esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns
## 3 Datasets  
Three publicly available intrusion detection datasets were used in this paper: NSL-KDD [34], Kyoto 2006+ dataset [35], and CIC-IDS2017 [36]. NSL-KDD is a cleaned dataset that addresses the duplicated entry problems in the KDD'99 dataset [37]. The dataset includes approximately 150,000 records with 41 attributes. Twenty-four attack scenarios are grouped into four attack categories: DoS, R2L, U2R, and Probe. DoS denotes a Denial-of-Service attack that represents the attempts to make computing or network resources inaccessible. R2L (Remote to User) sends packets to another computing machine over the network to gain access to local user accounts. U2R (User to Root) gains a normal user account and exploits some vulnerabilities to gain root access to computing systems. Probe scans a network of computers to gather information and find vulnerabilities in computing machines. In this study, U2R was not considered due to the limited sample size (only 119 instances).  
The Kyoto 2006+ dataset was developed to respond to the need for generating new network intrusion datasets. Although the KDD'99 and NSL-KDD datasets have been popular in designing machine-learning-based intrusion detection systems, it does not reflect recent network traffic trends. The Kyoto 2006+ dataset includes millions of real traffic data records with 24 attributes [35]. The 14 commonly used features in intrusion detection studies were included. They are duration (length of connection), service type, source bytes, destination bytes, count, same_ser_rate, serror_rate, srv_server_rate, dst_host_count, dst_host_srv_count, dst_host_same_src_port_rate, dst_host_serror_rate, and flag. Additional 10 features were added to understand traffic patterns in the networks. The initial version of the dataset was collected for three years (2006-2009), and the data collection continued until 2015. Since the size of the Kyoto 2006+ dataset is extremely large, we used a portion of the recent dataset (January 2015). The CIC-IDS2017 dataset [36] was created at the Canadian Institute for Cybersecurity (CIC). The dataset captures current network patterns to address the limitations of existing intrusion detection datasets. The CIC-IDS2017 dataset was captured during the period of 3 to 7 July 2017. It contains about 2.8 million network events with about 0.5 million attack events. The dataset consists of eighty-five variables, including the original full packet payload dataset, the processed dataset with a network traffic flow analysis tool (called CICFlowMeter), and labeled network flows with time stamps, IP addresses, network ports, protocols, and attack information. We used the dataset generated on Tuesday, which includes 431,873 normal and 13,835 attack events. The attack events were generated from Brute Force attack scenarios. In the rest of this paper, we use the terms NSLKDD, Kyoto2006, and CICIDS2017 to indicate each dataset utilized in our study, respectively.
## 4 Results and Discussion
### Comparison and Analysis
As shown in Tables 2 and 3, the SHO-BP algorithm exhibits the highest Pearson correlation coefficient, indicating a strong correlation. The SHO-BP algorithm achieves the best performance in terms of errors across all metrics. Although the improved Edge-Counting method can achieve good results for some concepts, the large average error indicates that the overall performance of this method is poor. While the maximum error of the WOA-BP algorithm is close to that of the SHO-BP, there is a significant difference in the mean squared error. In the aforementioned experiments, the results of the SHO-BP algorithm and the WOA-BP algorithm are very close. To further compare the optimization performance of SHO and WOA algorithms on the BP neural network, standard test functions are used to evaluate the optimization effects of both algorithms. The convergence curves of the two algorithms under four test functions are shown in Figures 8-11, and the formulas of the test functions are given by Equations (19)-(22). \[f_{1}(x)=\sum_{i=1}^{30}x_{i}^{2},\ \ -100\leq x_{i}\leq 100 \tag{19}\]  
\[f_{6}(x)=\sum_{i=1}^{30}\left(\left|x_{i}+0.5\right|\right)^{2},\ \ -100\leq x_{i}\leq 100 \tag{20}\]  
\[f_{9}(x)=\sum_{i=1}^{30}[x_{i}^{2}-10\text{cos}(2\pi x_{i})+10],\ \ -5.12\leq x_{i}\leq 5.12 \tag{21}\]  
\[f_{9}(x)=-20\text{exp}(-0.2\sqrt{\frac{1}{30}\sum_{i=1}^{30}x_{i}^{2}})-\text {exp}(\frac{1}{30}\sum_{i=1}^{30}\text{cos}2\pi x_{i})+20+\text{c}, \tag{22}\]  
\[-32\leq x_{i}\leq 32\]  
