esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Experimental Situation  
The experimental scenarios of this study are the energy load data of local residents in spring (autumn), summer, and winter; hence, there are three sets of experimental modules in this research. The experimental results obtained by the proposed SSO-DE were compared with those found by other algorithms including hybrid of PSO-DE, hybrid of ABC-DE, and hybrid of iSSO-DE. The four abovementioned algorithms all execute 200 iterations in different seasonal data as the stopping conditions of the algorithm and repeat the test 10 times to obtain the average, best, worst, standard deviation, and running time of the fitness value. The experiment used python3.7.1 to write the program, equipped with AMD Ryzen5 1600 CPU, 16 GB RAM, and a GTX1060 graphics card; the parameter settings of all algorithms are based on the computer environment test used in this research and the parameter settings with better experimental results in the process of the experiment are shown in the following Table 16.  
Before the experiment, this study first used SSO alone to compare with SSO-DE by various \(CR\) values set in order to verify the effectiveness of using DE in SSO-DE for local search. In three different seasons of data, each setting was set to 150 particles, 200 iterations, and 10 repeated experiments. The parameter configuration of SSO adopted the best parameter setting of each season in the experimental design of the previous section and the difference lies in the parameter settings of DE, which were uniformly set to \(Y=100\%\) and \(F=0.1+0.0005\times N_{gen}\). The settings are as shown in Table 17.  
The comparison results of SSO and SSO-DE in each season and the box and whisker diagrams are shown in Tables 18-20, where each method represents different algorithms, \(CR\) is the evolution parameter for DE, best is the best fitness value obtained by the algorithm in ten repeated experiments, worst is the worst fitness value, mean is the average fitness value of ten repeated experiments, and, finally, std represents its standard deviation, Figures 7-9, respectively.  
In spring (autumn) and winter, the difference in performance between SSO and SSO-DE was very obvious, that is, the calculation efficiency of SSO-DE was far superior to SSO regardless of the setting of the \(CR\) value of DE. In the summer experiment, the performance of SSO-DE was also slightly better than SSO although the gap of difference in performance was not as huge as those in spring (autumn) and winter. In addition, the setting of the \(CR\) value of DE in SSO-DE between 0.7 and 1 had the most eye-catching performance. These are the reasons that the initial \(CR\) value in the previous section of the experimental design was set to 0.85 and gradually increased to 0.95 with the number of iterations.
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Empirical Regression Models for GPP, ER, and NEE
EVI2, NDWI, LST, PPFD, WTD, and W\({}_{\text{s}}\) were fitted as predictor variables against EC NEE measurements. However, this approach wasnot selected for further analysis, because PPFD and WTD were not available at all sites and years, and it did not provide a consistent improvement compared to the non-linear NEE model (Equation (9)). See Supplementary Materials for more details. In the first stage of the analysis, the free parameters for the GPP, ER, and NEE models were estimated separately for each site using all the available years of flux data (Table S4), and in the second stage, the parameters were estimated for all the sites together (Tables S5-S7). To obtain one parameter set for all the sites, leave-one-out cross-validation (LOOCV) was used, i.e., the models were fitted using data from all the sites except for one site-year, which was used to validate the fitted models. After all the LOOCV runs, the mean model parameter values were applied to all the sites.
# Typhoon Track Prediction Based on Deep Learning
## 2 Materials and Methodology
### Typhoon Dataset  
#### 2.1.1 Dataset Acquisition  
Because of the different levels of development in each country and the different requirements for the collection of typhoon-related data, the typhoon-related observation data recorded in each country vary in type and quality. Therefore, the best track data set of typhoons in the northwest Pacific Ocean from the official website of the National Oceanic and Atmospheric Administration (NOAA) is selected to collect the information from observatories in several countries around the world. This dataset contains 239,594 data on the paths of all typhoons formed in the northwest Pacific Ocean from 1949 to 2021 and meteorological variables affecting the paths of typhoons. Because the research object is Hainan, only typhoons generated by the South China Sea region and requiring a relatively short landfall time are studied, so first, we need to filter their data and construct a new dataset from the data of typhoons formed by the South China Sea region among them. At the same time, because the data sources in this dataset are complex and many of the data types in it represent the same meaning, we need to merge data from these data variables that come from multiple institutions but have repeated meanings. For the convenience of presentation, we call the dataset obtained after data filtering and merging the best path dataset of typhoons in the South China Sea. The specific composition of this dataset is shown in Table 1.  
