esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Vegetable Consumption** & \multicolumn{2}{c}{**No Vegetable**} \\  & **(>1 Daily)** & \multicolumn{2}{c}{**Consumption**} \\ \hline  & **Boys** & **Girls** & **Boys** & **Girls** \\ \hline Age (years) & 11.1 (2.1) & 11.0 (2.1) & 11.4 (2.3) * & 11.4 (2.3) * \\ Body mass index (kg/m\({}^{2}\)) & 19.4 (3.8) & 19.2 (3.7) & 19.9 (3.9) * & 19.7 (3.8) * \\ Waist circumference (cm) & 71.0 (11.1) & 68.8 (10.1) & 71.9 (11.2)* & 69.8 (10.4) \\ WHtR & 0.32 (0.46) & 0.28 (0.45) & 0.33 (0.47) * & 0.29 (0.45) * \\ KIDMED score & 8.3 (2.2) & 8.2 (2.2) & 6.2 (2.3) * & 6.3 (2.1) * \\ Physical activity (h/wk) & 11.2 (5.9) & 9.2 (5.4) & 10.0 (5.8) * & 8.2 (5.1) \\ Screen time (h/wk) & 8.5 (8.7) & 7.1 (7.8) & 9.5 (8.8) * & 8.2 (8.1) * \\ Sleep time (h/day) & 8.7 (1.5) & 8.8 (1.6) & 8.4 (1.4) * & 8.4 (1.5) * \\
20 m shuttle run (laps) & 36.4 (20.7) & 25.8 (12.9) & 35.2 (20.4) * & 24.0 (13.1) * \\ \hline \end{tabular}  
* KIDMED: Mediterranean Diet Quality Index for Children and Adolescents; BMI: body mass index; WHtR: waist to height ratio;
* _p-_values < 0.01 for differences between increased (>1/day) and non-consumers of vegetables, from the same sex.  
\end{table} 
Table 2: Anthropometric and behavioral characteristics (mean \(\pm\) SD) according to more than once daily or no vegetable consumption, in participants from both sexes. Based on previous findings (Table 2) that show vegetable consumers had a better lifestyle profile than non-consumers, stepwise logistic regression analyses (four Models) were conducted to investigate potential associations between participant characteristics and vegetable intake (>1 per day vs. none) in both sexes. The results showed (Table 4, Model 1) that the likelihood of eating vegetables decreased for each year of increasing age, by 10% and 8% for girls and boys, respectively. Additionally, central fatness and overweight/obesity decreased the likelihood of eating vegetables by roughly 12% and 20%, respectively, for both sexes. After further adjusting for the KIDMED scores, the influence of overweight status and age wasunaltered but eating habits increased the likelihood of eating vegetables by 3.6 times (Table 4, Model 2). After adjusting for screen time and sleep duration, it was found that both excessive screen time and inadequate sleep reduced the probability of vegetableintake by almost 18% (Table 4, Model 3). When PA levels and aerobic fitness measurements were included in the analysis, the effects of the previous components remained (Table 4, Model 4). Moreover, the findings revealed that increased aerobic performance was associated with higher odds of eating vegetables, while low PA levels reduced the likelihood of eating vegetables by 30%. Discriminant analysis was employed to ascertain whether the predictors could more successfully distinguish between consumers and non-consumers. Standardised function coefficients (which show how heavily each variable is weighted to maximise group discrimination) showed that dietary habits (0.91), PA (0.44), and age (\(-\)0.29) were the more significant factors to distinguish vegetable consumers fromnon-consumers, for
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Cholangiocarcinoma
#### 2.2.2 Mediterranean Diet  
The Mediterranean diet consists mainly of plant-based ingredients: vegetable protein, plant polyphenols, and dietary fiber [45]. At least in part, it is these ingredients that may be responsible for the beneficial effects of the Mediterranean diet. Olive oil is the main source of fat in the Mediterranean diet, and its mono-unsaturated fatty acids, vitamin E, and antioxidants are associated with reduced cardiovascular and metabolic risks [46]. The fatty fish in the Mediterranean diet is a source of omega-3 fatty acids, which have anti-inflammatory and prebiotic effects [47]. In a randomized clinical trial, [PERSON]. [PERSON] et al. [48] compared the Mediterranean diet (MEDDiet) and the green Mediterranean diet (Green MEDDiet) in terms of the effect on steatosis in NAFLD patients. The