esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Transforming the Original Dynamics to Quadratic or to Bilinear Format
### Bilinear Systems  
We analyze bilinear time-invariant systems \(\mathbf{\Sigma}_{\text{B}}=(\mathbf{C},\mathbf{E},\mathbf{A},\mathbf{N},\mathbf{B})\) characterized by the following equations:  
\[\mathbf{\Sigma}_{\text{B}}:\ \ \begin{cases}\mathbf{E}\mathbf{x}(t)=\mathbf{A}\mathbf{x}(t )+\mathbf{N}\mathbf{x}(t)\mathbf{u}(t)+\mathbf{B}\mathbf{u}(t),\\ \mathbf{y}(t)=\mathbf{C}\mathbf{x}(t),\end{cases} \tag{2}\]  
where \(\mathbf{E},\ \mathbf{A},\ \mathbf{N}\in\mathbb{R}^{n\times n}\), \(\mathbf{B}\in\mathbb{R}^{n\times m}\), \(\mathbf{C}\in\mathbb{R}^{p\times n}\) and \(\mathbf{x}\in\mathbb{R}^{n}\), \(\mathbf{u},\ \mathbf{y}\in\mathbb{R}\). Here, we assume that the matrix \(\mathbf{E}\) is invertible and can be incorporated in the other matrices appearing in the differential Equation (2) as:  
\[\tilde{\mathbf{A}}=\mathbf{E}^{-1}\mathbf{A},\ \ \tilde{\mathbf{N}}=\mathbf{E}^{-1} \mathbf{N},\ \ \tilde{\mathbf{B}}=\mathbf{E}^{-1}\mathbf{B},\ \ \tilde{\mathbf{E}}=\mathbf{I}_{n},\]  
where \(\mathbf{I}_{n}\in\mathbb{R}^{n\times n}\) denotes the identity matrix of length \(n\). However, in practical scenarios in which the model is of large dimensions, we avoid explicitly computing the inverse of matrix \(\mathbf{E}\), and incorporate it as above. That is why we will interchange the two (equivalent) representations throughout the paper.  
For simplicity of exposition, we will treat the single-input, single-output (SISO) case. The multi-input multi-output case is indeed technically more involved; however, it is based on the same type of reasoning.
## 3 Materials and Methods
### Sediment Texture Analysis from Datasets of Borehole Samples
To calculate the particle-shape, the [PERSON] (1935) and the [PERSON] and Folk (1958) methods were used in this study. Both methods are based on the combination of three orthogonal axial lengths: L, I, and S (respectively, the lengths of the longest, intermediate, and shortest axes of individual clasts), so that L \(\geq\) I \(\geq\) S. The [PERSON] (1935) method allows for differentiating four classes of shapes (spheroids, blades, discoids, and rods) through a Cartesian coordinate system using the indices S/I and I/L. The ratio S/I discriminates blades and discs (low values) from rods and spheres (high values), while the ratio I/L separates blades and rods (low values) from discs and spheres (high values). To exhibit shape-sorting properties, the disc:rod index from Sneed and Folk (DRI = (L - I)/(L - S)) was applied. These authors separated oblate (discoid) shapes from prolate (blade and rod) shapes and suggested the combination of DRI with the ratio S/L to produce three end members of shape (disc, rod, and sphere) in a triangular diagram. In addition, we used the effective settling sphericity index of Sneed and Folk (1958) as the maximum projection sphericity of a particle (\(\psi_{\text{p}}\)), which is given by the equation \(\psi_{\text{p}}=(\text{S}^{2}/\text{L}\cdot\text{I})^{1/3}\). Void ratios were estimated from the grain size distribution, sphericity and roundness indices, and slenderness ratio [38]. These relationships have already been proven by various authors [39; 40]. A wider grain size distribution allows the particles to compact more densely, as the smaller particles fill in the gaps between their larger neighbors. Particles that are more angular pack less tightly, as sharp corners pull them apart. Increasing the slenderness of the particles can decrease the density as the thin particles close the gaps between the grains and create large open voids. To calculate hydraulic conductivity (HK) (m.s\({}^{-1}\)) we used the Kozeny-Carman equation from effective porosity and characteristic grain sizes, according to its most simplified form [35].
# Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review
## 5 Famous Privacy Enhancing Technologies That Are Widely Used for Privacy Preservation  
Apart from SD and attribute-centric anonymization, there exist many privacy-enhancing technologies (PETs) that are used to secure personal data enclosed in diverse formats. Figure 9 presents notable PETs that are being used by data owners to secure personal data.  
