2. Design of a typical DHS

#### 2.1. A typical DHS diagram

Due to the heated floor area of DHSs which become very larger, indirect DHSs are commonly formed in practice for major district heating field with substations. A typical DHS diagram is shown in Figure 1, and the meaning of symbols in it is given in the nomenclature.

Figure 1. A typical DHS diagram with control principle.

The hot water with high-level temperature (usually less than 150C in design condition) is supplied from the boiler (sometimes from CHPs) in the heat source and transfers heat to the substations; then the heat is released to the secondary side in the substation, and the temperature of the return water in the primary side is decreased. The radiators at the end-users receive transferred heat from the substations and then are emitted to the indoor air for space heating. The supplied heat should be continuously gained to maintain suitable zone air temperature due to the heat balance between the indoor and the outdoor environments. The threeway control valves installed in the primary side of the substations are utilized to regulate the water mass flow into the heat exchangers and to balance the heat supplied to the secondary systems. Note that the makeup water systems in the secondary side are same as it is in the primary side (drawing ignored).

#### 2.2. Subsystem of the DHS

become very powerful as simulation tools. Many businesses or academic software such as BLAST, EnergyPlus, DeST, DOE-2, RNSYS, PKPM-CHEC, eQuest, VisualDOE, ESP-r, Ecotect, IES, etc. [6] can be easily obtained from various channels. However, if considering inside of the software, most of them were developed based on steady-state approach. In research area, dynamics is normally applied to equipment or partial system simulations. Few researchers are working in the dynamic simulation field for developing entire DHS modeling and try to

In this chapter, a typical hot water DHS is considered and designed. Then, a dynamic mathematical model is developed based on the physical model and thermal dynamic principles. An actual model is developed by correcting an ideal model of the DHS. Following that, the characteristics of the DHS could be collected by using open-loop test (OLT) method. Finally, five types of control strategies are simulated and compared with the analysis of dynamic

Due to the heated floor area of DHSs which become very larger, indirect DHSs are commonly formed in practice for major district heating field with substations. A typical DHS diagram is

shown in Figure 1, and the meaning of symbols in it is given in the nomenclature.

utilize the models for system-level improvement [7–12].

response, energy consumption and zone air temperature responses.

94 Sustainable Buildings - Interaction Between a Holistic Conceptual Act and Materials Properties

1.4. Major focus on this chapter

2. Design of a typical DHS

Figure 1. A typical DHS diagram with control principle.

2.1. A typical DHS diagram

From Figure 1, it is realized that the structure of the indirect DHS includes the following subsystem such as heat source, pipe network in the primary side, substation, pipe network in the secondary side, heat emit system from terminal, indoor air and outside environment.


**Abstract**

Burn injuries are the fourth leading cause of death among accidental deaths, which is the third leading cause of death in humans. Early treatments are required to stop the degeneration of the muscles, which could lead to the complications of the patient, even death. A proper and in time, treatment can lead to a full recovery of the person. Professionals often mislead these early treatments due to the stress and the lack of sleep during their job. Thus, an artificial medical assisting method was developed to detect and classify burns. Skin burns in color images were accurately detected and classified according to burn degree to assist clinicians during diagnosis and early treatment. Detection and classification of burnt areas are performed using the sparse representation of feature vectors with over-redundant dictionaries.


*3.1. The Surface Microrelief Formation by Plasma-Electrolyte Treatment*

During plasma-electrolyte treatment, the surface relief formation occurs due to the combustion of individual microdischarges that melt the surface. For local melting, it is necessary that at the ignition point of the gas discharge, the temperature of the cobalt-chromium alloy S&S Scheftner (Mainz, Germany) becomes higher than the solidus temperature of 1170 ◦C, preferably above the liquidus temperature of 1390 ◦C. The melting process is beginning when the solidus temperature is exceeded. A completely liquid metal can be obtained only at temperatures above 1390 ◦C. It is known that the casting temperature recommended by the manufacturer is 1490–1540 ◦C. At these temperatures the alloy has good fluidity and there is no burnout of alloying elements. Insufficient invested discharge power will not allow to achieve the required heating of the sample and lead to local melting with the formation of microholes. And a higher power can lead to overheating of the entire sample and its melting with loss of geometry, which is unacceptable. It should also be noted that the treatment proceeds more intensively on sharp and protruding surfaces, this is explained by the greatest intensity of the electric field in these places.

Thus, when processing samples, it is necessary to take into account their geometry, mass and properties of the alloy. With a sample thickness of a metal plate of 1.8 mm, surface melting occurred at a voltage of U = 220 V and a current strength of I = 12 A, and at lower voltage values, samples with a roughness parameter Ra from 0.77 to 2.51 µm were obtained.

At the initial moment of the treatment, the electrolyte is in contact with the surface of the metal sample. Depending on the magnitude of the applied voltage and the temperature of the electrolytic anode, one of the following processes will take place: only electrolysis; electrolysis eventually turning into the combustion of discharges on the surface of the sample; initial combustion of discharges without the stage of electrolysis. In the case of electrolysis, a linear dependence of the current strength on the voltage will be observed. According to the Lenz-Joule law, when an electric current passes through a metal electrode (cathode), the amount of heat released is directly proportional to the square of the current, the resistance of the electrode and the time during which the electric current flowed. An increase in voltage leads to an increase in current. This leads to an increase in the degree of cathode heating. On the cathode surface the process of boiling the electrolyte begins, in addition to the electrochemical release of gas bubbles. Combustion of discharges occurs in gas bubbles. The flow of the above processes is determined by the currentvoltage characteristic (CVC) of the treatment, the behavior of which changes when the type of electrolyte, its concentration and temperature change. By changing the dependence of the current strength on the applied voltage, it is possible to determine the intensity of electrochemical reactions, vaporization near the surface of the metal electrode and combustion of single discharges.

Let us consider the CVC (Figure 4) of the plasma-electrolyte treatment of a cobaltchromium alloy obtained using 1, 3 and 5% sodium chloride solutions. The CVC consists

of two branches: the first is a gradual increase in voltage from 0 to 300 V, the second is a decrease in the applied voltage from 300 to 0 V. consists of two branches: the first is a gradual increase in voltage from 0 to 300 V, the second is a decrease in the applied voltage from 300 to 0 V.

Let us consider the CVC (Figure 4) of the plasma-electrolyte treatment of a cobalt-chromium alloy obtained using 1, 3 and 5% sodium chloride solutions. The CVC

at lower voltage values, samples with a roughness parameter Ra from 0.77 to 2.51 μm

At the initial moment of the treatment, the electrolyte is in contact with the surface of the metal sample. Depending on the magnitude of the applied voltage and the temperature of the electrolytic anode, one of the following processes will take place: only electrolysis; electrolysis eventually turning into the combustion of discharges on the surface of the sample; initial combustion of discharges without the stage of electrolysis. In the case of electrolysis, a linear dependence of the current strength on the voltage will be observed. According to the Lenz-Joule law, when an electric current passes through a metal electrode (cathode), the amount of heat released is directly proportional to the square of the current, the resistance of the electrode and the time during which the electric current flowed. An increase in voltage leads to an increase in current. This leads to an increase in the degree of cathode heating. On the cathode surface the process of boiling the electrolyte begins, in addition to the electrochemical release of gas bubbles. Combustion of discharges occurs in gas bubbles. The flow of the above processes is determined by the current-voltage characteristic (CVC) of the treatment, the behavior of which changes when the type of electrolyte, its concentration and temperature change. By changing the dependence of the current strength on the applied voltage, it is possible to determine the intensity of electrochemical reactions, vaporization near the surface of the

metal electrode and combustion of single discharges.

*Materials* **2022**, *15*, x FOR PEER REVIEW 8 of 20

were obtained.

**Figure 4.** CVC of plasma-electrolyte treatment for NaCl solution: By grey line is 1%, red line is 3%, blue line is 5%. **Figure 4.** CVC of plasma-electrolyte treatment for NaCl solution: By grey line is 1%, red line is 3%, blue line is 5%.

