esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Comparison of Pore Networks  
As shown in Table 4, a comparison of pore characteristics and physical characteristics was conducted between coal, shale, tight sandstone, and conventional sandstone, which illustrates the differences in lithology, material compositions, porosity, permeability, pore distribution, gas occurrence, and pore types. Shale and mudstone with exceptionally low permeability are the gas-bearing reservoirs as well as the cap rocks between other reservoirs in strata since coal and conventional sandstone have substantially better permeability, which is followed by tight sandstone reservoirs. In terms of pore networks, coal, shale, and tight sandstone reservoirs are significantly different in pore type, PSD, and connectivity at different scales. Therefore, a systematic classification and comparison of reservoir space are needed to better understand the similarities and differences between these unconventional reservoirs.  
The systematic classification of reservoir space from the nanoscale to fracture scale of high-rank coal, shale/ mudstone, and tight gas reservoirs is shown in Table 5. High-rank coal reservoirs are characterized by well-developed nanoscale OM pores (concentrated <100 nm) in coal matrix and well-connected fracture-clear networks because of the special mechanical properties of coal, but macropores and mesopores are underdeveloped with the more isolated spatial distribution. The major pore types in coal are molecular pores  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Intergranular pores, \\ dissolution pores, etc. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of pore characteristics of coal, shale, tight sandstone, and conventional sandstone gas reservoirs.  
of \(<\)10 nm, physical residual nanopores of 10 nm-100 nm, and fractures (including cleats) at the \(\mu\)m-mm scale, respectively. Shale has similar structural characteristics to coal, with well-developed nanoscale OM pores, but its microfractures and fractures are less developed compared to coal. This indicates a lower permeability and better sealing ability of shale reservoirs considering that the development of microfractures and fractures are the major influencing factors of nanoscale reservoirs. OM nanopores, clay mineral pores, and microfractures are the most important and developed pore types in shale. However, compared to coal and shale, tight sandstone reservoirs have higher porosity and permeability controlled by different pore types and PSD. Mineral-related pores, represented by intergranular pores, are the major pore types in tight sandstone, while intergranular pores develop better at the nanoscale and worse at the mesoscale. The connectivity between pores is mainly controlled by original fillings between particles and their changes during diagenesis.
## 1 Introduction
In addition, we propose our original Multi-Level Feature Fusion Module (MFFM) as an adaptive connection structure of feature fusion modules like MFFUs, which makes it possible for the model to fuse all the feature maps, from the lower-level feature maps to the higher-level feature maps, in adequate proportions. Finally, we propose the Adaptive Feature Fusion Detector (AFFDet) as the original adaptive feature fusion detector with strong multi-scale feature expression ability in which the MFFM and MFFU are embedded. The main contributions of this work include:  
* To solve problem 1, in our original MFFU, we adopt the same-scale feature maps connections with learnable weights horizontally, and different-scale feature maps connections with learnable weights vertically, and shortcut structures with learnable weights. As a result, our proposed MFFU can adaptively fuse multi-scale features;
* To solve problem 2, in our original MFFM, we adopt an innovative shortcut connection structure with learnable weights to connect multiple feature fusion modules such as MFFUs, at multiple levels, which is equivalent to adding a special attention mechanism. In brief, it can further adaptively fuse features that have been fused in feature fusion modules, such as MFFUs, at multiple levels;
* In order to solve the problem of inadequate utilization of features of other works, we propose a complete pedestrian detector based on our adaptive feature fusion idea. It can adaptively fuse multi-scale features at multiple levels, and has been proved to be a state-of-the-art one-stage pedestrian detector with competitive speed on the challenging Caltech benchmark.
## 2 Related Work  
### Ost-Hmds Capable of Occluding the Real World  
Conventional OST-HMDs such as HoloLens and Magic Leap One are incapable of attenuating the light arriving from the real environment. Therefore, these devices cannot accurately represent light and dark patterns created by shadow and caustics. Additionally, the virtual transparent objects produced by OST-HMDs are always brighter than the surroundings and are penetrated by the light rays present in the real environment.  
