esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 6 Conclusions  
Considering Earth re-entry and landing field constraints, an integrated design method for Moon-to-Earth transfer opportunity and precise transfer trajectory is developed. First, this paper innovatively proposes an analytical method to obtain the re-entry state that satisfies reliable ground landing by adopting the theory of spherical geometry. Next, the Moon-to-Earth transfer opportunity is effectively determined by the permitted range of the perilune altitude according to the inverse integration of the obtained re-entry state. Then, the three-impulse Moon-to-Earth transfer strategy is adopted to satisfy the Moon departure condition from any initial inclination in the obtained opportunity. The simulation shows that there is little difference in the width of the Moon-to-Earth transfer opportunity in each month under the fuel constraint. When the deviation limit is 50,000 km, the width of the transfer opportunity in a year is about 74 days. Compared with the traditional  
Figure 11: Relationship between velocity increment and perilune altitude.  
Figure 10: Relationship between transfer duration and velocity increment and re-entry velocity. (a) \(\Delta V\) to the transfer duration; (b) The re-entry velocity to the transfer duration.  
patched-conic method, the application of the Earth and Moon gravitational model and the high-precision dynamic model can ensure higher reliability and accuracy of the resulting transfer opportunities. Moreover, the longitude of the landing field is relevant to the optimal transfer moment in a day, while the latitude influences the date distribution of the opportunity. The transfer duration and the altitude of the perilume are both closely related to the fuel consumption of the mission. The proposed design method can provide an effective Moon-to-Earth transfer opportunity and trajectory determination reference for the next stage of the Moon missions of China and other countries.  
[PERSON] investigated the status of the Moon-to-Earth transfer opportunity and trajectory design. [PERSON], [PERSON] and [PERSON] studied the integrated determination method, performed the simulations, and revised the manuscript. [PERSON] provided research recommendations and checked and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the pre-research project for manned space program (Grant No. 202220141031), the National Natural Science Foundation of China (Grant No. 12002028), and the Beijing Institute of Technology Research Fund Program for Young Scholars (Grant No. XSQD-202101012).  
Not applicable.  
The authors declare no conflict of interest.
## 7 Embryonic Imaging by Phase-Contrast X-ray Imaging  
CXI is suitable for high-resolution embryonic imaging, as shown in Table 5. Also, the sample size that can be imaged with CXI is suitable for whole human embryos. The human embryo samples used in this study were obtained from the Kyoto Collection, which is one of the largest collections of human embryo specimens in the world and one of the few human samples that are ethically permitted for use [49]. When we planned to take images of human embryos using CXI, samples other than human embryos were used to be fixed directly to acrylic pranks, and the samples were discarded after imaging. For the reason that human embryos are precious, the imaging of CXI must be nondestructive. Therefore, we first developed a method for embedding samples in agarose gels. The gel was poured into a PVC tube approximately 1 cm in diameter and length, and the sample was fed into the tube as it hardened, resulting in a cylindrical sample encased in the gel. This gel cylinder was removed from the PVC tube, fixed directly to an acrylic plate with instant adhesive (Figure 10a), and mounted on an acrylic cell (Figure 10b). Imaging was performed in these states, and the resultant images were acquired at a resolution suitable for analysis. The sample was rotated slightly during imaging. To further reduce the gel shaking that occurred during this process and improve the image quality, a cup-shaped part was made of Kapton polyimide film (Figure 10c), into which the gel was poured, and the sample was fixed (Figure 10d). This method is currently used for imaging purposes.  
These innovations have made it possible to perform stable imaging of human embryos using CXI, and morphological analysis has been performed on the images obtained. Figure 11 shows images from representative studies [50; 51; 52; 53; 54; 55].  
