esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 The Immune Response and Its Interaction with the Gut Microbiota
### Microbial Metabolites and Immune Modulation  
The gut microbiota influences immune function through the production of bioactive metabolites derived from dietary components, host-secreted factors, or microbial metabolic interactions. These metabolites exert immunoregulatory effects and contribute to the protective functions of commensal bacteria. Among these, SCFAs, tryptophan-derived metabolites, and bile acid derivatives have been extensively studied for their immunomodulatory properties [74].  
SCFAs--primarily acetate, propionate, and butyrate--are produced through bacterial fermentation of dietary fiber. These metabolites reinforce intestinal barrier integrity by stimulating the production of mucus and antimicrobial peptides by IECs. Additionally, SCFAs promote the maturation and expansion of regulatory T (Treg) cells in the colon, thereby mitigating excessive inflammatory responses to microbial antigens [75]. SCFAs also support intestinal homeostasis by facilitating epithelial repair through the proliferation and differentiation of intestinal epithelial cells. Furthermore, they contribute to the expansion of innate lymphoid cells (ILC3), which secrete interleukin-22 (IL-22), a cytokine that enhances epithelial cell production of antimicrobial molecules [76].  
Tryptophan metabolites, particularly indoles, are derived from the microbial metabolism of dietary tryptophan and serve as ligands for the aryl hydrocarbon receptor (AhR), a key regulator of mucosal homeostasis. Deficiencies in these metabolites have been associated with the pathogenesis of inflammatory bowel disease (IBD) [77; 78]. Similarly, bile acid derivatives influence host immunity by modulating signaling pathways involving the farnesoid X receptor (FXR) and the G-protein-coupled bile acid receptor (TGR5). The enzymatic activity of bacterial bile salt hydrolases (BSHs) contributes to the metabolic transformation of bile acids. Notably, a reduction in BSH gene prevalence has been linked to an increased risk of IBD [79; 80].  
Mucosal homeostasis within the gastrointestinal tract is characterized by a dynamic interplay among gut microbiota, microbial metabolites, and host immune components. This precisely regulated state of low-grade inflammation is essential for maintaining immune tolerance and determining host susceptibility to infections [81; 82].
# Metabolism and Regulation of Ascorbic Acid in Fruits
## 2 Metabolic Pathway of Ascorbic Acid  
The main dietary sources of AsA for humans are fruits. The AsA contents of some fruits are organized, such as tomato, kiwifruit, orange, strawberry, carrot, sweet pepper, and so on (Table 1) [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Because of the important role of AsA in plants, in-depth studies have been carried out on the metabolic pathways of AsA in plants (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Common Name** & **Cultivar** &
\begin{tabular}{c} **Content of AsA** \\ **(mg/100 g FW)** \\ \end{tabular} & **Reference** \\ \hline Tomato & _Solanum pennellii_ & 4.40–17.61 & [18] \\  & Cherry & 31.98–42.68 & [19] \\  & Monika & 41.41–53.10 & [19] \\  & Isabella & 41.19–48.65 & [19] \\  & HLY & 5.58–18 & [20] \\  & Rio Grande & 6.78–10.8 & [20] \\ \hline Kiwifruit & Hayward & 51.3–79.7 & [21] \\  & Jiangxi79-1 & 53.8–93.6 & [21] \\  & Awaji & 22.2–28.8 & [21] \\  & Kosui & 31.5–50.3 & [21] \\ \hline Strawberry & Paratchatan & 63.73–72.57 & [22] \\  & Sagahonoka & 56.8 & [23] \\  & Sugyeong & 108.1 & [23] \\ \hline Pear & Banda & 10.2 & [24] \\  & Limon & 10.1 & [24] \\  & Incir & 4.4 & [24] \\ \hline Orange & Mandarin & 41.3 & [25] \\  & Hamlin & 62.7 & [25] \\  & Salsustiana & 56.8 & [25] \\ \hline Watermelon & Crimson sweet & 11.86–15.27 & [26] \\  & Giza & 15.58–30.45 & [26] \\  & Dumara & 10.84–23.34 & [26] \\ \hline Lemon & Ovale di Sorrento & 29.91 & [27] \\  & Sfusato Amalfitano & 27.71 & [27] \\  & Femminello Cerza & 26.90 & [27] \\  & Femminello Adamo & 26.69 & [27] \\ \hline Apple & _M. punila_ ‘Saivaiahong’ & 0.96 \(\pm\) 0.06 & [28] \\  & Yantai Fuji No. 3 & 0.37 \(\pm\) 0.09 & [28] \\  & Xinshiji & 0.25 \(\pm\) 0.05 & [28] \\  & Liuyuehong & 0.20 \(\pm\) 0.04 & [28] \\  & [PERSON] & 0.10 \(\pm\) 0.06 & [28] \\  & [PERSON] & 0.05 \(\pm\) 0.02 & [28] \\ \hline Grape & Vrboska & 0.46 & [29] \\  & [PERSON] & 0.41 & [29] \\  & [PERSON] & 0.36 & [29] \\ \hline Pepper & Segana & 71.99 & [30] \\  & [PERSON] & 68.56 & [30] \\  & [PERSON] & 61.22 & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 1: AsA content in fruits of different cultivars.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 3 Results
### Imagery  
The scenes presented in this subsection highlight a few examples of the high-quality imagery collected by CMIS. Figure 7 depicts a scene in Florida (Case M) from science flight #2 in which an agricultural field is undergoing a controlled burn. The images show calibrated, geo-referenced irradiance for the three CMIS spectral wavelengths. Since the aircraft was not constrained to fly along cardinal direction, the data swath was rotated relative to a standard latitude-longitude Mercator grid. The images required approximately 65 s to collect. The remaining images will be presented on an AT-XT grid for convenience. The undulations at the boundaries of the image result from the aircraft motion during the autopilot course corrections.  
Figure 7: Calibrated, geo-registered irradiance of a controlled agricultural burn at 27.85\({}^{\circ}\)N, 80.756\({}^{\circ}\)W for Case M over Florida during Science Flight #2 on 19 January 2021. The figure present images from the (**a**) 2.25 \(\upmu\)m, (**b**) 3.75 \(\upmu\)m (nadir), and (**c**) 4.05 \(\upmu\)m channels, respectively. The direction of flight was toward the southeast.  
Some of the radiances from the 3.75- and 4.05-\(\upmu\)m bands are saturated in the high temperature regions associated with smoke and gases adjacent to the flames, because of a long integration time used in the data acquisition. Fewer 2.25-\(\upmu\)m pixels are saturated because of the narrow bandwidth (50 nm) centered on 2.25 \(\upmu\)m and the temperature of the fire. Dual gains can be implemented in the future to reduce and potentially eliminate saturated pixels.  
Figure 8 presents calibrated radiance from the St. Lucie Nuclear Power Plant on the east coast of Florida (Case N) on 19 January 2021. As expected, radiance is higher at 3.75 \(\upmu\)m than at 4.05 \(\upmu\)m due to higher reflectivity at the shorter wavelength. The overall scene contrast at 3.75 \(\upmu\)m is larger because of greater reflectivity and higher quantum efficiency at that band than at 4.05 \(\upmu\)m. Temperature differences indicated by differences in radiance for waters near the power plant are quite visible at both wavelengths, particularly for a single, wide warm-water outlet and two narrow cold-water inlets. Warming of the ocean waters adjacent to the St. Lucie nuclear power plant is indicated by the line of enhanced radiance extending horizontally from the warm water outlet. Cooler temperatures of the Atlantic Ocean are also clearly evident near the top of the image.  
The radiance of Earth includes roughly equal contributions from reflected and emitted radiation in the MWIR. At night, MWIR bands have sufficient signal to directly estimate temperature for such terrestrial features as clouds, ocean, and terrain. During the day, temperature can still be extracted, but the retrieval requires additional processing to remove reflected solar radiance [7].
