esa-sceva/satcom-chunk-collection huggingface dataset:
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 2 Signal Model and Principle of the Range and Velocity Estimation
### Principle of the Range and Velocity Estimation  
Consider there are \(D\) targets moving at radial velocity \(v_{0i}(i=1,2,\cdots,D)\) relative to the radar. For FMCW radar signals, the radial velocity of the target relative to the radar is positive when the target is far away from the radar. This is because a positive Doppler shift is observed when the target is moving away from the radar. Ignoring the target movement within a chirp period, the time delay corresponding to the \(i\)th target can be expressed as:  
\[\tau_{i}=\frac{2(R_{0i}+(m-1)v_{0i}T_{l})}{c} \tag{4}\]  
where \(R_{0i}\) is the range between the \(i\)th target and the radar, \(c\) is the velocity of the electromagnetic wave in the air, \(T_{l}\) is the chirp duration with \(T_{l}=T_{c}+T_{z}\), \(1\ll m\ll M\), and \(M\) is the number of chirps. Substituting (4) into (3), ignoring the \(1/c^{2}\) term, the phase of the \(i\)th target can be expressed as:  
\[p_{i}(t)=2\pi\biggl{(}\mu\tau_{0i}t+\frac{2v_{0i}(m-1)T_{l}}{\lambda}+\frac{2 \mu v_{0i}(m-1)T_{l}}{c}t\biggr{)} \tag{5}\]  
where \(\tau_{0i}=2R_{0i}/c\), \(\lambda=c/f_{0}\) is the wavelength of the transmitted signal.  
Employing the central linear expansion formula, i.e., \(t=T_{l}/2\), \((m-1)T_{l}=(M-1)T_{l}/2\), the third term in (5) can be expressed as follows:  
\[\frac{2\mu v_{0i}(m-1)T_{l}}{c}t\approx\frac{\mu v_{0i}}{c}((M-1)T_{l}t+T_{l} ^{2}(m-1)+\frac{(M-1)}{2}T_{l}^{2}) \tag{6}\]Substituting (6) into (5), where the \(T_{l}^{2}\) term can be neglected, the phase in (5) is expressed as:  
\[p_{i}(t)=2\pi(f_{ri}t+f_{vi}(m-1)T_{l}) \tag{7}\]  
where \(f_{ri}=\mu(\tau_{0i}+v_{0i}(M-1)T/c)\), \(f_{vi}=2v_{0i}/\ A\ +\mu v_{0i}T_{l}/c\) are frequencies related to the range and velocity of the \(i\)th target. The information of \(\tau_{0i}\) and \(v_{0i}\) is simultaneously contained in \(f_{ri}\), while the information of \(v_{0i}\) is only contained in \(f_{vi}\). Therefore, after estimating \(f_{ri}\) and \(f_{vi}\), \(\tau_{0i}\) in \(f_{ri}\) can be calculated from \(v_{0i}\) in \(f_{vi}\) and in turn \(R_{0i}\) is obtained.  
When the targets move slowly, i.e., \(c\gg v_{0i}\), \(f_{ri}\) and \(f_{vi}\) can be, respectively, simplified as follows:  
\[f_{ri}=\mu\tau_{0i}\text{=}\frac{2\mu R_{0i}}{c} \tag{8}\]  
\[f_{vi}=\frac{2v_{0i}}{\lambda} \tag{9}\]  
Therefore, the discrete signal can be expressed as:  
\[\begin{split} x(m,n)=&\sum_{i=1}^{D}a_{rt(i)}\exp \{j2\pi(f_{ri}nt_{s}+f_{vi}(m-1)T_{l})+\hat{\varphi}\}+n_{z}(nt_{s})\,\\ &\text{for}\ 0\leq n\leq N-1\,1\leq m\leq M\end{split} \tag{10}\]  
where \(t_{s}=1/f_{s}\), \(f_{s}\) is the sampling frequency, \(N=f_{s}T_{c}\) is the number of samples. When a moving target approaches the radar, the schematic diagram of the time delay and Doppler shift generated by the moving targets is shown in Figure 1.  
