esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 3 PQ-DPoL
### Block Compression  
In blockchain systems, replacing ECC (elliptic-curve cryptography) with PQC (post-quantum cryptography) introduces significant challenges due to the increased size of signatures and public keys. As shown in Table 2, NIST PQC algorithms like Falcon and Dilithium offer relatively smaller public key and signature sizes compared to other PQC options, such as SPHINCS+, which has significantly larger signatures. However, even with Falcon and Dilithium, the increase in key and signature sizes can substantially reduce the number of transactions that can be stored within a fixed-size block, thereby decreasing the transactions per second (TPSs) by more than \(10\times\). Although Falcon and Dilithium present a more balanced trade-off between security and performance, their adoption still necessitates careful consideration of the impact on blockchain scalability and efficiency. The use of these algorithms helps to mitigate some of the performance overhead associated with larger cryptographic parameters, but the reduction in TPSs remains a significant challenge for maintaining the performance and scalability of blockchain networks.  
To address this reduction in TPSs and ensure the blockchain network remains efficient, block compression is employed. Compression mitigates the impact of the larger key and signature sizes introduced by PQC, allowing the network to maintain high TPSs.  
Block compression helps PQ-DPoL mitigate performance bottlenecks caused by large post-quantum signatures, allowing the system to maintain scalability and efficiency.  
For block compression, Snappy, a fast compression algorithm commonly used in real-time data processing, is utilized [25]. Snappy, known for its balance between compression speed and efficiency, is chosen to handle the larger data sizes introduced by PQC. This ensures that PQ-DPoL can maintain high throughput and performance by compressing data before transmission, minimizing the impact of larger signatures and keys without compromising security or integrity. Its rapid compression capabilities ensure that even with the larger sizes introduced by PQC, the network can maintain high throughput. This compression step enables faster transmission and processing while maintaining the security and integrity of transactions. However, while the chosen Snappy compression algorithm provides fast processing speeds, its compression ratio may be lower compared to other high-compression algorithms. This could potentially result in insufficient reduction of data size, leading to limitations in storage efficiency within the blockchain network. To assess this, we analyze the compression efficiency of the Snappy algorithm when applied to blocks in Section 4.2.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 4 Results  
### Analysis of the Controllers Performances with \(Svy\)  
In Section 3, the synthesis approach for structural active control involved identifying a controller that reduced the structured singular value (SSV) of the structure's uncertain model, enabling the system to manage the most severe disturbances. We then formulated the control using the minimization problem of the transfer function, which connected the controlled signals (displacements, accelerations, etc.) to the disturbances represented by the stochastic seismic signal.  
Based on this concept, the structural active control problem could be compared to solving a disturbance rejection problem, where the synthesized controller must minimize  
Figure 6: Time domain representation of the seismic acceleration (El Centro, 1979).  
the closed-loop gain of the weighted combination of the outputs and control effort by ensuring a value of 1 (Equations (14) and (16)). The SSV \(\mu\) was then the effective guarantee to reach the required robustness in the performance of the controlled system against structured uncertainties.  
Thus, a high SSV \(\mu>1\) indicates increased sensitivity to uncertainties, which may be undesirable. Conversely, a low SSV \(\mu<1\) indicates enhanced robustness to uncertainties. More precisely, as a rule of thumb, if \(\mu\) is less than 0.8, then the achievable performance can be improved; when it is greater than 1.2, the desired closed loop bandwidth is not achievable for the given amount of system uncertainties.  
In conclusion, the value continues to serve as the most reliable indicator of the performance achievable under actual implementation conditions. To evaluate the controllers' performance achieved with the AMD and ABSs, the one realizing the closest SSV \(\mu\) to one and respecting the conditions presented above was considered more effective and robust against uncertainties.  
The first controller developed, \(\mu_{\text{C-MD}}\), reached a SSV \(\mu\) of 1.0907, which was a value included in the secure interval (0.8 \(<\mu<\) 1.2), Inducing the guarantee that the controller can meet the desired performance objectives (structure responses' mitigation to seismic load).  
