esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Independent Variables and Data Collection  
Eurostat provides data of GHG emissions generated in each MS since 2000. However, the EU has undergone a great evolution in these two decades due to legislative changes aimed at mitigating the effects of climate change. Therefore, to develop a more accurate and updated predictive model, only the decade between 2010 and 2020 is adopted. In addition, Eurostat does not offer public data in years near to 2000 in some of the independent variables needed to develop the algorithm.  
The analysis considers data taken from the Eurostat database for 27 EU countries (EU-27): Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland and Sweden. Every country is expected, in addition to the Paris Agreement, to transpose the European Directives, with a 2030 target to reduce GHG emissions by 55% compared to 1990 (Fit For 55), and ultimately be carbon neutral by 2050. This, together with comparable growing economies and similar population densities of the EU member states, guarantees a solid and consistent analysis.  
During the preliminary analysis required for the development of the algorithm, several metrics have been analysed: gross domestic product (GDP), country surface area, population, construction activity, energy consumption, renewable energy share (RES), employment in construction, number of green buildings certifications, GHG emissions and heating degree days. However, the variables that appear to influence the most GHG emissions are three, as highlighted in the literature review [20; 21; 22; 23; 24; 25; 26; 27; 28]: GDP, population and RES. For this, data of GDP (in million euros), population (in million people) and RES (in % of renewables over total energy use) of each of the analysed years are collected from Eurostat. If polluting gas emissions like particulate matter were considered, a methodology similar to the one followed by [PERSON] et al. [29] would have been adopted.  
Moreover, the Pearson correlation between the GHG emissions and the three factors is analysed, and results are presented in Table 1.  
The findings indicate a moderate correlation (\(\geq\)0.40) of the variable RES, while GDP and population obtained a higher correlation with the GHG emissions (>0.95). As a result, the correlation in the three independent variables analysed is considered significant (sig. < 0.05) with 95% confidence, and therefore the three independent variables are used in the algorithm.
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
#### Limitations of Related Work and Our Contributions  
Table 1 summarizes the problems in existing approaches. Previous methods have at least one of the following weakness:  
* Models discussed in [44; 45; 46] were tested on less diverse databases.
* The work in [47; 48; 49] is experimented with a small number of classes.
* Dependence on a fixed set of handcrafted features, which requires extensive knowledge about the image characteristics [50]. They rely on texture analysis, where a limited set of local descriptors computed from an image is fed into a classifier such as random forest, etc. Despite a good level of accuracy in some works, these techniques have limitations in generalization and transfer capabilities in inter-database variability.
* Inefficient algorithms, resulting in higher computational costs and time utilization [38].
* The model in [51] uses a semi-automatic process to select features and, by doing so, important features might be missed that might help in better classification.
* Though the model discussed in [52] gives very good accuracy, it misclassifies two classes: anger and sadness.
* The model discussed in [53] generates too many interest points, which might be crucial when handling large databases.  
Contrary to the previous works, the proposed approach does not rely on a semi-automatic process for feature selection but computes features automatically. In addition, the proposed approach correctly classifies the two classes, sadness and anger. Further, the use of a diverse and large number of classes and images in the proposed method makes the model more reliable. In this paper, we present the following major contributions:  
* We have proposed the hybrid model: a fusion of CNN and SVM for facial expression data.
* To extract facial motion characteristics efficiently, deep learning models are used for the dense facial motion flow, which was the extracted optical flow from the initial frame (neutral expression) to the given frame of sequence.
* We extracted the displacement direction and strength of the facial landmark points for the geometric displacement, which capture the characteristics of the local displacement of facial expressions efficiently for SVM.  
We have used an optimal weighting combination of the hybrid classifiers, which provides better facial expression recognition results than individual classifiers.
* In order to evaluate the model, the CK+ and BU4D databases were used. We secured 99.69% recognition performance for the CK+ database and 94.69% recognition performance for the BU4D database.
