esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital SI Cancellation Using Adaptive Filter
By taking expectation on the square norm of time difference of (18), the following expression can be obtained:  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]0  
where \(\mu_{\text{X}}(n)\) equals \(\mu\) in the LMS case and equals \(\mu^{\prime}\) in (17) in the nLMS case. In steady-state, the difference expression (19) should be \(0\), which results in the steady-state MSE of LMS as  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]1  
where \(\bar{P}_{\text{T}}=\mathbb{E}\{||\mathbf{s}(n)||^{2}\}\) and \(q_{v}\) is the background noise power. The function \(\beta(n)\) can be expressed as  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]2  
where \(\mathbf{K}(n)=\mathbb{E}\{\Delta(n)\Delta^{\text{T}}(n)\}\) and \(q_{\epsilon}(n)=\text{tr}\{\mathbf{K}(n)\mathbf{R}_{\text{ss}}\}\). The upper bound of the convergence time of the LMS algorithm is  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]3  
where \(\beta_{\text{max}}\) is the maximum value of \(\beta(n)\), \(q_{\epsilon v}\) is a given excess noise power, and \(\theta(n)=\sqrt{\mathbb{E}\{||\Delta(n)||^{2}\}}\). For nLMS, by similar derivations, the steady-state MSE can be expressed as  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]4where  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]5  
and  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]6  
with \(\epsilon(n)=-\mathbf{s}^{\mathrm{T}}\Delta(n)\) and \(P_{\mathrm{T}}(n)=||\mathbf{s}(n)||^{2}\). The upper bound of the convergence time of the nLMS algorithm is then  
\[y(n)=v(n)-s^{\prime\prime}(n), \tag{2}\]7  
where \(\gamma_{0,\min}\) is the minimum value of \(\gamma_{0}(n)\). By analyzing the steady-state MSE and convergence time of LMS and nLMS algorithms, nLMS can reach a lower steady-state MSE within a smaller convergence time. The details about the analysis for the LMS and nLMS algorithms can be found in [36]. ```
1 Input:\(\mathbf{s}(n)\), \(n=0,1,\cdots\);
2 Output:\(y(n)\), \(n=0,1,\cdots\);
3 Initialization:\(\mathbf{h}_{0}=zeros(N_{\mathrm{taps}})\);
4%Iterative LMS adaptive filtering process;
5 for\(n=0,1,\cdots\)do
6\(\mathbf{s}(n)=[s(n),s(n-1),\cdots,s(n-N_{\mathrm{taps}}+1)]^{\mathrm{T}}\);
7\(s^{\prime\prime}(n)=\mathbf{h}_{n}^{\mathrm{H}}\mathbf{s}(n)\);
8\(y(n)=v(n)-s^{\prime\prime}(n)\);
9\(\mathbf{h}_{n+1}=\mathbf{h}_{n}+\frac{\mu y^{*}(n)\mathbf{s}(n)}{\mathbf{s}^{ \mathrm{H}}(n)\mathbf{s}(n)+\delta}\);
10 end
```  
**Algorithm 1**nLMS adaptive filter algorithm.
# Satellite Image Categorization Using Scalable Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.1 Image Normalization  
Before entering the feature extraction phase, the images are normalized. Input image \(\aleph\) is mapped to a 3-dimensional \(\mathbb{R}^{3}\) space with \(\mathbb{M}\times\mathbb{N}\times\mathbb{C}\) dimensions, where \(\mathbb{M}\) characterizes the number of rows, \(\mathbb{N}\) is the number of columns in image \(\aleph\), and \(\mathbb{C}\) is the number of channels of the input image. Mapping function \(\mathfrak{k}\) is applied to input image \(\aleph\). Mapping function \(\mathfrak{k}:[0,255]\rightarrow[0,1]\) is defined as  
\[\mathfrak{k}\left(\aleph(i,j)^{c}\right)=\frac{\aleph(i,j)}{max_{s,t}\aleph(s,t)} \tag{1}\]  
\(\mathrm{i}\in[1,\mathbb{M}],\mathrm{j}\in[1,\mathbb{N}],\mathbb{C}\in\mathbb{ R},\mathrm{G},\mathrm{B},\) and \(s,t\) range between all the values of \([1,\mathbb{M}]\) and \([1,\mathbb{N}]\).  