The only way to attenuate or shield OST-HMDs is to use spatial light modulators (SLMs), which are electrically programmable devices that can modulate light at the pixel level. These include liquid crystal displays (LCDs), digital micromirror devices (DMD), and liquid crystal on silicon (LCOS) displays. [PERSON] et al. [5; 6] developed occlusion-capable OST-HMDs using LCDs. In their approach, the real environment is occluded by the placement of LCDs on the display focal plane following the formation of the image from the real environment on the focal plane. However, this approach adds to the bulk and weight of the device because it increases the optical path length. Furthermore, the fact that the depth of the real environment is fixed at the display focal plane limits the advantage of the OST-HMD system. [PERSON] et al. [7] developed the thinnest occlusion capable OST-HMDs system to date, wherein instead of increasing the optical path length, their system achieves occlusion by compensating the blur of the occlusion regions, which is accomplished by overlaying the texture of captured real environment onto the display.  
Following a different approach, a system employing light fields has been proposed as a method for achieving occlusion [8; 9]. This system can shield the light produced by the real environment at the light ray level. However, this system cannot currently be adapted to the use of a clear mask for occlusion because the image elements lack the proper resolution.  
Although progress in the development of an occlusion-capable OST-HMD is certainly occurring, the use of SLMs can result in the loss of user vision in the event of a malfunction or failure. Accordingly, in this study, we attempted to develop a system for use with OST-HMDs without SLMs.
## 4 Proposal--DDQN with Sensor Fusion
### Proposed Fusion Methods
Given the nature of our input data, our custom CNN architecture was designed to meet these specific requirements. The architecture comprises three convolutional layers, strategically configured with varying filter sizes and pitches to efficiently process grayscale images with enhanced depth. The first layer, with its larger core (5 \(\times\) 5) and width of 5, serves to quickly reduce spatial dimensions while capturing the broader features and depth variations present in stacked images. Subsequent layers with smaller cores and advancements work to refine these features and enhance finer details. This design ensures that the network remains lightweight and computationally efficient, a critical consideration for deployment on the NVIDIA Jetson Nano, which, while powerful, has limited computational resources compared to larger GPU configurations. Reduced network complexity facilitates faster training and real-time inference, crucial for the timely and accurate decision making required in robotic navigation. By adapting our CNN to the specific characteristics of our depth-integrated grayscale images and the computational \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN Layer** & **Filters** & **Kernel** & **Stride** \\ \hline
1 st Layer & 16 & (5, 5) & 5 \\ \hline
2 nd Layer & 32 & (3, 3) & 2 \\ \hline
3 rd Layer & 32 & (2, 2) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CNN structure. constraints of our hardware, we achieve a balance between performance and efficiency, ensuring robust and responsive behavior in real-world robotic applications. The neural network proposed to learn the \(\bar{Q}\) function is composed of 2 Fully Connected layers (DDQN-FC) located after the sensor fusion network, as shown in Figure 2. The two layers have, respectively, 512 ReLU activation function neurons and 3 linear activation function neurons. The evaluation metric (loss function) is the Mean Square Error (MSE) between the calculated \(Q_{target}\) (see Equation (1)) and the one estimated by the neural network \(\bar{Q}\). Figure 3 presents the scheme of the Interactive Fusion method adapted to the navigation problem. It is worth noting that in Late Fusion, the characteristics obtained through the CNN are added (concatenated) directly with those obtained by the non-visual sensors. In the Interactive method, the main idea is to apply attention mechanisms to the image that guides the robot's pose, as well as the direction it should follow.
## 5 Matrix Converters Contribution towards the Innovation of Electrical Drives  
Traditionally, electric drives are composed by two distinct sub-assembles: the electric machine and its corresponding controlled power converter. Over the last decades there has been a shift from these physically separated systems to more compact, efficient and robust drives [92; 93]. The future trend is to achieve a fully integrated and smart machine that consists in a single plug and play drive.  