Therefore, the range and velocity of the \(i\)th target can be estimated as:  
\[\hat{R}_{i}=\frac{f_{ri}c}{2\mu} \tag{11}\] \[\hat{\vartheta}_{i}=\frac{\lambda f_{vi}}{2} \tag{12}\]
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Anomaly Detection Approach
#### 3.2.4 Mathematical Formulation of Anomaly Scores  
In the Isolation Forest (IF) model, anomaly scoring is based on the path length of data points across a set of randomly constructed binary trees. The anomaly score \(s(x)\) for a sample \(x\) is computed as  
\[s(x)=2^{-\frac{E(h(x))}{c(x)}} \tag{1}\]  
where \(E(h(x))\) is the average path length across all isolation trees, \(c(n)\) is the average path length in a binary search tree, approximated as  
\[c(n)=2H(n-1)-\frac{2(n-1)}{n} \tag{2}\]  
and \(H(i)\) is the harmonic number, with \(H(i)\approx In(i)+\gamma\) (Euler-Mascheroni constant).  
In the LSTM Autoencoder (LSTM-AE), anomaly scores are based on the reconstruction error of the input time series. The model is trained to minimize the mean squared error:  
\[\mathcal{L}_{MSE}=\frac{1}{T}\sum_{t=1}^{T}\mid x_{t}-\hat{x}_{t}\mid^{2} \tag{3}\]  
where \(x_{t}\) is the original input at time step \(t\), \(\hat{x}_{t}\) is the reconstructed output at time step \(t\), and \(T\) is the length of the input time window. An input window is flagged as anomalous when the reconstruction loss exceeds a dynamic threshold defined from the training error distribution.  
In order to detect anomalies using the LSTM-AE, we define an anomaly score for each input sequence as its reconstruction loss. During training, we calculate the distribution of mean squared errors (MSE) over normal data and select a threshold \(\theta\) corresponding to a high percentile (e.g., 95 th). An input is flagged as anomalous when  
\[\mathcal{L}_{MSE}(\mathrm{x})>\theta \tag{4}\]  
To classify an input sequence as anomalous, we define a dynamic threshold \(\theta\) based on the reconstruction loss values obtained from the training set. Specifically, we set \(\theta\) as the 95 th percentile of the training reconstruction loss distribution:  
\[\theta=\mathrm{Percentile}_{95}\mathcal{L}_{\mathrm{train}} \tag{5}\]  
During inference, an input window is flagged as anomalous if its reconstruction loss \(\mathcal{L}(x)\) exceeds \(\theta\). This unsupervised strategy allows the system to adapt the decision boundary to each environment's normal behavior, without requiring labeled anomalies.
## 3 Results
XGBoost, on the other hand, offers superior performance in terms of both accuracy and computational efficiency by leveraging gradient boosting with regularization techniques such as shrinkage, making it more effective in reducing bias. These metrics are significant as they directly impact the models' ability to predict ionospheric scintillations with high precision and generalization, essential for understanding and forecasting space weather phenomena. Figure 4: Linear regression plot of the ML models during the training phase; (**a**) the RF model; (**b**) the XGBoost model.
## 5 Comparison with Other Methods  
Table 4 explores a comparative study between the present study with other proposed models.  
This section shows how well our feature-selection model works compared to the previous works in this field. [PERSON], [PERSON] et al. [47] used a genetic algorithm with logistic regression for feature selection and used logistic regression for classification. They used data for training and testing, achieving accuracy and F1-scores of 94.55% and 93.56%, respectively, with 22 features. Table 4 shows the results obtained from different works. The result of a work [36] based on the usage of neural networks is 75.19% accurate. The use of genetic algorithms to optimize models [46; 47] resulted in a substantial improvement in the results obtained; the best accuracy of such models is 94.55%. The authors of [38] introduced a novel hybrid model that combined neighborhood-components analysis, a genetic algorithm, and a support-vector machine classifier (NCA-GA-SVM). The results revealed a classification accuracy of 96.36% and an F1-score of 95.52%. Experimental results and other research regarding HCC demonstrated that RFE-GB-RF exhibits acceptable performance. Therefore, while analyzing various metrics, it is concluded that the proposed work shows better results than other works in the literature. We used the same data for training and testing and achieved accuracy and F1-score values of 96.66% and 95.66%, respectively, with minimal features.Many other related work can be found in [38; 39; 46; 47].  
