esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
(1) When dealing with complex structures and topography such as loess plateaus, the targeted techniques employed by the CGG processing system, including tomographic static correction, effectively address the long-wavelength static correction issue. Through refined stacking velocity analysis and residual static correction iterations consistent with surface conditions, the shorter wavelength static correction problem is effectively tackled. Additionally, utilizing stacked velocity gathers and profiles for quality control ensures accurate velocity extraction. Guidance from geological variations aids velocity analysis, ensuring the velocity field conforms to geological principles, thereby enhancing velocity interpretation precision. After multiple iterations, the optimal velocity model is obtained, enabling seismic record processing to more accurately reflect subsurface structures, thus providing a reliable foundation for subsequent detailed interpretation of mined-out areas.  
(2) This paper employs 5D interpolation techniques to interpolate conventional seismic data, enhancing the spatial sampling rate and coverage to improve seismic imaging quality. 5D interpolation effectively reduces the occurrence of \"windowing\" phenomena in seismic profiles, especially in shallow coal seams. This method's advantage lies in enhancing the signal-to-noise ratio of the seismic data, resulting in the clearer imaging of subsurface structures and aiding accurate identification of mined-out areas and their boundaries. Regularization processing of seismic data in the OVT domain is performed to eliminate disparities between actual and ideal spatial sampling attributes, crucial for ensuring seismic migration remains unaffected by spatially irregular sampling, thereby enhancing imaging accuracy and resolution of subsurface structures. Through 5D interpolation and OVT domain processing, seismic data quality and sampling rates are effectively improved, resulting in clearer and more accurate subsurface imaging.  
(3) Consistency and feature variations of spectral decomposition slices: different frequency spectral decomposition slices exhibit high overall consistency. The 45 Hz spectral decomposition profile shows a distinct energy loss band between mined-out areas and normal coal seams, directly indicating mined-out area boundaries. Spectral decomposition slice characteristics of the mined-out area boundaries are consistent with those of actual excavated mined-out areas but exhibit different energy strengths.
## 3 Proposed Method
### Generative Model for Data Adjustment  
In various cases, ECG datasets often face data imbalance in minor classes. To generate synthesized data, we use a generative model, GAN [25; 26; 27]. Generally, GAN is formulated into two modules: (1) generator and (2) discriminator. The generator takes source samples with random noise, \(z\). This random noise \(z\) learns the distribution of the source data \(\mathcal{D}_{s}=(\mathcal{X}_{s},\mathcal{Y}_{s})\) and outputs synthetic samples \(\mathcal{D}_{f}=(\mathcal{X}_{f},\mathcal{Y}_{f})\). The output of the discriminator is usually the synthetic and original data. The discriminators' objective is to distinguish the synthetic and original data precisely. Basically, the generator is trained to generate synthetic data to classify the original by the discriminator. On the other hand, the discriminator tends to recognize the synthetic and original data as accurately as possible. The training objective of GAN can be described as the value function \(\mathcal{V}(\mathcal{D},\mathcal{G})\), where the generator value function \(\mathcal{V}(\mathcal{G})\) is expected to maximize and the discriminator value function \(\mathcal{V}(\mathcal{D})\) is expected to minimize:  
\[\begin{split}\min_{\mathcal{G}}\max_{\mathcal{D}}\mathcal{V}( \mathcal{G},\mathcal{D})=E_{x\sim P_{data(x)}}[log\mathcal{D}(x)]\\ +E_{z\sim P_{z(z)}}[log(1-\mathcal{D}(\mathcal{G}(z)))].\end{split} \tag{1}\]  
Here, \(\mathcal{D}(x)\) and \(\mathcal{G}(z)\) are denoted as the probability of \(x\), which belongs to the original data distribution \(P_{data}\), and a substantial mapping function obtained from the noise vector to the generated vector, respectively. However, the optimal parameters can be obtained by maximizing and minimizing the value function of the discriminator and generator, respectively. Discriminator \(\mathcal{D}\) is trained with both positive and negative ECG samples in each iteration. And the generator \(\mathcal{G}\) is updated with policy gradients two times, while \(\mathcal{D}\) is updated once; we consider this strategy from [25]. Training converges until \(\mathcal{G}\) is indistinguishable. Generated synthetic samples on the class-deficient category alleviate this issue, and certainly make the dataset considerably balanced. We use the balanced dataset in the local structure module (C). Adversarial loss and cross-entropy loss are adopted for the GAN module. Table 1 depicts the model description.  
