \\
13 & 0.4 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: BEMF values by harmonics.  
Figure 7: BEMF calculation at 480 r/min using FEM. **(a)** Voltage curves; **(b)** FFT analysis.  
8%, but there was no significant difference in the overall inductance distribution trend except for in the low-current section. Table 5 shows the maximum inductance values analyzed in LPM and FEM.
## 4 Future Developments and Access  
Developments are underway at all facilities such that the sensitivity and the accuracy of the technique can be improved. This could be done, for example, by increasing the solid angle coverage of the detectors (i.e., the instrument setup relatively easy) and by  
Figure 12: The number of events at (**a**) 66 keV (Aluminum), (**b**) 75 keV (Carbon) and (**c**) 131 keV (Oxygen), corresponding to the position of the material in the sample (**d**). Originally published in JPS Conf. Proc. 21, 011042 (2018). Reprinted with permission from Ref. [53] © 2018 The Author(s) of Ref. [53].  
Figure 13: (**a**) Scheme of the sample; (**b**) position of the fluorine in the sample, within the range from 27 keV to 35 keV. Originally published in JPS Conf. Proc. 21, 011044 (2018). Reprinted with permission from Ref. [54] © 2018 The Author(s) of Ref. [54].  
increasing the muon flux (accelerator development, much harder). However, plans are in place to increase data rates by at least one order of magnitude to several orders of magnitude, which will enable a rapid expansion of the technique and/or new instrument plan at muon facilities, such as ISIS and J-PARC. As stated before, the technique utilizes a large accelerator, so the material of interest be to be moved to the facility. There are various routes to obtain access to these facilities, that are dependent on the facility. However, in general for academic research these are free at the point of use and are peer-reviewed; yet, other routes are available, including paying for access.
# Worldwide Coverage Mobile Systems for
## 1 Introduction
Among others, the most relevant indicators they analyse are out-of-band radiation, spectral performance, bit and carrier synchronization, end-to-end latency of the physical layer, arrogance, complexity of the transceiver, and fundamental parameters to guarantee QoS. M2M communications are having a rapid growth in mobile networks where they provide the opportunity to satisfy the quality of service (QoS) requirements for massive machine communication systems (mMTC) with limited capabilities. Therefore, one of the problems that the mobile network must face concerns the massive use is the allocation of available resources. In order to guarantee this quality, in [13] the authors propose a fast and efficient algorithm that exploits the periodicity of MTC traffic based on the persistent allocation of resources. Given the expected demand for services supported by mMTC using satellite communication, the development of cost-effective devices with a tiny size is required, and the antenna is the main component impacting the size factor. In this investigation, the operation frequency bands are those commonly used in _cubesat_ constellations, as well as the _spaceX_ and _One Web_ systems, i.e., 10.7-11.7 GHz band for RX (down-link) and 14-14.5 GHz band for TX (up-link) [14]. The remaining part of this paper is organized as follows. Section 2 summarizes the main features of the various types of satellite constellations, remarking those characteristics, which can affect worldwide mobile communications' reliability and performance, and highlights the ongoing developments to provide worldwide coverage, pointing out their differences. Section 3 states the design constraints that are focused on the terrestrial segment requirements, given that they affect the antenna design. Afterwards, Section 4 introduces the most relevant types of antennas and design methods for satellite communications. In Section 5, the main results of the investigation are discussed, while Section 6 discusses the findings and highlights the advantages and drawbacks. Finally, Section 7 draws the main achievements and remarks on the paper contribution.
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 1 Introduction
### STC OTDM Receiver  
At the receiver side, the received signals pass through STC decoder as shown in Figure 2.  
Figure 2: STC OTDM receiver technique.  
The received symbol matrix in the MIMO system can be represented by [9]  
\[\underbrace{\left[\begin{array}{c}Y_{11}\\ Y_{21}\\ Y_{12}^{*}\\ Y_{22}^{*}\\ Y_{22}^{*}\end{array}\right]}_{Y}=\underbrace{\left[\begin{array}{cc}\alpha^{ \ast}H_{11}&\alpha^{\ast}H_{12}\\ \beta^{\ast}H_{21}&\beta^{\ast}H_{22}\\ \alpha H_{12}^{*}&-\alpha H_{11}^{*}\\ \beta H_{22}^{*}&-\beta H_{21}^{*}\end{array}\right]}_{Heq}\underbrace{X_{1} }_{X_{1}}+\underbrace{\left[\begin{array}{c}z_{1}\\ z_{2}\\ z_{3}\\ z_{3}\end{array}\right]}_{Z} \tag{13}\]  
where \(\alpha\)= \(H_{11}\)+\(H_{12}\) and \(\beta\) = \(H_{21}\)+\(H_{22}\).  
