esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Hence the present approach implements optimal feature selection methods that enable the machine learning algorithm to get trained faster and interpret the results easier by reducing the model complexity. Furthermore, it reduces overfitting and improves the accuracy of the model by mapping CSI traces to the ground truth values and by a selection of a robust subset of cumulant features. The idea proposed in the present work differs from the state-of-the-art method as it uses feature selection methods derived from information theory. Feature selection methods include mutual information feature selection (MIFS) [20], minimum-redundancy maximum-relevancy (mRMR) [21; 22], conditional informative feature extraction (CIFE) [23], joint mutual information (JMI) [24], conditional mutual information maximization (CMIM) [25], double input symmetrical relevance (DISR) [26], interaction capping (ICAP) [27] and conditional redundancy (CONDRED) [28]. HOS-Re uses a secondary dataset, SignFi [29] for validating the proposed methodology. SignFi adopts a convolution neural network (CNN) [29] to measure the recognition accuracy. HOS-Re adopts multilevel support vector machine classifier that uses gaussian radial basis function (RBF) kernel. In summary, the contribution of the paper is as follows:  
1. This paper proposes a HOS-based third order cumulant feature extraction from raw CSI traces and applies various feature selection methods derived from information theory, which effectively extracts and selects a robust feature subset considering the non-Gaussian signal distribution. 2. HOS-Re measures the recognition performance using an ML classifier with feature inputs from feature selection methods. The experiment explores most of the mutual information based feature selection methods on cumulant features and identifies methods having higher recognition accuracy. 3. To the best of our knowledge, this is the first reported work to use the combination of cumulant features + feature selection method for machine learning in the HGR domain using CSI traces and achieve significant performance. In the remainder of the paper, Section 2 reviews the related work; Section 3 provides the preliminaries of this work. Section 4 gives an overview of the system under study, and details on feature extraction, selection, and classification. Section 5 discusses the implementation of the proposed methodology and evaluates the performance of HGR, and Section 6 concludes the discussion on HGR.
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 6 Conclusions  
Based on the principle of the Stackelberg game, fully considering the decision preferences of airlines in the \"collaborative\" allocation process and aiming at further improving the fairness of resource allocation, a Stackelberg game model between ATC and airlines is established, and the study of the selection preferences between ATC and airlines in the process of selecting the final allocation plan is conducted. Based on the data of en-route network in East China, an analysis of selection preferences of airlines is conducted on the initial allocation plans, narrowing down the range of optimal allocation plans based on the game results of airlines. Finally, combined with the overall optimal principle of air  
Figure 9: Comparison of delay cost under each plan.  
traffic control, the optimal allocation plan was locked in. The results showed that when considering the main base airline's time and space management, they tend to prioritize the airline's flights. Different airlines have different preferences, resulting in different optimal allocation plans. This provides a decision-making reference for further improving airspace operation efficiency and improving resource allocation fairness, thereby enhancing the airline's participation in the implementation process of the CTOP.  
In the complete implementation process of the CTOP, after air traffic control formulates the allocation plan, airlines can also choose whether to modify the TOS of the corresponding flight based on whether the plan provided by air traffic control meets their own preferences. If some airlines choose to modify it, they need to consider the new flight information and reallocate the trajectory and time slot resources. Airlines can choose to modify it at any time, and for flights that have not yet been executed at the time of modification, new allocation situations need to be considered. This process has a significant impact, so in subsequent research, it is necessary to consider the impact of TOS updates on resource allocation and the development of new allocation plans.  
Conceptualization, W.T. and X.Z.; methodology, X.Z.; software, Q.F.; validation, W.T., X.Z. and Q.F.; formal analysis, X.Z.; investigation, X.Z. and Q.F.; resources, W.T. and Y.Z.; data curation, X.Z.; writing--original draft preparation, Q.F.; writing--review and editing, X.Z.; visualization, M.Y.; supervision, W.T. and Y.Z.; project administration, W.T.; funding acquisition, W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2022 YFB2602403, the National Natural Science Foundation of China, grant number 71971112, and the School Innovation Plan Project of Nanjing University of Aeronautics and Astronautics, grant number xczjh20230718.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 5 Unstructured Background Models
### Kernel-Based Models
#### 5.3.1 Kernel RX detector  
In [26], a non-linear anomaly detector that adopts a normal model in a higher dimensional feature space is presented. The RX algorithm in the feature space can be represented as  
removed background pixels \(\underline{\Phi}\big{(}\underline{\underline{X}}\big{)}\) in the feature space. That enables implementation of (25) without knowledge of the mapping function \(\underline{\Phi}(\cdot)\); the only requirement that remains is selecting the kernel function \(k\), which produces a positive definite Gram matrix. However, this can not be easily foreseen in real applications. Performance of the kernel RX detector is generally limited by the following: (1) it is sensitive to background contamination with anomalous pixels and noise, and (2) the inverse of \(\underline{\underline{\hat{K}}}\) is usually rank-deficient [90]. A Gaussian background purification approach adapted to background data samples probability distribution and an inverse-of-matrix-free method based on kernel principal component analysis (PCA) [91] were proposed in [90] to address the aforementioned kernel RX limitations. To improve the memory and time efficiency of the kernel RX detector, two families of techniques for approximation of the kernel function with either the data-independent random Fourier features or the data-dependent basis with the Nystrom approach were proposed in [92].