Referring to Figure 9, encryption is widely used in dynamic environments (e.g., cloud computing) to prevent personal data from disclosure [122]. Recently, encryption techniques were amalgamated with AI techniques to preserve the privacy of personal data [123]. DP is widely used to preserve the privacy of data while permitting the analytics of data. It has become one of the famous PETs of modern times and is widely used in federated learning environments to preserve the privacy of data/parameters [124, 125]. When privacy requirements are very high, only some statistics in the form of histograms are shared with the data analysts [126, 127]. Federated learning is one of the modern PETs that does not aggregate personal data in central environments but allows AI model training. FL has become a SOTA solution for privacy preservation when data is located in various places [128, 129]. Blockchain is another famous PET that can protect the privacy of the data as well as of participants. Blockchain is widely used to share data with multiple parties while preventing privacy breaches [130, 131]. Few short learning is a famous PET for training large AI models with limited data, leading to privacy preservation in AI applications [132, 133]. Trusted environment and secure enclaves are hardware-based approaches to guarantee the privacy of user data [134, 135, 136]. These approaches are becoming  
Figure 9: Overview of famous PETs used to secure personal data (e.g., privacy preservation).  
famous PETs due to the rapid rise in AI-based applications. Pseudonymization is a widely used technique for privacy preservation in cloud-based environments such as smart home environments, smart grids, and medical domains [137; 138]. Finally, masking techniques are widely used to hide identity or sensitive information in a variety of data formats (e.g., images, videos, etc.) [139; 140]. All of these technologies play a vital role in preserving individual privacy in static or dynamic environments. In some cases, hybrid PET is used to provide stronger privacy guarantees than individual techniques [141]. Recently, many PETs were also developed to provide privacy guarantees in epidemic handling systems that are being developed to fight the ongoing pandemic [142; 143; 144; 145]. Apart from these developments, more secure PETs are required to provide privacy guarantees in personal data handling.
## 2 Related Work
In addition to the research focusing on simulation environments, there are currently a few proposals of hybrid V2X systems that are based on real implementations. In [11], the authors propose an alternative system for V2X communication that does not rely on WAVE/DSRC and instead propose a hybrid system that embodies a mix of cellular network and Wi-Fi Direct. Their system provides seamless connectivity and vehicle monitoring through long-range cellular technology combined with local data transmission via Wi-Fi Direct, and incorporates a solution for reducing the long connection time of Wi-Fi Direct. In their paper, the authors only focus on optimizing the Wi-Fi Direct technology to reduce discovery times and the hybrid concept is limited to using a management system in the cloud to control local Wi-Fi Direct networks via long range technology. The performance of the proposed system is evaluated in a real environment for three different scenarios, but they only use smartphones in their evaluation. Finally, the technology company Continental announced that it is developing a hybrid V2X platform that allows mobile network communication (4G/5G) to be combined with direct V2X communication technologies such as DSRC and C-V2X PC5 [12]. This allows the vehicle manufacturers to integrate in their vehicles a system that would allow V2X communication on a global scale. According to Continental, combining short- and long-range communications results in a system applicable for any type of networking. For instance, short-range communications could be used to enhance the driving safety. On the other hand, long-range communications could enable infotainment access to the drivers and passengers, and could disseminate the safety related messages over longer distances, enhancing the awareness of drivers and automated vehicles. However, this platform is a closed commercial \"black box\" system supporting a set of existing standardized services. In contrast to existing solutions, the CAMINO framework is an open system that offers flexible support for standardized, future and custom C-ITS services. Additionally, CAMINO can be integrated with any existing and future V2X wireless technology in a vendor agnostic way. This means that commercial V2X technology modules from different vendors can be used within the framework. Furthermore, the CAMINO framework provides rich logging capabilities, allowing to evaluate the performance of the V2X technologies and services running on top of them. In Table 1, the CAMINO framework is compared with the solutions described in this section in terms of several key features.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in America  
#### 3.2.1 Detection and Attribution of Vegetation Growth  
In the American dryland, most areas also show the significant greening trend except west and south areas (New Mexico, Arizona, California, Utah, Nevada, Idaho). The maximum and significant positive trend areas are mainly located in the north (Figure 9). CO\({}_{2}\) shows the obvious positive effects and impact on North Dakota. For most areas in Colorado, Kansas and California, LU shows negative effects. Overgrazing followed by nutrient depletion is considered by many studies as a cardinal element in land degradation  
Figure 8: **(a)** The regional mean and magnitude (mean absolute value) of the different drivers of positive change in NDVI\({}_{m}\) of Chinese dryland areas. The error bars show the SD of grid cells. **(b)** The same as **(a)** for negative change.  
