esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Generative Adversarial Networks (GAN)  
Generative Adversarial Networks, commonly abbreviated as GAN, is a machine learning framework in which there are two working neural networks, namely generators and discriminators [12; 13]. These two components are composed of several layers, one of which is CNN as a layer for image segmentation. However, the function of these two neural networks is different. The generator will create a fake sample, and the discriminator will evaluate whether the sample is genuine or fake in the form of a loss value. Both models will be trained until they reach stability [13].  
The stability between the discriminator and generator may occur because the loss value generated by the discriminator in each epoch changes the weights in both models. In each epoch, the generator will create a sample of the input, which will be the input along with the ground truth of the generator input. The discriminator will assess the results generated by the generator and classify whether the sample is genuine or fake. This process will be repeated until the generator and discriminator obtain the most optimal weight. After the discriminator cannot distinguish between fake and real samples, it can be said that the GAN has reached a stable point or Nash equilibrium where the generator and discriminator have been optimized.  
In recent years, GAN has become the best architecture for synthesizing a natural image based on the dataset used to train the GAN model. Some of the applications of GAN include image imputation, where the GAN model will try to increase the resolution of the image or complete the missing parts of an image, and image-to-image translation, where the GAN will perform an image translation, such as converting a sketch into an image. Image-to-image translation aims to obtain a mapping relationship between the input image and the output image through a neural network model and generate a new image associated with that relationship. Since this study aims to generate realistic photo images of Indonesian faces from sketches, this research is included in the category of image-to-image translation, or generative model.
## 2 Materials and Methods
### Virtual Inertia Optimization Allocation Model
#### 2.2.4 Optimization Process  
After obtaining the dynamic optimization model of the system, the next step is to solve the optimization model. Based on the content described in Sections 2.2.1-2.2.3, the overall optimization process is shown in Figure 3. To solve this optimization problem, an optimization strategy based on the genetic algorithm (GA) is adopted and implemented through MATLAB R2021b coding, ultimately outputting the virtual inertia time constant that meets the constraint conditions. The initial population is generated by setting the feasible range of virtual inertia. Under typical disturbance scenarios, the frequency stability indicators of each scheme are obtained. Based on the preset constraint conditions, the individuals in the population are screened: individuals that meet the constraints directly enter the fitness evaluation and population update stage, while those that do not meet the standards undergo genetic operations such as chromosome crossover, gene recombination, and mutation. During the iterative process, the quality of the population is continuously optimized through the real-time verification of stability constraints until convergence. Finally, the virtual inertia allocation scheme that meets the dynamic stability requirements and has the best fitness is output. This process, through the collaborative mechanism of constraint verification and genetic search, takes into account both the optimization efficiency and feasibility of the solution.  
Figure 3: Optimized process flow.
# FPE-Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection
###### Abstract  
The increase in cybersecurity threats has made attack detection systems critically important. Traditional deep learning methods often require large amounts of data and struggle to understand relationships between features effectively. With their self-attention mechanism, Transformers excel in modeling complex relationships and long-term dependencies. They are also adaptable to various data types and sources, making them advantageous in large-scale attack detection scenarios. This paper introduces the FPE-Transformer framework, leveraging the strengths of the Transformer architecture. FPE-Transformer incorporates an innovative feature positional encoding mechanism that encodes the positional information of each feature separately, enabling a deeper understanding of feature relationships and more precise attack detection. Additionally, the model includes a ClassificationHead for enhanced accuracy and complex pattern recognition. The framework's performance was validated using the NSL-KDD and CIC-IDS2017 datasets, demonstrating its superiority over traditional methods in detecting diverse attack types and improving overall performance. This study highlights FPE-Transformer's innovative approach and ability to address key limitations of traditional deep learning methods, establishing it as a robust solution for modern attack detection challenges.  
