esa-sceva/satcom-chunk-collection huggingface dataset:
# Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components
## 4 Numerical Simulations  
### Numerical Conditions  
Numerical simulations were carried out on the microgrid model, which was already illustrated in Figure 1, to verify the validity of the authors' proposal. Figure 2 shows profiles of the forecasted net load and the price in electricity trade. As shown in Figure 2, the aggregated PV on sunny day was referred to as the aggregated VREG. The imbalance penalty was set to a large value to avoid the electricity surplus/shortage in the operation stage. Specifications of the controllable components in the microgrid model are summarized in Tables 1 and 2. Here, the ESSs and the CLs were aggregated into one large-scale component, respectively, to simplify discussions. These were made by referring to [26; 27; 33; 36].  
Time interval, \(\Delta t\), was set to 1 h, and daily operation schedules (\(t=1,2,\ldots,24\)) were determined. However, the period of optimization target was set from 8:00 AM (\(t=1\)) to 7:00 AM (\(t=24\)) considering operation of the time-constrained components in the microgrid model. The available duration of the CLs was set from 9:00 PM to 7:00 AM. The initial SOC level of the aggregated ESS was set to 50% of its capacity, and the level had to be returned to the original state until the end of the scheduling period. On the other hand, the initial SOC level of the aggregated CL was set to 50%, and it had to be 100% at the end of the scheduling period.  
In Table 3, the operation schedule of Case 1 was the best result for the forecasted net load. It means that the operational cost increased in the other cases for securing the reserve margin against the uncertainty in the actual operation. Besides, it was found that the expected values of the operation costs became worse than the operation costs for the forecasted net load in each case. However, in Case 3, the increase in the objective function was not so large as compared to that in Case 2 in spite of the potential risk that was reflected in both cases. As a result, the expected cost in Case 3 was the smallest as opposed to the operation cost for the forecasted net load. From these results, we can conclude that the authors' proposal functioned well.
## 5 Transmission Spectrum and Radial Displacement Field Study Analysis
### Radial Vibration Displacement Field Analysis of the Flexural Wave  
To further explore the mechanism of flexural wave very low-frequency broadband generation in AREM structures, the vibrational displacement fields along the radial direction at \(780\) Hz in the transmission spectrum were calculated for eight cycles of the AREM and WREM, as shown in Figure 14a,b, respectively.  
Figure 13: (**a**) Flexural wave dispersion relation curve of WREM; (**b**) flexural wave transmission spectrum of WREM; (**c**) longitudinal wave dispersion relation curve of WREM; (**d**) longitudinal wave transmission spectrum of WREM.  
As can be observed in Figure 14a, in the structure of the AREM, when the incident wave enters the structure from the right end of the first AREM cell, the wave undergoes an energy convergence phenomenon as it passes through the ABH region, and only a very small portion of the wave continues to propagate forward, while the vast majority of the wave is compressed in the central region of the AREM, which is consistent with the energy convergence effect of an ABH [5]. The vibration displacement of the center of the first AREM is the largest at this time, which is four times the given displacement. The displacement of the center of the second AREM structure is only less than 0.1 m. And as can be observed from Figure 14b, in the structure of the WREM, after the incident wave enters the structure from the right end of the first WREM cell, more waves continue to propagate forward, so it can be seen that the left half of the first WREM cell has quite a lot of energy. And the displacement size of the center of the second WREM structure is nearly 0.3 m, which is three times the displacement size of the center of the second AREM structure. The vibration displacement of the first WREM center is only less than two times the given displacement. It is also because of the significant convergence effect of ABHs on the flexural waves that the AREM needs only two cycles to attenuate the incident waves to a small size. On the other hand, the WREM requires three to four cycles to achieve the same effect. In summary, in the attenuation of flexural waves, the AREM has a more obvious advantage. The excellent attenuation performance of the AREM on the flexural wave is derived from the ABH effect of the AREM.