The second controller developed, \(\mu_{\text{C-BS}}\), reached a SSV \(\mu\) of 1.1861, which was also a value included in the secure interval (0.8 \(<\mu<\) 1.2).  
Based on the SSV realized, the two controllers presented very good indications in their ability to respond to the fixed control requirements; however, one should explore more results to compare their performances and attest which one of the two system used presented the best results.
# The Impact of Optogenetics on Regenerative Medicine
## 3 Biomedical Applications of Optogenetics
### Applications in Heart Failure  
Optogenetics in heart pathologies and therapy could achieve significant advantages [34]. In 2016, [PERSON] et al. [35] performed a study to evaluate the induction of ventricular fibrillation (VF) through light-induced excitation of opins present in the myocardium. Through electrophysiological simulations in a human ventricular model, specific ChR2 was injected and irradiated by blue light 470 nm, 0.4 mW/mm\({}^{2}\). This experimental model allowed observing a VF induced by the optogenetic device, demonstrating that opsin is a determining factor to consider for future therapies of severe VF [36]. Several studies have been also carried out on the hearts of mice, especially in the field of atrial fibrillation (AF). The idea was to modulate the heart rhythm, using light radiation on genetically modified ChR2 proteins [37]. These proteins can activate or inhibit the potassium (K) channel, thus shortening the atrial refractoriness and improving the atrial conduction. The in vivo results obtained with the optogenetic technique will allow for creating new applications to treat AF [38].
## 4 Experiments
### ML on BUZZ2  
The four standard ML methods (logistic regression, k-nearest neighbors (KNN), support vector machine (SVM), and random forests) were trained on the training dataset of BUZZ2 (7582 audio samples) and tested on the testing dataset of BUZZ2 (2332 audio samples). The training and testing took place on the hardware and with the same parameters as on BUZZ1 (see Section 4.2). Since, as was discussed in Section 4.2, feature scaling did not improve the performance of the ML methods on BUZZ1, no feature scaling was used with the four ML methods on BUZZ2. Tables 15-18 give the confusion matrices for logistic regression, KNN, SVM OVR, and random forests, respectively, on the BUZZ2 testing dataset.  
As these tables show, the total accuracy of KNN (98.54%) was the highest followed by random forests (96.61%). The total accuracies of logistic regression (89.15%) and SVM OVR (81.30%) were considerably lower, which indicates that these models did not generalize well on the testing data completely separated from the training data by beehive and location. When these results are compared with the results of the same ML methods on the BUZZ1 testing dataset in Section 4.2, one can observe that the accuracy of logistic regression fell by almost 10% (99% on BUZZ1 vs. 89.15% on BUZZ2), the accuracy of SVM OVR decreased by almost 20% (99.91% on BUZZ1 vs. 81.30% on BUZZ2), and the accuracy of random forests dropped by over 3% (99.97% on BUZZ1 vs. 96.61% on BUZZ2).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{_True_} & \multicolumn{5}{c}{_Predicted_} \\ \cline{2-6}  & **Bee** & **Noise** & **Cricket** & **Total** & **Testing Accuracy** \\ \hline Bee & 895 & 3 & 0 & 898 & 99.66\% \\ Noise & 5 & 929 & 0 & 934 & 99.46\% \\ Cricket & 0 & 26 & 474 & 500 & 94.80\% \\ Total Accuracy & & & & & 98.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Confusion matrix for KNN (\(n~{}=~{}5\)) on BUZZ2 testing dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{4}{l}{**Training Samples: 7582, Testing Samples: 2332**} \\ \hline
**Model Name** & **Training Loss** & **Training Accuracy** & **Testing Loss** & **Testing Accuracy** \\ \hline RawConvNet & 0.00348 & 99.98\% & 0.14259 & 95.67\% \\ ConvNet 1 & 0.47997 & 99.99\% & 0.55976 & 94.85\% \\ ConvNet 2 & 0.64610 & 69.11\% & 0.57461 & 95.50\% \\ ConvNet 3 & 0.65013 & 69.62\% & 0.58836 & 94.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Raw audio ConvNets on BUZZ2 train/test dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{_True_} & \multicolumn{5}{c}{_Predicted_} \\ \cline{2-5}  & **Bee** & **Noise** & **Cricket** & **Total** & **Testing Accuracy** \\ \hline Bee & 673 & 221 & 0 & 898 & 74.94\% \\ Noise & 2 & 932 & 0 & 934 & 99.78\% \\ Cricket & 0 & 26 & 474 & 500 & 94.80\% \\ Total Accuracy & & & & 89.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Confusion matrix for logistic regression on BUZZ2 testing dataset.