Keywords:Transformer models; attack detection; cybersecurity; deep learning; FPE-Transformer
# Dependence of Dispersion on Metamaterial Structural Parameters and Dispersion Management
###### Abstract  
This paper compares the dispersion in metamaterials (MMs) and some Thorlabs' conventional glass, and finds that MMs may exhibit much more substantial dispersion (e.g., three orders of magnitude larger dispersion). With such large dispersion, a transmission more than 22 km is impossible because of pulse splitting resulting from the third-order dispersion. However, MMs are artificial materials with their electric and magnetic plasma frequencies tunable depending upon their structures. We take advantage of such tunability to tailor the dispersive response of MMs and investigate the dependence of dispersion on the MM structural parameters. We make dispersion management by (1) searching for the existence of some 'good' dispersion points and numerically demonstrating 90 km long transmission with almost no pulse width expansion and any impact from a higher order dispersion in the MM we designed; and (2) searching for the possibility for group-velocity dispersion (GVD) compensation and demonstrating 120 km transmission by configuring the dispersion-engineered MM.  
metamaterial; dispersion; nonlinear Schrodinger equation; Gaussian pulse; dispersion compensation +
Footnote †: journal: Applied Physics Letters  
_Applied_  
_Applied_  
+++ ==WARNING: Truncated because of repetitions==  
++  
Recent advances in nanofabrication and breakthroughs in the field of MMs [6; 7; 8; 9; 10; 11; 12] have opened up a new range of possibilities for obtaining optical properties on demand in MMs [13]. There is a research group now demonstrating that the group-velocity dispersion can be compensated by designing the phase-engineered MM, thus developing a new dispersion compensation technology [14]. The dispersive properties of most MMs are determined by the electric and magnetic plasma frequencies through the Drude model [15; 16; 17]. MMs are artificial materials with their electric and magnetic plasma frequencies tunable, depending upon their constituents and structures. Reconfigurable MMs provide flexibility for tuning them and result in tailored dispersive properties for dispersion management purposes. This paper studies the dependence of dispersion on the electric and magnetic plasma frequencies, and searches for the possibilities of dispersion management. Two approaches for the dispersion management are proposed. Demonstrations of a 90 km long transmission, with almost no pulse width expansion and any impact from a higher order dispersion in the MM we designed, and a 120 km transmission, by configuring the dispersion-engineered MMs, are numerically realized.
## Appendix D Strain Tensor and Coordinate System  
In Figure 14\(xy\)-axes are principal axes. Tensile force acts on the block along the \(x\)-axis. Consequently, [PERSON]'s law stretches the block along the \(x\) axis in proportion to the applied force and compresses along the \(y\)-axis according to [PERSON]'s effect. Hence, \(u\) and \(v\) represent the \(x\) and \(y\) components of displacement vectors (\(\mathbf{u}\) and \(\mathbf{v}\)) resulting from this tensile load, and in this case \(v\) is negative. Since \(xy\) is the principal axis coordinate system, the \(y\) component of \(\mathbf{u}\) and the \(x\) componentof \(\mathbf{v}\) are null. Coordinate system \(x^{\prime}y^{\prime}\) is rotated from \(xy\) coordinate system by angle \(\theta\). It is clear that the same physical vectors \(\mathbf{u}\) and \(\mathbf{v}\) have both \(x^{\prime}\) and \(y^{\prime}\) components when expressed by the non-principal axis coordinate system. The same argument holds for the stress vector. Formulaically, the appearance of shear components in \(x^{\prime}y^{\prime}\) coordinate system can be expressed as follows.  
\[\epsilon^{\prime}_{xy} = \epsilon_{xy}\left(\cos^{2}\theta-\sin^{2}\theta\right)+\left( \epsilon_{yy}-\epsilon_{xx}\right)\cos\theta\sin\theta \tag{11}\] \[\sigma^{\prime}_{xy} = \sigma_{xy}\left(\cos^{2}\theta-\sin^{2}\theta\right)+\left( \sigma_{yy}-\sigma_{xx}\right)\cos\theta\sin\theta \tag{12}\]  
In Equations (11) and (12), \(\epsilon_{ij}\) and \(\sigma_{ij}\) represent the strain and stress tensor components.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 2 Overview of Agriculture 4.0
### Advantages of Agriculture 4.0  
The effects of smart agriculture are covered in the following sections.  
Production volume: The use of smart technology in agriculture can significantly boost the number of products produced on the farm. This will contribute to feeding a growing population.  
Production quality: The wellness and nutrition of people from all socioeconomic strata in the nation can be significantly impacted by the quality of food produced. A country's population will live longer and in better health with better food, which will improve its citizens' economic contribution.  
Effectiveness of farming practices and resource use: The application of smart technologies to conventional agricultural operations can increase their effectiveness. In turn, this encourages more efficient use of agricultural resources.  
