esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection
## 3 Proposed Approach
### Feature Selection  
Feature selection is the process of identifying the most relevant subset of features from a given dataset [24]. In today's context, real-world datasets are often abundant, containing a large number of features. However, the sheer quantity of features does not imply that every feature is essential or beneficial. Some features may be redundant or irrelevant. A dataset containing noisy, redundant, and irrelevant information can lead to overfitting, diminishing the overall performance, accuracy, and learning speed of any machine-learning model [10]. Feature selection reduces the computational cost by reducing the dimensionality of data. Therefore, feature selection is crucial. Feature selection involves choosing a subset of features from the original set without modifying them, thus preserving the original features' meaningful interpretations. Traditionally, feature selection techniques are categorized into three categories: filter method, wrapper method, and embedded methods [24]. Apart from these algorithms, metaheuristic algorithms can also be used for feature selection. Metaheuristic algorithms are inspired by natural phenomena and are crafted to tackle intricate optimization problems [25]. They belong to a category of optimization algorithms crafted to discover approximate solutions for challenging optimization problems. These algorithms prove especially beneficial when addressing complex problems where obtaining exact solutions is either impractical or computationally demanding. Serving as high-level strategies, metaheuristics direct the exploration and exploitation of solution spaces, enabling efficient searches for near-optimal solutions. In our proposed work, we have employed a differential evolution algorithm for feature selection, which is an evolutionary based metaheuristic algorithm [26; 27]. Evolutionary algorithms are based on [PERSON]'s theory of evolution. Some algorithms of this category are Genetic Algorithm (GA), Evolutionary Programming (EP), Evolution Strategies (ESs), Genetic Programming (GP), and differential evolution (DE). Previous studies have shown that differential evolution (DE) achieves a better performance than traditional FS approaches in other fields [28; 29; 30].
## 4 Results and Discussion
### Prediction Phase Results  
In this section, the robustness of the proposed techniques is thoroughly analyzed in detecting unseen data generated at different AWGN power ranging from \(-140\) to \(-125\) dBm. The performance of the classifier varies depending on the noise level and the existence of jammers. To detect the type of the received signal, the trained MLP using 10-fold cross-validation has a higher generalization accuracy in detecting unseen data generated at the same noise level as the training data. Therefore, the prediction accuracy degrades by increasing AWGN power. Table 9 shows the results of evaluating the trained classifier's performance to recognize the type of received signal at different AWGN powers.  
Tables 10-13 show the effect of noise on the trained classifiers in predicting the type of new data. As is shown, two main factors concerning the prediction accuracy are the type of jammer and noise power. Apparently, in the presence of MCWI and CI, the classifiers cannot perform precisely.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AWGN Power (dBm)** & \(\mathbf{-140}\) & \(\mathbf{-135}\) & \(\mathbf{-130}\) & \(\mathbf{-125}\) \\ \hline \hline
**MLP + 10-fold cross-validation** & \(81\)\% & \(53\)\% & \(34.73\)\% & \(15.30\)\% \\
**MLP + PCA** & \(78\)\% & \(55\)\% & \(35.70\)\% & \(15.70\)\% \\
**MLP + GA** & \(83.50\)\% & \(54\)\% & \(36\)\% & \(15.25\)\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: ML-based prediction results for AMC1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AWGN Power (dBm)** & \(\mathbf{-140}\) & \(\mathbf{-135}\) & \(\mathbf{-130}\) & \(\mathbf{-125}\) \\ \hline \hline
**MLP + 10-fold cross-validation** & \(81\)\% & \(53\)\% & \(34.73\)\% & \(15.30\)\% \\
**MLP + PCA** & \(78\)\% & \(55\)\% & \(35.70\)\% & \(15.70\)\% \\
**MLP + GA** & \(83.50\)\% & \(54\)\% & \(36\)\% & \(15.25\)\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: ML-based prediction results for AMC2.  
Figure 7: Comparative training time for MLP (21.81 min), MLP + GA (310 min) and MLP + PCA (9 min).