For a 1% aqueous solution of sodium chloride, the combustion of single discharges with a pink-purple glow color occurs at a voltage of 120 V (grey line in Figure 4). When the applied voltage exceeds 100 V, local air-gas jets are observed departing from the cathode surface by 3–4 mm from the surface. On the CVC curve in this section, the beginning of a decrease in current strength is observed. A gradual increase in voltage leads to an increase in the number of single discharges. At a voltage of 177–181 V, local discharges of a different glow (yellow-orange) are additionally observed. At a voltage of 193 V, only yellow-orange glow discharges remain. The process of intensive heating of the electrode begins, it heats up to a temperature of 850–900 °C. At a given temperature of the For a 1% aqueous solution of sodium chloride, the combustion of single discharges with a pink-purple glow color occurs at a voltage of 120 V (grey line in Figure 4). When the applied voltage exceeds 100 V, local air-gas jets are observed departing from the cathode surface by 3–4 mm from the surface. On the CVC curve in this section, the beginning of a decrease in current strength is observed. A gradual increase in voltage leads to an increase in the number of single discharges. At a voltage of 177–181 V, local discharges of a different glow (yellow-orange) are additionally observed. At a voltage of 193 V, only yellow-orange glow discharges remain. The process of intensive heating of the electrode begins, it heats up to a temperature of 850–900 ◦C. At a given temperature of the electrode, a film boiling of the electrolyte will occur. Film boiling presupposes the presence of a stable vapor-air shell that covers the surface of the electrode. However combustion of discharges leads to a violation of the film boiling of the electrolyte and its splashing. The glow at the tip of the electrode turns white and corresponds to approximately a temperature of 1300 ◦C, in other areas of the surface it turns yellow and corresponds to a temperature of 1000 ◦C. Combustion of "yellow" discharges stops to prevail on the second branch of the CVC with a decrease in voltage to 216 V, mainly the combustion of electrical "pink-violet" discharges. Combustion of individual "yellow" discharges of low intensity occurs with a decrease in voltage to 163 V. When the voltage drops below 90 V, combustion stops and electrochemical reactions occur with boiling of the electrolyte near the electrode surface.

The use of a 3% aqueous solution of sodium chloride as an electrolyte changes the CVC curve while maintaining its outlines (red line in Figure 4). Namely, at 45 V—sound vibrations appear, at 60 V—the beginning of combustion of individual discharges on the cathode surface is observed. As the voltage increases, the number of discharges increases. The glow has a pink-purple color. At 100 V, larger yellow discharges appear. At 113 V, only yellow discharges burn. With increasing voltage, the intensity of radiation and acoustic vibrations increases. There are strong current fluctuations associated with intense splashing of the electrolyte near the cathode electrode. With a decrease in the applied voltage, the reverse transition occurs at 83 V. Local melting of the electrode surface is observed.

An increase in the concentration of sodium chloride in the electrolyte to 5% contributes to the displacement of the CVC to the region of lower voltages and an increase in the current strength (blue line in Figure 4). A voltage to 65 V increase leads to acoustic vibrations in the form of a crackle. At 67 V the combustion of individual discharges on the electrode surface begins. The color of the discharge radiation is yellow. At 88 V acoustic vibrations of a different frequency occur. With a decrease in voltage, the reverse transition from the combustion of discharges to the flow of only electrochemical reactions is observed at 63 V.

For all three CVCs with different concentrations of electrolyte, the presence of hysteresis is characteristic, with a reverse decrease in voltage, the current strength becomes significantly less. This is due to the heating of the electrolyte and the processed metal electrode. However, to a greater extent from the heating of the electrode, since it creates a film boiling that separates the electrolyte from the metal surface, and the absence of contact no longer allows electric current to be conducted due to electrochemical reactions. The current flow occurs only due to combustion of electrical discharges.

The obtained results of the CVC curves of plasma-electrolyte treatment indicate that a 3% solution of sodium chloride is the most optimal for use. With this sodium chloride content, there is no flow of high current densities for both 5% of the solution concentration and the risk of sample melting is reduced, and the use of 1% solution requires the use of higher voltage values to achieve the required energy supply. Based on the CVC curve for a 3% solution, the samples were processed to create roughness in the range from 120 to 200 V.

Varying plasma-electrolyte regimes generate different values of roughness. So, data of voltage, the temperature of the electrode, and the received roughness was interpolated by Delaunay triangulation (the natural neighbor interpolation method in Matlab). The obtained results are shown in Figure 5. The received interpolation allows selecting the plasma-electrolyte treatment mode to obtain a given roughness. *Materials* **2022**, *15*, x FOR PEER REVIEW 10 of 20

**Figure 5.** Roughness distribution depending on voltage and temperature of the electrode. **Figure 5.** Roughness distribution depending on voltage and temperature of the electrode.

### *3.2. Analysis of the Microrelief of the Surface after Treatment 3.2. Analysis of the Microrelief of the Surface after Treatment*

It should also be noted the gradient nature of the formation of the microrelief of the surface along the immersion of the sample in the electrolyte. The maximum size of the holes at the end of the sample, however, as they gradually move away from the edge, their size decreases. Using the example of changing the surface roughness parameters of the samples, it can be said that Ra in the longitudinal direction changed as follows: 2.734→2.064→1.728→1.375 μm. It should also be noted the gradient nature of the formation of the microrelief of the surface along the immersion of the sample in the electrolyte. The maximum size of the holes at the end of the sample, however, as they gradually move away from the edge, their size decreases. Using the example of changing the surface roughness parameters of the samples, it can be said that Ra in the longitudinal direction changed as follows: 2.734→2.064→1.728→1.375 µm.

Figure 6 shows pictures of the characteristic treated surface of the sample obtained by scanning electron microscopy. The images show that, depending on the depth of immersion of the electrode, the morphology of the surface changed. Conventionally, the surface can be divided into several sections, differing in morphology and size of micro-Figure 6 shows pictures of the characteristic treated surface of the sample obtained by scanning electron microscopy. The images show that, depending on the depth of immersion of the electrode, the morphology of the surface changed. Conventionally, the surface can be divided into several sections, differing in morphology and size of microholes. The first

holes. The first region covers the end of the sample with a length of 2.5 mm, its morphology of the surface of the 1st region is very developed (Figure 6a). The diameter of

morphology is developed. The diameter of microholes varies from ~1.5 μm to ~5 μm.

Figure 6c shows the 3rd area of study of the sample with 2000× magnification, which is removed from the edge of the sample by 5 mm, its morphology is very developed. The

It is established that the magnitude of the applied voltage strongly affects the nature of the plasma processes and heat generation. At insufficient voltages, the processing effect is not observed, and at higher voltages, melting of the electrode may occur. The most optimal voltage range is 160–190 V. Also an interesting fact is the strong heating of the electrode and its glow to red when using a new electrolyte at room temperature at the initial moment of gas discharge combustion, but after a certain time the effect of incandescence of the cathode passes, and it acquires a natural color. At the end of the process, if the electrode is removed from the electrolyte in a heated state, without contact with water and its natural cooling in air, then it is covered with a dark oxide film. If the electrode is cooled in the electrolyte, there is a transition from film boiling to contact boiling, with local near-electrode boiling of the electrolyte, and the formation of a black oxide film

diameter of microholes varies from ~500 nm to ~2 μm.

does not occur.

region covers the end of the sample with a length of 2.5 mm, its morphology of the surface of the 1st region is very developed (Figure 6a). The diameter of microholes varies from ~3 µm to ~10 µm. Figure 6b shows the 2nd research area with 1000× magnification, which is 2.5 mm away from the edge of the sample, its surface morphology is developed. The diameter of microholes varies from ~1.5 µm to ~5 µm. *Materials* **2022**, *15*, x FOR PEER REVIEW 11 of 20

**Figure 6.** SEM images of the sample surface after treatment: (**a**)—the 1st research area with 500× magnification, (**b**)—the 2nd research area with 1000× magnification, (**c**)—the 3rd area of study of the sample with 2000× magnification. **Figure 6.** SEM images of the sample surface after treatment: (**a**)—the 1st research area with 500× magnification, (**b**)—the 2nd research area with 1000× magnification, (**c**)—the 3rd area of study of the sample with 2000× magnification.

AB125. The PZ group was divided into three groups according to Ra values: PZ1, PZ2,

and PZ3. The values of Ra in groups are described in details in Section 3.3.

Figure 6c shows the 3rd area of study of the sample with 2000× magnification, which is removed from the edge of the sample by 5 mm, its morphology is very developed. The diameter of microholes varies from ~500 nm to ~2 µm.

It is established that the magnitude of the applied voltage strongly affects the nature of the plasma processes and heat generation. At insufficient voltages, the processing effect is not observed, and at higher voltages, melting of the electrode may occur. The most optimal voltage range is 160–190 V. Also an interesting fact is the strong heating of the electrode and its glow to red when using a new electrolyte at room temperature at the initial moment of gas discharge combustion, but after a certain time the effect of incandescence of the cathode passes, and it acquires a natural color. At the end of the process, if the electrode is removed from the electrolyte in a heated state, without contact with water and its natural cooling in air, then it is covered with a dark oxide film. If the electrode is cooled in the electrolyte, there is a transition from film boiling to contact boiling, with local near-electrode boiling of the electrolyte, and the formation of a black oxide film does not occur.

