esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Neural Fuzzy Inference System  
ANFIS is a hybrid of ANN and fuzzy logic to apply the learning ability to the fuzzy system. ANFIS is composed of five layers [24], as presented in Figure 2.  
Figure 1: The first level decomposition filter process.  
Figure 2: Equivalent ANFIS architecture.  
Layer \(1\) represents the fuzzification layer. It calculates the membership function:  
\[\mu_{Ai}(x)=\frac{1}{1+\left[\left(\frac{x-C_{Ai}}{a_{Ai}}\right)^{2}\right]b_{ Ai}} \tag{8}\]  
\[\mu_{Bi}(y)=\frac{1}{1+\left[\left(\frac{y-C_{Bi}}{a_{Bi}}\right)^{2}\right]b_{ Bi}} \tag{9}\]  
where the \(C_{Ai}\), \(C_{Bi}\), \(a_{Ai}\), \(a_{Bi}\), \(b_{Ai}\), and \(b_{Bi}\) are the bell function parameters.  
Layer 2 defines the rules layer. The output is the firing strength of each node:  
\[W_{i}=\mu_{Ai}(x)*\mu_{Bi}(y) \tag{10}\]  
Layer 3 denotes the normalization layer. It normalizes the calculated firing strength:  
\[\overline{W_{i}}=\frac{W_{i}}{W_{1}+W_{2}} \tag{11}\]  
Layer 4 represents the consequent layer. The output of this layer is the product of normalized firing strength and the fuzzy rules consequent polynomial:  
\[\overline{W_{i}}f_{i}=\overline{W_{i}}(p_{i}x+q_{i}y+r_{i}) \tag{12}\]  
where \(p_{i}\), \(q_{i}\), and \(r_{i}\) are the consequent parameter sets.  
Layer 5 defines the defuzzification layer. Its output is the overall ANFIS output:  
\[f=\sum\limits_{i}\overline{W_{i}}f_{i}=\frac{\sum\limits_{i}W_{i}f_{i}}{\sum \limits_{i}W_{i}} \tag{13}\]  
Tuning all the adaptable parameters is the task of the learning algorithm in order to make the training data match the ANFIS output. The ANFIS model is trained to obtain the minimum Root Mean Square Error (RMSE) between the predicted values and actual measurements. RMSE can be defined as [25]:  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(x-y\right)^{2}} \tag{14}\]  
where \(x\) denotes the actual measurement, \(y\) is the predicted value and \(n\) presents the number of samples.
###### Abstract  
Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When \(r_{m}=0.600\) and \(r_{n}=0.700\), the correlation coefficient (\(r\)) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.  
GNSS-IR; soil moisture; empirical modal decomposition; CCSS; MRE 2023  
17 June 2023  
19 June 2023  
21 June 2023
## 3 Results and Discussion
### Comparative Adsorbent Data  
Table 6 presents a comparative assessment of adsorbents, including sol-gel materials, activated carbon, graphene oxide nanoplatelets, and others; bulk polymerization MIPs stand out as a promising alternative due to their unique molecular recognition capabilities. While sol-gel materials and certain adsorbents like activated carbon and graphene oxide nanoplatelets exhibit high adsorption capacities, they often suffer from limitations such as limited selectivity and susceptibility to fouling [41]. For instance, activated carbon, despite its high adsorption capacity, lacks molecular specificity and may be prone to fouling, reducing its efficiency over time. Similarly, graphene oxide nanoplatelets, while offering moderate to high adsorption capacities, pose concerns regarding availability and potential toxicity. In contrast, MIPs offer tailored molecular recognition, allowing for precise binding to specific target molecules. This specificity ensures efficient removal from complex  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Templates** & \(K_{d}\)**MIP** & \(K_{d}\)**NIP** & \(K\) (MIP)** & \(K\) (NIP) & \(K^{\prime}\) \\  & \(\mathbf{(mg\cdot g^{-1})}\) & \(\mathbf{(mg\cdot g^{-1})}\) & \(\mathbf{K}\) (MIP) & \(\mathbf{K}\) (NIP) & \(\mathbf{K^{\prime}}\) \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} Emtricitabine \\ Tenofovir \\ disoproxil \\ Naproxen \\ Diclofenac \\ \end{tabular} } & 1.78 & 1.65 & 1.16 & 1.06 & 1.08 \\  & 1.78 & 1.77 & 1.44 & 1.44 & 1.00 \\  & 1.77 & 1.76 & 1.14 & 1.14 & 1.00 \\  & 1.79 & 1.78 & 1.15 & 1.14 & 1.00 \\  & 1.68 & 1.59 & 1.09 & 1.02 & 1.06 \\  & 1.81 & 1.74 & 1.17 & 1.12 & 1.05 \\  & 1.56 & 1.55 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Template selectivity data in the presence of a competitor.  
