esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Image Completion through Iterative Low-Pass Filtering
## 3 Experimental Results
### Benchmark Results  
In this section, we present a benchmark analysis comparing an Iterative Low-Pass Filter (ILF) with a Non-Local Spatial Propagation Network (NLSPN) [24], utilizing the NYU Depth V2 dataset [39]. The dataset comprises video sequences capturing various indoor scenes recorded by both the RGB and Depth cameras from the Microsoft Kinect. It includes labeled pairs of aligned RGB and depth images, along with ground-truth depth images. The input depth images for the benchmark were sampled at a 42.77% filling rate.  
For our analysis, we selected 9409 raw depth matrices with clearly defined boundaries using the Extraction of Boundaries and Surfaces (EBS) method. These matrices include defect areas affected by infrared sources, reflecting surfaces, absorbing materials, overex  
Figure 8: Curved wallpaper completion results. (**a**) Rendered point cloud of raw depth matrix. (**b**) Rendered point cloud of completion result.  
Figure 7: Real Scene completion results. (**a**) Input depth image for Kinect 1.0. (**b**) Completion depth image for Kinect 1.0. (**c**) Point cloud of the completion depth image for Kinect 1.0. (**d**) Input depth image for Kinect 2.0. (**e**) Completion depth image for Kinect 2.0. (**f**) Point cloud of the completion depth image for Kinect 2.0.  
posed scenes, and dusky scenes. To ensure robustness, each depth matrix was confined by the operation block, and the boundaries of operation blocks were carefully adjusted to avoid segmentation errors. In this comparison, ILF emerges as superior in recovering data characterized by these challenging conditions.  
Figure 9 showcases three examples: a plush floor, a glossy floor, and a black reflective table. The Iterative Low-Pass Filter (ILF) excels in performance, offering superior details in these challenging areas. The highlighted scenes include reflective surfaces, absorbent furnishings, dim illumination, and overexposed RGB signals. Notably, the Non-Local Spatial Propagation Network (NLSPN) is susceptible to lighting conditions and surface properties, as it extracts features from both RGB and depth signals.  
In dim lighting conditions, NLSPN yields inaccurate results compared to the ground truth, as evident in the color maps. The glossy floor, reflecting the scene's RGB signal, leads to the mirroring or creation of the scene's depth in the reflective surface, resulting in higher values in quantitative evaluation. In the case of the absorbent black carpet, the system erroneously derived depth from the coffee table. In contrast, ILF demonstrated minimal effects from these specialized surfaces, as evidenced by the color maps, where ILF's results closely aligned with the ground truth.  
The depth-image completion results of the 9409 raw depth matrices were evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Absolute Relative Error (Rel), presented in Table 1. In direct comparisons, ILF consistently achieved smaller values in RMSE, MAE, and Rel, underscoring its superior performance.
