esa-sceva/satcom-chunk-collection huggingface dataset:
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 6 Experiments
### Discussion  
It can be observed that the proposed method achieves the best clustering performance on the three real application data sets compared to the baseline methods. While the baseline methods may perform exceptionally well in some cases, they do not consistently do so. In terms of computational speed, the proposed algorithm converges quickly and the time cost of clustering is also highly competitive. In data reconstruction, it can be seen that the proposed method is able to retain the key features of the original data. It is observed that the learned representation matrix has clear block diagonal structures. Simultaneously, as can be seen from the t-SNE visualization, the clustered sample points transition from a dispersed state to a compact formation. The ablation study verifies the effectiveness of embedding Markov transition probability information into the self-expression framework. Simultaneously, the ability of the proposed manifold term to handle nonlinear data is also demonstrated in the ablation study. The impact of connectivity constraints on the group structure is also validated in the ablation study. In the parameter sensitivity study, the proposed method maintains good insensitivity across the five clustering evaluation metrics. Overall, the proposed algorithm exhibits excellent capabilities in terms of clustering performance, key feature capture, and computational speed.
## 2 Materials and Methods  
### Hybrid Homomorphic Deconvolution  
Observed seismic data \(X\) can be modeled mathematically as a convolution  
\[X(t)=G(t)*H(t) \tag{1}\]  
where \(G(t)\) is [PERSON]'s function, \(H(t)\) is the seismic source time function (STF), and \(*\) represents the convolution operation. The complex cepstrum of a seismogram \(X(t)\) is defined as follows [16] ([PERSON] and [PERSON], 1999):  
\[\overset{\wedge}{X}(t)=\frac{1}{2\pi}\int\limits_{-\pi}^{\pi}\log\left[\int \limits_{-\infty}^{\infty}X(t)e^{-i\omega t}dt\right]e^{i\omega t}d\omega \tag{2}\]  
The inverse transform is defined as follows:  
\[X(t)=\frac{1}{2\pi}\int\limits_{-\pi}^{\pi}\exp\left[\int\limits_{-\infty}^{ \infty}\overset{\wedge}{X}(t)e^{i\omega t}dt\right]e^{-i\omega t}d\omega \tag{3}\]  
The series in the complex cepstrum is real, and the complex cepstrum domain is called the quefrency domain [17]. A indicated in (2) and (3), the cepstrum of a seismogram is the sum of the cepstra of its convolutional components; that is,  
\[\overset{\wedge}{X}(t)=\overset{\wedge}{G}(t)+\overset{\wedge}{H}(t) \tag{4}\]  
According to previous studies [16; 18], the STF tends to occupy low quefrency regions, whereas the Green function with a rapidly varying spectrum occupies a high quefrency region. The STF is expected to be concentrated near \(t=0\) in the quefrency domain, and structural responses are assigned to a high quefrency region. Therefore, for recovering \(H(t)\) and \(G(t)\), the first step is to apply linear filtering to cepstrum \(\overset{\wedge}{X}(t)\) for separating \(\overset{\wedge}{G}(t)\) and \(\overset{\wedge}{H}(t)\). The second step involves taking the inverse transform of them such that the corresponding convolutional components \(G(t)\) and \(H(t)\) of the seismogram \(X(t)\) can be separated. However, the phase unwrapping problem may occur in the inverse transform, as in (2), owing to the log operation in the cepstrum. To improve the phase unwrapping computation of the signal \(X(t)\), we use a method proposed by [PERSON] et al. (2003) [19] based on polynomial factoring algorithms. If \(H(t)\) is calculated, we can further modify \(H(t)\) with the constraints proposed by [PERSON] et al. (1997) [20], including nonnegativity, causality, and finite duration, to fit the physical meanings of STFs. To avoid amplitude differences due to the radiation patterns of earthquake events, we imposed a constraint on \(H_{i}(t)\), which denotes one earthquake STF estimated at \(i\)-th station must have the same seismic moment as that at all stations.