Figure 13: Random Forest method for k-NN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Missing-Value Replacement** & **Classification Algorithm** & **Methods** & **\# Selected Features** & **Accuracy** & **F1-Score** \\ \hline \multirow{4}{*}{Mean and Mode} & Logistic & \begin{tabular}{c} Lasso \\ Regularization \\ \end{tabular} & 28 & 95.16 & 94.71 \\ \cline{2-6}  & Regression & \begin{tabular}{c} Ridge \\ Regularization \\ \end{tabular} & 17 & 91.66 & 90.91 \\ \cline{2-6}  & \begin{tabular}{c} Recursive- \\ Feature \\ Elimination \\ \end{tabular} & \begin{tabular}{c} Gradient \\ Boosting \\ \end{tabular} & 3 & 95.12 & 93.75 \\ \cline{2-6}  & \begin{tabular}{c} Random Forest \\ \end{tabular} & 17 & 95.12 & 94.11 \\ \hline \multirow{4}{*}{k-NN} & Logistic & \begin{tabular}{c} Lasso \\ Regularization \\ \end{tabular} & 25 & 94.11 & 93.33 \\ \cline{2-6}  & Regression & \begin{tabular}{c} Ridge \\ Regularization \\ \end{tabular} & 18 & 90 & 90.01 \\ \cline{2-6}  & \begin{tabular}{c} Recursive \\ Feature \\ Elimination \\ \end{tabular} & \
\begin{tabular}{c} Gradient \\ Boosting \\ \end{tabular} & 7 & 96.66 & 96.55 \\ \cline{2-6}  &
\begin{tabular}{c} Random Forest \\ \end{tabular} & 9 & 95 & 94.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of different feature-selection techniques.
## 5 Analysis of the Influence of Different Factors on the Adjacent Pile Foundation
### Analysis of the Influence of Inclined Pile Tilt Angle on the Adjacent Pile Foundation  
In order to understand the effect of the inclined pile tilt angle on the adjacent pile foundation, the vertical pile bending moment and the pile displacement behavior at 5 m from the excavation surface are investigated at 21.75 m from the pit angle, with different angles of the inclined pile as shown in Figures 16 and 17, respectively.  
Figure 16: Pile bending moments.  
From Figure 16, it can be noted that the pile foundation reaches the maximum bending moment at 9 m for different inclination angles, and the bending moment shows a trend of increasing and then decreasing with the increase of soil depth. In addition, the maximum bending moment is \(-672.38\), \(-391.84\), and \(-688.11\) when the inclination angle of the inclined pile is \(10^{\circ}\), \(15^{\circ}\), and \(20^{\circ}\), respectively. Finally, when the inclination angle increases from \(10^{\circ}\) to \(15^{\circ}\) the maximum bending moment decreases by \(71.6\%\) and by \(75.6\%\) when the inclination angle increases to \(20^{\circ}\).  
According to Figure 17, it can be concluded that under different inclination angles, the pile foundation reaches the maximum horizontal displacement at 8, 9, and 10 m, respectively. In addition, the minimum pile displacement occurs at the pile head and the pile displacement shows a trend of increasing and then decreasing with the increase of soil depth. The maximum horizontal displacement of the pile body is \(49.5\), \(45.3\), and \(55.5\) mm, respectively, when the inclination angle of the inclined pile is \(10^{\circ}\), \(15^{\circ}\), and \(20^{\circ}\). With the increase of inclination angle of the supporting structure, the displacement of the pile body near the pile foundation also shows the trend of decreasing first and then increasing. Also, when the inclination angle increases from \(10^{\circ}\) to \(15^{\circ}\), the maximum pile displacement decreases by \(9.3\%\), and by \(22.5\%\) when the inclination angle increases to \(20^{\circ}\). Lastly, it can be seen that under the same change of inclination angle, the larger the inclination angle of the support structure, the larger the change of horizontal displacement of the adjacent pile foundation. Since the top of the pile near the pile foundation is the least change of horizontal displacement when using the inclined and straight combined pile support structure, attention should be paid to the displacement below the top of the pile near the pile foundation in the actual process. When safety control is carried out in the construction stage, the displacement of the pile top cannot be used as the criterion for judging whether the deformation of the adjacent pile foundation is safe or not.
