esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Comparison with Analytical Models
Overall, the numerical results of \(T_{GR,max}\) were significantly lower than the analytical results, with the exception of Zaeske(Tri) for \(s=2.0\) m. This was firstly due to the active earth pressure not being fully mobilised in the numerical simulations, giving lower values of \(T_{GR,max}\) in the analytical models. Secondly, the magnitudes of GR deflection were less in the simulation than in the analytical calculations, and resulted in lower values of \(T_{GR,max}\) due to greater simulated subsoil support. In conclusion, the results of the analytical calculations show that the numerical simulations are reliable in terms of load distribution in the embankment. The numerical and analytical results of \(Q_{head}\) were in good agreement overall. The numerical results of \(T_{GR,max}\) are deemed reasonable, though low compared with the analytical results. As expected, the more advanced analytical models (Zaeske and CA) were better at predicting the numerical results. The results of the Zaeske model came closest to the numerical results of \(Q_{head}\). However, the difference in the results between the square and triangular pile arrangements was larger for the Zaeske model than for the numerical models. The CA model was the most consistent of the analytical models in the comparison, showing almost the same trend as the numerical results for not only \(Q_{head}\) but also \(T_{GR,max}\). In contrast, the Extended Carlsson model greatly overestimated the numerical results, especially the results for \(T_{GR,max}\). Thus, it would be more suitable to use the CA model instead of the Extended Carlsson model for the design of lightly piled embankments. However, due to its simplicity, the Extended Carlsson model could still be used for a first estimation if \(Q_{head}\) is substituted for \(Q_{archd}\). Figure 19: Comparison of the pile head load (\(Q_{head}\)) from the numerical and analytical results, as well as the tributary load, plotted against \(s\). The discussed guidelines (Table 3) present geometrical limits for stable arch formation in terms of \(s\), embankment height \(H\) and square pile head width (\(a\)) or pile/pile head diameter (\(d\)). These geometrical limits were compared with the hypothesis of an increased maximum allowable value of \(s\) (\(s_{max}\)) in TK Geo 13 [16] for lightly piled embankments, currently at 1.2 m. EBGEO presents geometrical limits for both square and triangular pile arrangements. Table 4 shows a summary of \(s_{max}\) for \(H\) = 2.5 m according to each of the studied guidelines; \(s_{d}\) is the diagonal centre-to-centre pile spacing, equal to \(\sqrt{2}\cdot s\) for the square pile arrangements and \(2s\) for the triangular pile arrangements (longest diagonal between four piles). The results show that \(s_{max}\) = 1.6 m satisfies all the geometrical limits in Table 4. For square and triangular pile arrangements, \(s_{max}\) equals to 2.2 or 1.6 m, respectively. This supports the hypothesis that \(s_{max}\) could be greater than 1.2 m for lightly piled embankments.
###### Abstract  
The influence of the discharge cone size on the characteristic parameters of a rotating gliding arc, e.g., arc length, arc rotation frequency, and regeneration frequency was investigated when the grounding electrode was equipped with an extension tube. In addition, the effect of air flow rate (100-400 standard liter per minute, SLPM) on the characteristic parameters was also studied (with an extension tube, using a No. 5 discharge cone). The results showed that the disturbance of the outlet flow was intense without an extension tube, which led to the occurrence of the short-circuit regeneration of the gliding arc when the discharge gap was small, and the short-circuit regeneration of the gliding arc could be effectively avoided by adding an extension tube at the grounding end. The existence of the extension tube could significantly increase the arc length by up to 140 mm (250 SLPM, No. 3 discharge cone). The arc lengthening was mainly caused by the unilateral extension of the high voltage side, and the arc shape changed from a \"C\" to an \"L\" shape. This also reduced the regeneration frequency to one-quarter of the arc regeneration frequency without extending the tube. Without lengthening the tube, the arc could not achieve complete circumferential rotation. The arc distribution in the annular area between the anode and the cathode was discrete, and the arc distribution was extremely uneven. The existence of the extension tube made the arc rotate uniformly, and the maximum rotation frequency was 228 Hz. The influence of the air flow rate on arc length was mainly reflected in the small flow rate range. When the air flow rate was less than 200 SLPM, the arc length increased first, and then decreased with the increase of the air flow rate, reaching the maximum arc length of 143.6 mm at 150 SLPM. The arc frequency was positively correlated with the air flow rate, but the rotation frequency increased faster than the regeneration frequency: when the flow rate was 150 SLPM, the gliding arc rotated one circle in a tensile fracture period; when the flow rate reached 400 SLPM, the gliding arc rotated two circles in a tensile fracture period.  
