esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodologies and Dataset
### Assessment Indicators for Predicting Performance  
To evaluate and compare the forecasting capability of ARMA, RF and the proposed RF-IMF model the following evaluation metrics were applied. In the performance metrics explained below, \(x_{i}\) represents the actual value, \(\hat{x}_{i}\), its predicted value, \(\overline{x}\) is the mean value and N represents the number of testing datasets.  
1. Mean absolute error (MAE): It represents the mean of the absolute difference between the actual and forecasted values. \[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert x_{i}-\hat{x}_{i}\rvert\] (18)
2. Mean squared error (MSE): It is defined as the average of the squared difference between the actual and predicted values, or, in other words, it is the measurement of the variance. \[MSE=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2}\] (19)
3. Root mean square error (RMSE): It is the square root of the MSE or it is the measurement of standard deviation. \[RMSE=\left(\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2}\right)^{0.5}\] (20)  
Figure 3: Rolling forecast model.  
4. Mean absolute percentage error (MAPE): It is the percentage equivalent of MAE. This can easily be interpreted and explained. \[MAPE=(100/N)\sum_{i=1}^{N}\left|\frac{(x_{i}-\hat{x_{i}})}{x_{i}}\right|\] (21)
5. Coefficient of determination or R2: R-squared represents the proportion of variance in the dependent variable. It indicates how well a model fits the given dataset or it analyses how well a variable predicts another one. It lies between 0 and 1 and a larger value indicates a better fit between the predicted and actual value. R-squared value explains the strength of linear relationship between two variables. It can be used for evaluating trend analysis [27]. \[R^{2}=1-\frac{\sum_{i=1}^{N}(x_{i}-\hat{x_{i}})^{2}}{\sum_{i=1}^{N}(x_{i}-\overline {x_{i}})^{2}}\] (22)
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
## 1 Introduction
Since elitist/repair strategies are utilized regardless of performing exploration or exploitation, FHGWJA actually optimizes the balance between these two phases;
3. The positions of leading wolves are dynamically updated to avoid stagnation and convergence to local optima. For that purpose, FHGWJA utilizes a mirroring strategy based on the concept of descent direction, which is much simpler than the strategies documented in the literature;
4. FHGWJA evaluates constraints only after having verified that the new trial design can effectively improve the current best record in the current or subsequent iterations. Should this not occur, repair strategies are activated. This allows for reduced computational costs of optimization to a great extent;
5. FHGWJA does not require any internal parameters aside from population size and a limit to the number of iterations. The elitist strategies and convergence criterion implemented via FHGWJA actually make it unnecessary to specify the limit to the number of iterations. The proposed FHGWJA algorithm was successfully tested in seven \"real world\" engineering problems. The selected test cases, including up to 29 optimization variables and 1200 nonlinear constraints, regarded, in particular, the following: (i-ii) 2D path planning (minimization of trajectory lengths, respectively, with 7 or 10 obstacles); (iii) shape optimization of a concrete gravity dam with additional earthquake load (volume minimization); (iv-v) calibration of nonlinear hydrologic models (the Muskingum problem solved with 3 or 25 unknown model parameters to be identified); (vi) optimal crashworthiness design of a vehicle subject to side impact; (vii) weight minimization of a planar 200-bar truss structure subject to three independent loading conditions. The present algorithm was compared with the best performing algorithms indicated in the literature for each test problem and advanced variants of state-of-the-art MHAs. The rest of this manuscript is structured as follows. Section 2 describes the new hybrid optimization algorithm FHGWJA developed in this study. Test problems and optimization results are presented and discussed in Section 3. Section 4 summarizes the main findings of this study and outlines directions of future research.
## 4 Model of the Composite Deck
### Geometric Model  
The 3D nonlinear model was developed using the software SAP2000 Version 18 [30]. The cross section of the deck (Figure 2) was chosen according to typical composite bridges in Italy and consists of three double T steel profiles (with h = 1300 mm, top flange width \(\mathrm{b_{tf}}\) = 350 mm and thickness \(\mathrm{t_{tf}}\) = 25 mm, bottom flange width \(\mathrm{b_{bf}}\) = 650 mm and thickness \(\mathrm{t_{bf}}\) = 55 mm, and web thickness \(\mathrm{t_{w}}\) = 12 mm) spaced at 2950 mm and a concrete slab of 300 mm thickness and 6250 mm width. Four transverse elements are located at the ends of the bridge and 1/3 and 2/3 along the length of the bridge. The original bridge has a deck skewed with an angle of 60\({}^{\circ}\) in plan defined with respect to the end side orthogonal to the axis, but in this study, to better understand the effect of connection parameters such as ductility and strength, the symmetric model is also introduced. The steel beams are restrained on one side by fixed supports and on the other side by transversal-free supports.  
