esa-sceva/satcom-chunk-collection huggingface dataset:
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 1 Introduction  
Bicuspid Aortic Valve (BAV) is the most common congenital heart defect, affecting approximately 1-2% of the population [1, 2]. Characterized by the presence of two cusps in the aortic valve instead of the normal three, as shown in Figure 1, BAV can lead to significant clinical implications, including an increased risk of aortic stenosis, regurgitation, and aneurysms [3]. Although often diagnosed incidentally during imaging studies, its impact on cardiovascular health can be profound, making early diagnosis and management essential to prevent complications such as aortic dissection and heart failure. Genetic factors play a crucial role in BAV pathogenesis, as shown in Figure 2, offering insights into disease etiology and progression, while imaging provides a non-invasive means to assess structural and functional changes.  
In recent years, significant progress has been made in understanding the genetic basis of BAV. Key mutations in genes such as NOTCH1, GATA, and SMAD6 have been linked to abnormal valve development, calcification, and aortopathy. However, genetics alone cannot fully capture disease progression. Advances in imaging modalities, including echocardiography, cardiac MRI, and 4D Flow MRI, offer detailed visualization of valve morphology, flow patterns, and hemodynamic forces such as Wall Shear Stress (WSS). BAV displays a unique interplay between genetic mutations and hemodynamic changes contributing to disease progression. Mutations in genes like NOTCH1 can accelerate disease processes when paired with altered hemodynamic forces observed through imaging. Techniques such as 4D Flow MRI provide a detailed view of flow abnormalities, WSS, and energy loss, helping connect these genetic changes to measurable structural and functional outcomes. Integrating genetic findings with imaging biomarkers has emerged as a promising strategy to improve risk stratification, identify high-risk phenotypes, and personalize treatment approaches for BAV patients [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16].  
This review aims to consolidate and critically evaluate the current state of genetic and imaging research in BAV. We aim to identify key genetic variants and elucidate their functional implications in disease development and progression. Additionally, we explore the role of imaging biomarkers in linking genetic predispositions to observed hemodynamic and structural changes in BAV. By highlighting these connections, this review emphasizes the clinical utility of integrating genetic screening and advanced imaging modalities to enable earlier diagnosis, optimize risk stratification, and guide tailored management strategies.
###### Abstract  
Sustainable and profitable crop production has become a challenge due to frequent weather extremes, where unstable crop yields are often followed by the negative impacts of agronomic practices on the environment, i.e., nitrate leaching in irrigated and nitrogen (N)-fertilized crop production. To study this issue, a three-year field study was conducted during quite different growing seasons in terms of weather conditions, i.e., extremely wet, extremely dry, and average years. Over three consecutive years, the irrigation and N fertilizers rates were tested for their effect on grain yield and composition, i.e., protein, starch, and oil content of the maize hybrids; soil N level (%); and nitrate leaching. The results showed that the impact of the tested factors and their significance was year- or weather-condition-dependent. The grain yield result stood out during the extremely wet year, where the irrigation rate reduced the grain yield by 7.6% due to the stress caused by the excessive amount of water. In the remainder of the study, the irrigation rate expectedly increased the grain yield by 13.9% (a2) and 20.8% (a3) in the extremely dry year and 22.7% (a2) and 39.5% (a3) during the average year. Regardless of the weather conditions, the N fertilizer rate increased the grain yield and protein content. The soil N level showed a typical pattern, where the maximum levels were at the beginning of the study period and were higher as the N fertilizer rate was increased. Significant variations in the soil N level were found between weather conditions (r = -0.719) and N fertilizer rate (r = 0.401). Nitrate leaching losses were expectedly found for irrigation and N fertilizer treatments with the highest rates (a3b3 = 79.8 mg NO\({}_{3}\)\({}^{-}\) L).  
