esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 3 Proposed Method
### Segmentation  
In every image analysis, the segmentation stage is crucial. If the segmentation is not performed correctly, the rest of the image analysis will be complicated. Furthermore, segmenting the optic disc (OD) in a retinal image is critical in the diagnosis of certain ophthalmological abnormalities. Segmentation is a method of categorizing data based on pixel similarity. In terms of grayscale, color and intensity, the pixels in the nearby region have different values. When it comes to the automatic classification of disease, segmentation accuracy is crucial. Any inaccuracy in the segmentation stage leads to misclassification for diagnosing the pathological sign [26].  
In recent years, researchers have used a variety of ways for segmenting retinal vasculature. The methods have been divided into two groups, namely supervised and unsupervised methods. There are various algorithms for unsupervised approaches, such as morphological processing, model-based, matched filtering and vessel tracking. Notwithstanding, the unsupervised approaches have a limitation of handcrafted features for the representation and segmentation of vessels. As a result, supervised approaches outperform unsupervised ones when it comes to calculating segmentation accuracy.  
To filter unwanted noise and geometrical objects based on vessel structure, the researchers used a modified version of [PERSON]'s approach [27]. [PERSON]'s method is typically used to determine a threshold for distinguishing vessel and non-vessel pixels on a local or global scale across the entire image. Applying Otsu thresholding to the entire image at once produces poor results, which is why the modified version of OTSU's thresholding is applied. Both thin and thick vessels become more visible in this way. A single improved image that is then subjected to more local thresholding is obtained. The vessel-based thresholding is employed to define a new threshold in local thresholding, which is dependent on vessel locality. To suppress noise more effectively for vessels in the vicinity of wide vessels, we added some offset to the global threshold [28].  
We set a lower threshold than the global for other places away from wide vessels by subtracting an offset from it to recover small or thin vessels from the low-intensity background. The threshold values of sigma are set for enhancing thick and thin vessels in retinal images. For obtaining the binary image, global Otsu thresholding is applied. Then, the fusion of thick and thick vessels is applied to obtain the improved image [29]. Then, morphological operations are performed for effective the segmentation of blood vessels. The proposed methodology for the segmentation of blood vessels is shown in Figure 4.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 3 Results
### Correlation Analysis Between U and Sea Surface Parameters
SSS reaches the maximum in the region of 60\({}^{\circ}\) E-70\({}^{\circ}\) E, 0-4\({}^{\circ}\) N (Figure 2(g4)). D20 deepens in the eastern equatorial region, accompanied by westerly wind (Figure 2(h4)). Figure 3 illustrates the spatial correlations between various parameters and zonal velocity (U) from OSCAR data during 1993-2022. In the equatorial region, wind stress parameters exhibit the most pronounced influence on U. Specifically, Tx demonstrates a high positive correlation with U, reaching a correlation coefficient of 0.8 (Figure 3c). Ty also shows a positive correlation with U, with a correlation coefficient of 0.6 south of the equator, albeit slightly lower than that of Tx (Figure 3d). Curlz demonstrates a low positive correlation with U north of the equator and a high negative correlation with U south of the equator (Figure 3b). Both MLD (Figure 3d) and SSS (Figure 3g) display moderate positive correlations with U, and their correlation coefficients are less than 0.5. Both SLA (Figure 3a) and D20 (Figure 3h) demonstrate low negative correlations with U west of 70\({}^{\circ}\) E and positive correlations with U east of 70\({}^{\circ}\) E, with their correlation coefficients less than 0.2. In contrast, SST exhibits weak negative correlations with U in the equatorial region, with correlation coefficients less than \(-\)0.2 (Figure 3f), suggesting their relatively limited influence on WJ dynamics. Figure 3: Spatial correlations between various parameters and the zonal velocity (U) from OSCAR data during 1993–2022: (**a**) sea level anomaly (SLA), (**b**) wind stress curl (Curlz), (**c**) zonal wind stress (Tx), (**d**) meridional wind stress (Ty), (**e**) mixed layer depth (MLD), (**f**) sea surface temperature (SST), (**g**) sea surface salinity (SSS), and (**h**) 20\({}^{\circ}\)C isotherm depth (D20).
