esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Method for Self-Healing Efficiency Estimation  
The proposed method for self-healing efficiency estimation is based on the assumption that propagating Lamb waves interact with any material discontinuities. The more altered the material is in a particular area, the more changes it will cause in propagating Lamb waves in this area. Anomalies of Lamb wave propagation due to material discontinuities may manifest themselves as:  
* reflections;
* wave scattering;
* changes in wave velocity;
* changes in wavelength;
* changes in wave amplitude;
* Lamb wave entrapment inside delaminated region;
* mode conversion.  
These anomalies are mostly quite subtle and not easy to measure, extract and quantify. However, there is a positive correlation between local material degradation and local guided wave variation in this area. Taking this into account, changes in the wavefield might be used to monitor not only damage appearance but also its severity [19]. This opens up the possibility for nondestructive monitoring of the self-healing process. During the self-healing process, local wavefield changes are decreasing while the healing process progresses.  
The main idea behind the proposed technique lies in filtering the full wavefield images. A special adaptive image filtering technique is used to remove the main component of propagating Lamb waves, leaving only information about changes in the wave pattern. This procedure is possible in the wavenumber domain in which a special filter is designed and applied to all wavefield images. Maps presenting the spatial distribution of wavefield changes are used for damage assessment.  
In order to give not only qualitative results by observing how those maps change with the healing process, quantitative results are evaluated by summing the energy over the considered area.  
The scheme of the proposed nondestructive self-healing efficiency estimation method is presented in Figure 1. Three main blocks may be distinguished, which are marked with dashed lines: adaptive filter preparation, measured data processing and self-healing efficiency estimation. The first part is applied only once to reference data. The second block is repeated for every measurement with the use of filter prepared in the first step. After processing of all measured data, a self-healing process efficiency is estimated in the third step.  
A more detailed description of the proposed procedure is described in successive sections.
## 4 Clustering and Information Content
### Defining Cluster Variation and Entropy  
Cluster variation: The within-cluster variation, denoted as \(W_{y}\), for a specific cluster \(y\) is the sum of squared distances of the data points within that cluster to its center. Mathematically, for a cluster \(y\) with centroid \(c_{y}\), this is given by  
\[W_{y}=\sum_{x\in y}||x-c_{y}||^{2}.\]  
The total cluster variation \(W\) is the summation of the within-cluster variations for all clusters.  
Entropy: Entropy, denoted by \(H(Y)\), is a measure of the uncertainty or randomness of a random variable. In the context of clustering, \(Y\) is the random variable representing cluster assignment. The Shannon entropy for a discrete random variable with probability distribution \(p(y)\) is defined as  
\[H(Y)=-\sum_{y=1}^{K}p(y)\log(p(y))\]  
where \(K\) is the number of clusters.
## 6 Case Study
### Results of the User Importance Ranking  
Based on the proposed user importance model in this paper, 115 users were ranked by importance. Figure 6 shows the ranking results.  
Figure 5: Distribution network topology map at \(t=3\).  
Figure 6: User importance ranking results.  
The gradient from deep blue to deep red represents the increasing importance of users. As shown in Figure 6, the LV users connected to LP10 generally have higher importance levels because LP10 serves an industrial user area, where the electricity consumption and various evaluation indices are typically higher. Conversely, the importance of users connected to LP1 is relatively low because LP1 serves a residential user area, which has a lower importance level. Therefore, the proposed multi-dimensional user importance indicator system in this paper can accurately reflect the actual user situation, make it applicable for practical user importance level assessments, and offer significant practical value.  
To illustrate the score distribution of different indices among LV users and the weight of each indicator, 10 LV users connected to LP20 and LP21 are taken as examples.  
Figure 7 shows specific score distributions for these users across different indices.  
