esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods  
### Data Acquisition  
One-hundred randomly chosen late-gadolinium enhanced MRI cases (20 healthy, 80 with attested MI) from the University Hospital of Dijon were included in this study. Gadolinium contrast solution (Dotarem, Guerbet, France) was administered to the patients between 8 and 10 min before conducting the study. Balanced cases of acute and chronic myocardial infarctions were included. Besides, thirty-five percent of infarcted cases (\(n=28\)) presented micro-vascular obstruction areas. For all patients, a short-axis stack of cardiac images covering the whole left ventricle was acquired using one of the two clinical MRIdevices with magnetic fields of 1.5T and 3T (Siemens Healthineers, Erlangen, Germany). A T1-weighted phase sensitive inversion recovery sequence with slice thickness of 8 mm and slice gap of 2 mm was performed (TR = 3.5 ms, TE = 1.42 ms, TI = 400 ms, flip angle = 20). Voxel size differed among scans between 1.25 mm\(\times\) 1.25 mm to 1.91 mm \(\times\) 1.91 mm.  
The dataset ground truths were delineated in each slice by an expert of the institution (AL) with more than 15 years of expertise in the field. The endocardial and epicardial boundaries were contoured (papillary muscles were included in the cardiac cavity as recommended [44]) and in pathological cases the scar tissue was annotated taking separate contours for enhanced and MVO areas. Although the concentration of the contrast agent in the myocardium is influenced by several external factors (such as the type of contrast media and dose, the renal clearance, the delay between contrast injection and image acquisition, the MRI sequence parameters, etc.) [45], in our dataset the contrast media injection protocol and the MR acquisition parameters have been preserved. As such, the MRI signal intensity variability was reduced to the remaining external factors only. In late enhancement studies (as performed in this work), the fibrotic area appears bright in T1-weighted MRI. The MVO areas in T1-weighted MRI appear, instead, hypo-intense (i.e., black) regardless of the time-delay between the contrast agent injection and the image acquisition [45]. When present, the hypo-intense MVO areas are typically surrounded by hyper-intense (bright) regions.  
For assessing intra- and inter-rater annotations variability, a random subset of pathological cases (50%, \(n=40\)) were fully re-contoured by the same expert as well as by a second one ([PERSON], a cardiologist with 5 years of experience in the field).
# Shadow Hunter: Low-Illumination Object-Detection Algorithm
## 2 Related Work
### Attention Mechanism  
The attention mechanism has gained widespread use in deep learning for assigning varying weights to input information, allowing models to focus on crucial features. This mechanism has proven effective in improving both the performance and efficiency of deep learning models. In particular, the attention mechanism has gained significant traction in the field of computer vision, demonstrating its effectiveness in achieving favorable outcomes.  
One of the pioneering works in this field is the Recurrent Models of Visual Attention [25], which first introduced the utilization of attention mechanisms in computer vision. Their model, based on recurrent neural networks, dynamically selects regions of interest in images to enhance image classification performance. Since then, attention mechanisms have been further developed and applied in various ways to improve a wide range of computer vision tasks.  
For instance, in a notable work by [26], the spatial attention mechanism was introduced. This mechanism assigns distinct weights to feature maps corresponding to different spatial locations within a convolutional neural network. By doing so, the network can better focus on spatial locations that are more useful for the current task. Likewise, the channel attention mechanism, as presented by [27], assigns varying weights to feature maps across different channels. This facilitates the enhancement of task-relevant information and mitigates the influence of irrelevant information, leading to improved performance in target tasks.  
To combine both spatial and channel information, ref. [28] proposed the CBAM module, which infers attention weights sequentially along both channel and spatial dimensions. This lightweight and general-purpose module has been shown to effectively improve the performance and generalization of the network. Additionally, Fcanet [29], BAM [30] and SKnet [31] borrowed ideas from CBAM as well.  
