**2.1. Delamination**

**1. Introduction**

94 Solar Panels and Photovoltaic Materials

economic losses.

nance strategies.

is in no way easy.

The operation and maintenance of a photovoltaic (PV) power plant is of extreme importance to guarantee its optimal performance. Effective maintenance involves at the least semi-automatic analysis and alerts. In this way, the maintenance operator is capable of making immedi-

Moreover, monitoring is not just a regular recording of data but involves a more detailed analysis in order to prevent possible malfunctions associated with power and, in the end,

Thus, the development of automatic supervision tools to help maintainers to carry out an effective supervision of the power plant and, what is more important, to monitor the evolution of the modules behavior in an easy and feasible way is of great interest in the industry. Furthermore, the detection of operating failures in a timely fashion through the evaluation of panels over time means that automated monitoring is, in fact, absolutely necessary. Such monitoring will lead to the early replacement of poorly performing components, preventive

For device manufacturers, performance evaluations of their products can be used as a benchmark of their quality manufacturing processes. On the other hand, researchers and R&D divisions from companies can take advantage of such evaluations, using that information to identify future needs in the industry and to test real working conditions. For PV plant promoters and owners, realistic performance data is essential for investment decision-making.

Although several techniques for the detection of real and/or potential failures in the PV field can be found in the literature [2–4], few of them attempt to detect anomalies such as power loss, module failure, or health and safety dangers until after they occur and the effects are felt. These techniques, moreover, do not address preventative maintenance strategies or effective economical programs for the replacement of components. In addition, these systems do not integrate geo-references that may help to improve the application of preventative mainte-

New methodologies are needed to locate and analyze performance and malfunction of plant components on a global scale. Despite the great value of analysis carried out in the laboratory, it can often be of little help when applied to the real operation of maintenance plans. Test conditions in a laboratory may allow for a complete analysis of PV components, but owners can ill afford to close an entire installation or part thereof for equivalent testing in the field. Besides, laboratory test conditions are unlikely to fully typify working conditions in a real field. As such, laboratory test results and, more importantly, any conclusions drawn from them are likely to be decontextualized. On the other hand, carrying out systematic procedural techniques in the field under changing environmental and climatic conditions

Integrated geographic information system (GIS) platforms will allow test-related information to be comprehensively organized and geo-referenced, providing significant benefits. One such benefit is the fact that the impact of a single defective component on the overall

ate decisions to solve safety problems and minimize power losses [1].

maintenance policies, and better management of plants.

This consists of the loss of adhesion between the glass, the encapsulation, the active layers, and the subsequent layers [6], which can cause loss of current (power) in the photovoltaic modules. Loss of adhesion may occur for various reasons. Large thin film modules and certain other types of modules sometimes contain an additional transparent conductive oxide (TCO) layer, which may lose adhesion with the adjacent glass layer [6]. If the loss of adhesion is due to contamination, perhaps from cleaning, or environmental factors, then delamination will often take place, followed by moisture entering and, in due course, corrosion. Delamination leads to reflection of light and a subsequent loss of power in the modules.

In most cases delamination can be detected by visual inspection, with the degree of layer detachment being quantified by use of a reflectometer. Some delaminations, however, cannot be identified in this way and so methods such as pulsed active thermography or lock-in thermography can be used, while smaller delaminations can be detected with ultrasound scanners and X-ray tomography. The latter methods are slow [7] but provide a much higher resolution.


**Recovery of High Purity Lignin and Digestible Cellulose from Oil Palm Empty Fruit Bunch Using Low Acid-Catalyzed Organosolv Pretreatment**

**Kinanthi Mondylaksita 1,2, Jorge A. Ferreira 1 , Ria Millati 2 , Wiratni Budhijanto 3 , Claes Niklasson 4 and Mohammad J. Taherzadeh 1,\***

Received: 11 April 2020; Accepted: 7 May 2020; Published: 11 May 2020

**Abstract:** The lignocellulosic residue from the palm oil industry, oil palm empty fruit bunch (OPEFB), represents a challenge to both producing industries and environment due to its disposal difficulties. Alternatively, OPEFB can be used for the production of valuable products if pretreatment methods, which overcome OPEFB recalcitrance and allow tailored valorization of all its carbohydrates and lignin, are developed. Specifically, high-value applications for lignin, to increase its contribution to the feasibility of lignocellulosic biorefineries, demand high-purity fractions. In this study, acid-catalyzed organosolv using ethanol as a solvent was used for the recovery of high-purity lignin and digestible cellulose. Factors including catalyst type and its concentration, temperature, retention time, and solid-to-liquid (S/L) ratio were found to influence lignin purity and recovery. At the best conditions (0.07% H2SO4, 210 ◦C, 90 min, and S/L ratio of 1:10), a lignin purity and recovery of 70.6 ± 4.9% and 64.94 ± 1.09%, respectively, were obtained in addition to the glucan-rich fraction. The glucan-rich fraction showed 94.06 ± 4.71% digestibility within 18 h at an enzyme loading of 30 filter paper units (FPU) /g glucan. Therefore, ethanol organosolv can be used for fractionating OPEFB into three high-quality fractions (glucan, lignin, and hemicellulosic compounds) for further tailored biorefining using low acid concentrations. Especially, the use of ethanol opens the possibility for integration of 1st and 2nd generation ethanol benefiting from the separation of high-purity lignin.

**Keywords:** oil palm empty fruit bunch; lignin recovery; lignin purity; digestible cellulose; organosolv pretreatment


**4. UV light generation by SHG**

UV light sources are interesting in many fields such as biophotonics/chemical photonics, material processing, and optical data storage. Although diode lasers based on AlGaN have been demonstrated around 340 nm in pulse mode recently (Yoshida et al., 2008a, 2008b), frequency doubling of a red laser beam through a nonlinear crystal is still an efficient method to generate CW light in this wavelength range (Mizuuchi & Yamamoto, 1996; Mizuuchi et al., 1997, 2003a, 2003b; Knappe et al., 1998). UV light around 340 nm has been achieved by single pass SHG in bulk periodically poled LiTaO3 (Mizuuchi & Yamamoto, 1996; Mizuuchi et al., 1997) and MgO:LiNbO3 (Mizuuchi et al., 2003a) crystals and also achieved in a periodically poled MgO:LiNbO3 ridge waveguide (Mizuuchi et al., 2003b); but so far these periodically poled devices are not commercially available, and no UV light shorter than 340 nm has been demonstrated with these first-order periodically poled devices.

Here UV light around 337.5 nm will be generated using the external-cavity tapered diode laser system developed above as a pump source. The generated CW UV light source will be used as the excitation source for fluorescence diagnostics. Compared with other UV laser sources around 337 nm, such as a CW krypton-ion laser and a pulsed nitrogen laser, the UV laser source based on a tunable tapered diode laser system is far more simple, compact, and easy to operate.

A BIBO nonlinear crystal is used for frequency doubling of the 675.2 nm red light to the 337.6 nm UV light due to its relatively high effective nonlinear coefficient. The 10 mm long BIBO crystal with an aperture of 4 mm × 4 mm is cut with *θ* = 137.7º and *φ* = 90º for type-I phase matching (*eeo*) and antireflection coated on both end surfaces for 675/337.5 nm. The spectral bandwidth of both laser system B and C is narrow enough for frequency doubling through the BIBO crystal (the acceptable spectral bandwidth of this crystal is around 0.2 nm). Laser system C is chosen as the pump source for the frequency doubling experiment due to its better spatial beam quality.

Furthermore, we compare the results from the three laser systems based on different tapered gain devices. This is important for us to choose tapered gain devices for different

Parameter A B C Max. power (W) 1.38 1.25 1.05

power (nm) 668.35 675.34 675.67 Tunable range (nm) 659-675 663-684 666-685

(nm) <0.07 <0.05 <0.07

Table 2. Summary of the main parameters for diode laser system A, B and C.

*M*2 value 2.00±0.01 (1.27 W) 2.07±0.02 (1.0 W) 1.13±0.02 (0.93 W)

UV light sources are interesting in many fields such as biophotonics/chemical photonics, material processing, and optical data storage. Although diode lasers based on AlGaN have been demonstrated around 340 nm in pulse mode recently (Yoshida et al., 2008a, 2008b), frequency doubling of a red laser beam through a nonlinear crystal is still an efficient method to generate CW light in this wavelength range (Mizuuchi & Yamamoto, 1996; Mizuuchi et al., 1997, 2003a, 2003b; Knappe et al., 1998). UV light around 340 nm has been achieved by single pass SHG in bulk periodically poled LiTaO3 (Mizuuchi & Yamamoto, 1996; Mizuuchi et al., 1997) and MgO:LiNbO3 (Mizuuchi et al., 2003a) crystals and also achieved in a periodically poled MgO:LiNbO3 ridge waveguide (Mizuuchi et al., 2003b); but so far these periodically poled devices are not commercially available, and no UV light shorter than 340 nm has been demonstrated with these first-order periodically poled

Here UV light around 337.5 nm will be generated using the external-cavity tapered diode laser system developed above as a pump source. The generated CW UV light source will be used as the excitation source for fluorescence diagnostics. Compared with other UV laser sources around 337 nm, such as a CW krypton-ion laser and a pulsed nitrogen laser, the UV laser source based on a tunable tapered diode laser system is far more simple, compact, and

A BIBO nonlinear crystal is used for frequency doubling of the 675.2 nm red light to the 337.6 nm UV light due to its relatively high effective nonlinear coefficient. The 10 mm long BIBO crystal with an aperture of 4 mm × 4 mm is cut with *θ* = 137.7º and *φ* = 90º for type-I phase matching (*eeo*) and antireflection coated on both end surfaces for 675/337.5 nm. The spectral bandwidth of both laser system B and C is narrow enough for frequency doubling through the BIBO crystal (the acceptable spectral bandwidth of this crystal is around 0.2 nm). Laser system C is chosen as the pump source for the frequency doubling experiment

Laser system

applications.

Wavelength with max

Spectral bandwidth

devices.

easy to operate.

due to its better spatial beam quality.

