esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Image Segmentation on 2D Echocardiogram
## 2 Related Work
### Image Segmentation in Cardiology  
In cardiology, image segmentation is crucial for analyzing images from various modalities, including echocardiography, cardiac Magnetic Resonance Imaging (MRI), and cardiac Computed Tomography (CT) [9]. For instance, deep learning automates the segmentation of key structures such as the left ventricle, left atrium, and aortic valve in echocardiography [14]. Manual segmentation in echocardiography is labor-intensive and prone to inter-observer variability, making automation particularly valuable. However, challenges such as poor image quality, speckle noise, and edge dropout complicate segmentation in echocardiography [14].  
Extensive studies have utilized CNNs and U-Net architectures for segmenting key structures like the left and right ventricles, myocardium, and scar tissue from cardiac MRI images, yielding state-of-the-art performance. For example, [PERSON] et al. [12] employed a deep CNN for real-time segmentation in minimally invasive surgery, showcasing the versatility of CNNs in medical imaging tasks, including MRI segmentation. Similarly, [PERSON] et al. [15] applied CNN-based unsupervised techniques to cardiac MRI for multi-modal myocardial segmentation, achieving a Dice coefficient of 0.89. [PERSON] et al. [16] focused on myocardial segmentation in cardiac BOLD MRI using CNN-based unsupervised models, with their method demonstrating Dice scores as high as 0.91. These examples underscore CNNs' effectiveness in extracting features from MRI images and accurately delineating anatomical structures, even in challenging conditions.  
U-Net, a specific CNN architecture, has also played a pivotal role in cardiac MRI segmentation due to its encoder-decoder structure, which preserves spatial information while facilitating pixel-wise predictions. U-Net's skip connections ensure the retention of high-resolution spatial features during upsampling, making it particularly suitable for segmenting complex anatomical structures like the myocardium. [PERSON] et al. [10] demonstrated U-Net's application in 3D image-guided surgery for cardiac MRI, while [PERSON] et al. [13] employed a U-Net architecture for real-time 3D ultrasound segmentation, further illustrating its adaptability across imaging modalities. Moreover, public datasets such as ACDC have accelerated advancements in this domain by providing a benchmark for model performance, fostering further improvements in cardiac segmentation using both CNN and U-Net-based approaches [9; 14].  
Moreover, deep learning is also employed to segment cardiac structures and coronary arteries from cardiac CT images. Accurate segmentation is vital for quantifying stenosis, assessing plaque burden, and guiding interventions like angioplasty or stenting [14].
# The Chromosome Number and rDNA Loci Evolution in _Onobrychis_ (Fabaceae)
## 2 Results
### Inferences of the Patterns of Chromosome Number Evolution  
The basic chromosome number \(x=8\) was recovered as the ancestral state for all studied _Onobrychis_ species and as the ancestral state for all distinguished clades and subclades (Figure 3). Although in subclade Ib most of the species had \(x=8\), three events of descending dysploidy (from \(x=8\) to \(x=7\)) were inferred (in _O. ptolemiaa_, _O. hpargyrea_ and _O. grandis_ evolutionary lineages). In subclade Ia, only three diploid species had \(x=8\) (_O. crista-galli_, _O. persica_, _O. iberica_), while the remaining species had \(x=7\), and only one event of descending dysploidy was retrieved. Three events of chromosome number duplication were reconstructed: (i) in the evolutionary lineage of _O. subacaulis_ (clade II); (ii) for common ancestor of _O. arenaria_, _O. transcaucasia_ and _O. ibbersteinii_ and one accession of _O. vicifolia_ and (iii) for common ancestor of _O. altissima_, _O. cyri_, _O. internis_ and the second accession of _O. vicifolia_ (Figure 3 and Figure S1).  
Figure 2: Phylogenetic relationships among analysed _Onobrychis_ species based on the nrITS data set. Bootstrap support values are shown above the branches. The tetraploid species are shown in bold. The tree was rooted with _Hedysarum candidissimum_.
## 5 Conclusions  
In this study, the land-use change and the spatial and temporal evolution pattern of the ESV before and after the implementation of the GGP in Shaanxi Province were evaluated using land-use data from Shaanxi Province from 1990, 2000, 2010, and 2020. The following are the primary conclusions:  
Farmland, forestland, and grassland are the three primary land-use types in Shaanxi Province. Between 1990 and 2000, the three land-use types--farmland, forestland, and grassland--underwent massive changes in Shaanxi Province due to the GGP. The area of farmland decreased, with most of it being converted to forestland or grassland, while the areas of forestland and grassland gradually rose.  