Figure 7: The regional mean and magnitude (mean absolute value) of the different drivers of change in NDVI\({}_{m}\) of Chinese dryland areas. The error bars show the SD of grid cells.  
in the west of the USA. Shrubs are viewed as mainly competitors when the grass is destroyed by overgrazing [14]. CC and CV show smaller effects than LU and CO\({}_{2}\). For most areas, CV has negative effects on vegetation growth. Previous research found that there is a decrease in summer rains, which benefit grass growth, and a concomitant increase in winter rain, which benefits shrub rains in the drylands of the USA [11].
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## Appendix A Proof of Proposition 1  
Proof.: Consider the estimation error \(\bar{\epsilon}=\mathbf{\omega}_{e}-\hat{\mathbf{\omega}}_{e}\) and its successive derivatives together with the observer dynamics (13) and the dynamics (11). Then, estimation error \(e\) satisfies the following linear differential equation:  
\[\bar{e}^{(3)}+L_{2}\bar{\epsilon}+L_{1}\hat{e}+L_{0}\bar{\epsilon}=J^{-1}\bar {\xi} \tag{13}\]  
The space state realization of (13) is the following:  
\[\Sigma_{EO}:=\begin{cases}\dot{x}=Ax+B\bar{\xi}\\ \bar{\epsilon}=Cx\end{cases} \tag{14}\]  
where \(x_{1}=\bar{\epsilon}\), \(x_{2}=\dot{\bar{e}}\), \(x_{3}=\ddot{\bar{e}}\), and \(x=(x_{1}\ x_{2}\ x_{3})^{T}\)  
\[A=\begin{pmatrix}0_{3}&I_{3}&0_{3}\\ 0_{3}&0_{3}&I_{3}\\ -L_{0}&-L_{1}&-L_{2}\end{pmatrix}\quad B=\begin{pmatrix}0_{3}\\ 0_{3}\\ \mathsf{J}^{-1}\end{pmatrix}\quad C=\begin{pmatrix}0_{3}\\ 0_{3}\\ I_{3}\end{pmatrix}^{T} \tag{15}\]  
The variation of parameters formula gives the following solution:  
\[\begin{split} x(t)&=\exp(At)x(0)+\int_{0}^{t}\exp(A(t-\tau))B \bar{\xi}d\tau\\ \bar{\epsilon}(t)&=C\exp(At)x(0)+C\int_{0}^{t}\exp(A(t-\tau))B \bar{\xi}d\tau\end{split}\] (  
\(\{L_{2},L_{1},L_{0}\}\) are selected such that the matrix \(A\) is Hurwitz, then one has the following bound [61]  
\[\left\|e^{At}\right\|\leq e^{-\lambda_{M}t},\;\forall t\in\mathbb{R}_{\geq 0}\]  
where \(\lambda_{M}=\lambda_{max}(\text{Sym}(A))\) is the maximum eigenvalue of \(\text{Sym}(A)\) with \(\text{Sym}(A)=(1/2)(A+A^{T})\). Using this fact in (A5), one obtains:  
\[\begin{split}\|\vec{e}(t)\|&\leq\exp(-\lambda_{M} t)\|\vec{e}(0)\|+\frac{K_{0}}{J_{1}}\int_{0}^{t}\exp(-\lambda_{M}(t-\tau))d\tau\\ &\leq\exp(-\lambda_{M}t)\|\vec{e}(0)\|+\frac{K_{0}}{J_{1}\lambda_ {M}}(1-\exp(-\lambda_{M}t))\end{split}\] (A6)  
Consequently \(\|\vec{e}(t)\|\) converges exponentially to a sphere with radius \(\rho=\frac{K_{0}}{J_{1}\lambda_{M}}\) when \(t\to\infty\).
## 3 System Model and Problem Statements
### Problem Formulation
#### 3.2.1 The IoT Layer  
As shown in Figure 2, the system is made up of various IoT cells, and \(E\) edge nodes are considered. Moreover, the functions of IoT objects should be completed on time to provide a positive user experience and to direct subsequent actions taken remotely or locally. The IoT object includes EH modules and electricity storage elements such as RF energy harvesters and photovoltaic modules. A significant number of IoT objects are deployed and connected to the local controller in each cell. These IoT objects collect relevant local data during working hours and regularly offload the collected data to the local controller. IoT objects, for instance, regularly send power consumption [38] data to their local controller. Additionally, there is an MC assigned to gather the different IoT data from all local controllers at each time slot \(t\). The MC then processes and packages the data before being sent to the blockchain system for data consensus and recording.
