**3. Experimental Details**

The RUSW system included a lateral-driven, high-power ultrasonic welder (4.0 kW) and an inverter resistance power supply (Figure 3a). The high-power ultrasonic welder used a lateral spot welder (Telsonic M5000) with a vibration frequency of 20 kHz. A rectangular sonotrode tip with dimensions of 7 mm × 5 mm was used in this study (Figure 3b). To avoid the occurrence of arcs and marks on the Cu/Al interface, the shape of the sonotrode tooth was trapezoidal, instead of triangular. A resistance spot welder with a maximum electrical current supply of 4000 A was used. A zero-to-peak amplitude of 24 μm, current of 3900 A, clamping force of 1975 N and welding time of 0.2 s were selected for the hybrid welding. The interval between each sonotrode tooth was 0.9 mm. In order to avoid high current density occurring at the anvil/Al interface, the interval of the anvil tooth was 0.1 mm larger than that of the sonotrode tooth. For the conventional HPUSW, the welding time was 0.5 s while the clamping force was set to 1575–1975 N. The specimens were 6061-T6 aluminum alloy and pure copper, cut into 100 × 25 × 0.8 mm3 pieces. The overlapping area of the specimens was 25 × 25 mm2. In this work, prior to welding, the samples were ultrasonically cleaned with acetone to remove surface contaminants. The ultrasonic vibration direction (VD) was perpendicular to the length direction of the workpiece (Figure 3d).

The fracture surface morphology was observed by a JEOL JSM-7001F field emission gun scanning electron microscope (FEG SEM) (Jeol, Tokyo, Japan), equipped with an energy-dispersive X-ray spectrometer (EDS). X-ray diffraction (XRD) analysis was carried out using a PANalytical Empyrean (Malvern PANalytical, Almelo, The Netherlands) diffractometer to identify the phases of IMC at the welding interface. The XRD spectra were measured from 20◦ to 90◦, with a step size of 0.02◦ and a scanning speed of 0.02 ◦/s. The tensile tests were performed using a Shimadzu AGS-X electronic testing machine (SHIMADZU, Kyoto, Japan). Tensile-shear tests were carried out at a tensile speed of 1 mm/min. At least three samples were tested for each process condition. The mechanical strength of

the joints was evaluated according to tensile-shear strength, which was calculated by dividing the maximum tensile-shear force by the sonotrode area of 7 mm × 5 mm.

The temperature was measured using 0.1 mm K-type thermocouples, inserted through a semicircular groove of radius 0.5 mm on the aluminum sheet surface. Thermocouple tip was placed at 1.5 mm from the center of the Cu/Al interface. Figure 3c,d show a schematic of the tensile-shear and thermocouple temperature measurement tests, respectively.

**Figure 3.** (**a**,**b**) The RUSW machine setup; (**c**) configuration of the tensile-shear, and (**d**) temperature measurement tests setup.


**1. Introduction**

Charging stations or other types of energy storages need organization when they are used for special targets. Considering the grid's situation, an organized program should cover the supply and demand needs of the grid. Supply needs, such as low loss operation without side effects, and demand needs, such as abundance in the charge station, low cost, and increased power quality, are required to be fulfilled. All of these needs and similar problems, issues or remodeling methods have created a basis to providing organization for electric vehicles (EV) charge stations. Factors, such as societal, populace, grid infrastructure, and renewable conditions [1], critical power range, and financial analysis of a location, play important roles in determining the grid condition. However, supply/demand power and coordination, and the estimation of equipment is the telling factor, which needs focus to meet the incessant load demand.

