esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Purple Carrot and Prevention of MetS and Cancers
### Anti-Diabetic Potential of the Purple Carrot  
It has recently been proposed that purple carrot compounds can influence the metabolism of glucose. Anthocyanins from purple carrot, especially cyanidin 3-xylosyl galactoside, were found to be good inhibitors of \(\alpha\)-amylase, \(\alpha\)-glucosidase, and dipeptidyl peptidase IV [65]. [PERSON] et al. [66] also found that \(\alpha\)-glucosidase inhibition is not significantly affected by the glycosidic side of cyanidin chains (monoglucide has a similar effect compared to diglicoside). However, a markedly stronger action of the acylated structures compared to anthocyanin-3-glycos was found. About 58% of purple carrot anthocyanins are present in an acylated form [66; 67]. Not only anthocyanin but also other phenolic compounds could affect carbohydrate digestion enzymes. As proved by [PERSON] et al. [45], phenolic acid from purple carrot fractions (containing mainly chlorogenic acid) was the strongest \(\alpha\)-amylase inhibitor. Moreover, purple carrot extract caused a moderate inhibition of intestinal glucose uptake in Caco-2 cells. The authors did not observe impaired Caco-2 and enteroendocrine GLUTTag cell viability due to purple carrot extract up to a concentration of 100 ug/mL or 50 ug/mL, respectively.  
These properties could be associated with slowing down glucose absorption and metabolism. Inhibition of these enzymes involved in carbohydrate digestion could therefore partially reduce postprandial blood glucose levels and prevent hyperglycemia [45].  
It was found that increased insulin resistance in body tissues could be associated with type 2 diabetes development. Fermented extracts of purple carrot with _Aspergillus oryzae_  
Figure 2: A graphical representation of the nutraceuticaltical benefits of purple carrot against MetS and cancer.  
and _Lactobacillus plantarum_ exhibit strong anti-diabetic potential by preventing insulin resistance. They also improved first-phase insulin secretion and insulin sensitivity in the hyperglycemic state in type 2 diabetic rats with dementia [68; 69].  
Another study based on animal models explained and assessed purple carrot phenolic compound contributions in antioxidant defense, glucose metabolism, and renal and hepatic toxicity in rats. The positive effects of thirty days of consumption of purple carrot juice were a significant reduction in blood triglyceride concentration and superoxide dismutase activity in both dose-dependent and time-dependent manners. Therefore, no major impact on blood glucose concentration and no toxicity was observed concerning the functioning of the kidneys and liver [35]. Additionally, [PERSON] et al. [70] conducted a study that demonstrated the effectivity of anthocyanin-rich supplementation in the reduction of insulin resistance by improving signal transduction using the insulin receptor substrate-1/Akt pathway in adipose tissue. Similarly, anthocyanins also lower insulin resistance by decreasing insulin concentrations in blood [71].
# Ship Network Traffic Engineering Based on Reinforcement Learning
## 3 Experiment
### Traffic Balancing
The above algorithm may cause high-importance instructions to be transmitted through a longer path, reducing the ship command system's QoS. Therefore, we considered the priority transmission of high-importance instructions in our optimization goal and designed the following reinforcement learning algorithm.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Explanatory Variables
#### 3.2.2 Weather  
Table 3 shows the reasons for abnormal flights in China in 2019, issued by the Civil Aviation Administration of China, showing that 46.49% of delays were related to bad weather. Meanwhile, airlines account for 21.14% of the reasons.  
The weather data was downloaded from the website of rp5.ru ([[http://rp5.ru](http://rp5.ru)]([http://rp5.ru](http://rp5.ru)), accessed on 23 May 2022), including air temperature, atmospheric pressure at weather-station level, relative humidity at a height of 2 m above the earth's surface, wind direction, wind speed, and horizontal visibility. Because it is difficult to characterize the weather during the entire flight process, we used the weather conditions at PEK (departure airport) to describe the impact of weather on flight delays.
