esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 3 Training of CESDD
### Feature Extraction  
For in-the-wild vehicle detection, vehicles exhibit various appearances, resulting from geometrical differences, various orientations, tremendous illumination variations, etc. Adoption of descriptive features robust against these influences is critical to effective classification and vehicle detection. Two essential properties of sample appearance should be emphasized here: shape and color. Shape information generally resides in two scales: contours and other geometrical patterns on a macro scale, and textures on a micro scale. In this work, several types of feature extraction schemes are tested and compared regarding their performances in classification and detection, as could be seen in subsequent experiment. Based on the testing results, it is discovered that the combination of features describing all three types of appearance properties (macro shape, micro texture, and color) gives the best classification performance; specifically, we adopted FHOG for description of macro shapes, cell-structured LBP (CSLBP) for micro textures, and color names (CN) for color information.  
The concatenated features make up high dimensionality, which is time-consuming when processed and may contain redundant components that are useless in classification. To deal with this issue, \(\ell_{1}\)-based feature selection with the assistance of a linear SVM is utilized and works well with the proposed features, as could be seen from subsequent experiments.
## 3 Results and Discussion
### Experiment 3: The Effect of Missing Data in the Training Data  
In fact, due to probable presence of clouds in some of the images, information was not available for cloudy pixels in the classification of multi-temporal images if these cloudy pixels are removed. Thus, cloudy pixel data, as missing data, will not participate in the classification process. Retrieval of missing data in images, such as plant greenness peak images, which play a major role in the classification, is of particular importance and provides information used for classification that is closer to reality. As described in Step 2 of the Methodology section, all cloudy pixels in training and testing data were retrieved by the k-nearest method and then classified. Table 7 provides classification results in the case of eliminating missing data and also in the retrieval mode, then the retrieved pixels were employed in the RF classifier process. The overall accuracy in the retrieval of missing values is 95.4%, and in the absence of missing data is 93.2%. The results show an increase of 2.2% overall accuracy and, in total, the improvement of classification results in the retrieval mode of missing data in the feature space. It should be noted that all time-series images were used in this experiment and k was set as 7 in this study.
###### Abstract  
The aim of the present study was to evaluate the prebiotic potential of _Plantago major_ L leaves water-extractable polysaccharide (PWPs) and its lower molecular fractions. The structure of PWPs was investigated by high pressure anion exchange chromatography (HPAEC), size exclusion chromatography coupled with multi-angle laser light scattering detector (SEC-MALLS) and Fourier-transform infrared (FTIR) spectroscopy. The chemical composition and monosaccharide analyses showed that galacturonic acid was the main monosaccharide of PWPs followed by glucose, arabinose, galactose, rhamnose and xylose. FTIR study indicated a strong characteristic absorption peak at 1550 cm\({}^{-1}\) corresponding to the vibration of COO\({}^{-}\) group of galacturonic acid. The PWPs was subjected to hydrolysis using commercial enzymes to obtain _P. major_ low molecular fraction (PLM) which was successively separated by size exclusion chromatography on Biogel P2. PWPs and PLM were examined for in vitro prebiotic activity using various assays. Results gave evidence for changes in optical density of the bacteria cells and pH of the growth medium. A heterofractative process with a lactate/acetate ratio ranged from 1:1 to 1:5 was observed. The ability of PLM to stimulate the production of certain probiotic bacteria glycolhydrolases and to be fermented by _Lactobacillus_ sp. strains was successfully proved.  
_Plantago major_ L; polysaccharides; oligosaccharides; enzymatic hydrolysis; prebiotic activity
## 4 Results and Discussion
### Hyperparameter Tuning  
After determining the selected features for the model, the collected dataset is used for model construction. However, when building the graduate admissions prediction model, the initial random forest (RF) model requires the manual tuning of several hyperparameters  
Figure 1: Correlation analysis.  
to ensure the model's predictive accuracy. The selection of hyperparameters requires a high level of expertise and experience from engineers. For those with less experience or data sensitivity, finding the most suitable hyperparameter values for the graduate admissions prediction problem within a short time frame can be challenging.  