The length parameter plays a crucial role in determining the strength and ductility demands of the connection in the bridge analysis. To accurately assess these factors, three specific lengths were examined: 25 m, 37 m, and 50 m. The influence of the shear-slip law on the bridge's response and the impact of the skewness parameter were specifically studied for the intermediate length of 37 m. Instead, the linear connection design was analyzed using the 25-meter-long model.  
To obtain a detailed model, shell thin elements with four nodes were used to define the concrete and steel elements of the composite concrete-steel bridge, while nonlinear links were implemented for the studs. The mesh of shells had dimensions of 295 mm \(\times\) 150 mm, 295 mm \(\times\) 220 mm, and 295 mm \(\times\) 150 mm for bridges with lengths of 25, 37, and 50 m, respectively, after a sensitivity analysis of the results to the mesh dimensions. In particular, the variation in the results using shell elements with half sides is lower than 1%.  
A nonlinear analysis of the case studies was carried out considering a uniform load applied to the entire slab because the main aim is to compare the effect of different connector properties on global behavior, with some information about the distribution of the interface shear and the ductility demand in the studs. The shape of the live load, for example, the concentrated axes of trucks on the bridge, can give different numerical results, but the role of the studs remains the same in terms of comparative contribution due to their strength and ductility.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 2 Materials and Methods
### Network Architecture
#### 2.3.1 Geometry-Aware Adaptive Feature Extraction Block (GAFEB)
(**c**) Illustration of the PAConv operation: convolution kernels are adaptively weighted based on spatial relationships within each local neighborhood, enhancing the representation of diverse geometric structures. stabilizes gradient flow, alleviates feature degradation, and facilitates effective multi-scale feature integration. By stacking four consecutive GAFEB modules, the encoder progressively abstracts hierarchical geometric features, offering a consistent and discriminative shared representation for tasks involving instance and semantic segmentation.
# Software Project Risk Identification Based on Scrum Artifacts
## 3 Risk Identification Methodology for Scrum Projects
### Adapting the Framework for Use in the Specific Project  
The first stage of applying the framework is adapting the proposed lists of artifacts, attributes, and risks to suit the specific project (Figure 7). While the proposed framework structure can be used as-is, the team should assess whether it fully aligns with the specifics of their project. The team should start by selecting the relevant artifacts if adaptation is needed. In the proposed framework structure, attributes and their associated risks are already mapped to each artifact. However, the team has the flexibility to customize this structure by adding new artifacts, attributes, risk categories, or specific risks as needed. While customizing the artifacts, risks, and attributes for the project, the team must ensure that all selected artifacts have at least one attribute assigned (so that it would be possible to track the artifact status). The framework offers complete flexibility, allowing the team to create a structure and mappings that best reflect the project situation. From a practical standpoint, if teams within a company follow similar Scrum principles and use consistent tooling, they can adapt the framework once and reuse it across multiple projects, thus saving time and ensuring consistency.  
The important step in the adaptation stage is defining external sources that provide data about the project's progress (Step 1.2 in Figure 7). The team needs to select a source file for each artifact and map the required attribute values and expressions from the chosen source. In Scrum projects, Jira is commonly used for project management, and.csv files exported from Jira can be the source of project execution information. The framework's attributes must then be mapped to specific cells in the exported spreadsheet. While the framework includes default mappings, the team can select their particular data sources and define their mappings. Additionally, the team must specify the time intervals for gathering data from sources, ensuring that the risk-related information is updated regularly. This step also enables the creation of derived attributes based on the values of other attributes. For example, the deviation of velocity can be calculated using the standard deviation formula applied to the last three velocity values gathered.  
Figure 7: The stage of adapting the framework project.  
The adaptation stage's final step is defining threshold values to determine when an attribute is considered risky (Step 1.3 in Figure 7). Both minimum and maximum threshold values can be set if needed; however, at least one of these thresholds must be defined.  
Once the initial framework adaptation is complete, the team moves on to the execution of the project. During this phase, they collect and analyze project execution data from the defined sources. This information is the foundation for identifying risks, as outlined in the next section.
# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 3 Method
### Lsdm
#### 3.3.3 Global Relationship Enhancement Module  
The information representing the graph structure can be propagated among all of the nodes in GCN. The utilization of the standard GCN formula, which aggregates information from first-order neighborhoods, leads to limited expressive capabilities of GCN and a lack of global information integration. Furthermore, increasing the depth of GCN layersdoes not necessarily guarantee improved performance. This may result in the inability of GCN alone to establish global label semantic dependencies. To address this issue, we propose a Global Relationship Enhancement Module (GREM), as illustrated in Figure 3. In the Global Relationship Enhancement Module (GREM), the input tensor \(X_{f}\) is first linearly transformed to generate the Query (\(Q\)), Key (\(K\)), and Value (\(V\)) tensors. These transformations can be expressed as follows:  
\[Q=W_{Q}X_{f}+b_{Q},\quad K=W_{K}X_{f}+b_{K},\quad V=W_{V}X_{f}+b_{V}, \tag{25}\]  
where \(W_{Q}\), \(W_{K}\), and \(W_{V}\) are the weight matrices for the Query, Key, and Value tensors, respectively; and \(b_{Q}\), \(b_{K}\), and \(b_{V}\) are the corresponding bias terms. Next, the Query tensor \(Q\) undergoes a MaxPooling operation, which is followed by a repeat operation to match the original dimension of \(Q\):  
\[Q^{\prime}=\text{MaxPool}(Q),\quad Q^{\prime\prime}=\text{ Repeat}(Q^{\prime}), \tag{26}\]  
where \(Repeat(\cdot)\) represents the repeat operation and \(MaxPool(\cdot)\) is the maxpooling operation. The **attention mechanism** is applied by computing the dot product of the Query and Key tensors, which is followed by a Softmax operation to compute the attention weights:  
\[\text{Max-Atten}=\text{softmax}\bigg{(}\frac{QK^{T}+Q^{\prime\prime}}{\sqrt{ d_{k}}}\bigg{)}V, \tag{27}\]  
where \(d_{k}\) is the dimensionality of the Query tensor. The output is then multiplied by the Value tensor \(V\). For each attention head, the output is denoted as \(H_{1},H_{2},\ldots,H_{h}\), where \(h\) is the number of attention heads:  
\[H_{i}=\text{Max-Atten}_{i}\quad\text{for}\quad i\in\{1,2,\ldots,h\}. \tag{28}\]  
After computing the attention outputs for each head, they are concatenated to form the final tensor \(X_{f}^{\prime}\):  
\[X_{f}^{\prime}=\text{Concat}(H_{1},H_{2},\ldots,H_{h}). \tag{29}\]  
Finally, the output of GREM, \(f_{GREM}\), is obtained by applying a fully connected layer:  
\[f_{GREM}=X_{f}^{\prime}W^{o}+b^{o}, \tag{30}\]  
where \(W^{o}\) is the weight matrix and \(b^{o}\) is the bias term of the fully connected layer. In summary, GREM utilizes multiple attention heads to enhance the global relationships among nodes in the graph, with each head focusing on different parts of the graph and their interactions. The operations are designed to strengthen the global features and improve the model's representation of distant label dependencies.
## 2 Methodology
### Establishment of the Transformation Matrix  
The positions of all of the DOFs of the elements refined from the coarse element are contained within the coarse element. The unknown variables at any position in an element can usually be interpolated from the nodes through the shape function. So, taking a coarse element and the refined elements contained within it as an example, the DOFs contained in the coarse element nodes are the master DOFs and the rest of the DOFs in the refined elements, which are regarded as the slave DOFs that can be interpolated by the shape function. For the temperature unknown variable, each slave DOF can be calculated as  
\[T_{s}=\sum_{i=1}^{n}N^{i}T_{m}^{i} \tag{31}\]  
where, \(T_{s}\) is the temperature unknown variable of the slave DOF, \(T_{m}^{i}\) is the temperature unknown variable of the \(i\)-th master DOF in the coarse element that contains \(T_{s}\), \(n\) is the number of the nodes in the coarse element, and \(N^{i}\) is the shape function component of the \(i\)-th DOF in the coarse element.  
