esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Knowledge on Energy Refurbishment of Buildings and Green Procurement through Living Labs
## 3 Results
### Level of Knowledge Gain in LLs Specialized in Funding  
As seen from the left part of Figure 4, for LLs specialized in funding areas, a significant enhancement of knowledge after participating in LLs can also be observed.  
As illustrated in Figure 4 (the right part indicating average values (\(\overline{x}_{B},\overline{x}_{A}\)) and differences (\(\Delta\overline{x}_{A-B}\))), the lowest level of knowledge before participating in the LLs was observed for the topic of \"New financial mechanism for energy management\" (\(\overline{x}_{B}=1.78\)), while the highest level of knowledge was noted for \"Funding opportunities corresponding to my needs\" (\(\overline{x}_{B}=2.49\)). This indicates that stakeholders generally had a lower initial understanding of new financial mechanisms specifically tailored for energy management compared to general funding opportunities that align with their needs. After participating in LLs, the highest level of knowledge was observed for the topic of \"Good practices on funding for eco-innovations and energy refurbishment of buildings\" (\(\overline{x}_{A}=3.55\)). In relative terms, knowledge levels improved significantly across all areas, with the highest relative improvement being observed for the field of \"Good practices on funding for eco-innovations and energy refurbishment of buildings\"(\(\Delta\overline{x}_{A-B}=1.39\)), followed by \"New financial mechanism for energy management\" (\(\Delta\overline{x}_{A-B}=1.37\)) and \"New types of financial partnership\" (\(\Delta\overline{x}_{A-B}=1.36\)). This demonstrates a substantial increase in stakeholders' knowledge after participating in LLs, particularly in understanding new financial mechanisms and best practices for funding eco-innovations and energy refurbishment projects.  
Figure 3: Level of knowledge regarding green policy among stakeholders before and after participating in LL (%) (\(n=38\)). Likert scale: 1—Poor, 2—Fair, 3—Good, 4—Very good, and 5—Excellent.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 2 Data Preparation
### Worldwide EOR Project Distributions
After the data analysis, eight reservoir/fluid parameters were selected as the input to the model, which includes: porosity, permeability, depth, oil gravity, oil viscosity, oil temperature, net thickness, and oil saturation at the start of the project. The reasons to choose these parameters are because these parameters are commonly available during the exploration and drilling process, and these parameters could effectively reflect the reservoir and fluid conditions before the implementation of the EOR technique. Figure 3: Distribution of EOR reservoir/fluid parameters.
# Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation
## 3 Methodology
### Attention Gates  
Inspired by the work on Attention U-Net [26], we design a 3D attention gate for 3D data processing, combining it with the standard V-Net network. The 3D attention gate is applied before each level of skip-connection, which can make the network to put more weight on the characteristics associated with skip-connection. As shown in Figure 2, the 3D attention gate has two inputs: one is the feature map \(x\) transmitted from the extended pathway through a skip-connection, and the other is the feature map \(g\) output by the previous neural layer. Both \(x\) and \(g\) are sent to the \(1\times 1\times 1\) convolution, turning them into the same number of channels without changing the size. After the upsampling operation to change the number of channels the same, they are accumulated along the direction of the channel and passed through the ReLU. Then, the output through another \(1\times 1\times 1\) convolution and a sigmoid. Finally, we obtain an attention weight score, attention coefficients, \(\alpha_{i}\in[0,1]\), which can identify the salient features in the image. The output of the 3D attention gate is the element-wise multiplication of input feature-maps and attention coefficients: \(\hat{x}_{i,c}^{l}=x_{i,c}^{l}\cdot\alpha_{i}^{l}\). In a default setting, a single scalar attention value is computed for each pixel vector \(x_{i}^{l}=R^{F_{l}}\) where \(F_{l}\) corresponds to the number of feature-maps in layer \(l\). The weight information can be added to the input feature map of this layer to eliminate the influence of irrelevant information in the skip-connection. As shown in Figure 2, the output of the 3D attention gate is connected to the next encoder through concatenate operation to integrate contextual information, where \(C=C_{x}+C_{g}\), \(H=H_{x}=H_{g}\), \(W=W_{x}=W_{g}\), \(D=D_{x}=D_{g}\). Therefore, the 3D attention gate module could help to achieve better segmentation performance.  