To enhance the model's predictive accuracy and reliability, this study employs an improved sparrow search algorithm (ISSA) to adjust and optimize the hyperparameters of the random forest algorithm. Through optimization by the ISSA, the model can identify more suitable hyperparameters, thereby better adapting to the graduate admissions prediction task.  
Figure 2 shows the changes in the RF model RMSE after multiple hyperparameter selections using the ISSA algorithm. Evidently, with the increase in the number of ISSA iterations, the RMSE of the integrated ISSA-RF model gradually nears the optimal value. After the 14 th iteration, the RMSE of the combined model stabilizes. This indicates that the ISSA algorithm has a significant optimization effect on the RF model, and that the chosen hyperparameters effectively enhance the RF model's information processing capability, thereby improving its predictive accuracy and applicability.  
In summary, the ISSA algorithm demonstrates excellent performance in optimizing the RF model's hyperparameters. By leveraging the global search capability of the ISSA algorithm and the strong predictive power of the RF model, the combined ISSA-RF model shows more precise and reliable performance in the graduate admissions prediction task.
## 4 BER Computational Strategies for Decision Unit--Background Tutorial  
In this section, before presenting our novel technique for CNN-based bit error rate (BER) estimation technology, we need to briefly present tutorial descriptions for the conventional BER estimation regimes, which include some familiar approaches: the Monte Carlo (MC) prediction method, the Log-Likelihood Ratio-based BER model, and the modified MC-based estimation approaches.
# Simulation of Fluid Dynamics Monitoring Using Ultrasonic Measurements
## 2 Numerical Models for Wave Equation
Instead of solving this equation directly, SPECFEM3D solves an equation with a second-order time derivative of scalar potential \(\chi\):  
\[p=-\ddot{\chi}. \tag{2}\]  
The advantage of this implementation is that it automatically suppresses numerical artifacts that are known for appearing in the displacement formulation [50]. By combining Equations (2) and (1) and using the acoustic bulk modulus \(\lambda=\rho c^{2}\) with a density \(p\):  
\[\frac{1}{\lambda}\ddot{\chi}=\frac{1}{\rho}\
abla\cdot\
abla\chi, \tag{3}\]  
which is the acoustic wave equation that SPECFEM3D actually solves. Rather than solving wave Equation (3) directly, the \"weak form\" equation is solved. It is derived from Equation (3) by multiplying a test function \(\omega\), integrating by parts and using [PERSON]'s first identity:  
\[-\int_{\Omega}\frac{1}{\rho}\
abla\chi\cdot\
abla\omega d\Omega+\int_{\Gamma} (\
abla\chi\cdot\mathbf{n})\omega d\Gamma=\int_{\Omega}\frac{1}{\lambda}\ddot{ \chi}\omega d\Omega, \tag{4}\]  
where \(\Gamma\) is the boundary of the domain \(\Omega\), and \(\mathbf{n}\) is an outward normal vector unit along \(\Gamma\). In SPECFEM3D, this weak formulation is solved in a discretized form with the continuous Galerkin method. For a further explanation of the discretization, refer to [45; 51]. This type of Gaussian integral is well known in the context of finite elements, but instead, SEM uses a generalized Gaussian integration, the so-called Gauss-Lobatto-Legendre integration, which has the supplemental collocation points at both ends of the discretization interval. Detailed explanations of such Gauss-type polynomials can be found in [52; 53]. Functions on a given spectral element are approximated using the Lagrange polynomial with Gauss-Lobatto-Legendre (GLL) points. Figure 1 shows an example of a 2D first-order finite element and a fourth-order spectral element with a total of 25 GLL points.
