esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this article is to propose a new configuration based on OCDMA and Fast-OFDM techniques for access network applications. A hybrid intensity modulation with direct detection, fast orthogonal frequency-division multiplexing-code division multiplexing access (IM/DD Fast-OFDM-CDMA) system is analytically and numerically evaluated for an amplifier-free access network. Therefore, system performance is analytically investigated in terms of bit error rate/Q-factor as a function of simultaneous users, fiber length and launched optical power. Firstly, the proposed analytical model includes the overlapping effect among OFDM subcarriers, the peak-to-average power ratio (PAPR), and multiple access interference (MAI). Secondly, a simulation setup is performed, allowing four simultaneous users operating at 40 Gb/s in a passive optical network (PON) context. Furthermore, a power budget analysis is made between IM/DD Fast-OFDM-CDMA, all-optical IM/DD OFDM-CDMA (IM/DD AO-OFDM-CDMA) and OCDMA wavelength division multiplexing (OCDMA-WDM) configurations. It is shown that at 40 Gb/s and by using 2D-hybrid coding (2D-HC), the maximum achievable transmission-reach of IM/DD Fast-OFDM-CDMA is 142 km, which is 34 km and 60 km higher than those provided by the IM/DD AO-OFDM-OCDMA and OCDMA-WDM PON configurations, respectively.  
Fast-OFDM; optical CDMA; MAI; PAPR; access network; passive optical networks; security +
Footnote †: journal: Applied Sciences
# Theoretical Study of SOF\({}_{2}\) Adsorption on Pd/Pt-Ni(111) Bimetallic Surfaces
## 4 Conclusions  
In this paper, DFT calculations are used to investigate the adsorption ability of Pd/Pt-Ni(111) bimetallic surfaces for SOF\({}_{2}\) adsorption. In order to analyze the adsorption mechanism, the adsorption energy, charge transfer, density of electronic states, and electron density difference were calculated. The results show that the adsorption ability of SOF\({}_{2}\) are in the sequence of Pt-Ni(111) \(>\) Pd-Ni(111) \(>\) Ni(111) \(>\) Ni-Pd-Ni(111) \(>\) Ni-Pt-Ni(111). It is verified that Pd and Pt composite layers on Ni(111) can significantly enhance the adsorption capacity. The adsorptions of SOF\({}_{2}\) on Pt-Ni(111) and Pd-Ni(111) surfaces are strong chemisorption, which are irreversible adsorption processes. Although adsorption structures on Pt-Ni(111) surface are similar to those on Pd-Ni(111) surface, Pt-Ni(111) shows the most stable structure for its largest adsorption energy (\(-\)2.123 eV). The adsorption process on Ni-Pd-Ni(111) surface and Ni-Pt-Ni(111) surface belong to physical adsorption for its small adsorption energy, which is easy to desorb under external energy excitation. In summary, our research provides important theoretical support for developing an ideal SOF\({}_{2}\) adsorbent.  
[PERSON] and [PERSON] proposed the project and analyzed the simulation results. [PERSON] contributed to the DFT simulations. [PERSON] performed the analysis of the data and provided some revision of the manuscript. All authors read and approved the final manuscript.  
This research was funded by Natural Science Foundation of Inner Mongolia Autonomous Region (Grant recipient: Shiming Gan).  
The authors declare no conflict of interest.
