esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The goal is to finally provide a set of features that may be used to induct a target decision tree, the backbone of a fast classifier able to work in real-time in intrusion detection systems. As the target machine learning model, decision tree classifiers are chosen. They are constructed using previously selected features. They are characterized by several properties that make them an optimal classifier for IDSs [7; 8; 9]. First, compared with other classification models, they are computationally less intensive, allowing for faster network traffic classification. It is essential in real-time applications where decisions must follow the data flow. They are also easy to interpret [10], which is crucial in network traffic analysis, where understanding the reasoning behind a classification is as important as the classification itself. In addition, they are robust to noise in the data, can handle non-linear relationships, and are easy to update as new data are collected. Because the machine learning model requires the data to perform training efficiently [11], datasets were carefully chosen to be used in these experiments. The proper choice of data is crucial to obtain a useful classification model in all machine learning application fields [12; 13; 14], including cybersecurity. As the network traffic data vary and might differ in various types of networks, there are datasets--UNSW-NB15, BoT-IoT, ToN-IoT, and CIC-CSE-IDS2018--that collect various types of internet traffic, which have been combined into a single NF-UQ-NIDS-v2 dataset [15]. The latter dataset comprises almost 76 million records and 43 features, making it a good choice for training machine learning-based intrusion detection models. The key contributions of this study are the following:  
* The novel scheme of traffic features ordering, consisting of the selection of classifiers, their hyperparameter optimization, individual feature rankings of the best variant of classifiers, and finally, weighted feature ranking, results in an ultimate ordered set of features. * The analysis of the NF-UQ-NIDS-v2 datasets' features was sorted using the proposed scheme in view of their influence on the decision-tree-based classification, considering both accuracy and computational efficiency. * The final decision-tree-based classifier with a mechanism allowing for optimizing the speed-to-accuracy rate. By selecting various thresholds on the ordered feature list, one may increase the accuracy, lowering the inference speed and reversely. The rest of the paper is organized as follows. Section 2 is the literature survey. Section 3 introduces the dataset used with their proper features. In Section 4, there is a description of the proposed feature selection approach. Section 5 presents and discusses the obtained results, whereas Section 6 concludes the paper.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 4 Our Proposed Method  
### Motivation  
For domain adaptation, methods based on feature representation learning can be applied to a broader set of scenarios because of the loose restrictions on the source data. Furthermore, among representation-learning-based domain adaptation methods, some typical supervised and unsupervised deep learning models, such as convolutional neural networks and the autoencoder, have achieved fairly good performance. However, there are two main problems that have prevented the further development of these methods. The first is the training problem associated with deep neural networks. Some efficient models, such as graph regularization and sparse constraint, cannot be applied directly in supervised convolutional networks. In addition, although some optimization methods have been proposed [46; 47; 48], they have not been demonstrated to be effective in unsupervised deep networks for domain adaptation tasks. The second problem is data redundancy of image data. As the adjacent pixels of an image inside a local area are highly correlated, high-dimensional features of image data are inevitably affected by performance degradation in representation learning. For example, in a local receptive field neural network, the local relationship of replication features leads to a non-uniform distribution of edge detectors. To address these two problems, we propose a stacked convolutional sparse autoencoder method for unsupervised domain adaptation. In contrast to previous autoencoder-based methods that rely on the classical structure or the integration of regularization terms into the objective function, path-wise training is used to optimize the model of the sparse autoencoder and then the convolutional kernels are used to reserve the local relevance for learning abstract representations. Furthermore, the reconstruction independent component analysis (RICA) algorithm with whitening is introduced to pre-process the original datain both the source and target domains to remove correlations inside the local area for representation learning.  
