esa-sceva/satcom-chunk-collection huggingface dataset:
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 6 Probabilistic Bound on Node Existence  
We develop an upper probabilistic bound on the sensor node's power level based on the node model described in Section 4. Given the problem formulation from Section 4, and the probability discharge function \(P\), the expected charge level of each node is defined as \(E(X)\):  
\[E(X)=W-\sum_{t=1}^{T}P(t)g(t)=W-\sum_{t=1}^{T}(p\times r+(1-p)\times 0),\]  
\[E(X)=W-prT. \tag{1}\]  
For unbiased values of \(p\), \(E(X)=W\) is the highest possible value, while highly improbable. The lowest possible value \(E(X)=W-rT\) is also highly improbable.  
Our interest is to be able to identify the probability of a node being at or below a certain power level, \(Y\). Several tail probability estimation methods exists [50]. While Markov inequality can easily be applied, it provides a relatively loose bound. Other tighter bounds may also be applied, like Chebyshev Bounds, or Chernoff Bound known as the tightest bound. Since the nodes are independent and the discharge at each step is both random and independent, the Chernoff Bound [51; 52] conditions are satisfied, and the bound may be applied to give an upper bound to this probability. We use the multiplicative form of the Chernoff Bound:  
\[P_{r}[X\leq(1-\varepsilon)E(X)]\leq e^{-\frac{r^{2}}{2}E(X)}. \tag{2}\]Let us assume we wish to identify the probability of a node being at power level \(Y\) or less, where \(Y\) is always less than \(E(X)\). This is equivalent to setting  
\[\varepsilon=\frac{E(X)-Y}{E(X)}.\]  
The probability becomes  
\[P_{r}[X\leq Y]\leq e^{\frac{(W-pY-Y)^{2}}{2(pY-W)}}. \tag{3}\]  
This probability is a function of four parameters, the probability \(p\) of a node discharging at any time instance, the average discharge rate \(r\), the time elapsed \(T\) since the moment the sensor node power level was \(W\), and the node maximum power level \(W\).  
Equation (3) gives us an upper bound on the probability of finding a certain node at power level \(Y\) or less. Section 10.1 shows how close our simulations are to this bound. This bound can be utilized to assist an algorithm in identifying nodes at low power levels with high certainty.
## 5 The ATLAS MAC Protocol
### Parameter Selection  
#### 5.3.1 The Value of \(\eta\)  
The \(\eta\) is used to deduce the nominal portion of the radio capacity \(c\) for each cluster-head (Section 5.1), which we derived from the given per-node capacity (denoted by \(\lambda\)) of a random network, as deduced in [25],\[\lambda<\frac{k\times R_{tx}}{\rho}\times\frac{1}{L}=\frac{c\times R_{tx}}{n\times L} \tag{2}\]  
where, \(k\) is a constant, \(\rho\) is the node density, \(n\) is the number of nodes within the transmission range (\(R_{tx}\)) \(n=\rho\pi R_{tx}^{2}=\frac{20}{30\times 30}\times\frac{22}{7}\times(15)^{2} \approx 15\)), and \(L\) is the expected physical path length in the network. The value of \(\rho\) depends on the area of the network given by \(A\), and \(A=30\times 30\) square meters here. We also use a few of our simulation parameters to deduce an upper bound for \(\eta\) using Equation (2). Thus, for a deployment of 20 nodes in \(A\) with \(c=250\) kbps, \(R_{tx}=15\) m, \(n=15\), and \(L=2\), we obtain an upper bound of \(\eta\) that is equal to 0.47.
# Field Investigation and Rapid Deterioration Analysis of Heavy Haul Corrugation
## 4 Multi-Crack Expansion Analysis Considering Grinding Marks
### Stress Intensity Factor Calculation Based on Displacement of Singular Element  
Regarding the problem of rapid corrugation deterioration discussed in previous sections, there are some basic issues that should be considered such as RCF cracks, crosses between the grinding marks, and the influence of the cracks on the corrugation et al. By considering the applicability of the methodology and the accuracy of the solution, a stress intensity factor (SIF) based on the displacement of the singular element is selected to analyze surface crack characteristics of a rail under grinding mark conditions.  