The estimation of power, especially on the demand side of the grid, is very difficult [2]. Increasing or decreasing the rate of grid power does not go on determined patents, such as battery open circuit voltage estimation by Extended Kalman Filter (EKF) or similar estimation operations [3]. Supply/demand and coordinating for EVs charge station estimations have a lot in common, so they can contribute to an increase in power quality in the grid, directly when the grid infrastructure is ready. The benefits of this work are not limited to some factors, such as the decrease in green-house gas (GHG) emissions. Fuel transmission between oil refineries to gas stations consume electric power, especially in gas stations where compressed natural gas (CNG) stations use electric convertors. At present, too many researchers are trying to create and optimize algorithms to harvest fine results, but the methods and algorithms should be fed by trustworthy numerical data. In addition, the experiments are to be simulated using simulation software, such as MATLAB. In one Finnish study, to meet Finland's target of 100% renewable energy usage by 2050's (funded and presented by Ministry of Economy and Employment), the research on Energy PLAN (created by Aalborg University) modelling has focused on energy potential and generation from a perspective of economy, the rate of fuel usage, and CO2 emission according to decrease in GHG emission policy [4]. The result of this investigation shows that the energy future, based on 100% renewable energy, is entirely possible. In another study focused on Delhi, India's energy sector, the Japanese Society for the Promotion of Science (JSPS) investigated the future of energy and its optimization methods in Delhi. Their study found that integrated-systematic modelling should be used for energy flow in urban energy systems. They have used an optimal energy scenario and found an energy gap between supply and demand. This research paid attention to government energy policy in improving the demand side of their energy sector [5]. However, government policy and social behavior strongly influence the GHG emission in the transportation sector, in many countries, such as Austria [5]. The emission of CO2 is one of the primary problems in China and the CO2 condition will be in 2030 with the use of control or uncontrolled EVs, V2G and similar transportation systems. Studies have shown that the application of these transportation systems provide advantages, such as decreasing peak load, which is used for EV charging; generating power from renewable energy sources (RES); and reducing energy generation and EV charging costs [6]. Some of the literature has focused on the distance of charge stations with respect to the most capable grids. A Taiwanese study looked into helping customers receive better service from the grid by creating charge stations at reasonable distances in Taiwan [7]. Therefore, it is possible to determine probable locations for EV charging stations, based on the grid and transportation technology of the time, rather than based on any specific algorithm.

Several algorithm-based research are still used in smart grid and V2G operations for special conditions or purposes which are explained below. One study focused on uncertain situations with respect to V2G operation pricing, using the robust game-theory to treat uncertain issues. This algorithm was named ENTRUST (Energy Trading under Uncertainty in Smart Grid Systems) [8]. This study achieved reliability and cost e fficiency in terms of energy management. Another grid lead system is known as multi-agent energy management. In this system, all elements of demand side of the grid (home, building, industries, and vehicles) are connected. It can improve autonomy, connectivity, diversity, and appearance of the systems in the grid. Another study in Great Britain (GB) [9], created a dynamic virtual energy storage systems (VESS) to solve the dynamic frequency response. This model uses V2G and similar technology storages to notify grid whenever it needs emergency energy or whenever the rhythm of the energy flow has been disturbed. This model considered the response capacity of an EVs cluster. Researchers have furthermore developed some algorithms for EV charge stations, including the flow refueling location model (FRLM) [10] maximum covering location problem (MCLP) [11], nonlinear auto-regressive (NAR) [12], Genetic Algorithm (GA) [13], Genetic Algorithm-particle swarm optimization (GA-PSO) [14], and Genetic Algorithm-binary particle swarm optimization (GA-BPSO) [15].

Each research has coordinated points by special parameters, which is the dominant factor in supporting the system objectives. Previously described models, scenarios, and algorithms are the key to managing the grid and to increasing responsibility for EVs, charge stations and interconnected storage. The coordination of EV charging stations at first sight needs these methods to solve some pre-requisites. This paper considers Ankara's metropolitan electric infrastructure and the conditions dependent on Ankara's electricity infrastructure to determine potential locations for the charging stations. In Ankara's case, the grid has not been updated to be smart enough to include online sensors, which may be used in determined algorithms. This study attempts to determine the location by specifications of the grid infrastructure, categorizing the capacity of transformers and their other parameters. A method, based on the Genetic Algorithm is designed to verify the probability of a location meeting the requirement is shown.

The rest of the paper is organized as follows. Section 2 discusses the case study, particularly considering four locations in Ankara. Section 3 takes grids in Ankara in consideration to nominate them as the station location. Section 4 brings transformers into inspection by delineating several parameters to select transformers capable of matching the EV application. Section 5 presents the corresponding transformer indices and parameters to aid the selection process. Considering this framework, the layout of the estimation algorithm with respect to the Genetic Algorithm is explained in Section 6. Finally, conclusions are drawn in Section 7.


