esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 5 Conclusions  
In infrared images of the sea surface, the presence of complex backgrounds such as the sea, sky, and islands poses a challenge for target detection. Factors like sea wind disturbance and intense sunlight reflection further contribute to the complexity of the sea background. Ship targets in such environments often exhibit low intensity, a specific size, and a uniform grayscale on imaging devices, and the sea surface also contains patches of varying brightness and darkness, and landmasses or islands exhibit low grayscale values, making the robust detection of ship targets highly challenging. This paper proposes a dual-channel segmentation method based on local low-intensity region detection and geometric feature analysis. The image is traversed using a convolutional kernel with a negative center point to identify the low-intensity regions. These regions are subjected to adaptive threshold segmentation, incorporating shape and area features, to achieve the fine segmentation of potential target regions. In the other channel, a rough low-intensity region is obtained by applying adaptive threshold segmentation to the preprocessed image, and large area regions with similar grayscale values, such as mountains, are filtered out based on area features. Finally, the two segmentation results are fused to generate the detection outcome. Through qualitative and quantitative comparisons with three other methods on test sequences, the proposed algorithm demonstrates superior performance and improved detection capability for infrared ship targets in complex sea scenes. It effectively enhances the detection capability of ship targets in challenging infrared sea surface backgrounds, particularly in flare environments. However, it is necessary to acknowledge that this paper focuses specifically on dark-polar targets. In some scenarios, both bright and dark polar targets may coexist. Therefore, future work will enhance the algorithm's applicability and robustness by addressing the detection of both bright and dark targets, thereby expanding its effectiveness in diverse scenarios.  
Conceptualization, D.L.; methodology, J.T.; software, J.T.; validation, J.T.; formal analysis, M.W. and L.T.; investigation, J.T.; resources, D.L.; data curation, D.L. and J.T.; writing--original draft preparation, J.T.; writing--review and editing, D.L.; supervision, L.W. and G.G.; project administration, L.W. and G.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 5 Conclusions  
Here, the continuous ground measurements of SIF and the environmental and crop growth parameters of four plots under different levels of droughts for wheat crops were carried out, thus systematically exploring the potential of F760 tot for drought monitoring. The results indicated that:  
1. F760 tot was capable of distinguishing the differences in different drought levels and responded quickly to the onset of moderate droughts compared with other variables, which appeared to have the greatest decrease;
2. compared with F760 toc, F760 tot appeared to be more related to the physiology and was subjected to the canopy structure less, but these relationships varied in extreme droughts; and
3. F760 tot contained effective information on SM in terms of the correlations in short time lags, and the VIs were more strongly correlated with SM in the longer time lags. The results of this study demonstrate that F760 tot was more sensitive to moderate droughts that usually appeared in the early stages of drought stress in plants, which may be attributed to the representation of the total emitted SIF and less influence from the canopy structure.  
Conclusively, F760 tot has great potential for early drought detection and monitoring. Our findings may contribute to mitigating the risk of agricultural droughts. In addition to these findings, because of the reabsorption effect, further works can be conducted to explore the performance of the total emitted SIF at the red band for drought monitoring. Aside from that, structural equation modeling (SEM) is a statistical technique that can be used to reveal the relationships of the observed and latent variables, and further studies are needed to use this method to illustrate the potential relationships between SIF and other variables, thus contributing to understand its driving factors.  
Conceptualization, J.L., Q.S. and J.W.; data curation, J.L. and Q.S.; funding acquisition, J.W.; investigation, L.L. and W.Z.; methodology, J.L., Q.S., L.L. and W.Z.; project administration, J.W.; resources, J.W. and L.L.; software, J.L. and W.Z.; supervision, J.W.; validation, J.L., Q.S., J.W. and L.L.; visualization, J.L.; writing--original draft, J.L.; writing--review and editing, J.L., Q.S., J.W., L.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 42071401 and 41671424, and the National Key Research and Development Program of China, grant number 2017 YFB0504102.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Theoretical Foundation and Numerical Model Construction
### Numerical Model
#### 3.2.3 Boundary Conditions and Physico-Mechanical Parameters  
The model boundaries are constrained using roller supports: the lateral boundaries restrict normal displacement while remaining free in the \(z\)-direction; the bottom boundary is fixed in the \(z\)-direction but unrestrained horizontally. Under water impoundment conditions, hydrostatic pressure varying with elevation is applied to the slope surface below the water level, and corresponding pore water pressures are assigned to the rock mass below the phreatic surface. The physical and mechanical parameters used in the numerical model are listed in Table 1.  
Figure 6: Measured pore water pressure variation with depth chart.
