esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In summary, a bi-layered metasurface-based THz dual-wide-band LP-to-CP converter based on transmission characteristics is proposed. For incident linear X or Y polarized waves, it transmits dual polarized dual-band circularly polarized waves. It works over 1.16 THz to 1.634 THz (34% fractional bandwidth) and 3.935 THz to 5.29 THz (29% fractional bandwidth) within two bands for an axial ratio of 3 dB. Considerable energy transfer is observed in dual bands except for the band 5 THz to 5.29 THz. Close agreement between HFSS (FEM) and CST (FDTD) results ensures the validity and reliability of the proposed design. Physical mechanisms, electrical circuit representation and quantitative explanation of the working principle behind the dual-wide-band LP-to-CP conversion justify the designed structure. Such a simple and dual-wide-band, dual polarized LP-to-CP converter can be used in potential applications in THz wireless communication, including satellite communication, ground segment, imaging, spectrometry, antenna technology, and many other THz manipulating devices.  
Figure 12: Effect of (**a**) \(d\) and (**b**) \(c_{1}\) on the performance of dual-band LP-to-CP converter.  
**Author Contributions:** Writing-Original Draft Preparation, A.K.F.; Writing-Review and editing, C.R. and K.C.; methodology, A.K.F.; software, A.K.F.; validation, K.C.; and C.R.; supervision, C.R., project administration, C.R.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61831001, the High-Level Talent Introduction Project of Beihang University, grant number ZG216S1878, and the Youth-Top-Talent Support Project of Beihang University, grant number ZG226S1821.  
**Acknowledgments:** Authors convey their thanks to Prof. [PERSON] in the Vacuum Electronics National Laboratory, Beijing Vacuum Electronics Research Institute, China for his sincere supports for the CST software.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Effects of Ambient Humidity for Outdoor Measurement  
In this study, as seen in Supplementary Figure S1, we observed that the PM\({}_{2.5}\) concentration of IRMs were significantly increased with the increase in relative humidity level. The slope obtained from a simple regression line of FEM on IMRs, that is, (BAM = Slope * ESCORTAIR + intercept) at a relative humidity above 80%, was smaller than the slope at \(<\) 20.0%, 20.1-40.0%, 40.1-60.0%, 60.1-80.0%. The degree of decreasing trend was larger with IRMs, compared to two RGMs (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Indoor—Pan-Frying** & **Indoor—SHS** & **Outdoor—Urban Traffic Hotspot** \\  & **(n = 50)** & **(n = 60)** & **(n = 240)** \\ \hline GRIMM & 153.2 (46.2–409.7) & 23.5 (15.9–107.1) & NA \\ BAM & NA & NA & 9.0 (4.0–22.0) \\ ESCORTAIR & 86.8 (17.8–254.4) & 20.9 (17.4–156.6) & 13.7 (7.3–21.2) \\ PA & 104.9 (43.9–228.2) & 31.2 (14.4–194.3) & 19.7 (9.3–35.8) \\ PDR-1500 & 236.2 (49.3–648.7) & 28.4 (12.8–314.0) & 13.8 (6.8–34.8) \\ SIDEPAK & 261.3 (71.5–800.0) & 50.0 (21.0–652.0) & 29.1 (15.6–59.9) \\ Temp. (\({}^{\circ}\)C) & 21.7 (21.1–21.7) & 20.1 (19.7–20.5) & 30.7 (25.6–40.7) \\ RH (\%) & 37.0 (35.0–39.0) & 37.0 (35.0–38.0) & 56.4 (34.8–71.4) \\ \hline \hline \end{tabular}
\end{table}
Table 2: PM\({}_{2.5}\) mass concentration (\(\mu\)g/m\({}^{3}\)) (median, IQR) measured by real-time sensor devices and the federal equivalent method as well as the temperature and relative humidity throughout the sampling period.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Indoor-Pan-Frying**} & \multicolumn{4}{c}{**Indoor-SHS**} & \multicolumn{4}{c}{**Outdoor Urban Traffic Hotspot**} \\ \hline  & **FEM** & **E** & **PA** & **P** & **S** & **FEM** & **E** & **PA** & **P** & **S** & **FEM** & **E** & **PA** & **P** & **S** \\ \hline FEM & 1 & & & & & 1 & & & & & 1 & & & & \\ E & 0.97 & 1 & & & & 0.92 & 1 & & & & 0.85 & 1 & & & & \\ PA & 0.97 & 0.93 & 1 & & & 0.86 & 0.85 & 1 & & & 0.88 & 0.93 & 1 & & \\ PDR & 0.98 & 0.95 & 0.99 & 1 & & 0.96 & 0.93 & 0.94 & 1 & & 0.84 & 0.93 & 0.99 & 1 & \\ S & 0.98 & 0.99 & 0.96 & 0.98 & 1 & 0.88 & 0.86 & 0.88 & 0.93 & 1 & 0.91 & 0.91 & 0.99 & 0.99 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scatter plots and Spearman correlation coefficients between real-time PM\({}_{2.5}\) monitoring devices and FEM.
