esa-sceva/satcom-chunk-collection huggingface dataset:
# Accuracy, Time, and Comfort of Different Intraoral Scanners: An In Vivo Comparison Study
## 2 Materials and Methods
### Intraoral Scanning Protocol  
The digital impressions were taken with each intraoral scanner. Each scanner was calibrated accurately before the scanning procedure.  
The intraoral scanning impressions were repeated by an experienced and trained examiner with five years of experience in orthodontics and a subspecialization in digital dentistry. The examiner performed each scan once with TRIOS (T), once with iTero 5D Element (E), and once with iTero Lumina (L).  
A standard protocol was created to make the scanning procedure repeatable: the digital scanning impression started from the occlusal surfaces of the second left upper permanent molars, then the occlusal areas and the incisal margin of the upper arch. The scanner was then positioned into the palatal surfaces from the left second molars to the other part and then was shifted into the buccal surfaces. The palatal vault was completely recorded during the digital scanning. Each virtual cast was accepted only if all tooth surfaces were completely evident and no alterations were found. The scanning procedure was repeated if any crack line or any variation appeared or if the palatal vault was not fully recorded.  
A sample of 90 digital casts was collected (30 digital casts made by TRIOS Color, 30 digital casts scanned by iTero Element, and 30 digital casts made by iTero Lumina).
## 4 Sensitivity Analysis for Single Factor
### The Effect of VDO Height  
The height of the auxiliary ventilation duct directly affects the position of airflow diffusion. In this section, the ventilation airflow velocity was adjusted to 8.5 m/s, the air outlet was 0.4 m away from the WF, the insulation layer length was 0.6 m and its thickness was 0.01 m, the thickness of the insulation layer was 0.01 m, and the ventilation duct heights were changed to 0.025 m, 0.075 m, 0.105 m, and 0.150 m for research. The cooling effects of different ventilation duct heights are shown in Figure 8a.  
The cooling effects vary with the changes in the ventilation duct arrangement height. The final stable temperature shows a fluctuating trend with a rise in the auxiliary ventilation duct height, and this law applies to the temperature distribution of any section in the working area. When the height is 0.075 m, the cooling effect is the most obvious, and the ATWA at this time is 26.2 \({}^{\circ}\)C, which is 4.4 \({}^{\circ}\)C lower than the highest temperature in the same group of experiments. This is because the duct is located in the center of the PTI layer and has a certain distance from both the top and bottom plates of the surrounding rock. When the pipe is at 0.15 m, the average temperature drops slowly at first, then quickly after ventilation. This is due to the upper surrounding rock in the front having a high heat transfer and cooling consumption (50 s) and the proximity of the air supply outlet to the tunnel vault. Because of the low-speed airflow near the vault and the insulation layers on the side wall, the heat transferred from the rock can be reduced. Additionally, the airflow  
Figure 8: The changes in the ATWA. (**a**) The ATWA under different ventilation duct heights. (**b**) The ATWA under different insulation lengths.  
can supplement the cold capacity in a timely manner, meaning that the ATWA is lower when the ventilation pipe is 0.150 m high than when it is 0.105 m high. Further, when the air duct is arranged in the middle position, the airflow can be fully developed after shooting in, and the mixing between the cool airflow and the original air within the tunnel will be more uniform, which could allow the air duct to make full use of the cooling capacity. In general, the height of the air inlet pipe in an excavation project is usually arranged between 0.5 m and 1.5 m. According to the scale model test conversion, a better ventilation and cooling effect can be achieved at a position of 0.75 m. The actual project should be adjusted according to the process needs and the area of personnel activities.