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 4 Sensitivity Analysis of Carbonation Prediction Approach
### Optimized Number of Field Inspection Data  
In order to identify the inspection on-site data required, a sensitivity analysis for an optimized number for Bayesian analysis has been performed in this study. Table 3 shows the depth of carbonation  
Figure 4: Changes in posterior predicted values of carbonation depth according to changes in the number of Latin hypercube sampling (LHS) values.  
in bridges regularly examined in the field [30]. The carbonation depth of on-site data was measured following the guidelines for inspection and assessment of infrastructure safety [31].  
The prior prediction of carbonation depth using design variables was updated according to the different numbers of on-site data. Figure 5 shows prior prediction and updated prediction models as increasing numbers of inspection data in the Gajwa IC vi  
The sensitivity analysis to identify the optimized number for the proposed approach has been compared with increasing on-site inspection data from three different bridges in Table 3. Figure 6 presents the results of a sensitivity analysis for the optimized number of on-site data. The results show that the updated prediction converges while considering more than two on-site data in the analysis. Thus, two field inspection data are required to effectively predict the progress of concrete carbonation in the proposed approach.  
Figure 5: Updated prediction model considering different numbers of on-site data of the Gajwa IC viaduct.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bridge** & \
\begin{tabular}{c} **Environmental** \\ **Condition** \\ \end{tabular} & **Measured Point** & **Age (Years)** &
\begin{tabular}{c} **Carbonation Depth** \\ **(mm)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Gajwa IC viaduct} & \multirow{4}{*}{On land} & \multirow{2}{*}{Pier} & 11 & 5 \\ \cline{3-5}  & & & 16 & 5 \\ \cline{3-5}  & & & 19 & 7 \\ \cline{3-5}  & & & 24 & 7.3 \\ \hline \multirow{4}{*}{Noryang bridge} & \multirow{4}{*}{Above the river *} & \multirow{2}{*}{Pier} & 10 & 6 \\ \cline{3-5}  & & & 17 & 6.5 \\ \cline{3-5}  & & & 22 & 8.4 \\ \cline{3-5}  & & & 27 & 8.44 \\ \hline \multirow{4}{*}{Geoje bridge} & \multirow{4}{*}{Above the sea} & \multirow{4}{*}{Girder} & 27 & 23 \\ \cline{3-5}  & & & 34 & 30 \\ \cline{3-5}  & & & 39 & 32 \\ \cline{3-5}  & & & 44 & 26.4 \\ \hline \hline \end{tabular}  
* \({}^{*}\) Located in the center of the city.  
\end{table}
Table 3: Carbonation on-site data of subject bridges.
# Optimization Method for Stiffened-Plate Layout in Box Structures Based on Load Paths
## 2 Structural Optimization Method Based on Load Path
### Optimization Methods for Box Structure and Plates Layout  
Differing from traditional finite element stress analysis, regarding material redundancy areas and stress concentration areas, load paths not only visually depict the transmission route of loads but also allow for the determination of the type of internal stresses within the structure based on the posture or direction of the path, offering guidance for design. According to the load path schematic shown in Figure 2, a detailed classification of the force transfer paths was made according to the path departure and termination points, and the paths were roughly classified into six types, corresponding to serial numbers 1 to 6.  
In this context, Path 1 fully connects the load point with the constraint point and is reversible in direction. Path 2 forms a U-shaped path around the area where the constraint is applied. Path 3 starts from the load application area and terminates at the structural boundary, also being reversible in direction. Path 4 starts from the structural boundary terminates at the load constraint area and is reversible in direction. Path 5 begins at the boundary, then returns to terminate at the boundary. Regarding Path 6, which represents a vortex-like force flow path, as indicated by [PERSON] et al. [27], it is understood to result from lower stresses, leading to a recirculating structure in the normalized pointing vectors. It is recommended to eliminate the regions occupied by these vortices during the design process.  
Applying the classification principle of force transmission effectiveness, effective load paths are defined as those load paths that establish a complete connection between the load point and the constraint point. While the load paths without a complete connection between load points and constraint points are labeled as invalid load paths. Clearly, Paths 1 and 2 fall into the category of effective load paths, while Paths 3 to 6 belong to ineffective load paths. Effective load paths, serving as the primary load carriers, constitute the efficient region for load transmission, and the layout of stiffeners can be designed based on such path distributions. In contrast, ineffective load paths exhibit low load transmission efficiency or a self-balancing posture, allowing for reduced stiffener distribution in the respective areas and even the removal of original materials to achieve weight reduction. Through this classification method, on one hand, the categorization of the six paths is established, and on the other, this method can provide guidance for optimizing structural design.
## 3 ANN Drag Model and Training Strategy  
### The Construction of ANN  
A proper design of the ANN structure can greatly improve the computing efficiency and prediction accuracy of the neural network. In order to obtain a reasonable ANN frame for modeling the drag force, the learning algorithm, activation function, hidden layers, and input variables of ANN will be discussed based on the TensorFlow to build the patterning drag model.  
