esa-sceva/satcom-chunk-collection huggingface dataset:
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
## 2 Preliminaries
### The Learning with Errors (LWE) Problem  
Our scheme is based on the hardness of the LWE problem. The LWE problem, introduced by [PERSON] [23], is a well-known lattice-based hard problem that provides security against quantum attacks and allows for efficient implementation. In lattice-based public key cryptosystems, solving the average-case LWE problem is at least as hard as solving certain well-known worst-case lattice approximation problems.  
Definition 2 (LWE Problem): Given a security parameter \(\lambda\), let the lattice dimension \(n=n(\lambda)\), let the prime \(q=q(\lambda)\) be integers, and let \(\chi=\chi(\lambda)\) be an error distribution over \(\mathbb{Z}\). Let \(\mathbf{A}\) be a uniformly sampled matrix, \(\mathbf{A}\stackrel{{ R}}{{\leftarrow}}\mathbb{Z}_{q}^{m\times(n-1)}\). The goal of the LWE\({}_{n,\mathcal{A},\chi}\) problem is to distinguish between the two distributions as follows:\(\mathcal{D}_{0}\): Uniformly randomly sample \(\mathbf{s}\xleftarrow{R}\mathbb{Z}_{q}^{n-1}\) and \(\mathbf{e}\xleftarrow{R}\chi^{m}\), and let \(\mathbf{b}=\langle\mathbf{A},\mathbf{s}\rangle+\mathbf{e}\). Then \((\mathbf{A},\mathbf{b})\in\mathbb{Z}_{q}^{m\times n}\). \(\mathcal{D}_{1}\): Uniformly randomly sample \(\mathbf{b}\xleftarrow{R}\mathbb{Z}_{q}^{m}\). Then \((\mathbf{A},\mathbf{b})\in\mathbb{Z}_{q}^{m\times n}\). Then, for any probabilistic polynomial time (PPT) adversary \(\mathcal{A}\), the probability of solving the \(LWE_{n,\mathcal{A},\mathcal{X}}\) problem (i.e., distinguishing between these two distributions) is negligible:  
\[\Pr\left[\begin{array}{cc}\mathcal{A}(\mathbf{A},\mathbf{b})\rightarrow\mathcal{D}_ {\mathcal{W}}&:&\begin{array}{c}(\mathbf{A},\mathbf{b})\leftarrow\mathcal{D}_{0}\\ \cdot&(\mathbf{A},\mathbf{b})\leftarrow\mathcal{D}_{1}\\ \cdot&\in\{0,1\}\end{array}\right]\leq\frac{1}{2}+negl(\lambda).\]  
**Lemma 3** ([23]).: _[PERSON] [23] proposed a reduction of the LWE problem from the worst case to the average case. For any integer lattice dimension \(n\), integer prime \(q=q(n)\), and bound \(B=B(n)\geq 2n\), there exists a \(B\)-bounded distribution \(\chi_{B}\) that can be efficiently uniformly sampled. Therefore, if a quantum algorithm exists that can efficiently solve the \(LWE_{n,q,\mathcal{X}}\) problem, then a quantum algorithm also exists that can efficiently solve the \(\widetilde{O}(qn^{1.5}/B)\)-approximate worst-case problems SIVP and GapSVP on the lattice._
## 3 Results  
On the first and third days, cell proliferation in all of the groups was insignificant compared to the control group, so the energy density in all of the groups showed no significant difference; however, the wavelengths were significant compared to the control group (\(p\) < 0.05). The 808 nm wavelength was significantly higher than the other three wavelengths with regard to cell proliferation (Tables 2 and 3) (Figures 1 and 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Groups** & **Wavelength (nm)** & **Power (mW)** & **Power Density (mW/cm\({}^{2}\))** & **Energy Density (J/cm\({}^{2}\))** \\ \hline
1 & 635 & 220 & 0.33 \\ \hline
2 & 660 & 150 & 0.25 \\ \hline
3 & 808 & 250 & 0.4 \\ \hline
4 & 980 & 100 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 1: Laser characteristics of the study groups.  
