esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of Samples  
#### 2.4.1 Fourier-Transform Infrared Spectroscopy (FTIR)  
In order to characterize the PE-g-GMA samples, FTIR measurements were performed. There were two distinguishable peaks belonging to PE-g-GMA at wavenumber 1735 cm\({}^{-1}\), which corresponded to the carbonyl group of GMA and the methylene group of PE at wavenumber 722 cm\({}^{-1}\). To determine the amount of GMA grafted onto the polymer matrix, the ratios of these FTIR peak heights were considered. Each sample was measured from 600-4000 cm\({}^{-1}\), using a resolution of 4 cm\({}^{-1}\), 2-4 times.  
A calibration curve was obtained from a commercial PE-g-GMA (Sigma Aldrich, Darmstadt, Germany) sample with 8 wt.% GMA, which was diluted with LDPE pellets and blended in the Brabender plasticorder at 155 \({}^{\circ}\)C for 8 min at 60 rpm (see Appendix A, Figure A1). The ratio between the peak heights was plotted as function of the GMA content in the samples to the calibration curve to obtain the functionalization degree (FD).  
Furthermore, the efficiency of the grafting process, E, was defined by dividing the value of [GMA]\({}_{\text{g}}\) (grafted) by [GMA]\({}_{\text{i}}\) (initial concentration) according to:  
\[\text{E}\;(\%)=\frac{\left[\text{GMA}\right]_{\text{g}}}{\left[\text{GMA} \right]_{\text{i}}}\times 100 \tag{1}\]
## 5 Conclusions  
In this paper, a global and local feature fusion network with a biencoder (GLFFNet) is proposed in order to solve the problems of low edge accuracy, small segmentation error, and assimilation of small objects by large objects in complex remote sensing scenes. Based on Transformer structure, atrous convolution, gated convolution, and the spatial pyramid architecture, this paper proposes SFA and SPAC encoders for multi-scale feature extraction and then fusion feature information for subsequent decoding to generate segmentation results. At the same time, in order to transfer the pre-training weight of the classification dataset to the segmentation model better and give full play to the performance of the multi-scale structure of the semantic segmentation model, a multi-head loss block is designed to assist training. Finally, the experimental results of GLFFNet reaching 87.96% mIoU on the Potsdam dataset show that when GLFFNet is applied to the semantic segmentation task of high-resolution remote sensing, GLFFNe achieves higher accuracy than most of the semantic segmentation models released so far. Therefore, this paper believes that GLFFNet has its rationality and necessity in the existing semantic segmentation models in the field of remote sensing. In the future, we will continue to explore new ways to integrate convolution and Transformer and continue to optimize the model to further improve the overall training cost and interface time of the model so that it can be applied in important fields such as environmental monitoring.  
Conceptualization, Q.T., F.Z. and Z.Z.; methodology, Z.Z. and F.Z.; software, F.Z.; validation, Q.T. and F.Z.; formal analysis, F.Z. and H.Q.; investigation, Z.Z.; resources, Z.Z. and F.Z.; data curation, Z.Z. and F.Z.; visualization, F.Z.; writing--original draft preparation, F.Z.; writing--review and editing, Z.Z. and F.Z.; supervision, Z.Z.; project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by National key research and development program of China (2020 YFB1600702).  
Not applicable.  
The authors declare no conflict of interest.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 2 Study Area and Data
### Dataset  
#### 2.2.1 FY-4A Data  
FY-4A, the China's new generation geostationary orbit meteorological satellite, was launched on 11 December 2016. The Advance Geostationary Radiation Imager (AGRI) is one of the primary payloads on FY-4A and has 14 channels including two VIS, four NIR, and eight IR channels (Table 2). The AGRI imager acquires 40 full-disk maps and 165 regional maps of China on a daily basis, with a regional observation range of 3-55\({}^{\circ}\) N and 60-137\({}^{\circ}\) E. The FY-4A/AGRI data are publicly available from the National Satellite Meteorological Center data service network ([[http://satellite.nsmc.org.cn/PortalSite/Default.aspx](http://satellite.nsmc.org.cn/PortalSite/Default.aspx)]([http://satellite.nsmc.org.cn/PortalSite/Default.aspx](http://satellite.nsmc.org.cn/PortalSite/Default.aspx))(accessed on 10 November 2021)). In this study, FY-4A / AGRI L1 (level 1) data with a 4 km resolution was used.
