esa-sceva/satcom-chunk-collection huggingface dataset:
# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 2 Related Work
### Selection of a Representative Value for Data Duplication Processing
In the case of mode, the error of no calculation can occur if none of the values among the redundant values are the same. In the case of percentile, the calculated result can differ according to \(k\) as the value of percentile defined by a user. If the \(k\) value is 0%, 25%, 50%, 75%, or 100%, the calculated result is the same as the quartile value. In the case of trimmed mean, a different calculated result appears according to the k value as the mean of the user-defined \(k\) values, excluding the maximum and minimum values [9]. Accordingly, in a heterogeneous healthcare platform, a representative value of data is selected from the effective mean, median, and quartile values. In this case, the second quartile produces the same result as the median, which is excluded. Algorithm 1 illustrates the selection algorithm of the representative values. ```
Inputdk\({}_{C}\) // unique value of the variable c collected on device \(k\)  Vc // eigenvalues of the variable \(c\)  dkmatrix[c] // variable c data matrix of device \(k\)  Vcmatrix // data matrix of variable \(c\) Output(i) Umatrix/ // data matrix of user \(a\) Step 1. Device scan of variables  for k = 1 to num_decvice do  for c = 1 to num_variable do  if Vc == dk\({}_{C}\) then  Vcmatrix <- dkmatrix[c] // Store the variable c matrix of the device k in the data matrix of the variable \(c\) Step 2. Selection of a data matrix for variable \(c\)  for c = 1 to num_variable do  find representative value from Vcmatrix[t] save data matrix return
```  
**Algorithm 1** Selection of representative values  
Table 3 presents the selection of representative values using the mean, median, and quartile when the redundancy value of step count is \"3\". As shown in this table, the step count calculation was based on smartphone, health band, and travel distance data. The first time step is shown at t001. At t001, the step count calculated by the smartphone was 4011, 4237 by the health band, and 3678 by travel distance. At this time, as representative values, the mean is 3975, the median is 4011, the first quartile is 3678, the third quartile is 4124, and the fourth quartile is 4237. Smartphones and health bands count steps by means of their gyro sensor. Travel distance is the accumulated travel distance according to the GPS. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Member**} & \multicolumn{6}{c}{**Number of Data Duplication**} \\ \cline{3-6}  & & **Sleep** & **HR** & **Steps** & **Temp.** & **Hum.** \\ \hline A & 11 & 0 & 3 & 3 & 2 & 2 \\ \hline B & 5 & 3 & 2 & 3 & 2 & 2 \\ \hline C & 5 & 0 & 3 & 4 & 3 & 3 \\ \hline D & 4 & 3 & 2 & 3 & 3 & 3 \\ \hline E & 1 & 4 & 3 & 3 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data duplication count by device type.
## 3 Contribution of Selected Yeasts to Emerging Winemaking Demands
### Enhancing the Aroma of Wine
Using commercially available strains, it was also reported how the sequential inocula of the industrial _M. pulcherrima_ AWRI1149 and AWRI305 strains, together with _S. cerevisiae_, produced Shiraz wine with increased intensity of several desirable aromas and lower off-flavors [98]. Similar results were reported recently by [PERSON] et al. [99], who examined the effect of _M. pulcherrima_ inocula on the aroma and sensory complexity of Catarratto wines. Specifically, they realized a noticeably increased aromatic richness of the inoculated wines, which was also attributed to the presence of esters, the most prevalent of which was ethyl decanoate. _Hanseniospora_, specifically _H. guillermondii_, _H. vinae_, and _H. uvarum_, are also relevant species from an enological point of view thanks to their high production of aromatic compounds, such as acetate esters, 2-phenylethyl acetate, and ethyl acetate, and their high enzymatic activities, including beta-glucosidase and beta-xylosidase [100]. In Ecolly and Cabernet Sauvignon wines produced by using _H. uvarum_, specific varietal and fermentation volatile compounds were found. Particularly, sequential inocula enhanced the aromatic profile of the final product by notably boosting fruity and flowery aromas. Nevertheless, excessive levels of _H. uvarum_ affected the fermentation kinetics, producing wines with a -tail-polish-like aroma due to higher levels of acetate esters and volatile phenolics [101]. Moreover, strains of _H. uwarum_ may produce an excessive amount (up to 1 g/L) of acetic acid, imparting the typical vinegary smell [102]. Lately, the use of _Starmerella bacillaris_ (synonym _Candida zemplina_) has recently become widespread. Generally, this yeast is capable of resisting high concentrations of ethanol, and thus it is commonly present in the intermediate/final stages of must fermentation [103]. The most important peculiarities of this species are its high glycerol production (up to 14 g/L) [17] and its fructophilic character [104]. The mixed culture of _S. bacillaris_ and _S. cerevisiae_ seems to enhance the concentration of fatty acids, esters, and higher alcohols compared to the pure culture of _S. cerevisiae_[105]. High amounts of monoterpenes, such aslinalool, geraniol, ocimene, and nerol, were also detected in Sauvignon Blanc wines [106], although the presence of these compounds is highly grape variety-dependent [50].
