esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of Fermentation Parameters on Volatile Compound Production
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & _Sc/Lt Ratio_ & **Nitrogen** & **Oxygenation** & **Temperature** & **Residuals** \\ \hline \multirow{3}{*}{Acetaldehyde} & Stand. effect & 0.031 & \(-\)0.215 & \(-\)0.092 & \(-\)0.400 & \multirow{3}{*}{93.30\%} \\  & _p_-Value & 0.977 & 0.843 & 0.932 & 0.716 & \\  & Var. contrib. & 0.03\% & 1.44\% & 0.27\% & 4.97\% & 93.30\% \\ \hline \multirow{3}{*}{Benzaldehyde} & Stand. effect & \(-\)2.424 & 1.286 & 2.128 & \(-\)0.148 & \multirow{3}{*}{93.30\%} \\  & _p_-Value & 0.094 & 0.289 & 0.123 & 0.892 & \\  & Var. contrib. & 39.00\% & 11.00\% & 30.00\% & 0.14\% & 19.90\% \\ \hline \multirow{3}{*}{_Ethyl benzaldehyde_} & _Stand. effect_ & _1.000_ & \(-\)_1.000_ & \(-\)_1.000_ & _1.000_ & \multirow{3}{*}{93.30\%} \\  & _p-Value_ & _0.391_ & _0.391_ & _0.391_ & _0.391_ & \\  & _Var. contrib._ & _14.30\%_ & _14.30\%_ & _14.30\%_ & _14.30\%_ & _42.90\%_ \\ \hline \multirow{3}{*}{(E)-cinnamaldehyde} & Stand. effect & \(-\)0.414 & 1.242 & \(-\)1.656 & \(-\)0.207 & \multirow{3}{*}{40.00\%} \\  & _p_-Value & 0.707 & 0.302 & 0.196 & 0.849 & \\  & Var. contrib. & 2.29\% & 20.60\% & 36.60\% & 0.57\% & 40.00\% \\ \hline \multirow{3}{*}{6-Methyl-5-hepten-2-one} & Stand. effect & 0.016 & 0.016 & 0.379 & \(-\)3.838 & \multirow{3}{*}{16.80\%} \\  & _p_-Value & 0.988 & 0.988 & 0.730 & 0.031 & \\  & Var. contrib. & 0.00\% & 0.00\% & 0.80\% & 82.40\% & 16.80\% \\ \hline \multirow{3}{*}{\(\gamma\)_Butylrolactone_} & _Stand. effect_ & _1.000_ & \(-\)_1.000_ & _1.000_ & _1.000_ & \multirow{3}{*}{16.80\%} \\  & _p-Value_ & _0.391_ & _0.391_ & _0.391_ & _0.391_ & \\  & _Var. contrib._ & _14.30\%_ & _14.30\%_ & _14.30\%_ & _14.30\%_ & 42.90\% \\ \hline \multirow{3}{*}{Acetic acid} & Stand. effect & 20.805 & \(-\)0.813 & 11.375 & 23.103 & \multirow{3}{*}{16.80\%} \\  & _p_-Value & 0.000 & 0.476 & 0.001 & 0.000 & \\  & Var. contrib. & 39.40\% & 0.06\% & 11.80\% & 48.50\% & 0.27\% \\ \hline \multirow{3}{*}{Isovaleric acid} & Stand. effect & 6.826 & 2.246 & \(-\)1.155 & 0.381 & \multirow{3}{*}{5.35\%} \\  & _p_-Value & 0.006 & 0.110 & 0.332 & 0.729 & \\  & Var. contrib. & 83.00\% & 8.99\% & 2.38\% & 0.26\% & 5.35\% \\ \hline \multirow{3}{*}{Octanoic acid} & Stand. effect & 1.722 & 1.457 & \(-\)0.662 & \(-\)1.987 & \multirow{3}{*}{93.30\%} \\  & _p_-Value & 0.184 & 0.241 & 0.555 & 0.141 & \\  & Var. contrib. & 73.10\% & 15.20\% & 0.61\% & 0.33\% & 10.80\% \\ \hline \multirow{3}{*}{Decanoic acid} & Stand. effect & 3.250 & 1.207 & 1.061 & 3.100 & \multirow{3}{*}{19.90\%} \\  & _p_-Value & 0.048 & 0.314 & 0.367 & 0.053 & \\ \cline{1-1}  & Var. contrib. & 41.00\% & 5.65\% & 4.37\% & 37.30\% & 11.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of fermentation parameters on the production of different volatile compounds (aldehydes, ketones, acids). The \(Sc/Lt\) ratio had a significant positive impact on the production of carboxylic acids and esters (stand. effect 6.199 and 2.614, respectively) and a negative impact on alcohol