## 1 Introduction  
The study of solar-influenced space weather and, consequently, extreme weather events, climate change, protection, and preservation are of particular interest in modern science because they are acknowledged as being important for sustainable development [1; 2; 3; 4; 5]. As a result, a high-priority question in this increasingly technological world is whether we can predict the extent of the impact of intense solar radiation on the Earth, the human population, electronic devices, and telecommunications, and, furthermore, whether we can assess the consequences. The answer to this two-part question is not straightforward; it is highly complex. Consequently, the research requires a multidisciplinary approach, along with the application of various models from the different areas of science and industry [6; 7]. As a result, it was necessary to connect the theory with the experimental research that produces the data, and the people who use it. For this reason, a call was issued for submissions of suitable papers for this specific Special Issue.  
This Special Issue brings together new findings in the area of solar radiation, its influence, and diagnostics. This will advance our knowledge in this area so we can better comprehend its significance for upcoming investigations, using cutting-edge equipment, and it will examine the current state of knowledge in this field of research.
# Analysis of the Changes in Volatile Components During the Processing of Enshi Yulu Tea
###### Abstract  
Volatile constituents are critical to the flavor of tea, but the changes in Enshi Yulu tea during the processing have not been clearly understood. Using headspace solid phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) techniques, we analyze the aroma components of Enshi Yulu tea and changes in them during the processing stages. In total, 242 volatile compounds were identified. From fresh leaves to the shaping process in tea production, there are significant decreases in overall aroma substances, followed by increases after drying. Linalool is the dominant aroma component in Enshi Yulu tea, with a proportion of 12.35%, followed by compounds such as gernaid (7.41%), 2,6-dimethyl-5-heptene (6.93%), phenylmethanol (5.98%), isobutyl acetate (4.16%), hexan-1-ol (3.95%), 2-phenylacetaldehyde (3.80%), and oct-1-ene-3-ol (3.34%). The number of differential volatile components varied by production stage, with 20 up- and 139 down-regulated after steaming, 24 down-regulated after rolling, 60 up- and 51 down-regulated after shaping, and 68 up- and 13 down-regulated after drying. Most variation in expression occurred because of steaming, and the least during the rolling stage. PLS-DA analysis revealed significant differences in aroma components throughout processing and the identification of 100 compounds with higher relative contents, with five distinct change trends. Phenylmethanol, phenylacetaldehyde, (_2E_)-non-2-ean, oct-1-ene-3-ol, and _cis_-3-hexenyl hexanoate could exert a profound influence on the overall aroma quality of Enshi Yulu tea during processing. The results offer a scientific foundation and valuable insights for understanding the volatile composition of Enshi Yulu tea and its changes during the processing.  
Enshi Yulu tea; steamed green tea; volatile components; variety 10 November 2024  
16 December 2024  
17 December 2024  
Published: 9 December 2024
## 4 Applications of Graph Attention Networks
### Anomaly Detection  
Anomaly detection, which can also be referred to as outlier detection or novelty detection, is a method of identifying exceptional items, events, or observations that significantly deviate from the majority of the data and do not adhere to a well-defined notion of normal behavior. Such instances may raise suspicion of being generated by a distinct mechanism or seem incongruuous with the remaining set of data. Anomaly detection is utilized in various domains such as cyber security, medicine, machine vision, statistics, neuroscience, law enforcement, and financial fraud detection [174]. Due to their robustness and versatility, graph attention networks (GATs) have been proposed for anomaly detection tasks. GAT architectures have been proposed and investigated for anomaly localization in microwave imaging [175], video anomaly detection [53], time series anomaly detection [176; 177], and BGP anomaly detection [90], among others. Through these endeavors it is evident that GATs have the potential to be effective in detecting and localizing anomalies in diverse datasets.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 5 Challenges and the Way Forward
