esa-sceva/satcom-chunk-collection huggingface dataset:
# The Extraction of Maximal-Sum Principal Submatrix and Its Applications
## 6 Applications
### Construct a Optimal Stock Portfolio  
Another application related to the \(k\)-order MSPS is the stock portfolio selection. An optimal stock portfolio is here defined as the minimum correlation degree of stock returns, which is regarded as a kind of investment risk [25]. The minimum correlation degree means increased diversification of investment items. Our goal is to select a portfolio with the minimum correlation degree from the candidate stocks. The algorithm, named find a portfolio with the minimum correlation degree (FP-MCD), mainly includes the following steps to achieve this goal:  
1. Building a negative correlation coefficient matrix: Since MSPS-RU deals with the maximal problem, the minimum correlation degree can be converted to the maximal problem of the negative correlation coefficient matrix.
2. Using MSPS-RU algorithm to extract \(k\) stocks which have the maximal negative correlation degree.
3. Proposing investment suggestions. The pseudocode of the algorithm (Algorithm 3), FP-MCD, has been described as follows.  
```
Input:\(R_{t\times n}\);//The return rates of \(n\) candidate stocks in continuous \(t\) days; \(k\);//the number of stocks in the portfolio \(t_{1}\);//\(t_{1}\) days used for learning, constructing the correlation coefficient matrix; \((t-t_{1})\) days used to test portfolio returns Output:\(I^{*}\);//the optimal index set of the \(n\) candidate stocks
1 Let \(R_{t\times n}=\begin{bmatrix}P_{t_{1}\times n}\\ Q_{(t-t_{1})\times n}\end{bmatrix}\);
2 Construct the correlation coefficient matrix \(\text{T}_{n\times n}\) using \(P_{t_{1}\times n}\);
3 Build the negative correlation coefficient matrix \(\text{M}_{n\times n}=-\text{T}_{n\times n}\);
4 Use MSPS-RU to obtain the optimal portfolio \(I^{*}\)
5 Test portfolio returns with \(I^{*}\) and data \(Q_{(t-t_{1})\times n}\);
6 If the \(I^{*}\) is accepted
7 return\(I^{*}\);
8 else
9 return \"Not recommended\";
10 endif
```  
**Algorithm 3** FP-MCD algorithm
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Mimo
#### 3.4.1 Ica  
Researchers have proposed an ML-based method in [72], namely joint IQ independent component analysis (ICA), to settle spatial multiplexing problems in VLC MIMO system and enhance spectral efficiency (SE).  
The model in [72] is a 2 \(\times\) 2 MIMO-VLC system where superposed signals are generated. A 16-quadrature amplitude modulation (16-QAM) signal is transmitted at the Tx1 and a quadrature phase-shift keying (QPSK) is transmitted at the Tx2. As the power ratio of two Txs--namely the scaling factor--changes, the superposed constellation (SC) could be different. Blind source separation (BSS) using ICA could be adopted to separate and recover the two independent data streams from two Txs. ICA assumes that the subcomponents that compose the mixed observed signal are non-Gaussian and are statistically independent of each other. Moreover, the observed mixed signal is assumed to be the linear combination of the source signals. The proposed 2 \(\times\) 2 MIMO-VLC model with different SCs just meets those assumptions mentioned above. If we assume the source signal matrix as s, the observed matrix as x, and the mixing matrix as A, where x = As, then we can obtain the recovered signal by searching for the unmixing matrix W that can linearly transform x (which is whitened) so that the estimated subcomponents are independent of each other: s = Wx. The goal of ICA is to find the unmixing matrix W which is approximately equal to the A-1. It should be noted that the mixing matrix A is of full rank. Two mixed time-domain signals in MIMO CAP-modulated system could not be separated since they share the same pulse-shaping filter pairs and consequently lost the mutual independence features at every time slot. Therefore, the joint IQ ICA method deals with the SCs at the receiver side.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 3 Results  
Python software 2020.1 was used to calculate NDVI, DVI, RVI, RDVI, and GCI at any two wavelength combinations at the flag stage, flowering stage, grain-filling stage, and maturity stage of wheat, and the correlation analysis with the chlorophyll content in wheat was carried out. The correlation coefficients between NDVI, DVI, RVI, RDVI, and GCI and canopy chlorophyll content were distributed diagonally at (400, 400) and (1000, 1000), and the two-dimensional distribution map of the correlation coefficient R was generated for each growth period, and different colors were assigned according to the size of R [32]. As shown in Figure 4, the horizontal and vertical coordinates are the hyperspectral wavelengths of the wheat canopy, and the wavelength range is 400-1000 nm. In the figure, each point is the value of the correlation coefficient between the vegetation index and the chlorophyll content in the canopy constructed by the combination of the two wavelengths of the horizontal and vertical axes corresponding to the point. The shift in chlorophyll content and spectral index color from blue to red in wheat was indicated by a change from a negative correlation to a positive correlation.  
