esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Localization Technology
### Range-Based Techniques
#### 3.1.2 Time of Arrival (ToA)  
ToA is the time it takes for a wireless signal to travel from the transmitter to the receiver. It can be used to estimate the distance between the device and the anchor by using the following equation.  
\[d=T\times c. \tag{2}\]  
\(T\) represents the propagation delay time, \(d\) represents the distance between the device and the anchor, and \(c\) represents the speed of light. Two methods using the ToA technique, one-way ranging (OWR) and two-way ranging (TWR), are widely used, as shown in Figure 3.  
In the case of OWR, ToA can be obtained through the duration of transmission and reception time. While OWR calculates the time on a single path, TWR calculates it using RTT, which means the time from the moment the transmitter transmits the wireless signal to the moment the receiver receives the signal and then retransmits it to reach the transmitter. Through this, ToA can be calculated by subtracting the receiver waiting time (such as network delay time within RTT) from RTT and dividing it by half. Comparing the two methods, OWR is relatively fast and consumes less power in distance measurement; however, TWR does not require precise synchronization for the localization network and has the advantage of lower initial configuration cost as it is robust against network synchronization.  
In the case of the ToA technique, if the reference clocks of the anchor and the device are different, it is not possible to obtain the absolute time and the two reference clocks must be identical.
## 3 The Proposed MPPT Algorithm
### Negative Power Slope  
The value of the negative requested power slope must be chosen in order to ensure the rapid recovery of the MPP, after it has been overcome during the positive slope, and keeping the capacitor voltage variation within a reasonable value. The circuit of Figure 2a can be considered for the calculation of the capacitor voltage, assuming that the PV current is held to the MPP value by the inductor presence. As the \(v_{c}\) variation is expected to be very low, the following law can be assumed for the \(i_{r}\) current:  
\[i_{r}=f\left(t\right)=\left(i_{mpp}+\Delta I\right)-\frac{n_{slope}}{v_{mpp}} \cdot t, \tag{10}\]  
Solving the first-order differential equation, the \(v_{c}\) variation results as follows:  
\[\Delta v_{c}\left(t\right)=v_{c}-v_{c,0}=-\frac{\Delta I}{C}\cdot t+\frac{n_{ slope}}{2v_{mpp}C}\cdot t^{2}, \tag{11}\]  
Therefore:  
\[\left|\Delta v_{c}\right|_{\text{max}}=\frac{v_{mpp}\left(\Delta I\right)^{2} }{2n_{slope}C}, \tag{12}\]  
By limiting the maximum \(v_{c}\) variation, it results as follows:  
\[\left|\Delta v_{c}\right|_{\text{max}}<\delta_{\Delta xc}\cdot v_{mpp}\quad \Rightarrow\quad n_{slope}>\frac{1}{\delta_{\Delta xc}}\frac{\left(\Delta I \right)^{2}}{2C}\enspace, \tag{13}\]
## 4 Results
### Centralized Learning vs. FL Based on Edge-Fog-Cloud Architecture
#### 4.2.3 FL on Fog Layer Performance  
After analyzing the detection performance at the edge layer, we proceed to examine the detection performance at the fog layer. The results of the detection performance at the fog layer are illustrated in Figure 9.  
Figure 9: Loss, accuracy, precision, recall, and F1-score across fog layer; Figure (**a**) represents the trend of loss value in Fog Devices 1–5; Figure (**b**) represents the trend of accuracy value in Fog Devices 1–5; Figure (**c**) represents the trend of precision value in Fog Devices 1–5; Figure (**d**) represents the trend of recall value in Fog Devices 1–5; Figure (**e**) represents the trend of F1-score value in Fog Devices 1–5.  
