esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
## 2 The GAN and CGAN Model  
The goal of the GAN model is to estimate a generative model using an adversarial process [1]. This is achieved by simultaneously training two models. Firstly, a generative model \(G\), that, in the case of data forecasting, learns past patterns in the data and infers the predictive values. Secondly, a discriminative model \(D\), that determines how likely a sample was to originate from the 'true' training data, compared to being sampled from the generator. The generator is, hence, matched against an adversary, the discriminator (whose goal is to detect the difference between a true data sample and one created by the generator). Components of the model are then trained (via an optimization process) to maximize the probability of the discriminator being unable to distinguish true from generated data samples. Typically, including the approach we take here, the generator and the discriminator are both constructed as multi-layer perceptrons, with stochastic gradient methods being employed to obtain optimization.  
We start by formulating the definitions which we use in common across the GAN models we test. We consider a time series, \(y_{t}\). Our aim is to estimate the forecast of some \(y_{t^{\prime}>t^{\prime}}^{\prime}\) conditioned on a set of observations which we denote \(\mathbf{x}_{t}\). The inputs to the generator network are \(\mathbf{x}_{t}\) and \(\mathbf{z}_{g}\), where \(\mathbf{z}_{g}\) is a collection of samples from a normal distribution, \(p(z_{n})_{g}=\mathcal{N}(0,\mathrm{var}_{\mathrm{data}})\). During model training, the output from the generator, \(y_{t^{\prime}}^{\prime}\), as well as the true forecast sample \(y_{t^{\prime}}\), are fed to the discriminator, whose role is to discriminate between them, i.e., to identify \(y_{t^{\prime}}^{\prime}\) as the 'fake' sample.  
In an unconditioned GAN model, there is no control over the data that is generated. In the CGAN model, in contrast, by conditioning the model on additional information, it is possible to direct the data generation process [2]. Schematics of the GAN and CGAN models are depicted in Figure 1.
## 4 Results and Discussion
### Broadening Coefficient, O\({}_{2}\)  
The measuring process for NO-O\({}_{2}\) broadening was the same as for the other studies. Pure O2 was employed as the buffer gas here (purity 5.0, Linde). NO will react with O\({}_{2}\) to form NO\({}_{2}\) during the experiment as well as the gas mixture preparation process. NO concentration deduction was observed when filling O\({}_{2}\) into the gas cell. The maximum pressure used in NO-O\({}_{2}\) broadening measurement was less than 400 mbar. As an example, Figure 5a depicts an absorption spectrum of NO in O\({}_{2}\) measured at total pressures of 144.1 mbar and 295.9 mbar. The observed spectrum was averaged over 20 scans, and the Voigt profile was used to fit it. The obtained NO-O\({}_{2}\) broadening coefficient is \(\gamma_{\rm O2}\) = 57.68(66) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm and 50.88(53) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm for Peak 1 and Peak 2 according to the slopes of the linear regression of the Lorentzian widths versus pressures.  
Figure 3: (**a**) Measured NO absorption spectra in N2 at pressures of 896.1 mbar and 198.6 mbar (20-scan averaged). The residual between the measured and fitted data is depicted below, yielding an SNR of \(\sim\)300. (**b**) Experimentally obtained N\({}_{2}\)-induced pressure broadening of the NO absorption lines with a linear fitting model. The residual between fit and data is depicted in the graph below.  
Figure 4: (**a**) Measured NO absorption spectra (20-scan averaged) in Ar at pressures 938.3 mbar and 143.4 mbar. (**b**) Experimentally obtained Ar-induced pressure broadening of the NO absorption lines fitted with a linear model. The residual between the data and the fit is displayed below.  
Figure 5: (**a**) Measured NO absorption spectra (20-scan average) in O\({}_{2}\) at pressures 144.1 mbar and 295.9 mbar. Below is a display of the residual between the measured and fitted values. (**b**) Obtained NO P(5) (1\(\leftarrow\)0) pressure broadening absorption line caused by O\({}_{2}\) and fitted with a linear model. Below is a display of the fit-to-data residual.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
###### Abstract  
In upcoming smart urban environments, various things can be interconnected, and the Internet of Things (IoT) can be used to construct a safer and more convenient urban environment. Things in the IoT need an addressing system that can uniquely identify each one; internet protocol (IP) addresses can be used for this purpose. The IP address the two roles of an identifier and a locator. However, this binding has problems related to mobility and multihoming, and it is hard to deploy on a legacy IP system because of some limitations of sensor devices. To solve the problem, we propose a design for software-defined networking (SDN)-based identifier-locator separation architecture on IoT networks. In the proposed scheme, Internet Protocol version 6(IPv6)-based addresses are used for the identifiers and locators. The network is partitioned into a host identity domain for local routing and an IP domain for global routing. The host identity domain operates as an overlaid network over the IP domain, and it makes the unrouteable identifiers routable with a distributed hash table (DHT)-based routing strategy. For the evaluation of the proposed scheme, a packet forwarding cost and signaling cost model is calculated, and the results show that the proposed scheme is conjugable to an IoT network environment.  