**4. UV light generation by SHG**

A 30 dB optical isolator is inserted between the aspherical lens and the cylindrical lens in the output beam to avoid feedback from the optical components and the nonlinear crystal, as shown in Fig. 6. A biconvex lens of 75 mm focal length is used to focus the red fundamental beam into the BIBO crystal. The available output power of the fundamental beam in front of the crystal is 650 mW. The size of the focus *w*s × *w*f is around 70 µm × 35 µm, where *w*s and *w*f are the beam waists (diameters at 1/e2) in the slow and fast axes, respectively. The elliptical beam is used to reduce the effects of walk-off in the BIBO crystal. The walk-off angle in our crystal is 72.9 mrad, corresponding to a heavy walk-off parameter *B* of 15.1. The elliptical beam was proved to be optimum in the experiments, in good agreement with the theory of frequency doubling using elliptical beams (Boyd & Kleinman, 1968; Steinbach et al., 1996). The slight change in astigmatism with output power will cause the focusing conditions to vary slightly at different power levels. In the experiments, the astigmatism was corrected at maximum pump power. Two dichroic beam splitters separate the fundamental beam from the second harmonic output beam.

The wavelength of the fundamental beam is tuned to 675.16 nm, and the temperature of the crystal is 19.8 ºC. Figure 12 shows the measured second harmonic power as a function of fundamental power. The curve represents a quadratic fitting. A maximum of 109 µW UV light is obtained with a fundamental pump power of 650 mW. The conversion efficiency *η* is 0.026%W-1, compared to a conversion efficiency of 0.019%W-1 for a single-pass frequency doubling through a 15 mm long LiIO3 bulk crystal (Knappe et al., 1998), and the theoretically calculated value is 0.040%W-1 (Steinbach et al., 1996).

Fig. 12. Second harmonic power as a function of fundamental power. The squares are measured data, and the curve is a quadratic fit.

Red Tunable High-Power Narrow-Spectrum

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**2. Raman scattering monitoring of mechanical strain on graphene**

To date, several approaches to creating strain in graphene have been proposed [23, 24, 26, 28]. Although the graphene deformation methods are different, the Raman spectroscopy is always accepted as the most powerful and efficient tool to reveal the strain condition on graphene. In this section, we employ Raman scattering technology to observe biaxial strain on monolayer graphene. The strain was induced by deformation from a substrate with an array of SiO2 nanopillars. The nanopillars (1 cm2 in area, 80 nm in height, and 40 nm in pitch) were fabricated by employing a self‐assembled block copolymer through simple dry etching and deposition processes. The graphene sheet was subsequently transferred to the array of SiO2 nanopillars. The creation of biaxial tensile strain in graphene was validated based on high‐resolution micro‐ Raman spectroscopy, and the strain values were quantitatively calculated through the Grüneisen parameter. Atomic force microscopy (AFM) and accompanying finite element simulations were employed to confirm the accuracy and reliability of the Raman spectroscopy monitoring results.

**Figure 1** shows the schematic process to fabricate SiO2 nanopillars patterned by self‐assembled block copolymer. The fabrication began with forming periodic nanopatterns on 300 nm, thermally grown SiO2/Si substrate by using a self‐assembled block copolymer as a pattern template [50, 51]. A block copolymer layer, poly(styrene‐*block*‐methyl methacrylate) (PS‐b‐ PMMA), on top of a ~5 nm neutral PS brush layer was used to define periodic nanopatterns (**Figure 1(a)**–**(c)**). The PMMA cylinders were selectively removed by exposure to UV irradia‐ tion and rinsing with acetic acid (CH3COOH) to form the cylinder‐shaped nanopattern template layer. The brush layer in the patterned cylinders was removed by O2 plasma to allow the following evaporated material to deposit directly to the substrate in the next step. **Figure 2(a)** shows the block copolymer nanotemplate with a pitch (center‐to‐center distance between cylinders) size ~40 nm. A layer of 15 nm thick chromium (Cr) was deposited by e‐ beam evaporation and lifted off (**Figure 1(e)**–**(f)**). The array of Cr nanodots was uniformly created over the entire surface of SiO2/Si substrate (**Figure 2(b)**). Then, the underlying SiO2 layer was etched by Cr nanodots as a mask in the inductively coupled plasma (ICP) and the reactive ion etching (RIE) combination system. Finally, an array of SiO2 nanopillars (80 nm in height and 40 nm in pitch) was formed on Si substrate after removing the Cr nanodots, as shown in **Figure 2(c)**.

**Figure 1.** SiO2 nanopillar fabrication process with assistant of self‐assembled block copolymer. (a) original SiO2/Si sub‐ strate, (b) PS coated on substrate, (c) self‐assembled block copolymer coated on PS layer, (d)nanotemplate formed on substrate, (e) Cr evaporated on sample, (f) Cr nanodot on substrate after lift‐off, (g) SiO2 nanopillar formed after etch‐ ing with Cr nanodot mask, and (h) clean SiO2 nanopillar substrate after removing Cr nanodots.

**Figure 2.** (a) Self‐assembled block copolymer nanotemplate on SiO2/Si substrate, (b) Cr nanodots after lift‐off on SiO2/Si substrate, (c) obtained SiO2 nanopillars on SiO2/Si substrate, and (d) CVD grown monolayer graphene on Cu foil sub‐ strate. From Ref. [52].

The monolayer graphene that was transferred onto the array of SiO2 nanopillars was grown on a 25 μm thick 1″ × 1″ copper (Cu) foil by a chemical vapor deposition (CVD) [53, 54]. **Figure 2(d)** shows the SEM image of the grown monolayer graphene on Cu foil. A layer release‐ transfer method was employed to transfer the monolayer graphene film onto the pre‐pre‐ pared SiO2 nanopillar substrate as shown in **Figure 3**.

Firstly, in order to form a 150 nm thick PMMA supporting layer, the graphene layer on the back side of Cu foil was removed by oxygen plasma, followed by spin‐coating PMMA on the top side of the graphene sample and baked at 130°C for 3 min (**Figure 3(a)**–**(c)**). Secondly, the PMMA‐coated sample was dipped into 0.25 mol of Fe3Cl etchant for 4 h to remove the Cu foil completely, and then the left PMMA/graphene membrane was floated on the solution surface (**Figure 3(d)**–**(e)**). After cleaning the sample in DI water, the floating PMMA/graphene membrane was scooped on the SiO2 nanopillar substrate, and the PMMA layer was removed by acetone to only leave the graphene film on a SiO2 nanopillar substrate (**Figure 3(f)**–**(h)**). The graphene/SiO2 nanopillar substrate was dried on a hot plate at 105 °C for 1 min to evaporate solvent and water from the sample. With the help of water capillary action and van der Waals forces, the graphene film can be driven to follow the substrate profile. Namely, when the graphene/PMMA film was transferred to the SiO2 nanopillar substrate, a 150 nm thick PMMA layer made it remain flat on the SiO2 nanopillars. However, the graphene layer started to sag as the PMMA layer was removed and the capillary action of the water and the van der Waals forces between the graphene layer and the nanopillars helped the graphene layer depress and follow the profile of nanopillars. The schematic illumination for this process is shown in **Figure 4(a)**. During the SiO2 nanopillar fabrication process, we intentionally left several unpatterned regions on the substrate to attach the graphene film tightly without strain after the transfer, which also can be used as unstrained reference regions in the following Raman spectroscopy study. **Figure 4(b)** is the 45°‐angled SEM image showing that the deformed graphene film was supported by numerous and periodic SiO2 nanopillars.

**Figure 1.** SiO2 nanopillar fabrication process with assistant of self‐assembled block copolymer. (a) original SiO2/Si sub‐ strate, (b) PS coated on substrate, (c) self‐assembled block copolymer coated on PS layer, (d)nanotemplate formed on substrate, (e) Cr evaporated on sample, (f) Cr nanodot on substrate after lift‐off, (g) SiO2 nanopillar formed after etch‐

**Figure 2.** (a) Self‐assembled block copolymer nanotemplate on SiO2/Si substrate, (b) Cr nanodots after lift‐off on SiO2/Si substrate, (c) obtained SiO2 nanopillars on SiO2/Si substrate, and (d) CVD grown monolayer graphene on Cu foil sub‐

The monolayer graphene that was transferred onto the array of SiO2 nanopillars was grown on a 25 μm thick 1″ × 1″ copper (Cu) foil by a chemical vapor deposition (CVD) [53, 54]. **Figure 2(d)** shows the SEM image of the grown monolayer graphene on Cu foil. A layer release‐ transfer method was employed to transfer the monolayer graphene film onto the pre‐pre‐

strate. From Ref. [52].

126 Raman Spectroscopy and Applications

pared SiO2 nanopillar substrate as shown in **Figure 3**.

ing with Cr nanodot mask, and (h) clean SiO2 nanopillar substrate after removing Cr nanodots.

**Figure 3.** Transfer process of CVD graphene to prepared SiO2 nanopillar substrate. (a) Graphene grown on Cu foil, (b) sample with back side graphene removed, (c)PMMA coated on graphen, (d) sample sunk in FeCl3 solution to remove Cu, (e) PMMA/graphene layer in DI water for clean, (f) PMMA/graphene layer picked up by prepared SiO2 nanopiller substrate, (g)Flat PMMA/graphene on top of SiO2 nanopiller substrate, and (h) deformed graphene on SiO2 nanopiller after removing PMMA.

**Figure 4.** (a) Schematic illumination of graphene deformation process and (b) deformed graphene sheet standing on top of SiO2 nanopillars.

A Horiba micro‐Raman spectrometer (resolution of 0.045 cm−1) with a 50× objective lens (a spot size of about 1 μm) and 18.5 mW of He‐Ne (633 nm) was used to investigate the strain on the transferred monolayer graphene. The graphene on both flat regions and the SiO2 nanopillars was examined at several different positions. From the Raman spectra as shown in **Figure 5(a)** and **(b)**, the intensity of the 2D band is about two times of that of the G band, which is corresponding to monolayer graphene Raman spectra [25, 54]. The graphene on the flat regions (no nanopillars) of the substrate without clear deformation was used as reference to study the strain on graphene. It is clear that there are substantial differences in the Raman spectrum G and 2D bands between the graphene on SiO2 nanopillars and on the flat region. In **Figure 5(a)** and **(b)**, compared to the flat graphene, there are downshifts in the G band (average shift, 7.8 cm−1) and 2D band (average shift, 16.3 cm−1) of graphene on nanopillars in the peak positions. The downshift of the G band and the 2D band is attributed to the local strain on graphene induced by SiO2 nanopillars.

