esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Solving the Dilemmas
### Anticipatory Troubleshooting Dilemmas as a Markov Decision Process
#### 5.2.3 Solving the MDPs  
Both \(MDP_{\text{FixOrReplace}}\) and \(MDP_{\text{ReplaceHealthy}}\) are finite-horizon MDPs, where the horizon size is the granularity level. Following a trajectory in either MDP corresponds to possible future failures of components over the time steps up to \(T_{limit}\), and the selection of repair, replace, or ignore actions accordingly. Thus, the sum of rewards collected until reaching a terminal state is exactly the long-term maintenance cost corresponding to such an execution. A solution to each of these MDPs is a policy \(\pi\) that specifies the action the troubleshooting agent should perform in every possible state \(s\). An optimal policy is one that minimizes the expected long-term troubleshooting costs with respect to the underlying component \(C\).  
One can use an off-the-shelf MDP solver to obtain a policy for each MDP, thereby solving the dilemma for which it is created. The size of each of these MDPs is \(O(4^{n})\), since in each of the \(n\) time steps we consider there are two possible actions and two possibleoutcomes for each action (fault in the next time step or not). Thus, for small values of \(n\) one can run classic MDP algorithms such as value iteration [23], and for larger values of \(n\) one can use more sophisticated MDP algorithms, such as real-time dynamic programming (RTDP) [24] or upper confidence bound for trees (UTC) [25].  
Algorithm 1 summarizes our MDP-based solution, which uses both types of MDP (\(MDP_{\text{FixOrReplace}}\) and \(MDP_{\text{ReplaceHealthy}}\)) to solve both dilemmas. It is called when a component \(C\) fails and needs to be repaired. First, we choose whether to fix or replace \(C\) by solving \(MDP_{\text{FixReplace}}\) (line 1). Then, for every healthy component \(C^{\prime}\) we choose whether to replace it or not by solving \(MDP_{\text{ReplaceHealthy}}\) (line 4). Finally, all the chosen repair actions are applied. The overall worst-case complexity of Algorithm 1 is \(O(m\cdot(4)^{n})\), since it involves solving \(MDP_{\text{FixReplace}}\) once and solving \(MDP_{\text{ReplaceHealthy}}\)\(|COMPS|-1\) times (represented by \(m\)), i.e., one per healthy component.  
```
Input: C--the faulty component that needs to be repaired Input:\(n\)--desired level of granularity \(RepairAction\leftarrow\) SolveMDP\({}_{\text{FixOrReplace}}(C,n)\) \(ToReplace\leftarrow\emptyset\) foreach Component \(C^{\prime}\
eq C\)doifSolveMDP\({}_{RepalectHealthy}(C^{\prime},n)=Replace\)then Add \(C^{\prime}\) to ToReplace Apply RepairAction to repair \(C\) Replace every component in ToReplace
```  
**Algorithm 1**MDP-based anticipatory troubleshooting.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 3 RQ1: Authentication Mechanisms Based on LoA  
As described in Article 8 of the eIDAS regulation, a notified eID scheme shall specify the corresponding LoA (Low, Substantial, High), which shall meet the following authentication requirements (as defined in the ISO 29115 [47]):  
**LoA Low (L):**: comparable with LoA2 of ISO 29115, it provides a limited degree of confidence in the asserted identity of a person, single-factor authentication is acceptable.
**LoA Substantial (S):**: comparable with LoA3 of ISO 29115, at least two authentication factors should be used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
**LoA High (H):**: comparable with LoA4 of ISO 29115, at least two authentication factors should be used, and it should guarantee protection against duplication and tampering by attackers with high potential.  