In Figure 1, a stacked convolutional sparse autoencoder is illustrated as the proposed unsupervised domain adaptation method. The SCSA consists of several levels: for example, there are two layers in Figure 1; layer 2 is a repeat of layer 1 for more abstract feature learning; each layer is composed of two components. Firstly, the input data information is sphered according to the RICA with whitening. The overall goal of this part is to perform a separate part analysis of the imported data. Then, the transferable features are learned by training on patches with a sparse autoencoder. Secondly, the CNN feature maps are generated with the help of practical convolution operations and pooling in different domains. According to the projection layer, a classifier is built from the final features by transforming and reshaping them in the overall target domain.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 4 High-Quality DSM with Stereo Pairs Searching
### Searching for Optimized Stereo Pairs
After some iterations, the algorithm gradually converges to the optimal stereo imaging planning scheme, which realizes the optimization of the DSM accuracy by searching for high-quality stereo pairs.
## 4 Hyperplane Estimation  
Parameter estimation is the task of finding a suitable fit of data in a given search space to a model. A simple parameter estimation task is that of line fitting: given a set of points on a two-dimensional plane, can a set of points that form a line be estimated?  
Figure 1: SDS cluster stabilisation. (_a_) The threshold level; (_b_) the amount of tolerance; the x-axis describes the number of iterations, \(n\), while the y-axis describes the percentage of active agents.  
Figure 2: Simple line fitting by parameter estimation.  
Robust parameter estimation is an extension of parameter estimation that is able to fit a set of parameters in the presence of outliers; points that are not on the line, perhaps due to noisy observational data or the presence of multiple line candidates [18].  
A traditional technique for estimating these types of parameters is the Hough transformation [21].  
Latterly, the most popular robust estimators are those based on the stochastic principles introduced by the RANSAC algorithm [22].  
The main task that this research is concerned with is that of hyperplane estimation in multiple dimensions in the presence of increasing noise.  
A line can be seen as a hyperplane in two-dimensional space. Given a set of data points in two dimensions, the inliers are seen as those points that lie on the line, while the outliers are seen as those scattered points that do not fit the model. Generally, it is assumed that inliers are perturbed orthogonally from the hyperplane by a zero mean Gaussian with probability distribution:  
\[P_{i}(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{x^{2}}{2\sigma^{2}}} \tag{1}\]  
\(\sigma\) is the standard deviation and \(x\) the Euclidean distance from the hyperplane. The inliers are scattered around a true hyperplane, with the described amount of noise.  
A hyperplane in three dimensions can be seen as a sheet through three-dimensional space, analogous to a line through two-dimensional space. Hyperplanes in higher dimensions are difficult to visualise.  
Hyperplanes (and other manifolds) can be described using a minimal set of data. A hyperplane in two-dimensional space is able to be described using two points: termed the minimum set. A hyperplane in three-dimensional space can be described with three points. This relationship holds as the dimensions increase.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Infrastructure for Quantum Money Transfers  
Implementing a quantum teleportation-based system for money transfers requires a robust quantum infrastructure, utilizing both satellite-based and fiber-optic networks. This infrastructure would distribute entangled pairs to users over distances as needed for transactions. This approach aligns with the concept of satellite-based Quantum Information Networks (QINs), which connect quantum devices over long distances, significantly enhancing their inherent capabilities in computing, sensing, and security [33].