## 6 Conclusions  
Through the analysis of meteorological and pollution data in Macau, we conducted data analysis, investigated the correlation with meteorological factors, and developed and evaluated prediction models. The main conclusions include the following:  
1. In Macau, PM2.5 and PM10 concentrations exhibit significant seasonal patterns, with higher concentrations in winter and lower concentrations in summer.
2. Relative humidity, temperature, and sea-level pressure were identified as key meteorological factors influencing PM2.5 concentrations.
3. Through hourly analysis, daytime and early morning were determined as peak periods for PM pollution in Macau.
4. In Macau, PM is correlated with atmospheric stability but shows no significant correlation with boundary layer height.
5. A PM2.5 concentration prediction model based on Stacking ensemble learning was proposed, employing LSTM and XGBoost as base learners and LightGBM as the meta-learner, significantly improving prediction accuracy.
6. Experimental validation demonstrated that the constructed Stacking model can effectively capture the overall trends and patterns of PM2.5 concentrations, outperforming single models in evaluation metrics and reflecting the superiority of ensemble learning.  
We systematically analyzed the PM pollution issue in Macau, ranging from the temporal characteristics of pollutants and the correlation with influencing factors, to the development of an innovative prediction model, yielding valuable research outcomes. Our work provides technological support for improving air quality in Macau and the broaderregion. Our findings not only enrich the research content in the field of air pollution in Macau but also provide new insights for air quality monitoring and control in other cities, possessing significant theoretical value and practical significance.  
Conceptualization, H.T. and H.K.; methodology, H.T.; software, H.T. and H.K.; investigation, H.K., H.T. and C.W.; resources, C.W.; writing--original draft preparation, H.T.; writing--review and editing, H.K. and H.T.; visualization, H.T.; supervision, H.K. and C.W.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Macau Foundation under its research fund (grant no. MF2302), Macau China.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank the Macao Meteorological and Geophysical Bureau for providing high-temporal-resolution hourly data, and we are grateful to the Macau Environmental Protection Bureau for providing daily average pollution data, which are available on the website [[https://www.dspa.gov.mo/envdata.aspx](https://www.dspa.gov.mo/envdata.aspx)]([https://www.dspa.gov.mo/envdata.aspx](https://www.dspa.gov.mo/envdata.aspx)) (accessed on 1 April 2023).  
The authors declare no conflicts of interest.