From Figure 2, we can find the data types in the dataset are not the same, so it is necessary to digitize the character type data except for the tropical cyclone number and typhoon name, i.e., to convert the data types to a numeric type. The purpose of converting the data types is to facilitate the Granger causality test and to ensure that all data in the dataset can be input as a feature that can be learned by the neural network model. At this point, the dataset to be used in this study, the best path dataset of typhoons in the South China Sea, has been constructed. The dataset includes the path information and multiple variables of all typhoons generated by the South China Sea region from 1949 to 2021.
## 3 Results
In the AEF observation experiment at Beijing Thirteen Hill Station, the researchers use the EFM as described above. The instrument is grounded well and mounted on top of a 2.5 m high cottage with a roof that is sufficiently wide and flat, surrounded by no grass or other protruding objects, with the nearest trees more than 10 m away and no shading; thus, these environmental factors do not affect the near-surface AEF measurements. Sunlight is plentiful here, so the EFM is powered by solar energy, and the support mast and other instrument details are the same as those used in Antarctica. After using a similar method as described above and a fair-weather criterion, the authors selected 37 fair days from a period of 5 months, as detailed above (1 March-1 August 2022). These two different hourly average curves are shown in Figure 5. The vertical coordinate is the percentage of each AEF value/mean AEF (%), and the horizontal coordinate indicates the time in UT. Then, the blue line shows the percentage curve of daily variation for Antarctica, while the red line shows the percentage curve of daily variation for Beijing. The error bar corresponds to the standard deviation of the instrument. For the average daily variation curve in Beijing, the curve is a \"double-peak, double-valley\" curve, with the initial peak value of 112% of the mean AEF (0.19 kV/m) at 7:00 UT and the other peak value of 116% of the mean AEF (0.19 kV/m) at 24:00 UT, the minimum valley value of 79% of the mean AEF (0.13 kV/m) at 4:00 UT and the other valley value of 86% of the mean AEF (0.14 kV/m) at 15:00 UT. From 4:00 UT to 7:00 UT, the aerosols gathered near the ground. Additionally, the aerosol concentration and AEF increased, with the maximum peak appearing at approximately 7:00 UT. From 7:00 UT to 15:00 UT, due to strong solar radiation, the vertical transport of thermal convection and turbulence was active, aerosols were continuously transported upward from near the ground, the near-surface aerosol concentration continuously decreased, and the near-surface AEF reached the minimum valley at 15:00 UT. Then, from 15:00 UT to 24:00 UT, the upper aerosol concentration was greater than that near the ground. Due to atmospheric pressure, aerosols were vertically transported, and the aerosol concentration near the ground again increased. Then, the AEF also increased, reaching another peak at 24:00 UT. From 24:00 UT to 4:00 UT, the atmosphere gradually stabilized, and the aerosol concentration decreased. In addition, the AEF decreased to another valley at 4:00 UT. Figure 5: Results of the atmospheric electric field daily variation comparison between the Antarctic Chinese Zhongshan Station and Beijing Thirteen Hill Station. The vertical coordinate is the percentage of atmospheric electric field/mean atmospheric electric field, and the horizontal coordinate indicates UT. The red curve represents the percentage curve of daily variation at the Antarctica Chinese Zhongshan Station, while the blue line represents the percentage curve of daily variation at the Beijing Thirteen Hill Station. Error bars represent the corresponding standard deviations.
## 3 Results  
### Comparison of Cellular Responses between COST-Jet and nspDBD Treatment of Jurkat T Lymphocytes  
#### 3.1.1 COST-Jet and nspDBD Treatment Cause Comparable Reductions in Jurkat T Lymphocyte Viability  
To establish a common basis for comparing the two plasma sources, we sought treatment parameters necessary to achieve a predefined biological endpoint--comparable reductions in viability.  
Jurkat cells were treated by plasma from the nspDBD [33] or the COST-Jet [34] (He + 0.6% O\({}_{2}\)), using conditions described in previous studies (Table 1). In these studies, reduced viability was correlated with frequency for the nspDBD and treatment time for the COST-Jet; thus, we treated from 30-75 Hz with the nspDBD and up to 5 min with the COST-Jet. Cells were analyzed 24 h post-treatment. The propidium iodide (PI) viability assay was used to identify plasma parameters for each source that caused comparable reductions in cell viability.  
Jurkat cell viability, normalized to the untreated controls (for the COST-Jet controls, cells were exposed to the feed gas only), was reduced with increasing frequency for nspDBD and increasing treatment time for the COST-Jet. A 5-min exposure to the feed gas (He + 0.6% O\({}_{2}\)) did not affect cell viability, in agreement with other COST-Jet studies [34; 35].  