The following sections characterize the adopted authentication methods based on the different LoAs. Table 2 summarizes the results, where the considered MSs are referred to by using the two letters country code based on ISO Alpha-2.  
\begin{table}
\begin{tabular}{c c
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 4 Microfluidic Platforms Applied to Axonal Guidance
### Topographical Guidance  
Topographical guidance method is to construct micro-topography in the environment of axon to induce the growth direction of axon tips (growth cones). [PERSON] and [PERSON] [80] proposed an axon guidance method based on micro-topographical and biological cues on 3D substrates. The 3D substrate is constructed by using Matrigel and poly-d-lysine (PDL) to coat on PDMS. Directing axon growth by utilizing the preference of neuronal cells for growth cones that tend to grow toward the most favorable substrate in the absence of chemotactic factors. [PERSON] et al. [81] proposed a PDMS microfluidic chip integrated with a light patterned substrate to achieve isolated and unidirectional axonal outgrowth of human pluripotent stem cells derived neurons. This microfluidic device achieves isolation of the axon from the soma and dendrites and robust growth of the axon toward the adjacent axon region by optimizing the cross-sectional area and length of the PDMS micro-channels. Axon regrowth is guided by the photoinscribed nanotopography on an azobenzene-containing molecular film. The innovative point of this microfluidic device is the integration of nanotopographic patterns with a compartmentalized microfluidic chip, which creates a model based on human neuron that supports superior axonal alignment in isolated microenvironment. In the research of [PERSON], [PERSON] et al. [79], PLGA-based nanofibers were electron-pinned and coated on the bottom layer of the chip, shown as Figure 6A. These nanofibers form porous, bead-free, uniform scaffolds that guide axonal growth.  
Figure 5: Microfluidic platforms for neurite outgrowth. (**A**) The microfluidic culture platform to simulate axon entrapment by [PERSON] The left half of the figure shows the schematics of the microfluidic device. The right half part shows the normal SCs and GDNF-overexpressing SCs with axonal trapping in the middle chamber. (**B**) The microfluidic device to study the regeneration of injured CNS axon by [PERSON].
## 3 Results and Discussion
### TDOS (Total Density of States) Calculation  
The TDOS was simulated by convoluting molecular orbitals with Gaussian curves. Convolution is a mathematical operation that combines two functions to produce a third one, often representing some property of interest. Gaussian functions are often used in computational chemistry and physics to represent distributions. In this context, it seems like Gaussian curves are being used to smooth or broaden the individual molecular orbitals. Gaussian curves with unit height typically mean that the area under each Gaussian curve is normalized to one. This is a common practice to ensure that the convolution operation does not alter the overall intensity or scale of the original molecular orbitals. Each discrete vertical line in the simulated spectrum likely corresponds to a specific molecular orbital. The position of these lines reflects the energy levels associated with each orbital. The dashed line highlights the position of the HOMO (Figure 13). This is a key parameter in understanding the electronic structure of the system [67]. The UV-VIS spectra for all the studied molecules are shown in Figure 14. For the calculations, we used the TDDFT method with the B3 LYP/6-311++G(d,p) basis set. The most intensive peak in the DIT-SA UV-VIS spectrum corresponds to a transition from HOMO-1 to LUMO+1.  
Figure 13: Total density of states (TDOS) for all studied molecules.  
Figure 14: Comparison of UV-VIS spectra of dithranol, salicylic acid, and DIT-SAL calculated using the time-dependent DFT method.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 4 Cauchy Similarity Test
### Static Load Test  
A concentrated load was applied at the mid-span of the model to evaluate the static stiffness of the model through the load-deflection curve. The load was increased from zero and then decreased back to zero, with deflection changes at the mid-span and the quarter-span being recorded. When the concentrated load at the mid-span of the model reached its maximum value of 38 kg, the mid-span deflection of the scale model also reached its maximum value of 2.40 mm. Compared to the numerical model results, the maximum deformations at the mid-span for the high-precision numerical model and the simplified numerical model were, respectively, 2.26 mm and 2.16 mm, as illustrated in Figure 6. Linear regression analysis of the experimental data produces the deflection curves and their slopes at the mid-span and quarter-span, representing the static stiffness of the structure, as shown in Figure 7. The slopes of the deflection curves at the mid-span for the scale model, high-precision numerical model, and simplified model are, respectively, 0.0630, 0.0570, and 0.0562; at the quarter-span, they are, respectively, 0.0374, 0.0344, and 0.0340. The deflection changes in the scale model and numerical models show good consistency with a relative error of less than 10%, indicating that the static stiffness of the model satisfies the Cauchy similarity.  
Figure 5: High–fidelity numerical model. (**a**) Geometric model; (**b**) meshing.
