esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 1 Introduction
The versatility of the architecture and its robustness in handling different types of image data make it a powerful tool in diverse fields. However, its application in flap monitoring has been limited [29], with a notable absence in the analysis of thermographic flap images. This research gap presents a significant opportunity for improving postoperative flap monitoring, as existing methods often lack the precision and objectivity needed for early detection of complications. Current methods of flap monitoring, including clinical assessment and portable Doppler, while valuable, are subjective and intermittent, potentially delaying the detection of flap failure. More advanced methods such as implantable Doppler [30] and near-infrared spectroscopy provide continuous monitoring but are invasive or require direct contact with the flap, increasing the risk of infection and patient discomfort. To address these limitations, we have developed an AI program based on the U-Net architecture for autonomous identification and monitoring of flaps using thermographic images. Our approach offers several key advantages over existing methods. It provides non-invasive and continuous monitoring through thermographic imaging, allowing for constant, non-contact observation of flap temperature. The use of thermographic imagesensures more objective data, as they are less susceptible to environmental variations, providing more consistent and reliable information [31]. Furthermore, our system's ability to accurately segment flaps and determine their average temperature enables the potential for earlier detection of temperature anomalies indicative of flap compromise compared to traditional methods [32]. To overcome the challenges of medical data collection, we collaborated with a healthcare institution to create a unique dataset of thermographic flap images. Using this dataset, we developed a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model. Our model incorporates two types of attention modules [33; 34] in the bottleneck and decoding path, enabling better detection of complex flap shapes. This novel approach represents a significant advancement in the field of postoperative flap monitoring. By leveraging ML and thermographic imaging, we aim to provide clinicians with a more reliable, objective early-warning tool for detecting potential flap failures. Our method has the potential to improve patient outcomes by enabling timely interventions, which is a critical factor in salvaging compromised flaps. The emergence of ML in plastic surgery reflects a paradigm shift towards precision medicine and personalized treatment strategies. However, the successful implementation of ML in clinical practice requires rigorous validation and continuous refinement to ensure reliability and safety [35].
## 2 Methodology
### Rainfall Analysis  
Rainfall data were obtained from recorded (or in situ) instrumental data and from a global base of estimated values. Priority was given to in situ measurements, whose data were provided by [11]. However, since the analyses required continuous and error-free historical series for the period covered by EN 15-16, many of them were pre-evaluated and discarded. To obtain a grid of points that allowed the spatial evaluation of the phenomenon, we used data from the CHIRPS project, which provides a database of global estimates with a resolution of 0.05\({}^{\circ}\), and is generated from conventional records and satellite observations [12]. Fifteen series were obtained from in situ measurements and seventeen from the CHIRPS project (Figure 2 and Table 1). The hypothesis of intense landscape changes due to rainfall during EN 15-16 was tested by analyzing daily, monthly, and cumulative data. The six-month cumulative was the selected statistic, and the results of this exploratory analysis are presented in the results section.  
Figure 2: Location of the study area (Northern Parana, Brazil), rainfall stations and CHIRPS estimates used in the study. Sources: [8,11].  
A probabilistic analysis was performed to determine how rare the six month accumulation of rainfall was, based on the exceptionality observed in the period. Historical series were used for the adjustment of statistical distributions and parameters were applied to calculate the probability of an event equal to or greater than that observed in February 2016 for each series. It was noted that the gamma distribution was the most appropriate, therefore it was necessary to obtain the shape (\(\gamma\)) and scale (\(\beta\)) parameters, specific for each series.  
The length of all the historical series and the existence of gaps were verified, resulting in a 36-complete-years-period set for this work, between January 1981 and December 2016. After calculating the probability for each of the 32 historical series, the return period (RP) was calculated based on the following equation:  
\[\text{RP}=\frac{\frac{1}{\text{P}}}{12} \tag{1}\]  
where RP is the return period (in years) and \(p\) is the probability. The value 12 is for conversion from months to years.
## 4 Simulation Results and Analysis  
### Complexity Analysis  
This section analyzes the complexity of the proposed algorithm and traditional basis coefficient estimation algorithms, which is our motivation to improve the SBL-based framework for basis coefficient estimation.  
