esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 3 Experiment and Discussion
### Theoretical Analysis  
This section is used to discuss the performance of the proposed method based on different trend synthetic data. The unchanged data has constant outputs throughout the life of the system, which cannot provide useful information to facilitate prediction [1]. Most of the systems show a slow aging trend in the early stage and a fast aging trend in the later stage. Therefore, the synthetic data is constructed with exponential functional form (Equation (21)).  
\[S_{syn}(t)=\exp(t)+Gaussian(t), \tag{21}\]  
where, \(\exp(t)=e^{a_{exp}t+b_{exp}}\) is the exponential function to generate exponential data for different trends by adjusting parameter \(a_{exp}\) and \(b_{exp}\). \(Gaussian(t)\) is the Gaussian random variables with the mean \(\mu_{g}\) and the variance \(\sigma^{2}\) to simulate noise. The probability density of Gaussian noise follows the standard normal distribution (Equation (22)).  
\[N_{exp}(t)=\frac{1}{\sqrt{2\pi}\sigma}e^{\frac{-(t-\mu_{g})}{2\sigma^{2}}}. \tag{22}\]  
The normal distribution is symmetrical about \(t=\mu_{g}\). \(\mu_{g}\) and \(\sigma\) are used to adjust the data fluctuation. Therefore, different parameter settings, \((a_{exp},b_{exp},\mu_{g},\sigma^{2})\), are used to generate synthetic data for different trends. In the theoretical analysis, small scale samples are used for simulation analysis. The parameter settings of synthetic data samples are shown in Table 1.  
The dataset has 40 synthetic data samples, which have four different exponential forms and forty different Gaussian random variables. When \(a_{exp}>0\), the trend is ascending and the trend is descending for \(a_{exp}<0\). Then, the synthetic dataset is used to study the sensitivity of the proposed method to different types of input data. The results are shown in Figure 4.  
RMSE are (0.100, 0.101, 0.102, 0.103) and scores are (0.174, 0.120, 0.131, 0.149) for different exponential forms \((a_{exp}=0.01\), \(b_{exp}=0.25)\), \((a_{exp}=0.02\), \(b_{exp}=0.5)\), \((a_{exp}=-0.04\), \(b_{exp}=1.3)\), \((a_{exp}=-0.06\), \(b_{exp}=1.6)\). Experimental results show that the method has good prediction performance on samples with different trends. The conclusions in the section are used as the basis for sensor selection during the experimental phase. More discussion on the method performance will be given in the experimental simulation section based on NASA's dataset.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
###### Abstract  
Quantum cryptography is a well-stated field within quantum applications where quantum information is used to set secure communications, authentication, and secret keys. Now used in quantum devices with those purposes, particularly Quantum Key Distribution (QKD), which proposes a secret key between two parties free of effective eavesdropping, at least at a higher level than classical cryptography. The best-known quantum protocol to securely share a secret key is the BB84 one. Other protocols have been proposed as adaptations of it. Most of them are based on the quantum indeterminacy for non-orthogonal quantum states. Their security is commonly based on the large length of the key. In the current work, a BB84-like procedure for QKD based on double quantum teleportation allows the sharing of the key statement using several parties. Thus, the quantum bits of information are assembled among three parties via entanglement, instead of travelling through a unique quantum channel as in the traditional protocol. Asymmetry in the double teleportation plus post-measurement retains the secrecy in the process. Despite requiring more complex control and resources, the procedure dramatically reduces the probability of success for an eavesdropper under individual attacks, because of the ignorance of the processing times in the procedure. Quantum Bit Error Rate remains in the acceptable threshold and it becomes configurable. The article depicts the double quantum teleportation procedure, the associated control to introduce the QKD scheme, the analysis of individual attacks performed by an eavesdropper, and a brief comparison with other protocols.  
