esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Low temperature deposition techniques are becoming essential for many types of opto- and microelectronics fabricated in flexible substrates. Several approaches are currently being utilized to avoid damage on fragile substrates, such as plasma-enhanced chemical deposition (PECVD) and laser chemical deposition (LCVD) [1, 2]. However, malleable substrates are subjected to high energetic ionic bombardment caused by plasma generation in the PECVD system, producing cracking and unwanted stoichiometry ratios in the deposited material [3, 4, 5]. In an LCVD system, lasers are costly and provide only a small area deposition about several cm\({}^{2}\). Alternatively, an incoherent UV source can be used in many processing applications, since most organic/inorganic materials readily absorb photons in wavelengths \(<\) \(\sim\)250 nm. However, the utilization of traditional UV sources (UVC) in chemical dissociation is still limited by insufficient photon energy associated with reactants' chemical bonds, and ends in slow process rates (kinetics) unless it uses photo-catalysts. A new VUV (172 nm) flat lamp [6] has distinct benefits such as high radiation power, large and uniform area coverage that was not possible in the conventional photon sources for chemical reactions. In this research article, the experiment for dissociation of CS\({}_{2}\) by a VUV lamp will be described in the different experimental conditions. As a conductive polymer, CS\({}_{2}\) has been used as a sulfur dopant in organic chemistry and a solvent for various polymers [7, 8]. In addition, CS\({}_{2}\) can be used as an etchant of the carbon layer when it is used with plasma [9]. It has been known that CS\({}_{2}\) can be readily dissociated to CS + S fragments through photons, catalytic reactions, and plasma processes in the vapor phases [10, 11, 12]. However, direct photodissociation of a free standing and volatile CS\({}_{2}\) droplets on a substrate using high energy VUV photons (>7.2 eV) to deposit a thin layer that has never been explored. In addition, the conversion of the photo-fragments to a thin layer deposit in a scaled area of Si substrate is introduced. Being the first report of its kind for an in-situ high energy UV deposition methodology, the low temperature and simple deposition technology of organic precursors will lead to the cost-effectiveness of microelectronics processes.
# The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
## 3 Basics on Ant Colony Optimization  
Modeling how ants behave and interact helps solve many optimization problems. The first ant algorithm (Ant Colony Optimization) was presented by [PERSON] in 1992 [8]. The strength of ants lies in their numbers and the cooperation. The cooperation between individuals ensures the survival of the entire community. Each ant can find the shortest path from the anthill to the food source without analyzing the visible terrain that surrounds it. Ants easily adapt to new conditions. When the road is blocked by an obstacle, they can avoid it, when the place where the food was located becomes inaccessible, they will start looking for a new source of food.  
An ant that has reached the food and returns to the anthill leaves a pheromone trail behind. Depending on what signal the ant wants to send to others, the smell and the intensity of the pheromone varies. Any other ant, sensing the pheromone in its immediate neighbourhood and analyzing its intensity, is able to determine which direction to go in order to reach the food. The more ants pass along the path from food to the anthill, the stronger the smell of the pheromone will remain on that path, making it the most attractive path. The paths that are less traveled are forgotten over time, and even if they led to food, the pheromone will not be enough to guide the ants to their destination.  
The structure of the ant algorithm consists of three parts: main transition rule, global update rule, local update rule.