## 6 Simulation Results and Discussion  
This section presents the simulation results for the mean acquisition time of the scanning methods: spiral, raster, square spiral, and hexagon. The dwell time, \(\Delta t\), and the beam divergence, \(\theta_{b}\), were set to 0.1 s and 25 microradians, respectively, for different values of the standard deviation of the satellite's position and the size of the uncertainty area to be scanned during the simulations. The satellite platform vibration model by ESA and NASDA were utilized to generate random vibration signals for the standard deviation  
Figure 8: Relationship between the probability of acquisition and the ratio \(\frac{u}{\sigma}\).  
estimation using a sliding window [69]. For all the computations, Python version 3.12.3 and MATLAB version 9.7.0.1190202 (R2019b) were utilized.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 2 Lumped-Parameter Modeling of Piezoelectric Metamaterial  
In this section, a semi-analytical investigation of one-dimensional piezoelectric metamaterial integrated with LC shunt circuits is formulated. Firstly, the configuration of the unit-cell is described, followed by energy analysis based on continuum mechanics characterization. A lumped-parameter model of the piezoelectric metamaterial is formulated by incorporating the periodic boundary condition of the unit-cell, where a representative circuitry configuration, i.e., parallel-connections of the piezoelectric transducers, is considered. The dispersion relations of the piezoelectric metamaterial are then established.
## 3 Results and Discussion
### Catalytic Activity of Various NPs for Dyes  
Under visible light irradiation, the degradation efficiency of various dye solutions increased with reaction time, ultimately reaching a plateau. When Au-Ag alloy NPs were  
Figure 5: Wound healing capacity of Au-Ag alloy NPs. Data are presented in terms of mean \(\pm\) standard deviation values (\(n\) = 30). A \(p\) value of >0.05 indicates a nonsignificant between-group difference.  
used, the degradation efficiency of VBR stabilized at the 5 th minute, achieving complete degradation (100%). The degradation efficiency of MO and MG plateaued at the 8 th minute, each exceeding 99.97%, whereas that of RY4G reached a plateau at the 9 th minute, with an efficiency exceeding 99.96%. Furthermore, the degradation efficiency of MB, CR, and RhB stabilized at the 11 th minute, each exceeding 99.93%.  
The NP-mediated decolorization reaction of dyes followed pseudo-first-order kinetics [54]. To calculate apparent rate constants for catalytic reactions involving various NPs, the logarithm of dye concentration was plotted against reaction time. Table 4 presents the apparent rate constants for the degradation of different dyes by Au, Ag, and Au-Ag alloy NPs. Notably, Au NPs exhibited the highest catalytic activity, followed by Au-Ag alloy NPs and Ag NPs. Numerous studies have demonstrated that the stability and catalytic performance of Ag NPs are inferior to those of other metal NPs [55]. Nevertheless, the Ag NPs synthesized in our study retained a certain level of catalytic activity, likely because of the presence of the MQLE coating on their surface, which facilitated efficient electron transfer between MQLE and the dyes [48].  
VBR was the most effectively degraded dye, followed by MO, MG, RY4G, MB, CR, and RhB. The challenges associated with degradation primarily stem from the complexity of the chemical structures and the solubility of the dyes [54,56]. In the degradation of MG, the catalytic activities of Au-Ag alloy NPs synthesized using cell-free extracts of _B. safensis_ and those synthesized using _Deinococcus radiodurans_ protein extract were 92.6% and 83.68%, respectively [48,57]. The apparent rate constant for chlorypriphos degradation by Au-Ag alloy NPs synthesized using _P. longifolia_ leaf extract was 0.405 d\({}^{-1}\)[13]. The apparent rate constants for MO and MB degradation by chemically synthesized Au-Ag alloy NPs, utilizing NaBH\({}_{4}\) as a reducing agent, were 0.888 and 0.69 min\({}^{-1}\), respectively [58]. Typically, three methods are used for the degradation of dyes with NPs: NaBH\({}_{4}\) addition, ultraviolet light irradiation, and visible light irradiation [59]. Among these methods, visible light irradiation is the most convenient and sustainable option. The green-synthesized NPs investigated in this study effectively degraded various dyes under visible light irradiation, achieving degradation efficiency higher than or similar to that reported in the literature, which highlights the advantages of this synthesis technology.
