esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Mass Balance
#### 5.4.2 Exergy Flow Analysis  
The exergy flow of the HDRSF process without or with waste heat recovery are shown in Figures 8 and 9, respectively.  
Figure 8: Exergy flow analysis of the HDRSF process without waste heat recovery.  
Figure 7: Energy flow analysis of the HDRSF process with waste heat recovery.  
Overall, the exergy income of the HDRSF process comes from two sources: chemical exergy brought in by external reducing gas and physical exergy brought in by high-temperature reducing gas leaving the electric heating furnace. According to the conservation of elements, whether or not there is waste heat recovery does not change the reducing gas consumed by the reduction reaction in the shaft furnace. Therefore, when there is a reduction gas purification and recovery process, the chemical exergy of the reducing gas consumed in the production process is unchanged, while the chemical exergy is the main source of the overall exergy income of the process. The process's exergy income has not changed much in comparison with the energy consumption induced by waste heat recovery, and its exergy investment has decreased from 1.08 \(\times\) 10\({}^{7}\) kJ in the absence of waste heat recovery to 1.05 \(\times\) 10\({}^{7}\) kJ. However, it is essential to note that after waste heat recovery, the shaft furnace's energy income decreased from 2.48 \(\times\) 10\({}^{7}\) kJ to 1.86 \(\times\) 10\({}^{7}\) kJ due to a decrease in the demand for reducing gas in the stove. Lowering the volume of gas and top gas also has a positive effect on HDRSF cost reduction.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 3 PL-Based RAIM Availability Assessment
### Deficiency of Classic- and Enhanced-PL-Based RAIM Availability Assessment
#### 3.2.2 Intersection  
In this situation, there are also three kinds of positional relationships among the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve, the \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve and the \(lgP_{r}(\text{HMI}|1F)\) line, as shown in Figure 3.  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve intersects with the \(lgP_{r}(\text{HMI}|1F)\) line, and is higher than the blue dashed \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the brown \(P_{1}(\text{HMI}|1F)\) curve in Figure 3. In this situation, RAIM is unavailable and the VPL is larger than the VAL, i.e., \(\text{VPL}\geq\text{VAL}|H_{1}\), meaning a successful detection of \"RAIM being unavailable\".  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve intersects with the \(lgP_{r}(\text{HMI}|1F)\) line, and is lower than the blue dashed \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the black \(P_{2}(\text{HMI}|1F)\) curve in Figure 3. In this situation, RAIM is unavailable, but the VPL is smaller than the VAL, i.e., \(\text{VPL}<\text{VAL}|H_{0}\), meaning an optimistic assessment.  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve is separated from the \(lgP_{r}(\text{HMI}|1F)\) line, and is lower than the blue dashed \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the blue curve \(P_{3}(\text{HMI}|1F)\) in Figure 3. In this situation, RAIM is available, and the VPL is smaller than the VAL, i.e., \(\text{VPL}<\text{VAL}|H_{0}\), meaning the successful detection of \"RAIM being available\".  
Therefore, for the condition that the \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve intersects with the \(lgP_{r}(\text{HMI}|1F)\) line, the RAIM availability might be optimistically assessed.  
Figure 3: Intersection.
###### Abstract
Presently, they are traced by leading organizations, such as the MASC-CBRN--an initiative funded by the European Commission (promotes the development of an integrated and comprehensive approach to CBRN events) [23], or CBRN Central, an expert portal on the latest news on homeland security and preparedness for CBRN and explosive (CBRNe) threats [24]. The article aims to identify and analyze innovations related to CBRN threats that can contribute to sustainable development. The study relies on mixed-method research: bibliometrics, as well as computer-aided and manual analyses of data retrieved from the Scopus database from 2017 to 1 April 2022. The following research question (RQ) and corresponding hypothesis (H) were formulated:  
RQ: What are the directions of CBRN innovations which can support sustainability? H: Due to the growth of pollution, migrations, and climate change threatening biodiversity [25; 26], it is hypothesized that the directions of innovations in the field of CBRN will concern mainly environmental and health issues. The Sustainable Development Goals related to the notions will be supported. The rest of the paper is organized as follows: The next section discusses the classification of CBRN agents to provide a generic view of the topic, and then the methodological part describes the process of material retrieval and data analysis. In the Results, the bibliometric data and text exploration data are presented using NVivo and VOSViewer tools. Finally, codes and selected innovations described in the literature are juxtaposed with the Sustainable Development Goals. The last part, the discussion, which precedes brief conclusions, concentrates on the relevance of the topic and future research directions.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Problem Statement  
Deep learning-based OFDM receivers have been shown to improve performance [26; 27; 28; 29; 30; 31; 32; 33; 34]. However, the characteristics of the UWA channel are highly dynamic and depend on the specific underwater conditions. Therefore, existing deep learning-based channel estimation faces serious performance degradation due to variation in communication environments [26], which degrades performance and requires retraining of the network model. In particular, the performance of deep learning models degrades when they are applied to environments that differ from the ones they were trained on, due to model mismatch. One such method that has been proposed to address these challenges is the transfer learning network.