## Data and Method  
### Data  
NASA's Aqua satellite is part of the A-Train orbit satellite constellation that is equipped with six different Earth-observing instruments to measure various parameters related to land, ocean, and Earth's atmosphere and biosphere, with a concentration on water [19]. The Atmospheric Infrared Sounder (AIRS) can conduct high-precision atmospheric detection under sunny and cloudy conditions [20]. At present, the data detected by AIRS includes three versions: V5, V6 and V7 [21]. In this paper, the AIRS Version 6 Level 3 product was used to extract the TCWV, the Temperature of the Atmosphere at the Earth's Surface (TAS) and the Surface Skin Temperature (SST) measurements from September 2002 to February 2022 over China (15\({}^{\circ}\) N-55\({}^{\circ}\) N, 70\({}^{\circ}\) E\(\sim\)140\({}^{\circ}\) E). The product provides the monthly TCWV, TAS and SST retrievals at a 1 deg. (longitude) \(\times\) 1 deg. (latitude) resolution.
# Convergence Gain in Compressive Deconvolution: Application to Medical Ultrasound Imaging
## Appendix B Proof of Theorem 1  
To prove this main result, we require two lemmas. The first key lemma provides a lower bound on a specially constructed functional in terms of a quadratic term involving the matrix \(Q\) defined in Equation (16) (It should be noted that the proof framework is based on [PERSON]'s classical scheme [PERSON] (2013): First, carefully construct a semi-positive matrix (18) based on customized relaxation factors \(\rho\) and \(\varrho\) and then find the discrepancy between the current iterative point and the optimal point through their professional variational inequalities scheme (Inequality (A1)). The contractive property of iterative sequences is finally proven via their specially constructed identity. Since the proof line is straightforward and is similar to our previous work [PERSON] (2013), we give convergence lemmas and theories without detailed proof.).  
For a given \(w^{k}\in\Omega\), let \(w^{k+1}\) be generated by Equation (26) and let \(\tilde{w}^{k}\) be defined in Equation (27). Then we have \(\tilde{w}\in\Omega\) and  
\[\theta(u)-\theta(\tilde{u}^{k})+(w-\tilde{w}^{k})^{\mathsf{T}}F(\tilde{w}^{k} )\geq(w-\tilde{w})^{\mathsf{T}}Q(w^{k}-\tilde{w}^{k}),\quad\forall w\in\Omega\] (A1)  
where \(Q\) is defined in Equation (16).  
In the next lemma, we further analyze the right-hand side of Inequality (A1) and reformulate it as the sum of some quadratic terms. This new form is more convenient for our further analysis.  
Let \(w^{k}\) be generated by Equation (26) and let \(\tilde{w}^{k}\) be defined in Equation (27). Let \(Q\), \(H\), and \(G\) be defined in Equations (19), (18), and (21), respectively. Then, for any \(w\in\Omega\), we have  
\[(w-\tilde{w}^{k})^{\mathsf{T}}Q(w^{k}-\tilde{w}^{k})=\frac{1}{2}(\|w-w^{k+1} \|_{H}^{2}-\|w-w^{k}\|_{H}^{2}))+\frac{1}{2}\|w^{k}-\tilde{w}^{k}\|_{G}^{2}.\] (A2)
## 3 Methodology of Adaptive Gaussian Mixture Model Using Virtual Sample Generation
### Gaussian Component Splitting  
Once the nonlinearity is detected, then the splitting is to be executed. Current splitting algorithms can be summarized into two categories: (1) Split a Gaussian PDF in all directions or (2) Split a Gaussian PDF by applying a univariate splitting library in a specified direction [48]. Solving an L2-minimization optimization problem is usually required when using the first kind of method, which incurs additional computational consumption. In this paper, splitting in one direction is employed. The multivariate Gaussian PDF is split by applying a univariate splitting library in the direction of maximum eigenvalue, which is given per the following steps:  
First a standard normal distribution \(p(x;0,1)\) is split into \(N^{\prime}\) homoscedastic Gaussian distributed components as \(N^{\prime}p(x;0,1)\approx\sum_{i=1}^{N^{\prime}}\tilde{\alpha}_{i}p_{g}\left( x;\tilde{m}_{i},\tilde{\sigma}_{i}^{2}\right)\). This step is independent from the concerned problem; therefore, parameters \(\left(\tilde{\alpha}_{i},\tilde{m}_{i},\tilde{\sigma}_{i}\right)\) can be computed offline and saved as a splitting library. The computation of the splitting parameters is well-provided by [42], and one case with is illustrated in Table 2.  