The integration of power electronic converters and controller modules inside electrical machines brings challenges due to size constrains and thermal management issues. Currently, typical power converters for electric drives require a relatively high volume of passive components such as inductors and capacitors. The challenges are more strict on the DC link capacitors, that in most cases are electrolytic capacitors, whose lifetime is substantially reduced under high temperature operation as usually found inside electrical machines, greatly compromising the drive lifespan [92].  
The search for drives with improved performance and fault tolerance contributed to the rise in popularity of multiphase machine drives (more than 3 phases) and open-end winding (OEW) configurations [94]. While all these configurations improve the drive fault tolerance, multiphase machines are normally characterized by allowing torque ripple reduction while achieving higher power density and efficiency than traditional drives [95; 96]. OEW configurations can improve input to output voltage ratio utilization increasing the drive speed range [97], while allowing multilevel operation and the ability to eliminate common mode voltage (CMV) that is pointed to as the major cause for premature fails of converter supplied electric machines [98].  
In this section, matrix converter topologies and modulation schemes for multiphase and OEW machine applications are presented.  
Figure 16: Matrix converters applications for offshore wind farms. (**a**) fully modular architecture based on M3C topology. (**b**) Redesigned offshore wind farm architecture featuring a medium frequency transformer.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 6 Experimental Results and Discussion  
### System Configuration and Implementation  
Three separate NSL KDD, UNSW NB 2015, CIC_IDS2017, and CICDDOS2019 datasets were utilized in implementing the reimplemented approach. The reimplemented system was tested using the remaining 30% of the datasets after 70% had been used for training. The reimplemented work was pushed through 3 sets of selected features--13, 30, 13, 35, and 13, 40, all employing CFS RF for the NSL KDD dataset, UNSW NB2015, CIC IDS2017, and 13, 30 for the NSL KDD dataset, respectively--in order to gauge how well it performed. When employing CFS and collecting these s, it carried out RF with penalizing characteristics for these s, randomly selecting 10 estimators (10 subsets), and an ensemble.  
Evaluation learning datasets compute correlations between features. It calculates Wi for each set by using the highest weight and disregarding inferior weights. In the end, only the most influential subset of features--those that impact the performance of intrusion detection--were chosen. The dataset's dimensionality was decreased, and unnecessary attributes were removed using the hybrid CFS-RF approach. To this end, for NSL_KDD, 35 features, UNSW_NB2015, 40 features, CIC_IDS2017, and 40 features, CICDDOS2019, were obtained through the analysis and dissemination of the datasets from the planned CFS-RF.  
After that, HABBAs and two other classification forms (binary and multi-class) of a confusion matrix were used to identify potential invasions. Precision, recall, DR, FNR, and FAR are distinct metrics for assessing system performance. They were implemented utilizing the following technical specifications for computer hardware and software: Skiearn Library with Python 3.8 and Colab, IntelCore i9-11900K Processor, 64 GB RAM, 16 GB (NVIDIA GeForce RTX 3090) CPU, 1 TB of fast SSD Storage, and Windows 11 64-bit OS.  
As we intend to examine the complexity time, we used Google Colab locally to have more control over our model environment, allowing us to estimate our performance parameters, so we preferred to work offline. The Google Colab has been installed locally by installing the colab package using the pip manager on top of the Jupyter Notebook.
Analysis of Three-Dimensional Circular Tracking Movements Based on Temporo-Spatial Parameters in Polar Coordinates  
[PERSON]  
1 Department of Information and Computer Engineering, National Institute of Technology, Gunma College, Maebashi 371-8530, Japan 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3  
the frontal and sagittal planes (relative to the subject). However, only the Cartesian coordinate spatial error was used to analyze the tracking movement accuracy in our previous study. The temporospatial relationship between the tracer and target should be investigated for a comprehensive study of the motor control characteristics of circular tracking movements. In this study, we redesigned the experiments to evaluate the impact of various target speeds.  
