esa-sceva/satcom-chunk-collection huggingface dataset:
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### 2d-Gft  
In order to obtain high separability, the Graph Fourier Transform (GFT) of ECG signals is investigated from the graph spectrum domain. 2D-GFT helps enlarge the nuances among different classes. The impulse amplitude of the eigenvalue spectrum of the graph signal reflects the information of different components of the graph signal. The impulse amplitude of the eigenvalue is inversely proportional to the maximum value of the orthogonal eigenvector, that is, there are different degrees of amplification effects. With the increase of vertex number for graph signal, the maximum of orthonormal eigenvectors of Laplacian matrix will become smaller gradually. Effect of magnifying the difference should be more obvious [30].  
The GFT matrix parameter is set to 64. Figure 6 is the whole mesh figure of four types (N, S, V & F beats) in ECG recordings. Figure 7 is top view of part of Figure 6.  
Figure 6: Mesh plots of 4 types of ECG segments after 2D-GFT. (**a**) N beat (No. 100); (**b**) S beat (No. 232); (**c**) V beat (No. 208); (**d**) F beat (No. 213).  
As shown in Figure 6, there is also the symmetrical characteristic in 2D-GFT results. In Figure 7, graphics change greatly with the transformation of types. In the enumerated range, the eigenvalue spectrum of Class V is much more complete than that of Class F in Figure 7. That means the values in Class V are relatively concentrated, Class S and Class N in the middle, then Class F the most decentralized. GFT helps expand the gap among all types.  
GFT graph spectra distributions for the bispectrum of four types in ECG records are obviously different in the same region, the features can be extracted according to this characteristic.
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 4 Dynamic Model of Balloon-Borne UAVs
### Dynamic Stability  
The weight of the aircraft used is 90.0 kg and the wing area is 14.5 m\({}^{2}\). Combined with the aircraft aerodynamic data in \(Ma=0.064\) and \(H=10,000\) m, matrices A and B in Equation (1) are calculated, as shown in Equations (2) and (3). Following matrix A and B, the corresponding characteristic roots of Equation (1) are shown in Table 2. We can see that there are two pairs of complex roots, including \(-3.6256\pm 2.7985i\) and \(-0.0405\pm 0.3807i\). The first pair, \(-3.6256\pm 2.7985i\), corresponds to short-term motions, which includes the oscillation mode in the short-term and fast attenuation. The damping ratio is 0.7916 and the natural frequency is 4.58. The second pair, \(-0.0405\pm 0.3807i\), corresponds to the phugoid mode with slow attenuation. The damping ratio is 0.1058, and the natural frequency is 0.3828. Overall, the system is dynamically stable.  
\[\mathbf{A}_{lon}=\left[\begin{array}{cccc}-0.0476&6.9404&0&-9.8000\\ -0.0623&-2.6351&1.0000&0\\ -0.0864&-8.6787&-4.6495&0\\ 0&0&1.0000&0\end{array}\right] \tag{2}\]  
\[\mathbf{B}_{lon}=\left[\begin{array}{cccc}-0.0615&0\\ -0.0852&0\\ -7.0362&0\\ 0&0\end{array}\right] \tag{3}\]
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology
### GNSS Positioning Solution  
The system's data processing engine runs in the Cloud on AWS lambda which provides serverless computing resources anytime and anywhere. GNSS data processing is undertaken with RTKLIB (version demo5 b34i), an open-source toolkit for both real-time kinematic (RTK) and PPK solutions [17; 31]. Both RTK and PPK are relative positioning techniques accurately processing GNSS data from a site together with GNSS data from a nearby GNSS base station by reducing atmospheric error and eliminating receiver and satellite clock error. Another advantage of the PPK mode in RTKLIB is its ability to apply corrections both forwards and backwards in time, which helps to detect and correct anomalies such as cycle slips, thereby improving overall accuracy. The system is designed for configurations where two or more devices are deployed, with a baseline distance of less than 2 km between devices. The main RTKLIB parameter settings used are listed in Table 2.  