We use additive attention [27] to obtain the attention weight coefficient, and the additive attention is formulated as follows:  
\[\begin{split}& q_{att}^{l}=\psi^{T}\left(\sigma_{1}\left(W_{x}^{T }x_{i}^{l}+W_{g}^{T}g_{i}+b_{g}\right)\right)+b_{\psi}\\ &\alpha_{i}^{l}=\sigma_{2}\Big{(}q_{att}^{l}\Big{(}x_{i}^{l},g_{ i};\Theta_{att}\Big{)}\Big{)}\end{split} \tag{1}\]  
Figure 1: The proposed framework.  
where \(\sigma_{2}(x_{i,\epsilon})=\frac{1}{1+\exp(-x_{i,\epsilon})}\) correspond to sigmoid activation function. The 3D attention gate is characterized by a set of parameters \(\Theta_{att}\) containing linear transformations \(W_{x}\in R^{F_{1}\times F_{\text{int}}},W_{g}\in R^{F_{g}\times F_{\text{int}}},\varphi\in R^{F_{\text{int}}\times 1}\) and bias terms \(b_{\psi}\in R,b_{g}\in R^{F_{\text{int}}}\). The linear transformations are computed using channel-wise convolution for the input tensors. Furthermore, \(q_{att}\) defined the transformation operation of two inputs \(x\) and \(g\) under the parameters \(\Theta_{att}\).
## 4 Proposed Method
### See-Then-Grasp: Two-Stage Robotic Full 3D Reconstruction Pipeline  
Our proposed NBV pose optimization enables access to all viewpoints within a spherical view space for complete 3D reconstruction of an object. Previous studies, which used a fixed-base single manipulator system and a static object, often failed to achieve full coverage, especially for hidden surfaces. This system can only observe specific parts of the object, limiting the view space and preventing full 3D reconstruction of hidden surfaces like the bottom or back side. To overcome this limitation, we propose a novel two-stage reconstruction method utilizing a single robot manipulator and two cameras that directly interact with the object. The summarized method is represented in Algorithm 1.  
```
1:Voxurf model, Initial training set \(\{\mathcal{I}_{i},\mathcal{T}_{i}\}_{i=1}^{1}\)
2:Initialize Voxurf model and start training with \(\{\mathcal{I}_{i},\mathcal{T}_{i}\}_{i=1}^{1}\)
3:Stage 1: Accessible View Collection
4: Constrain NBV view space \(\theta,\phi\)
5:while\((\mathcal{U}_{\mathbf{\tau_{\Theta}}})>\epsilon_{1}\)do
6:if Training iteration mod \(t_{\text{NBV}}=0\)then
7: Optimize \(\mathcal{L}_{\text{NBV}}\) to find NBV pose \(\mathcal{T}_{\mathbf{\Theta}}\)
8: Move robot hand-eye camera to \(\mathcal{T}_{\mathbf{\Theta}}\)
9: Capture image \(\mathcal{I}_{t}\) and apply mask \(\mathcal{M}\)
10: Update training set \(\{\mathcal{I}_{i},\mathcal{T}_{i}\}_{i=1}^{t+1}\leftarrow\mathcal{I}_{t}, \mathcal{T}_{\mathbf{\Theta}}\) and retrain model
11:endif
12: Optimize \(\mathcal{L}_{\text{recon}}\)
13:endwhile
14: Generate \(\mathcal{T}_{\text{grasp,ref}}^{\text{base}}\) and grasp the object
15:Stage 2: Grasp-Assisted Full Coverage
16: Release the constraints \(\theta,\phi\)
17:while\((\mathcal{U}_{\mathbf{\Theta}})>\epsilon_{2}\)do
18:if Training iteration mod \(t_{\text{NBV}}=0\)then
