esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Digital Skills and Infrastructure in EU Countries Based on DESI 2024 Data
###### Abstract  
This paper presents an analysis of digital skills and network infrastructure in the European Union (EU) countries based on data from the Digital Economy and Society Index (DESI) 2024. We analyze the current state of digital skills and network infrastructure in EU countries, which in the DESI framework is called digital infrastructure, identifying key trends and differences between EU member states. The analysis shows significant differences in the relative share of citizens with a certain level of digital skills across countries, both among ordinary users of digital services and among information and communication technology professionals. The analysis of digital infrastructure includes fixed broadband coverage, mobile broadband, and edge networks, the latter of which are expected to become an important component of future digital infrastructure. The results highlight the importance of harmonizing the development of digital skills and digital infrastructure to support the EU's digital transformation. Significant attention is paid to 5G technology. The feasibility of including a new additional indicator in DESI for next-generation 5G technology in the frequency range of 24.25-52.6 GHz is shown. The value of this indicator can be used to assess the readiness of the EU economy for technological leaps that place extremely high demands on reliability and data transmission delays. The results of the current state and the analysis of digital skills and infrastructure contribute to understanding the potential for the future development of the EU digital economy.  
information technology; information and communication technology; telecommunications networks; digital infrastructure; DESI; fiber coverage; 5G technology; edge networking +
Footnote †: journal: Future Internet
# 3D DNA Nanostructures: The Nanoscale Architect
## 3 Dynamic Mechanical Control and Motion
### Controlling DNA Nanomechanics  
Most logic operations or state transitions for operating nanomechanical DNA devices will depend on toehold-mediated strand displacement (TMSD), which is a fundamental technique that can integrate DNA nanostructures with signals and processing power from the rich field of DNA computing, as has been thoroughly reviewed in previous articles [57; 58]. In TMSD, a duplex is formed where one strand is slightly longer than the other, which exposes a short region of unhybridized DNA known as the toehold. Another strand with a sequence domain complementary to the toehold can initially bind to this region. Then, if the incoming strand is further complementary to the remaining sequence of the duplex, it will competitively displace the currently bound shorter strand of the duplex via branch migration.  
One of the most common uses of this principle is to switch a hollow DNA nanostructure passively between its opened and closed states. These typically feature two separable pieces that are pinned together at two points: a hinge and a TMSD-based latch. Upon the latch being released, one half begins to float away while fixated at the hinge, as if swinging open. The overall structure can also be designed in the open configuration, such that the hinge is pre-strained to return to that configuration when the latch is undone. Moreover, strand displacement works regardless of whether the displacing molecule is the complementary oligonucleotide [7] or if the toehold is part of an aptamer sequence that specifically binds a target molecule [59]. These are typically examples of passive motion, that is, the latch determines a state (open or closed), but the movement of pieces relies on diffusion. Thus, while the logical state of the structure is determined, its actual physical state may not be.  
TMSD can also be used to implement an autonomous system where a DNA device can move through sequential states by a likewise sequence of TMSD reactions. The earliest demonstrations of autonomously transitioning through configurable physical states in DNA nanostructures include a DNA tweezer by [PERSON] et al. [55] and a DNA walker by the [PERSON] group in collaboration with the [PERSON] group [60]. In the presence of fuel strands, which were not inputs but a consumed reactant for the TMSD reactions to repeatedly occur, a series of distinct hybridizations could be triggered to autonomously convert the DNA system through a series of distinct physical states. However, the physical scale of such demonstrations was otherwise small.
# Polymer@gold Nanoparticles Prepared via RAFT Polymerization for Opto-Biodetection
## 1 Introduction  
The optical properties of colloidal gold nanoparticles (NPs) have been widely explored in diverse applications and in certain cases associated to polymer coatings. Very often, such hybrid polymer/gold nanostructures comprise Au cores decorated with polymer shells. While the Au NPs are thought to provide optical response due to their plasmonic behavior, the resulting polymer coatings might confer robustness, stability, functionality, responsiveness and biocompatibility, to the final nanocomposite (Figure 1). These potential benefits demand judicious control over the morphological properties of the Au cores and also on their surface modification when using polymers. The latter can be achieved by a number of strategies that include living radical polymerization techniques such as RAFT (Reversible Addition-Fragmentation chain Transfer).  