## 3 Pattern Selection with SymCHM
### SymCHMMerge: Improved Pattern Selection  
An analysis of the basic pattern selection algorithm showed lack of diversity in the found patterns, as the patterns were often very similar and overlapping. We improved the algorithm by merging redundant patterns and adjusting the learning and inference parameters, and named the resulting model SymCHMMerge.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Measurement of Mixer
#### 2.4.2 Linearity Measurement of Mixer  
The 1 dB compression point (P1 dB) is defined as the linear gain deviation of the mixer IF signal power as the RF input signal power increases. When the RF signal power increases to a certain level, the IF signal power no longer increases as the linear gain, and the IF signal power deviates from the RF signal power corresponding to the linear change. Theoretically, as the input RF signal power level increases, the output IF power level of third-order intermodulation increases three times faster than that of the fundamental. The third-order intermodulation point (IP3) is the intersection of the fundamental signal in an ideal linear system when the amplitude of the output signal is equal to the third-order intermodulation component, namely IP3, where the power of the third-order intermodulation is equal to the extrapolated value of the fundamental. The input and output power of the IP3 corresponds to IIP3 and OIP3, respectively. In this work, we measured the fundamental and third-order intermodulation scatter diagrams at 1:1 and 3:1, respectively. The intersection was extrapolated to obtain IP3.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 3 Methods for Real-Time Food Monitoring
### \(O_{2}\) Detectors  
Modified atmosphere packages (MAPs) have been broadly employed to prolong the shelf-life of various edible products [66; 67]. In a typical MAP, the product is sealed in a gas mixture where oxygen, nitrogen, and carbon dioxide concentrations are controlled. In this regard, different sensors able to monitor the concentration of oxygen within MAPs have been developed [39; 40; 68; 69]. Luminescence-based indicator systems based on fluorogenic molecules embedded in a polymer film that is permeable to gas and/or ions, (e.g., silicon rubber, poly(vinyl chloride), etc.) modify their photoluminescence upon oxygen exposure. However, when photo-excited with environmental light sources, this kind of sensor lacks a response that is significantly discernible by the naked human eye, thereby requiring intense photo-excitation sources (e.g., light-emitting diodes or lasers) and appetite detectors [70; 71; 72]. Alternatively, colorimetric redox sensors exploiting simple reversible redox chemistry [73; 74; 75; 76] and consisting of a redox dye (e.g., methylene blue) and a strong reducing agent (e.g., glucose in an alkaline environment) are able to change color upon small oxygen variations (i.e., 0.1%). Figure 1a schematizes the color variation achievable in a commercial system upon the presence of oxygen [77]. The sensor changes its color from pink to blue within a few hours upon oxygen exposure. Despite their high sensitivity and easy transduction, most of these sensors require to be stored and handled in anaerobic conditions, which considerably reduces their large-scale applicability in MAPs. Nevertheless, when in contact with high moisture environments typical of food packages, these redox dyes may be released from the water-insoluble polymer matrix in which they are embedded and diffuse into the packaging, hence posing health concerns. In this sense, the cation-binding capability of alginate, a polysaccharide extracted from brown algae and widely exploited in biomedical, pharmaceutical, wastewater remediation, and food applications [78; 79; 80; 81; 82; 83], has been used to prevent dye leaching [84].
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
## 3 Results and Discussion
### Optimization Measures
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slope Reinforcement Measures** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{Retaining walls} & Low-cost, simple construction process. & The wall itself is heavy, and finding suitable rock materials with appropriate strength in highly weathered rare earth mine areas is challenging. \\  & Setting anti-slide piles in unstable sliding areas can resist slope instability by increasing the anti-slide force. & Poor permeability of the pile foundation, which to some extent affects the seepage and diffusion movement of the local ore body solution. \\  & Effectively reinforces the slope by building brick structures or pouring concrete. & Due to its significant weight, the discharge of liquids is impeded, which severely affects the collection and injection of liquids in rare earth mines. \\  & Artificially changes the shape of the slope through earth & Excavation of the slope incurs substantial costs and leads to environmental degradation of the mine, undermining the objective of in situ leaching. \\  & & \\ \multirow{3}{*}{Slope Cutting and Unloading} & \multirow{3}{*}{Measures are taken to resist c, thereby securing the ecological environment.} & Ensuring the survival rate of plants and frequent maintenance to keep them vigorous is required, primarily by reinforcing only the topsoil layer. \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of slope reinforcement measures.