19: Optimize \(\mathcal{L}_{\text{NBV}}\) to find NBV pose \(\mathcal{T}_{\mathbf{\Theta}}\)
20: Move robot end-effector to \(\mathcal{T}_{\text{move}}\)
21: Capture image \(\mathcal{I}_{t}\) and apply mask \(\mathcal{M}\)
22: Update training set \(\{\mathcal{I}_{i},\mathcal{T}_{i}\}_{i=1}^{t+1+\text{stage}\,1}\leftarrow \mathcal{I}_{t},\mathcal{T}_{\mathbf{\Theta}}\) and retrain model
23:endif
24:endif
25: Optimize \(\mathcal{L}_{\text{recon}}\)
26:endwhile
```  
**Algorithm 1** See-Then-Grasp: Two-stage Robotic Active 3D Reconstruction  
Figure 4: Visualization of our proposed gradient-based NBV pose optimization. Starting from the blue-colored pose added in the previous optimization step, we obtain a camera trajectory that moves to the uncertain red-colored pose while optimizing \(\mathcal{L}_{\text{NBV}}\) (**left side**). Repeating this optimization step 50 times and adding it to the training set yields refined 3D reconstruction results (**right side**).
# Cascaded Dual-Inpainting Network for Scene Text
## 5 Conclusions  
In this study, a cascaded dual-inpainting network is proposed for scene text restoration, which consists of two specialized modules designed to enhance downstream text recognition: the SGM and the SRM. The SGM initially extracts structural representations and performs preliminary restoration of degraded text regions. Subsequently, the SRM refines the foreground structure reconstruction through structure-guided refinement based on the guidance generated by the SGM. The experimental results demonstrate the framework's effectiveness in reconstructing degraded scene text across various degradation patterns. The proposed network achieves an improvement in recognition accuracy compared to baseline methods when applied to incomplete scene text recognition tasks.  
In future research, studies will focus on improving the scene text inpainting performance, which mainly involves two aspects. One is to develop a comprehensive scene text dataset that covers different languages, various complex styles, and real-world text images. The other is to design inpainting strategies integrated with more powerful models, such as transformers, GANs, and DDPMs.  
This research is funded by the Innovation Program of Shanghai Municipal Education Commission under grant 202101070007E00098 and the National Natural Science Foundation of China under grant 624723130.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. TI-ST datasets can be found here: [[https://github.com/blackprotoss/GSDM](https://github.com/blackprotoss/GSDM)]([https://github.com/blackprotoss/GSDM](https://github.com/blackprotoss/GSDM)) (accessed on 18 January 2025) [30].  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Station** & **AUC** & **PA** & **PP** & **RP** & **PN** & **RN** \\ \hline
1 & MB & 0.65 & 0.65 & 0.59 & 0.73 & 0.72 & 0.58 \\
2 & LB & 0.76 & 0.76 & 0.77 & 0.79 & 0.75 & 0.72 \\
3 & ML & 0.73 & 0.75 & 0.81 & 0.57 & 0.72 & 0.89 \\
4 & EMS & 0.70 & 0.68 & 0.59 & 0.89 & 0.85 & 0.50 \\