This review explores the plasmonic properties of Au NPs aiming at the development of sensing platforms for biodetection, focusing on the surface functionalization of Au NPs using polymers prepared via RAFT polymerization. Therefore, the first part of the review presents a brief overview of the synthesis and properties of colloidal Au NPs as well as of surface functionalization methods applied to such colloids. In the second part, the main concepts underlying RAFT polymerizations are presented and the approaches that have been used to prepare polymer/Au nanocomposites are discussed. The third part of this review concerns the use of RAFT to prepare polymer coated Au NPs as optical biosensors and eventual multifunctionality in theranostic methods applied for example in cancer therapy.
## 4 Simulations  
The performance of the proposed controllers will be demonstrated by simulating a reconfiguration scenario and comparing the fuel consumption in two underactuated cases. The chief satellite flies in elliptical orbits and the orbital elements for the chief are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Orbit Element** & **Value** \\ \hline Apogee altitude/m & \(3\times 10^{6}\) \\ Perigee altitude/m & \(5\times 10^{5}\) \\ Inclination/deg & 40 \\ Right ascension of ascending node/deg & 60 \\ Argument of perigee/deg & 270 \\ True anomaly/deg & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial orbital elements of the chief spacecraft.  
The initial mass of the deputy is \(m=10\,\mathrm{kg}\), the deputy's estimated mass is \(\hat{m}=8\,\mathrm{kg}\). The matrix \(\mathbf{C}\in\mathbb{R}^{12\times 5}\) in RBFNN is chosen as:  
\[\mathbf{C}=\begin{bmatrix}\overline{\mathbf{C}}_{1}\\ \overline{\mathbf{C}}_{2}\end{bmatrix}, \tag{39}\]  
with  
\[\overline{\mathbf{C}}_{1}=\begin{bmatrix}-1000&-500&0&500&1000\\ -600&-300&0&500&1000\\ -1000&-500&0&500&1000\\ -0.5&-0.25&0&0.25&0.5\\ -1.0&-0.5&0&0.5&1.0\\ -1.0&-0.5&0&0.5&1.0\\ -1.0&-0.5&0&0.5&1.0\end{bmatrix}\mathbf{\overline{C}}_{2}=\begin{bmatrix}-250&-125& 0&125&250\\ -600&-300&0&500&1000\\ -500&-250&0&250&500\\ -0.3&-0.15&0&0.15&0.3\\ -0.5&-0.25&0&0.25&0.5\\ -0.5&-0.25&0&0.25&0.5\end{bmatrix}. \tag{40}\]
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 2 Related Works and Background
### Weight-Based Enhancements to DV-Hop
#### 2.1.3 HW-DV-Hop Localization Algorithm  
The HW-DV-Hop approach (Hyperbolic Weighted DV-Hop) is another improved method based on DV-Hop. HW-DV-Hop uses the following steps: (i) the anchors disseminate their precise locations; (ii) a new technique improves the average hop size; and (iii) the hyperbolic 2D schema is used to estimate the position of unknown nodes instead of the usual method.  
The first stage of HW-DV-Hop is similar to the DV-Hop and W-DV-Hop approaches. Each anchor \(A_{i}\) sends a message that includes its position and number of hops between the anchor nodes. In the second step, \(AHSize_{i}\) is determined using a new formula:  
\[AHSize_{new}=\frac{\sum\limits_{i=1}^{m}W_{i}\times AHSize_{i}}{\sum\limits_{ i=1}^{m}W_{i}}, \tag{17}\]  
where  
\[W_{i}=\frac{1}{\sum\limits_{j\
eq i}\left|AHSize_{i}-AHSize_{j}\right|}. \tag{18}\]  
In the third phase, rather than applying the multilateration approach, the 2D hyperbolic location schema [44] is used to estimate the locations of the unknown nodes.  
This approach assumes that \((x_{i},y_{i})\) represents the coordinates of anchor node \(A_{i}\) and \((x_{u},y_{u})\) represents the coordinates of unknown node \(U\). The following formula is used to calculate \(d_{i,u}\), which is the approximate distance between \(A_{i}\) and \(U\).  
\[d_{i,u}^{2}=(x_{i}-x_{u})^{2}+(y_{i}-y_{u  
Equation (19) can be represented in matrix form as  
\[GA=b, \tag{21}\] \[\text{where }A=[x_{u},y_{u},S_{u}]^{T},\text{G}=\begin{bmatrix}-2x_{1}&-2 y_{1}&1\\ -2x_{2}&-2y_{2}&1\\ \vdots&\vdots&\vdots\\ -2x_{k}&-2y_{k}&1\end{bmatrix},\text{b}=\begin{bmatrix}d_{1,u}^{2}-R_{1}\\ d_{2,u}^{2}-R_{2}\\ d_{k,u}^{2}-R_{k}\end{bmatrix}.\]  
According to Equation (20), \(A\) can be solved using the following formula.  
