esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Materials
### Algorithm of the Model Development  
The algorithm to develop the stoichiometric models with correction factors and the new empirical correlation for the reaction equilibrium constants is shown in Figure 2. The first stage in developing the models was determining \(n_{1}\)-\(n_{8}\), In \(K_{i}\), \(f_{k}\), and finally, to obtain the empirical correlation of \(K^{*}\). Table 6 and Supplementary Table S1 were used as the inputs for this determination. The second stage was validating the models and testing their accuracy against the secondary data. Table 7 and Supplementary Table S2 were used as the inputs in the second stage.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biomass**} & \multicolumn{4}{c}{**Ultimate Analysis (\% wt)**} & \multicolumn{4}{c}{**Proximate Analysis (\% wt)**} & \multirow{2}{*}{**HHV**} & \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Number**} \\ \cline{2-2} \cline{5-12}  & **C** & **H** & **O** & **N** & **S** & **VM** & **FC** & **Ash** & & **MS** & **(MJ/kg)** & **Name** \\ \hline Woodchips & 50.60 & 6.50 & 42.00 & 0.20 & 0.10 & 72.40 & 19.20 & 0.70 & 7.70 & 20.973 & 4 & [20] \\ Wood pellets & 46.78 & 5.96 & 45.44 & 0.32 & 0.09 & 83.01 & 15.66 & 1.34 & 12.23 & 18.631 & 5 & [56] \\ Sawdust & 51.33 & 6.13 & 41.97 & 0.12 & 0.02 & 77.76 & 20.44 & 1.80 & 9.3 & 20.765 & 2 & [45] \\ Corn & 44.70 & 6.30 & 45.20 & 1.20 & 0.09 & 66.30 & 16.60 & 2.40 & 14.7 & 18.295 & 4 & [57] \\ Wood pellets & 49.80 & 5.80 & 42.20 & 2.00 & 0.06 & 81.00 & 18.40 & 0.70 & 6.30 & 19.817 & 3 & [55] \\ Palm kernel oil & 44.58 & 4.53 & 48.80 & 0.71 & 0.07 & 83.50 & 15.20 & 1.30 & 16.00 & 15.824 & 1 & [43] \\ Sawdust & 48.91 & 5.80 & 45.11 & 0.18 & 0.00 & 80.63 & 17.27 & 2.10 & 0.00 & 19.197 & 1 & [49] \\ Wood pellets & 50.00 & 6.00 & 42.60 & 3.19 & 0.00 & 77.80 & 14.00 & 0.30 & 7.9 & 20.065 & 2 & [53] \\ Municipal solid waste & 46.30 & 5.20 & 44.80 & 2.90 & 0.86 & 47.00 & 8.50 & 0.00 & 14.30 & 17.701 & 2 & [55] \\ \hline Total & 9 & & & & & & & & & & & & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ultimate and proximate analyses of the biomasses used for validating the models and testing their accuracy.
## 4 Results and Discussion
### Angular Resolution Analysis of Wind Climatology  
In order to consider the effect of angular resolution of wind climatology on the power output estimation and to compare the optimization results with a different wind farm design method in a more systematic and convincing way, the obtained optimal wind farm layouts with the setting of 15\({}^{\circ}\) wind direction division are they further evaluated with a finer 3\({}^{\circ}\) angular resolution. The comparative results with different wind farm design methods are shown in Table 5. As can be seen, when the finer wind direction division is applied for power output evaluation, the discrepancy of optimized total wind farm power output becomes even smaller and the power output of the densified 40 \(\times\) 60 grid method and the coordinate method are very close to each other. Nevertheless, the coordinate method still yields slightly more power than the 40 \(\times\) 60 grid method, followed by the 20 \(\times\) 30 grid method. Quantitatively, when a finer angular resolution is applied for power output evaluation, the performance of wind farm layout optimization decreases from approximately 1.5% to 0.5%. Furthermore, by comparing the power output results with the original 15\({}^{\circ}\) and a finer 3\({}^{\circ}\) angular resolutions, it is found that the overall power output decrease is approximately 0.1%-0.3%, depending on the number of wind turbines optimized.
# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 1 Introduction  
### General Context  
A cognitive radio (CR) is a smart technology that can efficiently solve limited access issues in wireless networks. Its concept was first proposed by [PERSON] in 1998 and several entities such as the IEEE, the International Telecommunications Unit (ITU) and the National Telecommunications and Information Administration (NTIA) have since established a general definition for the same [1; 2; 3].  
The main function of CR consists in granting access to the spectrum using a dynamic strategy: the opportunistic exploration in the space-time dimensions of the network. Unlike traditional networks, CR has two types of users: the primary user (PU) who uses the frequency bands in a licensed manner and the secondary user (SU) who uses the spectrum opportunistically [4; 5; 6].  
Cognitive radio networks (CRN) operate with a management model that performs smart adaptations based on progressive learning and information exchange [7] to implement a dynamic and opportunisticaccess method. Spectrum decision making is a key function of CRN. However, it has not been investigated as much as other functions of the cognitive cycle [4; 8; 9]. The decision-making process aims to choose the best spectral opportunity among a finite set of possibilities, allowing the SU to generate a sequence of actions that lead to the completion of their objectives [9; 10; 11]. Inadequate decision-making processes can significantly affect the parameters of quality, latency, throughput, reliability, signalization, PU interference, energy efficiency, bandwidth, signal to interference plus noise ratio (SINR), and error rate [12; 13; 14].  
Collaborative strategies have delivered new models for the efficient use of radio resources, as well as decision-making processes for CRN. In collaborative decision making, unlicensed users communicate with each other to exchange local measurements regarding availability and interference (as well as other variables) to take advantage of spatial diversity [15; 16; 17]. This collaborative approach offers more advantages because the diversification of spectrum observation avoids possible interference. One of the challenges in spectrum selection is how to combine this shared information in a collaborative manner while the transmission is carried out [12; 18; 19].
## 3 Parameter Optimization and Performance Test
### Test Summary  
Based on the above results of the seedling dropping performance bench test, although the mechanism did not carry out the field planting performance test with the ditching device, the tray feeding mechanism, the seedling taking and launching mechanisms, etc., with reference to the definition of the planting frequency of the high-speed transplanter in JB/T 10291-2013 _Dry Land Planting Machinery_, the seedling dropping mechanism studied in this paper carried out the bench seedling dropping test at the planting rate of 100 plants/(min \(\cdot\) row), and the seedling dropping success rate was 100%. The average variation coefficient of the seedling dropping interval at the different positions was 6.25%, and the seedlings fell evenly. The average matrix damage rate was less than 10%, and the pot seedlings had no obvious damage. The seedling dropping mechanism had a good effect and could meet the requirements of uniform and low-loss seedling dropping in the operation. Under a planting frequency of 75-108 plants/(min \(\cdot\) row), the uniform dropping seedling operation could be better completed. If higher planting requirements are needed, the installation method of the drive cam needs to be improved, or the tension of the tension spring to the seedling plate should be appropriately reduced to avoid a large angular displacement of the drive cam and the seedling plate due to the large impact at the moment of contact, which will affect the uniformity of the subsequent seedling dropping interval.
# Compact and Accurate Scene Text Detector
## 2 Related Work
### Segmentation-Based Approach  
The segmentation-based approaches classify every pixel into one of two classes (e.g., text and not text). Such Methods mainly adopt FCN to obtain segmentation results and require post-processing that converts the results into word boxes. For example, in [3] the segmentation results are converted into word boxes using link prediction. In [2] a model was proposed, namely character region awareness for text detection (CRAFT), that represents inter-character relationships using a heat map and links the characters using a connected component labeling (CCL) algorithm. In [19] a progressive scale expansion algorithm was suggested that finds word boxes at various scales and merges nearby word boxes. As described above, the segmentation-based approaches indirectly infer word boxes via post-processing that converts the segmentation results into word boxes. Such an indirect mechanism may lead worse word boxes because it strongly relies on the results of the segmentation model.