## 4 Materials and Methods
### Ascorbic Acid Extraction and Analysis by HPLC  
Ascorbic acid was extracted and determined essentially as described in [PERSON] et al. [46]. Briefly, 0.5 g of tissue was homogenized for 1 min using a Polytron homogenizer (Polytron, Kinematica, Eschbach, Germany) with 0.1% metaphosphoric acid (4 mL). The homogenate was then centrifuged for 10 min at 4500\(\times\)\(g\) at 4 \({}^{\circ}\)C. The supernatant was passed through a C\({}_{18}\) cartridge (SepPak, Waters, Barcelona, Spain), previously activated with 4 mL of methanol, 4 mL of water and 4 mL of 2% metaphosphoric acid. The extract was subsequently filtered through a 0.45 \(\upmu\)m nylon filter (25 mm diameter, Analisis Vinicos, Tomelloso, Spain). This filtrate was directly used for AsA determination, while a 500 \(\upmu\)L aliquot of the same filtrate was incubated for one hour in the dark with 5 \(\upmu\)L of DTT (500 mM) for the determination of total vitamin C. Determination of total vitamin C content and AsA was carried out using a Dionex HPLC system with a photodiode array detector (PDA) and Chromleon software (Dionex, Thermo Fisher Scientific, Barcelona, Spain). An Ultrabase C\({}_{18}\) column (100 \(\times\) 4.6 mm, 2.5 \(\upmu\)m) and a mobile phase of methanol: water pH 2.5 (adjusted with metaphosphoric acid, 15:85, \(v/v\)), 0.2 mL min\({}^{-1}\) flux and injection volume 10 \(\upmu\)L. The temperature of the column was set at 35 \({}^{\circ}\)C. The method was calibrated with a curve of an ascorbic acid standard solution in 2% metaphosphoric acid, with concentrations between 1 and 100 \(\upmu\)g/mL. Dehydroascorbic acid content was calculated as the difference between total vitamin C and AsA contents.
# Model for Quantifying the Quality of Secure Service
## 5 Case Studies of Multi-Channel QoS
### Implications of Results  
The most difficult aspect of developing the QoSS metrics is making assumptions about the network characteristics. For these examples, we began with an assumption that \(P(dcr)=1\) was a baseline value that an adversary would be able to access all critical data. What does this mean for \(P(dcr)=1\)? Perhaps the assumption implies that no encryption is used, despite the fact that the use of encryption is strongly encouraged for all communications systems.  
Similarly, is this possible for \(P(dcr)=0\)? This assumption implies that the encryption is unbreakable at this time and under these communication and environmental conditions. The fact that we do not know the adversary's fullest capabilities, nor do we know the adversary's intentions, are considerations that must be included, within a range, in the estimate for the probabilistic aspect of our metrics. More accurately, we estimate what is possible within the current state-of-the-art and under a set of operational characteristics.  
Adversarial intention is much more difficult to estimate; intentions may change rapidly or may vary on a case-by-case basis. In light of that, we have attempted to reflect all the adversarial intentions, whether it is jamming, spoofing, or eavesdropping, within the generalized probability of confidentiality and probability of integrity. With these estimations, both adversarial capability and intention are difficult to concretely quantify in the initial pass, and they are, thus, cast in probabilistic terms.  
This version of the QoSS model is a single snap-shot in time; a time-varying QoSS model is in development in which the model estimations may be updated based on new research, information, or changing environmental and systemic conditions. As the QoSS model becomes more mature and broadly adopted, future iterations will benefit from increased understanding of these probabilistic approaches and an initial coarse estimate for design requirements may converge to refined security requirements if applied in an iterative manner.  
These iterations point directly to the eventual need for a simulation environment and all the supporting protocols that allow for the verification and validation of the security metrics. To achieve that end, the network performance will need to be influenced by a simulated adversarial actor, and the amount of data leakage or corruption will be directly quantified based on the amount of transmitted data. Only with that final step of validation will we be certain that the model portrays a realistic version of a communication network.
## 2 Materials and Methods
### Characterisation Techniques
Bronze specimens with artistic artificial patinas were investigated also by means of Voltammetry of Immobilised Micro-Particles (VIMP) during a previous work [64]. Linear Polarisation Resistance (LPR) and Electrochemical Impedance Spectroscopy (EIS) were performed on all the studied surfaces with a portable potentiostat Ivium Technologies CompactStat with Ivium(r) software 4.993 (Ivium Technologies B.V., Eindhoven, Netherlands), employing a three electrodes configuration, employing the ContactProbe proposed by [PERSON] [75], constituted by AISI316L stainless steel counter (CE) and pseudo-reference (RE) electrodes embedded in a PTFE case. With such setup, the constant presence of the electrolyte on the surface during the measurements is granted by using a sponge with long \"tails\" soaked in an electrolyte reservoir. For all measurements, an oligomineral water with pH around 8 and conductivity around 200 \(\mu\)S/cm was used as electrolyte. The use of mineral water for this aim was proposed in the literature as a good alternative to the traditionally used salt solutions for the measurements on artistic surfaces. In fact, it is characterized by a good conductivity, and guarantees at the same time the lowest invasiveness with respect to the analysed surface. LPR measurements were performed after 10 min of monitoring time (MT) of the open circuit potential (OCP). The potential was then varied of \(\pm\)10 mV with respect to the measured E\({}_{\text{corr}}\), with a scan rate of 10 mV/min. EIS measurements were performed after 5 min of monitoring time (MT) and 5 min of stabilization of the surfaces through the application of few nA currents. The following setup was adopted: frequency range between 100 kHz and 10 mHz with \(\pm\)10 mV with respect to E\({}_{\text{corr}}\). Starting from LPR and EIS results, polarization resistance (RP) values were calculated. It can be calculated considering the slope of the line resulting from LPR, or it can be calculated as the difference in the impedance modulus \(|Z|\) at high and low frequencies [90; 106]. Such value is inversely correlated to the corrosion resistance of the analysed surface.
