esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
This study was undertaken in three broad steps. First, the satellite images were processed to generate the relevant measures for a representative date (8 January 2021) for the summer season. Second, a thresholding process was adopted to identify relative differences in surface temperature (Ts\(-\)Ta), surface moisture (STR) and vegetation status, which were then used to identify irrigated pixels. As a third step, the classified pixels were aggregated at farm scale so as to match the irrigation water supply information and to measure the classification accuracy.
## 4 Experiments
### Model Training and Performance Comparison
DBGNN achieves the best prediction performance, with 92.39% ACC-Door and 78.84% ACC-Wall, surpassing all baseline models in both wall and door link prediction tasks, demonstrating remarkable accuracy in bubble diagram link prediction. The proposed model employs a dual-branch approach to decouple bubble diagrams and leverages subgraph sampling strategies to enhance local feature learning. As a result, DBGNN achieves the highest accuracy in both sub-tasks, confirming its effectiveness for the unique requirements of bubble diagram link prediction. Moreover, we visualized the link prediction results of the four models in Figure 9. For easy comparison, real floorplans and bubble diagrams are displayed on the left. Due to insufficient learning of the structural information within bubble diagram data, GCN and GAT generated a significant number of incorrect links. GraphSAGE produced bubble diagrams of relatively higher quality, but there were still issues; for instance, the 3-3 balcony node only had a wall link and no door link, implying no accessible path to the balcony from other spaces. DBGNN provided satisfactory predictions with no incorrect door or wall links. Our dual-branch network architecture effectively decouples the bubble diagram, allowing for targeted handling of door and wall links. The central node (living room) is \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**ACC (\(\uparrow\))**} & \multicolumn{2}{c}{**AP (\(\uparrow\))**} \\ \cline{2-5}  & **Door** & **Wall** & **Door** & **Wall** \\ \hline GCN & 61.96\% & 65.93\% & 45.63\% & 69.32\% \\ GAT & 69.52\% & 70.77\% & 49.52\% & 74.08\% \\ GraphSAGE & 86.63\% & 76.24\% & 87.33\% & 86.43\% \\ DBGNN & 92.39\% & 78.84\% & 91.35\% & 88.36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of ACC and AP of DBGNN and baseline models. highly correlated with non-central nodes, as most rooms should have a door link to the living room for accessibility, which is enhanced by CNS. Additionally, local links between non-central nodes should follow basic spatial logic; for example, a bathroom should not only connect to a kitchen via a door. We used DNS to improve the learning of spatial adjacency. Furthermore, while maintaining logical local topology, the global number of links must be controlled within a certain range. We addressed this with a multi-task loss function that balances global and local link structures. As shown in the visual results, DBGNN accurately created local links and optimal global structures without extraneous links, regardless of the number of input nodes. These results demonstrate the model's notable predictive accuracy and robustness, affirming the superiority of our approach in the bubble diagram generation task.
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 2 Materials and Methods
### The Process of Monte Carlo Numerical Simulation
#### 2.3.2 The Movement and Scattering of the Photon
\(\omega_{0}\) is the ratio of the scattering coefficient \(b(\lambda)\) to the attenuation coefficient \(c(\lambda)\) known as the albedo, 0\(<\omega_{0}<1\). The initial weight \(\omega=1\), so with the increase of \(n\), its weight \(\omega_{n}\) decays gradually, as shown in Equation (13) and Figure 6.  
