esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
To determine roasted date pit metabolite profiles, samples were subjected to detailed metabolomics analyses targeting its volatile and non-volatile metabolites, in comparison to those of unroasted pits and roasted coffee seeds. Data from each platform was further analyzed using multivariate data analyses to assist in identification of specimen's markers or their classification in an untargeted manner  
Volatile Components and Their Contribution to the Aroma of Roasted Pits and Coffee via SPME Analysis  
Aroma volatiles produced during coffee seeds' roasting may be the most important determining factor of coffee quality and have been the subject of research interest for almost a century, with intensive profiling reports. Aroma analysis of roasted date pits was performed to identify how they differ from typical roasted coffee product. Roasted coffee's aroma compounds are comprised of several chemical classes which are: thiols and other sulfur compounds, pyrroles, pyrans, pyrazines, furans, monoterpenoids and aromatics [7]. Quantitatively, the most abundant classes in coffee were to furans and pyrazines, whereas qualitatively, sulfur containing volatiles and pyrazines are regarded as the most significant contributors to coffee's aroma [8]. These volatiles vary significantly among different coffee blends which makes flavor extremely complex, and explains why different coffee types may exhibit such diverse, unique and specific flavors. Date palm roasted pit as a coffee alternative is expected to exhibit a comparable, acceptable aroma and flavor considering its usage in the market. In this study, roasted date pit (RS) and two commercial products derived from roasted pit (P\({}_{1}\) and P\({}_{2}\)) were analyzed via solid phase micro-extraction SPME-GC along with coffee product (C) to assess differences between roasted date pits and coffee seeds. The complete the list of identified volatiles in home-prepared roasted dates (RS), commercial preparations (pit product two, P\({}_{2}\)) and those of coffee (C) samples are presented in Table 1, and a representative gas chromatogram is shown in Figure 1. It should be noted that raw date pits had no aroma and were thus not included in the SPME analysis. A total of 21 volatile compounds were detected in coffee specimens of which additionally four volatiles are identified for the first time in roasted coffee seeds.
## 3 Results and Discussion
### Dominance of Inoculated Yeasts  
A total of 540 colonies were isolated on WL, sequentially re-cultured on WL, and examined for colony color, colony topography, and microscopic characteristics [32]. All isolates were identified as presumptive _S. cerevisiae_ based on their macroscopic and microscopic traits. This identification was supported by analysis of the 5.8 S-ITS region, which produced bands of a size (880 bp) comparable to _S. cerevisiae_[67]. The classification of these isolates as _S. cerevisiae_ was confirmed by comparing the restriction fragment profiles with Cf_ol_, Ha_e_III, and _Hinf_f_1 to those reported by [PERSON] et al. [68]. The interdelta profiles of the isolates were compared, revealing a high dominance of the starter strains inoculated in the different trials, with dominance ranging from 93.3% to 94.8%. The interdelta pro  
Figure 6: Results of microbiological analyses for acetic acid bacteria of different samples. T0, T6, T12, and T18 represent the sampling times (in days) during alcoholic fermentation. Lowercase letters indicate statistically significant differences among treatments according to [PERSON]’s test (\(p\leq 0.001\)); treatments sharing the same letter are not significantly different.  
files of the three starter yeasts were found exclusively in the respective trials where they were inoculated.
# Massive Data Capture Approach for Modeling Existing Building Stocks
###### Abstract  
This research paper aims to develop an approach for the digitalization of non-heritage building stock. Existing building stocks in need of rehabilitation are still not subject to optimized, massive digital surveying processes. Thus, it is difficult to assess the performance of the stock in its current state and after potential retrofitting. While massive data capture is being used to model heritage cases with high precision for preservation and documentation projects, this research paper aims to develop an approach for the digitalization of non-heritage building stock that allows for broader implementation, quicker results, and higher scalability, reducing the time required for data capture but still being precise enough for rehabilitation processes. The novel approach combines a laser scanner, thermal infrared sensing, high-quality pictures (HQPs), and automatic frame extraction (AFE) from video. Data preparation for three-dimensional reconstruction is the main novelty of this approach, which has been validated to obtain the surroundings and building information model (BIM) of the reference building for Barcelona schools. The results coincide with previous projects regarding the high precision of a laser scanner and the coverage of photogrammetry. New findings indicate that HQPs are a highly efficient method. Its combination with AFE provides higher levels of coverage. The proposed approach moves forward from the manually modeled BIM misalignments and enables modeling entire clusters to obtain digital twin building stock to ease future management of existing buildings.