As the 243,260 training samples are the local drag forces for each individual granule, it is worth noting that with the changes of sampling location and time, the local drag force will be changed obviously. Moreover, the mesoscale heterogeneity, particle motion characteristics, fluid-solid density ratio, and other factors may affect the simulation results substantially. Thus, it is necessary to preprocess local drag force data by statistical averaging  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\varepsilon_{A}\  
methods, hoping to eliminate the influence of statistical fluctuation that is caused by incomplete consideration of influencing factors.  
The local drag forces were grouped in the 2-dimensional interval \([Re_{p,i},Re_{p,i+1}]\times[\varepsilon_{s,j},\varepsilon_{s,j+1}]\) (\(i=1,2,\cdots,1100\), \(j=1,2,\cdots,60\)), with an interval size of \(\delta_{Re_{p}}=1\) and \(\delta_{\varepsilon_{s}}=0.01\). The average local drag forces \(<F_{d,DNS}>\)[56] were calculated in each interval by:  
\[<F_{d,DNS}>=\frac{1}{MN}\sum_{p=1}^{M}\sum_{q=1}^{N}\left(F_{d,loc}\right)_{p, q}, \tag{13}\]  
where \(MN\) represents the number of data in the interval \([Re_{p,i},Re_{p,i+1}]\times[\varepsilon_{s,j},\varepsilon_{s,j+1}]\) (\(i=1,2,\cdots,1100\), \(j=1,2,\cdots,60\)), with an interval size of \(\delta_{Re_{p}}=1\) and \(\delta_{\varepsilon_{s}}=0.01\). After local drag force averaging, 66,000 samples are left.  
Due to the particle Reynolds number \(Re_{p}\), the solid volume fraction \(\varepsilon_{s}\) and slip velocity \(\mathbf{u}_{slip}\), which are used to characterize the movement characteristics of particles, have clear physical meaning and an explicit relationship with drag force [19]. Thus, the above variables will be chosen as the inputs of the ANN drag model. Moreover, the Sauter mean diameter \(<d>\) is selected as the input, considering the description of the polydisperse characteristics in the ANN drag model. The input-output variables relationship of the ANN is illustrated in Figure 2.
## 3 Results  
The following tables show the results for the determination of total microbial counts (colony-forming units--CFU/mL) for Group A (Table 1) and Group B (Table 2), respectively.  
Figure 2: Treatment of a primary superior central incisor after trauma—Group B. (**a**) Edema present in tooth 61. (**b**) Preoperative X-ray showing periapical radiolucency. (**c**) Photoactivation after 1 week placement of the 5-amilovelunic gel inside the root canal. (**d**) Sample collection using a sterile 0.30. (**e**) The one-month follow-up radiograph demonstrated bone healing in the periapical area of tooth 61. (**f**) Clinical examination confirmed the absence of symptoms, with complete resolution of the fistula.  
The results from determining the total microbial counts aligned with the current literature, confirming that photodynamic therapy (PDT) exerts potent antibacterial effects on bacteria colonizing root canals in teeth requiring endodontic treatment.  
For both group, the LSD test showed that the treatments significantly decreased (\(p<0.05\)) the CFUs, with respect to the sample collection after pulp chamber opening.  
In particular, a statistically significant reduction in bacterial count was observed from the initial T0 sample, collected before ALAD gel application during the first session in both groups, to the T1 sample. In Group A, this reduction occurred within 45 min of the same session; in Group B, it was observed one week later, following a temporary filling placement. These results highlight the inherent antibacterial properties of ALAD gel, even before photoactivation. This downward trend in bacterial count persisted at the T2 sampling after photoactivation, resulting in a statistically significant reduction in both groups, nearly eliminating the bacterial count.  
However, in one Group B case, the temporary filling failed between sessions, leading to an increased bacterial count at T1, underscoring the necessity of a secure hermetic seal for effective endodontic healing. Thus, for cases where professional endodontic treatment is not feasible, but the protocol is still applied to preserve the tooth temporarily in the arch, a restoration technique ensuring a durable hermetic seal is essential to maintain the therapeutic effects.