\[\omega_{n}=\omega*(\omega_{0})^{n}=(\omega_{0})^{n} \tag{13}\]  
When the photon's new position is determined and is still in front of the Reception Plane, it is still possible to be absorbed by the receiver. The scattering angle and azimuth angle of the photon are reselected to obtain the new Direction Cosine \(\left[\mu_{x}^{\prime},\,\,\mu_{y}^{\prime},\,\,\mu_{z}^{\prime}\right]\) in Equation (14), where \(\mu_{s}=cos\varphi^{\prime}\), \(\varphi^{\prime}\) and \(\psi^{\prime}\) are the new scattering angle and azimuth angle of the photon, respectively. \(\mu_{z}\) is one of the moving photon's Direction Cosine, and its square \(\mu_{z}^{2}\) must be more than 0. In addition, in the Equation (14), to ensure that \(\sqrt{1-\mu_{z}^{2}}\) is meaningful and rational, \(1-\mu_{z}^{2}\) must be greater than 0, namely, \(0<\mu_{z}^{2}<1\). \[ \begin{bmatrix}\mu_{x}^{\prime}\\ \mu_{y}^{\prime}\\ \mu_{z}^{\prime}\end{bmatrix} = \begin{bmatrix}\mu_{x}\mu_{z}\Big{/}\sqrt{1-\mu_{ z}^{2}}&-\mu_{y}\Big{/}\sqrt{1-\mu_{z}^{2}}&\mu_{x}\\ \mu_{y}\mu_{z}\Big{/}\sqrt{1-\mu_{z}^{2}}&\mu_{x}\Big{/}\sqrt{1-\mu_{z}^{2}}& \mu_{y}\\ -\sqrt{1-\mu_{z}^{2}}&0&\mu_{z}\end{bmatrix}\begin{bmatrix}\sqrt{1-\mu_{z}^{ 2}}cos\psi^{\prime}\\ \sqrt{1-\mu_{z}^{2}}sin\psi^{\prime}\\ \mu_{s}\end{bmatrix} ,\mu_{z}^{2}<1 \tag{14}\]
## 4 Model Training and Evaluation Methods
### Evaluation Metrics
In Table 3, it can be seen that SVR achieved the highest R\({}^{2}\) = 0.95 and the lowest MSE = 0.04 and RMSE = 0.20, indicating that it successfully explained 95% of the variance in the data set and minimized the prediction errors. GBM (R\({}^{2}\) = 0.94, MSE = 0.05, and RMSE = 0.23) and HGBR (R\({}^{2}\) = 0.94, MSE = 0.05, and RMSE = 0.22), closely followed SVR, offering high explanatory power and low mean-error performance. DT (R\({}^{2}\) = 0.87, MSE = 0.11) and K-NNs (R\({}^{2}\) = 0.9, MSE = 0.08) have a more limited explanatory power and relatively higher error values. The SVR model achieved the best result for MAE, with MAE = 0.16, while the GBM also performed well, with MAE = 0.18, as did the HGBR model, with MAE = 0.17. The lowest MSLE was observed in the SVR model, with a value of 0.007, and in the GBM model, with a value of 0.01. The DT model showed the poorest performance, with the highest MSLE value, 0.02. The SVR model showed that it is the most robust model against outliers in its predictions, with the lowest worst-case deviation, MaxEr = 0.97, and eMAX = 30.02. In contrast, K-NNs, with eMAX = 81.53, and AdaBoost, with eMAX = 58.47, have high worst-case errors. The MARE, MSRE, RMSRE, and RRMSE metrics also showed that the SVR and GBM duo produced lower relative deviations, compared to the other models. MBE remained around zero in all models, indicating no systematic bias; the smallest bias was observed in the GBM and ETR models. U\({}_{95}\) had the narrowest confidence interval, with a value of 0.02 in the SVR and GBM models, and the widest confidence interval, with a value of 0.03 in the DT model. The analysis of the t-stat reveals that SVR and GBM do not show statistically significant bias, while the K-NNs and DT models may have more variable errors.
## 3 Methodology
### Cross-Layer Attention Upsampling (CLAU)  
#### 3.3.1 Nearest Neighbor Sampling  
To fuse the low-resolution in-depth features with high-resolution shallow features, the deep feature map needs to be upsampled before performing the concatenation operation in order to achieve resolution alignment in the spatial dimension. The YOLOv5 network uses nearest neighbor upsampling, meaning that it directly copies the value of the pixel closest to the current location. This method does not involve any learning behavior, and memory consumption increases without any increase in the amount of feature information.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.4.1 Preprocessing  
The initial dataset consisted of 29,995 cases and 164 variables. In order to focus on detecting predictors for the CTO PCI (response variable), conceptual (variables irrelevant to the result of the PCI were excluded), relevance (variables with weak association with the response were filtered out), and literature (variables considered important in the literature were included) criteria were used to filter the initial dataset and exclude a large number of variables. In addition, variables with missing values over 20% were not assessed. No imputation method for the missing values was considered, in order for the analysis to be based only on observed data. The association of the predictor variables with the response was separately assessed for each predictor variable with binary logistic regression analysis. Associations with a \(p\)-value > 0.05 resulted in excluding the corresponding variables from the dataset. The final dataset included 22 out of the 164 original variables with 17,370 complete cases.  