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 2 Circuit Composition of Low Phase Noise Crystal Oscillator
### Bandgap Reference with a Start-Up Circuit
The conduction of NM\({}_{2}\) makes the \(V_{2}\) voltage drop, and the drop of \(V_{2}\) voltage causes the transistor NM\({}_{1}\) to turn off so that the startup circuit automatically turns off after completing the startup task. In the start-up circuit designed in this article, after the start-up circuit is automatically turned off, transistors PM\({}_{0}\) and NM\({}_{2}\) will still be on, which will consume a certain amount of current. To reduce current consumption, the \(W/L\) of transistor PM\({}_{0}\) designed in this design is very small, so the on-resistance of PM\({}_{0}\) will be large. Therefore, the current flowing through transistors PM\({}_{0}\) and NM\({}_{2}\) will be very small, and the simulation shows that the current is 1.7 \(\mu\)A, so the leakage current is negligible. Figure 5 shows the simulation result of the start-up circuit, and the process corner and temperature are set to tt (\(-40\)\({}^{\circ}\)C), tt 55\({}^{\circ}\)C, tt 125 \({}^{\circ}\)C, ss 125 \({}^{\circ}\)C, ff (\(-40\)\({}^{\circ}\)C). As can be seen from the figure, the design start-up circuit is fully capable of covering all process corners and temperatures and the bandgap reference can produce stable voltage after 1.62 \(\mu\)s in the worst case. Figure 4: Variation of \(V_{REF}\) with the temperature at ss process corners.
## 4 Discussions
### Limitations and Future Works  
Blockchain-based data storage solutions offer decentralization, security, and transparency, yet they still present several challenges. Scalability remains a major issue: while on-chain storage ensures high security, it suffers from high costs and limited transaction throughput. In contrast, off-chain storage methods enhance efficiency but introduce concerns regarding data availability and integrity. Additionally, security risks, such as access control vulnerabilities and key management complexities in decentralized storage systems, require further research. To address these challenges, future research should focus on the following:  
* Enhancing Scalability: Optimizing Layer-2 solutions (e.g., Rollups, State Channels, Plasma) to improve transaction speed and reduce costs [19; 20; 52; 95].
* Strengthening Security and Privacy: Improving blockchain-based access control mechanisms, such as Attribute-Based Access Control (ABAC) and Zero-Knowledge Proofs (ZKPs), for privacy-preserving storage [47; 109].
* Integrating Blockchain with AI and ML: Exploring AI-driven data retrieval and adaptive storage mechanisms for optimizing blockchain efficiency [102; 104].
* Expanding Industry-Specific Implementations: Investigating blockchain storage solutions for IoT-based secure data sharing and continuous authentication, as well as custom blockchain models for satellite communications and space data management [196].  
These advancements are expected to enhance the efficiency, scalability, and security of blockchain-based storage solutions while unlocking new opportunities for decentralized data management.
# Polycyclic Aromatic Hydrocarbons in Popcorn Corn Varieties and Popcorns
## 1 Introduction
[22], the levels of PAHs in rice are in relation to the source of heating. Another research work [23] investigated the influence of the drying process on the levels of PAHs in granola, chocolate granola, and milk-filled cereals. However, there are no data on PAH content in popcorns. Since popcorn corn is subjected to heat via the process of roasting, similar to coffee or cocoa beans, it is reasonable to assume that PAHs can be formed during the preparation of popcorns. Due to different levels of carcinogenic activity, the European Food Safety Authority (EFSA) has set limits for the levels of benzo[a]pyrene and the sum of the concentrations of four PAHs (benzo[a]pyren, benz[a]anthracene, benzo[b]fluoranthene, and chrysene (PAH4) [8]) as the main indicators of PAH toxicity through foodstuffs. According to the regulation [24], the maximum allowed level of benzo[a]pyrene in processed cereal-based foods should not exceed 1 ug/kg, and the \(\sum\)PAH4 levels should not exceed 1 ug/kg. Since there are no data on PAHs in popcorns, this research aimed to investigate whether popping corn can result in PAHs in popcorns, and to report and quantify the occurrence of different PAHs in different popcorn varieties and the popcorns popped from them during two consecutive years using a microwave oven. The used method for analysis was GC \(\times\) MS, described in detail in the Supplementary Material.