Figure 4: Pontoon tilting angle variations.  
Figure 5: Schematic of pontoon tilting impact on height.
## 2 Design Concepts
### Design Considerations for Antenna Components
#### 2.1.1 Ground Structure  
Due to its capacity for providing high gain with stable broadside radiation across a wide operating frequency band, a cavity-backed configuration was adopted for this design [13]. With a cavity-backed antenna, there is current on the ground cavity sidewalls.  
Figure 1: Geometries of cavity-backed radiating open prism antennas: (**a**) With an octagonal ground cavity; (**b**) With a conventional square ground cavity. (— conductive hollow open prism with no bottom face), -- supporting dielectric substrate, -- feedlines, -- conductive ground cavity).  
This can potentially extend the aperture field beyond the physical aperture of the antenna, consequently resulting in a bigger effective aperture area and a higher aperture efficiency than those of an antenna with a planar ground plane [24].  
A basic ground cavity is formed by adding vertical sidewalls on all the edges of a conventional planar ground plane. Therefore, the shape of a ground cavity is dictated by that of the horizontal base of the cavity. One of the most frequently used planar ground planes is square in shape, and as a result, ground cavities with square [10; 11] aperture openings are the most common. As shown in [16], compared to a square ground plane with sharp corners, a corner truncated planar ground plane allows lower cross-polarization. Since low cross-polarization is desirable for the applications of interest, a corner truncated ground plane cavity was adopted in this work.
# Environmental Effects on Normalized Gross Primary Productivity in Beech and Norway Spruce Forests
## 3 Results
### Effect of Meteorological Conditions on GPP
#### 3.2.1 GPP\({}_{\text{norm}}\) Prediction through Stepwise Multi-Linear Regression (SMLR)
Furthermore, at the beech forest site in Stitna, statistically significant (\(p<0.01\)) interactive terms such as T\({}_{\text{s}}\):SVWC, Tair:SVWC, Tair:PAR, CNI:PAR, VPD:T\({}_{\text{s}}\), and VPD:SVWC were all found to have effects on GPPnorm values.
# Stressing State Analysis of SRC Column with Modeling Test and Finite Element Model Data
## 2 Modeling Test/Simulation Data
### Modeling Internal Energy and Element Strain Energy  
This paper characterizes the evolution of the stressing state of the SRC column finite element model (EFM) based on the internal energy (\(IE\)) input to the whole structure and concrete, stirrups and vertical bars during the loading process, as shown in Equation (3):  
\[IE_{j,\text{norm}}=\frac{IE_{j}^{\text{c}}+IE_{j}^{\text{v}}+IE_{j}^{\text{s} }}{Max(IE_{j}^{\text{c}}+IE_{j}^{\text{v}}+IE_{j}^{\text{s}})} \tag{3}\]  
\[\Delta IE_{j,\text{norm}}=\frac{(IE_{j+1}-IE_{j})}{Max(IE_{j+1}-IE_{j})} \tag{4}\]  
\[\Delta^{2}IE_{j,\text{norm}}=\frac{(IE_{j+1}-2 IE_{j}+IE_{j-1})}{Max(IE_{j+1}-2 IE_{j}+IE_{j-1})} \tag{5}\]_IE_ represents the internal energy of each part output by the post-processing module of the SRC column's finite element model. The superscripts \(c\), \(v\), and \(s\) represent the concrete, vertical steel bar, and spiral steel bar of the SRC column, respectively. The subscript \(j\) represents the load step in the process of FEM analysis. As shown in Equations (4) and (5), in order to further reveal the mutation law in the process of stressing state evolution of SRC columns, the first-order characteristic parameters \(\Delta IE_{j,\text{norm}}\) and second-order characteristic parameters \(\Delta^{2}IE_{j,\text{norm}}\) are also defined in this paper to characterize the stressing state evolution of SRC columns.  
