esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Methodology for Assessing and Modeling Manufacturing Processes
## 1 Introduction
The aim of this study was to provide a conceptual model that enables the assessment of manufacturing operations with transparency regarding the areas, flows, and factors. The goal of the model is to satisfy product quality requirements with excellence in operational efficiency. Moreover, the conceptual model seeks to serve as a basis for digital twin models and systems for assessing, managing, monitoring, and improving decision making in manufacturing systems. Furthermore, there are in the literature integrated manufacturing models; however, the focus on different areas, flows, and parameters provides the novelty of the presented model. The model includes various flows from the literature, while adding the energy flow, the maintenance aspects, the different regulation elements in the order flow, as well as adding the consideration of the manufacturing process itself to the \"industrial dynamics\" methodology proposed by [PERSON]. Furthermore, it increases the resources to be considered, monitored, and assessed defined by [PERSON]. The methodology developed is a natural evolution of the methodological frameworks created for the Second and the beginning of the Third Industrial Revolution to a modeling framework toward the Fourth Industrial Revolution. As a result, the proposed methodology details the order flow considering more managerial aspects that can regulate the manufacturing process and, therefore, the complete system. When comparing the proposed model with more recent approaches, these have a static assessment status, a level-oriented approach, a data- and AI-focused approach, or a dynamic approach; however, none of them provide a potential evaluation of all areas, flows, and elements interacting in all manufacturing systems nor the potential regulation mechanisms to control and improve the system with an integrated, dynamic, and scalable approach. Therefore, this research provides a needed framework to subsequently initiate the level-oriented and data-oriented approaches or to perform a static snapshot analysis. Lastly, considering the above-mentioned arguments, the integrated manufacturing process model represents a novel approach to serve as a guide model for improving process management and related target indicators. Furthermore, knowledge of the impacts of flows and their factors on target indicators and stability is helpful, as this fundamental information allows one to develop maintenance and quality plans, which enable the prediction of critical factors in the process. This makes it possible to reduce operational risks by generating preventive maintenance plans for the different flows related to the manufacturing process. Accordingly, the study provides a conceptual model that can support the activities of equipment producers, service engineering suppliers, and production and assembly companies.
## 3 Results and Discussion
### Correlation Analysis of Flavor Chemicals and Microbial Communities
For other important flavor chemicals in Baijiu, such as hexanoic acid, ethyl ester; octanoic acid, ethyl ester; decanoic acid, ethyl ester; dodecanoic acid, ethyl ester; phenylacetic acid, ethyl ester; 3-(methylthio)-propionic acid ethyl ester; acetic acid, phenylethyl ester; hexanoic acid, butyl ester; benzaldehyde and 2,3-butanediol, there was no significant correlation with the three dominant fungal genera _Issatchenkaia_, _Byssochlamys_ and _Saccharomyces_ (Figure 8b), indicating that the relevant dominant fungal microorganisms had no direct contribution to the synthesis of these flavor chemicals. Microorganisms with relatively low abundance in fermented grains, such as _Thermomyces_, _Thermoascus_, _Aspergillus_, _Leiothecium_, and _Rasamsonia_, showed a positive correlation with benzaldehyde, except _Thermomyces_ with no correlation (Figure 8b). Only _Saccharomyces_ showed a positive correlation with 4-ethyl guaiacol, while _Cladosporium_, _Apiotrichum_, and _Leiothecium_ showed a negative correlation. In addition, many low-abundance fungal genera were positively correlated with the formation of these flavor chemicals (Figure 8b). Compared with bacteria, fungi played an important role in the synthesis of important flavor chemicals in the Baijiu fermentation system. As fungi in the fermentation system are closely related to Daqu, the results provided an important basis for the importance of Daqu for Baijiu fermentation (Figures 5 and 8).
