esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The detection of diabetic retinopathy (DR) is challenging, as the current diagnostic methods rely heavily on the expertise of specialists and require the mass screening of diabetic patients. The prevalence of avoidable vision impairment due to DR necessitates the exploration of alternative diagnostic techniques. Specifically, it is necessary to develop reliable automatic methods to enable the early diagnosis and detection of DR from optical images. To address the lack of such methods, this research focused on employing various pre-trained deep neural networks (DNNs) and statistical metrics to provide an automatic framework for detecting DR in optical images. The receiver operating characteristic (ROC) was employed to examine the performance of each network. Ethically obtained real datasets were utilized to validate and enhance the robustness of the proposed detection framework. The experimental results showed that, in terms of the overall performance in DR detection, ResNet-50 was the best, followed by GoogleNet, with 99.44% sensitivity, while they were similar in terms of accuracy (93.56%). ResNet-50 outperformed GoogleNet in terms of the specificity (89.74%) and precision (90.07%) of DR detection. The ROC curves of both ResNet-50 and GoogleNet yielded optimal results, followed by SqueezeNet. MobileNet-v2 showed the weakest performance in terms of the ROC, while all networks showed negligible errors in diagnosis and detection. These results show that the automatic detection and diagnosis framework for DR is a promising tool enabling doctors to diagnose DR early and save time. As future directions, it is necessary to develop a grading algorithm and to explore other strategies to further improve the automatic detection and diagnosis of DR and integrate it into digital slit lamp machines.  
diabetic retinopathy; fundus images; optical medical imaging; digital slit lamp machine; neural networks; deep learning; early detection and diagnosis 2025152684  
1
## 5 Prototype Fabrication and Measurement Result
### Measured Setup  
The simulations were conducted using CST Microwave Studio 2022, executed on a Windows 11 system with an Intel Core i7 processor (11 th Gen), 32 GB RAM, and NVIDIA RTX 3060 GPU. The full-wave electromagnetic simulations, including far-field radiation, current distribution, and S-parameter analysis, were completed within 8-12 h, depending on mesh accuracy and frequency sweep settings. The transient solver was used for radiation analysis, and the frequency domain solver was used for S11 simulations.  
The setup shown in Figure 10 consists of an E8361A Vector Network Analyzer (VNA) to record the S11 and radiation results. A PI motor-controlled rotator allowed for precise angular measurement--a solid setup ensuring reliable measurement of the antenna's polar response. Anechoic chamber shielding (blue foam) ensured a low-reflection environment, which improved the accuracy of radiation pattern readings. This setup ensures that the measured radiation characteristics are accurate representations of the antenna performance in free space. Figure 11 shows the OTA measurement setup to test the radiation patterns and gain in the laboratory. Figure 12 illustrates the fabricated 1 \(\times\) 6 linear micro strip patch antenna array, and Figure 13 confirms the OTA measurement setup to test the radiation patterns of the 1 \(\times\) 6 linear MPA array with an integrated power divider in the laboratory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Simulated Results** & **Measured Results** & **Interpretation** \\ \hline
**Resonant Frequency** & \(\sim\)28.2 GHz & \(\sim\)28.1 GHz & Excellent alignment \\
**Return Loss** & \(-\)22 dB & \(-\)20 dB & Good impedance matching \\
**Bandwidth (\(-\)10 dB)** & \(\sim\)2.2 GHz & \(\sim\)2.3 GHz & Wide enough for mm-wave 5G \\
**E-Plane Pattern** & Single lobe & Measured match & Unidirectional radiation \\
**H-Plane Pattern** & Multi-lobe & Measured match & Phase-sensitive behavior \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulated and measured results.  
Figure 11: The OTA measurement setup. (**a**) The 1 \(\times\) 6 antenna array; (**b**) 1 \(\times\) 6 linear array with integrated power divider.  
Figure 10: The 28 GHz OTA measurement setup to test the radiation patterns and gain in the laboratory.
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.8 Quality of Service (QoS)  
It includes a set of services, which must be considered when transmitting data. Due to features of VANET, it is very challenging to support the quality of services. Each application has unique QoS requirements. Therefore, it is necessary to create adaptive QoS routing methods to quickly form updated paths when breaking old paths. Note that the linkbreakage is due to the change in the speed and position of vehicles and dynamic network topology [26].