**4.3. Principles of biological treatment**

The principles of biological treatment of wastewater were stated by [3]. The following is a summary of the principles:

**Figure 4.** Microbial growth curves [1].


3.2.1. Shoreline Positioning Error

To investigate the relationship between shoreline classification and positioning in Bora Bora over time, the position of the shoreline was estimated manually using the available VHR imagery. The error of the shoreline position determined for each year is detailed by Table 2. The tracing error was estimated to be two meters for each date, notably due to uncertainties in the determination of the position of the shoreline in vegetated areas due to treetops, but also in the case of low spatial resolution leading to difficulties in distinguishing the land from the sea. The georeferencing error ranges from 0 (2019 is used as the reference) to 4.02 m (for 1955). The total error (Equation (1)) is maximum for 1955 (4.76 m) and minimum for 2019 (2.06 m), and is between 3.20 and 3.95 m for the other dates.

**Figure 5.** *Cont*.

**Figure 5.** Map of the coastline classification (sandy beaches, rocks, trees, grass areas, mangroves, road embankments, private embankments, quays) of the main island of Bora Bora for (**A**) 1955, (**B**) 1977, (**C**) 1987, (**D**) 1999, (**E**) 2006, (**F**) 2010, and (**G**) 019.

**Figure 6.** Percentage of the coastline as sandy beaches, rocks, trees, grass areas, mangroves, road embankments, private embankments, and quays, as well as overall percentage of natural (sandy beach, rocks, grass areas, and mangrove) for 1955, 1977, 1987, 1999, 2006, 2010, and 2019 on the main island of Bora Bora. A conservative error on these percentages, caused by coastline tracing subjectivity and classification uncertainty for older images, may be estimated as approximately 10% of their value (i.e., 46 ± 5% of the coastline is a private embankment and 1 ± 0.1% is a mangrove on Bora Bora in 2019).

#### 3.2.2. Overall Changes in Shoreline Position

The changes in shoreline position on Bora Bora between 1955 and 2019 (for transects every 5 m) have a median of 5.6 m (positive values correspond to net accretion) and an interquartile range of [−0.09, 19.5]: approximately 25% of the shoreline has undergone erosion, and 50% have experienced accretion of up to 19.5 m, while the remaining 25% have gained more than 19.5 m over the sea. In detail, the construction of embankments and quays has led to strong 'artificial' accretion in the most densely populated parts of the

island (Vaitape, Faanui, and Anau; often over 15 m, and up to 220 m gained over the sea; Figure 7). Neighbouring natural sections (most often corresponding to the tree category, which is the most common natural category) have undergone erosion, most commonly of 2 to 5 m, from 1955 to 2019 (cf. the red erosion zones interspersed by green 'artificial' accretion zones on Figure 7A). On the southern beaches, the East-exposed portions have undergone erosion (over 2 m of landward net sea movement from 1955 to 2019) while the other portions have undergone accretion (Figure 7B), either naturally or due to the construction of embankments at the southernmost tip (Figure 5).

**Figure 7.** Map of the Net Sea Movement between 1955 and 2019 extracted from the DSAS module on ArcGIS 10.8.1. (**A**) mostly urbanised section in Faanui, with alternating accretion (linked to the construction of seawalls) and erosion (in the nearby natural areas); (**B**) sandy beach zone of Matira.

Overall, Bora Bora has, hence, increased in size due to the construction of embankments and quays; natural shoreline areas that persisted from 1955 to 2019 have mostly undergone erosion (e.g., natural zones with trees near embankments).