extreme weather; irrigation; N fertilizer rate; maize yield; grain composition; soil N level; nitrate leaching +
Footnote †: journal: Applied Sciences  
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# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 3 Selected Use Cases
### Anticipated Cooperative Collision Avoidance (ACCA)  
Towards the autonomous vehicles, car manufacturers are adopting and developing sensors that allow vehicles to sense their environment and control the vehicles. Driving automation systems rely on a variety of sensors like cameras, radar, lidar, etc. Despite the increasing number of in-vehicle sensors, the environmental perception of the vehicle remains limited. In certain situations, typical stand-alone sensing systems will not be able to detect and localize dangerous events on the road with sufficient level of anticipations. In such situations, too late detection of a dangerous event will trigger a hard braking or a dangerous maneuver or potentially lead to a collision.  
The anticipated cooperative collision avoidance (ACCA) use case relates to the possibility to anticipate certain potentially critical events in order to reduce the probability of collisions in situations when typical sensors will have no visibility or a short detection range (a few 100 m) [21; 22]. The aim of the ACCA use case is to induce smoother and more homogeneous vehicle reactions by facilitating the anticipated detection and localization of temporarily static events such as traffic jams, high deceleration, emergency braking or unexpected maneuvers of vehicles in front, etc. An example of a typical scenario of the ACCA use case is depicted in Figure 3.
## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Wet Antenna Attenuation (WAA) Correction  
Rainfall not only scatters and absorbs microwaves, but other phenomena associated with it, such as increased water vapor, decreased temperature and antenna wetting, can lead to additional attenuation that cannot be eliminated by the baseline. Considering that attenuation due to temperature and water vapor variations is typically one to two orders of magnitude smaller than rain-induced attenuation (with the possible exception of the water vapor absorption band around 22 GHz), these can generally be ignored. However, the water film covering antennas can change the antenna's directivity, efficiency and reflectivity [67], introducing additional attenuation up to a few dB. The lack of correction for wet antenna attenuation (WAA) has been shown to lead to a significant overestimation of rainfall estimates based on CMLs [26].  
In order to eliminate the effect of WAA, an exponential model related to the measured attenuation was proposed by [PERSON] et al. [68]. Later, based on the fact that measured attenuation consists of path attenuation and WAA, the literature [69] extended the exponential WAA model to link it to path attenuation. [PERSON] et al. [70] assumed a power-law relationship between the water film thickness and the rain rate and calculated the WAA based on the water film thickness and electromagnetic scattering theory. Some studies [71] also treated WAA directly as a function of rain rate. [PERSON] et al. [49] suggested ignoring the process of antenna wetting and drying and treating WAA as constant. On the contrary, long antenna drying and wetting processes have also been observed, and time-dependent dynamic WAA models have been proposed [29; 72]. [PERSON] et al. [73] compared the above empirical models and found that the WAA model, which is directly related to the rain rate, performs better, and that because the parameters of the model are independent of the CML frequency and path length, it is portable to CMLs with similar antenna characteristics. In the absence of rainfall observations, [PERSON] et al. [74] suggest that WAA can be quantified by using short CMLs, or, if short CMLs are not available, by statistics from CMLs and rainfall climatology. On the basis of the analysis of WAA time series, the LSTM algorithm [75] has also been applied to WAA estimation. In addition, since WAA is mainly related to antenna reflectivity, [PERSON] et al. [76] have developed a microwave measurement system that can measure attenuation and WAA simultaneously, which may present opportunities for WAA correction.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 3 Results and Discussion
### Soil  
Soil properties were directly affected by irrigation water quality. Saline water increased the salinity level of the soil, especially in the cases of BS#03 and BS#04 which were irrigated with a salinity level above the lettuce's threshold (0.9 dS m\({}^{-1}\)) (Table 3). The final EC\({}_{\text{e}}\) values of BS#03 and BS#04 were 30.8 and 34.8 dS m\({}^{-1}\), respectively. Moreover, SAR and N\({}^{+}\) concentrations in soil showed similar results. The concentrations of EC\({}_{\text{e}}\), SAR, and Na\({}^{+}\) increased over time in both BS#03 and BS#04. However, there were no remarkable changes in the experiments with desalinated irrigation water (Table 3). EC\({}_{\text{e}}\), SAR, and Na\({}^{+}\) concentrations in the first phase of the experiment were higher than those in the second phase, except for AS#04 where irrigation water with the highest level of Na\({}^{+}\) concentration was used. Figure 2: Soilodicity potential risk, evaluated using the sodium adsorption ratio (SAR) and the electric conductivity (EC\({}_{\text{w}}\)) of irrigation water. BS: before water treatment using the BWD system, i.e., saline water irrigation; AS: after water treatment using the BWD system, i.e., desalinated water irrigation.