mixtures with minimal interference from other compounds. Moreover, MIPs demonstrate stability and can be regenerated and reused multiple times without significant loss of performance. Despite potential variations in adsorption capacities based on synthesis conditions, the advantages of molecular specificity and recyclability position MIPs as superior alternatives for applications requiring precise molecular recognition. Thus, MIPs offer a promising avenue for advancements in targeted adsorption tasks, overcoming the limitations posed by other materials.
# Thermally Induced Displacements and Rotations of Pillars for Precise Geodetic Measurements
## 4 Discussion  
While previous studies have recognized the influence of thermal effects on columns for precise geodetic measurements [3; 4; 5; 6] and have also used high-precision tilt measurements to monitor this effect [10; 11; 12; 13; 14], none have directly confirmed this relationship using simultaneous measurements of tilt and displacement. For this reason, our study goes beyond this by providing valuable insight into thermally induced displacements in reinforced concrete piers and highlighting the importance of temperature variations in influencing structural behavior. A key strength of this study is its systematic approach, utilizing both experimental and theoretical methods to assess the effects of thermal loading. The results contribute to a more comprehensive understanding of thermal effects in reinforced concrete structures, which is particularly relevant for applications in bridge construction, high-rise buildings, and other large structures exposed to significant temperature variations. In addition, the study focuses on the direct application of this knowledge and experience to maintain precision in high-accuracy geodetic measurements.  
We should mention that while displacements caused by solar radiation are relatively small and generally affect only precise measurements, their impact on cadastral measurements is negligible due to the broader scope and lower sensitivity of such measurements.  
Despite its contributions, this study has certain limitations. The analysis primarily considers idealized boundary conditions and material properties, which may not fully capture the complexity of real structures. Future studies could incorporate more detailed material modeling and experimental validation to refine the prediction accuracy of the proposed approach. Although this work provides a solid foundation for understanding thermally induced displacements, further research is needed to investigate long-term effects such as creep, fatigue, and thermal aging. The integration of digital twin technologies and machine learning approaches could provide promising avenues for testing predictive models in this area.  
The practical applicability of this research extends to structural health monitoring and precise geodetic measurements, where understanding thermal effects is critical to ensuring accuracy.
## 4 Numerical Results
### Quasi-Static Structural Analysis  
Table 4 shows the maximum total deformation, von Mises, and shear stress results generated by the FEMs of the three structural components. CASE I-3 showed the largest response, although the structural response under the three load conditions was very similar. The largest deformation occurred in the PDF, and the maximum von Mises stress occurred in the steel beam frame near the bottom of the water tank. The structural response of the concrete pad was considerably smaller than those of the other structures, and thus it was not considered in the following calculations. As an example, Figure 11 illustrates the total deformation and von Mises stress of the concrete pad, the PDF panel, and the steel beam frame in the quasi-static structural analysis of CASE I-3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Item** & \begin{tabular}{c} **Concrete** \\ **Foundation** \\ \end{tabular} & **PDF Panel** & **Steel Beam Frame** \\ \hline \multirow{6}{*}{\begin{tabular}{c} CASE I-1 \\ (No Plate) \\ \end{tabular} } & Total Deformation (mm) & 0.028 & 8.056 & 6.595 \\  & von Mises Stress (MPa) & 8.883 & 4.761 & 110.970 \\  & & XY-Plane & 0.594 & 0.785 & 25.228 \\  & Shear Stress (MPa) & YZ-Plane & 1.695 & 1.466 & 40.429 \\  & & ZX-Plane & 1.469 & 0.311 & 35.760 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} CASE I-2 \\ (No Plate) \\ \end{tabular} } & Total Deformation (mm) & 0.028 & 8.057 & 6.631 \\  & von Mises Stress (MPa) & 8.731 & 4.745 & 112.100 \\  & & XY-Plane & 0.586 & 0.774 & 23.922 \\  & Shear Stress (MPa) & YZ-Plane & 1.694 & 1.466 & 37.589 \\  & & ZX-Plane & 1.469 & 0.312 & 36.599 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} CASE I-3 \\ (No Plate) \\ \end{tabular} } & Total Deformation (mm) & 0.027 & 8.058 & 6.668 \\  & von Mises Stress (MPa) & 8.397 & 4.707 & 117.420 \\ \cline{1-1}  & & XY-Plane & 0.567 & 0.779 & 23.227 \\ \cline{1-1}  & Shear Stress (MPa) & YZ-Plane & 1.693 & 1.466 & 35.695 \\ \cline{1-1}  & & ZX-Plane & 1.469 & 0.313 & 38.363 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of structural response in quasi-static analysis.
