esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
## 4 Evaluation of Analysis Parameters for Depth Prediction
### Selection of Sound Area  
As discussed above, an inappropriate selection of a sound area may affect the calculations for depth accuracy, making this decision a crucial parameter when depth estimations have to be performed. As a result, different procedures were followed for the respective depth measurements in both the time and frequency domain analyses. The plot of Figure 13b presents the thermal contrast slope evolutions to time for defect A1, after selecting three different sound areas as illustrated in Figure 13a. In particular, sound area 1 was selected next to the defect A1, while sound areas 2 and 3 were selected next to the top and vertical edges of the surface, respectively. As can be seen from this indicative plotting result, the different sound areas produced contrast slope evolution curves with different behaviours.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Def. No**} & **Measured** & **Calculated** & \multirow{2}{*}{**Error (\%)**} & \multirow{2}{*}{**Def. No**} & **Measured** & **Calculated** & \multirow{2}{*}{**Error (\%)**} \\  & **Peak Slope** & **Depth in** & & & & **Peak Slope** & **Depth in** & \\  & **Time in s** & \(\times 10^{-1}\) mm & & & **Time in s** & \(\times 10^{-1}\) mm & \\ \hline
**A5** & \(0.0341\) & **1.97** & \(-\)**1.5** & **B5** & \(0.2045\) & **4.82** & **20.5** \\
**A4** & \(0.0341\) & **1.97** & \(-\)**1.5** & **B4** & \(0.1932\) & **4.69** & **17.2** \\
**A3** & \(0.0341\) & **1.97** & \(-\)**1.5** & **B3** & \(0.1818\) & **4.55** & **13.7** \\
**A2** & \(0.0341\) & **1.97** & \(-\)**1.5** & **B2** & \(0.2045\) & **4.82** & **20.5** \\
**A1** & \(0.0341\) & **1.97** & \(-\)**1.5** & **B1** & \(0.2159\) & **4.96** & **24** \\ \hline \end{tabular}
\end{table}
Table 2: Depth estimations for the defects of Series A and B.  
Moreover, the above described procedure was repeated in the frequency domain. An indicative plotting result for the defect C4 is presented in Figure 14. Similar to time domain analysis, the same sound areas 1, 2, and 3 were used as well as the overall surface as a sound area. The comparison of the different plots of Figure 14 is revealing that an enhanced similarity was produced regardless the selection of the sound area, indicating that this parameter has a negligible impact when working with phase data.
## 6 Challenges and Future Research Scopes
### Federated and Interoperable Service  
When the edge cloud service extends to multiple domains with multiple ownership, it becomes more complex. Design and develop protocols for interoperable services are another challenge in edge cloud networks. This is another active research topic in edge cloud networks. Some research works proposed algorithms for discovery of independent edge providers and negotiation of service deployment and agreement with the provider. [PERSON] et al. [81] proposed an algorithm to use the assets across different Internet Edge Protections (IEPs). In the study demonstrated by [PERSON] et al. [8], the authors presented a cross-domain connectivity management protocol for mobile edge cloud (MEC) in the control plane. Moreover, SDNs are evolving into software defined exchange (SDX) to apply software-defined concepts on large-scale interdomain networking operated by various organizations. SDX also includes features like application-specific peering, load balancing, and steering through network functions. Therefore, this crucial area of edge cloud presents numerous potential research opportunities.
###### Abstract  
Smartphone user authentication based on passwords, PINs, and touch patterns raises several security concerns. Behavioral Biometrics Continuous Authentication (BBCA) technologies provide a promising solution which can increase smartphone security and mitigate users' concerns. Until now, research in BBCA technologies has mainly focused on developing novel behavioral biometrics continuous authentication systems and their technical characteristics, overlooking users' attitudes towards BBCA. To address this gap, we conducted a study grounded on a model that integrates users' privacy concerns, trust in technology, and innovativeness with Protection Motivation Theory. A cross-sectional survey among 778 smartphone users was conducted via Amazon Mechanical Turk (MTurk) to explore the factors which can predict users' intention to use BBCA technologies. Our findings demonstrate that privacy concerns towards intention to use BBCA technology have a significant impact on all components of PMT. Further to this, another important construct we identified that affects the usage intention of BBCA technology is innovativeness. Our findings posit the view that reliability and trustworthiness of security technologies, such as BBCA are important for users. Together, these results highlighted the importance of addressing users' perceptions regarding BBCA technology.  