**Figure 5.** Raman spectra of the (a) G peak downshift and (b) 2D peak downshift taken on the flat surface (red) and on the nanopillars (blue). From Ref. [52].

In the Raman spectra of graphene, the G band is related to the doubly degenerated E2g at the center of the Brillouin zone, while the 2D band is related to the momentum conservation of the scattering of two phonons with opposite waver vectors [26, 27]. The mechanical strain can influence the phonon variation in the crystal [3, 55]. The center‐zone phonon vibration change due to the strain is shown in the G band shift, and the change in the double‐resonance condition due to the strain, which affects the actual corresponding phonon, can be reflected by the 2D band in the Raman spectrum measurements. The local biaxial tensile strain induced by SiO2 nanopillar can explain the downshift happening to G and 2D bands.

The Raman shifts were qualitatively studied to discover the biaxial strain through the Grü‐ neisen parameter, which describes the vibrational effect on the crystal lattice property, by the following equation [24, 27, 55]:

$$\gamma = -\frac{1}{\alpha\_0} \frac{\delta \alpha}{\delta \varepsilon\_h} \tag{1}$$

where *ω0* is the Raman band frequency without strain, *δω* is the Raman band shift, *γ* is the Grüneisen parameter for the corresponding band, and *εh* is the hydrostatic strain on the graphene film. For biaxial strain, *εh* can be expressed as *εh* = *εt* + *εl* and *εt* = *εl* , in which *εt* and *εl* are the transversal and longitudinal components of the strain. From Eq. (1), based on previously reported Grüneisen parameters and the downshift characteristic bands collected from the test positions, the biaxial tensile strain *εl* = *εt* can be calculated to have an average value of 0.135 % from the G band and 0.117 % from the 2D band.

**Figure 4.** (a) Schematic illumination of graphene deformation process and (b) deformed graphene sheet standing on

A Horiba micro‐Raman spectrometer (resolution of 0.045 cm−1) with a 50× objective lens (a spot size of about 1 μm) and 18.5 mW of He‐Ne (633 nm) was used to investigate the strain on the transferred monolayer graphene. The graphene on both flat regions and the SiO2 nanopillars was examined at several different positions. From the Raman spectra as shown in **Figure 5(a)** and **(b)**, the intensity of the 2D band is about two times of that of the G band, which is corresponding to monolayer graphene Raman spectra [25, 54]. The graphene on the flat regions (no nanopillars) of the substrate without clear deformation was used as reference to study the strain on graphene. It is clear that there are substantial differences in the Raman spectrum G and 2D bands between the graphene on SiO2 nanopillars and on the flat region. In **Figure 5(a)** and **(b)**, compared to the flat graphene, there are downshifts in the G band (average shift, 7.8 cm−1) and 2D band (average shift, 16.3 cm−1) of graphene on nanopillars in the peak positions. The downshift of the G band and the 2D band is attributed to the local strain on

**Figure 5.** Raman spectra of the (a) G peak downshift and (b) 2D peak downshift taken on the flat surface (red) and on

top of SiO2 nanopillars.

128 Raman Spectroscopy and Applications

graphene induced by SiO2 nanopillars.

the nanopillars (blue). From Ref. [52].

**Figure 6.** (a) Morphology of the transferred graphene film on the SiO2 nanopillar substrate measured by AFM and (b) deformed graphene on four adjacent nanopillars (unit area). From Ref. [52].

In order to confirm the Raman spectroscopy defection results, the wavy morphology of the transferred graphene film on the SiO2 nanopillars substrate was carefully measured by the noncontact‐mode atomic force microscopy (AFM) over a 500 nm× 500 nm area as shown in **Figure 6**. It was clearly observed that the surface morphology of the transferred graphene film followed the SiO2 nanopillar profile. The inserted 2D graphene surface profile in **Figure 6(a)** was taken from two adjacent SiO2 nanopillars, which indicates how much the graphene layer was deformed. The peak‐to‐valley distances are found to have a range of about 0.5–5 nm. A subtle difference in the arrangement of the nanopillars and graphene sag condition creates a different degree of deformation on the graphene film. As described in the previous section, just after transfer, the graphene/PMMA film was kept flat, and the length of the graphene between two pillars was L, while once the PMMA was removed, the graphene began to sag to follow the nanopillar profile, and the length of graphene between two pillars was changed to L + ΔL (**Figure 6(b)**). Then, the mechanical strain on graphene can be calculated by the following equation: strain = ΔL/L [24]. Based on the AFM‐measured morphology of the graphene on the nanopillars, the local strain in the graphene can be extracted in the range from about 0.04 to 0.9 % with an average value about 0.17 % measured in a 1 μm2 area.

**Figure 7.** (a) A schematic illustration of strained graphene on top of a nanopillar. Simulated biaxial tensile strain on transferred graphene transferred by COMSOL Multiphysics; (b) with 0.5 nm; and (c) with 5 nm deformation toward. From Ref. [52].

**Figure 8.** Comparison of the strain extracted from AFM and calculated from Raman band shift.

Furthermore, a strain distribution on graphene based on the deformation values we measured from the AFM was simulated by COMSOL Multiphysics. As shown in **Figure 7(a)**, we used the actual dimensions, structure, and material parameters in the simulation model. For graphene, we used Young's modulus of 1 TPa [11] and Poisson's ratio of 0.3 [56]. We took the two extreme cases, the largest and the smallest deformations which were the 0.5 and 5 nm of the peak‐to‐valley distances. For the smallest graphene deformation, the maximum strain in the graphene near the top part of the nanopillar is about 0.3 %, and most of the region shows an average strain of about 0.07 % as shown in **Figure 7(b)**. On the other hand, for the largest graphene deformation, the minimum strain in the graphene near the top part of the nanopillar is as high as 3.5 %, and most of the region shows the average strain about 0.8 % as shown in **Figure 7(c)**. The simulated results confirm the analysis from the AFM measurement.

just after transfer, the graphene/PMMA film was kept flat, and the length of the graphene between two pillars was L, while once the PMMA was removed, the graphene began to sag to follow the nanopillar profile, and the length of graphene between two pillars was changed to L + ΔL (**Figure 6(b)**). Then, the mechanical strain on graphene can be calculated by the following equation: strain = ΔL/L [24]. Based on the AFM‐measured morphology of the graphene on the nanopillars, the local strain in the graphene can be extracted in the range from

**Figure 7.** (a) A schematic illustration of strained graphene on top of a nanopillar. Simulated biaxial tensile strain on transferred graphene transferred by COMSOL Multiphysics; (b) with 0.5 nm; and (c) with 5 nm deformation toward.

area.

about 0.04 to 0.9 % with an average value about 0.17 % measured in a 1 μm2

**Figure 8.** Comparison of the strain extracted from AFM and calculated from Raman band shift.

Furthermore, a strain distribution on graphene based on the deformation values we measured from the AFM was simulated by COMSOL Multiphysics. As shown in **Figure 7(a)**, we used the actual dimensions, structure, and material parameters in the simulation model. For graphene, we used Young's modulus of 1 TPa [11] and Poisson's ratio of 0.3 [56]. We took the two extreme cases, the largest and the smallest deformations which were the 0.5 and 5 nm of the peak‐to‐valley distances. For the smallest graphene deformation, the maximum strain in the graphene near the top part of the nanopillar is about 0.3 %, and most of the region shows an average strain of about 0.07 % as shown in **Figure 7(b)**. On the other hand, for the largest graphene deformation, the minimum strain in the graphene near the top part of the nanopillar

From Ref. [52].

130 Raman Spectroscopy and Applications

The Raman shift and strain conditions collected from Raman and AFM are shown in **Figure 8**. This biaxial strain value obtained from Raman spectroscopy detection matched to that obtained from accurate AFM measurement. This result demonstrates the accuracy and effectiveness for the Raman scattering technology to monitor the strain on graphene, even if the strain is induced by subtle nanostructures.


**5. Coins**

John's reign had three indictional tax cycles in which new coins were produced: 1118–1122, 1122–1137 and 1137–1143, and coins were produced at the two imperial mints of Constantinople and Thessaloniki [15] (*DOC*, pp. 1, 11, 30, 39, 41–47, 96–128, 181–274, esp. 245, 251, 253–254), (*DOC*, p. 97; [24] (pp. 48–49, 57, 107–108), [25–34]. In 1092, John's father Alexios had reformed Byzantine coinage after many years of devaluation, but John further refined this system: he introduced more middle denomination coins that enabled a greater amount of economic exchange than previously, and consequently, there is a great variety of coins on which to see his iconography [15] (*DOC*, pp. 169, 255–256, 259–260. On imperial costume and its iconography on coins in general, see: pp. 143–176. Images on Plate IX). A noticeable trend in the iconography of his gold coins in particular is that they appear to evidence John's growing ambitions, which could bear relation to these coins being minted for military expenditure.

Most of John's coinage displays an enthroned Christ on the obverse, where he is dressed in the typical iconographic classical clothing of the *chiton* and the *himation*. Christ has a cruciform halo, holds a gospel book, and lifts a hand in blessing (Figures 4–8). In John's lower denomination coins, Christ is either depicted standing, or we only see his head and shoulders. Some variant coinage displays an enthroned Virgin and Christ Child (Figure 9), or some coins from Thessaloniki display the head and shoulders of St Demetrios, warrior saint and patron of the second city of the empire (Figure 10).

**Figure 4.** First Indiction Hyperpyron Nomisma, 1118–1122; impression on gold; New York; 1b.1. Whittemore Collection. Published at [15] (*DOC*, pp. 256–259, 260–261. Images on Plate VIII).