Three polar coordinate kinematic parameters (\(\Delta R\), \(\Delta\theta\), and \(\Delta\omega\)) are widely used in circular tracking movement analysis [13; 14]. Our previous study analyzed the motor control and impact of speed and visual target feedback in 2D tracking movements based on these parameters [14]. In other words, \(\Delta R\) allows us to examine spatial motor control characteristics in polar coordinates as a circular movement performance evaluation. However, \(\Delta\theta\) and \(\Delta\omega\) allow us to analyze motor control temporal characteristics in polar coordinates as a circular tracking movement evaluation of the positional and velocity-control precision. This inspired us to examine two planar circular tracking movements in 3D space from a motor control perspective based on temporospatial parameters.  
Therefore, in this study, we analyzed the motor control characteristics of circular tracking movements in 3D space using three kinematic parameters in polar coordinates: difference in fixed pole distance, \(\Delta R\); position angle difference, \(\Delta\theta\); and angular velocity difference, \(\Delta\omega\). We investigated the parameter differences between circular tracking movements relative to the subject on the frontal and sagittal planes. We also examined parameter-based changes in motor control for four different target speeds.
## 3 Materials and Methods
### Multimodal Systems and Fusion Strategies
```
Input:Train-FP,Train-FV, number of epochs \(E\), training hyperparameters Output: Trained hybrid model \(M_{2 LF}\)
1 Initialize CNN base model to produce both features and scores ;
2 Create models \(M_{FP}\) and \(M_{FV}\) ;
3 foreachepoch \(e\in\{1,\dots,E\}\)do
4 foreach batch \(B_{FP}\), \(B_{FV}\) from Train-FP and Train-FV do
5\(F_{FP}\gets M_{FP}^{features}(B_{FP})\) ;
6\(F_{FV}\gets M_{FV}^{features}(B_{FV})\) ;
7\(S_{FP}\gets M_{FP}(B_{FP})\) ;
8\(S_{FV}\gets M_{FV}(B_{FV})\) ;
9\(f_{fused}\leftarrow\mathtt{Concat}(F_{FP},F_{FV})\) ;
10\(S_{fused}\gets S_{FP}+S_{FV}\) ;
11\(\hat{g}\leftarrow\mathtt{JoinLayer}(F_{fused},S_{fused})\) ;
12 Compute loss \(L\) between \(\hat{g}\) and \(y_{8}\) ;
13 Update \(M_{FP}\) and \(M_{FV}\) via backpropagation ;
14
15 return\(M_{2 LF}\)
```  
**Algorithm 1**Hybrid Fusion Model Training  
Accuracy measures the proportion of correctly classified instances out of the total number of predictions. FER (false error rate) is defined as the proportion of authentication attempts that result in an incorrect decision (either false acceptance or false rejection):  
\[\text{FER}=\frac{\text{Number of incorrect decisions}}{\text{Total number of attempts}}\]  
FRR (false rejection rate) refers to the proportion of genuine authentication attempts that are incorrectly rejected:  
\[\text{FRR}=\frac{\text{Number of false rejections}}{\text{Total number of genuine attempts}}\]  
FAR (false acceptance rate) is the proportion of impostor attempts that are incorrectly accepted:  
Figure 6: Hybrid-level fusion model. \[\text{FAR}=\frac{\text{Number of false acceptances}}{\text{Total number of impostor attempts}}\]  
EER (equal error rate) is the point at which FAR and FRR are equal and is commonly used as a summary indicator of system performance.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 5 Simulations
### Parameter Estimation  
With the K-means clustering, we can obtain the ultimate estimations of Hough parameters for the EHT. Figure 8 shows the estimated parameters of the EHT with different secondary thresholds \(\eta^{\text{Hough}}=10\) and \(\eta^{\text{Hough}}=3\), wherein the symbol \"\(\bigstar\)\" denotes the clustering center and \"\(\cdot\)\" denotes the secondary threshold crossings. It can be seen that, when the Hough threshold \(\eta^{\text{Hough}}\) is relatively high, only a few points can pass the threshold, and the estimation accuracy of parameters will be satisfactory. However, this is not the case for a lower threshold. In Figure 8b, the secondary threshold is set to \(\eta^{\text{Hough}}=3\), and we can observe that the clustering center deviates vastly from the true value.  