The ZF equalizer is used to recover the data symbols by considering \(\dot{H}_{eq}\) Y, where \(\dot{H}_{eq}\) is the pseudo-inverse of \(H_{eq}\) which can be represented as  
\[\dot{H}_{eq}\ =\ \frac{1}{\Lambda}\ H_{eq}^{H} \tag{14}\]  
where \(\Lambda\) is the diversity gain represented as [7]  
\[\Lambda=\ |\alpha|^{2}(\sum_{m=1}^{2}\lvert H_{1m}\rvert^{2})\ +\lvert\beta\rvert^{2}(\sum_{m=1}^{2}\lvert H_{2m}\rvert^{2}) \tag{15}\]  
So, the estimated data symbols can be represented by  
\[\hat{X}=\left[\begin{array}{c}\hat{S}_{1}\\ \hat{X}_{2}\end{array}\right]=\left[\begin{array}{c}\alpha H_{11}^{*}&\beta H _{21}^{*}&\alpha^{\ast}H_{12}&\beta^{\ast}H_{22}\\ \alpha H_{12}^{*}&\beta H_{22}^{*}&-\alpha^{\ast}H_{11}&-\beta^{\ast}H_{21} \end{array}\right]\left[\begin{array}{c}Y_{11}\\ Y_{21}\\ Y_{12}\\ Y_{22}\end{array}\right]\text{and} \tag{16}\]  
\[\hat{X}_{n}=X_{n}\ \Lambda+Z. \tag{17}\]  
After that, \(\hat{X}\) is passes through the S/P converter followed by the STC decoder. The receiver applies the SVD on the Toeplitz matrix of its channel response. The receiver uses the Hermitian \(U_{n}\) as inverse basis functions to extract the data symbols from the received symbols without interference where \(n=1,2\). This can be enforced as  
\[\hat{s}_{n}=\sum_{i=0}^{\text{N+L-1}}\hat{s}_{n}[\hat{a}\lvert u_{n}^{\ast} \lvert\hat{\rvert}\cdot\ldots\cdot\in\mathbb{C}^{\lvert\frac{n}{2}\lvert+ \text{L}\lvert\text{L}\lvert\text{L}\lvert\text{L}\lvert\text{L}\lvert\text{ L}\  
Therefore, the total BER can be represented as [10]  
\[\text{BER}=2\ Q(\sqrt{\frac{P_{2}}{\sigma^{2}}})\left(1-\frac{1}{2}\ Q(\sqrt{\frac{P _{2}}{\sigma^{2}}})\right) \tag{22}\]  
where \(Q(.)\) is the standard Q-function.  
Finally, from Equation (22), we proved that the proposed system enhances the performance of the BER.
## 3 The Dominating Set Problem  
Definition 1 provides the characterization of a dominating set.  
**Definition 1**.: _Consider \(G=(V,E)\) to represent a graph, and if \(D\subseteq V\), \(\forall v\in V\ v\in D\) or \(v\in V\backslash D\) and \(v\in N(D)\), N is the set of neighbours of nodes in \(D\), then, \(D\) is called a dominating set. A dominating set encompasses all adjacent nodes within its neighborhood._  
**Definition 2**.: _Consider \(G\), \(D\), \(N\), and \(V\) as in Definition 1; when \(D\) possesses the smallest possible number of elements, it is referred to as the MDS._  
The minimum dominating set problem is to determine dominating set of minimum size, and try to find its cardinality. The dominating set problem falls into the NP-hard category, meaning that it is almost impossible to find the most suitable solution quickly with current computer technology. More detailed information can be found in the study [11] about the dominating set. It can be seen that solving the MDS problem with a polynomial-time algorithm is NP-hard with the proof by the following contradiction via the SAT problem [37]:  
**Assumption 1**.: _Assume that the MDS problem is not NP-hard and there is an algorithm, running in polynomial time, to solve this problem._  
**Contradiction 1**.: _If there is an algorithm to solve the MDS problem with polynomial time complexity, then this algorithm should solve the SAT problem with polynomial time complexity. However, it is well known that the SAT is an NP-hard problem, which leads to a contradiction._  
Proof.: The Boole Satisfiability Problem (SAT) is the problem of determining whether a given Boole formula is satisfiable, and it is NP-hard. This problem is expressed as \(F=(C_{1}\wedge C_{2}\wedge\ldots\wedge C_{m})\), where each \(C_{j}\) is a clause consisting of a set of literals (for example, \(C_{1}=(x_{1}\vee\overline{x_{2}}\vee x_{3})\)). To reduce the SAT problem to the MDS problem, we follow these steps:  
* For each variable \(x_{i}\), create a node \(v_{i}\).