## 4 Discussion
The lower the Wilks' \(\lambda\) and the higher F value, the better the variable discriminates. Table 5 shows the DSC parameters that had the best discrimination power according to this criterion. Figure 2: PCA analysis and loading plots for PC1 and PC2 with projection of the variables: \(\Sigma\)SFA—total saturated fatty acids; \(\Sigma\)MUFA, total monounsaturated fatty acids; \(\Sigma\)PUFA, total polyunsaturated fatty acids. DSC parameters: T\({}_{\rm m}\)1, T\({}_{\rm m}\)2, T\({}_{\rm m}\)3, and T\({}_{\rm m}\)4—peak temperatures; h1, h2, h3, and h4—peak heights; H\({}_{\rm m}\)1, H\({}_{\rm m}\)2, H\({}_{\rm m}\)3, and H\({}_{\rm m}\)4—peak enthalpies determined at 1 \({}^{\circ}\)C/min (**A**), 2 \({}^{\circ}\)C/min (**B**), and 5 \({}^{\circ}\)C/min (**C**) and projection of cases showing distribution of hemp seed oil samples analyzed at 1 \({}^{\circ}\)C/min (**D**), 2 \({}^{\circ}\)C/min (**E**), and 5 \({}^{\circ}\)C/min (**F**). As can be seen in Table 5, the \(\Delta\)H\({}_{\rm m}\)2 parameter has the highest discriminatory power, and the h3 parameter has the lowest discriminatory power. Since the model was built to differentiate between two groups, a single statistically significant (\(p<0.05\)) discriminant function was obtained (Equation (1)), with a high canonical correlation coefficient (R = 0.96). \[D=-7.53\times\Delta\rm{H}_{\rm m}2+7.7\times\Delta\rm{H}_{\rm m}2+2.18\times T _{\rm m}4+1.76\times T_{\rm m}3-0.71\times h3 \tag{1}\]  
The analysis resulted in two classification functions:  
\[CF_{\it{Fresh\;seq\;olis}}=-4.1-20.1\times\Delta\rm{H}_{\rm m}2\ +20.6\times\Delta\rm{H}_{\rm m }3\ +5.8\times T_{\rm m}4+4.7\times T_{\rm m}3-1.9\times h3 \tag{2}\]  
\[CF_{\it{Fresh\;seq\;olis}}=-8.9+30.2\times\Delta\rm{H}_{\rm m}-30.9\times\Delta \rm{H}_{\rm m}3-8.8\times T_{\rm m}4-7.1\times T_{\rm m}3\ +2.9\times h3 \tag{3}\]  
Using these classification functions (Equations (2) and (3)), all oil samples were correctly classified. Thus, the ability to build discriminant model based on obtained data was 100%. It is obvious that in order for it to be possible to create a universal model estimating the freshness of oil, a  
Figure 3: Cluster analysis (CA) and loading plots representing results obtained at 1 \({}^{\circ}\)C/min (A), 2 \({}^{\circ}\)C/min (B), and 5 \({}^{\circ}\)C/min (C). \begin{table}
\begin{tabular}{c c c} \hline \hline
**DSC Parameter** & **F** & **Wilks\({}^{\prime}\)\(\lambda\)** \\ \hline \(\Delta\)H\({}_{\rm m}\)2 & 174.4214 & 0.182737 \\ \(\Delta\)H\({}_{\rm m}\)3 & 173.2217 & 0.18377 \\ T\({}_{\rm m}\)4 & 103.8601 & 0.272994 \\ T\({}_{\rm m}\)3 & 45.5087 & 0.461491 \\ h3 & 9.4849 & 0.804375 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Wilks\({}^{\prime}\)\(\lambda\) and F tests of group means. larger number of samples is needed (seeds from different regions and different years); however, as shown in this paper, DSC parameters can be successfully used for such a purpose.
## 3 Results and Discussion
### Evaluation and Optimization of the Arg-Sensitive Bioelectrodes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bioelectrode** & **Sensitivity, A\(\cdot\)M\({}^{-1}\)-m\({}^{-2}\)** & **Linear Range, \(\mu\)M** & **Response Time, s (95\%)** & \(K_{M}{}^{app}\), mM** & **Reference** \\ \hline Arginase/ & & & & & \\ urease/PANi/Pt & \(110\pm 1.3\) & 70–600 & 10 & 1.27 \(\pm\) 0.29 & [21] \\ \({}^{1}\) p-cells/urase/PANi/Pt & \(14\pm 1.2\) & up to 600 & 60 & 0.51 \(\pm\) 0.05 & [22] \\ Arginase-nAu- & & \(357\pm 24\) & 10–700 & 30 & 0.45 \(\pm\) 0.09 & [25] \\ p-cells/urase/PANi/Pt & \(684\pm 32\) & 3–200 & 15 & 0.31 \(\pm\) 0.05 & [26] \\ ArgO/nCeCu/GE & \(1630\pm 92\) & 5–100 & 60 & 0.32 \(\pm\) 0.05 & Current work \\ ArgO/nNiPtPd/GE & \(578\pm 5\) & 10–250 & 50 & 0.35 \(\pm\) 0.03 & Current work \\ ArgO/gCuHCF/GE & \(602\pm 42\) & 10–100 & 50 & 0.12 \(\pm\) 0.02 & Current work \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analytical characteristics of the developed ABSs for Arg determination. Figure 5: Amperometric characteristics of ArgO/gCuHCF/GE: (a) dependence of amperometric signal on concentration of Arg; (b) calibration graph for Arg determination. Conditions: working potential \(-150\) mV vs. Ag/AgCl/3 M KCl in 50 mM phosphate buffer, pH 7.0.  