Figure 2: Illustration of our SF-ECG framework. Here, GAN model is used to generate synthetic samples from the training distribution, and the local structure module clusters the generated samples in a local neighborhood. Lastly, another GAN model exhibits the prediction without explicitly requiring the source data.
## 5 Experimental Results
### Implementation  
The selection and configuration of appropriate hardware is essential for the effective execution of video analysis models. To accommodate the processing demands of analyzing large volumes of video data, a system with robust technical specifications was selected. The incorporation of a Graphics Processing Unit (GPU) was necessary to accelerate parallel processing and improve inference times for the models. The technical specifications of the equipment used for conducting the experiments are detailed in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **False Positive** & **False Negative** \\  & **Rate** & **Rate** \\ \hline RWF2000 & 16.0\% & 21.0\% \\ \hline RLVS & 22.5\% & 18.2\% \\ \hline CrimeDetection dataset & 16.1\% & 10.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Resulting false positive rates and false negative rates.  
The models demonstrated an average prediction time of approximately 90 ms, indicating their suitability for real-world applications. This result is particularly significant for public safety and video surveillance applications, where rapid detection and response to anomalous events are crucial. This performance metric confirms that the proposed models can be effectively deployed in real-world environments, thereby enhancing public safety and contributing to the overall well-being of the community.
## 2 Physical and Mechanical Test
### Rock Mechanics Test
#### 2.4.2 Water Effect  
The softening coefficient of phyllitic slate is generally about 0.3, that is, the uniaxial compressive strength of saturated slate is only 30% of that of natural intact slate. Rocks are greatly affected by water and have a strong softening effect. Rocks are easy to soften and break when immersed in water [40]. After tunnel excavation, the surrounding pressure of the loose rock mass is low, and the strength will decrease seriously once the rock meets water. Therefore, it is crucial to control the water entering the surrounding rock. When there is water in the tunnel, we should drain the water out of the cave as soon as possible to avoid water immersion in the rock mass.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rock Types**} & **Weathering Degree** & \begin{tabular}{c} **Unconfined Compressive** \\ **Strength (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Elastic** \\ **Modulus** \\ **(GPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Poisson's Ratio** \\ \end{tabular} \\ \hline \multirow{2}{*}{Phyllitic slate} &
\begin{tabular}{c} Micro-weathering \\ or new rock mass \\ \end{tabular} & 6.9–25.3 & 0.1–0.2 & 0.8–1.2 & 0.32–0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of rock mechanics test.  
Figure 6: Rock mechanics test: (**a**) laboratory rock test; (**b**) point load test.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 8 Sink Trajectory  
Successive retransmissions in cooperative routing lead to excessive energy consumption of the network nodes, which is unfavorable due to limited battery capacity of nodes. In order to mitigate this issue, we use mobile sink which moves on an elliptical trajectory [44]. AUV cooperates with MNs and GWs such that it goes to the near locality of GWs, receives the data packets and conveys them towards the surface sink. In this way, the overall communication distance is minimized. We consider an elliptically trajectory with horizontal major axis, such that;  
\[\frac{(x-x_{o})^{2}}{M_{AUV}^{2}}\ +\ \frac{(y-y_{o})^{2}}{m_{AUV}^{2}}\ =\ 1 \tag{16}\]  
and;  
\[M_{AUV}\ =\ 2\ \times\ m_{AUV} \tag{17}\]  
Foci points for non centered ellipse are defined at \((\pm c,y_{o})\) and \((\pm c,y_{o})\) and vertices of ellipse are at \((\pm a,y_{o})\) and \((\pm a,y_{o})\) whereas, \(c\ =\sqrt{M_{AUV}^{2}\ -\ m_{AUV}^{2}}\) and \(x\) and \(y\) show the running time coordinates of AUV and they are scaled using the following equations:  
\[x\ =\ x_{o}\ +\ M_{AUV}\ Cos(\theta) \tag{18a}\] \[y\ =\ y_{o}\ +\ m_{AUV}\ sin(\theta) \tag{18b}\]  
where \(\theta\) is the angle that AUV makes with the center of ellipse as shown in Figure 6.