## 2 System and Channel Models  
### System Model  
The coherent THz communication system physically combines the received THz wave with the local THz wave to recover signal information from the electric field. This system can provide good background noise suppression and provide better spatial and frequency selectivity. A basic coherent THz link is shown in Figure 1, we assume that both the transmitting and receiving ends are equipped with a high-directional THz transmitter and receiver. After propagating through the atmospheric turbulence channel, the THz signal at the output of the coherent receiver can be modeled as  
\[y=hx+n \tag{1}\]  
where \(x\) represents the transmitted signal, \(h\) denotes the channel state, \(y\) is the synthesized electrical signal at the receiver, and \(n\) indicates additive white Gaussian noise (AWGN).  
It is noted that the pointing error and atmospheric attenuation are independent. Therefore, the channel gain \(h\), which comprehensively represents the various interactions--such as path loss, absorption, scattering, and reflection--that THz waves undergo while propagating through the medium from the transmitter to the receiver, can be expressed as \(h=h_{l}h_{p}h_{a}\), where \(h_{l}\) represents the path loss, remaining constant under specific weather conditions and link distances. The term \(h_{p}\) denotes the pointing error loss factor, while \(h_{a}\) indicates the atmospheric attenuation loss factor. We observe that \(h_{l}\) is deterministic, whereas both \(h_{a}\) and \(h_{p}\) are random variables (RVs).
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 3 Results
### Statistics and Analysis
#### 3.1.2 Impact of Spectral Information Elements  
Figure 8 shows the results of organ classification using individual spectral information elements (Sentinel-2 bands and spectral indices) with single temporal data. The results varied across different days, with the best outcome for each element selected (OA between 0.631 and 0.855). While high-resolution Sentinel-2 bands (10 m) generally showed greater importance, all elements provided acceptable results (OA > 60%). Notably, despite B1 having a lower resolution (60 m), it still proved relevant for detection. The lowest performance was observed with spectral band B10, which also explains the minimal differences observed between Level 1C and Level 2A data. These findings suggest that the choice of spectral information is somewhat flexible, as most elements lead to similar detection accuracy, supporting the idea that combining various spectral data may increase the overall model performance.
## 4 Experiment
### Stacking Base Classifier Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Non-Spam**} & \multicolumn{3}{c}{**Spam**} \\ \hline
**No.** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & 57.3\% & 94.5\% & 71.3\% & 81.2\% & 27.8\% & 41.4\% \\
2 & 57.1\% & 93.7\% & 71.0\% & 80.1\% & 28.2\% & 41.7\% \\
3 & 56.5\% & 92.1\% & 70.0\% & 77.8\% & 28.2\% & 41.4\% \\
4 & 57.5\% & 95.8\% & 71.9\% & 86.1\% & 27.6\% & 41.8\% \\
5 & 57.6\% & 94.2\% & 71.5\% & 81.7\% & 29.1\% & 42.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Prediction results of spam and non-spam information when high-dimensional features of social attributes are input. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Base Learner**} & \(\chi^{2}\)**Chi-** & \multirow{2}{*}{\(\chi^{2}\)**\(p\)-Value**} & \multirow{2}{*}{**Pearson**} & \multirow{2}{*}{**Spearman**} \\ \cline{1-1}  & & **Squared** & & & & \\ \hline
1 & Logistic & 259.81 & 1.88 \(\times\) 10\({}^{-58}\) & 1.92 \(\times\) 10\({}^{-1}\) & 1.92 \(\times\) 10\({}^{-1}\) \\
2 & SVM & 394.25 & 9.81 \(\times\) 10\({}^{-88}\) & 2.37 \(\times\) 10\({}^{-1}\) & 2.37 \(\times\) 10\({}^{-1}\) \\
3 & MLP & 479.53 & 2.70 \(\times\) 10\({}^{-106}\) & 2.61 \(\times\) 10\({}^{-1}\) & 2.61 \(\times\) 10\({}^{-1}\) \\
4 & 1D-CNN & 572.35 & 1.72 \(\times\) 10\({}^{-126}\) & 2.85 \(\times\) 10\({}^{-1}\) & 2.85 \(\times\) 10\({}^{-1}\) \\