# Wavelength Conversion Enhancement Achieved by Using Resonance in an Array of Nanocylinders
## 2 Upconversion Material Nanocylinder
### Tailoring the Wavelengths of ED and MD Resonances  
Resonant wavelengths of the Mie resonance in a nanoparticle depend on the geometry and the structure parameters. We calculated the normalized scattering cross section of a single UC nanocylinder to determine the dependences of ED- and MD-resonant wavelengths on the cylinder geometry, i.e., the height (\(l\)) and diameter (\(d\)), so that the emission and absorption wavelengths of the UC process match those of the ED and MD resonances. Figure 2 shows the Q\({}_{\text{Scat}}\) spectrum of single UC nanocylinders with different heights (360 nm, 380 nm, and 400 nm) and a fixed diameter (400 nm), as well as for different diameters (360 nm, 380 nm, and 400 nm) with a fixed height (400 nm). The resonant wavelengths of the ED and MD red-shift as the size of the nanocylinder, i.e., height or diameter, increases. However, the wavelength changes of ED and MD differ depending on if the diameter or height is varied. When the height changes from 360 nm to 400 nm by an amount of 40 nm while the diameter is fixed at 400 nm, the ED wavelength increases by 40 nm, and the wavelength shift of MD is larger at 50 nm. In contrast, when the diameter increases from 360 to 400 nm with a fixed height of 400 nm, the wavelengths of ED and MD increase 30 nm and 60 nm, respectively. Using two different dependencies, the ED and MD resonances are allowed to match with the emission and absorption wavelengths, respectively, of the UC material.
## 2 Materials and Methods  
### Extraction Pilot Plant  
The trials were carried out by processing Ogliarola and Coratina cultivars harvested at the end of October and in the first week of November (2019) at medium-early and early ripening stages, respectively. Olive fruits (1800 kg of each cultivar) were harvested from the same plot of land (Puglia region, Italy) and processed within 48 h. The ripening indexes, 0.93 for Coratina and 1.57 for Ogliarola olive fruits, were determined according to the method described by [PERSON] et al. [54].  
The extraction plant for the production of EVOOs was composed of the following parts:  
* Defoliator and washing machine (Mori TEM, Tavarnelle Val di Pesa, Italy);
* Hammer crusher mod. TEM 200 (Mori TEM, Tavarnelle Val di Pesa, Italy) equipped with a flow meter with a sensitivity of \(\pm\)0.05 L/min only usable for oxygen gas (TEKNOM a/m 145, Figline e Incisa Valdarno, Italy) for oxygen measurement and addition in crushing;
* TCM ViscoLineTM XS, a viscous paste heat exchanger for rapid thermoconditioning of olive paste (Alfa Laval Corporate AB, Monza, Italy);
* Malaxer module, 200 kg (Mori TEM, Tavarnelle Val di Pesa, Italy);
* Decanter oliomio (Mori TEM, Tavarnelle Val di Pesa, Italy) at 300 kg/h;
* Vertical centrifuge separator Alfa Laval UVPX 305 AGT 14 (Alfa Laval S.p.A., Tavarnelle Val di Pesa, Italy).  
The concentration of oxygen must be related to the extraction plant capacity, in particular the crusher capacity. In all the tests, the flow of olives at the inlet of the crusher was near 640 kg/h. As a result, the quantity of oxygen was 0.019 L for each kg of olives at an addition flow of 0.2 (L/min), 0.038 L for each kg of olives at an addition flow of 0.4 (L/min), and 0.057 and 0.076 L for each kg of olives, respectively, at 0.6 and 0.8 (L/min) of oxygen in addition during crushing. Thus, the quantities of oxygen that were tested during crushing were 0.019 L, 0.038 L, 0.057 L and 0.076 L supplied at addition flows of 0.2 L/min, 0.4 L/min, 0.6 L/min, and 0.8 L/min, respectively. The oxygen was put inside the crusher chambers using a 1/4\" ball valve at a distance of 170 mm from the axle of the crusher, inside the sieve.  
After the crushing phase, the olive paste was pumped to the viscous paste heat exchanger at a temperature of 18 \({}^{\circ}\)C. Then, the olive paste merged into the malaxer at 25 \({}^{\circ}\)C for 30 min of malaxation. The malaxer was filled to its maximum level and hermetically sealed from the outside atmosphere. The filling time was approximately 19 min. After malaxation, the oil was separated using a decanter centrifuge. The feed flow to the decanter was 200 kg/h of olive paste. The oil was finally purified from water, solids, and colloids by the vertical separator centrifuge. The samples were withdrawn at the halfway point of the malaxer's discharge to avoid contamination with the previous sample for each trial.