## 3 Methodology
### Data Pre-Processing  
After obtaining the data in the measurement stage, it was necessary to process them to obtain two critical metrics for the development of the work: received power and distance between the transmitter and receiver.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 6 Evaluation
### Performance of Lookup Table  
To better analyze the effectiveness of our proposed scheme, we also needed to further analyze the lookup table, and the data storage structure in the fog nodes, to ensure that our scheme was practical and feasible. We conducted experiments on the query time of the lookup table. With the increase in querying data files, the query time can be seen in Figure 9.  
The blue line in Figure 9 represents the one-to-one lookup table method, and the red and grey lines represent the lookup tables for \(n=2\) and \(n=3\), respectively. It can be seen that the search time increases with the increase of search times. For \(n=1\), because the number of data files in the table is only one, the fog node can easily determine the important priority of data according to the access query times of each data and infer the meaning of the data representation (and even the encrypted data). At the same time, compared with \(n=2\) and \(n=3\), since the number of searches is determined, once a piece of datum is found, the search process will end immediately, which leads to a significant decrease in the average search time. When \(n=2\), it takes about 46 s to query the description files 200 times and the average time for each query is 0.23 s, which is within a reasonable limit. It can also be seen from the graph that the time spent on the look-up table corresponding to \(n=3\) has increased by approximately 50% compared to \(n=2\), but there is no improvement in terms of functional safety. Therefore, the one-to-two look-up table was ultimately chosen as the optimal choice.
## 2 Bullet Low-Overlap Point Cloud Registration Based on Line Feature Detection
### Voxelization Downsampling  
Due to the large number of point clouds on the surface of the captured warhead, the running time will be greatly increased, and the presence of noise will reduce the data quality. Point cloud filtering can well solve the above problems [25], and the filtered point cloud will use fewer points to describe warhead information than the original point cloud collection, as shown in Figure 2. Through investigation, we can consider bullet marks as linear features [1] to describe the same linear bullet marks. The original dense point cloud requires 10 point clouds to describe and contain noise points, while the filtered point cloud only requires 3 points to describe the linear features.  
In order to reduce the size of the point cloud, this paper uses voxel filtering to create a three-dimensional voxel grid, filters the point cloud with a uniform resolution of 0.003 mm, adopts automatic cyclic voxel filtering, calculates the maximum and minimum values of the \(x\)-, \(y\)-, and \(z\)-axes of the input point cloud, establishes a three-dimensional boundary box according to these values, and divides the boundary box into small cubes with specified voxel sizes. While the actual acquisition of the bullet point cloud data is usually in the order of millions, a reasonable filtering of the dense point cloud can help to improve the alignment efficiency.
## 2 Background Study
### Slant Path in Rain Cell
#### 2.4.1 Rain Height  
In many rain attenuation models [32; 33; 34], the rain height is used to determine the slant link length. However, the rain height is not uniform in every location of the world, so it is crucial to estimate the rain height in a specific geographical area. For a low rainfall rate, the minimum rain height is about 4 km, although there are variations in different models. However, some of the models estimate rain heights with elevation angles and rainfall rate as well. Table 3 lists most of the estimation techniques for rain height determination.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 2 Data and Methods
### MODIS Cloud Data  
MODIS Aqua Level 2 cloud mask product (MYD35) collection 6 was obtained from the Level-1 and Atmosphere Archive & Distribution System (LAADS) of NASA. MODIS has 36 bands covering the visible-to-infrared spectral range. The cloud mask product was derived using a fuzzy-logic scheme and a series of threshold tests to classify each 1 \(\times\) 1 km\({}^{2}\) pixel into \"cloudy\", \"probably cloudy\", \"probably clear\", or \"confident clear\" [12; 13; 14]. The first two types of pixels were assigned to be cloudy and the last two types were assigned to be cloud-free. The cloud mask product also provides the underlying surface-type (Figure 1). In this study, to distinguish the difference among surface-types, the comparison was done for dark, bright, and water surfaces. A bright surface is arid and desert surfaces, while a dark surface is a vegetated surface [13]. The cloud fraction was calculated from the cloud mask. MYD35 cloud mask data were geolocated using MODIS Level 1A collection 6 geolocation product MYD03. MYD03 also has solar zenith angle (SZA) data, which were used to investigate their impact on MODIS and CALIOP cloud detection. The MODIS CTH, COT, and cloud phase were from the Level 2 collection 6 MYD06 product [31]. Cloud phases include water, ice, and undetermined phases. All those data have a 1 \(\times\) 1 km\({}^{2}\) spatial resolution.  