Figure 10: Preparation for human embryo imaging. (**a**): A gel cylinder containing the human embryo sample is fixed to an acrylic plank; (**b**): the sample from (**a**) is placed in an acrylic cell ready for imaging. Samples mounted in the gel were different between photo (**a**) and photo (**b**); (**c**): the gel cylinder containing the human embryo sample is fixed to an acrylic plank. Kapton polyimide film is molded into a cup and fixed to an acrylic plank; (**d**): the gel and sample in (**c**) are placed in an acrylic cell and ready for imaging.  
\begin{table}
\begin{tabular}{c c c} \hline  & **CXI** & **7-T MRI** \\ \hline Volumetric resolution & \((0.013)^{3}\) mm\({}^{3}\) & \((0.036)^{3}\) mm\({}^{3}\) \\ Acquisition time & 2 h & 69 h \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of CXI and MRI for embryonic imaging.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 4 Results
### Evaluation Metrics  
This study employs accuracy, sensitivity, specificity, PPV, F1 score, and the ROC curve to measure the performance of the classification model. Accuracy refers to the ratio of the number of correctly predicted samples to the total number of samples; sensitivity, also known as recall, indicates the model's ability to identify positive cases correctly; specificity refers to the model's ability to identify negative cases correctly; PPV represents the proportion of samples predicted as positive that are positive; the F1 score considers both the precision and recall of the model, representing their harmonic mean. The ROC curve is a graphical tool used to evaluate the performance of binary classification models. The area under the ROC curve (AUC) is often used to quantify the model's overall performance. An AUC value closer to 1 indicates better model performance and a more remarkable ability to distinguish between positive and negative samples.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 3 AI-Based Optical Performance Monitoring
### AI-Based QoT and Impairment Monitoring
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithms** & **Features** & **Monitoring Targets** \\ \hline Artificial Neural Networks [14] & Empirical asynchronously sampled signal amplitudes & OSNR, CD, PMD \\ \hline Deep Neural Networks [57] & Asynchronous sampled raw data & OSNR \\ \hline Convolutional Neural Network [58] & Constellation-diagram & OSNR, Modulation Format \\ \hline Convolutional Neural Networks [13; 59] & Horizontal and vertical polarization, in-phase and quadrature-phase components of optical signals & OSNR, Modulation Format \\ \hline Artificial Neural Networks [37] & Launched power, EDFAs’ input powers, EDFAs’ output powers, EDFAs’ gains, EDFAs’ NFs, etc. & OSNR \\ \hline Principal Component Analysis, Artificial Neural Networks [60] & Asynchronous delay-tap plots & OSNR, CD, Differential Group Delay (DGD), Joint Bit-rate and Modulation Format Identification (BR-MRI) \\ \hline Principle Component Analysis, Artificial Neural Networks [61] & Asynchronous single channel sampling data & OSNR, Modulation Format \\ \hline Deep Neural Networks [62] & Signals’ amplitude histograms & OSNR, Modulation Format \\ \hline Kernel-based Ridge Regression [63] & Asynchronous delay-tap sampling data & CD, DGD \\ \hline Long Short-Term Memory Neural Networks [64] & Four-ribituary digital output & OSNR \\ \hline Long Short-Term Memory Neural Networks [65] & Frequency domain signal & OSNR, Nonlinear Noise Power \\ \hline Support Vector Machine [66] & Eye diagrams & CD, PMD, Noncoherent Crosstalk \\ \hline Artificial Neural Networks [67] & Amplitude noise and phase noise correlation (ANC, PNC), number of WDM channel, total CD & Nonlinear SNR \\ \hline Artificial Neural Networks [68] & Accumulative logarithmic ANC (ALANC), number of WDM channel, total CD, noise tangential and normal component & Nonlinear SNR \\ \hline Support Vector Regression [69] & Amplitude noise correlation, logarithmic accumulated CD & Nonlinear SNR \\ \hline \multirow{4}{*}{Artificial Neural Networks [5]} & Nonlinear SNR from GN model, span number, maximum span length, average span length, the launch power, link length, net CD, average gamma of fiber spans, average attenuation of fiber spans, number of WDM channels, ANC, PNC & Nonlinear SNR \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the ML-based monitoring techniques discussed in Section 3.1.