5 & XJX & 0.66 & 0.66 & 0.60 & 0.68 & 0.72 & 0.64 \\
6 & DF & 0.65 & 0.65 & 0.68 & 0.66 & 0.62 & 0.65 \\
7 & XXM & 0.65 & 0.65 & 0.70 & 0.59 & 0.61 & 0.71 \\
8 & LDDZ & 0.65 & 0.66 & 0.69 & 0.54 & 0.64 & 0.77 \\
9 & YAJJ & 0.66 & 0.68 & 0.62 & 1.00 & 1.00 & 0.32 \\
10 & LSBS & 0.69 & 0.69 & 0.78 & 0.52 & 0.64 & 0.85 \\
11 & HYA & 0.85 & 0.84 & 0.75 & 1.00 & 1.00 & 0.70 \\
12 & MSQS & 0.67 & 0.70 & 0.68 & 0.52 & 0.71 & 0.83 \\
13 & EMGQ & 0.68 & 0.73 & 0.95 & 0.37 & 0.69 & 0.99 \\
14 & MBMZ & 0.68 & 0.63 & 0.92 & 0.41 & 0.53 & 0.95 \\
15 & MBRD & 0.75 & 0.76 & 0.77 & 0.80 & 0.75 & 0.70 \\
16 & MBYJ & 0.69 & 0.69 & 0.65 & 0.63 & 0.73 & 0.74 \\
17 & WTQ & 0.77 & 0.77 & 0.69 & 0.79 & 0.83 & 0.74 \\
18 & NJWYYLZ & 0.66 & 0.66 & 0.61 & 0.71 & 0.71 & 0.60 \\
19 & YBYX & 0.98 & 0.98 & 0.95 & 1.00 & 1.00 & 0.95 \\
20 & LSFZJZ & 0.75 & 0.76 & 0.76 & 0.65 & 0.75 & 0.84 \\
21 & ZGDA & 0.75 & 0.75 & 0.69 & 0.76 & 0.80 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction results of LSTM on area2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Station** & **Mag\_Mae** & **Distance\_Average (km)** \\ \hline
1 & \(\mathrm{T}\mathrm{H}\) & 0.06 & 42.34 \\
2 & \(\mathrm{XC}\) & 0.26 & 101.01 \\
3 & \(\mathrm{D}\mathrm{C}\) & 0.15 & 99.96 \\
4 & \(\mathrm{DHZ}\) & 0.23 & 42.08 \\
5 & \(\mathrm{XCXM}\) & 0.68 & 117.21 \\
6 & \(\mathrm{DLSL}\) & 0.27 & 113.03 \\
7 & \(\mathrm{YL}\) & 0.30 & 55.52 \\
8 & \(\mathrm{YM}\) & 0.31 & 44.14 \\
9 & \(\mathrm{HA}\) & 0.06 & 109.35 \\
10 & \(\mathrm{YX}\) & 0.16 & 79.27 \\
11 & \(\mathrm{LJYS}\) & 0.05 & 78.47 \\
12 & \(\mathrm{LJGC}\) & 0.04 & 54.87 \\
13 & \(\mathrm{DQZ}\) & 0.03 & 59.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results of LSTM model for magnitude and epicenter on area3.
## 4 Discussion
### Data Partition Characteristics
#### 4.2.2 Vertical Federated Learning (VFL)  
Data partition in vertical federated learning (VFL) is depicted in Figure 7. In this data partition setting, two nodes shared the same users' profile but different features information. The nodes could be different health institutions or health data application providers. VFL aims to develop a global model by integrating patient features from different institutions without directly sharing patient data. Each node shares different patients' features \(\mathcal{X}\) and labels \(\mathcal{Y}\) information but has the same sample data \(\mathcal{I}\)[26]. VFL can be denoted as:  
\[\mathcal{X}_{j}\
eq\mathcal{X}_{k},\ \ \mathcal{Y}_{j}\
eq\mathcal{Y}_{k},\ \ \mathcal{I}_{j}=\mathcal{I}_{k},\ \ \forall\mathcal{D}_{j},\ \mathcal{D}_{k},\ \ j\
eq k \tag{4}\]  
Figure 6: The typical medical data partitions scenario for horizontal federated learning (HFL). Each node is a medical institution data silo or wearable medical device. They share the same feature of medical diagnosis \(\mathcal{X}_{j}=\mathcal{X}_{k}\) but have different patients index \(\mathcal{I}_{j}\
eq\mathcal{I}_{k}\).  