To clearly express the weights of different indices in terms of user importance, normalized data sizes were used in the figure. In Figure 7, the initial score of the fifth LV user of LP20 is lower than that of the third LV user of LP21; however, Figure 6 shows that LP20(5) is more important than LP21(3). This discrepancy arises because the daily load rate score for LP21(3) is 1, whereas that for LP20(5) is only 0.005. For the demand response rate, LP21(3) and LP20(5) score 0.69 and 0.90, respectively. In the AHP-EW calculation, the weight of the demand response rate in terms of user importance is lower than that of the daily load rate.
# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 2 Materials and Methods
### Methods
The values 1, 2, 3, 4, and 5 were taken into account. 3. Maximum variables: indicates the maximum number of variables (chosen at random) that are taken into consideration when partitioning a node. Usually, \(\surd{\rm n}\) is used as a standard parameter, where \(\rm n\) is the number of total variables, but \(\surd{\rm n}-1\), \(\surd{\rm n}\), and \(\surd{\rm n}+1\) were taken into account. In the case of _MLP_, the model was built from an input layer made up of 7 neurons (coinciding with the dimensionality of the data), a first hidden layer made up of 128 neurons, a second hidden layer made up of 64 neurons, and an output layer made up of only 3 neurons (matching the number of classifications). The neural network created was dense, a network formed by neurons that were each connected to all possible neurons belonging to contiguous layers. The activator used in the process was the rectifier or _ReLU_ activator, except in the last layer where the _softmax_ function was used. In addition, _hyperparameter tuning_ was applied, taking into account the following parameters:  
1. _Epochs_: indicates the number of times that the neural network reads the data from the training sample in order to adjust to them (translated into a successive update of its parameters). The values 25, 50, 75, 100, 150, and 200 were taken into account;
2. _Batches_: indicates the speed with which the network parameters are updated as the epochs progress. The values 15, 25, 50, 75, 100, 150, and 200 were taken into account. For the validation of the different models and obtaining the results, the methodology used with each of the 4 classifiers is shown in Figure 5.  
In the case of _CNN_, it was built with a similar approach to the _MLP_. In order to find enough patterns to solve this problem, we have used a convolutional layer with 64 filters and a kernel size of 3 \(\times\) 3. As to reduce the dimensionality, we also used a maxpool layer. Finally, a dense layer of 128 was introduced. In this architecture, we did not use dynamic parameter optimization. The networks need a high number of epochs to train (around 1000). We use the Nadam Optimizer [33] since it is the best in the tests that we have executed. We also exploit early-stopping, stopping the training when we do not have obtained better results in a certain number of epochs. For the validation of the different models and obtaining the results, the methodology used with each of the five classifiers was as follows:  
1. For _KNN_ and _SVM_: 1. Test the classifier with the best hyperparameters (previously calculated) on 50,000 random samples sized: 55-training, 5-test. 2. For _RF_ and _MLP_: 1. Apply hyperparameter tuning from 200 random samples sized: 55-training and 5-test. 2. Test the classifier with the best hyperparameters on 50,000 random samples sized: 55-training and 5-test. 3. For _CNN_: 1. Test the classifier with the best hyperparameters (manually settled) on 100 random samples sized: 55-training and 5-test.
## 6 Conclusions  
In this article, HSC, a low-latitude satellite constellation, is designed to overcome the challenges in acquiring precise spatial information at low-latitude locations with frequent cloudy/rainy conditions. HSC prioritizes spatial coverage based on the small inclination and large swath width. It combines the multispectral, hyperspectral, radar, and ship AIS payloads and embeds the onboard real-time ship detection system. The constellation will provide the high-frequency coverage in low-latitude regions once it is fully operational.  
The satellite networking of the launched HN-1 01/02 and WC-1 01/02 can enhance the observation coverage in Hainan Province, thereby facilitating ocean observation. In this constellation, the YOLOv5 algorithm for ship detection was integrated with the dual-line array push-sweep imaging mode. It realized both onboard image processing and the real-time detection of moving ships. Hence, only the sliced images containing identified ships can be transmitted to users, which reduces the downlink data effectively.  