days of storage, and likewise the ArgO/gCuHCF/GE--after three days. Contrary, the reported ADI/PANi/Nafion/Pt-SPE-based biosensor was very stable. It kept more than 90% stability after 35 days of storage [26]. This phenomenon may be explained by several reasons: (1) Highly purified recombinant enzyme ADI with the high specific activity was used for construction of the ADI-based ABS, whereas in this work, we used only partially purified ArgO; (2) ADI in the biosensing layer was stabilized with BSA, but in the present work ArgO was not stabilized additionally; (3) Proteins ADI and BSA in the biosensing layer were covalently immobilized on PANi/Nafion by cross-linking using glutaraldehyde. In the present work, ArgO was immobilized on the surface of a NZ-modified electrode by physical adsorption and covered by Nafion. The selectivities of the proposed ArgO/NZ-based ABSs to the target analyte (Arg) are of great importance, especially for analysis of real samples of beverages. The selectivity of the constructed ArgO/nCeCu/GE was estimated in relative units (%) as a ratio of the detected signal to the value of the highest current response (Figure A4): no signals were observed for most of the tested amino acids (L-Glu, L-Gln, D, L-Leu, L-Asp). Only L-Lys and D,L-Phe induced a nonsignificant (less than 10%) analytical signal.
# An Overview of Hydrogen's Application for Energy Purposes in Lithuania
## 3 Hydrogen Value Chain
Gas pipelines are also much cheaper than transporting hydrogen as the end products of methanol or ammonia in highly-pressurized containers or via transport (trucks) designed to move chemical substances. For larger distances (e.g., overseas), ships are considered to be the best option. Rail is one current transportation modes evaluated as a possible option. According to [22], the hydrogen delivery method depends on geographical and market characteristics, such as distribution network of refuelling stations, market availability of fuel cell vehicles and population density. In the medium term, a new market for green hydrogen transport is developing; other applications might emerge in the long term, such as the specifically designed hydrogen gas grid [40,41]. The green hydrogen value chain ends with end use/application. Use of green hydrogen depending on an end user can be grouped into a few different categories:  
* Power to mobility (green hydrogen-powered vehicles with integrated fuel cells and hydrogen storage tanks that eventually can substitute unabated fossil fuel vehicles);
* Power to power (green hydrogen can help to avoid electric grid imbalance. Hydrogen could be produced during the nighttime through water electrolysis using cheap electricity from offshore wind farms, stored and used later during the peak moments in winter months);
* Power to gas (green hydrogen, proportionally up to 25% due to technological and safety constraints [42], can be mixed and injected into existing natural gas networks. That would help reduce CO\({}_{2}\) emissions while heating various buildings);
* Power to industry (green hydrogen can be used as a feedstock in heavily polluting refineries, fertilisers, and the cement and steel production industries, to replace the currently used grey hydrogen). An overview of the current global hydrogen market shows that currently about 99.9% of all hydrogen produced each year is fossil fuel-based hydrogen used in industry. The whole market totals to ~77 million tons a year, using this hydrogen primarily for the refining and ammonia industries. Following the global energy transition, hydrogen demand is foreseen to increase dramatically by 2050 and due to its versatility green hydrogen will inevitably play a significant role in implementing carbon-free energy scenarios by transforming existing energy value chains from production to consumption.
## 2 Numerical Simulation  
### Train-Sound Barrier Modeling and Meshing  
In this study, the high-speed train model was built based on the geometric features of CRH 2A. Previous studies indicate that the aerodynamic characteristics of a 3-car train formation passing through the sound barrier are comparable to those of an 8-car and a 16-car train formation passing through the sound barrier, including the peak wind pressure [25]. [PERSON] et al. and [PERSON] et al. [26; 27] pointed out that the cross-section of the middle region of each train formation remains relatively constant and is situated within a stable flow field region. Hence, in order to minimize computational complexity and enhance efficiency [28], the present numerical simulation employs a three-section train configuration model, comprising a head car, a middle car, and a tail car. The head and tail cars of this train model each have a length of 25.5 m and the middle car is 25 m long, resulting in a total length of 76 m. The cross-sectional width of the train body measures 3.38 m, the height is 3.7 m, and the nose length of both head and tail cars is 6.62 m, as illustrated in Figure 2.  