## 4 Experimental Results and Analysis  
### Datasets  
To verify the effectiveness of the proposed SSAMMA, we used two benchmark datasets in our related experiments: Indian Pines (IP) and the University of Pavia (UP).  
In 1992, the AVIRIS sensor obtained an IP dataset by photographing agricultural areas in Northwest Indiana. This dataset has a spectral wavelength range of 0.4-2.45 \(\upmu\)m, a spatial resolution of 20 m, and 16 feature classes. The dataset size is 145 \(\times\) 145 pixels, including 220 bands. After excluding 20 bands with severe water absorption, the remaining 200 bands WERE used as the study objects. Figure 2 shows the false-color composite image and the ground-truth map of the IP dataset. Table 1 shows the number of each ground object category in this dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category Number** & **Category Name** & **Sample Number** & **Color** \\ \hline
1 & Alfalfa & 46 & \\
2 & Corn-notill & 1428 & \\
3 & Corn-mintill & 830 & \\
4 & Corn & 237 & \\
5 & Grass-pasture & 483 & \\
6 & Grass-trees & 730 & \\
7 & Grass-pasture- & 28 & \\  & moved & 28 & \\
8 & Hay-windrowed & 478 & \\
9 & Oats & 20 & \\
10 & Soybean-notill & 972 & \\
11 & Soybean-mintill & 2455 & \\
12 & Soybean-clean & 593 & \\
13 & Wheat & 205 & \\
14 & Woods & 1265 & \\
15 & Buildings-grass-trees & 386 & \\
16 & Stone-steel-towers & 93 & \\ \hline Total & & 10,249 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground object category for the IP dataset.  
Figure 2: IP dataset: (**a**) false-color composite image; (**b**) ground-truth map.  
The UP dataset was acquired via the ROSIS sensor at the University of Pavia, Italy, in 2003. It has a spectral range of 0.43-0.86 \(\upmu\)m, a spatial resolution of 1.3 m, and 9 feature classes. The dataset has a size of 610 \(\times\) 340 and includes 115 bands. After removing 12 noisy bands, the remaining 103 bands were used as the objects of study. Figure 3 shows the false-color composite image and ground-truth map of the UP dataset. The ground objects of the UP dataset are shown in Table 2.
## 3 Model Development
### Rockburst Prediction Model
Overall, the binary predictions of levels II and III were weaker than those of levels I and IV. It demonstrated that the extreme difference in the rockburst data of levels II and III was greater than those of levels I and IV, causing the model misclassification. Due to the change in threshold values, the TPR value increased with the FPR value and showed an overall upward convex trend. In addition, the PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost had higher AUC values than the other models, and the AUC values for different rockburst classes of PSO-BPNN-XGBoost were better than that of PSO-BPNN-AdaBoost. Therefore, it is clear that the integrated PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models achieved accurate and tolerable AUC values. Using raw data, we analyzed the model's performance. However, due to the imbalance in the dataset, the results may be accidental. It was necessary to introduce the SMOTE algorithm to explore its impact on the model performance. The accuracy before and after SMOTE processing is shown in Figure 15.  