# Characteristics of Coherent Optical Phonons in a Hexagonal \(\mathbf{YMnO_{3}}\) Thin Film
## 5 Summary  
We have reviewed the characteristics of the coherent optical phonon in the hexagonal YMnO\({}_{3}\) thin film and related optical studies on coherent phonons, ultrafast incoherent responses and steady-state optical transitions. In the hexagonal YMnO\({}_{3}\) thin film, the fundamental absorption band was observed near 1.7 eV and it showed a lower energy shift with an increase in temperature. The spectral profile and peak shift property of the band are consistent with those in other \(R\)MnO\({}_{3}\) compounds. The coherent phonon in the YMnO\({}_{3}\) thin film was assigned to the lowest-frequency A\({}_{1}\) optical phonon generated by the DECP mechanism. The symmetry and generation mechanism are identical to those in LuMnO\({}_{3}\), which indicates the similarity between YMnO\({}_{3}\) and LuMnO\({}_{3}\). On the other hand, the temperature dependence of the oscillation intensity is oppositely different between these compounds; the coherent phonon intensity in YMnO\({}_{3}\) (LuMnO\({}_{3}\)) is damped above (below) the Neel temperature. These behaviour can be explained by the isostructural transition depending on the rare-earth atom. In addition, we discussed incoherent responses of excited electron states that offer a clue for deeper understanding of coupling phenomena. We found the relation between the ultrafast incoherent response and pump photon energy. This reflects the distribution of photogenerated electrons depending on the energy difference between the pump photon energy and absorption band peak. The results presented in this review offer a new point of view for understanding ultrafast phenomena in hexagonal \(R\)MnO\({}_{3}\) compounds and for applying ultrafast applications.  
This research received no external funding.  
Author acknowledges [PERSON] for measurements of fundamental optical transition properties and valuable discussions. Author is also grateful to [PERSON] for the sample preparation and valuable discussions.  
The author declares no conflict of interest.
###### Abstract  
Medium- and large-scale heat sinks are critical for thermal load management in high-performance systems. However, their high heat flux densities and limited space complicate cooling, leading to risks of overheating, performance degradation, or failure. This study employs the Cascaded Lattice Boltzmann Method (CLBM) to enhance their thermal performance. This numerical approach is known for being stable, accurate when dealing with complex boundaries, and efficient when computing in parallel. The numerical code was validated against a benchmark configuration and an experimental setup to ensure its reliability and accuracy. While previous studies have explored mixed convection in cavities or heat sinks, few have addressed configurations involving side air injection and boundary conditions periodicity in the transition-to-turbulent regime. This gap limits the understanding of realistic cooling strategies for compact systems. Focusing on mixed convection in the transition-to-turbulent regime, where buoyancy and forced convection interact, the study investigates the impact of Rayleigh number values (\(5\times 10^{7}\) to \(5\times 10^{8}\)) and Reynolds number values (\(10^{3}\) to \(3\times 10^{3}\)) on heat transfer. Simulations were conducted in a rectangular cavity with periodic boundary conditions on the vertical walls. Two heat sources are located on the bottom wall (\(T_{h}=50\)\({}^{\circ}\)C). Two openings, one on each side of the two hot sources, force a jet of fresh air in from below. An opening at the level of the cavity ceiling's axis of symmetry evacuates the hot air. Mixed convection drives the flow, exhibiting complex multicellular structures influenced by the control parameters. Calculating the average Nusselt number (Nu) across the surfaces of the heat sink reveals significant dependencies on the Reynolds number. The proposed correlation between Nu and Re, developed specifically for this configuration, fills the current gap and provides valuable insights for optimizing heat transfer efficiency in engineering applications.  
mixed convection; correlation; CLBM; cooling; heat sink; turbulent regime +
Footnote †: journal: Applied Sciences  
Article
# The Combined Compact Difference Scheme Applied to Shear-Wave Reverse-Time Migration
## 5 Conclusions  
In this paper, we proposed a fourth-order time difference solution for the shear-wave equation using the supercompact difference scheme and the combined supercompact difference scheme. We carried out a detailed comparison of their accuracy in numerical simulations and reverse-time migrations. Thus, we reached the following conclusions:  
The combined compact difference scheme (CCD) has the characteristics of low numerical dispersion, high stability, and simulation accuracy. It is suitable for the numerical simulation of the seismic wavefield with large space and time steps. It provides an effective method for simulating shear-wave propagation and implementing shear-wave reverse-time migration.  