**Figure 5.** Second Indiction Hyperpyron Nomisma, 1122–1137; impression on gold; Amsterdam; 2.2 Peirce 1948 from Schulman i.30. Published at [15] (Ibid, pp. 261–274. The issues from the mint at Thessalonike have St Demetrios, the patron saint of the city, while those from Constantinople have St George. Images on Plates VIII and IX).

**Figure 6.** Third Indiction Hyperpyron Nomisma, 1137–1143; impression on gold; Dumbarton Oaks, Washington D.C.; 3b.3 Shaw 1947. Published at [15] (Ibid, pp. 261–274. The issues from the mint at Thessalonike have St Demetrios, the patron saint of the city, while those from Constantinople have St George. Images on Plates VIII and IX).

**Figure 7.** *Aspron Trachy Nomisma*, 1118–1143; impression on electrum; Dumbarton Oaks, Washington D.C.; 8b.2 Schindler 1960 (Kallai i.33). Published at [15] (Ibid, pp. 261–274. The issues from the mint at Thessalonike have St Demetrios, the patron saint of the city, while those from Constantinople have St George. Images on Plates VIII and IX).

**Figure 8.** Aspron Trachy Nomisma, 1118–1143; impression on electrum; Dumbarton Oaks, Washington D.C.; 8e.1 Peirce 1948 (H. Pl. 10.5). Published at [15] (Ibid, pp. 261–274. The issues from the mint at Thessalonike have St Demetrios, the patron saint of the city, while those from Constantinople have St George. Images on Plates VIII and IX).

Beginning with his gold coinage (*Hyperpyron Nomisma*), his first issue (1118–1122, Figure 4) depicts John in a ceremonial *loros* and *divitision* costume on the reverse, holding a patriarchal cross with the Virgin Mary. The hand of God also hovers above his head, and John holds in his other hand the *anexikakia* (a silk covered scroll filled with dust to symbolise humility) [15] (*DOC*, pp. 256–259, 260–261. Images on Plate VIII). These coins emphasise his divinely ordained succession and his co-rule with the *Theotokos*, along with his personal humility, but his next two issues display a progression from this humble piety.

His second issue (1122–1137, Figure 5) drops both the hand of God and the patriarchal cross in favour of John holding the military *labarum*, and he is crowned directly by the Virgin while still holding the *anexikakia*, while his third (1137–1143, Figure 6) shows him being not only crowned by the Virgin, but this time John holds a *globus cruciger* [24–34]. John's earlier humility was first militarised and later (as he set out on his eastern expedition to Cilicia and Syria) replaced with an emphasis on the universal rule assigned him by the Virgin.

In addition to these major coin issues, there was also a commemorative coin or medal produced by Alexios, assumedly in 1092, to celebrate John's coronation as infant: it displays a beardless John in imperial regalia and holding the gospels on one side, and his imperial parents Alexios and Eirene on the other, much like a modern commemorative coin (since it not covered by a major coin catalogue, Magdalino has made the suggestion to me that it may be a commemorative medal rather than a true coin [35,36]).

**Figure 9.** First Indiction Aspron Trachy Nomisma, 1118–1122; impression on billon; Dumbarton Oaks, Washington D.C.; B.11.1 Bertelè 1956 (H. Pl. 11.3). Published at [15] (*DOC*, pp. 224, 226, 228, 231, 234, images on Plates VIII and IX), [37].

**Figure 10.** First Indiction Tetaraton, 1118–1122; impression on copper; Oxford; HCR53126. Published at [15] (*DOC*, pp. 224, 226, 228, 231, 234, 248–249, 264–267. Images on Plates VIII-X), [37].

John's lower denomination coins evidence signs of his ambition even earlier on. On John's much produced electrum coins (silver/gold alloy with c.75% silver, known as *Aspron Trachy Nomismata*), we find John holding a patriarchal cross or the *labarum* with the martial saints George or Demetrios on the obverse, with the saint dressed in armour and with a drawn sword in his left hand (Figures 7 and 8 respectively) [15] (*DOC*, pp. 224, 226, 228, 231, 234, images on Plates VIII and IX; *DOC*, pp. 224, 226, 228, 231, 234, 248–249, 264–267. Images on Plates VIII-X, this image at https://hcr.ashmus.ox.ac.uk/collection/ 4?page=65) (accessed on 10 February 2022), [37]. On some versions we the patriarchal cross is depicted on three steps, drawing on iconography thought to relate to the cross that Emperor Theodosius II (408–450) erected on Christ's crucifixion site of Golgotha, which was common on Byzantine coins and seals in the few centuries after him (Figure 7).

In John's billon coinage (6–10% silver to copper alloy), John stands alone in a short military cloak, the *sagion*, rather than the *loros*, holding both a labarum and an *anexikakia* or *globus cruciger* (with earlier coinage displaying the *anexikakia* and later coinage the *globus cruciger*), while the obverse shows the Virgin and Christ child (Figure 9) [15,37].

On John's lowest denomination coinage, the tetaraton (copper), we see either a standing John with labarum, crown, and military cloak, akin to Figure 9, or his head and shoulders, as in Figure 10, that also displays the head and shoulders of St Demetrios on the obverse [15,37].


**1. Introduction**

Embryo implantation, the process of attachment and invasion of the uterus endometrium by the conceptus, is a complex physiological process tightly regulated by multiple biological molecules. Implantation requires a well orchestrated interaction between maternal and foetal tissues and consists of a fine balanced cross talk of cytokines, hormones and chemokines [1,2].

Chemokines play important key roles in several physiologic and pathologic aspects of human reproductive system, among them, the menstrual cycle, ovulation, implantation, cervical ripening, preterm labour and endometriosis [3].

The chemokine fractalkine (FKN) is synthesized as a 373 amino-acid transmembrane molecule. It is the only CX3C-chemokine which has been described [4,5]. FKN exists as both membrane-anchored and soluble forms. As an individual chemokine, its function is not merely chemoattraction, but it also acts as an adhesion molecule and is capable of regulating the immune response via CX3CR1 corresponding receptor interaction [5–7].

CX3CR1 belongs to a family of G protein-coupled receptors [7], expressed on cytotoxic effector lymphocytes, including NK cells and cytotoxic T lymphocytes [8]. The FKN-CX3CR1 axis is a crucial regulator of the microglia, the immune cells of the central nervous system (CNS) [9,10].

FKN and its receptor are expressed in numerous reproductive tissues, comprising testis, uterus, ovaries and Fallopian tubes [11,12]. According to previous studies, fractalkine might have an important involvement in the feto–maternal communication during gestation since the trophoblast cells express CX3CR1 and the endometrium cells produce FKN [13,14].

CX3CR1 controls three major signalling pathways, the PLC-PKC pathway, the PI3K/AKT/NFκB pathway and the Ras-mitogen-activated protein kinases (MAPK) pathways (p38, ERK1/2 and JNK) [15]. MAPKs are one of the oldest known signal transduction pathways [16,17] regulating proliferation, growth, migration and apoptosis [18]. PLC-PKC pathway can trigger the MAPK pathway and the secretion of various hormones [15]. The PI3K/AKT signalling pathway is activated via growth factors and plays a critical role in regulating diverse cellular functions including cell growth, metabolism, proliferation, survival, transcription, protein synthesis and mitogenesis [19,20].

Cell migration and invasion are pivotal processes for endometrial cells during implantation, cell trafficking and embryonal morphogenesis [21]. The endometrium undergoes different highly regulated physiological changes during implantation [22,23]. The main supporters of migration are protein kinases, including extracellular signal-regulated protein kinase (ERK)1/2, the phosphatidylinositol 3 kinase (PI3K), the focal adhesion kinase (FAK) and other kinases that can be activated by different cytokines, growth factors and the extracellular matrix [24]. The JNK and p38 pathways are activated in mouse preimplantation development [25].

Progesterone receptor (PR) is involved in the proliferation and differentiation processes by regulating the transcription of specific genes (c-myc, p21, EGFR etc.). The human progesterone receptor has two classical isoforms—A and B—but non-classical intracellular progesterone receptor variants have also been detected (PRC, PRM, PRS and PRT) [26]. PRA and PRB are transcribed from the same gene by using two distinct promoters. The PR can be phosphorylated at basal phosphorylation sites, as well as in response to progesterone. Different protein kinases, including mitogen-activated protein kinases (MAPK), are known to phosphorylate PR at specific serine amino acid residues and modify PR's activity [27].

Trophoblast invasion into the uterus is an essential step at implantation of the human blastocyst. The action is facilitated by the degradation of the extracellular matrix of the endometrium/decidua by various proteinases, including the matrix metalloproteinases (MMPs) [28]. The tissue inhibitors of matrix metalloproteinases (TIMPs) are key regulators of the metalloproteinases. They inhibit the activity of the MMPs by binding to the highly conserved zinc-binding site of active MMP [29]. It is suggested that MMPs and their regulators control many aspects of reproductive function. The expression of MMP2 and MMP9 depends on the activation of the aforementioned MAPK pathways [30,31] that are regulated by CX3CR1, as well. MMPs have been localized most strongly to the placental bed in early pregnancy suggesting that these proteins are involved in the invasion of trophoblasts [32,33]. MMP expression is also regulated by other factors, e.g., activin signalling and BMP2 signalling pathways [34–36].

Activins secreted by endometrial cells belong to the pleiotropic family of the transforming growth factor beta (TGFβ) superfamily of cytokines [37] and are potential factors for maternal–embryo interactions [38]. Activin/Nodal signalling through activin receptors plays an important role at implantation [34,38]. Activin is antagonized by another secreted molecule, called follistatin, which is a key regulator of the biological actions of activin [37]. Follistatins are able to bind directly and irreversibly to activins, neutralizing the ligand [39]. In early development, many processes are contingent on BMPs signalling for cell growth and differentiation [40]. Some follistatin isoforms can bind bone morphogenetic proteins (BMPs) and hinder their biological activities [41].