The traditional basis coefficient estimation algorithms based on LS and LMMSE all have the operation of matrix inversion; hence, the complexity is noted as \(\left(QL\right)^{2}\times MN+\left(QL\right)^{3}\). As previously stated, matrix inversion operations are present in the SBL algorithm while solving the a posteriori probability of the basis coefficients. The number of multiplication operations are dominated by mean and variance, i.e., \(\left(QL\right)^{2}\times MN+\left(QL\right)^{3}\) and \(\left(QL\right)^{2}\times MN+\left(QL\right)^{3}\), respectively, which are comparable to the complexity of the traditional method.  
Obviously, the complexity of GAMP-SBL algorithm is mainly determined by the E step in SBL, where the complexity is mainly matrix multiplication. In the GAMP algorithm, each iteration is multiplication between elements and does not involve the operation between matrices; thus, the complexity of the product operation of each iteration is approximately \(\left(MN\right)+\left(QL\right)\).  
In addition to the analytical results, the simulation runtime is also a useful indicator of complexity. Under the same constraints, when the signal noise ratio (SNR) is 20 dB, we illustrate the runtimes of the different algorithms as the number of subcarriers \(M\) in Figure 2, where the convergence error of both EM and GAMP iterations is \(10^{-6}\). The runtimes of both SBL and LMMSE increase exponentially as \(M\) increases, which is due to the fact that matrix inversion occurs in both algorithms. The runtime of SBL is higher than that of LMMSE with regard of the presence of the iterative calculation. However, the GAMP-SBL proposed in this paper avoids matrix inversion by decoupling the matrix into scalars using approximation messages. Therefore, as the number of subcarriers \(M\) increases, it does not cause the runtime of this algorithm to increase to a large extent, which implies the superiority of the proposed algorithm.
## 4 Results and Discussions
### Discussion
#### 4.4.2 Parametric Analysis  
The results in Figure 13a,b show that with the increase in specific heat capacity, the maximum and minimum heat power of the FRPC pile increased. When the specific heat capacity increased by 25% and 20%, the maximum heat power increased by 3.4% and 2.8%, showing a linear relationship. The minimum heat power increased by 10.0% and 6.13%, respectively. The maximum tensile stress decreased by 4.7% and 4.5%, respectively. This  
Figure 13: Optimization results for different thermal conductivities and specific heat capacities. (**a**) Heat power of different specific heat capacities. (**b**) Structural maximum tensile stress of different specific heat capacities. (**c**) Heat power of different thermal conductivity. (**d**) Structural maximum tensile stress of different thermal conductivity.  
phenomenon was because the energy density of the FRPC pile increased with the increase in specific heat capacity when the thermal conductivity was unchanged. This slowed down the temperature rise of the pile body. Thus, a higher temperature difference between the pile and the water inlet was maintained. The temperature difference between the pile and the water inlet improved the heat power of the pile.  
The results in Figure 13c,d show that, similar to the law of specific heat capacity, the maximum and minimum heat power of the FRPC pile increased with the increase in thermal conductivity. When the thermal conductivity increased by 18.7% and 15.8%, the maximum heat power increased by 3.75% and 3.2%, the minimum heat power increased by 2.9% and 2.1%, and the maximum tensile stress decreased by 13.2% and 11.7%, respectively. As the thermal conductivity rose, the heat transfer efficiency and the amount of heat exchange simultaneously increased, which caused the increase in heat exchange.  
Increasing thermal conductivity and specific heat capacity are effective solutions to improving the heat transfer capacity of phase change concrete piles. During long-term operation, the heat transfer power is close to the minimum heat power, so increasing the specific heat capacity that is more sensitive to the minimum heat power can effectively improve the heat transfer capacity of piles. The volume content of phase change material can improve the specific heat capacity and energy density, and the energy pile that was enhanced with the high thermal conductivity PCM is a good choice to improve the long-term operation performance of energy piles.
## 2 Theoretical Basis
### The Models of Elementary Volume of Saturated Soils  
Several variants of the structure of the elementary volume of porous saturated soils are considered (Figure 2), i.e., if the soil consists of: (1) soil matrix and pore water; (2) solid matrix, pore water and gas; (3) solid matrix, pore water, gas and ice. Most sites on land and in water areas are characterized by the second variant of the soil structure. However, the third variant of the soil structure is preferred for sites in permafrost zones.  