Internet of Things; IoT network; id-locator separation; SDN; software-defined network +
Footnote †: journal: Applied  
0000-0001-6000-0000-00001-6000][PERSON]
# Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
## 2 Experiment
### Explosion Experiment on the Pressure Reduction Module Effect  
#### 2.1.1 Linear Tunnel with Expansion Chamber  
A linear tunnel 140 cm long with a square cross-section of 30 \(\times\) 30 cm was combined with a 60 \(\times\) 60 \(\times\) 60 cm expansion chamber for an explosion experiment. The cross-section dimension of the expansion chamber was four times the section of the linear tunnel. The pressure transducers were mounted on the specimen sidewall at 2, 30, 90, and 170 cm away from the tunnel portal. In order to investigate the pressure reduction effects under different quantities of explosives, we used five quantities of C-4 explosives (100, 150, 200, 250, and 350 g), and the C-4 explosive was hung at 30 cm aboveground and detonated at 60 cm away from the tunnel portal. The experimental configuration is shown in Figure 1. In order to know the blast attenuation characteristic of the expansion chamber, as designed by expanding the cross-section, we analyzed and discussed the transfer of the blast inside the tunnel and the pure linear tunnel explosion experiment.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.2.2 Scenario Two  
If the maximized profit of the project is required to be 1054.18 MB, the required house-type one must be 214 units, while house-type two must be 96 units and house-type three must be 117 units. Table 6 shows the sensitivity analysis results that are expected to be beneficial for the owner or decision maker in planning a construction project, as shown in this table, and it depends on the constraints of the owner with the question, what is the difference in the demand for planning the numbers of houses in each type and which direction will affect the maximum profit of the project?  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Project’s Data** & **Scenario 1** & **Scenario 2** \\ \hline House-type 1 (unit) & 211 & 214 \\ House-type 2 (unit) & 100 & 96 \\ House-type 3 (unit) & 116 & 117 \\ Maximized profit of the project (MB) & 1053.91 & 1054.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensitivity analysis results and scenarios’ applications of the model.  
Figure 7: Comparison of number house types between optimal solution with actual data.  
According to the final result of this research, we found the maximized profit of the housing development project where the model was clarified by using a case study located in Bangkok and the perimeter in Thailand, as mentioned, and we verified the model for gaining satisfied answers. Then, it is extremely important and beneficial to the housing development of both the project in Thailand and other projects in the world that the model can be used for large and densely populated cities to obtain the most suitable answer.  
Thus, it is useful for construction planning and is especially significant for business in the present and future research, because this model will help reduce errors in the construction management of each project, which will affect the profits.
# Demonstration of Wastewater Recycling in a Slaughterhouse
## 2 Materials and Methods
### Analytical Methods  
Various parameters were continuously monitored and analyzed to assess the efficiency of the SWW recycling process. Notably, pH values, electrical conductivity, chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), total solids (TS), total suspended solids (TSS), sludge volume (SV), and a selection of free cations and anions were monitored to assess the effectiveness of the treatment.  
The pH value was determined with a FiveEasyTM FE20 pH meter from Mettler-Toledo AG (Schwarzenbach, Switzerland). The mobile conductivity meter Cond 340i from the company WTW (Weilheim, Germany) was used for the conductivity measurements. The COD was measured by means of the QuickCODlab-03D0318 from the company LAR Process Analysers AG (Berlin, Germany) via a thermal disintegration process. The measurement of TOC and TN was performed with the Analytik Jena TOC analyzer multi N/C 3100 (Jena, Germany). The TS were determined in accordance with DIN 12880, by drying the sample for 6 h, and then, weighing it. TSS were measured in the same way according to DIN 38409-2 only by prior filtration. The ions were measured by means of ion chromatography from Metrohm (Herisau, Schweiz) using a Metrosep A Supp 17-150/4.0 column for anions and a Metrosep C 4-150/4.0 column for cations. The SV was determined using a conical 1 L measuring cylinder.  