5 & Decision tree & 686.05 & 3.23 \(\times\) 10\({}^{-151}\) & 3.12 \(\times\) 10\({}^{-1}\) & 3.12 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test results of the high-dimensional features extracted from social attributes. performance of user profiling significantly improves. The Chi-squared test results for decision trees demonstrate the highest correlation when all attributes are used, as corroborated by the correlation analysis. In Table 11, the high-dimensional features extracted by decision trees show a correlation of over 0.9 with the sample labels. Table 12 shows the classification results when the high-dimensional features extracted by different classifiers are used as input. The precision for non-spam information exceeds 85% for all classifiers, with Logistic, SVM, and MLP achieving over 90%. Additionally, these three base learners achieve recall values over 90% for spam information, demonstrating their strong capability to support the model when high-dimensional features based on all attributes are used. Therefore, we incorporated the results from Logistic, SVM, and MLP as supplementary features for user profiling.
## 3 Results and Discussion
On the contrary, in the case of useful heat power, the gradient of change is the largest when \(L_{abs}\) = 150 mm (Figure 2). However, the value of \(\eta\), in addition to useful heat power, also depends on \(Q_{sun}\) (Equation (3), Section 2.2.1). The influencing term \(Q_{sun}\) grows faster than the influencing term \(Q_{heat}\) when the surface of the absorber increases because the solar potential of the structure also increases (\(G_{tot}\), Equation (12), Section 2.2.4). Figure 3 is practical for another reason--it can be used for the initial prediction (with satisfactory accuracy) of the thermal behavior of solar collectors of the same or similar design. This is made possible by the graphic display of characteristic \(t_{abs}\) isotherms (dashed straight lines), designed using simple linear regression (SLR). Following the presented methodology of using a double MCA (Section 2.3), the following diagram in Figure 4 (for \(d_{gt}\) = 3 mm, \(d_{gt}\) = 4 mm and \(d_{gt}\) = 5 mm) shows the functional dependence between the MCA final ranking for useful heat power and Labs (the first group of results) and the functional dependence between the MCA final ranking for thermal efficiency and Labs (the second group). Regardless of the thickness of the glass (while taking into account geometric, economic and ecological indicators), the MCA final ranking showed that the useful heat power is the highest when \(L_{abs}\) = 150 mm: 0.083 (for \(d_{gt}\) = 3 mm), 0.07 (\(d_{gt}\) = 4 mm) and 0.061 (\(d_{gt}\) = 5 mm). On the other hand, the MCA final ranking rated the \(L_{abs}\) = 50 mm option as the best when it comes to thermal efficiency: 0.121 (for \(d_{gt}\) = 3 mm), 0.103 (\(d_{gt}\) = 4 mm) and 0.091 (\(d_{gt}\) = 5 mm). For this reason, both independent groups of specific indicators (Section 2.3) are represented by logarithmic functions (logarithmic curves)--with the aim of noticing that the first group has a decreasing trend and the second has a scattered trend. It is clear that the optimal performance of the SGVTC is achieved when the useful heat power and thermal efficiency are in balance. A multidisciplinary approach showed  
Figure 4: Geometric optimization of the SGVTC using the double MCA. that the balance is achieved if \(L_{abs}\) is between 87 and 88 mm, with the preference being given to glass with a thickness of 3 mm (Figure 4). In other words, the optimal MCA final ranking for the analyzed cases is as follows: 0.078 (for \(d_{gt}\) = 3 mm), 0.066 (\(d_{gt}\) = 4 mm) and 0.057 (\(d_{gt}\) = 5 mm). In a theoretical sense, it is desirable for it to be less thick, but then its mechanical characteristics and weather resistance would be called into question. Therefore, the lower limit is limited to this value.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 4 Model Situation
### Results of Evacuation Models for Each Scenario
Figure 6: Model showing the route of the emergency services for Scenario 1.  
