esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Solar Radiation
#### 4.2.2 The Effect of Radiation Conditions on Radiant Heat Errors  
Simulating a comparative analysis with the sensor without radiation conditions at an ambient temperature of 233.15 K allows us to derive the effect of radiation on the temperature rise of the sensor. The ambient air pressure was 50 Pa, the wind speed was 100 m/s, the initial temperature of the temperature sensor was 236.15 K, and the radiation values were 0 W/m\({}^{2}\), 500 W/m\({}^{2}\), 1000 W/m\({}^{2}\), 1500 W/m\({}^{2}\), and 2000 W/m\({}^{2}\).  
Figure 8 shows the temperature variation of the Pt thin film under five different radiation conditions. It can be seen in the figure that the temperature of the Pt thin film increases as the amount of radiation increases. For example, the temperature of Pt thin film under five different radiation conditions at 600 ms was 233.39 K, 233.42 K, 233.46 K, 233.49 K, and 233.53 K. This is because the more significant the radiation, the greater the radiation heat generated by the Pt thin film, can be seen in Equation (10).
# Quantum Key Distribution in Kubernetes Clusters
## 6 Test and Validation  
In order to validate our solution, we estimated the throughput and the exchange time in both PTP and MH case scenarios. We also evaluated the routing algorithm besides the QSS infrastructure. For the tests regarding the key exchange, we used a scenario with three Kubernetes clusters deployed on three different physical nodes with the following characteristics: CPU Intel Core i5-5300U 2.30 GHz (marketed by Intel Corporation, Santa Clara, California, U.S.), 16 GB of RAM DDR3 1600 MHz (marketed by Kingston Technology Corporation, Fountain Valley, California, U.S.), Ubuntu 20.04.3 LTS, K3s version 1.22.5 and containerd v1.5.8 as the container runtime. We used a single virtual machine to test the routing algorithm where the QKD network nodes were simulated as Docker containers. The spawned VM had the following characteristics: 16 vCPUs, 40 GB of RAM, Ubuntu 18.04.5 LTS, and Docker CE version 20.10.5.
## 4 Experiment
### Metrics
#### 4.1.1 Peak Signal-to-Noise Ratio  
The \(PSNR\) is a metric used to evaluate image quality [40]. It measures the reference value of image quality between the maximum signal and the background noise. When given a grayscale image I and a noise image \(K\) of size \(m\times n\), the mean-squared error (\(MSE\)) formula is used.  
\[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I(i,j)-K(i,j)]^{2} \tag{42}\]  
The \(PSNR\) is defined as follows:  
\[PSNR=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right) \tag{43}\]  
The units of the \(PSNR\) are decibels, and a higher value indicates less image distortion. Typically, a PSNR higher than 40 dB suggests that the image quality is nearly identical to the original image. A range of 30-40 dB usually indicates an acceptable image quality distortion loss level. However, a \(PSNR\) between 20 and 30 dB signifies relatively poor image quality, while a \(PSNR\) lower than 20 dB indicates significant image distortion.
## 3 The PMT Readout and Trigger System
### CITIROCs  
The internal structure of CITIROCs is shown in Figure 3.  
An amplification stage is present on each channel, featuring two independent charge preamplifiers with different gain ranges: the high-gain preamplifier, with a gain from 10 to 600 and the low-gain preamplifier, with a gain from 1 to 60. The presence of two independent acquisition chains ensures a wide dynamical range, which is essential in the case of HEPD-02 due to the wide energy range required by design.  
A configurable shaper follows the preamplifier with shaping times that can be selected between 12.5 and 87.5 ns with 12.5 ns steps.  
The shaper's output can be stored in analogue memory circuits which can work in two operating modes: the Track and Hold mode, which samples the output of the shaper at a specific instant, and the Peak Detection mode, which follows the shaper's output in a defined time window.  
Since the scintillators of HEPD-02 have very different timing characteristics, the PMT&T board uses the Peak Detection mode.  
The FPGA uses the \"Time_trigger\" signals produced by the two CITIROCs to generate the trigger for the experiment. This enables the analogue output of the CITIROCs and starts the ADC conversions.
