esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Joint Motion Planning Based on Modified Hermite Interpolation
### Requirement for C\({}^{2}\) Continuity of Cubic Hermite Interpolation Curves  
The requirement for C\({}^{2}\) continuity at the connecting point between two adjacent cubic Hermite curves \(y_{i}(x)\) and \(y_{i+1}(x)\) is shown as the following Equation (19):  
Figure 6: Piecewise cubic Hermite interpolation curves.  
\[\ddot{y}_{i}(x_{i})=\ddot{y}_{i+1}(x_{i}) \tag{19}\]  
According to Equation (18), we can obtain Equations (20) and (21) below.  
\[\ddot{y}_{i}(x_{i})=\frac{6(y_{i-1}-y_{i})+2(x_{i}-x_{i-1})\dot{y}_{i-1}+4(x_{i} -x_{i-1})\dot{y}_{i}}{(x_{i}-x_{i-1})^{2}} \tag{20}\]  
\[\ddot{y}_{i+1}(x_{i})=\frac{6(y_{i}-y_{i+1})+4(x_{i+1}-x_{i})\dot{y}_{i}+2(x_{ i+1}-x_{i})\dot{y}_{i+1}}{(x_{i+1}-x_{i})^{2}} \tag{21}\]  
By sorting out Equations (19)-(21), formula (22) can be obtained below. First-order derivatives \(\dot{y}_{i-1}\), \(\dot{y}_{i}\) and \(\dot{y}_{i+1}\) at every three adjacent knots must meet formula (22) to guarantee C\({}^{2}\) continuity at connection points.  
\[(x_{i+1}-x_{i})\dot{y}_{i-1}+2(x_{i+1}-x_{i-1})\dot{y}_{i}+(x_{i}-x_{i-1}) \dot{y}_{i+1}=3\frac{(x_{i+1}-x_{i})}{(x_{i}-x_{i-1})}(y_{i}-y_{i-1})+3\frac{( x_{i}-x_{i-1})}{(x_{i+1}-x_{i})}(y_{i+1}-y_{i}) \tag{22}\]  
formula (22) can also be rewritten as formula (23) as below.  
\[\Delta_{i+1}\dot{y}_{i-1}+2(\Delta_{i}+\Delta_{i+1})\dot{y}_{i}+\Delta_{i} \dot{y}_{i+1}=3\frac{\Delta_{i+1}}{\Delta_{i}}(y_{i}-y_{i-1})+3\frac{\Delta_{ i}}{\Delta_{i+1}}(y_{i+1}-y_{i}) \tag{23}\]  
It can be seen from formula (22), it is almost impossible that arbitrarily specified \(\dot{y}_{0}\), \(\dot{y}_{1}\), \(\ldots\), \(\dot{y}_{n-1}\) at knots happen to meet formula (22). That is to say, when \(\dot{y}_{0}\), \(\dot{y}_{1}\), \(\ldots\), \(\dot{y}_{n-1}\) at knots are constrained arbitrarily, second-order derivatives of cubic Hermite curves at connecting points are discontinuous. In fact, as for cubic Hermtime interpolation curves, only one of them can be satisfied, constraining first-order derivatives willfully at knots or C\({}^{2}\) continuity. In order to solve the matter, a kind of modified cubic Hermite interpolation with willful constraints on first-order derivatives at knots is presented while the interpolation curves are C\({}^{2}\) continuous.