## 5 Transmission Rate Control Scheme Based on Network Node Bandwidth Allocation
### Scheme Description  
The rate control scheme based on node bandwidth allocation is achieved through rate negotiation and adjustment messages. Consider the simplified network model shown in Figure 1, where the _Server_ is the data-sending node and the _User_ is the data-requesting and receiving node. \(RouterA\) and \(RouterB\) are intermediate forwarding nodes. After completing the connection establishment, the _Server_ sends a negotiation request message before sending data. It then sends a termination message after the end of data transmission. \(RouterA\) and \(RouterB\) can process and forward the negotiation request message, and the _User_ replies to the _Server_ to complete the rate negotiation. After the negotiation, \(RouterA\) and \(RouterB\) can notify the _Server_ to adjust the transmission rate through the adjustment message.  
The header format of the negotiation message is shown in Figure 2, with the _Type_ field used to identify the type of signaling, while 0x0011 represents the negotiation request message, 0x0012 represents the negotiation success message, and 0x0013 represents the negotiation failure message. The _Max_ field is used to identify the maximum transmission rate currently achievable for this transmission. The _Min_ field indicates the minimum transmission rate accepted for this transmission. If the actual transmission rate is lower  
Figure 1: Bandwidth allocation-based transmission rate control model.  
than this value, then it is considered meaningless, and the transmission is abandoned. If there is no minimum rate requirement, then it can be set to zero. The \(ID\) field is used to identify different transmissions or different subflows of the same transmission. The \(Rtt\) field represents the current round-trip delay time of the link.  
The rate adjustment message is shown in Figure 3, with the _Type_ field used to identify the type of signaling, while 0x0021 represents the reduce transmission rate adjustment message, 0x0022 represents the increase transmission rate adjustment message, and 0x0023 represents the termination message. The \(ID\) field is used to identify different transmissions or different subflows of the same transmission. The _Speed_ field represents the new transmission rate.
# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Results and Discussion
The effectiveness of particle swarm optimization algorithm has also been proven on FC100 and CIFAR-FS. Figure 8a shows the relationship between the maximum accuracy and average accuracy of the CIFAR-FS dataset and the generation in meta-testing under the one-shot and five-shot strategies. Figure 8b displays the relationship between the maximum accuracy and average accuracy of the FC100 dataset and the generation in meta-testing under the one-shot and five-shot strategies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**CIFAR-FS 5-Way**} & \multicolumn{2}{c}{**FC100 5-Way**} \\ \cline{3-6}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline \multirow{2}{*}{MAML [17]} & \multirow{2}{*}{32-32-32-32} & 58.9 \(\pm\) 1.9 & 71.5 \(\pm\) 1.0 & - & - \\ Prototypical Networks [8] & 64-64-64-64 & 55.5 \(\pm\) 0.7 & 72.0 \(\pm\) 0.6 & 35.3 \(\pm\) 0.6 & 48.6 \(\pm\) 0.6 \\ Relation Networks [9] & 64-64-128-128 & 55.0 \(\pm\) 1.0 & 69.3 \(\pm\) 0.8 & - & - \\ R2D2 [33] & 96-192-384-512 & 65.3 \(\pm\) 0.2 & 79.4 \(\pm\) 0.1 & - & - \\ TADAM [34] & ResNet-12 & - & - & 40.1 \(\pm\) 0.4 & 56.1 \(\pm\) 0.4 \\ Shot-Free [47] & ResNet-12 & 69.2 \(\pm\) n/a & 84.7 \(\pm\) n/a & - & - \\ TEWAM [48] & ResNet-12 & 70.4 \(\pm\) n/a & 81.3 \(\pm\) n/a & - & - \\ Prototypical Networks [8] & ResNet-12 & 72.2 \(\pm\) 0.7 & 83.5 \(\pm\) 0.5 & 37.5 \(\pm\) 0.6 & 52.5 \(\pm\) 0.6 \\ MetaOptNet [38] & ResNet-12 & 72.6 \(\pm\) 0.7 & 84.3 \(\pm\) 0.5 & 41.1 \(\pm\) 0.6 & 55.5 \(\pm\) 0.6 \\ GEFS [23] & ResNet-12 & 71.5 \(\pm\) 0.8 & 86.0 \(\pm\) 0.5 & 42.6 \(\pm\) 0.7 & 59.1 \(\pm\) 0.6 \\ Ours & ResNet-12 & **71.91 \(\pm\) 0.85** & **86.77 \(\pm\) 0.63** & **44.16 \(\pm\) 0.77** & **62.17 \(\pm\) 0.