## 3 Results and Analysis
### Results and Analysis of Modified Bitumen Mixture Test
#### 3.3.3 Water Stability Test  
The results are shown in Tables 8 and 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bitumen Mixture** & \multicolumn{2}{c}{**DS/Time/mm**} \\ \cline{2-3}  & **Testing Value** & **Technical Requirements** \\ \hline Virgin Bitumen & 1204 & \\
3\% Sasobit & 2870 & \\
5\% XT-W3 & 2294 & \(\geq\)1000 \\
0.6\% Evotherm & 2571 & \\ SBS & 5012 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the high-temperature ruting test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bitumen Mixture** & \multicolumn{2}{c}{**Marshall Stability/KN**} & \multicolumn{2}{c}{**MS\({}_{\textbf{\theta}}\)/\%**} & \multicolumn{2}{c}{**Technical Requirements/\%**} \\ \cline{2-5}
**Mixture** & **MS** & **48 h MS\({}_{\textbf{1}}\)** & & **Requirementmentment & \\ \hline Virgin Bitumen & 10.46 & 8.78 & 83.94 & \(\geq\)75 \\
3\% Sasobit & 16.83 & 16.31 & 96.91 & \(\geq\)85 \\
5\% XT-W3 & 13.64 & 12.01 & 88.05 & \(\geq\)85 \\
0.6\% Evotherm & 14.21 & 12.42 & 87.40 & \(\geq\)85 \\ SBS & 14.73 & 13.16 & 89.31 & \(\geq\)85 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the soak Marshall test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bitumen Mixture** & \multicolumn{2}{c}{**RT\({}_{\textbf{1}}\)/MPa**} & \multicolumn{2}{c}{**RT\({}_{\textbf{2}}\)/MPa**} & \multicolumn{2}{c}{**TSR/\%**} & \multicolumn{2}{c}{**Technical Requirements/\%**} \\ \hline Virgin Bitumen & 0.947 & 0.786 & 83.00 & \(\geq\)70 \\
3\% Sasobit & 1.005 & 0.844 & 83.98 & \(\geq\)80 \\
5\% XT-W3 & 0.985 & 0.854 & 86.70 & \(\geq\)80 \\
0.6\% Evotherm & 0.846 & 0.739 & 87.35 & \(\geq\)80 \\ SBS & 1.184 & 1.054 & 89.03 & \(\geq\)80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of the freeze-thaw splitting test.  
Tables 8 and 9 show that compared with the virgin bitumen mixture, the residual stability MS\({}_{0}\) and the splitting strength of the Sasobit-modified bitumen mixture, XT-W3 modified bitumen mixture, and Evotherm-modified bitumen mixture were significantly higher than TSR, and the residual stability MS\({}_{0}\) increased by 15.5%, 4.9%, and 4.1%, respectively, and the splitting strength increased by 1.2%, 4.5%, and 5.2%, respectively. This shows that the water stability of the bitumen mixtures improved after mixing with modifiers. Among them, the Sasobit-modified bitumen mixture has the highest residual stability but lower splitting strength than the TSR, probably because the Sasobit modifier contains waxy components, and its nature constantly changes under the low- and high-temperatures environment, which negatively affects the splitting tensile strength of the bitumen mixture, which is related to the incorporation of the Sasobit modifier to reduce the void ratio of the bitumen mixture. Thus, this phenomenon and the Sasobit modifier to reduce the mix void ratio improve the bitumen mixture resistance to water damage and the results of each other, which ultimately makes the Sasobit-modified bitumen mixture freeze-thaw split strength compared to the virgin bitumen mixture not particularly obvious.