This dataset was randomly partitioned into two sub-datasets, namely the training and the test datasets, which corresponded to 70% (\(n\) = 12,160) and 30% (\(n\) = 5210) of its cases, respectively. The training dataset was used for model development. The test dataset was used for model assessment. Since a strong class imbalance was observed between the two categories of the PCI result, failure and success, both in the initial dataset (\(n\) = 29,995 cases;14% failure, 86% success) and in the filtered dataset (\(n=17\),370 cases; 12.4% failure, 87.6% success); the latter distribution remaining the same in the training dataset due to random partition, downsampling was applied. Particularly, the training dataset was randomly downsampled to address the class imbalance between the two classes of the response variable. The downsampling procedure employed randomly excluded cases with operation success until resulting in a dataset with equally sized classes (success and failure) for the response variable (\(n=3026\)). Despite losing information, downsampling was selected, since it was assumed that it is of equal interest to correctly predict both the successful and unsuccessful operation results. In addition, by selecting downsampling compared to other approaches (e.g., upsampling), the likelihood of overfitting and consequent negative impact in the prediction model's performance was avoided [14]. The above procedure is depicted in a flow chart (Figure 1).
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 4 CMOS Transimpedance Amplifier (TIA)  
The TIA is the first electronic circuit that directly interfaces with a photodetector (PD) for a current-to-voltage conversion. Therefore, the overall performance of the receiver is predominantly determined by that of the TIA. There are four main parameters defining the performance of the TIA: gain, bandwidth, noise, and power. Further, these performance parameters are strongly interdependent and strong tradeoff relations exist among them. Thus, for example, we cannot improve the gain and the bandwidth simultaneously without sacrificing the power.  
Traditionally, compound semiconductor devices were preferred for implementing optical interface circuits due to their high-speed capability. However, significant improvements in CMOS technologies have reduced the performance gap between these two technologies. Moreover, recently, further advancements in circuit technologies have led to CMOS implementations of extremely high-speed optical receivers whose operating speeds are higher than 40 Gb/s [53; 69].  
Besides enjoying the advantage of CMOS scalability, the success of CMOS-compatible silicon photonics also mandates the CMOS implementation of electronic circuits. Although most optical receivers still rely on hybrid integration, which interconnects multiple ICs from different technologies through bonding, monolithic integration is gaining increasing intention and some remarkable results have been demonstrated as previously mentioned. If realized, monolithic integration would be the best solution since it provides many benefits, such as enhanced signal integrity, small area, and reduced packaging cost, when compared to hybrid integration. In this section, we focus on CMOS realizations of the TIA by providing a brief overview of various TIA topologies reported so far.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 3 Results and Discussion
### AAE on Episode and Non-Episode Days  
The AAE plays an important indicator role in air pollution, and it can be used to distinguish aerosol particle types. The theoretical AAE of BC is close to 1.00 while a higher AAE between 1.00 and 2.00 indicates a significant increase in urban pollution by organic carbons because the atmospheric pollutants are usually mixed with strongly absorbing particles. Dust aerosols are likely to increase when the AAE increases to about 3.00 [65]. The Grade II of Chinese Ambient Air Quality Standard (CAAQS) for a daily mean of PM\({}_{2.5}\) is 75 \(\upmu\)g/m\({}^{3}\); thus, the daily average PM\({}_{2.5}\)\(\gamma\) 75 \(\upmu\)g/m\({}^{3}\) is defined as PM\({}_{2.5}\) episode days [66; 67]. According to the previous statistical research, average PM\({}_{2.5}\) concentrations during the episode days are 2-3 times higher as compared to PM\({}_{2.5}\) concentrations during the non-episode days [66]. The air pollutants monitoring data were provided by the China Air Quality Online Monitoring and Analysis Platform [68].  