# Model Updating of Bridges Using Measured Influence Lines
## 2 Materials and Methods
### Finite Element Model Updating (FEMU): Error-Domain Model Falsification (EDMF)  
For the FEMU, the EDMF methodology was employed instead of traditional approaches such as Bayesian model updating or residual minimisation. Introduced in 2013 for bridge load testing [28], EDMF is a structural identification methodology that leverages the falsification concept. Outlined by [28], it has deep roots in scientific methods, formalised in the 1930s by [PERSON], who asserted that scientific models cannot be entirely verified by data, but can only be falsified. Using this framework, EDMF determines plausible values for FE model variables based on experimental field data and defined uncertainty levels. The methodology involves creating various FE model instances, each characterised by a unique set of variable values. These are then assessed by comparing the model's predictions with actual sensor data collected during experiments. Models that deviate beyond the set uncertainty thresholds are deemed falsified, narrowing the range of potential variable values.  
According to [29], using falsification thresholds makes EDMF robust against the assumptions typically made about the correlation of uncertainties and incorporates adjustments for model biases based on engineering heuristics. This approach has been proven to offer more accurate identification and predictions, particularly where there is considerable systematic uncertainty, outperforming traditional methods like Bayesian model updating and residual minimisation. Its efficacy and growing acceptance are evidenced by its application in nine bridge-related studies, four on buildings, and two on geotechnical projects from 2015 to 2022 [29].
## 2 Materials and Methods
### Data Sources and Reprocessing
#### 2.2.2 NDVI Datasets  
The MODIS data (MOD13Q1) used in this study are derived from the longtime series vegetation index dataset from 2001 to 2020 provided by the LAADS DAAC data center ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)), accessed on 1 September 2022), with a temporal resolution of 16 d and a spatial resolution of 250 m. The data contained noise due to environmental conditions, sensor accuracy, etc. Therefore, the NDVI data are processed by de-clouding, aerosol, and masking. Finally, the time series NDVI dataset is generated.  
Figure 1: Maps of topographic (**a**) and vegetation types (**b**) in Northeastern China.
## 5 Discussion
The higher and more consistent fill factor scores for the MG excavation indicate that operators were able to execute more precise and confident excavation cycles, thereby reducing the need for corrective rework. This was in contrast with the conventional method, where a W-shaped excavation pattern emerged as the operators cautiously avoided over-excavation, ultimately leading to longer excavation times. Furthermore, the reduced frequency of relocation demonstrates that MG enabled the operators to maximize excavation from a single position, thereby reducing machine downtime and unnecessary repositioning. The demonstrated ability to excavate confidently at extended reach without relying solely on close-range manual verification suggests that MG improved the measurement of depth and the accuracy of leveling, further diminishing the need for frequent repositioning. The observed differences in excavation behavior indicate that MG encourages a more assertive and systematic excavation strategy. Unlike the conventional method--where the operators frequently adjusted the excavation depth out of caution--the MG users exhibited greater confidence in reaching the target depths, completing tasks more efficiently with fewer interruptions. Real-time feedback played a critical role in this enhanced performance. It enabled the operators to continuously adjust the bucket trajectories, by providing immediate, accurate information about depth and slope. This facilitated rapid corrections during excavation, minimized redundant passes, and ultimately led to fewer excavation cycles and improved fill factor stability. The mechanism effectively reduced both overshooting and undershooting of the target excavation profile, ensuring more efficient and precise operation. This implies that MG is not only able to reduce excavation time and cycle counts but also minimizes the need for constant manual verification, making it particularly beneficial for projects requiring precision excavation, such as preparation of foundations or trenching operations. While this study did not directly evaluate operator learning curves, the excavation patterns observed in Footing #4 suggest that the MG operators completed the final footing faster and with greater precision, indicating increasing familiarity and confidence in the system. Future studies should further investigate the potential learning effects of MG systems on excavation efficiency over longer timeframes.
