esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
###### Abstract  
Medical image analysis is critical for diagnosing and planning treatments, particularly in addressing heart disease, a leading cause of mortality worldwide. Precise segmentation of the left atrium, a key structure in cardiac imaging, is essential for detecting conditions such as atrial fibrillation, heart failure, and stroke. However, its complex anatomy, subtle boundaries, and inter-patient variations make accurate segmentation challenging for traditional methods. Recent advancements in deep learning, especially semantic segmentation, have shown promise in addressing these limitations by enabling detailed, pixel-wise classification. This study proposes a novel segmentation framework Adaptive Multiscale U-Net (AMU-Net) combining Convolutional Neural Networks (CNNs) and transformer-based encoder-decoder architectures. The framework introduces a Contextual Dynamic Encoder (CDE) for extracting multi-scale features and capturing long-range dependencies. An Adaptive Feature Decoder Block (AFDB), leveraging an Adaptive Feature Attention Block (AFAB) improves boundary delineation. Additionally, a Spectral Synthesis Fusion Head (SFFH) synthesizes spectral and spatial features, enhancing segmentation performance in low-contrast regions. To ensure robustness, data augmentation techniques such as rotation, scaling, and flipping are applied. Laplacian approximation is employed for uncertainty estimation, enabling interpretability and identifying regions of low confidence. Our proposed model achieves a Dice score of 93.35, a Precision of 94.12, and a Recall of 92.78, outperforming existing methods.  
deep learning; medical image analysis; uncertainty estimation; segmentation; radiomics 2020 ac academic Editor: [PERSON]  
1  
[PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON] Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation. _Appl._**2015**, 15, 2222. [[https://doi.org/10.3390/appl502222](https://doi.org/10.3390/appl502222)]([https://doi.org/10.3390/appl502222](https://doi.org/10.3390/appl502222))
## 3 Results
### Results of Situation Awareness
#### 3.4.2 SA Under Partial eHMI Penetration Condition  
The mean and SD of SA across different eHMI information content conditions under the partial eHMI penetration condition are presented in Table 9. Based on the results of the Shapiro-Wilk normality test, significant deviations from normality were observed for the three-level low-medium-high (\(p=0.029\)) and two-level medium-high (\(p=0.023\)) conditions, suggesting that these data do not follow a normal distribution. Therefore, a Friedman test was conducted, revealing a statistically significant difference among the six conditions (\(\chi^{2}(5)=11.37\), \(p=0.045\)). However, post hoc pairwise comparisons using the Wilcoxon signed-rank test with Bonferroni correction did not reveal any statistically significant differences between individual condition pairs.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Odball** & **Low** & **Medium** & **High** & **Low-Medium** & **Medium-High** & **Low-Medium-High** \\ \hline Mean & 3.00 & 4.58 & 3.00 & 2.75 & 3.75 & 4.83 \\ SD & 2.83 & 4.68 & 3.57 & 2.86 & 3.91 & 5.13 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean and SD of oddball counting error rate across different eHMI information content conditions under full penetration condition.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Odball** & **Low** & **Medium** & **High** & **Low-Medium** & **Medium-High** & **Low-Medium-High** \\ \hline Mean & 1.80 & 2.30 & 3.40 & 3.70 & 5.00 & 1.90 \\ SD & 2.82 & 2.16 & 4.48 & 3.33 & 4.52 & 1.79 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mean and SD of oddball counting error rate across different eHMI information content conditions under partial penetration condition.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**SA** & **Low** & **Medium** & **High** & **Low-Medium** & **Medium-High** & **Low-Medium-High** \\ \hline Mean & 4.25 & 6.25 & 5.58 & 5.92 & 5.00 & 5.50 \\ \hline SD & 2.73 & 3.36 & 3.03 & 2.87 & 2.52 & 2.61 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean and SD of SA across different eHMI information content conditions under full penetration condition.
