esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of fSFP on Expression of Genes Associated with Lipid Metabolism
#### 3.9.2 Changes in the Expression of mRNA in Adipose Tissue  
In the HFD control group, there was a significant upregulation (_p_ < 0.01) in the expression of leptin, CCAAT Enhancer-Binding Protein (C/EBP\(\alpha\) and C/EBP\(\beta\)), Fatty Acid Synthase (FAS), Sterol Regulatory Element-Binding Protein 1c (SREBP1c), and peroxisome proliferatorator-activated receptor (PPAR) compared to the NFD control group. Moreover, a significant (_p_ < 0.01) decreases of UCP2, adiponectin and PPAR mRNA expressions in adipose tissue was observed in HFD control as compared to NFD control. However, administration of all three doses of fSFP, including in the metformin treatment group, reversed these effects, as observed when compared with the HFD control group (Figure 9).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{2}{c}{**Hepatic Tissue (Relative to Control/GAPDH)**} \\ \cline{2-4}  & **ACC1** & **AMPK\(\alpha\)1** & **AMPK\(\alpha\)2** \\ \hline Controls & & & \\ NFD & 1.00 \(\pm\) 0.05 & 1.00 \(\pm\) 0.05 & 1.00 \(\pm\) 0.05 \\ HFD & 5.25 \(\pm\) 0.89 \({}^{\text{a}}\) & 0.26 \(\pm\) 0.05 \({}^{\text{a}}\) & 0.24 \(\pm\) 0.05 \({}^{\text{a}}\) \\ Reference & & & \\ Metformin & 2.84 \(\pm\) 0.59 \({}^{\text{ac}}\) & 0.50 \(\pm\) 0.11 \({}^{\text{ac}}\) & 0.43 \(\pm\) 0.08 \({}^{\text{ac}}\) \\ Test materials—fSFP & & & \\
400 mg/kg & 1.90 \(\pm\) 0.28 \({}^{\text{bc}}\) & 0.68 \(\pm\) 0.17 \({}^{\text{ac}}\) & 0.60 \(\pm\) 0.13 \({}^{\text{ac}}\) \\
200 mg/kg & 2.75 \(\pm\) 0.77 \({}^{\text{ac}}\) & 0.50 \(\pm\) 0.11 \({}^{\text{ac}}\) & 0.44 \(\pm\) 0.08 \({}^{\text{ac}}\) \\
100 mg/kg & 3.56 \(\pm\) 0.61 \({}^{\text{ac}}\) & 0.44 \(\pm\) 0.10 \({}^{\text{ac}}\) & 0.40 \(\pm\) 0.06 \({}^{\text{ac}}\) \\ \hline \hline \end{tabular} The values are depicted as the mean + SD for a sample size of 10 mice. Analysis underwent DT3 testing, revealing noteworthy distinctions (a) and (b) with \(p\) < 0.01 and \(p\) < 0.05, respectively, compared to NFD control, and (c) with \(p\) < 0.01, respectively, compared to HFD control.  
\end{table}
Table 3: Changes on lipid metabolism-related gene mRNA expressions in liver of mice.  
Figure 9: Effects on mRNA expressions associated with lipid metabolism in the adipose tissue due to fSFP administration. DT3 test results indicated significant differences (a) with \(p\) < 0.01 compared to NFD control, and (b) and (c) with \(p\) < 0.01 and \(p\) < 0.05 compared to HFD control, respectively. The values are depicted as the mean \(\pm\) SD for a sample size of 10 mice.
## 2 Materials and Methods
### Terrain Classification Methodology
#### 2.2.2 Terrain Classification Module  
An online terrain classification system is needed to collect information on terrain through the Kinect sensor and then the key points are extracted from a color image of the terrain. Since the terrain classifier is based on the bag-of-words (BoW) module [26], all extracted features are processed by a clustering algorithm to ensure that the clusters have high similarity. These cluster centers are the visual vocabulary. Then, the terrain images are encoded to form the visual dictionary and a visual vocabulary frequency histogram corresponding to each terrain type. Finally, the information is used to train the support vector machine (SVM) [27] and an optimal hyperplane of each terrain type is divided to classify all terrain types. The algorithm can grasp the key samples and eliminate many redundant samples.  