#### 3.2.3. Changes in Shoreline Position on the Southern Beaches

Placing a focus on the southern beaches, End Point Rate values (change in shoreline position in meters per year) were averaged over 10◦ intervals. In detail, between 1955 and 1999, accretion mostly occurred on the south-west facing beaches (azimuths between 0 and 90◦; Figure 8) and erosion on the east- and north-east facing beaches (azimuths between 220 and 0◦). Between 1999 and 2006, there was limited erosion on the north-facing beaches and accretion was predominant on all other azimuths. In 2006–2010, erosion occurred on the

south-west facing beaches with relatively important rates (up to 1.27 ± 0.10 m y−1 between 10 and 20◦, the maximum rate across all azimuths and time periods) while accretion was limited (with one outlier at 1.02 ± 0.40 m y−1 corresponding to an additional seawall). Lastly, from 2010 to 2019, only one averaged azimuth experienced erosion (−0.53 ± 0.22 between 220 and 230◦), while accretion was limited elsewhere (mostly below 0.5 m y−1).

**Figure 8.** Radar plot of the average (dot) and standard error (whiskers) of the End Point Rate (in meters per year, from 0 to 1.4 m y−1 on the plot) for 10◦—averaged azimuths on the southern beaches of Bora Bora. Blue colours indicate accretion (positive EPR) and orange colours indicate erosion (negative EPR). An azimuth of 0◦ corresponds to a south-facing beach (land on the north side, sea on the south side of the shoreline). The values below the date intervals correspond to the overall average and standard error of the End Point Rate across the beaches.

There were significant contrasts between some years (Table 3). There notably are significant negative linear correlations between 2006–2010 (after the construction of the largescale embankment) and each time range before 1999 (1955–1977, 1977–1987, 1987–1999). There is also a strong positive linear correlation (0.78) between 1955–1977 and 1987–1999, which may indicate a lack of sedimentary regime change between those dates. There are no significant differences between the other dates.

**Table 3.** Linear correlation coefficients r for the 10◦ average End Point Rate values calculated for Bora Bora's southern beaches of Matira.

Threshold for 5% significance: r = ±0.43.

#### **4. Discussion**

#### *4.1. Drivers of the Evolution of the Shoreline Classification and Potential Impacts on Sedimentary Processes on Bora Bora from 1955 to 2019*

Throughout the second part of the 20th century, infillings to build public and private properties onto the fringing reef (notably on the densely populated western side of the island; Figures 2 and 5) have been the prominent factor of shoreline category and position changes on Bora Bora. Trees and vegetation were removed and replaced by private embankments to consolidate infillings (from 3% or 1.2 km in 1955 to 46% or 20.3 km in 2019). Similar evolutions have been noted on other French Polynesian islands, notably an increase from 12% in 1977 to 57% in 2018 of embankments on the island of Moorea, which has similar geological, geomorphological, and socio-economical characteristics [3]. Through interviews with long-term Bora Bora residents performed in March 2021, it emerged that these embankments were initially linked to a drive to extend the island's land surface outwards and construct houses on the highly sought-after seaside, notably until the 1990s. For instance, a large fraction of the houses as well as public infrastructure on the west side of Bora Bora, most notably in Vaitape, is constructed on infilled fringing reefs (Figures 2 and 5). However, since the 2000s, the main reason for embankment construction (with or without building licences) has been the reinforcement of private land boundaries. This reinforcement is perceived as necessary in the face of continuous coastal erosion, linked to background swell, storms and cyclones (notably in reaction to the passage of cyclone Oli in 2010), and to anticipate future sea level changes (which are forecasted to be up to 0.76 m higher in 2080–2100 with respect to 1986–2005 for an intermediate global warming scenario [20]).

Modifying Bora Bora's coastline may have had strong impacts on the island's sedimentary regimes and, along with drainage canals and other land features altering sediment transport from the island to the sea, it may be a factor leading to heightened erosional effects [3]. Infillings and walls may reroute and strengthen currents and waves, leading neighbouring zones to experience stronger erosion [17] (Figure 7A). As visible on the aerial images and confirmed by interviews with residents, landowners in the vicinity of newly constructed coastal structures eventually have to build embankments as well to protect their gardens from erosion, leading to a ripple effect and a progressive artificialisation of the shoreline (Figure 5).