\[A=((G^{T}G)^{-1})G^{T}b \tag{22}\]  
Thus, the coordinates of node \(U\) are calculated using the formula below.  
\[\begin{cases}x_{u}=A(1)\\ y_{u}=A(2)\end{cases} \tag{23}\]
# Soil Erosion Assessment and Prediction in Urban Landscapes: A New G2 Model Approach
## 2 Materials and Methods
### Soil Erosion Using the G2 Model
#### 2.4.2 Vegetation Retention (Factor V)  
Vegetation retention factor (V) has similar characteristics to factor C in the RUSLE model. Factor V, by definition, represents the degree to which the presence of vegetation and its density are expected, as well as the way in which the soil is managed to protect it from erosion [29, 65]. Unlike factor C, which is obtained on the basis of empirical tables or by remote sensing, factor V in the G2 model is obtained by combining both. The input parameters are a time series of vegetation indices and constant empirical parameters of land use according to the formula:  
\[\mathrm{V}=\mathrm{e}^{(\mathrm{LU}\cdot\mathrm{FCover})} \tag{3}\]  
V--vegetation retention (normalized, dimensionless); LU--empirical parameter for land use (dimensionless); FCover--fraction of vegetation cover (dimensionless).  
Factor V is dimensionless and has a value from 1 to +\(\infty\). If the value is V = 1, it represents an unfavorable way of land management, i.e., the occurrence of bare land, while the values V > 1 represent a favorable way of land management, from the aspect of reducing erosion processes. Empirical parameter LU was created based on the interpretation of empirical data sets from the Erosion Potential Method [31] and the coefficient of watershed management X:a. Parameter LU has a value from 1, for soil that is bare and subject to erosion processes, to 10, for soils that are protected from natural erosion processes. The values of the empirical parameter of land use were assigned to the land cover obtained by the method of remote detection according to the values developed by [PERSON] and [PERSON] [32]. Fractional vegetation cover (FCover) represents a vegetation bio-geophysical parameter with normalized values from 0 to 1 (completely bare to dense vegetation). Fractional vegetation cover was obtained by applying the spectral vegetation index NDVI according to the formula [66, 67]:  
\[\mathrm{FCover}=\frac{\mathrm{NDVI}-\mathrm{NDVI}_{\mathrm{soil}}}{\mathrm{ NDVI}_{\mathrm{veg}}-\mathrm{NDVI}_{\mathrm{soil}}} \tag{4}\]  
NDVI--normalized difference vegetation index; NDVI veg--value representing vegetation in the vegetation index layer; NDVI soil--the value that represents bare land in the vegetation index layer  
The NDVI spectral indices were obtained for two time periods, and a set of 12 images for each month in 2001 and 2019 was used to obtain average values. In 2001, satellite images of the Landsat 5 and Landsat 7 missions were used, while satellite images of the Landsat 7 and Landsat 8 missions were used in 2019.
# A Novel Non-Parametric Spatiotemporal Scan Statistic: An Application to Detect Disease Outbreaks
## 3 Experimental Results
### A Simulation Study
** In the samples where the exact zip code was not detected in the most likely cluster, it was detected in the first secondary cluster, implying that the spatiotemporal scan statistic has an ability to detect the exact outbreak zip code in one of its significant clusters.
## 2 Numerical Model  
The Fire Dynamics Simulation (FDS) is a code developed by the NIST in cooperation with the VIT research center of Finland. The code numerically solves the Navier-Stokes equations and finds application in low velocity thermally driven flows, smoke production, and mass and heat transfer. The FDS code is a low Mach number Large Eddy Simulation solver (the Mach number is under 0.3). To define the turbulent characteristics, the Smagorinsky model is applied.  
The governing equations for mass and momentum conservation are:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdotp\overset{\rightarrow}{u}=\dot{m} _{b}^{\prime\prime\prime} \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho\overset{\rightarrow}{u})+\
abla\cdotp \overset{\rightarrow}{u}\overset{\rightarrow}{u}+\
abla\cdotp\overset{ \rightarrow}{p}=\rho g+\overset{\rightarrow}{f_{b}}+\
abla\cdotp\cdotp \tau_{ij} \tag{2}\]  
where \(\rho\) is the density, \(g\) is the gravity acceleration, \(u\) is the velocity, \(p\) is the pressure, \(t\) is the time, and \(\tau_{ij}=\mu\big{(}2S_{ij}-\frac{2}{3}\delta_{ij}(\
abla u)\big{)}\) is the stress tensor, where \(\mu\) is the dynamic viscosity of the fluid, \(S_{ij}\) is the strain tensor, and \(\delta_{ij}\) is the Kronecker delta.  