## 3 Dataset and Methodology
### Input Parameters
#### 3.1.1 Initial Bathymetry  
Generating an initial paleobathymetric map is the most critical step in forward stratigraphic modelling, as it controls the overall accommodation space and the geometry of stratigraphic units. It is also one of the most difficult input parameters to simulate, due to the presence of compaction, tectonic evolution, subsidence, and erosion in basins. It is particularly challenging in ancient systems, although in modern environments, defining paleobathymetry can be easier, as more data are typically available, such as the thickness of modern sediments and geophysical surveys. In addition, the effects of burial by the overlying units can be ignored. In the Fraser River Delta, the thickness of modern deltaic sediments is used, e.g., [33; 34], to generate a realistic initial bathymetry. Based on these data, the bathymetry profile resembles an overall fjord-like basin, where the main depo-center is a concave, bowl-shape basin (Figure 2). It should be noted that the model does not extend further south due to limited data availability. In this model, subsidence is neglected, although some minor anthropogenic subsidence has been reported in the delta plain [33].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameter** & **Value** \\ \hline Domain length (x-axis) (km) & 60 \\ \hline Domain length (y-axis) (km) & 40 \\ \hline Grid size (km) & \(0.5\times 0.5\) \\ \hline Time steps (yrs) & 5000 \\ \hline Sediment supply (km\({}^{3}\)/Ma) & 10,000 \({}^{\rm 81}\); 9000 \({}^{\rm 82}\); 9000 \({}^{\rm 83}\) \\ \hline River discharge (m\({}^{3}\)/s) & 6000 \({}^{\rm 81}\); 4500 \({}^{\rm 82}\); 4500 \({}^{\rm 83}\) \\ \hline Coarse sand (\%) & 5\({}^{\rm 81}\); 5\({}^{\rm 82}\); 5\({}^{\rm 83}\) \\ \hline Sand (\%) & 20 \({}^{\rm 81}\); 20 \({}^{\rm 82}\); 20 \({}^{\rm 83}\) \\ \hline Silt (\%) & 45 \({}^{\rm 81}\); 45 \({}^{\rm 82}\); 45 \({}^{\rm 83}\) \\ \hline Clay (\%) & 30 \({}^{\rm 81}\); 30 \({}^{\rm 82}\); 30 \({}^{\rm 83}\) \\ \hline \multicolumn{3}{c}{**Sediment transport parameters (km2/ky)**} \\ \hline \multirow{2}{*}{\(\rm K_{gravity}\)} & Coarse sand & Sand & Silt & Clay \\ \hline \multirow{2}{*}{\(\rm K_{gravity}\)} & Continental & 20 & 40 & 60 & 80 \\ \cline{2-2}  & Marine & 0.008 & 0.0110 & 0.0150 & 0.02 \\ \hline \multirow{2}{*}{\(\rm K_{water}\)} & Continental & 25 & 50 & 75 & 100 \\ \cline{2-2}  & Marine & 2 & 4 & 5 & 6 \\ \hline \multirow{2}{*}{\(\rm K_{best}\)} & Continental & 25 & 45 & 65 & 85 \\ \cline{2-2}  & Marine & 0.01 & 0.02 & 0.022 & 0.024 \\ \hline \end{tabular}  
\(\rm K_{gravity}\): gravity-driven diffusion coefficient; \(\rm K_{water}\): water-driven diffusion coefficient; \(\rm K_{best}\): high-energy short-term transport. \({}^{\rm 81-83}\); Steps 1–3.  
