esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Why GAN Cryptography?
### GAN Cryptography  
Compprising two distinct neural network components, the generator and the discriminator, GANs have demonstrated unprecedented abilities in generating data that are almost indistinguishable from real data.  
The generator is responsible for creating synthetic data, learning to mimic the true data distribution. It often includes multiple layers of neural networks, utilizing activation functions, batch normalization, and other advanced techniques to generate intricate data patterns [15].  
The discriminator, on the other hand, functions as a critic, discerning between real and generated data. It is often structured as a deep neural network that incrementally learns the features and characteristics that define authentic data [16].  
The synergy between these two neural network components leads to a continuous arms race where both entities evolve, giving rise to complex models that can recreate reality with astounding fidelity.  
In the realm of cryptography, GANs have found an innovative and transformative application. [PERSON] and [PERSON] in 2016 were among the first to explore neural cryptography, using adversarial training to allow neural networks to devise encryption protocols. This pioneering work opened the door to the amalgamation of deep learning with traditional cryptographic objectives.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 2 Surrogate Model of the Electrical Performance and Hybrid Geometric Errors  
The analysis of the whole array antenna is exactly the same with the subarrays, and each subarray has the same parameters, such as the flatness, the position tolerance and the amplitudes of the elements. Therefore, the following analysis is achieved on the \(8\times 8\) subarray.  
First the hybrid errors of the array antenna were analyzed, then the second order Taylor expansion was derived based on the performance's importance.
## 3 Dataset and Methodology
### Comparison of DInSAR and Optical Based Inventories  
The obtained inventory was compared to the available Argentinian National Glacier Inventory (NGI, www.glaciaresargentinos.gob.ar). The NGI was created by the Argentine Institute of Nivology, Glaciology and environmental sciences (IANIGLA-CCT, Mendoza) using optical images. The number of inventoried landforms and the degree of activity attributed to them were compared. The NGI includes the debris-covered glacier-rock glacier category as a landform type. The main reason for this is that sometimes it is difficult to separate both landforms using only the visual analysis of satellite images. Therefore, to perform the inventory comparison, both the active rock glacier category and the debris-covered glacier-rock glacier category have been considered. The same comparison was not possible for the Chilean side because the available inventories do not include the degree of activity in their classification (General Water Directorate, [[http://www.geoportal.cl](http://www.geoportal.cl)]([http://www.geoportal.cl](http://www.geoportal.cl))) or do not contain information about the landform area [1].
## 2 Problem Description
### Modelling
#### 2.4.1 Emergency Facility Location Model  
The k-means clustering machine learning algorithm is utilized as the basis for determining the location of distribution centers and grouping demand points. The algorithm divides a set of N objects into K clusters based on the similarity between members within each cluster and dissimilarity with members in other clusters [38]. The similarity to the cluster is measured by the proximity of each object to the cluster's mean value, known as the cluster centroid.  
The core concept of the K-means algorithm involves iteratively searching for cluster centers. The cluster center is determined based on the distance between each data point and the cluster center. The clustering process begins by identifying the data to be clustered, denoted as \(x_{ij}\) (\(i=1,\ldots,n;j=1,\ldots,m\)), where \(n\) represents the total number of data points to be clustered and \(m\) represents the number of variables. At the start of the iteration, each cluster's center, \(c_{ij}\) (\(k=1,\ldots,K;j=1,\ldots,m\)), is assigned independently and arbitrarily.  
Next, the distance between each data point and each cluster center is computed. The Euclidean formula is commonly used to calculate the distance \(d_{ik}\) between the \(i^{\text{th}}\) data point \((X_{i})\) and the \(k^{\text{th}}\) cluster center \((C_{k})\). If the distance between a data point and the center of the \(J\)-cluster is smaller than the distance to the centers of other clusters, the data point is assigned as a member of the \(J\)-cluster.  
After assigning data points to their respective clusters, the next step is to group the data that belong to each cluster. The new cluster center value can be calculated by determining the average value of the data points that are members of the cluster.  