## 4 Discussion  
### Impact of Climatic Variables on NPP Trend  
During the 16-year study period, the terrestrial NPP in Mongolia increased from 0.16 Pg C year\({}^{-1}\) in 2003 to 0.18 Pg C year\({}^{-1}\) in 2018, at the rate of 0.00053 Pg C year\({}^{-1}\) (\(p\)-value = 0.065). The increasing NPP trend in the region indicates that Mongolia plays a crucial role in the global carbon budget. The variability of growing season NPP was probably caused by the combined effects of climatic variables. In order to investigate the influence of the main climatic parameters, namely temperature, precipitation, and radiation, on the inter-annual variability of NPP, we calculated the trends of these parameters from 2003 to 2018 using daily reanalysis data from ECMWF ERA-Interim. The results based on the Mann-Kendall test showed that 48.85% of the country experienced a decreasing trend in precipitation (up to \(-\)2.83 mm per year), while 51.15% of the area indicated an increasing trend (up to 2.33 mm per year) (Figure 10(a)). In the regions, increased precipitation may inhibit plant growth by decreasing solar radiation. While analyzing the growing season temperature change between 2003 and 2018, the increased rate (up to 0.18 \({}^{\circ}\)C) accounted for 99.88% of the country (Figure 10(b)). In the case of net radiation, about 90.86% of the areas exhibited a decreasing trend. Of these, 59.14% were statistically significant, and they mainly occurred in the southern regions of the country (Table 3). The increasing precipitation and temperature trends resonated with the overall trends of average annual temperature and precipitation worldwide from 2000 to 2014 ([PERSON] et al., 2016). Typically, the combination of warmer temperature, higher precipitation, and elevated CO\({}_{2}\) concentration during the early growing season possibly increased NPP, partly by lengthening the growing season ([PERSON] et al., 2016). However, previous studies have indicated that drought is the main primary influential factor for NPP in Mongolia ([PERSON] et al., 2016).  
In addition, we investigated land use change based on the ESA land cover map from 2003 to 2018, which showed that barren land had declined over the study period. Earlier, [PERSON] et al. (2016) reported that barren lands were being restored to non-degraded  
Figure 8: Time series of growing season total NPP (blue) and area percentage of drought-affected areas (red) in Mongolia from 2003 to 2018. The gray column represents the major drought events (area >60% of the total land).  
lands. A decline in barren land is another possible reason for an increase in total NPP in Mongolia. Therefore, our study suggests that further studies should focus on the potential effects of drought in a more historical manner by considering attributes such as overgrazing, urbanization, deforestation, land use change, and wildfire, which can limit the NPP variation. However, this study provides an insight into how droughts affect the terrestrial NPP in Mongolia, a region that is critical to global climate change.
# Potential Synergistic Inhibition of _Enterococcus faecalis_ by Essential Oils and Antibiotics
## 3 Results
### Antibacterial Effects of Antibiotics  
The effectiveness of four antibiotics (kanamycin, streptomycin, gentamicin, and ampicillin) in inhibiting the growth of _E. faecalis_ was evaluated using the disk diffusion test. For each antibiotic, the stock solution was diluted in distilled water to 10% and 5% since these concentrations produced ZOI values that were similar in range with the ZOIs observed for CN and CL EOs tested previously (between 4 mm and 12 mm). The ZOI for 5% antibiotic solution of kanamycin (2.5 mg/mL), streptomycin (2.5 mg/mL), gentamicin (1.5 mg/mL), and ampicillin (5 mg/mL) were 5.50 \(\pm\) 0.28 mm, 8.00 \(\pm\) 0.40 mm, 8.25 \(\pm\) 0.47 mm, and 9.75 \(\pm\) 0.25 mm, respectively. The ZOI for 10% antibiotic solution of kanamycin (5 mg/mL), streptomycin (5 mg/mL), gentamicin (3 mg/mL), and ampicillin (10 mg/mL) were 9.50 \(\pm\) 0.64 mm, 10.75 \(\pm\) 0.25 mm, 9.75 \(\pm\) 0.25 mm, and 13.00 \(\pm\) 0.70 mm, respectively (Figure 2B). All antibiotics showed significant increase in ZOI between 5% and 10% concentration where kanamycin, streptomycin, and ampicillin showed robust increases (34% to 72%). There was significant difference among the antibiotic treatment groups (F (7, 24) = 24.16, \(p<0.0001\)), and the Tukey post-hoc test showed significant paired differences between 5% and 10% for each antibiotic solution (\(p<0.01\)).