gliding arc discharge; extension tube; flow rate; frequency +
Footnote †: journal: Applied Soft 2019, 9, 1347; doi:10.3390/app9071347
## 5 Applications
### Energy Storage
CQDs/CoS\({}_{2}\) nanocomposite exhibited superior electrochemical performance than the pure CoS\({}_{2}\) material. The nanocomposite showed a specific capacity of 808 F g\({}^{-1}\) at 1 A g\({}^{-1}\) current density.
## 2 Method
### Model Formulation
We now define \(Z=C_{1}-C_{2}\) and \(V(F)=K_{1}(F)-K_{2}(1-F)\), and subtract expression (2) from expression (1) to obtain:  
\[Z^{t}=\beta V\Big{(}F^{t-1}\Big{)}+(1-\beta)Z^{t-1} \tag{4}\]  
\[F^{t}=\alpha S\big{(}Z^{t}\big{)}+(1-\alpha)F^{t-1} \tag{5}\]  
Substitution of expression (4) for \(Z^{t}\) into expression (5) yields the following two-dimensional nonlinear dynamical system:  
\[Z^{t}=\beta V\Big{(}F^{t-1}\Big{)}+(1-\beta)Z^{t-1} \tag{6}\]  
\[F^{t}=\alpha S\Big{[}\beta V\Big{(}F^{t-1}\Big{)}+(1-\beta)Z^{t-1}\Big{]}+(1- \alpha)F^{t-1} \tag{7}\]  
whose fixed points correspond to states \(\left(\widetilde{Z},\widetilde{F}\right)\) such that:  
\[Z^{t}=Z^{t-1}=\widetilde{Z} \tag{8}\]  
\[F^{t}=F^{t-1}=\widetilde{F} \tag{9}\]  
Using conditions (8) and (9), expressions (6) and (7) can be rewritten as:  
\[\widetilde{Z}=\beta V\bigg{(}\widetilde{F}\bigg{)}+(1-\beta)\widetilde{Z} \tag{10}\]  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]0  
or, equivalently:  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]1  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]2  
and, substituting (12) for \(\widetilde{Z}\) in expression (13), we obtain the following fixed point conditions:  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]3  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]4  
Next, we address the specification of a route choice function \(S\) incorporating both _direct_ and _contrarian_ behaviors, as defined in the introductory remarks of Section 1. To this end, we propose a formulation encompassing the popular _Logit_ model in its well-known conventional form (16) as a representation of direct choice behavior, and in a modified form, presented hereafter, as a representation of contrarian choice behavior. The two forms are then merged into a single model through a weighted combination reflecting the fractions of travelers adopting the two types of behavior. According to this approach, the probabilities of choosing route 1 are determined by means of expressions (16) and (17), respectively, for direct \(\left(P_{1}^{DIR}\right)\) and for contrarian \(\left(P_{1}^{CON}\right)\) subjects:  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]5  
\[C_{2}^{t}=\beta K_{2}\Big{(}1-F^{t-1}\Big{)}+(1-\beta)C_{2}^{t-1} \tag{2}\]6  
where \(\mu\) (>0) represents the dispersion parameter of the Logit model. While the standard Logit model (16) is based on the assumption of cost-minimizing route choice behavior, expression (17) entails that the probability of a contrarian traveler choosing route 1 is an increasing function of the route's expected cost. This is intended to model the propensity of contrarians to _deliberately_ choose the route having higher _expected_ cost, based on the belief that the majority of other travelers are likely to behave as cost minimizers. If this kind of strategic guess is correct, the route having the higher expected cost may turn out to be, _after trips have been made_, the less costly choice.
# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 4 Results and Discussion
### Comparisons with State-of-the-Art Methods
#### 4.1.1 Results on CUB-200-2011  
The accuracies of different comparison methods on the CUB-200-2011 dataset are listed in Table 2. The experimental results show that the method in this paper achieves an excellent performance of 92.3% accuracy on this dataset, and the accuracy is much higher than that of Part-based R-CNNs, PoseNorm, KERL, Mask-CNN, and BCNN. FBSD and API-Net outperform other CNN-based classification algorithms by 89.8% and 90.0%, but both lag behind the methods in this paper. This is largely attributed to their simplistic approach to feature extraction. While these methods focus on local information, they lag in capturing inter-regional correlations and relative importance, which limits their accuracy. Transformer-based methods such as TransFG, IET, MP-FGVC, and ACC-ViT have demonstrated strong performance, highlighting the importance of global modeling capabilities. However, the self-attention mechanism has disadvantages in extracting fine-grained details. Therefore, this paper adopts ConvNeXt as the backbone network, combined with a cross-layer attention interaction module and a random selection strategy, achieving superior performance.  
Figure 5: The ACC-Epochs curves of the LDGNet during the training and testing phases. (**a**) represents the training phase, and (**b**) represents the testing phase.
## 2 Methods
### Lightweight Network Design  
#### 2.1.1 Backbone Network Improvement  
To meet the detection task requirements in space environments, this paper chooses the relatively lightweight YOLOv8s as the baseline model after weighing detection accuracy and speed. EfficientNet-B0 [40] is used as the main backbone feature extraction network for LTSCD-YOLO. EfficientNet-B0 is a lightweight yet efficient convolutional neural network structure, mainly comprising three modules: the first module is a convolutional layer with a kernel size of \(3\times 3\) and a stride of 2; the second module is the Mobile Inverted Bottleneck Convolution (MBConv) module; the third module consists of a convolutional layer with a kernel size of \(1\times 1\), an average pooling layer, and a fully connected layer. Among them, the MBConv is the core module, where the number following MBConv (1 or 6) represents the channel expansion multiple for each module's input feature matrix. The channel expansion multiple refers to how the number of channels in the input feature matrix is increased or kept the same through convolution operations in the neural network. Specifically, an expansion multiple of 1 means that the number of channels in the input feature matrix remains unchanged, while an expansion multiple of 6 means that the number of channels is expanded to 6 times the original, thereby enhancing the model's capacity for representation. The \(3\times 3\) or \(5\times 5\) indicates the kernel size for the Depthwise Convolution. The structure of the MBConv is shown in Figure 3 and mainly includes: (1) \(1\times 1\) convolution with an upscaling function; (2) \(k\times k\) depthwise convolution; (3) the Squeeze-and-Excitation (SE) module; (4) \(1\times 1\) convolution with a downscaling function; and (5) the Dropout layer. The \(k\times k\) in the depthwise convolution corresponds to the \(3\times 3\) and \(5\times 5\) structures in EfficientNet-B0.  
In the MBConv module, the input satellite image features first undergo a \(1\times 1\) convolution with an upscaling function, followed by a \(k\times k\) depthwise convolution to reduce computational load. Subsequently, an adaptive attention operation is performed through the squeeze-and-excitation module to obtain more effective image feature information, followed by a dimensionality reduction operation on the current features. Finally, a random dropout operation is executed through the dropout layer, enabling the model to learn more robust feature representations and enhancing the model's generalization ability.  
Figure 2: The network architecture of LTSCD-YOLO.
## 2 Materials and Methods
### Method
#### 2.4.2 Feature Encoder  
This study selects ConvNeXt as the feature encoder. As a significant breakthrough in the visual field in recent years, ConvNeXt maintains the efficient computation of convolutional neural networks while integrating the advantageous design elements of Transformer [37]. It possesses powerful feature extraction capabilities and an optimized network architecture, characteristics particularly suitable for processing complex features in mangrove remote sensing images. The specific structure is shown in Figure 6.  
Figure 5: Overall MFA-UperNet Network Structure (**A**. Feature Encoder, **B**. Auxiliary Edge Neck, **C**. Semantic Decoder).  
Specifically, the input mangrove remote sensing image (\(512\times 512\)) first passes through a 4 \(\times\) 4 convolutional layer, followed by a normalization layer. After these two layers of processing, the image resolution becomes one-fourth of the original (\(128\times 128\)). The features are then processed through 4 stages of ConvNeXt Blocks, where each ConvNeXt Block includes two-dimensional convolution (Conv2d), layer normalization (LN), GELU activation function, and Layer Scale and Drop Path regularization layers. Each stage contains 3, 3, 9, and 3 blocks, respectively. Through the ConvNeXt feature extractor, four different-sized multi-scale feature information sets are extracted, denoted as (\(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\)), providing multi-scale feature input for subsequent Auxiliary Edge Neck and semantic decoders.  