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 4 Principles of Image Restoration
Given that \(\frac{dJ_{r}(f)}{df}=\left(M^{T}+M\!f\right)f\) and \(M\) are a symmetric matrix, \(\frac{dJ_{r}(f)}{df}=2M\!f\). Equation (8) can be simplified as:  
\[f^{\!+1}=\frac{f}{1+\lambda M\!f^{\!f}}\cdot\left(h^{T}\frac{g}{h\!f}\right) \tag{9}\]  
where \(M\!f^{\!+}=\sum\limits_{j}\left(f^{\!t}-C_{k}^{j}\!f^{\!t}\right)\). The regular term \(J_{r}(f)\) takes more pixels in neighborhood \(S\) into account, thereby allowing the RL algorithm to accurately locate discontinuous points in the image. Given that \(J_{r}(f)\) imposes a heavy penalty factor on the details in the image, the following piecewise power function was used to modify this term:  
\[f(x)=\left\{ \begin{array}{cc}(x/255)^{scale}&\quad if\ 0\leq t_{1}\leq(x/255) \leq t_{2}\\ 0&\quad else\end{array} \right. \tag{10}\]where \(scale\in[-1,1]\), and \(t_{1}\) and \(t_{2}\) are two thresholds. Replace \(M\!f^{\prime}\) in Equation (9) with \(f\big{(}M\!f^{\prime}\big{)}\), which represents the point-to-point operation of vector \(M\!f^{\prime}\). The following RL algorithm for local segment regularization is then obtained:  
\[\mathbf{f}^{+1}=\frac{\mathbf{f}}{1+\lambda f\big{(}M\!f^{\prime}\big{)}}\cdot\left( \mathbf{h}^{T}\frac{\mathbf{g}}{\mathbf{h}\!f^{\prime}}\right) \tag{11}\]  
To further protect the edge information in the image, the residual deconvolution algorithm is used for the image restoration. The result of the regulated RL algorithm with local segmentation is denoted by \(f_{p}\). The remaining blurred image is modeled as  
\[\Delta\mathbf{g}=\mathbf{g}-\mathbf{h}\!f_{p} \tag{12}\]  
The local piecewise regularization RL algorithm used in image restoration involves the following steps:  
(1) Adopt the local piecewise regularization RL algorithm to obtain the preliminary result \(f_{p}\); and  
(2) Calculate the remaining blurred image \(\Delta\mathbf{g}=\mathbf{g}-\mathbf{h}\!f_{p}\), and then use the local piecewise regularization RL algorithm to obtain the remaining image \(\Delta\mathbf{f}\). The final restored image is \(f_{p}+\Delta\mathbf{f}\).
## 5 Finite Element Models Arrangement
### Validation of the Finite Element Results
#### 5.4.2 Major Axis  
A bending frame arrangement was used for the numerical assessment of the upright major axis test. Figure 28 compares the normalized bending moment-deflection curves between the FE model and experimental results. Figure 29 illustrates the linear regression of the FE model mean curve with the experimental mean curve. Table 6 presents the evaluation criteria for the accuracy of the FE model predictions.  
Figure 28: Finite element model and experimental curves for major axis test set-up: (**A**) non-reinforced frame types; (**B**) 200 mm reinforced frame types; (**C**) 300 mm reinforced frame types.  
Figure 27: Comparison of failure modes: (**A**) non-reinforced upright type; (**B**) 300 mm reinforced upright type.  
According to Figure 29 and Table 6, the FE results had high accuracy and compatibility with the test results. Figure 30 compares the FE failure modes of the frame with the experimental failure modes  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{Non-reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Standard deviation & 0.124180809 \\ \cline{2-3}  & Pearson (r) & 0.998891538 \\ \cline{2-3}  & R\({}^{2}\) & 0.9978 \\ \hline \multirow{2}{*}{200 mm reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Standard deviation & 0.135695798 \\ \cline{2-3}  & Pearson (r) & 0.995707933 \\ \cline{2-3}  & R\({}^{2}\) & 0.9914 \\ \hline \multirow{2}{*}{300 mm reinforced model} & \multicolumn{2}{l}{Evaluation criteria} \\ \cline{2-3}  & Standard deviation & 0.122399695 \\ \cline{2-3}  & Pearson (r) & 0.999038872 \\ \cline{2-3}  & R\({}^{2}\) & 0.9981 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Finite element model vs experimental results accuracy details in terms of evaluation criteria.  
Figure 29: Comparison of finite element model against major axis test results along with the linear regression: (**A**) and (**B**) non-reinforced model; (**C**) and (**D**) 200 mm reinforced model; (**E**) and (**F**) 300 mm reinforced model.  
where the developed FE model has well predicted the overall deformed shape. These upright frames failed by distortional and local buckling mode.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Impact of Varied Wind Speed on System Transient Stability
#### 4.3.2 For Wind Speed at 15 m/s  
In this case, wind speed of the turbine is set to 15 m/s. As shown by the omega curve in Figure 13, the first peak approaches the same value 1.008 p.u. during the disturbance and settles down in 8 s. Now considering generator angle curves in Figure 14, it can be noticed that the first positive and negative peaks are very similar to those in Figure 12 and all generator angles finally settle down after 9 s.