The main structure of the terrain classification system is demonstrated in Figure 3. The system is mainly divided into two steps: training and testing. In the first step, the information of all terrain types is collected and stored in memory and the data flow is presented as shown on the right in Figure 3. Then, local features of images in memory are extracted and extracted features are clustered by the k-means algorithm to generate a certain number of visual words [28]. Then, terrain images are encoded using the BoW module to form the visual dictionary and the visual vocabulary frequency histogram corresponding to each terrain type. Then, the information is used to train the SVM. With the aim of validating the terrain classification system established in the training part, the testing part is introduced, shown on the left in Figure 3. The local features from terrain images are extracted in the testing image set and the visual word dictionary is encoded. The images are converted to the frequency histograms that are input in the trained SVM to obtain the terrain label. This part is used by the hexapod robot for terrain recognition. The hexapod robot's gait transform algorithm is guided by terrain identification.  
Figure 3: Diagram of terrain classification system. BOW: bag-of-words; SVM: support vector machine.  
In this paper, a dataset was created using six common terrains: grass, asphalt, sand, gravel, tile, and soil. Each terrain image set contained 50 samples, which were acquired by a Kinect camera. A set of samples of terrain images with different illuminations and weather conditions is shown in Figure 4. The \(K\)-fold cross-validation was used and \(K\) = 5 [29]. All images were randomly partitioned into five equally sized groups. Each group was chosen as validation data for testing the classifier and other 4 groups for training set.
## 4 Discussion
### Behavior of Residual Space Charges
#### 4.1.3 Pressure and Gas Spacing
This may be because there could still be residual space charges remaining from the preceding breakdown within a recovery time of 0.5 ms. These residual charges will participate in the subsequent breakdown development, enhancing the corona-stabilization effect and causing significant changes in the breakdown voltage with gas pressure, thereby weakening the saturation characteristics. When the operating frequency decreased, the residual space charges almost completely disappeared, and their corona-enhancement effect on subsequent breakdown became very weak. Therefore, the saturation characteristic of the breakdown voltage-pressure curve becomes more pronounced. The breakdown stability of the switch is optimal at low-pressure conditions; however, it gradually deteriorates with an increase in operating pressure. This trend contradicts that of insulation recovery performance. This may be because as the working pressure increases, the gas density also increases, leading to a decrease in space charge mobility. Meanwhile, the rate of the applied voltage increase remains unchanged. Therefore, during breakdown in a high-pressure gas switch, it becomes difficult for space charges to move to appropriate positions and fulfill their role in improving the spatial electric field distribution. Furthermore, under high pressure, a significant number of positive space charges accumulate around the rod electrode and form a well-defined corona layer. Although this increases the electric field in area I and further strengthens the intensity of corona discharge, the corona hinders breakdown from occurring along the shortest distance (axis) of the electrode gap and forces it to bypass the stable corona layer. Consequently, channels of breakdown develop with a more tortuous morphology and over a longer path, as shown in Figure 14a. When the gap spacing is very small (1.5 mm), the difference in breakdown voltage between different frequencies is minimal, but this difference gradually increases as the gap spacing increases. The small gap spacing may hinder the formation of a stable corona layer around the high-voltage electrode, thereby limiting the effectiveness of corona discharge in preventing premature breakdown. As a result, gas density replaces the corona-stabilization effect and dominates the recovery of insulation strength within 500 us. The stabilization effect of corona discharge gradually becomes more prominent and dominates the insulation strength of the gap with increasing gap spacing, resulting in an approximate linear increase trend in breakdown voltage. However, as the gap spacing increases further, the distance between the corona layer and plate electrode (i.e., low-field region) also increases correspondingly, as shown in Figure 17. This results in an electric field that is more inhomogeneous in the low-field region, which weakens the corona-stabilization effect and leads to a decrease in repetitive breakdown voltage.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Mycotoxins and Food Safety
#### 2.5.8 Why Fungi Produce Mycotoxins
* Inhibition of mycotoxin absorption in the gastrointestinal tract  
Adding non-nutritional adsorbents to the meal to bind mycotoxins in the gastrointestinal tract and lower their bioavailability is one of the newest strategies for reducing mycotoxicosis in cattle. The most commonly researched adsorbents with a high affinity for mycotoxins are activated carbons (ACs), hydrated sodium calcium aluminosilicate (HSCAS), zeolites, bentonite, and certain clays [146]. Adding adsorbents to feeds is a common technique to protect animals against mycotoxins.
## 2 Experimental Set-Up
### Experimental Equipment and Data Collection  
The equipment used for temperature measurement consisted of 27 type K thermocouples embedded in the walls, featuring a refractory steel sheath with a diameter of 6 mm. These thermocouples are mass-isolated, flexible, equipped with G-type mounting nipples and a thread size of 3/8, \"male-female\" connectors, and cover a measurement range of 0 to 1200 \({}^{\circ}\)C, with a length of 400 mm. The thermocouples were grouped into 7 columns at 5 different heights, as shown in Figure 3, forming 5 temperature monitoring layers, each assigned one of three colors for easier result interpretation.  