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 2 Properties and Potential of Plant Growth-Promoting Rhizobacteria (PGPR)
### ACC Deaminase  
The occurrence of the ACC deaminase enzyme is another important direct mechanism of plant growth promotion [70]. The action of this enzyme is carried out with the inactivation of the ethylene precursor ACC, generating ammonia and alpha-ketobutyrate. High levels of ethylene can inhibit plant growth and even kill them. During saline or other environmental stress, ACC synthase and ACC oxidase activity increase ethylene synthesized by the plant. Therefore, the bacterial enzyme ACC deaminase helps plants decrease the negative effects of stress by facilitating adaptation and survival [71].  
It is also interesting to underline the interaction between the activity of the ACC deaminase enzyme and the action of auxin IAA because the latter can activate the transcription of the ACC synthase enzyme, which leads to an increase in ethylene levels. The increase in the level of ethylene leads to an inhibitory response to the production of IAA, which inhibits the positive effect on plant growth. However, the presence of the ACC deaminase enzyme, which lowers the level of ethylene, counteracts the inhibition, as mentioned earlier. Many of the PGPB recognized to positively affect metals contamination in constructed wetlands can synthesize both ACC deaminase and IAA [72].
## 3 Results
### Analytical Figures of Merit  
Applying the final conditions for TFME, calibration curves were constructed using an 0.01 M NaCl matrix spiked with analytes at concentrations of 50-200 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for CPS and TCS and 500-4000 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for TCS. The analytical figures of merit are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **CPS** & **TCS** & **AHTN** \\ \hline Calibration Curve & y = 9715.6x – 138,832 & y = 8869.4x + 478,841 & y = 13,362x – 370,528 \\ Determination & 0.9991 & 0.9853 & 0.9993 \\ coefficient (R\({}^{2}\)) & 50–200 & 500–4068 & 50–200 \\ LOD (\(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\)) & 1 & 10 & 1 \\ LQQ (\(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\)) & 5 & 50 & 5 \\ \hline \end{tabular}
\end{table}
Table 4: Quality parameters for the proposed TFME method.  
Figure 6: Elution results obtained using 1 mL of ethyl acetate under orbital stirring and ultrasound (\(n\) = 4). Volume of solution of organic compounds 10 mL spiked at 203 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for CPS and AHTN and 2000 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\) for TCS.  
The linearity of the calibration curves was verified using an analysis of variance (ANOVA). All the calibration curves exhibited small \(p\)-values, indicating a strong correlation between the independent and dependent variables. LODs were calculated using a signal-to-noise (S/N) equal to 3 criteria. The LODs obtained, although 10 times higher, are not in disagreement with those reported in an earlier report by our group [15] considering that, in the previous work, exhaustive extraction conditions (6 h), a larger volume of the water sample, and a more sensitive instrument were employed. The main advantage here is that  
Figure 7: Comparison of the results for three different films with 10% MWCNTs: (**a**) CPS; (**b**) TCS; (**c**) AHTN. Volume of solution 10 mL spiked at 203 \(\upmu\)g L\({}^{-1}\) for CPS and AHTN and 2000 \(\upmu\)g L\({}^{-1}\) for TCS. The number of replicates for each film was 3, except for TCS R3 (\(n=1\)).  
shorter extraction times are required, and there is less contamination due to the leaching of the plasticizer. The values found are promising and could be further lowered by modifying some of the experimental conditions, in particular by increasing the film area and reducing the volume of the eluent.  
A chromatogram at the lowest concentration from the working range studied is shown in Figure 8. The compounds were well identified, with negligible noise. It is worth mentioning the pronounced tail of the chromatographic peak for TCS, which makes the integration more difficult with greater variability of the results. This is also evident from the lowest determination coefficient in Table 4.
