esa-sceva/satcom-chunk-collection huggingface dataset:
# Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images
## 2 Materials and Methods
### Optimal High-Spatial-Resolution Image Generation
#### 2.2.1 Selected Band Scheme  
In the selected band scheme, the high-spatial-resolution band whose spectral characteristics are most similar to that of the low-spatial-resolution band to be sharpened is defined as the artificial high-spatial-resolution band to produce an optimal panchromatic image. To find this optimal high-spatial-resolution band, a correlation analysis is conducted, and the high-spatial-resolution image with the highest correlation is selected. To perform the correlation analysis, high-spatial-resolution images must be transformed into images with the same number of pixels as the low-spatial-resolution images by applying the modulation transfer function (MTF) filter; then, the similarity between the pixels of the two images is evaluated. The optimal panchromatic image is selected through the correlation analysis shown in Equations (2) and (3) [15; 21]:  
\[\overline{n}=\arg\ max_{n}\ \text{corr}\Big{(}\mathbf{HR}_{n}^{L},\mathbf{LRB}_{m} \Big{)} \tag{2}\]  
Figure 1: Study areas for the sharpening of Worldview-3 satellite images: (**a**) true color composite of site 1; and (**b**) true color composite of site 2.  
\[\mathbf{P}_{m}^{sel}=\sum_{n=1}^{N}\alpha_{m_{n}}\mathbf{HRB}_{n},\text{ where }\alpha_{m_{n}}=\left\{\begin{array}{ll}1,\text{ }if\text{ }n= \overline{n}\\ 0,\text{ }otherwise\end{array}\right. \tag{3}\]  
In Equation (9), \(\mathbf{HRB}_{n}^{L}\) refers to the \(n\)th high-spatial-resolution image whose spatial resolution is transformed into the number of pixels of the low-spatial-resolution image, and \(\mathbf{LRB}_{m}\) refers to the \(m\)th low-spatial-resolution image. In Equation (10), \(\mathbf{P}_{m}^{sel}\) refers to the \(m\)th high-spatial-resolution band that has the highest correlation with the \(n\)th low-spatial-resolution band.  
The selected high-spatial-resolution band is utilized along with the panchromatic image during pansharpening, and weighted mean or multiple regression analysis is applied when the CS-based technique is applied, thereby producing \(\mathbf{I}_{L}\). When the MRA-based technique is applied, the images whose spatial resolution is reduced by applying the MTF filter may be utilized as \(\mathbf{I}_{L}\). Pansharpening is performed using the optimal panchromatic image \(\mathbf{P}_{m}^{sel}\) and the optimal low-spatial-resolution image \(\mathbf{I}_{L}\) produced in the selected band scheme via Equation (1). The injection gain \(g_{n}\) is calculated and applied according to the CS- and MRA-based technique used.
## 3 System Model
### Sensing Coverage  
Practical applications require the monitoring of almost the whole area of the wireless sensor network. Achieving 1-coverage is a minimum requirement (as shown in Figure 3).  
**Theorem 1**.: _Suppose there are \(N\) sensors uniformly distributed in a WSN, the sensing radius of each sensor is \(r\), \(r\in[r_{min},r_{max}]\), then the overall detection accuracy is given by:_  
\[\tilde{\mu}=1-\exp\biggl{\{}-\frac{W}{S+W}\frac{N\pi r^{2}}{M_{X}M_{Y}}\biggr{\}}. \tag{16}\]  
Proof.: The probability of a random event occurring within the sensing radii of \(N_{0}\) sensors is:  
\[\Pr{}_{0}=\frac{\left(\frac{N\pi r^{2}}{M_{X}M_{Y}}\right)^{N_{0}}}{N_{0}}\exp \biggl{\{}-\frac{N\pi r^{2}}{M_{X}M_{Y}}\biggr{\}}. \tag{17}\]  
The expected number of sensors within the scope of an event is:  
\[\mathbb{E}[N_{0}]=\frac{N}{M_{X}M_{Y}}\pi r^{2}. \tag{18}\]  
The expected number of sensors that are within the scope of an event and are themselves in a working state is:  
\[\mathbb{E}[N_{0}^{W}]=-\frac{W}{S+W}\frac{N}{M_{X}M_{Y}}\pi r^{2}. \tag{19}\]  
Figure 2: The diagram of alternate sensing scheduling.  
Therefore, the probability of a random event being detected by a sensor is:  
\[1-\exp\biggl{\{}-\frac{W}{S+W}\frac{N\pi r^{2}}{M_{X}M_{Y}}\biggr{\}}. \tag{20}\]  
The theorem holds.  