## 5 Simulation and Discussion  
To verify the effectiveness of the improved AUKF estimation algorithm and the superiority of the designed dynamic adaptive compensation sliding mode control (DASMC) algorithm compared to the conventional equivalent sliding mode control (CESMC) algorithm under low-adhesion icy and snowy road conditions, slip ratio controller models based on DASMC and CESMC, as well as adhesion coefficient estimation models, were established in MATLAB/Simulink and Carsim for joint simulation analysis.  
Under icy and snowy conditions, the road surface can take on different forms, with compacted snow and ice being the most common. In this study, compacted snow with low adhesion and slippery asphalt roads are used as typical roads to evaluate the control performance of the two ASR schemes [35]. Additionally, to test whether the controller can respond quickly, two simulation scenarios are selected where the vehicle accelerates from a standstill to the target speed on split roads and joint roads. In the first scenario, the test vehicle model accelerates on a split road, with the left side on low-adhesion compacted snow and the right side on slippery asphalt. In the second scenario, the test vehicle model accelerates from slippery asphalt to compacted snow. To ensure fairness in the simulations, all other parameters are set identically. The vehicle model parameters are shown in Table 1.
## 3 Proposed System
### Dog Tracking
#### 3.3.1 SORT and DeepSORT  
SORT is a practical multi-object tracking method that can effectively track objects in consecutive frames. The SORT model proposed herein uses Faster-RCNN and a Kalman filter to detect an object's position and to predict the object's position in the next frame, respectively. Thereafter, the model calculates the Mahalanobis distance between an object's location and its predicted location in the next frame and uses the Hungarian algorithm [45] for matching to enable multi-object tracking. Therefore, the overall SORT process involves the detection, estimation, data association, and creation and deletion of tracked identities.  
Although SORT is a simple and effective multi-object tracking method, it compares only the size and position of a predicted object and does not consider the object's features. To address this limitation, the proposed system incorporates DeepSORT, which improves upon the detection method of SORT and accounts for the object's features, thus enhancing the accuracy of object tracking. DeepSORT applies SQRT's object tracking to pedestrian tracking. DeepSORT is based on SORT's multiple object tracking (MOT) architecture and uses the Kalman filter to predict a given pedestrian's position in the next frame. The model calculates the Mahalanobis distance between the region of the predicted pedestrian and the region in which other pedestrians may be located. Thereafter, a CNN is used to extract and calculate the minimum cosine distance between the pedestrian's features and the features of all the pedestrians in the next frame. Finally, the Hungarian algorithm is used for matching to enable multi-pedestrian tracking. Accordingly, DeepSORT involves the detection, estimation, feature extraction, data association, and the creation and deletion of tracked identities.
# Depth Image Super Resolution Based on Edge-Guided Method
## 2 Proposed Method
### Edge-Guided Depth Interpolation
#### 2.2.2 Discrimination of pixels distribution  
Firstly, the set \(\mathbf{C}_{e}\) is used to store the pixels on the edge. The pixels on the line segment between pixels p and q are stored in set \(\mathbf{L}\). Pixels \(p\) and pixel \(q\) are on the same side of the edge if the intersection of sets \(\mathbf{C}_{e}\) and \(\mathbf{L}\) is null, as shown Figure 1(a). The distribution of pixels \(p\) and \(q\) may have two situations when the intersection of sets \(\mathbf{C}_{e}\) and \(\mathbf{L}\) is not null. Pixels p and q are not on the same side of the edge in Figure 1(b), but they are on the same side in Figure 1(c). In this situation, we divide each neighborhood window into some sets according to the edge. The process is summarized in Algorithm 3.  