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 6 Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Image Pairs** & **Clients** & **Our Algorithm** & **RANSAC** & **MSAAC** & **LO-RANSAC** & **Co-RANSAC** & **Co-RANSAC** & **Co-RANSAC** \\ \hline \multirow{6}{*}{**Image Pairs**} & \(\mathcal{D}\) & 123 & **100** & **100** & 100 & 502 & 6000 & 301 \\  & \(\mathcal{P}\) & 147 & 84.7 & 89.1 & 97.4 & 37.7 & 97.7 & 94.9 \\  & \(\mathcal{P}\) & 0.071 & 0.002 & 0.077 & 0.744 & **0.011** & 0.403 & 0.117 \\  & \(\mathcal{D}_{L}\) & **2.3** & 204.9 & 371.3 & 76.3 & 723.4 & 93.0 & 94.9 \\  & \(\mathcal{P}\) & **0.077** & 333.884 & 300.300 & 223.133 & 373.228 & 294.800 & 274.439 \\ \hline \multirow{6}{*}{**Image Pairs**} & \(\mathcal{D}\) & **99** & 100 & 100 & 100 & 502 & 27.926 & 301 \\  & \(\mathcal{P}\) & 407 & 88.4 & 73.3 & 89.9 & 3.8 & 92.0 & 83.5 \\  & \(\mathcal{P}\) & **0.000** & 3.40 & 1.986 & 2.681 & 0.051 & 7.403 & 2.863 \\  & \(\mathcal{D}_{L}\) & **13.37** & 80.6 & 74.2 & 70.1 & 806.2 & 96.1 & 94.6 \\  & \(\mathcal{P}_{L}\) & **1.388** & 2.20 & 2.774 & 1.288 & 469.102 & 4.675 & 4.134 \\ \hline \multirow{6}{*}{**Image Pairs**} & \(\mathcal{P}\) & **74** & 100 & 100 & 100 & 502 & 2340 & 301 \\  & \(\mathcal{P}\) & 373 & 44.2 & 45.5 & 90.3 & 21.9 & 960 & 77.6 \\  & \(\mathcal{P}\) & 0.258 & 0.25 & 0.252 & 93.7 & **0.033** & 4.25 & 3.008 \\  & \(\mathcal{D}_{L}\) & **10.05** & 38.2 & 91.5 & 119.6 & 120.5 & 20.0 & 131.4 \\ \hline \multirow{6}{*}{**Image Pairs**} & \(\mathcal{P}\) & **327** & 50.000 & 5015 & 8005 & 507 & 303.000 & \\  & \(\mathcal{P}\) & 24.9 & 5.5 & 2.7 & 36.8 & 8.4 & 38.0 & \\ \cline{1-1}  & \(\mathcal{P}\) & 4.001 & 4577.040 & 4.632 & 5.953 & **0.306** & 9.67 & \\ \cline{1-1}  & \(\mathcal{D}_{L}\) & **139.3** & 1278.5 & 367.6 & 500.0 & 173.3 & 554.0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
**Author Contributions:** [PERSON] performed the theoretical developments. Unless otherwise mentioned in the paper, she programmed the software packages for performing the experiments and analysing the results. Furthermore, she prepared the first draft of this manuscript. The manuscript was revised and modified by the other authors. All of the authors assisted in planning the experiments and analysing the results. [PERSON] and [PERSON] provided the direction and supervision of this study. They provided their constructive comments and consults to perform this study. **Conflicts of Interest:** The authors declare no conflict of interest.
# The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
## 1 Introduction
Existing research mainly focuses on performance analysis under ideal reflection conditions, usually assuming that RIS hardware can achieve precise phase control while ignoring the phase error problem caused by hardware accuracy limitations, which may have a significant impact on system performance in practical applications. In view of these deficiencies, this paper uses the DDPG algorithm to conduct an in-depth study on the performance of RIS-assisted coal mine tunnel signal transmission. The DRL algorithm is applied to the coal mine RIS communication system for optimization design, and the DDPG algorithm is used to jointly optimize the base station beamforming matrix and the RIS phase shift matrix to maximize the system link rate. This article also compares the performance of DDPG with other DRL algorithms. Under the same conditions, the DDPG algorithm is superior to A3C and DQN in terms of convergence speed and stability. In addition, the article explores the impact of the system link rate under the two modes of RIS, ideal reflection and non-ideal reflection, as well as the impact of neural network parameter settings on algorithm performance.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 5 Requirements for SFS Characterization
### Expansion Potential
* _ASTM D4792 \"Standard Test Method for Potential Expansion of Aggregates from Hydration Reactions.\"_ In this test method, unbound aggregates are compacted in a cylindrical mold and then submerged in 70 \({}^{\circ}\)C (158 \({}^{\circ}\)F) water, and the expansion is measured over time. This test has been shown to be effective as a comparative or an index test to assess relative expansion of SFS [305, 306, 307, 308]. For use as a graded material in pavement bases, ASTM D2940 recommends that the expansion of SFS aggregates should be \(\leq\)0.50% by ASTM D4792. Because of the potential for significant expansion, some researchers have proposed pretreating the SFS prior to its use as an aggregate in bound applications, such as by outdoor weathering [56, 79, 99, 309, 310, 311], carbonation [114, 121, 124, 152], steam aging [79, 129, 311, 312], and so forth. The expansion tests detailed above are suitable candidates to confirm the degree of reaction or expansion before using the SFS.