The calculation of stress intensity factor based on the displacement method can be carried out by Formula (1):  
\[\left\{\begin{array}{c}K_{\mathrm{I}}=\frac{\mu\sqrt{2\pi}}{\sqrt{r}(2-2\
u )}(v_{\mathrm{c}}-v_{\mathrm{a}})\\ K_{\mathrm{II}}=\frac{\mu\sqrt{2\pi}}{\sqrt{r}(2-2\
u)}(u_{\mathrm{c}}-u_{ \mathrm{a}})\\ K_{\mathrm{III}}=\frac{\mu\sqrt{\pi}}{\sqrt{2}}(w_{\mathrm{c}}-w_{\mathrm{a}}) \end{array}\right. \tag{1}\]  
where \(\mu\) is the Shear Modulus; \(\
u\) is Poisson's ratio; \(r\) represents the distance between the analytically calculated node and the crack tip; Based on the 1/4 three-dimensional singular element in FEM, the Formula (1) can be optimized as:  
\[\left\{\begin{array}{c}K_{\mathrm{I}}=\frac{\mu\sqrt{2\pi}}{\sqrt{r}(2-2\
u )}[4(v_{\mathrm{b}}-v_{\mathrm{d}})+v_{\mathrm{e}}-v_{\mathrm{c}}]\\ K_{\mathrm{II}}=\frac{\mu\sqrt{2\pi}}{\sqrt{r}(2-2\
u)}[4(u_{\mathrm{b}}-u_{ \mathrm{d}})+u_{\mathrm{e}}-u_{\mathrm{c}}]\\ K_{\mathrm{III}}=\frac{\mu\sqrt{\pi}}{\sqrt{2}}(w_{\mathrm{c}}-w_{\mathrm{a}}) \end{array}\right. \tag{2}\]  
where \(v_{i}\), \(u_{i}\), \(w_{i}\) (\(i\) = a-e) are the vertical, lateral and longitudinal displacements at the calculation point \(i\); and a-e represent the numerical integration points in FEM, as shown in Figure 6.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 3 The UWA-OFDM-Channel-Estimation Framework Based on Deep Learning  
This section outlines the UWA-OFDM-channel-estimation framework based on deep learning. According to the shortcomings of insufficient channel samples, we first used the data-enhancement method to increase the number of channel samples, and we then used deep learning for channel estimation. The overall framework is shown in in Figure 2.
# Natural Photosensitizers in Clinical Trials
## 4 Natural Photosensitizers Pending Clinical Trials
### Alkaloids  
Alkaloids are a naturally occurring group of organic compounds [99; 100; 101]. One of them is berberine (BRB). It is a compound belonging to the group of isoquinoline alkaloids of plant origin, found naturally in such plant species as _Berberis vulgaris_, _Berberis aristata_, _Malonia quifolium_, _Hydrastis anandensis_, _Coptis chinensis_, and _Arcangelisa flava_. Due to its potent biological activity, BRB has been used in traditional Chinese medicine for many years. Berberine also has photosensitizing properties, so it has been tested as a potential photosensitizer in PDT [24; 101; 102; 103; 104]. Berberine's mechanisms of action may include inhibition of cell proliferation, DNA damage, and changes in protein and gene expressions. It readily binds to DNA, increasing the efficiency of singlet oxygen generation. Upon photoexcitation, BBR causes guanine-specific oxidation in DNA, leading to DNA damage that can induce apoptosis or cell death by necrosis [105]. Berberine has been shown to be an effective photosensitizer in PDT of various cancers, including cervical cancer, melanoma, and glioblastoma kidney cancer [102; 103; 104; 105; 106; 107; 108; 109].
# A Circularly Polarized Non-Resonant Slotted Waveguide Antenna Array for Wide-Angle Scanning
## 2 Configuration of the Antenna Element
### Design of Circular Polarizer
Figure 5: (**a**) The relationship between the cutoff frequencies and ridge height \(d1\) and (**b**) the relationship between the cutoff frequencies and ridge height \(d2\).
## 3 Automated Diagnosis of COVID-19
### Pixel-Level Diagnosis: Segmentation-Based Models
#### 3.2.3 Segmentation  
There are two different segmentation tasks in COVID-19 diagnosis models: lung region segmentation and lung lesion segmentation. Lung region segmentation is used to separate the whole lung region from the background, and lung lesion segmentation is used to distinguish the lesion region from other lung regions. The first lung region segmentation is usually performed as a preprocessing step. Here, we only focus on the methods that have been used in lung lesion segmentation.  