We begin by observing the loss values in the fog layers, which exhibit significant variations across fog devices, indicating a heterogeneous performance landscape. Subsequently, we delve into the analysis of accuracy performance in the proposed system. The analysis of fog devices' performance across rounds unveils intriguing trends regarding accuracy. Initially, in the first round, each device demonstrates stable starting accuracies exceeding 90%. Fog Device 2 displays a unique pattern, commencing and concluding with high accuracy but encountering a notable drop to nearly 0% accuracy in round 4. This sharp decline suggests a potential failure or misconfiguration that was subsequently rectified, leading to a swift recovery in accuracy. The rapid rebound of Device 2 hints at a robust error correction or recalibration process employed to efficiently restore its performance levels. Similar patterns are observed in precision, recall, and F1-score metrics. Notably, there is a slight deviation in the precision pattern when Fog Device 3 achieves a recall value of 1.0 in round 4.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 5 Analysis of Experimental Data: High-Speed Machining (HSM)  
The investigation results of L\({}_{16}\) performed at a high speed of machining are presented in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment. No.**} & \multirow{2}{*}{\(\mathbf{v}\) (**m/min**)} & \multirow{2}{*}{\(\mathbf{f}\) (**mm/rev**)} & \multirow{2}{*}{\(\mathbf{d}\) (**mm**)} & \multirow{2}{*}{\(\mathbf{SCE (J/mm^{3})}\)} & \multirow{2}{*}{\(\mathbf{Ra}\) (\(\mathbf{\mu}\)m)} \\ \cline{5-6}  & & & & & **Ave** & **Std. Dev.** & **Ave** & **Std. Dev.** \\ \hline
1 & 1750 & 0.1 & 1 & 0.75 & 0.005 & 1.99 & 0.12 \\
2 & 1750 & 0.2 & 2 & 0.62 & 0.005 & 2.86 & 0.04 \\
3 & 1750 & 0.3 & 3 & 0.62 & 0.005 & 5.22 & 0.08 \\
4 & 1750 & 0.4 & 4 & 0.37 & 0.005 & 7.89 & 0.95 \\
5 & 2000 & 0.1 & 2 & 0.75 & 0.005 & 2.00 & 0.02 \\
6 & 2000 & 0.2 & 1 & 0.66 & 0.005 & 2.96 & 0.23 \\
7 & 2000 & 0.3 & 4 & 0.43 & 0.005 & 5.19 & 0.07 \\
8 & 2000 & 0.4 & 3 & 0.43 & 0.005 & 7.10 & 0.10 \\
9 & 2250 & 0.1 & 3 & 0.75 & 0.012 & 1.61 & 0.00 \\
10 & 2250 & 0.2 & 4 & 0.56 & 0.005 & 2.93 & 0.27 \\
11 & 2250 & 0.3 & 1 & 0.61 & 0.012 & 3.53 & 0.38 \\
12 & 2250 & 0.4 & 2 & 0.56 & 0.005 & 7.53 & 0.09 \\
13 & 2500 & 0.1 & 4 & 0.78 & 0.005 & 2.03 & 0.29 \\
14 & 2500 & 0.2 & 3 & 0.67 & 0.008 & 2.28 & 0.04 \\
15 & 2500 & 0.3 & 2 & 0.69 & 0.005 & 4.71 & 0.12 \\
16 & 2500 & 0.4 & 1 & 0.61 & 0.012 & 6.05 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of confirmatory experiments for transitional speed machining.  
Figure 7: Main effect plot showing the influence of cutting parameters on Ra in TSM.
## 4 Narrowband Interference Suppression Algorithm Based on SVD with Guidance Signal
### SVD for Narrowband Interference Suppression  
SVD is an important signal separation method. By singular value decomposition and selective reconstruction of the Hankel matrix of the noisy PD signal, the narrowband interference signal can be effectively suppressed. The specific steps are as follows:  
(1) Set the noisy PD signal \(X\) according to the following:  
\[X=[x(1),x(2),\cdots,x(N)] \tag{25}\]  
\[x(k)=n(k)+p(k) \tag{26}\]  
In (26), \(n(k)\) is the periodic narrowband interference signal; \(p(k)\) is the PD signal; and \(k=1\), \(2\), \(\ldots\), \(N\) is the number of sampling points.  
Construct Hankel matrix \(A_{m\times n}\) for sampling series \(X\) as follows:  
\[A=\begin{bmatrix}x(1)&x(2)&\cdots&x(q)\\ x(2)&x(3)&\cdots&x(q+1)\\ \vdots&\vdots&&\vdots\\ x(p)&x(p+1)&\cdots&x(N)\end{bmatrix} \tag{27}\]  
Matrix \(A\) is a matrix of \(p\times q\), and its rank is \(r\).  
(27) can be further expressed as:  
\[A=A_{n}+A_{p}=\left[\begin{array}{cccc}n(1)&n(2)&\cdots&n(q)\\ n(2)&n(3)&\cdots&n(q+1)\\ \vdots&\vdots&&\vdots\\ n(m)&n(m+1)&\cdots&n(N)\end{array}\right]+ \tag{28}\] \[\left[\begin{array}{cccc}p(1)&p(2)&\cdots&p(q)\\ p(2)&p(3)&\cdots&p(q+1)\\ \vdots&\vdots&&\vdots\\ p(m)&p(m+1)&\cdots&p(N)\end{array}\right]\]  
In (28), \(A_{n}\) is the PD signal subspace and \(A_{p}\) is the narrowband interference subspace.  