In this paper, 10 elements in the concrete part of the SRC column finite element model, 14 elements in the high-strength stirrup part, and 12 elements in the longitudinal steel bar are selected to establish the stressing state sub-modes. Based on the element strain energy output by the ABAQUS post-processing module, a sub-mode of the stressing state is established as shown in Equation (6):  
\[\mathbf{S}_{j}^{c}=[ESE_{1}^{c},ESE_{2}^{c},\ldots,ESE_{10}^{c}],\ \mathbf{S}_{j}^{v}=[ESE_{1}^{v},ESE_{2}^{v},\ldots,ESE_{12}^{v}],\ \mathbf{S}_{j}^{s}=[ESE_{1}^{s},ESE_{2}^{s},\ldots,ESE_{14}^{s}] \tag{6}\]  
In order to eliminate the influence of the numerical value of the element strain energy, the evolution law of the stressing state of the structure can be more directly characterized. Normalize the element strain energy (\(ESE\)) of the sub-mode to obtain the normalized stressing state sub-mode. The expression of normalized \(ESE\) is shown in Equation (7):  
\[ESE_{j,\text{norm}}=\frac{ESE_{j}}{ESE_{M}} \tag{7}\]  
The subscript \(M\) represents the maximum value of \(ESE\) when the SRC column finite element model is loaded from 0 to the ultimate bearing capacity in axial loading. By further modeling the element strain energy of the SRC column, the corresponding characteristic parameters can be obtained.  
Figure 1: Process of SRC column’s stressing state analysis.
## 2 Materials and Methods
### Characterization  
#### 2.3.1 Properties of the HCLPPs  
All forms of the prepared hyper-crosslinked materials were characterized using the following methods: Textural characteristics, including the specific surface area, pore volume, and pore diameter distribution, were determined based on nitrogen adsorption-desorption isotherms measured at 77 K using a commercial automated volumetric gas adsorption analyzer ASAP 2050 (Micromerities, Norcross, GA, USA). Before the measurements, the samples were degassed under deep vacuum at 373 K for 4 h. The morphology and elemental composition were evaluated using a scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) spectrometer (Quantax, Bruker, Bremen, Germany) in the range of 5-30 kV. Using this technique, the films were analyzed at an accelerating voltage of 15 kV. Thermogravimetric analysis (TGA) of samples was performed using a TG 209 F1 Libra equipment (NETZSCH, Selb, Germany) in the temperature range from 25 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C in an inert argon atmosphere with a flow rate of 50 mL\(\cdot\)min\({}^{-1}\). The samples were heated from ambient temperature to 1000 \({}^{\circ}\)C at a heating rate of 4 \({}^{\circ}\)C min\({}^{-1}\), the weight of each sample was approximately 7 mg.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
###### Abstract  
Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the \"Land Cover Aerial Imagery\" (LICAI) dataset for semantic segmentation. The study focuses on the Francicorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture.  
land cover mapping; semantic segmentation; deep learning; satellite imagery; pre-trained backbone +
Footnote †: journal: Applied sciences  
Article
## 4 Discussion  
### Chlorella Vulgaris Extraction Yield  
Findings by previous researchers indicated that extraction yield depends on solvent the solvent used, pH, extraction time and temperature as well as on the chemical compositions of the sample [34; 35; 36]. A study by [22] has reported significant differences in extraction yield in cyanophytic algae reared using different growth media. The difference in _C. vulgaris_ extraction yield in this study implies that the different cultivation media used could have affected the extraction yield. The lower extraction yield recorded in ANM may be due to its high carotenoids contents, as it was reported that, carotenoids are lipophilic compounds [24], making their extraction difficult with polar solvents such as ethanol [37]. As compared to results of the present study, [38] observed almost similar yield (11.8 \(\pm\) 0.000%) in mangosteen (_Garcinia mangostana_ Linn), although a recent study by [22] detected higher yield (21.63 \(\pm\) 0.04%) in extracts from Spirulina (_Arthrospira fusiformis_) using ethanol as a solvent. These differences in extraction yield could be explained by variations in the chemical composition of the sample.