Figure 6: Simplified process flow diagram of the reverse osmosis.
# Improving the Solving of Optimization Problems:
## 3 Quantum-Compliant Problem Formulations
### Hubo/Pubo  
The QUBO formulation can be extended to handle higher-degree polynomials while preserving its features, resulting in Higher-Order Unconstrained Binary Optimization (HUBO) or Polynomial Unconstrained Binary Optimization (PUBO). These models minimize polynomial pseudo-Boolean functions:  
\[\text{minimize},f(\mathbf{x})=\sum_{V\subseteq 1,2,\ldots,N}q_{V}\prod_{i \in V}x_{i}, \tag{6}\]  
where \(V\) represents subsets of indices from \(1,2,\ldots,N\). The Ising model's higher-order extension is known as the Higher-Order Ising model.  
Formulating higher-order polynomials in QUBO/Ising models requires polynomial reduction, introducing auxiliary variables. This expands the search space and reshapes the energy landscape, adding small barriers that make exploration more challenging [23].  
Therefore, higher-order models for problems that are naturally represented by polynomial cost functions scale more efficiently with the problem size and offer a smoother search space, simplifying exploration compared to reduced formulations.
## 3 Secure and Precise DM Scheme Based on A Single-Carrier Multiple Antenna Arrays Model
### The Architecture of BS Based on a Single-Carrier Multiple Antenna Arrays Model
Therefore, the transmit beamforming vector \(\mathbf{v}_{k}\) can be designed as  
\[\mathbf{v}_{k}=\mathbf{h}(\theta_{dk}). \tag{4}\]  
Obviously, the transmit beamforming vector \(\mathbf{v}_{k}\) has the normalization characteristic, i.e.,  
\[\mathbf{h}^{H}(\theta_{dk})\mathbf{v}_{k}=1. \tag{5}\]  
To prevent the inserted AN to interfere the confidential information \(s_{k}\), we project the AN onto the null space of the steering vector \(\mathbf{h}(\theta_{dk})\) for the desired direction. Therefore, the projection matrix \(\mathbf{T}_{k}\) can be devised as  
\[\mathbf{T}_{k} =\mathbf{I}_{N}-\mathbf{h}(\theta_{dk})\mathbf{h}^{H}(\theta_{dk}). \tag{6}\]  
Apparently, the projection matrix \(\mathbf{T}_{k}\) has the orthogonal property, i.e.,  
\[ \begin{split}\mathbf{h}^{H}(\theta_{dk})\mathbf{T}_{k}& =\mathbf{h}^{H}(\theta_{dk})\{\mathbf{I}_{N}-\mathbf{h}(\theta_{ dk})\mathbf{h}(\theta_{dk})\}\\ &=\mathbf{h}^{H}(\theta_{dk})-\mathbf{h}^{H}(\theta_{dk})\\ &=\mathbf{0}.\end{split}  \tag{7}\]  
After up-converting to RF using the same carrier \(f_{c}\) for all transmit antenna arrays, the radiating signal \(\mathbf{x}_{k}=[x_{1k},x_{2k},\cdots,x_{Nk}]^{T}\) for the \(k\)th \(N\)-element antenna array at time \(t\) can be given by  
\[\mathbf{x}_{k}(t)=\mathbf{u}_{k}(t)\cdot e^{\beta 2\pi f_{c}t}. \tag{8}\]  
Finally, all radiating signals for each symbol vector \(\mathbf{s}\) can compose a transmitted signal vector, i.e.,  
\[\mathbf{x}(t)=\sum_{k=1}^{K}\mathbf{x}_{k}(t)=e^{\beta 2\pi f_{c}t}\cdot\sum_{k=1}^{K} \Big{[}\beta_{1}\sqrt{P_{\mathbf{s}}}\mathbf{h}(\theta_{dk})s_{k}+\beta_{2} \sqrt{P_{\mathbf{s}}}\mathbf{T}_{k}\mathbf{z}_{k}\Big{]}. \tag{9}\]  
It is noted that the BS transmits every symbol of \(\mathbf{s}\) to the LU from different directions with the same carrier simultaneously. Evidently, only the LU can receive all the useful signals, while the Eve cannot receive any useful information at all.