(2) Singular value decomposition of matrix \(A\):  
\[A=U\Lambda V^{T}=\begin{bmatrix}U_{n}&U_{p}\end{bmatrix}\begin{bmatrix}\Lambda _{n}&0\\ 0&\Lambda_{p}\end{bmatrix}\begin{bmatrix}V_{n}&V_{p}\end{bmatrix}^{T} \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **True** & **Relative Error of the Direct** & **Relative Error of the** & **Relative Error of the** \\  & **Value/MHz** & **Estimation Method/\%** & **Rife Method/\%** & **Improved Rife Method/\%** \\ \hline
1 & 1.11 & 0.90 & 0.22 & 0.05 \\
2 & 1.53 & 0.32 & 0.41 & 0.02 \\
3 & 2.26 & 0.44 & 0.10 & 0.00 \\
4 & 4.14 & 0.24 & 0.06 & 0.01 \\
5 & 6.23 & 0.08 & 0.12 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of narrowband interference frequency obtained by each method.  
In (29), \(U\) and \(V\) are left and right orthogonal eigenvector matrices, respectively; and \(V\) is a diagonal matrix, \(\mathbf{\Lambda}=diag(\lambda_{1},\lambda_{2},\lambda_{3},\ldots,\lambda_{p})\), where \(\lambda_{i}\) is the singular value of the matrix \(A\), which is arranged in descending order.  
(3) Reconstruct the PD signal.  
According to a large number of papers [18], narrowband interference signals correspond to large singular values, and each narrowband interference frequency corresponds to two non-zero singular values. Therefore, after obtaining the number \(m\) of narrowband interference from the frequency-amplitude mean curve of FSWT, narrowband interference suppression can be achieved by setting the first \(2m\) singular values to zero. The reconstructed PD signal is shown in (30).  
\[A_{p}{}^{\prime}=U\begin{bmatrix}0&0\\ 0&\Lambda_{p}\end{bmatrix}^{\prime}V^{T} \tag{30}\]
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 2 Related Works
### Pose Estimation Methods Based on RGB-D Images  
Pose estimation methods utilizing RGB-D information primarily leverage depth data for preprocessing and postprocessing while relying on RGB images for pose estimation. [PERSON] et al. [54] analyzed why occlusion occurs due to the loss of topological information when projecting the three-dimensional real world into a two-dimensional image. To address this, they introduced a method that incorporates depth-enhanced object detection results as input for pose estimation. In this approach, depth information is obtained by computing the disparity between images captured from two horizontally aligned RGB cameras [55]. The depth data are then used to segment the background, and within the segmented regions, RGB information is employed for object segmentation. The segmented object regions are subsequently refined and provided as input to the CPM network to generate confidence maps for each joint. This method adopts a top-down pose estimation approach, leveraging depth information for human region detection to enhance robustness against occlusions caused by overlapping individuals. However, similar to conventional top-down methods, the computational cost increases proportionally with the number of people in the image, limiting its scalability. [PERSON] et al. [28] proposed an approach that enhances pose estimation performance by integrating features extracted from depth images into the OpenPose model. However, the use of depth images was limited to enhancing the backbone feature representations, while it was not separately utilized for estimating joint locations or their connectivity.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 5 Machine Learning Models for Identification of the LOS, NLOS, and MP Conditions
### Support Vector Machine Classifier for the UWB Localization System  
SVM is a supervised machine learning technique suitable for solving both classification and regression problems [43; 44]. It is strongly based on the framework of statistical learning theory [45]. SVM has also been recognized as one of the most frequently used classification techniques in the machine learning community in the past due to its robustness and superior performance without the need to tune several parameters compared to deep neural networks [9]. In short, SVM takes the data as an input and determines a hyper-plane that separates the data into predefined classes. The hyper-plane was established in the SVM algorithm by maximizing the margin between the separable classes as wide as possible. Table 2 presents the comparison of four kernel types in SVM using the UWB measurement data and extracted features examined in Section 4.  