The YOLOv5 model is an efficient architecture, and the overall precision of it reached 92.3% after training with the ship images gathered in Hainan. In the real scenarios, the moving ships with the sizes of 70 m and 25 m can be recognized precisely in the images with the spatial resolutions of 5 m and 25 m, respectively.  
The four launched satellites are all equipped with remote sensing sensors and AIS onboard, which enables the retrieval of spatial and temporal information regarding images and AIS. Synergy of onboard sensors with optical instruments and ship AIS can be effective for obtaining ship parameters when the ship position is matched.  
However, given the limitations of optical remote sensing, SAR and hyperspectral satellites will likely to be added to HSC at a later stage. This will also help provide data-driven guidelines and support for the ongoing construction of the Hainan Free Trade Port.  
Conceptualization, L.W., T.Y., T.W., C.W. and X.-M.L.; methodology, L.W., T.Y., C.W. and X.-M.L.; validation, L.W., T.Y., T.W. and C.W.; writing--original draft preparation, L.W.; writing--review and editing, L.W., T.Y. and N.L.; funding acquisition, L.W., T.Y. and X.-M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hainan Province Science and Technology Special Fund, grant number ZDKJ2021019.  
Not applicable.  
Data Availability StatementData are contained within the article.  
Author [PERSON] was employed by the company Hainan International Commercial Space Launch Co., Ltd. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Signal Processing  
#### 2.5.1 Electrocardiography  
The ECG is a non-invasive way to measure the electrical activity of the heart, allowing us to extract relevant information to evaluate its state [24].  
Most of the parameters computed in this study are derived from the identification of the R-peaks. Due to the ECG's characteristics changing in time and frequency, maximal overlap discrete wavelet transform (MODWT) filters have become a trend [25], being the selected method to apply to the collected signals. These filters decompose the signal into basis functions (wavelets) that contain information in both time and frequency of a signal at different resolution levels. By choosing the appropriate frequency level, we are able to remove undesired components present in the signal [25]. MODWT was preferred over traditional wavelet decomposition due to its better alignment of the reconstructed signal with the original time-series [26]. The most suitable wavelet was chosen based on the quantification of how much the R-peaks were enhanced using the maximization of the energy-to-Shannon entropy ratio criterion, proposed by [PERSON] et al. [27]. Level 4 decomposition coefficients were stored and used to perform the inverse wavelet transform to reconstruct the signal and thus give us the filtered result, which corresponds to a frequency band of 11-22 Hz. The R-peak detection algorithm started with baseline shift removal and normalization of the signal amplitude. Next, the Shannon energy was computed, followed by its envelope, thus retrieving the locations of the signal where energy concentration is higher; that is, where the QRS complexes are, as carried out in [28; 29]. The envelope was computed using a moving root mean square (RMS), with a length of 70 samples that was also normalized. The envelope was further transformed into a squared signal. Finally, the filtered signal was multiplied by the obtained square signal, and the R-peaks were detected using scipys's [30] findpeaks function, using a minimum distance of 120 samples and a minimum height of 0.15.  
Finally, the metrics of HRV were computed, all based on the time-series of the distance between consecutive R peaks--the RR intervals. These include standard deviation of the RR intervals (SDRRi), square root of the mean sum of the square differences between RR intervals (RMSSD) [31], and SD1 and SD2, extracted from the Poincare plot (plot of the current RR intervals against the previous ones) [32]. Moreover, the cardiovascular load indicators were extracted from these signals, based on reserve HR (maximum HR--resting HR) including RHR, cardiovascular strain (CVS), and cardiovascular load (CVL), since they have already been used to evaluate the cardiovascular response in the occupational context [33].