Figure 1: The research region of Eastern China. Green, yellow, and blue backgrounds show dark, bright, and water surfaces on May 29, 2015, respectively. Red dots are environmental monitoring stations.  
In order to investigate the difference between MODIS and CALIOP cloud detection for different cloud types, MODIS cloud types are classified using the method of International Satellite Cloud Climatology Project (ISCCP) [32]. ISCCP defined 9 cloud types according to CTP and COT, and they are cumulus (Cu), stratocumulus (Sc), stratus (St), altocumulus (Ac), altocratus (As), nimobstratus (Ns), cirrus (Ci), cirrostratus (Cs), and deep convective (Dc). The first three (Cu, Sc, St) are low clouds, the middle three are middle clouds (Ac, As, Ns), and the last three are high clouds (Ci, Cs, Dc).
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 2 Preliminaries
### Random Forest Classifier  
In general, random forest can be seen as a set of unpruned inference trees, who together will vote for the final decision in classification tasks, namely, \"majority vote\". Assume a training dataset consisting of \(N\) samples with \(M\) features in full dimension. Each inference tree in random forest (RF) grows with \(n(n<N)\) bootstrap samples randomly selected from the same dataset, which can be described as random variable selection; note that only \(n\) training samples randomly selected from the dataset is used to build a single tree. Due to the two selection randomness of both the training sample and feature subsets and the advantage of the ensemble learning method, random forest obtains the capability of avoiding the problem of overfitting, high noise immunity, and outstanding computational accuracy. Furthermore, RF can also achieve extremely speedy computation on a parallel architecture, which allows each inference tree to run independently on a CPU core. Because of the excellent classification performance, random forest classifier has been widely used in various applications and tasks.
## 4 Quantitative Methods for the Detection of AD
### Principal Component Analysis & Scaled Subprofile Modeling
By applying SSM-PCA to amyloid images of sMCI and pMCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), they used logistic regression to select PCs with the greatest explanatory power at follow-up. Results reported from an independent test set using combined FDG, amyloid, and non-imaging variables showed significant group separation between converters and non-converters using the PCs obtained through SSM-PCA and Cox voxel-wise regression. A further follow-up study in 2021 from the same group reports that their model has strong predictive power in estimating the risk time to conversion from MCI within the AT(N) framework [119]. [PERSON] et al., used SSM-PCA on combined fMRI and tau-PET data on amyloid-positive patients and discovered a spatially dependent pattern of tau retention within patient group precunei, posterior cingulate cortices, and dorsolateral prefrontal cortices [120]. This pattern had 82.6% specificity and 79.1% sensitivity in differentiating AD HC patient groups. A positive correlation between tau retention and canonical resting-state functional networks was also discovered, but a negative correlation was reported between tau retention and seed-based analysis originating in both the precuneus/PCC and left dorsolateral prefrontal cortex. [PERSON] et al., used SSM-PCA to identify an Alzheimer's-related pattern from a combination of FDG-PET images, CSF protein markers, and neuropsychological information in a group of 20 AD and 20 HC subjects. The pattern was then validated on a group of 261 patients with AD, a behavioral variant of frontotemporal dementia (bvFTD), MCI, and HC subjects. They report differentiating AD HC groups with AUC = 0.95 and MCI vs. bvFTD from AD with AUC = 0.76 to 0.85 between internally and externally validated sample groups [120]. [PERSON] et al., also used SSM-PCA to develop a disease-specific pattern to differentiate DLB from AD patients with an AUC = 0.87. This result is notable due to the significant clinical comorbidity of DLB and AD symptoms [121]. [PERSON] et al., used SSM-PCA to generate disease-specific patterns for AD from amyloid-PET images [122]. They reported that amyloid-PET images were sufficient to differentiate between healthy controls and AD patients, but that FDG-PET or regional cerebral perfusion-weighted images were necessary to distinguish between AD, pMCI, and sMCI groups with statistically significant accuracy, leading them to suggest that perfusion-weighted images can be used as a surrogate for FDG-PET when performing SSM-PCA analysis.