Sandblasting and new plasma-electrolyte treatments were compared. The surface roughness parameter Ra of cobalt-chromium samples after various processing methods presented in Figure 7. Grouping in ascending order of Ra value: PL, MC, AB50, AB90, AB125. The PZ group was divided into three groups according to Ra values: PZ1, PZ2, and PZ3. The values of Ra in groups are described in details in Section 3.3. *Materials* **2022**, *15*, x FOR PEER REVIEW 12 of 20

**Figure 7.** Surface roughness parameter distribution of samples, where bars represent mean (circle) **Figure 7.** Surface roughness parameter distribution of samples, where bars represent mean (circle) and standard deviation (bars).

and standard deviation (bars). A comparison of the roughness parameters of standard surface treatment methods with plasma-electrolyte microrelief formation shows that this method is superior in its A comparison of the roughness parameters of standard surface treatment methods with plasma-electrolyte microrelief formation shows that this method is superior in its capabilities to sandblasting, milling and surface polishing. Thus, by adjusting the parameters of the processing process, it will be possible to obtain the required surface roughness.

capabilities to sandblasting, milling and surface polishing. Thus, by adjusting the parameters of the processing process, it will be possible to obtain the required surface roughness. A complex relationship exists between surface roughness and adhesion. As expected, when a larger particle size abrasive is used, the Ra value is seen to increase. A complex relationship exists between surface roughness and adhesion. As expected, when a larger particle size abrasive is used, the Ra value is seen to increase. However, the average roughness increases as the larger particles create higher peaks and deeper valleys in the surface profile [39]. Also in [39] it was shown that as the Ra value increases, the Rz and Rq values increase proportionally.

However, the average roughness increases as the larger particles create higher peaks and deeper valleys in the surface profile [39]. Also in [39] it was shown that as the Ra value increases, the Rz and Rq values increase proportionally. A comparative assessment of the surface morphology for various processing methods was carried out. SEM images of the surface for various processing methods presented in Figure 8. The surface after polishing is presented in Figure 8a–c. Irregulari-A comparative assessment of the surface morphology for various processing methods was carried out. SEM images of the surface for various processing methods presented in Figure 8. The surface after polishing is presented in Figure 8a–c. Irregularities in the form of cavities formed by the abrasive of the polishing tool are detected at 2000× and 3000× magnification. The surface after smearing the surface layer with a processing tool presented in Figure 8d–f. Surface after sandblasting using sand with a dispersion of 50 µm presented in Figure 8g–i. In these images can be observed the remains of sand particles

ties in the form of cavities formed by the abrasive of the polishing tool are detected at

persion of 50 μm presented in Figure 8g–i. In these images can be observed the remains of sand particles pressed into the metal base. The morphology of the surface has many sharp protruding irregularities left as a result of the sliding of sand particles on the metal

Figure 8j–l show images of the surface after sandblasting using sand with a dispersion of 90 μm. Figure 8m–o show images of the surface after sandblasting using sand with a dispersion of 125 μm. Sand particles are found on all the studied surfaces. The

Figure 8p–r show images of the surface after plasma-electrolyte formation. A porous structure is observed, the protrusions have a spherical shape. The surface obtained by this method is fundamentally different in morphology from standard processing methods in the absence of sharp ledges, which later, when applying ceramics, can act as stress

main difference is the size of the formed irregularities.

concentrators and lead to chipping of ceramics under cyclic loads.

surface.

pressed into the metal base. The morphology of the surface has many sharp protruding irregularities left as a result of the sliding of sand particles on the metal surface.

*Materials* **2022**, *15*, x FOR PEER REVIEW 13 of 20

**Figure 8.** *Cont*.

**Figure 8.** SEM images of the surface for various processing methods: (**a**–**c**)—surface after polishing (PL group); (**d**–**f**)—surface after milling (MC group); (**g**–**i**)—surface after sandblasting with an abrasive size of 50 μm (AB50 group); (**j**–**l**)—surface after sandblasting with an abrasive size of 90 microns (AB90 group); (**m**–**o**)—surface after sandblasting using with an abrasive size of 125 microns (AB125 group); (**p**–**r**)—surface after plasma-electrolyte treatment (PZ groups). **Figure 8.** SEM images of the surface for various processing methods: (**a**–**c**)—surface after polishing (PL group); (**d**–**f**)—surface after milling (MC group); (**g**–**i**)—surface after sandblasting with an abrasive size of 50 µm (AB50 group); (**j**–**l**)—surface after sandblasting with an abrasive size of 90 microns (AB90 group); (**m**–**o**)—surface after sandblasting using with an abrasive size of 125 microns (AB125 group); (**p**–**r**)—surface after plasma-electrolyte treatment (PZ groups).

*3.3. Apparent Shear Strength*  The distribution of shear strength by groups is presented in Figure 9. There was a significant decrease in adhesion strength for the PL group (*p* < 0.05). Shear strength in group AB90 was significantly higher than in group AB50 (*p* < 0.05) and insignificant Figure 8j–l show images of the surface after sandblasting using sand with a dispersion of 90 µm. Figure 8m–o show images of the surface after sandblasting using sand with a dispersion of 125 µm. Sand particles are found on all the studied surfaces. The main difference is the size of the formed irregularities.

higher than in group AB125. Groups AB50, AB125, and MC did not differ significantly. In plasma-electrolyte groups the shear strength of PZ2 was significantly higher than in PZ1 and PZ3. Ra and shear strength results in groups are presented in Table 2. Figure 8p–r show images of the surface after plasma-electrolyte formation. A porous structure is observed, the protrusions have a spherical shape. The surface obtained by this method is fundamentally different in morphology from standard processing methods in the absence of sharp ledges, which later, when applying ceramics, can act as stress concentrators and lead to chipping of ceramics under cyclic loads.


**5. Conclusions**

The use of lateralized RSA with a low neck-shaft angle humeral stem results in significantly improved external rotation compared to medialized RSA with a 155◦ humeral stem, even in cases of severe fatty infiltration of the infraspinatus and the teres minor. Humeral lateralization and a low neck-shaft angle should be favored when planning an RSA in patients without a functional posterior rotator cuff. On the other hand, the medialized humerus with a 155◦ inlay stem contributed to a greater anterior forward flexion than the other configuration. However, the change in ROM amongst groups did not affect the postoperative clinical outcome.

**Author Contributions:** Conceptualization: A.L. and P.C.; methodology: H.B.; formal analysis: H.B.; investigation: S.O., H.K. and M.N.; resources: P.C.; data curation: S.O. and H.B.; writing: M.N., R.T. and A.L.; original draft preparation: M.N., S.O. and H.K.; writing—review and editing: P.C., A.L. and R.T.; project administration: P.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was founded by a grant from FORE (Foundation for Research and Teaching in Orthopaedics, Sports Medicine, Trauma and Imaging in the Musculoskeletal System): grant number 2021-1.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Recherche Clinique Vivalto Santé (protocol code CERC-VS-2018-06-1, date of approval: 29 June 2018).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** All the available data have been presented in this study. Details regarding where data supporting reported results can be requested at the following e-mail address<EMAIL_ADDRESS>
**Conflicts of Interest:** Alexandre Lädermann reports grants and personal fees from Arthrex, grants and personal fees from Medacta, grants and personal fees from Stryker, outside the submitted work. He is the founder of the FORE, of BeeMed, and Med4cast. Philippe Collin reports that he is a paid consultant for Stryker and Arthrex and receives royalties from Stryker. Rihard Trebše reports personal fees from Zimmer Biomet, personal fees from Medacta, personal fees from De Puy. Other authors declare no conflict of interest.


**4. Conclusions**

In this study, we extended the previous study and applied six machine learning algorithms: NB, RPART, NNET, SVM, RF and GBM to evaluate the surface water extraction using a Landsat 8 OLI images in Nepal. Using the previous reference dataset and Landsat scene, six different models were developed using the CARET package in R software. Cross-validation was completed to minimize the overfitting then train the model to predict the surface water and validate the full reference dataset. With three secondary bands: Slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. The results were compared, case by case, and the following conclusions were drawn from the test scene and applied machine learning algorithms:

It seems that machine learning methods could be very useful for the accurate automated binary classification of surface water in Nepal. The use of RF with original LS8 data or with the addition of the slope or NDWI with another algorithm can be undertaken. Based on this and previous work [13], it is recommended to segment the study area with and without snow or low and high elevation, then apply RF or GBM for better performance.

For further investigation, this study aimed to evaluate the application of convolutional neural networks or deep learning for better accuracy. In addition, individual original bands and secondary bands with the RF and GBM can be evaluated so that high accuracy can be achieved with minimum bands.

**Author Contributions:** Conceptualization, T.D.A.; formal analysis, T.D.A.; investigation, T.D.A. and A.S.; methodology, T.D.A.; resources, D.H.L.; software, T.D.A.; supervision, D.H.L.; validation, A.S.; visualization, A.S.; writing—original draft, T.D.A.; writing—review and editing, A.S. and D.H.L.

**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A2B6009363).

**Acknowledgments:** The authors are grateful to the U.S. Geological Survey server (http://glovis.usgs.gov) for providing the Landsat data that was used in this manuscript freely. The authors would also like to thank the anonymous reviewers for their constructive comments and improving this manuscript.