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
###### Abstract  
Recently, low-light image enhancement has attracted much attention. However, some problems still exist. For instance, sometimes dark regions are not fully improved, but bright regions near the light source or auxiliary light source are overexposed. To address these problems, a retinex based method that strengthens the illumination map is proposed, which utilizes a brightness enhancement function (BEF) that is a weighted sum of the Sigmoid function cascading by Gamma correction (GC) and Sine function, and an improved adaptive contrast enhancement (IACE) to enhance the estimated illumination map through multi-scale fusion. Specifically, firstly, the illumination map is obtained according to retinex theory via the weighted sum method, which considers neighborhood information. Then, the Gaussian Laplacian pyramid is used to fuse two input images that are derived by BEF and IACE, so that it can improve brightness and contrast of the illuminance component acquired above. Finally, the adjusted illuminance map is multiplied by the reflection map to obtain the enhanced image according to the retinex theory. Extensive experiments show that our method has better results in subjective vision and quantitative index evaluation compared with other state-of-the-art methods.  
low-light image enhancement; retinex theory; image fusion Article
# Synthesis of Silicon Carbide Powders from Methyl-Modified Silica Aerogels
## 2 Materials and Methods  
### Preparation of Silica Aerogel  
Silica aerogel and carbon black (Raven425, Birla Carbon, Seoul, Korea) were used as starting materials for the carbothermal reduction. Silica aerogel powders were prepared from water glass (silica content, 28-30 wt.%; SiO\({}_{2}\)/Na\({}_{2}\)O = 3.52:1; Young IL Chemical, Incheon, Korea) and n-hexane (95%, Samchun Pure Chemicals, Pyeongtaek, Korea) using an emulsion polymerization technique. The detailed procedure is described elsewhere [27]. Silica aerogel and carbon black were mixed in n-hexane using an ultrasonic bath. Homogeneous slurry was obtained after 10 min and dried at 100 \({}^{\circ}\)C in an electric oven. The C/SiO\({}_{2}\) molar ratios were 0, 0.5, 1, 2, and 3. The SiO\({}_{2}\)/C powder mixture was poured into an alumina (Al\({}_{2}\)O\({}_{3}\)) boat, which was placed inside a tube furnace under a constant argon flow (100 sccm). The carbothermal reduction was performed at 1450-1525 \({}^{\circ}\)C for 1 and 5 h. The synthesized powders were further heat-treated at 800 \({}^{\circ}\)C for 1 h in air to remove residual carbon black.
###### Abstract  
To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state-action interactions. In particular, closed-loop experiments, which involve continuous interaction between an agent and their driving environment, serve as a critical framework for improving the practical applicability of DRL algorithms in autonomous driving systems. This study empirically analyzes the capabilities of several representative DRL algorithms--namely DDPG, SAC, TD3, PPO, TQC, and CrossQ--in handling various urban driving scenarios using the CARLA simulator within a closed-loop framework. To evaluate the adaptability of each algorithm to geographical variability and complex traffic laws, scenario-specific reward and penalty functions were carefully designed and incorporated. For a comprehensive performance assessment of the DRL algorithms, we defined several driving performance metrics, including Route Completion, Centerline Deviation Mean, Episode Reward Mean, and Success Rate, which collectively reflect the quality of the driving in terms of its completeness, stability, efficiency, and comfort. Experimental results demonstrate that TQC and SAC, both of which adopt off-policy learning and stochastic policies, achieve superior sample efficiency and learning performances. Notably, the presence of geographically variant features--such as traffic lights, intersections, and roundabouts--and their associated traffic rules within a given town pose significant challenges to driving performance, particularly in terms of Route Completion, Success Rate, and lane-keeping stability. In these challenging scenarios, the TQC algorithm achieved a Route Completion rate of 0.91, substantially outperforming the 0.23 rate observed with DDPG. This performance gap highlights the advantage of approaches like TQC and SAC, which address \(Q\)-value overestimation through statistical methods, in improving the robustness and effectiveness of autonomous driving in diverse urban environments.  