## 3 Modeling of the System and Optimization
### Exergy Model
#### 3.2.2. Exergy Destruction  
Destruction of exergy occurs due to several reasons. Friction between the viscous fluid and the flow passage causes some pressure drop which accounts for some portion of exergy destruction [14].  
\[\dot{E}_{d,\Delta P}=-\frac{\dot{m}\Delta P}{\rho}\frac{T_{a}\ln(\frac{T_{out}} {T_{a}})}{(T_{out}-T_{in})} \tag{21}\]  
Heat transfer from high temperature sun to the relatively low temperature absorber plate causes some entropy generation, which accounts for some portion of exergy destruction as follows [14]:  
\[\dot{E}_{d,\Delta T_{s}}=-\eta_{o}I_{T}A_{a}T_{a}(\frac{1}{T_{p}}-\frac{1}{T_{ s}}) \tag{22}\]  
Heat transfer between the flowing fluid and absorber plate also accounts for some portion of exergy destruction which is as follow [14]:  
\[\dot{E}_{d,\Delta T_{f}}=-\dot{m}C_{p}T_{a}(\ln(\frac{T_{out}}{T_{in}})-\frac{ (T_{out}-T_{in})}{T_{p}}) \tag{23}\]  
Using Equations (11)-(19), after some mathematical simplification, the exergetic efficiency equation in terms of utilized energy can be written as [14]:  
\[\varepsilon=\frac{\dot{m}[C_{p}(T_{out}-T_{in}-T_{a}\ln(\frac{T_{out}}{T_{a}}) )-\frac{\Delta P}{\rho}]}{G_{T}A_{a}(1-\frac{T_{a}}{T_{s}})} \tag{24}\]  
Conversely, in terms of exergy losses, it can be written as:  
\[\begin{array}{c}\varepsilon=1-\{(1-\eta_{o})+\frac{\dot{m}C_{p}T_{a}(\ln( \frac{T_{out}}{T_{in}})-\frac{(T_{out}-T_{in})}{T_{p}})}{G_{T}A_{a}(1-\frac{T_ {s}}{T_{s}})}+\\ \frac{U_{l}(T_{p}-T_{a})(1-\frac{T_{s}}{T_{p}})}{G_{T}(1-\frac{T_{s}}{T_{s}})} +\frac{\dot{m}\Delta P_{s}\ln(\frac{T_{out}}{T_{in}})}{\rho G_{T}A_{a}(1-\frac {T_{s}}{T_{s}})(T_{out}-T_{in})}+\frac{\eta_{o}T_{a}(\frac{1}{T_{p}}-\frac{1}{ T_{s}})}{(1-\frac{T_{s}}{T_{s}})}\}\end{array} \tag{25}\]  
Dimensionless Exergy term can be written as [26]:  
\[\dot{E}^{\prime}=\frac{\dot{E}}{\dot{E}_{in,solar}} \tag{26}\]Equations (19)-(23) and (25) can be rewritten as [14; 26]:  
\[\dot{E}^{\prime}_{l,q}=-\frac{U_{l}(T_{p}-T_{a})(1-\frac{T_{a}}{T_{p}})}{G_{T}(1- \frac{T_{a}}{T_{s}})} \tag{27}\]  
\[\dot{E}^{\prime}_{l,\rho}=-(1-\eta_{o}) \tag{28}\]  
\[\dot{E}^{\prime}_{d,\Delta P}=-\frac{\dot{m}\Delta PT_{a}\ln(\frac{T_{out}}{T _{a}})}{\rho G_{T}A_{a}(1-\frac{T_{a}}{T_{s}})(T_{out}-T_{in})} \tag{29}\]  
\[\dot{E}^{\prime}_{d,\Delta T_{s}}=-\frac{\eta_{o}T_{a}(\frac{1}{T_{p}}-\frac{ 1}{T_{s}})}{(1-\frac{T_{a}}{T_{s}})} \tag{30}\]  
\[\dot{E}^{\prime}_{d,\Delta T_{f}}=-\frac{\dot{m}C_{p}T_{a}(\ln(\frac{T_{out}}{ T_{in}})-\frac{(T_{out}-T_{in})}{T_{p}})}{G_{T}A_{a}(1-\frac{T_{a}}{T_{s}})} \tag{31}\]  
\[\varepsilon=1-(\dot{E}^{\prime}_{l,q}+\dot{E}^{\prime}_{l,\rho}+\dot{E}^{ \prime}_{d,\Delta P}+\dot{E}^{\prime}_{d,\Delta T_{s}}+\dot{E}^{\prime}_{d, \Delta T_{f}}) \tag{32}\]  
Exergy destruction ratio (EDR) is defined as the ratio of total exergy destruction in the system to exergy of the outlet water [27]. Mathematically,  
\[EDR=\frac{(\dot{E}_{d,\Delta P}+\dot{E}_{d,\Delta T_{s}}+\dot{E}_{d,\Delta T_{ f}})}{\dot{E}_{out,f}} \tag{33}\]
# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 5 Conclusions and Future Work  
In this paper, we introduced a multi-criteria model based on supervised learning, called NetSel-RF, to cope with incorrect network selection decisions that lead to unnecessary handovers,  
Figure 8: AP Selection.  