## 3 Numerical Simulation of the Debris Impact on Satellite Tank
### Velocity Attenuation of Debris  
The velocities of debris at different times obtained by the simulation results are shown in Figure 5. When the debris moves in the liquid hydrazine, it is subjected to drag force, leading to a decrease in velocity [22]. The debris velocity attenuated sharply from 7000 m/s to 805 m/s at \(0<t<200\)\(\upmu\)s. The kinetic energy lost by the debris was mainly converted into the kinetic energy of the liquid hydrazine  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\rho\) **(kg/m\({}^{3}\))** & \(C_{l}\) **(m/s)** & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(\gamma\)o \\ \hline Liquid hydrazine & 1008 & 1500 & 1.979 & — & — & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Materials parameters of liquid hydrazine.  
Figure 4: The simulation model of the debris impacting the satellite tank at the velocity of 7000 m/s; the right side is the model cut in half.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\rho\) **(kg/m\({}^{3}\))** & \(E\) **(GPa)** & \(\upsilon\) & \(\sigma_{s}\) **(MPa)** & \(\sigma_{b}\) **(MPa)** \\ \hline Titanium & 4500 & 118 & 0.34 & 820 & 890 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material parameters of titanium.  
and the deformation energy of the tank walls. Liquid hydrazine begins to flow, and perforations and bulges occur on the front wall. The debris velocity decreased slowly at 200 \(\upmu\)s \(<t<\) 800 \(\upmu\)s, and the kinetic energy of debris was mainly used for the cavity expansion. At \(t>\) 800 \(\upmu\)s, the debris velocity remained basically unchanged. When \(t=\) 1196 \(\upmu\)s, the debris passed through the tank, the velocity decreased slightly, and a perforation occurred on the back wall. The residual velocity of the debris was 263 m/s, which indicates that the debris velocity is significantly reduced by the hydrodynamic ram effect.
# PET/CT Radiomics in Lung Cancer: An Overview
## 2 Methodology
### Feature Extraction
#### 2.4.2 Texture Features  
The objective of texture features is to quantify the variability of the grey-scale levels in the region of interest. They are therefore able to assess intra-lesion heterogeneity, which is considered a strong indicator of malignancy and poor prognosis [30; 31].  
Texture features commonly used in radiomics are first- and second-order statistics. First-order statistics describe the overall variation of the signal in the region of interest regardless of the relative position of the voxels. As a consequence, these features are fairly invariant to geometric transforms, therefore robust to image reconstruction and filtering. They include basic statistics such as mean, median, range, standard deviation, skewness, and kurtosis [32]. Second-order statistics model the joint signal variation between pairs of voxels lying at a predefined relative displacement between each other--among them are: Grey Level Co-occurrence Matrices (GLCM [33]), Grey Level Run-Length Matrices (GLRLM [34]), and Neighborhood Grey-Tone Difference Matrices (NGTDM [35]).  
Other texture features different from the above have also been investigated for characterizing PET/CT images in lung cancer--as, for instance, Gabor filters [36], Laws' masks [37], Local Binary Patterns [38], and wavelets [32].
## 3 Proposed 2D DW-ZCC Code
#### Code Properties
However, the proposed 2D-spectral/spatial DW-ZCC code is developed with a technique that yields limited weight and length in comparison with the existing counterpart. Consequently, the proposed code can offer a relatively high level of security by \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\text{E}_{g,h}\) & \(\text{R}^{(1)}\)(g,h) & \(\text{R}^{(2)}\)(g,h) & \(\text{R}^{(3)}\)(g,h) & \(\text{R}^{(4)}\)(g,h) \\ \hline \(g=1,h=1\) & \(w_{1}w_{2}\) & 0 & 0 & 0 \\ \hline \(g=1,h\
eq 1\) & 0 & \(w_{1}w_{2}\) & 0 & 0 \\ \hline \(g\
eq 1,h=1\) & 0 & 0 & \(w_{1}w_{2}\) & 0 \\ \hline \(g\
eq 1,h\
eq 1\) & 0 & 0 & 0 & \(w_{1}w_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation properties for 2D-EMD code with \(w_{1}=3\) and \(w_{2}=2\). employing adjacent code sequences and a multi-dimensional code structure with efficient performance parameters. The proposed approach employs FSO channels to transmit the signal between the sensors and the medical center. One of the problems associated with FSO is its short range and dependance on weather conditions. However, these can be overcome by the proposed unique code sequence with multiple dimensions, which can mitigate MAI between adjacent subscribers and support high-capacity communication over large distances. Nevertheless, it is pertinent to mention that the overall capacity and cardinality of the system is dependent on weather conditions, and although the proposed model is able to provide desirable performance in adverse environments, channel conditions might affect performance in certain conditions.
