esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analyses
### Comparison of Predicted Results  
As shown in Figure 10 and Table 4, the RMSM, MAE, MAPE, and R\({}^{2}\) of the temperature prediction results based on the LSTM model were 0.105, 0.103, 0.010, and 0.988, respectively. In addition, the RMSM, MAE, MAPE, and R\({}^{2}\) of the temperature prediction results based on the physical model were 3.857, 2.554, 0.258 and 0.346, respectively. The accuracy of the temperature prediction model based on the LSTM was significantly higher than that of the physical model; the RMSE, MAE, and MAPE were reduced by 3.752, 2.451, and 0.248, respectively, the R\({}^{2}\) improved by 0.642, and the relative error in predicting the moment of CCB (cold chain breaking, which means the value of temperature rises to an unsuitable range for freshness) was reduced by 3.92%. The predicted temperature trend based on the LSTM model closely matched the actual temperature changes, indicating that the LSTM model is more adept at forecasting time-series ambient temperature fluctuations than the physical model of the temperature-controlled container. In addition, the physical model describes changes in heat transfer, but hardly for more in-depth explanations, such as the effect on temperature of some factors like the direction of airflow in the interior and the complex changes in the rate of cold energy release during phase transitions, which may be some of the reasons for the difference between the actual value and the predicted value.  
Figure 8: Training set results.  
Figure 9: Test set temperature prediction result.
###### Abstract  
In this research, supercritical CO\({}_{2}\) extraction is applied to extract bioactive compounds from _Laminum album_ (white dead nettle, _Lamiacene_). Extraction was performed at various temperatures (40, 50, and 60 \({}^{\circ}\)C) using methanol as co-solvent at a constant flow rate of CO\({}_{2}\), methanol, and pressure. The collected extracts were characterized in terms of antioxidant capacity by using DPPH, ABTS and FRAP in vitro antioxidant activity assays, whereas the Folin-Ciocalteu procedure was employed to estimate the total phenols content (TPC). On the other hand, phenolic compounds in the extracts were quantitated by liquid chromatography coupled with a photodiode array detector (UPLC-PDA) and confirmed with a mass detector (TQD). The extracts have shown high TPC ranged between 234.17 to 650.17 mg GAE/g extract. DPPH scavenging of the extracts was estimated and obtained EC50 values ranged from 0.12 to 0.37 mg/mL of solution. The ABTS radical scavenging activity ranged from 43.20 to 44.53 ug TE/g. The FRAP value was found within the range of 19.48 to 44.74 umol TE/g of extract. Differences between extraction conditions were observed. In this research, 50 \({}^{\circ}\)C/250 bar was efficient for the TPC, DPPH, ABTS, and FRAP assays; moreover, statistically, TPCs and FRAP assay showed significant differences between the conditions at \(\alpha\) = 0.05. The identification of phenolic compounds in the obtained extract of _Laminum album_ flowers, using UPLC/PDA, revealed that chrysign, pinostrobin, myriction, and _trans_-3-hydroxycinamic acid were the significant molecules present, which may be responsible for the high content of polyphenols and antioxidant activity. The results obtained indicated that SC-CO\({}_{2}\) could be considered an alternative method for extracting bioactive compounds of _Laminum album_. High antioxidant activity and the presence of various bioactive compounds indicate the potential of this plant from the _Lamiacene_ family and the possibility of its application in various industries, including agriculture, food technology, or pharmacy.  
Keywords:white dead nettle; polyphenols; bioactive compounds; antioxidant assays; TPC; DPPH; ABTS; FRAP; supercritical fluid extraction; UPLC/FDA +
Footnote †: journal: _Sci._
## 4 Results and Discussion
### Error Analysis  
We calculated error at different altitude instances to further evaluate our results and it supports the above analysis that with force conditions, the operator performance for the altitude hold task was better, i.e., resulted in low error values. The mean values of errors are shown in Figure 14.  