## 5 Performance Evaluation
### AllReduce  
In the AllReduce experiment, the data type was Integer and the size of transferred data was 256 MB. Figure 10 graph shows the result of AllReduce communication time. Like the previous experiments, in the graphs of Figure 10, C indicates MPI communication in C environment, All_B indicates MPI buffer communication in Python environment, and All_O indicates MPI object communication in Python environment.  
Figure 9: Gather communication time results according to the experiment environment: (**a**) Host; (**b**) one Docker Container; (**c**) two Docker Containers; (**d**) four Docker Containers.  
Figure 10: _Cont._  
Figure 10 is the result of executing AllReduce. Figure 10a is the Host environment, Figure 10b is the one Docker Container environment, Figure 10c is the two Docker Containers environment, and Figure 10d is the four Docker Containers environment. In the experiment, the communication result between the experimental environments is the same as the previous experiments. With DDR4 in Flat mode, All_B showed slower performance than C. However, in MCDRAM of Flat mode, the communication performance of All_B and C was similar. A special feature of the AllReduce experiment is seen in Python's object communication. Most of the previous communication experiments showed the fastest performance in MCDRAM of Flat mode. In AllReduce, object communication All_O in Cache mode showed faster performance than object communication All_O in MCDRAM of Flat mode. This result seems to be due to the feature of AllReduce, which combines values from all processes and distributes the results to all processes. Unlike the previous experiments, AllReduce has a data operation task. It seems that the utilization of cache has increased for computation. Therefore, it was confirmed that object communication All_O in Cache mode improved the performance by up to 76% compared to object communication All_O in MCDRAM of Flat mode.  
The All_B communication in MCDRAM of Flat mode showed up to 162% performance improvement over All_B communication in DDR4 of Flat mode. In Cache mode, All_B communication is up to 110% faster than DDR4 communication in Flat mode. The Host environment (Figure 10a) and one Container environment (Figure 10b) showed almost similar performance. Like the previous experiments, when the number of Containers increased, the poor communication performance was confirmed.
## 2 Governing Equations and Methods
### Rough Interfaces Model Generation
Equation (7) was modified to replace \(\Delta x\) with \(\Delta x/4\), which matched the roughness parameter values for \(\delta\) and \(\lambda_{0}\). This change led to a significant increase in simulation runtime. For the regular models and \(T=120\) us, the time-step \(\Delta t\) calculated from Equation (7) was \(\Delta t=9.7\times 10^{-10}\) s, leading to around 124,000 time steps. However, for the rough crosses, \(\Delta t\) was reduced to \(2.4\times 10^{-10}\) s, leading to nearly 500,000 time steps and a run-time approximately four times longer. The incident wavelength is of the order of the size of the cross-shaped hole, so it is expected that interaction with the defined roughness, which is realistic for additive manufacturing methods but around sixty times smaller, should have a minimal effect on the exotic wave effects observed for the regular, periodic structure.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 4 Conclusions
Data Availability Statement: The GNSS observation data can be downloaded from [[https://cddis.nasa.gov/archive/gnss/data/daily/](https://cddis.nasa.gov/archive/gnss/data/daily/)]([https://cddis.nasa.gov/archive/gnss/data/daily/](https://cddis.nasa.gov/archive/gnss/data/daily/)), accessed January 2019. The orbit products can be downloaded from [[https://cddis.nasa.gov/archive/gps/products/](https://cddis.nasa.gov/archive/gps/products/)]([https://cddis.nasa.gov/archive/gps/products/](https://cddis.nasa.gov/archive/gps/products/)), accessed January 2019, [[ftp://igss.gnsswhu.cn/pub/gnss/products/mgex/](ftp://igss.gnsswhu.cn/pub/gnss/products/mgex/)]([ftp://igss.gnsswhu.cn/pub/gnss/products/mgex/](ftp://igss.gnsswhu.cn/pub/gnss/products/mgex/)), accessed January 2019 and [[http://ftp.aiub.unibe.ch/CODE_MGEX/](http://ftp.aiub.unibe.ch/CODE_MGEX/)]([http://ftp.aiub.unibe.ch/CODE_MGEX/](http://ftp.aiub.unibe.ch/CODE_MGEX/)), accessed January 2019. Questions and requests for materials can be addressed to [EMAIL_ADDRESS] or [EMAIL_ADDRESS]. The authors would like to extend their sincere gratitude to IGS/MGEX for providing the relevant data. The numerical calculation in this study have been done on the supercomputing system in the Supercomputing Center, Shandong University, Weihai. The authors declare no conflict of interest.