Notation:  
\(m\): number of distribution centers (DC) with \(i=1,\ldots,n\)  
\(n\): total of demand points with \(j=1,\ldots,n\)  
\(c\): latitude and longitude coordinates  
\(K_{jk}\): \(c\) is the coordinates (lat, long) of the demand point \(j\)  
Variables:  
\(f_{ic}\): coordinates \(k\) of DC \(i\)  
\(D_{ij}\): distance between DC \(i\) and demand point \(j\)  
\(X_{ij}\): 1 if demand point \(j\) is assigned to DC cluster \(i\), and 0 otherwise  
Formulation  
When the location of the demand point \((n)\) is known, the determination of the distribution center's location \((m)\) is formulated with a single objective.  
\[\text{min}{\sum_{ij}D_{ij}y_{ij}} \tag{1}\]  
Subject to:  
\[\sum_{j}y_{ij}=1,\forall_{i} \tag{2}\]  
\[D_{ij}=\text{euclidean distance}\big{(}f_{i},d_{j}\big{)},\forall_{i,j} \tag{3}\]  
\[y_{ij}\in(0,1) \tag{4}\]Equation (1) minimizes the distance between distribution center \(i\) and the collection of demand points \(j\), while Constraint (2) ensures that all demand points \(j\) are in the same cluster as distribution center \(i\). Constraint (3) defines the distances between distribution center \(i\) and demand point \(j\), while Constraint (4) represents binary decision variables.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 2 Channel Model
### Composite Irradiance Model
Figures 3 and 4 illustrate the ABER versus electrical SNR for the same approximations, using OOK, and 4-PPM schemes respectively, with either wavelength, spatial, or time diversity. A significant decrease, in OP and ABER, for both modulation schemes is evident as the normalized outage threshold or the average electrical SNR increase, respectively, despite the stronger PE impact for the GG distribution, compared to the NE approximation. This is well argued, since the NE distribution describes saturated conditions of turbulence, while the GG one refers to weaker turbulence conditions, due to the selected values of \(a\) and \(\beta\). In the scenario where the MG approximates the Malaga distribution, we consider even a stronger PE impact. Although the Malaga describes weaker turbulence conditions compared with the NE case, a decreased performance in both terms OP and ABER is observed since the strong PE effect dominates over turbulence. Moreover, the 4-PPM scheme is more efficient than OOK in all cases examined. Furthermore, in all scenarios it is clear that the SIMO system outperforms the system with only one transmission. Figure 4: ABER for 4-PPM scheme versus the expected SNR. Figure 3: ABER for OOK scheme versus the expected SNR. Figure 2: OP performance versus the normalized outage threshold for NE case.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
Cellular automata represent a distinctive computational paradigm that provides a facile, extensible, and efficient framework for simulating extensive systems and conducting intricate computations grounded in environmental evidence. Cellular automata have been applied in various fields for modeling and computation. Cryptography involves secure communication through the use of codes and ciphers. While cellular automata are not a standard tool in traditional cryptography, they have been explored for certain cryptographic applications. For instance, cellular automata may be used in the design of stream ciphers or in generating pseudorandom sequences, contributing to cryptographic protocols. The objective of this paper is to develop an innovative encryption system utilizing finite-state machines and their invertible properties.
# ARMONIA: A Unified Access and Metro Network Architecture
## 4 Simulation Results
### Traffic Prediction
The static provisioning scenario, though not presented in Figure 9, results up to 20% more utilization of the processing resources both in the layer 1 and 2, while for the highest load it would require the full utilization of the resources. Lower, on average, nodes' processing utilization results in the use of fewer processing nodes, reducing the respective capital cost and improving the overall energy savings. It is clear that the use of machine learning algorithms has several advantages. ARMONIA resource allocation algorithms leverage the QoT estimator that provides highly accurate QoT metrics, and the traffic predictor that provides a reliable forecast for the expected future traffic loads, allocating just enough network resources. Consequently, the joint operation and the dynamic reconfiguration of the different network parts results in significant savings in network resource utilization. These savings are  
Figure 8: (**a**) The number of utilized transponders and (**b**) the bandwidth utilization in the actual, machine learning assisted and static provisioning scenarios for various traffic loads. Figure 9: Average utilization of the processing edge and core nodes in the actual and ML assisted provisioning scenarios for different traffic loads. translated to CAPEX savings when network dimensioning decisions need to be taken. OPEX are also reduced by using smaller overprovisioning margins during the resource allocation and adaptively changing them based on the forecasted traffic.