behavioral biometrics; continuous authentication; privacy concerns; protection motivation theory
## 2 Mathematical Model of the Hybrid MG System
### PEM Fuel Cell System  
The FC stack output voltage comprising \(N_{\mathrm{FC}}\) cells can be mathematically determined using the following equation [32; 33]:  
\[V_{\mathrm{FC}}=N_{\mathrm{FC}}\times V_{Cell} \tag{3}\]  
The power produced by the PEMFC can then be calculated using (4) [32; 33]:  
\[P_{\mathrm{FC}}=V_{\mathrm{FC}}\times I_{\mathrm{FC}} \tag{4}\]  
\(I_{\mathrm{FC}}\) refers to the fuel cell current measured (A), and \(V_{cell}\) represents the output voltage of an individual cell. The expression for \(V_{cell}\) is given in (5) [32; 33]:  
\[V_{Cell}=E_{nerst}-V_{act}-V_{ohm}-V_{conc} \tag{5}\]  
In (3), \(E_{nerst}\) represents the electric potential of the cell, commonly referred to as the Nernst equation. The operating temperature of the PEMFC, represented as \(T\) and measured in _Kelvin_, is a key parameter. The partial pressures of hydrogen and oxygen are denoted as \(P_{H2}\) and \(P_{O2}\), respectively.  
\[E_{nerst}=1.229-8.5\times 10^{-4}(T-298.15)+4.308\times T\times ln\left(P_{H2} \sqrt{P_{O2}}\right) \tag{6}\]  
\(V_{act}\) represents the voltage drop due to the reaction rates at the electrodes and is determined by (5). The fuel cell's performance is influenced by several critical coefficients, denoted as \(\sigma_{1},\ \sigma_{2},\ \sigma_{3}\) and \(\sigma_{4}\). CO\({}_{2}\) represents the concentration of dissolved oxygen at the catalytic interface is computed using (8).  
\[V_{act}=\sigma_{1}+\sigma_{2}T+\sigma_{3}Tln(C_{O2})+\sigma_{4}T\ln(I_{ \mathrm{FC}}) \tag{7}\]  
\[C_{O2}=\frac{P_{O2}}{(5.08\times 10^{6})e^{\frac{-98}{1}}} \tag{8}\]  
\(V_{ohm}\) indicates the voltage loss caused by resistance to proton flow in the electrolyte, as described by (6). The electrode resistance ( \(R_{m}\)) is determined using (9), which accounts for the length of the electrolyte (\(L\)). In addition, \(\mu_{m}\) denotes the membrane water content.  
\[V_{ohm}=I_{\mathrm{FC}}\ R_{m} \tag{9}\]  
\[R_{m}=\frac{181.6\ L}{A}\times\left(\frac{1+0.03\left(\frac{I_{\mathrm{FC}}} {A}\right)+0.0062\ \left(\frac{T}{303}\right)^{2}\left(\frac{I_{\mathrm{FC}}}{A}\right)^{2.5}}{ \mu_{m}-0.634-3\left(\frac{I_{\mathrm{FC}}}{A}\right)e^{\left(\frac{T-303}{T }\right)}}\right) \tag{10}\]  
Lastly, \(V_{conc}\) accounts for the voltage loss resulting from the decrease in gas concentration and is calculated as follows:  
\[V_{conc}=\frac{-RT}{nF}ln\left(1-\frac{I_{\mathrm{FC}}}{J_{\mathrm{FC}}^{max }\ A}\right) \tag{11}\]  
The gas constant, denoted as \(R\), is measured in _mol/Kelvin_ is important in thermodynamic calculations. The Faraday constant, represented by \(F\), is another essential parameter fundamental to electrochemical reactions. The maximum current density of the fuel cell is indicated by \(J_{\mathrm{FC}}^{max}\), with units of Amp/cm\({}^{2}\), reflecting the highest achievable current per unit area. Additionally, A represents the cell area, measured in cm\({}^{2}\), which directly influences the overall performance and efficiency of the fuel cell. Each of these parameters is integral to understanding and optimizing the fuel cell's operation.
## 2 Results and Discussion
### Method Validation
#### 2.1.1 Chromatographic Parameters  
To evaluate the suitability of the HPLC-DAD system according to European Pharmacopeia [45], the retention factor, peak asymmetry, resolution, specificity, and number of theoretical plates were calculated using the Chromeleon 7 Chromatography Data System (Thermo Fisher Scientific, Waltham, MA, USA) Software Version 7.3 (Table 1). The resolution should be >2 between the peak of interest and the closest eluting possible interference, such as impurity, related substance, degradation product, excipient, and internal standard, while the theoretical plates should be >2000 [48]. The high number of theoretical plates (N > 11,500) confirmed the high column efficiency [48]. The asymmetry factors close to 1 indicated a small tailing but precise quantification measurements, according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (T \(\leq\) 2) [45; 48].