Normalization is a frequent pre-processing step in computer vision tasks to standardize the pixel values of images, which can help enhance the performance and convergence of machine learning models.  
Figure 3: Architectural diagram of the proposed methodology.  
Figure 2: Dataset visualizations: (**a**) T-SNE visualization; (**b**) doughnut chart for category distribution.
## 1 Introduction  
In the increasingly connected world of the 21 st Century, Internet technology is spreading into developing areas where access to the World Wide Web was previously limited or non-existent. Over time, a range of social benefits will derive from this connectedness, not least of which will be the provision of educational services to the people living in those areas. However, connectedness is only one half of the proposition; the other half is the growing catalogue of free educational materials that is now available from top-rated universities in the form of MOOCs (Massive Open Online Courses), lectures from the Khan Academy, TED Talks (Technology, Entertainment and Design Conference) and YouTube EDU, which in one year alone partnered with over 300 universities and other providers to offer more than 65,000 free lectures.  
The scale of this free on-line content is impressive by any standard, with TED Talks, for example, being viewed by over 450,000 people a day [1]. World experts in their subject area present ideas on a wide range of topics and these high-quality videos can be used as a free learning resource by anyone with an Internet connection, irrespective of their location. The student only needs to have sufficient digital literacy to access the video. However, in order to be fully effective, on-line education should go beyond the presentation of content alone; it needs to go further by providing assessment tasks that allow the student to reinforce and integrate the content into their knowledge-base. This is achieved by applying the knowledge acquired in assessment tasks. Once submitted and marked, the student needs to be given feedback, which allows them to see the difference between their performance and the ideal, and how they can close the gap [2]. This is the essence of good assessment feedback.  
This case study examines whether the process of delivering feedback can be automated by using the assessment feedback function that comes standard in Learning Management Systems (LMS). This strategy, were it to be successful, will address the issue of delivering quality feedback, which has, historically, been problematic and resulted in student expectations for feedback not being met [3]. It is a labor-intensive process, with the time and expense adding considerably to the cost of delivering education. The feedback in this case study, while generic, is informed and structured by best practice principles [4]. We argue that the efficiencies of this method will allow for the scalability of course delivery to make it cost-effective to deliver education to people in the developing and developed world alike.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid
### Modifications of Scheme
#### 3.1.1 Changing Relay Settings or Relay Type  
The simple solution to protect against changes in the power system topology is the readjustment of the protective relay settings. In blinding zones, the relay cannot pick-up the fault and these zones can be covered by increasing the sensitivity of the relay through reducing the pickup current value. It should be noted that changing relay setting may cause problems in the protection coordination [29; 55]. In Reference [68], a method of microgrid protection has been proposed by using low voltage ride through (LVRT) operation. The protective relay setting values have been corrected in the outcome of the simulation results. Reference [69] has presented an improved OC protective relay based on the compound fault acceleration factor and the beetle antennae search (BAS) optimization in microgrids. The method has enhanced the speed of the OC protection and the coordination between the primary and backup relays.  
A DOC relay has been proposed to protect the DG feeder from sympathetic tripping as a result of fault on the adjacent feeder [46]. In particular, the DOC relay is required to prevent the unwanted tripping in neighboring feeders [55]. Modern protective relays are digital and multifunctional with the capability of activating multiple protections simultaneously.  
Figure 8: Traditional coordination of protective relays.
## 5 Conclusions  
In this study, laser cutting experiments were conducted on a 5 mm thick PMMA sheet using a CO\({}_{2}\) laser. The input parameters considered were LP, CS, and FPP, while the output responses focused on the dimensions of the kerf geometry. The data obtained from the experiments were subjected to a DOE analysis, leading to the following conclusions:  
1. A decrease in the FPP results in a notable reduction in the top kerf width, while an increase in the LP and a decrease in the CS contribute to a substantial widening of the kerf.