## 3 Methods
### Efficient Attention  
In the self-attention mechanism (7), the feature map is typically subject to two dot-product operations. First, calculate the similarity between pixels in the image through \(S=QK^{T}\); then, perform a weighted sum through \(D=SV\). The calculation complexity is \(O(n^{2})\). The high computational complexity limits the use of attention mechanisms in image processing.  
\[S(Q,K,V)=Soft\max(QK^{T})V \tag{7}\]  
Therefore, [PERSON] et al. proposed the efficient attention mechanism (8) to address the issue of high computational complexity in self-attention mechanisms. This is an attention mechanism that performs similarly to the standard attention mechanism but with lower computational complexity. \(\rho()\) represents the Softmax normalization function.  
\[E(Q,K,V)=\rho(Q)(\rho(K)^{T}V) \tag{8}\]  
In contrast to the self-attention mechanism, the efficient attention mechanism notices that, in the normalization process, if the channel dimension of \(Q\) and the spatial dimension of \(K\) are normalized separately, this is equivalent to normalizing \(QK^{T}\) as a whole. Therefore, the efficient attention mechanism distributes the normalization of \(QK^{T}\) to the query and key individually and utilizes the associativity of matrix multiplication \((QK^{T})V\to Q(K^{T}V)\) to reduce the calculation complexity from \(O(n^{2})\) to \(O(d_{k}d_{v})\), where \(n\) represents the input size and \(d_{k},d_{v}\) represents the channel dimensions of the key and value. In practical applications, \(d_{k}\) and \(d_{v}\) are often significantly smaller than \(n\).
## 6 Simulation Results and Analysis
### Simulation Scenario
Figure 7 demonstrates the relationship between the computational capability of relay satellites and the system cost in the proposed LEO satellite edge computing network. As the computational capability of relay satellites increases, the system cost decreases significantly due to improved task processing efficiency and reduced communication overhead. However, the curve exhibits a diminishing return effect, where further increases in computational capability yield only marginal reductions in system cost. This highlights the importance of balancing computational resources to achieve cost-effective performance. The figure underscores the efficiency of the proposed joint task offloading and resource scheduling strategy in optimizing system performance while minimizing costs. Furthermore, an increase in IoT device numbers also results in uneven decreases in system costs. This is due to the reduction in task computation time as satellite computation improves, allowing the task queue to remain stable within a reasonable timeframe, thus lowering costs. Moreover, a rise in device data quantity leads to more tasks for the system to process, adding to the costs. The cost via our proposed methodology notably surpasses that of previous approaches [40], and with enhanced satellite computation, our method's cost increase is slower than that of existing methods, underscoring the benefits of our approach. Figure 8 evaluates the relationship between the average task arrival rate and system cost under different numbers of IoT devices for the proposed algorithm in this chapter and the algorithm from [40]. As shown in the figure, as the task arrival rate increases, the system cost decreases. This is because a higher task arrival rate leads to greater utilization of the network's computational, communication, and caching resources, resulting in increased system costs. Moreover, the system cost achieved by the proposed algorithm in this chapter is lower than that of the algorithm in [40]. Furthermore, as the task arrival rate increases, the cost under the proposed algorithm increases at a relatively slower rate. Figure 6: System cost versus maximum queue length of relay satellites. The system cost metric used in this study incorporates energy consumption, task processing delays, and other operational factors. By minimizing system costs, the proposed method achieves significant energy savings in addition to reducing delays and improving task completion rates. This holistic evaluation provides a comprehensive assessment of the system's performance. Figure 8: System cost versus the average arrival rate of the tasks. Figure 7: System cost versus computing capability of relay satellites.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 7 Future Directions
### Interannual Variability and Long-Term Trends of SUHI  
The spatial, diurnal, and seasonal variations of the SUHI have been extensively studied, but the inter-annual variability and long-term trends remain poorly understood primarily due to data limitations. The AVHRR sensor provides the longest record at a high temporal resolution, but it was utilized to analyze the SUHI in the early literature because of its coarse spatial resolution and low data quality [30; 62]. The Landsat archive has provided LST observations for more than 30 years at a high spatial resolution, but the long revisiting cycle impedes the wide application in detecting the SUHI's inter-annual variability of SUHI [119; 213]. As a result, the previous efforts focused mainly on the impacts of urban expansion on LST using the data in a few representative years and days of the year [88; 90; 252; 253; 254; 255]. The increasing duration of the MODIS LST record, with high temporal resolution and data quality, allow us to examine both inter-annual variability and long-term trends of SUHI after 2000. However, such efforts have been restricted to a few regions like China [76; 109; 121], India [256], Mediterranean [179], and Thailand [257] due to the relatively short observation time and coarse spatial resolution. An integrated use of multi-source data (e.g., MODIS and Landsat) could generate LST observations at both high spatial and temporal resolutions, but at the cost of accuracy [215; 258]. It is foreseeable that the inter-annual variability and long-term trends of the SUHI will attract rising interests from researchers when more data and new data fusion techniques become available.