Figure 4: \(Cont.\)  
Figure 4: Phase properties of key growth period and chlorophyll content.
## 2 Materials and Methods
### Statistics  
Statistical analysis of datasets was performed using the software SPSS 15.0 for Windows (SPSS Inc., Chicago, IL, USA). Data are expressed as mean \(\pm\) standard error of the mean (s.e.m.) and analyzed using the unpaired \(t\)-test. Statistical analyses for the xCELLigence measurements were performed using GraphPad PRISM Version 7.02 for Windows (GraphPad Software Inc., La Jolla, CA, USA) with the Mann-Whitney test for three time-points (0.5, 1.0, and 1.5 h). Differences were considered statistically significant at \(p<0.05\) (\({}^{\circ}\)\(p<0.05\), \({}^{\circ\!\!  
For the visualization of the changes in the HA pericellular coat, HaCaT cells were stained for HA by biotinylated HA binding protein and streptavidin-FITC and detected by confocal laser scanning microscopy. Representative images of HaCaT cells after 6 h in PAM and control media are shown in Figure 3. The HA pericellular coat was impaired in cells cultivated in PAM--especially in PAM60--but did not show any changes for cells in AAM. The influence of PAM on the HA coat depended on the plasma treatment time used for the generation of the PAM. HaCaT cells treated with HAdase completely lost their HA coat after 6 h of incubation.  
After culture for 3 or 6 h in PAM, HaCaT cells showed an impaired HA pericellular coat. Because HA plays an important role in cell attachment, it was interesting to investigate if the impairment of the HA coat by culture in PAM has a significant effect on the adhesion and the viability of HaCaT cells.  
Figure 1: Flow cytometry determination of the hyaluron (HA) expression in non-adherent HaCaT cells after cultivation in plasma-activated media (PAM) or control media for 3 h. Note that the fluorescence intensity for HA was reduced depending on the plasma treatment time used for generation of the PAM (mean \(\pm\) s.e.m., n = 3, unpaired \(t\)-test, \({}^{*}\)\(p\)\(<\) 0.05, \({}^{**}\)\(p\)\(<\) 0.01). Abbr.: FL1-H: fluorescence intensity, DMEM: [PERSON]’s modified Eagle’s medium, PAM30: 30 s plasma-treated DMEM, PAM60: 60 s plasma-treated DMEM, AAM: 60 s argon gas-treated DMEM, Hadase: DMEM with hyaluronidase.  
Figure 2: Flow cytometry determination of size and granularity of HaCaT cells after cultivation in DMEM (**left**) or PAM60 (**right**) for 3 h. No decisive differences can be seen between the dot plots of forward (FSC) and sideward scatter (SSC). Abbr.: DMEM: Dulbecco’s modified Eagle’s medium, PAM60: 60 s plasma-treated DMEM.
## 2 Optimized Link State Routing Protocol  
The IETF MANET group introduced OLSR as a proactive routing protocol designed for Ad-Hoc networks. In this protocol, optimization of network broadcasting is achieved through the utilization of nodes referred to as Multi-Point Relay (MPR). Each MPR should broadcast topology information and forward messages to destination nodes. The MPR concept aims to minimize message flooding overhead by reducing redundant retransmissions within the same area. Figure 2 shows the different types of messages required by the OLSR protocol to function as described in RFC 3626:  
* HELLO-messages are responsible for link sensing, neighbor detection, and MPR signaling.
* TC messages are used for topology notification (advertisement of link states).
* MID-messages notify the presence of multiple interfaces on a node.  
