esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 3 Proposed Method
### Segmentation
#### 3.2.1 OTSU Thresholding  
The ideal threshold is the one that produces the minimum potential segmentation error. Otsu is a technique that can be utilized to obtain optimal thresholding outcomes. Otsu thresholding provides several advantages over other procedures, including computational speed, good capabilities when combined with other methods for performance improvement and a consistent performance. Based on the assumption that each picture pixel contains two classes or a bimodal histogram, Otsu thresholding will determine the appropriate image thresholding. By decreasing class variance, the Otsu technique [30] searches thoroughly. The image is segmented based on pixels into two parts, C0 and C1 (foreground and background), according to the intensity level, where C0 varies from 0 to p and C1 varies from p + 1 to 255. As mentioned in [31], let \(\sigma_{wc}^{2}\), \(\sigma_{\beta}^{2}\), \(\sigma_{TM}^{2}\) be within-class, between class and the total variance, respectively. The minimization of \(\sigma_{wc}^{2}\) should be present to achieve optimal thresholding. The pseudocode of OTSU thresholding is described as follows (Algorithm 5):  
Figure 4: Proposed methodology for blood vessel segmentation.  
For excluding unconnected non-vessel pixels, we employed pixel/area-based thresholding. Noise causes certain small, isolated regions in the segmentation findings, and these regions are sometimes mistakenly identified as vessels. We deleted less than or equal to 30 disconnected pixels that were judged non-vessels or part of the background noise based on the connectivity of the retinal vessels. After that, mathematical morphology is applied for effective vascular segmentation.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 4 Results
### Data Analysis Results
#### 4.1.3 Acquisition of Turning Time Ratio  
The ratio of turning times was derived from the analysis of the turning times of the measured OS trajectories. Table 2 shows the turning time of the OS trajectory. The greater the rudder angle and the faster the vessel speed, the smaller the turning time. The maximum turning time (51.58 min) and the minimum turning time (4.78 min) were found in combinations of \(\delta_{1}\) and \(\
u_{1}\), and \(\delta_{7}\) and \(\
u_{6}\), respectively.  
Figure 8a,b shows the trajectories of OS for different turning starting points S. Figure 8c,d show the difference in the \(x\)-axis distance value and the \(y\)-axis distance value difference, respectively, and the difference is seen according to the rudder angle within \(\pm\)300 m. The difference between these distance values was used to calculate the ratio ROT of the difference between the turning angle and the turning time for turning time correction. Figure 8e explains the difference in turning angle between the circle with radius R and the OS trajectory, and Figure 8f explains the ratio ROT of the turning time calculated using this difference in turning angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rudder Angle 1** & \(D_{x}\) & \(D_{y}\) & \(D_{y}/D_{x}\) & **R** \\ \hline \(\delta_{1}\) & 2423.57 & 2420.23 & 0.9986 & 1210.95 \\ \(\delta_{2}\) & 1665.31 & 1672.44 & 1.0043 & 834.44 \\ \(\delta_{3}\) & 1416.06 & 1415.77 & 0.9998 & 707.96 \\ \(\delta_{4}\) & 1286.98 & 1283.36 & 0.9972 & 642.59 \\ \(\delta_{5}\) & 1212.04 & 1207.09 & 0.9959 & 604.78 \\ \(\delta_{6}\) & 1169.41 & 1164.40 & 0.9957 & 583.45 \\ \(\delta_{7}\) & 1150.81 & 1146.48 & 0.9962 & 574.32 \\ \hline \hline \end{tabular} \({}^{1}\)\(\delta_{1}\), 5.0\({}^{\circ}\); \(\delta_{2}\), 10.0\({}^{\circ}\); \(\delta_{3}\), 15.0\({}^{\circ}\); \(\delta_{4}\), 20.0\({}^{\circ}\); \(\delta_{5}\), 25.0\({}^{\circ}\); \(\delta_{6}\), 30.0\({}^{\circ}\); \(\delta_{7}\), 35.0\({}^{\circ}\).  
\end{table}
Table 1: Calculation of diameter, ratio, and radius of measured OS trajectory (unit: m).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rudder Angle 1**} & \multicolumn{6}{c}{**Speed 2**} \\ \cline{2-7}  & \(\
u_{1}\) & \(\
u_{2}\) & \(\
u_{3}\) & \(\
u_{4}\) & \(\
u_{5}\) & \(\
u_{6}\) \\ \hline \(\delta_{1}\) & 51.58 & 25.78 & 17.19 & 12.89 & 10.32 & 8.60 \\ \(\delta_{2}\) & 37.28 & 18.64 & 12.43 & 9.32 & 7.46 & 6.22 \\ \(\delta_{3}\) & 32.65 & 16.33 & 10.88 & 8.16 & 6.53 & 5.44 \\ \(\delta_{4}\) & 30.43 & 15.2
# Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows
## 3 Proposed Dynamic Window Approach  
DWA is one of the commonly used methods for local obstacle avoidance. During an iteration, the algorithm can calculate all feasible speed groups for the next iteration. The optimal motion combination is then selected by the evaluation function. The algorithm adopted in the present paper differs from the original algorithm as described below:  
Figure 1: Agent coordinate system.  