## 3 Results
### External Load
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**MD+2**} & \multicolumn{2}{c}{**MD-4**} & \multicolumn{2}{c}{**MD-3**} & \multicolumn{2}{c}{**MD-2**} & \multicolumn{2}{c}{**MD-1**} & \multicolumn{2}{c}{**MD**} \\ \hline  & & **Coeff** & **(SE)** & **\(p\)** & **Coeff** & **(SE)** & **\(p\)** & **Coeff** & **(SE)** & **\(p\)** & **Coeff** & **(SE)** & **\(p\)** \\ \hline  & G1 & 6.84 & & 24.38 & & 19.36 & & 12.13 & & 12.35 & & 26.61 & \\  & G1 & (0.95) & & (0.85) & & (1.01) & & (0.71) & & (0.62) & & (0.61) & \\ \cline{2-11} ACC & G2 & 18.11 & a ***, & 24.44 & & 19.05 & & 14.86 & a ***, & 13.09 & & 14.41 & a ***, \\  & G3 & (0.98) & b *** & (0.90) & & (1.08) & & (0.74) & b *** & (0.65) & & (0.67) & b ***, \\  & G3 & 20.15 & & 22.93 & & 16.71 & & 14.09 & & 13.21 & & 15.93 & \\  & G1 & (0.87) & & (0.84) & & (1.05) & & (0.693) & & (0.62) & & (0.61) & \\ \hline  & G1 & 5.27 & & 20.11 & 18.45 & & 9.77 & & 9.45 & & 32.28 & \\  & G1 & (0.94) & & (0.84) & & (0.99) & & (0.73) & & (0.65) & & (0.64) & \\ DEC & G2 & 19.05 & a ***, & 20.23 & & 18.19 & b ***, & 12.81 & a ***, & 10.94 & & 13.78 & a ***, \\  & G2 & (0.96) & b ***, & (0.88) & & (1.04) & c ** & (0.75) & b ***, & (0.67) & a ** & 13.78 & a ***, \\  & G3 & 18.90 & & 18.50 & & 14.33 & & 12.26 & & 10.18 & & (0.69) & c ***, \\  & G1 & (0.86) & & (0.84) & & (1.02) & & (0.71) & & (0.65) & & (0.65) & \\ \hline  & G1 & 310 & & 464 & 561 & 409 & & 374 & & 892 & \\  & G2 & (18.1) & & (16.1) & & (19.2) & & (13.6) & & (12.0) & & (11.7) & \\ PLoad & G2 & 587 & a ***, & 474 & & 554 & & 471 & a ***, & 411 & a ***, & 395 & a ***, \\  & G3 & (18.6) & b ***, c * & (17.0) & & (20.4) & & (14.1) & b *** & (12.4) & b *** & (12.8) & b *** \\ \cline{2-11}  & G3 & 545 & & 467 & 529 & 473 & & 415 & & 402 & \\  & G3 & (16.5) & & (16.0) & (19.8) & (13.1) & & (11.9) & & (11.8) & \\ \hline  & & & & & & & & & & & \\ \end{tabular} Notes. Coeff = coefficient; SE = standard error; MD+2 = two days after match; MD-4 = four days before match; MD-3 = three days before match; MD-2 = two days before match; MD-1 = one day before match; MD = match day; ACC = number of actions accelerating above 3 m\(\cdot\)s\({}^{2}\); DEC = number of actions accelerating above \(-\)3 m\(\cdot\)s\({}^{2}\); PLoad = player load; G1 = starter players; G2 = non-starter players; G3 = players who did not participate in the match. a = significant differences between G1 and G2; b = significant differences between G1 and G3; c = significant differences between G2 and G3. * \(p<\) 0.05, ** \(p<\) 0.01, *** \(p<\) 0.001.  
\end{table} 
Table 2: Between-groups differences according to variables related to mechanical demands.
# Plasma-Activated Tap Water Production and Its Application in Atomization Disinfection
## 3 Experimental Results  
### Discharges of Plasma Sources in Tap Water  
Figure 4 shows DBD source discharges in tap water. The discharge started at about 16 kV (peak-to-peak), and a blue-purple uniform filamentary discharge appeared in the glass tube. As the bubbles extended outward from the small holes in the outer glass tube (10 L/min air provided by an air pump, Figure 1), part of the plasma extended to the tap water outside the tube through the channel formed by many bubbles, generating many bubbles with plasma. The extended volume of the plasma outside the tube increased significantly as the voltage increased.  