## 3 Materials and Methods
### Soil Salinity Prediction and Accuracy Assessment
The RK method with a linear function and without a covariable factor was applied to predict ECe. In this study, IDW, OK, and OCK methods were conducted using ESRI ArcMap software, and the RK method was conducted with SAGA software. After that, four standard statistical measurements, (1) mean prediction error (ME), (2) root mean square error (RMSE), (3) coefficient of determination (R\({}^{2}\)), and (4) percent of bias (PBIAS), were used to assess the accuracy of each interpolation method based on the testing dataset, with 121 points, as suggested by [PERSON] et al. [56] and [PERSON] et al. [57] Mathematically, the general form of ME, RMSE, R\({}^{2}\), and PBIAS can be expressed in the following equations:  
\[ME=\frac{\sum_{i=1}^{n}(Predicted\ value-Measured\ value)}{n} \tag{8}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(Predicted\ value-Measured\ value)^{2}}{n}} \tag{9}\]  
Figure 5: Spatial distribution of soil moisture content in the study area. \[\mathrm{R}^{2}=\left\{\frac{\sum_{i=1}^{n}(\mathrm{Predicted\ value}-\mathrm{Mean\ predicted\ value})(Measured\ value-\mathrm{Mean\ measured\ value})}{\left[\sum_{i=1}^{n}(\mathrm{Predicted\ value}-\mathrm{Mean\ predicted\ value})^{2}\sum_{i=1}^{n}(\mathrm{Measured\ value}-\mathrm{Mean\ measured\ value})^{2}\right]^{0.5}}\right\}^{2} \tag{10}\]  
\[\mathrm{PBIAS=}\frac{\sum_{i=1}^{n}(\mathrm{Measured\ value}_{i}-\mathrm{Predicted\ value}_{i})}{\sum_{i=1}^{n}(\mathrm{Measured\ value}_{i})}\times 100 \tag{11}\]  
where n is the number of measured and predicted points.
## 3 Calculation of Critical Speeds  
Based on the global motion equation established in the preceding context, without considering the SFD oil film force and the nonlinear restoring force of the supports, the beam model was solved using both custom-coded software and ANSYS 2023 R1. The resulting Campbell diagram and critical speeds were obtained and compared to verify the correctness and accuracy of the code. As depicted in Figure 2, the overall model comprises the Euler-Bernoulli beam model, eight concentrated masses, and three supports. Furthermore, the concentrated masses represent the six-stage compressor and two-stage turbine.  
As shown in Equation (58), the global stiffness matrix, mass matrix, and gyroscopic matrix of the model were assembled according to the nodal degree of freedom ordering, and the finite element code written was then utilized to solve the model.  
The subsequent calculations of the Campbell diagram were performed using both custom code and ANSYS. The first three critical speeds were then extracted from the diagram for comparison.  
As shown in the Campbell diagram (Figure 3) and Table 1, the first three critical speeds calculated by the self-developed code and ANSYS for a beam length of 2 m differ by less than 2%, demonstrating the correctness and accuracy of the established model.  
In Figure 3, FW denotes forward rotation, BW denotes backward rotation, and F=1\(\times\)spin represents a line indicating one-fold constant rotational speed.
# Rapid Characterization Method for SMC Materials for a Preliminary Selection
## 4 Experimental Results
### Resistivity: Inorganic Layer  
The resistivity in SMCs coated by nano-ferrites was studied in the green state and after heat treatment at 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C in different environments (vacuum, air and steam). Specimens have 0.2 wt% of the nano-ferrites insulating layer and were compacted at 800 MPa. Compacting pressure of 800 MPa, as highlighted in the previous paragraph, ensures a good balance between resistivity and mechanical properties; this is  
Figure 9: Resistivity variations in SMCs with different percentages of epoxy and phenolic resins (wt%) at the same pressure level 800 MPa. (wt% shown: 0.05; 0.075; 0.1; 0.2; 0.3; 0.4; 0.5 and 0.75).  
always true because the reduction in resistivity with the increase in compacting pressure has the same magnitude.  
Figure 11 shows the resistivity of SMCs coated with nano-ferrites (0.2 wt% and 800 MPa) in the green state and after heat treatment at 400 \({}^{\circ}\)C at different environmental conditions. As attended, the resistivity in the green sample resulted in the higher value (102 \(\upmu\Omega\)m); despite that, the heat treatment must be performed to increase the mechanical properties. Furthermore, stress relieving causes a beneficial effect on magnetic properties (coercivity and permeability). For this reason, a heat treatment at 400 \({}^{\circ}\)C was performed for a limited time of 15 min. Adopting the air environment obtained the best result (14.2 \(\upmu\Omega\)m), while the steam usage resulted in the lower result (2.2 \(\upmu\Omega\)m). In general, the high decrease in the resistivity for the green specimen values was due to the layer fracture during the stress relieving. Vacuum and steam environments cause less oxidation with respect to the air treatment, reducing the final resistivity of the sample.  