Figure 7: Throughput drops.  
throughput drops, and complexity in the current HM solutions. We constructed a dataset, performed data preparation, carried out feature selection, and developed our model by using RF. Our model, based on multi-criteria (coming from the network, user preferences, end-devices, and applications) and supervised learning, outperformed the SSF approach regarding several metrics, such as the number of handovers (67%) and throughput drops (50%). Moreover, our model behaved similarly to AHP-TOPSIS regarding the metrics mentioned, but selecting a new AP without waiting for the current AP is beyond the capability of the model. Our approach is proactive, and AHP-TOPSIS is reactive; therefore, NetSelf-RF makes a hand-off to an AP with better conditions before the mobile device loses connection, in contrast to AHP-TOPSIS.  
As future work, we intend to consider various devices carrying out the handover process at the same time. In this way, we plan to carry out the handover as a group, reducing the control messages that arrive at the AP. We also plan to evaluate the impact of proactive migrations in the throughput.  
Conceptualization, D.A.E., A.J.C. and O.M.C.; Investigation, D.A.E., A.J.C., F.Y.V., O.M.C. and A.O.; Methodology, F.Y.V. and A.O.; Writing--original draft, D.A.E. and A.J.C.; Writing--review & editing, O.M.C., F.Y.V., and A.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Colombian Government throuho the PhD scholarship of the student [PERSON] and the Postdoctoral Fellowship programme of [PERSON] (80740-468-2019).  
The authors would like to thank the Universidad del Cauca and the Fundacion Universitaria de Popayan.  
The authors declare no conflict of interest.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### Second Set of Tests
#### 5.3.4 Comparison  
Figure 20 shows the number of increments observed in the limits when V2 is used. It is the same as in the first test case: the minimums tend to increase less than the maximums. In tests using 30 kHz, since outliers are detected, the same amount of increment is reported.  
The generated _safe zone_ margins for the second set of tests are demonstrated in Figure 21. As expected, V2 always provides a greater _safe zone_ margin. This is especially important in the case of method three, since generally, the minimums that are required for detection are well above the average value, while the maximums are close. The benefit of using V2 is clearly illustrated in Figure 19, where upon testing with the 34-kHz frequency, the maximum detection value of V2 in both tests is much closer to the calculated average value.  
Figure 19: Limits achieved using method three on the second set of tests.  
Figure 18: Limits achieved using method two on the second set of tests.  
The average detection lengths achieved using all of the methods are shown in Figure 22 using the optimum frequency (37 kHz) for detecting Defect 2. Both methods two and three achieve the same detection length for 5% CSA; nonetheless, when the defect size decreases, method three tends to have a higher detection length. In the tests with a 34-kHz excitation frequency, method two achieved a higher detection length, since the average value is not set mathematically. Using the optimum frequency, it can be concluded that method three is the best method, since it generated the most detection length with the highest _safe zone_ margin.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 2 Related Work
### Examples of AI-Based Malware Detection Technology
#### 2.5.1 Kaspersky Lab's ML-Based Malware Detection Technology Research
Similarity hashing classifies malicious files into an appropriate bucket according to the hash value of the feature information, and it performs in-depth detection when a normal/malicious file is mixed in a bucket. In deep detection, a similarity hashing function is trained with all the extractable lightweight features and then classifies files as normal/malignant using an  
Figure 4: ML: segmentation of object space. Figure 3: ML: detection algorithm life cycle. ensemble algorithm. If normal/malicious files are mixed in the classification result, the rare malicious code learning step is performed. The extracted feature information is applied to ML and deep learning (DL) models that detect rare malicious codes and similar malicious codes. If it is difficult to classify malicious files in the pre-execution stage because of encryption or obfuscation, the post-execution stage is performed. The post-execution stage detects malicious code by training a DL model with the information collected by executing the malicious file. As shown in Figure 6, it creates behavior graphs and behavior patterns using the process and behavior records collected when executing malicious code, extracts key characteristic information about the behavior patterns created with a DL-based model, and then classifies and judges them as normal/malignant through the classification model. An experiment showed that considering the robustness, scalability, throughput, and explainability of the system, a good detection performance was guaranteed even for small changes in malicious code. In addition, a system was built that is suitable for processing a large amount of malicious code and allows a user to understand the detection results [13]. Figure 5: Pre-execution basic detection and deep detection processing process. Figure 6: Post-execution processing process.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude
#### 3.4.1 Effect of Epicenter Distance on Magnitude Estimation Accuracy  
According to [24], in the empirical formula for magnitude estimation using waveforms with different time windows after P-wave, the results were significantly improved by adding the distance term, and the training convergence speed of the network and the impact on the training set were better than the network without the introduction of the epicenter distance feature. In contrast, the normalization process adversely affects the model's acquisition of accurate amplitude information.  