On a larger scale, a stark example of the impact of urbanisation on coastal sedimentation is the effect of a large protective embankment in Matira (southern tip of Bora Bora) on the neighbouring sandy beaches. This protective embankment to stabilise the shoreline position at the tip of the peninsula was first built on a small scale before 1999 and then extended to most of the peninsula between 2005 and 2006. It may have modified currents and sedimentary processes and altered the erosional regime in the area (Figure 8, Table 2), leading to enhanced erosion on previously stable sections of the beaches. Before the construction, the sandy beaches underwent net erosion on the northeast-facing side and accretion on the southwest-facing side. This movement corresponded to the average wind-driven surface currents (the main wind regime is West to East in Bora Bora [13]) and, hence, may have been mostly of natural causes. During the construction, the signal was blurred (1999 to 2006), and was then inversed after 2006. However, the development of hotels, seaside gardens, and planting of coconut trees has artificially led to a net accretion between 2010 and 2019 on the beaches due to the definition of the shoreline position used in this study (sea-side edge of vegetation). Overall, these erosional processes remain limited (generally under 1 m y−1) and may partly be noise due to the shoreline positioning error of this study (2.06 to 4.76 m).

The time series used here enables the assessment of overall long-term changes in coastal typology and position, which can be linked to human activities, as sea level rise was small (~2 cm) between 1955 and 2019 in Bora Bora. However, the temporal resolution of the time series used here (4 to 22-year gaps between each image) is too broad to assess changes due to most intense short-term events. An exception is the passage of Cyclone Oli in February 2010, two months before Bora Bora was imaged by satellite (Table 1). Although the island of Bora Bora is protected from strong swells by a barrier reef and circled by a wide lagoon, extreme weather events can have damaging impacts along the shoreline of the main island. The passage of this storm may notably have led to the strong erosion of the sandy beaches of Matira (Figure 8) and additional erosion in other natural sections of the coastline, although this cannot be certain in the absence of other images between 2006 and 2010.

The recent availability of data from satellites with daily and high spatial resolution imaging capacity (e.g., [21]) as well as the LiDAR campaign conducted on Bora Bora in 2015 [22] could be combined for future high-resolution 2D or 2.5D monitoring (see review by [9]) of the evolution of the coastline of the island and help to disentangle the individual effects of various factors (background waves, storms, constructions) on sedimentary processes.

#### *4.2. Further Consequences of Coastal Urbanisation and Perspectives for Management*

Beyond impacting coastal erosion, infillings and embankments are costly and can lead to economic and biological issues. Firstly, infillings and embankments are built over shallow fringing reef ecosystems, which typically house numerous juvenile fish and invertebrate organisms [23]. These constructions and the associated modified currents and sedimentology are, hence, accompanied with a removal and degradation of ecosystems. This could have wide-reaching impacts on marine communities throughout Bora Bora's lagoon and, hence, impact local fisheries and livelihoods. Furthermore, requesting building permits is a lengthy administrative process, which nowadays most often results in a refusal. In addition, using adequate building materials, such as volcanic rocks, leads to additional expenses. As a result, many private infillings and embankments are built illegally [24] using tyres, construction rubble, or rusty metal spikes. Beyond altering seascapes on an island that is famous worldwide for its scenery, these may spread pollutants into the nearby marine ecosystems [25].

In addition to the numerous private embankments, public infrastructure, most notably roads and quays, are associated with seawalls. In particular, the main road of Bora Bora is a belt road which, in many areas, is located only a few meters away from the shoreline. This road is protected from the sea by embankments that represent approximately 10% of the island's shoreline (Figures 5 and 6). This road, hence, plays a non-negligible role in the artificialisation of the coastline, damage to coastal ecosystems, and changes in sedimentary regimes. In some areas, no wall has yet been constructed to protect the belt road, but there are projects to implement some (e.g., along the high school of Bora Bora located between Vaitape and Matira), notably because the road can be flooded during strong swell events. Instead of seeking to protect a belt road located close to the sea, building a new road further inland wherever possible, and restoring natural shoreline types by removing the walls, could be efficient solutions to mitigate the impact of urbanisation on Bora Bora's shoreline.