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 3 Localization and Mapping
### Global Alignment  
Given a set of frame-to-frame odometry observations, \(\{\Delta\xi_{1},\Delta\xi_{2},\ldots,\Delta\xi_{m}\}\), where each \(\Delta\xi_{i}=(x,y,\theta)^{\mathsf{T}}\in\mathfrak{se}(2)\), we seek to align a pose graph constructed from the odometry observations with a set of global position readings from a GPS receiver. We assume odometry observations to be locally consistent but subject to drift and assume GPS coordinates to be locally bounded by a Gaussian distribution specified by an arbitrarily large covariance matrix that captures the expected errors due to multi-pass signals and geometric dillution of precision. We denote a GPS coordinate as \(\mathbf{g}=(x,y)^{\mathsf{T}}\in\mathbb{R}^{2}\), where \(x\) and \(y\) represent the global position estimate in meters within the Universal Transverse Mercator (UTM) coordinate system. We also convert the translation component of the odometry observations to meters for compatibility with the UTM coordinate frame.
## 3 Method Description  
The MD-SERel model has three components: an encoding layer, a multi-dimensional labeling framework layer, and a decoding layer. The overall process is as follows: First, the text sequence is input into the encoding layer, where the semantic features of each token are extracted using RoBERTa and a multi-head self-attention mechanism. At the same time, syntactic features are captured using Graph Convolutional Networks (GCNs), integrating semantic and syntactic information to generate the final token vector representation for each token. Next, the generated token vectors are fed into the multi-dimensional labeling framework layer, where a scoring function performs parallel scoring in a single stage, producing a relation-dimension-based scoring matrix. Based on this matrix, fine-grained classification obtains the relation label classification matrix, enabling single-stage label mapping. Finally, in the decoding layer, the relation label classification matrix decodes the final triplet recognition results based on the head and tail entity association classifications. The model framework is illustrated in Figure 1.  
Figure 1: MD-SERel model.
## 3 Computational Aeroacoustic (CAA) Model of Human Phonation
### CAA Results
#### 3.3.2 Wave Propagation  
In this section, the sound pressure levels (SPL) in the frequency domain are compared. Six signals (three CFD simulations and two types of vocal tract models) were used for spectral analysis, with the frequency resolution \(\Delta f=5\) Hz and the Hanning windowing. The spectra of the acoustic pressure evaluated at the acoustic probe mic1 (see Figure 7) are depicted in Figure 9 for two types of vocal tracts. The vowel [u:] is characterized by formant frequencies \(f_{F1}^{u}=270\) Hz, \(f_{F2}^{u}=1000\) Hz and \(f_{F3}^{u}=2484\) Hz [53]. The tolerable bandwidths are defined as \(B_{F1}=~{}~{}100\) Hz and \(B_{F2,F3}=~{}~{}200\) Hz. The usage of SGS models does not modify positions of formant frequencies, but it modifies considerably the SPLs (\(L_{F}^{u}\) and \(L_{F}^{\dot{u}}\)), see details in Table 4. The intensity of the third formant \(L_{F3}^{u}\) computed using the WALE model reached 42 dB, compared to 36 dB in the aeroacoustic simulation based on the One-Equation SGS model. All formant frequencies computed by the One-Equation SGS model are weaker compared to the WALE SGS model and the laminar model by 5-7 dB and 6-17 dB, respectively.  
In the case of the vowel [i:], the formant frequencies reported in the literature are \(f_{F1}^{\dot{u}}=289\) Hz, \(f_{F2}^{\dot{u}}=987\) Hz, \(f_{F3}^{\dot{u}}=2299\) Hz. Similar to the previous case, the WALE SGS model has computed the \(L_{F2}^{\dot{u}}\) and \(L_{F3}^{\dot{u}}\) stronger compared to the One-Equation SGS model by 6 dB and 15 dB, respectively.  