## 4 Experimental Results and Discussion  
### Creating a User-Defined Glove Dataset  
Currently, there are no publicly available datasets on nitrile medical gloves. The detection effect in real nitrile glove defect detection scenarios can be affected by complex conditions such as light and angle. In this paper, the data collection work was carried out on actual defect samples in the production workshop to ensure that the user-defined glove dataset represents real variation in defects. To better detect small scratch and tear targets and enhance the model generalization ability, defect data were enhanced before model training. The image was preprocessed for defect data, including clipping, noise, dimness, brightness, and rotation. The image obtained after data enhancement is shown in Figure 5.  
The customized nitrile glove dataset includes frontal and vertical shooting, each with tear and scratch defect types, and contains 39,941 images. From the data, 31,953 training sets and 7988 test sets were randomly selected. The sample dataset is shown in Figure 6.  
Figure 5: Partial dataset after data enhancement.  
Table 1 shows that after 1000 iterations of training, the data-enhanced dataset improved the generalization ability and robustness of the model. Upon noise, rotation, and clipping data enhancements based on geometric transformation, the differences in scale, position, and perspective between the training set and test set could be eliminated. By adjusting the data enhancement based on color space transformation, such as brightness, the differences in illumination, color, and brightness between the training set and test set could be eliminated. The data enhancement method adopted in this study made the amplified training data as close as possible to the real distributed data, thus improving the detection accuracy.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 3 Stealth Communication
### Future Perspectives  
#### 3.3.1 Wireless Stealth Communication and Hybrid with Interference Management  
The related work has demonstrated the stealth system in fiber links and free space optical communication systems. One of the future perspective stealth methods is photonic stealth communication with RF signal carriers for wireless communications. Since the security of the system relies on the bandwidth of the noise, future stealth communication systems should be tested with RF carrier frequencies beyond 25 GHz, which provides 10 GHz to 100 GHz bandwidth. The interferometer can be scaled to the wireless system (Figure 7). The RF signal is modulated on optical carriers to generate long-range and frequency-independent delays (D1 and D2 in Figure 7). Compared with the optical link, the RF version has the advantage of using MIMO transmitter and receiver to separate the reference signal \(\cos[\omega_{c}+n(t)]\) with \(\cos[\omega_{c}+s(t)+n(t)]\), which removes the combination in Figure 5 and improves the signal to noise ratio. The separation is performed with BSS methods described in Section 2.1. The BSS process does not affect the security of the system, since it only separates the linear combination of \(\cos[\omega_{c}+n(t)]\) and \(\cos[\omega_{c}+s(t)+n(t)]\), and does not separate the nonlinear combination of signal and noise. The noise can only be cancelled when physical keys, D1 and D2 are matched.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Wireless Resource Customisation  
To customise wireless resources, network slicing techniques can be used as an open door. Wireless resource customisation alludes to the usage of streamlined application-specific wireless resource allocation techniques within the assigned slice. Mostly, either human-related or machine-related communications are dependent on the traditional wireless resource allocation strategies. Such strategies may not be helpful for a TI application, but these traditional strategies mainly concentrate on the configuration of the uplink and downlink of tactile communication. Nevertheless, combined uplink or downlink configuration is alluring for an application considering the control loop [73]. Except for a few ongoing types of research [51; 74; 75], there are still loopholes (unexplored areas) of wireless resource allocation for TI applications.  
Few works have been done to allocate resources with emerging technologies such as SDN, NFV, MA techniques, etc. The work in [76] focuses on understanding the traffic requirements of the TI environment, wherein strategies are discussed to control and allocate the bandwidth of the network. The standard FibreWireless (FiWi) infrastructure is merged with SDN to mobilise the bandwidth allocation. The SDN controller programmatically allocated the required resources based on the current global traffic status. Hence, the required QoS was also achieved under different TI environments.