Figure 5 captured the simultaneous discharge of four DBD devices driven by 20 kV pulses. The 4-DBD discharge array generated a stable discharge below the water surface, producing aqueous RONS, which can generate 500 mL PATW at one time.  
Figure 4: Discharges in the tap water at different applied voltages (Pictures taken by Nikon D750, exposure time 50 ms).  
Figure 3: The disinfection door based on the atomized PATW (Spray volume of PATW: 10 L/h).  
The voltage and current characteristics of the discharge array are shown in Figure 6. The peak-to-peak voltage was 20 kV, the frequency was 10 kHz, and the peak-to-peak current was about 100 mA. The plasma power calculated by Lissajous graphic was 17.5 W.  
The optical emission spectrum (OES) is shown in Figure 7. The emission spectra of N\({}_{2}\) and N\({}_{2}\)\({}^{+}\) in excited states dominated the OES. Because the DBD discharge formed in the air injection zone underwater (Figure 5), the OES was dominated by N\({}_{2}\) species as common air plasma [29; 30]. This measurement result is consistent with the mass spectrometry measurement result of the discharge when the air humidity was high [31]. Mass spectrometry shows that N\({}_{2}\) in vibrational states easily reacted with O, O\({}_{3}\), and OH to generate NO and NO\({}_{2}\) and further generated particles such as NO\({}_{2}^{-}\), NO\({}_{3}^{-}\), and NO\({}_{3}^{-}\)(H\({}_{2}\)O)\({}_{n}\).  
Figure 5: Four DBD devices discharge simultaneously under 20 kV high voltage drive (Pictures taken by Nikon D750, exposure time 10 ms).  
Figure 6: The voltage and current waveform of the 4\(-\)DBD tube array.
# A Novel Fiber Optic Sensor for Microparticle Velocity Measurement Using Multicore Fiber
## 1 Introduction
Multicore fiber (MCF) is a special kind of optical fiber that has multiple cores in the same cladding [14; 15; 16]. As core distances are set during the fiber preform fabrication, they can be checked precisely. The standard deviation of the spacing between adjacent cores is usually small (e.g., 1.5 \(\upmu\)m for some commercial MCFs [17]), which should be better than that of a fiber array. In this paper, we report a cost-effective and compact MCF probe to fulfill the two-dimensional (2D) single particle velocity measurement.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 3 Finite Element-Based Analytical Model of Phase-to-Ground Line Spacing for Ice-Covered Lines
### Ground Arc Pendant Modeling and Solution Methods
#### 3.1.2 Nonlinear Finite Element Method  
Transmission line shape determination using the finite element method is a common approach to calculate the prestress distribution and geometry of the conductor and ground wires. By setting initial shapes and stresses, an iterative process is used to approximate an accurate solution. The horizontal tension and displacement at the lowest point serve as convergence criteria to ensure accuracy and stability. To expedite convergence, a reduced elastic modulus (about 1 \(\times\) 10\({}^{-3}\) order of magnitude) is utilized during shape-finding, which is restored to the true value of subsequent static and dynamic analyses.  
Figure 3: The force diagram of a transmission line with two suspension points at different heights.  
The conductor model LGJ-630/45 and the ground wire model OPGW-185 are used, with the basic parameters listed in Table 1. For a span of 400 m, the conductor's sag is compared between the suspended chain calculation (Equation (4)) and the finite element simulation, as shown in Figure 4. The line in Figure 4 represents the error between the theoretical calculation values and the finite element simulation results. The difference between the computed and simulated values is minimal, with an error margin within 0.2% and a minimum of 0.028%. This confirms that the nonlinear finite element model accurately predicts the sag of overhead transmission lines.
# NN-Based 8 FSK Demodulator for the Covert Channel
###### Abstract  
In this article, a superposition-based covert channel and its demodulator were proposed and examined. As a covert waveform, an 8 FSK modulation was selected. The impact of the channel estimation error and resulting imperfect SIC operation (successive interference cancelation) on the covert information demodulation process was considered. Especially for this imperfection, an NN-based demodulator was proposed. The superiority of this solution over the traditional 8 FSK correlator-based receiver was examined for various cases, including the hard- and soft-decision detectors. It was proven that, although NN does not provide BER values equal to zero, even for the perfect SIC, it generally overcomes the traditional correlator-based 8 FSK demodulator. Simulation results showed that the NN-base demodulator, in the case of additional covert channel coding, provides error-free demodulation, even for four-times greater channel gain error.  