ConvNeXt plays a crucial role in this study's mangrove semantic segmentation task. First, its powerful feature extraction capability enables the network to effectively capture multi-level information of mangroves from local textures to global structures. Shallow features retain rich spatial details, helping precisely locate mangrove boundaries, while deep features contain rich semantic information, facilitating distinction between different categories. Second, ConvNeXt's optimized architectural design is particularly suitable for processing complex textures and multi-scale features of mangrove remote sensing images, ensuring both inference speed and high feature expression capability. Finally, through deployment, it achieves faster inference speed while maintaining high accuracy, demonstrating the model's efficiency in practical applications. This efficient multi-scale feature extraction mechanism provides a solid feature foundation for MFA-UperNet, significantly improving model performance in mangrove semantic segmentation tasks, showing notable advantages, especially in handling complex backgrounds and ambiguous boundaries.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Resource Recycling
#### 2.7.3 Gaseous Phase Products  
The gaseous products primarily consist of carbon dioxide (CO\({}_{2}\)), nitrogen (N\({}_{2}\)), and other small molecular gases, possibly including trace amounts of combustible gases such as methane and hydrogen. These gases can be collected and purified for various applications. For instance, the produced carbon dioxide can be utilized for greenhouse gas management, as a resource in the production of carbonated beverages, or dry ice, or to enhance plant photosynthesis. Nitrogen, being an inert gas, finds use in packaging, metal processing, and the electronics industry. Additionally, the gases generated during hydrothermal oxidation can be harnessed for thermal energy recovery, providing energy for electricity generation and heating within the plant, thereby reducing overall energy consumption while generating economic benefits. For example, [PERSON] et al. found that, during wet air oxidation (WAO) of digested sludge, insoluble organic matter is converted into volatile fatty acids (VFAs), ammonia, nitrates, nitrites, organic nitrogen, and carbon dioxide. The study also indicated that the WAO method can decompose digested sludge, with subsequent anaerobic digestion (AD) further producing methane [42].  
Overall, the solid-phase products of sludge treated with HTO technology are primarily inorganic compounds. The liquid-phase products mainly include non-volatile fatty acids (with acetic acid as the main component), aldehydes, ketones, and aromatic compounds, while the gas-phase products consist mostly of carbon dioxide and nitrogen. Under ideal reaction conditions, these intermediate products can eventually be converted into acetic acid and carbon dioxide. However, in engineering practice, excessively pursuing organic conversion can significantly increase energy consumption and operational costs, making it impractical for real-world production needs. As a result, achieving the ideal outcomes may not always be feasible.  
This highlights the necessity of predicting the products of sludge treatment under different conditions, designing well-planned treatment schemes, and preventing the formation of unexpected byproducts. For example, [PERSON] developed a detailed kinetic model for the wet oxidation of municipal sludge. This model can predict the concentrations of intermediate products that are critical to the biological nutrient removal (BNR) process under various reaction conditions, providing insights into the concentration of key intermediates during wet oxidation [71].  
Therefore, to successfully apply HTO technology in industrial settings, continuous experimental research is essential to further refine and optimize the technology.
## 2 Related Works
### Deblurring  
Image blur is a significant distortion of document images. For example, images captured with moving or vibrating cameras may cause \"motion blur\"; the atmospheric turbulence can cause \"Gaussian blur\", and \"defocus blur\" may be caused by the so-called lens aberrations [29].  
The main problem of deblurring is the so-called deconvolution. Deblurring is an ill-posed problem. Thus, the final solution or clearing of the deconvolution should be optimized according to the specific type of image commonly used for deblurring. This type of optimization was introduced by [PERSON] et al. [30], and it was later extended by other authors who added and involved some ad hoc steps in providing an approximate solution [15; 31; 32; 33]. For example, [PERSON] et al. [34] added specific constraints to the problem to provide a correct solution to the so-called PSF (point spread function) problems. Therefore, using ad hoc steps and understanding well the precise nature of images at stake is crucial for finding appropriate solutions to the deconvolution problem.  