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 4 Discussion
### Model-Based versus Empirical Calibration  
The results presented in the previous section indicate that a calibration determined by opto-physiological modeling provides a reasonable estimate for a subset of the evaluated wavelength combinations. Especially for the evaluated wavelength combinations with 760 nm, the miscalibration leads to an RMSE of >4%. The calibration model determined by the proposed data-driven approach yields a much smaller RMSE of <2%, well within the required accuracy of the ISO standard [21]. The results indicate that the calibration model determined by modeling could be used as a starting point for the empirical approach. This would allow narrowing of the search range for the various wavelengths, which could greatly reduce the number of computations.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 1 Introduction
Commercially available chillers powered by thermal energy offer cooling outputs ranging from 5 kW to megawatts, in addition to suppliers for drying and cooling without refrigeration systems with air volume flows starting from 4000 m\({}^{3}\)/h. Nevertheless, according to statistics from the end of 2015, only approximately 1350 cooling systems had been installed globally [40]. According to past studies, it can be stated that from the different types of solar cooling, photovoltaic vapour compression cooling shows increased promise for application in buildings from the economic and energy conservation perspectives. It is generally recognised that they offer cooling systems with an increased efficiency that can be conveniently integrated into standard buildings. Additionally, solar electric vapour compression heating functions in a same manner as solar electric vapour compression cooling and has been investigated for applications in winter months in the aforementioned studies. Therefore, by combining PV electric heating and cooling in a PVAC, this could provide a more beneficial option for buildings that require both cooling and heating. It is essential for future sustainable developments that the occupants incorporate some adaptive techniques to save energy, such as opening the windows, using low-energy fans, using external shading to prevent the summer sun from heating the cooled areas and wearing suitable clothes instead of totally relying on the air conditioning. Many studies indicate that using these measures could save up to 50% of the cooling energy [41; 42; 43]. Similar to the majority of other countries, it is anticipated that the need for air conditioning in Jordan will double by 2030 compared to 2015 levels [44]. This increased demand is caused by various factors: an overall rise in the standard of living, economic development transformation in architectural design, the increased utilisation of glazing in constructions and the gradually rising temperatures caused by global warming. Consequently, there will be a drastic increase in emissions, which will make a further contribution to climate change.
## 2 Materials and Methods
### Road DEM Construction Method
#### 2.3.3 DEM Optimization Considering the Water Catchment Characteristics of the Road Surface  
Urban roads are the main carriers of urban traffic, and the urban drainage system is also laid mainly along the road; consequently, the road is in the main area of the urban surface catchment process. Because the urban road is relatively flat overall, the simulation of the catchment process on the road surface based on the classic surface runoff model causes serious disorder of the catchment path [30; 31; 32], which leads to failure of the catchment simulation on the road surface. This issue is also a main problem in the simulation of the urban terrain catchment process. In this study, to achieve the correct catchment function of the road surface, a DEM correction method based on the original DEM data was proposed so that the modified road DEM would be able to correctly simulate the catchment path. The main processing steps were as follows:  
Step 1: Collect the water outlet in the road according to the UAV clear photographic image, read the road DEM, determine the row and column position of the water outlet in the DEM, and mark it as the water outlet grid, as shown in Figure 6(1).  
Step 2: Check the grids marked as adjacent to the outlet grid, and determine the flow direction of the surrounding grids for each grid. If the current grid flows into the marked outlet grid, the grid will continue to be marked as the correct grid. Otherwise, the grid will be marked as the incorrect grid until all grid traverses are completed, as shown in Figure 6(2).  
Step 3: For each grid marked incorrectly, if it flows into the grid marked as correct, mark the grid as correct. Otherwise, do not process until all incorrect grids are traversed, as shown in Figure 6(3).  
Step 4: For incorrect grids that were not processed in step 3, check the surrounding grids that are marked as correct, and calculate the minimum value of the difference between the elevation of this grid and the
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 3 Problem and Algorithm Description  
### Problem Description  
The clustering routing algorithm in UWSNs generally forwards data from the cluster nodes to each cluster head node. The data are sent to the sink node through the manner of single or multiple hops, thereby leading to a higher energy consumption of the cluster head node than other nodes in the cluster. This discrepancy in consumption amount causes the unbalanced energy consumption of the network. It also leads to the premature death of cluster head nodes. To solve this problem, [PERSON] et al. [9] proposed DC-PSO. Although dual-cluster head and cluster multiple-hop routing methods can effectively reduce the energy consumption of cluster heads, they fail to fundamentally solve the problem of \"hot zones\".  