Additionally, 405 m of compensation cables were used for connecting the thermocouples, featuring silicone rubber insulation and sheath, with dimensions of 2 \(\times\) 0.5 mm\({}^{2}\), along with indicator controller devices that ensure temperature measurement and control, including selectable heating and cooling functions and adjustable temperature compensation, with an accuracy of \(\pm\)0.25% full-scale or \(\pm\)3 \({}^{\circ}\)C. The 5 planes were color-coded according to the height where they were mounted.  
The values measured by the thermocouples were consolidated by the data acquisition module and subsequently processed for real-time access by the data collection station, both illustrated in Figure 4, which presents a detailed horizontal section of the experimental setup.
## 4 Findings Results
### Descriptive Findings
#### 4.1.7 Insights from the Most Globally Cited Documents
[PERSON] [29] discussed the sustainability dimensions of demand forecasting using DL models, considering environmental factors to balance economic and environmental practices in green supply chains. This comprehensive analysis underscores the progress from traditional methods to sophisticated AI/ML techniques and the growing importance of sustainability in supply chain management. Each paper has its strengths but also faces challenges, such as computational costs, complexity, and data requirements, which provide strategic direction for both researchers and practitioners keen to stay at the forefront of developments in demand forecasting. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author(s) and** & **Research Goals** & **ML/DL Models** & **Dataset** & **Key Findings** & **Advantages** & **Limitations** \\ \cline{3-6}
**Year** & & & **Used** & **Characteristics** & & AI/ML models & \\ \hline \multirow{3}{*}{**[PERSON]**[24] & To integrate demand forecasting with AI or enhanced accuracy in supply chain management & Various ML models like random forest, SVM, and neural networks & Large-scale retail dataset with diverse product categories & significantly outperform traditional forecasting methods & Improved accuracy and adaptability & High computational cost and complexity \\ \hline \multirow{3}{*}{**[PERSON]**[28] & To apply ML models to predictive analytics in supply chain demand forecasting & Hybrid models combining regression and neural networks & Industrial production and sales data & reduce forecasting errors up to 20\% standard methods & Effective in capturing complex patterns & Requires extensive data preprocessing \\ \cline{1-1} \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of AI/ML techniques in supply chain demand forecasting.
# Assessment of Dental Arch Reproduction Quality by Using Traditional and Digital Methods
## 2 Materials and Methods
### Statistical Analysis  
Tests used for analysis included the t-test for two independent samples, ANOVA test, and Kruskal-Wallis test, Mann-Whitney U test, as well as the Shapiro-Wilk normality distribution test.  
A significance level of 0.05 was used for statistical analysis. When \(p\)-values less than 0.05 were obtained, statistical differences were deemed significant. The STATISTICA 13.3 program was used to perform the calculations.  
The following variables were considered in the statistical analysis:  
* Root Mean Square (RMS): \(\mathbf{RMS}=\ \frac{1}{\sqrt{n}}\bullet\sqrt{\sum\limits_{i=1}^{n}\left( \mathbf{x}_{1,i}-\ \mathbf{x}_{2,i}\right)^{2}}\), where \(\mathbf{x}_{1,i}\) is measuring point \(i\) on the reference model, \(\mathbf{x}_{2,i}\) is measuring point \(i\) on the test model, and \(n\) is the total number of measurement points per model.
* Standard deviation (SD) is the standard deviation of the deviations.  
The trueness of the pattern model reproduction was assessed by determining the average of RMS values among all models from each group separately.  
The results of deviations of individual linear dimensions: LD1, LD2, LD3 were analyzed. Therefore, the \"Dev. LD1\", \"Dev. LD2\", \"Dev. LD3\" were analyzed to identify differences between segment lengths on models under study and segment length on the reference model for each linear dimension.  
In addition, \(\mathrm{V}_{\mathrm{LD1}}\), \(\mathrm{V}_{\mathrm{LD2}}\), and \(\mathrm{V}_{\mathrm{LD3}}\) were analyzed. The \(\mathrm{V}_{\mathrm{LDi}}\) coefficient determines what percentage of reference values are deviations of the tested linear dimensions, where  
\[\mathrm{V}_{LD_{i}}=\left(\mathrm{LDi}\ \mathrm{dev./}\mathrm{LDi}\ \mathrm{ ref.\ value}\right)\bullet 100\%,\ i=1,2,3,\]  
LDi ref. value = segment length in the reference model for the \(i\)th linear dimension;  
LDi dev. = difference between the segment length obtained in the tested method and the segment length determined on the reference model for the \(i\)th linear dimension.  