## 3 Results
### ERA5 Vertical Wind Speed Assessment  
Figure 3a,b display the absolute deviations of vertical wind speed at various heights. In Figure 3a, for January, February, and March, most absolute deviations of vertical wind speed at 50 m range between \(-\)1 m/s and 1 m/s, with several blue outliers related to dust storms and sandstorms, as highlighted in different colors in Figure S2. The median absolute deviation at 100 m is near 0 m/s, while at 200 m, 500 m, and 1000 m, the median absolute deviation is below 0 m/s, indicating an underestimation of vertical wind speed by ERA5. The scatter plots in Figure S2(a2-i2) show a low correlation between ERA5 vertical wind speed and lidar observations. Previous studies also noted significant discrepancies between ERA5 vertical wind speed and actual observations [18].  
In Figure 3b, for July and August, most absolute deviations at 50 m range between \(-\)1 m/s and 1 m/s, with some blue outliers exceeding 1 m/s, due to local heating from industrial activities in Baotou, causing air expansion and ascent. Strong convective weather also contributes to deviations, with thunderstorms and heavy rain on 17 July, 25 July, and 18 August leading to underestimations by ERA5. Figure 3c,d show large relative deviations at various heights in both winter and summer.  
The relatively low spatial resolution of ERA5 data may smooth out localized strong convective or turbulent phenomena, leading to an underestimation of vertical wind speeds. In contrast, observational data are capable of capturing fine-scale localized vertical motions,  
Figure 3: Absolute and relative deviations of vertical wind speed at various heights. (**a**) Absolute deviations of vertical wind speed (ERA5 reanalysis data minus Doppler wind lidar observations) for January, February, and March; (**b**) absolute deviations for July and August; (**c**) relative deviations (ERA5 reanalysis data minus Doppler wind lidar observations, divided by Doppler wind lidar observations) for January, February, and March; (**d**) relative deviations for July and August. Red circles and blue dots indicate the distribution of deviation data, with deeper colors indicating denser areas. Blue dots represent outliers, and the light green box plot shows the interquartile range (75 th percentile, median, and 25 th percentile).  
particularly in areas characterized by intense convection and turbulence. As a result, such localized dynamics may not be adequately represented or resolved in the ERA5 reanalysis, potentially leading to discrepancies when compared with observational datasets. Overall, the vertical wind speed from ERA5 reanalysis data shows larger deviations during both winter and summer.
# Quantitative Characterization of Complex Systems--An Information Theoretic Approach
## 5 Case Study--Entropy Metric Formulation and Analysis of Simulated Complex Networks  
### Entropy to Understand Interaction Dynamics  
To understand the dynamic structure of a given complex network as a whole, it is important to analyze and explore the main conceptual underpinnings of a network, which are the nodes and their interactions. Because complex networks are continually emergent and non-linear in nature, exploring the dynamics of constituent nodes and their interaction patterns helps in providing insight about how a network structure evolves. Considering the degree distribution of every individual node in a network, let \(P_{k_{i}}\) be the dynamic degree of a node where it gives the probability of a particular node having \(k\) links at a given time-step \(i\). An entropy measure for a static network is first defined, which then is expanded to consider the dynamic nature of complex networks.  
Suppose there are \(n\) nodes in a network space \(N\), \(N=~{}(N_{1},N_{2},\ldots,N_{n})\). Now let \(K\) be a class of \(N\) where it defines the number of links of each observed node \(n\) of a network. \(K=~{}(K_{1},K_{2},\ldots,K_{n})\). Let \(I\) be the proportion of interactions that are represented by the number of links associated with each node \(n\):  
\[I_{n}=\frac{K_{n}}{(K_{1}+K_{2}+\ldots+K_{n})}=\frac{K_{n}}{\sum_{i=1}^{n}K_{ i}} \tag{2}\]  
Entropy is mathematically represented as:  
\[H(X)=-\sum_{i=1}^{m}p(x)\log_{2}~{}p(x) \tag{3}\]  
where \(0<H(X)<\log_{2}m\) and \(0<H(X)<\log_{2}H(X)\). It is to be noted that the value of \(P~{}\log P=0\) when \(P=0\).  