Figure 1: Comparing the effect of wavelengths and energy densities on the proliferation of BFP stem cells on the first day.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Energy Density** & **1 J/cm\({}^{2}\)** & **1.5 J/cm\({}^{2}\)** & **2.5 J/cm\({}^{2}\)** & **4 J/cm\({}^{2}\)** \\ \hline \multicolumn{4}{c}{Wavelength} \\ \hline control & 100 & 100 & 100 & 100 \\ \hline
635 nm & 81.6018 & 105.898 & 95.8477 & 98.8391 \\ \hline
660 nm & 100.561 & 105.862 & 100.796 & 100.602 \\ \hline
808 nm * & 102.007 & 109.765027 & 119.181 & 117.269 \\ \hline
980 nm & 103.347 & 97.3933 & 93.1032 & 101.313 \\ \hline \end{tabular}
\end{table}
Table 2: The viability of cells (%) on the first day (items marked with * are \(p\) < 0.05 in comparison to the control group). [One-way ANOVA and Dunnet’s supplementary tests were used to compare the study groups with the control].  
Figure 2: Comparing the effect of wavelengths and energy densities on the proliferation of BFP Stem Cells on the third day.
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Data Acquisition  
In this work, we employed a TMD 3719 sensor-based system containing a white LED (consisting of three wavelengths: blue (465 nm), green (525 nm), and red (615 nm)) and a photodetector. The CareSens II blood glucose meter was used to measure the actual corresponding blood glucose level and a simultaneous PPG signal was recorded for three minutes. The sampling rate for PPG data was 24 Hz. Data were collected from a total of 34 subjects. The entire data collection process was supervised and approved by the institutional review board (IRB) at Kookmin University, Seoul, Korea, and the IRB protocol number is KMU-20211-BR-286. Table 1 shows the dataset demographics of the 34 subjects. The male to female ratio of subjects is 50:50.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measurement** & **Blood Glucose (mg/dL)** & **Age (Years)** & **HbA1c (\%)** \\ \hline Min & 86 & 23 & 5.1 \\ Max & 170 & 86 & 8.8 \\ Mean \(\pm\) SD & 120.82 \(\pm\) 25.12 & 55 \(\pm\) 21.92 & 5.87 \(\pm\) 0.63 \\ \end{tabular}
\end{table}
Table 1: Dataset description.  
Figure 1: Proposed system architecture.
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### Model Development  
The multivariate regression model establishes a link between an object's desired physical, chemical, or biological attributes and its spectrum response. Multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLSR) are the most used regression methods in quantitative analysis. MLR analysis aims to find a relationship between a response variable and an explanatory variable. One disadvantage of the MLR model is that the spectra often have high co-linearity, which can lead to overfitting the calibration models. When there is a linear relationship between the spectra and the quantitative features of the sample, PLSR emerges as the most reliable and robust tool, built on the linear algorithm and giving good performance [74]. The model's performance is judged by its higher determination coefficients; other statistics include lower root mean square errors for cross-validation (RMSECV) and prediction (RMSEP) [75].
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 3 Results
### Simulated Study  
#### 3.1.1 Point Spread Function  
Figure 6 shows the beamformed responses of 10 point targets located at depths 15-30 mm using CPWC and our CPWC + SVD method over a 60 dB display dynamic range. As seen from Figure 5(a), the simple CPWC mode has high sidelobes. Although the sidelobe artifacts in the upper area are suppressed by the Hamming apodization, the artifacts at the bottom are strengthened in Figure 5(b). Meanwhile, the Hamming window apodization enlarges the mainlobe and lowers the lateral resolution. We also see from Figure 5(c) that the proposed beamformer presents the same resolution as CPWC, and much better performance in terms of the sidelobe level.  
Figure 7 shows the lateral variation of beamformed responses of simulated point spread function (PSF) and confirms these qualitative observations. The target here is the central point located at the depth \(z=20\) mm. As the results show, the proposed beamformer presents the same narrow mainlobe and lower sidelobe levels, with respect to CPWC. In terms of sidelobe levels, the first side level of the proposed beamformer is about 6.2 dB lower than that of CPWC. Combined with the Hamming window, CPWC loses its advantage in resolution. Specifically, the width of the mainlobe almost doubles.  
The quantitative results of the FWHM and PSL are given in Table 2. While the FWHMs of CPWC and CPWC + SVD are nearly the same, our method obtains a sidelobe 6 dB lower than CPWC, which indicates its validity for sidelobe suppression with respect to point targets in a homogeneous medium. Apodization has a similar performance as CPWC + SVD in the PSL, but its effect for the FWHM is serious.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beamformer** & **FWHM (mm)** & **PSL (dB)** \\ \hline CPWC & 0.46 & \(-25.62\) \\ CPWC + Hamming & 0.89 & \(-31.76\) \\ CPWC + our SVD filter & 0.46 & \(-31.80\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Full-width at half-maximum (FWHM) and peak sidelobe (PSL) for beamformed responses at \(z=20.0\) mm.  