## 6 Discussion  
The RF with Bayesian hyperparameter optimization method presented in Section 4 shows better performance in terms of precision, recall, and accuracy of an urban land cover classification example. The RGB band features and full multispectral band features have been discussed and examined by three different classifiers including SVM, RF, and RF with Bayesian optimization. In order to evaluate the performance of different models, pixel-wise classification is used in this paper. Then, Equations (2) and (3) are used to estimate the evaluation values (accuracy, precision, and recall) from the confusion matrices. From the confusion matrices, all three classifiers show improved performance by incorporating more related band information. The classification accuracy of SVM is increased by 47.1%, RF is improved by 9.1%, while the RF with Bayesian optimization is enhanced by 10.4%. Compared with RGB bands, NIR bands and SWIR bands (multispectral band features) provide more precise results (the minimum overall precision provided by SVM is over 93.2%).  
Meanwhile, in terms of precision, recall, and accuracy, the RF with Bayesian hyperparameter optimization gives the best results. For the overall precision of an urban land cover classification example given in Section 3, the RF with Bayesian optimization is 0.5% higher than RF and 41.8% higher than SVM by using RGB band features, respectively. Simultaneously, the RF with Bayesian optimization is 1.8% higher than RF and 5.1% higher than SVM by using multispectral band features, individually. Moreover, quantitative results are also presented to compare the differences of the performance provided by different models through the classification map for labeled areas with all band features and full band features. Both classification maps show that the RF with a Bayesian hyperparameter optimization model generates the best land cover visualization results with less noises and error classified pixels.  
There are also a number of issues that are worth investigation when the proposed method is to be applied in real-world applications. For instance, the spatial resolution of Sentinel-2 satellite is about 10 m; as a result, some pixels (in particular the ones at boundaries of surface classes) are actually mixed pixels involving different surface classes. The classification performance is therefore not accurate enough for these mixed pixels and a better result may be obtained with a higher spatial resolution. In addition, the cloud may have adverse effects on classification performance. This can be partially addressed by either taking the satellite image with a low cloud coverage or taking the median value of the satellite images within a time interval.