## 4 The WBANs Communication Stack
### Routing Protocols
The classification and state-of-the-art routing protocols for WBANs can be divided into several important sub categories, which include multipath routing, QoS-aware routing, cluster-based routing, mission-critical reliable routing, and others that are shown in Figure 17, and explained in the following sections.
# Ab Initio Study of Martensitic Transformation in NiTiPt High Temperature Shape Memory Alloys
## 3 Results and Discussion
### Electronic Structures  
In order to investigate the electronic mechanism behind the martensitic transformation pathways, the partial density of states (PDOSs) of the B19 and B19\({}^{\prime}\) structures of Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) alloys are calculated and plotted in Figure 5. For NiTi, the electronic states below the Fermi level are mostly contributed by the Ni d states, while electronic states above the Fermi level come mainly from Ti d states. This feature is consistent with other DFT study [46]. Furthermore, the hybridization between Ti d and Ni d states in the structure of B19\({}^{\prime}\) is stronger than that of B19. Moreover, compared with the Ti d of B19 NiTi, the high energy peaks above Fermi level of B19\({}^{\prime}\) shift towards the Fermi level. This suggests that B19\({}^{\prime}\) is more stable than B19 in NiTi, in accordance with the results of \(E_{\text{f}}\) (see Figure 4).  
Figure 5 shows that, due to the Pt dopants, a new set of peaks centered at \(-\)4.5 eV emerges in the PDOSs of the B19 and B19\({}^{\prime}\) of Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) serials. Moreover, these set of peaks become stronger and broader with the increase of Pt content. The doping of Pt gives rise to the strong resonant states between the Ni and its nearest neighbor Pt atoms in the energy region from \(-\)3 eV to \(-\)1.8 eV, and enhances the interaction between Ti and Ni (Pt) atoms. The peaks of Ti atoms and Ni-Pt resonated atoms shift towards lower energies slightly. These indicate that both the B19 and B19\({}^{\prime}\) phases become more stable as the Pt increases, in line with the data of \(E_{\text{f}}\). The electronic interaction in B19\({}^{\prime}\) is slightly stronger than that of in B19.  
However, for the \(x\) = 18.75 alloy, the peaks of Ni d states below the Fermi level located at about \(-\)1.8 eV in the PDOSs of B19 are higher than those of B19\({}^{\prime}\), which implies that the hybridization between Ni (Pt) d and Ti d states in the B19 phase is stronger than that of B19\({}^{\prime}\). This reveals that the B19 orthorhombic phase is more stable compared with the B19\({}^{\prime}\) monoclinic phase, which is in accordance with the formation energy results that the orthorhombic phase is the martensitic stable structure for Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) alloy, as \(x>15\).
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 4 Routing Path Formation
### String Formation in Case of Node Failure
#### 4.4.2 Scenario 2: What will Happen If a Node in a String Goes down after the Initialization?  
A mechanism is required to forward the packets in case one of the nodes of a string is down. First, the nodes above and below the failed node in that string should be informed about the failure so they can make an alternative path. Timeout threshold and beacon signals are used to address this issue. If a node does not transmit a packet for a certain period, i.e., 10 WPs, then it should transmit a beacon packet to indicate that it is still alive. The nodes should also expect to receive a data or a beacon packet  
Figure 9: A node is down at the time of routing initialization.  
from the nodes above and below them within the threshold period. If a node fails to transmit data or beacon packets, then the nodes above and below in the string conclude that the node has died and start the process of finding the alternative path.