The application of the SMOTE preprocessing technique led to a significant improvement in the accuracy of all evaluated models, as depicted in the figure. Notably, the PSO-BPNN-AdaBoost model exhibited the most pronounced enhancement in accuracy, with an impressive 8.9% increase. This substantial improvement underscores the model's sensitivity to data imbalance and the effectiveness of SMOTE in addressing this issue. By generating synthetic samples for the minority class, SMOTE mitigated potential errors caused by data imbalance, thereby enhancing the AdaBoost framework's capability to identify and classify patterns belonging to the minority class. Figure 15 further illustrates  
Figure 14: ROC curves of different models under each rockburst class sample. the varying degrees of improvement observed among different algorithms. Models such as KNN and BagKNN, which are known to be highly sensitive to data distribution, showed considerable improvements in accuracy. This highlights the pivotal role of SMOTE in bolstering the learning capabilities of these algorithms. In contrast, algorithms like random forest (RF) and bagging-based methods exhibited relatively minor changes, suggesting their inherent robustness to data imbalance. Notably, the PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models consistently outperformed other methods, both before and after preprocessing, showcasing their superior predictive performance. Moreover, the figure underscores the importance of data preprocessing techniques such as SMOTE in balancing datasets and enhancing model performance, particularly for algorithms that are sensitive to data imbalance. Additionally, the results demonstrate that integrating ensemble learning techniques such as AdaBoost and XGBoost with optimization algorithms like PSO can achieve high accuracy and robust predictive performance in imbalanced datasets. These findings suggest that addressing the challenges of data imbalance requires careful consideration of preprocessing techniques and algorithm selection to achieve optimal performance in machine learning tasks.
## 4 Results
### Comparative Analysis of CBA Methodologies and Results
#### 4.1.2 Comparison and Evaluation of the Applied GHG Emission Factors in CBA Methodologies  
GHG emission factors are expressed differently in the CBA methodologies. The CBA methodology in the Slovak Republic applies emission factors expressed in grams per 1 kg of fossil fuel consumed; the CBA methodology in Austria also applies a mass expression of emission factors related to the amount of fuel consumed (3.15 t CO\({}_{2}\)/t fuel). The CBA methodology in Austria refers to the fuel consumed (t) and one CO\({}_{2}\) emission factor; it does not differentiate between different fuel types. For increased use of non-fossil fuel energy (e.g., hydropower for trolleybuses, trams, or railways), separate studies are needed. The methodology in Hungary applies emission factors in grams of CO\({}_{2}\)es per vehicle-km. The methodology in the Czech Republic applies the expression of emission factors in grams per 1000 vehicle-km.  
Two CBA methodologies provide GHG emission factors for specific fuels, the Slovak Republic and Hungary. The Slovak methodology applies GHG emission factors for diesel, petrol, and electricity. The Hungarian methodology uses GHG emission factors for diesel, petrol, CNG, and LPG.  
Table 11 compares selected parameters and characteristics of the GHG emission factors from road transport in the CBA methodologies.  
For electric vehicles, the CBA methodology in the Slovak Republic considers indirect emissions (WtT) in grams per kWh of electricity consumption, based on the three categories of emission factors according to the voltage published by the European Investment Bank (EIB). In Hungary, for public transport using electricity, it considers indirect emissions (WtT) in grams per kWh of electricity consumption.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollutant** & **SK** & **H** & **A** & **CZ** \\ \hline CO\({}_{2}\) & Yes & & & Yes \\ CH\({}_{4}\) & Yes & & & No & Yes \\ N\({}_{2}\)O & Yes & & & No & Yes \\ Conversion to CO\({}_{2}\)e & Yes & & Yes & No & Yes \\ \hline \hline \end{tabular} Note: The methodology in the Czech Republic considers CH\({}_{4}\) and N\({}_{2}\)O emissions, but the values of emission factors for CH\({}_{4}\) and N\({}_{2}\)O are not given in the methodology.  
\end{table}
Table 10: Comparison of the types of GHG emissions from road transport applied in the CBA [58; 61; 62; 63].