# Iterative Receiver Design for the Estimation of Gaussian Samples in Impulsive Noise +
## 4 Signal Estimation
### Upper FG Half: Kalman Smoother  
Suppose that the lower part of the FG sends messages \(p_{u}(s_{k})\) consisting of a single Gaussian distribution, with mean \(\hat{s}_{u,k}\) and variance \(\hat{\sigma}_{u,k}^{2}\),  
\[p_{u}(s_{k})=g(s_{k};\hat{s}_{u,k},\hat{\sigma}_{u,k}^{2})\quad. \tag{27}\]  
Under this assumption and based on the SPA, the forward and backward Equations (17) and (18) along the upper line of the FG coincide with those of a Kalman smoother [18]. To compute the forward and backward messages, we assume that \(\eta_{f}\) and \(\eta_{b}\) are respectively the means of the Gaussian messages \(p_{f}(s_{k})\) and \(p_{b}(s_{k})\) and \(\sigma_{f}^{2}\) and \(\sigma_{b}^{2}\) are their variances. Equations (14), (17), and (27) yield:  
\[p_{f}(s_{k}) = \int g(s_{k-1};\eta_{f,k-1},\sigma_{f,k-1}^{2})g(s_{k-1};\hat{s}_ {u,k-1},\hat{\sigma}_{u,k-1}^{2}) \tag{28}\] \[\cdot g(s_{k};a_{1}s_{k-1},(1-a_{1}^{2})\sigma_{s}^{2})ds_{k-1}\] \[= g(s_{k};\eta_{f,k},\sigma_{f,k}^{2})\]  
in which \(\eta_{f,k}\) and \(\sigma_{f,k}^{2}\) can recursively be updated as:  
\[\eta_{f,k} = a_{1}\eta_{f,k-1}+a_{1}\frac{\sigma_{f,k-1}^{2}}{\sigma_{f,k-1}^ {2}+\hat{\sigma}_{u,k-1}^{2}}(\hat{s}_{u,k-1}-\eta_{f,k-1}) \tag{29}\] \[\sigma_{f,k}^{2} = (1-a_{1}^{2})\sigma_{s}^{2}+a_{1}^{2}\frac{\sigma_{f,k-1}^{2}}{ \sigma_{f,k-1}^{2}+\hat{\sigma}_{u,k-1}^{2}}\hat{\sigma}_{u,k-1}^{2} \tag{30}\]In the same way, the backward recursion can be obtained from (14), (18), and (27):  
\[p_{b}(s_{k}) = \int g(s_{k+1};\eta_{b,k+1},\sigma_{b,k+1}^{2})g(s_{k+1};\hat{s}_{u,k +1},\hat{\sigma}_{u,k+1}^{2}) \tag{31}\] \[\cdot g(s_{k+1};a_{1}s_{k},(1-a_{1}^{2})\sigma_{s}^{2})ds_{k+1}\] \[= g(s_{k};\eta_{b,k},\sigma_{b,k}^{2})\]  
where:  
\[\eta_{b,k} = \frac{1}{a_{1}}\eta_{b,k+1}+\frac{1}{a_{1}}\frac{\sigma_{b,k+1}^{ 2}}{\sigma_{b,k+1}^{2}+\hat{\sigma}_{u,k+1}^{2}}(\hat{s}_{u,k+1}-\eta_{b,k+1}) \tag{32}\] \[\sigma_{b,k}^{2} = \frac{1}{a_{1}^{2}}(1-a_{1}^{2})\sigma_{s}^{2}+\frac{1}{a_{1}^{2 }}\frac{\sigma_{b,k+1}^{2}}{\sigma_{b,k+1}^{2}+\hat{\sigma}_{u,k+1}^{2}}\sigma _{u,k+1}^{2} \tag{33}\]  
Thus, the message sent from the variable node \(s_{k}\) to the factor node \(p(y_{k}\mid s_{k},i_{k})\), here named \(p_{d}(s_{k})\), is Gaussian as well,  
\[p_{d}(s_{k})=p_{f}(s_{k})p_{b}(s_{k})=g(y_{k};\hat{\eta}_{k},\hat{\gamma}_{k}^ {2}) \tag{34}\]  
where, as in all Gaussian product operations, the variance and mean,  
\[\hat{\gamma}_{k}^{-2} = \sigma_{f,k}^{-2}+\sigma_{b,k}^{-2} \tag{35}\] \[\hat{\gamma}_{k}^{-2}\hat{\eta}_{k} = \sigma_{f,k}^{-2}\eta_{f,k}+\sigma_{b,k}^{-2}\eta_{b,k}\quad, \tag{36}\]  
are found by summing the precision (i.e., the inverse of the variance) and the precision weighted mean.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 5 Accuracy Validation
### Deformation Modeling Accuracy  
According to the literature [34], the magnitude of the residual phase can be used as an effective indicator of the degree of fit between the InSAR modeling monitoring results and the real deformation, which in turn reflects the modeling accuracy of the model. When the residual phase in the interferometric pair is smaller, the modeling accuracy is higher. The residual phase can be calculated from the difference between the differential interferometric phase and the phase component of the low-pass deformation, as shown in Equation (18). Here, the residual phases of the InSAR-3D-CTPIM model constructed in this paper and the linear model in SBAS-InSAR were counted and compared, and Figure 11 shows the corresponding residual phase values in the interferograms obtained by the two methods. As can be seen from Figure 12, the residual phases of both models were less than 0.7 rad, and the residual phases of InSAR-3D-CTPIM were less than 0.5 rad. By counting the STD of the residual phases of the two models corresponding to the high-coherence points, it can be concluded that the RMSE of the traditional linear model was 0.13 rad and that of the STD of InSAR-3D-CTPIM was 0.08 rad. In comparison, the modeling accuracy of InSAR-3D-CTPIM was improved by 34.3% compared with that of the traditional linear model, which indicates that InSAR-3D-CTPIM has higher accuracy in modeling the deformation process of the coal mine.