While maintaining the overall aerodynamic characteristics of the train, the modeling approach involves the appropriate simplification of underfloor elements, roof structures, door handles, etc. The modeling excludes subtle components such as bogies, pantographs, and air-conditioning outlets [29], with a focus on smoothing the train's surface. The head and tail cars, with their relatively complex curved surfaces, are meshed using polyhedral unstructured meshing [28]. The grid control size for the train surface is set at 0.2 m, and  
Figure 1: Nose lengths of different high-speed trains: (**a**) CRH 2A [21]; (**b**) Germany ICE 3 [22]; (c) CRH 380A [23]; (**d**) CRH 400 BF [24].
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 3 Magneto-Optical Transparent Nano-Ceramics
### Potential Application  
Transparent nano-ceramic is the core of modern magneto-optical materials, which provides a broad development prospect for the Faraday isolator. [PERSON] et al. [59] compared characteristics of Faraday isolators with different materials of magneto-optical medium, which are listed in Table 2. It can be seen from Table 2 that there is still a certain gap in the isolation between TGG nano-ceramic ceramics and TGG crystals. TGG crystals can obviously work effectively in higher-power lasers and achieve a stable isolation ratio. Although the isolation measurement power is lower for TAG and Ce:TAG transparent nano-ceramics, it can be estimated by calculation that the extinction ratio of the TAG ceramic samples can be maintained above 30 dB when the laser power reaches the kilowatt level.  
In 2021, [PERSON] et al. [60] fabricated a TGG/sapphire composite magneto-optical element Faraday isolator, whose maximum operating power with an isolation ratio higher than 30 dB was estimated to exceed 2 kW, which was almost three times that of a single TGG crystal. In addition, they also performed experimental (shown in Figure 9) and numerical studies on thermally induced depolarization and thermal lensing of composite elements, and the optimized structure could be successfully operated at a high radiation power. Experimental results showed that an isolation ratio of 34 dB in the composite elements was achieved at a laser power of 700 W, which was 5 dB higher than the classical single element. This indicated that TAG transparent nano-ceramics could be used to prepare kW Faraday isolators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Medium**} & **Isolation Ratio\(\oplus\)** & **Isolation Ratio\(\oplus\)** & \multirow{2}{*}{**Water Cooling**} \\  & **Laser Power** & **Laser Power** & \\ \hline TGG crystal & 30 dB@650 W & 6.5 m@340 W & Optional \\ TGG transparent & 30 dB@340 W & 6.5 m@340 W & Optional \\ nano-ceramic & 38 dB@300 W & 8 m@300 W & Required \\ Ce: TAG transparent & 31 dB@300 W & 3.8 m@300 W & Required \\ nano-ceramic (0.1 at.\%) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of characteristics of Faraday isolators with different magneto-optical media [38; 56]. Reprinted with permission from Ref. [38]. Copyright 2018, copyright CNKI. Reprinted with permission from Ref. [56]. Copyright 2017, copyright OSA.
## 5 Conclusions  
The contents of SOC and LOC fractions (EOC, POC, DOC, and MBC) decreased with increasing soil depth. After a long-term positioning test, SOC content exhibited notable differences across various treatments. Non-tillage treatments significantly increased the contents of soil organic carbon and labile organic carbon fractions in the 0-60 cm soil layer compared to tillage treatments, with FB performing the best, followed by GB. Conversely, tillage treatments notably reduced the contents of these carbon fractions, with MSR showing the lowest levels. Continuous cropping resulted in higher SOC levels than rotation cropping. Furthermore, FB processing improved the CPMI value. Under traditional tillage practices, the SOC content across all layers in the humus layers of mollisols in farmland was lower than that in GB and FB. This observation suggests that farmland conditions are not conducive to the fixation of organic carbon in mollisols, while also indicating that mollisols in farmland possess a greater carbon sequestration potential. A significant amount of organic carbon is returned to the grass and forest belts, leading to higher organic carbon storage in these areas compared to farmland. In conclusion, in the mollisols region of Northeast China, non-tillage treatments may be more effective in enhancing the stability of organic carbon and reducing the risk of carbon loss. The findings indicate that vegetation restoration in this region has substantial carbon sink potential, and that improving soil fertility--particularly through restoration models centered on FB as the primary land use pattern--can serve as a crucial strategy for enhancing soil fertility in this area.  