Positive values indicate an increase and negative values indicate a shorter length of a given section compared to the reference model.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 4 Discussion
### Approximate Comparisons between Model Estimation and Experimental Data  
[PERSON] and [PERSON] recently proposed a theoretical model of \(\mu_{\text{k}}\) based on the thermodynamic process of ice friction [43] and verified it using several laboratory experiment data. Here, the field experiment data in current work was used to compare approximately with the estimations of physical model. In the physical model, the differences between melting point and the temperatures of slider and substrate need to be input. Because of reaching thermal equilibrium with the air, the temperatures of sliders were the air temperature. The mean value of the temperatures of surface and bottom of ice cover was employed as the temperature of ice track, and they were assumed as the air temperature and melting point respectively. Because of the low salinity of sea ice in current study, the physical parameters of sea ice used in the model were chosen as those of fresh ice (see Table 1 in [43]), except that the ice density was measured as 913.2 kg\(\cdot\)m\({}^{-3}\). The only unknown parameter in the model is contact size, and it was employed as 1 mm here based on the assumption in [43]. The ice-ice and steel-ice experimental data were compared with the modelled estimations, and Figure 13 shows the results. The modelled values agreed well with the measured data for ice-ice friction with a correction coefficient of 0.57, and the modelled values were only 7.2% greater than the measured data. However, for steel-ice friction, the modelled values underestimated measured data by 70.0% at velocity higher than 0.75 m\(\cdot\)s\({}^{-1}\) and overestimated measured data by 115.3% at velocity lower than 0.75 m\(\cdot\)s\({}^{-1}\). While the relationship between modelled and measured values showed a relative stronger correlation coefficient at the former velocity regime (\(r=0.55\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Friction** & \(\mu_{0}\) & \(C\) & \(R^{2}\) \\ \hline SS-ice & 6.6 \(\times\) 10\({}^{-5}\) & 7.5 \(\times\) 10\({}^{-3}\) & 0.83 \\ CS-ice & 7.0 \(\times\) 10\({}^{-3}\) & 7.4 \(\times\) 10\({}^{-3}\) & 0.85 \\ ice-ice & 6.2 \(\times\) 10\({}^{-4}\) & 3.3 \(\times\) 10\({}^{-3}\) & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Empirical constants \(\mu_{0}\) and \(C\) and coefficient of determination \(R^{2}\) of each contact fitted by the Equation (4) with significance less than 0.05.  
Figure 12: The velocity dependence of \(\mu_{\text{k}}\). The colored points are the experimental data, and the colored dashed lines are trends fitted based on Equation (4).
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 2 Related Work  
This section describes other literature on debris detection or classification. Both deep-learning and other techniques such as SVM have been used for garbage detection, but primarily only for outdoor situations with solid garbage. Also, most of these techniques consider only solid garbage. [PERSON] et al. [27] developed a smartphone app to detect and localize the outdoor garbage. The authors used a pre-trained AlexNet CNN model for detecting garbage from outdoor captured images where Bing image search is used to collect the debris data set. The model has a classification accuracy of 87% for this application. However, their approach does not provide any information regarding the type of garbage and uses segmentation to detect regions, which is not very accurate. Another approach involved using an SVM with SIFT features to segregate garbage into recycling categories is provided in [19]. This method uses cropped images of solid garbage only for classification but does not detect the location of the trash. The method has an accuracy of 94% for the given task. The overfeat-googlenet model was trained with debris present in outdoor environments by [PERSON] et al. [28]. The authors used 18,672 images of various types of litters and wastes to train CNN for identifying the outdoor environments solid debris such as leaves, newspapers, food packages, can's etc. The scheme achieved a precision of 68.27% for debris detection in this application. CNN architectures have also been used for underwater debris detection using FLS images [18]. This model was trained using images of common marine debris. Recently, [PERSON] et al. [15] evaluated various deep-learning frameworks for implementing the marine debris detectors in autonomous underwater vehicles. The authors realize that CNN and SSD have higher accuracy when compared to YOLOv2 and Tiny YOLO frameworks. However, SSD requires higher processing times.
