esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hopf Bifurcation Analysis  
In actual drilling, self-excited vibration system will be associated with the Hopf bifurcation dynamic phenomenon. So the stable cutting zone is not well judged by the system linear stability analysis. Hopf bifurcation refers to the phenomenon whereby the equilibrium point is changed from stable to unstable and form limit cycles when the system parameter changes pass a critical value. It is closely related to the production of self-excited vibration in engineering. The self-excited vibration with Hopf bifurcation will cause instability in the drilling system. The limit cycle exists when the value of bifurcation parameter is less than the threshold and it is the subcritical Hopf bifurcation. Otherwise it is supercritical Hopf bifurcation.  
The Hopf bifurcation feature is shown in Figure 11; the amplitude of the drilling system is a function of axial dimensionless cutting depth \(\psi\). The linear stability boundary of the drilling system is \(\psi_{CS}\); the cutting of system is stable when \(\psi<\psi_{CS}\), otherwise it is unstable. In a subcritical case, when an unstable limit cycle (periodic orbit) coexists with the stable equilibrium (stationary cutting), the system may produce stick-slip vibration. In a supercritical case, a stable limit cycle coexists with the unstable equilibrium, so the system is stable.
# Self-Sensing Six-Phase PMSM Drive Based on Back-EMF Measurement
## 3 Field-Oriented Control with Vector Space Decomposition  
The control structure shown in Figure 1 is based on the vector space decomposition procedure [16]. Field-oriented control is achieved by controlling only one torque- and flux-productive subspace \(d\)-\(q\) for the whole six-phase machine. The transformation of vector space decomposition from phase coordinates to partial orthogonal subspaces is defined in (12)-(17). The coefficient \(K\) represents the amplitude-invariant behavior for the six-phase (\(K=0.5\)) and three-phase (\(K=1\)) operations.  
\[\begin{bmatrix}i_{a1}\\ i_{\beta 1}\end{bmatrix}=\frac{2}{3}\begin{bmatrix}1&-\frac{1}{2}&-\frac{1}{2}\\ 0&\frac{\sqrt{3}}{2}&-\frac{\sqrt{3}}{2}\end{bmatrix}\begin{bmatrix}i_{A}\\ i_{B}\\ i_{C}\end{bmatrix} \tag{12}\]  
\[\begin{bmatrix}i_{a2}\\ i_{\beta 2}\end{bmatrix}=\frac{2}{3}\begin{bmatrix}\frac{\sqrt{3}}{2}&-\frac{ \sqrt{3}}{2}&0\\ \frac{1}{2}&\frac{1}{2}&-1\end{bmatrix}\begin{bmatrix}i_{D}\\ i_{E}\\ i_{F}\end{bmatrix} \tag{13}\]  
\[i_{a} =\left(i_{a1}+i_{a2}\right)K \tag{14}\] \[i_{\beta} =\left(i_{\beta 1}+i_{\beta 2}\right)K\] (15) \[i_{z1} =\left(i_{a1}-i_{a2}\right)K\] (16) \[i_{z2} =\left(i_{\beta 2}-i_{\beta 1}\right)K \tag{17}\]The resultant \(\alpha-\beta\) subspace is transformed to the synchronous reference frame utilizing the standard Park transformation (18), where \(\theta_{e}\) is the electrical position of the rotor flux.  
\[\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}=\begin{bmatrix}\cos(\theta_{e})&\sin(\theta_{e})\\ -\sin(\theta_{e})&\cos(\theta_{e})\end{bmatrix}\begin{bmatrix}i_{\alpha}\\ i_{\beta}\end{bmatrix} \tag{18}\]  
The transformation of reference voltages in the inverse path is realized similarly to the transformations shown in (12)-(18), considering the inverse form of the transformation matrices. The subspace \(z1\)-\(z2\) is controlled in the stationary reference frame. Currents \(i_{z1}\) and \(i_{z2}\) are controlled to zero to keep the six-phase operation in balanced conditions. The difference between amplitudes of phase currents in partial three-phase systems (the current sharing problem) and the harmonics of the order \(6k\pm 1\) (\(k=1,3,5,\,\dots\) ) are then eliminated. Since the required values for the \(z1\)-\(z2\) subspace are constant DC values (zero), the \(PI\) controllers can be used for the control of currents \(i_{z1}\) and \(i_{z2}\). Otherwise, resonant controllers would be required. The speed loop of the field-oriented control has been set by the pole placement method, where the transfer function of the current loop has been considered as the unity gain. The current controllers for \(d\)-\(q\) currents have been set by the zero-pole cancelation method, where the integration time constant of the \(PI\) controller is equal to the time constant of the \(RL\) circuit represented by the machine winding.