Conceptualization, methodology, J.W. and X.D; software, X.D. and W.C.; validation, J.W. and W.C.; formal analysis, X.D.; investigation, X.D. and W.C.; resources, J.W.; data curation, X.D. and W.C.; writing--original draft preparation, X.D.; writing--review and editing, J.W.; visualization, X.D. and W.C.; supervision, project administration, funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Plan Project: Regulation Mechanism of Key Process of Agricultural Soil Organic Matter Transformation-Soil Structure Transformation in Black Land (No: 2022 YFD1500103).  
Not applicable.  
The data presented in this study are available upon request from the first author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 2 Related Works
### Local Descriptors  
#### 2.1.1 Local Binary Patterns  
The Local Binary Patterns (LBP) descriptor describes the spatial structure of a local patch containing a center pixel \(x_{c}\) surrounded by \(p\) equally-spaced neighbors [15]. Given a texture image, \(I\), the LBP patterns of each pixel can be computed by assigning a binary code to the center pixel, \(x_{c}\) of a \(3\times 3\) patch with eight neighbors and computing an LBP histogram used to characterize the image. In [16], a complete version of LBP was developed to allow for a variable number of neighbors \(p\) located on a circular radius \(r\). The LBP pattern is defined as  
\[LBP_{r,p}=\sum_{j=0}^{p-1}S(x_{j}-x_{c})2^{j} \tag{1}\]  
where \(S(A)\) is defined as  
\[S(A)=\begin{cases}1&\text{if }A\geq 0\\ 0&\text{else}\end{cases} \tag{2}\]  
Since its introduction as a textured-based descriptor in 2002, LBP has undergone several improvements to produce a more robust descriptor. For example, [PERSON] et al. [17] improved LBP by dividing the face image into sub-regions of various sizes and positions, and the AdaBoost algorithm was used to learn the most discriminative LBP histograms. The so-called Boosted-LBP features were then classified as expressions using machine learning techniques such as template matching, Support Vector Machines, Linear Discriminant Analysis, and linear programming. LBPTOP extends LBP to encode temporal changes and spatial changes [18]. This method extracts LBP patterns from three orthogonal planes: the spatial plane, the vertical spatio-temporal plane, and the horizontal spatio-temporal plane. In [19], a new kernel-based manifold learning method called kernel discriminant isometric mapping is proposed to reduce the dimension of LBP features. The features are then classified using the nearest neighbor classifier. Recently, [PERSON] et al. [20] proposed the Extended Local Binary Patterns on three Orthogonal Planes (ELBPTOP) for spontaneous micro-expressions recognition, and in [21], a smaller LBP feature vector that is also resistant to noise is presented. The novel descriptor considers four neighbors and diagonal neighbors separately and an adaptive window and averaging in radial directions to improve the feature extraction.
## 2 DPF Model Considering Governor Delay of SG
### SG Model with Governor Delay in DPF  
In traditional DPF, the output of an SG participating in the primary frequency regulation is given by,  
\[\Delta P_{\rm G,M}=-K_{\rm G,M}\Delta f \tag{3}\]  
However, the response of the governor system of the SG takes time, thus adjustment of the output of the SG has some delay, which may be described by the \(1\)st order as shown in Figure 1, where \(T\) is the time constant [22].  
With a step signal \(r(t)\) as the input, the response of the governor is given by,  
\[h(t)=1-e^{-\frac{t}{T}} \tag{4}\]  
Figure 1: First-order model of governor delay.  
At each time step, \(\Delta f\) is obtained, and the output of the SG is adjusted with \(\Delta f\), as shown in Figure 2.  
By combining (3) and (4), the SG's output at the \(m\)-th time step is given by,  
\[\Delta P_{\text{G,M}m}=-K_{\text{G,M}}\Delta f_{m}\Big{(}1-e^{-\frac{t}{t}}\Big{)} \tag{5}\]  
where \(\Delta f_{m}=f_{m}-f_{m-1}\).  
Based on the superposition principle, the total active power output of the SGs with the governor delay is given by,  
\[\Delta P_{\text{G,M}}=-\sum_{m=1}^{n}K_{\text{G,M}}\Delta f_{m}\Big{(}1-e^{-(n +1-m)\frac{\Delta f}{t}}\Big{)} \tag{6}\]  
where \(n\) is the time interval in the DPF analysis.