The results in Figure 6 show that the overall error distribution gradually tends to concentrate as the epicenter distance increases, while the mean error for large earthquakes is skewed towards negative values, i.e., underestimation. In the first second, the errors of large earthquakes are significantly larger than the mean errors, and the maximum underestimation reaches about magnitude 4. The magnitude underestimation is more serious in remote sites. However, as the time window lengthens, at about 5 s, the mean of the estimation errors for large earthquakes improves considerably, and the underestimation of events at distant epicentral distances gradually returns to normal levels. The overall mean error is less than 0.5, and the error of large earthquakes is also less than 1. Although the universal magnitude error of large earthquakes is larger than the mean magnitude error of all data, the magnitude errors of large earthquakes show a positive correlation tendency with the increase in the source distance, which is in line with the objective law that the source information accounts for more in the near-seismic waveforms, and decreases in remote earthquakes due to the increase in the path effect.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 4 Experiments  
### Datasets and Metrics  
The COCO dataset [23] is widely regarded as one of the most representative benchmarks for object detection and human pose estimation. It encompasses various challenging scenarios, including small objects, low-resolution scenes, and high-resolution images, and contains over 330k images, each labeled with object bounding boxes, object categories, and keypoint locations.  
TinyPersonv2 [24] is a benchmark dataset designed for detecting extremely small human instances, containing 1610 images with 72,651 annotated instances. Most human instances in TinyPersonv2 are smaller than 20 pixels, with challenges such as occlusions, low resolution, and cluttered backgrounds.  
In object detection, we compute the mean average precision (mAP) by averaging over various intersection-over-union (IoU) scores, measuring the bounding box overlap. Additionally, we report the AP (small) metric, as small objects are particularly sensitive to high-frequency information loss due to their limited pixel representation. For human pose estimation, we use the mAP averaged across object keypoint similarity (OKS) scores. We also report the [EMAIL_ADDRESS] and [EMAIL_ADDRESS] metrics to enable a more comprehensive comparative analysis.
## 4 Results  
The results in Figure 3 illustrated that except for MB alone, all experimental groups could decrease the viability of _E. faecalis_ in the root canal biofilm model, compared with the control group (\(p<0.001\)). As shown in Table 1, a highly significant difference was found between the MB alone and the groups treated with MB in combination with 660 nm diode laser, SWEEPS, or both (\(p<0.001\)). Additionally, there was no significant difference found between the MB plus 660 nm diode laser and MB plus SWEEPS (\(p=0.982\)). Compared to the HP alone, the bacterial counts in the HP + 980 nm diode laser group (\(p=0.014\)), HP + SWEEPS group (\(p=0.002\)), and HP + 980 nm diode laser + SWEEPS group (\(p<0.001\)) were significantly lower, while those in the HP + 980 nm diode laser group and HP + SWEEPS group did not differ significantly (\(p=0.982\)). In addition, no bacteria were detected in the presence of 5.25% NaOCl (\(p<0.001\)).  
Figure 3: Effect of different treatment groups on cell viability of Enterococcus faecalis biofilm. * Significantly different from the control group, \(p<0.001\). MB: methylene blue, HP: hydrogen peroxide, DL: diode laser, nm: nanometer, SWEEPS: shock wave enhanced emission photoacoustic streaming, NaCl: 5.25% sodium hypochlorite.
## 6 Specific Application of Topic Prediction Models and Algorithms
### Deep Learning or Machine Learning Analysis Models and Algorithms Based on Time Sequence
#### 6.1.3 SVM Model  
SVM is a common machine learning algorithm. It adopts the SRM (structural risk minimization) strategy and has high prediction accuracy, and it is often used in various classification and regression tasks.  
[PERSON] et al. used NSF graphene project data as the data source to propose a new trend prediction method for fund projects based on time series analysis and SVM model [37]. [PERSON] and [PERSON] proposed a support vector machine prediction model optimized with the improved particle swarm optimization algorithm to predict development trends in the graphene field [28].