## 4 Results and Discussion
### Correlation Analysis  
After the initial processing of the data, correlation analysis of all input features is needed to improve the accuracy of the model. The purpose of this step is to determine if there is an excessive correlation between the features [57; 58; 59]. If the correlation value between two features is too high, it indicates that the relationship between them is strong, i.e., when one feature changes, the other feature may also change. This situation can affect the accuracy of the graduate student admission prediction model, as highly correlated features can introduce redundant information into the data set, which can degrade the performance of the model [60; 61; 62].  
By analyzing the correlation matrix (Figure 1), we found that most of the correlation values between features are below 0.5, indicating weak correlations between them. However, there is an exception where the correlation value between the university rating and SOP is 0.5, showing a moderate level of correlation. Nevertheless, this correlation value remains within an acceptable range and will not have a significant impact on model training. The results of this correlation analysis indicate that the selected input features generally meet the requirements for model training and effectively mitigate the impact of feature relationships on the prediction model's accuracy, providing a solid foundation for constructing an accurate and reliable graduate admissions prediction model.
# The Cerebral Arterial Wall in the Development and Growth of Intracranial Aneurysms
## 3 Cerebral Arterial Wall Anatomy  
The cerebral arterial wall represents the host of IAs and is the substrate of their birth and development. The wall is made up of several interconnected layers that have unique characteristics with different compositions and specific functions, such as interacting with the perivascular environment.  
The inner layer of cerebral arteries is the tunica intima, which is composed of a single layer of endothelial cells directly connected to the internal elastic lamina (IEL). The next layer is the tunica media, which is predominantly composed of smooth muscle cells with some elastin and collagen fibers. The outermost layer is the tunica adventitia, which is mostly made of collagen fibers, fibroblasts, and associated cells, such as perivascular nerves, particularly in large and small pial arteries [16]. Moreover, in comparison to the systemic arteries, cerebral ones have fewer elastic fibers (muscular type) and a thinner adventitia, but they have a well-developed IEL. They do not have an external elastic lamina nor a vasa vasorum [16]. The perivascular environment of the arterial wall is made up of blood cells flowing within its lumen and cerebrospinal fluid together with the glymphatic system along the subarachnoid space. The arterial wall is in continuous connection with these elements through the modulators of the arterial wall activity and reactions, different signaling pathways, and carriers of systemic molecules, inflammatory proteins, and cytokines [17].
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 3 Results and Discussion
### STEPDISC-PCA-BP Model Construction and Performance
The calculation results are shown in Table 9.  
In a multi-level classification task, \(Micro-F1\) can calculate the accuracy of each level by taking all levels into account at once and is often used to quantitatively assess the overall accuracy of the model prediction, with values ranging from 0 to 1, the closer to 1, the better the model performance. Equations (13) and (14) were used to average all levels of each of the three models to obtain values of \(\overline{TP}\), \(\overline{TN}\), \(\overline{FP}\), \(\overline{FN}\), and then find the values of \(Micro-P\) and \(Micro-R\), and finally substitute into Equation (15) to calculate the values of \(Micro-F1\). \[Micro-P=\frac{\overline{TP}}{\overline{TP}+\overline{FP}} \tag{13}\]  
\[Micro-R=\frac{\overline{TP}}{\overline{TP}+\overline{FN}} \tag{14}\]  
\[Micro-F1=\frac{2\times(Micro-P)\times(Micro-R)}{(Micro-P)+(Micro-R)} \tag{15}\]  
After calculation, the \(Micro-F1\) value of STEPDISC-PCA-BP model was 0.855, and the overall correct rate was 85.5%. The STEPDISC-PCA-BP model has a simple structure, high prediction accuracy, strong generalization ability, and robust network, which is feasible for the prediction of future air quality levels.
## 4 Results
### Static Results
#### 4.1.2 Number of Satellites  
In order to measure the number of satellites that could be seen for each of the devices, measurements were carried out as mentioned in the testing procedure. As seen in Figure 6, the number of satellites that the device could see with linear technology obtained a higher number of satellites in all cases.  