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite** & **Spatial** & **Spectral Resolution** & **SNR** & **Predicted** & **Retrieved** & **Type of** & **Reference** \\  & **Resolution** & **(Average FWHM)** & **SNR** & **(kg/h)** & **(kg/h)** & & **Dispersive (prism)** & [11] \\ \hline PRISMA & 30 & -10 & 200 & 639.37 & 644.92 \(\pm\) 365.99 & Dispersive (prism) & [11] \\ EnMAP & 30 & -7.8 & 230 & 707.43 & 710.60 \(\pm\) 403.24 & Dispersive (prism) & [12] \\ EMIT & 60 & -8.4 & 750 & 894.73 & 899.94 \(\pm\) 510.92 & Dispersive (grating) & [23] \\ GF5B-AHSI & 30 & 8.25 & 200 & 684.70 & 670.69 \(\pm\) 380.60 & Dispersive (grating) & [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Retrieved emission rate (kg/h) based on different parameters of hyperspectral imagers. eters used by EMIT and GF5B-AHSI have more complex optical systems [43; 44]. On the other hand, the interferometric imaging spectrometers, represented by Fourier transform spectrometers, offer intrinsic advantages. Their high spectral resolution and efficient energy utilization makes them well-suited for applications demanding fine spectral detail. From a performance perspective, dispersive spectrometers provide high spectral resolution and accuracy while maintaining a relatively simple and compact design, especially the prism spectrometers. In contrast, interferometric spectrometers generally face challenges for data transmission and processing. In addition, due to their more complex optical design and larger volume, interferometric spectrometers are typically better suited for large satellite missions. However, the Fabry-Perot interferometer stands as an exception to this, which could achieve fine spectral resolution detection in a compact volume; however, it is limited to specific spectral bands [45]. Considering launch costs and the limitations of payload mass and volume, dispersive spectrometers are well-suited for most methane plume requirements, as shown in Table 1. For achieving finer spectral resolution, the Fabry-Perot interferometer is an alternative, as well.
# Application of Machine Learning to Assess the Quality of Food Products--Case Study: Coffee Bean
## 1 Introduction
In the literature, from the point of view of the problem posed, many researchers have tried to apply CNNs using digital images to assess the quality of green coffee beans during harvesting, for example, by the team of [PERSON] et al. (2020) [18], among others, who achieved a coffee grading rate of 93%. On the other hand, the team of [PERSON] et al. (2020) [32], based on green coffee beans, similarly made a quality assessment according to defects, obtaining a grading rate of min. 72.4%. Research was also conducted by [PERSON] et al. (2021) [16] to effectively build an intelligent system based on deep learning and computer vision to assist in the detection of defects, including mold, fermentation, insect bites and crushed coffee beans, obtaining neural models with recognition quality rates of 78% and 93% [16]. In view of a number of research works aimed at improving the evaluation of the quality of harvested coffee beans, the current technological solutions in the process of coffee harvesting and roasting are still being sought to be made more effective. In this work, as mentioned above, the focus was on recognizing three quality classes of coffee beans because of the coffee roasting process. The assumption of the study was to assess the authenticity based on the observation of the color properties of selected Arabica coffee samples obtained through the processing of digital images. The utilitarian purpose was to develop convolutional networks to assist in the identification of quality classes of Arabica coffee beans. The application of modern methods using the Python 3 environment seems to be a key aspect. This is due to the fact that it is easier to implement this type of solution in the food industry than traditional statistical methods. The designed convolutional models, according to the machine learning criterion, have high scalability, which makes it possible to adapt to applications in the food industry.