Attention mechanisms are a potent tool that empowers deep learning models to process input data more effectively, thereby enhancing their performance and efficiency. With ongoing development and continual improvements, attention mechanisms will continue to hold a crucial role in a wide range of deep learning tasks across diverse domains. Their versatility and effectiveness make them a valuable asset in advancing the capabilities of deep learning models.
## 3 Application to the Case Study
### Analysis with FEM  
Modeling, simulations and FEM analysis were performed by means of the Finite Element package Ansys(r) (Ansys, 2019) [38]. Given the complexity of the model, an ad hoc algorithm was written in the programming language Ansys Parametric Design Language (APDL). The geometrical model used in the numerical simulations (Figure 3b) has been identified on the basis of observations surveys carried out by the authors and summarized in Figures 4 and 5 and Table 1. On account of the helicoidal symmetry of the stair, efforts have been made to design and generate the geometry of the step with a central support zone, in order to represent the spiral staircase. In particular, some parameters like the step rise, the inner and outer radii of curvature delimiting the walkable surface, the angle defined by each step as well as the number of them, have been used to generate the parametrical model. The first step has been fixed at the ground and a rigid behavior has been assigned to the surfaces between adjacent steps by imposing appropriate constrain equations. The physical model was discretized using 249,000 linear tetrahedral elements, i.e., SOLID 185, and 57,000 nodes with three translational degrees of freedom each.  
Mechanical properties: for the sake of simplicity, the behavior of masonry was assumed to be elastic with the following values for the modulus of elasticity, the Poisson modulus and the density of the material, respectively: \(E=21\) GPa, \(\
u=0.3\), \(\rho=25.5\)\(\mathrm{KN}/\mathrm{m}^{3}\).  
Boundary conditions: given the uncertainty related to the connection of the stairs to the surrounding wall, the analyses were carried out with reference to two different sets of boundary conditions representing the limiting cases through which the real behavior of the stair of Nisida can range:  
Figure 7: Perspective view of a typical step with line 4 inside.
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 3 Closed Form Solutions of Thermal Buckling
### Case 2 Temperature-Independent FGM Thin Plates  
In this case, the FGM thin plates have temperature-independent material properties which vary with the thickness following the power law in Equation (16). For a uniform temperature field, \(\hat{G}_{0}\) follows from Equation (14) that  
\[\hat{G}_{0}=\frac{\Delta T\cdot\int_{-h/2}^{h/2}\frac{\alpha E}{1-v}\text{d}z}{ \int_{-h/2}^{h/2}\frac{Ez^{2}}{1-v^{2}}\text{d}z-\frac{\left(\int_{-h/2}^{h/2} \frac{Ez}{1-v^{2}}\text{d}z-\Delta T\cdot\int_{-h/2}^{h/2}\frac{\alpha Ez}{1-v }\text{d}z\right)^{2}}{\int_{-h/2}^{h/2}\frac{E}{1-v^{2}}\text{d}z-\Delta T \cdot\int_{-h/2}^{h/2}\frac{\alpha E}{1-v}\text{d}z}} \tag{37}\]  
Then, the critical buckling temperature is  
\[\Delta T_{\text{cr}}=\frac{\hat{G}_{0\text{cr}}J_{0}J_{1}+\hat{G}_{0\text{cr}} J_{2}J_{5}-2\hat{G}_{0\text{cr}}J_{3}J_{4}+J_{0}J_{5}-\sqrt{\Theta}}{2 \left(\hat{G}_{0\text{cr}}J_{0}J_{2}-\hat{G}_{0\text{cr}}J_{4}{}^{2}+J_{0}{}^ {2}\right)} \tag{38}\]  
where  