## 3 DSPOT-Enhanced Self-Evolving Neural Networks
### DSPOT-Based Ramp Classifier  
Based on extreme value theory, it is likely that extreme events follow a generalized Pareto distribution (GPD) [43], which is observed in wind power ramps in Figure 4. Thus motivated, we will develop a data-fitting technique using the GPD model to determine the dynamic threshold \(z_{q^{cat}}(t)\) for different ramp events, where the index \(cat\in\{up,down\}\) denotes the category of ramp events and \(q^{cat}\) is the quantile of the corresponding ramp event distribution used to determine the threshold \(z_{q^{cat}}(t)\). The idea is to first estimate the parameters of the GPD and then use the estimated GPD to find \(z_{q^{cat}}(t)\) based on the quantile \(q^{cat}\). To account for the time-varying dynamics of ramp events, the parameters of the GPD will be updated using the recent observed wind power in a moving window with size \(d\).  
Specifically, let \(P_{class}(t)\) denote the wind power output at time \(t\), where the index \(class\in\{GE,Mitsubishi\}\) represents the class of wind turbines. In a specified time period \(l\), ramp-up and ramp-down events can be separately expressed as:  
\[\begin{split} P_{class}(t)-P_{class}(t-l)&=\Delta P _{class}^{l}(t)>z_{q^{qp}}(t),\\ P_{class}(t)-P_{class}(t-l)&=\Delta P_{class}^{l}( t)<-z_{q^{down}}(t),\end{split} \tag{1}\]  
where \(l\) and \(q^{cat}\) are parameters to be tuned by BO (see Section 3.3) to determine the ramp events.  
Based on the above definitions of ramp events, we classify the original dataset into ramp-up, ramp-down, and non-ramp datasets, i.e., 3 different datasets for each class of wind turbine. Let \(\mathcal{X}_{i}^{class},i\in\{up,down,non\}\) denote these 3 datasets, where \(\mathcal{X}_{up}^{class}\) denotes the ramp-up dataset, \(\mathcal{X}_{down}^{class}\) the ramp-down dataset, and \(\mathcal{X}_{non}^{class}\) the non-ramp dataset. These datasets will be used to train NNs in Section 3.2. Clearly, the quality of these datasets (i.e., how well different ramp events can be separated) depends on the values of \(z_{q^{up}}(t)\) and \(z_{q^{down}}(t)\). In this section, we determine \(z_{q^{qp}}(t)\) and \(z_{q^{down}}(t)\) using the GPD model. For ease of presentation, we present how to calculate the dynamic threshold \(z_{q}(t)\) for ramp-up events by omitting the index \(cat\) in the following. Correspondingly, the dynamic threshold for ramp-down events can be determined using the same procedure.
## 3 Data and Methodology
### Methodology
#### 3.2.3 Description of the Methodology  
The main objective of this work is to predict the next \(h\) future values, called the prediction horizon, of a time-series \([x_{1},x_{2},\ldots,x_{t}]\).  
The predictions are based on \(w\) previous values, or, in other words, on a historical data window. This process is called multi-step forecasting, as various consecutive values have to be predicted. The aim of multi-step forecasting is to induce a prediction model \(f\), and in our case \(f\) is obtained by using a deep learning strategy, following the equation:  
\[[x_{t+1},x_{t+2},\ldots,x_{t+h}]=f(x_{t},x_{t-1},\ldots,x_{t-(w-1)}). \tag{3}\]  
Unfortunately, frameworks that provide deep learning networks model, such as H20, does not support this multi-step formulation.  