Then, consider the spectral factorization of the covariance matrix \(\mathbf{P}_{i}\) as:  
\[\mathbf{P}_{i}=\mathbf{V}\mathbf{\Lambda}_{i}\mathbf{V}^{T} \tag{16}\]  
where \(\mathbf{\Lambda}_{i}=\text{diag}\{[\lambda_{1},\cdots,\lambda_{k},\cdots,\lambda_ {n}]\}\) is the eigenvalues matrix with \(\lambda_{k}\) being the \(k\)-th largest eigenvalue of the covariance matrix \(\mathbf{P}_{i}\). The \(i\)-th component is approximated by \(p_{g}(\mathbf{x};\mathbf{m}_{i},\mathbf{P}_{i})\approx\sum_{j=1}^{N^{\prime}}\tilde{ \alpha}_{j}p_{g,j}(\mathbf{x};\mathbf{m}_{i,j},\mathbf{P}_{i,j})\) with mean and covariance matrix given as:  
\[\mathbf{m}_{i,j}=\mathbf{m}_{i}+\sqrt{\lambda_{k}}\tilde{m}_{j}\mathbf{v}_{k} \tag{17}\]  
\[\mathbf{P}_{i,j}=\mathbf{V}\tilde{\mathbf{\Lambda}}_{i}\mathbf{V}^{T} \tag{18}\]  
where \(\mathbf{v}_{k}\) is the \(k\)-th eigenvector of \(\mathbf{P}_{i}\) and \(\mathbf{\Lambda}_{i}=\text{diag}\{[\lambda_{1},\cdots,\lambda_{k},\cdots,\lambda_ {n}]\}\) is the set of eigenvalues of the \(i\)-th new component.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component \(i\)** & \(\mathbf{\alpha}_{i}\) & \(\tilde{\mathbf{m}}_{i}\) & \(\tilde{\mathbf{v}}_{i}\) \\ \hline
1 & 0.0763216491 & \(-\)1.6899729111 & 0.4422555386 \\
2 & 0.2474417860 & \(-\)0.8009283834 & 0.4422555386 \\
3 & 0.3524731300 & 0 & 0.4422555386 \\
4 & 0.2474417860 & 0.8009283834 & 0.4422555386 \\
5 & 0.0763216491 & 1.6899729111 & 0.4422555386 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Five-component splitting library with \(\beta=2\) and \(\lambda=0.0025\)[42].
## 3 Results and Discussions
### Mechanism Analysis of RAP Emulsification-Separation  
#### 3.7.1 Action of Surfactants  
In Figure 16, it can be observed that, after using anionic surfactants to formulate emulsions, the water-oil amphiphilicity of the surfactant in the emulsion effectively reduced the mutual exclusion phenomenon between the hydrophilic group of the surfactant and the hydrophobic pollutants and improved the solubilizing ability of the emulsion on the pollutants. The solubilizing effect of the lotion on the aged asphalt of the RAP surface gradually softened the asphalt. Then, as water penetrated, the adhesion between the aged asphalt and the aggregate was weakened, causing the asphalt to separate from the aggregate surface. The emulsification-separation method proved its effectiveness by destroying the colloidal structure of the aged asphalt with surfactants. It is recommended to apply it to modified asphalt, different types of matrix asphalt, etc. Its basis is derived from its amphiphilic molecular structure and its adsorption behavior at the interface. The specific effects of different materials will be explored in future research. The surface polarity of stone materials, such as basalt or granite, is strong, leading to an increase in reagent consumption, but the effect and change rule after treatment are basically the same as those of limestone.  