As shown in Figure 2, white lines represent the edge pixels, the whole black portion is a to-be-divided area, and an image patch will be area-divided based on the connectivity of the black area. In addition, there are some special edge curve formats that need to be stated clearly. As shown in Figure 3, if the edge curve is not traversing the entire image patch, we think this is a special form of connectivity, that is, a form where interior space which is enclosed within the edge pixels is zero. Furthermore, the details of the algorithm are as follows.  
Figure 3: Some special forms of the edge curves.  
Figure 2: Distinguish of two pixels near edge.  
After determining the distribution of pixels, pixels \(p\) and \(q\) can be discriminated easily whether on the same side of the edge. They are on the same side of the edge when they belong to the same pixel set, otherwise they are not on the same side. Once the kernel functions of bilateral filter are determined, the HR depth image can be interpolated using Equation (9). When interpolation is performed, the Gaussian kernel \(f_{\mathrm{s}}(\cdot)\) also suppresses some noise for the depth values. In addition, with the guidance provided by the indicator \(f_{r}(\cdot)\), only pixels at the same side of the edge will be considered during interpolation so that edges can be well preserved.
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 4 Results and Discussion  
### Distribution of Pressure along the Flow Direction  
The pressure distributions along the flow direction for different particle volume concentrations are shown in Figure 4, where the pressure values are relative to the atmospheric pressure at the pipe outlet. It can be seen that four curves have the same change trend. The pressure at the inlet is the maximum, and it then decreases gradually to atmospheric pressure at the outlet because it is the flow caused by the pressure difference between the inlet and outlet. It can be divided into three areas according to the changes in the pressure. The pressure is high and changes slowly in the inlet area (\(0\leq z/L\leq 0.2\)). The pressure change begins to increase in the transition area (\(0.2\leq z/L\leq 0.8\)). The pressure decreases approximately linearly as the pipe length increases in the fully developed area (\(0.8\leq z/L\leq 1\)) where the pressure drop per unit length is a constant that increases as the particle volume concentration increases. The distinction between the three areas is more obvious as the particle volume concentration increases.
## 3 Evaluation Results and Discussion
### Information Flow and Product Traceability Performance of the Smart System
The smart system was developed to effectively gather, monitor, store, and analyze data to improve pruning logistics management (even though the tool could be utilized for any biomass supply chain). As indicated in Table 8, this integrated system enables improved performance of pruning supply chain management and product traceability by reducing biomass loss (in terms of quality and quantity), increasing the quality of solid fuel and delivery service, and reducing logistics and transaction costs [2; 11; 14]. Therefore, evaluation results indicate that the SLS is important tool for management of the biomass value chain and trading system. It enables tracing and tracking of the product, controls product quality, facilitates information flow, reduces management cost, and enables managers to manage the entire supply chain. Table 8 indicates that most of the major functional features performed satisfactorily.
## 2 Materials and Methods
### Determination of the Crop Water Stress Index (CWSI) and Water Stress Index (WSI)  
Two indices were derived from the thermal imagery obtained over the experiment. The Crop Water Stress Index (CWSI) was calculated by normalizing the canopy temperature (_Tc_) with the air temperature (_Ta_) and vapor pressure deficit (VPD), according to the methodology proposed by [PERSON] _et al._ ([46]; Equation (1)).  
\[CWSI=\frac{(Tc-Ta)-(Tc-Ta)_{LL}}{(Tc-Ta)_{UL}-(Tc-Ta)_{LL}} \tag{1}\]  
where (_Tc_ - _Ta_)\({}_{\rm LL}\) is the lower limit and corresponds to the _Tc_ - _Ta_ value of a canopy that is transpiring at its potential rate and (_Tc_ - _Ta_)\({}_{\rm UL}\) is the upper limit and corresponds to the _Tc_ - _Ta_ value of a canopy where the transpiration is completely halted. The lower limit was determined by the Non-Water Stress Baseline proposed by [PERSON] [47], with the following equation: (_Tc_ - _Ta_)\({}_{\rm LL}\) = \(-\)3.25 DPV + 3.38.  