In order to solve this issue, a different methodology has been proposed [9]. The basic idea is to divide the main problem into \(h\) prediction sub-problems. Then a forecasting model will be induced for each of the sub-problems, as shown in Equation (4).  
\[x_{t+1} = f_{1}(x_{t},x_{t-1},\ldots,x_{t-(w-1)}) \tag{4}\] \[x_{t+2} = f_{2}(x_{t},x_{t-1},\ldots,x_{t-(w-1)})\] \[\ldots = \ldots\] \[x_{t+(h-1)} = f_{(h-1)}(x_{t},x_{t-1},\ldots,x_{t-(w-1)})\] \[x_{t+h} = f_{h}(x_{t},x_{t-1},\ldots,x_{t-(w-1)})\]  
Notice that in this way, we lose the time relationship between consecutive records of the time-series. For instance, instants \(t+1\), \(t+2\), \(t+3\) or \(t+4\) will not be considered when forecasting \(t+5\).  
On the other hand, considering such values for the predictions could increment the forecasting error. This is because values for \(t+1\), \(t+2\), \(t+3\) or \(t+4\) are based on predictions, and they would have a negative effect on the forecasts if the values were not precisely estimated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operator** & **Value** \\ \hline Population size & 50 \\ Generations & 100 \\ Limit of generations & 50 \\ Crossover probability & 0.8 \\ Mutation probability & 0.1 \\ Elitiss probability & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Genetic algorithm (GA) parameter setting.  
It follows that a search for optimal parameters should be carried out for each sub-problem, where the evaluation of each individual corresponds to the error made by the neural network in the training phase. This means that the computational time needed to train the complete model is high. However, the capability of H2O to perform distributed computation decreases the total computational time required.
# Security Implications for Ultra-Low Power Configurable SoC FPAA Embedded Systems
## 4 Embedded FPAA Security Concerns
### Positive FPAA Security Attributes  
The FPAA structure has a number of good security aspects. The FPAA uses FG devices to store the device state without any SRAM loading vulnerability, particularly from an external IC. Once the  
Figure 4: Device Lifetime available for wireless sensor nodes The graph shows the opportunity for energy harvesting systems at the size of 1 cm\({}^{2}\) and 10 cm\({}^{2}\) form factors; most energy harvesting systems output 10 \(\upmu\)W of power per cm\({}^{2}\), with the exception of solar cells in direct sunlight in a desert.  
FG values on the chip are programmed loaded the FPAA code is secure, unless one can scan out the states of the FG elements. FG programming an IC will have minimal changes over the lifetime (e.g., 10 year rating) of the part. The programming code is not the IC \(\upmu\)P SRAM, but only used for programming, then purged after programming. Analog values hard to measure without disturbing the values significantly, and digital computation can be encoded with analog computation and storage. Further, very low power circuits are challenging to externally measure due to the low circuit currents (e.g., pA and nA). These transistors do not have enough current or field to generate light to measure transistor behavior, and become very hard to measure the external fields.  
On the other hand, the FPAA structure is a platform for creating secure applications. The SoC FPAA structure is a generic structure, openly published, and built from general components. None of the particular components are unknown or confidential. IC Layout says almost nothing about the programmed IC function. The motivation to _steal_ the knowledge of on-chip FPAA circuits is minimal. The infrastructure can measure the analog behavior at any given node in the FPAA. FPAAs allow for scanning every hardware node internally to the circuit (e.g., [3]). If the core FG programming on the IC is verified, effectively part of the calibration procedure and measurement [18], then the entire IC can be verified. Secure analog and digital code can be programmed in a secure space.  
The IC could have intelligence, using internal signals and voltages, to choose to erase its contents. If tampering is suspected, the operating device could pull up on the tunneling voltage line(s) in an attempt to erase the previous operating code. The device parallel erase occurs from a combination of electron tunneling and reverse tunneling. The result leaves little chance of recovering any previous code even with a short erase cycle. One is more likely to pick up device mismatch patterns rather than anything of the previous code.
# A Ku-Band Fully Differential Low-Power High-Input TdB Low-Noise Amplifier
## 4 Conclusions  
In this paper, we designed and implemented a low-power and high-input P1 dB low-noise amplifier operating in the Ku-band. The proposed LNA achieved a high input P1 dB and a low noise figure by adopting a fully differential structure and large transistor. Additionally, by applying class AB bias at the output stage, we optimized power consumption while securing a high P1 dB. In addition, the fully symmetric differential structure improves the rejection of common-mode noise. The results of this paper demonstrate the best input P1 dB performance compared to the latest Ku-band LNAs.  