Figure 9 also shows that the simulations using the WALE SGS model for both vowels result in significantly enforced higher harmonic frequencies \(nf_{0}\) compared to the One-Equation SGS model for both vowels. In the case of vowel [u:], the harmonic frequencies above 1000 Hz for the WALE SGS model are even stronger than the aeroacoustic output based on the laminar CFD simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Case & \(L_{f_{o}}\) & \(L_{f_{1}}\) & \(L_{f_{2}}\) & \(L_{1235}\) & \(L_{F1}^{u_{i}}\) & \(L_{F2}^{u}\) & \(L_{F3}^{u_{i}}\) \\ \hline LAM-u & 4
## 2 Materials and Methods
### Nanofluids Preparation  
To prepare the Ag-nanofluids, 55 mL of vegetable oil was taken in a glass beaker and the Ag target was immersed in it. A convex lens with a 50 cm focal length was used to concentrate the laser energy on the target. The immersed target within the lubricant was kept 30 cm from the lens. Laser pulse energy/area gives the fluence. An energy meter was used to measure the laser energy (300 mJ/pulse). The normal laser spot size was 10 cm. A lens with a 50 cm focal length was used in this study. The target was set at 30 cm apart from the lens. The actual laser spot size on the Ag target was 4 mm in diameter. The laser energy per area (fluence) was calculated as 2.4 J/cm\({}^{2}\). This laser fluence was used to prepare the nanostructures in various vegetable oils. After aligning the laser using the operation mode, and Q switch mode to obtain 10 nanosecond pulses with high energy, the pulsed laser enhanced mode was used for the ablation process using nanosecond pulses. The laser ablation was performed for 5 and 10 min to obtain two different concentrations of nanofluids. After every minute, a new spot on the target was ablated to avoid the effects of continuous irradiation at the same spot. Stable Ag-nanofluids were obtained in the lubricants. Ag-nanostructures were synthesized in isopropyl alcohol (IPA) for the same ablation time. The Ag nanostructures were evaluated for their morphology by scanning electron microcopy (SEM), average size by dynamic light scattering (DLS), concentration by an inductively coupled plasma-optical emission spectroscopy (ICP-OES), and elemental composition and chemical states by X-ray photoelectron spectroscopy (XPS).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **Properties** & & & & \\ \hline
**Vegetable** & **Density @** & **Viscosity @** & **Viscosity @** & **Viscosity @** & **Refractive** \\
**Fluids** & **20 °C (g/cm\({}^{3}\))** & **24 °C (m Pa s)** & **40 °C (m Pa s)** & **100 °C (m Pa s)** & **Index** \\ \hline
**Soybean Oil** & 0.9604 & 54.3 & 32.93 & 6.79 & 1.47 \\
**Corn Oil** & 0.9100 & 52.3 & 30.8 & 6.57 & 1.40 \\
**Sunflower Oil** & 0.9197 & 68.0 & 40.05 & 8.65 & 1.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material Properties.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 1 Introduction
### Our Goal  
Dynamic data replication in distributed network systems can accelerate information spread in a specific area. However, possible issues with replication-based routing include: network congestion in clustered areas leading to a long delay, being wasteful of network resources (including bandwidth, storage and energy), and network scalability. Since network resources may quickly become constrained, deciding how to replicate the messages plays critical roles in many routing protocols. Meanwhile, some algorithms, such as epidemic and gossip algorithms, could cause significant network overhead by essentially passing around redundant information multiple times, which brings additional communication overhead.  
On the other hand, by increasing the diversity of pairwise exchanges, data replication algorithms improve upon the convergence speed of data dissemination. As load balancing is an important goal in ad hoc networks, we let the data carrier distribute the data dissemination task to multiple vehicles in the area, and we hope every vehicle in the network can carry an approximately equal amount of data dissemination tasks. To achieve this, at each step, every vehicle computes a weighted average (mean of the two numbers) of its own value with values received from some of the other nodes until the network converges to a global average. The global average is calculated as the total number of message copies divided by the number of vehicles being averaged. The average calculation can be utilized to measure the convergence rate and complexity when network consensus is achieved in replication-based data dissemination. It is interesting to investigate how such nodes average their values, trying to achieve a general consensus in the shortest possible time.  
To reduce unnecessary transmissions and improve communication overhead, the number of messages that can be replicated should be bounded when a message is disseminated to a specific area. Besides, through distributed averaging, the dissemination delay will decrease when the computing and communication burdens are distributed among the vehicles, which motivates us to combine distributed averaging with replication-based data dissemination. Therefore, we need to develop a data replication scheme that can control the number of message copies and combine distributed averaging with data dissemination to evaluate network convergence rate through data replication.