Based on the analysis of the convergence curves with experimental data, it can be observed that the SHO algorithm demonstrates a rapid convergence capability when computing the ontology-based semantic similarity of transportation infrastructure. Compared to the WOA algorithm, SHO algorithm can more quickly approach the optimal solution. These findings indicate that the SHO-BP algorithm achieves superior performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **SHO-BP** & **WOA-BP** &
\begin{tabular}{c} **Improved** \\ **Edge-Counting** \\ \end{tabular} & **Improved IC** & **HOM** & **HowNet** \\ \hline Pearson correlation coefficient & 0.9236 & 0.9074 & 0.8676 & 0.84015 & 0.7234 & 0.5721 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pearson correlation coefficient of results obtained by different calculation methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error** & **Maximum** & **Average** & **Standard Deviation** \\ \hline
**SHO-BP** & **0.09617** & **0.02379** & **0.02136** \\
**WOA-BP** & 0.10201 & 0.02880 & 0.02879 \\
**Improved** & 0.20158 & 0.05520 & 0.04035 \\
**Edge-Counting** & 0.14863 & 0.06452 & 0.06162 \\
**HOM** & 0.16044 & 0.03171 & 0.03592 \\
**HowNet** & 0.46873 & 0.18268 & 0.18007 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The maximum, average, and standard deviation of different method results. (The smallest value in each column is highlighted in bold). Figure 8: Convergence curve under test function \(f_{1}(x)\). Figure 10: Convergence curve under test function \(f_{9}(x)\). Figure 9: Convergence curve under test function \(f_{6}(x)\).
_Appl. Sci._**2012**, \(2\), 485-495; doi:10.3390/app2020485  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Polydimethylsiloxane (PDMS) Coating onto Magnetic Nanoparticles Induced by Attractive Electrostatic Interaction**  
**[PERSON], [PERSON] and [PERSON] ***  
Physicochimie des Electrolytes, Colloides et Sciences Analytiques, UMR 7195, 4 place Jussieu - cc51 75252 Paris cedex 05, France  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-01-4427-4309; Fax: +33-01-4427-3228.  
_Received: 16 March 2012; in revised form: 7 May 2012 / Accepted: 21 May 2012 / Published: 29 May 2012_  
**Abstract:** In this article, we present an efficient synthesis pathway of polydimethylsiloxane (PDMS) coated magnetic nanoparticles from hydrophilic polyacrylate coated ferrofluids (NPPAA). A block copolymer based on polydimethylsiloxane is selected for its propensity to interact with the carboxylate functions on the NPPAA. The interaction is due to negative charges on NPPAA and positive ones on the amphiphilic copolymer. The synthesis is achieved by interfacial interaction, simplifying the purification of the PDMS-coated nanoparticles (NPPDMS) from subproducts such as ions and water. NPPDMS are well dispersed in hydrophobic solvents (toluene, diethyl ether) and can then be embedded into a curable PDMS polymer.  
**Keywords:** magnetic nanoparticle; PDMS; amphiphilic copolymer; electrostatic complexation; polydimethylsiloxane composite; aminopropylsiloxane
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 1 Introduction
### Difficulties in Optical Remote Sensing Image Object Detection  
Remote sensing images exhibit complexity, covering a wide range with large image scales. While they provide valuable visual features for detection models, they also introduce complex and irrelevant background information that hinders accurate detection. Firstly,background interference arises from similar objects in the scene, leading to potential misjudgment. For example, small islands with similar colors and thin buildings on land can be mistakenly identified as ships. Secondly, self-interference occurs due to the diverse textures, similar colors, and wake features of ships, affecting the final recognition results. Thirdly, the random orientation of ships in remote sensing images poses another difficulty. Unlike natural scene images captured from a horizontal perspective, satellite images are taken from a bird's-eye view, resulting in ships appearing at any position with any orientation, making detection challenging.  
Furthermore, vehicles, boats, helicopters, airplanes, and large structures such as stadiums appear much smaller in remote sensing images compared to natural scene images captured from a horizontal viewpoint. Consequently, existing visual models often struggle to achieve accurate detection for these objects. For instance, in the optical remote sensing image dataset DOTA [4], small objects, such as cars, occupy approximately 30 \(\times\) 50 pixels, large trucks occupy around 35 \(\times\) 90 pixels, and the smallest yacht of interest is merely about 35 \(\times\) 60 pixels in size.