## 3 Application Example  
### Experimental Equipment  
This study validates the axis motion trajectory feature extraction method using data obtained from a custom-built multi-disk rotor test bench. As depicted in Figure 8a, the test bench comprises a servo motor, disk rotors, a base, and supports. It can achieve a maximum speed of 1300 r/min and supports three different rotor configurations, with each rotor disk capable of holding up to eight weights.  
Figure 6: Calculation of distance data sequences, where \({}^{\alpha}\) ((1), 2, 3, 4\({}^{\alpha}\) in the figure correspond to \(L\)(1), \(L\)(2), \(L\)(3), \(L\)(4) in Equation (5).  
Figure 7: Calculation of distance data sequences.  
During the experiment, two sets of eddy current displacement sensors collected vibration displacement signals at a sampling frequency of 16 kHz. For each experimental condition, a single sensor recorded 65,536 data points per sampling session.  
As illustrated in Figure 8b, the sensors were arranged perpendicularly to each other to measure the displacement of the object in the vertical direction. The experiment focused on the third disk rotor to obtain vibration displacement signals representing the operating state of the entire rotor system.
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 2 Methods and Evaluation Index
### Training Model and Parameter Setting  
Since this paper deals with 1D seismic data, considering the amount of data and the performance of the machine itself, Resnet18 is the chosen network, based on Resnet18, changing the convolutional layer, pooling layer, and batchnormal to 1D, while keeping the rest of the settings unchanged. As for the network parameter settings, according to the amount of data to be processed, the batch size is set to 100 and the initial learning rate range is [\(10^{-6}\),\(9\times 10^{-6}\)]. During the training process, the learning rate decreases to 90% of the original rate every 10 epochs.  
The experiments in this paper were run on a machine equipped with an Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50 GHz and an NVIDIA GeForce RTX 2080 Ti.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### Flavonoids  
Flavonoids are low-molecular-weight phenolic compounds based on the flavan nucleus [78], responsible for the aroma and antioxidant potential of honey [46]. Their biological effects span a wide range, including antibacterial, anti-inflammatory, anti-allergic, and antithrombotic actions [76]. According to the floral and geographic sources of the honey, flavonoid profiles typically vary greatly [35; 78; 79]. The following Jara honey samples had the highest flavonoid content: JH-2 (321.9 mg/kg), JH-3 (307.9 mg/kg), JH-11 (299.7 mg/kg), and JH-10 (291.4 mg/kg). They were relatively lower in JH-18 (267.1 mg/kg), JH-4 (265.8 mg/kg), and JH-17 (255.3 mg/kg) (Table 6).
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 2 Methodology
### Reservoir Simulation  
To test the inversion methodology, we produced a set of synthetic reservoir models based on the Gato do Mato model. The simulated injection well is located below the salt formation at a depth of 6000 m and in a horizontal position of 10,000 m. The region of the reservoir, the target region for inversion, is indicated with a red rectangle in Figure 1. Empirical data from the industry suggest that, for the Brazilian pre-salt, the maximal production-induced velocity perturbation in the reservoir is around 3%. In addition, the average dimension of the reservoir is 2000 m in the horizontal direction and 200 m in the vertical direction.  