## 2 Materials and Methods
### Original Artwork and Experimental Design
#### 2.1.3 Application of Protective Coatings  
Four protective coatings were formulated using chitosan and cellulose nanocrystals (CNC), both of which are biocompatible, renewable polysaccharides widely studied for their film-forming, mechanical, and barrier properties [54; 55; 56; 57]. To synthesise the nanocomposite, chitosan was dissolved in an aqueous solution, as described by [PERSON] et al. (2001) [57], while nanocellulose was produced using a ball mill and subsequently dispersed in water at a concentration of 0.7%, as reported by [PERSON] et al. (2019) [56]. The coatings were prepared by mixing chitosan with three different concentrations of CNC to investigate the influence of filler content on coating performance. A preservative (2.5% \(w/w\) sodium benzoate) and UV stabiliser (Tinuvin 292, 1-1.5% \(w/w\)) were added to all formulations. Nanocellulose was produced via a top-down approach using a Retsch PM 100 planetary ball mill, operating at 500 rpm for 30 min with 5 mm diameter grinding balls [58]. Chitosan and sodium benzoate were purchased from Sigma-Aldrich (Sigma-Aldrich Corporation, St. Louis, MO, USA), while Tinuvin was supplied by CTS Conservation (CTS srl, Altavilla Vicentina, Italy).  
The four resulting coatings were as follows:  
* Chitosan (CS);
* Chitosan with 1.5% nanocellulose dispersion (CS/CNC1.5);
* Chitosan with 3% nanocellulose dispersion (CS/CNC3);
* Chitosan with 6% nanocellulose dispersion (CS/CNC6).  
Each coating was applied by brush in a uniform layer over the surface of the painted experimental banknotes. After application, the samples were dried at ambient conditions (22 \(\pm\) 2 \({}^{\circ}\)C, 45 \(\pm\) 5% RH) for 7 days.
## 3 Results and Discussion
### Mixing Behavior in Laser Cold-Wire Test Welds
The results given in Figure 16 show that the V-groove weld configuration gave the highest average filler metal mixing values of the laser cold-wire welding trials. Average filler metal mixing values in percent were in the range 58-62% and 60-67% at measurement lines 1 to 4 of the test welds LCW7 and LCW8, respectively. Comparison of leading and trailing wire feeding based on the chromium weight-% mapping images of Figure 15g-h and the filler metal mixing profiles in Figures 19 and 20 indicates that the mixing homogeneity is best in weld LCW8, which was made with trailing wire feeding. Overall, test welds LCW6 and LCW8 gave the best results, corresponding to homogenous filler metal distribution across the whole weld cross-section. Both LCW6 and LCW8 were produced using trailing wire feeding. Figure 19: Filler metal mixing results from EDS stepwise characterization. (**a**–**d**) Filler metal mixing-% profiles of measurement lines 1–4 (ML1–ML4) from test weld LCW7 (**e**). In figures (**a**–**d**) one step between adjacent measurement points in the horizontal axis equals 100 micrometres. Figure 18: Example of mixing behaviour in a laser cold-wire weld produced in I-groove configuration with closed gap. Micrographs (**b**,**c**) and filler metal mixing profiles (**d**,**e**) at corresponding locations (**a**) showing inhomogeneous mixing occurred in laser cold-wire test weld LCW1.
## 3 Doppler OCT for Otology Applications
### In Vivo Experiments
#### 3.3.1 Small Animal Studies  
[PERSON] et al. [73] presented the first measurement of vibrating motion from the apex of an intact mouse cochlea in vivo. To minimize the motion artifact caused by animal motion, a pre-experiment of collecting the data of six different sound stimulus levels and nine frequencies for averaging was conducted. This study demonstrated the advantage of phase-sensitive OCT to detect vibrations of intracochlear tissues in vivo without the risk of opening compared to the laser Doppler vibrometry.  