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
###### Abstract  
Background: Sjogren's syndrome (SS) is a systemic autoimmune condition marked by significant dry eye disease (DED), leading to considerable corneal changes. These modifications, encompassing punctate epithelial erosions, chronic epithelial abnormalities, and corneal ulcers, significantly impact eyesight and quality of life. Progress in comprehending the corneal pathophysiology associated with SS has prompted innovative diagnostic and treatment approaches. Aim: This narrative review aims to examine developing trends in the pathogenesis, diagnostic methods, and treatment strategies for [PERSON]'s syndrome-associated corneal changes. Methods: The study was based on a narrative review of the current literature available on PubMed and Cochrane from Jan 2000 to December 2024. Results: Corneal changes associated with [PERSON]'s syndrome result from a multifactorial interaction of ocular surface inflammation, tear film instability, and epithelium degradation. Recent research underscores the significance of immune-mediated pathways, such as T-cell-induced inflammation and cytokine dysregulation, as crucial factors in corneal disease. Innovations in diagnostic instruments, including in vivo confocal microscopy and tear proteomics, provide earlier and more accurate identification of subclinical alterations in the corneal epithelium and stroma. Therapeutic developments concentrate on meeting the specific requirements of SS-related DED. Biological treatments, especially tailored inhibitors of interleukin-6 and tumor necrosis factor-alpha, show potential in mitigating inflammation and facilitating epithelial repair. Moreover, regenerative approaches, such as autologous serum tears and mesenchymal stem cell therapies, provide innovative methods to repair ocular surface integrity. Advanced drug delivery technologies, including nanoparticle-loaded eye drops, enhance bioavailability and therapeutic efficacy. Conclusion: Recent developments in comprehending SS-related corneal changes have transformed the management approach to precision medicine. The combination of improved diagnostics and innovative therapy approaches offers potential for reducing disease progression, maintaining corneal health, and enhancing patient outcomes. Subsequent investigations ought to concentrate on enhancing these tactics and examining their long-term safety and effectiveness. Clinicians and researchers must adopt these developments to successfully tackle the difficulties ofSS-related corneal illness, providing hope for improved care and higher quality of life for those affected.  
Sjogren's syndrome; autoimmune eye diseases; dry eye disease; keratoconjunctivitis sicca; ocular surface; cornea; corneal pathologies; corneal medical and surgical therapies
## Conclusions: Low Temperature
### High Temperature: \(22\,^{\circ}\)C-\(40\,^{\circ}\)C
#### 7.3.2 AT-FTSP  
AT-FTSP (see Figure 7) shows a stable behavior during the first \(1,000\) s of the test, providing an average synchronization error of about \(1.266\)\(\mu\)s.  
A temperature variation at instant \(1,000\) is induced. This change is shown in Figure 7 at instant \(1,100\) s, where the maximum average synchronization error reaches the value of \(15\,\mu\)s. In a short period of time, the proposed clock skew module works to obtain a better synchronization error, minimizing its value to \(3.679\)\(\mu\)s for the second interval (\(1,000\)\(s\)-\(3,000\)\(s\)).  
This test ends with an average synchronization error of \(2.685\)\(\mu\)s, and this means an improvement of \(159.40\%\) with respect to FTSP.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **FTSP** & **AT-FTSP** & **A2T-FTSP** \\ \hline Error & 7.432 \(\mu\)s & 2.685 \(\mu\)s & 1.349 \(\mu\)s \\ Standard Deviation & 8.757 \(\mu\)s & 4.941 \(\mu\)s & 2.064 \(\mu\)s \\ Maximum Error & 44.5 \(\mu\)s & 35.5 \(\mu\)s & 22.5 \(\mu\)s \\ \(<\) 95\% Error & 22.5 \(\mu\)s & 12 \(\mu\)s & 5.5 \(\mu\)s \\ \hline \end{tabular}
\end{table}
Table 5: Results with temperature variation: \(22\,^{\circ}\)C–\(40\,^{\circ}\)C.  
Figure 7: High temperature results.
# Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro's Lagoon
###### Abstract  
Maritime navigation safety relies on preventing accidents, such as collisions and groundings. However, several factors can exacerbate these risks, including inexistent or inadequate buoyage systems and nautical charts with outdated bathymetry. The International Hydrographic Organization (IHO) highlights high costs and traditional methods as obstacles to updating bathymetric information, impacting both safety and socio-economic factors. Navigation in inland and coastal waters is particularly complex due to the presence of shallow intertidal zones that are not signaled, where navigation depends on tidal height, vessel draw, and local knowledge. To address this, recreational vessels can use electronic maritime sensors to share critical data with nearby vessels. This article introduces a low-cost maritime data sharing system using IoT technologies for both inland (e.g., Ria de Aveiro) and coastal waters. The system enables the collection and sharing of meteorological and oceanographic data, including depth, tide height, wind direction, and speed. Using a case study in the Ria de Aveiro lagoon, known for its navigational difficulties, the system was developed with a Contextual Design approach focusing on sailors' needs. It allows for the real-time sharing of data, helping vessels to anticipate maneuvers for safer navigation. The results demonstrate the system's potential to improve maritime safety in both inland and coastal areas.  