A bilaminar co-culture system is used to study cell–cell interactions and the technique can be modified for co-culturing any variety of adherent cell types. In this system, the two cell types can connect to each other physically. Therefore, they are able to communicate with each other not only via their released signalling molecules, but also through different cell surface molecules. This way, we can get a better view of action/reaction both of the examined cells [42–45]. *Int. J. Mol. Sci.* **2020**, *21*, x FOR PEER REVIEW 3 of 19 via their released signalling molecules, but also through different cell surface molecules. This way,

In this study, we focused on the molecular mechanisms governing FKN supporting HEC-1A/JEG-3 interaction and influencing the expression of implantation-related genes in an in vitro co-culture model. Understanding how these mechanisms contribute to implantation might open new targeted medical therapies to give reassurance to women suffering from its failure. we can get a better view of action/reaction both of the examined cells [42–45]. In this study, we focused on the molecular mechanisms governing FKN supporting HEC-1A/JEG-3 interaction and influencing the expression of implantation-related genes in an in vitro coculture model. Understanding how these mechanisms contribute to implantation might open new


Abstract

Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling.

Keywords: metabolism, unstructured kinetic models, black box models, gray box models


*2.3.1. Ultrathin nanosheets of MoS2 through electrochemical lithium-intercalated exfoliation process*

To control the step of the formation of LixMoS2 compound, Zhang's group developed a simple method—electrochemical lithiation process—to control Li-intercalated and subsequent exfoliation in water or ethanol [15]. Lithium intercalation of MoS2 is performed in a test cell. The bulk MoS2 is prepared as cathode. The electrochemical intercalation is performed using galvanostatic discharge at a current density of 0.05 mA. After the discharge process, the product is washed with acetone to remove any residual electrolyte, followed by exfoliation and ultrasonication in distilled water or ethanol. Eventually, the suspension is centrifuged and washed many times with water.

As shown in **Figure 3b**–**d**, high-quality and ultrathin MoS2 nanosheets can be obtained by this simple method. The lateral size of nanosheets is about several hundred nanometers with a near transparency. Electron diffraction pattern of a flat area of the nanosheet (**Figure 3c**) and the corresponding high-resolution transmission electron microscope (HRTEM) image (**Fig‐ ure 3d**) show that the hexagonal lattice structure is formed by this method, showing clearly a lattice spacing of 2.7 Å assigned to the (100) planes. This strategy can also been applied to prepare other single-layer semiconducting nanosheets, such as WS2, TiS2, TaS2 or graphene.


4.3 Mesoscopic composite model of the simulation of creep cavity damage and fracture

The flow diagram structure is shown in Figure 10; the FE model of the polycrystalline case is shown in Figure 11 [31].

At the time of 78.9 hours, there were seven grain boundary elements that failed. If that is deemed as creep fracture time, then it agrees with the majority of all

#### Figure 9.

Inverse U0 and stress level for P91 at 625°C.

#### Table 4.

The values of nucleation coefficient and growth coefficient and lifetime prediction.

Modeling of Creep Deformation and Creep Fracture DOI: http://dx.doi.org/10.5772/intechopen.89009

Figure 10. The flow diagram structure [31].

Figure 11. The FE model of the polycrystalline case study [31].

#### Figure 12.

The location of the first seven failed grain boundary elements [31].

#### Table 5.

The sequence and time of fracture [31].

#### Figure 13.

The damage evolution with time of the seven failed grain boundary elements [31].

uni-axial creep tests conducted [9]: In uniaxial test, one specimen is fractured at 16.6, 17.9, and 58.3 hours, respectively. It is worthy to mention that the simulation was conducted for plane strain case; hence a longer lifetime at the same applied stress is expected.

The location, the sequence of fracture, and the time of fracture of grain boundary element are shown in Figure 12 and Table 5, respectively. The creep cavity damage evolution with time of the first seven failed grain boundary elements is

#### Modeling of Creep Deformation and Creep Fracture DOI: http://dx.doi.org/10.5772/intechopen.89009

#### Figure 14. The evolution of normal stress with time [31].

shown in Figure 13. The whole all normal stress evolution and the creep cavity damage evolution are shown in Figures 14 and 15, respectively.


**3. Computation of urban albedo: remote sensing calibration of DART**

Bottom of atmosphere irradiance and exitance are essential terms in the 3D radiative budget of urban and natural landscapes. In the short‐wavelength spectral domain, the ratio of exitance and irradiance is simply the albedo. DART can compute these terms for any time, date, and spectral band Δ*λ*, by using urban database inputs and actual atmosphere conditions that can be derived from satellite images or data from meteorological centers (e.g., ECMWF) or in situ measurements (e.g., AERONET network). The urban database, presented to DART in a \*.obj file format, must contain all buildings of the considered area, as well as the relevant information about urban vegetation in that area (i.e., geographic locations and physical dimensions of trees). It also requires distinction between roofs, walls, and streets, which means each facet in the database has to be registered in one of these groups. DART uses this registration to assign optical properties of the different scene elements. An example of a 3D urban model in nadir and an oblique view, which was built based on the urban database of the city of Basel, is shown in **Figure 7**.

**Figure 7.** DART 3D view of the city of Basel.

The accuracy of DART simulated albedo *ADART* ,Δ*λ* and exitance *MDART* ,Δ*λ* corresponds with accuracy of the presented optical properties. **Figure 8**, which shows various views of DART simulated images of Basel in natural colors, illustrates importance of specific optical properties. Roofs and walls appear with different colors. These colors depend on the roof and wall optical properties. A major problem is that it is practically impossible to know the spatial distribution of these properties accurately beforehand, due to their spatial variability. In addition, they vary also in time. In consequence, a major objective of the URBANFLUXES project is to derive maps of optical properties per type of Earth surface element. The approach relies on the use of atmospherically corrected remote sensing data (in our example, Landsat‐8 images), in order to compute the 3D radiative budget accurately. Two calibration methods were developed to compute products albedo more accurately: (1) a direct and (2) an iterative calibration.

**Figure 8.** DART simulated images of Basel, Switzerland: (a) pushbroom image with zoom, (b) airborne camera image, (c) satellite RGB image, (d) satellite TIR image.

### **3.1. DART calibration with satellite images**

*LDART,Δλ(xDART, yDART, Ωv)* the per‐pixel simulations of radiance *LDART,Δλ, n(xDART, yDART, Ωv)* and cross section *σn(xDART, yDART, Ωv)* images of each type of scene element n in discreet directions

**3. Computation of urban albedo: remote sensing calibration of DART**

Bottom of atmosphere irradiance and exitance are essential terms in the 3D radiative budget of urban and natural landscapes. In the short‐wavelength spectral domain, the ratio of exitance and irradiance is simply the albedo. DART can compute these terms for any time, date, and spectral band Δ*λ*, by using urban database inputs and actual atmosphere conditions that can be derived from satellite images or data from meteorological centers (e.g., ECMWF) or in situ measurements (e.g., AERONET network). The urban database, presented to DART in a \*.obj file format, must contain all buildings of the considered area, as well as the relevant information about urban vegetation in that area (i.e., geographic locations and physical dimensions of trees). It also requires distinction between roofs, walls, and streets, which means each facet in the database has to be registered in one of these groups. DART uses this registration to assign optical properties of the different scene elements. An example of a 3D urban model in nadir and an oblique view, which was built based on the urban database of the city of Basel, is shown

The accuracy of DART simulated albedo *ADART* ,Δ*λ* and exitance *MDART* ,Δ*λ* corresponds with accuracy of the presented optical properties. **Figure 8**, which shows various views of DART simulated images of Basel in natural colors, illustrates importance of specific optical properties. Roofs and walls appear with different colors. These colors depend on the roof and wall optical properties. A major problem is that it is practically impossible to know the spatial distribution of these properties accurately beforehand, due to their spatial variability. In addition, they vary also in time. In consequence, a major objective of the URBANFLUXES project is to derive maps of optical properties per type of Earth surface element. The approach relies on the use of atmospherically corrected remote sensing data (in our example, Landsat‐8 images), in order

along the sensor viewing direction *Ωv)*.

234 Sustainable Urbanization

in **Figure 7**.

**Figure 7.** DART 3D view of the city of Basel.

D*irect calibration* is a straightforward method that calibrates DART without assessing the optical properties of the surface elements per pixel [23], while *iterative calibration* uses an iterative procedure in order to spatially derive the optical properties of the scene elements. In theory, this approach gives more accurate results, but is computationally more complex. Both methods are demonstrated on the example of DART simulation of the city of Basel (Switzer‐ land) calibrated using a Landsat‐8 multispectral image, with band specifications presented in **Table 1**, corrected of atmospheric effects by DLR using the ATCOR atmosphere correction model [24].

**Table 1.** Landsat‐8 band specifications.

It is important to note that the image of the 6th Landsat‐8 band was omitted, because of its spectral coincidence with the atmospheric absorption bands. However, this omission has no impact on presented methods as they are designed to work with any type of satellite data, with a number of spectral bands ≥2 and with any spatial resolution.

## *3.1.1. Direct method*

The direct method consists of the following five steps:

**Step 1.** Reflectance images corresponding to the satellite bands used for calibration are simulated. For these simulations, we set the optical properties to a realistic, but not exact value, as this piece of information is unavailable. The spatial resolution of images is equivalent to the size (*xDART* , *yDART* ) of the DART voxels (in this example to 2.5 m).

	- **◦** The corrected image corresponds to the so‐called direct sun illumination. Hence, bottom of atmosphere (BOA) irradiance is direct, without diffuse component. The reflectance is noted ρdd,BOA.
	- **◦** The corrected image corresponds to the actual BOA sun (direct) and atmosphere (diffuse) irradiance. The reflectance is then noted ρsd,BOA. It would be ρhd,BOA (hemispheric‐direct) if the atmosphere irradiance was isotropic.