wireless communication; co vert channel; dirty constellation; steganography; LPD; security; neural network +
Footnote †: journal: _Sensors_  
1
Footnote 1: email: {[PERSON]Tel. : +48-261-885-509}
## 1 Introduction
To the best of our knowledge, this is the first detailed theoretical research on the introduction of RF/FSO into deep space communications. Subsequently, [PERSON] et al. applied an empirical scintillation model to study the BER performance of the hybrid RF/FSO deep space communications system [17]. Inspired by the above researches, this paper applies the hybrid RF/FSO system to deep space communication based on the scintillation model proposed in [7], and discusses the end-to-end communication performance. We propose a terrestrial-deep space hybrid RF/FSO system using hybrid \(L\)-pulse position modulation-binary phase shift keying-subcarrier intensity modulation (\(L\)-PPM-BPSK-SIM) scheme, where the RF link obeys Rayleigh fading, and the FSO link follows Gamma-Gamma distribution. When discussing the channel model, so as to obtain the relationship between solar scintillation and BER more accurately, we neglected the attenuation factor of the troposphere. Then considering the influence of the coronal turbulence during the superior solar conjunction period, the background model of the non-Kolmogorov spectrum solar wind turbulence was introduced in detail through the weak electromagnetic scintillation theory, and the research on amplitude fluctuations was conducted. As far as we know, there have been many studies on the influence of solar scintillation on radio wave communications [18; 19; 20], but studies for the hybrid RF/FSO system are relatively rare. On this basis, considering the pointing error, the outage probability, BER, and average channel capacity expressions of the hybrid RF/FSO system in the deep space are derived. According to the closed analytical formula, we further analyzed the effects of the modulation scheme, coronal parameters, and system parameters on solar scintillation and system performance. The rest of this paper is arranged as follows: Section 2 introduces the amplitude scintillation theory based on the non-Kolmogorov coronal turbulence model, the RF/PSO hybrid system communication model and hybrid \(L\)-PPM-BPSK-SIM scheme. Section 3 further deduces the formula of outage probability, BER, and average channel capacity of the system. Then in Section 4, the theoretical results are simulated and discussed. Finally, Section 5 provides some concluding remarks.
###### Abstract  
Enamel matrix derivative (EMD) is applied for periodontal therapy. We created a synthetic amelogenin peptide (SP) derived from EMD, and have previously investigated the biological function of SP. However, it is unknown whether SP affects odontoblastic differentiation. In this study, we tested the effects of SP in the odontoblast-like cells, KN-3 cells. KN-3 cells were cultured with SP (0 to 1000 ng/mL) and then cultured for 3, 8, 24, or 48 h in order to determine the effects of SP on cell proliferation and detect its optimum concentration. KN-3 cells were treated with SP in odontogenic differentiation medium cultured for 3 or 7 days. Odontogenic markers were measured by the detection of alkaline phosphatase (ALP) activity and dentin silalo phosphoprotein (DSPP) expression, the calcified nodule formation, and calcium deposition. The addition of SP significantly promoted cell proliferation at 100 ng/mL, generating the greatest change in cell proliferation. SP also showed increased odontogenic expression markers and mineralization. These results suggest that SP, derived from EMD, could have potential for application in dental pulp capping.  
emdogain; amelogenin; odontoblast; differentiation; mineralization 1
## 3 Results and Discussion
### High-Risk Zones Created by Cruise Ships' Routes
#### 3.1.2 Bisevo Island  
Bisevo island is part of the longitudinal and transversal corridor that cruise ships use. Longitudinal southeasterly routes proceed to the south Adriatic east-coast destinations, while northwesterly routes head to north and central Adriatic ports. Transversal routes in northerly and southeterly directions connect the port of Split with southern Adriatic destinations and the Italian coast.  
During the research period, 21 cruise ships passed less than 3 M from Bisevo island with average retention periods of 19.23 min, 51 cruise ships passed with 3 M to 6 M distances from Bisevo island, 6 cruise ships kept 6 M and more from Bisevo island and 43 cruise ships passed through the Svetac and Bisevo passage (Table 11). Traffic monitoring showed that two cruise ships met less than 3 M from Bisevo island's shores five times in a 90 min period.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 3 Data-Partitioning Strategy Based on Grid Segmentation  
With the continuous refinement of grid models in CFD applications, the resolution of numerical simulations has been increasing, leading to a rapid growth in the volume of flow-field data from terabytes (TB) to petabytes (PB) or even higher orders of magnitude. Large-scale flow-field data pose a series of challenges for visualization processing, such as storage and I/O issues. The most commonly used approach to address the data access and computational challenges in visualizing large-scale flow-field data is leveraging high-performance computing techniques, including parallel I/O hardware and software, as well as parallel visualization algorithms. This involves partitioning the massive computational tasks and evenly distributing them across numerous computing nodes of supercomputers.  
