esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Preparation of Test Fuel

Table 1: Properties of the oily phase and aqueous phase of FPBO (Fast pyrolysis bio-oil). \begin{table}
\begin{tabular}{c c c c} \hline
**Peak No.** & \(t_{R}^{b}\) **(min)** & **Component** & \multicolumn{2}{c}{**Area (\%)**} \\ \cline{3-4}  & & & **Oily Phase** & **Aqueous Phase** \\ \hline
1 & 1.3 & Water & 0.61 & 15.29 \\
2 & 1.6 & Acetone & 0.14 & 0.99 \\
3 & 1.8 & Hydroxy-acetaldehyde & 0.08 & 0.27 \\
4 & 1.9 & Acetic acid & 0.16 & 2.10 \\
5 & 2.6 & 1-Hydroxy-2-propanone & 0.16 & 1.72 \\
6 & 3 & Propionic acid & 0.21 & 0.64 \\
7 & 3.9 & Pyridine & 0.11 & 0.24 \\
8 & 4.6 & 1-Hydroxy-2-butanone & 0.06 & 0.17 \\
9 & 5 & 2-Hydroxytetrahydrofuran/profuran & 0.05 & 0.13 \\
10 & 5.2 & Cyclopentanone & 0.06 & 0.16 \\
11 & 5.4 & Butyric acid & 0.08 & 0.17 \\
12 & 6.3 & 2-Cyclopenten-1-one & 0.05 & 0.24 \\
13 & 6.8 & Furfuryl alcohol & 0.23 & 0.88 \\
14 & 7.7 & Butylrolactone & 0.24 & 0.71 \\
15 & 8.4 & 3-Methyl-2-cyclopenten-1-one & 0.17 & 0.32 \\
16 & 8.7 & Phenol & 0.52 & 0.53 \\
17 & 9.2 & 2-Hydroxy-3-methyl-2-cyclopenten-1-one & 0.34 & 0.72 \\
18 & 9.3 & 2,3-Dimethyl-2-cyclopenten-1-one & 0.21 & 0.33 \\
19 & 9.4 & Cresol & 0.38 & 0.37 \\
20 & 9.8 & 2-Methoxyphenol & 1.05 & 1.78 \\
21 & 10.8 & Pyrocatech & 1.52 & 2.32 \\
22 & 11.3 & Hydroquinone & 0.35 & 0.47 \\
23 & 11.5 & Methyl benzenediol & 0.92 & 1.07 \\
24 & 11.9 & 2,6-Dimethoxy-phenol & 0.42 & 0.44 \\
25 & 12.5 & Levoglucosan & 0.39 & 0.61 \\
26 & 15 & Caffeine & 1.51 & 1.57 \\
27 & 15.1 & Hexadecanentirile & 0.85 & 0.33 \\
28 & 15.4 & Palmitic acid & 6.22 & 0.42 \\
29 & 16.2 & Linoleic acid & 1.63 & 0.22 \\
30 & 16.4 & Stearic acid & 1.66 & 0.29 \\ \hline \end{tabular}
\end{table} 
Table 2: List of GC-MS (Gas Chromatography-Mass Spectrometry) area percentages of compounds in oily phase and aqueous phase (Reproduced with permission from [29], Elsevier, 2014). Table 3 illustrates the properties of diesel, FPBO (50 vol % oily-phase oil + 50 vol % aqueous-phase oil), and the emulsion of 5% FPBO + 95% diesel. The properties of the FPBO (50 vol % oily-phase oil + 50 vol % aqueous-phase oil) are measured based on the test methods of ASTM (American Society for Testing and Materials) D7544 (including gross calorific value, water content, pyrolysis solids content, kinematic viscosity, density, sulfur content, ash content, pH, pour point and flash point). By comparison, the FPBO had a higher oxygen content but a lower carbon and hydrogen content as well as a lower heating value than the diesel. Moreover, the FPBO had much higher viscosity than was the case for the diesel fuel.
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 3 Results
### Performance Comparison Test and Analysis of the BPNN Algorithm
#### 3.1.2 Forest Fire Image Recognition Effect Test  
Various algorithms are used to compare the forest fire image data processing speed and to process videos with a duration of 4 min and 16 s. The video has 29 images/s, and the size of each frame is 960\(\times\)540, for a total of 7424 images. The processing speed and delay rate are calculated using the BPNNFire algorithm, BPNN, interframe difference, background elimination, and Vibe algorithm, presented in Figure 8.  