Here, the atmospherically corrected satellite image corresponds to the case ρsd,BOA. The corresponding DART simulated term of interest is noted:

$$\rho\_{DART,\lambda} \left( \mathbf{x}\_{DART}, \mathbf{y}\_{DART}, \boldsymbol{\Omega}\_{\boldsymbol{S}}, \boldsymbol{E}\_{\boldsymbol{S},\boldsymbol{B}\boldsymbol{M}} \left( \boldsymbol{\Omega}\_{\boldsymbol{S}} \right), \boldsymbol{L}\_{\boldsymbol{a}\boldsymbol{m}} \left( \boldsymbol{\Omega} \right), \boldsymbol{\Omega}\_{\boldsymbol{s}\boldsymbol{a}}, t\_{\boldsymbol{s}\boldsymbol{a}} \right),$$

where—*xDART* , *yDART* : coordinates of a given point in the DART simulated scene,

**Step 2.** Obtained DART image *ρDART, Δλ(xDART, yDART, ΩS, ES, BOA(ΩS), Latm(Ω), Ωsat, tsat)* was georeferenced and resampled to the spatial resolution of the Landsat‐8 image (*xsat*, *ysat*) to ensure exact correspondence in size and geolocation.

**Step 3.** A calibration factor *K*Δ*λ*(*xsat*, *ysat*, *tsat*) is computed per DART pixel (*xsat*, *ysat*). The objective of this factor is to mitigate the use of approximate optical properties. We use the following equation:

$$K\_{\Delta\lambda} \left( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sat}}, t\_{\text{sat}} \right) = \frac{\rho\_{\text{sat}\Delta\text{ur}, \ \lambda} \left( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sat}}, \mathbf{\Omega}\_{\text{S}}, \mathbf{\Omega}\_{\text{sat}} \right)}{\rho\_{\text{std}\Delta\text{RRT}, \ \lambda} \left( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sat}}, \mathbf{\Omega}\_{\text{S}}, \mathbf{\Omega}\_{\text{sat}}, t\_{\text{sat}} \right)}. \tag{1}$$

**Step 4.** The map of urban spectral albedo *A*Δ*λ*, is calculated per spectral band Δ*λ* with:

$$\begin{split} \boldsymbol{A}\_{\boldsymbol{\Delta}\boldsymbol{\lambda}} \Big( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sur}}, \boldsymbol{\Omega}\_{\text{S}}, \boldsymbol{E}\_{\text{S,BOL,\lambda\lambda}} \Big( \boldsymbol{\Omega}\_{\text{S}} \Big), \boldsymbol{L}\_{\text{ann},\text{\lambda}\boldsymbol{\lambda}} \Big( \boldsymbol{\Omega} \Big), \boldsymbol{t}\_{\text{sur}} \Big) &= \boldsymbol{K}\_{\boldsymbol{\Delta}\boldsymbol{\lambda}} \Big( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sur}}, \boldsymbol{t}\_{\text{sur}} \Big), \\ \boldsymbol{A}\_{\boldsymbol{\Delta}\boldsymbol{\mathcal{R}}\boldsymbol{\Gamma},\boldsymbol{\lambda}\boldsymbol{\lambda}} \Big( \mathbf{x}\_{\text{sur}}, \mathbf{y}\_{\text{sur}}, \boldsymbol{\Omega}\_{\text{S}}, \boldsymbol{E}\_{\text{S,BOL,\lambda}} \Big( \boldsymbol{\Omega}\_{\text{S}} \Big), \boldsymbol{L}\_{\text{ann},\text{\lambda}} \Big( \boldsymbol{\Omega} \Big), \boldsymbol{t}\_{\text{sur}} \Big), \end{split} \tag{2}$$

where *ADART* ,Δ*λ* is the albedo computed by DART, for spectral interval Δ*λ*:

It is important to note that the image of the 6th Landsat‐8 band was omitted, because of its spectral coincidence with the atmospheric absorption bands. However, this omission has no impact on presented methods as they are designed to work with any type of satellite data, with

**Step 1.** Reflectance images corresponding to the satellite bands used for calibration are simulated. For these simulations, we set the optical properties to a realistic, but not exact value, as this piece of information is unavailable. The spatial resolution of images is equivalent to the

**•** The DART reflectance image of interest *ρDART* ,Δ*λ*(*xDART* , *yDART* , Ω*sat*) is simulated in the viewing direction of the available satellite image. It is important to note that this approach can also work with an image which has not been atmospherically corrected. In this case, the simulated image corresponds to a "top of atmosphere" (TOA) data in a "direct‐direct" configuration, for which the incident and exiting radiative fluxes are following a single direction (ρdd,TOA). Classical atmosphere correction methods lead to two types of images:

**◦** The corrected image corresponds to the so‐called direct sun illumination. Hence, bottom of atmosphere (BOA) irradiance is direct, without diffuse component. The reflectance is

**◦** The corrected image corresponds to the actual BOA sun (direct) and atmosphere (diffuse) irradiance. The reflectance is then noted ρsd,BOA. It would be ρhd,BOA (hemispheric‐direct) if

Here, the atmospherically corrected satellite image corresponds to the case ρsd,BOA. The

**Step 2.** Obtained DART image *ρDART, Δλ(xDART, yDART, ΩS, ES, BOA(ΩS), Latm(Ω), Ωsat, tsat)* was georeferenced and resampled to the spatial resolution of the Landsat‐8 image (*xsat*, *ysat*) to

( *xy E L t* , ,W W WW , , ( ), ,, ( ) *sat sat*)

*DART DART DART S S BOA S atm* ,Δ

where—*xDART* , *yDART* : coordinates of a given point in the DART simulated scene,

a number of spectral bands ≥2 and with any spatial resolution.

size (*xDART* , *yDART* ) of the DART voxels (in this example to 2.5 m).

The direct method consists of the following five steps:

the atmosphere irradiance was isotropic.

r

— *L atm*(Ω): atmosphere radiance,

— *tsat*: time of the satellite acquisition.

corresponding DART simulated term of interest is noted:

l

— Ω*S* , Ω*sat*: sun and satellite view direction, respectively,

ensure exact correspondence in size and geolocation.

— *ES* ,*BOA*: sun irradiance at the bottom of the atmosphere (BOA),

*3.1.1. Direct method*

236 Sustainable Urbanization

noted ρdd,BOA.

$$\begin{split} \boldsymbol{A}\_{\text{DART},\boldsymbol{\lambda}}\left(\mathbf{x}\_{\text{DART}},\mathbf{y}\_{\text{DART}},\boldsymbol{\Omega}\_{\text{S}},\boldsymbol{E}\_{\text{S}\_{\text{BOM}}}\left(\boldsymbol{\Omega}\_{\text{S}}\right),\boldsymbol{L}\_{\text{ann}}\left(\boldsymbol{\Omega}\right),\boldsymbol{t}\_{\text{sat}}\right) &= \\ \frac{\rho\_{\text{d}\boldsymbol{\theta}}\boldsymbol{E}\_{\text{S},\boldsymbol{\text{BOM}},\boldsymbol{\lambda}}\,+\,\big[\rho\_{\text{d}\boldsymbol{\theta}\text{-}\boldsymbol{\Lambda}\boldsymbol{\lambda}}\big(\boldsymbol{\Omega}\big)\boldsymbol{L}\_{\text{ann\\_\text{AL}}}\left(\boldsymbol{\Omega}\right)\boldsymbol{\cos}\left(\boldsymbol{\theta}\right)d\boldsymbol{\Omega}\boldsymbol{\Omega}}{\boldsymbol{E}\_{\text{S},\boldsymbol{\text{BOM}},\boldsymbol{\lambda}}\,+\,\big[\boldsymbol{L}\_{\text{ann\\_\text{AL}}}\big(\boldsymbol{\Omega}\big)\boldsymbol{\cos}\left(\boldsymbol{\theta}\right)d\boldsymbol{\Omega}}.\end{split}\tag{3}$$

In (3), *θ* is the zenith angle corresponding to the viewing direction Ω.

**Step 5.** Desired urban surface albedo *A* is calculated as the integral of all the spectral albe‐ dos *A*Δ*λ*(*xsat*, *ysat*, Ω*S* , *ES* ,*BOA*,Δ*λ*(Ω*S* ), *L atm*,Δ*λ*(Ω), *tsat*) across the entire spectrum, weighted by the BOA irradiance *ES* ,*BOA*,Δ*λ*(Ω*S* ).

**Figure 9.** Landsat‐8 atmospherically corrected image for an NIR band (864.6 nm) over the city of Basel, acquired on the 24th April, 2015.

Here, we consider an application of the method to the city of Basel. **Figure 9** shows the atmospherically corrected Landsat‐8 band 5 (**Table 1**) image.

The original DART image of the same spectral band without calibration, at the simulation resolution of 2.5 m, is shown in **Figure 10**. This image has been georeferenced using the spatial information retrieved from the Landsat‐8 satellite image.

**Figure 10.** DART reflectance image for an NIR band (864.6 nm), over the city of Basel, corresponding to a Landsat‐8 image, with erroneous (but realistic) optical properties used as input.

Image in **Figure 10** was resampled to the Landsat‐8 native spatialresolution using ILWIS image processing software. After computation of the calibration factor (step 3) and the albedo per spectral band, we integrated the final broadband albedo product as shown in **Figure 11**. In **Figure 11**, the color bar indicates the albedo values, and the axes simply indicate the pixels positions.

**Figure 11.** Urban surface albedo image computed using the direct calibration method for the city of Basel, with a Landsat‐8 multispectral image.

## *3.1.2. Iterative method*

The original DART image of the same spectral band without calibration, at the simulation resolution of 2.5 m, is shown in **Figure 10**. This image has been georeferenced using the spatial

**Figure 10.** DART reflectance image for an NIR band (864.6 nm), over the city of Basel, corresponding to a Landsat‐8

Image in **Figure 10** was resampled to the Landsat‐8 native spatialresolution using ILWIS image processing software. After computation of the calibration factor (step 3) and the albedo per spectral band, we integrated the final broadband albedo product as shown in **Figure 11**. In **Figure 11**, the color bar indicates the albedo values, and the axes simply indicate the pixels

**Figure 11.** Urban surface albedo image computed using the direct calibration method for the city of Basel, with a

information retrieved from the Landsat‐8 satellite image.

image, with erroneous (but realistic) optical properties used as input.

positions.

238 Sustainable Urbanization

Landsat‐8 multispectral image.