deep reinforcement learning; TQC; CrossQ; SAC; TD3; DDPG; PPO; autonomous driving; CARLA simulator +
Footnote †: journal: Applied sciences
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Segments** & \multicolumn{2}{c}{**Bias**} & \multicolumn{2}{c}{**SD of Bias**} & \multicolumn{2}{c}{**LoA**} \\ \hline  & **D1** & **M90** & **D1** & **M90** & **D1** & **M90** \\ \hline Head & 30.88 & 28.95 & 20.67 & 39.74 & \([-9.63;71.40]\) & \([-48.94;106.8]\) \\ \hline Trunk & 22.20 & 6.22 & 14.80 & 6.37 & \([-6.80;51.21]\) & \([-6.27;18.71]\) \\ \hline Shoulder & 3.51 & \(-20.46\) & 3.62 & 31.23 & \([-3.58;10.62]\) & \([-81.67;40.74]\) \\ \hline \hline \end{tabular} Standard deviation (SD); limits of agreement (LoA).  
\end{table} 
Table 6: Data from Bland–Altman analysis for all kinematic variables. (\(p>0.05\)). This result reveals that for the principal segment of this exercise--the shoulder--the application demonstrates kinematic agreement with the 3D system. Globally, the results of the present study seem to suggest that the application could be a valuable tool for supporting home-based rehabilitation in individuals with extensive movement experience and postural control. Future studies are required involving populations with low physical activity levels, limited movement awareness, or even associated chronic pain to test the suitability of this system for these subjects [36]. Some limitations were identified in this study. The fact that the sample was recruited voluntarily led to the existence of selection bias, resulting in a small sample size while hindering the representativeness of the population and decreasing the statistical power, thus compromising the external validity of this study. Regarding the questionnaires used to characterize the sample, all were subject to memory bias since some questions referred to past events. For future studies, we suggest the recruitment of a larger sample and it would also be relevant to use more stringent inclusion and exclusion criteria to control confounding variables and allow data reproducibility. Given postural variability, it would be interesting to include specific populations (e.g., those with shoulder pain and those without), different tasks, and data collection protocols [18]. To our knowledge, very few studies have validated 2D video systems integrated into smartphones against 3D optoelectronic systems. A study conducted by [PERSON] et al. (2018) [37] aimed to validate a mobile technology for assessing shoulder range of motion. The study involved a comparison between a motion-based machine learning software and a goniometer for only four specific arcs of shoulder motion. While the study showed promising levels of accuracy, it is important to note that it did not employ gold-standard equipment, such as a 3D system. This methodology could potentially have an impact on the study's findings and results. Most existing research, as indicated by several systematic reviews [38; 39] primarily examines tasks such as running or mechanical lifting, rather than those directly related to upper limb functionality. This underscores the innovative nature of our study, and it can represent a starting point for further research into this matter.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.4 Genetic Algorithm  
A Genetic Algorithm (GA) is a search-based optimization technique inspired by natural selection and genetics. This type of approach was popularized by [PERSON] in the 1970s, especially through his book Adaptation in Natural and Artificial Systems [46]. These metaheuristics are part of the broader category of evolutionary algorithms (EA). GAs are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators. A GA repeatedly modifies a population of individual solutions, called individuals or phenotypes, to evolve toward better solutions. Each individual has a set of properties or chromosomes, that can be mutated and altered [47, 48].  
The key steps involved in GA are the following [47, 48]:  
* Step 1: An initial population of candidate solutions (chromosomes) is initialized in the search space.
* Step 2: These chromosomes are evaluated using a fitness function that measures the quality of the solution. After this, a subset of individuals from the population is selected based on their fitness values using a mechanism such as roulette wheel selection or tournament selection.
* Step 3: The selected chromosomes are combined to create a new solution by recombining their genetic information.
* Step 4: Random changes (mutations) are introduced to the offspring in order to increase their genetic diversity.
* Step 5: The least fit individuals in the population are replaced with the new offspring, creating a new generation.
* Step 6: Steps 2-5 are repeated until a stopping criterion is reached, such as a maximum number of generations or a satisfactory solution being found.