The ESV in Shaanxi Province was USD 3802.82 billion in 1990, USD 3814.90 billion in 2000, USD 3836.20 billion in 2010, and USD 3806.50 billion in 2020, with a pattern of initial growth followed by decreases. The value provided by hydrological regulation is the highest among the ESVs, whereas the value provided by water supply is the lowest. The high-value areas are mainly distributed in the south, and the land-types are mainly forestland and grassland. The low-value areas are mainly distributed in the middle, and the land-type is mainly built-up land.  
The GGP has enhanced the ESVs of forestland and grassland, but the continual development of built-up land has limited the extent of other land-types, resulting in a decrease in the total ESV of Shaanxi Province. In the future, existing policies should be appropriately adjusted, forestland and grassland preservation should be enhanced, and essential farmland development should be strengthened. Polices should improve the efficiency of land use, rationalize the planning of urban and rural built-up land, and enhance the region's sustainable development.  
Conceptualization, B.Z.; Data curation, K.L.; Funding acquisition, B.Z.; Project administration, B.Z.; Resources, K.L.; Software, K.L.; Writing--original draft, K.L.; Writing--review and editing, B.Z. and K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 41701025), the Postgraduate Student Scientific Research Innovation Project of the Jiangsu Normal University of China (Grant No. 2021 XKT0057), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 1 Introduction
### Organization  
The remainder of this study is organized as follows: Section 2 outlines the frame structure and details of the MIMO receiver with one-bit ADCs. Section 3 introduces channel and the sample-based likelihood probabilities. In addition, an optimization problem is designed to minimize the MSE between the true- and proposed likelihood probabilities. Section 4 describes the proposed solution to the optimization problem. Section 5 presents simulation results and demonstrates the effectiveness of the proposed solution. Finally, Section 6 concludes the study and discusses the main findings.
## 2 Antenna Design  
This section describes the antenna design and its radiation modes. The antenna's design guidelines are given considering the variations in its surface current density and the electric field. The antenna optimization is also considered to create a realistic design for on-body applications.
# The Generalized Mohr-Coulomb Failure Criterion
## 1 Introduction
Thus, there are practical needs to establish a new failure criterion for the analysis of strength problems and deformation problems in geotechnical engineering. The M-C failure criterion is widely used in geotechnical engineering because of its practicality, simple expression, and strength parameters that have physical significance and are easy to obtain using in situ or laboratory tests. However, it neglects the effect of \(\sigma_{2}\). In fact, the influence of \(\sigma_{2}\) on the failure behavior of geomaterials is important during engineering construction where high stress might be created [28]. In this study, we generalize the M-C criterion by simply regarding its parameters of cohesion \(c\) and friction factor \(f\) as the functions of the coefficient \(b\) of the intermediate principal stress, written as \(c(b)\) and \(f(b)\), respectively. Functions \(c(b)\) and \(f(b)\) can be obtained simply by the Lagrange interpolation or any other data fitting techniques to a series of \(c_{i}\) and \(f_{i}\) measured under different \(b_{i}\). In this way, the effect of \(\sigma_{2}\) can be naturally reflected in the failure criterion. In the simplest case, conventional triaxial tests are enough to establish a true triaxial failure criterion by linearly interpolating \((c_{0},f_{0})\) measured at \(b=0\) (triaxial compression) and \((c_{1},f_{1})\) at \(b=1\) (triaxial extension), which will be expounded upon shortly. Like its classical version, the generalized M-C criterion has a non-smooth failure surface. However, we believe this is just the nature of geomaterials, and prove from true triaxial tests and the characteristic theory of stress tensors that even for isotropic geomaterial, the yield locus should be non-smooth per se in the state of \(\sigma_{1}\!\geq\!\sigma_{2}=\sigma_{3}\) (\(b=0\)) or \(\sigma_{1}=\sigma_{2}\!\geq\!\sigma_{3}\) (\(b=1\)). The authors are conscious that this is not in agreement with the \"consensus\" and are prepared for being questioned. The existing literature, such as [PERSON] and [PERSON] [29] and [PERSON] [30], claims without rigorous proof that the failure surfaces of isotropic geomaterials are smooth. By comparing the proposed failure criterion with those celebrated failure criteria on the experimental data in the literature, it is demonstrated that the predictions by the proposed criterion agree best with experimental results.