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiments were performed on CIFAR-FS and FC100 and our approach was compared with existing methods. Average meta-learning classification accuracies (%) with 95% confidence intervals on CIFAR-FS and FC100 meta-test splits. a-b-c-d denotes the size of the layer in the backbone. Figure 7: The relationship between the accuracy and the generation in meta-testing on miniImageNet. The 0-th generation represents the \(n\) groups of feature fusion weights randomly generated during population initialization. To further verify the effectiveness of the method, we performed experiments on the Miner Unsafe Behavior dataset by extracting video key frames. Random seed number was fixed to ensure the experiment verifiability. The population was set up with 10 individuals and the iterations were set at 20 generations. The experimental results obtained by comparing the meta-learning accuracy before and after feature fusion are shown in Table 3. This method achieves good performance.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 4 Statistical Overview
Other approaches, such as literature analyses and conceptual work, were evenly distributed across all fuel groups, demonstrating their universal nature. These conclusions confirm that while the selection of calculation methods may depend to some extent on the type of fuel, the research methodology remains independent of this factor and is mainly based on experiments, regardless of the fuel area analyzed. While the analysis confirms a growing interest in the application of machine learning and genetic algorithms in combustion research, many studies show recurring methodological weaknesses. Limited data availability often results in narrow model training, which raises concerns about robustness and generalization. Furthermore, performance metrics are frequently reported without benchmarking against traditional methods, obscuring whether AI-based approaches offer real improvement. The lack of open data, source code, and detailed reporting also limits reproducibility and transparency. These critical gaps should be addressed in future work to ensure that AI tools can be reliably integrated into combustion system design and control in real-world scenarios. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Biodiesel** & **Biomass** & **Coal** & **Gasoline** & **Hydrogen** & **Natural Gas** & **Total** \\ \hline Total & 11 & 10 & 16 & 30 & 17 & 16 & 97 \\ \hline \multicolumn{8}{c}{Methods and Algorithms} \\ \hline CFD & 0 & 0 & 4 & 12 & 3 & 4 & 23 \\ Decision trees & 2 & 3 & 4 & 5 & 1 & 2 & 17 \\ Genetic algorithms & 3 & 1 & 7 & 14 & 4 & 12 & 39 \\ Mean square error & 3 & 1 & 2 & 4 & 3 & 1 & 12 \\ Neural networks & 7 & 4 & 4 & 11 & 11 & 5 & 42 \\ PCA & 0 & 1 & 3 & 1 & 0 & 1 & 6 \\ Random forests & 1 & 1 & 4 & 2 & 0 & 0 & 8 \\ Regression analysis & 1 & 2 & 1 & 3 & 6 & 2 & 13 \\ SVM & 2 & 0 & 4 & 5 & 7 & 2 & 18 \\ \hline \multicolumn{8}{c}{Research Methodology} \\ \hline Experiment & 9 & 9 & 14 & 28 & 16 & 16 & 89 \\ Literature analysis & 3 & 5 & 4 & 6 & 6 & 4 & 28 \\ Conceptual & 6 & 7 & 10 & 25 & 10 & 14 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of publications by fuel type, methods used, and research approach.