2. The bottom kerf width is significantly influenced by the quadratic term of the CS\({}^{2}\) and the interaction effect between the CS and the FPP (CS \(\times\) FPP).
3. CS, FPP, and the quadratic term of the laser LP\({}^{2}\) are identified as significant factors affecting the ratio of the upper kerf to the lower kerf.
4. The cut kerf angle exhibits noteworthy linear effects of the LP and the FPP, accompanied by a significant quadratic effect of the LP\({}^{2}\).
5. Optimal conditions for the LP, CS, and FPP fall within the ranges of 48-60 W, 4-7 mm/s, and \(-1\) to 1 mm, respectively.  
All the authors contributed to this study's conception and design. Material preparation, data collection, and analysis were performed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The first draft of the manuscript was written by [PERSON] and all the authors commented on previous versions of the manuscript. The editing and reviewing of the manuscript was carried out by [PERSON], [PERSON] and [PERSON]. The conceptualization and administration of this project were performed under the supervision of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Overlay plots for the comparison of the correlation among all the input parameters.  
**Funding:** [PERSON] acknowledges the AGAUR Fellowship (FI-SDUR-2020) of the Generalitat de Catalunya for its financial support.  
**Acknowledgments:** The authors respectfully acknowledge the technical support provided by the University of Northampton (UON), the Center for Structural Integrity, Micromechanics, and Reliability of Materials (CIEFMA), and the Materials Science department of the Polytechnic University of Catalunya (UPC).  
**Conflicts of Interest:** The authors have no relevant financial or non-financial interests to disclose.
## 5 Applications and Outlook
### Aggregate Void Ratio and Gradation Design  
Asphalt mixtures are multiphase composites consisting of asphalt binder, coarse aggregate, fine aggregate, mineral filler and other additives. The properties of asphalt mixtures are closely related to the volume fraction and spatial distribution of these components. The void fraction is one of the important volumetric properties that affect the stability and durability of asphalt mixtures [154]. [PERSON] et al. [155] quantified the inhomogeneity of pore distribution by measuring the internal structure of rotating compacted specimens and roller compacted specimens with gamma rays.  
X-ray CT and image analysis techniques allow non-destructive and accurate analysis of void distribution in asphalt mixtures. [PERSON] et al. [156] applied DIP techniques based on X-ray CT technology to reconstruct the 3D microstructure of asphalt specimens and determine the specimen tensile strength. During the compaction process, the 3D fractal dimension of the pores showed a trend of first changing and then changing. A hypothesis based on the evolution of the 3D fractal dimension of the pores is proposed to explain the phenomenon.  
[PERSON] et al. [157] evaluated the physical and mechanical properties and sound absorption characteristics of porous concrete by evaluating the design based on the target void ratio and recycled aggregate content and found that the difference between the target void ratio and the measured void ratio was less than 1.7%. [PERSON] et al. [158] performed a statistical analysis of tensile cracking in asphalt mixes based on the Weibull model to test the void ratio for different parameters. The study results found that the average fracture toughness values and the Weibull parameter decreased by increasing the porosity content.  
Mineral gradation is the main factor affecting the road performance of asphalt mixtures, and gradation is one of the important characteristics of the mix. [PERSON] et al. [159] studied the effect of aggregate gradation variation on the rutting characteristics of asphalt concrete mixes. [PERSON] et al. [160] conducted experiments on six asphalt concrete mixes and tested their different gradations to study the effect of gradation variation on the properties of the mixes.  