Because all the temperature unknown variable of slave DOFs is computed by linear combinations of the temperature unknown variable of master DOFs and the shape function component, the transformation matrix between the DOFs array and the master DOFs array is actually the combination of the shape function component and the identity matrix elements. Additionally, the shape function components are easily decided by the refining process of the initial coarse mesh.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 5 Conclusions  
This study analyzes the response of NDVI and SIF to meteorological drought in the Yellow River Basin from 2001 to 2020. The results show that: from the spatial distribution of different seasons, the significant improvement area of SIF (66.49%, \(p<0.05\)) is higher than that of NDVI (50.7%, \(p<0.05\)). The spatial distribution of vegetation growth monitored by NDVI and SIF was consistent throughout the annual and different seasons. SPEI-12 has the largest proportion of negative values and has a significant periodic variation. The significant positive correlation areas of SIF-SPEI in spring, summer and autumn (R \(>\) 0, \(p<0.05\)) were 7.00%, 28.49%, and 2.28% respectively, which were higher than the significant positive correlation areas of NDVI-SPEI (spring: 1.79%; summer: 20.72%; autumn: 1.13%). SIF responded more strongly to SPEI in summer, and farmland SIF was significantly correlated with SPEI (0.3424, \(p<0.01\)). The results indicates that SIF was superior to NDVI in detecting drought impacts.  
Conceptualization, F.Q. and Z.P.; methodology, J.L. and Z.L.; data curation, J.L. and M.X.; writing--original draft, J.L.; writing--review and editing, J.L. and M.X.; visualization--J.L. and Z.H.; supervision, F.Q. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by High Resolution Satellite Project of the State Administration of Science, Technology and Industry for National Defense of PRC, grant number 80-Y50G19-9001-22/23; The National Science and Technology Platform Construction, grant number 2005 DKA32300; The Key Projects of National Regional Innovation Joint Fund, grant number U21A2014; The Ministry of Education, grant number 16 JJD770019; and The Open Program of Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province, grant number G202006.  
Not applicable.  
The authors declare no conflict of interest.
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 3 Results and Discussion
### NRII and ERMI per Capita Limits Estimation  
The ANN model represents an accurate possible outcome of road investment interventions on ARA, for a given economy, demography, and transport system. It could serve as a useful tool for exploring effective and potential road investment limits that will result in the minimum accident rate. The best NRII/capita and ERMI/capita are the combination of NRII/capita and ERMI/capita that correspond to the lowest ARAs for a given set of demographic, economic, passenger, and freight conditions. Sample NRII/capita and ERMI/capita values corresponding to the minimum ARAs for the population of 10 million illustrated in the previous section are estimated and shown in Table 6. The per capita NRII and ERMI ratio and their relative proportions were also estimated. The NRII/ERMI ratio and their relative proportion are better when the budget is limited to below the best estimates. This is because the ratio or relative proportions are also better than random values. Safer NRII/capita and ERMI/capita limit results for populations of 5, 15, and 30 million with different demographic, economic, freight, and passenger conditions were also estimated. These results are presented in the Appendix A in Tables A1-A3, respectively. It can be seen that the safe NRII/capita limit is only greater than the safe ERMI/capita limit for cases in which there is a higher working population (approximately 70%), GDP per capita (USD 30,000 and above), or a combination of a higher CNVR and GDP. Otherwise, the ERMI/capita should be prioritized over the NRII/capita for a lower ARA. Interpolation can be used to estimate in between results with some reasonable degree of accuracy.  
Figure 7: Annual road accident vs. ERMI and NRII per capita for a population of USD 10 million 40,000 GDP and Working Population of 70%.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
###### Abstract  
Building change detection monitors building changes by comparing and analyzing multi-temporal images acquired from the same area and plays an important role in land resource planning, smart city construction and natural disaster assessment. Different from change detection in conventional scenes, buildings in the building change detection task usually appear in a densely distributed state, which is easy to be occluded; at the same time, building change detection is easily interfered with by shadows generated by light and similar-colored features around the buildings, which makes the edges of the changed region challenging to be distinguished. Aiming at the above problems, this paper utilizes edge information to guide the neural network to learn edge features related to changes and suppress edge features unrelated to changes, so as to accurately extract building change information. First, an edge-extracted module is designed, which combines deep and shallow features to supplement the lack of feature information at different resolutions and to extract the edge structure of the changed features; second, an edge-guided module is designed to fuse the edge features with different levels of features and to guide the neural network to focus on the confusing building edge regions by increasing the edge weights to improve the network's ability to detect the edges that have changed. The proposed building change detection algorithm has been validated on two publicly available data (WHU and LEVIR-CD building change detection datasets). The experimental results show that the proposed model achieves 91.14% and 89.76% in F1 scores, respectively, demonstrating superior performance compared to some recent learning change detection methods.  
change detection; deep learning; feature fusion; edge features; optical remote sensing images +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images.  
_Appl. Sci._**2024**, _14_, 5415. [[https://doi.org/10.3390/app14135415](https://doi.org/10.3390/app14135415)]([https://doi.org/10.3390/app14135415](https://doi.org/10.3390/app14135415))  
doi:10.3390/app14135415