# A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks
## 2 Related Works
It adopts an iterated local search approach in deriving the hop sequence of multiple traveling MAs over the deployed source nodes. In recent years, the use of a mobile Sink which moves on a pre-specified trajectory has been extensively investigated by several researchers [13; 14; 29]. In the prospective of the mobility of mobile Sink, the works are divided into three categories [30; 31]: Random Mobility; the movement of mobile Sink is on a random trajectory; Fixed Mobility, where the Sink moves on a fixed pre-specified path; Controlled Mobility, where the trajectory of the mobile Sink is controlled, depending on its position and density of data in its vicinity.
# Sub-Diffraction Focusing Using Metamaterial-Based Terahertz Super-Oscillatory Lens
## 4 Conclusions  
We designed and successfully fabricated a metamaterial-based 0.1 THz SOL that generates hot spots repeatedly in the direction perpendicular to the lens and experimentally demonstrated its performance. The fabricated lens, with a 101.6 mm diameter and 7.5 mm focal length, overcomes the 2.46\(\lambda\) diffraction limit of conventional lenses by focusing THz radiation onto a small spot of 0.67\(\lambda\) diameter. Moreover, hot spots are formed by each 3875 \(\times\) 3875 \(\upmu\)m\({}^{2}\) unit pattern composed of five 127.5 \(\upmu\)m diameter circular slits. The SOL does not only provide significantly higher resolution than conventional lenses and a working distance that is more than 2\(\lambda\), it also allows the means to simultaneously focus  
Figure 9: Frequency dependence of the lens: (**a,b**) calculation results using (**a**) 0.097 THz and (**b**) 0.103 THz incident beams; (**c,d**) experimental results using (**c**) 0.097 THz and (**d**) 0.103 THz; and (**e**) frequency dependence of the interval between focal points: black line represents the calculated results using the Talbot principle while the red squares show the calculated results by the finite element method. The green circle shows the experimental results.  
a THz wave on multiple spots. Furthermore, the SOL can focus incident THz beams with different frequencies and thereby exhibit variable focal lengths, allowing convenient adjustments to the working distance without requiring any lens design changes. Therefore, this lens design is ideal for implementing THz-based industrial inspection applications due to its demonstrated sub-diffraction limit focal spot size, long and adjustable working distance, long depth of field, and multiple hot spots.  
Conceptualization, A.I.; methodology, A.I.; software, A.I.; formal analysis, A.I.; investigation, A.I.; writing--original draft, A.I.; writing--review & editing, M.I., V.K.M.-u., V.C.A. and M.N.; visualization, A.I.; supervision, M.I. and M.N. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by JSPS KAKENHI Grant Number JP20H02206.  
Not applicable.  
[PERSON] is grateful to [PERSON], [PERSON], [PERSON], and other members of the Davis Millimeter Wave Research Center (DMRC) of the University of California, Davis for their hospitality during his visit when the main results of this paper were obtained.  
The authors declare no conflict of interest.