Loose soils are of particular interest from a geotechnical point of view, including sandy and clayey soils. In the model, the sandy soils were considered non-cohesive and clayey soils--as cohesive. Non-cohesive soils with a solid matrix are characterized by a negligible change in porosity, while fluid saturation of the porous space can vary significantly. For cohesive soils, since their fluid saturation coefficient is close to unity, an increase in moisture leads to an increase in the volume of the pore space [1].  
According to [PERSON]'s theory, since the fluid and solid matrix particles move together, the bulk density of the porous medium can be averaged. If the soil consists only of the solid matrix and pore water, then the averaging takes the following form:  
\[\rho_{b}=\phi\rho_{l}+(1-\phi)\rho_{s} \tag{22}\]  
where \(\rho_{l}\) and \(\rho_{s}\) are the density of pore liquid (water) and solid grains, respectively.  
If the soil consists of a solid matrix, pore water, gas and ice, then the averaging takes the following form:  
\[\rho_{b}=\phi S_{S}\rho_{S}+\phi(1-S_{S})\rho_{l}+(1-\phi)\rho_{s} \tag{23}\]  
where \(\rho_{S}\) is the density of pore gas, and \(S_{S}\) is the fraction of gas in pore volume.  
If the soil consists of a solid matrix, pore water, gas and ice, then the averaging takes the following form:  
\[\rho_{b}=\phi S_{S}\rho_{S}+\phi S_{i}\rho_{i}+\phi(1-S_{S}-S_{i})\rho_{l}+(1- \phi)\rho_{s} \tag{24}\]  
where \(\rho_{i}\) is the density of pore ice, and \(S_{i}\) is the fraction of ice in pore volume.  
The bulk density can be expressed as a function of the dry density and the moisture coefficient as follows:  
\[\rho_{b}=\rho_{dry}(1+W) \tag{25}\]  
\[W=\frac{S_{r}e\rho_{l}}{\rho_{S}} \tag{26}\]  
where \(\rho_{dry}\) is the dry density, \(W\) is the moisture coefficient of soil, \(e\) is the void ratio, and \(S_{r}\) is the saturation factor (saturated pore volume fraction).  
Figure 2: Schematic representation of the elementary volume of cohesive and non-cohesive soil with different moisture values and different soil structure: (**a–c**)—if soil consists of soil grains and pore liquid (water); (**d–f**)—if soil consists of soil grains, pore liquid and gas; (**g–i**)—if soil consists of soil grains, pore liquid, gas and ice.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 2 System Model and Conventional Method
### Measurement Model of Target  
Assume that in the monitoring area, the deployment coordinate of the network node \(s_{i}\) is \((x_{i},y_{i})\), and the effective communication coverage area is a circular area with the radius of the center of the network node itself as \(r_{i}\). When the moving target appears within the coverage, \(h_{i}(X(k))=\sqrt{\left(x(k)-x_{i}\right)^{2}+\left(y(k)-y_{i}\right)^{2}}\) can be used to express the distance between the moving target \(t\) with the position \((x(k),y(k))\) and the deployed node \(s_{i}\) with the position \((x_{i},y_{i})\). Assuming that the sensor measurement of the target is disturbed by external environmental disturbance noise, the measurement of the network node \(s_{i}\) at time \(k\) can be modeled as  
\[Z_{i}(k)=h_{i}(X(k))+U_{i}(k) \tag{3}\]  
where \(X(k)\) is the state of moving target at time \(k\). \(U_{i}(k)\) is the measurement noise of the network node \(s_{i}\) at time \(k\), which is a Gaussian white noise with a mean of zero and a variance of \(R_{i}(k)\).