internet of things; IoT systems; maritime data; meteorologic data; maritime communities; safe navigation; Raspberry Pi; ESP8266
## 1 Introduction
These SFs are used to study the Nakagami-m and Nakagami-hoyt fading channels in [18,19], respectively. A SOFS-based analysis of channel simulators implementing Nakagami fading is presented in [20]. A detailed analysis on characterization of angular spread and SOFS of air-to-ground and different outdoor land-mobile radio communication channels for both empirical and geometric channel models is conducted in [21, 22, 23, 24, 25]. A study conducted in [26], presents an analysis on SOFS of mmWave communication channel in outdoor radio propagation environment at 28 GHz. No such studies, for angular-spread and SOFS quantification of outdoor mmWave (38 GHz) and indoor mmWave (28 GHz) radio propagation channels are available in the literature, to the best of authors' knowledge. Therefore, there is a significant scope to conduct a thorough study for quantification of angular spread and characterization of fading statistics of outdoor and indoor mmWave radio propagation channels. In this paper, the measurement results presented in [4,6,7] for plain AoA statistics of different outdoor and indoor radio propagation scenarios at 28 and 38 GHz frequency bands are extended to the quantification of dispersion of energy in azimuthal domain and characterization of SOFS of the radio channel. The conducted analysis is of high significance in designing receivers, antennas spacing, interleaving schemes, interference mitigation, modulation schemes, and error-correction codes, etc. The details of considered measurement data-sets, propagation scenarios, and the proposed work are presented in Table 1. A part of this work was presented in [27] where only one indoor measurement dataset was used to analyze the azimuthal spread and SOFS of indoor radio propagation channels. However, this paper extends the work by including analysis on three more measurement data-sets for different indoor and outdoor mmWave radio propagation scenarios. A detailed analysis on the impact of variations in Doppler frequency, link-distance, and observation amplitude level on the AFD, LCR, CD, and spatial auto-covariance is conducted. This study focuses on the angular spread analysis only in the azimuth plane. The remaining of the paper is organized as follows: Measurement setup for all the cases is explained in Section 2. The detailed channel characteristics, proposed methodology and discussion on obtained results are given in Section 3. Finally, the conclusion is given in Section 4.
## 3 Methodology
### Analysis Process  
By integrating the XGBoost algorithm with the MPA, it is possible to define a more accurate relationship between PC and other mining-related influencing factors through machine learning techniques. Furthermore, the SHAP algorithm is applied to interpret the impact of various mining influencing factors on PC. The detailed implementation procedure is illustrated in Figure 5, and the specific steps are outlined as follows:  
1. Collect data related to mining influencing factors and construct a dataset. Subsequently, randomly divide this dataset into a training subset (comprising 80% of the dataset) and a testing subset (constituting the remaining 20%). This random partitioning ensures that the model can generalize effectively to unseen data during the training phase.
2. Define an objective function aimed at optimizing the XGBoost parameters, focusing on num_trees, max_depth, and eta. In this study, the cross-validation score calculated across multiple optimization iterations serves as the evaluation metric for the objective function.
3. Employ the MPA to iteratively optimize parameter selection for XGBoost. During each iteration, evaluate the performance of each parameter set. Once the stopping criteria are satisfied, output the optimal parameters. Through this process, the best-fittingparameters for XGBoost are determined, enabling the construction of the coupled MPA-XGBoost model. This model is then utilized to establish a regression analysis framework linking PC with other mining-related influencing factors.
4. The performance of the MPA-XGBoost model is analyzed and compared with that of other machine learning models using the Taylor diagram methodology. Simultaneously, SHAP modeling is utilized to further elucidate the relationships and significance of various mining influencing factors on PC.