As shown above, the void ratio and gradation characteristics of aggregates often affect their road performance. By visualizing the morphological characteristics of the aggregates, the researchers quantified the internal structure and spatial distribution of the aggregates and investigated the mechanisms affecting the road performance of the aggregates. The iterative revision of the research results has effectively improved aggregate performance.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 1 Introduction
Despite the fact that super-Gaussian pulses are much better approximation to real pulses, they have two weaknesses: they cannot be implemented in Non-Return-to-Zero (NRZ) protocols and they do not have a close-form analytical solution. It is the objective of this paper to assemble important pulses profile, whose propagation in dispersive medium can be expressed in analytical terms. It is shown that there are profiles, which are much better candidates to simulate real pulses, but their dynamics can be formulated in a closed-form analytical expressions. Moreover, it is shown that the dynamics of almost any practical pulse profile in dispersive media can be simulated and approximated by analytical expressions. In the first part of the paper, after the introduction of the general theory (Section 2) and the presentation of fundamental theorems (Section 3), the propagation of a Gaussian pulse is presented (Section 4). In the second part, it is shown that it is possible to present, analytically, the propagation of singular pulses (Section 5), which can be written initially with exponentials and step functions (like rectangular pulses and exponential-step pulses). In the third part, it is shown that every step function can be replaced with a smooth step function, and therefore any singular pulse, which is presented in the previous part of the paper, can be replaced with its counterpart smooth version (Section 6). In particular, smooth rectangular pulses can replace super-Gaussian ones. The fourth part is dedicated to the important pulse, which is singular in the spectral domain, i.e., the Nyquist-Sinc pulse (Section 7). First, the analytical expression for its propagation in dispersive medium is presented, and then the analytical expression of the propagation of its spectrally-smooth, i.e., physical, counterpart is presented as well. In the last part of the paper, accelerated-Airy pulses and attenuation compensating pulses are presented, as well as their physical counterparts (Section 8).
## 2 Metamaterial Absorber Design
### Equivalent Circuit Model  
The proposed MMA equivalent circuit model was designed and simulated via ADS (advanced design system) [56], as shown in Figure 6[27; 57; 58; 59]. Each resonant peak is constituted by the inductance and capacitance of separate elements, such as the inner Jerusalem cross, the two splits ring, and the external microstrip line, indicated different colors in Figure 6a. An RLC circuit is considered for each resonant frequency in the equivalent circuit design shown in Figure 6b. The series capacitances, C1, C2, C3, and C4,  
Figure 5: (a) Imaginary permittivity, (b) real permittivity, (c) imaginary permeability, (d) real permeability, (e) imaginary normalized impedance, and (f) real normalized impedance for different MMA designs.  
are calculated by Equation (8), where the resonant frequency is \(f\), and the associated series inductance is \(L\) (L1, L2, L3, and L4).  
\[C=\frac{1}{4\pi^{2}f^{2}L} \tag{8}\]  
The inductance (L1, L2, L3, and L4) is generated by different elements, which are calculated by Equation (9), where the substrate length is \(l\); and \(w\) and \(t\) are the width of the strapline and the substrate thickness, respectively, in inches.  
\[L(nH)=0.00508l\left[\ln\left(\frac{2l}{w+t}\right)+0.5+0.2235\left(\frac{w+t}{ l}\right)\right] \tag{9}\]  
Figure 6: (**a**) Different resonating parts of MMA, (**b**) equivalent circuit model, and (**c**) \(S_{11}\) plot from ADS and CST.  
The coupling capacitance (C5, C6, C7, and C8) between the elements and ground is estimated by Equation (10), where \(d\) is the gap between the strip, and \(A\) represents the area of the strip. Associated series resistance is estimated by tuning for the increment and reduction in the \(S_{11}\) value. Figure 6c is an \(S_{11}\) plot of both CST and ADS simulations.  