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 2 Related Work
### CBCT Toth Segmentation  
Segmenting tooth models from CBCT images holds significant importance for the subsequent diagnosis and treatment of dentists [1; 2]. For example, accurate tooth models assist dentists in detailed assessments of the morphology, density, and structure of the alveolar bone, aiding in the diagnosis of various bone disorders such as fractures, cysts, or inflammation. In dental implant surgery, CBCT tooth segmentation provides crucial information for planning the position, angle, and depth of implants. By precisely segmenting tooth and alveolar bone structures, doctors can evaluate the suitability of implants and determine the optimal surgical approach. Before the advent of deep learning, people commonly used variants of level-set methods [5; 6] and incorporated prior knowledge for tooth segmentation. With the emergence of deep learning, automated tooth segmentation from CBCT using neural networks has been explored [10; 11; 12]. However, accurate tooth segmentation from CBCT images remains a challenging task due to the following reasons: (1) The shape and size differences between different teeth in CBCT images, as well as their intersections with surrounding tissues, make the segmentation task complex. (2) Acquiring a large amount of three-dimensional CBCT image data is relatively difficult, leading to overfitting and insufficient generalization ability of deep learning models in small sample situations. (3) CBCT images provide rich three-dimensional information, but traditional two-dimensional segmentation methods may not fully leverage this information, making it challenging to effectively process and integrate three-dimensional information.
## 4 Discussion  
The simulation results indicate that the three-stripe configuration proposed here yielded better performance compared to the common configuration (one stripe). Performance parameters such as reflected beam gain, SLL, beam-steering accuracy, and steering dynamic range showed better results. Figure 7 shows a 3 dB better reflected gain and SLL for the three-stripe configuration in comparison to the common one-stripe configuration. The larger the beam-steering angle, the better the reflected gain, SLL, and bandwidth in the three-stripe configuration, compared to the one-stripe configuration. The reason for this is the smaller phase dynamic range of each stripe in the three-stripe configuration (type A and type B) compared to the one-stripe configuration where the phase dynamic range was larger (Figure 4). In addition, another combination of the three-stripe configuration was investigated, in which the three-stripe configuration was again superior to the one-stripe configuration (Figures 11 and 12). The accuracy of the reflected angle for different frequencies around the operating frequency (bandwidth) was found to be better for the three-stripe configuration compared to the one-stripe configuration, as shown in Table 4. A 50% better accuracy was demonstrated (Figures 9 and 10).  
The construction of a three-stripe MS could be realized by using a plastic frame holder in the front and a plastic board on the back side for mechanical strength, as shown in Figure 13. In Figure 13, the incident beam \"sees\" the whole MS uniformly, aligned at \(Z=0\) (the front side of the MS). On the back side, it is different, but no effect on the reflected beam was found.  
Figure 12: Beam steering far-field radar cross section (RCS) results at 28 GHz in the three-stripe configuration. (**a**) Steering angle simulation results of 12\({}^{\circ}\). (**b**) Steering angle simulation results of 31\({}^{\circ}\).
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 6 Discussions
### Comparison to Average Ensemble and Individual Components of the Ensemble  
Table 11 compares the best results obtained by the PTA ensemble to the ones obtained by the average ensemble and by the algorithms of the ensemble. The evaluation metrics presented in the table were computed as the averages of those obtained for each fold.  
As shown in the table, the PTA ensemble returned the best RMSE results for each dataset compared to the other approaches, which were based on the average ensemble and on the individual components of the ensemble, namely, the RFR, the GBR, the AdaBoost, and the ETR. For each dataset, the PTA ensemble returned the \(R^{2}\) values which were the closest to one, which means that it fit the data the best. Regarding the MAE values, it also returned the best results. However, in the case of the MAPE values, there were exceptions for the Cooling Dataset, in which the values returned by the RFR and the ETR were better, and for the Standardized Cooling Dataset, in which the value returned by the GBR was better. The differences were very small or insignificant. These differences can be justified by the objective function used by the ensemble, which gave more attention to the minimization of the RMSE.