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## Abbreviations  
\begin{tabular}{l l l} Symbol & Description & Units \\ C\({}_{\text{P}}\) & Heat capacity of air at constant pressure & kJ m\({}^{-3}\)\({}^{\circ}\)C\({}^{-1}\) \\ [CO\({}_{2}\)] & CO\({}_{2}\) concentration & umol mol\({}^{-1}\) \\ DAE & Days after emergence & d \\ E & Chamber evaporation rate & mmol m\({}^{-2}\) s\({}^{-1}\) \\ Ecan & Canopy transpiration rate & mmol m\({}^{-2}\) s\({}^{-1}\) \\ ET = \(\Delta\)X\({}_{\text{h20}}\)*MF & Chamber evapotranspiration & mmol m\({}^{-2}\) s\({}^{-1}\) \\ gs & Single leaf stomatal conductance & mol m\({}^{-2}\) s\({}^{-1}\) \\ g\({}_{\text{A}}\) & Canopy aerodynamic conductance & mol m\({}^{-2}\) s\({}^{-1}\) \\ G & Soil heat flux & W m\({}^{-2}\) \\ G\({}_{\text{SFC}}\) & Canopy surface conductance & mol m\({}^{-2}\) s\({}^{-1}\) \\ G\({}_{\text{S}}\) & Canopy stomatal conductance & mol m\({}^{-2}\) s\({}^{-1}\) \\ H & Sensible heat flux & W m\({}^{-2}\) \\ LE & Latent heat flux & W m\({}^{-2}\) \\ \(\uparrow\)Lc & Longwave radiation emitted by the canopy & W m\({}^{-2}\) \\ \(\downarrow\)Lg & Longwave radiation emitted by the glass of the water filter & W m\({}^{-2}\) \\ MF & Mass flow rate of air & mol s\({}^{-1}\) \\ NPSW\({}_{\text{abs}}\) & Absorbed non-photosynthetic shortwave radiation & W m\({}^{-2}\) \\ Offset & Difference between \(\Delta\)T\({}_{\text{A}}\) and \(\Delta\)T\({}_{\text{IR}}\) & \({}^{\circ}\)C \\ P & Energy storage in photosynthesis & W m\({}^{-2}\) \\ P\({}_{\text{net}}\) & Canopy net photosynthetic rate & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ PPF\({}_{\text{o}}\) & Incident photosynthetic photon flux & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ PPF\({}_{\text{abs}}\) & Fraction of incident PPF absorbed by the canopy & \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) \\ R\({}_{\text{net}}\) & Net radiation & W m\({}^{-2}\) \\ s & Slope of the relation between saturation vapor pressure and temperature & \\ T\({}_{\text{air}}\) & Mean air temperature measured above the canopy & \({}^{\circ}\)C \\ T\({}_{\text{Aero}}\) & Canopy aerodynamic temperature & \({}^{\circ}\)C \\ T\({}_{\text{canopy,IR}}\) & Canopy brightness temperature measured by IR transducers & \({}^{\circ}\)C \\ T\({}_{\text{glass, T}}\) & Water filter glass and chamber wall temperatures & \({}^{\circ}\)C \\ T\({}_{\text{R}}\) & Canopy radiometric temperature & \({}^{\circ}\)C \\ T\({}_{\text{Sky}}\) & Composed of 20\% chamber T\({}_{\text{wall}}\) and 80\% T\({}_{\text{glass}}\) & \({}^{\circ}\)C \\ \end{tabular}
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Meshing  
ANSYS Mesh software (V16.0) is utilized to discretize the solid domain of the bearing chamber-bearing system, as well as the fluid and solid domains of Chambers 1, 2, and 5. Given the intricate internal structure of the models, both the solid and fluid regions are meshed using a tetrahedral unstructured mesh.  