[118] compared multiple models and expanded the number of recognizable pest categories to nine, yet universal generalizability remained unattainable. Weed detection faces similar issues. Weeds are typically small, morphologically diverse, and sometimes resemble crops, factors that significantly increase the difficulty of accurate identification. Studies like [125] have proposed using multi-scale convolution or attention layer mechanisms to enhance feature extraction and focus on weeds, thereby improving detection accuracy and generalizability. However, misidentification still occurs in heavily occluded environments, where non-weed plants may be incorrectly classified. Developing models capable of handling multi-scale targets while improving generalizability remains a critical challenge. Agricultural field environments are complex, and dynamic environmental interference affects model accuracy. In real-world scenarios, issues such as target overlap, occlusion, and background interference persist. These factors, along with variations in lighting, temperature, humidity, and background conditions, influence the input data acquisition for models. Consequently, there are discrepancies between practical application results and laboratory test performance, indicating a need for improved method adaptability [15]. Studies such as [14; 86] have enhanced detection models to achieve better accuracy in complex environments, though these improvements remain condition-specific. To strengthen model stability in challenging conditions, research can also focus on input data optimization. Acquiring high-quality image data and constructing comprehensive datasets that encompass diverse real-world scenarios represent a growing trend for improving model precision. As discussed in Section 2, integrating technologies like structured-light cameras and infrared spectroscopy can effectively enhance data quality. However, the use of such high-performance equipment increases costs significantly. To address this, some studies [82] employ data augmentation techniques to improve data quality and testing accuracy without requiring expensive hardware. This approach balances performance and cost-effectiveness while maintaining model reliability in variable agricultural environments. Future work should continue exploring robust data acquisition and processing methods to bridge the gap between controlled testing and real-world deployment.
## 2 Materials and Methods
### Verification Step of the Modified Tier 3 Approach  
Subsequent to the calculation of the TF, there is a need for a validation step, where the AIMD is placed inside a phantom similar to the one where the TF was measured, and the temperature increase is noted for various routings (trajectories). In ISO/TS 10974:2018 [18], examples of coil-phantom configurations and routings that generate non-uniform in vitro incident electric fields, which could be used for the TF validation, are shown. Then, the measured temperature increase can be compared to the corresponding one calculated using the TF and the incident electric field. However, this procedure presents some problems with the necessary consistent distance between the temperature probe and the tip, as well as with the possible interaction of a lead trajectory with itself [34]. Thus, other methods to achieve linearly independent incident electric fields have been proposed, such as the repositioning of the phantom inside the birdcage coil, the dual-transmit birdcage body coil, the use of passive scatterers as dielectric padding and the use of small local RF coils [34; 35; 36; 37; 38].  
As the proposed modified Tier 3 approach is a numerical procedure, a verification step was established and a set of routings was considered, based on the Hadamard matrix, whose rows are mutually orthogonal [39]. Thus, the use of electric fields as they are mapped into this matrix could provide low correlations among different routings. In particular, we set eight plane wave sources along the lead, with the incident electric field corresponding to the rows of the following matrix:  
\[H_{\text{8}}=\begin{bmatrix}1&1&1&1&1&1&1&1\\ 1&0&1&0&1&0&1&0\\ 1&1&0&0&1&1&0&0\\ 1&0&0&1&1&0&0&1\\ 1&1&1&1&0&0&0