## 2 Design of Ferrite-Loaded AMC  
### Concept of the AMC Design  
An AMC is a type of periodic array structure with properties resembling those of PMCs. It consists of a ground plane and periodic metal patches, usually referred to as FSSs. Each patch can be connected to the ground with either via pins, as shown in Figure 2a, or via-less. The operating frequency and reflection characteristics of an AMC can be obtained by controlling the geometrical parameters, such as the height and shape of the metal patches or the spacing between them, or the material properties. In general, an AMC can be characterized by a parallel inductor and capacitor (\(LC\)) resonator, as shown in Figure 2b. In the LC circuit, the gap between the metal patches can generate the capacitance (\(C_{P}\)), and the inductance (\(L_{p}\)) can be generated from the current flow to the ground through the via from the metal patch layer.  
The operating frequency (\(f_{c}\)) and surface impedance (\(Z_{S}\)) of the AMC are determined from Equations (1) and (2) [19].  
\[f_{c}=\frac{1}{2\pi\sqrt{L_{p}C_{P}}} \tag{1}\]  
\[Z_{S}=\frac{j\omega L_{p}}{1-\omega^{2}L_{p}C_{P}} \tag{2}\]  
From Equation (1), the resonant frequency can be reduced by increasing the \(L_{p}\) and \(C_{p}\). Equation (2) indicates that the surface impedance of the parallel resonant circuit is directly proportional to \(L_{p}\) and inversely proportional to \(C_{P}\). The surface impedance of the parallel resonant circuit decreases when the \(C_{P}\) increases. Thus, in general, the miniaturization of the AMC is based on the values of \(L_{p}\) and \(C_{P}\). The value of \(L_{p}\) is usually controlled by the dimension of the patch and height of the substrate, while the capacitance is controlled by the fringing field that occurs at the gap between the metal patches. In general, the additional lumped capacitors are connected between the patch elements as a means of shifting down the operating frequency. There is usually a trade-off between the choice of \(L_{p}\) and \(C_{P}\) in terms of bandwidth and unit cell size. However, if the parameters such as the substrate height, dielectric constant, and effective length of the patch are kept constant, only by increasing the capacitance will the frequency be lowered with the reduced impedance and bandwidth. Therefore, it would be challenging, or even impossible, to design a dielectric substrate-only-based AMC compact for the target applications of 6.78 MHz using lumped capacitance, despite its simpler configuration and lower manufacturing cost.
## 3 Comparative Analysis of Different Binarization Methods
### Texture-Sensitive Binarization Methods  
Binarization Method Based on Edge Detection Operator  
In addition to grayscale and background difference methods, edge detection operators are also widely used for binarization of X-ray images. Edge refers to the position where the gray value changes sharply, and during the image formation process, due to the difference in physical factors such as brightness, texture, color, shadow, etc., the gray value of the image is abruptly changed, and then the edge is formed. Edges are quantified by examining each pixel's neighborhood and quantifying its grayscale change, which is equivalent to the difference between the square derivative or discrete series of continuous functions in calculus.  
Edge detection is mostly based on the convolution method based on directional derivative mask [19] (gradient directional derivative), and the convolution operators to calculate the change in gray level are based on the convolution method based on the directional differential convolution kernel. When the edge detection operator consisting of two or more convolution kernels is used, it is assumed that there are n convolution kernels; there are usually multiple ways to measure the edge intensity of the final output. The sum of the absolute values of corresponding positions is selected as the edge intensity of the final output.  
In this paper, the Sobel operator [20; 21; 22; 23] is used, which is an improvement on the Prewitt operator [24; 25; 26; 27], and a weight of two is used on the central coefficient, as shown in Formulas (4)-(7):  
\[g_{x}\ =\ \frac{\partial f}{\partial x}\ =\ (z_{7}+2z_{8}+z_{9})-(z_{1}+2z_{2}+z_{3}), \tag{4}\]  
\[g_{y}\ =\ \frac{\partial f}{\partial y}\ =\ (z_{3}+2z_{6}+z_{9})-(z_{1}+2z_{4}+z_{7}), \tag{5}\]  
Figure 9: (**a**) RGB and binarized RGB difference image. (**b**) Open operation on the RGB difference image after binarization.  