Figure 15: SEM images of aggregates after emulsification–separation.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Forecasts Combination
#### 2.3.3 Additive Quantile Regression Averaging  
A hybrid regression model based on Additive Quantile Regression (AQR) model consists of GAM and QR. Its first use was seen in the work of [PERSON] et al. [41], extended by [PERSON] et al. [42], used in the work of [PERSON] et al. [12] and given in Equation (12).  
\[y_{iq}=\sum_{k=1}^{p}s_{k,q}(x_{ik})+\epsilon_{ik}\quad q\in(0,1), \tag{12}\]  
where \(x_{ik}\) are \(p\) covariate terms, from \(x_{i1}\), \(x_{i2,\cdots}\), \(x_{ip}\), \(S_{k,q}\) are smooth functions and \(\epsilon_{iq}\) are the error terms. Smooth function (\(s\)) can be expressed as given in Equation (13)  
\[s_{k}(x)=\sum_{q=1}^{j}\beta_{q,k}bs_{qk}(x_{ik}), \tag{13}\]  
where \(bs_{qk}\) represents the \(k\)th basis function with \(j\) dimension and \(\beta_{qk}\) is the \(k\)th parameter. Parameters of Equation (12) are estimated by minimising the expression in Equation (14):  
\[Q_{y|x}(q)=\sum_{i=1}^{N}\rho q\big{(}y_{i,q}-\sum_{k=1}^{p}s_{k,q}(x_{ik}) \big{)}, \tag{14}\]  
where \(\rho_{q}\) is the pinball loss function; however, the loss function it minimises is that of Equation (8).
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology
### Meteorological Variable Mapping  
SOS API publishes the available observedProperty in the SOS response to inform clients of the data availability. The names of the climate variable of AmGSD are mapped to the observedProperty of SOS. These observed properties are published for securing the interoperability of climate data. observedProperty is also used as a filtering parameter in querying data through SOS API.  
Climate variables of AmGSD are named internally. To understand the meaning of each name, clients have to go through the AmGSD manual. From the viewpoints of clients, the names published need to be comprehensible by humans and machines, so that data flow can be done automatically and smoothly.  
We proposed a naming concept of agro-environmental elements, and the Japanese government declared it as a standard guideline, which is available on their website [17]. According to the guideline, the meteorological variables are renamed to be self-descriptive names, as shown in Table 2. Clients can clearly understand the property of the data without going through specification of AmGSD. These names are used in SOS API for publishing and filtering services.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AmGSD** & **Procedure of SOS** & **Description** \\ \hline AmGSD; 6 areas & \begin{tabular}{c} urn:NARO1 km:AgroWeather \\ (National Agriculture and Food \\ Research Organization) \\ \end{tabular} & Referring to the whole AmGSD \\ \hline Each grid is referred by Japanese & \
\begin{tabular}{c} urn:NARO1 km:AgroWeather:simple \\ public 3 rd mesh ID \\ \end{tabular} &
\begin{tabular}{c} Clients can refer to each grid by \\ concatenating the simple mesh ID \\ after the procedure value \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mapping the coverage and grid of AmGSD (Agro-meteorological Grid Square Data system) to SOS (Sensor Observation Service).
## 3 Security and Privacy Challenges
### AI Role in EC Security
#### 3.3.1 Machine Learning for Data Security and Privacy  
Machine learning (ML) indicates algorithms and statistical models for carrying out specific tasks without explicit instructions. An ML algorithm puts up a mathematical model of user data, also known as a \"training set\" capable of making predictions or decisions. ML can be used to detect suspicious activity by analyzing user behavior to detect patterns that may indicate malicious activity and ensure data security and privacy requirements [159]. Machine learning (ML) techniques have the potential for enhanced detection of data security and privacy threats while dealing with huge amounts of data coming from IoT end devices. [PERSON] et al. mention that the training dataset utilized in the development of ML models is itself vulnerable to a possible data security threat [160]. Usually, the data owners and end users are against the sharing of their sensitive data, which becomes a bottleneck in the development of trusted ML models. To circumvent such issues,  
Figure 10: EC and AI: benefitting each other [154].  
classification protocols utilize ML classifiers over encrypted data to protect the privacy of end users and service providers.  
The training approach in centralized ML modeling involves the collection as well as the storage of data in a central location or server. Additionally, in a centralized approach, the intended model is trained using a complete dataset on a central server. This type of approach is practical when the training entity owns the data or has authorization to use it. As shown in Figure 11a, each participant computes their part of the ML model, and subsequently a reduced function finalizes the desired model. However, this technique has disadvantages, including privacy issues due to the distribution of sensitive data with a central cluster of servers and the training process becoming a bottleneck as the dataset grows. Various researchers have proposed an edge-based security system by combining ML with cryptography techniques, which monitors and detects suspicious activities on the network and takes appropriate countermeasures. The deployed ML models include Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM) [161; 162].  
A huge amount of data is required for the training of AI models, and quite often, user-sensitive data become exposed in the process. The integration of AI models with differential privacy ensures the accuracy of models with or without the inclusion of user-sensitive data. The traditional ML models might be robust against data attacks but lack feature extraction from the data and fail to detect attacks that have undergone minor modifications [154]. The study in [163] reviews ML frameworks like TensorFlow Lite, Apache MXNet, and Core ML, along with hardware platforms such as Nvidia Jetson and Google Edge TPU, focusing on their efficiency and accuracy in data processing within edge environments.