\end{table}
Table 1: Parameters used in modeling.  
Figure 2: Initial bathymetric model characterizing the Fraser Valley before the delta progradation. The main depo-center is the “fjord-like” basin. This model was created using the thickness of modern sediments e.g., [33; 34].
###### Abstract  
Network operators have been dealing with the necessity of a dynamic network resources allocation to provide a new generation of customer-tailored applications. In that sense, Telecom providers have to migrate their BSS/OSS systems and network infrastructure to more modern solutions to introduce end-to-end automation and support the new use cases derived from the 5G adoption and transport network slices. In general, there is a joint agreement on making this transition to an architecture defined by programmable interfaces and standard protocols. Hence, this paper uses the iFusion architecture to control and program the network infrastructure. The work presents an experimental validation of the network slicing instantiation in an IP/Optical environment using a set of standard protocols and interfaces. The work provides results of the creation, modification and deletion of the network slices. Furthermore, it demonstrates the usage of standard communication protocols (Netconf and Restconf) in combination with standard YANG data models.  
Keywords:Software Defined Networking; network slicing; 5G; network automation; L3 VPN +
Footnote †: journal:can be now enabled, allowing more sophisticated services to emerge and co-exist in a commonly shared infrastructure.  
In order to enable this flexible consumption of network capabilities, it is necessary to develop new forms of network control to make possible a dynamic partitioning and assignment of resources end-to-end. Therefore, several initiatives have been discussed across the industry, including the IP network resources management (L2 [3] and L3 VPNs [4]), the optical layer management (Transport API [5]) or the network devices configuration (Openconfig [6]).  
This paper extends the work presented in the latest OFC conference [7]. In addition, this work adds a set of tests; Including creating two isolated network slices, prefixes addition and modification to each slice and slices deletion. A last set of tests included the service continuity evaluation after a physical device reset. For the whole set of experiments, a hierarchical control architecture over multi-layer IP over DWDM networks was used.  
This paper is organized as follows: The authors describe those concepts in Section 2. Section 3 described the whole control architecture used for the network slice instantiation in a service provider environment. Section 3.5 describes the proposed architecture's implementation choices. The authors selected one of those choices and made a proof-of-concept in Telefonica and CTTC Laboratories, Section 4 includes the results.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
###### Abstract  
In this study, with an FPGA-board using VHDL, we designed a secure chaos-based stream cipher (SCbSC), and we evaluated its hardware implementation performance in terms of computational complexity and its security. The fundamental element of the system is the proposed secure pseudo-chaotic number generator (SPCNG). The architecture of the proposed SPCNG includes three first-order recursive filters, each containing a discrete chaotic map and a mixing technique using an internal pseudo-random number (PRN). The three discrete chaotic maps, namely, the 3D Chebyshev map (3D Ch), the 1D logistic map (L), and the 1D skew-tent map (S), are weakly coupled by a predefined coupling matrix M. The mixing technique combined with the weak coupling technique of the three chaotic maps allows preserving the system against side-channel attacks (SCAs). The proposed system was implemented on a Xilinx XC7Z020 PYNQ-Z2 FPGA platform. Logic resources, throughput, and cryptanalytic and statistical tests showed a good tradeoff between efficiency and security. Thus, the proposed SCbSC can be used as a secure stream cipher.  