For example, two distinct healthcare organizations exist in the same region: one hospital and one health insurance company. Users of these two healthcare organizations may mostly be the same because they are the region's residents. However, the user features may not have anything in common because healthcare insurance records users' income and medical reimbursement, while hospitals keep users' medical treatment histories. VFL data partition securely combines different features sets to enhance the performance of the model. Thus, the VFL data partition increases feature dimension in data training.  
In contrast to HFL, there are a few published VFL-based studies applied in medical applications. One such an example was proposed by [PERSON] et al. [56]. The authors developed an autoencoder federated learning model for the vertically partitioned medical data. An autoencoder model is used for transforming user features in each client into a latent dimension. The proposed method does not share any raw medical data but latent dimensions as secure perturbed data. After receiving the clients' latent dimensions, the aggregate server concatenates all latent dimensions for training the global model. However, this approach is prone to reverse-engineering, which could discover the original medical data from the latent dimensions. In addition, the proposed method needs all the clients to perform data alignment, which means the user data has the same row indices in all data silos (first row data on clients \(k\) must be the same as client \(j\)).
# A New Model for Emotion-Driven Behavior Extraction from Text
## 3 Task Definition  
This section introduces the proposed task of emotion-driven behavior extraction (EDBE), which comprises three sub-tasks: emotion extraction, behavior extraction, and emotion-driven behavior identification.  
To illustrate the proposed task, we compared it with two related tasks, namely ECE and ECPE, as shown in Figure 1. The ECE task aims to identify cause clauses corresponding to a given emotion keyword, such as \"happy\". In the example depicted in Figure 1, the cause clauses related to the keyword \"happy\" are \"a policeman visited the old man with the lost money\" and \"told them that the thief was caught\". In the ECPE task, the goal is to extract both the emotion clause and its corresponding cause clause. In contrast, the EDBE task aims to extract the emotion and the behavior driven by that emotion. Specifically, the task involves extracting all behaviors, such as \"visited the old man with the lost money\", \"told them that the thief was caught\", \"thanked the policeman\", and \"deposited the money in the bank\", along with the emotion \"happy\" in the document. Emotion-behavior pairs are then established by matching the keyword \"happy\" with the behaviors it drives, resulting in identified emotion-driven behaviors.  
As shown in Figure 1, the first sub-task focuses on identifying emotion spans in the document, which correspond to text fragments expressing emotions. The second sub-task involves identifying behavior spans in the document, which represent actions or events driven by emotions. Finally, the third sub-task aims to identify emotion-driven behaviors, which are pairs of emotional spans and the corresponding behavior spans that are causally related.  
The ultimate objective of these sub-tasks is to extract emotion-driven behaviors from the text. The following subsections provide detailed explanations of the design of EDBE and the sub-tasks of emotion extraction, behavior extraction, and emotion-behavior pair filtering.  
Figure 1: Comparison of ECE, ECPE, and EDBE tasks with an example.
## 3 Method  
### SBAS-InSAR Technology  
The SBAS-InSAR technology combines multiple master images to create interferograms with short temporal and spatial baselines. The problem of reducing independent images due to the inability to interfere between images caused by long baselines is solved by this technique improving the quantity and quality of interference image pairs in this way [22; 23].  