# System Studies on the Fusion-Fission Hybrid Systems and Its Fuel Cycle
## 3 Results and Discussion
### Neutron Cross-Sections Analysis  
The obtained spectra were used for calculating the average cross-sections by means of the FISPACT-II program. As shown in Figure 7, there are three important actinides (Np-237, Am-241, Am-243) that have a capture to fission ratio \(\alpha\) higher than 1 despite there being very hard neutron spectra inside the transmutation area (\(\overline{E}_{n}>3\) MeV). At the same time, it is necessary to highlight that  
Figure 6: Normalized neutron spectrum in the transmutation area of DEMO-FNS.  
this ratio is just slightly higher than 1, compared to the same ratio for light-water and fast reactors (Table 1). This feature of \(\alpha\) for some of the actinides has the following explanation. Most MA have almost equal fission cross-section which is higher than capture cross-section for neutron energy higher than 1 MeV, while for some of them (Np-237, Am-241, Am-243), the capture cross-section is significantly larger than the fission cross-section for neutron energy less than 1 MeV. At the same time, 60% of neutrons have energy between 0.001 and 1 MeV. This causes an offset of the average fission cross-section to a low energy region where it is less than the capture cross-section.  
As follows from Figure 7, CO\({}_{2}\) is the best option for MA transmutation as for this coolant all main actinides have the lowest \(\alpha\) value. However, there is a problem for MA transmutation in the case of this coolant that will be shown below.
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.5 Integration with Emerging Technologies  
To fully realize the potential of DTs, they must be integrated with other emerging technologies, such as blockchain, the Internet of Things (IoT), and 5G communication networks. Blockchain technology can provide secure and transparent data management for DTs, enhancing traceability and integrity throughout the lifecycle. This is particularly important for applications like smart mobility, where real-time data from autonomous vehicles and smart infrastructure must be shared and processed securely.  
Similarly, IoT devices can enhance DT systems by enabling detailed real-time data collection from various sources, such as traffic sensors, environmental monitoring stations, and vehicle telematics. The integration of 5G networks can further support this by providing high-speed, low-latency communication channels, making DT applications more responsive and reliable throughout the infrastructure lifecycle.
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
###### Abstract
It is well expected that the mini-slot frame structure will be adopted in future 3 GPP standard versions (i.e., B5G/6G). Since the control information in power systems is normally a type of short and bursty packet, the mini-slot frame structure and scheduling mechanism perfectly match the characteristics of such a type of information. Thus, the idea of a mini-slot frame structure will be employed in this paper to reduce the transmission latency. Recently, the design and analysis of uRLLc networks have attracted much interest, most of which seek the solutions from the perspective of information theory or the upper layer techniques without considering the implementation from the PHY and MAC [14; 15; 16; 17]. In contrast, in this paper, we achieve the uRLLc target with respect to the signal waveform and resource scheduling, which is the fundamental infrastructure in the whole communication network. To be specific, a reliable and secure transmission scheme, i.e., OFDM with a hopped subcarrier (so-called OFDM/FH), is designed with respect to the wireless PHY. The reliability of the system is achieved by flexibly choosing the subcarriers (or sub-channels) with good quality and avoiding the jammer/interferers. In addition, the system can accommodate the multiple nodes by assigning various FH sequences, i.e., OFDM with FH multiple access (OFDM/FHMA). On the basis of the OFDM/FHMA signal, the resource scheduling scheme and automatic repeat request (ARQ) based on the mini-slot structure are adopted, which can guarantee the low latency with respect to the wireless MAC. Then, the numerical tradeoff between reliability in terms of the BER and latency resulting from (re)transmission is investigated by semi-analytic simulations. It is worth noting that the proposed OFDM/FHMA based on the mini-slot frame structure could become the PHY and MAC technique in future B5G/6G networks, and it can achieve the stringent requirements of reliability and latency for the critical services in power systems. The remainder of this paper is organized as follows. In Section 2, a new OFDM/FHMA system based on the mini-slot frame architecture is designed, which includes the design of the reliable OFDM/FH signal and the integration with the mini-slot-based scheduling mechanism. Then, in Section 3, the signal processing is analyzed to explain how the transmission reliability is enhanced by OFDM/FHMA. This is followed by the derivation of the theoretic latency due to (re)transmission under the predetermined reliability. Finally, as for various applications in power system, we present the simulation results of reliability in terms of the BER and latency, which validate that the proposed OFDM/FHMA based on the mini-slot frame architecture can perfectly meet the transmission requirements of these applications.