# Study on Solar Radiation and the Extreme Thermal Effect on Concrete Box Girder Bridges
## 2 Calculation Method of Solar Radiation and Field Temperature Observing
### The Variation and Impact of Solar Radiation  
#### The Solar Radiation Variation  
A clear day in summer (16 July) was selected as the research example, as the solar effect is obvious. On that day, the sunrise and sunset time are 06:07 and 19:44, respectively. Therefore, the sunshine period from 7:00 to 19:00 was chosen for the solar radiation variation study. Solar radiation changes with time, the total radiation is calculated as Formula (5), and it is also related to structural azimuth angle, solar altitude, and solar azimuth. According to the actual situation, the solar radiation boundary condition of the box girder surfaces can be considered as five types, shown in Table 1 and Figure 5.  
The vertical solar radiation condition only exists at the sunny side of the web, and the area is changed with time as the wing plate shelters. The shadow area length at different moments is calculated as  
\[l_{s}=\frac{l_{w}\cdot tan\ h}{cos(\psi-\psi_{a})} \tag{17}\]  
where \(h\) is the solar altitude, \(\psi\) is the solar azimuth, \(\psi_{a}\) is the azimuth angle of the outer surface normal direction (from the south direction to the east is positive and to the west is negative), and \(l_{w}\) is the length of the flange. The solar angles diagram is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solar Radiation Type** & **Number of Surfaces** \\ \hline Horizontal solar radiation & 7 \\ Vertical solar radiation & 2,6 \\ Only horizontal scattering and reflection & 1,4,5 \\ Only vertical scattering and reflection & 3 \\ No solar radiation & 8–11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solar radiation types of different surfaces.  
Figure 4: The field observation station.  
According to Formula (5), in addition to the solar radiation intensity, the solar radiation on the surface is also related to the solar incidence angle \(\varphi\) (the direction is the same as \(\psi\) and \(\psi_{a}\)). Based on the geometric theory, the solar incidence angles of the surfaces with different orientations are calculated as  
\[cos\ \varphi=sin\ hsin\ h_{a}+cos(\psi_{a}-\psi)cos\ hcos\ h_{a} \tag{18}\]  
where \(h_{a}\) is the angle between the normal direction of the sunshine surface and the horizontal surface.  
Figure 7 illustrates the variation of the measured air temperature and the calculated solar radiation intensity in a day.  
Figure 5: Heat transfer conditions and the web shadow area.  
Figure 6: Diagram of solar angles.  
Figure 7 also shows that the solar radiation changes obviously with time and surface orientations. Solar radiation arrives at the maximum value and keeps stable from 10:00 to 16:00 on the top slab. On the sunlight side of the web, it reaches the maximum at 9:00 and begins to descend rapidly then. For the surfaces without direct sunlight, it changes slowly due to scattering radiation and reflection.
## 5 Simulation Results  
In this section, we provide simulation results to validate the performance superiority of the spectrum-saving transmission method. The system is assumed to be working in Ku band with \(B=1\) Hz where \(\lambda=c/f=3\times 10^{8}/(14\times 10^{9})\) m. In this paper, we consider a geostationary Earth orbit satellite where the orbit height is set to \(d=36\),000 km. Moreover, the normal-direction gains of transmit and receive beams of the satellite are set to \(E_{t}=120\) dB and \(E_{r}=50\) dB, respectively. Considering the strong capability of the HS, we set \(G_{t,0}=82\) dB and \(G_{r,0}=61\) dB, while the normal-direction gains of UT UPAs are set to \(G_{t}=45\) dB and \(G_{r}=37\) dB. In addition, \(\eta_{i}\) and \(\kappa_{i}\) are randomly generated in \([0.5,1]\).
