esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 2 Background and Related Works
### Feature Extraction
#### 2.2.1 Wavelet Packet Transform  
Wavelet packet transform (WPT) is an extension of wavelet decomposition (WD) [49]. This method is chosen for its efficiency for non-stationary signal denoising, compression and classification. It also allows the features to have more discrimination power than the features from discrete wavelet transform.  
The wavelet packet transform function of a signal \(f(x)\) can be defined as [50]:  
\[W_{s}f(x)=f(x)*\Psi_{s}(x)=\frac{1}{s}\int_{-\infty}^{+\infty}f(t)\Psi(\frac{ x-t}{s})dt, \tag{9}\]  
where \(s\) is a scale factor. \(\Psi_{s}(x)=\frac{1}{s}\Psi(\frac{x}{s})\) is the dilation of a basic wavelet \(\Psi(x)\) by the scale factor \(s\).  
Figure 5: Feature Extraction for the Proposed Model.  
The scale factor acts as a linear operator and divides the signals into two components: approximation and detail. The approximation then can split itself into another approximation and detail. The process can be repeated till the signal that correlates well with the frequencies required for the classification of the signal is retained in the wavelet coefficients [51]. Figure 6 shows the wavelet tree for the ECG signal from one of the datasets.  
However, with wavelet packet decomposition, the massive size of the decomposed coefficient can be a hurdle for classification. Entropy has been introduced to tackle such issues in WPD. It is prevalent in measuring the uncertainty of data in signal processing. There are different types of entropy. Some of them are Shannon's entropy (SE), Log Energy Entropy (LEE), Renyi Entropy (RE), and Tsallis entropy (TE) [52].  
The entropy function is defined is as follows:  
\[E_{ij}=\sum_{k=1}^{N}E_{ijk} \tag{10}\]  
\[E_{ij}=<x(i),x(j),x(k)>=|d_{jk}|^{2}, \tag{11}\]  
where k is the number of coefficients, \(j\) is the number of nodes and \(i\) is the level on the node. The probability of the coefficients can be calculated as follows:  
\[Px_{i}=\frac{E_{ijk}}{E_{ij}}. \tag{12}\]  
The entropy can be defined as follows:  
\[H(X)=-\sum_{i=1}^{n}Px_{i}log_{2}Px_{i}, \tag{13}\]  
where \(P\) represents the probability, \(x_{i}=1,....,i\) represents the possible outcomes, being:  
\[0<Px_{i}<1,\sum_{i=1}^{n}Px_{i}=1. \tag{14}\]  
Figure 6: ECG Wavelet Tree.
## 3 Results and Discussion
### Effect of Different Factors on the Stability of the Hybrid Nanofluids
#### 3.1.1 Influence of pH  
The pH of nanofluids is usually adjusted to increase the charges on the surface of the nanoparticles, which keeps the surface potential of the nanoparticles away from isoelectric point so as to enhance the electrostatic repulsion and stability of the nanofluids. The pH adjustment is suitable for electrostatic stabilization mechanism of colloidal particles that can be described by the DLVO (Derjaguin, Landau, Verwey and Overbeek) theory, i.e., the total interaction between electrostatic stabilized particles is the combination of van der Waals attraction and electrostatic repulsion [64].  
Hybrid nanofluids of 0.1% mass fraction with pH = 3, 7, and 11 were prepared and ultrasonically dispersed for 60 min. Figures 4 and 5 show, respectively, the condition of the nanofluids after one and seven days. The hybrid nanofluid with pH = 3 has completely precipitated after 7 days, and the hybrid nanofluid with pH = 7 had a large amount of precipitation, while the hybrid nanofluid with pH = 11 had no obvious change and showed very good stability. The same trend is observed for bottom precipitation, as shown in Figure 5. The pH had a great influence on the stability of the hybrid nanofluid, and the hybrid nanofluid of pH = 11 was shown to have the best stability. This is mainly due to the fact that Al\({}_{2}\)O\({}_{3}\) nanoparticles hydrate to produce hydroxyl groups, and the hydroxyl groups dissociate to charge the surface of the nanoparticles [65]. There are many hydrophilic acidic functional groups on the surface of the graphene oxide nanoparticles, which have strong ability to adsorb anions. Under alkaline conditions, a large amount of OH\({}^{-}\) ions were continuously adsorbed on the surfaces of Al\({}_{2}\)O\({}_{3}\) and GO nanoparticles to form double electric layers around the nanoparticles, which produced repulsive forces between the particles and prevent agglomeration and precipitation caused by collision between particles. Moreover, larger electrostatic repulsion also increased the distance between particles, which decreased van der Waals attraction and, thus, reduced the probability of particles agglomerating and sedimentation, and further improved the stability of the nanofluids [66].  