# Seismic Response Analyses of a Large-Span Powerhouse Cavern Considering Rock-Structure Interaction
## 4 Numerical Modeling
### 2D and 3D Finite Element Modeling  
Continuum modeling by the finite element method (FEM) with the Midas/GTS code was performed in two- and three-dimensional conditions. The implicit modified Newton-Raphson method in Midas/GTS code is used to capture the nonlinearity of the complex rock-structure system, in which the tangent stiffness is updated in each time step based on deformation [23]. Because of the dimension and complex geometry of this cavern, the three-dimensional analysis of the cavern was also performed with the purpose of validating the two-dimensional analysis, as shown in Figure 4. The seismic response of the underground cavern was studied by applying the above-selected accelerograms (Table 4).  
Zero-thickness Goodman interface elements are used to represent the rock-structure interaction behavior [24]. High normal and shear stiffnesses were set to avoid the occurrence of overlapping. To ensure that the most appropriate methods of modeling are used, the following assumptions/conditions are introduced:  
No sliding at the rock-structure interface is assumed to occur (no-slip condition);  
Kinematic rock-structure interaction effects are ignored;  
Appropriate mechanical damping, representative of the materials under study, has to be determined. The critical damping ratio is taken to be equal to 5%, according to the average value of natural material damping in dynamic conditions;  
For accurate representation of wave transmission through a numerical model, the spatial element size, I, must be smaller than approximately one-tenth to one-eighth of the wavelength associated with the highest frequency component of the input wave, which can be expressed by [25; 26]:  
\[1\leq\frac{\lambda}{8\sim 10} \tag{5}\]  
Appropriate boundary conditions should be applied in the analysis: in order to simulate an infinitely extended medium, viscous boundaries are introduced at both the vertical sides of the model;  
The recorded accelerograms are assumed directly applied at the bottom of the numerical model.  
In both the 2D and 3D models, the bottom boundary was fixed in the vertical direction and viscous boundaries were applied along the lateral boundaries. The filtered input motions were applied at the bottom boundary of the models.  
Figure 4: Reinforcement system adopted in the three-dimensional numerical model.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 5 Conclusions  
This paper introduces the YOLOv8 model and proposes an improved model, the EB-YOLOv8 model, to overcome the current problems of steel surface defect detection. The model adds the EMA module to make the model update and converge more smoothly, to reduce the noise in the gradient descent by weighted average model weights, to solve the problem of large data fluctuations in complex scenes, and to increase the stability of the model. The channel dimensions are also grouped into several sub-features to reduce computational overheads. The original PANet structure is substituted with the BiFPN in combination with Concat in YOLOv8, which achieves effective multi-scale feature fusion and increases its accuracy. EB-YOLOv8 combines these two structures to enhance the detection accuracy and stability of the model.  
In the field of steel defect detection, there has been relatively little research focused on imbalanced datasets like SSD, with most studies concentrating on well-distributed datasets. However, the distribution characteristics of samples significantly impact model performance. Therefore, future research should further investigate these issues to enhance model robustness and generalization capability.  