Data organization and partitioning play a crucial role in the parallel visualization process of large-scale flow fields. They serve as essential preprocessing techniques aimed at effectively managing and organizing massive data, enabling efficient access to large-scale datasets. Commonly employed methods for data organization and partitioning include multiresolution-based approaches and block-based approaches. Multiresolution-based methods, albeit effective in reducing the overall data volume, may introduce a trade-off by compromising data quality. These methods employ approximate compression techniques that lower data precision or discard fine-grained details, potentially leading to a loss of data fidelity. On the other hand, block-based approaches may encounter challenges related to imbalanced data partitioning. Significant variations in data volume across different grid blocks can result in uneven distribution, thereby impacting load balance during computation and affecting overall performance.  
Both of the aforementioned approaches can impact the effectiveness and quality of parallel visualization. Therefore, this paper proposes a data-partitioning strategy based on grid segmentation. This involves preprocessing flow-field data files to construct metadata (for the sake of convenience, metaInfo is used to refer to this series of data structures when metadata is mentioned later), constructing grid and coordinate-mapping models, reorganizing grid topology, and dividing geometric models by grid type. By parsing the coordinate-mapping model and grid-mapping model and reading flow-field attribute data in parallel, efficient data loading is achieved. In addition, we implement a static load-balancing configuration to address the problem of load imbalance that can occur during grid segmentation. Figure 1 describes the parallel reading process.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 2 System Model
From (1), the received SNRs at [PERSON] and [PERSON] are written as  
\[\gamma_{n}=P|H_{n}|^{2}\Big{/}\sigma_{n}^{2} \tag{4}\]  
According to (2) and (4), the PDF of \(\gamma_{n}\) is given by  
\[f_{\gamma_{n}}(r)=\frac{A_{n}}{2}\sum_{j=1}^{\beta_{n}}a_{j,n}\left(\frac{ \sigma_{n}}{\sqrt{P}}\right)^{\frac{\alpha_{n}+j}{2}}r^{\frac{\alpha_{n}+j}{4 }-1}K_{\alpha_{n}-j}\left(2\sqrt{\frac{\alpha_{n}\beta_{n}\sigma_{n}\sqrt{r}}{ \sqrt{P}\left(\xi_{g,n}\beta_{n}+\Omega_{n}^{\prime}\right)}}\right),r\geq 0 \tag{5}\]  
Moreover, the CDF of \(\gamma_{n}\) can be derived as  
\[F_{\gamma_{n}}(r)=A_{n}\sum_{k=1}^{\beta_{n}}a_{k,n}\bigg{(}\frac{\sigma_{n}} {\sqrt{P}}\bigg{)}^{\frac{\alpha_{n}+k}{2}}\int_{0}^{\sqrt{r}}\!\!m^{\frac{ \alpha_{n}+k}{2}-1}K_{\alpha_{n}-k}\!\!\left(\!2\sqrt{\frac{\alpha_{n}\beta_{ n}\sigma_{n}m}{\sqrt{P}\left(\xi_{g,n}\beta_{n}+\Omega_{n}^{\prime}\right)}}\right) \mathrm{d}m \tag{6}\]  
According to (14) in [20], \(K_{\circ}(x)\) in (6) can be rewritten as Meijer's G-function, and then using (26) in [20] for (6), the CDF of \(\gamma_{n}\) is given by  
\[F_{\gamma_{n}}(r)=\frac{A_{n}}{2}\sum_{j=1}^{\beta_{n}}a_{j,n}\left(\frac{ \sigma_{n}}{\sqrt{P}}\right)^{\frac{\alpha_{n}+j}{2}}r^{\frac{\alpha_{n}+j}{4 }}G_{1,3}^{2,1}\bigg{[}\sqrt{P}\left(\frac{\alpha_{n}\beta_{n}\sigma_{n}\sqrt {r}}{\sqrt{P}\left(\xi_{g,n}\beta_{n}+\Omega_{n}^{\prime}\right)}\bigg{|} \frac{\alpha_{n}-j}{2},\frac{\frac{\alpha_{n}+j}{2}}{2},-\frac{\alpha_{n}+j}{ 2}\bigg{]} \tag{7}\]