The choice of the kernel types in SVM had a strong influence on its accuracy regarding our particular measurement of UWB data. The results in Table 2 show that the radial basis function (RBF) kernel reached the highest accuracy with 82.96%, while the sigmoid function provided the poorest with 50.59%. Both linear and third-order polynomial functions had comparable results. In terms of training and test times, the linear function achieved the lowest time per sample while the sigmoid function showed the worst performance with the highest time per sample. In all circumstances, the training and test times in SVM were in the order of milliseconds. This meant that SVM had the poorest performance in terms of test time compared to RF (Section 5.2) and MLP (Section 5.3).
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization  
### Optimizing Methods  
It is very important to choose the optimization algorithm that is best suited to the task being solved. The goal of optimization is to minimize (or maximize) the objective function under certain constraints. Optimization using the directed search approach can be achieved by using an iterative algorithm consisting of two steps. In the first step, the \"improvement\" direction is determined at the iteration point, followed by a one-dimensional search in which the point of the next iteration is calculated by finding the minimum in the \"improvement\" direction that was determined earlier. Among different types of directional search algorithms, the most common are the steepest descent (SD), Newton, CG, and quasi-Newton methods [143]. As a rule, SD is the first choice in many applications because of its simplicity and globally convergent behavior. However, in many other applications that require a large number of variables, CG and quasi-Newton methods are preferred, which also globally converge, because of their better performance, especially at the optimal points. The most commonly used one-dimensional search algorithms are the equal interval search, golden ratio search, dichotomous search, Fibonacci search methods, and parabolic fitting method [144]. If a fixed interval of uncertainty with a minimum number of function estimates is required, the Fibonacci search algorithm is the preferred choice. However, the genetic algorithms (GA) [145] and simulated annealing methods [146], which are stochastic rather than deterministic in nature, represent two alternatives to the traditional optimization algorithms. In addition, they use a directed random search to find optimal solutions in complex domains.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Results  
The datasets are partitioned into three sets using an 8:1:1 ratio, consisting of training sets, validation sets, and test sets. The training set is used to train the parameters and features of the model; the validation set is used to monitor the model's performance and conduct hyperparameter tuning; and the test set is exclusively reserved for evaluating the model's performance. Regarding the model's parameter settings, the optimization algorithm chosen is the Adam algorithm with a learning rate of 0.0001. Dropout layers are incorporated into every layer except the fifth layer to mitigate overfitting, with a dropout rate of 0.25 in the first layer and 0.5 in the subsequent layers. To strike a balance between computational efficiency and dataset size, the batch size is set to eight, and the number of training epochs is adjusted based on real-time monitoring of the loss function curve. Four sets of data are fed into the neural network for training, resulting in the development of four independent models.
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 2 The Wavelet Transform
### Discrete Wavelet Transform  
The initial application of the Discrete Wavelet Transform (DWT) was to use discrete data as the scale parameter, \(c\), in the Equation (1). Commonly, the dyadic grid was used, resulting in \(c=2^{k}\). After [PERSON]'s developments [26], many derivations of the DWT appeared. These were based on the use of digital filters to optimize the computational process, generating information in frequency bands. This tool consists of the processing of a discrete signal, \(x(i)\), at different positions and scales (_i.e._, different frequencies and resolution levels), decomposing the signals in approximation (\(A\)) and detail (\(D\)). The approximation information is obtained by means of a low pass filter, and the detail information using a high pass filter, as it can be observed in Figure 2.  
After applying filters to a signal, \(S\), of a frequency band \([0,\pi]\) and number of samples, \(N\), the frequency band is halved obtaining both approximation information (\(A\)) \([0,\pi/2]\) and detail information (\(D\)) \([\pi/2,\pi]\). Therefore, applying the Nyquist rule [47], downsampling by two can be justified without losing any relevant information; this gives a resulting number of \(N/2\) samples [48].  
The recursive application of the filtering algorithm resulted in Multiresolution Analysis (MRA) and the Wavelet Packet Transform (WPT).  
The main application of this tools was in crack detection in the feature extraction. The DWT can represent a signal with a limited number of coefficients that can be directly used as features.  
Figure 2: DWT decomposition of a signal (S) in approximation information (A) and detail information (D) using filters.  