As shown in Figure 2, two operating conditions were identified between the two plasma sources where cell viability was comparable:  
1. 2-min for the COST-Jet (93.7% \(\pm\) 3.9%) and 45 Hz for the nspDBD (89.8% \(\pm\) 10.01%)
2. 4-min for the COST-Jet (65.7% \(\pm\) 3.4%) and 75 Hz for the nspDBD (61.9% \(\pm\) 9.4%).  
These results demonstrated that completely different plasma devices could be operated to achieve the same biological endpoint. These two discharge conditions were used as the basis for further comparative investigations because similar responses were observed despite the fact that the cells were exposed to different magnitudes of species and conditions unique to each source (gas flow for the COST-Jet; charged species, UV radiation, and electric field for the nspDBD).
# Advances in Tissue Engineering for Disc Repair
## 3 Tissue Engineering for IVD Regeneration
### Tissue Engineering for AF and NP Restoration and Maintenance
#### 3.2.3 NP-AF Regeneration and Tissue Engineering  
NP-AF tissue engineering combines two approaches: NP replacement and AF repair. There are three ways to manufacture NP-AF, namely, using NP and AF cell-seeding scaffolds, integrated biphasic NP-AF scaffolds, and scaffolds made with decellularized natural IVD [11]. Scaffolds seeded with NP and AF cells were separately prepared and assembled into a composite construct. [PERSON] et al. manufactured a biphasic construct using electrospun MSCs seeded on a PLLA scaffold and HA hydrogel [96]. [PERSON] et al. generated a biphasic NP-AF scaffold with integrated collagen and glycosaminoglycans. The biphasic scaffold was composed of collagen-glycosaminoglycan, which coprecipitates as an NP-like core, and encapsulated in multiple lamellae of photochemically cross-linked collagen membranes that made up AF-like lamellae [79]. This scaffold exhibited mechanical characteristics similar to those of native discs with a ring-independent height recovery of 82-89%. [PERSON] et al. generated a scaffold consisting of chondrocytes and AF cells, which were respectively seeded into a scaffold consisting of hydrogel in the center and silk protein in the periphery, respectively [97]. [PERSON] et al. manufactured an IVD scaffold by inversely reconstructing the structure of native IVD and bioprinting bacterial cellulose nanofibers using a high-throughput-optimized micropattern screening microchip in rats [98]. [PERSON] et al. made a 70% endogenous cell-removing scaffold that preserved the glycosaminoglycan content, collagen fibril structure, and mechanical properties of the IVD by altering chemical and physical decellularization [99]. [PERSON] et al. created a natural NP-AF composite scaffold using decellularized bovine tail IVD and confirmed the presence of type II collagen and glycosaminoglycan in the NP region and the native angle-ply collagen microarchitecture in the AF region [100].
###### Abstract  
During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm's capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%-20%. Moreover, the methods demonstrate robustness.  
Keywords:intelligent mining; autonomous haulage; planning map; Hybrid A* algorithm; path planning +
Footnote †: journal: Applied Sciences
## 1 Introduction
In addition, the required storage capacity for the system is quite large. In this paper, a joint error estimation method based on the LS method for coarse estimation offline and the recursive least squares (RLS) method for accurate estimation online is proposed to address the problems mentioned above based on the Volterra series. The obtained error parameters are then used to reconstruct the error signal, and the error-free signal is obtained by subtracting the reconstructed error signal from the error-containing output signal by means of digital post-compensation. The time complexity of the LS method is \(O\left(n^{3}\right)\), but that of the RLS method is \(O\left(n^{2}\right)\). Thus, the joint error estimation method proposed in this paper can greatly reduce the computational complexity compared with the traditional LS method, thus reducing the requirement of the calibration method in terms of the computational power of the calibration system. At the same time, the traditional LS method needs to store about \(n^{2}\) data points but this paper only needs to store \(n\) data points, which reduces the requirement of the calibration method in terms of the storage capacity of the calibration system. It  
Figure 1: (**a**) TIADC system diagram; (**b**) time sequence diagram of each ADC in a TIADC system. also performs fewer complex field operations when calculating the kernels of Volterra series, thus improving the efficiency of the system. After the offline error estimation, the system uses the RLS method online to approximate the more accurate error parameters. The RLS method is more suitable for the online estimation compared to the LS method because it only needs to input the current state for simple recursive operations, whereas the LS method needs to perform a large number of complex field operations after adding windows when estimating the mismatch error in the TIADC system, thus involving high computation complexity and slow computation speed.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 1 Introduction
The application of new mathematical methods for the solution of engineering problems can not only introduce the methods but also shorten the distance between the methods and applications. In this study, the SRM and the WPA are combined for the SSA. As in [42], the distribution of maximum equivalent plastic strain points in this study is regarded as a signal function, which is filtered by wavelet packet denoising technology to obtain a smooth CSS. Figure 1: Three-layer decomposition. (**a**) Wavelet; (**b**) Wavelet packet. The implementation step is as follows: First, the SRM is used to bring the slope into the LES and calculate the FOS. Second, deploying a series of parallel lines approximately perpendicular to the slope surface, and picking out the points of these lines with the maximum equivalent plastic strain points, is noted as \(y_{i}\) (\(x_{i}\)). Third, the wavelet packet filter is used to smooth \(y_{i}\) (\(x_{i}\)) and the location of the CSS is accordingly determined. Three numerical examples including a homogeneous slope, a slope containing a weak intercalated layer, and a soil-rock mixture slope are employed to validate the proposed method.