## 4 Results
### Groundwater Resource Evaluation  
This section completes the third goal of this paper, i.e., the groundwater resource evaluation in the NSUS. Excluding formations behaving as aquicludes (bedrock) and aquitards (g1, g2, and g3), the average PGS in Adra town is around 1.77 Mm\({}^{3}\). This figure varies in the 0.12-5.49 Mm\({}^{3}\) range when the minimum and maximum thickness and effective porosity values are used (Table 2). In the NSUS, g1 and g2 confine the aquifer formed by WB, t1, and t2. Excluding bedrock, g1, and g2, average PGS is around 0.58 Mm\({}^{3}\) and its range is 0.05-1.71 Mm\({}^{3}\) (Table 2). The actual groundwater contribution to the SFUS must be less than average PGS, which is the theoretical maximum value. Since groundwater exploitation is virtually null, the NSUS groundwater discharge can be considered a reliable proxy of the groundwater contributed from the NSUS to the SFUS. For groundwater discharge evaluation through the Darcy's Law formulation, the hydrogeological formations in the NSUS were grouped into three aquifers (1) that formed by WB, t1, and t2 (called NSUS aquifer); (2) the northern sector of the colluvial formation; and (3) the anthropogenic filling patches. In each aquifer, input data were saturated thickness, discharge section, hydraulic gradient, and permeability (Table 3). Average saturated thickness is total thickness minus the difference in topography and piezometry deduced from open wells and springs. Average saturated thickness of the NSUS aquifer is around 0.85-fold the average total thickness described in Table 2. This figure results from weighting full-saturated aquifer sectors such as those shown in the hydrogeological cross-sections I-I' to III-III' (Figure 7) and others fully desaturated. Average saturated thickness of colluvial and anthropogenic filling formations are 0.7- and 0.5-fold the average total thickness, respectively. The aquifer discharge sections were deduced from the hydrogeological map (Figure 6). The desaturated aquifer sectors were excluded from this calculation. After the two flash campaigns carried out in September 2014 and June 2015, piezometry in open wells W1 to W6 (Figure 2) and groundwater discharge elevation in springs S1 to S5 (Figure 2) were used to delineate the piezometric level and define the hydraulic gradient. Average hydraulic gradients varied in the 0.017-0.044 range and coarsely followed the topographic gradient as 0.035 in the NSUS aquifer, 0.017 in the colluvial formation, and 0.004 in the anthropogenic filling formation (Table 3). Average permeability for these formations is in Table 2.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 4 ACA Integrated with the APF Method
### Improvement of Pheromone Volatilization Factor \(\rho\)  
The pheromone evaporation coefficient measures how much pheromone evaporates from the pathway taken. A numerical change will change how much the level of pheromone is on that path, which greatly affects ants' decision-making behavior when selecting paths and the performance trajectory of the whole ant colony. In normal ACAs, it is kept at a constant value. If it is too large, the rate at which pheromone evaporates will become too fast and at risk of becoming stuck in a local optimum. If it is too small, then variations in concentrations of pheromone along paths will be less, resulting in slow convergence. We adjusted the regulation of pheromone by adopting a dynamic volatilization strategy--the dissipation rate of pheromone is automatically adjusted according to the search process. This adaptive mechanism not only effectively prevents the search process from stagnating in the suboptimal solution region but also significantly improves the exploration efficiency of the system in the solution space. The specific implementation mechanism is as follows:  
\[\lambda=1-e^{-\frac{N_{c}}{N_{max}}} \tag{16}\]  
\[\rho^{\prime}=\rho\times\lambda \tag{17}\]  
In Equations (16) and (17), \(\lambda\) is the evaporation factor adjustment coefficient, and \(\lambda\in(0,1)\); \(N_{c}\) is the current iteration number of the ant colony; and \(N_{max}\) is the maximum iteration number of the ant colony.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 5 Discussion  
Due to the importance of accuracy in the field of scene classification, this paper only introduced the influence of each parameter on accuracy in this section. Learning rate, Dropout rate, the feature length of the fully connected layer and the training iterations are all important parameters of CNN and play an important role in classification accuracy. In Section 5.1, the influence of four different learning rates on classification results is discussed. In Section 5.2, experiments were conducted for seven different Dropout rates to analyze the best accuracy. In Section 5.3, the feature length of the fully connected layer is evaluated. In Section 5.4, the test results corresponding to different iteration steps in the training process are analyzed.
## 2 Measurement Environments and Methods
### Frequency Domain Measurement Method  
The spectrum analyzer used was the N9010B spectrum analyzer manufactured by Keysight Company, and its measurement range was 10 Hz-44 GHz. In measurement, the parameter setting of the spectrometer has a great influence on the accuracy of test results, so reasonable parameter setting is very important. The occupied BW (bandwidth) mode of the spectrum analyzer was used in the frequency domain measurement method. We measured the frequency occupation from 300 MHz to 3 GHz at these three positions. The horizontal polarization and vertical polarization of the antenna were measured, respectively. The preamp was set to the open state. The parameters of frequency domain measurements are listed in Table 1.