Figure 4: Photos of 0.1 wt % hybrid nanofluids after: (**a**) 1 day and (**b**) 7 days.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 2 Methods
### Motion Artifact Reduction in Reconstruction  
To reduce motion artifacts, iterative blind deconvolution algorithm is inserted into the iterative process of OSEM reconstruction. Hence, Equation (1) can be modified as  
\[\mu_{j}^{n+1}=\frac{\left(f^{n}*h^{n}\right)_{j}}{\sum_{i\in S_{b}}a_{ij}}\sum _{i\in S_{b}}a_{ij}\frac{y_{i}}{\sum_{l}a_{il}(f^{n}*h^{n})_{l}}. \tag{6}\]  
In Equation (6), it is assumed that the estimation of degraded image (\((f^{n}*h^{n})\)) is still a Poisson distribution. The corresponding projection of estimated motion-degraded image are compared with the measured projection data to estimate the ideal image and PSF. The overall process of the BD-OSEM method is shown in Figure 1.  
As shown in Figure 1, \(px\) represents input image size. The result of each OSEM-iteration is used as the degraded image for the BD-iteration (\(g_{0}(\mathbf{S})=\mu_{j}^{n}\)), while the result of the BD-iteration is returned to the OSEM-iteration as intermediate value for the next iteration. To stop the iterations, a difference measure between two successive iterations is defined [29]. If the difference is smaller than a threshold, the computation is stopped, and the last estimation is the best one. The criterion is defined as the following:  
\[\tau^{n+1}=\frac{\sum_{\mathbf{S}=1}^{\mathbf{N_{s}}}\bigl{|}\hat{\mu}^{n+1}( \mathbf{s})-\hat{\mu}^{n}(\mathbf{s})\bigr{|}}{\sum_{\mathbf{S}=1}^{\mathbf{N_ {s}}}\hat{\mu}^{n}(\mathbf{s})}<r, \tag{7}\]  
Figure 1: Flow chart of the combination of blind deconvolution and ordered subset expectation maximization reconstruction (BD-OSEM).  
\[\tau_{k+1}=\frac{\sum_{\mathbf{S}=1}^{\mathbf{N_{s}}}\left|\hat{f}_{k+1}(\mathbf{s })-\hat{f}_{k}(\mathbf{s})\right|}{\sum_{\mathbf{S}=1}^{\mathbf{N}}\hat{f}_{k}( \mathbf{s})}<r. \tag{8}\]  
In Figure 1, if \(\tau_{k}\geq r\), the result of the BD-iteration will be returned to the next BD-iteration. If \(\tau_{k}<r\) and \(\tau^{n}\geq r\), the result of the BD-iteration will be inserted to the iteration of OSEM, which is used as intermediate value. Note that the process of BD-OSEM is stopped at two or more iterations; hence, \(\tau_{k}<r\) and \(\tau^{n}<r\) should be satisfied.
## 4 Distributed Hotspot Network Architecture
### Network Servers/Operators  
Network servers/operators are the entities, the standard ISPs, that can be used in this architecture as the domain name servers or the path forwarders. Such providers enable wireless bearer services, especially cellular services for the end applications or users. To accomplish this task, the operators perform radio frequency allocation, end-user support, network process maintenance, and network equipment provisioning. These operators are also able to generate revenue and charge end-applications or customers according to their agreement or network usage policy. In addition, mobile virtual network operators (MVNOs) can be used as leased network service providers under the major network operators.