1. DWA is extended to a 3D dynamic environment.
2. New evaluation items are added to the original evaluation function and therefore a new evaluation function is established. Fuzzy logic is also introduced to adjust the weight of each evaluation item according to the working conditions. The distance from the agent to the goal and the distance between the agent and the obstacle are the basis for assessing the adjustment.
3. Two different velocity windows are evaluated at the same time. To ensure the safety of the trajectory, the maximum velocity window is adopted to calculate the braking distance and the distance to the obstacle.
4. Local goals are used to avoid large obstacles or U-shaped obstacles.  
Figure 2 summarizes the algorithm workflow.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 4 Merging Pollution Data over a Grid on the City Map
### Impact of the Different Pollutants Based on User Profiles  
Up to this point we have seen in a generalised way the interpolation and mapping process of the different pollutants. However, in practice, the application is focused on the user profile.  
Figure 4 depicts the flow chart and methodology used in our proposal. Initially, the user asks for a specific route, selecting the mode: walking or cycling. Based on his/her profile (or medical history), we retrieve information about pollutants that should be avoided by the user. The user is provided with relevant and critical information, empowering  
Figure 3: Example of a generic theoretical variogram, showing the _nugget effect_, _sill_, and _range_.  
him/her to proactively make informed personal decisions about their route choices and identify potentially unhealthy travel environments.  
Since the user's profile is confidential, to preserve privacy we anonymise any personal information but we have access to metadata that indicate based on his/her medical history which pollutants are considered critical or relevant. For instance, there are some common profiles, such as people with asthmatic problems, pregnant women, and people with chronic obstructive pulmonary diseases with respiratory infections. In the case of asthma, this order usually is considered PM2.5, O\({}_{3}\), SO\({}_{2}\), and NO\({}_{2}\)[27; 28]. In the case of pregnant women, we include all pollutants in this order NO\({}_{2}\), CO, PM2.5, PM10, and O\({}_{3}\)[29; 30; 31]. Finally, with chronic obstructive pulmonary diseases with respiratory infections, we mainly focus on PM2.5, PM10, and NO\({}_{2}\).
## 4 Implementation and Testing
### Evaluation Process  
The designed and optimized proof of concept was exported as an executable (.exe). For the evaluation, a keystroke-level model (KLM) analysis is adopted. To date, KLM has been employed for the evaluation of interactive applications in a broad variety of domains. The actual KLM process involves setting goals as scenarios and then testing the physical actions involved in completing activities to achieve the goals, such as the number of keys pressed, mouse movements, or mental preparation. Table 1 denotes the main activities collated in the KLM analysis for this study. The KLM output provides an overview of any aspects of the application that may be overly time consuming or challenging for the end users. For example, if users spend a substantial amount of time to locate the virtual gallery and the virtual historian, the addition of virtual signs to navigate the users may be considered an option. Furthermore, if mental preparation times are high, it might indicate complex steps that could be simplified with clearer prompts.  
For the testing, a scenario is devised, as follows: Scenario A: Interacting with the Historian after locating the gallery in the virtual world. In each of these scenarios, the user goals and tasks involve navigating into the virtual world, navigating from the outdoors into the gallery, interacting with the virtual AI-powered NPC, and exploring the digitized artworks. Table 2 provides an overview of the KLM actions involved in Scenario A.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Description** & **Time (s)** \\ \hline
**Keystrokes (K)** & Pressing keys and interacting with the interface. & \(\sim\)0.2 \\
**Pointing (P)** & Moving a mouse or controller to a target. & \(\sim\)1.1 \\
**Homing (H)** & Switching between input devices, like keyboard to mouse. & \(\sim\)0.4 \\
**Mental Prep. (M)** & Pausing to think about what to do next. & \(\sim\)0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: KLM Overview.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Action Description** & **KLM Actions** \\ \hline
**Locate Gallery** & User guided throughout the digital world & P\(n\) + M \\  & and locates the gallery & \\
**Locate Historian** & User guided throughout the gallery and & P\(n\) + M \\  & locates historian & \\
**Approach** & User moves toward historian in order for & K\(n\) \\  & the plug-in to be activated & \\
**Start Dialogue** & Click on historian to initiate (press enter & \\  & to type your question or T to ask via mic) & K\(n\) + M \\
**Ask Question** & Think of a question to ask and address it & Delay \\
**Listen** & Wait for response & K\(n\) + M \\
**Locate artefacts** & User guided throughout the digital world & P\(n\) + M \\  & and locates the digitized artefacts & \\ \hline \hline \end{tabular}
\end{table}
Table 2: KLM Overview for Scenario A.  