## 3 Results and Discussion
### Effects of Fermentation Process on Volatile Arroma Components  
In S3 vs. F group, 19 of these volatiles were significantly upregulated, including dimethyl sulfide, (E,E)-2,4-heptadienal, benzaldehyde, benzyl alcohol, etc. The other nine volatiles were significantly downregulated, including 1-octen-3-ol, nerolidol, indole, etc.  
Among the above 28 differential compounds, 13 substances belonged to fatty acid degradation products. The fresh tea leaves were crushed by rolling, and the unsaturated fatty acids underwent enzymatic hydrolysis to form C\({}_{6}\)-C\({}_{9}\) alcohols, aldehydes, ketones, and esters [33]. During fermentation, most alcohols ((E)-2-hexen-1-ol, (Z)-2-penten-1-ol, 1-penten-3-ol) and aldehydes ((E,Z)-2,6-nonadienal, (E,E)-2,4-heptadienal) were upregulated, while esters ((Z)-3-hexenyl butyrate, hexyl butyrate) were downregulated. Most of these upregulated fatty acid degradation products had the aroma characteristics of fresh, fruity, and sweet aroma [4; 33], which contributed to the presentation of the aroma quality of black tea.  
Four volatile components were associated with amino acid degradation, including benzaldehyde, benzyl alcohol, 2-methylbutyratealdehyde, and 2-methylpropanal, and these aroma components were all upregulated during the fermentation process. These substances presented floral, fruity, and delicate aromas [4; 7], and the increase in their content might contribute to the formation of the floral and fruity aromas of black tea. Benzaldehyde and benzyl alcohol were products of the shikimate pathway, which were produced by phenylalanine under the action of phenylalanine decarboxylase [49]. They had sweet, fruity, and citrus-like aromas and were reported to be the main aldehyde in black tea [50].  
During fermentation, metabolites of the amino acid metabolic pathway increased significantly, and free amino acids were confirmed to be degraded to volatile compounds in black tea [26; 34]. Both phenylethyl alcohol and benzeneacetaldehyde were biosynthesized via the phenylalanine metabolism pathway. Phenylethyl alcohol with honey, spice, rose, and iliac smell and benzeneacetaldehyde with honey and sweet flavor were very important flavor compounds in various teas [26]. Isoleucine and valine underwent Strecker degradation to form 2-methylbutanal and 2-methylpropanal [33], respectively, which were the main reasons for the increase of these two volatile aroma components during the fermentation process of black tea. It could be seen that the upregulated volatile compounds during the fermentation of black tea were mainly fatty acids and amino acid degradation products, which presented sweet, floral, and fruity flavor. The aroma substances formed in these fermentation processes laid a certain foundation for the formation of the flavor quality of black tea.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Unsupervised Upsampling  
Existing deep learning-based upsampling methods mainly focus on supervised learning. However, because it is difficult to collect point clouds of the same object with different resolutions, the low-resolution point clouds in the training set are often obtained by downsampling the real point clouds. Therefore, a trained point cloud upsampling model inevitably learns the reverse process of downsampling. To learn upsampling without introducing manual downsampling priors, researchers have increasingly focused on unsupervised upsampling models. We briefly introduce several existing unsupervised point cloud upsampling models.  
To learn the point cloud's entire structure and local structure simultaneously, [PERSON] et al. (2016) proposed a new autoencoder, local to global autoencoder (L2G-AE). The benefit of local to global reconstruction design is that L2G-AE can be applied to the application of unsupervised point cloud upsampling. This was the first method to use deep neural networks for unsupervised upsampling. Unlike the results of PU-Net and EC-Net obtained from the input upsampling in a supervised manner, L2G-AE obtains the local reconstruction result and downsamples it to the target level. L2G-AE is not as effective as the first two networks in some categories, due to its unsupervised learning method and the inability to see ground truth labels.  