OLSR is designed to work in a completely distributed manner and does not depend on any central entity. The protocol doesn't require reliable transmission of control messages. Each node sends control messages periodically and can therefore sustain a reasonable loss of any messages. Such losses occur frequently at radio interfaces due to collisions or other transmission issues. Also, it does not require the sequential delivery of messages. Each control message contains a sequence number, which is incremented for each message. Thus, the recipient of a control message can, if required, easily identify which information is more recent.  
Figure 1: Workflow of the proposed approach.  
Figure 2: OLSR data flow.  
The OLSR protocol is intended to operate in a fully decentralized manner, with no need for a central authority. It doesn't require reliable transmission of control messages, as each node sends them periodically and can withstand a certain degree of message loss, which is common in radio interfaces due to collisions or other transmission problems. Additionally, there is no requirement for messages to be delivered in a specific order, as each control message contains a sequence number that is incremented for each message. This allows the recipient of a control message to easily determine which information is the most recent, if necessary.  
Thus, nodes control traffic by exchanging two types of messages: HELLO and TC messages. Each one is used to update specific information, as we are going to explain in the next section.
# A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks
## 5 Simulation and Analysis  
In this section, we evaluate the performance of the proposed handover strategy and compare it with other handover schemes.
## 8 Conclusions
We should consider the following aspects:  
* Fairness and Equity: If specific drivers are given preferential treatment based on their utility requirements, it could potentially disadvantage others (routing unfairness). It is crucial to ensure that any differentiation is based on justifiable criteria and does not result in discrimination or unfair advantages for specific individuals or groups. * Safety: Any traffic system's primary objective should be ensuring all road users' safety. Introducing differentiated traffic network views should not compromise safety standards. The system should prioritize factors such as avoiding congestion, minimizing accidents, and adhering to traffic laws, rather than solely focusing on individual utility requirements. * Efficiency and Network Performance: A well-functioning traffic network benefits everyone by reducing travel times, congestion, and environmental impact. By tailoring traffic network views to individual drivers' utility requirements, it may be possible to optimize overall network performance. However, this approach should not come at the expense of fairness, safety, or the public interest. * Data Privacy and Security: It is crucial to ensure that personal information is protected and used responsibly. Using traffic flows enables this. Informed driver consent to use the system should be addressed. * Transparency and Accountability: The traffic system operator must be transparent about the principles and algorithms used to differentiate traffic network views. The decision-making process should be clear, and drivers should be able to understand and challenge the system's outcomes. Accountability mechanisms should be in place to address any potential biases or errors in the system.
## 2 Materials and Methods
### Continuous Approach: Optimal Control
#### 2.2.2. Collocation Discretization  
Exemplarily, we consider a discretize-then-optimize approach based on direct collocation with the midpoint rule. Let \(0=\tau_{0}<\dots<\tau_{n}=1\) be a time grid, \(X_{h}=\{x\in H^{1}([0,1])\mid x|_{[\tau_{i},\tau_{i+1}]}\in\mathbb{P}_{1},i=0, \dots,n-1\}\) and \(V_{h}=\{v\in L^{2}([0,1])\mid v|_{[\tau_{i},\tau_{i+1}]}\in\mathbb{P}_{0},\,i=0,\dots,n-1\}\) the piecewise linear and piecewise constant ansatz spaces for positions and velocities, respectively. We discretize (1) by looking for solutions \(x_{h}\in X_{h}^{2}\) and \(v_{h}\in V_{h}^{2}\) and require the state equation \(\dot{x}(\tau)-T(v(\tau)+w(x(\tau)))=0\) to be satisfied only at the interval midpoints \(\tau_{i+1}/2:=(\tau_{i}+\tau_{i+1})/2\) for \(i=0,\dots,n-1\). Representing \(x_{h}\) by its nodal values \(x_{i}=x_{h}(\tau_{i})\) and \(v_{h}\) by its midpoint values \(v_{i}=v_{h}(\tau_{i+1/2})\), we obtain the large nonlinear program  
\[\min_{T,x_{h},v_{h}}T\quad\text{s.t.}\quad c_{h}(T,x_{h},v_{h})=\left[\begin{array} []{c}x_{0}-x_{O}\\ x_{n}-x_{D}\\ x_{1}-x_{0}-(\tau_{1}-\tau_{0})T\left(v_{0}+w((x_{0}+x_{1})/2)\right)\\ \vdots\\ x_{n}-x_{n-1}-(\tau_{n}-\tau_{n-1})T\left(v_{n-1}+w((x_{n-1}+x_{n})/2)\right) \\ v_{0}^{T}v_{0}-\vartheta^{2}\\ \vdots\\ v_{n-1}^{T}v_{n-1}-\vartheta^{2}\end{array}\right]=0. \tag{5}\]
# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 1 Introduction
### Challenges and Value Proposition  
Despite the multiple benefits of implementing these technologies in industry, they are not yet the norm in all industries due to several factors, such as the following:  
* High implementation costs: Given that these technologies require specialized hardware, such as virtual reality glasses and computer equipment of the necessary range to enable the development of the environment, together with the handling of a considerable amount of data, these aspects represent a barrier for a significant number of companies [13].