To perform an inversion task using ML, we needed an adequate training set, which, in our case, is a set of reservoir velocity models with a good variability in shape and velocity. In our algorithm, we considered the initial velocity model \(m_{init}\), as presented in Figure 1, and the reservoir velocity perturbation model \(m_{pert}\) that represents the changes in the reservoir due to oil and gas production. The perturbations in the reservoir are related to water, oil, or gas displacement in the media; all these factors induce changes in the physical properties of the rocks and consequent changes in acoustic velocity [2]. To model the reservoir anomaly, we employed a Gaussian statistical distribution centered around the injection well. The standard deviation of the Gaussian followed an aleatory distribution from 300 m to 500 m. The center of the Gaussian was sampled around the point \((x,z)=(10\text{ km},6\text{ km})\).
## 2 Method  
First-principles calculations are performed using the Vienna ab initio simulation package (VASP) [32; 33; 34] based on the density functional theory (DFT). The projector augmented wave (PAW) [35; 36] pseudopotentials with the exchange correlation energy term from Ceperley-Alder [37] and Perdew-Zunger [38] (CA-PZ) type local density approximation (LDA) [39] are used in this work. To account for the strong-correlations in \(d\)-orbitals, we include the on-site Coulomb repulsion \(U\) (LDA + \(U\)) [40] with \(U=3.5\,\mathrm{eV}\), \(J=0.6\,\mathrm{eV}\) for Ru ions [20]. Different \(U\) and \(J\) values for Ru ions are also considered to see the effects. The reasonable U value of Ti ions ranges from 3.5 to 8.0 eV [41; 42; 43; 44]. Because the lattice constants and the bulk modulus of bulk STO agree better with experimental results [42], we thus adopt the on-site \(U=5.0\,\mathrm{eV}\), \(J=0.64\,\mathrm{eV}\) for Ti ions [41; 42]. Owing to the same ionicity of Ru(Ti) ions and the same local RuO\({}_{6}\)(TiO\({}_{6}\)) octahedral structure, the same \(U\) and \(J\) values of Ru(Ti) ions are adopted for both bulk and monolayer systems. The \(k\)-mesh and energy cutoff are \(12\times 12\times 8\) (\(8\times 8\times 1\)) and 400 eV for bulk SRO (SRO/STO superlattice). In all calculations, the lattice structure is optimized until the total energy converges within \(10^{-3}\) eV. The Curie temperature (\(T_{\mathrm{C}}\)) of the ferromagnetic phase are simulated through the Monte Carlo method based on the Heisenberg model using the VAMIIRE package [31]. A \(50\times 50\times 50\) (\(50\times 50\times 1\)) supercell for bulk SRO (SRO/STO superlattice) is used in Monte Carlo simulations.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 5 Discussions and Limitations
### Limitations  
This study only focused on the derivation of the adaptive scaling factor under fast-changing environments. Subsequently, GMPP tracking under PSC has been left as a future work for this paper. However, the proposed method can be integrated with some GMPP candidate interval locating algorithms to enhance its tracking performance under PSC. Besides, the optimal scaling factor is obtained using tentative simulations in this study. An intelligent algorithm may be designed to be integrated into the VSS INC MPPT algorithms for automatically solving the optimal design.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 5 PUF Stability Evaluation and Automatic Detection of Unstable Bits
### Further Temperature Compensation
#### 5.5.2 Re-Sampling the Reference and Stability  
Throughout the prior sections, the EoA PUF has not only been calibrated for an offset of the two ROs' mean frequencies but also (auto-detected) unstable bits were eliminated from the response. Thus, it is obvious that only an offset re-calibration over temperature cannot solve all the additional bit errors over temperature variations.  