## 1 Introduction
As to an object rotating or spinning at high speeds, a special kind of stability around its rotation axis can be generated, such as spins and propellers, of which the phenomenon is generally called the gyroscopic effect in physics. [PERSON] et al. [20] used the gyroscopic effect mechanism to explain why the maximum torque values are required when getting through the transition curves. They found that the gyroscopic moment is related to an additional rotational velocity around the x-axis caused by the superelevation ramp of the track. However, whether the gyroscopic effects exist on the IRW with both two wheels rotating at high speeds on straight tracks or what unique dynamic characteristics would appear at high speeds were not deeply investigated before. Motivated by the above considerations, the present study deduced an improved mathematical model of the IRW through the Euler-Lagrange dynamic analysis method. Considering that the translational forward speeds of the wheels are different, the creepages and creep forces were calculated separately on the wheels of each side. The effects of gravitational restoring forces and gyroscopic moment were also considered. Then, comparisons with a model generated in the software SIMPACK 2018 were conducted to verify the validity of the mathematical model. Further, the dynamic characteristics were analyzed and summarized with variations in the longitudinal speed and yaw suspension coefficients. In addition, a scaled IRW experiment system was built to observe and verify the existence of the dynamic characteristics, especially the slight self-guidance ability at high speeds.
_Article_  
**Combined Process of Ozone Oxidation and Ultrafiltration as an Effective Treatment Technology for the Removal of Endocrine-Disrupting Chemicals**  
**[PERSON] 1,2, [PERSON] 3,* and [PERSON] 1**  
Civil Engineering Department, North China Institute of Aerospace Engineering, No. 133, Aimin East Road, Langfang 065000, China; [EMAIL_ADDRESS] (X.S.); [EMAIL_ADDRESS] (S.H.)  
Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application, Langfang 065000, China  
Shandong Institute of Geophysical and Geochemical Exploration, Jinan 250013, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-186-3166-2049  
check for updates  
**Abstract:** Endocrine-disrupting chemicals (EDCs) in the secondary effluent discharged from wastewater treatment plants are of great concern when water reuse is intended. The combined process of ozone (O\({}_{3}\)) and ultrafiltration (UF) is a promising EDC removal method. The removal efficiency of five EDCs using O\({}_{3}\), UF and their combination were investigated and compared. The five EDCs were estrone, 17\(\beta\)-estradiol, estriol, 17\(\alpha\)-ethynyl estradiol and bisphenol A, which are typically present in secondary effluent. Results showed that organic matters in secondary effluent became easier to be removed by the combined process, with ultraviolet absorbance reduction enhanced by 11%-18% or 24%-26% compared to the UF or O\({}_{3}\) alone. The removal efficiency of EDC concentration, estrogenicity and acute ecotoxicity by the combined process was 17%-29% or 54%-92%, 19% or 73%, 40% or 60% greater, respectively, than that of the O\({}_{3}\) or UF alone. Particularly, when EDCs were treated by the combination of O\({}_{3}\) and UF, about 100% EDC removal efficiency was achieved. Overall, the combined application of O\({}_{3}\) and UF offers an effective approach to control the concentration and toxicity of EDCs in secondary effluent.  
**Keywords:** endocrine disrupting chemicals; secondary effluent; ozone; ultrafiltration; removal efficiency
# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
###### Abstract  
As an important instrument for olfactory detection in non-destructive testing (NDT), the electronic nose plays an important role in simulating olfactory detection. However, its performance is often affected by drift phenomena, including changes in sensor performance caused by environmental factors and olfactory fatigue due to long-term use. Although the effect of drift on the performance of electronic noses is widely recognized, there is still a relative lack of research on how drift affects feature optimization. This study presents a novel idea that drift factors not only affect the direct readings of the sensor but may also have a profound effect on the feature optimization process and hence the compensation of the electronic nose. To explore this concept, we chose temperature and humidity, the two most common environmental drift factors, for our experimental study. In our study, we verified the impact of drift factors on feature optimization and found a positive correlation between the concentration of sensor scores and the correct classification rate. Moreover, we adopted an innovative quadratic feature optimization method, which aims to reduce the influence of the drift factor on the feature optimization process and thus improve the drift resistance of the electronic nose. In our experiments, we found that the unweighted quadratic feature optimization method performs best in reducing the drift effect. After the optimization process, the recognition rate reaches 100% in the training set and 96% in the test set, which indicates that the electronic nose has improved significantly in terms of drift resistance. In summary, this study explores the effect of drift factors on feature optimization and proposes an effective treatment method, which provides a reference direction for the development and optimization of electronic nose technology.  