\[\begin{array}{ll}\Theta=&\hat{G}_{0\text{cr}}^{2}J_{0}^{2}J_{0}^{1}-2\hat{G} _{0\text{cr}}^{2}J_{0}J_{1}J_{2}J_{5}-4\hat{G}_{0\text{cr}}^{2}J_{0}J_{1}J_{3} J_{4}+4\hat{G}_{0\text{cr}}^{2}J_{0}J_{2}J_{3}^{2}\\ &+4\hat{G}_{0\text{cr}}^{2}J_{1}J_{2}^{2}J_{5}+\hat{G}_{0\text{cr}}^{2}J_{2}^{ 2}J_{5}^{2}-4\hat{G}_{0\text{cr}}^{2}J_{2}J_{3}J_{4}J_{5}-2\hat{G}_{0\text{cr} }^{2}J_{1}J_{5}\\ &+4\hat{G}_{0\text{cr}}J_{0}^{2}J_{2}^{2}+2\hat{G}_{0\text{cr}}J_{0}J_{2}J_{5} ^{2}-4\hat{G}_{0\text{cr}}J_{0}J_{3}J_{4}J_{5}+J_{0}^{2}J_{2}^{5}\end{array} \tag{39}\]  
The critical buckling temperature for linear and nonlinear temperature fields can be obtained similarly. Table 3 presents the integrals \(J_{0}\sim\)\(J_{5}\) which can be calculated numerically, and \(f(z)\) is given in Equation (23). It is noteworthy that the critical buckling temperature is irrelevant to the initial temperature for temperature-independent materials.
## 3 Methodology
### Performance Evaluation of Methods  
To evaluate the prediction performance of the GA-LSTM model before the BiLSTM and GRU reference approaches, the criteria described by [PERSON] and [PERSON] (2018) were used in our research, consisting of the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) [39]. These evaluation measures can be calculated as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(yi-\hat{y}i)^{2} \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert yi-\hat{y}i\rvert \tag{4}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\Bigl{|}\frac{\lvert yi-\hat{y}i\rvert}{yi} \Bigr{|}x100 \tag{5}\]  
where residual error \(=yi-\hat{y}i\); \(yi\) is the actual output; \(\hat{y}i\) is the predicted output; and \(n\) is the number of predictions.  
The paired \(t\)-test formula is as follows [40]:  
\[t=\frac{\overline{d}-0}{S_{\overline{d}}/\sqrt{n}} \tag{6}\]  
where \(\overline{d}\) is the observed mean difference; \(S_{\overline{d}}\) is the standard error of the observed mean difference; and n is the number of differences.  
The selection of the evaluation metrics MSE, MAE, and MAPE in unemployment prediction allows a comprehensive evaluation of the performance of the proposed model.  
The MSE is used to calculate the means of the squared differences between the observed values and the predicted values, providing a measure of the spread of the squared errors and their average. On the other hand, the MAE is used to determine the mean of the absolute differences between the observed values and the predicted values, which is useful to evaluate the overall accuracy of the model in terms of absolute deviation. In addition, the MAPE is responsible for measuring the average percentage differences between the observed values and the predicted values. It is especially useful in tasks where sensitivity to relative variations is more important than sensitivity to absolute variations [41].  
In general, even small discrepancies in the values of these metrics can influence decisions and actions taken in the economic and social spheres. It is vital to consider the limitations of these metrics: compared to MAE, MSE may be more affected by outliers due to its quadratic nature; MAE may not accurately reflect model performance if the data set used for testing contains many outlier values; and with respect to MAPE, its use is restricted to strictly positive data by definition, as well as its bias towards low forecasts, which makes it inappropriate for predictive models where large errors are expected [42].