## 3 Results
### Behavior Classification
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \multicolumn{2}{l}{**Temperature Range Used to Device Model**} & \multicolumn{2}{l}{**Evaluated at 29 °C**} & \multicolumn{3}{l}{**Evaluated at 27 °C**} & \multicolumn{3}{l}{**Evaluated at 35 °C**} \\ \multicolumn{2}{c}{} & \multicolumn{2}{l}{**Accuracy**} & \multicolumn{1}{l}{**Precision**} & \multicolumn{1}{l}{**Accuracy**} & \multicolumn{1}{l}{**Precision**} & \multicolumn{1}{l}{**Accuracy**} & \multicolumn{1}{l}{**Precision**} \\ \hline
20\({}^{\circ}\)C & Eating & 76 & 61 & Eating & 50 & 4 & Eating & 52 & 18 \\  & Lying & 76 & 85 & Lying & 52 & 88 & Lying & 50 & 56 \\  & Lying and & 73 & 37 & Lying and & 49 & 10 & Lying and & 51 & 21 \\  & Bounding & 64 & 34 & Sounding & 49 & 52 & Standing & 50 & 8.6 \\  & Bounding and & 64 & 29 & Standing and & 50 & 0 & Standing and & 50 & 0 \\  & Bounding & 64 & 30 & Runninging & 82 & 72 & Eating & 48 & 7.2 \\ \hline
27 °C & Eating & 50 & 10 & Eating & 82 & 72 & Eating & 48 & 7.2 \\  & Lying & 50 & 52 & Lying & 78 & 81 & Lying & 50 & 50 \\  & Lying and & 51 & 40 & Lying and & 76 & 64 & Lying and & 49 & 40 \\  & Bounding & 49 & 0 & Standing & 68 & 39 & Standing & 50 & 1.4 \\  & Bounding and & 50 & 0 & Standing and & 68 & 32 & Standing and & 50 & 0 \\  & Bounding & & & Runninging & & & Runninging & & \\ \hline
35 °C & Eating & 50 & 47 & Eating & 48 & 5 & Eating & 75 & 60 \\  & Lying & 50 & 35 & Lying & 48 & 34 & Lying & 78 & 79 \\  & Bounding and & 50 & 48 & Lying and & 49 & 50 & Lying and & 77 & 68 \\  & Bounding & 50 & 6.5 & Standing & 51 & 85 & Standing & 70 & 44 \\  & Bounding and & & Standing and & & Standing and & & Standing and & & Standing and \\  & Bounding & 50 & 0 & Runninging & 50 & 0 & Runninging & 67 & 17 \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy and precision of the random forest algorithm fit in each temperature across the temperature ranges throughout the experiment period on the four animals fitted with the wearable sensors.
# Topology Optimization with Explicit Components Considering Stress Constraints
###### Abstract  
Topology optimization focuses on the conceptual design of structures, characterized by a large optimization space and a significant impact on structural performance, and has been widely applied in industrial fields such as aviation and aerospace. However, most topology optimization methods prioritize structural stiffness and often overlook stress levels, which are critical factors in engineering design. In recent years, explicit topology optimization methods have been extensively developed due to their ability to produce clear boundaries and their compatibility with CAD/CAE systems. Nevertheless, research on incorporating stress constraints within the explicit topology optimization framework remains scarce. This paper is dedicated to investigating stress constraints within the explicit topology optimization framework. Due to the clear boundaries and absence of intermediate density elements in the explicit topology optimization framework, this approach avoids the challenge of stress calculation for intermediate density elements encountered in the traditional density method. This provides a natural advantage in solving topology optimization problems considering stress constraints, resulting in more accurate stress calculations. Compared with existing approaches, this paper proposes a novel component topology description function that enhances the deformability of components, improving the representation of geometric boundaries. The lower-bound Kreisselmeier-Steinhauser aggregation function is employed to manage the stress constraint, reducing the solution scale and computational burden. The effectiveness of the proposed method is demonstrated through two classic examples of topology optimization.  