quantum information; quantum cryptography; Quantum Key Distribution; BB84 protocol; teleportation +
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# Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
## 2 Graphs and Graph Spectrum  
Graphs are the mathematical structure underpinning the formulation of networks. Let \(G=(V,E)\) be an undirected graph with vertex set \(V=\{v_{i}\}_{i=1}^{n}\). Each edge between two vertices \(v_{i}\) and \(v_{j}\) carries a non-negative weight \(w_{ij}\), the \(ij\)-th entry of the adjacency matrix \(\mathbf{W}\). For unweighted graphs we set \(w_{ij}=1\) for two connected nodes and \(0\) for two disconnected nodes. The degree of a vertex \(v_{i}\in V\) is defined as \(d_{i}=\sum_{j=1}^{n}w_{ij}\), and the degree matrix \(\mathbf{D}\) is defined as a diagonal matrix that contains the degrees of the vertices along diagonal, i.e., \(\mathbf{D}_{ii}=d_{i}\). The unnormalised graph Laplacian matrix is defined as \(\mathbf{L}=\mathbf{D}-\mathbf{W}\). As \(G\) is undirected (\(w_{ij}=w_{ji}\)), the unnormalised Laplacian is symmetric. As symmetric matrices are special cases of normal matrices, they are Hermitian matrices and have real eigenvalues. Another common variant of the Laplacian matrix is the normalised Laplacian [8],  
\[\mathbf{L}_{\mathrm{norm}}=\mathbf{D}^{-\frac{1}{2}}\mathbf{L}\mathbf{D}^{- \frac{1}{2}}=\mathbf{I}-\mathbf{W}_{\mathrm{norm}}=\mathbf{I}-\mathbf{D}^{- \frac{1}{2}}\mathbf{W}\mathbf{D}^{-\frac{1}{2}}, \tag{1}\]  
where \(\mathbf{W}_{\mathrm{norm}}\) is known as the normalised adjacency matrix (strictly speaking the second equality only holds for graphs without isolated vertices). The spectrum of the graph is defined as the density of the eigenvalues of the given adjacency, Laplacian or normalised Laplacian matrices corresponding to the graph. Unless otherwise specified, we will consider the spectrum of the normalised Laplacian.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 1 Introduction
### Related Works
#### 1.1.1 Traditional Forecasting Methods  
Regression analysis involves the establishment of a regression equation to predict the future trend of the dependent variable based on the analysis of both dependent and independent variables. This model is straightforward to construct and offers quick predictions. For instance, in 2021, [PERSON] et al. [4] introduced a kernel function to the lasso linear regression method, applying it to nonlinear problems to address the regression analysis of non-time series data. Its accuracy improved compared to standard lasso regression, yielding enhanced results. [PERSON] et al. [5], in their research, introduced a hybrid support vector regression approach. They investigated the coupling and inter-dependency between the model parameters. Moreover, they optimized the hyper-parameters and altered the model's configuration using nested strategies and state-transformation algorithms. This method was successfully applied to predict medium- and long-term electric loads in a real industry in China. The essence of the regression method is to analyze vast amounts of actual data to understand the relationship between these factors and the electric load. The goal is to use this relationship to predict the future trajectory of the electric load. These influencing factors are straightforward to analyze and understand, and the prediction accuracy is commendably high. However, these influencing factors can change due to environmental shifts, policy alterations, and other uncertainties. These elements introduce a high degree of randomness. If anomalous data are used as the foundation for prediction, they can adversely affect the accuracy of future power load forecasts.  
The time series method is an illustrative approach based on historical electricity data. The amount of data required for this method allows for electricity forecasting without the need for an enormous scale. For example, In 2021, [PERSON]. [PERSON] et al. [6] investigated the impact of noise on the inadequate identification of autoregressive integrated moving average (ARIMA) model factors and proposed a series of solutions. They experimented with actual power load samples derived from Poland, evaluating the robustness of ARIMA models to noise in predicting the time series of electric loads. Additionally, they determined the limiting noise level for predictive capability. Time series are ordered sequences of data points over time, and they predict future changes based on past electricity loads. Oneadvantage of this model is its simplicity in understanding and execution. However, if forecasting relies solely on changes in electricity loads, achieving accurate predictions becomes challenging. This is mainly because electricity loads exhibit randomness and seasonality, making the forecast less than ideal.
# Children's Functional Clothing: Design Challenges and Opportunities
## 4 Functional Textiles
### Functional Textiles for Improved Protection and Wellbeing
#### 4.1.4 Waterproofing Textiles  
Waterproof textiles are specially engineered fabrics treated or designed to repel liquids, preventing penetration into the material. Waterproofness represents an extreme level of water resistance [38]. Waterproof clothing for children goes beyond mere protection from the water; it contributes to their health, safety, and enjoyment, fostering a positive and active lifestyle. Investing in quality waterproof clothing ensures that children can explore and play freely, regardless of the weather.  