## 3 Results  
### Subject Characteristics and PM\({}_{2.5}\) Concentrations  
Body weight, body mass index, SBP, DBP, and HR were not different between HPM\({}_{2.5}\) and LPM\({}_{2.5}\) condition visits before the treadmill running (Tables 1 and 2). Both outdoor and indoor PM\({}_{2.5}\) concentrations in HPM\({}_{2.5}\) condition visits were 8.6- and 7.6-fold higher than the LPM\({}_{2.5}\) condition visits, respectively (_p_ \(<\) 0.001; Table 1). Average indoor PM\({}_{2.5}\) concentration during the exercise in HPM\({}_{2.5}\) condition visits was higher than the 24 h PM\({}_{2.5}\) standard (35 \(\mu\)g/m\({}^{3}\)) of the US Environmental Protection Agency (EPA) and categorized as \"unhealthy for sensitive groups\" or \"unhealthy\" by the US air quality index levels.  
\begin{table}
\begin{tabular}{c c c} \hline  & **HPM\({}_{2.5}\)** & **LPM\({}_{2.5}\)** \\ \hline Age, years & 24.6 \(\pm\) 0.4 & \\ Height, cm & 177.4 \(\pm\) 1.5 & \\ Body weight, kg & 77.9 \(\pm\) 1.6 & 77.5 \(\pm\) 1.8 \\ BMI, kg/m\({}^{2}\) & 24.8 \(\pm\) 0.6 & 24.7 \(\pm\) 0.7 \\ Outdoor PM\({}_{2.5}\) concentration, \(\mu\)g/m\({}^{3}\) & 150.9 \(\pm\) 27.1 * & 17.6 \(\pm\) 4.7 \\ Indoor PM\({}_{2.5}\) concentration, \(\mu\)g/m\({}^{3}\) & 59.0 \(\pm\) 2.1 * & 7.8 \(\pm\) 1.0 \\ \hline \end{tabular} Values are mean \(\pm\) SE. HPM\({}_{2.5}\), high particulate matter 2.5; LPM\({}_{2.5}\), low particulate matter 2.5; BMI, body mass index; PM, particulate matter. *_p_ \(<\) 0.001 vs. LPM\({}_{2.5}\).  
\end{table}
Table 1: Subject characteristics (n = 9) and PM\({}_{2.5}\) concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**HPM\({}_{2.5}\)**} & \multicolumn{2}{c}{**LPM\({}_{2.5}\)**} & \multicolumn{2}{c}{**_p-Value**} \\ \cline{2-7}  & **pre** & **Post** & **pre** & **Post** & **C** & **T** & \(\mathbf{C\times T}\) \\ \hline SBP, mmHg & 114.2 \(\pm\) 2.0 & 120.3 \(\pm\) 2.3 & 116.0 \(\pm\) 2.3 & 119.7 \(\pm\) 2.4 & 0.47 & \textless{}0.001 & 0.22 \\ DBP, mmHg & 70.3 \(\pm\) 2.6 & 73.6 \(\pm\) 2.6 & 69.8 \(\pm\) 3.0 & 73.0 \(\pm\) 2.7 & 0.66 & \textless{}0.001 & \textgreater{}0.99 \\ PP, mmHg & 43.9 \(\pm\) 1.5 & 46.8 \(\pm\) 1.5 & 46.2 \(\pm\) 1.3 & 46.7 \(\pm\) 2.0 & 0.49 & 0.07 & 0.26 \\ HR, bpm & 59.3 \(\pm\) 2.4 & 76.9 \(\pm\) 1.9 & 60.3 \(\pm\) 2.8 & 76.1 \(\pm\) 2.8 & 0.94 & \textless{}0.001 & 0.39 \\ \hline \end{tabular} Values are mean \(\pm\) SE. HPM\({}_{2.5}\), high particulate matter 2.5; LPM\({}_{2.5}\), low particulate matter 2.5; C, condition; T, time; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; HR, heart rate.  
\end{table}
Table 2: Blood pressure and heart rate at pre- and post-exercise intervention.
## 4 Health Prognostics
### Empirical Degradation Model
#### 4.3.3 Hybrid Failure Prognostics  
Similar to the fault diagnosis model, the hybrid method, which combines the degradation model and prognostics method, overcomes the problem of a single degradation model or prognostics method that could not accurately predict the health condition. The hybrid method can use their advantages and makeup or minimize their weaknesses [224; 281; 282]. Therefore, the hybrid of signal processing, degradation model, and prediction method is a new trend in RUL prediction. According to different hybrid contents and forms, hybrid methods mainly include degradation model fusion, prediction methods fusion, and degradation model and prediction methods fusion.