## The Living Building Blocks of MESs
### Direct Extracellular Electron Transfer (DEET)  
Microbial communities enriched at anode surfaces placed in the environment are commonly dominated by proteobacteria, of which _Geobacter_ spp. is a member [49]. The direct transfer of  
Figure 1: Mechanisms of interaction of electroactive microorganisms (EAMs) with the anode of microbial electrochemical systems (MESs) include direct extracellular electron transfer (DEET) through membrane bound cytochromes, PEET by using electroconductive pili, the use of soluble electron shuttles (SEET), and interactions between different species, directly transferring electrons between one another (DIET).  
electrons to electrodes has been most heavily investigated in _G. sulfurreducens_[6] The direct exchange of electrons from microbes to an electrode is achieved by membrane-bound cytochromes such as OmcZ [50]. _G. sulfurreducens_ has been demonstrated to have a substantial diversity of cytochromes, suggesting the potential for flexibility in cytochrome function, and of these, OmcZ has been the most well characterized cytochrome in the direct transfer of electrons to electrodes in MFCs [6; 50; 51; 52; 53; 54]. The importance of OmcZ to electron transfer to electrodes and subsequent current generation became apparent after the deletion of OmcZ was observed to decrease the ability to exchange electrons to the anode, while deletions of other cytochromes did not have as significant an effect [51].
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 3 Method
### Learning-Based MVS  
In order to acquire 3D geometry for large scale scenes, we explore the learning-based MVS method to estimate depth maps for multiple images. R-MVSNet [6], a deep learning architecture with capability to handle multi-scale problem, has advantages in processing high-resolution images and large-scale scenes. Moreover, R-MVSNet utilizes the Gated Recurrent Unit (GRU) to sequentially regularize the 2D cost maps, which reduces the memory consumption and makes the network flexible. Thus, we follow the framework of R-MVSNet to generate corresponding depths of the source images and train it on the DTU [28] dataset. Camera parameters and image pairs are determined by the implementation of COLMAP-SfM [25], while depth samples are chosen within \([d_{min},d_{max}]\) using the inverse depth setting. The network returns a probability volume \(\mathbf{P}\) where \(P(x,y,d)\) is the probability estimation for the pixel \((x,y)\) at depth \(d\); then, the expectation depth value \(d(x,y)\) is calculated by the probability weighted sum over all hypotheses:  
\[d(x,y)=\sum_{d=d_{min}}^{d_{max}}P(x,y,d)\cdot d. \tag{1}\]  
However, as with most depth estimation methods, the coarse pixel-wise depth data \(d(x,y)\) generated by R-MVSNet may contain errors. Therefore, before point cloud fusion by the depth maps, it is necessary to perform a denoising process on the depth data. In this paper, we apply the bilateral  
Figure 1: General pipeline of our work. Three branches are implemented to process the reconstruction dataset. The upper branch is the semantic segmentation branch to predict the semantic probability map; the middle branch is SfM to calculate the 3D odometry and camera poses; the lower branch is to estimate the depth map. Then, semantic fusion is applied to fuse them into a coarse point cloud. The last step is to refine the point cloud by local and global methods.  
filtering method to improve the quality of depth maps with edge preservation; the refined depth data \(d^{\prime}(x,y)\) are obtained by:  
\[d^{\prime}(x,y)=\frac{\sum_{i,j}\omega(x,y,i,j)\cdot d(x,y)}{\sum_{i,j}\omega(x,y,i,j)} \tag{2}\]  
where \(\omega(x,y,i,j)=exp(-\frac{(x-i)^{2}+(y-j)^{2}}{2\sigma_{j}^{2}}-\frac{||d(x,y )-d(i,j)||^{2}}{2\sigma_{g}^{2}})\) is the weighted coefficient; \(\sigma_{f}\) and \(\sigma_{g}\) are the variance of domain kernel \(f(x,y,i,j)=exp(-\frac{(x-i)^{2}+(y-j)^{2}}{\sigma_{j}^{2}})\) and range kernel \(g(x,y,i,j)=exp(-\frac{||d(x,y)-d(i,j)||^{2}}{\sigma_{g}^{2}})\) respectively. As shown in Figure 2, the depth map becomes more smooth with edge preservation after bilateral filtering.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