At the start of an experiment, when operators were asked to only hold an altitude at 1000 ft, the exaggerated force feedback condition gave optimum results with less error values, as in Figure 14 and less % overshoot, Table 5, which is a good performance sign. Similarly, for increasing and decreasing altitudes at 1000 ft and 2000 ft, realistic and exaggerated force feedback conditions yielded better performance by improved handling qualities. The mean error values presented in Figure 14 validate our analysis.
## 2 Theoretical Background on the ANSYS DesignXplorer(c) Probabilistic Methods
### ANSYS DesignXplorer(\(\copy\) for Structural Reliability Analysis  
In order to perform the SRA, a stochastic parametric FEA model is firstly built in the ANSYS environment such that the different input parameters are assigned their corresponding distributions. Then, the developed FEA model is employed in performing a series of FEA simulations on the OWT support structure through the DoE module packaged in the DesignXplorer(\(\copy\) facility in ANSYS, in order to map the response domain and then derive an appropriate response surface model. The purpose of the response surface is to interpolate the values in the multiple dimensions characterized by the DoE. There are several types of response surfaces as packaged in DesignXplorer(\(\copy\)[53] which include the genetic aggregation, standard response surface full second-order polynomials, kriging algorithms, non-parametric regression, and the sparse grid. However, in this study, only the standard response surface full second-order polynomials with manual refinements is applied, along with the selection of the specification of three verification points. The response surface is interpolated from design points in the DoE. Also packaged within the response surface module is the goodness of fit metric, which is calculated for the DoE points and can be calculated for verification points to check how accurate the response surface can predict the design points. In all limit state cases, the predicted vs. observed chart should be checked to show the goodness of fit data for one or more outputs. Furthermore, the output data should be observed in order to ascertain whether most of the points fall on or near the line, which implies that the response surface predicts the values for most of the design points within its range including the verification points accurately. This indicates that the response surface is a good fit for the DoE points.  
In this study, the six sigma analysis function of the DesignXplorer(c) module in ANSYS is used for the probabilistic assessment. Latin hypercube sampling (LHS) with number of samples equal to \(1\times 10^{7}\) is defined and applied to all the design constraint cases. The cumulative distribution function (CDF) is used to assess the probability of failure and, hence, the reliability of the component. The cumulative distribution function value at any given point expresses the probability that the respective parameter value remains below that point. Whether this probability represents the failure probability or the reliability of the component depends on how failure is defined [53]. The corresponding reliability index \(\beta\) is evaluated through appropriate statistical transformation [55].
## 7 Conclusions  
A gradient three-interval equalization image enhancement algorithm combined with improved adaptive contrast enhancement was proposed to enhance the contrast and edge details of X-ray images. The algorithm adjusts the intensity of contrast enhancement based on local features and improves the calculation method of the gain factor in the adaptive contrast enhancement algorithm. Lastly, it introduces a weight calculation strategy for the three gradient intervals. The degrees of contribution of different gradient levels to the image details are adopted as the weights, so the image enhancement process is more targeted. X-ray images of BiFeO\({}_{3}\) melting and solidification processes were used for testing the proposed algorithm. The experimental results show that, from the images enhanced by the GTIE-IACE, the detail changes during melting and solidification can be more clearly observed. It should be pointed out that the details of image regions with small differences in brightness changes are also well enhanced. Compared with the traditional image contrast enhancement algorithms, the proposed GTIE-IACE effectively avoids issues such as over-enhancement of global contrast and insignificant contrast enhancement of image regions with small gradient changes. From the processed X-ray images of BiFeO\({}_{3}\) melting and solidification processes, distinct solid-liquid interfaces, and crystal growth processes can be clearly observed. By enhancing the contrast and edge details of images, researchers can analyze the phase transition behavior of crystalline materials more easily. This is of great significance for carrying out research based on real-time observation of high-temperature material experimental processes on the space station.  