## 4 Evaluation
### Experimental Results
DUA may select an inefficient routing path, due to its nature, to upgrade virtual distance too high as described in section 2 (for details refer [20]). So, as shown in Figure 20, in the case of closed void, DUA follows a longer route than VAA. VAA travels the best route in either case. Figure 17: Length of routing path when there is one open void. Figure 18: Closed void. VAA exchanges neighbor information via the Hello message of the neighbor discovery protocol including distance update information. Thus, there is no additional control overhead. Hello messages broadcast once in every five seconds. However, before sending the actual data packet, all nodes assure their next hop node to BS. For this, VAA takes less time than DUA. DUA takes significantly more time when there is more than one void. DUA also takes more time, because it upgrades virtual distance up to the source from the void node. Figure 21 shows control packet overhead before sending the actual data packets. The graph shows VAA has 40% less overhead than DUA where a void exists. There is no prior setup in GPSR so it does not have control packet overhead, before sending actual data packet, except for the neighbor discovery protocol. GPSR's energy expenditure ratio is higher, because it follows a longer routing path than VAA. DUA follows a similar path, but in some cases of the closed void, it follows a longer path. DUA has a more control overhead (generated by routing) and in case of the topology changes, all nodes up to the destination have to change the virtual distance. Thus, more energy is consumed in DUA than VAA. Figure 22 shows the energy expenditure in different cases. Where voids exist, energy consumption is 28% - 39% more in GPSR than VAA. However, DUA consumes 8% more energy where there are three voids. The ratio increases as the number of voids increases. Energy efficiency of VAA is due to its low control overhead and low path length, as shown previously. Figure 21: Control overhead (generated by routing) before sending data packets. Figure 20: Average length of routing path in different types of void. Figure 23 shows the simulation in random scenarios for the average number of distance upgrade required to circumvent the voids. These outcomes are the average of 50 simulation results of the network consists of 350 nodes to 700 nodes confined in a 500 \(\times\) 600 m\({}^{2}\) area. In denser scenarios, when average node degree ((\(\pi\)r\({}^{2}\)n)/A, where, n is the number of nodes within a node n\({}_{i}\)'s transmission range r and A is a total coverage area) is high, only a few upgrades are required. On the other hand, when the average node degree decreases there are more chances to be dead end nodes and concave nodes and it also requires more upgrades in virtual distance to circumvent voids. However, in any of the cases majority of the voids can be avoided within four upgrades or less. In average cases it requires only around three upgrades to circumvent. Figure 23: Average number of upgrades required to circumvent voids with different average node degree. Figure 15.0 Energy consumption.
## 2 Materials and Methods
### Plane Wave [PERSON]'s f-k Method
Because of the time delay \(\tau(x)\) in the temporal domain, the Fourier transform should be followed by a phase shift \(e^{i2\pi\sin(\theta)x/c}\) in the frequency domain, as follows:  
\[G_{\theta}(x,z=0,f)=\int_{-\infty}^{+\infty}\psi_{\theta}(x,z=0,t)e^{-i2\pi ft }e^{i2\pi\sin(\theta)x/c}dt \tag{6}\]  
Then the phase-shifted spectrum is Fourier transformed in the lateral dimension  
\[G_{\theta}(k_{x},z=0,f)=\int_{-\infty}^{+\infty}G_{\theta}(x,z=0,f)e^{-i2\pi k _{x}x}dx \tag{7}\]  
where \(k_{x}\) refers to the lateral spatial wavenumber on the transducer surface. [PERSON] et al. [23] modified the process of beamforming by applying the phase shift once before lateral FFT, instead of applying another phase shift after migration. The core of the spectrum migration is to estimate initial spectrum \(\phi_{\theta}(k_{x},k_{z},t=0)\) from the obtained spectrum \(G_{\theta}(k_{x},z=0,f)\) as the boundary condition. \(\phi_{\theta}(k_{x},k_{z},t=0)\) is the Fourier transform over actual coordinate \((x_{s},z_{s})\). This procedure can be divided into two steps. The first is mapping \(G_{\theta}(k_{x},0,f)\) to \(\hat{\phi}_{\theta}(k_{x},k_{z},0)\), where \(\hat{\phi}_{\theta}(k_{x},k_{z},0)\) refers to Fourier transform over virtual source \((\hat{x}_{s},\hat{z}_{s})\). The second step is to shift \(\hat{\phi}_{\theta}(k_{x},k_{z},0)\) to \(\phi_{\theta}(k_{x},k_{z},0)\). To map \(G_{\theta}(k_{x},0,f)\) to \(\hat{\phi}_{\theta}(k_{x},k_{z},0)\), [PERSON] et al. [21] provided the solution as  