Figure 2: Abstract fundamental representation of a complex network.  
Applying this measure to the number of nodes constituent in the network space we obtain:  
\[H(X)=-\sum_{i=1}^{n}I_{n}\log_{2}\ I_{n} \tag{4}\]  
Now, considering the dynamic nature of a complex network, let \(j\) be the number of time-steps over which a network is observed. At every time-step \(j\), each and every node \(n\) of the network will have a degree \(k\). Now, considering the degree of nodes at individual time-steps of a network, entropy is given by:  
\[H(X)=\sum_{t=1}^{j}(-\sum_{i=1}^{n}I_{n_{t}}\log_{2}\ I_{n_{t}}) \tag{5}\]  
Here, a complex network is quantified to be a function of the number of nodes present (\(N_{n}\)) and the number of links (\(I_{n}\)) associated with the nodes. The uncertainty based on the distribution of the node degrees over a time period is used as a base indicator to calculate network entropy. This also takes into consideration the diversity in network interactions. When calculated, this index represents absolute order in the network when it takes a value of 0, and represents disorder and absolute diversity when it takes a maximum value (the maximum value depends upon the type of complex network being considered).
## 2 A Brief Overview of the Related Concepts  
### Pulmonary Function Test (PFT)  
Spirometry is considered the \"gold standard\" for the measurement of respiratory volumes as well as diagnosing both obstructive and restrictive lung diseases [9; 14; 15]. However, the surge in technology has led to the development of a variety of methods for monitoring PFT and other respiratory measures, such as chest wall motion, airflow and respiratory volumes, respiratory rate, resultant transcutaneous oxygen saturation (SpO\({}_{2}\)), and respiratory carbon dioxide (CO\({}_{2}\)) elimination. Notably, the authors of [16] developed a wearable patch-type strain sensor that facilitates real-time lung function measurements, such as forced volume capacity (FVC) and forced expiratory volume (FEV), alongside breath monitoring, which is particularly pertinent in the context of the COVID-19 pandemic. The authors of [17] introduced a novel wearable sensor device employing a multi-modal wireless sensor for monitoring pulmonary health status, showcasing a new approach for remote primary diagnostics. A comprehensive wearable sensor system utilizing a multi-sensor strategy for lung function monitoring was presented in [18], demonstrating the potential for non-invasive estimation of tidal lung volume from electromyogram (EMG) measurements. Furthermore, a machine learning-assisted system with wireless wearable sensors for the real-time evaluation of respiratory behaviors was proposed in [19], marking a significant advancement in tracking the progression of respiratory disorders and diseases. Also, the authors of [20] reported on a wearable miniaturized non-dispersive infrared sensor for continuous monitoring of transcutaneous CO\({}_{2}\), which could revolutionize the remote assessment of pulmonary gas exchange efficiency. These innovations underscore the rapid advancements in wearable sensor technologies, offering new avenues for respiratory health monitoring and disease management. The equipment and methods for assessing respiration are classified in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Measured Technique** & **Category** & **Measured Parameters** & **Sensor Position** \\ \hline \multirow{3}{*}{Spirometry} & \multirow{3}{*}{Lung function testing} & Forced expiratory volume in 1 s (FEV\({}_{1}\)), forced vital capacity (FVC), peak expiratory flow rate (PEF), forced expiratory flow (FEF) & \multirow{3}{*}{Mouthpiece or nasal/oral area} \\ \cline{3-3} \cline{5-5}  & & Total lung capacity (TLC), residual volume (RV), functional residual capacity (FRC) & \\ \cline{3-3} \cline{5-5}  & & Electrical activity of inspiratory muscles) & Surface electrodes on the (e.g., diaphragm and thectscal muscles) & \\ \cline{3-3} \cline{5-5}  & & Thoracic and abdominal & Straps around the chest and abdomen \\ \cline{3-3} \cline{5-5}  & & & and abdomen \\ \hline \hline \end{tabular}
\end{table}
Table 1: Respiratory monitoring devices and methods [16; 17; 18].