* For each clause \(C_{j}\), create a node \(c_{j}\).
* If a variable \(x_{i}\) appears positively in a clause \(C_{j}\), add an edge between \(v_{i}\) and \(c_{j}\). If the negation \(\overline{x_{i}}\) appears in \(C_{j}\), also add an edge between \(v_{i}\) and \(c_{j}\).  
After this transformation, all nodes in the graph are connected such that the Boolean formula is satisfied. Specifically, each clause node \(c_{j}\) must be adjacent to at least one variable node \(v_{i}\). If the resulting graph has a dominating set, this set corresponds to a set of variables that satisfy the SAT problem. Thus, if the graph has a dominating set, the formula \(F\) is satisfiable.  
Since the SAT is an NP-hard problem, solving it in polynomial time is impossible. Therefore, the assumption that the MDS problem can be solved in polynomial time leads to a contradiction. The assumption that the MDS problem is not NP-hard is false. Hence, the MDS problem is NP-hard. \(\square\)
# The Total Phenolic Content and Antioxidant Activity of Nine Monofloral Honey Types
## 1 Introduction  
The use of natural foods in the daily diet has significantly increased in the modern world. These foods contain a variety of antioxidant compounds, including flavonoids and phenolic acids, which have beneficial effects on human health [1]. Honey, a totally natural food, could not be an exception, as it provides dietary antioxidants in a highly palatable form [2; 3]. Additionally, honey has been used worldwide in the treatment of diseases, because it has been proved to possess several biological properties, such as antibacterial, anti-inflammatory and anti-cancer effects, as well as stimulation of wound and burn healing [4; 5; 6; 7].  
In particular, the antioxidant activity of honey has been evaluated by various authors using different methods and it has been shown to depend on the botanical and geographical origin of honey and its processing [8; 9; 10; 11]. Furthermore, several studies have found a linear correlation between the antioxidant activity of honey and its color, with the darker honeys showing higher antioxidant potent [12; 13]. While there are numerous articles that explorethe antioxidant properties of honey, there remains a lack of extensive information on monofloral honeys, as most studies rely on samples obtained from the commercial market.  
One of the most studied honeys, especially regarding its antibacterial activity, is manauka honey, coming from the New Zealand manuka tree (_Leplosperrum scaparium_), which has been recommended for wound treatment [14; 15; 16]. Previous studies reported that manauka honey consists of a high amount of phenolic compounds, such as flavonoids [17]. Manuka honey also contains a bioactive ingredient called methylglyoxal that exhibits non-peroxide antibacterial activity [18].  
In Greece, there is a great number of plant species giving various monofloral honeys, which are distinguished for their microscopic, sensory and physicochemical characteristics. However, scarce literature exists regarding the biological activities of Greek monofloral honeys, mostly referring to their antimicrobial properties, while even fewer are the references regarding their antioxidant potential [19; 20; 21].  
Considering all of the above, the aim of the present study was to collect fresh and unprocessed Greek honey samples directly from beekeepers, to select representative samples of nine specific monofloral Greek honey types and to analyze their total phenolic content and antioxidant activity. Their antioxidant capacity was also compared with that of Manuka honey. Lastly, the findings were correlated with color and electrical productivity data.
## 4 Execution of MAMC-MAC Protocol
### Allocation of TDMA Time Period  
The reason behind using the TDMA schema is to allocate time frames in one-hop inter-distance radius to every cluster by supporting contention less channel access. Anyhow, until the arrival of transmission time, the cluster members have to wait when there are a large number of nodes at the destination receiving messages. We remove this latency by preferring time allocation to clusters in decreasing order depending on its mobility. The procedure for frame allotment must satisfy the condition that it is allowed to process the packet to only one neighboring hop, in the reverse direction of the node movement.  