\[\hat{\phi}_{\theta}(k_{x},k_{z},0)=\frac{\hat{ck_{z}}}{\sqrt{{k_{x}}^{2}+{k_{z }}^{2}}}G_{\theta}(k_{x},0,f(k_{x},k_{z})) \tag{8}\]  
where \(k_{z}\) is the axial spatial wavenumber perpendicular to the transducer \(k_{x}\) and \(k_{z}\) are related to \(f\) by  
\[f=\xi sign(k_{z})\sqrt{{k_{x}}^{2}+{k_{z}}^{2}} \tag{9}\]  
Then \(\hat{\phi}_{\theta}(k_{x},k_{z},0)\) should be transformed to \(\phi_{\theta}(k_{x},k_{z},0)\) for Fourier transform of the real coordinates. \[\phi_{\theta}(k_{x},k_{z},0)=\hat{\phi}_{\theta}(k_{x},(k_{z}-k_{x}\gamma)/ \beta,0) \tag{10}\]  
\(\phi_{\theta}(k_{x},k_{z})\) is the Fourier transform over actual coordinates. Then the final image can be obtained by applying 2-D Fourier inverse transform over \(\phi_{\theta}(k_{x},k_{z})\)  
\[\psi_{\theta}(x,z)=\int\int_{-\infty}^{+\infty}\phi_{\theta}(k_{x},k_{z})e^{i2 \pi(k_{x}x+k_{z}z)}dk_{x}dk_{z} \tag{11}\]  
\(\psi_{\theta}(x,z)\) is the final image data after beamforming. This procedure augments computational speed because it replaces time-delay calculation with FFT in the Fourier domain. This model assumes that the reflected sound wave of the scatterers only propagates in the direction of the sensor, which makes the principle of spatial spectrum coordinate migration different from the model proposed by [PERSON] [18]. In reflective ultrasound equipment, the performance of the scatterers as a reflection model is more suitable to the actual situation than the pure scattering model, so this method improves the imaging quality.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 5 A QKD Protocol Based on a Shared Collaboration among Three Parties
### Action and Impact of a Possible Eavesdropper under an Individual Attack
In fact, performing a numerical analysis based on a Monte Carlo simulation about the average value of \(P_{E}\) inside the gray region for the random selection of \(\omega_{1}\) and \(K\) defined by the threshold of \(P_{j}\geq 0.9\), we get: \(\overline{P}_{E_{H}}=0.076\) for \(\theta=0\) and \(\overline{P}_{E_{D}}=0.173\) for \(\theta=\frac{\pi}{2}\). It clearly shows a notable advantage against individual attacks of Types B and C. Such outcomes could increase only if [PERSON] has strict control over the knowledge about the period when [PERSON] already has performed her control measurement and only if [PERSON]\({}_{1}\) maintains such resource without use.
## 2 Some Existing Methods of Estimating the Lithium-Ion Battery State of Charge
### Data-Driven Method  
Data-driven approaches are model-free black-box methods that, for a given dataset, can provide rapid and accurate results given enough training data. These applications are the fruit of outstanding progress in machine learning in the recent past, which has motivated a renewed interest in applying data-driven algorithms to the field of the battery state-of-charge estimation. Indeed, data-driven methods take temperature as an input characteristic, which avoids the establishment of a new thermal coupling model. Nevertheless, in the studies of ref. [43], the authors utilized a BP neural network to estimate the state of charge of lithium-ion batteries. In this regard, the estimation error of the state of charge was around 5%. On the other hand, the work performed in ref. [44] proposed another method to estimate the battery state of charge using Support Vector Machine (SVM). In this case, the authors established an average error of 3%. Furthermore, ref. [45] proposed the use of LS-SVM to correct the cumulative error of the ampere-hour integration method, which favors online state-of-charge estimation. However, these approaches do not take into consideration the issue of the number of training data. Thus, the work of ref. [46] suggests that using an LSTM method to estimate the state of charge can reduce the demand for training data for modeling to some extent. However, in modeling state-of-charge estimation under different conditions, data-driven methods, including RNNs, do not have great advantages. Generating sufficient training data is important but difficult because battery cell chemistry can change at any time, making existing training data non-representative.