## 3 Results and Discussion
### Anaerobic Digestion of the BWW and PE Mixture
Other acids such as propionic, isobutyric and butyric acids are also present in all influents in addition to acetic acid, although in lower concentrations. All these acids were removed efficiently under the tested operating conditions, suggesting that microbial communities could break down the more complex acids into acetate and convert them into biogas/methane, as a result of the active working balance between hydrogen-producing bacteria and hydrogenotrophic methanogens. Despite the evidence of the good performance of the hybrid anaerobic reactor digesting the mixture of two complementary effluents, through the verified biogas quality and the VFA removal, for instance, the resulting methane yield did not exceed 0.118 L CH4/g CODIIN (Table 4). The results obtained confirm the fact that the process efficiency decreases with the reduction in HRT and with the consequent increase in the organic load. Comparatively, the methane yield obtained at 6.7-d and 3-d HRT conditions is compatible with that reported in another hybrid unit treating dairy effluent under an HRT of 15 d [58]: 0.238 L and 0.195 L CH4/ g VSIN (OLR = 2.2 and 5.0 g VS/L-d, respectively) versus 0.191 L CH4/g VSIN (OLR = 7.30 g VS/L-d). By applying another reactor type (UASB) to digest surplus yeast and brewery wastewater, higher values were reported by [PERSON] and [PERSON] [13] who found a methane production of 0.301 L CH4/g CODIN, however digesting a substrate much less concentrated than the one applied in this work (influent 30 versus 4 g/L COD). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & & **HRT 6.7 d** & **HRT 3.0 d** \\ \hline COD (g/L) & IN & \(30\pm 1\) & \(30\pm 1\) \\ COD (\%) & R & \(52\pm 2\) & \(12\pm 3\) \\ TS (g/L) & IN & \(23\pm 1\) & \(23\pm 1\) \\ TS (\%) & R & \(8\pm 4\) & \(54\pm 2\) \\ VS (g/L) & IN & \(15\pm 1\) & \(15\pm 1\) \\ VS (\%) & R & \(22\pm 5\) & \(68\pm 1\) \\ TP (g/L caffeic acid) & IN & \(0.4\pm 0\) & \(0.4\pm 0\) \\ TP (\%) & R & \(38\pm 0\) & \(38\pm 1\) \\ pH & IN & 7.0 & 7.0 \\ pH & OUT & 8.1 & 7.9 \\ TKN (g/L) & IN & \(1.1\pm 0.0\) & \(1.1\pm 0.0\) \\ TKN (\%) & R & \(-4\pm 0.0\) & \(21\pm 0.0\) \\ NH4\({}^{+}\)-N (g/L) & IN & \(0.8\pm 0.0\) & \(0.8\pm 0.0\) \\ NH4\({}^{+}\)-N (\%) & R & \(-0.7\pm 0.0\) & \(6\pm 2.0\) \\ Total VFA (g/L Hac) & IN & 4.24 & 4.24 \\ Total VFA (\%) & R & 87 & 95 \\ \hline Acetic acid (g/L) & IN & 2.63 & 2.63 \\  & OUT & 0.24 & 0.15 \\ \hline Propionic acid (g/L HAc) & IN & 1.08 & 1.08 \\  & OUT & 0.25 & 0.02 \\ \hline Isobutyric acid (g/L HAc) & IN & 0.23 & 0.23 \\  & OUT & 0.03 & 0.01 \\ \hline Butyric acid (g/L HAc) & IN & 0.29 & 0.29 \\  & OUT & 0.02 & 0.01 \\ \hline \hline \end{tabular} HRT—hydraulic retention time; COD—chemical oxygen demand; TS and VS—total and volatile solids; TP—total phenols; TKN—total Kjeldahl nitrogen; VFA—volatile fatty acids; HAc—in terms of acetic acid; IN—input; OUT—output; R—removal.  
\end{table} 
Table 3: Anaerobic digestion performance: brewery wastewater and piggery effluent mixture.