# Spin Dependent Transport through Driven Magnetic System with Aubry-Andre-Harper Modulation
## 2 Magnetic Junction, TB Hamiltonian and Theoretical Formulation
### Junction Setup and the Hamiltonian
Within the minimal coupling scheme the irradiation can be simplified through a vector potential \(\vec{A}(\tau)\) like [59; 60]  
\[\vec{A}(\tau)=\left\{A_{x}\sin(\Omega\tau),A_{y}\sin(\Omega\tau+\phi)\right\} \tag{4}\]  
where \(A_{x}\) and \(A_{y}\) represent the amplitudes and \(\phi\) corresponds to the phase. Depending on these parameters we get linear, elliptical and circularly polarized lights. The vector potential satisfies the relation \(\vec{A}(\mathcal{T}+\tau)=\vec{A}(\tau)\) where \(\mathcal{T}\) (=2\(\pi/\Omega\)) is the time period of the driving field. \(\vec{a}\) is the lattice vector and \(j=\sqrt{-1}\). As we are working with a strictly 1D system, \(\Lambda\) simplifies to \(\Lambda=A_{x}a\), and, \(A_{y}\) and \(\phi\) do not have any explicit roles in our analysis. The other three sub-Hamiltonians of Equation (1) can be written in a much simpler way as they do not include any kind of magnetic interaction and irradiation. They are expressed as:  
\[H_{\mathrm{S}}=\sum_{i\leq-1}a_{i}^{\dagger}\epsilon_{0}a_{i}+\sum_{i\leq-1} \left(a_{i+1}^{\dagger}t_{0}a_{i}+h.c.\right) \tag{5}\]  
\[H_{D}=\sum_{i\geq N+1}b_{i}^{\dagger}\epsilon_{0}b_{i}+\sum_{i\geq N+1}\left(b _{i+1}^{\dagger}t_{0}b_{i}+h.c.\right) \tag{6}\]  
\[H_{tun}=c_{1}^{\dagger}t_{S}a_{-1}+c_{N}^{\dagger}t_{D}b_{N+1}+h.c. \tag{7}\]  
where \(a_{i}\) and \(b_{i}\) are of the similar form like \(c_{i}\), and they contain usual fermionic creation and annihilation operators. The electrodes \(S\) and \(D\) are parametrized by on-site energy \(\epsilon_{0}\) and \(t_{0}\), respectively. The parameters \(t_{S}\) and \(t_{D}\) represent the coupling strengths of S and D with the magnetic chain, respectively.
## 2 Materials and Methods
### Feasibility Pilot Study
#### 2.2.1 Sample  
We enrolled 30 consecutive patients after ischemic or hemorrhagic stroke, aged between 47 and 82 years (mean time since onset: \(120\pm 46\) days). Patients were recruited at the two centers of Don Gnocchi Foundation Onlus of Rome: (i) Santa Maria della Provvidenza, where the robotic/technological devices were installed (Robotic Center), and (ii) Santa Maria della Pace (Conventional Center). Inclusion criteria were: sub-acute patients (time latency since stroke ranging from two weeks to six months) after only one ischemic or hemorrhagic stroke, verified by MRI or CT; between 40 and 85 years old; time latency since the stroke between two weeks to six months; and the ability to understand simple instructions. Exclusion criteria included: fixed contraction deformity in the affected limb that would interfere with active therapy (ankylosis, Modified Ashworth Scale = 4) and severe deficits in visual acuity. Patients from the Robotic Center were treated by means of robotic devices for the upper limb (robotic group, RG, \(n=16\)), while patients from the Conventional Center were treated according to conventional rehabilitation protocols for the upper limb (conventional group, CG, \(n=14\)).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Weight in the Ranking Formula** \\ \hline Provides outcome measures & 3 \\ Provides normative values & 1 \\ Safety issues & 1 \\ Contrainidications & 1 \\ Literature supporting efficacy & 3 \\ Purchase priority & 3 \\ Level of impairment & 2 \\ Customizable exercises & 2 \\ Efficiency level & 4 \\ \hline \end{tabular}
\end{table}
Table 3: Weights used in the formula for calculating the score of a robotic solution.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ANOVAANalysis Of VAriance  
APARAbsorbed PAR  
ASFYApparent Spectral Fluorescence Yield  
ChlChlorophyll  
ChlFChlorophyll Fluorescence  
DOYday of the year  
ECEEEEdydCovariance  
ESAEuropeanSpaceAgency  
fAPARfraction of APAR  
FLDFraunhofer Line Discrimination  
FRFFar Red Fluorescence  
FRSIFFarRedSIF  
FWHMFullWidth at Half-Maximum  
GPPGrossPrimaryProduction  
LAILeafAreaIndex  
LUELightUseEfficiency  
NDVINormalizedDifferenceVegetationIndex  
NEENet EcosystemExchange  
PARPhotosyntheticallyActiveRadiation  
PRIPhotochemicalReflectanceIndex  
PSIIPhotosystemIPhotosystemII  
RFRedFluorescence  
RSIFRedSIF  
SFMSpectralFittingMethod  
SFYSpectralfluorescence yield  
SIFSun-InducedFluorescence  
TOCTopOfCanopy  
UTCCoordinatedUniversalTime