## 1 Introduction  
The diversity of _Prunus_ spp. L. nectariferous honey plants from the family _Rosaceae_ in Croatia is pronounced, and includes the following species: _P. armeniaca_ L., _P. avium_ L., _P. cerasifera_ [PERSON], _P. cerasis_ L., _P. dulcis_ (Mill.) [PERSON], _P. fruticosa_ [PERSON], _P. mahaleb_ L., _P. padus_ L., _P. spinosa_ L. and _P. tenella_ [PERSON]. [1]. Mahaleb (_Prunus mahaleb_ L.) kernels [2] contain coumarin, herniarin (7-methoxycournarin), dihydrocournarin and a small amount of amygdala (mandelonitrile-\(\beta\)-gentio-bioside). Furthermore, the kernels contain ca. 40% fatty oil with unusual conjugated fatty acids, such as 9,11,13-octadecatrienoic acid (_cis-_, _trans-_, _trans-_ and _cis-_, _trans-_, _cis-_ isomers) and _ca_. 31% proteins [2]. The ratio of coumarin and herniarin in different parts of this plant and their biosynthesis pathways were investigated previously [3]. The plant is robust and insensitive to diseases and is used as a stock in the grafting of cherry and marasca. [4]. The kernel (embryo) of the seed is soft-textured and tastes bitter and aromatic, especially after chewing for some time, when a subtle bitter almond flavour develops. Mahaleb kernels are used in folk medicine as a tonic and antidiabetic, as well as a flavouring agent. [5]. The volatile compounds of _P. mahaleb_ leaves, flowers, stem-bark and wood were isolated by steam distillation and subsequently analysed by GC and GC-MS [6]. One hundred and thirty components were identified. Aliphatic hydrocarbons (e.g. C\({}_{20}\), C\({}_{21}\) and C\({}_{28}\)), alcohols, carbonyls, fatty acids (e.g. dodecanoic, tetradecanoic, hexadecanoic and linoleic acids), terpen, C\({}_{13}\)-norisoprenoids and phenylpropane derivatives (e.g. coumarin) were detected. _P. mahaleb_ is also listed as a botanical source of honey in Europe [7], but with no detailed data on the chemical characterization of the honey is available. Wild-growing _P. mahaleb_ in Croatia served as the main nectar source of unifloral honey samples for the presented research.  
In addition to traditional approaches, examination of the volatiles profile might be considered as a strategy enabling honey authentication, since its composition is known to vary widely with the floral origin and way of processing [8]. Finding specific or nonspecific biomarker compounds is a powerful tool in the determination of the honey botanical origin [8-11]. For this purpose, the volatile fraction of _P. mahaleb_ honey was investigated in present paper for the first time by means of headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE) followed by gas chromatography and mass spectrometry analyses (GC, GC-MS). The comparison with results of our previous research [6] on volatiles from _P. mahaleb_ flowers, leaves, bark and wood is also presented.
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 6 VLSI Design and Comparative Analysis  
A chip which implements an LR-aided K-Best detector targeting a 2 \(\times\) 2 MIMO system was synthesized using 32 nm standard CMOS technology. The frequency of operation was set to 253 MHz at a supply voltage of 1 V. To estimate the power consumption of the MIMO decoder hardware (\(P_{hw}\)) under normal operation conditions, first we computed the channel matrix (H) based on free space medium. The latter was then applied to the MIMO decoder along with large number of typically received inputs (Y), the average power consumption has then been estimated for all test cases.  
The minimum transmission power requirement is estimated using Equation (9) based on free space communication medium, where we have considered the minimum acceptable BER of \(10^{-3}\), this is because received signals with BER higher than this figure may not be reliably detected [20]. We have considered two study cases: 4G and 5G wireless communication networks. The operation parameters of 4G systems are obtained from the third-generation partnership project which provide detailed information on the operating conditions of such networks [21], where we consider a 4G network transmission frequency of 1.8 GHz. The operation parameters of 5G networks are still in the development stage; initial estimates of the transmission frequencies indicate they are within the range 10-100 GHz [22]. For the purpose of this work we have considered the first successful implementation of a 5G wireless communication network recently announced by Ericsson [22], where in the transmission frequency was set to 15 GHz. In both of the above cases we consider  
For the first case study, i.e., the 4G wireless mobile network, Figure 5 shows that the proposed MIMO can significantly reduce the transmission power requirements at the expense of increased power consumption of the MIMO decoder; therefore, its overall power costs is comparable to existing approaches. However for the second case study, i.e., the 5G wireless network, our decoder exhibits remarkably higher power efficiency, because it reduces the overall power cost: by two- and seven-fold, compared with conventional K-best implementations in [13; 14], respectively. These large power saving are expected to be higher as the carrier frequency increases. Figure 7 show the relationship between the transmission carrier frequency and the overall power consumption costs for the four schemes.  
Finally, it is worth noting that the throughput of the proposed design is comparable to existing approaches as outlined in Table 1.