The coefficient of turbulent consistency in [PERSON]'s analysis is defined as:  
\[\mu_{LES}=\rho(C_{s}\Delta)\bigg{(}2S_{ij}\ :S_{ij}-\frac{2}{3}(\
abla\overline{ \mathrm{u}})^{2}\bigg{)}^{\frac{1}{2}} \tag{3}\]  
where \(\Delta\ =(\delta x\delta y\delta z)^{\frac{1}{3}}\) is the dimension of the spatial filter proportional to the dimension of the grid.  
The energy conservation equation is written as:  
\[\frac{\partial}{\partial t}(\rho h_{s})+\
abla\rho h_{s}u=\frac{Dp}{Dt}+q^{ \prime\prime}-\dot{q}_{b}^{\prime\prime}-\
abla q^{\prime\prime}+\epsilon \tag{4}\]  
where \(h_{s}\) is the sensible enthalpy, \(\dot{q}_{b}^{\prime\prime}\) is the energy transferred to particles during evaporation (change of phase), and \(q^{\prime\prime}\) are the radiative and conductive heat fluxes.  
The equation for species conservation is written as:  
\[\frac{\partial}{\partial}(\rho Y_{a})+\
abla\cdot\rho Y_{a}\dot{\overline{u} }=\
abla\rho D_{a}\
abla Y_{a}+\dot{m}_{a}^{\prime\prime\prime}+\dot{m}_{b}^{ \prime\prime\prime} \tag{5}\]  
where \(\dot{m}_{b}^{\prime\prime\prime}=\sum\limits_{a}\dot{m}_{b,a}^{\prime\prime}\) is the species production rate as particles or droplets.  
The time step used is variable and adjusted by the FDS code. During the calculation, the time step is adjusted so that the CFL (Courant, Friedrichs, Lewy) condition is satisfied. The default value of the size of the time step is [31]:  
\[DT=\frac{5(\delta x\delta y\delta z)^{1/3}}{\sqrt{gH}} \tag{6}\]  
where \(\delta x\), \(\delta y\), and \(\delta z\) are the dimensions of the smallest mesh cell, \(H\) is the height of the computational domain, and \(g\) is the acceleration of gravity.
# Crack Growth and Delamination Analysis in GFR Composite Materials
###### Abstract  
The modeling of the structural behavior of composite materials is an interesting but complex task since the response of the material to loading structural may be difficult to predict, and the failure may be manifested in different forms. In cross-ply fiber-reinforced composites, the major failure mechanisms include: (i) the failure of the matrix material (transverse cracking), (ii) delamination and (iii) the breakage of the fibers. The process of the transverse cracking is a well studied damage mechanism and can be used in numerical simulations, in order to study the effects of various parameters on the crack density. In this paper, the finite element modeling of a cross-ply composite under uniaxial loading in tension is performed using ABAQUS software, considering all the potential damage mechanisms. The model takes into account shear-lag effect for the determination of the stress transfer and furthermore it adopts a homogenization procedure for the calculation of elastic and viscoelastic material properties. Stochasticity is introduced by assigning various interfacial strengths that follow a Gaussian distribution, so as to predict the cracking sequence up to saturation in the transverse to the 0\({}^{\circ}\) layers. The results are directly compared with available experimental measurements showing reasonable agreement. Finally, a cross-ply RVE model was created and loaded in uniaxial tension and crack propagation is modelled with the Extended Finite Element Method (XFEM). The stress concentration calculations around the crack tips are in agreement with the mesoscale model.  