Conceptualization, M.P. and Y.L.; methodology, S.B.; software, M.P.; validation, M.P., Y.L. and S.B.; formal analysis, M.P.; investigation, Y.L.; re-sources, Y.L.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, Y.L. and S.B.; visualization, M.P.; supervision, Y.L.; project administration, Y.L. and S.B.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No. 21606099), the Innovative and Entrepreneurial Talents Foundation of Jilin Province (No. 2023 QN31), and the Natural Science Foundation of Jilin Province (No. YDZJ202301 ZYTS157, 20240304097 SF).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### HPLC Analysis  
All samples were filtered through 0.22 \(\upmu\)m polytetrafluoroethylene (PTFE) filter before the injection. HPLC analyses were performed on the Agilent 1200 Series HPLC system (Agilent, San Jose, CA, USA) equipped with a diode array detector (DAD) according to [PERSON] et al., with small modifications [26]. Polyphenols were separated on a Phenomenex C18 column (Kinetex 150 mm \(\times\) 4.6 mm, 2.6 \(\upmu\)m, 100 A) using the mobile phase, consisted of 2% HCOOH in H\({}_{2}\)O (solvent A) and methanol (solvent B) at a flow rate of 0.8 mL/min. Gradient conditions were as follows: 3-8% B linear 0-13 min, 8% B isocratic 13-18 min, 8-10% B linear 18-20 min, 10% B isocratic 20-45 min, 10-100% B linear 45-50 min, 100% B isocratic 50-54 min, 100-3% B linear 54-55 min, 3% B isocratic 55-60 min. The wavelength used for the quantitative determination of catechins and phenolic acids was 280 nm, while flavonoid derivatives were detected and quantified at 360 nm. Individual phenolic compounds were identified by comparing their spectral data and retention times with those of the authentic external standards. Calibration curve of external standard was used for their quantification. The calibration curves of standards were made by diluting the stock standards with methanol. Parameters of linear regression, LOD and LOQ for phenolic compounds by HPLC analysis are presented in Table 2.
## 6 Experiments
Processing time is calculated by the logs of the captured image data and the remoted voltage meter in the experiments. Figure 11: GIS map in the network simulator in the experiments. The results are shown in Figure 12; Figure 13. Firstly, Figure 11 shows the results of the data delivery rate in comparison with the epidemic DTN routing with the nondirectional antenna by DHCP configuration and the proposed DTN routing with the AAA system based on the results from the prototype system specifications. The results show the proposed AAA system is more effective than the nondirectional antenna because the delivery rate in the proposed AAA reaches nearly 95 percent even if there are only 25 vehicles. Moreover, even if the number of the vehicle in the city is increased, the percentage stays at nearly 100 percent on the proposed methods, while the nondirectional antenna group shows 80 percent in the delivery rate. Next, Figure 12 shows the results of the latency from the Takizawa City Office to Iwate Prefectural University. As with the previous results, the latency of the proposed AAA system is rapidly decreased to about 1000 s, and the latency keeps around 500 s when the vehicles are increased. On the other hand, the nondirectional antenna groups stay around 2000 s even if the number of vehicles is increased in the simulation. \begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & \multicolumn{1}{c}{**Setting Values**} \\ \hline Fixed Nodes & Two Stations; IPU (Iwate Prefectural University) and Takizawa City Office. \\ \hline \multirow{3}{*}{Mobile Nodes} & 25, 50, 75, 100, 125, 150 vehicles. \\  & The vehicles randomly move with 10–50 km/h, and the movement pattern is based on the random walk model. \\ \hline DTN & Epidemic vs. Data Triage(Only one level of the priority) \\ \hline \multirow{3}{*}{Wireless Interfaces} & Non-directional antenna with IEEE802.11b (2.4 GHz, 11 Mbps, 100 m) vs. The proposed AAA system with IEEE802.11b (Maximum Distance 2000 m) \\  & TTL (21,600 s) \\ \hline \multirow{3}{*}{Data} & Data are carried from IPU to Takizawa City Office. \\  & 0.5–1.0 MB data (text data) are created in every 50–60 s at IPU. \\ \hline Resources & 2 GB storage. \\ \hline Simulated Duration & 6 h (21,600 s). \\ \hline Process Time for the Connection & Non-directional antenna (5 s for DHCP configuration) vs. Proposed AAA (1 s for the beam-forming and the wireless establishment). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The scenario of the network simulator in the experiment. Figure 12: Results of the data delivery rate in the experiments. The results indicate the effectiveness of the proposed methods in the winter road surveillance in the local areas and the additional experiments, including the evaluation of the proposed DTN routing in future studies.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## The Proposed Scheme
### RST Attack Correction Based on SIFT
#### Notation Attack Correction  
The image is rotated by a certain degree, resulting in information destruction and loss. The match between rotated image and watermarked image is shown in Figure 5.  