## 3 Special Model: Exponentiated Exponential-Pareto Distribution (EEP)
### Quantile Function  
The quantile function of a continuous random variable \(T\) is defined as the inverse function of its CDF \(F_{T}\). Since the explicit form of \(F_{T}\) is derived in (10), we have the quantile function an EEP random variable \(T\) as follows:  
\[F_{T}^{-1}(p;\theta,\eta)=\begin{cases}[\ln(\frac{c}{c-p})\frac{\theta}{a+1}]^{ \frac{1}{\eta}}&p<c[1-e^{-(a+1)}]\\ \\ \left(\frac{c}{1-p}\right)^{\frac{1}{a\eta}}\theta^{\frac{1}{\eta}}&p\geq c[1- e^{-(a+1)}].\end{cases} \tag{17}\]  
In fact, given that the values of \(c\) and \(\alpha\) are fixed, the value of \(c[1-e^{-(\alpha+1)}]\) could be obtained. Notice that \(c[1-e^{-(\alpha+1)}]<0.5\). Hence, the median (0.5 quantile) of an EEP random variable could be expressed as follows, in terms of \(\theta\) and \(\eta\):  
\[M(\theta,\eta) =F_{T}^{-1}(p=0.5;\theta,\eta)\] \[=(2c)^{\frac{1}{a\eta}}\theta^{\frac{1}{\eta}}\]  
The median of EEP distribution is plotted in Figure 4. Similar to what we observed in Figure 2, the median of an EEP distribution is an increasing function in \(\theta\) and a decreasing function in \(\eta\).  
Figure 4: Median plot of EEP distribution with different parameters.
# Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions
###### Abstract
Thus, the present article focusses on how to visualize salient aspects of voice production, ordered according to the three subsystems that constitute the vocal apparatus: respiratory, oscillatory and resonator. The visualization of key components within these subsystems facilitates the connection between perceived voice qualities and their underlying physiological, aerodynamical and acoustical correlates, as illustrated in Figure 1. The ultimate goal is the development of both vocal and expert listening competences in singing students. With respect to the respiratory subsystem, we will discuss real-time visual feedback of breathing patterns. This is relevant to voice pedagogy because these patterns control lung volume, which affects subglottal pressure (\(p_{\text{sub}}\)), i.e., the air pressure in the lungs [18]. Variation in \(p_{\text{sub}}\) results in changes in both sound pressure level and spectrum tilt, which determine vocal loudness [20]. With regard to the oscillatory subsystem, technology used to visually monitor variations in the tension, extension and adduction of the vocal folds will be discussed. These are  
Figure 1: Schematic representation of the subsystems that constitute the vocal apparatus and underlying physiological, aerodynamical, acoustical and perceptual correlates. relevant as they determine the number of vocal fold oscillations per second and glottal resistance. Both will determine fundamental frequency (\(f_{\circ}\)) and phonation types, respectively, which are significant to qualities such as pitch, roughness, vibrato and voice timbre [18]. Finally, concerning the resonator's system, movements of the larynx, jaw, soft palate, lips and tongue, are associated with modifications of sound radiation and vocal tract resistance. Acoustically, this leads to various vocal tract transfer functions that differentiate vowels, consonants and voice timbre, characteristics that can be visualized in real-time by spectrographic displays.
## 4 SSO, Crowding Distance, and Elite Selection  
The proposed tSSO, based on SSO [18], is used to update solutions from generation to generation intelligently; the crowding distance [19] is used to rank temporary nondominated solutions systematically, and the elite selection [34] is used to select solutions to act as parents. Hence, SSO, crowding distance, and elite selection are introduced briefly in this section.