## 3 Results
### Improvement of SI with PNN Method  
It can be inferred from Section 3.2 that the PNN method can provide more accurate rain area delineation compared with the threshold method used in the traditional SI precipitation retrieval algorithm. Therefore, we investigated whether the PNN method could be used to improve the performance of SI by reducing the misdetected and false alarm precipitation.  
Figure 7: **(a)** Mean absolute error (MAE), **(b)** root mean squared error (RMSE) and **(c)** coefficients of determination (\(R^{2}\)) of the precipitation estimated by the Scatter Index using the rain areas delineated by probabilistic neural network (PNN) and delineated by the threshold method.  
In our study, the precipitation of the test dataset in each of the 16 cases was estimated according to Equation (3) using the rain area delineated by PNN and SI_threshold, respectively. The estimated precipitation datasets were termed as rain_PNN and rain_threshold. Figure 7a-c presents the mean absolute error (MAE), root mean squared error (RMSE) and coefficients of determination (R2) of rain_PNN and rain_threshold. As presented by Figure 7, the ranges of the MAE, RMSE and R2 of the Rain_threshold were 0.11-0.84 mm (mean value is 0.45 mm), 1.33-2.21 mm (mean value is 1.33 mm) and 0.11-0.50 mm (mean value is 0.29 mm), respectively. In contrast, the MAE and RMSE of rain_PNN reduced to 0.07-0.8 mm (mean value is 0.39 mm) and 0.42-2.11 mm (mean value is 1.23 mm), respectively, whereas the R2 of rain_PNN increased to 0.16-0.58 mm (mean value is 0.37 mm). The results indicated that the performance of SI can be improved by using the PNN method to delineate rain areas instead of the threshold method.
## 4 Non-Model-Based Ultrasound Envelope Statistics Parametric Imaging Techniques
### Ultrasound Entropy Imaging  
The information-theoretic entropy was first introduced into the biomedical ultrasound field by [PERSON] [57]. Shannon entropy \(H\) is defined by [58]  
\[H\equiv-\int\limits_{r_{\min}}^{r_{\max}}w(r)\log_{2}[w(r)]dr, \tag{5}\]  
where \(r_{\min}\) and \(r_{\max}\) are the minimum and maximum envelope amplitudes, respectively; \(w(r)\) is the PDF of envelope amplitude \(r\). Shannon entropy reflects the uncertainty of random signals. A larger value of Shannon entropy indicates a greater uncertainty, representing that the ultrasound backscattered signals change from regular to random and even complex states [58].  
Clinical studies demonstrated that Shannon entropy increases with increasing severity of hepatic steatosis [22; 59; 60]. [PERSON] et al. [59] investigated ultrasound entropy imaging for assessing hepatic steatosis of 107 patients, using an ultrasonographic scoring system [36] as the reference standard. A correlation \(r=0.63\) (\(p<0.0001\)) was reported. [PERSON] et al. [60] assessed hepatic steatosis of 394 patients with ultrasound entropy and ASQ imaging; the reference standard was an ultrasonographic fatty liver indicator [61]. Correlations of \(r=0.713\) (\(p<0.0001\)) and \(r=-0.630\) (\(p<0.0001\)) were reported for entropy and ASQ imaging, respectively. [PERSON] et al. [22] investigated small-window entropy imaging for characterizing hepatic steatosis in liver donors (\(n=53\)) and patients (\(n=142\)); the reference standards were MR spectroscopy and histopathology for liver donors and patients, respectively. A correlation \(r=0.74\) with MR spectroscopy measurements was reported. For liver patients, the AUC was 0.80, 0.90, and 0.89 for \(\geq\)mild, \(\geq\)moderate, and \(\geq\)severe, respectively.