e-nose; drift compensation; feature optimization; random forest; artificial neural networks +
Footnote †: journal: Applied sciences
## 1 Introduction
### Contributions of the Paper  
This paper puts forward a system where several hardware and software components work together in an interweaved manner despite their distributed and heterogeneous nature. Considering the design, development and testing works carried out, and the solutions that are available in the existing published literature, it is the authors' opinion that the paper provides the following contributions:  
1. The usage of a UAV as a tool to take measurements from wireless networks in mobile communications. The usage of such a solution offers several advantages over the existing procedures: (a) it is faster to deploy a UAV or even a swarm of them than having teams of technicians and engineers performing measurements; (b) it is a far cheaper solution than having the aforementioned teams perform a similar functionality; (c) the mobility, positioning and speed that a UAV equipped with the correct sensors would have greatly surpasses what humans could perform, even with their set of measurement equipment. It must also be considered how UAVs can be used in locations that would be impossible for humans to access, or in a hazardous environment that could put human lives at risk. This tool not only performs extremely accurate measurements of 4G mobile networks, but also provides a way to visualize them and effectively offers a front-end Graphical User Interface (GUI).
2. Usage of Galileo as a high accuracy Global Navigation Satellite System (GNSS) for the UAV that has been built. Galileo presents several features that, as will be explained in Section 3, make it a desirable option as a GNSS system to both provide accurate positioning of the UAV and exact readings from mobile networks. Therefore, hardware that is capable of establishing communications based on Galileo services has been put to use.
3. A UAV built from scratch for the purpose of high-accuracy mobile network signal measurements: the UAV that has been built for the purposes of this paper has been done so from scratch. This was necessary due to several reasons. While a commercial solution equipped with the required sensors and a Galileo-enabled microcontroller would also be viable, it was chosen to use the presented UAV because the authors had tighter control over what was added to the UAV by mounting up the components themselves. This became especially important with the controller (Navio2) used with the UAV, as the authors were able to set what kind of hardware could use Galileo as the GNSS of choice and make use of its features.
## 2 EO Satellite and In Situ Observations for Earth Surface
### SEASAT and Its Legacy  
The Seasat was the first ocean dedicated satellite launched in June 1978 although it operated only for 106 days. Earth-orbiting satellites such as TOPEX/Poseidon, and scatterometers on NASA Scatterometer (NSCAT), QuickSCAT, all launched in the 1990s and Jason-1, launched in 2001, Jason-2, launched in June 2008, and Jason-3 launched 17 January 2016 followed the initial mission and continued its legacy. The MetOp and its successor MetOp-SG programmes are the current and future legacy to enhance and expand the European EO capability.
## 4 Determination of Battery Capacity  
The advantages of the installed battery is a smooth power output and the ability to provide the required power supply to the load on a cloudy or rainy day. However, it is better to reduce the capital cost of the battery. Therefore, optimal capacities of the battery and inverter are determined by simulation results in this research. In this simulation, it is assumed that \(I_{in}\) = 0 kW/m\({}^{2}\). Additionally, customers will use hot water for 3 h (three persons) per day. In Figure 2, the vertical axis indicates operational cost per day \(C_{day}\), and the horizontal axis indicates the capacity of the battery \(C_{Bx}\). According to inverter capacity \(P_{I}\) described in Figure 2, the case of \(C_{day}\) = 0 is not satisfied with the constraint condition. Similar results are found in Figure 2b. Therefore, Figure 2 shows that the optimal combination is \(P_{Iopt}\) = 6 kW and \(C_{Bopt}\) = 25 kWh.  
Figure 2: Optimal dimension of storage battery. (**a**) 10 \(\mathrm{kWh}\)–100 \(\mathrm{kWh}\); (**b**) 20 \(\mathrm{kWh}\)–30 \(\mathrm{kWh}\).