Ideal cost of cultivation: It is achieved when there is a balance between quantity, quality, and efficiency in the procedures used. This results in a higher price for the agricultural output that is produced.  
Reducing wastage: The farming industry, one of the main economic sectors, is in great part to blame for the massive quantities of food and other secondary resources that are wasted. Smart technology could be employed to track and cut down on this waste.  
Ecologically sustainable: The reduced agricultural waste and improved agricultural process efficiency directly reduce the environmental and ecological footprint.  
Effective use of Time: Timely delivery of the necessary insecticides, fertilizers, and other chemicals can provide timely and high-quality agricultural output with fewer losses thanks to smart agriculture.  
The farming industry may be at threat by the following while deploying agricultural 4.0 technology:  
* Theft of business and customer data.
* Taking resources under the control of sensors and gadgets.
* Destroying the objects that devices control.
* Damage to reputation if a data breach is disclosed.  
Agriculture 4.0 may be at risk from infrastructure damage, sensor failures may affect poultry and cattle breeding, and control system hacks may affect greenhouse farming. Allof these could cause problems or malfunction in agricultural operations by harming the IoT architecture's hardware and software. In addition, data-collecting technologies face challenges from malicious assaults, unauthorized access, privacy breaches, and other issues.  
The landscape has shifted, with AI and ML research concentrating on agro-based contexts, water management, livestock, and farmlands. Monitoring, control, and decision-making alternatives in the irrigation field repeatedly tried to save water and enhance productivity [8; 9; 10; 11; 12; 13]. Some research concentrated on plant leaf disease [14], horticulture [15], vineyards [16], hydroponic [17], alert facilities [18], integration of IoT technological innovations [19], tracking resources [20], and cloud control [21].
## 3 Performance Evaluation
### The Hoek-Brown Criterion  
Similar to the Mohr-Coulomb case, the evaluation of the performance of ANNs was carried out on the 15% of the whole data series. By investigating different one- and two-layered neural ANNs, the 15-5-15-1 and 17-3-12-1 architectures were found to be the most efficient ANNs in the prediction of the \(P_{i}\) of tunnels in the Hoek-Brown case. As indicated in Table 4, the 17-5-15-1 MLP-ANN had a coefficient of determination of 99.91%, an \(RMSE\) of 0.179285 MPa, and a \(MAE\) of 0.12516 MPa. On the other hand, the most accurate RBF-ANN had an \(R^{2}=93.18\%\), an \(RMSE=1.558064\) MPa, and a \(MAE=1.078099\) MPa. Although the developed ANNs for the Mohr-Coulomb case are more accurate compared to the Hoek-Brown case in terms of occurred errors, the ANNs suggested for the Hoek-Brown modeling were still useful and efficient. Also, it was shown that that MLP-ANN are more rigorous in the prediction of the \(P_{i}\) of circular tunnels excavated in the elasto-plastic, strain-softening, Hoek-Brown rock mass.  
Figure 4: (**a**) ANN-based predicted \(P_{i}\) versus calculated \(P_{i}\); (**b**) differences between ANN-based predicted \(P_{i}\) and calculated \(P_{i}\) for the best RBF-ANN (15-15-1) for the Mohr-Coulomb criterion.  
The architecture and the topology of the most efficient MLP-ANN proposed for the modeling of the \(P_{i}\) in the Hoek-Brown case 17-5-15-1 is shown in Figure 5.  
For the best constructed MLP-ANN, the predicted and calculated \(P_{i}\), along with the relative differences of predictions and calculations for each independent exemplars are, respectively, shown in Figure 6. As clearly illustrated, the predictions are similar to the exact values of the calculations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{6}{c}{**ANN Type**} \\ \cline{2-7}  & \multicolumn{2}{c}{**MLP-ANN**} & \multicolumn{2}{c}{**RBF-ANN**} \\ \cline{2-7}  & **R2 (\%)** & **RMSE (MPa)** & **MAE (MPa)** & **R2 (\%)** & **RMSE (MPa)** & **MAE (MPa)** \\ \hline
17-3-12-1 & 99.72 & 0.268343 & 0.204655 & 93.18 & 1.558064 & 1.078099 \\
17-3-15-1 & 93.37 & 1.36379 & 0.965679 & 84.51 & 2.5
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 3 Experiments  
### Experiment Environments  
This study evaluated the performance of the Rmap in two environments: The simulation environment and the real environment. The memory efficiency of the Rmap was evaluated in a simulation environment using a Player/Stage simulator. The Rmap algorithm was implemented into the Player/Stage. Moreover, the grid map generated by DP-SLAM was used for comparison, as shown in Figure 8b.  