The V-Net-based segmentation model VNET-IR-RPN17 [107] was trained for pulmonary tuberculosis purposes; it was verified to be good enough to separate candidate patches from viral pneumonia. [PERSON] et al. [47] employed an inception residual recurrent convolutional neural network with a transfer learning approach for COVID-19 detection and NABLA-N network model for segmenting the regions infected by COVID-19. [PERSON] et al. [194] used the aggregated residual transformations to learn a robust and expressive feature representation and applied the soft attention mechanism to achieve the automated segmentation of multiple COVID-19 infection regions. [PERSON] et al. [196] trained a JCS system, which includes a segmentation branch that is trained with accurately annotated CT images, performing fine-grained lesion segmentation. [PERSON] et al. [197] proposed a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) that can automatically identify infected regions from chest CT slices. [PERSON] et al. [123] proposed a novel online attention module with a 3D Convolutional Network (CNN) to focus on the infection regions in the lungs when making decisions of diagnoses. [PERSON] et al. [195] built a 2D seg mentation model using the U-Net architecture, which gives the output by marking out the region of infection. [PERSON] et al. (2018) developed a Dual-branch Combination Network (DCN) for COVID-19 diagnoses that can simultaneously achieve individual-level classification and lesion segmentation. [PERSON] et al. (2019) proposed federated semi-supervised learning for COVID region segmentation in 3D chest CT. The framework is designed to leverage unlabeled data for federated learning.
## 3 Results
### Environmental Controls of NEP, GPP, and Reco  
The statistical relationships between the NEE and meteorological, soil, and management variables are presented in Table 3. The NEE correlates strongly negatively with R\({}_{\rm g}\) (R = \(-\)0.8) on the monthly scale, followed by T\({}_{\rm a}\) and VPD (R = \(-\)0.6). The SWC and Prec show weak correlations with NEE, likely due to the soil being classified as wet for most of the period and precipitation not showing seasonality. On the annual scale, NEE shows moderate correlations with T\({}_{\rm s}\) and Prec and weak correlations with R\({}_{\rm g}\), T\({}_{\rm a}\), VPD, and SWC (R \(<\) 0.3). For the meteorological seasons, correlations are significant in summer and spring, with only T\({}_{\rm s}\) strongly negatively correlated with the NEE in spring. For autumn and winter, the correlations with T\({}_{\rm a}\), VPD, T\({}_{\rm s}\) and SWC are not significant, while R\({}_{\rm g}\) and Prec present significant but weak correlations, except for Prec in autumn, which also moderately correlates with the NEE. Similar behavior is observed in the two management seasons, where only Prec correlates significantly with the NEE (R = \(-\)0.6) in the cold season, while in the warm season, all variables are weakly correlated but significant. Therefore, no single variable can explain the interannual and interseasonal variability in the NEE. The pasture height variable moderately correlates (R = \(-\)0.6) with the NEE in the warm season (Figure S1). The anomalies of the NEE in relation to the monthly average were analyzed comparatively against the behavior of environmental variables, seeking to identify possible causes of interseasonal variability. Once again, no strong correlations were found between these anomalies (R \(<\) 0.2), as illustrated in Figure S2.  
Figure 5: Cumulative annual and seasonal net ecosystem exchange (NEE) from 2015 to 2021 in a subtropical natural grassland of the Pampa biome grazed with cattle. (**a**) Annual; (**b**) Summer; (**c**) Autumn; (**d**) Winter; (**e**) Spring.
# Policy Similarity Measure for Two-Player Zero-Sum Games
## 3 Preliminaries  
### Two-Player Normal-Form Games  
A two-player normal-form game [37] involves two players, indexed by \(i\in\{1,2\}\). Each player selects an action \(a_{i}\) from a finite set \(\mathcal{A}_{i}\), and the pair of chosen actions then determines their payoffs through utility functions \(u_{i}:\mathcal{A}_{1}\times\mathcal{A}_{2}\rightarrow\mathbb{R}\). A mixed strategy \(\pi_{i}\) represents a probability distribution over player \(i\)'s actions. The expected utility for the joint strategy profile \((\pi_{1},\pi_{2})\) is expressed as  
\[G_{i}(\pi_{i},\pi_{-i})\ =\ \sum_{a_{i}\in\mathcal{A}_{i}}\sum_{a_{-i}\in \mathcal{A}_{-i}}\pi_{i}(a_{i})\ \pi_{-i}(a_{-i})\ u_{i}(a_{i},a_{-i}), \tag{1}\]  
where \(\pi_{-i}\) denotes the mixed strategy of the opponent. The best response against \(\pi_{-i}\) is  
\[\mathcal{BR}(\pi_{-i})\ =\ \arg\max_{\pi_{i}}\ G_{i}\big{(}\pi_{i},\ \pi_{-i}\big{)}, \tag{2}\]  
which reflects the strategy that maximizes player \(i\)'s expected utility against the opponent's strategy. Extensive-form games extend these formalisms to the multi-step sequential case (e.g., poker). Every finite extensive-form game has an equivalent normal form [38] but, due to its exponential size, most algorithms must be adapted to handle the sequential setting directly.  