Assuming that \(N+1\) SAR images are arranged according to time series (\(t_{0}\), \(t_{1}\),..., \(t_{n}\)), one of the images is selected to be the master image, to align the rest of the images with it, then the \(N+1\) images can generate \(M\) differential interferograms, and \(M\) satisfies the following conditions:  
\[\frac{N+1}{2}\leq M\leq\frac{N(N+1)}{2} \tag{1}\]  
The \(j\)th differential interferogram is generated by the interference between the image acquired at \(t_{A}\) and the image acquired at \(t_{B}\) (\(t_{B}>t_{A}\)); the interference phase of a pixel whose azimuth coordinate is \(x\) and distance coordinate is \(r\) can be expressed as:  
\[\delta\varphi_{j,def}=\varphi_{B}(x,r)-\varphi_{A}(x,r) \tag{2}\] \[\approx\frac{4\pi}{\lambda}[\mathrm{d}(t_{\mathrm{B}},x,r)- \mathrm{d}(t_{\mathrm{A}},x,r)]+\Delta\varphi_{topo}^{j}(x,r)+\Delta\varphi_{ APS}^{j}(t_{B},t_{\mathrm{A}},x,r)+\Delta\varphi_{noise}^{j}(x,r)\]  
where \(\varphi\) is the interference phase; \(\lambda\) is the radar signal wavelength; \(\varphi_{A}(x,r)\) and \(\varphi_{B}(x,r)\) are the SAR image phase values at \(t_{A}\) and \(t_{B}\); \(\mathrm{d}(t_{\mathrm{A}},x,r)\) and \(\mathrm{d}(t_{\mathrm{B}},x,r)\) are cumulative deformation in the line of sight direction at \(t_{A}\) and \(t_{B}\) relative to moment \(\mathrm{d}(t_{0},x,r)=0\); \(\Delta\varphi_{topo}^{j}(x,r)\) denotes the residual terrain phase of the differential interferogram; when a higher precision DEM is incorporated into the differential interference process, it can effectively eliminate the majority of the terrain phase; \(\Delta\varphi_{APS}^{j}(t_{B},t_{A},x,r)\) denotes atmospheric delay; \(\Delta\varphi_{noise}^{j}(x,r)\) denotes noise phase. By removing the delay, residual terrain phase and noise phase, the interference phase can be simplified as:  
\[\delta\varphi_{j}=\varphi_{\mathrm{B}}(x,r)-\varphi_{\mathrm{A}}(x,r)\approx \frac{4\pi}{\lambda}[\mathrm{d}(t_{\mathrm{B}},x,r)-\mathrm{d}(t_{\mathrm{A} },x,r)] \tag{3}\]  
Expressing Equation (3) in matrix form as:  
\[\mathbf{B}v=\delta\varphi \tag{4}\]  
where \(\mathbf{B}\) is the matrix of \(M\times N\). When the coefficient matrix \(\mathbf{B}\) is full rank, the deformation rate can be solved using the least-squares method (LSM), while when the coefficient matrix \(\mathbf{B}\) is a singular matrix, the generalized inverse matrix of the matrix \(\mathbf{B}\) can be obtained using singular value decomposition (SVD), which leads to the minimum norm of velocity vector [24; 25].
## 3 Results
### Prediction Results
Correspondingly, a negative absolute error indicates an overestimation of an observed value. A box plot is also used to show the dispersion of a set of data [34]. It is mainly used to reflect the characteristics of the original data distribution, but also to compare the characteristics of the distribution of multiple groups of data. In terms of the median, the SVM model has the best prediction with a median absolute error of 0 mm, the ENN model has a median absolute error of 5 mm, and the MMM model has a median absolute error of 4 mm. In terms of the fluctuation range, the normal range of absolute error for the SVM model is \(-72\) mm to \(65\) mm, the normal range of absolute error for the ENN model is \(-72\) mm to \(104\) mm, and the normal range of absolute error for the MMM model is \(-74\) mm to \(83\) mm. The SVM model has the least fluctuation in absolute error. The prediction accuracy of the SVM model, ENN model, and MMM model was further evaluated using the Taylor Diagram in Figure 12. The Taylor diagram was plotted based on the geometric relationship among the correlation coefficient (equal to the root of \(R^{2}\)), standard deviation, and \(RMSE\)[34]. The Taylor diagram accurately and efficiently reflects the performance of the prediction model, with a closer proximity to observations indicating better predictions [35]. All three metrics indicate that the SVM model predictions were  
Figure 11: Box plot of Absolute Errors. Figure 10: AE of MMM model. close to the observed values. Overall, the SVM model was closest to the observed values in comparison with the ENN model and MMM model. Through the above analysis of the prediction performance of each model, it is concluded that each model has its own advantages and disadvantages, and it is impossible to directly determine which model is better, so further research is needed to make a better choice.