###### Abstract
[PERSON] et al. extracted the features of EEG by calculating its MPE and the classification accuracy was 90.6% with a LDA classifier [30]. Furthermore, [PERSON] et al. proposed Multivariate Multi-Scale Permutation Entropy (MMPE) to incorporate the simultaneous analysis of multi-channel data as a unique block and applied it to a complexity analysis of Alzheimer's disease EEGs [31]. [PERSON] et al. came up with Multiscale Fuzzy Entropy (MFE) by combining FE and scale factor, and used rolling bearing fault type recognition [32]. At present, MFE is mainly applied on fault diagnosis and has shown its superiority to most complexity measures such as ApEn, SampEn, FE, PE, and so on. Recently, [PERSON] et al. proposed the so-called refined composite multivariate multiscale fuzzy entropy (RCMvMFE) based on MFE, and applied it to feature extraction on intracranial EEG data and fantasia data; the average classification accuracies on the two datasets were 96% and 75% with a SVM classifier, respectively [33]. However, there are few reports about the application of MFE in MI-EEG signal analysis. In addition, the same parameter values are employed to calculate MFE on multiple different scales using the MFE method. As a matter of fact, from the perspective of signal processing, the essence of the coarse-grained process of time series is to sample the signal after low-pass filtering, and each coarse-grained time series carries the characteristic information of the original signal on different scale factors and has its own complexity. Therefore, it is necessary to optimize and use the different parameters in the calculation of MFE on different scale factors. This will make it more reasonable to measure the complexity of a signal and enhance the adaptability of MFE. In this paper, MFE is improved by using independent optimization strategy for the parameters on different scale factors, and improved MFE (IMFE) is applied to the feature extraction of MI-EEG. The paper is organized as follows. In Section 2, the basic principles of FE, MFE, and SVM are briefly introduced. Section 3 describes the working process of IMFE in detail. In the next section, extensive experiments are conducted on a publicly available dataset. Section 5 concludes the paper.
# Embedded AI-Based Digi-Healthcare
## 4 Design and Implementation
### Hardware Development
#### 4.1.1 Sensor Descriptions  
The hardware prototype was developed while keeping in mind that the use case of this study is remote monitoring of cardiovascular patients. This means that the IoMT prototype was integrated with sensors that monitor the baseline parameters needed to ensure the good health of a cardiac patient. These vital parameters include heart rate, blood oxygen saturation, body temperature, and electrocardiogram (ECG) signal. This section highlights the brief working description of the sensors used for the hardware development:  
Figure 1: Proposed System Architecture.  
**MAX30102-Heart rate & blood oxygen saturation sensor:**  
Heart rate and SpO\({}_{2}\) are among the key vital parameters which are used to determine the health of a cardiac patient. The MAX30102 sensor [13] is a pulse oximeter Figure 2 which measures these vitals. This sensor uses IR and red LED to get the Photopethysmogram (PPG) signal which is used to measure the blood oxygen saturation. Contrarily, the sensor utilizes the IR to check for beats for heart rate monitoring. This device's small size makes it ideal for integration in wearable devices.  
2**MLX90614-Non contact IR Temperature Sensor:**  
This sensor [14] is an Infra-Red based, noninvasive thermometer. It can perform at a measurement resolution of 0.02. The non-contact attribute makes it suitable for hassle free wearable health solutions. Since this sensor is IR-based Figure 3, it gives much faster results than the conventional contact-based sensors.
## 2 Islanding Detection Methods
### Hybrid Methods
#### 2.3.3 Voltage Fluctuation Injection Method  
With this approach, the islanding is detected by combining the rates of frequency and voltage change. In the initial stage, the islanding is discovered by keeping an eye on the PCC's rate of change for both frequency and voltage. The DGs will trip if one of them exceeds the threshold value. A periodically switching high-impedance load is used to implement a voltage perturbation during the second step for verification. When themain grid is connected, the grid stabilizes the PCC voltage disturbance brought on by the switching of the high-impedance load. However, islanding is identified when the main grid connection is lost by monitoring the impact of the periodic perturbation at the PCC voltage. This method's detection time is less than 0.216 s and is independent of the quality factor. However, it is less effective when used in large DG units [20].