To evaluate how much a strategy profile \(\pi=(\pi_{1},\pi_{2})\) deviates from the equilibrium in a two-player zero-sum game, the exploitability metric known as NashConv [21] can be used. It is defined as  
\[\text{Expl}(\pi)\ =\ \sum_{i=1}^{2}\Big{[}G_{i}\big{(}\mathcal{BR}(\pi_{-i}),\ \pi_{-i}\big{)}\ -\ G_{i}\big{(}\pi_{i},\ \pi_{-i}\big{)}\Big{]}, \tag{3}\]  
When \(\text{Expl}(\pi)=0\), no player can improve their expected payoff by unilaterally deviating from \(\pi\), which indicates that \(\pi\) is an NE.
# Body-Part-Aware and Multitask-Aware Single-Image-Based Action Recognition
## 4 Experiments
### Evaluation of the Action Recognition Task on the Stanford 40 Actions Dataset
In Table 4, we evaluate our methods by comparing them with the existing ones on the Stanford 40 Actions Dataset. Among the other methods (Action-Specific Detectors [26], VGG-16&19 [42], TDP [29], ResNet-50 [23], and Action-Mask [28]), the performance of the method in [27] is the best. Part-Based Network in [27] and Part Action Network in [27] use the body joints information to localize seven body parts and feed the network with the person bounding box image along with the seven body part images. Moreover, the Part Action Network [27] uses manually labeled part action annotations on the Stanford 40 Actions Dataset. The Part-Based Network obtained 89.3% mAP and the part action network obtained 91.2% mAP, whereas our proposed method (91.91%) uses only three body parts and does not use additional annotations for body parts. The body joints in the proposed method are localized in a multitasking manner, unlike the method in [27], which uses an independent human pose estimator. \begin{table}
\begin{tabular}{l c} \hline \hline
**Feature Representation** & **mAP** \\ \hline Image-based feature stream & 85.57 \\ \hline Attention-image-based feature stream & 85.38 \\ \hline Part-image-based feature stream & 84.27 \\ \hline Concatenation-Based Action Decision (Image + Attention + Part) & 91.76 \\ \hline Proposed: Ensemble of (Image + Part + Concatenation) & **91.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of mAP obtained through feature representation from multiple streams for action recognition on the Stanford 40 Actions Dataset [15]. Bold font indicates the best result. Figure 6: Detailed performance evaluation (average precision) of our proposed method. FS, feature stream. Results reported are on the Stanford 40 Actions Dataset [15]. Only some action classes are represented in the figure.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
###### Abstract  
Bicuspid Aortic Valve (BAV) is a prevalent congenital heart defect, characterized by the presence of two cusps instead of three, leading to significant clinical implications such as aortic stenosis, regurgitation, and aneurysms. Understanding the genetic underpinnings of BAV is essential for early diagnosis and management, which can prevent severe complications like aortic dissection and heart failure. Recent studies have identified critical genes associated with BAV, including NOTCH1, GATA4, GATA5, SMAD6, NKX2.5, BMP2, and ROBO4, all of which play vital roles in aortic valve development and function. Imaging advancements, particularly in cardiac MRI and echocardiography, have enhanced the assessment of valve morphology and hemodynamics, with Wall Shear Stress emerging as a promising biomarker. This review consolidates current genetic and imaging research, elucidating the contributions of genetic variants to the etiology and progression of BAV, while emphasizing the importance of imaging biomarkers in clinical management. The findings aim to improve genetic screening strategies, facilitate early diagnosis, and guide the development of targeted therapies for individuals with BAV.  
Bicuspid Aortic Valve; genetic mutations; imaging biomarkers; aortic stenosis; wall shear stress; cardiac MRI; congenital heart defect; aortic regurgitation; valve morphology; aortopathy  
18 November 2024  
Revised: 6 January 2025  
1 Undergraduate Medical Education, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada; [EMAIL_ADDRESS]  
2025 by the authors.  