After the simulation, the coverage performance of the Rmap in real environments was evaluated. Figure 8c is the actual indoor layout of the house for the experiments. For the practical experiments, the authors built a robot with the S3C6410 board, URG-04 LX-UG01 laser sensor, and two DC motors, as shown in Figure 8d.  
Figure 8: The experiment environments. (**a**) The simulated environment using player/stage; (**b**) The grid map produced by DP-SLAM for (**a**) environment; (**c**) The real environment; (**d**) The robot the authors built.
# Factorial Numbers and Their Practical Applications
## Application of Factorial Numbers in Permutation Problems  
### Converting Factorial Numbers to Permutations  
A permutation is a sequence of different elements (numbers, letters) of finite length \(n\). The number of permutations of length \(n\) is equal to the factorial \(n!\). Permutations play an important role in mathematics--for example, there is abstract algebra based on permutations--and in numerous practical applications [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; 30]. The most complex unsolved combinatorial optimization problem to date, known as the traveling salesman problem, is the problem of finding a minimum on a set of permutations [30].  
The ability to obtain permutations based on factorial numbers is an important application, although not the only one. In [1; 2; 3; 4; 5; 22], an algorithm for obtaining permutations based on factorial numbers is described. It follows from the property of permutations, according to which all their elements are different, that accordingly, each subsequent element of the permutation must be different from all previous ones. The algorithm proposed below, which is characterized by greater simplicity and speed, takes into account the property of permutations, according to which the sum of the digits in the permutation is constant and equal to \((n\times(n-1))/\)when obtaining the last element of permutation [6; 7; 8].  
The algorithm contains the following steps:  
1. The value of the highest \(n-1\) digit of the factorial number is the first element of the permutation.
2. The value of the \(i\)-th digit of the factorial number is compared with all the values of the preceding digits of the permutation, and for each new comparison result in which it is equal to or greater than the value with which it is being compared, it is increased by one. The resulting sum of ones forms the \(i\)-th element of the permutation.
3. The last number of the permutation in the zero digit is found in the form of the difference between the previously known constant sum of \(n\) elements of the permutation and the sum of the \(n-1\) elements of the permutation already received.  
For example, if it is necessary to convert the factorial number 3 4 0 0 1 0 into a permutation, then in accordance with the given conversion algorithm, we obtain the permutation 3 5 0 1 4 2. There are no repeating digits in it. The sum of the digits is \((6\times 5)/2=15\). Therefore, the last digit is found as a result of subtracting \(15-13=2\).  
Table 3 for \(n=4\) presents all factorial numbers and the permutations obtained from them.
## 2 Materials and Methods
### Research Methods
#### 2.2.3 Walnut Growth Information Extraction  
After completing the classification of ground objects and the extraction of the planting area of walnuts in the research area, a mask of the extracted walnut planting area would be generated, and the mask area was calculated based on the MODIS image, to monitor the growth of walnuts in the research area annually from 2017 to 2021. In order to ensure data integrity and reduce the impact of clouds, in this study, Sentinel-2 and MODIS images were selected for combination. Firstly, images of Ganquan Town from April to mid-September were obtained by the projection method of Sentinel-2 images from 2017 to 2021; secondly, the maximum value of NDVI was synthesized with image data from April to mid-September every year; finally, walnut growth from April to mid-September in 2021 was compared to the NDVI value within the same period in 2020 using the NDVI difference model [29]. The NDVI difference model is shown in Formula (8):  
\[NDVI_{n}=NDVI_{N}-NDVI_{N-1} \tag{8}\]  
where \(\mathrm{NDVI_{N}}\) and \(\mathrm{NDVI_{N-1}}\) are the maximum NDVI values of the corresponding year; N and N \(-\) 1 represent the current year and the previous year, respectively.  
According to the calculation results of \(\mathrm{NDVI_{n}}\), walnut growth in Ganquan Town is classified into three categories based on normal statistical rules: when f(\(\mathrm{NDVI_{n}}\)) = \(-\)1, it indicates that growth is worse than in previous years; when f(\(\mathrm{NDVI_{n}}\)) = 0, it indicates that growth is the same as that of previous years; when f(\(\mathrm{NDVI_{n}}\)) = 1, it indicates that growth is better than in previous years.