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results
### Medium Radar Backscatter Areas (Amazon Rainforest)  
In order to further validate the radiometric comparability between both SAR instruments, the radar brightness \(\beta^{0}\) is derived from distributed targets from areas within the Amazon rainforest with medium radar backscattered and a sufficient SNR above 15 dB for copolarized and above 10 dB for cross-polarized channels. For this purpose, three regions are defined with a pixel size of 1000 by 700 pixels (range, azimuth) representing vignettes of about 23 km\({}^{2}\) in slantrange geometry. These distributed targets are spread over all three IW subswatches (Figure 3) and compose a wide coverage w.r.t. antenna elevation from near to far range. By visual inspection, these vignettes are found to contain mainly homogeneous forest areas without disturbing orographic features like rivers or deforested areas. Note that as radar brightness depends on the local incidence angle, slightly different average values are expected for each observation area.  
The derived radar brightness for the different observation areas are evaluated as a function of acquisition time over a two year observation period. Within Figure 4 the HV channel results are depicted for both SAR instruments: S-1A (blue) and S-1B (red). The related mean values and standard deviations for co- and cross-polarized channels are summarized in Table 2. The radar backscatter decreases from near range (IW1) to far range (IW3) due to the corresponding local incidence angle of the observed area. The radar brightness of each zone shows only slight variations and trends over time (Figure 4) which are expressed by standard deviations in the order of 0.2 dB (Table 2). Low differences  
Figure 3: Distributed targets (red rectangles) selected for each subswath of IW-mode SLC-products acquired over Amazon rainforest. The elevation for the background map was derived using the 90 m digital elevation model (DEM) from TanDEM-X [38] ([[https://tandemx-science.dlr.de](https://tandemx-science.dlr.de)]([https://tandemx-science.dlr.de](https://tandemx-science.dlr.de))). The overview map was created using generic mapping tool (GMT) [39].  
are found between S-1A and S-1B; the ratios between the averaged radar brightness are low between 0.05 dB and 0.44 dB.
## 3 Results and Discussion
### Overall Performance of Proposed Model for Root Rot Disease Identification in Ginsing Plants
Additionally, it achieved a high \(F_{1}\) score of 0.88 and accuracy of 0.89, which are higher than those of the other transfer learning models. These imply that the proposed proposed can accurately predict 89% of diseased plants in the created dataset. Furthermore, the training time and inference time of proposed model are comparatively lower, increasing the practicality of this model. Overall, the proposed model has several advantages in real-time deployment.
## 4 Electromagnetic Analysis of the Proposed Damper  
According to the results of the characteristic tests, an RHED with controllable and improved damping performance was designed. An electromagnetic finite element analysis was performed to predict the dynamic behavior and investigate the electromagnetic characteristics. For the simulation based on finite element analysis, ANSYS Electromagnetics suite (ver. 19.2, official license), a commercial analysis program widely used for magnetic and electric field analysis, was used.
## 3 Experimental Investigation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Tensile Coupon** & \begin{tabular}{c} **Base Metal Coupon** \\ **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Gauge Width** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Gauge Length** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Tensile Yield** \\ **Strength (\(\mathbf{v_{2}}\))** \\ \end{tabular} &
\begin{tabular}{c} **Ultimate Tensile** \\ **Strength (\(\mathbf{v_{n}}\))** \\ **(MPa)** \\ \end{tabular} \\ \hline
1.5 mm thickness & 1.48 & 20.0 & 50.0 & 200.0 & 264.0 & 594.0 \\ G316L-EN 1.4404 & 1.47 & 20.0 & 50.0 & 198.0 & 252.0 & 608.0 \\  & 1.49 & 20.0 & 50.0 & 197.0 & 261.0 & 595.0 \\  & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & 259.0 & 599.0 \\
2.0 mm thickness & 1.97 & 20.0 & 50.0 & 198.0 & 280.0 & 695.0 \\ G304L-EN 1.4307 & 1.99 & 20.0 & 50.0 & 200.0 & 293.0 & 709.0 \\  & 1.98 & 20.0 & 50.0 & 197.0 & 285.0 & 705.0 \\  & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & 286.0 & 703.0 \\
3.0 mm thickness & 2.98 & 20.0 & 50.0 & 197.0 & 291.0 & 730.0 \\ G304L-EN 1.4307 & 2.96 & 20.0 & 50.0 & 200.0 & 289.0 & 711.0 \\  & 2.97 & 20.0 & 50.0 & 198.0 & 293.0 & 731.0 \\  & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & \multicolumn{1}{c}{—} & 290.0 & 724.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material properties determined from tensile tests. Figure 3: Experimental and Finite Element failure modes of channels. (**a**) Experimental 3D view; (**b**) Experimental front view; (**c**) Finite element 3D view; (**d**) Finite element front view [11].