## 3 Numerical Simulations
### Summary of the Identification Results  
Table 4 shows the simulation cases with uniform random noise and Table 5 summarizes the identified loads under eight cases and three different noise levels. It can be observed that when only moving-load noise is present, the identification accuracy of the influence line area method (IL), the absolute value method (AIL), and the absolute value method with multiple measurement points (AIL-Modified) are the same. When the load spacing is small, but the total load magnitude remains constant, the identification accuracy remains nearly unchanged. However, when there is a uniform load on the bridge, the accuracy of the IL decreases significantly, whereas both the methods of AIL and AIL-Modified can meet engineering requirements.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 2 Multi-Source Fusion Model
### Integrated Navigation Systems and Error Models  
The navigation coordinate system is defined as an east-north-up (E-N-U) coordinate system. In this case, the system state vector is as follows:  
\[\mathbf{X}=[\delta\varphi_{E},\delta\varphi_{N},\delta\varphi_{U},\delta\varphi_{ E},\delta\upsilon_{N},\delta\upsilon_{U},\delta L,\delta\lambda,\delta h, \varepsilon_{x},\varepsilon_{y},\varepsilon_{z},\
abla_{x},\
abla_{y},\
abla _{z}]^{T} \tag{1}\]  
where \(\delta\varphi_{E},\delta\varphi_{N},\delta\varphi_{U}\) are the attitude angle errors, \(\delta\upsilon_{E},\delta\upsilon_{N},\delta\upsilon_{U}\) are the velocity errors, \(\delta L,\delta\lambda,\delta h\) are the longitude, latitude, and altitude errors, \(\varepsilon_{x},\varepsilon_{y},\varepsilon_{z}\) are the gyro drift errors, and \(\
abla_{x},\
abla_{y},\
abla_{z}\) are the accelerometer drift errors. By analyzing the errors of the inertial navigation system, the error state equation is obtained as follows:  
\[\mathbf{X}_{k}=\mathbf{\Phi}_{k|k-1}\mathbf{X}_{k-1}+\mathbf{G}_{k-1}\mathbf{W}_{k-1} \tag{2}\]  
where \(\mathbf{\Phi}_{k|k-1}\) is the state transition matrix of the inertial navigation system from time \(k-1\) to \(k\), \(\mathbf{X}_{k}\) is the error state vector at time \(k\), \(\mathbf{X}_{k-1}\) is the error state vector at time \(k-1\), \(\mathbf{G}_{k-1}\) is the noise coefficient matrix, and \(\mathbf{W}_{k-1}\) is the system noise vector.
## 4 Results
### Obtaining the Peak Load Profile
#### 4.1.1 Finding the Load Profile through the Maximum Entropy Principle
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multicolumn{2}{c}{**Beg-End of**} & \multirow{2}{*}{\(T_{m}\) [h]**} & \multirow{2}{*}{\(i_{m,stapt}\)} & \multirow{2}{*}{\(i_{m,stapt}\)} & \multirow{2}{*}{\(i_{m,stop}\)} & \multicolumn{2}{c}{\(R_{m}\)} & \multirow{2}{*}{\(r_{m}\)} & \multirow{2}{*}{\(R_{m}/T_{m}\) [reqs/h]} \\  & & & & & & & & & & & & \\ \hline
1 & 00:00–24:00 & 24 & 1 & 48 & 85,550 & 1 & 3564 \\
2 & 11:00–17:00 & 6 & 22 & 33 & 56,780 & 0.66 & 9463 \\
3 & 12:00–15:00 & 3 & 24 & 29 & 36,360 & 0.42 & 12,120 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample values of the load and time properties of subdivision located during a day. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(i\)} & \multirow{2}{*}{\(1\)} & \multirow{2}{*}{\(2\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(21\)} & \multirow{2}{*}{\(22\)} & \multirow{2}{*}{\(23\)} & \multirow{2}{*}{\(24\)} & \multirow{2}{*}{\(25\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(29\)} & \multirow{2}{*}{\(30\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(33\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(\ldots\)} & \multirow{2}{*}{\(\mathbf{48}\)} \\ \hline
2 & 0 & 0 & \(\ldots\) & 0 & 1 & 1 & 1 & 1 & \(\ldots\) & 1 & 1 & \(\ldots\) & 1 & 0 & \(\ldots\) & 0 \\
3 & 0 & 0 & \(\ldots\) & 0 Function **fmin** can find the minimum of any multivariate function subject to some constraints. It accepts the following parameters:  
* The name of a minimized multivariate function (here **minus_entropy**);