* Resistance to change: In many companies the non-implementation of new technologies is due to bad conceptions of these technologies and the requirement of training necessary for the handling of this equipment, without forgetting the learning curve involved for the operator when adopting these new technologies, which mainly causes rejection [11].
* Technological limitations: Factors such as latency, internet connection stability and data processing capacity are still, in many cases, obstacles that hinder the correct implementation of these technologies.  
Given this background, the need for comprehensive solutions that combine remote monitoring, virtual simulation and immersive training to address the current limitations in industrial maintenance is evident. The literature supports the potential of an immersive system based on digital twins with early warning capabilities as an answer to these challenges. The methodological section that follows proposes precisely such a technological architecture, which comprises a systematic way to implement these technologies for any industrial equipment, by collecting data based on previous analysis, and integrating a digital twin into an immersive virtual reality environment. This system will seek to anticipate faults and operational risks through early warnings, while facilitating continuous training and remote assistance to personnel through the use of open-source software and programs that are easy to understand for the user in order to expand the implementation of these technologies to any type of area in the industry, thus democratizing the use of these new technologies, as well as improving the reliability of the processes and safety in the different industrial fields.
# Easy, Fast, and Accurate Method of 3-Dimensional Mirror Plane Creation for Actual Clinical Users
## 2 Materials and Methods
### Creation of Mirror Plane  
Additional medical image software (3-matic(r), Materialize NV, Leuven, Belgium) was used to create the mirror plane, including functions of mirroring and surface-based registration. The creation of a unilaterally defected skull model was performed on the left side of the temporal area on the skull (Figure 1A,B). Three points were plotted on the defected skull model according to the commonly used reference landmarks from recent literature; Nasion, PNS, and Basion [9] (Figure 1C).  
Figure 1: Unilateral defect skull model (**A**). front view (**B**). top view (**C**). bottom view with three reference points; Nasion, PNS, Basion.  
These reference points were connected to establish an initial mirror plane as a temporary mid-plane to begin the mirroring process (Figure 2A). Reflecting the original defect model by the initial mid-plane created a mirrored defect mode (Figure 2B). Then, the original skull model and mirror skull model underwent surface base registration by a conjunctive scale of 5, 1, 0.5, 0.1, 0.05, 0.01, respectively (Figure 2C).  
Figure 2: (**A**). initial mirror plane (in blue) created from the three reference points. (**B**). Mirror image of the original defect model (**C**). Surface based registration was performed and moved mirror plane was obtained (in green).  
The three reference points of the mirror model moved along during the registration procedure and were used to construct a moved mirror plane. Bisecting the initial mid-plane and moved mirror plane enabled us to achieve a final mirror plane that was customized and optimized for the initial defect model (Figure 3).
# Antivirus Evasion Methods in Modern Operating Systems
## 3 Antivirus Detection Mechanisms
### Signatures  
Signature-based malware detection is a traditional method that has been used for a long time. A signature is a unique pattern or code template of a virus that can be used to identify it [15]. The signature is a short fragment that does not reveal any useful information for virus writers to use. Antivirus software can compare the signature of a scanned file with all the signatures from its blacklist of suspicious files, also known as the signature database. If the signature being compared exists in the database, the scanned file is marked as malicious. This method is effective for threats that are already known, making it a popular threat search technique. However, signature-based detection has some drawbacks [16]. It is powerless against new threats and susceptible to modifications that mask the virus. Scanning a new or modified threat may prove unreliable until the signature database is updated with new or modified signatures. Additionally, storing all signatures leads to an exponential increase in their number, resulting in lower performance and longer search times. However, if the virus software change does not modify the signature directly, the signature can still be useful in identifying the malware.