The combined supercompact difference scheme (CSCD) is extended and optimized from the combined compact difference scheme (CCD). Compared with its prototype, the supercompact difference scheme, the combined supercompact difference scheme further suppresses the numerical dispersion caused by the increase in spatial step length and is suitable for numerical simulation with even larger spatial step length.  
Finally, we carried out the reverse-time migration imaging of the Marmousi model of shear-wave velocity under isotropic conditions. The combined supercompact difference scheme (CSCD) yields the best shear-wave imaging results of the Marmousi model when compared with the other methods. These results reveal the potential for further extending the supercompact difference scheme and the combined supercompact difference scheme forward simulation of shear waves in complex media such as two-dimensional or three-dimensional anisotropic media and viscoelastic media. It is worth highlighting that the method presented in this paper is restricted to isotropic media at the moment. However, these results reveal the potential for further extending the supercompact difference scheme and the combined supercompact difference scheme forward simulation of shear-wave in complex media such as two-dimensional or three-dimensional anisotropic or viscoelastic media. Of course, the method in this paper also has its limitations, such as low computational efficiency, making it very challenging to apply to three-dimensional media, which is also the focus of our subsequent research.  
Conceptualization, C.Z.; Funding acquisition, X.L.; Formal analysis, W.Y.; Investigation, C.Z.; Methodology, C.Z., X.L. and P.S.; Project administration, W.W., X.L. and P.S.; Resources, W.Y.; Software, C.Z. and W.Y.; Supervision, P.S.; Validation, W.W.; Visualization, C.Z.; Writing--original draft, C.Z.; Writing--review & editing, W.W. and X.L. All authors have read and agreed to the published version of the manuscript.  
The study is supported by the Science and Technology Research and Development Project of CNPC (2021 DJ3506) and (2021 ZG03) and R&D Department of China National Petroleum Corporation (Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications, 2022 DQ0604-02).  
Not applicable.  
The authors declare no conflict of interest.
# Kamchatka Berry (_Lonicera caerulea_ L.) Pomace Bioferment as an Innovative Cosmetic Raw Material
## 1 Introduction  
Kamchatka berry (_Lonicera caerulea_ L.) is one of the most ecologically important species. It is found mainly in the northern hemisphere, in countries such as Russia, Canada, Japan and China [1].  
The fruits of the Kamchatka berry (_Lonicera caerulea var. kamtschatica_) are characterised by a blue-violet colouring of the skin, and an ellipsoid cylindrical or slightly oval shape. They contain sugars such as glucose and fructose and minerals such as potassium, iron and calcium. They also contain organic acids such as citric acid, malic acid and phytic acid. Kamchatka berries owe their name to their high content of biologically active compounds, including phenolic compounds or vitamins, which translates into their antibacterial, detoxifying and anti-inflammatory properties. In the food industry, they are mainly used in the production of jams, juices, timctures, wines and as natural colourings [2]. In contrast, no data were found in the literature on the use of Kamchatka berry pomace in cosmetic technology.  
Bioferments are preparations produced from natural raw materials by a fermentation process using appropriate strains of bacteria and/or yeast [3; 4]. They are mainly obtained by fermentation using lactic fermentation bacteria of the genus _Levilactobacillus_, with the strains used showing potential probiotic activity [5]. Fermentation increases the bioactivity of raw materials by breaking down high-molecular compounds into low-molecular compounds, so that fermented raw materials have a better effect on skin and hair than non-fermented raw materials [6]. The use of fermentation also refers to the enrichment of organoleptic properties such as colour or aroma [7; 8]. The range of applications of bioferments includes skin whitening, antioxidant, anti-aging, moisturising and anti-allergic properties [9; 10].  