###### Abstract  
This paper focuses on the fracture damage characteristics of styrene-butadiene-styrene (SBS)-modified SMA-13 specimens with basalt fiber under various freeze-thaw (F-T) cycles. SBS-modified stone mastic asphalt (SMA)-13 specimens with basalt fiber were prepared, first, using the superpave gyratory compaction method. Then, asphalt mixture specimens processed with 0-21 F-T cycles were adopted for the high-temperature compression and low-temperature splitting tests. Meanwhile, the acoustic emission (AE) test was conducted to evaluate the fracture characteristics of the asphalt mixture during loading. The results showed that the AE parameters could effectively reflect the damage fracture characteristics of the asphalt mixture specimen during the high-temperature compression and low-temperature splitting processes. The fracture damage of the asphalt mixture specimens during compression or splitting are classified into three stages based on the variation of the AE signals, i.e., when the load level is below 0.1-0.2 during the first stage and the load level is 0.1-0.9 or 0.2-0.8 during the second stage. The AE signal amplitude and count show clear correlations with the compression and splitting load levels. Meanwhile, the AE signal clarifies the formation, development, and failure of internal damage for the asphalt mixture specimens during the compression and splitting processes. The intensity (value and density) of the AE signal parameters of asphalt mixture decreases with increasing F-T cycles. It is evident that the F-T cycle has a significant adverse effect on the mechanical strength of asphalt mixture, which makes asphalt mixtures more likely to cause early failure.  
Keywords: asphalt mixture; basalt fiber; fracture characterization; acoustic emission; freeze-thaw +
Footnote †: journal: Applied Sciences
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### Segmentation Task in Histological Image Analysis
#### 5.5.4 Multiclass Segmentation: Tissues + Nuclei or Cells  
Some studies have sought to integrate the segmentation of multiple structures simultaneously, aiming for a more comprehensive understanding of the tissue microenvironment. [PERSON] et al. [86] propose a supervised GAN model with an integrated segmentation module for generating oligodendroglioma images, effectively incorporating segmentation into the synthesis pipeline. [PERSON] et al. [74] present a multi-task deep learning framework for virtual staining, segmentation, and classification of label-free photoacoustic images, demonstrating the potential of multimodal learning. [PERSON] et al. [97] introduce a GAN-based approach for stain-independent segmentation, facilitating generalization across domains. [PERSON] et al. [68] leverage generative diffusion models with self-supervision for histology segmentation, highlighting progress in self-supervised learning for segmentation tasks.  
Other studies focus on specific challenges: [PERSON] et al. [92] use cGANs for fibrosis detection and quantification. [PERSON] [76] proposes a region-aware GAN architecture for stain normalization, addressing key issues for segmentation consistency. [PERSON] et al. [72] introduce HistoStarGAN, a unified framework for stain normalization, stain transfer, and stain-invariant segmentation in renal histopathology, while [PERSON] et al. [94] focus on domain generalization through unsupervised domain augmentation with CycleGAN.  
[PERSON] et al. [70] present an intensely supervised two-stage GAN for stain normalization, while [PERSON] et al. [69] use CycleGAN-based augmentation to tackle stain variability. [PERSON] et al. [71] developed MiNuGAN, a conditional GAN model for simultaneous mitosis and nuclei segmentation on multi-center breast H&E images, demonstrating GANs' versatility for specific segmentation tasks. [PERSON] et al. [66] introduce SAFRON, a synthesis framework for colorectal cancer histology images, which also supports segmentation models. Lastly, [PERSON] et al. [83] apply domain adaptation techniques for automated tumor cell scoring and survival analysis on PD-L1 stained tissue images, linking segmentation with clinical outcome analysis.  
The field of histopathological image segmentation is advancing towards increasingly generalizable, efficient, and robust solutions, with potential direct impact on clinical applications and biomedical research.