As discussed in the IEEE 1609.4 WAVE standard, the developed schema rules out the implementation of channel switching during the synchronization interval. Irrespective of service channel interval (SCHI) and control channel interval (CCHI), the message is forwarded to all other channels. Here, the complete DSRC bandwidth (75 MHz) is divided into slots. Every cluster is assigned a frame for enabling multi-hop broadcast with minimal delay. Each frame is further categorized into slots. Frame assignment is performed based  
Figure 2: Mobility-dependent clustering scheme.  
on cluster mobility, in which priority is given to the highest mobility cluster. The accessing time to the channel is less for clusters with maximum speed. Figure 3 describes the frame assignment procedure based on clustering.  
In the MAMC-MAC protocol, for transmission, at the start of each TDMA time slot, the hop arbitrarily chooses a transmit time period in which each slot has the equal probability to be chosen by a node. To make it comparable to WAVE's control channel (CCH), every TDMA frame has 20 slots, with a time duration of 1 ms for immediate broadcast of the safety messages. The hops distribute the messages. Therefore, a slot becomes vacant within the assigned frame to allow the upcoming messages from neighboring hops. From this, the frame distribution is performed among the nodes within the network, since all nodes are familiar with the information on the unallocated slots. The clustering algorithm describes the number of frames per cluster that is fed as a MAC layer input. The slot assignment is performed in a manner that; when a hop collects a message moving in a specific direction, it processes it right away to its next hop in the similar direction [21]. For inter- and intra-cluster communication, the proposed MAMC-MAC designed a scheme by dividing the time periods of TDMA frame according to our requirements.  
From Figure 4, we can observe that a TDMA frame is divided into '\(q\)' time periods beginning with the \(0 th\) slot and ending at the '\(q-1 th\)' slot. Synchronization of the first TDMA frame is performed by the \(0 th\) slot. Later, it transfers the slot-assignment state (SAS) enclosed in the specified cluster such that each hop is provided with dedicated time slot  
Figure 3: Allocation of the TDMA time period depending on clustering.  
for data transmission. In the Figure 4, the \(1 st\) slot to \(q-1 th\) slots are used for transmission of data.
# Simulation of Cross-Correlated Random Fields for Transversely Anisotropic Soil Slope by Copulas
## 5 Simulation Process of Transversely Anisotropic CCRF  
To model ideal CCRFs, the random field is firstly discretized by the matrix decomposition method with consideration of the spatial variability of soil parameters. Then, copulas are employed to model the cross-correlation of random fields. Thus, the multivariate CCRFs are constructed to accurately represent the spatially variable soil profiles. The simulation procedures of transversely anisotropic CCRF are as follows:  
_Step 1_: Characterize the slope geometry and collect on-site data of mechanical parameters. Then the random variable \(\mathbf{X}\) of the soil parameters and corresponding statistics can be determined;  
_Step 2_: Determine the optimal marginal distribution \(F(\cdot)\) and copula function \(C(\cdot)\) of soil parameters. According to our previous studies, TN, LN, GB and WB are selected as candidate margins. Gaussian, Plackett, Frank and No. 16 copulas are chosen to be candidates. Via AIC and BIC, the optimal copulas and margins are sieved out;  
_Step 3_: Establish a geometric model of the studied slope. Boundary conditions are set and finite element meshes are divided. Then the finite element analysis model is established. Slope stability analysis is carried out in the \"job\" module. Develop a MATLAB program to extract the meshes, node coordinates and other related information from the \":inp\" file automatically generated by ABAQUS;  
_Step 4_: Substitute the node and element coordinates obtained in _Step 3_ into Equation (9) to calculate the autocorrelation matrix \(\mathbf{M}_{a}\). According to Equation (10), perform Cholesky decomposition and obtain the lower triangular matrix \(\mathbf{L}\). Subsequently, independent standard normal random fields \(\mathbf{H}^{IAG}\) are generated from Equation (11). It should bepointed out that the mesh size of CCRF model used in this paper is consistent with that of the finite element model;  
**Step 5**: According to the copula determined in _Step 2_, the corresponding CCRF algorithm is selected. Then the cross-correlation standard uniform random fields \(H^{CAU}\) are established. Via the inverse function \(F^{-1}(\cdot)\) of the margins of soil parameters determined in _Step 2_, \(H^{CAU}\) is converted into the expected cross-correlation non-normal random fields \(H^{CAN}\) by isoprobabilistic transformation.