GreenMEDDiet group consumed green tea (three-four cups/day), the Wolfia globosa aquatic plant strain (100 g/day frozen cubes), and a green shake with an additional portion of polyphenols (dose 1240 mg/day). Both the MEDDiet and reduced calorie Green-MEDDiet were shown to reduce body weight. Despite similar, moderate weight loss in both groups, the Green-MEDDiet group achieved a much greater loss of intra-hepatocyte fat (IHF%) (\(-\)38.9%) when compared to Mediterranean control (\(-\)19.6%; \(p=0.035\)) and the general principles of the standard of care group (\(-\)12.2% proportional; \(p<0.001\)). A greater loss of liver fat content was independently associated (\(p<0.05\)) with an increase in the consumption of Wolfia globosa and walnuts, a decrease in the consumption of red and processed meat,and an increase in serum folate changes in microbiome composition (beta diversity) [48]. Another randomized trial by [PERSON] et al. [49] tested the Mediterranean ketogenic diet on 14 obese men diagnosed with metabolic syndrome and NAFLD. At the end of the 12-week study, there was a significant decrease in alanine aminotransferase ALT (71.97 U/L vs. 37.07 U/L) and aspartate aminotransferase AST (47.71 U/L to 29.57 U/L). Additionally, there was a significant reduction in fatty liver in 92.86% of patients. Despite the small intervention group (\(n=14\)), this study shows that the macronutrient composition of the diet may play a significant role in NAFLD diet therapy [49]. Studies on people with metabolic syndrome indicate some beneficial effects of intermittent fasting. Early time-restricted feeding (eTRF) is a protocol whereby there is a strictly defined period in the first quarter of the day when one eats and fasts [50]. The impact of eTRF was investigated in a study first conducted by [PERSON] et al. [51] and all metabolic parameters (fasting glucose, fasting insulin, lipid profile, oxidative stress, hypertension, and postprandial insulin) were improved even without fat mass reduction [50; 51]. Other studies ([PERSON], [PERSON] et al., [PERSON] et al.) [52; 50] also indicate the possible influence of the time of eating and the circadian rhythm on metabolic parameters and steatosis.
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
###### Abstract  
Unmanned Aerial Vehicles (UAVs) provide exceptional flexibility, making them ideal for mitigating communication disruptions in disaster-affected or high-demand areas. When functioning as communication base stations, UAVs can adopt either orthogonal or non-orthogonal multiple access schemes. However, traditional Orthogonal Multiple Access (OMA) techniques are constrained by limited user access capacity and system throughput, necessitating the study of non-orthogonal access mechanisms for UAV-assisted communication systems. While much of the research on non-orthogonal multiple access focuses on Non-Orthogonal Multiple Access (NOMA), Rate-Splitting Multiple Access (RSMA), a novel non-orthogonal technique, offers superior throughput performance compared to NOMA. This paper, therefore, investigates the optimization of UAV-assisted downlink communication systems based on RSMA. We first develop a mathematical model of the system and decompose the primary optimization problem into multiple subproblems according to parameter types. To solve these subproblems, we propose an optimization algorithm that combines the Augmented Lagrange Method (ALM) with the Artificial Fish Swarm Algorithm (AFSA). The optimization algorithm is further enhanced by incorporating dynamic step size and visual strategies, as well as memory behaviors to improve convergence speed and optimization accuracy. To address linear equality constraints, we introduce a correction factor to modify the behavior of the artificial fish. The final optimization is achieved through cross-iterative solutions. Simulation results show that the system throughput under the RSMA strategy can be improved by 13.30% compared with NOMA, validating the effectiveness and superiority of RSMA in UAV-assisted communication systems.  