Indeed, in opposition to human-made constructions enhancing erosion, natural shoreline types such as mangroves and grass have widely acknowledged ecological functions [26]

and act as sediment accumulation zones on Bora Bora (Figures 5 and 7). This is partially due to the geographical position of these areas: mangroves and grass areas are located within bays on Bora Bora and directly receive sediment-rich runoff from the surrounding land following rainfall. Net accretion is enabled by the presence of plant roots slowing down currents and leading to sediment deposition [27]. Coastal management and restoration projects on Bora Bora could aim at removing erosion-inducing walls and revegetating the shoreline to consolidate it. There are numerous plants that can stabilise the shoreline: low-lying vegetation (grass, bushes), strong-rooted trees (local varieties such as aito—*Casuarina equisetifolia*, purau—*Hibiscus tiliaceus*, miki miki—*Pemphis acidula*, or even coconut trees—*Cocos nucifera,* although they are less efficient at trapping sediments). In addition, promoting coral growth on the fringing reefs—leading to hard structures that attenuate wave energy—could also be a positive management solution [28]. Expanding mangroves (*Rhizophora stylosa*) due to their efficient sediment-trapping roots, however, is debatable in the context of French Polynesian islands. Indeed, mangroves were absent from French Polynesia until the 1930s and were only introduced in the 1930s to promote oyster and crab production [29]. They are often viewed negatively, as invasive species (notably taking over adjacent grass areas, cf. Figure 4), and are actively removed by local inhabitants. Nevertheless, there are numerous alternative nature-based solutions available to replace walls without compromising shoreline stability. However, although most residents that were interviewed were aware of and suggested natural methods to prevent coastal erosion, a strong majority was opposed to removing walls and planting trees on their private lands. This opposition was mostly due to the costs involved and uncertainty in the resulting shoreline stability. Performing experiments in various parts of the island by removing walls and monitoring shoreline stability, communicating with the public about the results, and financially contributing to coastal restoration could provide incentives to remove private embankments around the island.

The important impact of embankments and seawalls on shoreline stability throughout Bora Bora underscores the sensitivity of the island's sedimentary regime to human-made structures. This type of human-induced shoreline destabilisation is common in French Polynesia (e.g., in the atolls of the Tuamotu Archipelago [17]; in Moorea, of similar geomorphology to Bora Bora [3]). The urbanisation of coastlines has profound impacts on physical processes around the islands, from modifying sedimentary processes to increasing vulnerability to coastal erosion, storm surges, and sea level rise [17]. Geologically younger volcanic islands such as Bora Bora [10] may be assessed as less vulnerable to coastal erosion than low-lying atolls such as in the Tuamotu Archipelago or even the motu dotted along the barrier reef of Bora Bora (Figure 1). However, shorelines with hard volcanic lithology, which are spared from erosion and accretion on decadal time scales, are rare on Bora Bora (rock category, under 3%, Figure 6). In addition, most seawalls and embankments on Bora Bora have a height of less than one meter. If the island's coastal lifestyle continues in the future, in the absence of sustainable coastal management solutions, there will be a need to keep infilling land and elevate seawalls to cope with rising sea levels. As demonstrated by the imagery timeline used in this study, increasing the artificialisation of the coastline may lead to even more coastal erosion in a positive feedback loop, and is not a sustainable solution. Lastly, beyond the economic importance of preserving white sandy beaches on the tourist island of Bora Bora, the strong human density and rarity of inhabitable areas in the steep inner parts of the island make coastal erosion a major challenge on Bora Bora, especially in the context of increasing human populations on the island [14]. There is a need to adapt lifestyles and public infrastructures to the changing climate, sea level rise, and more frequent and intense storm surges and swell [20]. There must be incentives to encourage islanders to move further inland when possible and revegetate the shorelines rather than fight a losing fight against erosion and aggravate the problem. Communicating with the public, developing management plans, and stabilising the coastline with nature-based solutions are required to tackle the issue head-on in Bora Bora and in similar contexts worldwide.


4.2.6. Dot Blotting

Dot blotting of Ub2(48)tau4RD(353) and Ub2(63)tau4RD(353) conjugates, tau4RDΔC, and Ub-tau4RD(353) at different aggregation times, using the anti-oligomer antibody A11 (ThermoFisher, Waltham, MA, USA), was carried out according to the protocol described in [30].