After the re-calibrated mean frequency drift, the different tuning vectors may result in minor changes in individual pairs and bit responses and thus change some prior unstable bits to become stable and vice versa. Therefore, the EoA-PUF intrinsic auto-detection of unstable bits was also used at all three calibration points to determine blanked bits for each evaluated region. Thereafter, the reference response (determined at 20 \({}^{\circ}\)C) was updated at the other two temperatures as the original reference might be invalid for bits that were unstable and became stable with changing temperature. This lowered the error rate at all three calibration points to almost zero, as can be seen in Figure 17 as blue curve. The evaluated temperatures next to the calibration points have a remaining error of around 1%, which can be expected as the offset calibration, masking, and reference response change with temperature and cause some bits to flip. In consequence, to reduce the overall error rate of the EoA-PUF, more than one calibration point can be selected to re-calibrate the new mean oscillation frequencies of the two challenged ROs and to auto-detect the unstable bits over a wider temperature range. The amount of stable bits stays constant over all calibration points (see Figure 13), but detected unstable bits vary in position. On average, the amount of stable bits over all calibration points is reduced by 8% compared to the reference temperature. Although no voltage variation can be conducted due to the lack of direct access to the power supply, it can be expected that constant voltage drops, which change the operation point, can also be compensated by this approach. Temporary fluctuations in the power supply can be seen as additional noise and eliminated by increasing the deadzone.  
In summary, the EoA-PUF--when calibrated only at one temperature--behaves as good as or even better than SotA implementations over temperature changes, while it provides a faster readout compared to SotA RO-PUFs. In addition, using some of its  
Figure 17: Temperature-dependent error rate of the presented EoA-PUF implementation, in green, compared to temperature-compensating approaches.  
unique features, like calibration and auto-detection of unstable bits, the temperature dependency can be minimized at distinct temperatures and thus overall flattened over a wide temperature range.
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 5 Discussion
### Roof Stability and Influencing Factors  
The Cantilever Beam Theory analysis highlights that the uncut roof's tensile stress is affected by roof thickness, coal seam dip angle, top-cutting height, and angle. The stress is inversely proportional to the dip angle and roof thickness, but directly proportional to the top-cutting angle. An optimal top-cutting height of 5.0 m and angle of 10\({}^{\circ}\) are optimal for roof collapse and stability. Tensile stress exceeding the roof's strength is crucial for effective top-cutting. Precise control of these parameters is essential to prevent ineffective collapse or excessive deformation, ensuring roadway safety.  
Figure 12: Roadway deformation monitoring results and development effectiveness: (**a**) roadway deformation; (**b**) roof delamination; (**c**) roadway development effectiveness.
# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
###### Abstract
A graph partitioning method with CNN used for earthquake detection is proposed [15]. In this method, they did not use the actual GNN method but only classic graph theory was used. Recently, a study by [1] used GNN with CNN for seismic event classification from multiple stations but they did not use any spatial information or meta-information about stations. Therefore, in this study, we propose a large-scale deep learning model batch normalization graph convolutional neural network (BNGCNN). The CNN part of the model extracts useful and relevant features from seismic raw waveform data collected from multiple stations and GNN processes spatial information and meta-information about multiple base stations. Batch normalization in CNN improves learning and reduces training time [13; 14]. In this way, the proposed model effectively process seismic data obtained from multiple networks and can predict earthquake efficiently and correctly. Our contributions to this study are summarized as follows:  
* We propose a deep learning-based model BNGCNN model for the early prediction of an earthquake. * For experiments, we use a seismological dataset having 1477 events collected from 187 stations. Event waveform data with location information have been collected from multiple seismic stations instead of a single station. * The performance of our model has been systematically analyzed by fine-tuning its several hyper-parameters. * We chose the model proposed in [18] as a baseline model to compare the results obtained from the proposed model. Results show the superiority of our model. The rest of the paper is organized as follows: In Section 2, we discuss related work including seismic data, convolutional networks, and graph networks. Section 3 discusses the architecture of the proposed model and its parameters. A brief introduction of the seismic dataset, challenges, and statistics are given in Section 4. Section 5 includes the experimental results of the proposed model and the baseline models. Conclusion and future work are given in Section 6.