chaos-based stream cipher; SPCNG; 3D chebyshev; logistic; skew-tent; FPGA; performance
## 2 Materials and Methods  
### Basic Theory  
Following the approach in [1], one can consider the one-dimensional propagation of an electromagnetic wave in the z-direction in an isotropic, homogeneous electrically conducting medium. Other than in [14], the transient phenomena at the very beginning of the electromagnetic excitation are ignored as they are very short compared to pulse lengths of 10 to 200 ms. Harmonic electromagnetic excitation is considered. If the electric field vector lies in the y-direction and is \(E_{y}\), the magnetic field vector in the x-direction is \(H_{x}\); the time dependence is harmonic, it can be described from [PERSON]'s law by  
\[\frac{\partial E_{y}}{\partial z}=-\mu_{0}\mu_{R}\frac{\partial H_{x}}{ \partial t}=-i\omega\mu_{0}\mu_{R}H_{x} \tag{1}\]  
and from [PERSON]'s law by  
\[\frac{\partial H_{x}}{\partial z}=-\sigma E_{y} \tag{2}\]  
where \(\omega\) is the frequency of the electromagnetic wave, \(\sigma\) is the electrical conductivity and \(\mu_{R}\) the relative high-frequency magnetic permeability, which is  
\[\mu_{R}=\left({\mu_{r}}^{2}+{\mu_{r}}^{n}{}^{2}\right)^{1/2}-{\mu_{r}}^{n} \tag{3}\]  
where \({\mu_{r}}^{\prime}\) and \({\mu_{r}}^{n}\) are the real and the imaginary part of the complex relative permeability at the frequency \(\omega\), respectively. At typical induction frequencies of 10 to 100 kHz, \(\mu_{R}\) is usually somewhat smaller than the static relative permeability [16].  
Here, \(Z=i\omega\mu_{0}\mu_{R}\) can be called the impedance parameter and \(Y=\sigma\) the admittance parameter.  
The wave propagation in the conducting medium is described by  
\[H_{x}=\frac{E_{y}}{Z_{0}}\exp(-\Gamma_{0}z), \tag{4}\]  
where  
\[Z_{0}=\sqrt{\frac{Z}{Y}}=\sqrt{\frac{i\omega\mu_{0}\mu_{R}}{\sigma}} \tag{5}\]  
is the characteristic impedance and  
\[\Gamma_{0}=\sqrt{ZY}=\sqrt{i\omega\sigma\mu_{0}\mu_{R}}=(1+i)\sqrt{\frac{ \omega\sigma\mu_{0}\mu_{R}}{2}}=\frac{1+i}{\delta} \tag{6}\]is the propagation coefficient of the medium. Here,  
\[\delta=\sqrt{\frac{2}{\omega\sigma\mu_{0}\mu_{R}}} \tag{7}\]  
is the electromagnetic skin depth.  
In the case of thermal waves, one considers the temperature oscillation \(\theta\) and the heat flow \(f\) instead of \(E\) and \(H\). From the definition of the thermal conductivity \(\lambda\), one obtains  
\[\frac{\partial\theta}{\partial z}=-\frac{1}{\lambda}f \tag{8}\]  
and from the temperature change in an elemental volume by a gradient of the heat flow one obtains  
\[\frac{\partial f}{\partial z}=-i\omega\rho c\phi \tag{9}\]  
Comparing (8) and (9) with (1) and (2), one can see the formal analogy of wave propagation with \(Z=1/\lambda\) and \(Y=i\omega\rho c\), and one obtains the thermal impedance  
\[Z_{0,th}=\sqrt{\frac{Z}{Y}}=\frac{1}{\sqrt{i\omega\lambda\rho c}} \tag{10}\]  
and the thermal propagation constant  
\[\Gamma_{0,th}=\sqrt{ZY}=\sqrt{\frac{i\omega\rho c}{\lambda}}=(1+i)\sqrt{\frac {\omega\rho c}{2\lambda}}=\frac{1+i}{\mu_{th}} \tag{11}\]  
where  
\[\mu_{th}=\sqrt{\frac{2\lambda}{\rho c\omega}} \tag{12}\]  
is the thermal diffusion length, the thermal analog of the electromagnetic skin depth.
## 2 Hyperspectral Imaging Configurations  
Hyperspectral imaging, broadly speaking, is a type of spectral imaging technology that integrates imaging and spectroscopy to obtain 3-D data cubes, which contain 2-D spatial and one-dimensional (1-D) spectral information from products. It is closely related to another spectral imaging technology, called multispectral imaging, which also has been used for food quality and safety assessment. The main difference between the two technologies resides in the number of wavelengths, spectral resolution and the way of acquiring image data. It is generally acknowledged that imaging at fewer than 10 discrete wavelengths or wavebands (usually broad wavebands) is termed multispectral, while imaging at more than 10 contiguous wavelengths or narrow wavebands is termed hyperspectral.