Conceptualization, X.L. and X.P.; methodology. X.L.; validation, Z.Y.; formal analysis, X.L. and X.P.; investigation, X.L. and X.P.; resources, Q.Y.; data curation, Q.Y.; writing--original draft preparation, X.L.; writing--review and editing, X.L., X.P., Q.Y. and Z.Y.; funding acquisition, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Key Research and Development Program No. AB23026105, Guilin major special plan No. 20220103-1, Special funds for local scientific and technological development under the guidance of the central government in 2021 (GuiKeZY21195030) and Chinese Manned space program.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 3 Application of Deep Learning Methods to Pig Posture Detection
### One-Stage Detection Models
By splitting pig posture detection into two separate modules, [PERSON] and [PERSON] (2018) applied YOLOv5 for pig localization and EfficientNet-B0 for posture classification, achieving an AP of 99.4% and a precision of 93% for the two tasks, respectively. In terms of AP\({}^{\text{IoU}}\) = 0.5, precision, and recall, this approach greatly outperforms the use of YOLOv5 alone in the detection of lying and not-lying posture. Another improved model, improved YOLOX ([PERSON] et al., 2019), was developed by incorporating effective tricks of YOLOv5 into YOLOX. Its performance was tested for detecting the standing, lying, and sitting postures of pigs, and it particularly exhibited improved performance in sitting recognition after simple data augmentation on sitting posture, with an AP0.5 of 90.9% and an AP0.5-0.95 of 82.8%. Compared with the most popular two-stage model Faster R-CNN, the improved YOLOX has about 10% higher mAP0.5-0.95, nearly 10-fold higher speed, and a much smaller model size, showing great superiority as a one-stage method. [PERSON] et al. (2019) designed a high-effect YOLO model that improved the performance of the feature extractor and integrated the dual attention mechanism. The high-effect YOLO model showed obvious improvement of mAP on the detection of standing, sitting, prone, and sidling posture compared with YOLOv3, SSD, and Faster R-CNN. Additionally, under occlusion and overlapping of pig bodies, the high-effect YOLO model displayed advantages in generalization and luminance robustness. Built on YOLOv5 architecture, a novel Light-SPD-YOLO model was developed by introducing an improved compressed block and a channel-wise mechanism, enhancing the speed and accuracy of the detection of five pig postures while lowering the model computation ([PERSON] et al., 2019). One-stage methods, as represented by YOLO series models, have exhibited powerful strength in detecting more complex postures on the basis of only using 2D images as shown in Table 3. Besides great superiority in speed, one-stage methods need only limited computer resources, but have comparable or even greater accuracy than two-stage methods, suggesting that one-stage methods may be more qualified for real application in commercial pig farms. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Posture Classification** & **Data Source** & **Detection Model** & **Evaluation Index** & **Results** & **Advantage** & **References** \\ \hline Nine types of posture and behaviors & YOLOv2 & Accuracy & 97\% & \begin{tabular}{c} Contribute high-quality \\ datasets for building deep \\ learning models; design the \\ livestock safety \\ surveillance system \\ \end{tabular}  & \multirow{4}{*}{[60]} \\ \cline{1-1} \cline{3-4} \cline{6-
# Resting-State Functional Connectivity in Mathematical Expertise
## 4 Discussion
### Mathematicians' Preconfigured Functional Connectivity for Their Expertise  
How efficiently the functional connectivity of resting-state fMRI updates to that of task-related fMRI is known to be closely associated with individuals' level of performance, suggesting that the brain's functional networks at rest need to be reconfigured when they are given specific tasks, getting ready for the successful performance of the tasks [77]. The fast and efficient reconfiguration and update of the functional network from rest to task is strongly interwoven with individuals' task performances. Moreover, the degree of similarity between resting-state functional connectivity and task-related functional connectivity exerts on task performance [77; 78]. For example, in high-performing individuals, their functional connectivity of resting state did not change much compared to that of task-related fMRI [77]. This reconfiguration efficiency or preconfigured