**Conflicts of Interest:** The authors declare no conflicts of interest.


Nevertheless, the potential annual average powers fluctuate between 15 and 30 kW/m, depending on the sector within the sweep. As stated in [50], approximately 20 kW/m will make wave energy economically viable. As wave energy is not as mature as other renewable energies (like solar or wind), the economic viability not only depends on the amount of power a location can provide. The cost of the WECs is an essential part of the cost of a wave farm. The cost of operation and maintenance is also high, as corresponds to a facility in the sea [51]. The maximum power occurs during the winter season at 73.25 W and 36.65 S, approximately 15 km from the coast. The seasonal average is 37 kW/m, corresponding to a wave height of approximately 2.9 m, with a standard deviation of 1.6 kW/m. The maximum height at this point was 4.96 m (108 kW/m). In summer (December-February), the values vary between 15 and 25 kW/m. The least energetic month is January, presenting a constant 20 kW/m, half the one obtained in winter. One can note that throughout the year, 75% of the time, the wave height is above 2.2 m, which means

that 75% of the time, the power will be a minimum of 22 kW/m. If this is carried out in terms of energy, it corresponds to a minimum of 193 MWh/m/year.

The SV and MV indices calculated presented values between 0.06–0.4 and 0.1–0.6, similar to those presented by [26]. These indices are relatively low compared to other studies that present similar potential magnitudes, such as [31,52]. Additionally, as mentioned before, the site selection must consider the wave power and variability of the area; here, the COV presents a lower variability than the considerable wave potential found. As a whole, the different indices make the area a more than convenient place for energy extraction.

We investigated the energy consumption of the areas close by. On average, a household consumes approximately 200 kWh per month, presenting a maximum peak in winter with values close to 230 kWh/month and a minimum in summer of 150 kWh/month [53]. Our work shows that this value is less than what can be extracted from a single grid cell, which is 2 × 2 km, since 75% of the time, the energy extracted would be larger than 16.6 MWh/month. Annually the maximum that could be collected is 270 MWh/m/year, and the lowest as perceived by the radar is 160 MWh/m/year. Considering a simple device, for example, an OWC with an efficiency of 40% [54,55], a single wave energy collector could have an approximate annual savings of USD 13,500, which would supply the average annual energy demand of approximately 33 houses, equivalent to avoiding 32.31 tonCO2eq/year [56]. The most significant advantage of wave energy is its high-power density, meaning that it can harness more energy than other renewable energies. Due to the novelty and newness of wave energy converters, it is impossible to provide a specific number of devices that can attain powers as big as wind or solar energy. For example, Ref. [51] shows several devices' performance, and the installed power varies between 6 kW and 15 MW. Thus, to reach 1 GW of installed power, the quantity of WEC can vary between 160,000 and 66. Likewise, the amount of area used to display the WECs also will change. Noteworthy, there are so many WECs types and locations where this can be installed that each case has to be evaluated case by case. Let us note something interesting to consider; hybrid systems, coupling waves and wind energy in the same facility [57]. It will provide an even more stable supply using the same area.

This work made it possible to estimate the wave power in front of the Biobío region, determining the area's suitability for its low variability and high energy concentration. With the high-resolution information provided by the HF radar, the best location for energy extraction can be identified. The utility of HF radars in energy terms relies on its high resolution, both temporal and spatial, given the ability to compare locations of interest within the same area. The possibility to implement a transportable radar to measure the high frequencies waves rank this instrument as an excellent asset to establish the wave power of a location.

As established throughout this work, even when a single radar can provide wave information, it can present ambiguities. This cannot be solved using only the radar's information. Of course, eventually, two radar systems would be ideal. However, not being cheap systems, we must enhance what we already have. Thus, one of our research's next stages could be to move the transportable HF radar close to the buoy to calibrate it correctly. Instead, we can use coastal video imagery for doing so [58,59]. We should also investigate an up-and-coming smartphone-based camera system recently assessed for coastal image classification [60].

Another great applicability of an HF marine radar system is its complementary use to satellite measurements or numerical models. In turn, radar data can be a valuable tool for the calibration of satellite data such as CFOSAT [61] and SWOT [62]. For example, SWOT's main objective is to measure ocean topography with centimeter-scale accuracy over kilometer-scale spatial resolution [63]. Therefore, HF radar measurements that also have a high resolution can be an accurate validation system. It shows the usefulness of HF marine radar as an instrument of remote sensing to study wave patterns and wave energy potential of vast areas at a high resolution.

**Author Contributions:** Conceptualization, V.M.-M. and R.A.-d.-R.; methodology, V.M.-M. and R.A. d.-R.; software, V.M.-M. and R.A.-d.-R.; validation, V.M.-M. and R.A.-d.-R.; formal analysis, V.M.- M. and R.A.-d.-R.; investigation, V.M.-M. and R.A.-d.-R.; data curation, V.M.-M. and R.A.-d.-R.; visualization, V.M.-M.; supervision, R.A.-d.-R.; project administration, R.A.-d.-R.; V.M.-M.; writing original draft: V.M.-M. and R.A.-d.-R.; writing—review and editing, V.M.-M., R.A.-d.-R., D.F., and J.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The National Agency for Research and Development (ANID)/Scholarship Program funded this research through grant DOCTORADO BECAS CHILE/2018–21181031.

**Data Availability Statement:** The HF radar fields resulting from this research work are available upon request to the first author.

**Acknowledgments:** We appreciated the reviewers' expert work that allowed this research, both in form and substance, to improve more than substantially. Buoy data was provided by the CENDHOC, Chilean Navy's Hydrographic and Oceanographic Service (SHOA).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

### *Letter* **Seasonal Variability of SST Fronts in the Inner Sea of Chiloé and Its Adjacent Coastal Ocean, Northern Patagonia**

**Gonzalo S. Saldías 1,2,\*, Wilber Hernández 3, Carlos Lara 4,5, Richard Muñoz 1, Cristian Rojas 3, Sebastián Vásquez 6, Iván Pérez-Santos 7,8 and Luis Soto-Mardones 1**

**Abstract:** Surface oceanic fronts are regions characterized by high biological activity. Here, Sea Surface Temperature (SST) fronts are analyzed for the period 2003–2019 using the Multi-scale Ultrahigh Resolution (MUR) SST product in northern Patagonia, a coastal region with high environmental variability through river discharges and coastal upwelling events. SST gradient magnitudes were maximum off Chiloé Island in summer and fall, coherent with the highest frontal probability in the coastal oceanic area, which would correspond to the formation of a coastal upwelling front in the meridional direction. Increased gradient magnitudes in the Inner Sea of Chiloé (ISC) were found primarily in spring and summer. The frontal probability analysis revealed the highest occurrences were confined to the northern area (north of Desertores Islands) and around the southern border of Boca del Guafo. An Empirical Orthogonal Function analysis was performed to clarify the dominant modes of variability in SST gradient magnitudes. The meridional coastal fronts explained the dominant mode (78% of the variance) off Chiloé Island, which dominates in summer, whereas the SST fronts inside the ISC (second mode; 15.8%) were found to dominate in spring and early summer (October–January). Future efforts are suggested focusing on high frontal probability areas to study the vertical structure and variability of the coastal fronts in the ISC and its adjacent coastal ocean.

**Keywords:** MUR SST; SST fronts; Inner Sea of Chiloé; northern Patagonia

#### **1. Introduction**

Oceanic fronts are relatively narrow regions with high gradients of physical, chemical, biological, and optical properties. They are generally associated with convergence at the surface [1] and high aggregation of organisms and biological activity e.g., [2,3]. Lately, enhanced submesoscale activity has been identified around fronts [4,5], which involves ageostrophic vertical circulation with increased vertical fluxes of tracers and momentum. In general, frontal features and currents such as jets and meanders e.g., [6,7], filaments e.g., [8,9], and river discharges e.g., [10,11] present distinct patterns of variation in strength and duration over multiple temporal scales and are identified from satellite sea surface temperature (SST) fields.

**Citation:** Saldías, G.S.; Hernandez, W.; Lara, C.; Muñoz, R.; Rojas, C.; Vásquez, S.; Pérez-Santos, I.; Soto-Mardones, L. Seasonal Variability of SST Fronts in the Inner Sea of Chiloé and Its Adjacent Coastal Ocean, Northern Patagonia. *Remote Sens.* **2021**, *13*, 181. https://doi.org/ 10.3390/rs13020181

Received: 30 November 2020 Accepted: 4 January 2021 Published: 7 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Remotely sensed data have been crucial in the study of the evolution of ocean fronts at multiple spatio-temporal scales [9,12–16]. The improved accuracy and spatial resolution of SST sensors over time have allowed the quantification and spatial distribution of sharp fronts in coastal regions [11,15,17–20]. Here, high-resolution (1 km) SST images are used to analyze the variability of SST fronts in the Inner Sea of Chiloé (ISC) and adjacent coastal ocean (northern Chilean Patagonia). The ISC is a long inner sea (about 260 km) with high heterogeneity of hydrographic conditions due to the influence of freshwater from several rivers and the marine influence by the active water exchange through Boca del Guafo and Chacao channel [21,22]. Several islands (Desertores Islands) promote contrasting hydrographic and SST characteristics between the northern and southern areas. In general, northern Patagonia is characterized by elevated surface chlorophyll-a concentration and primary productivity during spring-summer-fall [23]. Recently, harmful algal blooms events were described in the region in association with anomalous oceanographic conditions during summer 2016 [24]. Although the development of chlorophyll patches and blooms have been suggested to develop around frontal regions [25], there are no major insights into the evolution and distribution of surface fronts along the ISC.