Considering the problems cited in the preceding paragraph, this study sets clusters according to the distance between the cluster and base station. If the distance is large, the numbers of cluster are smaller. The purpose is to share the cluster head nodes' energy. In addition, the dynamic hierarchical mechanism is to avoid that the same node that is repeatedly selected as the cluster head. Krill swarm optimization is then applied to the master cluster heads and vice-cluster heads in the selection process. In sum, this paper proposes a dynamic layered dual-cluster heads routing algorithm based on KH optimization.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion
### Metaheuristic Parameters  
It is well known that the tuning parameters of metaheuristics largely influence the outcome of these algorithms. Thus, it is necessary to carefully tune and select the best possible tuning parameters. Various tuning parameters for all the algorithms selected in this paper are provided in Table 2. Each algorithm was independently run 20 times to account for its stochastic nature. In each run, the number of search agents was limited to 30, whereas the iteration limit was kept as 200, thereby encompassing 6000 function evaluations. Fixing the number of function evaluations would help in drawing a meaningful comparison between the algorithms under consideration.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Instances** & **Features** & **Source** \\ \hline Heart & 303 & 13 & [[https://archive.ics.uci.edu/ml/datasets/heart+Disease](https://archive.ics.uci.edu/ml/datasets/heart+Disease)]([https://archive.ics.uci.edu/ml/datasets/heart+Disease](https://archive.ics.uci.edu/ml/datasets/heart+Disease)) (accessed on 1 February 2023) \\ Ionosphere & 351 & 34 & [[https://archive.ics.uci.edu/ml/datasets/ionosphere](https://archive.ics.uci.edu/ml/datasets/ionosphere)]([https://archive.ics.uci.edu/ml/datasets/ionosphere](https://archive.ics.uci.edu/ml/datasets/ionosphere)) (accessed on 1 February 2023) \\ German & 1000 & 24 & [[http://www.liacc.up.pt/ML/old/statlog/datasets.html](http://www.liacc.up.pt/ML/old/statlog/datasets.html)]([http://www.liacc.up.pt/ML/old/statlog/datasets.html](http://www.liacc.up.pt/ML/old/statlog/datasets.html)) (accessed on 1 February 2023) \\ Breast & 699 & 9 & [[https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+)]([https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+))(diagnostic) (accessed on 1 February 2023) \\ Sonar & 208 & 60 & [[https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+](https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+)]([https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+](https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+))(Sonar,+Mines+vs.+Rocks) (accessed on 1 February 2023) \\ Ovarian & 216 & 4000 & [PERSON] et al. [57] \\ Australian & 690 & 14 & [[https://archive.ics.uci.edu/ml/datasets/statlog+](https://archive.ics.uci.edu/ml/datasets/statlog+)]([https://archive.ics.uci.edu/ml/datasets/statlog+](https://archive.ics.uci.edu/ml/datasets/statlog+))(australian+credit+approval) (accessed on 1 February 2023) \\ Colon & 62 & 2000 & [PERSON] et al. [58] \\ Diabetes & 768 & 8 & [[https://archive.ics.uci.edu/ml/datasets/diabetes](https://archive.ics.uci.edu/ml/datasets/diabetes)]([https://archive.ics.uci.edu/ml/datasets/diabetes](https://archive.ics.uci.edu/ml/datasets/diabetes)) (accessed on 1 February 2023) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the binary datasets.
# Profiling Croatian Whisky Using GC/MS-MS and UHPLC-QTOF
## 3 Results and Discussion
### Aromatic Profile of Croatian Whisky Determined by SPME-GC-MS
#### 3.3.5 Maturation and Flavor Development  
After distillation, the new-make spirit is colorless and neutral, but it contains many congeners that have either been transferred from the malt and/or created or concentrated during fermentation or distillation, as previously discussed. Maturation involves the transition of the new spirit into a more mature, mellow, and smoother product, usually achieved by diluting the spirit first in water and then transferring it to wooden casks for a minimum period of three years but often much longer. This maturation stage significantly enhances the complexity of the sensory characteristics of the whisky. In the studied samples, several congeners were detected. Glucogallin, the ester of gallic acid and glucose, which may contribute to astringency and could possess antioxidant properties. It is believed to be derived from oak wood aging in barrels such as those used for whisky pro-duction [38]. Quinic acid is a compound commonly found in various plants including the oak from whisky barrels, and it contributes to the bittermess and astringency of whisky. As a cyclic polyol, it can contribute to perceived acidity and astringency while acting as an antioxidant [37; 55]. Furthermore, threonine acid is a metabolite produced during the oxidation process of vitamin C. Its occurrence in whisky could potentially result from chemical reactions that occur during aging. Gallic acid, commonly derived from the breakdown of tannins, is a phenolic acid that influences the color, astringency, and antioxidant properties of whisky [1; 15]. Vanillin plays a significant role in the aroma of whisky, adding sweet and creamy hints reminiscent of vanilla. Its presence and levels are vital at the initial filling of the barrel and undergo changes during maturation. Extracted from lignin in oak wood as whisky matures, it gradually permeates into the spirit over time [31]. Syringic acid and syringaldehyde, also derived from the breakdown of lignin in oak wood, contribute spicy, sweet, and smoky notes, enriching the whisky's complexity.
## 5 Parameter Optimization of the Semi-Active Energy Regenerative Suspension with an EHA
### Optimization Result Analysis  
The above parameters are brought into the genetic algorithm optimization toolbox, and the optimization results of the semi-active energy regenerative suspension with an EHA are shown in Table 1.  