topology optimization; stress concentration; explicit components; stress aggregation function; method of moving asymptotes +
Footnote †: journal: Applied sciences
## 3 Proposed Model
### Obtain the Ranking of the Operation System Alternatives
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(d(\tilde{v}_{\tau j}\tilde{v}_{j}^{-})\) & \(\tau=1\) & \(\tau=2\) & \(\tau=3\) & \(\tau=4\) \\ \hline \(j=1\) & 0.0416 & 0.0169 & 0.0000 & 0.0353 \\ \(j=2\) & 0.0461 & 0.0057 & 0.0087 & 0.0461 \\ \(j=3\) & 0.0037 & 0.0010 & 0.0095 & 0.0037 \\ \(j=4\) & 0.0035 & 0.0000 & 0.0028 & 0.0027 \\ \(j=8\) & 0.0000 & 0.0137 & 0.0123 & 0.0148 \\ \(j=7\) & 0.0000 & 0.0015 & 0.0093 & 0.0007 \\ \(j=8\) & 0.0457 & 0.0156 & 0.0000 & 0.0320 \\ \(j=10\) & 0.0001 & 0.0032 & 0.0009 & 0.0035 \\ \(j=11\) & 0.0182 & 0.0000 & 0.0151 & 0.0175 \\ \(j=12\) & 0.0075 & 0.0000 & 0.0196 & 0.0066 \\ \(j=13\) & 0.0392 & 0.0012 & 0.0268 & 0.0392 \\ \(j=14\) & 0.0000 & 0.0054 & 0.0025 & 0.0056 \\ \(j=15\) & 0.0000 & 0.0080 & 0.0004 & 0.0083 \\ \(j=16\) & 0.0110 & 0.0005 & 0.0115 & 0.0110 \\ \(d_{\tau}^{\tau}\) & 0.2166 & 0.0729 & 0.1193 & 0.2269 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Distance of each operation system from FPIS. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(d(\tilde{v}_{\tau j}\tilde{v}_{j}^{-})\) & \(\tau=1\) & \(\tau=2\) & \(\tau=3\) & \(\tau=4\) \\ \hline \(j=1\) & 0.0000 & 0.0251 & 0.0416 & 0.0353 \\ \(j=2\) & 0.0000 & 0.0445 & 0.0413 & 0.0500 \\ \(j=3\) & 0.0083 & 0.0087 & 0.0000 & 0.0016 \\ \(j=4\) & 0.0000 & 0.0035 & 0.0012 & 0.0027 \\ \(j=6\) & 0.0127 & 0.0012 & 0.0029 & 0.0127 \\ \(j=7\) & 0.0093 & 0.0081 & 0.0000 & 0.0007 \\ \(j=8\) & 0.0000 & 0.0329 & 0.0485 & 0.0349 \\ \(j=10\) & 0.0071 & 0.0004 & 0.0029 & 0.0071 \\ \(j=11\) & 0.0000 & 0.0182 & 0.0031 & 0.0175 \\ \(j=12\) & 0.0102 & 0.0196 & 0.0000 & 0.0086 \\ \(j=13\) & 0.0000 & 0.0411 & 0.0152 & 0.0419 \\ \(j=14\) & 0.0033 & 0.0002 & 0.0031 & 0.0033 \\ \(j=15\) & 0.0083 & 0.0003 & 0.0079 & 0.0083 \\ \(j=16\) & 0.0005 & 0.0111 & 0.0000 & 0.0110 \\ \(d_{\tau}^{-}\) & 0.0596 & 0.2148 & 0.1415 & 0.1710 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Distance of each operation system from FNIS. most recommended system. This can be seen from Table 14, which shows that operation system 2 performs well in connectivity (SC4), cyber security (SC11), and brand preference (SC12), as its performance is the same as the Fuzzy Positive-Ideal Solution (FPIs) of each of these sub-criteria with \(d(\vec{v}_{\tau j},\vec{v}_{j}^{*})\) equals to 0. By observing \(d(\vec{v}_{\tau j},\vec{v}_{j}^{-})\) in Table 15, we can see that operation system 2 performs rather well in terms of graphic performance (SC2), market share (SC13), and voice assistant (SC8) since its position is farther away from the Fuzzy Negative-Ideal Solution (FNIS).