## 4 Results and Discussion
### Simulated Multiplex Networks
In the third scenario, 2-layer multiplex networks were generated with \(n=128\) nodes and \(c=10\) communities, to explore the performance of the proposed **Mx-CRTSA** with networks that consist of small-sized clusters. More examples of the generated networks from scenario 3 are shown in Figure 4a-d.  
Figure 2: Examples showing some of the simulated 2-layer multiplex networks generated with \(n=128\), \(c=4\) and variable \(\mu_{0}\). (**a,b**) Adjacency matrices with \(\mu_{0}=0.1\); (**c,d**) Adjacency matrices with \(\mu_{0}=0.2\). Figure 4: _Cont._  
Figure 3: Examples showing some of the simulated 3-layer multiplex networks generated with \(n=128\), \(c=4\) and variable \(\mu_{0}\). (**a–c**) Adjacency matrices with \(\mu_{0}=0.1\); (**d–f**) Adjacency matrices with \(\mu_{0}=0.6\). In all scenarios, the experiments were replicated 50 times and the variation in the average values of NMI and AR with respect to \(\mu_{0}\) for the different methods are reported in Figure 5a-f. As the figure shows, the proposed Mx-CRTSA method scores the best results in all networks in terms of NMI and AR compared to the other methods. This experiment reflects the robustness and effectiveness of the proposed Mx-CRTSA compared to other methods under comparison. These results indicate that the proposed algorithm mitigates the impact of noise by estimating the clean low-rank representation tensor which leads to more accurate clustering results. Figure 4: Examples showing some of the simulated 2-layer multiplex networks generated with \(n=128\), \(c=10\) and variable \(\mu_{0}\). (**a,b**) Adjacency matrices with \(\mu_{0}=0.1\); (**c,d**) Adjacency matrices with \(\mu_{0}=0.2\). Figure 5: _Cont._  
Scalability Analysis  
To test the proposed **Mx-CRTSA**'s scalability, a set of simulated networks were generated where the number of nodes in the networks was increased from 32 to 2048 on a logarithmic scale. The multiplex networks were generated following [32] with \(c=10\), \(M=2\) and \(\mu_{0}=0.1\). The comparison between the run time taken by **Mx-CRTSA** and the other methods is reported in Figure 6. As the figure demonstrates, **Mx-CRTSA** takes a longer time compared to most of the other methods and is comparable to **CSNMTF** and **DiMMA**. However, the **Mx-CRTSA** method obtains the best results over the compared methods in terms of community detection. Figure 5: Average NMI and AR scored by the different methods for simulated multiplex networks with \(n=128\) generated with variable \(\mu_{0}\). (**a**,**b**) Multiplex networks with \(c=4\) and \(M=2\); (**c**,**d**) Multiplex networks with \(c=4\) and \(M=3\); (**e**,**f**) Multiplex networks with \(c=10\) and \(M=2\).
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
###### Abstract  
Ground-based arc-scanning synthetic aperture radar (ArcSAR) is the novel ground-based synthetic aperture radar (GBSAR). It scans 360-degree surrounding scenes by the antenna attached to rotating boom. Therefore, compared with linear scanning GBSAR, ArcSAR has larger field of view. Although the feasibility of ArcSAR has been verified in recent years, its imaging algorithm still presents difficulties. The imaging accuracy of ArcSAR is affected by terrain fluctuation. For rotating scanning ArcSAR, even if targets in scenes have the same range and Doppler with antenna, if the heights of targets are different, their range migration will be different. Traditional ArcSAR imaging algorithms achieve imaging on reference plane. The height difference between reference plane and target in scenes will cause the decrease of imaging quality or even image defocusing because the range migration cannot be compensated correctly. For obtaining high-precision ArcSAR image, we propose interferometric DEM (digital elevation model)-assisted high precision imaging method for ArcSAR. The interferometric ArcSAR is utilized to acquire DEM. With the assist of DEM, target in scenes can be imaged on its actual height. In this paper, we analyze the error caused by ArcSAR imaging on reference plane. The method of extracting DEM on ground range for assisted ArcSAR imaging is also given. Besides, DEM accuracy and deformation monitoring accuracy of proposed method are analyzed. The effectiveness of the proposed method was verified by experiments.  
s 2019; 19, 2921; doi:10.3390/s19132921