## 3 Results
### Changing the Map and Its Obstacles  
In the following example from Figure 16 new obstacles (cyan circles) are integrated into the map. An update of the paths shows an automatic change of the paths including the detection of the passage width.  
The new paths are located further in the center of the geometry and can be built as a digital twin as explained in Section 3.5. All newly created paths are also represented topologically so that in addition to the suitable short paths, the alternative possibilities are calculated. In addition to the appearance of new obstacles, the map itself can also be extended. Figure 17 shows the path generation of an extended map with new tasks, obstacles, and robots.  
Figure 16: Adding new obstacles on the map: RGB map, newly generated paths, newly estimated clearance width.  
Figure 15: Repositioning the robot: map with the robot not on track, repositioning path for the robot.  
Likewise, the digital twin, including the clearance widths, for the new area is automatically acquired. Figure 18 shows the digital twin, the Euclidean distance transformation, and the additional clearance width of the new path.  
In addition to expanding the map and placing obstacles, path generation can also be analyzed for noisy input images. In the map below, there are 1-pixel holes in the traversable area. These holes can be removed in a preprocessing step, but for robustness analysis, they should be left unchanged. The generated paths in Figure 19 avoid the holes as they topologically contribute to the initial image.  
The results show that noise-free images should be applied for path generation. Otherwise, as shown in Figure 18, numerous branches are created, complicating the resulting digital twin. However, due to the robustness of skeletonization, paths can be found that accomplish the task while avoiding individual holes. Therefore, this method is very well suited to respond to changing inputs. While the map layouts in Figures 17 and 18 are very  
Figure 17: Map extension: RGB map, skeletonization of the extended map, path generation of the extended map.  
Figure 18: Digital twin for the extended map: graph generation, Euclidean distance, clearance width.  
Figure 19: Noisy map: path generation, Euclidean distance transformation, clearance width.  
simple, Figure 20 shows the application of path generation using a maze as an example. There are several tasks, a robot, and a blocked path.  
The paths including the path widths can be determined automatically. If a path is completely blocked, no path is created. It also can be determined automatically whether a task can be reached at all. The same procedure can be used for a more complicated maze. Figure 21 shows the path definition, the graph for the digital twin and the Euclidean distance transformation of the image for a more complicated maze.  
The path to the red marked task has been blocked so that the is completely unreachable for all other tasks.
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
## 2 Materials and Methods  
### Materials  
The raw material of silkworm excrement biochar (BC) was collected from the Guangxi Silk-Worm Technology Promotion Centre. The procedure of preparing biochar (BC) has been reported in our previous study [12]. Briefly, after dried naturally in a plastic tray, the silkworm excrement was transferred into a crucible. Then, the crucible was then heated in a muffle furnace (Nabertherm LT40, BRE, GER; heating rate: 20 \({}^{\circ}\)C/min; ventilatory rate: 2 mL N2/min) at 500 \({}^{\circ}\)C for 3 h, and then cooled to room temperature. BC was pulverized and stored in a glass container prior to characterization and pot experiment. The basic physicochemical properties of BC were determined, and the results were shown in Table 1.  
Ferrous sulfate (Fe(II)) was purchased from Shanghai Sinopharm Chemical Reagent Co. Ltd., China, which is analytical grade.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Item**} & \multirow{3}{*}{**pH**} & \multirow{3}{*}{\begin{tabular}{c} **Total** \\ **Organic** \\ **Carbon** \\ **(TOO \%** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Caution** \\ **Exchange** \\ **Capacity** \\ **(GC)C** \\ **end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Available** \\ **N mg/kg** \\ **(EC)** \\ **end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Available** \\ **P mg/kg** \\ **(ES)** \\ **end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Available** \\ **N mg/kg** \\ **(ES)** \\ **end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Available** \\ **As mg/kg** \\ **(ES)** \\ **end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Available** \\ **C mg/kg** \\ **(ES)** \\ **end{tabular} } \\  & 7.6 & 14.6 & 9.25 & 131.5 & 26.5 & 122.0 & 27.3 & 7.7 & 1.8 & 3.0 \\ Biochar & 9.4 & 227.8 & 71.59 & ND & ND & ND & ND & ND & ND \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil and biochar characteristics.  