We selected 13 monitoring cities from six regions (the TB: Aksu; the SB: Chengdu, Deyang, Chongqing; the PRD: Guangzhou, Shenzhen, Zhuhai; NS: Nanjing, Shanghai; the YRB: Puyang, Jinan; BT: Beijing, Tianjin) to study the diversities in the AAE between PM\({}_{2.5}\) episode days and non-episode days (Figure 12). As compared to other cities, more episode days and higher AAE values occurred in Aksu over the Tarim Basin during 2017 and 2018. As discussed above, the AAE values were significantly affected by dust aerosols from the Taklimakan Desert. Guangzhou, Shenzhen, and Zhuhai, belonging to the YRD, had the minimum number of episode days. Furthermore, the AAE values on these days were regularly low, especially in 2018 with the most obvious difference. Beijing and Tianjin in the BT area displayed no significant differences of AAEs between episode days and non-episode days. In 2018, the AAE\({}_{388-500}\) over industrial-emission-dominant regions like Guangzhou, Shanghai, and Beijing was 2.64 \(\pm\) 0.044, 2.71 \(\pm\) 0.18, and 2.72 \(\pm\) 0.13 for episode days, and 2.85 \(\pm\) 0.20, 2.83 \(\pm\) 0.13, and 2.72 \(\pm\) 0.12 for non-episode days. The AAEs obtained for episode days were generally lower than those in non-episode days.  
Figure 11: Monthly variations of the AAOD\({}_{388}\) (**a**), AAOD\({}_{500}\) (**b**), and AAE\({}_{388-500}\) (**c**) in six sampling areas.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 4 Discussion
### Topography of the Area  
The topography of a study area is the most important boundary condition for any model. In this research study, the measured topography data from 2002 of the Yangtze River Estuary were used. In the estuary of the Yangtze River, in addition to its natural evolution, two other channels exist which are known to be greatly affected by human activities. These channels are the NB and SB. Between the 1950s and 1990s, due to the closure of the shoal and deposition of the river bed (particularly [37] in the upper reaches), the terrain of the North Branch had undergone major changes. These changes led to the river discharge becoming greatly reduced. Also, the tidal ranges in the upper reaches of the river, along with the saltwater intrusions, were increased during that period [38]. However, since 2000, the NB has been less affected by human activities, which has resulted in fewer changes in the area's topography [37; 39]. Another major human activity in the NB was that a deep-water channel was dredged and reinforced in 1998. This was completed in order to increase the water depth to ensure the safety of the fairways. The Eastern WDP (south to north project) caused a decrease of the river discharges, and resulted in an enhancement of the salt-water intrusions. In particular, the sand bars around the river mouth were found to have experienced notable increases in their salinity levels [40].
## 6 Simulation Results
### Optimization Objectives Comparison  
The core focus of this study lies in the optimization of three key objectives: maximizing risk coverage and the longest street length, and minimizing the transition distance.  
To evaluate the performance of different algorithms, the mean values of these objectives were compared. Notably, the first two objective values were inverted to align with the real-world context of maximizing risk coverage and minimizing the longest street length, respectively. This approach ensures that the results are directly interpretable in the context of the practical problem being addressed.  
Figure 9 presents the average risk coverage values of patrol routes obtained by different algorithms across various scenarios. Each cell in the table represents the mean value of the risk coverage achieved by the respective algorithm for a specific scenario. The risk coverage metric reflects the ability of a patrol route to effectively encompass areas with higher incident risk, demonstrating the performance of each algorithm in addressing dynamic risk conditions.  
Figure 10 displays the average total street length of patrol routes generated by different algorithms across various scenarios. Each cell in the table represents the mean value of the summed street lengths covered by the respective algorithm for a particular scenario. A higher value indicates a greater street-level police presence, reflecting improved visibility and accessibility of law enforcement in the patrolled area. This metric serves as a critical indicator of the effectiveness of each algorithm in maximizing street coverage, thereby enhancing public safety and reassurance.  