Statistical parameters, and the energy of the coefficients, can also be used as features. The features selected can be all from the same decomposition level in the tree, this is called the single-level basis selection. Alternatively, the features can be selected from different levels, this is termed the multiple-level basis selection. The main problem when using these kind of transforms is in finding the best features for crack detection. In addition, the decision as to whether a defect does or does not exist requires a classification system. Using thresholding methods or intelligent classification systems, such as ANN, GA, or Support Vector Machines (SVM) is common.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 4 Results
### Analysis of Prediction Accuracy
Specifically, the AUC values for the PCA-BP, PCA-SVM, and PCA-RF models are 0.859, 0.864, and 0.747, respectively, and the AP values are 0.966, 0.841, and 0.854, respectively, all lower than those of the PCA-Elman model. The aforementioned results further confirm the effectiveness  
Figure 6: The confusion matrix of the PCA-Elman model, based on 5-fold cross-validation, for the prediction of 37 debris flow sample datasets from Yunnan Province. Furthermore, by comparing the accuracy rates of the Elman Neural Network model and the PCA-Elman model, it was found that the Elman Neural Network model had a lower predictive accuracy of 74.28%, which is below the 87.5% accuracy of the PCA-Elman model. This observation indicates that performing Principal Component Analysis on the original evaluation indicators before model establishment not only effectively removes redundant and correlated information between the indicators but also retains the necessary information between the original indicators, creating linearly independent composite indicators, and thereby enhancing the model's accuracy.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spatial Resolution
#### 3.1.2 Ground Sampling Distance (GSD)  
Due to the ability to point off-nadir to increase revisit time, geometric distortions, similar to the bowtie effect due to the constant angular resolution of the instrument, cause the GSD to increase with a larger off-nadir angle, and with it also the ground projected GRD in terms of meters [40]. The GRD degrades with an increase in off-nadir angle.We use Attitude Determination and Control System (ADCS) telemetry, a camera model, and other parameters to directly geo-reference the pixels of an image, from which the off-nadir angle varying GSD is determined on a per-image basis. Forming the product between the GSD in terms of meters and the LSF FWHM of the optics in terms of pixels, an image-specific GRD is determined.  
Figure 7 shows the GSD determined using direct georeferencing of three captures with different off-nadir angles. Table 4 shows more information about the three captures, in particular average GSD, in addition to the estimated GRD. The left plot in Figure 7 shows the along track GSD of the center pixels within each line of the data cubes (pixel index 341). The apparent noise is due to a time-stamping inaccuracy. The right plot shows the GSD across track of the middle line (line index 477) in the data cubes. The across track GSD increases as expected with a larger off-nadir angle. The along-track GSD is unchanged due to no dependence on off-nadir angle. The GSD close to the nadir is about 60-70 m, while it reaches ca. 500 m, 400 m on average across in the most off-nadir case. Approximately 63\({}^{\circ}\) is the highest off-nadir angle HYPSO-1 acquire hyperspectral scenes without viewing past Earth's horizon.  
Figure 6: Estimated LSF FWHM (black dots, right axis). The vertical lines indicate the 1\(\sigma\)-deviation. How many suitable edges used in the estimation per band is indicated by the blue histogram, left axis. An RGB composite of the image used in the sharpness assessment is shown to the right.
## 5 Conclusions  
We integrated the optical fiber sensing technique and the neural network approaches, and investigated the applicability for loads and AoAs identification of wing structures.  
We developed a 3.6-m semi-spanned wing model with eight trailing edge flaps. Two optical fibers with 30 FBGs were bonded on the upper side of the wing model along the main and aft spars. Aerodynamic loads were applied to the wing model by wind tunnel tests, and load identification was performed by using the neural network approach. The FBG data and eight flap angles were input to the neural network, and the estimated load distributions on the eight wing segments were output. The load distributions were successfully identified, even for data that were not used for training of the neural network. The error range was from \(-\)1.5-1.4 N with a standard deviation of 0.57 N.  
We solved the mechanical inverse problem by using the neural network. We also investigated the application of the neural network to solve the aerodynamic problem to identify the AoA. In this process, load distributions were first identified, and thereafter the AoA was determined by using the load distributions and the flap angles through another neural network. This process required only the FBG and flap angle data to be observed. The neural network successfully identified the AoA with error ranges from \(-\)1.03-0.46\({}^{\circ}\) with a standard deviation of 0.38\({}^{\circ}\).  
[PERSON] proposed the idea, and conducted the simulation and analysis. The experiment was conducted by [PERSON] and [PERSON]. The article was written and completed by [PERSON], and checked by [PERSON].  
This research was funded by JSPS KAKENHI, grant number JP17K14878.  
Figure 12: Design of NN-based identification process for load and AoA.  
Figure 13: Comparison between applied and estimated AoA.