The two keypoints of watermarked image are set as \(i_{\text{w}}\) and \(j_{\text{w}}\) separately, and two keypoints of rotated image are set as \(i_{\text{r}}\) and \(j_{\text{r}}\). According to the basic mathematics, the vector can be obtained by the calculation on coordinates between two points. Thus, the vector in the watermarked image and rotated image can be set as \(i_{\text{w}}^{\rightarrow}\) and \(i_{\text{r}}^{\rightarrow}\), respectively. For the correction, the rotation angle of the kth matching point is recorded as \(\phi_{k}\). So far, the corrected angle of rotation can be given in Equation (12):  
\[\beta_{\text{c}}=\frac{1}{m}\sum_{k=1}^{m}\phi_{k},\;\phi_{k}=\arccos\left( \frac{i_{\text{w}}^{\rightarrow}j_{\text{w}}\cdot i_{\text{r}}^{\rightarrow} j_{\text{r}}}{\left|i_{\text{w}}^{\rightarrow}j_{\text{w}}\right|\left|i_{ \text{r}}^{\rightarrow}j_{\text{r}}\right|}\right), \tag{12}\]  
where \(m\) is the number of valid matching points. Equation (12) means that any two pairs of matching points are picked out to calculate the rotation angle, and then the average value of the angles' sum is calculated. In this context, the corrected angle \(\beta_{\text{c}}\) is obtained. Thus, the attacked image at the receiving end should be rotated by the angle of \(\beta_{\text{c}}\) for the rotation correction.
# Unraveling the Complexity of Plant Trichomes: Models, Mechanisms, and Bioengineering Strategies
### Trichomes: Non-Glandular and Glandular Variants  
Trichomes are the most prominent epidermal appendages. They display a remarkable diversity of function and structure [12]. In terms of their ability to synthesize and secrete metabolites, they can be broadly categorized into non-glandular trichomes (NGTs) and glandular secretory trichomes (GSTs) [13] (Table 1).  
NGTs are found in angiosperms, gymnosperms, and mosses and function primarily to deter herbivores and insects. Cotton fibers are an example of a specialized non-glandular trichome found in cotton plants that provides pest resistance against _Silverleaf whiteflies_[14]. As a result of their ability to form physical barriers, NGTs play a crucial role in protecting plants from external threats.  
The GSTs, on the other hand, are specialized in synthesizing, secreting, or storing a wide variety of secondary metabolites with significant ecological, economic, and medicinal applications, as well as serving as a defense against external pathogens and pests [15]. _Cannabis sativa, Solanum Lyopersicum_, and _Artemisia annua_ exhibit a wide variety of trichome morphologies, ranging from stalked and bulbous structures to multicellular structures. Among the most effective malaria drugs, artemisinin is synthesized in the glandular secretory trichomes of _Artemisia annua_[16]. Similarly, cannabis glandular secretory trichomes produce cannabinoids, including tetrahydrocannabinol (THC) and cannabidiol (CBD), which have a number of therapeutic uses [17].  
In addition, GSTs are capable of producing menthol from mint, phenolic compounds from rosemary, and volatile oils from _Schizonepeta tenuifolia_[18]. The tomato GSTs (type IV) secrete sticky substances that trap fungal gnats and immobilize small arthropods, demonstrating their dual roles in plant defense and ecology [19].