Concerning the circular polarization case, the average number of satellites on those closer to the window was 4.2, which is very close to the minimum number of satellites required to obtain the geolocation. A value of 4 is needed to obtain the geolocation. The average satellite value significantly increased as soon as the device was brought within the structure, but it never reached the levels attained by the device using linear polarization.  
In the case of the linear polarization device (Figure 6), it followed the same tendency as the circular polarization device to have a higher average value in positions farther away from the window. Additionally, it should be taken into account that this was 6.5, a mean value significantly higher than 4, in the position where it had the worst average, demonstrating a higher level of robustness.
###### Abstract  
Traditional underwater acoustic reconnaissance technologies are limited in directly detecting underwater acoustic communication signals. This paper proposes a dual-feature ResNet-Transformer model with two innovative breakthroughs: (1) A dual-modal fusion architecture of ResNet and Transformer is constructed using residual connections to alleviate gradient degradation in deep networks and combining multi-head self-attention to enhance long-distance dependency modeling. (2) The time-frequency representation obtained from the smooth pseudo-Wigner-Ville distribution is used as the first input branch, and higher-order statistics are introduced as the second input branch to enhance phase feature extraction and cope with channel interference. Experiments on the Danjiangkou measured dataset show that the model improves the accuracy by 6.67% compared with the existing Convolutional Neural Network (CNN)-Transformer model in long-distance ranges, providing an efficient solution for modulation recognition in complex underwater acoustic environments.  
Keywords:automatic modulation recognition (AMR); underwater acoustic communication; transformer network; deep learning
## 5 Deployment and Integrability
### Cloud Dashboard  
A cloud-based dashboard is another option for institutions that prefer a fully managed cloud-accessible solution. A micro-service would run in a virtual private cloud (VPC) behind a DICOM-web gateway that enforces automatic de-identification. All patient identifiers, birth dates, and free-text (burned into the image or in DICOM headers) would be stripped in transit, ensuring that only anonymized data reaches the cloud, while reverse-mapping to the original study would be retained on-premises.  
This has the clear advantage of cloud accessibility from outside the hospital/clinic network, allowing for remote or mobile access (for instance, for consultation with senior physicians/radiologists), and dashboard hooks (push notifications to mobile devices when a new positive case is predicted), with the drawback of higher costs (cloud computation, storage, and anonymization) and potential data leaks.
## 4 Conclusions  
In summary, it is found that the dielectric breakdown voltage, color, 2-FAL, CO, CO\({}_{2}\), and C\({}_{2}\)H\({}_{2}\) under study could be represented by the normal distribution. The Weibull distribution is suitable for representing the IFT, acidity, water content, H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), and C\({}_{2}\)H\({}_{4}\). It is found that SDM can be utilized to estimate the HI of transformers while using individual condition parameters data. The predicted accuracy is subject to the obtainability of the data at various ages. Predominantly, the trends of the predicted HI are close to the computed HI. The hypothesis testing from the results using Chi-square shows that the \(X^{2}\) value of HI data is 12.94, where it falls outside the rejection area at 0.05 significance level. The overall average percentages of absolute errors in training and validation regions are 0.65% and 2.17%, respectively. The predicted HI of transformers based on SDM yields accuracy of about 97.83%.  
The research study was successfully completed with contributions from all authors. The main research idea, simulation works and manuscript preparation were contributed by A.M.S., N.A. contributed to the manuscript preparation and research idea. [PERSON], [PERSON], [PERSON] and M.S.Y. assisted with finalizing the research work and manuscript. M.A.T. provided several  
Figure 9: The absolute error percentage between the computed and predicted health index.  
suggestions from an industrial perspectives. All authors have read and agreed to the published version of the manuscript.  
The research fund was funded by the Ministry of Education and Universiti Putra Malaysia for the funding provided for this study under PUTRA Bermpak (GPB/2017/9570300) and FRGS scheme FRGS/1/2019/TK07/UPM/02/3 (03-01-19-2071 FR).  
Not applicable.  
The authors would like to thank TNB Research Sdn. Bhd. for the technical support.  
The authors declare no conflict of interest.