Figure 8 indicates that the processing speed is 7.54 fps for the BPNNFire algorithm, 7.26 fps for the BPNN algorithm, 7.13 fps for the interframe difference algorithm, 6.79 fps for the background elimination algorithm, and 0.87 fps for the Vibe algorithm. When the flight speed of the UAV is constant, owing to the limited change-of-scene information recorded in the video, preprocessing during video frame capture can reduce the number of frames to achieve a real-time data processing effect. When the video frame is set to 5 fps, the interframe difference algorithm and background elimination method are used to increase the processing speed to verify the accuracy of the forest fire recognition algorithm at a low frame rate. The test data in Figure 8 reveal that, under the same conditions, the BPNNFire algorithm has the lowest delay rate, and its processing speed can meet the real-time usage and accuracy requirements. Regarding algorithm processing results, other algorithms are used to process images. Figure 9 presents the recognition results of various algorithms.  
Figure 8: Processing speed and delay rate of the algorithms.  
Figure 7: Training and testing loss function curves.  
Figure 10 displays the accuracy of fire-image determination using various algorithms, revealing that the BPNNFire algorithm can meet real-time accurate forest fire recognition requirements under a low frame rate. The recognition accuracy rate reaches 84.37%, indicating that the BPNNFire algorithm is superior to other algorithms in recognition accuracy.  
Figure 10: Fire determination accuracy of various algorithms.  
Figure 9: Recognition results of various processing algorithms. (**a**) Original image; (**b**) BPNNFire algorithm; (**c**) BPNN algorithm; (**d**) Interframe difference algorithm; (**e**) Background elimination algorithm; (**f**) Vibe algorithm.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 4 Experimental Setup
### Battery Pack  
In order to make this study possible, a battery pack of four Li-ion cells in series was employed. Specifically, we used the B18650 CA cell from BAK (Shenzhen, China). Table 6 shows the main parameters of the implemented battery pack.  
To have a deeper understanding of the cells used, an electrochemical impedance spectroscopy (EIS) measurement was performed at 25 \({}^{\circ}\)C of each cell of the battery. The impedance data were fitted to an equivalent circuit model that is commonly used in the characterization of Li-ion cells, such as in the study presented in [33]. Figure 4 shows a schematic diagram of that model, and Figure 5 shows the data measured from all the cells.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Number of cells & 4 & - \\ V\({}_{\text{BAT}}\) & 10.8–16.8 & V \\ V\({}_{\text{CELL}}\) & 2.7–4.2 & V \\ Energy & 8.1 & Wh \\ Cell energy & 32.4 & Wh \\ Capacity & 2250 & mAh \\ Cell capacity & 2250 & mAh \\ \hline \hline \end{tabular}
\end{table}
Table 6: Battery characteristics.  
Figure 4: EIS equivalent model.  