This study aims to characterize the seasonal and interannual variability of surface thermal fronts along the Inner Sea of Chiloé and its adjacent coastal ocean. For the first time, high-resolution satellite images were used to achieve a frontal analysis of this region. Section 2 describes the data and methods. Section 3 resumes the principal results and discusses the seasonal and interannual evolution of SST fronts. Finally, a summary is presented in Section 4.

#### **2. Satellite Data and Methods**

We used daily SST data from the Multi-scale Ultra-high Resolution (MUR) product [18] obtained from the Physical Oceanography Distributed Active Archive Center (PODAAC; see Data Availability Statement below). MUR data were chosen over other SST products due to the increased data cover and improved performance detecting SST gradients in the coastal region [18]. The data have a spatial resolution of 0.01 × 0.01 degrees. A comparison with a near-surface temperature time series from a buoy located in Seno Reloncaví (see Figure 1) validates the performance of MUR SST in the ISC (Figure 1b,c). The buoy has been maintained by the research center i-mar since 2017 (see Data Availability Statement below).

The gradient magnitude (GM) and the Canny edge-detection algorithm [26] were used to detect and quantify the frontal regions and their evolution. The GM was computed following other studies e.g., [11,18]:

$$\nabla\_{\mathbf{x}} TSM\_{i} = \left( TSM\_{i-1} - TSM\_{i+1} \right) / \left( X\_{i-1} - X\_{i+1} \right) \tag{1}$$

$$(\nabla\_y T S M\_{\hat{\jmath}} = (T S M\_{\hat{\jmath}-1} - T S M\_{\hat{\jmath}+1}) / (Y\_{\hat{\jmath}-1} - Y\_{\hat{\jmath}+1}) \tag{2}$$

$$|\nabla TSM| = \left\{ \left( \nabla\_{\mathbf{x}} TSM\_{i} \right)^{2} + \left( \nabla\_{\mathbf{y}} TSM\_{j} \right)^{2} \right\}^{1/2} \tag{3}$$

where *xSSTi* and *ySSTj* are the zonal and meridional components of the SST gradient, respectively, and | *SST*| is the gradient magnitude. The Canny edge-detection algorithm [26] was applied on daily SST fields to identify coherent frontal segments and compare them with the regions of increased gradient magnitudes. The Canny method tracks the direction of SST gradients using a threshold value. The gradients are calculated using the derivative of a Gaussian filter. After studying the range of gradient magnitudes for all images (Figure 1d), we used a high threshold value of 0.1 ◦C/km, which separates most values in the low range of the PDF from the higher values associated with the formation of SST fronts. The probability of finding a front was calculated as the number of times a pixel is classified as a front divided by the total images considered in a time window (i.e., seasonal aggregates). Further details of the application of the Canny method can be found elsewhere [15,19].

**Figure 1.** (**a**) Map of the Inner Sea of Chiloé and its adjacent coastal ocean in northern Patagonia. The bathymetry is shown in a blue-white color scale. The position of the oceanographic buoy located in Seno Reloncaví is denoted by a red dot. The comparison between satellite SST and near-surface (1 m) temperature at the buoy is presented in (**b**,**c**). In situ temperature has been daily averaged to match satellite SST data. (**d**) Probability Density Function (in %) of the distribution of all SST gradient magnitudes for the period 2003–2019.

Satellite chlorophyll fluorescence (Fluorescence Line Height, nFLH) data from the Moderate Resolution Imaging Spectroradiometer (MODIS, on-board Aqua) and for the period 2003–2019 were obtained from the ocean color website (see Data Availability Statement below). These data were used to compute the seasonal climatology. We chose to use the chlorophyll fluorescence over the chlorophyll product because of the characteristic estuarine turbid conditions of the ISC and the relatively poor performance of default chlorophyll algorithms [27].

An Empirical Orthogonal Function (EOF) analysis was performed on the gradient magnitudes to separate the main modes of variability. The EOF was computed following the Singular Value Decomposition (SVD) approach to avoid a large covariance matrix associated with the high resolution of the images [28]. Please note that each time series is demeaned and detrended in the process.

#### **3. Results and Discussion**

The temporal pattern of SST variability is well-captured by MUR SST compared to the buoy measurements at Seno Reloncaví for the period 2017–2020, as shown in Figure 1b. However, some events with peaks in *in situ* temperature were not well recorded by the satellite data, especially during spring-summer (Figure 1b). There is a slight underestimation by MUR SST (Figure 1c). In general, MUR data reproduced the temporal SST variability with high correlation (r = 0.96), which gave us confidence that the MUR product is reasonably accurate in these coastal waters. Given the recent deployment of this buoy in Seno Reloncaví (starting in 2017), this is the first comparison of satellite SST and *in situ* temperature in the ISC.

The seasonal climatology of SST fields revealed a typical pattern for temperate ecosystems, i.e., marked spatial and seasonal variability. During austral spring and summer, the higher SST is observed in the northern area (north of Desertores Islands) and adjacent coastal ocean (Figure 2a,d). In fall, The adjacent coastal ocean showed the highest values compared to the ISC (Figure 2b), whereas the entire coastal ocean shows the lowest temperatures (<11 ◦C) in winter (Figure 2c). The persistence of these mean fields is variable depending on the location and season (Figure 2e–h). The mean warm pattern observed in summer in the northern ISC (Seno Reloncaví) is also highly variable (Figure 2e). This strong variability is also presented in the fall and spring (Figure 2f,h). In general, the ICS presents the largest SST variability (greater than 1.2 ◦C) in spring (Figure 2h). Notice that low variability south of Desertores Islands is associated with the lowest averaged SST fields in connection with the coastal ocean through Boca del Guafo (Figure 2b,d), characterized by intrusions of Sub-Antarctic waters (SAAW) [29,30]. Finally, winter represents the coldest season with the annual cycle's greatest spatial homogeneity (Figure 2c,g). The annual cycle of SST is coherent with previous studies in northern Patagonia [22,31,32].

The quantification of the climatological SST gradient magnitudes for the entire 17 years of study is shown in Figure 3. During spring and summer, the SST gradient magnitude fields suggest a high frontal activity in the northern area of the ISC (Figure 3a,d). Summer also represents a period with increased SST fronts in the coastal ocean with an extended meridional band of high gradients (Figure 3a). This would be linked with the development of a coastal upwelling front off Chiloé Island in summer due to predominant northward winds [30,33]. The extended band with medium values of gradient magnitude in fall would represent the weakening of upwelling-favorable winds, and consequently, a weaker upwelling front (Figure 3b). Medium values in the standard deviation corresponding to the upwelling front would explain a synoptic variability with pulses of upwelling events through summer and fall (Figure 3e,f). High values in SST gradient (>0.05 ◦C/km) and variability (>0.03 ◦C/km) on the southern area of the ISC in summer could be associated with the generation of SST fronts from the intrusions of oceanic waters through Boca del Guafo (~44◦S) (Figure 3a,e). In spring, the highest gradients are presented in the northern ISC with a band of increased variability near Desertores Islands (Figure 3d,h). Finally, SST's gradient magnitudes are less pronounced in winter (Figure 3c,g) which is coherent with a more homogeneous and cold temperature field (Figure 2c,g). Water column stratitication in Seno Reloncaví (not shown) presents a coherent annual cycle with maximum (minimum) stratification in spring-summer (fall-winter).

**Figure 2.** Seasonal climatology (2003–2019) of SST in the Inner Sea of Chiloé and its adjacent coastal ocean. (upper panels) averages and (lower panels) standard deviations for (**a**,**e**) summer (January, February, March), (**b**,**f**) fall (April, May, June), (**c**,**g**) winter (July, August, September), and (**d**,**h**) spring (October, November, December). For reference of locations see Figure 1a.

To further understand the generation and evolution of fronts along the ISC and its adjacent coastal ocean, the seasonal variability of frontal probability (FP) is shown in Figure 4. The use of an edge-detection algorithm is crucial for calculating the FP since fronts tend to be narrow and coherent bands of increased gradients of ocean properties [14], which can be overlooked and or not correctly identified through the gradient magnitude. The FP maps reveal that the formation of SST fronts off Chiloé Island in summer-fall (Figure 4a,b), potentially associated with the coastal upwelling, is consistent with the increased gradient magnitudes shown in Figure 3. The FP reaches its largest values (>9%) in the northern ISC in spring-summer (Figure 4a,d). Also, the lowest SST frontal activity in spring-summer is shown on the western section of the southern ISC (Figure 4a,d), which might be associated with a greater oceanic influence on the eastern side. The eastern side also has several river outflows, which could influence the temperature field, creating increased gradients. It is interesting to note that the FP in fall is <3% along most of the ISC, representing the season with the lowest SST frontal activity (Figure 4b). In contrast, the frontal probability values during winter indicate higher PF as compared to fall (Figure 4c vs. Figure 4b), especially at Boca del Guafo (values up to 7%), which was not demonstrated through the gradient magnitude (Figure 3).