## 5 ACTOR Design
### Routing Metric  
ETX is the most commonly used routing metric for RPL. We updated ETX to account for the transmission power control mechanism. Calculating ETX in the open implementation of Contiki consists of applying an _Exponentially Weighted Moving Average_ (EWMA) to the ETX, as in Equation (10). The factor \(\alpha\) determines the weight based on which new observations are preferred to the old ones.  
\[E\bar{T}X_{t}(p)=\frac{ETX_{t-1}(p)\cdot(Scale-\alpha)+PacketETX\cdot\alpha}{ Scale} \tag{10}\]  
ACTOR's calculation of ETX must consider that the transmission power is changing. For example, if a node temporarily tries to evaluate a link with a low transmission power, the ETX associated with this transmission power should not be advertised to the farther nodes. Hence, for each neighbor, ACTOR maintains a table (rather than a single value) for the ETX associated with each transmission power, according to Equation (11).  
\[ETX_{t}(p,a)=\frac{ETX_{t-1}(p,a)\cdot(Scale-\alpha)+PacketETX\cdot\alpha}{ Scale} \tag{11}\]  
When the trickle algorithm triggers the transmission of a DIO, the ETX value that is advertised is associated with the transmission power that maximizes the ETX index at that point in time.  
\[ETX_{t}(p)=\max_{A_{t}\in A}ETX_{t}(p,A_{t})\]
# Content Adaptation and Depth Perception
## 5 User Studies
### Results
#### 5.5.2 Accuracy  
Error rate is shown in Figure 10, split by condition and subtask. In the Order task, accuracy was 3.4% (SD = 7.56) for SliceView and 3.4% (SD = 11.1) for LookingGlass; no significant difference was found (t(21) = 0.015, \(p\) = 0.988). In the Range task, it was 9.2 (SD = 3.61) for SliceView and 10.31 (SD = 6.07) for LookingGlass; the difference was not significant (t(21) = \(-\)1.046, \(p\) = 0.307). In the Compare task, it was 2.04% (SD = 3.98) for SliceView and 10.9% (SD = 4.53) for Looking Glass; there was a significant difference (t(21) = \(-\)7.492, \(p\) = \(<\) 0.001). In the Readability task, it was 0.95% (SD = 0.95) for  
Figure 10: Task Completion Time and Accuracy split by condition and subtask. (** refers to \(p\)-value \(\leq\) 0.01; *** refers \(p\)-value \(\leq\) 0.001).  
SliceView and 1.22% (SD = 1.02) for Looking Glass; the difference was not significant (t(21) = \(-\)1.567, \(p\) = 0.137).  
Accuracy was similar in both conditions when performing ranging and ordering subtasks, but significant differences were observed when performing the comparing subtasks. We hypothesise that these differences may be caused by the differences in resolution between both displays and by the capabilities of both devices when providing vertical motion parallax.
## 2 Processing of Ceramics with Aurivillius Type Structure
### Doping Strategies  
A route followed during several years to increase the piezoelectric coefficients in sintered ceramics is the doping of the structure. Values of \(d_{33}\) higher than 30 pC/N have been published for titanates and niobates Aurivillius with different number of pseudo-perovskites layers by this strategy. Thus, [PERSON] et al. reported a value of \(d_{33}\) = 37 pC/N in a ceramic with multi-substitution Li\({}_{0.04}\)Ce\({}_{0.04}\)Na\({}_{(0.46-x/2)}\)Bi\({}_{(4.46+x/2)}\)Ti\({}_{(4-x)}\)Sc\({}_{x}\)O\({}_{15}\) after a thermal treatment in oxygen atmosphere [58]. [PERSON] et al. achieved values of \(d_{33}\) = 33 pC/N for Na\({}_{0.5}\)Bi\({}_{4.5}\)Ti\({}_{4}\)O\({}_{15}\) doped with Co [59]. d\({}_{33}\) values of 31 pC/N have been also reported for Na\({}_{0.5}\)La\({}_{0.5}\)Bi\({}_{2}\)Nb\({}_{2-x}\)W\({}_{x}\)O\({}_{9}\)[60] and (Li,Nd,Ce)\({}_{0.06}\)(Na,Bi)\({}_{0.44}\)Bi\({}_{2}\)Nb\({}_{2}\)O\({}_{9}\)[61] compositions. The reasons for the piezoelectric improvement by doping are the enhancement in local lattice distortion caused by LiNd and LiLa co-substitution and the high applied field due to fewer defects, as oxygen vacancies in the perovskite layers, which reduces the electrical conductivity. This leads to an increase of remnant polarization by reducing the leakage current. [PERSON] et al. reported d\({}_{33}\) values of 30 pC/N in SrBi\({}_{4}\)Ti\({}_{4}\)O\({}_{15}\) doped with Mn [62]. The enhanced piezoelectric performance can be attributed to the substitution of B-site Ti\({}^{4+}\) ions by Mn\({}^{3+}\) ions, which  
Figure 7: Piezoelectric constant, \(d_{33}\), of the random and textured Nb-doped Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\) sintered for 2 h showing the anisotropy of the piezoelectric properties (taken from [51]).  