NDVI measures the difference between near-infrared (NIR) and red light reflectance, providing information on vegetation greenness and photosynthetic activity. It is widely used to monitor vegetation density, health, and productivity over large areas. However, it suffers from various limitations for vegetation state retrieval, particularly in seasonal snow-covered environments andiferous forests [36]. The Plant Phenology Index (PPI) has been recently developed to overcome these limitations and improve plant phenology monitoring, notably in Arctic areas, and has shown promising performances [40; 42]. Unlike the NDVI, the PPI is physically based and derived from Beer's law, adapted for canopy reflectance. It has an almost linear relationship with the Leaf Area Index (LAI), making it a powerful proxy for phenological stage identification through remote sensing [40]. Recent studies have further explored the potential of PPI beyond Arctic regions, offering new insights into vegetation monitoring in arid ecosystems [43] and across Europe [39]. To our knowledge, the PPI performance has not been assessed specifically for complex mountainous environments. As in Arctic environments, mountain areas, and more specifically, the European Alps, face a seasonality marked by the presence of snow and low temperatures. Therefore, the PPI could provide new insights for coping with the limitations of NDVI and enhance the monitoring of vegetation in complex environments such as mountain areas. Considering these factors, the objectives of this paper are to investigate (1) how PPI compares to NDVI in retrieving phenological metrics in areas characterized by seasonal snow and coniferous forests, such as mountainous regions, and (2) whether PPI can address some of the known limitations of NDVI for monitoring vegetation dynamics in such environments. These aspects will be examined through an initial comparative assessment of phenological metrics derived from PPI and NDVI for monitoring vegetation dynamics across four vegetation types--deciduous trees, coniferous trees, grasslands, and shrublands--in European alpine ecosystems. The findings of this study may provide new perspectives on monitoring vegetation dynamics in complex environments, potentially linking long-term regional vegetation trends to annual phenological and growth responses to rising temperatures and climate stress in diverse topoclimatic conditions. Ultimately, this could aid in forecasting changes in ecosystem productivity and biodiversity.
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 4 Data Aggregation and Communication
### Data Embedding
#### 4.1.4 Constructing ET (X)  
While partitioning \(Xloc\) into 2 or 3 labels, \(Xloc+1\) is constructed by substituting every block, \(bl\), by \(hf(bl)\), where \(hf\) is a 1-1 naming hash function. It is essential to mention that  
Figure 3: Nodes’ formation based on landmark symbols.  
Figure 2: Landmark finding and alphabet reduction process.  
the units of distinct levels will call hash functions onto distinct domains to compute names. To this point, the focus is on any given level \(i\). Using the random property, the computation of \(hf()\) for a specific unit of size 2 or 3 will take \(O(1)\) time. This produces the series \(Xloc+1\); this process is reiterated till the series is of size one or till the tree root. Let \(P_{i}(X)\) represent total nodes at level \(i\) in \(ET(X)\). The original data, \(M_{0}(X)=|X|\), are used to design the first (leaf) level from the symbols. We have \(P_{i}(X)/3\leq P_{i+1}(X)\leq[P_{i}(X)/2]\). Hence, \(\frac{3}{2}|X|\leq P_{i}|X_{i}|\leq 2|X|\). Therefore, for every \(i\), \(|\Sigma_{i}|\leq|X|\) and, thus, \(log^{*}|\Sigma_{i}|\leq log^{*}|X|\) are shown in Figure 4.  
**Theorem 1**.: _Given the data, \(X\), the \(ET(X)\) can be calculated in \(O(|X|\ log^{*}|X|)\) time._
## 2 Materials and Methods  
### WSN Simulation Setup  
We adopt a similar WSN simulation setup from our previous work [13], in which multiple sensor nodes are randomly dispersed in a designated area. These nodes sense, process, and transmit data via cluster heads to a sink node or base station. A total of n nodes in the WSN are grouped into clusters denoted \(\mathrm{C_{i}}\), each associated with a cluster head (CH) labeled \(\mathrm{H^{i}}_{\mathrm{z}}\). For this paper, we utilized the K-means clustering algorithm to group the sensor nodes into clusters. K-means assigns nodes to clusters based on proximity, which helps minimize intra-cluster variance and enhances energy efficiency in data transmission.  
Data from each sensor node is aggregated at the cluster head and then routed to the sink node or BS through a CH-based routing technique. This approach extends the WSN lifespan by minimizing energy consumption through optimal CH selection and routing path optimization. The CH selection and routing path are dynamically adjusted using our proposed chronological wild geese migration optimization (CWGO) algorithm [13] and other comparison algorithms for efficient energy use, minimal delay, and reliable transmission. Figure 1 illustrates the WSN system model. In this WSN system model, sensor nodes are organized into clusters labeled Cluster 1 to Cluster 5. Each cluster has one designated cluster head (CH) in which all the data from its cluster members are being transmitted. The CHs then transmit this consolidated data to the BS using a single-hop or multi-hop data transmission through the CHs in the WSN.  