Liconses MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 1 Introduction  
Titanium and its alloys are widely used as aerospace materials owing to their excellent mechanical properties, particularly, their fatigue resistance. \(\beta\) titanium alloy has emerged as a promising titanium alloy and has been used in recent years in several landing gear systems in aircrafts such as Boeing 787 and Airbus A380 [1; 2; 3; 4].  
Generally, it is considered that when the applied stress is less than the fatigue limit, the alloy material exhibits an infinite fatigue life. In the past two decades, however, the occurrence of a fatigue fracture originating from inside the alloy material below the conventional fatigue limit has been reported in a very high cycle fatigue (VHCF) regime [5; 6]. Therefore, tremendous efforts have been devoted by several researchers to investigate the fatigue behavior of \(\beta\) titanium alloys and these studies have confirmed the occurrence of internal fractures in the VHCF regime [7; 8; 9; 10; 11].  
The propagation rate of the internal crack is commonly used to describe the crack behavior in the VHCF regime. However, the direct observation of the internal crack is extremely difficult. Consequently, several researchers have devoted considerable efforts to develop indirect approaches for obtaining the internal crack propagation rate. For example, [PERSON] et al. used a conventional fatigue test and fractography to obtain the crack propagation rate of a high-strength steel [12]. In addition, [PERSON] et al. developed fractographic models for estimating the fatigue life of steel in the VHCF regime [13; 14]. The most important step of these methods was to clearly determine the crack initiation area. However, these attempts have not successfully conducted on titanium alloys. [PERSON] et al. estimated the fatigue life of Ti-15V-3 Cr-3 Sn-3 Al using fractography and reported that there was a large deviation in internal fractures [15]. One of the reasons for this is, comparedwith inclusions which are the internal crack initiation sites of steels, it is more difficult to determine and identify multiple facets, which are commonly observed at the internal crack initiation sites of titanium alloys [15; 16; 17]. In addition, at the early stage of the internal crack propagation process, the facet initiation and crack propagation behavior are strongly affected by several factors such as microstructures and grain orientation, which cause a significant deviation in the fatigue life estimation of internal cracks. This indicates that it is important to develop a general model that can illustrate the facet initiation and propagation behavior of titanium alloys.  
In this study, we investigated the multiple facets growth (initiation and propagation) process of a \(\beta\) titanium alloy by fractography and applying the Tanaka-Akiniwa model [12] to derive the material constants for the Paris law and the propagation rate of internal cracks. Finally, a reverse fatigue life estimation was conducted to evaluate the accuracy of the obtained material constants.
## 1 Introduction
[PERSON] (2002) predicted next-day PM\({}_{2.5}\) using ANN with today air pollutants' (NOx, PM\({}_{10}\), PM\({}_{2.5}\), O\({}_{3}\)) concentrations and their variances and next-day precipitation and temperature as input variables [34]. [PERSON] and [PERSON] (2017) developed hybrid model next-day PM2.5 using PCA, LSSVM, and Cuckoo search [35]. The model developed in this study shows improved predictive performance compared to the models of the previous two studies and is presented in the results and discussion section. Among various ANN models, the multilayer perceptron artificial neural network in this study is a feedforward type ANN model that can be used universally, unlike the RBF, BP, PSO, and RNN-CMAQ mentioned above. Representatively, a PM\({}_{2.5}\) prediction study using a multilayer perceptron artificial neural network predicted hourly PM\({}_{2.5}\) using data from an hour ago using six measurement stations in Poland [36]. PM\({}_{2.5}\) prediction studies using many ANNs, including the above studies, have focused only on hourly predictions, not daily predictions. Studies that predicted daily PM2.5 concentration by ANN are insufficient. In addition, when accurate weather forecasts are available, verification of performance changes of the forecasting model is not performed, so it is necessary to verify this. Therefore, in this study, we developed a model that can predict each country nationwide and 16 administrative districts using ANN as a basic factor for air pollutant and meteorological data in Korea from 2015 to 2019. In addition, a one-day prediction model was developed, capable of next-day predictions of PM\({}_{2.5}\) concentration if the air pollution and weather information of the day is known. In addition, models that could predict PM\({}_{2.5}\) concentrations for NW and 16 administrative districts were developed using ANN.