esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Volatile Compounds of the Flower Petals of N. hybrid
#### 2.2.1 Identification and Concentration of the Volatile Compounds in _N. hybrid_
)** & & & **Formula** & & **Nh-1** & **Nh-2** & **Nh-3** & **Nh-4** \\ \hline \multicolumn{10}{c}{Ketones} \\ \hline
21 & 988 & 975 & 6-Methyl-5-heptene- & C\({}_{8}\)H\({}_{14}\)O & Citrus & nd & nd & 0.12 b  & 0.19 a  \\
22 & 1070 & 1067 & Acetophenone & C\({}_{8}\)H\({}_{9}\)O & Must, flower, almond & 0.36 b  & 0.29 b  & 0.38 b  & 0.51 a  \\
23 & 1204 & 1206 & (-)-Verbeneo & C\({}_{10}\)H\({}_{14}\)O & Spicy, mint & nd & 0.12 a  & nd & nd \\
24 & 1425 & 1426 & \(\alpha\)-Ionone & C\({}_{13}\)H\({}_{20}\)O & Wood, violet & 0.27 b  & 0.17 c  & 0.12 c  & 0.41 a  \\
25 & 1775 & 1772 & (E)-\(\alpha\)-AtAlantone & C\({}_{15}\)H\({}_{22}\)O & - & 0.51 a  & nd & nd & nd \\
26 & 1882 & 1883 & 2-Heptadecane & C\({}_{17}\)H\({}_{24}\)O & - & 0.71 b  & 3.00 a  & 0.44 b  & 0.22 b  \\ Total & & & & & 1.85 b  & 3.59 a  & 1.06 c  & 1.34 b  \\ \hline \multicolumn{10}{c}{All classes} \\ \hline
27 & 600 & 600 & Hexane & C\({}_{6}\)H\({}_{14}\) & Alkane & nd & nd & nd & 0.79 a  \\
28 & 700 & 700 & Heptane & C\({}_{7}\)H\({}_{16}\) & Alkane & nd & nd & nd & 0.79 a  \\
29 & 1300 & 1300 & Tridecane & C\({}_{13}\)H\({}_{28}\) & Alkane & 0.40 b  & 0.86 a  & 0.31 b  & 0.71 a  \\
30 & 1364 & 1366 & 2-Methylridecane & C\({}_{14}\)H\({}_{30}\) & - & nd & nd & nd & 0.17 a  \\
31 & 1371 & 1373 & 3-Methylridecane & C\({}_{14}\)H\({}_{30}\) & - & nd & nd & nd & 0.16 a  \\
32 & 1378 & 1381 & Farnesame & C\({}_{15}\)H\({}_{32}\) & - & nd & nd & nd & 0.13 a  \\
33 & 1405 & 1400 & Tetradecane & C\({}_{14}\)H\({}_{30}\) & Alkane & 0.49 a  & 0.43 a  & 0.44 a  & nd \\
34 & 1446 & 1450 & 2,6,10, & & & & & & & \\
35 & 1505 & 1500 & Pentadecane & C\({}_{15}\)H\({}_{32}\) & Alkane & 22.43 a  & 23.09 a  & 20.49 a  & 11.15 b  & 1600 & Hexadecane & C\({}_{6}\)H\({}_{34}\) & Alkane & 0.18 b  & nd & 0.21 a  \\
36 & 1706 & 1700 & Heptadecane & C\({}_{17}\)H\({}_{36}\) & Alkane & 2.64 a  & 2.85 a  & 1.41 b  & 0.39 c  \\
37 & 1712 & 1715 & 2-phenyl-Undecane & C\({}_{17}\)H\({}_{28}\) & - & 0.46 b  & 0.67 a  & 0.31 b  & nd \\
39 & 2106 & 2100 & Heneicosane & C\({}_{21}\)H\({}_{44}\) & Alkane & 0.57 a  & nd & 0.25 b  & nd \\ Total & & & & 27.17 a  & 27.91 a  & 14.80 c  \\ \hline \multicolumn{10}{c}{Alkenes} \\ \hline
40 & 1020 & 1198 & D-Limonene & C\({}_{10}\)H\({}_{16}\) & Citrus, mint & nd & 0.11 b  & 0.45 a  \\
41 & 1035 & 1022 & m-Cymene & C\({}_{10}\)H\({}_{14}\) & Solvent, gasoline, & & & nd & 0.11 a  \\
42 & 1392 & 1393 & 7-epi-Sesuithujene & C\({}_{15}\)H\({}_{24}\) & - & 0.88 b  & 0.12 c  & 0.83 b  & 1.26 a  \\
43 & 1411 & 1415 & Ced-8-ene & C\({}_{15}\)H\({}_{24}\) & - & nd & 0.12 a  & 0.11 a  \\
## 2 Related Work
### Enhancing Real Data with Generative Adversarial Networks (GANs)  
With the rapid development of deep learning technologies, generative adversarial networks (GANs) have shown considerable promise in image generation and data augmentation. In the domain of InSAR image processing--especially under data-scar conditions--GANs have been successfully utilized for data augmentation. By leveraging adversarial training between a generator and a discriminator, GANs are capable of producing synthetic images that closely approximate the distribution of real data, thereby compensating for the limited availability of authentic datasets. [PERSON] et al. [39] successfully generated InSAR images resembling real deformation signals