In an attempt to apply proper systems with a different lateral resolution for each application, [PERSON] et al. [74] complementary utilized Doppler OCT and Doppler OCM to measure the micromechanical mechanisms of the mammalian middle ear. The cochlear apexes of the gerbil, mouse, and guinea pig were well-measured by cross-section, motion, and phase map, which verified the applicability of Doppler OCT and Doppler OCM to in vivo vibration measurements.  
To measure the wide-range dynamics and vibration distribution of tissues, the multi-frequency swept-source OCM using wide-field heterodyne detection was developed in [PERSON] et al. [75]. To capture a full-field volumetric TM data and mapping the spatial information of vibration (i.e., amplitude and phase), an ultrahigh-speed CMOS camera and working-distance enhanced microscope were utilized. The results of this study showed the applicability of multi-frequency swept-source OCT for vibration measurement in vivo experiments and surgeries.  
In addition to the obtained wide-range in vivo vibrating motion data of the middle ear, [PERSON] et al. [76] measured the relative vibrating distribution of distinctive sections of the TM (e.g., malleus, thick region, and thin region) by applying external sound from 1 to 8 kHz. This study demonstrated the feasibility of Doppler OCT for measuring the vibration tendency and analyze the characteristics of each section in the middle ear according to the frequency of externally induced sound.  
To verify the feasibility of Doppler OCT as a tool for functional and structural assessment, [PERSON] et al. [66] measured the vibrating motion of both normal and pathologic TM, as well as the ossicles of rats in vivo. As shown in Figure 6, 3D reconstructed phase shift maps were obtained at the contact area (between the TM and malleus) in the normal condition and in the simulated effusion condition. The results of this study verify the possibility of Doppler OCT extracting the signal-by-signal demodulation method, which enables to distinguish the normal and abnormal bio-tissues.
## 4 Proposed Solution
### Multi-Sensor Sampling
#### 4.5.1 Finite Differences
 \tag{18}\]  
Applying the CFD, the gradient optimization method shown in the Equation (11) becomes:  
\[ \begin{split}{x_{r}}^{i+1}&=x_{r}^{i}+\gamma_{c}^{ i}\,\frac{\partial f}{\partial(RSS_{c})}\cdot\|\
abla_{x}RSS_{c}\|+\gamma_{s}^{ i}\,\frac{\partial f}{\partial(RSS_{s})}\cdot\|\
abla_{x}RSS_{s}\|\\ {y_{r}}^{i+1}&=y_{r}^{i}+\gamma_{c}^{i}\,\frac{ \partial f}{\partial(RSS_{c})}\cdot\|\
abla_{y}RSS_{c}\|+\gamma_{s}^{i}\,\frac {\partial f}{\partial(RSS_{s})}\cdot\|\
abla_{y}RSS_{s}\|\end{split}  \tag{19}\]  
where \(\gamma_{c}^{i}\) and \(\gamma_{s}^{i}\) are the learning rates of the gradient algorithm (at the \(i-th\) iteration) from the client and server sides, respectively. The values of the learning rates \(\gamma_{c}\) and \(\gamma_{s}\) are set to be high at initial iterations and low at the end of convergence [46]. A natural choice for the learning rates that exhibit such behavior  
Figure 4: Gradient approximation schemes: backward finite differences (BFD) (**left**); forward finite differences (FFD) (**center**) and central finite differences (CFD) (**right**). are \(\gamma_{c}=\frac{\kappa}{RSS_{c}^{2}}\) and \(\gamma_{s}=\frac{\kappa}{RSS_{c}^{2}}\) to enable the accelerated convergence when the relay node is farther from the optimum (lower RSS) and decelerated convergence and stabilization when the optimum (higher RSS) has almost been reached. The values of the learning rate constant \(\kappa\) is selected empirically. This learning rate selection will make sure the convergence to a local minimum as the learning rate asymptotically satisfies \(\sum\limits_{j=1}^{\infty}\gamma_{j}\rightarrow\infty\) and \(\gamma_{j}\to 0\) as \(j\rightarrow\infty\). Further, as provided in [19], the RSS gradient can be estimated with least squares given no direct measurements of the spatial gradient. \[X\beta=Z \tag{20}\] \[\beta=(X^{T}X)^{-1}X^{T}Z \tag{21}\]  
For \(X^{T}X\) to be non-singular for a unique solution of \(\beta\), geometrically, the spatial samples must be co-linear, which is inherent in the CFD sampling.