\[C=\varepsilon_{0}k\frac{A}{d} \tag{10}\]
# Fractal Dimension-Based Methodology for Discriminating Original Paintings from Replicas
## 3 Material and Methods
### The DBC-RGB Algorithm
As expected, the average FD values and the standard deviations are very close for all pairs of paintings, indicating a high degree of similarity between the originals and their replicas. For the Cleveland dataset, Figure 6 shows boxplots comparing the FD values between paintings by the same artist using the sliding window configuration with a 90% window overlap. Table 5 provides the corresponding results, including average FD values and standard deviations for each painting. Figure 5: Boxplots showing significant differences between the original and replica paintings in the Princeton dataset. **: \(p\)-value < 0.01 and ***: \(p\)-value < 0.0001. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Compared Paintings** & \multicolumn{4}{c}{**\(p\)-Value for Wilcoxon Test**} \\  & & **0\% wo** & **25\% wo** & **50\% wo** & **75\% wo** & **90\% wo** \\ \hline \multirow{8}{*}{Princeton} & 1 Or. vs. 1 Rep. & 1.0000 & 1.0000 & 0.9919 & **0.0196** & **0.0000** \\  & 2 Or. vs. 2 Rep. & 0.4599 & 0.1939 & **0.0021** & **0.0000** & **0.0000** \\  & 3 Or. vs. 3 Rep. & 0.7896 & 0.2296 & 0.1057 & **0.0000** & **0.0000** \\  & 4 Or. vs. 4 Rep. & 1.0000 & 1.0000 & 0.8386 & 0.0672 & **0.0000** \\  & 5 Or. vs. 5 Rep. & 1.0000 & 1.0000 & 1.0000 & 1.0000 & **0.0091** \\  & 6 Or. vs. 6 Rep. & 0.1498 & **0.0077** & **0.0000** & **0.0000** & **0.0000** \\  & 7 Or. vs. 7 Rep. & **0.0126** & **0.0007** & **0.0000** & **0.0000** & **0.0000** \\ \hline \multirow{8}{*}{Cleveland} & 1 A vs. 1 B & 0.1810 & 0.5550 & **0.0100** & **0.0000** & **0.0000** \\  & 1 B vs. 1 C & 1.0000 & 1.0000 & 0.0599 & **0.0000** & **0.0000** \\  & 1 A vs. 1 C & 1.0000 & 1.0000 & 1.0000 & 0.5669 & **0.0030** \\  & 2 A vs. 2 B & 1.0000 & 0.6239 & 0.5160 & **0.0389** & **0.0000** \\  & 2 B vs. 2 C & 1.0000 & 1.0000 & 1.0000 & 0.1300 & **0.0000** \\  & 2 A vs. 2 C & 1.0000 & 1.0000 & 1.0000 & 1.0000 & **0.2079** \\ \cline{1-1}  & 3 A vs. 3 B & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** \\ \cline{1-1}  & 3 B vs. 3 C & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** \\ \cline{1-1}  & 3 A vs. 3 C & 0.1640 & 0.4380 & **0.0109** & **0.0000** & **0.0000** \\ \cline{1-1}  & 4 A vs. 4 B & **0.0009** & **0.0000** & **0.0000** & **0.0000** & **0.0000** \\ \cline{1-1}  & 4 B vs. 4 C & **0.0350** & **0.0003** & **0.0000** & **0.0000** & **0.0000** \\ \cline{1-1}  & 4 A vs. 4 C & **0.0000** & **0.0000** & **0.0000** & **0.0000** & **0.0000** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(p\)-Values for the Wilcoxon test comparing the FD values between the original paintings and replicas. wo: window overlap. Statistically significant differences are indicated with a bold \(p\)-value.
## 1 Introduction
This technology is being developed and validated up to Technological Readiness Level (TRL) 4 through dedicated experimental campaigns performed at the component level. Numerical assessments are also envisaged at a full-scale level for a digital aero-vibro-acoustic model of an actual aircraft cabin, including the modeling of both the external disturbances and the primary and secondary components [25; 26; 27]. In this paper, the practical implementation of an active headrest system for aircraft cabin seats is presented. The concept is based on the use of a pair of speakers integrated into the headrest of a passenger seat and located on both sides of the passenger's head to attenuate certain low-frequency tonal noise components, thus creating a local zone of quiet (comfort area) around the passenger's ears. The work focuses on the experimental activities executed on a mock-up of a cabin seat equipped with the ANC headrest system to investigate the technological potential of this local noise-cancelling solution in a representative acoustic field. The active noise control logic is implemented on a laboratory DSP control system and associated hardware is tested by simulating the propeller-induced noise of a turboprop aircraft in a reference environment.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 1 Introduction
### Contributions of This Work  
1. A novel autonomous key generation method based on synthesized speech.
2. A low-bit-rate perfectly secret communication system combining MELPe, Vernam cipher, and real-time SKD.
3. Experimental validation of the proposed system, demonstrating its feasibility and security.
4. A discussion on the implementation challenges and scalability of the system in real-world applications.