## 4 Experimental Results
### Image Reconstruction Experiment of Pelvic Image
Different settings of these parameters could lead to different qualities of reconstructed images. In order to study the performance of our algorithm under different parameters, we only relax one or two free parameters while fixing the other parameters. During the experiment, we firstly set a smaller initial value empirically and observe the image quality, then it is found that the image is full of strong noise and artifacts. If the parameter values increase gradually, image quality is improved and the PSNR and SSIM values rise. However, if the values increase unrestrictedly, the image quality does not improve as expected--then, the image appears to be oversmoothed and to have lost details, at the same time the PSNR and SSIM values are also significantly decreased. The parameters selection experiments are conducted and relevant quantification curves are displayed in Figure 8. From the figure, it could be observed that the parameter values have a great impact on the image quality. If the values are too high or too low, the image quality will be affected greatly. Concretely speaking, when \(a\) is 0.5, the PSNR and SSIM achieve the best value. \(\gamma_{1}\) and \(\gamma_{2}\) control the quality of the reconstructed images; it can be seen from the figure that when the values increase, the PSNR and SSIM will  
Figure 4: 80 views reconstruction results of pelvic image: (**a**) ground truth, (**b**) FBP, (**c**) OS-SART, (**d**) TV, (**e**) PICCS, (**f**) TVPI-G, and (**g**) NPICCS. The display window is [\(-150\) 250] HU. Figure 5: The ROIs from 48-views and 64-views reconstruction results. The first row is from 48-views ROI. The second row is from 64-views ROI. (**a**) ground truth, (**b**) FBP, (**c**) OS-SART, (**d**) TV, (**e**) PICCS, (**f**) TVPI-G, and (**g**) NPICCS. The display window is [\(-150\) 250] HU. be higher, but if the values continue to increase, the indicators will obtain unsatisfactory results. Figure 6: Line intensity distribution of CT images reconstructed from 80-views at two areas. (**a**) Red line; (**b**) Blue line. Figure 5.0 Absolute difference image of different methods from 48-views. (**a**) FBP, (**b**) OS-SART, (**c**) TV, (**d**) PICCS, (**e**) TVPI-G, and (**f**) NPICCS.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### Two-Path Spatial-Temporal Feature Fusion Module
#### 3.2.1 Multi-Scale Feature Extraction (MSFE)  
Generally, a convolution layer utilizes multiple convolution kernels of the same size to carry out convolution operation, which leads to the limitation of feature extraction to a certain extent. In the two-path spatial-temporal feature fusion module, we propose multi-scale feature extraction (MSFE) to process the input silhouettes, which can extract more comprehensive and discriminative feature representations with different granularities. MSFE utilizes the multi-scale convolution (MSC) structure with multiple kernels of various sizes to extract gait features during convolution operation and merges the multi-scale features by different single-path convolution branches. The MSC can expand different perceptual fields and learn more fine-grained features. Moreover, multiple branches can fuse the fine-grained features and obtain more abundant and complete gait characteristics.  
The structure of MSFE is presented in Figure 2. MSFE comprises a three-layer structure, where the first layer is a MSC structure and the other two layers are the regular convolutions. The MSC structure comprises three parallel convolution branches, each of which is designed with distinct kernel sizes: (1,1), (3,3), and (5,5), respectively. Each branch processes the input gait sequences and transforms them into multi-scale feature maps. Then, the outputs of the parallel convolution operations at different scales are combined together by element-wise addition to obtain more abundant features. Subsequently, another two convolution layers are employed to optimize the comprehensive characteristics to achieve the appropriate number of channels, which can also balance the capabilities of the network more flexibly. Assuming that the input is \(X\in R^{C\times T\times H\times W}\), where \(C\) means the number of channels, \(T\) represents the length of the gait sequence and \((H,W)\) signify the height and width of each frame. The MSC feature \(F_{MSC}\) can be formulated below:  
\[F_{MSC}=F_{1\times 1}(X)+F_{3\times 3}(X)+F_{5\times 5}(X) \tag{1}\]  
where \(F_{1\times 1}\), \(F_{3\times 3}\) and \(F_{5\times 5}\) denote the 2D convolution operations with the kernel sizes of (1,1), (3,3) and (5,5), respectively.
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 1 Introduction  
Ionic liquids (ILs) are considered to be good substitutes for organic solvents with low vapor pressure and low flammability [1] and are widely used in various fields [2; 3]. However, their toxicity has been paid more attention in recent years [4; 5]. Benzothiazole ILs have been synthesized and have wide applications in synthesis [6], extraction [7], and separation [8; 9]. It has been shown that benzothiazole ILs have toxic effects on zebrafish, especially in acidic environments [10]. Most ILs are relatively stable and toxic, so the efficiency of natural degradation is very low [11; 12]. ILs are likely to become persistent pollutants and cause long-term damage to the ecological environment if discharged without treatment. There are few studies on the degradation of benzothiazole ILs. Therefore, it is of great significance to study methods for their effective treatment in wastewater.  