* The matrix \(A\), for the constraint from Equation (5);
* The vector \(R\), also for the constraint from Equation (5);
* The vector \(lb\), for constraints \(0\leq f_{i}\);
* The vector \(ub\), for constraints \(f_{i}\leq 1\). Function **minus_entropy** is multivariate function that returns the opposite value of the function given by Equation (7). A MATLAB-based program that implements Algorithm 1 and calculates \(F\_opt\) for the considered real example is presented below:  
A =... % values are presented in Table 3  
R = [1, 0.66, 0.42]'  
lb = zeros(n,1); up = ones(n, 1) % lower and upper boundaries for f_i  
F_initial = rand(1, n)./ n % initially, randomly around 1/n  
MinusEntropy = @(F) sum(F.* log2(F)) % defining of minus entropy function  
F_opt = fmincon(@MinusEntropy, F_initial,[], [], A, R, lb, ub)  
plot(F_opt)  
The function **fmincon**[38] is a MATLAB implementation of the function **fmin** from Algorithm 1. The work-step results for obtaining the minimum with constraints using **fmincon**[38] are presented in Figure 1. Linear interpolation was applied. The values of \(F\_opt\) found using Algorithm 1 are presented in Figure 2.
## 3 Results and Discussions
### Dispersion-Induced Focus Shift  
It is of great importance to evaluate how the mKZK model demonstrates its significance in HIFU applications. In Figure 5, comparisons are carried out to display how the wave distribution behaves differently before and after inserting the phantom sample into the wave propagation path. It can be observed that, for either data from experiments or from the mKZK model, although only a slight difference is seen in the axial wave profile when examining the fundamental components, an axial focus shift is more evident for the overall acoustic pressure since it is highly affected by the harmonic components. It is explained here that, due to the dispersive nature of the phantom, higher-order harmonic components require larger values of both sound velocity and attenuation coefficient in the KZK model, urging the overall wave profile to move forward to the transducer.  
In the present study, the focus shift distance was quantified for the overall axial acoustic pressure distribution and its decomposed components, and this is listed in Table 1.  
The focus shift, which is found to be of millimeter magnitude for the studied condition, cannot be ignored, especially for clinical HIFU studies. On the one hand, in typical HIFU applications, the much higher surface pressure would induce even stronger acoustic nonlinearity in the focus area,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Overall** & **Fundamental** & **2 nd Harmonic** & **3 rd Harmonic** \\ \hline mKZK & 1.47 & 1.41 & 1.51 & 1.62 \\ Experiment & 1.42 \(\pm\) 0.04 & 1.40 \(\pm\) 0.03 & 1.45 \(\pm\) 0.04 & 1.51 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Focus shift induced by acoustic-dispersive phantom sample (in mm).  
Figure 5: Comparison of the axial distributed acoustic pressure between the cases of with and without the phantom sample: (**a**) the overall pressure; (**b**) the fundamental component; (**c**) the second harmonic; (**d**) the third harmonic.  
and up to tens of orders of harmonic components might contribute to the overall acoustic responses. In that case, dispersion in acoustic velocity as well as the attenuation coefficient could cause larger focus shifts. On the other hand, even for the millimeter-level focus shift exhibited with the current setup, an impressive amount of acoustic energy would be deposited outside the designated focus area. As demonstrated in Figure 6, about 25% of the \(-3\) dB focal region would fall outside of the one predicted by the traditional KZK equation, when the phantom sample is introduced into the transducer axis. In addition, the absence of shockwaves in the experiment should be noted, and this indicates that the influence of shockwaves could be ignored in the theoretical model under such experimental parameters.