## 2 Materials and Methods
### False Nearest Neighbors  
In a reconstruction space of very low dimension, two points appear to be closer to each other than they are. Two points are considered as real neighbors if the distance between them remains constant as the reconstruction dimension increases. Conversely, the distance between false neighbors continues to increase as long as the reconstruction dimension remains too low.  
The underlying principle behind the method is to look for points in the time series that are neighbors in the reconstruction space, but that should not be, as their future time evolution is very different [38; 39]. The distances between all points, for different consecutive reconstruction space dimensions, \(m\)-dimensional and \((m+1)\)-dimensional spaces, must be estimated. If the ratio between both distances is higher than a threshold \(r\), it says that the neighbors are false neighbors.  
If the standard deviation of the data is \(\sigma\) and it uses the maximum norm, for computation speed reasons, the percentage of false neighbors \(\chi\) amounts to:  
\[\chi(r)=\frac{\sum_{n=1}^{N-m-1}\Theta\left(\frac{\left\|\mathbf{s}_{n}^{(m+1 )}-\mathbf{s}_{k(n)}^{(m+1)}\right\|}{\|\mathbf{s}_{n}^{(m)}-\mathbf{s}_{k(n) }^{(m)}\|}-r\right)\Theta\left(\frac{\sigma}{r}-\left\|\mathbf{s}_{n}^{(m)}- \mathbf{s}_{k(n)}^{(m)}\right\|\right)}{\sum_{n=1}^{N-m-1}\Theta\left(\frac{ \sigma}{r}-\left\|\mathbf{s}_{n}^{(m)}-\mathbf{s}_{k(n)}^{(m)}\right\|\right)}, \tag{12}\]  
where \(\mathbf{s}_{k(n)}^{(m)}\) is the nearest neighbor to \(\mathbf{s}_{n}\) in the \(m\)-dimensional reconstruction space. The subscript \(k(n)\) indexes the time series element, with \(k(n)\
eq n\), for which \(\left\|\mathbf{s}_{n}-\mathbf{s}_{k(n)}\right\|\) is minimal.  
The first term of the numerator in Equation (12), within the summation, is equal to one, if the nearest neighbor is false, that is if the distance increases by a factor greater than \(r\) when the reconstruction dimension is increased by one, from \(m\) to \(m+1\). The second term drops those pairs of points whose initial distance is already greater than \(\sigma/r\), since, by definition, they cannot be false neighbors, as on average, the points cannot be further away than \(\sigma\). Therefore, in the calculation, these points should not be taken and, thus, appear in the denominator's normalization factor.  
The right reconstruction dimension is that for which the percentage of false neighbors falls to approximately zero, as shown in Figure 9. Once it reaches the dimension, it is assumed that the attractor embraces its true spatial configuration, at least from a topological perspective. The results may depend on the chosen delay \(\tau\). In any case, if it seeks to be able to differentiate the chaos from the noise, it is essential to validate the results using surrogate data testing [29].
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 1 Introduction
In a similar way, an iterative algorithm that selects two nodes, one from a macro-cell and one from a small-cell, is proposed in [16]. It also uses the SINR to maximize the throughput obtained by UEs. On the other hand, the authors of [17] propose to dynamically add or remove the nodes which simultaneously serve the UE based on Channel State Information (CSI) and cell loads. This scheme aims at increasing the data rate, as well as decreasing the probability of radio link failure. Including the use of the UE location, a deep learning-based mobility management methodology is introduced in [18] that enables dual connection to improve the quality of service of UEs that are going to perform a handover. To this end, the methodology forecasts the future location of users based on mobility patterns. Besides the selection of nodes from two 3 GPP access technologies, other studies propose the use of a wireless local area network access point (WAP) to serve the UE in heterogeneous networks [19]. Therefore, both licensed and unlicensed spectrum is used in resource allocation. In this manner, the authors of [20] provide a methodology that dynamically balances the load between eNBs and WAPs. On the other hand, the nodes of these two radio access technologies (RATs) that provide the highest estimated QoE to video streaming users are selected by the approach introduced in [21]. In spite of these solutions, 3 GPP proposes to select the nodes based only on Reference Signal Received Power (RSRP) [22]. Given the simplicity of this solution, this paper proposes an alternative methodology to outperform 3 GPP approach. The proposed system uses the location of the UEs as well as multiple network metrics to select the eNB and gNB that will provide the maximum QoE to users. In this context, this paper proposes a location-aware solution for the selection of eNB and gNB that simultaneously serve an user in MR-DC-enabled scenarios. A system-level simulation tool is used to compare the proposed methodology and the 3 GPP-compliant solution in a real dense urban scenario. In this regard, several tests are carried out with different scenario conditions to demonstrate the feasibility of the proposed solution, which enhance the overall QoE perceived by users. The results also demonstrate the importance of location in properly assigning network nodes in MR-DC scenarios. The rest of the paper is organized as follows. Section 2 presents the scenario. The methodology proposed is detailed in Section 3 followed by the simulation assumptions in Section 4. The results are then depicted in Section 5. Finally, concluding remarks and future work are outlined in Section 6.