functional connectivity during resting state, specifically with the similarity to a task-related configuration, has been known to aid in updates in task-related networks and predictions of individuals' better task performance. Conforming to this, we also found that the resting-state functional connectivity in mathematicians was similar to the task-related functional connectivity when the same group of mathematicians had been involved in the processing of arithmetic calculation [1]. Mathematicians' task-based functional patterns were observed in the areas composed of the inferior, middle, and superior frontal gyrus and the inferior occipital gyrus, which were also found in the resting-state networks in the present study. On the contrary, non-mathematicians showed substantially different functional networks between resting-state and task-related fMRI. Their functional activations were observed mostly in the inferior parietal lobule, bilateral occipital gyrus, superior frontal gyrus, and superior medial gyrus [1], whereas the resting-state functional connectivity in the present study was composed of the connections largely in the medial frontal gyrus to precuneus and lateral orbital gyrus to thalamus. Therefore, our findings reconcile with the argument that experts' preconfigured resting-state functional connectivity, which is already similar to task-related functional connectivity even at rest, enables individuals to update their mental states for task performance more efficiently, leading to better behavioral performances compared to non-experts.
## 3 Experimental Results and Analysis
### Pile Bending Moment  
In the test, strain gauges were pasted symmetrically on both sides of each pile and sheet-pile wall along the inclined direction of the site. The specific distribution is shown in Figure 4. According to the Euler-Bernoulli beam theory [38], the bending moment, \(M\), can be obtained from the following form:  
\[M=\frac{EI(\varepsilon_{t}-\varepsilon_{\varepsilon})}{D} \tag{2}\]  
where \(EI\) (\(E\) = 2.8 GPa) is the bending stiffness of the pile or sheet-pile wall cross-section, \(\varepsilon_{t}\) and \(\varepsilon_{\varepsilon}\) represent the tensile and compressive strains at a particular measurement point measured by strain gauges, and \(D\) is the diameter of the pile or the thickness of the sheet-pile wall. In this paper, the bridge piles furthest away from the sheet-pile wall were called SA piles, the bridge piles near the sheet-pile wall were called SB piles, and the sheet-pile wall was called the SC wall.  
In previous experimental studies [39; 40], only the bending moment on the pile body has been analyzed, with no consideration of the variation in bending moment in the sheet-pile wall. Therefore, this section will explore the distribution law of the bending moment on the sheet-pile wall.  
Figure 10 shows the distribution of bending moments along the depth of the sheet-pile wall under the action of PGA = 0.10 g. Under the action of PGA = 0.10 g, the sheet-pile walls' maximum bending moments in the test and conventional groups were located in the middle (Sc3 measurement point) of sheet-pile walls, which were 1.663 N\(\cdot\)m and 2.803 N\(\cdot\)m, respectively, the latter being about 1.7 times greater than the former. The bending moment at the bottom (Sc1 measurement point) of the sheet-pile wall was larger than that at the top (Sc5 measurement point) in both sites, because the bottom of the sheet-pile wall was fixed with special glue and the bottom fixing of the sheet-pile wall could also withstand part of the bending moment. Overall, the bending moment of the sheet-pile wall in the test group was generally smaller than that in the conventional group, which indicates that the drainage structure on the sheet-pile wall in the test group could effectively drain the excess pore water and reduce the liquefaction of the soil around the sheet-pile wall. However, there was no drainage structure in the conventional group, so the excess pore water pressure around the sheet-pile wall could not be dissipated in time, resulting in a higher degree of soil liquefaction than that in the test group. In addition, the soil lost shear strength after liquefaction, producing more serious lateral spreading and greater lateral forces, which made the bending moment of the sheet-pile wall in the conventional group larger than that in the test group. The results indicate that sheet-pile walls reinforced by drainage structures are more stable during liquefaction.  