Iterative calibration method computes the actual optical properties for each type of element in the scene per pixel of the satellite image or per group of pixels, which provides the desirable spatial variability in optical properties of present elements. If N types of elements are present in a single pixel, the reflectance of this pixel is formed by a number of different optical properties, that is, by N unknowns. Therefore, a set of equations is required to resolve the contribution of every surface element in a final reflectance value, which involves the image per type of scene elements described in Section 2.2. Taking into account a number of pixels, one obtains a system of equations, where a number of equations are equal or greater than the number of unknowns.

Consecutive steps of this approach are presented below, with N being the number of types of scene elements present in the studied urban area and parameter k being the order of the iteration initialized at k = 1:

**Step 1.** Regular grid is laid over the satellite image, with a mesh size equal to M M satellite pixels and with *M* > *N* . This ensures that each cell of the mesh contains a number of satel‐ lite pixels equal or larger than N.

**Step 2.** DART simulates the total radiance image *L DART* ,Δ*λ*(*xDART* , *yDART* , Ω*sat*) of the scene and also the radiance images per scene element n *L DART* ,Δ*λ*,*n*(*xDART* , *yDART* , Ω*sat*) in the satellite viewing direction Ω*sat*. Consequently, each surface element n viewed by a DART pixel d along the satellite viewing direction Ω*sat* has a reflectance value equal to *ρn*,*d* ,Δ*λ k* (*xDART* , *yDART* ).

It is important to note that reflectance and radiance are proportional terms, linked by the BOA sun irradiance:

$$\left(\rho\_{DART\lambda}\left(\mathbf{x}\_{DART},\mathbf{y}\_{DART},\boldsymbol{\Omega}\_{\text{sat}}\right) = \frac{\pi \boldsymbol{L}\_{DART,\text{\AA}\boldsymbol{\ell}}\left(\mathbf{x}\_{DART},\mathbf{y}\_{DART},\boldsymbol{\Omega}\_{\text{sat}}\right)}{\boldsymbol{E}\_{DART,\text{ROA},\text{\AA}\boldsymbol{\ell}}}.\tag{4}$$

The first iteration k = 1 assumes that the reflectance of an urban elements *ρn*,*d* ,Δ*λ k* (*xDART* , *yDART* ) is constant across the entire DART scene. Its plausible value is provided by a reasonable first guess, taken, for instance, from a recent airborne spectral data. Next step is to compute the reflectance value *ρn*,*d* ,Δ*λ k* +1 (*xDART* , *yDART* ) of the urban surface elements, which we will be used by DART in the follow‐up iteration k + 1. Objective of each iteration is to bring the DART reflectance closer to the reflectance of a real satellite image. In a DART pixel d, mean irradi‐ ance of an element type n is given by:

$$E\_{\text{DART},\lambda,\eta,d}\left(\mathbf{x}\_{\text{DART}},\mathbf{y}\_{\text{DART}}\right) = \frac{\pi \, L\_{\text{DART},\lambda,\eta}\left(\mathbf{x}\_{\text{DART}},\mathbf{y}\_{\text{DATT}}\right)}{\rho\_{\text{s}}^{\text{k}}\left(\mathbf{x}\_{\text{DATT}},\mathbf{y}\_{\text{DATT}}\right)} \cdot \frac{\Delta\mathbf{x}\_{\text{DATT}} \,\Delta\mathbf{y}\_{\text{DATT}} \,\cos\theta\_{\text{sat}}}{\sigma\_{\text{s},d}\left(\mathbf{x}\_{\text{DATT}},\mathbf{y}\_{\text{DATT}},\,\,\Omega\_{\text{sat}}\right)},\tag{5}$$

where *θsat* is the zenith angle of the satellite viewing direction and *σn*,*d* (*xDART* , *yDART* , Ω*sat*) is the cross section of the element of type n, which is viewed by the DART pixel d along the satellite viewing direction Ω*sat*. It is also an output of the DART model. The ratio *∆xDART* .*∆ yDART* .*cosθsat σn*,*d* (*xDART* , *yDART* , Ω*sat*) had to be introduced because the radiance of any DART pixel is simulat‐ ed per effective square meter of the pixel area *∆xDART* .*∆yDART* .*cosθsat*, while the radiance of the element n is expressed per effective square meter of the area of the surface element itself *σn*,*d* (*xDART* , *yDART* , Ω*sat*). Both radiances are equal, if only a single element is present in the considered pixel.

**Step 3.** DART radiance *L DART* ,Δ*λ*(Ω*sat*) and element *L DART* ,Δ*λ*,*n*(Ω*sat*) images are resampled in the satellite image spatial resolution (Δ*xsat*, Δ*ysat*). If selecting an appropriate spatial resolu‐ tion of aDARTsimulation, one can make the assumption that each satellite pixel m is composed of an integer number *D* 2 of DART pixels. Therefore, for any given satellite pixel m, where d is the index ofthe DART pixels in m (*d* ∈ 1 *D* 2 ) and n is the index ofthe elementtype (*n* ∈ 1 *N* ), one can define:

**•** Radiance as:

$$L\_{\text{DARTA},\lambda,m} \left( \mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}}, \ \ \ \ \ \ \ \ \mathbf{D}\_{\text{sat}} \right) = \frac{\sum\_{d=1}^{D^2} L\_{\text{DARTA},\lambda,d}}{D^2},\tag{6}$$

**•** Radiance of scene element n as:

$$L\_{\text{DART}\downarrow\lambda,\eta,m} \left( \chi\_{\text{sart}}, \mathcal{Y}\_{\text{sag}\,\Omega\_{\text{an}}} \right) = \frac{\sum\_{d=1}^{D^2} L\_{\text{DART}\downarrow\lambda,\eta,d}}{D^2},\tag{7}$$

**•** Irradiance of elements of type n as:

$$E\_{\text{DART},\lambda,\eta,d} \left(\mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}}\right) = \frac{\sum\_{d=1}^{D^2} E\_{\text{DART},\lambda,\eta,d} \left(\mathbf{x}\_{\text{DART}}, \mathbf{y}\_{\text{DART}}\right) \sigma\_{\eta,d} \left(\mathbf{x}\_{\text{DATT}}, \mathbf{y}\_{\text{DATT}}\right)}{\sum\_{d=1}^{D^2} \sigma\_{\eta,d} \left(\mathbf{x}\_{\text{DATT}}, \mathbf{y}\_{\text{DATT}}\right)},\tag{8}$$

and

**•** Cross section of element n viewed by pixel m as:

Remote Sensing Studies of Urban Canopies: 3D Radiative Transfer Modeling http://dx.doi.org/10.5772/63886 241

$$
\sigma\_{n,m} \left( \mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}} \right) = \sum\_{d=1}^{D^2} \sigma\_{n,d} \left( \mathbf{x}\_{DART}, \mathbf{y}\_{DART} \right). \tag{9}
$$

For a first approximation, Eq. (8) can be written similarly as Eq. (5):

( ) ( )

r

p

,Δ , ,

*∆xDART* .*∆ yDART* .*cosθsat*

240 Sustainable Urbanization

considered pixel.

one can define:

**•** Radiance as:

of an integer number *D* 2

**•** Radiance of scene element n as:

**•** Irradiance of elements of type n as:

,Δ , ,

and

l

l

*DART n d DART DART k*

,Δ ,

*L xy x y cos E xy*

l

( ) ( )

D D = W (5)

q

*DART n DART DART DART DART sat*

 s

*xy xy*

. , . . , ., , , ,

where *θsat* is the zenith angle of the satellite viewing direction and *σn*,*d* (*xDART* , *yDART* , Ω*sat*) is the cross section of the element of type n, which is viewed by the DART pixel d along the satellite viewing direction Ω*sat*. It is also an output of the DART model. The ratio

*σn*,*d* (*xDART* , *yDART* , Ω*sat*) had to be introduced because the radiance of any DART pixel is simulat‐ ed per effective square meter of the pixel area *∆xDART* .*∆yDART* .*cosθsat*, while the radiance of the element n is expressed per effective square meter of the area of the surface element itself *σn*,*d* (*xDART* , *yDART* , Ω*sat*). Both radiances are equal, if only a single element is present in the

**Step 3.** DART radiance *L DART* ,Δ*λ*(Ω*sat*) and element *L DART* ,Δ*λ*,*n*(Ω*sat*) images are resampled in the satellite image spatial resolution (Δ*xsat*, Δ*ysat*). If selecting an appropriate spatial resolu‐ tion of aDARTsimulation, one can make the assumption that each satellite pixel m is composed

the index ofthe DART pixels in m (*d* ∈ 1 *D* 2 ) and n is the index ofthe elementtype (*n* ∈ 1 *N* ),

,Δ , 2 , , ,

,Δ , , , Ω 2 , , *sat*

( ) ( ) ( )

,. , , ,

*DART n d DART DART n d DART DART d*

*n d DART DART d*

, 1

s

2 ,Δ,, , 1

=

2

*L*

*D*

2

*L*

*D*

,Δ , 1

,Δ,, 1

( )

s

,

*x y*

l

*DART n d d*

l

= W = å (6)

= = å (7)

å (8)

*DART d d*

( )

*L xy D*

( )

*L xy D*

*E xy xy E xy*

l

*DART m sat sat sat*

*DART n m sat sat*

2

=

*D*

l

*DART n d sat sat D*

**•** Cross section of element n viewed by pixel m as:

= å

l

,

of DART pixels. Therefore, for any given satellite pixel m, where d is

*n DART DART n d DART DART sat*

$$E\_{\text{DART},\lambda,\text{\tiny{u}},\text{\tiny{u}}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}}\right) = \frac{\pi \, L\_{\text{DART},\lambda,\text{\tiny{u}}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}},\,\mathbf{\Omega}\_{\text{sat}}\right)}{\rho\_{\text{u},\text{u}}^{\text{k}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}}\right)} \cdot \frac{\Delta\mathbf{x}\_{\text{sat}}\,\Delta\mathbf{y}\_{\text{sat}}\,\cos\theta\_{\text{sat}}}{\sigma\_{\text{u},\text{u}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}},\,\mathbf{\Omega}\_{\text{sat}}\right)}.\tag{10}$$

In this expression, *ρn*,*m k* (*xsat*, *ysat*) is the reflectance of the scene element of type n in the satellite pixel m at iteration k, as computed in the last iteration or equal to the initial value of k = 1. It assumes that the reflectance value of each DART pixel d corresponding to the satellite pixel m is constant.