# Transmission Angle Analysis of a Type of Parallel Manipulators
## 3 Transmission Angle Analysis  
Unlike the multi-DOF serial manipulators, the results close to a singularity may destroy the mechanical construction of the parallel manipulators. Three different kinds of singularities employed for the closed kinematic chains are defined in the study [23]. Thus, we can obtain:  
\[\mathbf{I}_{q}\dot{\mathbf{q}}=\mathbf{I}_{x}\dot{\mathbf{x}} \tag{10}\]  
where \(\dot{\mathbf{q}}\) represents a vector with the active joint rates and \(\dot{\mathbf{x}}\) denotes a velocity vector of the moving platform. From Equations (8) and (9), the components of matrices \(\mathbf{J}_{qr}\) and \(\mathbf{J}_{qp}\) share a common characteristic that can be evaluated as the trigonometric values of an angle. As displayed in Figure 3, the angle can be obtained as:  
\[\cos\alpha_{i}=\frac{\mathbf{w}_{i}^{\text{T}}(\mathbf{v}_{i}\times\mathbf{u}_{i})}{|\bm {v}_{i}\times\mathbf{u}_{i}|} \tag{11}\]  
\[\cos\gamma_{i}=\mathbf{w}_{i}^{\text{T}}\mathbf{e} \tag{12}\]  
Figure 3: Angle relationship derived from matrices \(\mathbf{J}_{qr}\) and \(\mathbf{J}_{qp}\). It can be observed here that when \(\alpha_{i}\to 90^{\circ}\) and \(\gamma_{i}\to 90^{\circ}\), the results are \(\det(J_{qr})\to 0\) and \(\det(J_{qp})\to 0\). The phenomena of the direct kinematic singularities may inevitably appear. This angle related to the singularities can be regarded as the inner transmission angle (ITA) for the kinematic chain. Matrix \(J_{xr}\) or \(J_{xp}\) also has the same matrix dimension with \(n\times 3\) (\(n\geq 2\)). Since the moving platform has only translation in space, the moving platform possesses three degrees of freedom at most. If the number of kinematic chains exceeds the minimum required to drive the manipulator, the parallel manipulator is viewed as a redundant one. The Jacobian matrix of this parallel manipulator that maps an m-dimensional velocity vector in the operation space to n-dimensional joint velocity vector is no longer a square matrix. The determinant of \(J_{x}{}^{T}J_{x}\) is equal to 0. The parallel manipulators' \(i\)th redundant characteristics are inverse kinematic singularities when any of the minor Jacobians with \(3\times 3\) dimension extracted from \(n\times 3\) (\(n\geq 3\)) \(J_{x}\) are singular:  
\[J_{x}{}^{T}J_{x}= \begin{bmatrix}\sum\limits_{i=1}^{n}w_{ix}{}^{2}&\sum\limits_ {i=1}^{n}w_{ix}w_{iy}&\sum\limits_{i=1}^{n}w_{ix}w_{iz}\\ \sum\limits_{i=1}^{n}w_{ix}w_{iy}&\sum\limits_{i=1}^{n}w_{iy}{}^{2}&\sum \limits_{i=1}^{n}w_{iy}{}^{2}\\ \sum\limits_{i=1}^{n}w_{ix}w_{iz}&\sum\limits_{i=1}^{n}w_{iy}w_{iz}&\sum \limits_{i=1}^{n}w_{iz}{}^{2}\end{bmatrix}  \tag{13}\]  
The parallel manipulator is viewed as an inverse kinematic singularity when any of the minor \(J_{x}{}^{T}(2\times 2)\) matrices extracted from \(J_{x}(2\times 3)\) is singular:  
\[J_{x}{}^{T}= \begin{bmatrix}w_{1x}{}^{2}+w_{1y}{}^{2}+w_{1z}{}^{2}&w_{1x}w_{2x }+w_{1y}w_{2y}+w_{1z}w_{2z}\\ w_{1x}w_{2x}+w_{1y}w_{2y}+w_{1z}w_{2z}&w_{2x}{}^{2}+w_{2y}{}^{2}+w_{2z}{}^{2} \end{bmatrix}
## 4 Discussion
Modifications in the observed thermal properties of segment samples reflect the influence of oat flour PEF treatment on the transition temperature and enthalpy, which are related to gelatinisation and amylose-lipid complex melting in starch. The notable increase in transition temperatures that was displayed by flour segments collected from PEF-treated oat flour, at high specific energy input, (Table 4) substantiates higher temperature and longer time requirements for the facilitation of (1) starch swelling, (2) starch gelatinisation, and (3) amylose-lipid complex melting. Depending on the strength of PEF intensity applied, earlier studies reported either a higher or lower transition temperature with PEF-treatedstarches and PEF-assisted chemically modified starches [8; 41; 42]. In this study, the observed increase in transition temperature of segment samples was likely an effect of granule aggregation, which induced physical reorganisation in the samples and enhanced its structural stability. Alterations on the gelatinisation enthalpy exhibited by flour segments after PEF treatment of oat flour indicates some degree of change in the water-starch molecules association/interaction in the sample [43]. Lower gelatinisation enthalpy suggests that water molecules were able to react with