access for emergency teams. The blue arrows indicate the optimal path for rescue units, while the critical areas for intervention are marked with relevant hazard zone data. Table 8 provides specific data for the danger zones in Scenario 2, focusing on the intensity of thermal radiation. In the ERPG-3 zone (10.0 kW.m\({}^{-2}\)), the zone extended 12 m from the fire source and presented a potentially fatal risk within 60 s. The ERPG-2 zone (5.0 kW.m\({}^{-2}\)) extended 17 m and could cause second-degree burns within 60 s. The ERPG-1 zone (2.0 kW.m\({}^{-2}\)) extended 25 m, where exposure would result in pain within 60 s.  
The presented ammonia leak model for Scenario 1, combined with the pool fire risk in Scenario 2, emphasised the importance of precise evacuation planning and timely deployment of rescue services. The implications of these findings extend beyond theoretical modelling, offering practical insights for emergency response units tasked with safeguarding public health and safety. The model outputs, combined with real-time environmental data, enable rescue units to minimise exposure risks while ensuring efficient intervention strategies in hazardous situations. Ultimately, the integration of real-time environmental \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Danger Zone** & \multicolumn{2}{c}{**Thermal Radiation**} & \multicolumn{1}{p{113.8 pt}}{**Size and Range (m)**} & \multicolumn{1}{p{113.8 pt}}{**Consequences**} \\ \hline \hline
**LRPG-3** & 10.0 & 14 & Potential risk & **without cuts** \\ \hline ERPG-2 & 5.0 & 17 & 2 nd degree burns within 60 s \\ ERPG-1 & 2.0 & 25 & Pain within 60 s \\ \hline \hline \end{tabular} Note: Colour coding used for hazard zones—yellow (ERPG-1, low risk), orange (ERPG-2, moderate risk), and red (ERPG-3, high risk), reflects increasing levels of risk due to ammonia concentration.  
\end{table} 
Table 8: Danger zones for Scenario 2. Figure 7: Model showing the route of the rescue services for Scenario 2.  
data with advanced modelling techniques fosters a more resilient approach to industrial safety management.
## 1 Introduction
### Related Works and Research Gap  
At present, research on machine learning for MEC network optimization is focused on two main areas, one of which is direct optimization based on historical data in the network. For example, one study [3] proposed a network optimization algorithm based on single-cell edge computing. A task offloading strategy for wireless mobile networks assisted by UAVs was shown in another study [5]. Reference [7] describes a resource allocation method based on deep reinforcement learning in 5G ultra dense networks. Another study [16] put forward a deployment and task allocation method for a server reinforcement learning framework in a multi-access edge computing scenario. In addition, the authors of [21] proposed a federal learning mechanism under data-security-sensitive conditions. The other research focus is the discussion of spatial and temporal correlations in data and the prediction of unknown parameters based on historical network status. For example, one study [3] demonstrated a method for network prediction under the condition of unknown user status, as well as network optimization. Another study [22] investigated the prediction of network parameters and network optimization using a hybrid artificial neural network. It can be seen from the literature that many researchers have tried to use machine learning to promote the performance of MEC. However, there are still some deficiencies in the current research. Firstly, with an increase in network scale, the complexity of optimization increases and the time required for optimization is also significantly extended. It is still necessary to make this decision-making process more efficient. Secondly, it is worthwhile to carry out more research about the relevance of network data. On this basis, a corresponding network framework should be proposed to achieve this function. Subsequently, researchers can discuss how users can help further optimize the mobile network through mutual cooperation. Briefly, the content of this paper includes a service scenario for the MEC network and the cooperation of users in the network. Specifically, the network frame includes one MEC server providing service to users in multiple cells (mainly downlink tasks). Cooperation between users can optimize resource allocation by saving resources and allowing for task coordination, further improving network performance.