Figure 6: Accumulation effect of EHT (Scheme I, binary integration, [150, 250] s). (**a**) \(a-b\) space. (**b**) \(a\) dimension. (**c**) \(b\) dimension.  
Figure 7: Accumulation effect of EHT (Scheme II, Plane-constrained integration, with \(q=0.001\), [150, 250] s). (**a**) \(a-b\) space. (**b**) \(a\) dimension. (**c**) \(b\) dimension.  
Once the parameters in the Hough parameter space are obtained, they can be mapped back into the measurement space \(R-A-E-t\) or any other CSs, to show the time history and current position of the target. Figure 9 shows the corresponding detected curve, true trajectory, and raw measurements in a single plot, with Figure 9a the detection threshold \(\eta^{\rm Hough}=10\) and Figure 9b \(\eta^{\rm Hough}=3\). It can be seen from Figure 9a that, the presented EHT works well as the detection result approximates highly to the nominal true trajectory. In contrast to Figure 9a, Figure 9b shows that it has worse detection performance. Because its secondary threshold is relatively low, multiple crossings will be recorded and this will reduce the clustering performance. However, in reality, the secondary threshold \(\eta^{\rm Hough}\) is still a key design parameter and needs to be determined by experience or massive Monte Carlo simulations.
# AESOP: Adjustable Exhaustive Search for One-Pixel Attacks in Deep Neural Networks
## 4 Exhaustive Search for One-Pixel Attacks
### Exhaustive Search Algorithm  
A simple method for one-pixel attacks is to generate an input image different by a pixel for each pixel index and each pixel value, and to examine whether a given neural network classifies the one-pixel different input correctly. In the case of MNIST data, an exhaustive search explores all the search spaces (i.e., \(28\times 28\times 256\)) to determine whether a one-pixel attack exists for a given input image. Since an exhaustive search inspects all the possibilities of one-pixel attacks with respect to the pixel locations and values, we can capture all the characteristics of one-pixel attacks. Although this method should attempt a number of one-pixel different images, we can utilize the parallelism of GPUs. Algorithm 1 presents our exhaustive search algorithm for the one-pixel attack problem described in  
Figure 1: Example of a one-pixel attack.  
Section 3. First, it identifies the class \(t_{\mathbf{x}}\) of a given original input image \(\mathbf{x}\) (line 2). For each pixel \((i,j)\) and each possible integer value \(val\) of the pixel, our algorithm generates an input \(\mathbf{x}^{\prime}\) that is different only at the pixel \((i,j)\) with a value of \(val\) (lines 6-7). It then examines whether the output class \(t_{\mathbf{x}^{\prime}}\) of \(\mathbf{x}^{\prime}\) differ from the output class \(t_{\mathbf{x}}\) of the original input \(\mathbf{x}\) (line 9). The loops are executed in parallel with GPUs. After the loop terminates, it returns a set \(E\) of perturbations for a one-pixel attack (line 13).  
```
Input : a neural network \(f\) and an input image \(\mathbf{x}\) Output : a set \(E\) of perturbations
1\(E=\emptyset\);
2\(t_{\mathbf{x}}=f(\mathbf{x})\);
3 for\(i=0\) to 27 do in parallel
4 for\(j=0\) to 27 do in parallel
5 for\(val=0\) to 255 do in parallel
6\(\mathbf{x}^{\prime}=\mathbf{x}\);
7\(\mathbf{x}^{\prime}[i,j]=val\);
8\(t_{\mathbf{x}^{\prime}}=f(\mathbf{x}^{\prime})\);
9 if\(t_{\mathbf{x}^{\prime}}\
eq t_{\mathbf{x}}\)then\(E=E\cup\{(i,j,val)\}\);
10 endfor
11 endfor
12 endfor
13 return\(E\);
```  
**Algorithm 1**Ehaustive search for one-pixel attacks for MNIST data set