U
## 2 Categorization and Classification of Quality of Service Routing Protocols
### Probabilistic Routing Protocols (Soft Real Time)
In addition, MCBR involves two kinds of meta-routing strategies: search-based and constrained-flooding. However, the additional use of control packets for both types of routing strategies causes a significant overhead. In order to reduce this overhead, the QoS-aware learning-based routing protocol is proposed in [33]. Another probabilistic soft real time multi path routing protocol named QoS and Energy Aware Multi-Path Routing Algorithm (QEMPAR) was introduced to support real-time applications [34]. The goal of this protocol is to increase the network lifetime. The approach assumed that all of the nodes were randomly distributed in the intended environment. Each node was assigned unique ID. The node energy was considered equal at the beginning of the simulation. In addition, the nodes were aware of their location by using GPS and could handle the energy consumption. Based on this assumption, the nodes could communicate with other nodes beyond of their radio range. In this protocol the energy consumption model it used to determine the suitable link, path discovery, and paths assortment. In addition, the tiny packets were sent using different paths. The strength of this protocol is to prolong the network lifetime. However, throughput is affected due to increase of the latency. In addition, no mobility is considered in this protocol and using GPS makes it cost ineffective. Energy constrained multi-path routing (ECMP) is the extension of MCMP [35]. The ECMP is proposed to frame the QoS routing problem to reduce the energy consumption. The protocol focuses on the play-back delay, reliability, and geo-spatial path selection limitations. A tradeoff between less energy consumption and the minimum number of paths is shown for improving the QoS requirements. The main purpose of driving the ECMP model is to utilize the resource constraints efficiently in order to replicate not only resourceful bandwidth utilization, but, also, insignificant energy consumption in its stringent terms. The ECMP selects those paths in the network, which satisfy the QoS requirements. However, fulfilling the QoS provisioning, routing overhead is introduced in terms of additional energy consumption and computational complexity. The overhead can affect the performance of those applications that require a certain delay and a bandwidth. Quality-of-Service Routing (QoSR) is a deterministic soft real time protocol to determine the optimal path from the source node to base station consuming the minimum energy [36]. The nodes are particularly selected for multi-hop packet forwarding to maintain the energy efficiency. The QoSR aims to receive the successful packet to extend the network lifetime. QoSR also focused on the predefined level of reliability. The QoSR could be used in polynomial convolution with respect to the number of sensor nodes by using the Bellman-Ford algorithm. However, QoSR does not have scalability support and also does not explain how to achieve reliable data reception.
## 4 Conclusions  
In this paper, by simultaneously incorporating the LSK and SENet_v2 modules into the YOLOv8 model, the overall performance of the SL-YOLOv8 is significantly enhanced. The LSK module improves the ability to capture multi-scale features of the model, while the SENet_v2 module effectively integrates semantic and spatial information and optimizes the feature fusion strategy. Particularly in the identification of faults in transmission lines, the application of the LSK module allows the model to capture small and subtly defined targets more effectively, such as faults in transmission lines. The SENet_v2 module further enhances the sensitivity for fault features of the model. This combination not only improves the accuracy of fault detection but also significantly reduces false alarms, especially in complex situations and varying lighting conditions. Furthermore, the SL-YOLOv8 demonstrates enhanced real-time performance and scalability when handling large-scale transmission line images, providing more reliable technical support for the routine inspection and maintenance of power systems. Experiments conducted on the Break-ID-1632 and cable damage datasets demonstrate that the SL-YOLOv8 outperforms basic YOLOv8 and other typical algorithms in terms of accuracy, robustness, and versatility, effectively coping with data variations and noise. The SL-YOLOv8 extends the accuracy, speed, and model size. This enhancement validates the effectiveness of the synergistic action of the two modules in enhancing the model adaptability and performance, offering a new direction for further optimizing object detection algorithms.  
**Author Contributions:** Conceptualization, X.Z.; methodology, X.Z.; software, X.Z.; validation, J.Z.; formal analysis, X.Z.; investigation, X.Z.; resources, X.J.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z. and X.J.; visualization, [PERSON]; supervision, X.J.; project administration, X.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Our studies not involving humans or animals.  
**Informed Consent Statement:** Our studies not involving humans.  
**Data Availability Statement:** The data used in this paper can be obtained through the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Discussion
The rapid pace of urbanization may result in the conversion or degradation of farmland near residential areas, turning it into a wasteland. Additionally, changes in agricultural practices or crop types during urbanization may also contribute to the increased rate of farmland abandonment. There are several limitations to this study. First, although the high-resolution satellite imagery data used in this study exhibited a high level of accuracy, some inherent errors in the data remain unavoidable. Future research could enhance classification accuracy by exploring the subtle spatial and spectral variations across different land use types. Second, while the high-resolution satellite data employed in this study provides fine spatial resolution, it is limited in several aspects compared to data from other sensor types. Specifically, it has fewer spectral bands, a longer revisit period, a narrower swath, and a shorter operational lifespan. Moreover, the study area of Qiaotou Town is located in a region characterized by persistent cloud cover and rainfall, which hinders the acquisition of long-term, large-area temporal and spatial coverage. Future studies should focus on utilizing multi-temporal imagery and building time-series datasets through the integration of diverse data sources, which would enable a more comprehensive analysis of farmland abandonment over extended periods and broader regions. Lastly, although this study identified slope, water systems, roads, and residential area proximity as key determinants of farmland abandonment, it did not sufficiently account for the economic conditions or the behavioral motivations of farmers. Farmland abandonment is a complex and dynamic process influenced by a variety of interrelated factors. Future research should aim to incorporate a broader range of biophysical, socio-economic, and policy-related factors when selecting determinants. Additionally, methods such as field surveys, questionnaires, and structured interviews should be employed to better understand the underlying drivers of farmland abandonment. Furthermore, a more integrated theoretical framework is needed to evaluate strategies for the reclamation of abandoned farmland. Tailored management plans should be developed for abandoned farmland under varying conditions, with the goal of identifying practical and feasible solutions to address the issue of abandonment.