Figure 11: Comparison of resistivity between SMCs coated with nano-ferrites (0.2 wt% and 800 MPa): green and heat-treated SMCs as a function of the variations in heat treatment environments (400 \({}^{\circ}\)C 15 min).  
Figure 10: Resistivity variations in SMCs with phenolic resins (0.2 wt% and 800 MPa) for different technological processes.  
In Figure 12, the effect of temperature on resistivity was reported. The heat treatments in air, steam and vacuum were performed at 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C. As a result, the air environment remains the best choice in respect to steam and vacuum. On the other hand, it appears evident that higher temperatures negatively affect resistivity, especially in steam and vacuum environments. In the air environment, resistivity decreases from 400 \({}^{\circ}\)C to 500 \({}^{\circ}\)C and then increase from 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C. This behaviour is probably associated with the oxidation and diffusion processes.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 2 Materials and Methods
### Evaluation Metrics  
To quantify the quality of the reconstruction in each method, the root-mean-square error (RMSE), shown in Equation (8), was used. The error used in the RMSE calculation was the Euclidean distance in three-dimensional space \(\mathbb{R}^{3}\), and it was calculated after reprojecting the points in the point cloud, as shown in Equation (9).  
\[rmse=\sqrt{\frac{\sum\limits_{i=0}^{n}error^{2}}{n}} \tag{8}\]  
\[error=\sqrt{(x_{o}-x_{r})^{2}+(y_{o}-y_{r})^{2}+(z_{o}-z_{r})^{2}} \tag{9}\]  
The depth information received from the camera was able to have an error of 2%, thus increasing the RMSE. However, as this affected all methods similarly, it did not interfere with the final comparison.  
To measure the distance between the correspondents points, these points must be known, i.e, which point in one point cloud should match which point in the other point  
Figure 5: Charuco board with noise and artefacts due to the repetitive pattern.  
cloud. Since the point cloud from the RGB-D device did not contain information enabling us to identify which point was which, a method was proposed to label some points.  
In this work we used OpenCV [37], as it is a stable and robust library for computer vision. ArUco markers were used as fiducial markers and a charuco board was used as the pattern board for the pose estimation, as proposed by [PERSON] et al. (2014) [28].  
The ArUco module in OpenCV has all the necessary functions for pose estimation using the implemented charuco board [38].
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 4 Experiments
### Training Strategy  
In the experiment, an input image size of 640 \(\times\) 640 was used, with an initial learning rate of 0.01 and a batch size of 16, and the Adam optimizer was selected. The Mosaic data augmentation technique was employed for data pre-processing, which involves concatenating four images and randomly applying scaling, cropping, and shuffling the images. The number of training epochs was set to 150, as it was found that stability could be achieved after around 130 epochs based on the experimental results from the landslide dataset. We performed all the experiments using the PyTorch 1.8 deep learning framework on a Linux system with hardware equipped with an NVIDIA Tesla P100-16 GB GPU. The training process of LP-YOLO is shown in Algorithm 1.  
```
Input: landslide remote sensing images, labels for bounding box coordinates object class, \(B_{x}\), \(B_{y}\), width \(B_{w}\), height \(B_{h}\) Output: Class probabilities \(P_{c}\) and predicted bounding box coordinates
1 Initialize landslide dataset = train images (80%) + validation images (20%); batch size = 16; epochs \(E\) = 150; data augmentation = true;
2 Training stage: foreach epoch in \(E\)do
3 Load YOLO hyperparameters;
4 Randomly select batch size images;
5 Training on the landslide images with LP-YOLO algorithm (Figure 1);
6 Calculate the loss and update the parameters by backpropagation;
7 Evaluating detection performance of the algorithm using validation images;
8
9 end foreach
10 save optimal bestweight.pt
11 Testing stage: Use the trained LP-YOLO model to predict the test images;
12 predict \(P_{c}\), \(B_{x}\), \(B_{y}\), \(B_{w}\), and \(B_{h}\); display class \(P_{c}\) and predicted bounding box