starch molecules more easily in the crystalline region. In other words, the packing within the inner granule structure have been relaxed after PEF treatment and this can affect the accessibility of starch molecules. On the other hand, higher gelatinisation enthalpy of flour segments from PEF-treated oat indicates a more crystalline sample and therefore, a more compact starch granule packing that would require larger amount of energy to cause melting of starch crystals. Increased enthalpy associated with amylose-lipid complex melting, due to PEF treatment, was not previously reported in the literature. Flour segments from PEF-treated oat that exhibited higher enthalpy would demand increased energy requirements to melt the amylose-lipid complex in the system. It is likely that the hydrophobic tail buried in the inner part of the amylose-lipid complex helical structure increased the degree of entanglements among starch molecules [40; 44] and caused the observed increase in enthalpy. Moreover, the amylose-lipid complex forms the V-amylose crystalline units, which was reported to be the fifth type of resistant starch. This affirms the additional peak (13\({}^{\circ}\) at 20) that was observed in the XRD pattern of flour segments from PEF-treated oat flour, which was previously reported to be also related to formation of amylose-lipid complex [45].
## 3 Materials and Methods
### Data Processing
#### 3.4.3 SDEMs and TDEMs Generation in Agisoft Photoscan Pro  
Images in Photoscan can be aligned at _low_, _medium_, or _high_ accuracy depending on the scale of features of interest to be reconstructed [35]. The selection of alignment accuracy depended on snow surface type, photoset image quality and required processing time. For low-quality images (characteristic of low light conditions and from the minimal set-up) and for smooth snow areas, _medium_ accuracy is preferred due to the paucity of small-scale features to match and because reconstruction might be confounded by the associated digital noise. With better image quality from the advanced set-up, a _high_ reconstruction quality is preferable. However, we found this to be redundant considering the small GSD of 0.001-0.004 m pixel\({}^{-1}\) obtained and with _low_ reconstruction quality still providing centimeter-scale GSD in the downscaled images. A _high_ reconstruction quality is preferred for the summer areas to compensate for the poor camera positions of the land-based surveys.  
The processing time was noticeably reduced by the image pair preselection option (_generic_), which pre-matched image pairs with reduced matching constraints. The sparse clouds generated from the alignments were manually edited and filtered with Photoscan Pro built-in tools for removing evident outliers (cloud points detached from the overall reconstructed surface) and noise in the point clouds. The sparse clouds were georeferenced by manually identifying the GCP in the matched pictures and assigning them the coordinates from the GNSS post-processing. At this stage, also the natural CGCPs available and further identified (mainly common identifiable rocks), were used to co-georeference the winter and summer DEMs. Co-georeferencing consists of georeferencing both snow and terrain DEMs with common visible features of known precise location with the purpose of mitigating errors in the final snow depth estimation. These common recognizable points had been directly measured with the GNSS station at the ground level. The number of GCPs, and common GCPs (CGCPs) used for both the summer and winter DEMs are listed in Table 1.  
For the dense cloud reconstruction process, all areas were reconstructed at a _low_ or _medium_ reconstruction quality in Photoscan Pro because of the high computational costs. The reconstruction quality selection criteria were the same for the sparse reconstruction. Photoscan reconstruction quality affects the source imagery resolution by downsampling. For _low_, resolution is reduced eight times (to 12.5%) on each side reducing the imagery GSD. However, due to the millimeter GSDs photoset quality (Table 2), the image resolution still resulted in very detailed and accurate centimeter order geometry. Triangular meshes of the areas were finally generated from the dense clouds with a high polygon count using the standard proposed triangulation in Photoscan Pro and without any interpolation method.