## 2 Materials and Methods
### Definition of the Tank and Baffle Types  
In order to examine the effects of baffle geometries more closely, we conducted our studies on a prismatic tank whose volume and dimensions were close to those of a fuel tank (volume: 50.4 L; \(L\): 700 mm, \(W\): 300 mm, \(H\): 240 mm). Due to the symmetrical and homogeneous structure of the tank, we placed the pressure measurement points on only one side wall of the tank. In Figure 4, the structure of the tank and the placement points of the pressure points are shown. The tank oscillated horizontally. In the analysis, two types of excitation signals were applied to the tank. In the first eight of these analyses, a sinusoidal input with \(A=0.1\) m amplitude and \(f=0.8\) Hz (\(\omega=5.02\) rad/s) frequency was applied to the tank, taking into account the ES31100-01 engineering standard shaking test criteria. The filling rate (\(h_{liquid}/h_{tank}\)) was 50% in these analyses. \(h_{baffle}/h_{liquid}\) was set as 0.8, and this ratio was selected only for the third baffle structure because of the baffle's placement. Eight scenarios were carried out for the study. At the second stage of the analysis, the amplitude of the input signal was set as \(A=0.02\) m, and the angular frequency was calculated from Equation (6). We examined the sloshing behavior of the three baffled structures selected from the first eight scenarios and the unbaffled tank system for the lower amplitude input value by setting the tank displacement \(A=0.02\) m and \(\omega=\omega_{n}\). The last four scenarios were analyzed separately for 50% and 70% filling rates. In addition, since the tanks could be used in different engineering fields, the dynamic responses to be obtained if various fluids were used in the same tank system were also analyzed using water and kerosene fluids.  
The scenarios of analyses performed to examine the baffle structures at different frequencies and amplitude input values are presented in Table 1.  
Figure 4: Dimensions of the tank and the locations of the pressure measurement points.  
Figure 3: Comparative plots of pressure–time curves of points PT2, PT3 and PT4 (\(h_{\text{baffle}}/h_{\text{liquid}}=0.4\)).
## 5 Experimental Results and Discussion
### Experimental Results of Steel Cord conveyor Belt Defects
#### 5.2.2 Noise Reduction and Normalization of the Defects Signals  
Weak magnetic signals of the steel cord conveyor belt defects could easily be disturbed by strong and non-stationary noise in coal mine. Therefore, the noise of the defects signals was reduced using a noise reduction method, combining the wavelet packet and the RLS (Recursive Least Squares) adaptive filtering [24]. The noise reduction principle of the filtering algorithm is presented in Figure 10. \(x(n)\) was the input signal that includes the useful signal \(s(n)\) and the noise signal \(N_{1}(n)\). The sum of the multi-layer high frequency detail signal for \(\mathrm{x}(n)\) was decomposed by the wavelet packet. \(N_{2}(n)\) was used as the noise reference signal of the RLS adaptive filter algorithm. Finally, the noise of the defect signals were reduced by the RLS adaptive filtering algorithm.  
In order to eliminate the effect of the signals amplitude on classification, the signals after noise reduction were normalized. The normalized signals of the splice, the broken rope defect and the rope fatigue defect before and after noise reduction are presented in Figures 11-13.  
Figure 8: Scheme of the weak magnetic signals collection experiment platform.  
Figure 10: Noise reduction principle for a combination of the wavelet packet and the Recursive Least Squares (RLS) adaptive filter.  
Figure 9: Installing picture of the weak magnetic testing deviceFigure 11: Normalized weak magnetic signals of a splice before and after noise reduction.  
Figure 12: _Cont._  
Figure 12: Normalized weak magnetic signals of the broken rope defect before and after noise reduction.  
Figure 13: _Cont_.