induced BO\({}_{6}\) distortions. In addition, large grain size and high bulk densities reduced dielectric loss, contributing to the enhancement of the piezoelectric activity. All these coefficients were accomplished in materials with Curie temperature ranging from 500 to 796 \({}^{\circ}\)C. In those works, the authors prove that the ceramics are not depoled up to the transition temperature but the piezoelectric coefficients are not given at the potential working temperature. However, it is known that the piezoelectric activity decreases as the temperature increases, although values are high enough to allow their uses as high temperature piezoelectrics [63], as can be observed in Figure 8.
## 6 Discussion  
In a smart grid environment new challenges are posed to the power system operators by electric vehicles. Several studies have been proposed in the literature presenting control and optimization strategies for managing the charging/discharging of the electric vehicles' batteries [30]. However, the communication requirements still remain an open issue. In fact, different charging and discharging management strategies can be adopted ranging from a fully-centralized charge control decision, to distributed (or transactive), and to price control, which has limited communication requirements [31]. In the first case, the decisions are taken at the system level, hence it is generally accepted that it is the best control system in terms of, e.g., security of the power system. However, a more sophisticated communication infrastructure is needed that foresees bidirectional communication flow at the price of higher cost. Power line communication technology stands as a good candidate provided that its design is optimized. The design procedure presented in this paper, allows one to solve the bit-loading problem, finding a compromise among the conflicting objectives of minimal signal power and \(BER\), while maximizing the \(BR\). Among the Pareto-optimal solutions, provided by the optimization process, the designer can perform the definitive choice depending on the hardware and the communication requirements. Once this choice is made, the entire constellation system is designed.  
Conceptualization and analyses were done through the collaboration of all the authors. Software development was done by S.C. Data validation was done by [PERSON] wrote and reviewed the paper. All authors have read and approved the final manuscript.  
This research has been funded by \"Fondazione di Sardegna\" under project \"ODIS--Optimization of Distributed systems in the Smart-city and smart-grid settings\", CUP: F72F16003170002.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
## 3 Results
### Modelling Stress Build-up Associated with CTE Mismatch during Thermal Cycle Aging
Using (3), the tensile stress induced in the coating by the CTE mismatch between both layers (the substrate and the coating) is calculated:  
\[\left[\Delta\sigma_{x}\right]_{a}=\frac{E_{a}\;(\alpha_{s}-\alpha_{a})\Delta\;T }{(1-v_{a})+\frac{h_{a}}{h_{s}}\frac{E_{a}}{E_{s}}(1-v_{s})} \tag{4}\]  
We observe that Equation (4) includes thermal and mechanical components via the thermal dilatation coefficient \(\alpha\) and Young's modulus and Poisson's ratio of the coating and the substrate. Silicone coatings have a Young's modulus lower by multiple orders of magnitude than the PCB Young's modulus, and coating thickness is significantly less than substrate thickness. Therefore, it can be written in good approximation:  
\[\left[\Delta\sigma_{x}\right]_{a}=\frac{E_{a}\;(\alpha_{s}-\alpha_{a})\Delta \;T}{(1-v_{a})} \tag{5}\]  
With those simplification assumptions, we observe that tensile stress is a linear function of both Young's modulus of the coating and the CTE mismatch. In addition, we notice that lateral tensile stress no longer depends on the thickness of the coating and is not a function of the lateral position with respect to plate edges. Those well-known Formulas (4) and (5) [17; 23] are valid for minor deflection of the bi-layer sheet, an assumption valid in present conformal coating application because of the large difference in substrate and coating thickness and the low elastic modulus of silicone materials. Equation (5) provides a reasonable estimate of the lateral tension stress build-up associated with the difference in CTE between the two bi-layer materials. Lateral tension  
Figure 5: Conformal coating on PCB substrate schematic view picture. stress has no spatial dependence, assuming the stress is constant along the interface plane. Therefore, it cannot predict coating delamination often observed at the edge of the plate. Restricting material evaluation to Equation (5) does not consider the large shear stress that can develop close to the edges of the bi-material sheet. When moving away from the bi-layer plate edge, shear stress decreases to become negligible, and tension stress becomes dominant. Including localized spatial shear stress in the coating evaluation is important because high shear stress near the edge can initiate delamination. [PERSON] and [PERSON] have studied [24] the shear stress distribution considering thin film assumptions, the film being thin enough to be modeled as a membrane. Authors propose an integrodifferential equation predicting shear stress spatial distribution along the lateral plate's dimension (along x or y in Figure 5) in the function of an imposed strain (that can be associated with a CTE mismatch). They derived two approximated analytical solutions of the stress distribution equation, one solution valid close to the edge of the bi-layer plate and one valid far from the edge.