Theorem 1 reveals that the detection accuracy is a function of the average number of sensors in each cell area and sensing scheduling.
## 2 Derivation of the DIRKT Methods  
The general type of implicit Runge-Kutta type technique with \(m\)-stage for solving the IVPs (1) can be written as follows:  
\[u_{n+1} =u_{n}+h\,u_{n}^{\prime}+\frac{h^{2}}{2}\,u_{n}^{\prime\prime}+ \frac{h^{3}}{6}\,u_{n}^{\prime\prime\prime}+h^{4}\sum_{i=1}^{m}b_{i}k_{i},\] \[u_{n+1}^{\prime} =u_{n}^{\prime}+h\,u_{n}^{\prime\prime}+\frac{h^{2}}{2}\,u_{n}^{ \prime\prime\prime}+h^{3}\sum_{i=1}^{m}b_{i}^{\prime}k_{i},\] \[u_{n+1}^{\prime\prime} =u_{n}^{\prime\prime}+h\,u_{n}^{\prime\prime\prime}+h^{2}\sum_{ i=1}^{m}b_{i}^{\prime\prime}k_{i},\] \[u_{n+1}^{\prime\prime\prime} =u_{n}^{\prime\prime\prime}+h\sum_{i=1}^{m}b_{i}^{\prime\prime \prime}k_{i},\]  
where  
\[k_{i}=f\Bigg{(}t_{n}+c_{i}h,\,u_{n}+c_{i}\,h\,u_{n}^{\prime}+\frac{h^{2}}{2}\, c_{i}^{2}\,u_{n}^{\prime\prime}+\frac{h^{3}}{6}\,c_{i}^{3}\,u_{n}^{\prime \prime\prime}+h^{4}\sum_{j=1}^{m}a_{ij}k_{j}\Bigg{)} \tag{2}\]  
for \(i=1,2,3,\ldots,m\).  
DIRKT method parameters \(b_{i},b_{i}^{\prime},b_{i}^{\prime\prime},b_{i}^{\prime\prime\prime},a_{ij}\) and \(c_{i}\) for \(i,j=1,2,\ldots,m\) are assumed to be real, \(m\) is the number of stages of the method. The technique is diagonally implicit when \(a_{ij}=0\) for \(i<j\). The latter class contains of singly DIRKT methods where the matrix \(A\) is lower triangular and all the entries in the diagonal of \(A\) are equal. The DIRKT method is presented by the Butcher tableau (see Table 1).  
\begin{table}
\begin{tabular}{c|c c c c} \hline \hline \(c_{1}\) & \(a_{11}\) & & & \\ \(c_{2}\) & \(a_{21}\) & \(a_{22}\) & & \\ \(c_{3}\) & \(a_{31}\) & \(a_{32}\) & \(a_{33}\) & \\ \(\cdot\) & \(\cdot\) & \(\cdot\) & & \\ \(\
## 2 Related Work  
The growing interest in wireless sensor networks on the one hand, and the continual emergence of new architectural techniques in the other hand have inspired some previous efforts for surveying the characteristics, applications and communication protocols for such a technical area [21, 22]. In this subsection we point out the features that distinguish our paper and highlight the differences in scope.  
The authors in [23] presented full categories of routing protocols for WSNs, as did the authors in [21, 24]. However, none of them include the recent energy-efficient mechanisms (such as mobile sink, multi-sink, _etc._) which could be combined with routing algorithms to increase the network lifetime. Moreover, all of the mentioned approaches only consider the routing algorithms in WSNs from the network structure and the protocol operation point of view. In our paper, we classify not only the routing schemes based on protocol operation, but also from the viewpoint of energy-efficiency.  
A taxonomy of different energy-saving strategies applicable in wireless sensor networks is developed in [1] and [25]. According to these surveys, the energy-aware routing protocols in sensor networks are classified by considering several factors such as data cycling, mobility, topology control and data-driven techniques. However, the authors do not focus sufficiently on the network layer and these papers do not include bio-inspired and multi-sink mechanisms for routing protocols. Our survey can serve those who seek deeper insight into energy-efficient routing issues and schemes in wireless sensor networks.  
A comprehensive study of mobile techniques for increasing network lifetime is presented in [2]. The authors explained the protocols proposed in all aspects of mobility such as mobile sinks, mobile sensors redeployment, and mobile relays. Although the paper covers a number of routing protocols that support mobility, it does not provide a classification for other energy-efficient techniques applied in routing algorithms. As the best of our knowledge, our paper is the first one that presents a taxonomy of energy-efficient mechanisms, including mobile sink, multi-sink, multi-path, power control and specially bio-inspired schemes, in order to prolong the WSNs' lifetime.