\begin{table}
\begin{tabular}{l c c} \hline
**Index** & **Equation** & **Reference** \\ \hline OSAVI & \((1+0.16)\cdot\frac{R800-R670}{R800+R670+(0.16)}\) & [39] \\ MCARI1 & \(1.2\cdot[2.5\cdot(R800-R670)-1.3\cdot(R800-R550)]\) & [29] \\ MSAVI & \(\frac{1}{2}\Big{[}2\cdot R800+1-\sqrt{(2\cdot R800+1)^{2}-8\cdot(R800-R670)} \Big{]}\) & [40] \\ \hline \multicolumn{3}{c}{_Chlorophyll indices_} \\ \hline TCARI & \(3\cdot[(R700-R670)-0.2\cdot(R700-R550)\cdot R700/R670)]\) & [30] \\ TCARI/OSAVI & COMBINED TCARI/OSAVI & [30] \\ TVI & \(0.5\cdot[120\cdot(R750-R550)-200\cdot(R670-R550)]\) & [41] \\ SIPI & \(R800-R445\) & [42] \\ \cline{2-3}  & \(R800+R680\) & \\ G & \(R550\) & - \\ \cline{2-3}  & \(R670\) & \\ ZTM & \(R750\) & [43] \\  & \(R710\) & \\ VOG & \(R740\) & [44] \\ \hline \multicolumn{3}{c}{_Carotonoid indices_} \\ \hline CAR & \(R515\) & [35] \\  & \(R570\) & [45] \\ LIC3 & \(R440\) & [45] \\  & \(R740\) & \\ RARS & \(R746\) & [36] \\  & \(R513\) & \\ PRI & \(R570-R531\) & [32] \\  & \(R515-R531\) & [33] \\ PRI515 & \(R515+R531\) & [33] \\  & \(PRI\) & \\ PRIn & \(R700\) & [34] \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The Water Deficit Index (WDI) was calculated according to the procedure developed by [PERSON] _et al._[48]. It is based on the vegetation index/temperature trapezoid (VIT) and has been developed to combine the spectral vegetation indices with temperature measurements in order to apply CWSI theory to partially-vegetated fields without knowledge of foliage temperature.
## 4 Experimental Design
### Comparative Experiments with Other Algorithms  
To further ensure the reliability of the results, YOLO-TC is trained and validated on this paper's dataset by comparing it with the object detection algorithms Retina-Net, Center-Net [24], YOLOv5, YOLOX, YOLOv7 and YOLOv8. The final comparative results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**YOLOv7** & **ECA-CBAM** & **HA-PANet** & **Detection Head Reconstruction** & **MPDIIoU** & **Params/10\({}^{6}\)** & **[EMAIL_ADDRESS]/\% \(\uparrow\)** \\ \hline \(\sqrt{}\) & & & & & 36.54 & 85.1 \\ \(\sqrt{}\) & \(\sqrt{}\) & & & & 38.10 & 87.4 \\ \(\sqrt{}\) & & \(\sqrt{}\) & & & 39.27 & 86.1 \\ \(\sqrt{}\) & & & \(\sqrt{}\) & & 33.47 & 85.9 \\ \(\sqrt{}\) & & & & \(\sqrt{}\) & 36.54 & 85.5 \\ \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & & & 43.32 & 87.9 \\ \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & & 38.67 & 88.5 \\ \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & \(\sqrt{}\) & 38.67 & 88.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments.  
Figure 9: Precision–recall curve for attentional mechanisms. (**a**) CBAM. (**b**) ECA-CBAM.  
The data in the table indicate that YOLO-TC presented in this study surpasses the accuracy of existing object recognition algorithms in identifying both phone and cigarette objects. Compared to other object detection algorithms, the mAP of the proposed algorithm is improved by 14.7% compared with Center-Net, which is the best performer among the first-stage object detection algorithms, and the \(\text{AP}_{\text{C}}\) and \(\text{AP}_{\text{P}}\) are improved by 18% and 10.7%, respectively. Compared with YOLOX, which adopts the anchorless frame method in the YOLO series of algorithms, the mAP is improved by 10.5% and \(\text{AP}_{\text{C}}\) and \(\text{AP}_{\text{P}}\) by 10.7% and 7.6%, respectively; and compared with YOLOv7, which has higher detection accuracy, the mAP is improved by 3.7%. Compared to the YOLOv8 model, our model does not outperform it in terms of parameter count and inference speed. However, in terms of mAP, our model shows an improvement of 1.7% over YOLOv8. Notably, for small objects such as cigarette butts, our model achieves an AP improvement of 2.5% over YOLOv8. The method presented in this study, while not ideal regarding parameter count (38.67 MB) and FPS (64), maintains a commendable equilibrium between detection accuracy and velocity, rendering it competitive overall performance. The accuracy-recall curve and the visualized confusion matrix of YOLO-TC in this paper are shown in Figure 10.