## Abstract  
The last few years, many studies have been published on the 3 rd-order inverse Volterra series transfer function nonlinear equalizer (IVSTF-NLE) in long-haul optical communication systems. Nonetheless, no experimental work has been published on investigating the potential of the 3 rd-order IVSTF-NLE for the compensation of Kerr nonlinearities in a long-haul wavelength division multiplexing (WDM) system consisting of high-bit rate super-channels, as high as 400 Gb/s. In this paper, we study experimentally the performance of a 3 rd-order IVSTF-NLE in a coherent optical WDM system, with a central, 400-Gb/s, 4-band, dual-polarization (DP), 16-ary quadrature amplitude modulation (QAM) orthogonal frequency division multiplexing (OFDM) super-channel. We compare its performance against the performance of the digital back-propagation split-step Fourier (DBP-SSF) method for the compensation of nonlinearities after 10 \(\times\) 100 km of ITU-T G.652 standard single mode fiber (SSMF). In the second part of this paper, we compare, via Monte Carlo simulations, the performance of the 3 rd-order IVSTF-NLE and the DBP-SSF method, in terms of reach extension and computational complexity, after propagation through ITU-T G.652 SSMF and a ITU-T G.655 large effective area fiber (LEAF). By means of both experimental evaluation and simulations, we show that, in the presence of strong nonlinear effects, the 3 rd-order IVSTF-NLE, which uses a single step per span, performs similarly with the two-steps-per-span DBP-SSF, whereas the eight-steps-per-span DBP-SSF is only marginally better but at the vast expense of computational complexity.  
nonlinear equalizers; volterra series transfer function; digital back-propagation
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 4 Communication-Assisted Stopping Distance
### Stopping Distance
\begin{table}
\begin{tabular}{c c c} \hline
**Road Type** & **Coeff of Friction with ABS** & **Without ABS** \\ \hline Asphal—Dry & 0.8–0.9 & 0.75 \\ Asphal—Wet & 0.5–0.7 & 0.45–0.6 \\ Concrete—Dry & 0.8–0.9 & 0.75 \\ Concrete—Wet & 0.8 & 0.7 \\ Snow & 0.2 & 0.15 \\ Ice & 0.1 & 0.07 \\ \hline \end{tabular}
\end{table} 
Table 1: Coefficient of friction. Using these equations, we predict the effect of using our maximum communication latency of 2.92 ms as the reaction time in place of the typical driver reaction time, which is suggested to be between 0.67 s and 2 s. The reaction time depends on driver state, such as age, driver experience, and state of mind. In this work, 1.5 s is chosen as the reaction time as this is the time reported by Brake to most accurately represent most drivers [46]. Figure 2 highlights the stopping distance reduction that could be observed when using communication-assisted systems or autonomous braking systems. The results for this section show the difference between the stopping distance when using each method of measuring stopping distances, and they show the second has a slight reduction in distance needed. The second finding is that the reduction in distance is large when using the communication reaction time. This information can used in critical braking scenarios, such as debris on the road causing an emergency braking situation, a vehicle breaking down, a crash occurring, or sudden braking by the lead vehicle. Table 3 shows the distance that can be saved at each distance with the use of communication-based reaction time when the consecutive losses are 0. This table also shows the percentage of distance that is saved and at its lowest is a decrease of over 30%. Through the use of the two formulas, it can be seen that Equation (3) is more accurate showing the braking distances for any vehicle that is chosen. Our experiments used a small hatchback, but the equation can be altered to represent any vehicle necessary. For these reasons, this will be the method used in our experiments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Value** & **Range** & **Chosen Value** \\ \hline W, Weight Of Vehicle & N/A & 1800 kg (Citrogen DS3) \\ g, Gravitational Acceleration & N/A & 9.8 (m/s\({}^{2}\)) \\ P, Air Density & N/A & 1.35 \\ Af, Projection Area & Height * Width & 2.562 \\ Cd, Air Drag Factor & 0.15–0.5 & 0.4 \\ V, Velocity (m/s) & N/A & (mph/2.237) \\ \(\eta_{\text{b}}\) Brake Efficiency & 0.8–1.0 & 0.7 \\ \(\mu\), Friction Factor & N/A & 0.8 \\ \(\theta\), Road Slope & N/A & 0 \\ \(f_{r}\),Roll Factor & 0.012–0.015 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 2: Stopping distance comparison.