Figure 6: Beamformed responses of the simulated point spread function (PSF): (**a**) CPWC; (**b**) CPWC with Hamming window; and (**c**) CPWC + SVD. All images are shown with a dynamic range of 60 dB.  
Figure 7: The lateral variation of CPWC, CPWC + Hamming window and CPWC + SVD at the depth of 20 mm.
## 5 Proposed WNoC Design
### Antenna  
The THz band is notorious for its high path loss, which poses challenges. To address this, an array of antennas appears promising, but it requires additional feeding circuits. Research has demonstrated that carbon-based materials like graphene or carbon nanotube (CNT) antennas support the propagation of surface plasmon polaritons (SPPs) across a wide range, from microwave to the infrared region. These materials offer advantages in terms of tunability, bandwidth, and size. However, with current fabrication methods, where chips are integrated using the CMOS process, using graphene material presents additional challenges. Various antenna structures, such as triangular monopoles [31], dipoles, patches [46], and zigzags [2], have been previously studied at lower frequencies of up to 70 GHz. Some have also been explored at mm-Wave and THz frequencies, but there is a lack of full chip-scale modeling and analysis of results, such as E-field distribution, path loss, and budget link examples, in high-frequency communication within chip cores and between chips. In this paper, a dipole and monopole zigzag antenna has been chosen due to its small size and omnidirectional radiation pattern, leading to easy integration in the chip. The specifications for these dipole antennas are illustrated in Figure 4, and their \(|S_{11}|\) parameter in the THz band is shown in Figure 5. The on-chip zigzag antenna is 353 \(\upmu\)m long, while the dipole antenna is 149.9 \(\upmu\)m long, for operation in the THz band. For simulation purposes, a lumped port is applied to excite the antenna, and the size of the antenna is optimized using Ansys HFSS to resonate in the THz band. The \(|S_{11}|\) parameter of both antennas under a polyimide substrate indicates that the dipole antenna has a wider  
Figure 3: Transmission scenario across cores in a chip.  
bandwidth compared with the zigzag antenna, while both antennas exhibit approximately the same value of \(|S_{11}|\).
## 3 Numerical Model for Multi-Cluster Hydraulic Fracturing in Shale Reservoirs with Natural Fractures
### Multi-Cluster Hydraulic Fracturing in Shale Reservoirs with Natural Fractures
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Parallel to** & **Vertical to the** & **Parameter** & **Value** \\  & **the Bedding** & **Bedding** & & \\ \hline Compression modulus \(E_{c}\) (GPa) & 28.13 & 28.57 & Reservoir vertical ground stress \(\sigma_{v}\) (MPa) & 32 \\ Tensile modulus \(E_{t}\) (GPa) & 4.81 & 3.64 & Maximum horizontal ground stress of reservoir \(\sigma_{h}^{max}\) (MPa) & 30 \\ Poisson’s ratio \(\
u\) & 0.13 & 0.15 & Minimum horizontal ground stress of reservoir \(\sigma_{h}^{min}\) (MPa) & 28 \\ Compressive strength \(\sigma_{v_{\text{C}}}\) (MPa) & 191.08 & 123 & Reservoir filtration coefficient \(c_{1}\) (m/s\({}^{1/2}\)) & \(1\times 10^{-14}\) \\ Shear stiffness (GPa/m) & 3000 & 3000 & Natural fracture filtration coefficient \(c_{2}\) (m/s\({}^{1/2}\)) & \(1\times 10^{-13}\) \\ Tensile strength \(\sigma_{t}\) (MPa) & 6.04 & 13.98 & Natural fracture tensile strength \(\sigma_{t}^{\prime}\) (MPa) & 2 \\ Hydraulic fracture energy \(G_{c}\) (Pa-m) & 5000 & 5000 & Natural fracture energy \(G_{c}\) (Pa-m) & 1000 \\ Density \(\rho\) (g/cm\({}^{3}\)) & 2.5 & 2.5 & Injection rate \(Q\) (L/min) & 60 \\ Initial porosity \(e\) (\%) & 3 & 3 & Fluid viscosity \(\eta\) (mPa-s) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of multi-cluster hydraulic fracturing of shale reservoirs with natural fractures (Case 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{\(\sigma_{v}\) (MPa)} & \multirow{2}{*}{\(\sigma_{h}^{max}\) (MPa)} & \multirow{2}{*}{\(\sigma_{h}^{min}\) (MPa)} & \multirow{2}{*}{\(Q\) (L/min)} & \multirow{2}{*}{\(\eta\) (m\(\cdot\)Pa)} \\ \hline Case 2 & 34 MPa & 30 MPa & 28 MPa & 60 L/min & 1 mPa-s \\ Case 3 & 32 MPa & — & 25 MPa & — & — \\ Case 4 & — & — & 28 MPa & 30 L/min & — \\ Case 5 & — & — & — & 30 L/min & 20 mPa-s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Material parameters of the other four cases.