## 4 Colorectal Cancer
A natural flavonoid compound (Dihydromyricetin) extracted from the leaves of a Chinese medicinal herb known as Vitis heyneana has shown very potent antitumor power by combating chemoresistance by inhibiting Nrf2/MRP2 signaling in CRC cells [48]. Flavones have also been studied as modulators of P-glycoprotein in the LoVo cell line and in the LoVo/DX DOX-resistant line. Specifically, baicalein and luteolin have been shown to induce apoptosis in these cell lines, although it has been found that baicalen could be a substrate of this drug transporter [49]. From Citrus jambhiri Lush and Citrus pyriformis Hassk (Rutaceae), nine naturally occurring compounds were isolated. Of all of them, limonin, deacetylnomilin, hesperidin, neohperidin, stigmasterol, and \(\beta\)-sitosterol-O-glucose were the ones that inhibited the efflux of P-glycoprotein, being even more potent than verapamil. An in vitro study carried out on Caco-2 cells showed that these compounds increased the sensitivity to DOX and completely reversed DOX resistance by inhibiting P-gp, limonin being the most potent [50]. Another study has revealed 17 lamellarin compounds isolated from Australian Didemmun species that represent new non-cytotoxic P-gp inhibitor pharmacophores [51]. One of the compounds that has been extensively studied as an antitumor agent in different types of tumors, including CRC, is quercetin. In addition, it has also been shown to be capable of inhibiting P-gp-mediated MDR in various tumor cells. A larger study showed that quercetin increased the cytotoxicity of DOX in the resistant cell line SW620/Ad300 by inhibiting ATP transport that activates P-gp, which in turn increased the intracellular accumulation of DOX. Additionally, this study demonstrated that quercetin could reverse MDR by significantly blocking D-glutamine and D-glutamate metabolism. Specifically, it downregulated the expression of the glutamine transporter solute carrier family 1, member 5 (SLC1A5) in SW620/Ad300 cells [52]. Like quercetin, curcumin has also been extensively studied as an antitumor agent in different types of tumors. Curcumin has been shown to inhibit a series of molecular pathways in the SW620/Ad300 line, which in turn decreased the antioxidative stress ability and P-gp transport activity [53]. Similarly, in the colon tumor cell line HCT-8/5-FU, it has been shown that curcumin acted as a regulator of P-gp, thus reversing the resistance of this cell line to the chemotherapeutic drug 5-Fu [54]. Despite the good results obtained, more studies are needed to reinforce the activity of these bioactive compounds of plant origin and to elucidate new compounds or derivatives thereof that can modulate MDR.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Dataset
Due to the lighting conditions, there are often problems of underexposure or overexposure. The reflection on the surface of the spacecraft and the difficulty of identifying the structural parts of the spacecraft due to insufficient light will increase the difficulty of accurate target segmentation. Considering the influence of those mentioned above, multi-scale, multi-attitude, noise, and illumination imbalance, as well as the common complex backgrounds and weak targets in some public datasets, we selected the data when establishing the spacecraft segmentation dataset. Among them, there are 80 small-scale spacecraft images and 81 images of spacecraft in poor lighting conditions. There are 70 images of spacecraft with complex backgrounds. Our dataset contains more hard examples. The proportion of hard examples in the dataset reaches 38%. Some hard examples are shown in Figure 10 below. It can be seen that our spacecraft segmentation task is challenging and hard. Our dataset is a small-sample dataset. However, compared with public datasets commonly used for image segmentation, such as the Pascal VOC 2012, the number of images of our various objects is of the same magnitude. At this order of magnitude, our network can achieve segmentation well.
## 3 Results  
### Content of Selected Biologically Active Substances in the Extract  
The total flavonoid content (expressed as epicatechin equivalents) in the hemp extract (100%, 1:1) was determined to be 1.55 mg\(\cdot\)mL\({}^{-1}\), whereas the polyphenol content (calculated as gallic acid equivalents) in the analyzed extract measured 149.65 mg\(\cdot\)mL\({}^{-1}\) (Table 1). Additionally, the antioxidant activity, represented as a percentage of inhibition, reached 22.93%, equivalent to 32.05 mM Trolox.  