**Step 4.** Resampled DART radiance is compared with the satellite radiance of all considered image pixels. If the difference between the two is acceptable, the procedure is terminated, and the desired urban albedo is the one computed by DART in the iteration of its simulation. Similarly to the direct calibration method, the albedo product is computed from all the spectral albedos. However, if the difference between the two radiances is according to user require‐ ments too large, the computation enters in a next step.

**Step 5.** The actual calibration of DART for each cell of the mesh defined in step 1 is conduct‐ ed for all *M* 2 satellite pixels and in each cell u of the mesh. Since *M* 2 > *N* , the number of pixels m analyzed in each cell u is larger than the number of different surface element types. Therefore, a deconvolution could be applied to retrieve the optical properties of each surface element present in the studied cell. Each cell u contains *M* 2 satellite pixels m, leading to a system of *M* 2 equations verifying if the DART image and the satellite image are equal:

$$L\sum\_{\mathfrak{u}=1}^{N}L\_{\text{DART},\mathfrak{u},\mathfrak{u},\mathfrak{u}}\left(\mathbf{x}\_{\text{sar}},\mathbf{y}\_{\text{sar}},\boldsymbol{\Omega}\_{\text{sar}}\right) = L\_{\text{sar},\mathfrak{u},\mathfrak{u}}\left(\mathbf{x}\_{\text{sar}},\mathbf{y}\_{\text{sar}},\boldsymbol{\Omega}\_{\text{sar}}\right), \forall m \in \left[1\,M^2\right].\tag{11}$$

Obviously, the verification is negative if the two images differ. At iteration k, the DART radiance values *L DART* ,Δ*λ*,*n*,*m*(*xsat*, *ysat*, Ω*sat*) are computed with inaccurate approximated optical properties *ρn*,*u k* (*xsat*, *ysat*). If we define *L DART* ,Δ*λ*,*n*,*m* ' (*xsat*, *ysat*, Ω*sat*) as the DART radiance value computed from *ρn*,*u k* +1 (*xsat*, *ysat*), and if we accept the fact that radiance values are proportional to reflectance values [Eq. (10)], then we can write:

$$\frac{L\_{\text{DARTA},\text{A},\text{u},\text{u}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}},\boldsymbol{\Omega}\_{\text{sat}}\right)}{\boldsymbol{\rho}^{k}\_{\text{u},\text{u}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}}\right)} = \frac{L^{\top}\_{\text{DARTA},\text{A},\text{u},\text{u}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}},\boldsymbol{\Omega}\_{\text{sat}}\right)}{\boldsymbol{\rho}^{k\cdot\text{1}}\_{\text{u},\text{u}}\left(\mathbf{x}\_{\text{sat}},\mathbf{y}\_{\text{sat}}\right)}.\tag{12}$$

Consequently, we can rewrite the system of Eq. (11) to a system of *M* 2 equations, in which the unknowns are the expected reflectance values *ρn*,*u k* +1 (*xsat*, *ysat*):

$$\sum\_{n=1}^{N} \frac{\rho\_{n,\boldsymbol{u}}^{k+1}(\mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}})}{\rho\_{n,\boldsymbol{u}}^{k}(\mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}})} \cdot L\_{\text{DART},\lambda,\mathbf{u},\mathbf{u}}(\mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}}) = L\_{\text{auța }\lambda,\mathbf{u}}(\mathbf{x}\_{\text{sat}}, \mathbf{y}\_{\text{sat}}), \forall m \in \left[1M^{2}\right]. \tag{13}$$

Resolving this system for each cell u of the mesh leads to the new desired reflectance values *ρn*,*u k* +1 (*xsat*, *ysat*), which is used in the follow‐up iteration starting in step 2. This system will find no solutions, if one of the satellite pixels of the cell u contains N types of elements, but all other pixels contain just a single unique element of the same type. In this case, the solution needs a new adaptation of the mesh grid size.

The iterative method has been executed on the same case as the direct method, but so far we retrieved the new optical properties after only a single iteration. We show in **Figure 12** the preliminary result assessing the operational functioning of the method. On the left, we see the spectral Landsat‐8 image used for the calibration, for the 5th spectral band (864.6 nm). In the center is the corresponding initial DART reflectance image, resampled to the satellite resolu‐ tion. On the bottom is the new DART reflectance image, with updated optical properties for each element, on the new grid.

**Figure 12.** (Top left) Landsat‐8 image used for the calibration (NIR band). (Top right) DART image with initial optical properties (NIR band), resampled in the satellite resolution. (Bottom) DART image with updated optical properties (NIR band).

After one iteration, the computed mean relative error between the DART image and the Landsat‐8 image went from *rel* =0.3519 to *rel*,*new* =0.0998. It improved by a factor of 3.5. A clear advantage of this method compared to the direct one is the actual computation of new optical properties, which we are able to use in the simulation of other satellite images, whereas the direct calibration only works for a single image. A better accuracy is also expected after several iterations.

#### **3.2. Albedo derived from climatological data**

Consequently, we can rewrite the system of Eq. (11) to a system of *M* 2

,Δ , , ,Δ , 1

l

*k DART n m sat sat sat m sat sat n*

( ) ( ) ( ) 1

*k* +1

, . , , , 1 . ,

= = " Î é ù å ë û (13)

 l

, 2

Resolving this system for each cell u of the mesh leads to the new desired reflectance values

The iterative method has been executed on the same case as the direct method, but so far we retrieved the new optical properties after only a single iteration. We show in **Figure 12** the preliminary result assessing the operational functioning of the method. On the left, we see the spectral Landsat‐8 image used for the calibration, for the 5th spectral band (864.6 nm). In the center is the corresponding initial DART reflectance image, resampled to the satellite resolu‐ tion. On the bottom is the new DART reflectance image, with updated optical properties for

**Figure 12.** (Top left) Landsat‐8 image used for the calibration (NIR band). (Top right) DART image with initial optical properties (NIR band), resampled in the satellite resolution. (Bottom) DART image with updated optical properties

(*xsat*, *ysat*), which is used in the follow‐up iteration starting in step 2. This system will find no solutions, if one of the satellite pixels of the cell u contains N types of elements, but all other pixels contain just a single unique element of the same type. In this case, the solution needs a

*x y L xy L xy m M*

(*xsat*, *ysat*):

unknowns are the expected reflectance values *ρn*,*u*

( )

*n u sat sat*

*x y*

,

+

r

242 Sustainable Urbanization

r

*ρn*,*u k* +1

(NIR band).

*k N n u sat sat*

new adaptation of the mesh grid size.

each element, on the new grid.

equations, in which the

Alternative methodology to derive urban albedo images has been adapted for a case when no satellite data of the area of interest are available. DART images are, in this case, simulated with optical properties of surface elements calibrated from the last available satellite image and with available atmospheric illumination conditions of direct sun irradiance *ES* ,*BOA*,Δ*λ* and diffuse sky irradiance *Eatm*,Δ*λ*. This information can be obtained from several sources including ECMWF (www.ecmwf.int), Meteo France (www.meteofrance.com), and other in situ mete‐ orological sensor networks.

In this alternative approach, DART calculates the so‐called white sky and black sky albedos.

The white sky albedo *ADART* ,*white sky*, Δ*λ*(*xsat*, *ysat*, *ES* ,*BOA*(Ω*S* ) =0, *L atm* = *Eatm π* , *tsat*) is computed for an irradiance coming solely from the atmosphere, that is, without any direct sunlight contri‐ bution. The black sky albedo *ADART, white sky, Δλ(xsat, ysat, ES, BOA(ΩS)=0, Latm=0, tsat)* is computed for the direct sunlight only.  Computation of thermal exitance images**

The calibration of the DART model in the thermal infrared domain, using satellite thermal infrared images, follows the same kind of approach. Indeed, the method will rely on spatial‐ ly setting up systems of equations over groups of pixels in the satellite image, to solve for the desired parameters. However, an additional difficulty comes from the fact that there are now 2 unknowns for each single measurement: the temperature *T* and the emissivity of the urban surface elements. Furthermore, the treatment of satellite pixels from thermal imagery only give information on the value of the product × *L* (*T* ), where *L* (*T* ) is Planck's law for temperature (*T* ). Hence, separating the variables by simply taking more pixels to create equations for the system is not adequate. The adopted strategy is to consider 2 thermal infrared images that correspond to 2 close spectral bands, with the assumption that emissivity is the same forthese 2 spectral bands. This approach leads to the determination of the thermodynamic temperature and emissivity per surface element of type n. Then by considering the other spectral bands, if the satellite image has more than 2 thermal infrared bands, it leads to the determination of the emissivity of each type of surface element in those spectral bands. In the URBANFLUXES project, we will work with satellite images that have several thermal infrared bands, as shown in **Table 2**.

**Table 2.** Landsat‐8 thermal infrared band specifications.


*2.2. Inorganic Nanobiomaterials*

Inorganic biomaterials are those lacking carbon element and they are widely employed for in vivo and in vitro biomedical research [222]. These crystalline or glass structured nanomaterials are used to replace or restore a body tissue [36]. The main applications of inorganic biomaterials, including bioceramics and bioglasses, are for orthopedics and dentistry. Modifications in composition and fabrication techniques may produce a range of biocompatible materials such as bioceramics [223]. Natural bone also includes inorganic materials like calcium (Ca) and phosphorus (P) in the form of hydroxyapatite (HA) crystals, as well as carbonate (CO3 2−), potassium (K), fluoride (F), chlorine (Cl), sodium (Na), magnesium (Mg), and some trace elements including copper (Cu), zinc (Zn), strontium (Sr), iron (Fe), and silicon (Si) [224]. Therefore, it is very logical to investigate on nanomaterials based on these inorganic constituents.

Table 2 presents a general classification of inorganic nanobiomaterials and summarizes representative applications of each material in tissue engineering. The following subsections present a general description of each nanomaterial type listed in Table 2 and a detailed literature survey on the corresponding developments for tissue engineering.

**Table 2.** Types of inorganic nanomaterials with their applications.