## 4 Methodology
### Validation Techniques
#### 4.6.2 Reliability Accuracy Measures  
The reliability of the models was assessed using two statistical techniques viz. mean absolute error (MAE) and root mean square error (RMSE). The MAE can be elaborate as the summation of difference between actual and predicted values divided by all total observations of a dataset. The MAE and RMSE have been illustrated in Equations (10) and (11).  
\[\text{MAE }=\ \frac{1}{\text{n}}\sum_{i\ =\ 1}^{\text{n}}\big{|}\text{I}_{ \text{predicted}}-\text{L}_{\text{actual}}\big{|} \tag{10}\]  
\[RMSE =\ \sqrt{\frac{1}{n}\sum_{i\ =\ 1}^{\text{n}}\big{(}L_{\text{ predicted}}-L_{\text{actual}}\big{)}^{2}} \tag{11}\]  
where n is the sample size for training or testing sub-set, Lpredicted and Lactual are the predicted and actual values of landslides (dependent factor). RMSE and MAE values less than 0.5 designate a better prediction ability of the models and greater than 0.5 resembles less ability [74].
## 5 Simulations
### Simulation Results  
Table 3 shows the optimization results of the accuracy rate, number of selected features, fitness function values, and CPU times achieved by various algorithms. Note that the best values among all comparison algorithms are highlighted in bold font. As can be seen, the proposed EBBA achieves the best accuracy rate on 7 datasets and achieves the best of selected feature number on 2 datasets. More intuitively, the proposed EBBA achieves the best fitness function values on 9 datasets, which means that the proposed EBBA is with the best performance among all benchmark algorithms. The reason may be that we enhance the EBBA by improving its exploration ability and balancing its exploration and exploitation abilities, which make the EBBA more suitable for solving feature selection problems. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset** & **Number of Features** & **Number of Instance** \\ \hline
1 & Breastcancer & 10 & 699 \\
2 & BreastEW & 30 & 569 \\
3 & Congress & 16 & 435 \\
4 & Exactly & 13 & 1000 \\
5 & Exactly2 & 13 & 1000 \\
6 & HeartEW & 13 & 270 \\
7 & SonarEW & 60 & 208 \\
8 & SpectEW & 22 & 267 \\
9 & tic-tac-toe & 9 & 958 \\
10 & Vote & 16 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Algorithm** & **Key Parameters** \\ \hline
1 & BPSO & \(c1=2\), \(c2=2\) \\
2 & BGWO & \(\alpha=[2,0]\) \\
3 & BDA & \(w=[0.9,0.4]\), \(s=[0.2,0]\), \(a=[0.2,0]\), \(c=[0.2,0]\), \(f=[0.2,0]\), \(e=[0,0.1]\) \\
4 & BBA & \(A=0.25\), \(Q_{max}=2\), \(Q_{min}=0\) \\
5 & EBBA & \(Q_{max}=2\), \(Q_{min}=0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key parameters of benchmark algorithms. In addition, Figure 2 shows the convergence rates obtained by different benchmark algorithms during the solving processes. As can be seen, the proposed EBBA achieves the best curves on most datasets, which performs the best convergence ability among all the comparison algorithms. On the other hand, we also evaluate the effectiveness of the proposed improved factors. Specifically, we combine the proposed [PERSON] flight-based global search method, population diversity boosting method, and chaos-based loudness method with conventional BBA, namely, EBBA-IF1, EBBA-IF2, and EBBA-IF3, respectively. Table 4 shows the optimization results of the accuracy rate, number of selected features, fitness function values, and CPU times achieved by various algorithms. Moreover, Figure 3 shows the convergence rates obtained by different EBBA versions during the solving processes. As can be seen, the