## 3 Tunnel Detection Method
### HTEM System and Data Acquisition  
The HTEM system is primarily composed of internal instruments and external pod devices in the engine room. The basic composition of the equipment is shown in Figure 3. The instruments in the aircraft cabin include a power supply, a transmitter, a receiver, and auxiliary instruments. The pod device carries the transmitting coil, compensating coil, and receiving sensor. It is attached to the bottom of the helicopter by a loose rope. The instrument in the cabin and the one on the pod device transmit signals through a long transmission cable tied to a sling rope. In the experiment, the diameter of the pod was 18.1 m. The emission current was a bipolar triangular wave with a pulse width of 2.4 ms and a peak current of 470 A. The turn-off time of the triangular wave was 1.16 ms, and current linearity was good during the turn-off period. The transmitting magnetic moment was 47.8 \(\times\) 10\({}^{4}\) A\({}^{2}\). The sampling frequency of the receiving system was 192 kHz.
# Investigation into the Uniformization of Proton Beams for FLASH Therapy
## 2 Materials and Methods  
### The Physical Principle of Beam Uniformization Using Nonlinear Magnets  
A pair of octupole fields, a pair of octupole and dodecapole pole fields, and a pair of step fields were inserted into transport lines, and their effects on the uniformity of the beam spot at the target (selected at the end of the transport line) were compared.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Principal Component Analysis (PCA)  
[PERSON] [24] and [PERSON] [25] developed PCA as a statistical method to reduce the dimensionality of a dataset. PCA projects the original dataset, x, from an M-dimensional space, into a new orthonormal N-dimensional space, resulting in the transformed data, y, which is a linear combination of the original data. In this transformation, the constants, \(a_{ij}\), are the weights along the axes of the new space, known as principal components (PCs) (see Equation (1)). The principle behind PCA is to identify the principal components (PCs) as the directions in which the original data exhibit the most variation. These directions are the eigenvectors of the covariance matrix of the original data, \(x\), corresponding to the largest eigenvalues. As a result, the PCs are ranked from those containing the most information to those containing the least. This enables a reduction in the original data to the PCs that account for a significant percentage of the variance. The weights of the variables in each PC indicate their relevance.  
\[y_{j}=\sum_{i=1}^{n}x_{i}\cdot a_{ij} \tag{1}\]  
In this research, PCA was applied to the original dataset of 11 variables to gain a deep understanding of these variables and their impact on the resulting port rankings. The explained variance of the original data by each principal component (PC) and the weights  
Figure 3: Box and whisker plot of the operational criteria.  
of each variable in these PCs were used for this purpose. Validity of the relevance analysis was verified by applying TOPSIS to the dataset reduced to different dimensionalities.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 1 Introduction
### Paper Contribution  
This paper proposes an IDS for providing security and privacy in smart buildings that simultaneously considers both network and sensor data. By incorporating FL-driven, anomaly-based techniques, the proposed method is future-proof and aligned with emerging 6G requirements. In particular, the main contributions of this paper can be summarized as follows.  
* Development of a lightweight FL solution that minimizes computational and energy overhead, supporting the sustainability goals of 6G networks;
* Integration of zero-touch automation compatibility, enabling seamless incorporation into MLOps processes for continuous learning and adaptation without manual intervention;
* Establishment of a trusted AI framework, securing the AI solution against current and future cybersecurity threats, thereby safeguarding system integrity;
* Emphasis on developing eXplainable AI (XAI) models that provide clear, human-understandable explanations for AI decisions, thereby improving user confidence in the AI system, which is crucial for human-centered applications like smart buildings.  
This paper is organized as follows. The background on FL is presented in Section 2, while Section 3 summarizes the related work. Section 4 introduces the proposed architecture, while Section 5 provides a comprehensive review of the dataset used in the simulation analysis. Sections 6 and 7 present the design and development of the FL-enabled IDS for smart buildings, respectively, starting from the data pre-processing and engineering system. Section 8 presents the performance evaluation of FL-enabled IDS using benchmarks initially established in a centralized context, while Section 9 discusses the results obtained for mitigating poisoning attacks against the presented system. Section 10 presents the conclusions of the paper.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 3 Interference Alignment in Cognitive Radio: First Paradigm  
The first paradigm that considers a CR network having a number of SU pairs and a single PU-Tx and PU-Rx pair, where the PU-Tx is assumed far from its Rx and the SU-Rxs are considered not to be influenced by the interference from the PU-Tx was presented in this section.