# Low-Rank Approximation of Frequency Response Analysis of Perforated Cylinders under Uncertainty
## Appendix A Parabolic Shell of Revolution
### Reissner-Naghdi Shell Model  
The two-dimensional shell model applied is the Reissner-Naghdi [37], where the transverse deflections are approximated with low-order polynomials. The resulting vector field has five components \(\mathbf{u}=(u,v,w,\theta,\psi)\), where the first three are the displacements and the latter two are the rotations in the axial and angular directions, respectively. Here, the convention that the computational domain \(D\) is given by the surface parametrisation and the axial/angular coordinates, which are denoted by \(x\) and \(y\), has been adopted.  
Deformation energy \(\mathbf{A}(\mathbf{u},\mathbf{u})\) is divided into bending, membrane, and shear energies, denoted by subscripts \(B\), \(M\), and \(S\), respectively.  
\[\mathbf{A}(\mathbf{u},\mathbf{u})=t^{2}\mathbf{A}_{B}(\mathbf{u},\mathbf{u})+ \mathbf{A}_{M}(\mathbf{u},\mathbf{u})+\mathbf{A}_{S}(\mathbf{u},\mathbf{u}). \tag{4}\]  
Bending, membrane, and shear energies are given as\[t^{2}\mathbf{A}_{\mathcal{B}}(\mathbf{u},\mathbf{u}) = t^{2}\int_{D}\Big{[}\
u(\kappa_{11}(\mathbf{u})+\kappa_{22}( \mathbf{u}))^{2}+(1-\
u)\sum_{i,j=1}^{2}\kappa_{ij}(\mathbf{u})^{2}\Big{]}A_{1}A _{2}dx\,dy, \tag{110}\] \[\mathbf{A}_{M}(\mathbf{u},\mathbf{u}) = 12\int_{D}\Big{[}\
u(\beta_{11}(\mathbf{u})+\beta_{22}(\mathbf{u }))^{2}+(1-\
u)\sum_{i,j=1}^{2}\beta_{ij}(\mathbf{u})^{2}\Big{]}A_{1}A_{2}dx\,dy,\] (111) \[\mathbf{A}_{S}(\mathbf{u},\mathbf{u}) = 6(1-\
u)\int_{D}\Big{[}(\rho_{1}(\mathbf{u})^{2}+\rho_{2}( \mathbf{u}))^{2}\Big{]}A_{1}A_{2}dx\,dy, \tag{112}\]  
where \(\
u\) is the Poisson ratio (constant). The scaling \(E/(12(1-v^{2}))\), where \(E\) is the [PERSON]'s modulus, has been omitted.  
Using the identities above, the bending, membrane, and shear strains [37], \(\kappa_{ij}\), \(\beta_{ij}\), and \(\rho_{i}\), respectively (with the curvature tensor values already inserted), can be written as  
\[\kappa_{11} =\frac{\partial\theta}{\partial x},\quad\kappa_{22}=\frac{ \partial\psi}{\partial y},\quad\kappa_{12}=\frac{1}{2}\bigg{(}\frac{\partial \psi}{\partial x}+\frac{\partial\theta}{\partial y}-\frac{\partial v}{ \partial x}\bigg{)},\] \[\beta_{11} =\frac{\partial u}{\partial x},\quad\beta_{22}=\frac{\partial v }{\partial y}+w,\quad\beta_{12}=\frac{1}{2}\bigg{(}\frac{\partial v}{\partial x }+\frac{\partial u}{\partial y}\bigg{)}, \tag{113}\] \[\rho_{1} =\frac{\partial w}{\partial x}-\theta,\quad\rho_{2}=\frac{ \partial w}{\partial y}-v-\psi.\]
## 1 Introduction
Without the need for paired HRR datasets, the proposed model enables HRR estimation by segmenting the flame regions, extracting the fire diameter and flame height, and applying the Zukoski correlation to estimate HRR. For the flame segmentation component, a novel YOLO-YCbCr model [32] was introduced, which integrates YCbCr color-space-based rule segmentation with deep learning-based object detection for enhanced efficiency and robustness. For comparative analysis, a deep learning-based segmentation model (YOLOv8 segmentation model) [33] with the original architecture was adopted and fine-tuned for flame segmentation using the authors' fire image dataset. The subsequent HRR prediction steps, flame size extraction and HRR estimation, were conducted using the same process as in the proposed model. Furthermore, real-time HRR prediction was conducted using six fire experiment videos provided by the National Institute of Standards and Technology (NIST), and the prediction performance was quantitatively assessed. In addition, the effect of incorporating average flame height calculations into the prediction model was analyzed to evaluate its impact on HRR prediction performance. Finally, the performance of the proposed models was compared with that of an existing sequence modeling-based AI-HRR prediction model.