Waterproof fabrics, besides blocking external water, must also provide breathability for effective water vapour diffusion. These dual characteristics are crucial for regulating body temperature, managing heat efficiently, and protecting against external elements [16]. According to [PERSON] et al. [47], testing methods, including hydrostatic pressure and simulated rainfall are used to determine fabric waterproofness. The common hydrostatic pressure test requires a minimum of 5000 millimetres (mm) of water column (water column represents the height of a column of water that the fabric can support before water begins to penetrate) to be considered waterproof, with high-quality materials achieving values up to 30,000 mm.  
Some studies have been carried out to improve the waterproof properties of fabrics. [PERSON] et al. [37] enhanced fabric waterproofness by coating woven polyester structures with solvent-based polyurethane (PU). This study also incorporated natural elements, adding anti-UV and antibacterial properties. In addition to improving water resistance, PU can be used to absorb UV radiation. It can also create a reflective barrier that reduces the amount of UV light absorbed by the fabric or increase the durability of UV protection. Another research on outdoor clothing fabrics found that applying a hydrophilic treatment to laminated nylon woven fabrics improved their waterproof-breathable properties [38].
## 2 Beam Dynamics Design
### Choice of Beam Dynamics Parameters
#### 2.2.2 Synchronous Phase  
The electric field generated by the RFQ is time-varying, with opposite polarities in adjacent cells. When a particle traverses precisely half an RF period within one cell, consistently entering each cell at the same phase, it can experience continuous acceleration at a constant phase during transmission. Such a particle is referred to as the synchronous particle, and its phase is called the synchronous phase \(\varphi_{s}\)[28].  
Choosing \(\varphi_{s}=-90^{\circ}\) provides the RFQ with the maximum longitudinal acceptance. Therefore, we set the initial synchronous phase to \(-90^{\circ}\) to effectively capture and bunch the beam. Then, we slowly increase the synchronous phase to bring it closer to the crest of the electric field waveform to enhance acceleration efficiency. In order to maintain optimal bunching and accelerating effects, the final synchronous phase is set at \(-30^{\circ}\).
## 4 Polyphenols
### Flavonoids
#### 4.1.1 Flavones  
The structural composition of flavones can be seen highlighted in structures **23**-**27** in Figure 4. The key structural components of flavones are localized to the aromatic C ring, also characterized by a ketone substitution at C-4. The position of hydroxy groups can vary; however, the flavones present in _U. tometosa_ have been noted to contain hydroxy groups in C-5 and C-7 of ring A and C-3\({}^{\prime}\), -4\({}^{\prime}\), -5\({}^{\prime}\), and -6\({}^{\prime}\) of ring B. Flavones putatively identified from _U. tometosa_ are apigenin (**26**) and luteolin (**27**). These compounds were identified upon analysis of an ethanol extraction of _U. tometosa_ using UV absorption peaks and retention time compared to standard references; however, no efforts were made to spectroscopically characterize the compounds [80]. These basic structures contain the same components of flavones coupled with hydroxy groups at C-5 and -7 of ring A and C-4\({}^{\prime}\) of ring B (highlighted in blue in Figure 4), with an additional hydroxy group at C-3\({}^{\prime}\) for compound **27**.  
Three additional flavones--artochamin C (**23**), 5\({}^{\prime}\)-hydroxycudraflavone A (**24**), and dihydroradlavone B (**25**)--were identified via a bioactivity-guided isolation of a methanol extract of _U. tometosa_. They were identified utilizing MS and \({}^{1}\)H and \({}^{13}\)C NMR spectroscopy [81]. These compounds possess additional substitutions when compared to the previously discussed flavones.  
Compound **23** contains a dimethyl 2H-pyran structure fused to the A ring via C-7 and -8, with an additional hydroxy on C-3\({}^{\prime}\) of ring B. Compounds **24** and **25** are structurally related, where **24** contains a dimethyl substituted 2-H pyran structure fused at C-6 and -7 of ring A, while **25** contains a saturated pyran. Structures **24** and **25** also contain an oxygen bound to C-6\({}^{\prime}\) of the B ring. In **25**, the oxygen is present as a hydroxy moiety, while **24** has an ether that links to the 2 methylpent-2-ene group emanating from C-3 of ring C, forming a 2H-pyran heterocycle.  