Recently, CNN is used extensively in blind blur enhancement of images [35], and also most state-of-the-art methods are based on CNN [36]. The flexibility and power of CNN provides the possibility to improve the blur of an image blindly, especially in edges (Super Resolution). The CNN is also used in different forms such as hierarchical [37] and pyramid networks [38] to deblur the images. For example, one of the approaches involves Generative Adversarial Networks (GAN) in many related applications, such as super-resolution, blind denoising, and deblurring [39]. [PERSON] et al. [40] have proposed such models to deblur text images.  
Most of these related studies show some image quality improvement, but they are mainly capable of deblurring images with only a low magnitude of blur and mostly for the types Gaussian blur or mean blur. Therefore, a new model which can be used for other types of blurs such as motion blur is needed. Beyond that, in many cases, besides blur, other distortions may be present simultaneously.
## 3 Definition of the FFP-IM  
In practical engineering applications, there is a general focus on the failure of structural systems. Therefore, this paper will establish the IM for the fuzzy structural system based on the FFP [16] mentioned in Section 2, which is called the FFP-IM.  
Set \(P_{I_{M}}\) represents the unconditional failure probability associated with the second-order LSF given in Equation (8). When the membership level \(\alpha_{i}\) of the fuzzy variable \(Z_{i}\) is taken as a realization value \(\alpha_{i}^{*}\), the unconditional failure probability value of \(M\) is denoted as \(P_{I_{M|Z_{i}}}\). At this point, the fuzzy variable \(Z_{i}\) converges to the interval variable \([z^{\text{L}}(\alpha^{*}),z^{\text{U}}(\alpha^{*})]\), which eliminates the fuzziness of the fuzzy variable \(Z_{i}\). Considering the impact of the membership level \(\alpha_{i}\) on FFP within its distribution domain, similar to the  
Figure 2: Significance of non-probabilistic reliability metrics [16]. (**a**) Structural systems must be reliable. (**b**) Structural systems may be reliable or failing. (**c**) Structural systems must fail.  
random SA on failure probability in Ref. [31], the IM index of the fuzzy variable \(Z_{i}\) on failure probability (also called FFP-IM) is defined as follows:  
\[\tau_{i}=E_{Z_{i}}[P_{t_{M}}-P_{t_{M|Z_{i}}}]^{2}=\int_{-\infty}^{+\infty}{[P_{ t_{M}}-P_{t_{M|Z_{i}}}]^{2}f_{\mathbf{\alpha}_{i}}(\alpha_{i})\mathrm{d}\mathbf{\alpha}_{i}} \tag{10}\]  
where \(f_{\alpha_{i}}(\alpha_{i})\) is the PDF of the membership level \(\alpha_{i}\) of the fuzzy variable \(Z_{i}\).  
The IM index of a set of input fuzzy variables \((Z_{i_{1}},Z_{i_{2}},\cdots,Z_{i_{r}})\) on failure probability is defined as follows:  
\[\tau_{i_{1},i_{2},\cdots,i_{r}}=E_{Z_{i_{1}},Z_{i_{2}},\cdots,Z_{i_{r}}}[P_{tM }-P_{tM1Z_{i_{1}},Z_{i_{2}},\cdots,Z_{i_{r}}}]^{2} \tag{11}\]  
The important properties of FFP-IM are derived as follows:  
Prop. 1: \(\tau_{i}\geq 0\);  
Prop. 2: If \(\tau_{i}=0\), then the fuzzy variable \(Z_{i}\) takes any membership level that does not influence the failure probability;  
Prop. 3: If \(\tau_{i}=\tau_{ij}\), it shows that the impact of the fuzzy variable \(Z_{i}\) on the failure probability is not increased by increasing the variable \(Z_{i}\);  
Prop. 4: \(\tau_{i}\leq\tau_{ij}\leq\tau_{i}+\tau_{j|i}\);  
Prop. 5: \(\tau_{\max}=\tau_{1,2,\cdots,n}\).  
The FFP-IM index \(\tau_{i}\) can characterize the mean impact exerted by the input fuzzy variable \(Z_{i}\) on the FFP as its membership level varies within the specified range.