The recovery methods of ILs in wastewater include extraction [13], adsorption [14; 15], and membrane separation [16; 17], but these methods cannot completely remove low-concentration ILs in wastewater. Degradation is considered to be an available method for the low-concentration ILs [18; 19]. At present, there are many degradation methods, such as photodegradation [19; 20], chemical oxidation [21; 22], electrochemical oxidation [23; 24], biodegradation [25], and thermal decomposition [26]. Thermal decomposition and biodegradation have certain limitations due tostable chemical structure and toxicity of ILs [12]. Electrochemical oxidation avoids the use of oxidants, but it is often necessary to add a suitable electrolyte into the aqueous IL solution to increase the conductivity of the solution, while electrolyte electrolysis may cause secondary contamination [27]. At present, photodegradation [28] and chemical oxidation are the most concerned methods for IL degradation. Previous studies have shown that photocatalysis can effectively degrade ILs. Complete degradation of ionic liquids (organic cations: BMIM and EMIM; inorganic anions: BF4\({}^{-}\), PF6\({}^{-}\), and Br\({}^{-}\)) can be achieved by combining hydrothermal and photocatalytic treatment [19]. Dye-sensitized photocatalysts (DCQ-TiO\({}_{2}\)/SiO\({}_{2}\)) have also been used to degrade imidazolium-based ionic liquids. The experimental results show that the synthesized photocatalyst has higher activity. Under the experimental conditions, the degradation rate of [BMIM]Br reached 95% at 180 minutes, and the photodegradation intermediate was identified as a harmless and easily biodegradable component [29]. Photocatalytic degradation is much faster than natural degradation (half-lives of IL cations ranged from 32 \(\pm\) 4 to 135 \(\pm\) 25 days) [11].  
In this study, low concentrations of benzothiazole ILs aqueous solutions were used as experimental objects for exploring a simple and effective method for the treatment of these ILS. Influence conditions of the degradation process and the degradation kinetics were studied.
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 2 Hygro-Thermal Model
### Hygro-Thermal Coupling
#### 2.3.1 Moisture Dependency of Thermal Parameters  
It is known that the heat capacity of a porous material is directly related to its moisture content. In particular, if the material is wet, the effect of moisture on heat capacity can be considered using:  
\[\rho C=\rho_{bulk}C_{p}+w\cdot C_{w}, \tag{7}\]  
where \(\rho_{bulk}\) [kg/m\({}^{3}\)] is the bulk density of the material, \(C_{p}\) [J/(kg\(\cdot\)K)] is the specific heat capacity of the dry material, and \(C_{w}\) [J/(kg\(\cdot\)K)] is the specific heat capacity of liquid water. The specific heat capacity of water has a weak thermal dependence for the range of temperatures of interest in the present study [39]. Therefore, it is taken as a constant, \(C_{w}=4182\) J/(kg\(\cdot\)K), which corresponds to the specific heat capacity of water at 20 \({}^{\circ}\)C.  
Additionally, the moisture influence on the thermal conductivity of a wet porous material can be determined through the following expression:  
\[\lambda=\lambda_{0}\big{(}1+b\cdot w/\rho_{bulk}\big{)}=\lambda_{0}\big{(}1+b\cdot w _{g}\big{)}, \tag{8}\]  
where \(\lambda_{0}\) [\(W\)/(m\(\cdot\)K)] is the thermal conductivity of the solid matrix, and \(b\) [-] is a material-dependent thermal conductivity supplement. The supplement \(b\) can be described as the fractional increase (sometimes expressed as a percentage) of the thermal conductivity per mass-percent moisture content, i.e., moisture mass per unit mass of dry material \(\times\) 100. Thermal conductivity has a minor dependence on temperature for normal environmental conditions, and therefore, its thermal dependency is disregarded [40].  
For practical applications, the thermal conductivity of porous materials is often given as a linear relationship based on experimental values determined for different moisture content conditions. For instance, an expression of the form \(\lambda=A+B\cdot w\) has been extensively used in the literature to describe the thermal conductivity of brick [41; 42; 43]. In the previous expression, the intercept \(\lambda(w=0)\) stands for the thermal conductivity of the dry material, \(\lambda_{0}\). In turn, the slope of the curve represents the increase in thermal conductivity per moisture content, and according to Equation (8), it must be equal to \(\lambda_{0}\cdot b/\rho_{bulk}\).