Table 7 presents the fitted parameters that were obtained through the EIS measurements. The resistance value R\({}_{\rm S}\) represents the ohmic resistance of the cell. In this instance, the mean value was 27.316 m\(\Omega\), which was considered during the design of the balancing circuits. It is important to note that a low R\({}_{\rm S}\) indicated that this cell model was suitable for this study. Nevertheless, further analysis is required to confirm its suitability for space applications, as conducted in [33]. The remaining parameters were not considered due to their negligible impact on the final performance of the equalizers.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 1 Introduction
However, it is worth noting that the stability of all recursive algorithms mentioned above should be carefully considered, and more efforts must be made to make the recursive algorithm stable [35]. The other way is to use the simplified structure firstly introduced in [36], which uses a diagonal channel structure of nonlinear filters and channel reduced strategy that only uses a few channels near the main one. Note that the choice of the number of the reduced channels may significantly influence the steady-state performance. This paper focuses on a group of adaptive Volterra filter-based algorithms, which are widely used in practice and perform well in a low-order nonlinear case. The major challenge of these traditional Volterra filter-based algorithms is that the computational complexity increases rapidly with the increase of the length of the memory and the order of the Volterra kernel. Recently, the Kronecker product decomposition has been introduced to linear system identification [37] and acoustic echo cancellation [38]. When using the Kronecker product decomposition, the computational complexity can be reduced dramatically in theory. In addition, it is demonstrated that the decomposable Volterra model outperforms the simplified structure in [36]. Motivated by these latest works, this paper proposes an adaptive Volterra-filtered error least mean square algorithm (DVMFELMS) that takes a decomposable Volterra model and filtered-error structure into account for the nonlinear active control problem. By comparing the computational complexity and the convergence performance of the proposed algorithm with the linear FELMS algorithm, VFELMS algorithm [11], FEGFLNN algorithm [19], and FEEMFNL algorithm [39], this paper shows that the proposed algorithm not only reduces the computational complexity, but also improves the convergence behavior slightly in the broadband noise cases. The remainder of this paper is organized as follows. Section 2 formulates the problem of nonlinear active noise control. Section 3 derives the proposed DVMFELMS algorithm, and Section 4 compares its computational complexity with some state-of-the-art nonlinear filter algorithms. Simulation and conclusions are presented in Section 5 and Section 6, respectively.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 5 Deflection Temperature Modelling
### Temperature Correction Model for the Deflection Basin
#### 5.2.2 Temperature Correction Based on the Load Center Deflection Rate  
This method looks for the relationship between the change rate before and after the temperature correction of deflections at other radial distances and the load center deflection change rate based on the change rate between the measured load center deflection and the calculated deflection at the load center after modulus correction.  
The plots of the deflection rate at the load center and each point at the radial distance from 20 to 90 cm were also analyzed, and the proportional function was used for fitting. The fitting results at different radial distances are shown in Figure 9. The ratio \(k\) and \(R^{2}\) of the deflection rate at each point and the load center are obtained, and the results are shown in Table 3.  
Figure 8: Fitting results between deflection difference at different radial distances and load center deflection difference: (**a**) 20 cm; (**b**) 30 cm; (**c**) 45 cm; (**d**) 60 cm; (**e**) 90 cm.  
Figure 9: Fitting results between deflection rate at different radial distances and load center deflection rates: (**a**) 20 cm; (**b**) 30 cm; (**c**) 45 cm; (**d**) 60 cm; (**e**) 90 cm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Radial** & **20** & **30** & **45** & **60** & **90** \\ \hline \(k\) & 0.5672 & 0.4662 & 0.4180 & 0.3867 & 0.3222 \\ \(R^{2}\) & 0.8886 & 0.8329 & 0.8341 & 0.8204 & 0.7685 \\ \hline \end{tabular}
\end{table}
Table 3: Adjustment factors for deflection temperature.  
From the fitting results, the fitting degree is better than that of the difference fitting in the previous section, and the results obtained are more accurate. Then, the deflection temperature correction model at different radial distances in the temperature-affected range is obtained, as shown in Equation (9). The meaning of each parameter is the same as above and will not be explained again.  
\[\left\{\begin{array}{ll}d_{0}=10^{0.004644(20-T)}\times d_{0T}\\ d_{20}=(0.5672\times\frac{d_{0}-d_{0T}}{d_{0T}}+1)\times d_{20T},R^{2}=0.8886\\ d_{30}=(0.4662\times\frac{d_{0}-d_{0T}}{d_{0T}}+1)\times d_{30T},R^{2}=0.8329\\ d_{45}=(0.4180\times\frac{d_{0}-d_{0T}}{d_{0T}}+1)\times d_{45T},R^{2}=0.8341\\ d_{60}=(0.3867\times\frac{d_{0}-d_{0T}}{d_{0T}}+1)\times d_{60T},R^{2}=0.8204\\ d_{90}=(0.3222\times\frac{d_{0}-d_{0T}}{d_{0T}}+1)\times d_{90T},R^{2}=0.7685 \end{array}\right. \tag{9}\]