## 3 Results
### Effect of DM Extracts on the NF-\(\kappa\)B and MAPK Signaling Pathway  
The pretreatment with DM extracts significantly inhibited the production of nitrite and PGE\({}_{2}\), mRNA expression level of iNOS and COX-2, and inflammatory cytokines level. To clarify the mechanism underlying the inhibition of inflammatory responses, regulation of the NF-\(\kappa\)B and MAPK signaling pathway was demonstrated using the extracts showing the highest anti-inflammatory activities (Figure 5), namely the 30% ethanol extract in May and 60% ethanol extracts in August and November (Figure 3c,d). The NF-\(\kappa\)B signaling pathway can be initiated by an external stimulus. The phosphorylation and successive proteasomal degradation of \(\kappa\)B are essential for classical activation [20]. The inhibitory \(\kappa\)B kinase (IKK) complex phosphorylates and degrades \(\kappa\)B, resulting in the translocation of NF- \(\kappa\)B to the nucleus. MAPK is involved in a wide range of signaling pathways, which are activated by various extracellular stimuli [21]. JNK, p38, and extracellular signal-regulated kinases (ERK) are included in the MAPK pathway. The LPS-treated group exhibited an increased phosphorylation of NF-\(\kappa\)B, IKK, JNK, and p38. NF-\(\kappa\)B phosphorylation was approximately 1.5, 1.8, and 1.5 times lower in the group treated with the extract from May, August, and November than in the group treated with LPS alone (\(p<0.05\)). IKK\(\alpha\) phosphorylation was approximately 1.5, 2.8, and 2 times lower in the group treated with the extract from May, August, and November than in the group treated with LPS alone (\(p<0.05\)). JNK phosphorylation was approximately 1.7, 2.8, and 2.4 times lower in the group treated with the extract from May, August, and November than in the group treated with LPS alone (\(p<0.05\)). The phosphorylation of p38 was approximately 2.2, 4.5, and 3.5 times lower in the group treated with the extract from May, August, and November than in the group treated with LPS alone (\(p<0.05\)). All DM extracts significantly inhibited phosphorylation of the NF-\(\kappa\)B signaling pathway-related protein. Among the extracts, the extract from August displayed the most prominent regulation ability.  
Figure 4: Concentration profiles of interleukin (IL)-6 (**a**), IL-1ß (**b**), and tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) (**c**) in the RAW264.7 cells stimulated with lipopolysaccharideacharide. W100, hot water extracts; E30, 30% ethanol extracts; and E60, 60% ethanol extracts. Data are presented as the mean \(\pm\) SEs of triplicate determinations of independent wells. Statistical analysis was performed using one-way ANOVA. \({}^{\star}\), \(p<0.05\) compared to the LPS-treated group.
## 4 Performance Evaluation
### Simulation Results
Accordingly, after a certain simulation time, the aggregate throughput of MCSS without WPT becomes lower than that of MCSS with WPT.
## 2 Modified Boost Converter (MBC)
### Converter Analysis
#### 2.1.1 Mode-I  
For a time-interval that the power switch receives the pulse and is in the short circuit state, both \(L_{X}\) and \(L_{Y}\) are magnetizing. This is done by inputting the voltage through diode \(D_{2}\) and the power switch for \(L_{X}\) and from \(C_{1}\) and switch for \(L_{Y}\). As can be seen in this mode, \(D_{1}\) is in mode-I. Based on this statement, currents of inductors are rising in the ON state. As predicted, the voltage value on capacitor \(C_{1}\) is discharging on \(L_{Y}\) through the power switch and decreases. Additionally, the voltage on the output capacitor is discharging on the output load in this mode. The current value of diode \(D_{1}\) is zero and because of the conducting condition for diode \(D_{2}\), its current will increase. Figure 3a shows the ON state of the power switch in the projected structure and Figure 3b illustrates the simplified form of Figure 3a.  