## 2 Related Work  
### Plant Disease Detection Method  
In the field of plant disease detection, various technologies and common approaches have been developed. Recent studies include [PERSON] et al. [17], who developed a Deep Convolutional Neural Network (DCNN) model for detecting four cucumber diseases, including anthracose, achieving a 93.4% accuracy rate while reducing the influence of multiple diseases on detection. Similarly, [PERSON] et al. [18] proposed a neural network-based model that integrates Depthwise Separable Convolution (DSC) and an attention mechanism, achieving 94.65% accuracy on four public rice datasets. This model outperformed six other deep learning models tested at the same time and demonstrated good adaptability to mobile devices. [PERSON] et al. [19] developed a method for identifying plant diseases in different plant datasets. The method effectively combines segmentation and classification and achieves a classification accuracy of 93.75% using genetic algorithms on CNN. [PERSON] et al. [20] introduced an enhanced YOLOv3 model for multi-scale feature detection of disease images. Through the adoption of multi-scale training strategy; a detection accuracy of 92.39% was achieved. [PERSON] et al. [21] presented an enhanced SE-YOLOv5 network architecture that incorporates attention mechanisms for extracting important features and optimization of the network model, resulting in a detection accuracy of 91.07%. [PERSON] et al. [22] adopted the relatively large architecture of InceptionV3 and VGG16 and slightly adjusted the model for rice disease detection. Meanwhile, due to the unsuitability of mobile devices for large-scale architectures, a two-stage small CNN architecture is proposed, resulting in a detection precision of 93.3%. [PERSON] et al. [23] introduced a strategy to enhance training samples by integrating GoogleNet and Deep Generative Adversarial Network (GAN) [24]. Samples processed by this network have better generalization effects compared to those processed by traditional data augmentation methods. This approach enhances the accuracy of tomato leaf disease detection, improving the model's effectiveness and achieving a detection accuracy of 94.33%. [PERSON] et al. [25] introduced a redesigned residual dense network that combines the advantages of deep residual networks and dense networks. This approach reduces the parameter volume and enhances the information flow, resulting in a detection accuracy of 95%. [PERSON] et al. [26] developed an enhanced Faster R-CNN model for detecting leaf spot disease on sugar beet leaves. By optimizing parameters according to the image and region of interest, detection efficiency was improved, resulting in an accuracy of 95.48%.
## 2 Establishment and Analysis of Damaged Components
### Components Damaged by Chemical Corrosion and Temperature  
Experiments show that the deformation and creep rates of rocks vary under the coupled effect of varied chemical corrosion and temperature [44]. Notably, pH and temperature affect the creep model's construction. Therefore, when constructing the creep model of rock under the coupled effect of varied chemical corrosion and temperature, it is necessary to consider the effect of pH and temperature on the rock's creep rate. The Nishihara model can accurately describe the stages of primary creep and steady-state creep in rock. Figure 1 shows the Nishihara model.  
The creep equation of the Nishihara model is given as follows:  
\[\begin{cases}\varepsilon=\frac{\sigma}{E_{1}}+\frac{\sigma}{E_{2}}\bigg{(}1-e ^{-\frac{E_{2}}{\eta_{2}}t}\bigg{)}&(\sigma\leq\sigma_{\rm s})\\ \varepsilon=\frac{\sigma}{E_{1}}+\frac{\sigma}{E_{2}}\bigg{(}1-e^{-\frac{E_{ 2}}{\eta_{2}}t}\bigg{)}+\frac{\sigma-\sigma_{\rm s}}{\eta_{1}}t&(\sigma>\sigma _{\rm s})\end{cases} \tag{1}\]  
where \(\sigma\) and \(\varepsilon\) are the total stress and strain of the Nishihara model, respectively; \(E_{1}\) and \(E_{2}\) are the elastic modulus of parts 1 and 3, respectively; \(\eta_{1}\) and \(\eta_{2}\) are the viscosity damping coefficients of parts 2 and 3, respectively, which are constant; \(\sigma_{\rm s}\) is the maximum yield stress of the rock; \(t\) is the time.  
The Nishihara model's variation law of creep rate with time is obtained by deriving Equation (1) as follows:  
\[\begin{cases}\dot{\varepsilon}=\frac{\sigma}{\eta_{2}}e^{-\frac{E_{2}}{\eta_ {2}}t}&(\sigma\leq\sigma_{\rm s})\\ \dot{\varepsilon}=\frac{\sigma}{\eta_{2}}e^{-\frac{E_{2}}{\eta_{2}}t}+\frac{ \sigma-\sigma_{\rm s}}{\eta_{1}}&(\sigma>\sigma_{\rm s})\end{cases} \tag{2}\]  
The Nishihara creep model cannot describe the accelerated creep stage of rock, according to the analysis of Equation (2). The creep rate of the rock material when the applied load is less than its maximum yield stress is only related to \(E_{2}\), \(\eta_{2}\), and \(\sigma\). To overcome the  
Figure 1: Nishihara model.  
problem that the Nishihara model cannot depict the influence of pH and temperature on the creep rate, a viscoelastic element under the coupled effect of pH and temperature (Figure 2) is constructed to replace the Kelvin body in the Nishihara model. This study replaces the viscoplastic elements in the Nishihara creep model with nonlinear viscoplastic elements to improve the description of the accelerated creep stage using the new creep model.