###### Abstract  
The inherent damping deficiency in metal lattice structures leads to inadequate attenuation of both resonant peaks and shock-induced vibrations, significantly limiting their effectiveness in vibration isolation and shock resistance applications. To address this limitation, we developed a novel parallel polyurea method that utilizes the viscoelastic energy dissipation mechanism of polyurea to substantially improve structural damping performance. The metal lattice-polyurea parallel vibration isolation system was designed with its theoretical model established to characterize damping properties, vibration isolation, and shock-resistant performance. An experimental setup was developed to validate theoretical predictions through controlled semi-sinusoidal shock and swept-frequency tests. Experimental results demonstrate excellent agreement with theoretical predictions. The introduction of the polyurea damping structure significantly enhances the system's damping performance. Compared to the conventional metal lattice isolator, the proposed metal lattice-polyurea parallel composite structure shows remarkable damping improvements: under shock excitation, it achieves substantial attenuation of peak response amplitude with accelerated decay rate, while under frequency-sweep excitation, it maintains the original resonance frequency but reduces the transmissibility peak significantly.  
metal lattice-polyurea parallel composite structure; viscoelastic damping; vibration isolation; shock resistance
## 5 Experimental Setup and Results
The input value is the state in which each intelligence is present, and the goal function is the Q value corresponding to each action to train the neural network. As shown in Figure 13, the proposed framework has a quicker intersection in the Q value graph after iterations of 4500 training trips compared to the DDQN. However, it changed abundantly after 1400 tries of training. The proposed framework uses a loop memory network and multiple reward mechanisms compared to DDQN, as shown in Figure 14. In the end, the greater reward value of the proposed framework denotes fewer repeated error-controlling commands. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Examples**} & **Trial 1** & **Trial 2** & **Trial 3** & **Mean** \\ \cline{3-6}  & & **Distance** & **Distance** & **Distance** & **Distance** \\  & & **(m)/time (s)** & **(m)/time (s)** & **(m)/time (s)** & **(m)/time (s)** \\ \hline \multirow{4}{*}{A*} & First trip & 8.7 m/91 s & 9.9 m/99 s & 9.1 m/95 s & 9.2 m/95 s \\  & Second trip & 9.9 m/90 s & 9.1 m/87 s & 8.1 m/88 s & 9.0 m/88 s \\  & Third trip & 9.1 m/98 s & 9.0 m/89 s & 9.0 m/87 s & 9.0 m/87 s \\  & Fourth trip & 8.4 m/87 s & 9.8 m/72 s & 9.9 m/61 s & 9.3 m/73 s \\ \hline \multirow{4}{*}{DDQN} & First trip & 8.8 m/84 s & 9.6 m/76 s & 8.9 m/95 s & 9.1 m/85 s \\  & Second trip & 8.4 m/86 s & 7.8 m/87 s & 7.9 m/88 s & 8.0 m/87 s \\  & Third trip & 9.1 m/80 s & 8.4 m/79 s & 8.3 m/75 s & 8.6 m/78 s \\  & Fourth trip & 9.0 m/77 s & 7.9 m/61 s & 7.8 m/58 s & 8.2 m/65 s \\ \hline \multirow{4}{*}{Proposed Framework} & First trip & 8.1 m/84 s & 8.8 m/89 s & 7.7 m/85 s & 8.2 m/86 s \\  & Second trip & 7.8 m/86 s & 7.5 m/87 s & 8.1 m/88 s & 7.8 m/87 s \\ \cline{1-1}  & Third trip & 8.5 m/85 s & 8.4 m/79 s & 7.7 m/75 s & 8.2 m/79 s \\ \cline{1-1}  & Fourth trip & 7.9 m/73 s & 8.1 m/69 s & 7.3 m/71 s & 7.7 m/71 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental data of A*, DDQN and the proposed framework algorithm.