Figure 4: Flavones (**23–27**) are characterized by aromaticity of the three A, B, and C rings and a ketone substitution on the C ring.  
Figure 5: Flavonols (**28–34**), with flavon-3-ols below (**35–37**). Sugars in red are \(\beta\)-D-glucose and \(\alpha\)-L-hamnose sugars are depicted in purple. Structures boxed in red indicates compounds mistakenly attributed to _U. tometosa_ in previous sources.  
The compounds **23-25** exhibited antibacterial activity when isolated via a methanol extraction. They were analyzed to determine MIC values for _S. aureus_, _B. subtilis_, _E. coli_, and _Klebsiella pneumoniae_. Of the compounds tested, **23** produced the best results with MIC values ranging from 4.1 to 6.7 \(\upmu\)g/mL, indicating an antibacterial effect on both Gram-positive and Gram-negative bacteria. The MIC values of **25** ranged from 5.1 to 8.3 \(\upmu\)g/mL and **24** was found to be least efficacious with values ranging from 9.8 to 16.9 \(\upmu\)g/mL [81].
## 2 Methods
### Wavelet Packet Decomposition  
Wavelet packet decomposition combines wavelet transform and multi-resolution approximation. More detailed features are extracted as the signal is subdivided step-by-step [28]. After performing a wavelet packet decomposition of level N, \(2^{N}\) different waveform signals \(D_{Nj}\), \((j=1,2,\ldots,2^{N}-1)\) with low to high frequencies are generated. The energy of each band signal can be calculated by Equation (8).  
\[E_{Nj}=\int\big{|}D_{Nj}(t)\big{|}^{2}dt=\sum_{k=1}^{n}\Big{|}d_{jk}\Big{|}^{2}, \tag{8}\]  
where \(d_{jk}\) is the amplitude of the \(k\) point of the reconstructed signal \(D_{Nj}\), and \(n\) is the number of discrete points. The energy of each frequency band can be calculated from Equation (8), and the feature vector \(S_{N}\) can be constructed from these energy values as in Equation (9).  
\[S_{N}=[E_{N0},E_{N1},\ldots,E_{Nj},\ldots,E_{N(2^{N}-1)}] \tag{9}\]  
\(S_{N}\) can be normalized by the min-max normalization method to obtain the new feature vector \(S_{N}^{{}^{\prime}}\).  
\[S_{N}^{{}^{\prime}}=[E_{N0}^{{}^{\prime}},E_{N1}^{{}^{\prime}},\ldots,E_{Nj^{ {}^{\prime}}}^{{}^{\prime}}\ldots,E_{N(2^{N}-1)}^{{}^{\prime}}] \tag{10}\]  
Figure 2 shows an example of decomposition with 4-layer wavelet packets from the experimental data in this work.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 2 Tracking Error Estimation
### Non-Coherent Pre-Filter Design  
The non-coherent pre-filter is based on a linear Kalman filter. The state variables of non-coherent pre-filter are code phase error (chip), carrier phase error (cycle), Doppler frequency error (Hz), and Doppler frequency rate error (Hz/s), respectively. The state variables of non-coherent pre-filter are shown in the following equation:  
\[X_{\rm nonCoh}=\left[\delta\tau\,\delta\varphi\,\delta f\,\dot{\delta}f\right] ^{\rm T} \tag{20}\]  
The state transition matrix is constructed by assuming a constant acceleration model for carrier phase error and a constant position model for code phase error. In this transition matrix, the carrier-aiding for DLL is used so that dynamic stress for DLL is overcome by PLL. Thus, the narrow bandwidth DLL can be used to decrease the code tracking error. The inherent relationship between state variables is fully exploited. The state transition matrix is expressed as follows:  
\[A_{\rm nonCoh}=\left[\begin{array}{cccc}1&0&\beta T_{\rm coh}&0\\ 0&1&T&\frac{T_{\rm coh}{}^{2}}{2}\\ 0&0&1&T_{\rm coh}\\ 0&0&0&1\end{array}\right] \tag{21}\]  
where \(\beta\) is the scale factor converting the carrier Doppler frequency to code chip rate.  