Although L2G-AE can perform unsupervised upsampling by reconstructing overlapping local areas, it focuses on capturing global shape information through local to global reconstruction. Limiting the network to capture the inherent upsampling mode generates a high-quality upsampling point set. For the shortcomings in L2G-AE, [PERSON] et al. (2016) proposed a new self-supervised point cloud upsampling model, SPU-Net. Its framework includes two main parts: point feature extraction and point feature expansion from coarse to fine. In point feature extraction, the self-attention module is combined with the graph convolutional network. The context information within and between the local regions is captured at the same time. In the point feature expansion, a hierarchical and learnable folding strategy is introduced to generate an upsampled point set with a learnable two-dimensional grid. To further optimize the noise points in the generated point set, the author proposes a new self-projection optimization, which is associated with joint loss, reconstruction loss, and uniform loss as a joint loss to promote self-supervised point cloud upsampling. SPU-Net does not require 3D ground truth dense point cloud supervision and can repeatedly upsample from the downsampled patch, is not limited by paired training data, and can retain the original data distribution.
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.9. Acuario Project--Magallanes Region  
The Acuario on-shore wind farm is a facility that will be located in the north of Punta Arenas, Magallanes region. It will have 417 wind turbines, the owner of which will be Consorcio Austral. Furthermore, the height of the towers from ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2500 MW, with an estimated capacity factor of 0.52. Figure 18 shows a panoramic image of the landscape of the project site, and Table 11 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Units** \\ \hline Location & San Gregorio Commune, North of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Consorcio Austral & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 417 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2500 & MW \\ \hline Capacity Factor & 0.52 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 2,437,500 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 1875 & MW \\ \hline GH2 Production & 215,625 & Tons/year \\ \hline NH\({}_{3}\) Production & 1,209,375 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 2,156,250 & Tons/year \\ \hline \end{tabular}
\end{table}
Table 11: Acuario GH2 plant main specifications.  
Figure 18: Panoramic view of the landscape in the Acuario project.  
Considering Figure 18, it is evident that the Acuario on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 11, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 2,437,500 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 1875 MW of installed capacity. In this sense, the GH2 production plant will generate 215,625 tons/year of GH2, and 1,209,375 tons/year of NH\({}_{3}\). Finally, this facility will save 2,156,250 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 3 PM2.5 Time Series Prediction Model
### Supplementary Leaky Integrator Echo State Network  
The echo state network, including the model proposed in this paper, has the ability to memorize historical information. However, due to the unpredictability of chaotic time series, it is difficult to accurately retain the information before the current state of the reservoir for a long time. Moreover, due to the complexity of PM\({}_{2.5}\) time series, its current concentration information may be more dependent on the most recent state. Inspired by the leaked integrator reservoir, the update of the reservoir state is strongly dependent on the historical state. In this paper, considering the impact of previous state on current state, the state update formula of reservoir neurons is improved, and SLI-ESN model is proposed. The state update formula of reservoir is as follows:  
\[\mathbf{x}(n+1)=a\times tanh(\mathbf{W}_{in}\times[1;\mathbf{U}(n+1)]+\mathbf{W}\times\mathbf{x}( n))+b\times\mathbf{x}(n)+(1-a-b)\times\mathbf{x}(n-1) \tag{9}\]  
As with LI-ESN, the meaning of the attenuation parameter \(a\) has not changed, and its satisfy \(0<a<1\). The parameter \(b\) is the supplement factor, and it also need to satisfy \(0<b<1\). More importantly, like the side length of a triangle, each coefficient must be greater than \(0\), and satisfy \(a+b<1\). Because the current state of reservoir is more dependent on the state of neighboring moment, this paper considers influence of the first two historical moments. When \(n\geq 2\), supplement \((1-a-b)\times\mathbf{x}(n-1)\) is established. Set \(\mathbf{W}_{back}=0\), the output layer is calculated as follows:  
\[\mathbf{y}(n)=\mathbf{W}_{out}\mathbf{x}(n) \tag{10}\]The calculation from reservoir to output layer is solved by ridge regression [29]. It is a biased estimation regression method for collinear data analysis. It overcomes ill-posed problem in least squares solution and prevents over-fitting problem. The expression is as follows:  
\[\mathbf{W}_{out}=\left(\mathbf{X}^{T}\mathbf{X}+\mathbf{I}\right)^{-1}\mathbf{X}^{T}\mathbf{Y} \tag{11}\]  
where \(\mathbf{k}\) is regularization parameter, \(\mathbf{I}\) is the identity matrix, \(\mathbf{x}(n)\) and \(\mathbf{y}(n)\) are column vectors of \(\mathbf{X}\) and \(\mathbf{Y}\), respectively.