Figure 8: Internal Gallery View and AI-based NPC Avatar.
###### Abstract  
Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions.  
flood; GRACE-FO; Pearl River Basin; reconstruction; generative adversarial imputation networks +
Footnote †: journal: _remote sensing_  
02024, 16, 4727. [[https://doi.org/10.3390/rs16244727](https://doi.org/10.3390/rs16244727)]([https://doi.org/10.3390/rs16244727](https://doi.org/10.3390/rs16244727))
## 5 Applications and Discussions
### Chaotic Time Series Prediction
#### 5.1.3 Comparison and Discussion  
Above all, both the tracking curve and the RMSE index indicate that the ABFT-SMM method runs efficiently in the working condition changed situation. It proves that the predictive outputs followed the process very well. At the same time, the modeling error, with respect to the data of condition 1, is listed in Table 3 along with the results from other methods as reported in [23, 26, 27, 28, 29]. Comparison between various methods shows that the ABFT-SMM can reach a better degree of accuracy. That is because different agents can collaborate to describe the nonlinear process, moreover, the membership values of the agents can be adjusted online when new data comes to the system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & \(\mathbf{r_{a}}\) & \(\mathbf{s_{i}}\) & **Centers** & RMSE \\ \hline
1 & 0.14 & 0.1 & 7 & 0.0037 \\
2 & 0.1 & 0.1 & 6 & 0.0044 \\
3 & 0.19 & 0.1 & 6 & 0.0040 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial values and the indexes of the prediction systems.  
Figure 1: Comparison of the ABFT-SM identification results with Mackey-Glass time series outputs. (**a**) is the tracking result for condition 1, (**b**) is the tracking result for condition 2, (**c**) is the tracking result for condition 3, and (**d**) is the varied condition situation.
# Inverse Approach of Parameter Optimization for Nonlinear Meta-Model Using Finite Element Simulation
## 2 Preprocess
### Inverse Method  
The FE model used in the inverse method was set up by dividing the tensile specimen into 100 equal parts along the length and 32 equal parts along the cross-section, and each FE was modeled as a solid type with eight integration points. Figure 5 shows the shape of the FE model. By simulating the practical test environment, a fixed condition was applied to one grip part of the FE model, and a forced displacement condition (5 mm/min) was applied to the opposite grip part. For applying the forced displacement condition, the displacement of the gage zone and the reaction force of the fixed part were simulated. The simulation condition was set in such a manner that the FE simulation would be terminated when the maximum strain of the gage zone derived by the tensile test was reached. The inverse method is a process of meta-model-based optimization using FE simulation. The optimization of the parameter range was performed by repeating the convergence test with the response surface method of the design of experiment. The procedure for the inverse method used in this study is shown in Figure 6. The parameter values of each meta-model through curve fitting described in the previous paragraph were set to the search range by the design of experiment method. The initialized parameters were optimized in the curve-fitting process based on the experimental data and ASTM E646. The design of experiment was performed using the D-optimal method to generate a response surface model by using the estimation algorithm, and the response surface was configured as a quadratic polynomial surface. The response obtained in each  
Figure 3: Illustration of parameter initialization for RMSE comparison. simulation was the RMSE (R) value determined by the genetic algorithm. If R \(\geq\eta_{max}\), the experiment table was rewritten by adjusting the parameter range around the optimum point. To prevent unnecessary iterations at R \(\geq\eta_{max}\), the maximum number of iterations was set to \(i_{max}\). \(\eta_{max}\) was 0.01, and \(i_{max}\) was 50. The iteration of the inverse method was stopped when the RMSE became lower than the \(\eta_{max}\) value or when the number of iterations reached the preset maximum value.
## 7 Results
### Optimization Process with In-Laboratory Measurements
#### 7.1.2 Linearity Test  
To test the dependence of the measured DS on the applied force, two measurement sessions were conducted varying the applied force in the range of 20-220 N, separately fastening the sample with plaster and concrete. The results presented in Figure 11 show that for a force lower than 100 N, the plaster offers a lower variability of results, but in the rest of the range, the concrete results are more constant with the variation of the force.  