## 5 Limitations and Future Work  
A few limitations were identified during the course of this research. One key constraint was the limited number of computing nodes available in our experimental environment, which restricted the scale of our implementation compared to other studies. Testing the implementation on bigger clusters would be necessary. Another limitation is the lack of a chain regeneration mechanism, which is tricky to implement in a distributed memory environment. This absence led to fewer generated chains then requested, which makes the table imperfect. Future work could address this issue and improve the rainbow table generation process. Another area for future work is to experiment with other parallelization techniques for rainbow tables generation, for example, utilizing the power of GPU to enhance the generation process.
## Materials and Methods
### Data Processing and Statistical Analysis  
We began by smoothing the raw 3D motion capture data using a five-point moving average filter with a weighted distribution (1-3-4-3-1), a function supplied by VICON software NEXUS 1.8.5 (Oxford Metrics Ltd., Oxford, UK). This process provided us with kinematic information, including marker positions, positional changes, velocities, and accelerations. Subsequently, we constructed a biomechanical body model consisting of 15 segments using these refined data [5,29,55]. Additionally, we utilized data from markers on equipment to model the racket, shuttlecock, and net. Employing fundamental principles of physics, we transformed basic positional data from consecutive frames of reconstructed information into the dynamic movements of the forehand smash.  
Figure 1: The full-body smash control revealed by a skilled player.  
In the realm of 3D body modeling, we determined the inertial characteristics for each segment by applying anthropometric regression equations that took individual body mass, body height, gender and race into account [55]. With our comprehensive biomechanical model, encompassing the racket, shuttlecock, and the entire body, we proceeded to quantify parameters related to the three smash quality parameters and the movement parameters of the forehand smash. The movement parameters included the dynamic data related to body positioning, racket angle, major joints, and the X-factor. The latter is determined by the separation angle between lines connecting the right and left shoulder joint centers and the right and left hip joint centers, respectively, in the transverse plane.  
All the parameters derived from the VICON motion capture system and the biomechanical modeling process were subjected to analysis using SPSS Statistics version 22.0 (SPSS Inc., Chicago, IL, USA). We present the results through descriptive statistics (average \(\pm\) standard deviation). Furthermore, we conducted independent t-tests to compare differences between novice and skilled players concerning both smash quality and movement parameters. Statistical significance was established at a threshold of \(p\) < 0.05. To express changes in a parameter as a percentage, we used the following formula: [(large value \(-\) small value)/small value]. This same formula was employed to calculate the percentage disparities between the groups.
## 3 The 6G Technology
### Sixth-Generation Key Enabling Technologies
#### 3.4.5 Quantum Computing (QC)  
Quantum computing is a technology that facilitates high-speed computation while ensuring the reliability, security, and energy efficiency of computational systems [4; 19]. QC is defined by three fundamental principles: superposition, entanglement, and interference [36; 100]. Other properties of QC include the quantum no-cloning theorem, and decoherence [101; 102]. In the 6G network, the propagation strength of a signal wave can be boosted or suppressed intelligently by QC [36]. A QC-assisted system possesses high data rates and communication security. It can simultaneously process multi-dimensional data through its super-speed computing capabilities [4], and can be effective for network optimization applications [103].  
The performance of the 6G network can be improved by integrating QC and ML algorithms for superior computational speed and complexity [19; 104; 105]. The resulting technology from the amalgamation of ML and QC is Quantum Machine Learning (QML) which was developed for real-time, computationally intensive application scenarios [4; 36; 37].