## 2 Materials and Methods
### Evaluation Metrics  
To evaluate the performance of the proposed model, this study uses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (\(R^{2}\)) as performance metrics to evaluate prediction accuracy. The RMSE, MAE and \(R^{2}\) formulas are:  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(p_{i}-\widetilde{p}_{i})} \tag{9}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}|p_{i}-\widetilde{p}_{i}| \tag{10}\]  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}(p_{i}-\widetilde{p})(\widetilde{p}_{i}- \widetilde{\widetilde{p}})}{\sqrt{\sum\limits_{i=1}^{n}(p_{i}-\widetilde{p})^ {2}}\times\sqrt{\sum\limits_{i=1}^{n}(p_{i}-\widetilde{\widetilde{p}})^{2}}} \tag{11}\]  
where \(p_{i}\) and \(\widetilde{p}_{i}\) represent the observed and simulated power at point i, respectively. \(\widetilde{p}\) and \(\widetilde{\widetilde{p}}\) represent the average of the observed and simulated power time series, respectively. n is the length of the time series.  
To evaluate the performance differences between different models, the improvement percentage of three performance metrics, namely \(P_{RMSE}\), \(P_{MAE}\) and \(P_{R2}\), are used. The subscript 1 is a model with better performance, and the subscript 2 is a model with normal performance. M is represented as the value of an evaluation index (\(P_{RMSE}\), \(P_{MAE}\), and \(P_{R2}\)) of the prediction model. The percent improvement metric between Model 1 and Model 2 is calculated as follows:  
\[P_{m}=\frac{|M_{1}-M_{2}|}{M_{2}}\times 100 \tag{12}\]
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Part of a Plant** & **Name of the Compounds** & **References** \\ \hline Roots & Phytoecdysteroids: ajugasterone, 20-hydroxyecdysone, castosterone, norcysterone, 16-reptansterone, 28-epi-sengosterone, 2-dehydroajugalactone. & [5; 16] \\ \hline  & Iridoids: ajugoside, reprotiside, 8-acetylharpagide, & \\  & harpagide, acubin, catalpol, harpagoside. & \\  & Phytosterols: B-sitosterol, stigmasterol. & \\  & Polypphenols: p-coumaric acid, ferulic acid, rutin, & \\  & luteolin, apigenin, quercetin. & \\ Above-ground parts & Diterpenoids: ajugareptensin, ajugavensin A, & [5; 13; 17; 18] \\  & ajugotientin,14,15-dehydroajugareptansin & \\  & 3\(\alpha\)-hydroxyajugamarin F4, areptin A, areptin B, & \\  & ajugatannin B1, ajugatannin D1. & \\  & Phytoecdysteroids: & \\  & 22-dehydro-12-hydroxycysterone, & \\  & 22-dehydro-12-hydroxy-29-norsengosterone. & \\ \hline  & Steroids: codysterone, ajugalactone, ajugasterone A, & \\  & ajugasterone B, 29-nornostosterone, & \\  & 29-norsengosterone, 25-hydroxycesdone acetate, & \\  & clerosterol, 22,23-didehydrocloreosterol, & \\  & reptanslactone A, reptanslactone B. & \\  & Diterpenoids: ajugaretplaneone A, ajugareptanzone & [5; 7; 13; 17; 19; 20] \\  & B, ajugachin A. & \\  & Iridoids: reposide, ajugol, ajureptazide A, & \\  & ajupertzide B, ajupertzide C, ajuretpazide D. & \\  & Phenylpropanol glycosides: teupolide, martinoside, & \\  & verbasoscalide, isovascoside. & \\ \hline Leaves & Ecdysteroids: B-ecdysone, ajuga-lactone. & \\  & Essential oils: 1-octen-3-ol, hexadecanoic acid, & [5; 7; 13; 17; 21] \\  & terpinolene and 6,10,14-trimethyl-2 pentadecanone. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Active compounds occurring in different organs of _A. reptans_. Finally, the photoprotective effects of the examined extracts were compared, and their safety for use was assessed. The plant material was prepared using two drying methods: lyophilization and conventional drying at 40 \({}^{\circ}\)C. The activity profiles of these two plant organs will allow for the assessment and comparison of the potential of both parts of the plant, so that its cosmetic potential can be fully utilized. Figure 1: The selected active compounds present in _A. reptans_ according to the literature: (**a**) 20-hydroxyso (**b**) harpagoside (**c**) stigmasterol (**d**) ajugavensin A (**e**) cedysterone (**f**) verbascoside (**g**) quercetin (**h**) 1-octen-3-ol.