## 3 Analysis and Results
### Qualitative Analysis
#### 3.2.5 Effects of BIM Application  
Researchers conducted in-depth interviews to investigate the effectiveness of BIM applications in solving problems based on work content categories during the pilot work at the construction site. For further detailed modeling, precise quantities could be calculated, and model quality could be improved through thorough inspections of individual objects, enabling comprehensive material management. In the process of construction feasibility reviews, errors could be identified to enhance the accuracy of information, and stability reviews could identify the risk areas, leading to design changes as required by the data. During process management, issues could be easily seen by others due to information sharing, promoting interdisciplinary communication. Task history records facilitated monthly manpower checks (M/M). When calculating quantity data, comparing and reviewing construction cost management tasks allowed for sharing the work status with the headquarters, enabling on-site managers to inspect monthly work progress and visually confirm the 4D construction status of each process (Table 8).  
Figure 5: (a) The percentage of BIM software usage in Project 1. (b) The percentage of BIM software usage in Project 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Effect** \\ \hline \multicolumn{3}{c}{Accurate quantities when reviewing quantities.} \\ \multicolumn{3}{c}{Through object separation, each object can be checked in detail.} \\ \multicolumn{3}{c}{Improve quality with detailed revisions and reviews, including interference and} \\ \multicolumn{3}{c}{dimensions between each structure.} \\ \multicolumn{3}{c}{Consolidate materials that were previously in different files.} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effects of working on pilot site application BIM.
# Towards Facial Biometrics for ID Document Validation in Mobile Devices
## 7 Conclusions  
This paper is devoted to the development of an efficient method for protecting ID and travel documents by augmenting them with a secured facial biometric template to be encoded in the machine-readable code. The approach is optimized for portable devices (e.g., smartphones) in terms of the CPU usage and solves the frontal face verification problem in the offline match-on-document scenario. Our demo application on an iPhone 7 is able to perform the complete card validation process (including detection, rectification, extracting templates and verification) in 0.2 s.  
We introduce the improved facial biometric descriptor and estimate the effect of its compression on the performance of the system in various experiments.  
As an additional contribution of this work, we introduce several print/capture datasets that may be useful for the research related to face recognition for mobile document security applications. They can be employed to analyze the robustness of face recognition algorithms to various distortions caused by the combined impact of printer and digital camera.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Template Size** & **Print/Capture** & **Print/Capture** & **Print/Capture** & **Print/Capture** \\
**Geometric + Texture** & **Aberdeen** & **Aberdeen v2** & **Utrecht** & **AR** \\ \hline
136 G + 160 T. & 0.999438/0.0027 & 0.999179/0.0036 & 0.99592/0.023 & 0.994624/0.029 \\  & 0.0047 & 0.0048 & 0.016 & 0.023 \\
120 G + 160 T. & 0.999465/0.0034 & 0.999394/0.0059 & 0.998035/0.027 & 0.992643/0.073 \\  & 0.0047 & 0.0073 & 0.018 & 0.018 \\
100 G + 160 T. & 0.999452/0.0027 & 0.999277/0.0063 & 0.995276/0.033 & 0.994577/0.078 \\  & 0.0054 & 0.0077 & 0.020 & 0.024 \\
136 G + 140 T. & 0.999262/0.0068 & 0.998991/0.0061 & 0.992228/0.044 & 0.987724/0.030 \\  & 0.0074 & 0.0077 & 0.024 & 0.043 \\
136 G + 120 T. & 0.999297/0.0075 & 0.99912/0.0081 & 0.991232/0.059 & 0.982557/0.038 \\  & 0.0081 & 0.0087 & 0.026 & 0.047 \\ \hline \end{tabular}
\end{table}
Table 1: Performance characteristics (AUC/FNMR@FMR = 0.01/Equal Error Rate(EER)) of classifiers for various sizes of compressed templates and datasets.  
Figure 10: FAR/FRR of ANN classifier for various compression of texture features (PCA components): (**a**) Print/Capture Aberdeen; (**b**) Print/Capture Aberdeen v2; (**c**) Print/Capture Utrecht; and (**d**) Print/Capture AR.  
The overall results show the high performance of the developed method against biometric impostor attacks. At the same time, it may be customized with the use of biographical data or adapted for other biometric characteristics (such as fingerprints and iris). The method may be applied without sophisticated equipment, in a very cheap and convenient way. Our future work will be directed towards increasing the robustness of the developed facial template, more detailed analysis of the performance with a multi-fold approach and adapting deep learning techniques for the match-on-document scenario.