In this study, five different algorithms for CH and routing path optimization, namely DE-SEP, E-CERP, DUCISCA, EECHIGOWO, and the proposed CWGO, were compared and integrated into the WSN simulation. Table 1 summarizes the detailed parameter settings for each algorithm, ensuring consistency across all simulation scenarios. These detailed configurations allow for a fair comparison of each algorithm's performance and facilitate reproducibility.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Proposed CWGO** & **DE-SEP** & **E-CERP** & **DUCISCA** & **EECHIGOWO** \\ \hline \multirow{2}{*}{Population Size} & \multirow{2}{*}{5} & \multirow{2}{*}{5} & \multirow{2}{*}{5} & Randomly initialized, evolves over generations & 10 solutions iterate over epochs \\ \hline \multirow{3}{*}{Iterations/Epochs} & Adaptive with & & & & \\  & synchronized flight, & & & & \\  & foraging & & & & \\ \hline Penalty for Low & Excludes node with & Excludes node with energy \textless{} 0.2 & Excludes node with energy \textless{} 0.2 & Excludes node with energy \textless{} 0.2 & Excludes node with energy \textless{} 0.2 & Excludes node with energy \textless{} 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and settings.  
Figure 1: The WSN system model.
## 4 Data Quality Checks and Experimental Corrections  
Before comparing the noise results obtained in these two anechoic facilities, a number of corrections should be applied to the data measured with the out-of-flow microphones to determine the acoustic characteristics of the noise source. In particular, sound pressure levels have to be corrected for insufficient signal-to-noise ratio, the effects of shear-layer refraction, atmospheric absorption, and geometric distance. These corrections were performed as follows:  
1. Background noise correction
2. Correction for shear-layer refraction
3. Correction for microphone directivity and frequency response
4. Atmospheric absorption
5. Geometric position (for AC-2 TsAGI data)
6. Strouhal scaling (for AC-2 TsAGI data)
## 8 Conclusions  
In this study, a new three-dimensional code for spectral/time/spatial was suggested based on the one-dimensional VWZCC code extension. Moreover, the proposed code is characterized by the property of zero cross-correlation (ZCC), high immunity against PINN noise and multiple access interference (MAI), supports a large number of simultaneous users, supports a very high data rate, and reduces system complexity due to the use of a single photodetector at the receiver level. The performance of our system demonstrates that the suggested code performs well compared to 3D-PD, 3D-PD/MD, and 3D-DCS/MD codes. Further, the comparison of this code from the 1D and 2D to 3D passage proved a better performance in terms of BER and SNR as well as a quality factor Q, which makes the reception of a received signal power higher than the noise power. According to the numerical results, the proposed code improves the optical system capacity up to 3.686, 3.234, 2.908, 5.112, and 2.248 times compared to 3D-PD, 3D-PD/MD, 3D-DCS/MD, 2D-VWZCC, and 1D-VWZCC codes. It saves a received power up to \(-7.04\) dBm and \(-5.9\) dBm compared to the 1D-VWZCC and 2D-VWZCC codes, respectively. Furthermore, the suggested system is successfully implemented using Optisystem software, where the code provides a low BER up to \(1.99\times 10^{-31}\) and a better quality reach, up to 11.56. The proposed code demonstrates high efficiency due to the absence of MAI and the low effect of PINN noise compared to previously published codes, which allows the system to meet the requirements of high-speed optical transmission networks with a simple transmitter/receiver structure and supports a large number of users at a very high data rate and low power consumption with a higher quality factor Q (greater than 6) and a very low bit error rate (less than \(10^{-9}\)), which makes it executable in future optical networks, such as the Wavelength Division Multiplexing (WDM), Next-Generation Passive Optical Network (NG-PON), free-space optics (FSO), etc.  
Conceptualization, M.R.; methodology, M.R. and A.C.; software, M.R. and A.C.; validation, A.C., A.S.K. and G.N.S.; formal analysis, M.R.; investigation, A.C., A.S.K., B.S.B. and G.N.S.; resources, A.S.K.; data curation, M.R. and A.C.; writing--original draft preparation, M.R.; writing--review and editing, A.C., A.S.K., B.S.B. and G.N.S.; visualization, A.C. and G.N.S.; supervision, G.N.S. and A.C.; project administration, A.C. and G.N.S.; funding acquisition, A.S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research is supported by the General Directorate of Scientific Research and Technological Development 'DGGST'--Algeria.  
The authors declare no conflict of interest.