## 3 Results
### Validation of Pleiades-Retrieved Water Turbidity  
Turbidity was computed from Pleiades data using the NIR band reflectance for test site 1, and 6 matchups with the Pauillac station were identified (3 May 2017, 24 May 2017, 1 June 2017, 10 June 2017, 14 June 2017 and 19 September 2018).  
Very good agreement between satellite and in situ turbidity was found for the range of 200-840 NTU (Figure 8b): RMSE of 43 NTU and MAPE of 10%. The linear regression slope is 0.99 and R\({}^{2}\) is  
Figure 7: Comparisons between MODIS/Terra (\(\rho\)555) and MODIS/Aqua (\(\rho\)555) (**a**), MODIS/Terra (\(\rho\)645) and MODIS/Aqua (\(\rho\)645) (**b**), MODIS/Aqua (\(\rho\)555) and Pleiades (\(\rho\)558) (**c**), MODIS/Aqua (\(\rho\)645) and Pleiades (\(\rho\)655) (**d**), MODIS/Terra (\(\rho\)555) and Pleiades (\(\rho\)558) (**e**), and MODIS/Terra (\(\rho\)645) and Pleiades (\(\rho\)655) (**f**) water reflectance values (test site 2) on 7 April 2017.  
0.95. These results show a large improvement over using top-of-atmosphere (MAPE 57%) (Figure 8e) or Rayleigh-corrected (MAPE 40%) (Figure 8f) reflectances, indicating the need for a full atmospheric correction even for extremely turbid waters.  
Turbidity retrieved using red band or band ratio algorithms with best-fitted exponential relationships show less agreement with in situ turbidity. Much lower turbidity values are obtained using the red band algorithm due to the saturation of the water reflectance signal in this spectral region (Figure 8a, Figure 5). The saturation of water reflectance is even more obvious for the green band (Figure 5), the use of which would also result in the underestimation of water turbidity. The NIR/Green and NIR/Red band ratio algorithms (Figure 8c,d) show better results than single red or green bands, with MAPE of 22%. This proves that an NIR spectral band is required to estimate SPM concentrations (or turbidity) over the wide range of SPM concentrations encountered in the GE (10 to \(>\) 1000 g.m-3) [16,19].  
Figure 8: Pleiades-derived turbidity in Nephelometric Turbidity Unit (NTU) as function of in situ measured turbidity in the Pauillac autonomous station when using red band (**a**), NIR band (**b**), NIR/Green band ratio (**c**) and NIR/Red band ratio (**d**) algorithms. Same comparisons when using the top of atmosphere (TOA) reflectance in the NIR band (**e**) and Rayleigh-corrected (RC) reflectance in the NIR band (**f**). The vertical error bars show the standard deviation within the selected ten Pleiades pixels (\(\pm\)3 to \(\pm\)23 NTU) and the horizontal error bars show half the range of the bounding in situ turbidity measurements (from \(\pm\)40 to \(\pm\)83 NTU).  
The temporal variability in the Pauillac site is larger than the instantaneous spatial variability around the site: compare the vertical (spatial variability) and horizontal (temporal variability) error bars in Figure 8.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 2 Related Work
### Verifiable Random Function
#### 2.2.2 Quantum Threats to Blockchain Security  
In 1994, [PERSON] proposed a groundbreaking algorithm that could be executed on a quantum computer and capable of solving problems such as integer factorization and discrete logarithms, which are practically unsolvable by classical computers within a reasonable time, in polynomial time. The security of elliptic-curve cryptography (ECC) is based on the elliptic-curve discrete logarithm problem (ECDLP). However, quantum computers utilizing [PERSON]'s algorithm have the potential to break the security of ECC, meaning that information encrypted using ECC could be easily decrypted with the advent of quantum computers.  