## 3 Results
### Indirect Signs of ACL Graft Insufficiency
#### 3.2.3 Anterior Tibia Subluxation
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{6}{c}{**Anterior Tibia Subluxation**} \\ \cline{3-8} Parameter & & \multicolumn{2}{c}{**Sagittal**} & \multicolumn{2}{c}{**Axial**} \\ \cline{3-8}  & & **Me** & **LI** & **UI** & **Me** & **LI** & **UI** \\ \hline Intercept & 0.14 & \(-\)0.09 & 0.38 & 0.15 & \(-\)0.09 & 0.39 \\ Week [L] & **0.85** & **0.65** & **1.04** & **0.87** & **0.66** & **1.08** \\ Week [Q] & **-0.18** & **-0.37** & **0** & **-0.26** & **-0.46** & **-0.07** \\ SNQ & **0.18** & **0.12** & **0.25** & **0.12** & **0.05** & **0.19** \\ SD & 0.03 & \(-\)0.02 & 0.08 & \(-\)0.01 & \(-\)0.06 & 0.04 \\ Sex & \(-\)0.15 & \(-\)0.35 & 0.06 & \(-\)0.17 & \(-\)0.39 & 0.05 \\ Age & \(-\)0.11 & \(-\)0.3 & 0.09 & \(-\)0.12 & \(-\)0.33 & 0.09 \\ \(\beta\) & PCLS at week 2 & **-0.48** & **-0.68** & **-0.27** & **-0.46** & **-0.69** & **-0.24** \\ Weight bearing & 0.05 & \(-\)0.18 & 0.3 & 0.07 & \(-\)0.18 & 0.32 \\ Week [L] : Sex & \(-\)0.04 & \(-\)0.18 & 0.1 & \(-\)0.05 & \(-\)0.19 & 0.1 \\ Week [Q] : Sex & 0.1 & \(-\)0.02 & 0.23 & 0.11 & \(-\)0.02 & 0.24 \\ Week [L] : Age & **-0.24** & **-0.39** & **-0.09** & **-0.21** & **-0.36** & **-0.05** \\ Week [Q] : Age & 0.05 & \(-\)0.09 & 0.19 & 0.03 & \(-\)0.12 & 0.17 \\ Week [L] : PCLS at week 2 & **0.21** & **0.07** & **0.35** & **0.17** & **0.03** & **0.31** \\ Week [Q] : PCLS at week 2 & 0.08 & \(-\)0.05 & 0.2 & 0.09 & \(-\)0.04 & 0.23 \\ Week [L] : Weight bearing & 0.15 & \(-\)0.04 & 0.34 & 0.21 & 0 & 0.41 \\ Week [Q] : Weight bearing & 0 & \(-\)0.19 & 0.18 & \(-\)0.05 & \(-\)0.24 & 0.14 \\ \hline Intercept & \(-\)1.04 & \(-\)1.19 & \(-\)0.88 & \(-\)1.01 & \(-\)1.16 & \(-\)0.85 \\ \(\sigma\) & Week [L] & \(-\)0.5 & \(-\)0.8 & \(-\)0.2 & \(-\)0.42 & \(-\)0.71 & \(-\)0.12 \\ Week [Q] & 0.38 & 0.06 & 0.71 & 0.55 & 0.23 & 0.86 \\ PCLS at week 2 & 0.05 & \(-\)0.1 & 0.21 & 0.06 & \(-\)0.1 & 0.2 \\ \hline \(\tau\) & \(\beta\) Intercept & 0.65 & 0.51 & 0.83 & 0.68 & 0.54 & 0.87 \\  & \(\sigma\) Intercept & 0.42 & 0.29 & 0.57 & 0.39 & 0.27 & 0.54 \\ \hline \(\alpha\) & & \(-\)0.34 & \(-\)1.99 & 1.03 & 0.21 & \(-\)1.41 & 1.82 \\ \hline R\textasci{2} & Population level & 0.49 & 0.36 & 0.58 & 0.45 & 0.32 & 0.56 \\ Total & 0.83 & 0.81 & 0.84 & 0.82 & 0.79 & 0.83 \\ \hline \end{tabular} Note: Me is median of posterior, and LI and UI are lower and upper bounds of 95% credible interval. [L] and [Q]—linear and quadratic trends. The \(\cdot\)\(\cdot\)\(\cdot\) symbol indicates interaction term. \(\beta\)—regression weight; \(\tau\)—patient random effect; \(\sigma\) residual standard deviation; \(\alpha\)—skewness coefficient. The coefficients are on a standard normal scale. Statistically credible effects are bolded.  