## 2 Computation Model and Methodology
### Thermodynamic Closure and Tabulation Details  
The closure of the above mentioned flow system can be achieved by a real fluid EoS, such as Peng-Robinson (PR) [34], Soave-Redlich-Kwong (SRK) [35; 36; 37] (they belong to the category of classical cubic EoS [38]) or Cubic-Plus-Association (CPA). Based on the understanding gained from the literature [27; 39; 40], the present work used CPA as the EoS for the closure. Using this CPA EoS, a uniform look-up table is generated over the entire range of the case's estimated thermodynamic states. Three inputs to the table are temperature (T), pressure (P) and species mass fraction (\(Y_{m},m=1,N_{s}-1\)), where \(N_{s}\) is the number of species in the mixture. The IFPEN-Carnot thermodynamic library, based on a robust isothermal-isobaric (TPn)-flash algorithm performs calculations for the vapor-liquid equilibrium (VLE) for each grid point of the look-up table in order to compute the thermodynamic and transport properties in each phase. Then, mixture rules are used to compute these properties for the all specified mixtures in the table. For the input (T,P,Y) parameters, there is a range of maximum values and the number of points to cover this range of input parameters. Thus, the table has a three-dimensional cubic array of values with P, T and \(Y_{m}\) as its axis. Each cell of this array stores more than ten calculated thermodynamic and transport property values. During the run time, for the values that the solver is looking for, a lookup method is performed with a linear interpolation based on the inverse distance weighting method. More details on this can be found in the works of [PERSON] et al. [41] and [PERSON] et al. [26].
# A Deep Reinforcement Learning Approach for Active SLAM
## Appendix A Proof of the Monotonicity Property of the Robot's Pose Uncertainty
### Monotonicity of Utility Functions  
In the literature different utility functions to quantify the uncertainty of the robot's pose have been proposed both from the perspective of the TOED and IT: (i) _T-opt_ (trace of the covariance matrix, or sum of its eigenvalues) [11, 48, 49, 50]; (ii) _D-opt_ (determinant of the covariance matrix, or product of its eigenvalues) [51; 52; 53]; (iii) _E-opt_ (maximum or minimum eigenvalue, depending on the notation) [54] criteria, and (iv) [PERSON]'s entropy [55; 56].  
As reported in [9; 57; 58], monotonicity (the property that states that uncertainty increases as the robot moves during exploratory trajectories) is essential for adequate decision making in the context of active SLAM. Moreover, [PERSON] et al. [9] demonstrated that monotonicity holds for any utility function when uncertainty is represented differentially, regardless of the chosen representation for the robot's orientation. Also, [PERSON] and [PERSON] [58] demonstrated that using a Lie-group based representation monotonicity also holds.  
Notably, from Equation (A14),  
\[\Sigma_{A}^{\prime}-\Sigma_{A}\simeq Ad_{\Upsilon_{AB}}\Sigma_{B}Ad_{\Upsilon_ {AB}}^{T}\] (A17)  
it holds that, because \(\Sigma_{B}\) is a positive semi-definite matrix, \(Ad_{\Upsilon_{AB}}\) is a non-singular matrix (with \(\det(Ad_{\Upsilon_{AB}})=1\)) and \(Ad_{\Upsilon_{AB}}\Sigma_{B}Ad_{\Upsilon_{AB}}^{T}\) is a positive semi-definite matrix. Also, from [59], if \(A\) and \(B\) are symmetric matrices contained in \(\mathbb{R}^{n\times n}\) it can be stated that,  
* \(A\geq B\Rightarrow\text{tr}(A)\geq\text{tr}(B)\).
* \(A\geq B\geq 0\Rightarrow\det(A)\geq\det(B)\).
* \(A\geq B\Rightarrow\lambda_{i}(A)\geq\lambda_{i}(B)\;\forall i=(1,...,n)\) with \(\lambda_{i}\) the \(i\)-th largest eigenvalue of \(A\) and \(B\), respectively.  
Therefore, because \(\Sigma_{A}^{\prime}\geq\Sigma_{A}\geq 0\), the four utility functions would satisfy monotonicity and, therefore, would result in a reliable decision making.