Figure 10: Distribution of the bending moment along the depth of sheet-pile walls (PGA = 0.10 g). (Unit: N\(\cdot\)m).
## 4 Discussion
Nonetheless, a few reports expressed the role of 3-carene against insect resistance in pine species [15; 16; 17; 18]. The amounts of 3-carene were associated with the tolerance of _Sitka spruce_ against white pine weevil [15; 16]. The high amounts of 3-carene were correlated with the resistance to Douglas-fir pitch moths in Lodgepole pine (_Pinus contorta_) [17]. In Scots pine (_Pinus sylvestris_), a high level of 3-carene was correlated with the survival of low larvae of Sawflies (_Dipiron pin_) [18]. In the current study, 3-carene emission was enhanced markedly and detected as the major monoterpene after PWN infection in _P. koriensis_. The selective enhance emission of 3-carene might have a relation to the chemical response and/or chemical defense against PWN infection. Previous studies found that the relative expressions of \(\alpha\)-pinene synthase in _P. pinea_ were distinctly enhanced by PWN infection, but no difference was observed between control and PWN infection in _P. pinaster_[20]. Two terpene synthase genes, \(\alpha\)-pinene and longifolene synthase, in _P. massonia_, were involved in the oleoresin defense strategy to PWD-resistant Masson pines. Upregulation of these genes in the stem supported their involvement in terpene biosynthesis as part of the defense against PWNs [48]. 3-Carene was characterized enzymatically in some conifer species (_Pica abies, Pica sitchenis, P. bankiana_, and _P. contorta_) [14; 22]. However, there are no reports on the characterization of monoterpene synthase in _P. densiflora_ and _P. koriensis_. 3-Carene synthase gene was isolated from _P. densiflora_ and functionally characterized by tobacco transformation. Transgenic tobacco overexpressing gene (_Pd3-car_) was isolated from _P. densiflora_ and showed 3-carene production in the transgenic tobacco lines. Extensive evidence has demonstrated the nematicidal activity of monoterpenoids and essential oils against PWN, although such compounds are not derived from pine species [49; 50; 51]. [PERSON] et al. [51] reported that thymol and carvacrol have a strong nematicidal activity to juvenile PWN with the LC\({}_{50}\) values of 0.096 and 0.099 mg/mL, respectively. Natural ester compounds (isobutyl angelate, 2-methylbutyl angelate, 2-methylbutyl isobutanoate, and linalyl acetate) have been reported for nematicidal activities in the Asteraceae plant essential oils [52]. 3-Carene was also reported to have weak nematicidal activity against _B. xylophilius_[53]. In one such study, \(\alpha\)-pinene, 3-carene, and D-limonene were identified as major constituents in the essential oil of _Angelica archangelica_ roots, which showed antifungal activity against 10 plant pathogenic fungi [26]. Although \(\alpha\)-pinene was reported for strong antifungal activity, this compound has no nematicidal activity and even attracts insect vectors of PWD [54; 55; 56]. In our current experiment, a mild nematicidal activity was exhibited against PWNs by treating 3-carene. However, 3-carene was found as a highly responded compound to _P. densiflora_ and _P. koriensis_ after PWN infection, but it exhibited strong antifungal properties.
# Low-Cost _Ka_-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies
## 2 Materials and Methods
### Experimental Setup
#### 2.1.2 The Indoor Unit
As an example, Figure 1 shows the measured power level of the KaSAT beacon and background noise for a day with three rain events. It is clear how the noise power increase marks the time limits of the rain events. Note that the signal levels in the ordinates do not correspond to actual power values in dBm; they are the numerical result of applying an FFT to the \(I\) and \(Q\) samples out of the analog-to-digital converter. In the series, the relative values between time samples have a physical meaning, but their absolute values are meaningless. In Figure 2, we illustrate the measured power level of the KaSAT beacon for the whole month of August 2018. Note in Figure 2 how the raw beacon level undergoes a daily cycle on days with a mostly clear sky.