\end{table} 
Table 6: Results of Bayesian multilevel skew- normal regressions with PCL subluxation as the dependent variable.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 2 Wind Turbine and Wind Speed Modelling and Wind Turbine Control
### Rotor and Aerodynamics  
The rotor and aerodynamics module is described by the following equation:  
\[T_{a}=0.5\rho\pi R^{3}V^{2}\frac{C_{p}(\lambda)}{\lambda}, \tag{1}\]  
where \(T_{a}\) is the aerodynamic or hub torque, \(\rho\) is the air density, \(R\) is the rotor radius, \(V\) is the effective wind speed (Section 2.1) and \(C_{p}(\lambda)\) is the power coefficient unique to the rotor design. \(\lambda\) is the tip-speed ratio given by  
\[\lambda=\frac{R\Omega}{V}, \tag{2}\]  
where \(\Omega\) is the rotor speed.
# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
## 4 Experiments
### Contrast Experiments  
In order to further verify the effectiveness of the proposed method, we train the base classifier SVM model, the SVM model based on transfer learning, the SVM model based on semi-supervised learning and the SVM model based on semi-supervised and transfer learning respectively to identify the unknown radar emitter samples. In addition, in order to verify the adaptability of the model to the measurement error, we introduce an error deviation level test algorithm [21]. The specific experiment results are shown in Figure 5.  
From the contrast experiment results, it can be known that when only using the base classifier SVM model for identification, the identification accuracy obtained is less than 55%. The main reason is that the known radar emitter data and the unknown radar emitter data do not satisfy the same-distribution hypothesis, resulting in an inability to obtain a valid classifier. When using the SVM model based on semi-supervised and transfer learning for identification, the optimal identification accuracy can be achieved within a certain measurement error range. Identification accuracy can reach more than 90% in the measurement error range of 15%, indicating that the method has good noise adaptability, and is obviously superior to the SVM model based on transfer learning, and the SVM model based on semi-supervised learning. The main reason is that the semi-supervised and transfer learning can make full use of sample information to achieve good performance without a lot of iteration.  
Figure 4: Impact of the transfer learning method on the semi-supervised learning method.  
The identification accuracy obtained by the SVM model based on transfer learning is slightly better than that obtained by the SVM model based on semi-supervised learning. The main reason is that there are not many available training samples in the target domain, which leads to the fact that only using the semi-supervised learning method cannot enhance the training effect. When the measurement error is greater than 10%, the identification accuracy of the transfer learning method and the semi-supervised learning method will be significantly reduced, indicating that their noise adaptability is not good.
# A Programmable ROADM System for SDM/WDM Networks
## 3 Design of Programmable SDM/WDM ROADM System  
The proposed programmable SDM/WDM ROADM system creates two integrated networks over the MCF-based fibre networks. Generally, each core in MCFs is treated as a typical SMF with fan-in and fan-out devices for each MCF. Inside of each MCF, some fibre cores are configured as bypass cores and the residual fibre cores are configured as WDM fibre links. The number of bypass cores and WDM cores can be reconfigured by a centralized controller. Therefore, these two integrated networks can be optimized according to the traffic request. Bypass core links, combined with WDM cores, generate programmable virtual topologies in the MCF-based optical networks.  
Figure 1 shows the concept of programmable bypass core configuration based on an exemplary SDM network. Nodes are interconnected with 7-core MCF links. Fibre cores in different MCF links that are directly connected without any wavelength switching in the intermediate nodes are defined as bypass cores. As shown in Figure 1, a bypass link is set up between node A to node F by connecting bypass cores directly to node C and node D. Similarly, other bypass links can be established between different nodes. As an example, to further illustrate, several cores in node C are configured as bypass cores, including node A to node E and node B to node D. The residual cores as shown in node D are used to achieve the Route-and-Select ROADM, such as the bypass link from node B bypassed node C and connected to RS ROADM in node D.  
The following sections will illustrate the detailed node structure including bypass connection and traditional WDM fibre links, programmable virtual topology, and hardware requirements.