The security of blockchain primarily relies on maintaining the integrity and trustworthiness of transactions, with each transaction protected by cryptographic signatures verified through consensus among nodes. However, if a quantum computer can decrypt ECC signatures, an attacker could forge or alter existing blockchain transactions, severely undermining the fundamental trustworthiness of the blockchain. This could pose a significant threat to the network's security and integrity, particularly if quantum computers become commercially viable. Existing ECC-based blockchain systems would no longer be secure, risking the collapse of the entire blockchain structure [15; 16]. Recent studies have explored quantum computing's impact on blockchain security and consensus mechanisms. The work in [17] demonstrates the potential profitability of quantum PoW mining. In [16], pre- and post-quantum cryptographic methods are compared, highlighting their resistance to quantum attacks. Ref. [18] reveals Bitcoin's vulnerability to [PERSON]'s algorithm, and [19] proposes a quantum-based identity authentication framework. The implementation of post-quantum blockchain systems faces significant challenges, particularly with respect to large ciphertext overheads and the compatibility of these schemes with existing blockchain infrastructures. Further research is required to optimize these cryptosystems and to standardize post-quantum cryptographic algorithms that can be seamlessly integrated into current blockchain networks.  
Therefore, the advancement of quantum computing presents a significant challenge to blockchain technology. To maintain the security of blockchain, it is essential to adopt new post-quantum cryptography (PQC) algorithms that can replace existing cryptographic methods like ECC.
## Materials and Methods
### Characterization Techniques
#### 2.5.3 Fractography via Scanning Electron Microscopy (SEM)  
Fractography was performed utilizing SEM and subsequent image analysis using a JEOL JSM 6510 LV, Oxford Instruments scanning electron microscope. The selected voltage was 5 kV while the specimens were stabilized on SEM holder with a carbon adhesive tape. Also, specimens were sputter-coated with an Au thin layer to avoid charging effects.
# SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
## 3 Method
### Saii  
SIPNet has been designed to improve the performance of object detection and segmentation as much as possible. However, high-resolution solar images with sizes of up to \(4096\times 4096\) pixels\({}^{2}\) demand more memory. If these original images are fed directly into the network, multiple downsampling operations will result in the object being reduced to a few pixels or even disappearing from higher level feature maps. For example, an object measuring \(16\times 16\) pixels\({}^{2}\) in a \(4096\times 4096\) pixels\({}^{2}\) image occupies only about one ten-thousandth of the entire image. When the original image is downsampled to \(1024\times 1024\) pixels\({}^{2}\), the size of the object is reduced to about \(3\times 3\) pixels\({}^{2}\). Similarly, downsampling to \(256\times 256\) pixels\({}^{2}\) will cause it to disappear. On the other hand, an image of this size would require a significant amount of GPU memory during forward propagation. It will greatly increase the load on the GPU and increase the risk of GPU memory overflow, even leading to the program's termination.  
To handle the problem of detecting ultra-small objects in high-resolution images while maintaining higher memory utilization, we integrated an open-source generic framework called Slicing Aided Hyper Inference (SAHI) into the trained SIPNet (SIPNet & SAHI for short). The main idea of SAHI is that slicing the input images into overlapping patches results in relatively larger pixel areas for small objects compared to the images fed into the network.  
The flow of SAHI during inference is detailed below and can also be seen in Figure 4. First, the original image \(I\) is sliced into 1 number of \(M\times N\) overlapping patches \(P_{1},P_{2},...P_{k}\). Then, each patch is resized while preserving the aspect ratio. After that, object detection forward passes by the trained SIPNet are applied to each overlapping patch independently. Meanwhile, an optional full-inference (FI) using the original image can be applied to detect larger objects. Finally, the overlapping prediction results are merged back to original size using NMS. During NMS, boxes with a higher IoU than a predefined matching threshold \(Tm\) (here, set to 0.5) are matched. For each match, detections with a detection probability lower than \(Td\) (here, set to 0.4) are removed.  
Figure 4: The inference process of the trained SIPNet with the integration of SAHI, where the yellow module represents the original image \(I\) cut into 1 \(M\times N\) overlapping patches \(P_{1},P_{2},..P_{k}\), while the blue module represents the trained SIPNet.