## 2 Materials and Methods
### Data Extraction and Synthesis  
A risk of bias assessment was conducted using a custom checklist informed by the PRISMA 2020 [12] guidelines to ensure the credibility of the findings. The assessment was applied to 60 selected studies, focusing on sample size adequacy, clarity of statistical or machine learning analysis, reproducibility of methods, and transparency of conflict of interest. Each domain was scored using a predefined three-point scale: \(2=\) low risk (methodologically sound, no concerns), \(1=\) moderate risk (some limitations present, but unlikely to alter findings), and \(0=\) high risk (serious methodological concerns or missing data). Total scores ranged from 0 to 8 across all domains. Studies scoring 7 or higher were classified as having a low risk of bias, while those scoring below 7 were deemed to have a moderate risk of bias. The scoring breakdown and detailed justifications for each study are provided in Supplementary Table S1.  
Among the 60 studies evaluated, 55 were categorized as low risk, and 5 as moderate risk, primarily due to limitations in reproducibility or insufficient transparency regarding data or code availability. Despite these moderate concerns, no studies were identified as having a high risk of bias. Therefore, all studies were deemed methodologically sound and were retained for inclusion in this review.  
Data extraction was then independently performed by the authors to minimize errors and ensure consistency. The extraction focused on key aspects of each study, including research objectives, methodological approaches, and reported outcomes. Following extraction, and in order to address the research questions, the data were synthesized according to a set of categories. These included application focus, key trends, dataset, ML techniques, evaluation metrics, key findings, challenges addressed, and suggested future directions.
## 1 Introduction
### MODIS-BIRD Data Comparison in a Fire Scene in Siberia Close to Lake Baikal  
For a fire scene in Siberia close to Lake Baikal, the quantitative fire parameters were retrieved from the BIRD and MODIS data in [8] using the same methodology in order to provide directly comparable results. The methodology included the following steps:  
* Consolidation of contiguous hot pixels in hot clusters;
* Retrieval of the effective fire temperature, effective fire area and FRP for the hot clusters;
* Estimation of the TIR background temperature variability (background clutter) and of the confidence intervals for the effective fire temperature, effective fire area, FRP and pronounced fire fronts;
* Estimation of the front length and radiative intensity (ratio of the FRP to the front length, characterizing the fire front strength).  
Figure 1 shows an area west of Lake Baikal, Russia, observed nearly simultaneously, i.e., within a 30-minute interval, by MODIS/\"Terra\" and BIRD on 16 July 2003. The comparison clearly shows that only the BIRD data allow an estimation of fire front attributes relevant for fire managers. MODIS MIR and TIR data have a 1 km ground resolution at the nadir. They do not allow estimating fire front attributes, except for the FRP. This comparison and the figures obtained from BIRD data only, presented in Table 4, provide a useful metric by which the enhanced performance of BIRD in terms of fire detection and characterization can be judged [9].  
Figure 1: Fragments of forest fire images at Baikal, obtained by MODIS and BIRD on 16 July 2003, with a time interval of 25 min. Images show the MIR image from each sensor, and detected hot clusters are color coded according to their Fire Radiative Power (FRP) in megawatts (MW). The arrows in the MODIS image fragment show probable false alarms (because real fires could hardly cool down sufficiently in the short time interval between MODIS and BIRD that the data take not to be seen as hot spots in the higher-resolution BIRD fire scene fragment).  
MODIS data allow the detection of larger fire fronts or only separate hot spots where one cannot recognize clear fire fronts. In contrast, the higher-spatial resolution BIRD data make it possible to recognize larger fire fronts and to estimate their attributes: FRP, fire front length, fire front strength and fire front effective depth.
## 4 Experiment and Verification  
The validity of the method is illustrated in Section 3 using data such as the gyroscope current under failure and the full-life temperature of a solar sail. In this research, we focus on the different types of variations in component health characteristic parameters. To provide a more comprehensive illustration, we present a complete case study using operational data from a real satellite's momentum wheels.
# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
###### Abstract  
The 3D point cloud reconstruction from photos taken by an unmanned aerial vehicle (UAV) is a promising tool for monitoring and managing risks of cut-slopes. However, surface changes on cut-slopes are likely to be hidden by seasonal vegetation variations on the cut-slopes. This paper proposes a vegetation removal method for 3D reconstructed point clouds using (1) a 2D image segmentation deep learning model and (2) projection matrices available from photogrammetry. For a given point cloud, each 3D point of it is reprojected into the image coordinates by the projection matrices to determine if it belongs to vegetation or not using the 2D image segmentation model. The 3D points belonging to vegetation in the 2D images are deleted from the point cloud. The effort to build a 2D image segmentation model was significantly reduced by using U-Net with the dataset prepared by the colour index method complemented by manual trimming. The proposed method was applied to a cut-slope in Doam Dam in South Korea, and showed that vegetation from the two point clouds of the cut-slope at winter and summer was removed successfully. The M3C2 distance between the two vegetation-removed point clouds showed a feasibility of the proposed method as a tool to reveal actual change of cut-slopes without the effect of vegetation.  
cut-slope assessment; image segmentation; colour index; U-Net; vegetation removal Article  
Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net. _Applied_  
[[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
app12010395  
Academic Editor: [PERSON] Received: 18 October 2021  
Accepted: 28 December 2021  
Submitted: 31 December 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Coyright:** 2021 by the authors. Licence MDP, 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/))).
## 4 Experimental Results
### Manual Set-Point Adjustment  
With the aim to test the stability of the emulator, the voltage response at different set-points has been analyzed. Figure 14 shows the manual change of current set-point from 65 to \(-\)65 A, with the emulator controlling the same set-point voltage at 300 V. It must be noticed that the slope of the current increment by the charger is 10 A/s, which is within the limits of the CHAdeMO standard [19]. Furthermore, the PI controller implemented in  
Figure 12: General overview of the testbench used for the power tests done with the proposed EVE. On the left, a 50 kW EVE together the EBox gateway; On the right the HUT system built up with a 50 kW V2G charger; and the power transformer in the centre.  
the emulator has a reduced velocity error during the transition of this current variation in the HUT.  
Figure 13: Simplified operation sequence diagram of the complete EVE test procedure.  
Furthermore, it is possible to verify the behaviour of the system for grid management purposes. For example, thanks to this test, it has been verified that this charger could be used to perform frequency grid operation for current set-point changes up to 50 A [26]. Furthermore, the smooth transition between the two set-points, indicates that it is possible to use this EVSE with V2G capability for operations such as peak shaving or management of renewable energy surplus.  
The variation of the set-point voltage from 300 V to 200 V is shown in Figure 15. These kinds of battery voltage fluctuations are abnormal in EV batteries, but they allow the user to know the stability and response of the charger to be tested. In this case, the emulator keeps the same set-point power, which produces an increment in the demanded current to the V2G charger. Depending on the time response of the chargers, maximum or minimum power peaks can appear during the voltage transition, which will be larger if the voltage transition is more abrupt.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 4 Results
### Analysis of Geographical-Optimal-Similarity Model Prediction Results
#### 4.2.2 Model Performance Evaluation  
The GOS model was evaluated and compared with other models. The evaluation metrics presented in Table 4 indicate that, compared to machine learning methods such as RF, LR, and BRFC, the BCS and GOS models based on geographical similarity exhibit significantly lower RMSE and mean absolute error (MSE) values, highlighting their superior predictive performance. This improvement suggests that incorporating geographical similarity effectively enhances the model's ability to capture spatial patterns and reduce prediction errors. Particularly, the GOS model demonstrates further optimisation over BCS, reinforcing its robustness and accuracy in landslide susceptibility assessment. The results (Figure 7) reveal that by setting the optimal threshold parameter, the AUC value of the GOS model reached 0.846, which was 9.64% higher than the BCS. This outcome showed that the GOS model effectively improved the landslide susceptibility prediction accuracy, outperforming the BCS model that used all sample data. Meanwhile, compared to machine learning methods like RF, LR, and RBFC, the GOS model's AUC value improved by 4.37%, 2.81%, and 8.92%, respectively, suggesting that the GOS model has stronger classification ability and prediction performance in landslide susceptibility assessment. In particular, using similarity evaluation and optimal sample selection, the GOS model reduces the impact of data noise and outliers, thus performing better in terms of model accuracy and stability. In addition, compared to conventional machine learning methods, the GOS model combines the unique advantages of geographic similarity, revealing higher applicability and robustness in diverse terrains and complicated geological environments.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **RMSE** & **MAE** \\ \hline RF & 0.56 & 0.43 \\ LR & 0.57 & 0.48 \\ BRFC & 0.58 & 0.49 \\ BCS & 0.12 & 0.09 \\ GOS & 0.10 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of model performance evaluation.  
Figure 6: Optimal similarity threshold.