## 2 Case Study of the Valencia Region  
### Description of Study Area  
The area selected for the comparative study is the Region of Valencia. This region is in the southeast of Spain (Figure 1) by the Mediterranean Sea. It consists of three provinces--Castellon (CST), Valencia (VLC), and Alicante (ALC)--and includes 542 municipalities. The area covers 23,260 km\({}^{2}\) and represents 4.60% of the national surface; it had a population of 5,072,000 inhabitants in the year 2021 (Spanish National Institute of Statistics). The relief of the area is influenced by its proximity to the Mediterranean, featuring a river system that cuts deeply into the land. The study area is mainly formed by Late Cretaceous carbonate rocks: limestones and dolomites with a significative presence of banks of marl. Materials forming the foothills consist of clay and silt from later Tertiary and Quaternary periods.
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 5 Results and Discussion
### Experiment 1  
In this experiment, efficiency (time to complete) was considered the major objective measure for the evaluation. The overall time to complete the experiment was noted down for each participant. A statistical analysis was conducted on both the interaction methods.  
Figure 8 shows the required mean time to complete the experiment including all the four variations (Table 3). On the basis of the time-to-complete data of the specified experiment, we inferred that for variation order 1 (L-R), GazeGuide took 39.72% (\(M=47.20\), \(SD=5.80\)) less time than RC (\(M=70.60\), \(SD=10.73\)). In variation order 2 (F-B), GazeGuide took 45.15% (\(M=49.67\), \(SD=7.02\)) less time than RC ((\(M=78.65\), \(SD=13.85\)), whereas in variation order 3 (R-L), GazeGuide took 42.28% (\(M=43.61,SD=8.25\)) less time than RC (\(M=67.00\), \(SD=16.79\)). For variation order 4 (B-F), GazeGuide took 59.85% (\(M=40.66,SD=7.91\)) less time than RC (\(M=75.41,SD=12.14\)) to complete the experiment. Overall, GazeGuide took 46.75% less time-to-complete the specific experiment than RC.  
Further, the paired \(t\)-test showed a highly significant difference in the time to complete the experiment using GazeGuide and RC. For variation 1 (L-R), \(T(14)=-5.95\), \(p=0.00021\), variation 2 (F-B) \(T(14)=-5.69\), \(p=0.00029\), variation 3 (R-L) \(T(14)=-3.82\), \(p=0.0040\), and variation 4 (B-F) \(T(14)=-5.69\), \(p=0.00029\). Overall (Figure 8), we can clearly see that when using GazeGuide, the participants significantly took less time to complete the experiment than when using RC.
# Ranking Authors in an Academic Network Using Social Network Measures
## 2 Related Work
### Academic Network Measures  
In order to find the productivity and the impact of an author, different indexing schemes are introduced. In this regard, [PERSON] [8] proposed the h-index to rank the authors and is considered a pioneer.The h-index takes into account both citation count and publication count. The h-index is intended to measure both the quality and quantity of a scientific output. On the one hand, the h-index is most widely-used indexing technique; on the other hand, it has some shortcomings. One of the shortcomings is that once a paper is selected in the h-index, no further importance is given to that paper even if the paper doubles its citation [14]. To address the issue of the h-index, [PERSON] proposed another indexing technique named the g-index. It is an improved version of the h-index, and it firstly arranges the documents in decreasing order of their citation received as in the h-index. Afterwards, the g-index is calculated, which is the largest document number such that the top publications collectively receive at least g\({}^{2}\) citations. Although the g-index is widely used to find the impact of authors, it could not gain popularity like the h-index, because one very highly cited paper may affect the values of the g-index. Moreover, using the h-index, new authors could not get the desired credit, leading to another shortcoming of the h-index. The m-index [10] solved this issue by the clear distinction between old and new authors. It is computed by dividing the h-index by the number of the research's years as an author. Although the m-index is an enhanced version of h-index, for the m-index, a small change in the h-index values leads to a big change in the m-quotient, which is one of the major drawbacks of the m-index. The third issue linked with the h-index is that it cannot differentiate between the authors having the same h-index, but variation in the citation received. To overcome this issue, [PERSON] et al. proposed a novel indexing technique named the R-index [11]. It is computed by calculating the h-index, adding all the citation counts involved in the h-index and finally calculating the square root of the cumulative sum of citations. Afterwards, a variation of the R-index was proposed by [PERSON] et al. [11], named the AR-index, which takes into account the age of the article. In this regard, another indexing technique called the DS-index [7] was proposed. It differentiates among the authors having a very small change in the citation count. In the related literature, [PERSON] and [PERSON] proposed a graph-based analytics framework [15] to assign scores and to rank the paper, venue and author. The graph-based analytics framework used an algorithm that only considered the linked structures of the underlying graphs.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 1 Introduction