**Figure 3.** Seasonal climatology (2003–2019) of gradient magnitude of SST in the Inner Sea of Chiloé and its adjacent coastal ocean. (upper panels) averages and (lower panels) standard deviations for (**a**,**e**) summer (January, February, March), (**b**,**f**) fall (April, May, June), (**c**,**g**) winter (July, August, September), and (**d**,**h**) spring (October, November, December). For reference of locations see Figure 1a.

We performed an EOF analysis to separate the main modes of variability of the SST gradient magnitude and evidence the periods when the development of SST fronts in the coastal ocean and the ISC is more likely to occur. The EOFs show that most of the variance is explained in the meridional band of high SST gradients off Chiloé (Figure 5a). These features, highly associated with the upwelling front [22,32], occurred most of the years during December-April (Figure 5c,d). The second EOF isolates the enhanced SST frontal activity in the ISC (Figure 5b), predominantly in spring and early summer (October– February; Figure 5c,d). The temporal oscillations of the EOFs reveal a persistent annual cycle, especially after the end of 2005 (Figure 5d). A maximum SST gradient magnitude in the coastal ocean occurred in early 2008 as seen through the peak of mode 1 (Figure 5d). In northern Patagonia, large-scale climatic influence has been suggested to produce changes in the coastal oceanography, and concomitantly, in ecological patterns around the Inner Sea of Chiloé [21,34]. Future studies focused on the interannual variability of SST fronts should consider the potential impact of climate variability e.g., [21] on the generation or blocking of SST fronts in the ISC.

**Figure 4.** Seasonal climatology (2003–2019) of SST frontal probability (FP; %), based on the Canny edge-detection algorithm, in the Inner Sea of Chiloé and its adjacent coastal ocean for (**a**) summer (January, February, March), (**b**) fall (April, May, June), (**c**) winter (July, August, September), and (**d**) spring (October, November, December). For reference of locations see Figure 1a.

**Figure 5.** EOF analysis of SST gradient magnitudes. (**a**) First and (**b**) second EOF modes. (**c**) Mean annual cycle of EOF time series shown in (**d**). The error bars in (**c**) correspond to the monthly standard deviations.

The use of satellite platforms to monitor oceanographic properties provides important insights into oceanographic fronts' spatial and temporal variability. An important feature of the northern Patagonian shelf is its significant freshwater inputs [35,36] which could also generate thermal variability and fronts. The river discharges in the northern ISC have been associated with phytoplankton blooms [37]. Freshwater discharge from large rivers and or glacial melting into the Inner Sea of Chiloé (41–45◦S) creates a freshwater plume with high levels of biological activity which extends into the coastal ocean [32,38]. While the local impacts of those freshwater discharges remain unknown, it is expected that they would favor the formation of oceanographic fronts and the aggregation of large organisms, such as whales, in the region [39]. Ocean fronts are hotspots of high biological activity [19,40–42] by which increased primary productivity is expected during spring-summer-fall and linked to the areas of high frontal activity (i.e., northern ISC, coastal band off Chiloé Island, southern border of Boca del Guafo). The seasonal climatology of chlorophyll fluorescence (Figure 6) suggests there is enhanced phytoplankton activity around the SST fronts in northern ISC and a maximum fluorescence in a meridional band off Chiloé in fall (Figure 6b), which would agree with the presence of a coastal upwelling frontal band in the coastal ocean (Figure 3b). Thus, northern Patagonia is a highly dynamic region where biophysical interactions over coastal waters remain largely unstudied. The use of satellite products across multiple spatial and temporal scales provides fundamental insights into the oceanographic processes around coastal fronts [43]. Future studies assessing the variability of coastal chlorophyll and productivity in northern Patagonia should focus on regions with enhanced frontal activity to further understand the biophysical coupling at ocean fronts.

**Figure 6.** Seasonal climatology (2003–2019) of chlorophyll fluorescence (nFLH; W m−2 μm−1 sr−1) in the Inner Sea of Chiloé and its adjacent coastal ocean for (**a**) summer (January, February, March), (**b**) fall (April, May, June), (**c**) winter (July, August, September), and (**d**) spring (October, November, December). For reference of locations see Figure 1a.

Potential mechanisms leading to the generation of these fronts could also be related to the bathymetry along the ISC. The northern basin (north of Desertores Islands) has average depths around 300–400 m, whereas the southern ISC is considerably shallower (Figure 1a) [44]. The presence of Desertores Islands limits the water exchange and the circulation between these two sub-regions with distinct regimes of environmental variability [21], which would explain the sharp contrast in SST and frontal activity between these areas (e.g., Figures 2 and 3). The dynamics at Boca del Guafo is likely a major factor influencing the intrusion of oceanic waters and generation of fronts in the southern ISC. Future studies, including field measurements, should focus on sampling the locations with

high FP (Figure 4) to understand better the vertical structure and variability of fronts in northern Patagonia.

#### **4. Summary**

This study presents the first analyses of SST frontal variability in the Inner Sea of Chiloé and its adjacent coastal ocean. A high correlation with an *in situ* time series in Seno Reloncaví, a region with enhanced frontal variability and high seasonal fluctuations, validates the use of MUR SST fields. The annual cycle of SST gradient magnitudes suggested enhanced frontal activity in northern ISC (north of Desertores Islands) in spring-summer, whereas the coastal ocean off Chiloé presented the highest average gradients in summer and fall. These seasonal patterns are, in general, confirmed by the quantification of the SST frontal probability. Maximum probabilities reached about 10% in northern ISC and off Chiloé. Overall, the southern ISC presented low SST gradient magnitudes and frontal probability yearlong, except for the southern side of Boca del Guafo. An EOF analysis clarified the dominant modes of variability of SST gradient magnitude, highlighting (i) a coastal band of enhanced SST gradients off Chiloé in summer-fall which is coherent with coastal upwelling events and fronts, and (ii) maximum SST fronts in northern ISC and around the southern side of Boca del Guafo in spring-summer. A preliminary inspection of the annual cycle of chlorophyll fluorescence suggests an increased physical-biological coupling around ocean fronts since the highest fluorescence is found in northern ISC and off Chiloé Island, where SST gradient magnitudes and frontal probabilities are maximum. Future studies are suggested to occur in the regions with high frontal activity and considering high-frequency field observations in the surface mixed layer to understand further the dynamics of ocean fronts and their biological implications in northern Patagonia.

**Author Contributions:** Conceptualization: G.S.S. and W.H.; Methodology: G.S.S., W.H., R.M., C.R.; Investigation: G.S.S., W.H. and L.S.-M.; Resources: G.S.S., C.L. and I.P.-S.; Data curation: G.S.S., S.V., I.P.-S. and L.S.-M.; Writing—original draft preparation: G.S.S. with contribution from all coauthors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by FONDECYT grant number 11190209 and 1190805. GSS was partially funded by the Millennium Nucleus Center for the Study of Multiple Drivers on Marine Socio-Ecological Systems (MUSELS) funded by MINECON NC120028. WH has been funded by MUSELS and Universidad del Bío-Bío. CL has been funded by MUSELS and UPWELL Millenium Nucleus (grant ICM NCN 19153). RM has been funded by Universidad del Bío-Bío. The oceanographic buoy at Seno Reloncaví was funded by FONDEQUIP Grant EQM160167.

**Data Availability Statement:** MUR data are available at https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1). Buoy data at Seno Reloncaví are available at http://www.cdom.cl/estaciones. php?estacion=RLCV&seccion=Oceano. Chlorophyll fluorescence data are available at NASA's ocean color website http://oceancolor.gsfc.nasa.gov.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

*Letter*

### **Comparison of Measured Surface Currents from High Frequency (HF) and X-Band Radar in a Marine Protected Coastal Area of the Ligurian Sea: Toward an Integrated Monitoring System**

#### **Lyuba Novi 1, Francesco Ra**ff**a 1,\* and Francesco Serafino 2**

Received: 31 July 2020; Accepted: 17 September 2020; Published: 19 September 2020

**Abstract:** Two different ground-based remote sensing instruments can be used for the near-real-time monitoring of surface waves and currents, namely the high frequency HF radar and the microwave X-band radar. The HF system reaches larger offshore distances at lower spatial resolutions and provides a poorer measurement of the wave-induced currents in very shallow waters. On the other hand, the X-band system achieves significantly higher spatial resolutions with a smaller offshore coverage. This study provides a preliminary comparison of the measured surface currents, obtained by the two different tools where they overlap. The comparison showed a good agreement between the measures with some discrepancies ascribable to the difference in the characteristics of the two radar technologies.

**Keywords:** wave radar; sea waves; model data; Mediterranean sea

#### **1. Introduction**

The observation and monitoring of marine coastal currents is an important task for coastal protection, erosion control, and flood mitigation as well as near-shore fishing management and marine operations such as installations of offshore wind farms or oil and gas plants [1].