\[{g_{x}}^{\prime}\ =\ \frac{\partial f}{\partial x}\ =\ (z_{2}+2z_{3}+z_{6})-(z_{4}+2z_{7}+z_{8}), \tag{6}\]  
\[{g_{y}}^{\prime}\ =\ \frac{\partial f}{\partial x}\ =\ (z_{6}+2z_{8}+z_{9})-(z_{1}+2z_{2}+z_{4}). \tag{7}\]  
The corresponding operator is  
\[{g_{x}}\ =\ \begin{bmatrix}-1&-2&-1\\ 0&0&0\\ 1&2&1\end{bmatrix},\,{g_{y}}\ =\ \begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix},\]  
\[{g_{x}}^{\prime}\ =\ \begin{bmatrix}0&1&2\\ -1&0&1\\ -2&-1&0\end{bmatrix},\,{g_{y}}^{\prime}\ =\ \begin{bmatrix}-2&-1&0\\ -1&0&1\\ 0&1&2\end{bmatrix}.\]  
Since the Sobel operator can carry more information about the edge direction and has a better suppression effect on noise, the Sobel operator in all directions is used to operate the gray image and sum the absolute value of the final result for binarization. The binarization image obtained in this way is more sensitive to gradient changes (i.e., texture features). The results of binarization based on the edge detection operator are shown in Figure 10. Compared with the above two methods, the image texture features extracted by the binarization method based on edge detection operators are more obvious.
## 6 Results and Discussion
### Analysis of Velocity Profiles  
Figure 6b shows the mean stream-wise velocity profiles for all ranges of DR at \(Re_{\tau_{0}}=395\). All of the profiles reduce to the linear distribution \(u^{+}=y^{+}\) in the viscous sub-layer. Further from the wall, the velocity profiles are well-captured for all ranges of DR.  
The model can also predict well a range of Reynolds numbers with varying rheological parameters as can be viewed in Figure 7a. This is extended in Figure 7b for high Reynolds numbers, where there is a significant improvement compared with the model of [PERSON] et al. [32]. This is a result of the new closure model for \(NLT_{xx}\) which scales well with Reynolds number and with reduced complexity.  
The advantage of the current model is the ability to capture all velocity profiles well within the model limits, with more simplicity with regards to model closures and without friction velocity dependence.  
Figure 5: Comparison of the rate of Newtonian dissipation of \(k\) between DNS data (\(\times\) crosses) and predictions with the new model (continuum lines), and \(v^{2}-f\) model of [PERSON] et al. [28] (dash lines). Each colour represents a different drag reduction regime: red (LDR case 16); orange (HDR case 22).  
Figure 6: Comparison of the (**a**) local eddy viscosity and (**b**) mean stream-wise velocity profile, between DNS data (\(\times\) crosses) and model predictions (continuum lines). Each colour represents a different drag reduction regime: red (LDR case 16); blue (IDR case 19); green (HDR case 20); orange (very HDR case 22).
## 2 Materials and Methods
### Transformer Module  
We know that the original model of the Transformer model includes both encoding and decoding parts. But due to the intrusion detection, traffic is characterized by the length of each datum which is of fixed length. Therefore, we have conducted it in our proposed model mainly based on the encoding part of Transformer model and modified some of his parameters [29]. This part includes a multihead attention mechanism and a feed-forward neural network. The attention mechanism in dot product attention contains three inputs which are query, key and value. The Transformer model computes the weight scores for each feature using query and key, and then computes the weighted sum of the weights and values for each feature to obtain the output. Using point active attention can be operated in parallel to reduce training time. Its calculation Formula (3) is as follows:  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{3}\]  
In Formula (3), \(Q\), \(K\) and \(V\) represent the three matrices of Query, Key and Value, respectively, and \(d_{k}\) is the dimension of Key. In this paper, in order to enrich the extracted features, a multihead attention structure is used. The calculation Formulas (4)-(6) of multihead attention are as follows:  
\[Q_{i}=QW_{i}^{Q},K_{i}=KW_{i}^{K},V_{i}=VW_{i}^{V},i=1,\ldots,n \tag{4}\]  
\[head_{i}=Attention(Q_{i},K_{i},V_{i}),i=1,\ldots,n \tag{5}\]  
\[MultiHead(Q,K,V)=Concat(head_{1},\ldots,head_{n})W^{Q} \tag{6}\]  
Since the _GELU_ activation function involves an error function (erf), and the error function is a nonlinear function, noise will be introduced. In some cases, introducing output randomness can be beneficial, as it can help the neural network explore more solutions and avoid overfitting. Therefore, using the Gaussian error linear unit activation function _GELU_ as the activation function increases randomness, as shown in Formula (7):  
\[GELU(x)=0.5x(1+\tanh(\sqrt{2/\Pi}(x+0.0444715x^{3}))) \tag{7}\]