## 3 Results
### Effect of Metal Ions on HoFucl Activity  
L-FucIs have a conserved metal-binding site (Figure S1 in Supplementary Materials) and commonly require divalent cations, such as Mn\({}^{2+}\), Mg\({}^{2+}\), and Co\({}^{2+}\), for their catalytic activities [2; 5; 6]. In particular, Mn\({}^{2+}\) plays a crucial role in the thermal stability and catalytic activity of L-Fucl [3]. In this study, we evaluated the effects of two different concentrations (1 and 10 mM) of Mn\({}^{2+}\), Mg\({}^{2+}\), and Co\({}^{2+}\) on the activity of _Ho_FucI. Without any metal supplementation (control), very low activity was observed, while the presence of metal ions boosted the enzyme activity (Table 1). Specifically, upon the addition of 1 mM Mn\({}^{2+}\) and Co\({}^{2+}\), the enzyme activities increased \(-5\) fold compared to that of the control, but the enzyme activities were lower at 10 mM Mn\({}^{2+}\) and Co\({}^{2+}\). In the case of Mg\({}^{2+}\), higher activity was observed at 10 mM Mg\({}^{2+}\) than at 1 mM, with an RA of 488%. However, EDTA, which is used as a metal chelator, had little effect on enzyme activity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metal Ions**} & \multicolumn{3}{c}{**Concentration (mM)**} \\ \cline{2-3}  & **0** & **1** & **10** \\ \hline EDTA & & 69.7 \(\pm\) 24.6 & 80.8 \(\pm\) 61.9 \\ Mn\({}^{2+}\) & & 555.7 \(\pm\) 78.7 & 346.5 \(\pm\) 7.4 \\ Mg\({}^{2+}\) & & 252.1 \(\pm\) 32.1 & 488.1 \(\pm\) 19.8 \\ Co\({}^{2+}\) & & 523.1 \(\pm\) 74.2 & 112.8 \(\pm\) 19.3 \\ \hline \hline \end{tabular} \({}^{1}\) Relative activity (%) represents the enzyme activity relative to that obtained without any supplementation of metal ions, set as 100%. \({}^{2}\) Experimental data represent the means \(\pm\) standard deviations of three replicates.  
\end{table}
Table 1: Effect of metal ions on the L-fuculose-isomerizing activity of L-fucose isomerase (l-Fucl) from _Halothermothrix oreniii_ (HoFucl) 1.  
Figure 4: (a) Effect of temperature on the L-fuculose-isomerizing activity of _Ho_FucI. Enzyme reactions were performed at various temperatures ranging from 30 to 100 \({}^{\circ}\)C with 7 \(\upmu\)g of _Ho_FucI and 10 mM L-fuculose for 5 min in 20 mM sodium phosphate (pH 7.0) buffer in the presence of 1 mM Mn\({}^{2+}\) and 0.5 M NaCl. The experimental data represent the means \(\pm\) standard deviations. Relative activity (%) represents the enzyme activity relative to that obtained at 60 \({}^{\circ}\)C, set as 100%. (b) Effect of pH on the L-fuculose-isomerizing activity of _Ho_FucI. Enzyme reactions were performed at various pH ranging from 4 to 11 with 7 \(\upmu\)g of _Ho_FucI and 10 mM L-fuculose at 60 \({}^{\circ}\)C for 5 min in the presence of 1 mM Mn\({}^{2+}\) and 0.5 M NaCl. The buffers used were 100 mM sodium acetate (pH 4, 5, and 6), 100 mM sodium phosphate (pH 7 and 8), and 100 mM glycine-NaOH (pH 9, 10, and 11). The experimental data represent the means \(\pm\) standard deviations. Relative activity (%) represents the enzyme activity relative to that obtained at pH 7, set as 100%.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 2: Postprocessing Filters
#### 3.2.2 Spatial Filter  
Multiple parameters were tuneable under the spatial filter settings. The spatial filter performed several one-dimensional passes horizontally and vertically over the frame for enhancement. This was selected by the filter magnitude, which was set to a maximum value of 5, in this scenario--it was the number of filter iterations. The alpha and delta factors were selected as 0.5 and 1, respectively. The alpha factor was the exponential moving average whereas the delta factor was the threshold depth value for which any neighbouring pixel exceeding this threshold caused the smoothening effect to temporarily turn off. The primary purpose of the spatial filter was to preserve the edges. The spatial filter in the pyrealsense2 library is an implementation of a domain transform, which efficiently performs the edge-preserving filtering of 2D images. The recursive equation for calculating the exponential moving average (EMA) is given below.  