Studies on heat generation during power transfers have shown evidence of material degradation and system efficiency reduction without suggesting solutions [33; 41; 42; 43; 44]. EMI in the deployment of IPT systems without adequate shielding can disrupt nearby electronic systems and compromise operational safety [31; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55]. The integration of magnetic concrete into transmitter designs has been proposed as a means of enhancing electromagnetic field containment. In general, shielding methods for IPT systems can be categorized into active and passive shielding. Active shielding involves the use of additional coils or circuitry to generate opposing electromagnetic fields, effectively canceling unwanted emissions [56; 57; 58; 59]. In contrast, passive shielding relies on materials with high permeability or conductivity to absorb and redirect stray magnetic fields [60; 61]. This paper describes research on the design of magnetic concrete for an IPT system developed for railway applications with a focus on its role in addressing challenges related to material degradation, thermal behavior, and electromagnetic shielding. In this paper, FEA is used to simulate the performance of magnetic concrete, providing insights into its suitability compared to traditional ferrite cores and normal concrete. These simulations are used to analyze temperature distributions, electromagnetic field interactions, and their combined impact on system performance and reliability. Laboratory tests of materials have also been conducted to validate some of the material properties necessary for numerical simulation and material characterization. Finally, a full-scale magnetic slab as an ancillary component of an IPT system has been constructed for validation. By addressing the challenges of fragility, thermal management, and shielding of the unique coil topology design presented, this study lays the groundwork for improving IPT infrastructure in sustainable and efficient rail transport systems.
# Time-Dependent, Two-Photon Rabi Frequency Shift Observed in Multi-Frequency Raman Spectra
## 2 Experimental Setup  
Figure 1 shows the schematic of our experimental setup. Two pump beams with spectra centered at 786 nm and 837 nm are generated from the dual-wavelength Tiaspphire laser system [15]. The frequency difference between the two pump beams is chosen as 23.25 THz to resonate with the vibrational Raman frequency of sulfur hexafluoride (SF\({}_{6}\)). The two beams, each with spectral bandwidth of \(\sim\)3 nm, are sent to the triple-grating compressor, where they are independently compressed. The time delay between the two pump beam pulses can be varied by translating the back mirror, M7. To reduce the effect of SPM, the grating compressors are set to have the two pump beams positively chirped with pulse duration of \(\sim\)1 ps. A half-wave plate and a polarizer are placed before the hollow fiber containing the SF\({}_{6}\) to adjust the total energy for the experiment and redirect part of the beam into a cross-FROG setup for electric field measurement.  
To ensure the uniformity of chirps in two pump pulses, we conducted second harmonic FROG measurements. In our experiment, we employed cross-FROG spectroscopy to investigate the first anti-Stokes order. In this context, cross-FROG serves as a valuable tool for simplifying the spectrogram analysis. It accomplishes this by utilizing the Stokes pulse as a reference. This reduction in unknown variables in the spectrogram greatly enhances our ability to extract essential information from the double-peaked first anti-Stokes order. For all the FROG calculations in this paper, the traces are 128 \(\times\) 128 pixels.  
Two sets of measurements of the electric field of the first anti-Stokes order were carried out. The first set varied the energy, leaving the time delay constant, and the second set varied the time delay with the energy constant. For the first data set, the FROG reconstruction measured the pump pulse duration to be 900 fs FWHM, with a chirp rate of 1.6T Hz/ps. The Stokes pulse had a duration of 830 fs FWHM and a chirp rate of 1.5 THz/ps. The two pump pulses had a time delay of 333 fs, with the higher frequency pump pulse leading the lower frequency Stokes pulse. With this delay, the instantaneous frequency separation was red-shifted by \(\sim\)0.5 THz. At this red-shift, the maximum number of Raman orders was observed as well as the maximum red-shifting of the second peak observed in each order. The total energy of both pumps varied from 2.2 mJ to 1.7 mJ and to 1.2 mJ of average energy. The energy is equally split between the two pump pulses. For the second set of measurements, the pump duration was 780 fs with a chirp of 1.2 THz/ps and the Stokes was 830 fs with a chirp rate of 1.2 THz/ps. The energy was left at the maximum, which was \(\sim\)2.2 mJ and the time delay was varied to have increased red-detuning from 0 to +666 fs in increments of 333 fs.