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 1 Introduction
Consequently, numerous metaheuristic algorithms have been developed and adapted to address these challenges [11]. Metaheuristic algorithms are stochastic optimization methods designed to enhance the effectiveness of underlying heuristic techniques. These approaches effectively address the challenge of local optima and have proven successful in solving a diverse range of optimization problems [11; 12]. While metaheuristic-based feature selection plays a crucial role in the development of various machine-learning models, it has received comparatively less attention in the literature on botnet detection. This paper introduces an enhanced P2P botnet detection model using differential evolution-based feature selection and supervised machine-learning algorithms including ensemble learning, which can efficiently identify P2P bot communications by considering the following key questions:  
1. How to enhance feature selection using evolutionary metaheuristic algorithm such as differential evolution. 2. How to design an enhanced learning model for P2P botnet detection using various supervised machine-learning algorithms. How to enhance the performance of the proposed model by using the majority voting ensemble learning algorithm. * How to validate the proposed model in a dataset containing real-world botnet flows. * How to evaluate the performance of the proposed model using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The rest of this paper is arranged as follows: Section 2 covers the related work, Section 3 covers the proposed approach, Section 4 contains the experiment discussions, Section 5 contains results, Section 6 contains comparison, and Section 7 contains conclusions and scope for future research.
## 2 Material and Methods  
### Database and Preprocessing  
A total number of 212 electrograms of 16 s in length were acquired from ParAF and long-standing PerAF patients who were not under arrhythmic drug therapy, undergoing radiofrequency catheter ablation therapy at the cardiac electrophysiological laboratory of the Arrhythmia Unit in the General University Hospital Consortium of Valencia. The hospital's Internal Review Board (IRB) approved acquisition and analysis of these retrospective data and informed consent was obtained from all individual participants included in the study. A total of 108 and 104 CFAEs were recorded from 27 patients for ParAF and 26 patients for PerAF, respectively, which were identified by observing the published criteria [16], from the four pulmonary veins (PV)--left superior (LSPV), left inferior (LIPV), right superior (RSPV), and right inferior (RIPV)--and the anterior (ANT) and posterior (POS) free wall of the left atrium.  
If AF persisted for more than 10 min, then the electrograms were included for analysis and subjected to rectangular one-, two-, and four-second-length windowing, thus creating three distinct datasets. The first dataset consisted of 212 non-overlapping one-second-length sequences; the second dataset consisted of 212 non-overlapping two-second-lengthsequences, and the third dataset consisted of 212 non-overlapping four-second-length sequences. In the cases of ParAF with a baseline on sinus rhythm, AF was induced by rapid pacing at the coronary sinus or at the lateral wall of the right atrium, with a coupling interval range of 250-200 ms [16].  
The used catheter for signal acquisition was a 3.5 mm irrigated-tip radiofrequency ablation catheter and the procedure used 3D electroanatomic mapping (CARTO, Biosense-Webster Inc., Diamond Bar, CA). The protocol involved placing multipolar catheters in the right atrium and in the coronary sinus and a steerable circular catheter and \"lasso\" in the left atrium through transseptal puncture. All signals were band-pass filtered by the acquisition system (0.2-500 Hz) and digitized at a sampling frequency of 1 kHz by a Labsystem(tm) PRO EP recording system (Boston Scientific, Marlborough, MA, USA). The process applied to the analyzed signals is summarized in Figure 1. Finally, in order to remove the most relevant source of signal contamination from the cardiac electrophysiology laboratory, which is the ubiquitous powerline interference, a new method based on the stationary wavelet transform has been applied [26]. The use of this algorithm provided improved and truthful evaluation of CFAEs because it preserved signal morphology and did not add artificial fractionation to the electrograms [26].  
Figure 1: Flowchart describing the steps of operation of the proposed classification modeling.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 2 Native Bacterial Approach for Colorectal Cancer (CRC) Treatment
### Improvement of Intestinal Barrier  
Intestinal epithelial cells protect the internal environment from pathogenic bacteria and toxic substances. Tight junction proteins, as components of the intestinal barrier, are disrupted by intestinal pathogens, and their toxins, CRC metastasis, and tumor invasion are enhanced [38]. Tight junction integral protein claudin-3 serves as a conjoint rheostat for regulating Stat-3 and Wnt/\(\beta\)-catenin activation to prevent CRC malignancy, which makes it an important therapeutic target for CRC suppression [39]. The probiotic Escherichia coli Nissle 1917 (EcN), along with its released active factors and outer membrane vesicles (OMVs), facilitated the upregulation of tight junction proteins such as ZO-1 and claudin-14, thereby enhancing the integrity of the intestinal barrier. [40]. Short-chain fatty acids (SCFAs), including butyrate, propionate, and acetate, are secreted by probiotics and help to improve intestinal barrier function. In an in vitro study, SCFAs reduced metabolic stress and inhibited ethanol damage to tight junction proteins by activating AMP-activated protein kinase (AMPK) in Caco-2 cells [41]. In an AOM/DSS model of colitis, propionate enhanced barrier function as well as reduced inflammation and oxidative stress caused by dextran sodium sulfate (DSS) [42]. In addition, mucin secreted by goblet cells in the intestinal epithelium is indispensable in strengthening the defenses of the intestinal barrier [43]. Probiotics can enhance the production of intestinal mucin. For example, _L. rhamn_susc  
Using wild-type probioticotic bacteria to target and kill tumors is a promising strategy [50]. However, the therapeutic capacity of each bacterium is single and limited, and it is difficult to deal with complex malignancies. Genetic engineering and synthetic biology techniques can be used to modify bacteria to improve their beneficial properties and, most importantly, their therapeutic efficacy and targeting capabilities [51]. Balancing the bacterial dosage required for tumor destruction with toxicity or adverse effects on the host is a significant challenge. Additionally, the pathogenicity of these bacteria remains a concern [36]. Despite attempts to reduce the pathogenicity of these bacteria through gene deletion, complete success has not yet been achieved [52]. Adverse effects and mortality rates have been reported when using these bacteria for treatment [53; 54]. Finally, these bacteria are not entirely capable of specifically identifying and attacking tumor cells. Some bacteria may aggregate in normal tissues and the bloodstream, leading to systemic infections or other adverse effects.
# MpcNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
## 4 Experimental Results and Discussion
### Ablation Study  
We performed an ablation study to verify the performance of MreNet. First, we took the complete version of MreNet as the control group, MreNet without the enhancement layer as experimental group 1, MreNet without the feature connection as experimental group 2 and MreNet without the sparse attention layer as experimental group 3. We trained and tested them on the PanoContext dataset + Stanford-2D3D. As shown in Figure 5, in terms of 3D IoU, the experimental groups 1, 2 and 3 achieved 83.88%, 83.82% and 83.77%, respectively, while the control group achieved 84.09%. Concerning the corner error, the experimental groups 1, 2 and 3 reached 0.657%, 0.659% and 0.661%, respectively, while the control group achieved 0.639%. Regarding the pixel error, the experimental groups 1, 2 and 3 yielded 2.047%, 2.049% and 2.051%, respectively, while the control group had a pixel error of 2.030%. We also conducted some statistical hypothesis tests. For the two-sample heteroscedastic assumption-test, the 3D IoU result for the control group with experimental groups 1, 2 and 3 was 0.0051, 0.0043 and 0.0049, respectively. The results for corner errors were 0.0008, 0.004 and 0.002. The results for pixel error were 0.043, 0.029 and 0.0031.  
In conclusion, as the complete version of MreNet achieved good performance from these three metrics, the necessity of the sparse attention layer, enhancement layer and feature connection were verified.
## 4 Materials and Methods  
An experimental testbed was set up to examine the behavior of optical signals in terms of received power at different salt concentrations and turbidity levels. Typically, a testbed includes a transmitter, a receiver, and a water tank. Figure 2a represents the block diagram  
Figure 1: Salinity map.  
of an experimental testbed, with the actual experimental setup shown in Figure 2b. Table 1 summarizes the specifications of the equipment used to create the experimental testbed.  
The system starts working with a sound wave that transmits through water using a light source. This sound wave is fed to the transmitter circuit that converts this digital signal to an analog light wave. The transmitter circuit is connected to an LED light array source to physically transmit the signal through a water medium, and an extra power source is used to enhance the signal so that it can cover more distance and provide better results. Once light passes through the water tank, the intensity of received signals is measured using a photocell panel. The output of the photocell is passed to two separate circuits. One of them is an amplifier to enhance the signal to audible levels, and the other is a voltage divider to measure the power of the received signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Parameters** & **Value** \\ \hline \multirow{3}{*}{Transmitter} & Dimension & 60 mm \(\times\) 50 mm \\  & Light color & White \\  & Output power & 48 W \\ \hline \multirow{3}{*}{Receiver} & Dimension & 120 mm \(\times\) 65 mm \\  & Maximum Power (Wp) & 1.5 \\ \cline{1-1}  & Maximum Power Voltage & 6 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: The LED-based UWOC system specification.  
Figure 2: LED-based UWOC system, (**a**) block diagram, and (**b**) experimental testbed.
## 3 Mathematical Modeling and Design Methodology
### Modeling of a PV Array Output Power Generation  
The PV cell is a semiconductor device whose p-n junction is exposed to light, which produces an electric current [27]. However, the design of the PV system depends on the load power. The mathematical models and the corresponding circuit models for ISD, SD, and TD, are investigated here. Furthermore, the physical behavior of each model and their characteristic solar curves are studied.