\[S_{t}=\begin{cases}Y_{1},&t=1\\ \alpha Y_{t}+(1-\alpha)S_{t-1},&t>1\text{ \it and }\Delta=|S_{t}-S_{t-1}|<\delta_{thresh}\\ Y_{t},&t>1\text{ \it and }\Delta=|S_{t}-S_{t-1}|>\delta_{thresh}\end{cases} \tag{2}\]
## 2 Results
### Association of the Key Volatiles in Different Types of Tea Samples via Co-Expression Analysis  
In order to understand the impact of cultivar features on the aroma characteristics of green tea and black tea regardless of processing methods, co-expression analysis was employed to explore the correlations of the 27 shared volatiles in the pairs of freeze-dried samples vs. green tea as well as freeze-dried sample vs. black tea. Figure 6 visualizes the correlations of the shared volatiles between different tea samples, as well as the internal correlations of the top 10 volatiles in green and black teas. Table S5 lists the IUPAC names of the symbols in Figure 6. The 27 shared volatiles included 11 alcohols, 7 aldehydes, 2 ketones, 4 esters, and 3 miscellaneous. Obviously, the contents of 3,7-dimethylocta-1,6-dien-3-ol (Alcohol-1), (\(2E\))-3,7-dimethylocta-2,6-dien-1-ol (Alcohol-2), 2-[(25,55)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol (Alcohol-3), (\(5E\))-3,7-dimethylocta-1,5,7-trien-3-ol (Alcohol-10), nonan-1-ol (Alcohol-11), (\(E\))-4-(2,6,6-trimethylcyclohex-1-yl)but-3-en-2-one (Ketone-1), and 3-methyl-2-[(\(Z\))-pent-2-enyl]cyclopent-2-en-1-one (Ketone-2) in freeze-dried tea samples had correlations with their contents in green teas and black teas (\(p<0.05\)), among which 3,7-dimethylocta-1,6-dien-3-ol (Alcohol-1) had the highest Pearson coefficients of 0.958 and 0.934 for green tea and black tea. For certain volatiles, much higher correlations with freeze-dried samples were observed in green tea compared with black tea, such as (\(5E\))-3,7-dimethylocta-1,5,7-trien-3-ol (Alcohol-10), dodecan-1-ol (Alcohol-6), (\(E\))-4-(2,6,6-trimethylcyclohex-1-yl)but-3-en-2-one (Ketone-1), (\(2Z\))-3,7-dimethylocta-2,6-dienal (Aldehyde-3), hexanal (Aldehyde-5), benzaldehyde (Aldehyde-6), methyl 2-hydroxybenzoate (Ester-4), and naphthalene (Miscellaneous-2). Most of the shared aldehydes in freeze-dried samples had greater correlations with those in green tea rather than black tea. By contrast, few compounds in freeze-dried samples had only correlations with those in black tea, e.g., hexan-1-ol (Alcohol-8), (\(Z\))-hex-3-en-1-ol (Alcohol-4), and 2,4-ditert-butylphenol (Miscellaneous-3). Concordant changes of three alcohol compounds, namely 2-[(25,55)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol (Alcohol-3), 3,7-dimethylocta-1,6-dien-3-ol (Alcohol-1), dodecan-1-ol (Alcohol-6), and one ester compounds of diethyl benzene-1,2-dicarboxylate (Ester-2) were observed in green tea, while 3,7-dimethylocta-1,6-dien-3-ol (Alcohol-1), 2-[(25,55)-5-ethenyl-5-methyloxolan-2-yl]propan-2-ol (Alcohol-3), and 2-[(15)-4-methylcyclohex-3-en-1-yl]propan-2-ol (Alcohol-5) changed congruously in black tea, together with the concordant changes of (\(2E\))-3,7-dimethylocta-2,6-dien-1-ol (Alcohol-2), 2,4-dimethylbenzaldehyde (Aldehyde-1), (\(2E\))-3,7-dimethylocta-2,6-dienal (Aldehyde-2), and bis(2-methylpropyl) benzene-1,2-dicarboxylate (Ester-1).