# A Method to Reduce Non-Nominal Troposphere Error
## 1 Introduction
In summary, the elevation method cannot maintain a good satellite geometry, the GDOP method could not minimize the non-nominal troposphere error, and the brute method is infeasible to implement because of its significant computational cost. To overcome the shortcomings of these methods,  
Figure 1: Skyplots of optimal subsets containing 13 satellites obtained with the Geometric Dilution of Precision (GDOP) method. this paper proposes a method to balance the psudorange error and the satellite geometry, while improving both the GBAS integrity under abnormal troposphere and the running rate. This paper analyzes troposphere meteorological data of eight International GNSS Monitoring Assessment System (iGMAS) stations and 10 International GNSS Service (IGS) stations in China and records the most serious conditions during 2015 and 2016. To eliminate the integrity risk caused by non-nominal troposphere error, this paper presents a method for bounding the conspiring error [10]. Given the influence of abnormal troposphere error on VPL, an effective method is proposed, which can greatly decrease the non-nominal troposphere error in positioning solution. The average VPL increase caused by non-nominal troposphere error is 1.15 m using the proposed method, which corresponds to a 1.17 m reduction. Moreover, compared with the brute method and the greedy method, the running rate of the proposed method is improved by 390.91% and 111.65%, respectively. In summary, the new method minimizes the non-nominal troposphere error in the positioning solution and airborne VPL under severe troposphere conditions, while improving both the GBAS integrity and the running rate. An illustration of the selected optimal subset is presented in Figure 2, which indicates that the final optimal satellite subset should achieve a balance between non-nominal troposphere error and satellite geometry rather than just choose the satellites with smaller non-nominal troposphere error. The rest of the paper is organized as follows: Section 2 analyzes troposphere meteorological data in China. Section 3 briefly introduces the non-nominal troposphere model and a method for calculating the error. Section 4 presents the method for bounding the non-nominal troposphere error and analyzes its impact on VPL. Section 5 proposes a new method to select optimal satellite subset under non-nominal troposphere based on the relationship among the VPL, pseudorange error and satellite geometry. Finally, Section 6 summarizes the main points and conclusions.
## 3 Results and Discussion
### Resistance to Permanent Deformation  
In Figures 6 and 7, the curves obtained in the permanent deformation tests and the results for the rutting depth (%) of mixtures after 30,000 cycles are provided, respectively.  
According to the results shown in Figures 6 and 7, the resistance to permanent deformation, considering 30,000 load cycles was highest for the 3% NC + 2% SBS mixture, followed by the SBS 60/85, 3% NC, and conventional mixtures. In proportional terms, in practice, it is estimated that the replacement of the conventional mixture containing the two modifiers would reduce the rutting depth in the field by up to 58%. Considering the replacement of the reference mixture with the SBS 60/85 and 3% NC mixtures, the corresponding reductions could reach up to 35% and 29%, respectively.  
The best behavior in terms of permanent deformation observed for 3% NC + 2% SBS mixture is in agreement with the performance prediction carried out in the empirical characterization of the binders (lower penetration) and the rheological study of the mixtures (higher stiffness and lower phase angles). In the same way, the results for the phase angles were also effective in the prediction of the performance hierarchy of the mixtures in relation to resistance to permanent deformation.  
About the rate of deformation increase, which can be measured by the WTS (wheel-tracking slope) parameter, Figure 6 shows similar behaviors between the mixtures CAP 50/70, SBS 60/85, and 3% NC + 2% SBS. On the other hand, it is observed that the 3% NC mix exhibits a lower initial deformation with a higher deformation increase rate. In this sense, considering about 3000 load cycles, for example, the deformation level was the same for the mixtures 3% NC and 3% NC + 2% SBS; however, due to a higher deformation increase of the mixture modified only by nanoclay, the scenario becomes different at the end of the test. High rates of deformation increase are considered a negative characteristic for asphalt mixtures performance, and according to the results obtained in this work, simultaneous action of SBS can improve this behavior of the 3% NC mixture.  
Regarding the maximum limits for the rutting depth established by the French [46] and the European [47] specifications, all of the asphalt mixtures under study met the 10% criterion for 30,000 load cycles. However, only the 3% NC + 2% SBS mixture satisfied the more restrictive criterion, corresponding to 5%.