```  
**Algorithm 1**Pseudocode of LP-YOLO algorithm.
## 2 Related Work
Overall, although these last papers exploit the histogram in their approaches, they are substantially different from our work, since they exploit the histogram for: (i) enhancing the image quality; (ii) adjusting the contrast; and (iii) extracting features to be used by a final classifier. Moreover, only a couple of these papers focus on COVID-19 disease. Our approach, on the other hand, directly uses the histogram information to measure the distance from a target histogram and then decides whether or not an unknown CXR or CT image is related to a patient who is infected with COVID-19. This makes our approach an unsupervised method, which exhibits very limited implementation complexity. A synoptic overview of the related work is provided in Table 1, that summarizes the main approaches pursued by the referred papers. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Family** & **Approach** & **Work** & **Image Type** \\ \hline Manual screening & CAD & [57] & CT \\  & & [58] & CT \\  & & [59] & CT \\  & & [60] & CT \\  & & [61] & CXR \\ \hline Hand-crafted features & Texture-based features & [33] & Cells \\  & Edge-based features & [34] & Histopathological \\  & & [35] & Cytological \\  & Graph mining & [36] & Histopathological \\  & Color-based features & [37] & Cells \\  & & [38] & Histopathological \\  & & [39] & Histopathological \\ \hline Deep learning & Review & [72] & CXR \\  & & [73] & CT \\  & & [74] & CT \\  & & [75] & CXR + CT \\  & & [65] & CXR + CT \\  & & [76] & CXR + CT \\  & & [77] & CXR + CT \\  & & [78] & CXR + CT \\ Segmentation & [79] & CXR \\  & & [80] & CT \\  & & [81] & CT \\  & & [82] & CT \\  & & [56] & CT \\  & & [83] & CXR \\  & & [84] & CXR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A synoptic view of the main related work.
## 7 Experimental Results
### Performance Measure on Raspberry Pi  
In this section of the paper, the performance of the proposed work is measured by implementing RM and EDF algorithms on a Raspberry Pi-based IoT device with specifications outlined in Table 2. These algorithms are performed using the same approaches we considered throughout the paper. The response time of the algorithms is measured in ms. Figure15a shows the results of the RM algorithm. Harmonization gives the best response time due to the low hyper-period, and this is expected because the Harmonization idea originally was coined to improve the performance of RM algorithm by minimising the hyper-period. However, the proposed optimal threshold is also reasonably good, and the performance is not degraded for an increasing number of tasks. A static threshold performs better for smaller task sets but when the task sets grow, the response time also rises, and this is due to the overhead of algorithms. Finally, the No Threshold approach works reasonably for smaller tasks, but, as the tasks set increases to more than 20, the response time rockets up and the system becomes unusable.  
Similarly, in the case of EDF in Figure 15b, the graph looks identical with that of Figure 15a, but, in this case, the proposed optimal threshold performs better than the harmonization. The reason for this is that the harmonization approach is introduced initially to improve the RM algorithm. Thus, for the dynamic-priority system, it is as impressive as in the case of RM. The other two approaches perform precisely similar to that of RM, i.e., performs reasonably for lower task sets but becomes unusable for massive task sets.  
As shown in Figure 15, the scheduling algorithms are run for an increasing number of tasks, and the performance comparison is carried out. The performance deteriorated in both systems with a growing number of tasks, but, in the proposed solution, the degradation is steady, and, in the existing works, it is degrading exponentially.  
Figure 15: _Cont._
## 3 Numerical Analyses
### Shell FE Model  
The shell FE model (Figure 4a) was implemented in Midas GEN [48]. Before performing the nonlinear analyses, an eigenvalue analysis was conducted to evaluate the fundamental vibration modes of the tower. Figure 5 presents the first three vibration mode shapes. A rather standard dynamic behavior can be observed: the first two vibration modes involve a high value of participant mass and are mainly characterized by a bending behavior with negligible torsional effects, while the third vibration mode shows a predominant torsional behavior. The value of the participant mass of the fundamental vibration mode is affected by the presence of the basement, which is characterized by large wall thickness when compared with the upper walls.  
Figure 4: (**a**) Shell FE model, (**b**) fiber FE model, and (**c**) solid FE model of the tower.  
Figure 3: BIM model of the masonry tower.  
The nonlinear behavior of masonry is simulated using the Concrete Damage Plasticity (CDP) model. The adopted compression and tensile stress-strain inelastic curves are illustrated in Figure 6a,b, while Table 2 reports the adopted CDP parameters, evaluated according to [49; 50], where \(\psi\) is the dilation angle, \(\varepsilon\) the eccentricity, \(\mathrm{f_{b0}/f_{c0}}\) is the ratio of initial equibiaxial compressive yield stress to initial uniaxial compressive yield stress, K is the ratio of the second stress invariant on the tensile meridian, and \(\mu\) the viscosity parameter.  