composite laminates; mean-field homogenization; cross-ply composites; transverse cracking; finite element analysis +
Footnote †: journal: Applied  
doi: 10.3390/
## 3 System Model
### Traffic Data Preprocessing Model
The correlation between each independent variable and the dependent variable is calculated by multiple regression. The independent variable with weak R-squared value is eliminated. The dimension of \(Q\) is reduced, which has a positive impact on the following prediction. In order to select the characters based on R-squared, we discuss the issue with two cases. **Case 1: The correlation measurement between traffic flow and single character. ** For \(\forall Q_{:j}\in Q_{:j}\
eq z\), the correlation between the traffic flow and the character \(j\) is denoted as follows:  
\[R^{2}[Q_{:j},Q_{:z}]=1-\frac{\sum_{i=1}^{z}(Q_{ij}-\widehat{Q_{iz}})^{2}}{ \sum_{i=1}^{z}(Q_{ij}-\overline{Q_{iz}})^{2}}. \tag{6}\]  
The correlation between traffic flow and the character \(Q_{:j}\) is strong as \(R^{2}[Q_{:j},Q_{:z}]>0.49\), moderate as \(R^{2}[Q_{:j},Q_{:z}]\in[0.25,0.49]\), and weak as \(R^{2}[Q_{:j},Q_{:z}]<0.25\). In this paper, we assume that character \(Q_{:j}\) has a correlation with traffic flow and should be reserved for prediction. Otherwise, the character \(Q_{:j}\) with R-squared less than 0.25 will be eliminated to reduce the prediction cost due to the weak correlation. **Case 2: The correlation measurement among multiple characters. **\(\forall Q_{:j},Q_{:p},\dots,Q_{:m}\in Q\), \(j,p,\dots,m\
eq z\), the matrix \(Q\) eliminates as \(Q^{{}^{\prime}}\) based on R-square, which is denoted as follows:  
\[Q^{\prime}=\left\{\begin{aligned} Q_{:}R^{2}& \geq 0.25\\ Q\cap\arg\max_{Q_{:k}}[R^{2}[Q_
## 2 Materials and Methods
### Notations  
We show the notations used in the paper in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Meaning** \\ \hline \(p\) & \(p\) is a large prime number. \\ \(Z_{p}^{*}\) & \(Z_{p}^{*}\) is a modulo-p multiplicative group. \\ \(Enc(M)\) & The result of encrypting the plaintext \(M\). \\ \(Dec(C)\) & The result of decrypting the ciphertext \(C\). \\ \(t\) & The number of participants in the protocol is \(t\). \\ \(P_{i}\) & The \(i\)-th participant. \(P_{1},\ldots,P_{t-1}\) are the clients, and \(P_{i}\) is the server. \\ \(S_{i}\) & Vector of datasets for participant \(P_{i},i\in\{1,2,\ldots,t\}\). \\ \(n_{i}\) & The dataset size of \(S_{i},i\in\{1,2,\ldots,t\}\). \\ \(S_{i}[j]\) & The \(j\)-th element in the dataset vector \(S_{i}\) of participant \(P_{i},i\in\{1,2,\ldots,t\},j\in\{1,2,\ldots,n_{i}\}\). \\ \(m\) & The length of the Bloom filter. \\ \(k\) & The number of hash functions used by the Bloom filter. \\ \(h_{u}\) & The \(u\)-th hash function, \(u\in\{1,2,\ldots,k\}\). \\ \(h_{u}(x)\) & The hash value of \(x\) by using the \(h_{u}\) that is in \(\{1,2,\ldots,m\}\), where \(u\in\{1,2,\ldots,k\}\). \\ \(BF_{i}\) & The Bloom filter obtained by mapping the dataset \(S_{i},i\in\{1,2,\ldots,t\}\). \\ \(BF_{i}[l]\) & The \(l\)-th bit of \(BF_{i},i\in\{1,2,\ldots,t\}\), \(l\in\{1,2,\ldots,m\}\). \\ \(RBF_{i}\) & The randomized Bloom filter obtained by randomizing \(BF_{i},i\in\{1,2,\ldots,t\}\). \\ \(RBF_{i}[l]\) & The \(l\)-th element of \(RBF_{i},i\in\{1,2,\ldots,t\}\), \(l\in\{1,2,\ldots,m\}\). \\ \(ERBF_{i}\) & The encrypted randomized Bloom filter obtained by encrypting \(RBF_{i},i\in\{1,2,\ldots,t\}\). \\ \(ERBF_{i}[l]\) & The \(l\)-th element of \(ERBF_{i},i\in\{1,2,\ldots,t\}\), \(l\in\{1,2,\ldots,m\}\). \\ \(rand()\) & Generate a random number between 1 and \(p-1\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of notations.
## 8 Performance  
The plot between potential and current density are used to evaluate the performance of cell after optimization of different parameters like type of component material, cell design, fuel, fabrication technology and appropriate temperature. Table 5 gives power densities with varying parameters such as different component materials, temperature and fuel. In the past, intense research has been carried out regarding configuration LSM-YSZ/YSZ/NiO-YSZ using hydrogen fuel. Later on LSCF is used as an alternative of LSM due to its higher ionic conductivity. Researchers also investigated the composite of electrolyte and LSCF in order to enhance ionic conductivity. A significant research has also been done to attain maximum power density with fuels other than hydrogen. Figure 9: Fuels for solid oxide fuel cell (SOFC).