In order to verify the effect of the optimized EHA's parameters on the performances of the semi-active energy regenerative suspension with the EHA, the performance indexes of the suspension before and after optimization are simulated and compared assuming that the vehicle is traveling at a speed of \(20\) m/s on a C-grade road and the simulation time is \(10\) s [24]. Additionally, the simulation results are shown in Table 2 and Figures 9-11.  
Table 2 shows that after optimizing the parameters, the RMS value of the sprung mass acceleration is reduced by \(18.60\%\), which indicates that the ride comfort of the vehicle has been improved to a certain extent, and the RMS value of the tire dynamic load is decreased by \(12.61\%\), which shows that the vehicle's driving safety has been enhanced; therefore, the vehicle's damping performance is improved. After optimizing the parameters, the RMS value of the energy regenerative power increases by \(49.67\%\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimal Parameters** & **Symbol** & **Value** \\ \hline The effective area of the hydraulic cylinder piston & \(A\) & \(7.66\times 10^{-4}\) m\({}^{2}\) \\ The displacement of the hydraulic motor & \(q\) & \(4.25\) mL/r \\ The back electromotive force constant of the generator & \(k_{e}\) & \(8.28\times 10^{-3}\) V \(\cdot\) min/r \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal parameters.  
and the energy regenerative efficiency increases from 18.68% to 27.26%, which demonstrates that the vehicle's energy regenerative performance was improved significantly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Performance Index**} & \multirow{2}{*}{**Symbol**} & \multicolumn{2}{c}{**Value**} & \multirow{2}{*}{**Control Effect/\%**} \\ \cline{3-3} \cline{5-5}  & & **Before Optimization** & & **After Optimization** \\ \hline Sprung mass acceleration & \(\sigma_{a}\) & 2.1585 m/s\({}^{2}\) & 1.7571 m/s\({}^{2}\) & \(-\)18.60 \\ Tire dynamic load & \(\sigma_{Fd}\) & 802.7 N & 701.5 N & \(-\)12.61 \\ Energy regenerative power & \(\sigma_{Preg}\) & 59.82 W & 89.53 W & 49.67 \\ Energy regenerative efficiency & \(\eta\) & 18.68\% & 27.26\% & 45.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results.  
Figure 10: Tire dynamic load response curve.  
Figure 9: Spring mass acceleration response curve.
## 4 Model Optimization Based on Stepwise Regression  
### Multiclinearity  
A multiple linear regression model \(y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{p}x_{p}\) has a basic assumption that is rank (\(X\)) = \(p+1\), where \(X\) is known as the design matrix. If there exist \(p\) numbers, making the following equation true, multicollinearity exists between the independent variables (\(x_{1},x_{2},\ldots,x_{p}\)) [32].  
\[c_{0}+c_{1}x_{i1}+c_{2}x_{i2}+\ldots+c_{p}x_{ip}\approx 0,i=1,2,\ldots,n \tag{16}\]  
Objectively speaking, when an event involves multiple factors, there is typically a certain degree of correlation between them. When a group of variables is strongly correlated with each other, it is considered to violate the basic assumption of the multiple linear regression model. In Equation (15), the terms of the full parameter model are trigonometric functions of the angular positions of the axes, which are interrelated with each other. Therefore, it is reasonable to infer that the explanatory variables of this model have significant collinearity.  
If the regression model has multicollinearity, the parameter estimates via OLSE are particularly unstable, and the variance of the coefficients rises more rapidly as the multicollinearity obtains more. This effect can result in a situation where some independent variable regression coefficients fail significance tests when the regression equation is highly significant, and the structural relationships between variables become distorted. Although multicollinearity has no influence on data fitting, it is worth noting that if the correlation of independent variables cannot be guaranteed to remain static in the prediction period, multicollinearity will have a significant negative effect on the regression prediction and misrepresent the prediction results.  
A vital technique for assessing the degree of multicollinearity and diagnosing is the variance inflation factor (VIF) methodology [33]. Let \(X^{s^{\prime}}X^{*}=(r_{ij})\) be the correlation matrix of the independent variables after central standardization, and then the diagonal elements of its inverse matrix are the variance inflation factors \(VIF_{j}=c_{ij}\). The formula is as follows.  
\[C=(c_{ij})=(X^{s^{\prime}}X^{*})^{-1} \tag{17}\]  
It is proven that the \(c_{ij}\) and the coefficient of multiple determination \(R_{j}^{2}\) are related :  
\[c_{ij}=\frac{1}{1-R_{j}^{2}} \tag{18}\]  
The stronger the linear correlation (measured by the \(R_{j}^{2}\)) of the independent variable \(x_{j}\) with the others, the more severe multicollinearity is. It is empirically shown that there is serious multicollinearity between the independent variables when \(VIF_{j}\geq 10\), which might greatly affect the least-squares estimates [32].