using GANs, presenting a novel approach to addressing sample scarcity. As noted by [PERSON] and [PERSON] [29], GANs represent one of the most promising techniques for data augmentation. Without question, GANs hold considerable potential for the generation and enhancement of InSAR imagery, particularly in applications involving surface deformation signals associated with volcanic or seismic activity. By generating samples that closely mirror real observational data, GANs can significantly enhance training performance and model accuracy.  
The realism and physical fidelity of InSAR interferograms generated by GANs involve inherent uncertainties. Compared to physically based data synthesis methods, GANs exhibit limited capability in accurately replicating real-world physical contexts and noise characteristics. Physical models, which incorporate complex processes such as seismic activity and topographic variations, can produce interferograms that maintain both physical consistency and accuracy. In contrast, GANs rely heavily on large volumes of real training data, and their performance often degrades significantly under data-scarce conditions. As a result, the generated interferograms may lack meaningful physical grounding, often leading to reduced output quality. Although GANs are capable of generating visually plausible images through adversarial training between the generator and discriminator [40], their model architectures inherently lack an understanding of physical processes such as seismic deformation. Consequently, the generated images may exhibit discrepancies when compared to actual physical phenomena.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 2 Design of UWB Antenna Unit
### Antenna Structure Design  
As UWB communications evolve and progress, ultra-wideband antennas should not only achieve miniaturization and low profile, but also take into account the simple structure and ease of integration required for processing design, as well as the more stringent requirements on impedance bandwidth. The main indexes of antenna design in this section are: working band covers 3.1-10.6 GHz, meeting VSWR \(<\) 2, realizing omnidirectional radiation and stable radiation characteristics. For the monopole antenna whose radiation patch is round, rectangular, triangular and other regular shapes, the cylinder approximation method can be used to estimate the minimum resonant frequency to meet the target requirements [20; 21], so it is enough to adjust the size of radiation patch and ground appropriately.  
Table 1a shows the model diagram of the UWB monopole antenna unit, where the red part is the rectangular radiating patch embedded with a metamaterial array, and the blue part is the surrounding ground structure. The antenna sample in Figure 1b is a single-sided printed antenna structure on a Teflon circuit board with an overall size of 25.63 mm \(\times\) 21.8 mm \(\times\) 1.524 mm, which is comparable to the size of One yuan coin, and the specific geometric parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Parameters** **L** & **W** & **1** & **w** & **W\({}_{\mathbf{1}}\)** & **gw** & **fw\({}_{\mathbf{1}}\)** & **fw\({}_{\mathbf{2}}\)** & **gl** & **fi\({}_{\mathbf{1}}\)** & **fi\({}_{\mathbf{2}}\)** & **m** \\ \hline Value/mm & 21.8 & 25.63 & 9.94 & 10.1 & 13.32 & 4.81 & 5 & 1.4 & 10.5 & 0.6 & 3.5 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters of the UWB monopole antenna.  
Figure 1: UWB monopole antenna: (**a**) structural diagram; (**b**) sample.
# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 5 Conclusions  
Targeting the composite fault diagnosis of rolling bearings, an improved SE-ResNet diagnosis method was proposed. Upon experimental verification using bearing data, the following conclusions were reached:  
1. An improved EEMD method based on GS-SVR with a window function was used for noise reduction of the original signal. The singular value method was used to filter and reconstruct the decomposed IMF components. The pre-processing made the signal easier to analyze.
2. The data were converted from Markov variation fields to a 2D image, which facilitated feature recognition by a convolutional neural network. The introduction of SE in the ResNet50 network yielded a great accuracy improvement with a small amount of computation.
3. The LMF method was utilized to obtain the sound and vibration signal modal correlation features, and the inter-modal interrelationships were considered. The sophistication of the improved SE-ResNet method for composite fault diagnosis of rolling bearings was demonstrated experimentally.  
Figure 10: Loss comparison.  
Figure 9: Training accuracy.  
Furthermore, this article collects data under the same time and rotation speed. The number of samples is controllable. In industrial equipment, the normal operation time is much longer than the fault time. This results in less fault samples. During operation, the noise of mechanical equipment has an immeasurable impact on sound signals. This issue deserves further investigation.  
Conceptualization, X.G. and C.L.; methodology, X.G. and D.L.; software, X.G. and Y.W.; validation, X.G. and Y.T.; formal analysis, Y.T.; investigation, Y.T.; resources, Y.T.; data curation, Y.W. and D.L.; writing--original draft preparation, X.G.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific Research Fund of the Department of Education of Liaoning Province, China (JYTMS20230183).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The raw/processed data needed to reproduce these findings cannot be shared publicly at this time, as they are also part of an ongoing study.  
The authors declare no conflicts of interest.
## 5 Simulation Results and Discussion
### Bit Error Rate Issue  
Using the parity check matrix provided in IEEE \(802.11n\) standard shown in Figure 5, comparative error performance studies have been taken for different encoding rates and are shown in Figure 9. Also the C-MIMO is compared with SISO in the \(\frac{1}{2}\) rate case. The codeword length is kept fixed and the number of decoder iteration is taken as \(100\). Bit error rate (BER) is taken as performance parameter in this paper. BPSK modulation and AWGN channel are used for the simulation. Like the other wireless channels, simulation using cooperative MIMO shows similar outcomes.  
Figure 8: Energy efficiency for different constellation sizes.  
Figure 9: Bit error rate over SNR curve for different encoding rate.
## 3 Results and Discussion
### Exergy Analysis  
The relative irreversibility of each component of the refrigeration system with different configurations and refrigerants is presented in Figure 8. The results revealed that the compressor was associated with 42% to 45% of the total system irreversibility in the SS cycle, followed by the expansion valve, which was associated with 26% to 29% irreversibility with different refrigerants. In the IHX cycle, the compressor had 42% to 45% of the total system irreversibility, similar to the SS cycle, followed by the condenser and expansion  
Figure 7: The influence of (**a**) IHX and (**b**) vapor injection on different performance parameters.  
value, with about 16% to 19% of the total irreversibility. In the third cycle with flash tank vapor injection, the compressor was associated with approximately 50% of the total system irreversibility, while the flash tank had a negligible RI value.  
The reason for the relatively higher RI value for the compressor compared with the other components could be that the compressor's highest mechanical and isentropic losses occurred in the vapor compression cycle. It is well understood that the component associated with a higher proportion of exergy destruction would be the main candidate for performance improvement, irrespective of its exergy efficiency value. Reducing the exergy destruction associated with components could lead to improvement in performance. The results showed that the modification of the compressor has a great potential to increase the system's efficiency.  
The influence of utilizing the IHX and vapor injection on exergy parameters is shown in Figure 9. It was revealed that the utilization of the vapor injection improved the exergy efficiency of the system by approximately 8% compared with the single-stage cycle. In comparison, the IHX could improve the exergy efficiency by between 2% and 3%. The utilization of vapor injection improved the system's performance remarkably, irrespective  
Figure 8: Relative irreversibility diagrams of each component of the refrigeration system for (**a**) SS, (**b**) IHX, and (**c**) ECO configurations and different refrigerants (T\({}_{\text{source}}\) and T\({}_{\text{sink}}\) of \(-5\) and \(35\)\({}^{\circ}\)C, respectively).  