The use of biomass waste from the agri-food industry has attracted the attention of researchers in many fields. Pomace, once used exclusively for the production of alcoholic beverages, vinegar or animal feed, now represents a potential for reuse as a valuable raw material [11]. In addition to their biological and chemical properties, plant biomass wastes are also a rich source of wild microorganisms with interesting fermentation capabilities and faster adaptation to less favourable environmental conditions [12].  
A bioferment using _Saccharomyces cerevisiae_ and _Lacticaseibacillus paracasei_ strains was prepared from pomace of Kamchatka berry varieties Aurora and Indigo Gem. The formulation was used to formulate a cosmetic paste on a commercial COSMOS certified shampoo base [13; 14].
## 2 Theoretical Foundation
### Signal Detection Techniques
#### 2.1.2 MMSE Detection  
MMSE detection balances noise suppression and interference reduction by considering noise variance, channel characteristics, and the correlation between received and transmitted symbols. Equation (7) represents the mean squared error (MSE) employed in MMSE detection, considering factors like channel setting, noise variance, and the correlation between transmitted and received symbols. The summation (\(\Sigma\)) iterates over \(n\) observations (\(i=1\) to \(n\)), \(\theta\) denotes the actual value, and \(\theta^{\prime}\) represents the estimated value.  
\[\frac{1}{n}\Sigma_{i=1}^{n}[(\theta-\theta^{\prime})^{2}] \tag{6}\]  
\[minE[(\theta-\theta^{\prime})^{  
the error between the estimated and real values. To find the average squared difference, divide the total squared errors by the number of observations using a factor of \(1/n\). The relation (7) helps minimize the average squared difference between estimated and actual values. MMSE detection seeks a balance between minimizing interference and reducing noise impacts.
## 4 Methodology
### Spectral Indices
#### 4.4.3 Differential Vegetation Index (DVI)  
Infrared and red band difference [44] as a vegetation index can differentiate between soil and vegetation regions. DVI was used in the seven-band dataset. The index may help separate impervious surface and non-cultivated soil areas, giving a similar result with SR as another related study example. Impervious surface regions belong to the artificial surfaces class, but non-cultivated soil areas belong to the forest and semi natural areas class in the study.
## 4 Results  
### Segmentation Procedure  
A cluster analysis was conducted to segment the maritime students according to their rating of the PEU of the VR engine room simulator (Figure 8). Since the number of segments was not known in advance, [PERSON]'s hierarchical cluster procedure with squared Euclidian distance was first used to determine the most appropriate number of clusters. The results of group membership, agglomeration coefficient, and associated dendrograms suggested two and three clusters. Consequently, a non-hierarchical K-means cluster method was employed to additionally test grouping alternatives. A solution with two clusters was deemed appropriate because the obtained segments displayed the most satisfactory characteristics regarding interpretability and measurability. An independent sample \(t\)-test and [PERSON]'s \(t\)-test result revealed that the segments' mean scores significantly differed on all four measurement items at the probability level \(p<0.001\) (Table 1). The first segment, labelled Proactivists, accounted for 65.5% of the total sample and was composed of students who mostly perceived the VR engine room's simulator PEU as positive (M > 4). The second segment, which comprised 34.5% of the total sample, was designated as Moderates because the average rating scores for all examined PEU items were between three and four (Figure 8).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & \multicolumn{4}{c}{**Segments**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Proactivists**} & \multicolumn{2}{c}{**Moderates**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \cline{2-7}  & **M** & **SD** & **M** & **SD** & & & \\ \hline Understandable a & 4.37 & 0.54 & 3.40 & 0.88 & 26.7 & 4.48 & **<0.001** \\ Effortless & 4.47 & 0.65 & 3.00 & 0.73 & 56 & 7.91 & **<0.001** \\ Easy to use & 4.29 & 0.52 & 3.20 & 0.77 & 56 & 6.43 & **<0.001** \\ User friendly a & 3.97 & 0.59 & 3.20 & 0.84 & 29.3 & 3.70 & **<0.001** \\ \hline \end{tabular} Note. a A Welch test was reported, as the assumption of homogeneity of variables has not been met.  