## 3 Discussions of Experimental Results
### Comparisons of Aerodynamic Characteristics of the DCFJ312 and CFJ312 Airfoil
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Airfoils** & \(C_{Lmax}\) & \(\triangle C_{Lmax}\) & \(AoA_{stall}\) & \(\triangle AoA_{stall}\) \\ \hline OA312 & 1.13 & — & 22\({}^{\circ}\) & — \\ CFJ312 & 1.76 & 55.8\% & 32\({}^{\circ}\) & 10\({}^{\circ}\) \\ DCFJ312-OF1/2 & 1.89 & 67.3\% & 34\({}^{\circ}\) & 12\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of maximum lift coefficients and stall \(AoA\)s of DCFJ312 and CFJ312 airfoils. In short, DCFJ312-OF1/2 airfoil has better aerodynamic performance than CFJ312 airfoil. CFJ and DCFJ technologies transport the energy to the main flow by the turbulent mixing between the jet and main flow, thereby improving the performance of OA312 airfoil. However, only the streamwise vortex structure is generated by CFJ technology while both streamwise and spanwise vortex structures are generated by DCFJ technology because of the existence of taps. Thus, DCFJ technology is more effective in the turbulent mixing than CFJ technology, and DCFJ technology has greater performance enhancement for OA312 rotor airfoil than CFJ technology.
## 2 Materials and Methods
### Pattern Recognition Methods  
#### 2.2.1 Support Vector Machines (SVMs)  
Support vector machines (SVMs) are supervised learning models, with associated learning algorithms that analyze the data used for classification and regression analyses [29; 30].  
A type of SVM for regression, support vector regression (SVR), was originally proposed by [PERSON] and his coworkers [28]. In SVR, the set of training data includes predictor variables and observed response values. The goal is to find a function _f(x)_ that deviates from \(y_{n}\) (sample labels) by a value no greater than \(\varepsilon\) (bias) for each training point _x_--that is, remain as flat as possible. Therefore, SVR is also known as tube regression. Its schematic diagram is shown in Figure 1.  
According to the literature on SVM linear regression [28], the solution to SVR is as follows:  
\[f(\textbf{{x}})=\sum_{n=1}^{N}\left(a_{n}-a_{n}^{*}\right)(\textbf{{x}}_{n}^{ \text{T}}\textbf{{x}})+b. \tag{1}\]  
Where \(x\) is the input feature vector, b is the distance parameter, \(a_{n}\) and \(a_{n}^{*}\) are the introduced Lagrange multipliers. However, some regression problems cannot be described adequately using a linear model. In that case, we can obtain a nonlinear SVR model by replacing the dot product \(\textbf{{x}}_{n}^{\text{T}}\textbf{{x}}\) with a nonlinear kernel function \(K(\textbf{{x}}_{1},\textbf{{x}}_{2})=\textless\rho(\textbf{{x}}_{1})\), \(\rho(\textbf{{x}}_{2})\textgreater\), where \(\rho(\textbf{{x}})\) is a transformation that maps \(x\) to a high-dimensional space. Therefore, the final solution to nonlinear SVR can be obtained as:  
\[f(\textbf{{x}})=\sum_{n=1}^{N}\left(a_{n}-a_{n}^{*}\right)K(\textbf{{x}}, \textbf{{x}}_{n})+b. \tag{2}\]  
Figure 1: The schematic diagram of support vector regression (SVR).