of the refrigerant type, while the improvement by the IHX depended on the type of the refrigerants. Therefore, these results agree with the COP results regarding vapor injection and the IHX presented in Figure 7.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.3 Forest Carbon Stocks of the Contiguous United States (2000-2009)  
[PERSON] et al. [63] mapped the contiguous United States forest carbon stocks between 2000 and 2009 at a 250 m resolution from FIA data, MODIS satellite imagery, and ancillary geospatial datasets using the Phenological Gradient Nearest Neighbor approach. The published datasets also contained total carbon stocks, live tree aboveground forest carbon, live tree belowground forest carbon, down dead wood carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon. Uncertainty maps were not provided with the carbon maps.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 4 Method--SNR of SGAD for Line Detection
### Definition of SGAD  
According to the work in [6], the gradient angle at each pixel can be derived as follows:  
\[\theta_{i}=\tan^{-1}\left(\frac{g_{r_{i}}}{g_{c_{i}}}\right), \tag{45}\]  
where \(g_{r_{i}}\) and \(g_{c_{i}}\) are the gradients at pixel \(i\) in the row and column directions, respectively.  
Then, the gradient angle difference (GAD) of two pixels is defined as the minimum positive angle between their gradient vectors as follows:  
\[\delta_{i,j}=\min\left(\text{abs}\left(\theta_{i}\text{-}\theta_{j}\right),2 \pi-\text{abs}\left(\theta_{i}\text{-}\theta_{j}\right)\right) \tag{46}\]  
The angle difference is calculated for the pairs of gradient vectors (Figure 6). Then, the measure, SGAD is derived by adding the gradient angle differences as follows:  
\[\text{SGAD}=\delta_{1,B}+\delta_{2,7}+\delta_{3,6}+\delta_{4,5}. \tag{47}\]  
Figure 5: Comparison of SNRs of the ED-based and the SD-based line detections. (**a**) \(\text{SNR}_{\text{PS}}(\text{line}_{\text{ED}})\) and (**b**) \(\text{SNR}_{\text{PS}}(\text{line}_{\text{SD}})-\text{SNR}_{\text{PS}}(\text{ line}_{\text{ED}})\). In panel (b), areas with negative values are denoted with white grid lines.
### Fundamental Heart Sound  
Traditionally, the stethoscope is used to observe heart auscultation. Recognizing pathological heart conditions requires a trained ear. Heart auscultation aims to determine any abnormality in the fundamental heart sound (FHS). A normal FHS consists of two phases, diastole and systole, referred to as the cardiac cycle. Each phase produces a sound based on the opening and closing of heart valves. Thus, in a healthy heart, two sounds occur in a healthy heart, S1 (lub) and S2 (dub), with a rhythm. S1 is produced due to the closing of mitral and tricuspid valves, indicating the start of the systole phase, and S2 is produced due to the closing of aortic and pulmonic valves, indicating the start of the diastole phase. Figure 1, demonstrates the activity of human heart during systole and diastole phase.  
Figure 1: Types of heart signals [7].  
Based on the reported research, Table 1 demonstrates the frequency range for various types of heart sounds that a PCG recording may contain.  
Several ailments can be indicated through abnormalities in FHS, such as heart arrhythmia, endocarditis, rheumatic heart disease, myocardial infarction, congenital heart disease, etc. [8]. Following heart auscultation, as a follow-up, several tests are carried out to obtain the exact condition of the heart based on invasive and non-invasive procedures. Primarily, ECG is the standard way to recognize early signs of heart abnormalities. However, due to the integration of electronics in the stethoscopes, heart sounds can be recorded as PCG. Moreover, resident doctors can use these recordings or feed into an expert system to support decision-making. The system extracts relevant features from the signal, ultimately helping diagnose [7].