\end{table}
Table 1: Characterization of the PEU-based segments.  
Figure 7: Instrument design, data collection process, and analysis procedure flowchart.  
A statistically significant difference between the two segments was recorded for perceived benefits of VR in learning for four out of six measurement items: comprehend the previously acquired knowledge, t(29.1) = 3.19, \(p\) < 0.001; controlling the learning process t(56) = 2.76, \(p\) = 0.008; improving learning effectiveness t(56) = 3.20, \(p\) = 0.012; and stimulating responsiveness and active learning, t(56) = 2.29, \(p\) = 0.026.  
As displayed in Table 2, Proactivists provided statistically significant higher mean scores for the intention to include the VR engine room simulator as a part of their learning activities, t(28.2) = 3.15, \(p\) = 0.009, and recommend it to others, t(56) = 2.35, \(p\) = 0.022. Compared to Moderates (M = 4.15), Proactivists expressed a higher level of overall satisfaction with the VR trial experience (M = 4.68); t(56) = 2.91, \(p\) = 0.005.
## 2 Study Sites and Database Description
### French Study Site
#### 2.1.3 In Situ Measurements  
Soil moisture (_mv_) was measured within a window of 2 h with respect to the S1 acquisition date. For each reference plot, twenty-five to thirty measurements of volumetric soil moisture were conducted in the top 5 cm of soil by means of a calibrated TDR (Time Domain Reflectometry) probe. All soil moisture measurements within each plot were averaged to provide a mean value for each plot. The range of the soil moisture value is between 7.0 and 36.3 vol %.  
The soil roughness parameter _Hrms_ (root mean surface height) was not measured. Only information about the roughness class was collected for each reference plot at each field campaign according to three classes: smooth areas (sowing) with a root mean square surface height (_Hrms_) lower than 1 cm, medium roughness areas (slightly plowed soil) with an _Hrms_ between 1 cm and 2 cm, and rough areas (well plowed soil) with an _Hrms_ higher than 2 cm. These roughness thresholds that are used for each class are obviously approximations, but because of our experience in collecting roughness measurements over the past 20 years, we will assume these are fairly reliable observations.  
Finally, our French database is composed of 289 elements with radar backscattering coefficients (in the VV and VH polarizations), NDVI values, and in situ measurements of soil moisture (_mv_) and roughness class. For a specific SAR acquisition date, each element of the real database represents a reference plot with an associated mean _mv_ value, SAR information (mean incidence angle and mean backscattering coefficients in the VV and VH), and mean NDVI value. Among the 289 database elements, 18 samples represent smooth areas, 259 are medium-rough areas, and 12 are rough areas. This database was only used to validate our inversion approach for soil moisture mapping.
## 2 Related Work
### Anomaly Detection and Classification
Several common time-series classification algorithms are distance-based metrics, such as the k-nearest neighbors (KNN) approach, which have proven to be successful in classifying multivariate time-series [47]. Plenty of research indicates Dynamic Time Warping (DTW) as the best distance-based measure to use along KNN [48]. Besides distance-based metrics, feature-based algorithms have been used over the years [49], which rely heavily on the features being extracted from the time-series data or modeling the time series with parametric methods. Hidden-State Conditional Random Field (HCRF) and Hidden Unit Logistic Model (HULM) are two successful feature-based algorithms that have led to state-of-the-art results on various benchmark datasets [50]. However, HCRF is a high computational efficiency algorithm that detects latent structures of the input time series using a chain of \(k\)-nominal latent variables. Furthermore, the number of parameters linearly increases the latent states required. To overcome this, HULM proposes using \(H\) binary stochastic hidden units to model \(2^{H}\) latent structures of the data with only \(O(H)\) parameters. Another approach for multivariate time-series classification is by applying dimensional reduction techniques or by concatenating all dimensions of a multivariate time series into a univariate time series [51].