For the entire bearing chamber-bearing system, the global mesh element size is set to 3 mm. However, the mesh element size for the bearings within the system is refined to 1.5 mm to capture the finer details of these critical components. As a result, the mesh of the overall bearing chamber-bearing system consists of 2,308,616 cells, with an average mesh quality of 0.836. The maximum mesh quality is 1 and the minimum mesh quality is 0.00396. In order to avoid computational failure due to poor transition between the bearing mesh and the housing mesh, the mesh size of each bearing is set to 1.5 mm, and the mesh behavior is set to soft--the cell size will be affected by the overall cell size to avoid the case of uneven mesh transition between different regions. This mesh quality is deemed sufficient to meet the calculation requirements. The discrete representation of the bearing chamber-bearing system is illustrated in Figure 5.  
For each individual bearing chamber, the computational region includes both fluid and solid domains. The global mesh element size of each chamber is set to 1 mm, while the mesh element size of the oil inlet in each chamber is refined to 0.1 mm. A mesh with 1,874,337 cells for Chamber 1 with an average mesh quality of 0.840, a mesh with 1,788,269 cells for Chamber 2 with an average mesh quality of 0.836, and a mesh with 2,272,938 cells with an average mesh quality of 0.837 for Chamber 5 are used for calculation. The discrete results of Chambers 1, 2, and 5 are shown in Figure 6.
###### Abstract  
This research proposes a methodology based on control engineering, transforming the simulation model of system dynamics into a mathematical model expressed as a system transfer function. The differential equations of a time domain present in the Forrester diagram are transformed into a frequency domain based on the Laplace transform. The conventional control engineering technique is used to present and reduce the dynamic system in a block diagram as a mechanism for determining the structure of the system. The direct path equation and the feedback equation are determined to obtain mathematical models that explain the trajectory of the behavior of each state variable through a transfer function in response to the different inputs of the system. The research proposal is based on presenting an alternative of analytical validation for more robust decision-making to systems dynamics models, based on the explanation of the system structure through a transfer function and its analysis of stability and external controllability for the system dynamics model under study. The results are visually analyzed in a root diagram.  
system dynamic; Forrester; block diagram; differential equations; transfer function; system stability
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 3 Results
### Estimation of Gas Flared Volumes  
Preliminarily to the model development, we evaluated the agreement between the computed radiometric variable (i.e., BTE) and the quantity to assess (i.e., gas flared volumes). To this aim, the temporal trends of official OPEC and EIA data on gas flared volumes (Figure 2) and the BTEs (Figure 7) were compared, revealing a very good accordance, showed by a linear correlation coefficient (R) of about 82% (computed in years 2000-2014) and 84% (computed in years 2005-2014) when OPEC and EIA datasets are considered.  
Table 2 includes, for each model, the corresponding coefficient of determination (R\({}^{2}\)) and the \(p\)-value as well as the amount of gas flared in Niger Delta estimated by the models in 2014-2016. Figure 9 show the scatter plots of both models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Temporal** & **Regression Model** & \multirow{2}{*}{\(\mathbf{R^{2}}\)} & \multirow{2}{*}{\(p\)-Value} & **Year of** & **Gas Flared** \\  & **Range** & & **Equation** & & **Estimation** & **Volume [BCM]** \\ \hline \multirow{2}{*}{OPEC} & \multirow{2}{*}{2000–2013} & Gas flared [BCM] = & & 2014 & 14.3 \\  & & 0.0005*BTE + 9.401 & & 2015 & 12.4 \\ \multirow{2}{*}{EIA} & \multirow{2}{*}{2005–2013} & Gas flared [BCM] = & & 2016 & 11.9 \\ \multirow{2}{*}{EIA} & \multirow{2}{*}{2005–2013} & Gas flared [BCM] = & & 2014 & 13.4 \\  & & 0.0008*BTE + 3.902 & & 2015 & 10.4 \\ \multirow{2}{*}{OPEC} & \multirow{2}{*}{2000–2013} & \multirow{2}{*}{67\%} & \multirow{2}{*}{\(<\)0.01} & 2016 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression models data.  
Figure 8: (**a**) Spatial distribution of BTE classes in the investigated states, (**b**) distribution of BTE classes between onshore and offshore.