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 4 Experimental Results
### Model Testing in Synthetic Datasets
The reason for this change is that the timeliness score decreases exponentially before the arrival of the next updated data. For example, the \(09^{\#}\) experiment always fluctuates around the set period \(T=7\), but the deviation degree \(t=9\) later than the set period is obviously greater than the deviation degree \(t=6\) earlier than the set period. In this case, the iterative update of the timeliness score tends to zero. This is because when \(t=9\), the value of \(f_{n+1}\) is negative \(Q_{1}\), and when \(t=6\), the value of \(f_{n+1}\) is positive \(Q_{2}\). At the same time, \(|Q_{1}|\) is greater than \(|Q_{2}|\), so the iterative change of timeliness score tends to zero. \begin{table}
\begin{tabular}{c c c} \hline
**Parameter Value** & **The Last\(f_{n}(t,A1A)\)** & **Iteration Times** \\ \hline \(06^{\#}\):\(\alpha=0.05\), \(fall=0.2\), \(t=6|8\) & \(1.000\) & \(899\) \\ \(07^{\#}\):\(\alpha=0.05\), \(fall=0.2\), \(t=5|9\) & \(1.003\) & \(227\) \\ \(08^{\#}\):\(\alpha=0.05\), \(fall=0.1\), \(t=5|9\) & \(1.001\) & \(337\) \\ \hline \(09^{\#}\):\(\alpha=0.05\), \(fall=0.1\), \(t=6|9\) & \(-0.001\) & \(284\) \\ \(10^{\#}\):\(\alpha=0.05\), \(fall=0.1\), \(t=5|8\) & \(1.007\) & \(139\) \\ \hline \end{tabular}
\end{table} 
Table 3: The number of iterations required for the convergence of the model when the data update time fluctuates around the set period (\(T=7\)). Figure 5: Change trend of score value when periodic data update fluctuates around the set time.
# Applications of Machine Learning and High-Performance Computing in the Era of COVID-19
## 3 Effectiveness of ML and HPC Techniques in the COVID-19 Era
### ML and HPC Techniques' Role in the Data Management Life Cycle  
The data life cycle (DLC) is an important building block of knowledge-based applications. It has seven phases, and each phase has unique functions to be performed on the data. We provide an overview of the phases of the DLC in Table 1.  
The list of phases provided in Table 1 is generic, and they can be adopted for COVID-19 applications/systems. We highlight the use of ML and HPC in the above-mentioned phases of the DLC in Figure 4. ML techniques are mostly used for extracting desired knowledge from data. In contrast, HPC techniques can be useful for storing intermediate results or processing large-scale data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase Number** & **Phase Name** & **Tasks Performed in a Respective Phase** \\ \hline
1. & Collection & Data are collected from relevant people. \\
2. & Storage & Data are stored in databases for later use. \\
3. & Preprocessing & Data are cleaned for later use. \\
4. & Analytics & Data are processed with ML and HPC for insights. \\
5. & Usage & Data are used for desired purposes. \\
6. & Distribution & Data are shared for research purposes. \\
7. & Archival & Data are archived for needs-based use. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concise overview of the data life cycle employed to combat the COVID-19.
## 1 Introduction
The main purpose is to enhance the available information for selecting the best technology to optimize user experience. To do this, we used two products with distinct characteristics, to assess the potential impact of the displayed product on the perception of usage properties. In order to conduct this analysis, it is important to define the usage properties that are of interest. This can be achieved by reviewing conceptual models and previous research [21; 22; 23; 24]. When considering the various usage properties of display technologies, the focus should not only be on functionality and usability, but also on emotional or pleasurable aspects [25], as well as characteristics related to media [23], because the importance that users attach to usage properties may be influenced by the type of property. Therefore, the following research questions are raised:  
**RQ1. **: _Do perceptions depend on the usage properties of the display technology? Do they depend on the product displayed?_
**RQ2. **: _Does the type of usage property influence the importance given to it?_
**RQ3. **: _Does display technology influence decision making and compliance with users' expectations?_  
A comparative analysis was conducted about the perceived importance and assessment of twelve usage properties by considering five different product display technologies: 2D, 360\({}^{\circ}\), AR, ni_VR, and i_VR. The assessed usage properties were selected from previous works to cover a variety of functional, usable, emotional, and media characteristics. Two additional variables were taken as indicators of decision and preference. Some preliminary partial results of the study are published in [26] but are presented in more breadth and depth in the present manuscript. This study also supplements a previous work [16], which analysed the influence of display technologies on the perception of product features so that an overview of the product presentation effect of display technologies on user interaction could be obtained. This information is interesting in many fields, such as online showcases and sales webs, tools for early assessments of product design and development, among others. Section 2 presents previous related work. Section 3 describes the materials and methods followed to carry out the study. Section 4 presents the results, which are discussed in Section 5. Finally, Section 6 indicates the main conclusions drawn from the work.