Table 2 shows the characteristics of hemp extracts in terms of cannabinoid content compared to a commercial product, i.e., CBD imp oil 20% \"Ultra Extract\".  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter (Units)** & **Result \(\pm\) SD** \\ \hline Flavonoids (mg\(\cdot\)mL\({}^{-1}\)) & 1.55 \(\pm\) 0.036 \\ Polypphenols (mg\(\cdot\)mL\({}^{-1}\)) & 149.65 \(\pm\) 0.797 \\ Antioxidant activity (\% inhibition) & 22.93 \(\pm\) 4.013 \\ Antioxidant activity (mM Trolox) & 32.05 \(\pm\) 6.328 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content of flavonoids (epicatechin equivalent mg/mL), polyphenols ( gallic acid equivalent mg/mL) and antioxidant activity in the basal extract from hemp inflorescences (100%, 1:1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Concentration (\%, _w/w_)** \\ \cline{2-3}
**Cannabinoids** & **Experimental Extract (100\%, 1:1)** & **Hemp Oil CBD 20\%** \\ \hline Cannabidiol (CBD) & & \\ \cline{2-3} \cline{1-  
In the experimental extract, the presence of cannabidiol (CBD) was identified at a concentration of 0.08%, and cannabidiolic acid (CBDA) at 0.04%. These concentrations were notably lower than those observed in the reference sample, where CBD and CBDA levels were 20.71% and 1.10%, respectively. The disparity can be attributed to the utilization of supercritical CO\({}_{2}\) in the commercial preparation, favoring the extraction of biologically active compounds with a higher affinity for cannabinoids. Furthermore, the analysis confirmed the existence of A9-THC (0.11%), CBC (0.22%), CBGA (0.06%), CBG (2.66%), CBDV (0.08%), and CBN (0.02%) in the commercial product. The conventional extraction methods for these components involve solvents with a high affinity for cannabinoids, such as ethanol, methanol, or hexane, and supercritical CO\({}_{2}\), while water, as used in this experiment, exhibits low affinity [31; 32]. The 70% ethanol employed in the experiment was subsequently evaporated during the extract concentration process, resulting in a water extract.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Cannabinoids**} & \multicolumn{2}{c}{**Concentration (\%, _w/w_)**} \\ \cline{2-3}  & **Experimental Extract** & **Hemp Oil CBD 20\%** \\  & **(100\%, 1:1)** & **“Ultra Extract”** \\ \hline delta-9-tetrahydrocannabinol acid (\(\Delta\)9-THCA) &
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### MQ7. What Are the Research Topics by RSA?  
Again, this mapping question may be subject to different interpretations, since a paper can address different topics or it can be in the borderline between some of them. Thus, a careful inspection of the literature was done to classify the papers in the most adequate category. As a result, the main problems detected are shown in Table 4. In the case of automatic crop harvesting, no papers were found in the present mapping study, possibly because they do not consider the use of remote images.  
The results indicate that the different categories are not equally distributed. The topics that received more attention are the estimation of agricultural parameters, the analysis of crop vigor, and the problems related to water usage. These represent more than 77% of the papers. The works related to detection of pathogens, diseases, and insect pests are about 10% of the total. Moreover, at the other end, the classes with relatively fewer publications are yield prediction, weed detection, and the analysis of nutrient status. Therefore, these types of problems represent a good opportunity to advance in RSA research.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Research Topic** & **Ref.** & **Total** \\ \hline Agricultural parameters & [79,91,95,96,104,108,110,111,114,120,122,125,126,129,136,142,147, \\ extraction & 148,150,153,154,157,161,175,178,181–184,188,191,196,201,202] & 34 \\ Growth vigor & [91,92,114–117,132,139,142,144,145,152,158,159,161,171,174,176, \\  & 177,184,189,190,194,195,197,199] & 26 \\ Drought stress, irrigation & [91,101,106,107,119,124,127,128,130,131,135,138,151,155,162,163, \\ and water productivity & 165–167,172,180] & 21 \\ Detection of pathogens, diseases & [133,141,143,146,155,156,173,187,192,200,203] & 11 \\ and insect pests & [91,134,140,151,152,159,179,186] & 8 \\ Yield prediction & [113,137,149,169,170] & 5 \\ Nutrient status & [118,121,151,198] & 4 \\ Automatic crop harvesting & - & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of the main types of research topics addressed in remote sensing in agriculture (RSA) papers.  
Figure 5: Frequency of the main types of capture platforms for the research in RSA.