Figure 5: Main vibration modes with respective periods and participant mass.  
Figure 6: Compressive (**a**) and tensile (**b**) inelastic behavior adopted for the masonry in the shell model.  
The seismic action is represented by means of an equivalent static loads system evaluated as:  
\[F_{i}=F_{h}\cdot z_{i}\cdot\frac{W_{i}}{\sum_{j}z_{j}W_{j}} \tag{1}\]  
\[F_{h}=S_{d}(T_{1})\cdot W\cdot\alpha/g \tag{2}\]  
where \(F_{i}\) is the force applied to the \(i\)-th mass, \(W_{i}\) and \(W_{j}\) are the weight of the \(i\)-th mass and \(j\)-th mass, \(z_{i}\) and \(z_{j}\) are the height of the \(i\)-th mass and \(j\)-th mass measured from the tower base, \(S_{d}(T_{1})\) is the value of the spectral acceleration from the design response spectrum for the first natural period \(T_{1}\), \(W\) is the total weight of the tower, \(\alpha\) is a coefficient equal to 0.85 if \(T_{1}\) < \(2T_{\rm c}\) or equal to 1 in all the other cases, and \(g\) is the gravity acceleration. The load profile and the involved parameters are reported in Table 3.  
The response of the tower was evaluated in all the principal directions because of the non-symmetric distribution of the openings in the perimeter walls. Figure 7 shows, for each principal direction of the XY plane, the areas of the tower where the tensile stress exceeds the tensile strength, together with the respective value of the scale factor \(\lambda\) of the equivalent static lateral loads. The damaged areas are mainly located near the openings or on the second and third vaults.
## 4 Discussions
### The Response of GPP to Driving Factors  
Daily sunshine duration in summer acted as a dominant factor for most regions in the GKM. The sunshine duration in meteorological observation is defined as the period during which direct solar irradiance exceeds a threshold value of 120 W/m\({}^{2}\)[74]. It is a general indicator of the cloudiness of a location [75]. According to Figure 13, a warming and drying trend has occurred in the GKM, so daily sunshine duration increased simultaneously. When compared with the impact of GSL on AGG by extending photosynthesis time in spring (advance of SOC) and autumn (delay of EOG), the daily sunshine duration in summer prolonged the photosynthesis time in summer when photosynthesis efficiency is high, and a slight extension of daily sunshine duration contributed to a large enhancement in AGG. Also, vegetation can absorb more solar radiation over a long sunshine duration, which is directly related to photosynthesis and was confirmed to be a limited factor in high latitude regions [76].  
Temperature had positive effects on GPP, while precipitation had negative effects on GPP, which were in agreement with relevant study results [40; 59; 60; 61]. GKM vegetation system was strongly constrained by low temperature, so the warming trend that is reported above was bound to increase GPP. Precipitation inhibited GPP across the GKM in most periods when precipitation was adequate, illumination time and temperature decreased with the thick cloud cover that was brought by precipitation, which weakened photosynthesis and reduced vegetation GPP; thus, the precipitation was not a direct factor for GPP. However, in spring when precipitation was rare, no obvious correlation between GPP and precipitation was found, due to the alimentation of snow.  
FPAR, instead of sunshine duration and temperature, was a dominant factor in part of northern GKM. Overlay analysis revealed a high correlation of the above-mentioned areas with the severely burnt zone caused by the fire in 1987 (Figure 15). Previous studies [63; 64; 65] demonstrated a secondary succession process in burnt areas. Variation in vegetation type and tree age during the succession process may have contributed to a non-significant correlation between climate changes and GPP interannual variation in the northern GKM. On the other hand, FPAR is an important parameter for photosynthesis, and one previous study [77] revealed a close relationship between FPAR and vegetation type. Therefore, GPP in the severely burnt area in the northern GKM was closely related with FPAR.
## 2 Background
### Theoretical Calculations  
In several of the cases reviewed herein, periodic DFT calculations were used (in combination with VTIR spectroscopy) in order to elucidate the structure of gas adsorption complexes [65]. These calculations were performed using a periodic model of the zeolite consisting of a one-unit cell or, sometimes, a double-unit cell.  
In each case, DFT calculations were performed by means of the VASP program package [66; 67; 68], using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional [69; 70] together with the projector augmented wave approximation (PAW) of Blochl [70] and the plane wave basis set with a kinetic energy cut-off at 400 eV. Further details can be found elsewhere [71; 72].