## 3 Case Study
### Crop Water Need  
According to Figure 6, the central band of the aquifer (labelled as Z1) has the highest concentration of irrigation plots: 79% of the irrigation surface of the entire aquifer. This part accounts for 257,456 ha and 58% of this surface (149,647 ha) has irrigated crops. In other areas, labelled as Z2, groups of plots have an irrigated vs. unirrigated ratio of around 75%, accounting for around 15% of the agricultural surface on the global aquifer. In addition, the average depth of the aquifer estimated in 2016 was 29.41 m, according to reference piezometers used by the Guadiana Hydrographic Confederation (Spain) and data from the SIG maps. Due to the initial conditions related to the present case study, two levels of crop water need are considered: 1500 m\({}^{3}\)/ha per year and 3000 m \({}^{3}\)/ha per year. Both values are the result of water constraints on crop irrigation with the aim of preserving the aquifer. Although there are crops that have higher water requirements, most crops in the study area (mainly vineyards) currently have an average water requirement within the selected range, by considering usual and real  
Figure 4: Location of study area and aquifer.  
Figure 5: Satellite image of regime of exploitation of water resources and chronological graph of groundwater aquifer level. Source: Authors’ elaboration through Google Earth images and CHG data.  
Figure 6: Irradiance solar resource and groundwater depth level: case study. Description of Z1 and Z2.  
grouping plots. Subsequently, both crop water need values (1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha per year) are representative of averaged crop irrigation necessities.
## 3 Results and Discussion
### Parametric Investigation of the Initial Reconstruction Result
Due to the combined effects above, the NRMSE shows a much smaller variation amplitude when the number of grids increases to a certain extent, indicating that increasing the number of grids appropriately is advantageous. Another metric of reconstruction performance is computation time. 200 operations were performed under the same computer conditions to obtain the average total computation time of the basis method. As illustrated in Figure 9, the total computation time increases exponentially as the number of grids increases, with a significant jump at \(n=12\). Considering the effect of grid configuration on the total computation time and reconstruction accuracy, a uniform grid configuration of \(11\times 11\) was selected as a trade-off in the basis method for the subsequent study in this paper, as shown in the red box in Figure 9.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 6 Conclusions  
In this paper, we have demonstrated an autonomous object-tracking system for UAVs using the Lightweight Deep Vision Reinforcement Learning (LDVRL) method. From the experiment and results discussed above, we can see that using the LDVRL method with DQN achieves good performance while maintaining a small model size and can be applied to real-world UAVs whose onboard processing unit is very power limited. We have compared our lightweight techniques with other related conventional techniques to see how much the processing resources are reduced. By using only the visual input captured by the front-mounted camera, the UAVs are not required to equip any additional lasers or sensors to accomplish the task.  
We also tested our framework with a DDPG Actor-Critic algorithm and compared it with the DQN. The result shows that the DDPG performs worse than the DQN in this task (with the states in continuous space and actions in discrete space). Our assumption to explain this surprising result is because a DDPG is more complicated with double the number of networks than a DQN. It requires much more work on tuning the parameters as well as more epochs to train. Since our target is to find the most lightweight but effective solution to perform object tracking with an UAV, we did not carry the experiment with the DDPG further. The DQN has shown that it can be trained with fewer epochs and is also smaller in size. Thus, the DQN reduces the required computational resources and is easier to be applied on physical UAVs.  
Moving forward, we plan to pursue testing our LDVRL method in real-world scenarios to further validate its efficacy and robustness. This will enable us to refine the system to better handle varying environmental conditions and unexpected challenges. Ultimately, our goal is to create a comprehensive and efficient autonomous object-tracking system for UAVs that is capable of functioning optimally in diverse real-world environments.  
Conceptualization, H.N.; Methodology, H.N.; Software, H.N.; Resources, S.T., H.D., K.M., R.V.; Supervision, S.T., K.M., R.V.; Formal Analysis, H.N.; Investigation, H.N., S.T., H.D., K.M., R.V.; Validation, S.T., H.D., R.V., K.M.; Writing-original draft, H.N.; Writing-review and editing, S.T., H.D., K.M., R.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Deakin University.  
The data used in this manuscript was generated during the study. Please refer to Section 4.9 of this manuscript for more details on the data.  
The authors declare no conflict of interest.