## 4 Discussion  
A performance summary and comparison of this work with other fabricated state-of-the-art from the literature are given in Table 1. In this comparison table, we have included both capacitive and inductive-based isolation systems. [PERSON] et al. [27] achieved the highest isolation voltage rating of 7.5 kV\({}_{\text{rms}}\) within 30 \(\upmu\)m thick polyimide using an on-chip transformer implemented in a 5 V 0.18 \(\upmu\)m CMOS, however, at the cost of higher energy consumption. The capacitive isolator implemented in a 1.8 V 0.5 \(\upmu\)m process has the lowest energy of 99 pJ/b [12]. The work of [PERSON] et al. [23] stands out among the others, with the smallest effective area using the pulse polarity scheme. Within the works presented in Table 1, our design achieved the maximum speed of 0.6 Gbps and a 0.8 ns minimum propagation delay for metal1-meta2 transformer implementation utilizing pulse polarity modulation implemented in a high-voltage 5 V 0.35 \(\upmu\)m CMOS process. The presented results show the effect of increasing the separation distance between coils, where it increases the isolation voltage from 0.5 kV\({}_{\text{rms}}\) to 1 kV\({}_{\text{rms}}\). However, this comes with a trade-off of reduced speed and increased propagation delay to 0.5 Gbps and 2.6 ns, respectively.  
Figure 16: (**a**) Data-dependent jitter (DDJ) vs. bit (first 2 bits zoomed); (**b**) composite jitter histogram showing the distribution of jitter in the system.  
Figure 15: (**a**) Measured real-time eye diagram of the fabricated inductive digital isolator chip output at 0.5 Gbps; (**b**) corresponding jitter bit error rate (BER) bathtub over one unit interval (UI) window.
## 5 RIS Applications
RISs can also be used to create virtual environments with multiple signal paths that can improve the performance of robots in complex environments [70; 71]. RISs can also be used in manufacturing to improve the efficiency and quality of production processes. RISs can be designed and placed strategically to optimize the absorption and reflection of specific frequencies of electromagnetic waves, which can improve the performance of industrial equipment and reduce energy consumption [69]. Moreover, RISs can also be used in entertainment and media to enhance consumers' viewing and listening experience. RISs can be designed to act as a screen or a speaker that can manipulate and control electromagnetic waves, improving the quality and immersion of audiovisual content [72]. Finally, RISs can also be used in education and research to improve the performance of scientific instruments and equipment. RISs can be designed and fabricated to act as a lens or a mirror that can focus or reflect electromagnetic waves, improving the resolution and accuracy of scientific imaging and sensing technologies [73].
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 3 Methodology  
When dealing with video anomaly detection, classic UNet architectures can inadvertently become too proficient at reconstructing input features, including anomalies. This reconstruction efficiency of UNets, especially due to their skip connections, often leads to the undesired effect of anomalies being rendered indistinct from normal data. As a result, the anomalies can become less discernible, making detection more challenging.  
In a traditional UNet setup, while the skip connections are crucial for preserving high-frequency details and mitigating the loss of information during the encoding process, they may also enable the accurate reconstruction of anomalies. This occurs because the detailed features from the encoder are directly carried over to the decoder, which may result in the anomalies being maintained in the output, thereby diminishing the model's ability to differentiate between normal and anomalous patterns.  
Incorporating the MaskedConv3D layer between these skip connections can alleviate these issues. The MaskedConv3D layer, considering the temporal dimension, works on a sequence of frames and applies a zero mask to a subspace of the centre kernel. This leaves the previous and next frames, as well as the boundary of the centre frame, intact, forming a sort of \"hollow ball\" around potential anomalies. This design forces the model to infer the reconstruction of this hollow space based on surrounding spatial and temporal information, rather than directly processing the anomalies themselves.  
This approach is underpinned by the logic that the model should be able to reconstruct normal sequences using the information from the boundary and adjacent frames, while struggling to do the same for anomalous ones. Therefore, this method prevents anomalies from being inadvertently reconstructed and normalised, ensuring they stand out for detection.
# Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network
## 3 Our Methodology
### Feature Enhancement Module (FEM)  
The feature enhancement module is mainly designed to prevent sharp features learned by corner points and edge points of non-flat regions (as shown in Figure 2) from being blurred. Given a sparse point cloud \(X\in\mathbb{R}^{N\times 3}\) and a scaling factor \(\lambda\), the FEM encodes it into an \(N\times C\) feature matrix, as shown in Figure 3. This encoding process enhances the  
encoding the coordinates of point \(x_{i}\), the normal vector, the neighborhood edge vector, and the inner product of normals between \(x_{i}\) and its neighbors \(x_{j}\) according to Equation (1).  
\[x_{ij}^{\prime}=(x_{i},x_{j},e_{ij},n_{i},n_{j},\alpha_{ij}) \tag{1}\]  
where \(e_{ij}=x_{i}-x_{j}\) (\(e_{ij}\in\mathbb{R}^{1\times 3}\)) is the edge vector. \(n_{i}\) and \(n_{j}\) denote normal vectors of points \(x_{i}\) and \(x_{j}\), respectively. \(\alpha_{ij}\) is the inner product between \(n_{i}\) and \(n_{j}\), i.e., \(n_{i}\cdot n_{j}\).  
The point cloud \(X\) with \(N\) points is encoded as \(X^{\prime}\) (\(X^{\prime}\in\mathbb{R}^{N\times k\times 14}\)) via Equation (1). The encoded features \(X^{\prime}\) are then processed through a series of shared multilayer perceptrons (MLPs) to yield a higher-order nonlinear local feature representation \(\hat{X}\) (\(\hat{X}\in\mathbb{R}^{N\times K\times C}\), where \(C\) is the channel count, \(C\geq 14\)) according to Equation (2).  
\[\hat{x}=\mathcal{A}(h_{\theta}(x_{ij}^{\prime})) \tag{2}\]  
where \(\hat{x}\in\hat{X}\), \(x_{ij}^{\prime}\in X^{\prime}\), \(h_{\theta}(\cdot)\) represents MLPs and \(\mathcal{A}(\cdot)\) is the symmetric function, which is the maximum pooling function in the paper.  
Second, to capture rotation-invariant global structures, a Gram matrix \(G(X)\) (\(G(X)=XX^{T}\), \(G(X)\in\mathbb{R}^{N\times N}\)) is constructed. A higher-order nonlinear global feature representation \(\widehat{X}\) (\(\widehat{X}\in\mathbb{R}^{N\times K\times C}\)) is derived by applying MLPs to the Gram matrix \(G(X)\).  
Finally, the local feature \(\widehat{X}\) is concatenated with the global feature \(\widehat{X}\), forming a new feature representation \(X^{*}\) (\(X^{*}\in\mathbb{R}^{N\times K\times 2C}\)). And the enhanced feature \(X_{local}^{*}\) (\(X_{local}^{*}\in\mathbb{R}^{N\times C}\)) is learned through MLPs and maximum pooling.
## 4 Conclusions and Future Work
Finally, we are aware that the mechanism currently used to combine the output of filtering modules and make a single decision is quite simple and does not provide enough flexibility compared to the mechanisms incorporated in other reactive spam filtering frameworks (e.g., SpamAssassin). We plan to improve the flexibility of this mechanism taking advantage of some optimizations introduced in previous studies [20] to achieve a powerful framework that can be used during SMTP time. **Author Contributions:** Conceptualization, S.G.-M. and J.R.M. ; Methodology, S.G.-M. and J.R.M. ; Software, C.G.M. and E.P.A.-C.; Validation, C.G.M., E.P.A.-C. and J.R.M. ; Formal analysis, C.G.M. and E.P.A.-C.; Investigation, S.G.-M., C.G.M., E.P.A.-C. and J.R.M. ; Writing--original draft preparation, S.G.-M.; Writing--review and editing, S.G.-M. and J.R.M. ; Visualization, C.G.M., E.P.A.-C. and J.R.M. ; Supervision S.G.-M. and J.R.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Conselleria de Cultura, Educacion e Universidade (Xunta de Galicia) under the scope of the strategic funding of Competitive Reference Group [grant number ED431C 2022/03-GRC]. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Datasets used for the experimentation included in this study are publicly available online and have been cited in the text. **Acknowledgments:** SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. **Conflicts of Interest:** The authors declare no conflict of interest.