## 4 Experiment
### Performance Evaluation of User Profiling with Integrated High-Dimensional Features
The attention layer output \(o_{t}\) is derived as follows:  
\[e_{t}=utanh(wh_{t}+b) \tag{5}\]  
\[a_{t}=\frac{exp(e_{t})}{\sum_{j=1}^{t}e_{j}} \tag{6}\]  
\[o_{t}=\sum_{t=1}^{i}a_{t}h_{t} \tag{7}\]  
Figure 3: User profile prediction network. Figure 2: High-dimensional feature extraction based on stacking ensemble learning. where \(u\) and \(w\) are weights, \(b\) is bias, and \(e_{t}\) is probability distribution. The user prediction results \(Y=\left[y_{1},\cdots,y_{s-1},y_{s}\right]^{T}\) are then generated through a sigmoid activation function. The final model's training results are shown in \[Z=ReLU(u\otimes W_{c}+b_{c}) \tag{1}\]0  
Figure 4 presents the training outcomes for models under three scenarios: (1) without high-dimensional features, (2) with basic high-dimensional features (population high and tweet high), and (3) with all high-dimensional features. The test set accuracies achieved are 77.23%, 81.82%, and 86.49%, respectively. Integrating all high-dimensional features yields the highest overall accuracy for the training curve. Although some fluctuations occur, the inclusion of high-dimensional features allows the model to capture more information for effective learning. Using only basic high-dimensional features (population high and tweet high), the initial training curve resembles that of the full-feature model. However, as training progressed, its performance gradually declined and exhibited greater volatility, indicating that relying solely on partial high-dimensional features might be insufficient to sustain model performance throughout training. Without integrating high-dimensional features and using only the raw training inputs, the training curve consistently lagged behind the other two curves, demonstrating that high-dimensional features provide crucial information for accurate profiling predictions. In summary, integrating multiple high-dimensional attribute features can effectively enhance the performance and stability of user profiling. This finding underscores the importance of considering the depth of profiling and hidden information during the profiling process. However, introducing too many variables could introduce noise, leading to minor oscillations in model training. Therefore, careful consideration of each variable's contribution to the profiling model is necessary.
###### Abstract
To mitigate these adverse effects, a strategy has been proposed to delay the onset of fiber breakage and ruptures by encasing the composite tubes within expanded Polypropylene (EPP) blocks to restrict lateral deformation. This study examines the energy efficiency and deformation behavior of EPP foam blocks reinforced with nested-tube reinforcements under quasi-static lateral loads. Previous discussions emphasize that foam is not a suitable filler material because of its poor energy efficiency, which is a result of the tubes' lateral expansion under these loading scenarios. In order to address this constraint, the current work creatively integrates CFRP and aluminum nested tubes into the EPP blocks. By severely limiting the tubes' lateral expansion under lateral pressure, this deliberate positioning is intended to improve the specimens' ability to absorb energy. It is evident from empirical findings that EPP foam specimens supplemented with nested-tube configurations perform significantly better in terms of crash efficiency than configurations in which nested tubes are used but EPP foam is not integrated. In particular, very good outcomes were obtained by combining lightweight nested CFRP tubes with EPP foam blocks. This study highlights the potential of lightweight composite materials in applications requiring high energy absorption and efficiency, while also adding to the body of knowledge by demonstrating the efficacy of nested-tube reinforcements in enhancing the energy absorption characteristics of EPP foam blocks.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 7 Evaluation of the Solutions
### Runs and Results
#### 7.2.3 Effect of the Group Size  
In random graphs with 70 nodes and using the following parameters, the effect of the group size on the optimal spanning structures was analyzed. The results are in Table 4. Using \(D_{min}=1\) and \(D_{max}=10\), 10% of the nodes were nodes with a degree bound of one. The edge costs was selected from the interval of \([1,4]\). The group size was varied from 2 to 47. For each configuration, 50 runs were executed.  
Note that the first line corresponds to the computation of the shortest path. By increasing the size of the group \(M\), the difference between the costs of the three spanning structures increases.  
An interesting result can be observed from the three series of calculations. Even though there are more arcs in the DCMStH then in the DCMStT, the cost of the DCMStH is more favorable. This is the consequence of the fact that DCMStHs, to circumvent the constraints, can return several times to some nodes and use favorable edges.