The contaminated soil was classified as a typical calcareous soil and collected in a paddy field adjacent to a mining in Daxin, Guangxi Zhuang Autonomous Region, China. Soil samples were dried naturally in shade. The pH, total organic carbon (TOC), cation exchange capacity (CEC), available N, available P, available K, As, and Cd contents of soil were determined, and the results were shown in Table 1.  
The rice variety (_Oryza sativa_ L.) is Baiyou1191, which purchased from the agricultural science and technology market of Nanning, Guangxi Zhuang Autonomous Region, China. The entire growing period of rice is about three months.  
The concentrations of Cd and as in the solutions were determined by inductively coupled plasma mass spectrometry (ICP-MS, iCAP Q, Thermo Scientific, MA, USA). ND indicates lower than the detection limit, Cd is 0.0125 mg/kg, and As is 0.025 mg/kg.
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Glucose or Xylose
#### 4.1.3 Biosynthesis of 3-HP via the \(\beta\)-alanine Pathway  
Researchers have also investigated the production of 3-HP via the \(\beta\)-alanine pathway, and the process of 3-HP synthesis from glucose or xylose as substrates has been developed using this strategy. [PERSON]'s team [60] constructed the biosynthetic pathway from \(\beta\)-alanine to 3-HP by overexpressing the optimal enzyme combination of BPAT from _B. cereus_ and HPDH from _E. coli_, and the engineered strain produced 13.7 g/L of 3-HP from glucose in fed-batch fermentation. Later, the same team introduced xylose utilization genes in _S. cerevisiae_ and then tested the ability to produce 3-HP from glucose or xylose via the \(\beta\)-alanine pathway or the malonyl-CoA pathway. It was shown that the \(\beta\)-alanine pathway was more advantageous in producing 3-HP from xylose, while the malonyl-CoA pathway produced a higher 3-HP titer using glucose as substrate [79]. The key precursor for 3-HP synthesis, \(\beta\)-alanine, can be produced through transamination from oxaloacetate in the anaperotic pathway, as well as from succinate in the TCA cycle. [PERSON] et al. [38] constructed the \(\beta\)-alanine pathway in a high succinate-producing strain of _E. coli_ via the overexpression of endogenous aspartate aminotransferase (AspA) and heterologous L-aspartate-\(\alpha\)-decarboxylase (PanD) from _C. glutamicum_, and the appropriate enzyme combination to convert \(\beta\)-alanine to 3-HP [80]. The engineered strain produced 31.1 g/L of 3-HP from glucose in fed-batch fermentation. Compared with other 3-HP biosynthesis pathways, the \(\beta\)-alanine pathway has a low titer; therefore, it is urgent to develop enzymes with high catalytic activity which can be used to more efficiently produce 3-HP in the \(\beta\)-alanine pathway.
# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
###### Abstract
In [24], an end-to-end YOLO network model is used and a more accurate position of component defects in the transmission line is obtained by adding a coordinate attention module. In [25], the features of insulators with different specifications were extracted based on a deep neural network. The INSU-YOLO detection method was proposed, and the insulator defect dataset was constructed to avoid the problem of network overfitting caused by insufficient data. During the training process of the selected target detection network model, the training results are susceptible to the influence of the dataset, where the one-stage network model improves the detection speed compared to the two-stage network model, but its detection accuracy needs to be improved. To solve the problems of insufficient accuracy and lack of robustness in the process of insulator defect fault detection, this paper proposes a YOLOv5 based on a receptive field module and multiscale. The main work is as follows: anchor frames are obtained that match the size of the detected target by k-means clustering to improve the detection accuracy of the network for target objects with different proportions; the low-level detail features are extracted from the network and fused with the deepest semantic features to the small-scale detection layer designed in this paper to improve the detection performance of the network model for small-area targets; a lightweight backbone network is built using the GhostNet [26] lightweight network to reduce convolution operations and improve the real-time performance of the model while ensuring detection accuracy; the channel receptive field block (CRF) receptive field module that integrates channel information is designed at the network head to replace the original SPP module [27], integrate channel information, fuse multiscale feature information, and use dilated convolution to reduce the calculation of redundant information.