Based on these situations, the current waveform passing through inductor \(L_{X}\) can be written as below:  
\[L_{X}\frac{di_{LX}}{dt}=V_{in}\Rightarrow\frac{di_{LX}}{dt}=\frac{V_{in}}{L_{ X}} \tag{1}\]  
The current of inductor \(L_{Y}\) is:  
\[L_{Y}\frac{di_{LY}}{dt}=v_{\text{C1}}\Rightarrow\frac{di_{LY}}{dt}=\frac{v_{ \text{C1}}}{L_{Y}} \tag{2}\]  
The voltage waveform for capacitor \(C_{1}\) can be gained as:  
\[C_{1}\frac{dv_{\text{C1}}}{dt}=-i_{LY}\Rightarrow\frac{dv_{\text{C1}}}{dt}=- \frac{i_{LY}}{C_{1}} \tag{3}\]  
Finally, we can find the voltage for the output capacitor as Equation (4):  
\[C_{2}\frac{dv_{0}}{dt}=-\frac{v_{o}}{R}\Rightarrow\frac{dv_{o}}{dt}=-\frac{v_ {o}}{C_{2}R} \tag{4}\]  
The steady-space matrix of the ON state for the proposed converter can be obtained by Equation (5)  
\[\left\{\begin{array}{l}L_{X}\frac{di_{LY}}{dt}=V_{in}(d)\Rightarrow\frac{di _{LY}}{dt}=\frac{V_{in}}{L_{X}}(d)\\ L_{Y}\frac{di_{LY}}{dt}=v_{\text{C1}}(d)\Rightarrow\frac{di_{LY}}{dt}=\frac{ v_{\text{C1}}}{v_{\text{C}}}(d)\\ C_{1}\frac{dv_{\text{C1}}}{dt}=-i_{LY}(d)\Rightarrow\frac{dv_{\text{C1}}}{dt}= \frac{-i_{LY}}{C_{1}}(d)\\ C_{2}\frac{dv_{o}}{dt}=-\frac{v_{o}}{R}(d)\Rightarrow\frac{dv_{o}}{dt}=-\frac{ v_{o}}{RC_{2}}(d)\end{array}\right\}\Rightarrow\left[\begin{array}{ccc} \overset{\bullet}{i_{LX}}\\ i_{LY}\\ \overset{\bullet}{v_{\text{C1}}}\\ \overset{\bullet}{v_{01}}\\ \overset{\bullet}{v_{01}}\\ \end{array}\right]=\left[\begin{array}{ccc}0&0&0&0\\ 0&\frac{1}{L_{Y}}&0\\ 0&-\frac{1}{C_{1}}&0&0\\ 0&0&-\frac{1}{KC_{2}}\\ \end{array}\right]\left[\begin{array}{c}i_{LX}\\ i_{LY}\\ \overset{\bullet}{v_{\text{C1}}}\\ \overset{\bullet}{v_{01}}\\ \end{array}\right]+\left[\begin{array}{c}\frac{1}{L_{X}}\\ 0\\ 0\\ 0\\ \end{array}\right][V_{in}] \tag{5}\]
## 5 Practical Application Research and Discussion of Results
### Results  
Based on the recorded data, errors in decision-making were identified in 17 out of 127 patients. Specifically, errors concerned the initial diagnostic and treatment processes for 7 out of 127 patients, while for 10 out of 127, errors were noted in the further stages of treatment, both in the nephrology ward and in the nephrology outpatient clinic. In 7 out of 127 patients, errors during the initial diagnostic phase were found in the process of ordering laboratory tests, as not all required tests were performed, such as lipid profiles, protein measurements in 24 h urine collections, glucose concentration, and hemoglobin A1c as screening tests for the presence of diabetes. In 2 out of 127 patients, a too low dose of steroids was applied, and in one patient, the appropriate dose of nephoprotective drugs--ACE inhibitors--was not administered. In the later stages of treatment, 10 out of 127 patients exhibited errors in the decisions made, with 7 of those errors being related to the failure to perform all recommended laboratory tests, including creatinine levels with eGFR measurements, serum protein levels, and the 24 h urine protein quantification, as well as hemoglobin A1c measurement as a risk factor for detecting post-steroid diabetes. Additionally, in the medical history, the weights and blood pressure readings of eight patients were not recorded. In 8 out of 127 patients, the steroid dose was reduced too slowly, and in 5 out of 127 patients, either an inappropriate dose of nephoprotective drugs was applied, or they were not used at all.