## 2 Materials and Methods
### Module 3: Preserving the Convective Regions  
Dealiasing errors tend to occur in vigorous small-scale convective regions that are often associated with high winds and sharp shear edges where the positive and negative signs of the radial velocity information change, resulting in large gradients. This kind of large but true gradient can be mistaken as being caused by dealiased velocities and thus, can trigger erroneous dealiasing. It is hence necessary to identify the convective regions and prevent incorrect dealiasing. In ADDA, an algorithm consisting of three steps was added to identify the regions of the true gradient associated with convection, following a similar method to that presented in [PERSON] and Sun [8]. The first step was discovering all the edges where the positive and negative signs of the radial velocity information changed, including those caused not only by aliased velocities, but also by atmospheric convection. At each valid point, a search was performed with the adjacent four points in the radial and azimuthal directions. If the signs of any two adjacent points were different, both were marked with flag 1. The second step was to distinguish the real wind shear boundaries from the boundaries from aliased points. This was done by selecting the points with absolute radial velocity differences between the two neighbor gates of less than 80% of the Nyquist radial velocity. This standard considered the fact that the real meteorological disturbance had a certain degree of continuity, with velocity values near zero in a narrow transition zone. All adjacent points meeting this condition were then marked with flag 2. The last step was to check whether the real shear regions were confined, which was done by setting all points to flag 3 in between two boundary points (with flag 2) that were separated by a distance of less than 60 km (a tunable parameter). Even through the choice of 60 km was empirical, it reflected the typical spatial scale of extreme winds that are associated with deep convective storms. All the points marked with flag 3 were defined as real meteorological disturbance regions that should not be dealiased. As will be shown later, this module was quite effective in reducing the potential error propagation that is often triggered by vigorous convection. Figure 1b expresses a case of the flag field after the three steps have been utilized for the original radial velocity of the 6.0\({}^{\circ}\) elevation angle (Figure 1a) observed by an S-band radar located in Guangxi, China.
## 3 Results and Discussion
### Effects of Enzyme Treatment on the Formation of Tea Cream in Rock Tea Extract  
\"Cold and muddy\" refers to the phenomenon where turbidity is created in the tea extract and precipitates are formed upon cooling. It is an important quality issue related to liquid tea extracts [33]. This turbidity and precipitation, known as tea cream, is primarily formed by the combination of catechins and tea polyphenols with proteins [34; 35]. For liquid tea extracts, eliminating the \"cold and muddy\" phenomenon is crucial to improving the taste of tea extracts.  
The status of tea extracts stored at room temperature and at 4 \({}^{\circ}\)C for 24 h are shown in Figure 5. The weight of tea cream in all enzyme-treated samples was significantly lower than that in the control group (\(p<0.001\)). When comparing the weight of tea cream obtained after the treatment with the three single enzymes (TAN, TYR, and LAC), tannase had the best effect on reducing precipitation, with a tea cream weight of 2.21 mg/L, which was 91.46% lower than that obtained in the control group (Figure 6). [PERSON] et al. also found that tannase can reduce the precipitation amount in green tea concentrate by 74.63% and improve the storage taste of green tea concentrate [19]. According to the results obtained for the combined enzymes, TAN-TYR-LAC showed the greatest decrease in tea cream weight (0.85 mg/L), corresponding to a 96.71% reduction compared with that of the control group. This was followed by TAN-TYR and TAN-LAC, which reduced the tea cream weight by 93.39% and 84.81%, respectively, compared with that of the control group. Among the combination enzymes, TYR-LAC led to the smallest tea cream weight reduction, with a decrease of only 60.11% compared with that of the control group (Figure 6). Gallated catechins, such as EGCG and ECG, contain more galloyl and B-cyclohydroyphenyl groups, which easily bind to caffeine and other compounds, making them more likely to form  
Figure 4: Radar map of the electronic tongue results obtained for the action of single enzymes (**a**) and combined enzymes (**b**) on the tea extracts. Aftertaste-B represented the bitterness aftertaste; Aftertaste-A represented the astringency aftertaste.  
precipitates than non-gallated catechins, such as EGC and C [19]. By reacting with tannase, tyrosinase, and lactose in tea extracts, gallated catechins are likely transformed into non-gallated catechins, thus weakening the binding ability of proteins and caffeine in the tea extracts containing gallated catechins [25]. Consequently, the tea cream weight is reduced. This is likely the reason why enzyme treatment positively affects the clarity of tea extract and the formation of tea cream.