## 3 Proposed PBFT Architecture and Model  
This section describes the proposed enhanced PBFT via dynamic hierarchy management and location-based clustering. Our enhanced PBFT provides improved reliability and scalability compared to the DLM-PBFT and typical PBFTs. Figure 2 shows an example of the proposed PBFT architecture.  
Due to blockchain networks' Peer-to-Peer (P2P) communication nature, communication latency tends to increase as the distance between nodes grows. Minimizing communication latency is crucial for reducing the consensus time [23]. In the case of private blockchains, where node locations are usually static, this characteristic can be leveraged for efficient clustering. Our proposed method clusters nodes based on location information, aiming to benefit from reduced communication latency. Such clustering minimizes network communication latency and reduces the time required to reach consensus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Communication Complexity** & **Clustering** & **Grouping** &
\begin{tabular}{c} **Layer** \\ **Management** \\ \end{tabular} \\ \hline PBFT & \(O(N^{2})\) & No & No & No \\ G-PBFT & \(O(N_{group}\times N_{g_{node}}^{2})\) & No & Yes & No \\ SDMA-PBFT & \(O(N\times K\times\log_{K}N)\) & No & Yes & Yes \\ DLM-PBFT & \(O(N\times K^{2})\) & No & Yes & Yes \\ Proposed PBFT & \(O(N_{cluster}\times N_{node}\times N_{g\_node}^{2})\) & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Consensus Algorithms.  
Furthermore, the proposed PBFT method enhances dynamic hierarchy management and grouping algorithms when compared to the DLM-PBFT. In other words, the proposed method can be applied to large-scale networks by utilizing a hierarchy formation algorithm based on location-based clustering [24].  
In the rest of this section, we will explain the blockchain network structure where the proposed method is applied, the types of nodes that constitute this blockchain network, and the roles of each node. Additionally, we will explain how clustering is configured in the proposed method, and how we dynamically change the hierarchical structure within each cluster to reduce the time required for consensus.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 4 Crypto-Engine Implementation in the EMERGE Project
### Discussion of Results  
The Key Generation in ECTAKS is noticeably slower than in similar research works (e.g., [69]), and it depends on the complexity of the topology of the network. On the other hand, the performance for encryption is in line with or better in comparison to the state of the art solutions, while providing the additional benefits of the topology-authenticated keys. In fact, when comparing the Crypto-Engine encryption and signature generation for a single message against the results shown in [70], our solution is faster: 27.14 ms (Table 2 in [70]) versus the sum of the 0.744 ms required by the Crypto-Engine to encrypt, e.g., 1500 bytes of data (theoretical maximum size for an Ethernet frame) and the 6.5 us required to create a digital signature. These results make Crypto-Engine an appealing alternative when the Key component distribution performance is not an issue. This may be the case when the ANT is either simple (i.e., few communication links) or when it can be fixed at deployment time. In comparison with HW and HW-accelerated solutions, the performance of a pure SW implementation of the Crypto-Engine is trivially disadvantaged. Moreover, it is difficult to provide an accurate comparison since ECC HW accelerator performance is often measured in terms of the single ECC point-operation performance, maximum achievable clock frequency, and chip area occupation [63] leaving out the performance bottlenecks that exist due to the communication bus and the device drivers responsible for providing the accelerator results to the upper layer of the communication stack.
## 2 Steady State Imaging Using Magnetic Nanoparticles  
Ultrasmall super paramagnetic iron oxide (USPIO) (a.k.a. magnetic nanoparticle (MNP)) is a steady state blood pool agent that creates contrast in magnetic resonance imaging (MRI) through magnetic susceptibility variations proximal to the vascular network [20, 21, 22, 23], which results in changes in inherent and induced transverse relaxation times defined as T\({}_{2}\) and T\({}_{2}\)*, respectively. Accurate evaluation of R\({}_{2}\)*(1/T\({}_{2}\)*) or R\({}_{2}\)(1/T\({}_{2}\)) necessitate defining two or more points on the natural or induced transverse relaxation decay curve. Addition of contrast agent increases R\({}_{2}\)*(1/T\({}_{2}\)*) thus causing limits on sampling as the T\({}_{2}\)* is shortened. It can be shown that:  
\(\Delta\)R\({}_{2}\)* or \(\Delta\)R\({}_{2}\)\(\propto\) log [S\({}^{\text{post}}\)/S\({}^{\text{pre}}\)]  
where, S\({}^{\text{pre}}\) ad S\({}^{\text{post}}\) are pre- and post-contrast MRI signal intensity [24]. A fundamental assumption is that the change in the transverse relaxation rate ([\(\Delta\)R\({}_{2}\) = 1/\(\Delta\)T\({}_{2}\)] and [\(\Delta\)R\({}_{2}\)* = 1/\(\Delta\)T\({}_{2}\)*]) relative to the preinjection baseline is proportional to the perfused local blood volume per unit tumor volume (V) multiplied by a function (f) of the plasma concentration of the agent (P) [25, 26, 27].  