In recent years, the monitoring of surface currents with remote sensing techniques has greatly improved, making it possible to even perform real-time observations over sea surface areas of different extension. Among these techniques, two different ground-based remote sensing instruments can be deployed for the near-real-time monitoring of surface waves and currents, namely the high frequency HF radar and the microwave X-band radar. They directly measure the directional wave spectra at a spatial resolution from 250 m to 15 km, which depends on the specific allocated bandwidth and antenna design.

The overall spatial coverage of these tools significantly differs, as well does their spatial resolution. The HF system reaches larger offshore distances at lower spatial resolutions and provides a poorer measurement of the wave-induced currents in very shallow waters. On the other hand, the X-band system achieves significantly higher spatial resolutions with a smaller offshore coverage. The inherent differences of HF and X-band radars open new routes toward an integrated monitoring technique, which exploits the complementary nature of the output provided separately by the two systems [2,3].

High frequency (HF) coastal radars are very powerful instruments, providing information on surface velocity in terms of hourly maps over extended regions (range up to 100 km) and with high spatial resolution (order of 1–3 km). This information can be used to address several societal needs such as navigation safety, search and rescue, oil spill or other pollutant tracking, marine protected areas and fishery management [4,5].

The X-band radar represents a practical remote sensing system for sea waves and current monitoring in coastal and shallow waters. It is used for the acquisition and the analysis of consecutive sea surface images [6,7]. The surface current is retrieved from a sequence of these radar images by an inversion procedure, that accounts for the modulation effects that depend on both the sea state and the radar parameters as well as on the acquisition geometry [7–11].

This work is devoted to explore whether an integrated monitoring system can be successfully employed to measure surface currents in near real time across a variety of spatial scales. By blending HF and X-band radar data such an integrated system aims at reaching a high near-shore spatial resolution still covering a large off-shore area. This study provides a preliminary comparison of the measured surface currents, obtained by the two different tools where they overlap. Measurements taken at a selected study site located in the Ligurian Sea were analyzed.

As the present work focused on comparing two different measuring tools, rather than to study the local marine dynamics, an analysis of the surface circulation of the Ligurian Sea was beyond our aim and is already quite a well-covered topic in literature [12].

#### **2. Materials and Methods**

#### *2.1. Study Site and Analyzed Sea Conditions*

The study site is located within the Eastern Ligurian Sea, in the North West Mediterranean Sea as depicted in Figure 1, along a 15 km-long coastline in an area situated in front of which bounds an important Marine Protected Area (Cinque Terre).

**Figure 1.** Study area in the Ligurian Sea: red circles indicate the High Frequency (HF) radar stations. The yellow circle indicate the X-band radar location.

A CNR-ISMAR HF Radar Network has been installed along the coast of Eastern Liguria, near La Spezia and Cinque Terre, in year 2016 and is composed by two CODAR SeaSonde HF radar stations operating in the frequency band of 25 MHz. A CONSILIUM/SELESMAR X-band radar was installed at Corniglia (SP) about 60 meters above sea level. The radar locations are shown in Figure 1.

At the installation site, the HF radar and the X-band radar both worked from 12 September 2017 to 1 April 2018.

The analysis was carried out as follows:

#### *2.2. HF Radar Data Collection and Analysis*

The HF radar network was designed, implemented, and managed through the efforts of Institute of Marine Sciences - National Research Council (ISMAR-CNR La Spezia) [13]. HF radar data were collected and processed by ISMAR-CNR within the Ritmare and Jerico–Next projects [14]. The datasets hereinafter considered were downloaded from the website http://ritmare.artov.isac.cnr.it/thredds/catalog.html. Depending on the sea state, estimated errors ranged from 3 to 10 cm·s−1 and explained only part of the rms difference of 10–20 cm·s−1 found between HF and the in situ current measurements. The rest was assumed to be due the differences of the quantities measured (e.g., the spatial averaging [15]).

The acquisition settings are listed in Table 1.

**Table 1.** HF Radar system parameters.

HF radar is appropriate to detect surface ocean currents due to the diffraction grating effects of the rough sea surface [16,17]. Just when the radar signal scatters off a wave that is exactly half the transmitted signal wavelength, and that wave is traveling in a radial path either directly away from or toward the radar, the radar signal will return directly to its source. The scattered radar electromagnetic waves coherently add up, resulting in a strong energy return at a certain specific wavelength. The returning signal exhibits a Doppler-frequency shift that would always turn up at a known position in the frequency spectrum in the absence of ocean currents. Nevertheless, the observed Doppler-frequency shift does not match up exactly with the theoretical wave speed. The Doppler-frequency shift includes the information of the principal ocean current on the wave velocity in a radial pathway, jointly with the theoretical wave speed. Total velocities are derived using least square fit, which maps radial velocities measured from individual sites on a Cartesian grid. The final result is a map of the horizontal components of the ocean currents, on a regular grid, in the area covered by two or more radar stations [14].

#### *2.3. X-Band Radar Data Collection and Analysis*

A CONSILIUM SELESMAR marine X-band radar was installed on the roof of the sewage treatment plant at Corniglia (SP) about 60 meters above sea level. The radar antenna was located at the coordinates 44◦07 10" N, 9◦42 20" E.

The radar system radiates a maximum power of 25 KW, operates in the short pulse mode (i.e., pulse duration of about 90 ns), and is equipped with an 9-ft (2.7 m) long antenna with horizontal polarization (HH). These features enable reaching a spatial resolution of about 9m and an angular resolution of approximately 0.9◦. The signal received by the antenna was converted through an analog–digital converter and interpolated on a Cartesian grid with a regular spacing of about 10 m to obtain two-dimensional (2D) sea surface images. The image sequence acquired by the X-band radar was stored and processed, and each raw data sequence consisted of 64 individual images stored every 2.4 s. The accuracy of the X-band radar in terms of measured velocities was of the order 10 cm s−1 [18].

The acquisition settings are listed in Table 2.

**Table 2.** X-band radar system parameters.

The image processing to extract the inhomogeneous surface current fields from the X-band radar data were based on the so called "Local Method", proposed in [19,20] and can be applied to data acquired in coastal areas, where the presence of coastlines and varying bathymetry cause a spatial inhomogeneity of the wave motion [3,21–23].

A block diagram of the inversion procedure is presented in Figure 2.

**Figure 2.** Block diagram of the current field reconstruction procedure, where 3D FFT is the fast Fourier transformto obtain the 3D radar sub-spectra and NSP is the normalized scalar product technique.

The partitioning procedure is needed to extract *Ns* spatially overlapping sub-areas, so it is possible to assume the waves' homogeneity and uniformity from the analyzed radar data temporal sequence. After that, the fast Fourier transform (FFT) is applied to the *Ns* temporal sub-sequences to obtain the 3D radar sub-spectra.

Each spectrum is expressed as 4 *Fj* ´ *k*, ω 5 *j*=1,...,*Ns* , where ´ *k* = *kx*, *ky* is the wave-number vectorand ω the angular frequency; spectra are then analyzedby applying the normalized scalar product (NSP) technique [7], in order to retrieve the local surface current vector through the following estimator:

$$V^{j}(\mathcal{U}) = \arg\max\_{h} \frac{\left< \left| F^{j}(\mathbf{\hat{k}}, \omega) \right|, G(\mathbf{\hat{k}}, \omega, \mathbf{\hat{u}}) \right>}{\sqrt{P\_{F}P\_{G}}} \tag{1}$$

where *G* ´ *k*, ω, *U*´ = δ ω − % *gk* − ´ *k*·*U*´ is the characteristic function based on the dispersion relation; δ(·) is the Dirac delta distribution;|*F*|, *G* represents the scalar product between the functions |*F*| and *G*; and *PF* and *PG* are the powers associated with |*F*| and *G*, respectively.

Once the local (sub-areas) current vectors have been estimated, it is possible to define the 'global' (applied to the whole radar spectrum) band-pass (BP) filter [3,20,21].

#### **3. Results and Discussion**

Figure 3 shows a qualitative snapshot of the HF and X-band surface velocity fields, time averaged over a sample period on the original spatial grids, with the purpose to qualitatively show the coverage overlaps and the overlapping points. More in detail, red (blue) arrows are located at the HF (X-band) grid points, whereas colored dots indicate the instruments overlaps.

**Figure 3.** Qualitative snapshot of surface currents from X-band (blue arrows) and HF (red arrows), time averaged from 12 September 2017 to 18 September 2017 on original grids. Overlapping points A and B are indicated by the black and greed dots respectively.

Due to the very different spatial resolutions, some differences in the spatial velocity patterns may arise between the HF and X-band, which capture different spatial scales. Strong spatial variability at the HF sub-grid level may not be completely captured by the low resolution radar, resulting in an over-smoothed surface circulation, especially in the coastal zone. In order to cover coastal waters with

HF measurements at a higher spatial resolution, a rather trivial option is to linearly interpolate the HF-derived currents on a finer grid to compensate for the missing locations. However, it of course does not improve the quality of data, as the sub-grid processes still remain unresolved. Although the main large-scale current direction is consistently measured by the two instruments, the HF-derived circulation pattern does not capture the details of the near-shore spatial variability, especially in the west–northwest portion of the domain. Here, the X-band measurements revealed the existence of a cyclonic branch at the western edge of the grid, which was instead missed by the HF-derived data at the same location.