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations
### Metrics of Interest  
The compared filters aim to reduce the loudspeaker's signal while preserving the desired signal. The minimal enhancement for white noise is also interesting, given the regularization parameter \(\alpha\) added to the problem. These are measured by the desired signal reduction factor (DSRF, or \(\xi_{x}\)), gain in SER (gSER), and gain in SNR (gSNR), respectively. We also observe the directivity index (DI, or \(\mathcal{D}\)), which measures the beamformer's behavior when employed in a spherically anisotropic noise field. Their time-dependent broadband formulations are given by  
\[\xi_{x}[l] =\frac{\sum_{k}\abs{X_{1,k}[l]}^{2}}{\sum_{k}\abs{Y_{f,k}[l]}^{2}} \tag{51a}\] \[\text{gSER}[l] =\frac{\sum_{k}\abs{S_{1,k}[l]}^{2}}{\sum_{k}\abs{Y_{f,k}[l]}^{2}} \cdot\frac{1}{\xi_{x}[l]}\] (51b) \[\text{gSNR}[l] =\frac{\sum_{k}\abs{R_{1,k}[l]}^{2}}{\sum_{k}\abs{R_{f,k}[l]}^{2}} \cdot\frac{1}{\xi_{x}[l]}\] (51c) \[\mathcal{D}[l] =\frac{\sum_{k}\abs{\hat{\mathbf{f}}_{k}^{\text{d}}[l]\mathbf{d}_{k}[l]}^{2} }{\sum_{k}\hat{\mathbf{f}}_{k}^{\text{d}}[l]\hat{\mathbf{\Gamma}}_{k}[l]\hat{ \mathbf{\Gamma}}_{k}[l]} \tag{51d}\]  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Source** & **Position** & **Signal** \\ \hline \(x[n]\) & \((2,5,1.8)\) m & 50\_male\_speech\_english\_ch8\_OmniPower4296.flac \\ \(s[n]\) & \((3,4,1)\) m & 69\_abba\_ch8\_OmniPower4296.flac \\ \(r[n]\) & \(\sim\) & wgn\_48 kHz\_ch8\_OmniPower4296.flac \\ \hline \hline \end{tabular}
\end{table}
Table 1: Source information for the simulations.  
Figure 1: Room layout for simulations.  
in which \(\mathbf{\Gamma}_{k}[l]\) is the spherical anisotropic noise field correlation matrix [31], and \(\mathbf{d}_{k}[l]\) is the steering vector between the desired source and the sensor array, both assuming a far-end free field environment. We are also interested in a time-average broadband formulation for these metrics, these being given by  
\[\xi_{x} =\frac{\sum_{l,k}\big{|}X_{1,k}[l]\big{|}^{2}}{\sum_{l,k}\big{|}X_{ \mathcal{J},k}[l]\big{|}^{2}} \tag{52a}\] \[\text{gSER} =\frac{\sum_{l,k}\big{|}S_{1,k}[l]\big{|}^{2}}{\sum_{l,k}\big{|}S_{ \mathcal{J},k}[l]\big{|}^{2}}\cdot\frac{1}{\xi_{x}}\] (52b) \[\text{gSNR} =\frac{\sum_{l,k}\big{|}R_{1,k}[l]\big{|}^{2}}{\sum_{l,k}\big{|}R_ {\mathcal{J},k}[l]\big{|}^{2}}\cdot\frac{1}{\xi_{x}}\] (52c) \[\mathcal{D} =\frac{\sum_{l,k}\big{|}\xi_{k}^{\mathbf{H}}[l]\mathbf{d}_{k}[l] \big{|}^{2}}{\sum_{l,k}\xi_{k}^{\mathbf{H}}[l]\mathbf{\Gamma}_{k}[l]\mathbf{\xi}_{k}[ l]}. \tag{52d}\]  
We chose the gains in SER and SNR metrics rather than the more common ERLE and WNG [32], given an _a priori_ knowledge of distortion on the desired signal. Therefore, the gains become more representative of the results.