Figure 10: Force distributions by the manual (**a**) and the mechanized (**b**) method using the TAD.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 3 Visual Saliency-Based JND Model  
In this section, we present the visual saliency-based JND model. The Human Visual System (HVS) is sensitive to many salient features that lead to attention being drawn towards specific regions in a scene and it is a well studied topic in psychology and biology [23]. Visual saliency model is based on the principle of HVS. According to HVS, the different areas of the image have different degrees, so we set different weights for an image. Image processing technology, combined with the visual saliency, can reflect the subjective initiative of the system more accurately. The visual saliency model is of great significance and broad application in many media information processing fields, e.g., segmentation [24], target recognition [25], image target relocation [26], and so on. The proposed model, the visual saliency-based JND model (as Figure 1 shown), is an efficient model to represent the perceptual redundancies. This model includes the contrast sensitivity function (CSF), luminance (LA) effect, and visual saliency (VS) effect. It is defined as:\[JND(n,i,j)=s\cdot N\cdot M_{base}\cdot M_{LA}\cdot M_{VS} \tag{4}\]  
where \(s\) is intended to account for the summation effect of individual JND thresholds over a spatial neighborhood for the visual system and is set to 0.14. N is the dimension of DCT. \(n\) is the index of a DCT block (we often set \(n=8\)) and \((i,j)\) is the position of \((i,j)\)-th DCT coefficient. The parameters \(M_{base}\), \(M_{LA}\), and \(M_{VS}\) will be detailed later.
## 3 Open-Jet Testing
This state-of-the-art facility is adept at generating authentic hurricane wind turbulence by realistically replicating the complete spectra of velocity at a large scale and high Reynolds numbers. The facility demonstrates proficiency in generating mean wind speed and turbulence profiles that emulate open-terrain environments [63]. A suite of advanced instruments, including cobra probes, load cells, laser displacement sensors, and a 256-channel pressure scanning system, are available at the WISE laboratory. These instrumental resources allow advanced testing to capture and scrutinize the data garnered during experimental trials. The large LSU WISE open-jet facility is a critical platform for researchers to validate their conceptual frameworks, advancing knowledge and fostering breakthroughs in science, hurricane engineering, materials, and structures. The goal is fortifying infrastructure, making it more resilient and sustainable. The LSU WISE open-jet facility is a cutting-edge establishment offering distinctive capabilities for subjecting buildings to realistic wind loads [42; 57; 63]. Noteworthy features and functionalities encompass high Reynolds number testing, authentic turbulence generation, comprehensive wind flow analysis, and large-scale experimentation, crucial for  
Figure 4: LSU WISE large-scale testing facility, featuring a test section measuring 4 m by 4 m.  
accurately simulating flow patterns around buildings in the ABL. It proficiently generates turbulence patterns that closely mimic real-world scenarios, enabling the precise characterization of wind loads on structures. Additionally, the facility accurately reproduces the entire wind flow characteristics encircling buildings, including mean and peak pressures, closely mirroring observations in actual environmental settings. This facility stands as an invaluable instrument for both researchers and engineers dedicated to structural wind engineering. Presented subsequently are three distinct open-jet studies.
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 4 Simulation Verification Analysis
### Early Failure of Outer Raceway  
Figure 33 shows the early fault sound signal of the bearing outer raceway. The analysis idea is the same as that of the bearing inner raceway. For noise reduction processing, the SBOA and CPO convergence curves obtained based on the minimum envelope entropy value are shown in Figures 34 and 35, respectively. After 47 iterations of the SBOA, FMD reached the optimal combination. The filter length \(L\) is 240, the frequency band cutting number \(K\) is 7, the mode number \(n\) is 7, and the cycle period \(m\) is 35. CPO was iterated 26 times, and VMD reached the optimal combination. The number of decomposition modes \(K\) is 5, and the penalty factor \(\alpha\) is 5784. The ISEI values of each mode are shown in Table 4.  
As shown in Table 4, F-IMF3 in S-FMD has the largest ISEI value and contains the most fault information. The envelope analysis of F-IMF3 is shown in Figure 36, and three peaks with prominent amplitude energy can be clearly seen, respectively corresponding to the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline F-IMF & IMF1 & IMF2 & IMF3 & IMF4 & IMF5 & IMF6 & IMF7 \\ ISEI & 1.53 & 2.34 & 5.02 & 1.20 & 2.17 & 2.58 & 4.08 \\ V-IMF & IMF1 & IMF2 & IMF3 & IMF4 & IMF5 & & \\ ISEI & 1.48 & 1.46 & 1.40 & 2.12 & 1.98 & & \\ \hline \end{tabular}
\end{table}
Table 4: ISEI values of the decomposed modes via parameter-optimized FMD-VMD (outer raceway fault).  
Figure 34: Convergence curve of the SBOA (outer raceway fault).  
Figure 35: Convergence curve of the CPO algorithm (outer raceway fault).  
outer raceway fault frequency \(f_{o}\) and harmonic components. The interference frequency of 25 Hz appears in the form of side frequency bands at \(f_{o}\) and \(2f_{o}\), indicating that the noise interference is serious, but the impact of environmental noise is still significant. The ISEI of V-IMF4 in C-VMD is the largest. Its analysis is shown in Figure 37. The fault frequency is not intuitive, and the harmonic component of \(f_{o}\) is not obvious.