## 3 Key Technologies of UAV-Assisted SAGINs  
### Routing Protocols  
In UAV-assisted SAGINs, routing protocols play a crucial role. Due to SAGINs' integration of multi-dimensional communication networks in the air, ground, and space, the design of their routing protocols must take into account the high dynamism of drones, frequent changes in network topology, and unique challenges in multi-dimensional environments. Effective routing protocols can ensure the efficient and reliable transmission of data packets in complex and ever-changing network environments. This section will provide an overview of several key routing protocols used in unmanned aerial vehicle-assisted SAGINs, including their working principles, advantages, limitations, and challenges in practical applications. Table 3 shows the analysis of common routing protocols.
# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 1 Introduction
This classifier was proposed because of its accuracy, speed and ability to train the multi-dataset of a transmission line fault within the shortest possible time. The model was used for its ability to handle heterogeneous data and its categorical features. It was also sensitive to hyperparameters and handled noisy data [31]. The uniqueness of the proposed model is its ability to train noise data without affecting the accuracy and performance of the system. The fault data was comprised of four fault conditions in different scenarios, and the analysis was divided into two parts: one was the modelling of the network to extract fault cases from the transmission line using Matlab/Simulink, and the other was to detect and classify the faults using the data generated from the simulations to detect and classify faults with the help of a trained classifier [32].
## 4 Analysis of the Entire Collapse Process
### LS-DYNA Explicit Dynamic Analysis
#### 4.1.2 Dynamic Characteristic Analysis  
The block Lanczos method was used to analyze the dynamic characteristics of the 3D finite element model of the super-large cooling tower. Figure 13 shows a curve of the 200-order natural frequency of the cooling tower as a function of the mode order. According to this graph, the odd and even order frequencies of the cooling tower are the same and have symmetry. The natural frequency distribution is very dense, the fundamental frequency is only 0.68 Hz, and the first 50 order mode frequencies are mainly concentrated between 0.6 and 1.9 Hz. To consider the influence of fluctuating wind pressure, the wind vibration coefficient used in the subsequent explicit dynamic analysis is set to 1.9 [31].  
Figure 12: The finite element model of the cooling tower.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Tower** & **Pillar** \\ \hline Elastic modulus E (GPa) & 32.5 & 33.5 \\ Yield stress (MPa) & 8 & 8 \\ Tangent modulus (MPa) & 150 & 150 \\ Hardening parameters H & 0 & 0 \\ Strain rate parameter C & 40 & 40 \\ Strain rate parameter P & 5 & 5 \\ Failure strain \(\varepsilon\) & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of material model parameters.  
Table 4 shows the typical mode shapes of the cooling tower structure. From the analysis of the graphs, it is found that the vibration modes of the tower are greatly different in the hoop and meridional directions; in particular, with the increase of the order, the number of the hoop and meridional harmonics increases significantly, while in the low-order mode, the meridional direction shows 1 to 3 harmonics and the hoop direction shows 6 to 12 harmonics. Among them, in the first few modes, the tower top vibration is the main type. In the bottom tower vibration mode, there are many ring-shaped harmonics and the tower top amplitude is small.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 3 Results
### Separability Assessment of Samples  
The Jeffries-Matusita (JM) distance between a pair of probability functions is a measure of the average distance between the two class density functions [75]. The JM distance between the two classes was closer to 2, indicating a classification accuracy. All the samples were generated in a grid-by-grid manner, whose spatial autocorrelation was reduced, and whose reliability was improved. After calculation, the JM distances of different samples were greater than 1.85 in this study, indicating that the reliability of sample quality was enhanced.