Figure 11 presents the average total transfer distance of patrol routes generated by different algorithms across various scenarios. Each cell in the table represents the mean value of the summed transfer distances computed by the respective algorithm for a specific scenario. A lower value indicates a reduction in non-productive (\"ineffective\") transfer distances, signifying improved efficiency in the allocation of police resources to active  
Figure 9: Comparison of objective 1 from simulation results of various algorithms.  
patrol tasks. Minimizing the transfer distance is crucial for optimizing the operational efficiency, as it ensures that a larger proportion of police time and effort is dedicated to actual policing activities rather than unnecessary movement between locations.  
Based on the results presented in Figures 9-11, which compare the performance of algorithms across the three optimization objectives, the PPS-MOEA/D algorithm maintains a dominant position overall. Although a few algorithms achieve slight superiority in certain scenarios for the first two objectives, PPS-MOEA/D consistently delivers a robust and superior performance across the majority of cases.
# Smart Gateway for Healthcare Networks Based on Beam Steering Technology
## 2 Smart Gateway Antenna
### Interferometric AOA Estimation Method
The analytical results reveal that the estimation error before the redesign is large due to the synergistic effect of the fluctuations in \(E_{\Delta}\) and \(E_{\Omega}\). On the other hand, the estimation error after the redesign is suppressed compared to that before the redesign, depending on the immovable reference characteristics, indicating that the proposed antenna can autonomously estimate the direction of the sensor device.
# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 4 Exploring Future Directions
### Real-Time Sign Language Interpretation in Livestreams  
An innovative direction for future research is the use of sign language recognition models for real-time interpretation during live streams. In this scenario, sign language can be interpreted on the fly and provide instant translation during live broadcasts, conferences, or events. This significantly increases accessibility for people with hearing impairments who participate in this type of real-time interaction.  
In this context, ChatGPT can be used to improve interpretation by rewriting the translated content in real-time, ensuring that the result is not only accurate but also contextually appropriate and clear to the audience. This can fill the communication gaps during conversations from events where the original output may be too literal or unclear. Future research could explore the technical challenges of implementing this system in real-time settings, focusing on latency, accuracy, and user feedback to ensure a smooth and effective experience for end users.
## 2 Proposed Method
### Detect AD Area
#### 2.3.1 Improved OTSU  
In 1979, [PERSON] [32] proposed the maximum inter-class variance threshold selection method. The most important feature of this algorithm is that it can adaptively determine the optimal segmentation threshold of the image. In the threshold selection process, the gray value corresponding to the variance between the target and the background of every pixel in the image, when the maximum variance is obtained, the segmentation threshold is optimal. However, the algorithm is not ideal for segmentation image while the two peaks of the gray histogram is not obvious. Therefore, this work adopts the improved Otsu algorithm in [33], it replaces the mean of the image in the Otsu algorithm with the average variance of the image, as follows:  
\[T=\text{Arg}\max_{0\leq T\leq L-1}\left[\omega_{0}\big{(}\sigma_{0}^{2}-\sigma ^{2}\big{)}^{2}+\omega_{1}\big{(}\sigma_{1}^{2}-\sigma^{2}\big{)}^{2}\right] \tag{14}\]  
Here, \(\sigma_{0}^{2}\) and \(\sigma_{1}^{2}\) are the variances of the gray values of the image pixels of \(L_{0}\) and \(L_{1}\), \(L_{0}\), and \(L_{1}\) are the gray values of two types of image pixels divided by the threshold \(T\), \(\sigma^{2}\) is the variance of the full picture pixel gray value, \(\omega_{0}\) and \(\omega_{1}\) are the probability of occurrence of pixel gray values in the range. By incorporating the average variance of the image into the Otsu algorithm, it is possible to effectively  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Original Image** & **Pre-Processing Image** & **NSCT Processing Image** \\ \hline ENL & 29.2806 & 1.6684 & **0.6016** \\ CII & 1 & 1.7489 & **2.2821** \\ \hline \hline \end{tabular} Bold font number indicates the best performance in each class.  
\end{table}
Table 2: Comparative analysis.  
Figure 5: (**a**) Original ROI; (**b**) AD area marked by expert; (**c**) pre-processing result; (**d**) NSCT processing result.  
segment images with high contrast. Because the pre-processing and NSCT processing enhanced the contrast of the mammograms, this method is in line with the requirements of this research work.