# Evaluation of Bridge Maintenance Priorities in Megacities
## 4 Application of the Proposed Model to Eight Megacity Bridges
### Bridge Selection with Maintenance Priority Mode  
In order to select bridges to which the maintenance priority model could be applied, the research team collected data on bridges in Daegu, one of Korea's major cities, where ADT data were available. There were a total of 18 bridges with ADT data in Daegu. Among the 18 bridges with an ADT value of 50,000 or more were targeted for the maintenance priority model, and a total of eight bridges were selected. Table 6 shows input information on eight bridges with the maintenance priority model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ADT \({}^{*}\) (Cars/Day)** & **Score** \\ \hline ADT \(<\) 20,000 & 0.1 \\ \hline
20,000 \(\leq\) ADT \(<\) 40,000 & 0.2 \\ \hline
40,000 \(\leq\) ADT \(<\) 60,000 & 0.4 \\ \hline
60,000 \(\leq\) ADT \(<\) 80,000 & 0.6 \\ \hline
80,000 \(\leq\) ADT \(<\) 100,000 & 0.8 \\ \hline
100,000 \(\leq\) ADT & 1.0 \\ \hline \hline \end{tabular} \({}^{*}\) ADT: Average daily traffic.  
\end{table}
Table 4: Score by criticality factor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison Factor** & **Content** & **Score** \\ \hline \multirow{3}{*}{Facility Classification} & Type 3 & 0.5 \\ \cline{2-3}  & Type 2 & 0.7 \\ \cline{2-3}  & Type 1 & 1.0 \\ \hline \multirow{6}{*}{Number of Lanes} & Lanes \(<\) 4 & 0.1 \\ \cline{2-3}  & 4 \(\leq\) ADT \(<\) 6 & 0.2 \\ \cline{2-3}  & 6 \(\leq\) ADT \(<\) 8 & 0.4 \\ \cline{2-3}  & 8 \(\leq\) ADT \(<\) 10 & 0.6 \\ \cline{2-3}  & 10 \(\leq\) ADT \(<\) 12 & 0.8 \\ \cline{2-3}  & 12 \(\leq\) ADT & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scores by comparison factor.  
All bridges applied to the proposed maintenance priority model are located in megacities, with completion years distributed from 1986 to 2003. The design loads are all identical to DB-24, and some bridges have seismic designs, and some bridges do not. The B safety grade constitutes seven bridges, and C grade constitutes one bridge, and ADT values range from 53,644 to 94,687. The facilities are classified into four places of Type 1 and four places of Type 2, and bridges have 6-10 lanes.
## 2 Materials and Methods
### Retrieval and Analysis of Spectral Indices  
In addition to the spectral bands, spectral indices have been developed and used to analyze remote sensing images. These indices are calculated using common equations and enhance some spectral features by minimizing effects of illumination and reducing noise [76; 77]. In this study, we used the bare-soil reflectance extracted from the Sentinel-2 images to calculate 40 spectral indices (Appendix A). Based on their application in previous studies, these indices can be grouped into four categories: vegetation, soil, geology, and water. To identify the indices that improved detection of soil variability in space and time, we performed multiple factor analysis (MFA) of the time series of indices for the 58 points of bare soil. This multivariate method, MFA [78] is based on principal component analysis (PCA) and used to summarize data described by several sets of variables organized into  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviation** & **Index** & **Equation** & **Reference** \\ \hline R1 & Radar cross ratio & \(\frac{VH}{VV}\) & [74] \\ R2 & Radar ratio 2 & \(\frac{VH+VV}{VV}\) & [74] \\ D & Radar difference & \(VH-VV\) & [75] \\ \hline \hline \end{tabular} _VV_: vertical–vertical polarization, _VH_: vertical–horizontal polarization.  
\end{table}
Table 1: Equations of the Sentinel-1 indices used.  
groups. Variables of the same group are normalized with a weight equal to the inverse of the first eigenvalue of the PCA, and each variable of the group has its own weight. In our study, each time series of indices was considered as a group. We selected twelve indices that contributed differently to the first two dimensions of the MFA in order to use them to calibrate the models (S2-indices) (Table 2). We then calculated the same 12 indices using the soil spectra measured in the laboratory and the central wavelengths of the Sentinel-2 bands (Lab-indices). To decrease the number of indices potentially included in the prediction models, we selected optimal indices that had the highest Pearson correlation coefficients with SOC content.