As clearly visible in Figure 3, at intermediate off-shore distances, an overlapping zone exists between the HF and X-band grids, where the time-series of surface velocities can be directly compared without any additional interpolation in space. In such an intermediate zone, X-band and HF derived data without spatial interpolation are expected to give similar results over time if the X-band surface currents are correctly derived. Seaward of these overlapping locations, the HF radar has the advantage of a long distance coverage suitable to capture larger scale circulation structures, whereas the X-band becomes advantageous shoreward of the overlapping areas, where smaller scale dynamics needs to be resolved.

Time series of the northward and eastward surface velocity components, derived by HF and X-band radar at overlapping points A and B, are reported in Figures 4 and 5, respectively, from 12 September 2017 to 1 April 2018. In each panel of Figures 4 and 5, the green line refers to the HF measurements, whereas the black line shows the X-band ones. Missing data in the time series corresponds to periods where the X-band radar system did not work or the surface dynamics in the near shore area, covered by the X-band radar, cannot be measured with enough accuracy due to low sea state or rain that affect the current field estimation.

**Figure 4.** Time series of the northward surface velocity components, derived by HF and X-band radar at overlapping points A and B. The values on top (Pij) denote the Pearson's correlation coefficient.

**Figure 5.** Time series of the eastward surface velocity components, derived by HF and X-band radar at overlapping points A and B. The values on top (Pij) denote the Pearson's correlation coefficient.

HF derived currents are provided as hourly means, whereas X-band measurements are obtained as instantaneous values at irregular time steps (multiple time steps per hour). In order to get a clearer comparison, the X-band data were therefore averaged over time to get hourly means. The HF values were then linearly interpolated in time in order to match the X-band hourly mean time spacing.

X-band derived velocity components display a good agreement with the HF counterpart throughout the sampling. A significant (*p* value << 0.01) positive correlation among the data was also indicated by the Pearson's linear correlation coefficients Pij, here computed. Pij was 0.675 and 0.54 for the U components in A and B, respectively, whereas it had a value of 0.67 and 0.8 for V in A and B, respectively. The root mean square errors of U at point A and B were 0.14 m/s 0.17 m/s, respectively, while for the northward components, it assumed the values of 0.14 m/s and 0.13 m/s in A and B, respectively. Figure 6 shows the resulting time signature of the velocity intensity at overlapping points A and B as derived by the two instruments; a close up of a shorter timeslot is shown in Figure 7 only for clearer visualization purposes. Figure 8 finally reports a scatter plot of the HF and X-band surface velocity components in A and B, separately.

As a final step, we show in Figures 9 and 10 a close up on the measured components over a reduced time range (12 September 2017 to 18 September 2017) characterized by the time-average condition reported in Figure 3 for that time range. The reported time-series are shown at regular hourly time spacing. The corresponding root mean square errors, for the shorter set, were 0.1 m/s and 0.12 m/s for U in A and B, respectively, and 0.07 m/s and 0.05 m/s for V in A and B, respectively. It is interesting to note the substantial disagreement in northward components at location A, occurring at 15 September and neighboring times (upper panel of Figure 9). Here, the X-band measurements showed a positive peak as opposed to the local decrease captured by the HF measurements. The computed spatial standard deviation of the northward component, at this time (and neighboring times), exceeded the 90% of its maximum values, revealing the existence of a high spatial variability that might be not completely captured by the low resolution grid of the HF radar.

**Figure 6.** Velocity intensities derived by HF and X-band radar at overlapping points A and B.

**Figure 7.** Velocity intensities derived by HF and X-band radar at overlapping points A and B. Close up of Figure 6 over a reduced time slot (10 December 2017 at 00:24 to 14 December 2017 at 23:23).

**Figure 8.** Scatter plot of the HF and X-band surface velocity components in A (**left**) and B (**right**).

**Figure 9.** Time-series of the northward surface velocity components, derived by HF and X-band radar at overlapping points A and B for a reduced time period (12–18 September 2017).

**Figure 10.** Time series of the eastward surface velocity components, derived by HF and X-band radar at overlapping points A and B for a reduced time period (12–18 September 2017).

Similarly, the eastward components at the same time locations were also characterized by a local overestimation of the velocity intensity by the HF radar compared to the X-band one, at location A (upper panel of Figure 10). Additionally, in this case, a field spatial standard deviation above 85% of the maximum level was found. At early times (i.e., between 12–13 September (Figure 9 and upper panel of Figure 10), discrepancies between HF and X-band data were associated with values of the spatial standard deviations ranging between 50% and 70% of the maximum value for the northward component, and around 50% of the maximum value for the eastward component, revealing a quite significant spatial variability that may affect the HF derived values in the analyzed sea condition.

#### **4. Conclusions**

In this work, the surface current fields measured by an X-band radar were compared with those provided by a HF-band radar. The comparison showed good agreement between the measures, although some discrepancies were also detected. At this stage, we cannot rigorously explain the nature of such differences among the data. A possible explanation resides in the different spatial scales that can be captured by the two tools. The HF radar is likely to over-smooth the small-scale features typically occurring in coastal waters or complex nearshore bathymetries. The high spatial variability of such features might not be completely captured by the HF coarse resolution, largely remaining a not-resolved sub grid process. On the other hand, the higher spatial resolution of the X-band radar allows for the capture of finer processes that are likely to carry higher sources of field local variance. The second possible source of differences between the HF and X-band derived currents is the inherent limitation of the latter, which loses accuracy when estimating surface currents in under-developed sea conditions. An important aspect that emerges from this work is the possible integrated usage of the two instruments, which exploits the advantages provided by the individual parts (wider spatial coverage for the HF band and higher spatial resolution for the X-band). Consequently, more accurate measurements

of surface currents can be carried out by the combined system on large spatial domains still capturing smaller scale effects in the nearshore area. The analysis carried out in this study represents a preliminary assessment for the system performance. Its effective usage aimed at unveiling the physical processes of oceanographic relevance still remains beyond the target of this work. A physically-oriented application aiming at enlarging the background of physical oceanography is clearly needed to fully exploit the system potential, and will be explored in a future study.

**Author Contributions:** F.S. proposed the idea. L.N. and F.S. developed the method. L.N. and F.R. analyzed the results and wrote the text. F.S. supervised the work. All authors commented on the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** For the HF radar data considered in the present letter, the authors are very grateful to the manager of the datasets of the HF radar data, collected and processed by ISMAR-CNR within the Ritmare and Jerico-Next projects.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**8. Neurofibrillary tangles (NFT)**

Tau, a protein associated with microtubules, is required for normal neuronal development and axonal expansion. However, frontal association cortices, the lateral parietotemporal area, and the mesial temporal lobe (especially the hippocampus) neurons are where hyperphosphorylated tau protein aggregates are most frequently found as helical filamentous NFT. The relationship between the density and distribution of tau NFT and the symptoms and severity of AD dementia emphasizes the critical role of NFT in AD pathology.

### **9. Loss of neurons and synapses**

Synapse loss and neuronal cell death have a similar distribution to NFT. Due to the death of neurons in the nucleus basalis of Meynert, acetylcholine (Ach), a neurotransmitter linked to memory, is decreased in typical AD. Most current therapies seek to remedy this cholinergic deficit. Serotonin and norepinephrine deficits result from the loss of neurons in the brainstem's locus ceruleus and median raphe. Dysfunctional serotonergic and adrenergic activity in the brain is probably the root cause of dysphoria and insomnia in AD [38, 39].


**About the Editor**

#### **Giorgio Gallinella**

Giorgio Gallinella, MD, PhD. Born on 20.06.1963. Full Professor of Microbiology at the Department of Pharmacy and Biotechnology at the University of Bologna, Italy. He is currently teaching Medical Microbiology in the Master Degree Program in Health Biology, and Microbiology and Vaccines in the Master Degree Program in Pharmaceutical Biotechnology. He obtained his Degree in Medicine and Surgery in 1989, a PhD in Microbiological Sciences in 1993, and a Medical Specialization Degree in Microbiology and Virology in 1997. In year 2000 he received the Abbott Murex Award, conferred by the European Society for Clinical Virology, for the original contribution in the field of virologic diagnosis. In his scientific activity, he has conducted research in the field of basic and clinical virology. A special interest has been devoted to the study of Parvovirus B19, both as a model system for research in basic virology, and also for its clinical implication as a human pathogenic virus. In this context, the research is mainly focused on the study of virus-cell interactions; the study of the pathogenesis and of the clinical and epidemiological aspects of infectious processes; the development of innovative methodologies and diagnostic assays; the development of novel antiviral drugs. He has organized the IX Parvovirus Workshop, Bologna, Italy, august 2002, and more recently, the XVIII Parvovirus Workshop, Rimini, Italy, June 2022.