## 3 Results  
This study uses the following three criteria to assess orbit precision: (1) Overlapping arcs for internal consistency [2; 11]. The precision evaluation of the overlapping arcs is conducted using the consecutive three-day arc orbit solutions. The orbit of the last day of the first three-day solution and the orbit of the middle day of the second three-day solution are compared; (2) Comparison with the GBM multi-GNSS orbit products from the GFZ analysis center [13], which can be download from the FTP web site ([[ftp://cddis.gsfc.nasa.gov/pub/gps/products/mgex/](ftp://cddis.gsfc.nasa.gov/pub/gps/products/mgex/)]([ftp://cddis.gsfc.nasa.gov/pub/gps/products/mgex/](ftp://cddis.gsfc.nasa.gov/pub/gps/products/mgex/))); (3) SLR independent accuracy assessment for external validation [30; 31]. SLR retro-reflector offsets for these satellites are given at ([[https://ilrs.cddis.eosdis.nasa.gov/missions/satellite_missions/current_missions/cmg1_com.html](https://ilrs.cddis.eosdis.nasa.gov/missions/satellite_missions/current_missions/cmg1_com.html)]([https://ilrs.cddis.eosdis.nasa.gov/missions/satellite_missions/current_missions/cmg1_com.html](https://ilrs.cddis.eosdis.nasa.gov/missions/satellite_missions/current_missions/cmg1_com.html))) on the ILRS website [31]. The SLR station coordinates are fixed to the a priori SLRF2008. The station displacement models are applied consistently with the GNSS solutions. The troposphere delays, relativistic effects, and the offset of the LRAs with respect to the satellites' centers of mass are corrected in the SLR observations [30]. The two-step data screening procedures are employed for SLR processing [30]. A comparison of the four strategies using the above three criteria is shown below to verify the influence of the ground station distribution and joint orbit determination strategy on the BeiDou POD.
## 3 Results and Discussion
#### 3.1.1 Amino Acids and Amines  
Several amino acids and amine derivatives were eluted early, as detected in chromatograms at Rt. 4-11 min. (Figure 1), and grouped in two major clusters, i.e., G and C, in negative and positive MNs, respectively (Figure 2 and Figure S1), from which 14 metabolites could be annotated in peaks 2-15 (Table 1). The identified metabolites included four amines and 10 derivatives of amino acids _viz._ lysine, iso/leucine, phenylalanine, and glutamine, in which decarboxylation (-CO\({}_{2}\), 44 amu), demethylation (-CH\({}_{2}\), 14 amu), deglycosilation, deamination (-NH\({}_{2}\), 17 amu) and dehydration (-H\({}_{2}\)O, 18 amu) were the major fragmentation pathways matching the reported literature, GNPS library, and HMDB and MassBank databases. All identified metabolites were common between both fruits except for peaks 14 and 15 (Table 1), which were detected in _L. siceraria_ at [M+H]\({}^{+}\) 267.1337, C\({}_{13}\)H\({}_{19}\)N\({}_{2}\)O\({}_{4}\)\({}^{+}\) and [M+H]\({}^{+}\) 245.1859, C\({}_{12}\)H\({}_{25}\)N\({}_{2}\)O\({}_{3}\)\({}^{+}\), identified as N-(methoxybenzyl)glutamine, which is previously reported in Cucurbitaceae [15] and Iso/Leucyl-Iso/Leucine dipeptide, respectively.