The process noise vector \(w_{1}\) of non-coherent pre-filter is given by:  
\[w_{1}=\left[w_{\rm code}\,w_{\rm clock}\,w_{\rm drift}\,w_{\rm accel}\right]^{ \rm T} \tag{22}\]  
where \(w_{\rm code}\) is the process noise for the code phase error to account for code multipath effects, \(w_{\rm clock}\) is process noise for the clock bias, \(w_{\rm drift}\) is the process noise for the clock drift, and \(w_{\rm accel}\) is the process noise for the phase acceleration (which is related to the receiver dynamics) [23]. The corresponding covariance matrix of system model for coherent pre-filter is given by  
\[Q_{\rm nonCoh}=E[w_{1}w_{1}{}^{\rm T}] \tag{23}\]  
The ATAN/ATAN2/NC-EMLE discriminator outputs are directly used as observation vector, shown as  
\[Z_{\rm nonCoh}=\left[\delta\tau_{\rm NC-EMLE}\,\delta\varphi_{\rm ATAN}\, \delta f_{\rm ATAN2}\right]^{\rm T} \tag{24}\]According to Equations (15)-(17), it is obvious that the observation matrix can be given in a very simple form as  
\[C_{\rm nonCoh}=\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\end{array}\right] \tag{25}\]
## 5 Experiments
### Parameter Selection Experiments and Results
This not only decreases the model's training efficiency but also, due to data scarcity, may prevent the model from being adequately trained, ultimately affecting its performance and generalization capability. When the backtrack window size is set to 7 days, the model achieves the best predictive performance, outperforming other window sizes across all evaluation metrics. In summary, a 7-day historical backtrack period is used for subsequent experiments. In the real-world home appliance market, accurate sales forecasting is crucial for strategic planning, inventory optimization, and flexible sales strategies. Consequently, it is  
Figure 10: Comparison curves of the evaluation metrics under different backtrack window sizes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backtrack Window Size** & **R\({}^{2}\)** & **MAE** & **RMSE** & **MAPE** \\ \hline
4 & 0.813 & 10.866 & 16.551 & 0.246 \\
5 & 0.822 & 9.936 & 16.158 & 0.214 \\
6 & 0.819 & 10.080 & 16.261 & 0.205 \\
7 & **0.844** & **9.295** & **15.048** & **0.171** \\
8 & 0.821 & 10.560 & 16.078 & 0.220 \\
9 & 0.793 & 10.836 & 17.230 & 0.213 \\
10 & 0.805 & 10.608 & 16.655 & 0.204 \\
11 & 0.788 & 10.892 & 17.344 & 0.210 \\
12 & 0.790 & 11.005 & 17.212 & 0.218 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results for different backtrack window sizes. necessary to further evaluate the performance of the proposed TransTLA model in multi-step forecasting. We employ a 7-day historical sliding window to generate predictions for the next 1 to 4 days, which are then compared to actual sales data. For instance, to predict the next 3 days, the model uses real data from the previous 7 days to forecast the sales for the next 3 days at once. Subsequently, the sliding window moves forward by 3 days, and the process is repeated. Figure 11 presents the home appliance sales forecast results of the TransTLA model on the test set under different prediction steps (1 to 4 days ahead). The blue lines represent the actual sales volumes, while the orange lines indicate the predicted values. The model demonstrates high accuracy and sensitivity for short-term forecasts of 1 and 2 days, closely tracking actual sales. As the prediction horizon extends, slight discrepancies emerge between the forecasted and actual sales volumes. This is expected due to the increased uncertainty associated with longer-term predictions. However, the model still captures the overall long-term sales trends effectively, especially during significant upward or downward trends, showing strong trend detection and robustness.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 3 Architecture Design
### MQTT for Event Transport in Edge Computing
#### 3.3.1 Protocol Comparison for Edge Computing  
CoAP (Constrained Application Protocol), AMQP (Advanced Message Queuing Protocol), MQTT, and HTTP (HyperText Transfer Protocol) are popular messaging protocols for IoT and edge computing [28]. While HTTP boasts widespread support and robustness, its resource demands outweigh its benefits in resource-constrained edge environments. BothCoAP and MQTT excel in low-bandwidth and resource-constrained settings, even running on 8-bit microcontrollers with minimal memory. However, MQTT provides superior throughput and more reliable data delivery options through its QoS levels across low- and high-traffic scenarios. In contrast, AMQP, although adept at complex messaging patterns, falls short in edge computing due to its larger header size and increased complexity [29].