Conceptualization, J.-H.K. and C.-Y.K.; methodology, S.-R.L., J.-H.K. and M.-S.B.; software, S.-R.L., J.-H.K. and M.-S.B.; validation, S.-R.L., J.-H.K. and M.-S.B.; formal analysis, S.-R.L. and M.-S.B.; investigation, S.-R.L., J.-H.K. and M.-S.B.; resources, C.-Y.K.; data curation, S.-R.L.; writing--original draft preparation, S.-R.L.; writing--review and editing, S.-R.L., M.-S.B. and C.-Y.K.; visualization, S.-R.L.; supervision, C.-Y.K.; project administration, C.-Y.K.; funding acquisition, C.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article.  
Figure 5: (a) Simulated and measured S-parameter, (b) simulated and measured noise figure, (c) measured input P1 dB, (d) simulated and measured K-factor (stability).  
Figure 6: Measured third-order intermodulation distortion (IMD3) at 14 GHz (100 MHz tone spacing).  
**Acknowledgments:** This work was supported by the Technology Innovation Program (RS-2024-00433901) funded by the Ministry of Trade, Industry & Energy (MOTIE, Republic of Korea); MSIT (Ministry of Science and ICT), Republic of Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-RS-2024-00436406, 50%) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 2 Fluctuating Nakagami Fading Channel Model  
The fluctuating Nakagami-m fading distribution was initially proposed in [18] as the ratio of two independent random variables: the Nakagami-distributed and power-transformed uniformly distributed one, with the channel coefficient envelope \(R\) given by  
\[R=\sqrt{\sum_{i=1}^{m}U^{-\frac{1}{m_{s}}}X_{i}^{2}+\sum_{i=1}^{m}U^{-\frac{1} {m_{s}}}Y_{i}^{2}}. \tag{1}\]  
It is assumed that the complex random variable \(X_{i}+\mathrm{i}\ Y_{i}\) (signal in each of the multi-path clusters) follows the zero mean circular symmetric complex normal distributions (i.e., \(X_{i}+\mathrm{i}\ Y_{i}\sim\mathcal{CN}(0,2\sigma^{2})\)) with \(m\) being a total number of such clusters. Each component is assumed to be shadowed (represented with a uniform distributed coefficient \(U\), i.e., \(U\sim U(0,1)\)) with a shadowing intensity \(m_{s}\).  
The resultant envelope probability density function \(f_{R}(r)\), cumulative distribution function \(F_{R}(r)\), and their instantaneous SNR counterparts (i.e., \(f_{\Gamma}(\gamma),F_{\Gamma}(\gamma)\)) are given by (see [18]):  
\[\begin{cases}f_{R}(r)=\frac{2m_{s}}{\Gamma(m)}r^{-1}G_{1,2}^{1,1} \left(\frac{m_{s}m}{(m_{s}-1)\Omega}r^{2}\right|\begin{array}{c}1-m_{s}\\ m_{s},-m_{s}\end{array}\right),\\ F_{R}(r)=\frac{m_{s}}{\Gamma(m)}G_{1,2}^{2,3}\left(\frac{m_{s}m}{(m_{s}-1) \Omega}r^{2}\right|\begin{array}{c}1-m_{s},1\\ m,0,-m_{s}\end{array}\right),\\ \\ \begin{cases}f_{\Gamma}(\gamma)=\frac{2m_{s}}{\Gamma(m)}\gamma^{-1}G_{1,2}^{1,1}\left(\frac{m_{s}m}{(m_{s}-1)\tilde{\gamma}}\gamma\right|\begin{array}{ c}1-m_{s}\\ m_{s},-m_{s}\end{array}\right),\\ F_{\Gamma}(\gamma)=\frac{m_{s}}{\Gamma(m)}G_{1,2}^{2,3}\left(\frac{m_{s}m}{(m_{ s}-1)\tilde{\gamma}}\gamma\right|\begin{array}{c}1-m_{s},1\\ m,0,-m_{s}\end{array}\right),\end{cases} \tag{3}\]  
where \(\Omega=\frac{2\sigma^{2}m_{s}m}{(m_{s}-1)}\) and the instantaneous SNR is related to the envelope as \(\frac{\Gamma}{\tilde{\gamma}}=\frac{R^{2}}{\Omega}\).
## 4 Second Step: Regional Pole Placement
### Linear Matrix Inequalities  
Once the reduced system is written in a state-space form, LMIs can be exploited. A brief explanation of the basic theory of LMI is provided in the present section. More details can be found in the literature, such as in book [22].  