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 1 Introduction
After processing, jobs must be returned back to banks where they will be charged for the provided service. In order to address financial data supply chain, this research covers all aspects of data batch processing, being that it is the tool that manages banks' financial data supply chain scheduling. The scheduling aspects of DBP are job priorities, precedence relationships, constraints in addition to cost. It is important to understand that cost consideration in data batch processing is vital due to the extremely high cost of the resources involved. However, previous research has overlooked the cost despite its importance, and the focus was on the effectiveness of the scheduling component of the process. The high cost of software and hardware resources used in the batch process and the patent rights of companies that own the platform made it not only beneficial, but also essential for DBP users to reduce the cost associated with it. Therefore, this research intends to bridge such a gap and provides a comprehensive study that covers the important aspects of data batch processing. Thus, the main contributions of this work can be summarized as follows:  
* Consider all types of costs related to the batch process which are the servers and software basic leasing cost, rental cost for additional resources needed in case of overload and extra work, penalty cost of failing to execute the batch process as per the Service Level Agreement (SLA), and the opportunity cost representing the cost of idling a resource for any period of time due to inefficient task allocation. * Develop an iterative dynamic scheduling algorithm (DCSDBP) to optimize the data batch processing considering the different costs, availability of resources, and customer service level agreement (SLA) along with the rest of the batch process factors such as the clients' priorities, tasks predecessors and time. * Utilize the created optimization model to address the problem of controlling the availability of resources such as processors and software required to process input batches and balance the usage of current owned resources and the need to rent additional ones while maintaining the lowest possible cost for the whole data batch processing. The rest of the paper is organized as follows: Section 2, the background related to this work is presented. The data batch algorithm is presented in Section 3. The illustrative example and sensitivity analysis are presented in Sections 4 and 5 respectively. Finally, the conclusions are presented in Section 6.
###### Abstract  
Fifth-generation (5G) heterogeneous network deployment poses new challenges for 5G-based cognitive radio networks (5G-CRNs) as the primary user (PU) is required to be more active because of the small cells, random user arrival, and spectrum handoff. Interweave CRNs (I-CRNs) improve spectrum utilization by allowing opportunistic spectrum access (OSA) for secondary users (SUs). The sum utilization of spectrum, i.e., joint utilization of spectrum by the SU and PU, depends on the spatial and temporal variations of PU activities, sensing outcomes, transmitting conditions, and spectrum handoff. In this study, we formulate and analyze the sum utilization of spectrum with different sets of channels under different PU and SU co-existing network topologies. We consider realistic multi-channel scenarios for the SU, with each channel licensed to a PU. The SU, aided by spectrum handoff, is authorized to utilize the channels on the basis of sensing outcomes and PU interruptions. The numerical evaluation of the proposed work is presented under different network and sensing parameters. Moreover, the sum utilization gain is investigated to analyze the sensitivities of different sensing parameters. It is demonstrated that different sets of channels, PU activities, and sensing outcomes have a significant impact on the sum utilization of spectrum associated with a specific network topology.  
5G networks; cognitive radio; interweave; sum utilization of spectrum; spectrum handoff; spatial and temporal variations +
Footnote †: journal:  
_Article_
## 4 Algorithm Validation
### Computation Time Analysis  
Table 1 presents the SE computation times for enclosures from Section 4.2 using the proposed generalized algorithm and FEM. Note that all calculations were performed on an outdated PC with a 6 th-generation quad-core Intel Core i5 processor and 16 GB of RAM. It can be seen that the SE calculation by FEM takes considerably longer than that using the generalized algorithm based on equivalent circuits. In all cases, the time difference is more than \(10^{6}\) times.  
Thus, the results of the algorithm validation prove that it has acceptable accuracy and low computation time. Consequently, using this algorithm in the early stages of designing electronic equipment, one can quickly evaluate the SE, which will significantly reduce the time for developing the final product.
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC) Analysis  
The thermal properties of BRS were tested using a differential scanning calorimeter (DSC 214 Polymer, NETZSCH, Bayern, Germany). BRS powders (10 \(\pm\) 0.5 mg) were dispersed into 50 \(\mu\)L distilled water in an aluminum pan, hermetically sealed, and equilibrated for 24 h before the measurement. Thermal transitions of samples for gelatinization were marked by T\({}_{\text{o}}\) (onset temperature), T\({}_{\text{p}}\) (peak temperature), T\({}_{\text{c}}\) (conclusion temperature), and \(\Delta\)T (gelatinization temperature range).