## 2 Materials and Methods
### Test Site  
The studied PF200 unit was commissioned on the premises of a full-scale water treatment facility, Cornell University Water Filtration Plant (CWFP). The plant is located in Ithaca (NY) and is responsible for providing water to Cornell University's campus and the local community. Owned and operated by Cornell University, CWFP uses Fall Creek as the primary water source and relies on a conventional coagulation/filtration/sedimentation process for water treatment. It operates in compliance with US EPA requirements, exceeding the water standards enforced by the National Primary Drinking Water Regulations [45]. During the period of the tests, CWFP was operating below its design capacity of 3.6 Mgal/day, treating water at an average flow rate of 1.7 Mgal/day. As a result, the facility was only running one or two shifts per day, with stopovers every night. The previously described PCH-180 sPACI coagulant that was used in the pilot plant is also used for the treatment process at CWFP and was sourced from the facility for the pilot tests.  
Besides the regular quality monitoring conducted by CWFP, Fall Creek water quality is also monitored by the Community Science Institute (CSI). At their website, CSI maintains a public archive that contains water quality data from Central New York stream and lake samples collected by local volunteer partners [46]. Table 1 provides a compilation of the most important water quality parameters sourced from the CSI database. The provided data were based on the measurements collected from 2010 to 2024 in the lower section of Fall Creek, downstream from the hamlet of McLean. Relevant information regarding the materials and methods used by CSI is provided on their website.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Unit** & **Mean** & **Median** & **P\({}_{\$}\)** & **P\({}_{\$}\)** & **Std Dev** \\ \hline pH & - & 7.93 & 7.75 & 7.58 & 8.70 & 0.49 \\ Turbidity & NTU & 21.26 & 6.20 & 2.00 & 68.00 & 64.12 \\ Total suspended solids & mg/L & 23.07 & 6.00 & 0.77 & 115.25 & 61.36 \\ Total dissolved solids & mg/L & 248 & 230 & 216 & 297 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Raw water quality parameters for Fall Creek, the raw water source for the PF200 drinking water system study; P\({}_{\$}\) and P\({}_{\$}\) indicate the 5 th and the 95 th percentile.  
Figure 2: Schematic drawing of the experimental setup. Key unit operations are labeled (_Raw water_ source, _Rapid mix_, _Flocculator_, _Clarifier_, and _Sand filter_), and auxiliary elements are denoted by _NTU_ (turbidimeters), _CV_ (control valves), \(q\) (flow gauges), and \(p\) (pressure gauge/level sensor). Solid lines indicate the hydraulic connections, and the dashed lines indicate signal loops.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.4 Floor Plan Vectorization  
Vectorization was a task performed only on 2D images of architectural floor plans. As the input data in such scenarios consists of a rasterized image, they are hardly scalable, with processing possibilities limited to computer vision techniques. Vectorization of such a plan changes its representation from a matrix of pixels into a structured file of vector descriptions. The processed image should preserve quality (even if scaled up) and simplify further data processing. A sample result is presented in Figure 8. Chronologically, the first article found was that of [PERSON] et al. [117]. The \"Raster-to-Vector\" approach they proposed grounded the processing in a convolutional neural network and integer programming techniques. The later articles were similar and differed mainly in the proposed algorithm and the learning method. [PERSON] et al. [120] used a combination of many different neural networks, such as Global Convolutional Network (GCN), Pyramid Scene Parsing Network (PSPNet), DarkNet53, and Deeplab v3. [PERSON] et al. [122] researched a processing method integrating UNet, Faster-RCNN, statistical component analysis, and the Ramer-Douglas-Peucker algorithm. The last article found, published by [PERSON] et al. [104], relied on a newly designed version of a Generative Adversarial Network (GAN) called EdgeGAN, which was found to process data significantly faster than other compared solutions.  
Figure 7: Visualization of the Description Generation task. An input floor plan (**left**) is analyzed and converted to a set of sentences describing its components and their location (**right**).