\(\Delta\)R\({}_{2}\) = kf(P)\(\cdot\)V  
Therefore, the capability of MNP to modulate the effective transverse relaxation rate, R\({}_{2}\)*, is exploited where the difference image of R\({}_{2}\)* before and after administration of MNP will be useful in determining microvessel density [28].  
A gradient echo (GE) MRI sequence allows blood volume (BV) assessment in relatively shorter data acquisition times and has the advantage of minimizing artifacts associated with flow effects. A recent report with a brain tumor model also showed that the R\({}_{2}\)* is more sensitive to the MNP induced susceptibility change [29] and can be used to estimate tumor BV. Although BV is linearly proportional to \(\Delta\)R\({}_{2}\) and \(\Delta\)R\({}_{2}\)*, the proportionality constant was shown to be contrast agent dose-dependent [30]. The apparent BV values of tissues with different composition of arteries, capillaries, and veins were shown to be different [30] since \(\Delta\)R\({}_{2}\) and \(\Delta\)R\({}_{2}\)* are affected by vessel size [31]. The \(\Delta\)R\({}_{2}\) peaks for vessels 1-2 \(\mu\)m in diameter whereas \(\Delta\)R\({}_{2}\)* is fairly independent of vessel size beyond 3-4 \(\mu\)m. These studies thus provide the rationale for spin-echo imaging being more sensitive to microvasculature, while gradient-echo imaging is more sensitive to total vasculature.
## 3 Results  
### Physical Characteristics of the Biosorbents  
The pore volume and pore size of the N-PPF and C-PPF samples were determined using the BET method. As shown in Table 1, the pore volumes of N-PPF and C-PPF were 0.00314 and 0.01069 cm\({}^{3}\)/g, respectively. The pore sizes of N-PPF and C-PPF were 30.1640 and 450.2094 A, respectively. Figure 2 depicts images for comparison of untreated and chemically treated (KOH) materials analysed by SEM. Figure 2a shows the fibres in the presence of lignin and hemicellulose components. KOH treatment created many pores and increased the pore size of the C-PPF sample. The increase in pore volume and pore size after chemical treatment are consistent with the results of previous studies. This shows that KOH can be used in the modification process to provide a developed pore structure and more active adsorption sites, as shown in Figure 2b [5; 6; 14; 15; 22; 23; 24].  
\begin{table}
\begin{tabular}{c c c} \hline
**Biosorbent** & **Pore Volume (cm\({}^{3}\)/g)** & **Pore Size (Å)** \\ \hline N–PPF & 0.00314 & 30.1640 \\ C–PPF & 0.01069 & 450.2094 \\ \hline \end{tabular}
\end{table}
Table 1: Specific surface area, pore volume, and pore size of the biosorbents.  
Figure 2: The SEM images show the surface morphology of N–PPF (**a**) and C–PPF (**b**).  
The functional groups of cellulose, hemicellulose and lignin were studied in the literature and are shown in Table 2[25].  
The comparison of FT-IR spectra between N-PPF before adsorption and C-PPF before adsorption were analyzed by the PerkinElmer FT-IR (model Spectrum 2), operating in attenuated total reflection (ATR) mode used to recorded the FT-IR spectra at wavenumbers 4000-400 cm\({}^{-1}\), which are shown in Figure 3.  
In Figure 3, it was found that the FT-IR spectra of the adsorbent shown at 1636-1640 cm\({}^{-1}\) corresponded to the peak of -C-OH group stretching [14; 15], 1270-1232 cm\({}^{-1}\) may be attributed to the C-O-C stretching of lignin [15], and 1000-1100 cm\({}^{-1}\) corresponded to the peak of the -C-OH group of hemicellulose [21].  
The treated fibres showed a reduction in the amount of hemicellulose and lignin, as can be seen from Figure 2, when comparing the fibre spectra before treatment (Figure 3a) and after treatment (Figure 3b).