Let \(\mathcal{D}\) be a subset of the complex plane. A first-order system \(\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}\) is \(\mathcal{D}\)-stable if all its poles lie in \(\mathcal{D}\). There is a class of regions that have a straightforward characterization of \(\mathcal{D}\)-stability: the so-called LMI regions. \(\mathcal{D}\subseteq\mathbb{C}\) is denoted as a LMI region if there exist a symmetric matrix \(\mathbf{R}\in\mathbb{R}^{d\times d}\) and a matrix \(\mathbf{S}\in\mathbb{R}^{d\times d}\) (for a suitable size \(d\) depending on the shape of the LMI region) such that  
\[\mathcal{D}=\{z\in\mathbb{C}:\ f_{\mathcal{D}}(z)<0\}, \tag{17}\]where \(f_{\mathcal{D}}(z)=\mathbf{R}+z\mathbf{S}+\overline{z}\mathbf{S}^{T}\), and \(<0\) means negative definite. LMI regions are convex and are symmetric respect to the real axis. For example, conic sectors, vertical half-planes, horizontal strips, ellipses, parabolas and hyperbolic sectors are LMI regions, as well as any intersection of the above. For instance, the left half-plane is an LMI region obtained by setting \(\mathbf{R}=0\) and \(\mathbf{S}=1\). Some samples of LMI regions, and their relations with the properties of the system dynamic response, are shown in Figure 1.  
The following theorem, whose proof is provided in [13], characterizes \(\mathcal{D}\)-stability for LMI regions.  
**Theorem 1:**_Let \(\mathcal{D}\) be an LMI region. The system \(\dot{\mathbf{x}}=\mathbf{Ax}\) is \(\mathcal{D}\)-stable if and only if there exists a symmetric matrix \(\mathbf{X}\succ 0\) such that_  
\[\mathbf{R}\otimes\mathbf{X}+\mathbf{S}\otimes(\mathbf{AX})+\mathbf{S}^{T} \otimes(\mathbf{AX})^{T}<0. \tag{18}\]  
The symbol \(\otimes\) is the Kronecker product and \(\mathbf{R},\mathbf{S}\) are the matrices used to define the LMI.  
Theorem 1 generalizes the Lyapunov stability, that is a special case of Equation (18) (by setting \(\mathbf{R}=0\) and \(\mathbf{S}=1\)).
# The Impact of Hollow Wear on the Stability of High-Speed Railway Vehicles
## 1 Introduction
In addition, because of the nonlinear interaction between the wheel and the rail, it is possible for Hopf bifurcation and limit cycle motion to take place while the wheelset is in motion. So, the bifurcation configuration method can also be used to evaluate the stability of vehicles [10; 11]. [PERSON] [12; 13] presented a computation of bifurcation diagram and the influence of nonlinearities of the vehicle system on the type of Hopf bifurcation investigated. For a detailed model of a railway passenger coach, he used two different methods to analyze the oscillation behavior depending on operating speed, wheel-rail contact conditions, and different model configurations. [PERSON] [14] studied the influence of different nonlinear equivalent conicity functions on bifurcation characteristics. [PERSON] [15] used the root locus method to calculate the natural vibration frequency and natural damping of each rigid body in the vehicle system at different operating speeds. This method is a linear frequency domain calculation method, where different trajectory lines represent the natural frequency changes of different rigid bodies in the calculation model at different speeds. When the natural damping coefficient of a certain root locus is less than 0, it indicates that the system has become unstable. Due to the neglect of the influence of nonlinear factors in the system, the calculated critical speed value of the vehicle is relatively high, and this article will not continue to discuss the root locus method. Compared to other methods, the velocity-reducing method is simpler. By establishing a vehicle-track dynamics model, the convergence of lateral vibration of wheelsets can be simulated, thereby the critical speed of the vehicle instability can be calculated [16]. In the vehicle design, improvement, and maintenance processes, the velocity-reducing method, bifurcation configuration method, and measurement of lateral acceleration of the bogie frame have been widely used to assess vehicle stability. However, previous studies have not focused on whether the stability assessment results obtained from these three methods are consistent and the differences among them. To investigate the impact of hollow wear on vehicle stability, the measured worn wheel profiles were matched with standard rails, and dynamic simulations were conducted using these methods under identical wheel hollow wear conditions in this study. In addition, the characteristics of these methods were elucidated. The insights are expected to inform more effective maintenance strategies and safety assessments for high-speed rail vehicles.