 Welcome back. Welcome back to the attic. Things are getting really on fire. You're asking fantastic questions. Please send them sooner. Otherwise, you know, we don't have time at the end. So we remind you of our hashtag. We have a hashtag big th 20. We have the gamification in the platform. Don't forget it. And the questions to our speakers. So on the prices and everything. So we're going to jump on to our third keynote of the day at the attic. Pre COVID, the aviation sector contributed 2% of global greenhouse emissions. But there is a commitment to reduce by half these emissions by 2050. Considering the project growth in passengers numbers, one of the many ways to achieve this is to optimize fuel efficiency and in a big way. Our next speakers believe that the way to monitor in this is by tokenizing emissions and allowing them to be tracked through a blockchain. Does this sound super interesting? To tell us more, we have with us Pedro Artida Fernández and Alberto Uceda Aguilar. They are both consultants with ISDEFE. Hello. Welcome. Hi, how are you? Here they are. Others who have been very disobedient and have decided to speak in Spanish. Nothing happened. Welcome. Well, this topic is super interesting. So I invite and remember our guests. Remind you of questions at the end in Spanish or in English. I translate them if it is necessary, but our speakers understand English perfectly, but prefer to speak in Spanish. Guys, we are looking forward to hearing from you and your questions as soon as possible. Until now. Until now. Well, I think I'm going to start. I think it looks good. You can hear me. See, right? Well, I'm not going to do a presentation as good as Elena has done. First, we wanted to thank you for letting us participate in this conference. We are very happy to participate and we are delighted to be here with all of you. Although I am in the living room of my house, we are very happy and we are going to tell you a story that I hope will be of your interest and your gift. Well, as you can see, the story we are going to tell is about mission tokenization. It is about mission calculation and creating the tokens for what are the missions that generate all the flights and what the air transport means. We have divided the talk into two. The first one I am going to give you. And well, I am going to tell you a little bit about what this tokenization is. And then the second talk is about my colleague Pedro García. And well, he is going to tell you how to perform those mission calculations, how to tokenize them, and then he is going to show you the result that we have put in the different frames of demand that we have designed in this project. We are going to start. Well, first we have to introduce ourselves. We are going to tell you who we are, both my colleague Pedro García, like me. We work or belong to the company of ISDEFE. ISDEFE means engineering systems for the defense of Spain. Well, it is a company that is public, it is written to the defense ministry and it is considered my own medium and technical service. ISDEFE is a reference in the field of defense, security, and also of the State General Administration. ISDEFE offers services of engineering consultancy to public organizations, both national and international, in areas of technological and strategic interest. ISDEFE has a team of more than 1,500 people. And well, what am I going to say? It is the best ally for the organizations and entities of the State General Administration and civil and military organizations. You are going to forgive me, but I really have not been able to avoid the attempt to make a spoiler about the second part that my colleague Pedro is going to do. As you can see here, we have one of the frames of demand that we have designed in the project. And well, I am not going to go into more detail. I am going to let him tell you everything, but he really does it very well. And surely you are going to see it and it is going to be very interesting, all the talk that Pedro is going to do. So I am not going to continue with the spoiler, I am not going to continue with more details. As we have seen, the talk that is about all the issue of emissions, which causes what is all the air transport, right? So, well, as we are going to talk about this issue, what we are going to do is to give you an idea of what is equivalent to a ton of carbon dioxide. Well, we have put a series of reference units so that you can have an idea and well, we are going to start with the drawing on the left. As you can see, for a year, a vehicle can generate and emit 4 tons of carbon dioxide, while a single family of four members can get to emit 2.5 tons of carbon dioxide. These solutions are the ones that would correspond to a trip, to a flight that would take place between Madrid and London. It would be a trip of idea and return. In 2019, all that was the air transport came to emit 915 million tons of carbon dioxide. And how much is equivalent to these emissions? We are also going to use the same reference units to give us an idea of how much is equivalent to all these emissions that were emitted in 2019. And as you can see, these emissions correspond to a number of 229 million cars. That number of cars has been emitted. In addition, these emissions emitted in 2019 correspond to the emissions of 360 million dollars. And to be able to compensate for all these emissions, we should plant 2,980 million trees from London. If these trees are pine trees, it is much better because you know that the pine tree has a great chance of getting rid of carbon dioxide. Once we know how much carbon dioxide is equivalent to a ton of carbon dioxide, we are going to focus a little more on the emissions that the air transport emits. And we are going to show you a series of numbers to give you an idea of what this means of transport. As you can see, 2% of the emissions caused by the human being correspond to the emissions emitted by the air transport. However, the 12% of these emissions of the air transport correspond to all those that have emitted all the air transport in general. As I mentioned earlier, in 2019, 915 million tons were emitted. That corresponds to a 6% more than those that were emitted in 2017. 80% of these emissions correspond to routes of a distance of more than 1500 km. And as you can see, they are routes with distances that are difficult to go to alternatively to carry out by other means of transport. Well, it seems that the world has finally awakened and realized the problem that all these emissions have and the aspects that it produces on the earth. And well, at an international level, several conferences have been celebrated, in particular, you know one of the works, the Decoto Protocol, which was an agreement celebrated on December 11, 1997, an agreement that was carried out by the main industrialized countries. And in it, the emissions of carbon dioxide were reduced to at least 5% with respect to the emissions emitted in the year 1990. In addition, more recently, more closely produced to date, the Paris Agreement was also carried out on December 12, 2015, and it reached an agreement to reduce the temperature of the earth to 1.5 degrees. Well, if we focus on the emissions produced by air transport, there are two programs that have been created by what these international organisms are. There are two programs, one that has been created by ACI, which is called Corsia, and the other has been created and promoted by the European Union, which is the EU ETS. Well, it does not go into detail exactly what each of these programs does, but what I do want to emphasize is that both came to the goal of reaching the 2005 to a reduction of 50% in the 2050, sorry, they established as an objective to reduce the 50% of the emissions that were emitted in 2005. Of course, considering that if it was going to reach an increase, or if it was going to multiply by four, the volume of passengers. So, well, we know that in these two programs that I have commented, that they are focused on what are the calculations of air transport emissions, and well, to be able to make that comparison and in this way reduce everything that is the carbon dioxide. These two programs, well, we know that the calculations that they do are quite rudimentary, they are quite archaic, and they are still using Hasessel, and, well, the information is still being added per year, and this is the reason why the ARETA project was born. ARETA, which is what it means, as you can see, Avation Real-Time Emission Token Accreditation, is born with the idea of serving as a tool to help these two programs, and in this way they can calculate in an automatic way all those emissions in real-time. The ARETA project, as I have mentioned, comes as one of the winning ideas of the CASTACIEN program, and it manages ideas for research, development and innovation that took place in 2018 in ISDF. And as I have mentioned, the goal for which we created this project was to estimate the real-time emissions of all the flights produced by the airlines. Also, what we wanted to achieve with ARETA was to create digital assets or tokens that can be commercialized against any of the CO2 emissions. Also, what is intended with ARETA is for an editor to verify the assets of CO2 that have been created. In addition, we also want to make the arrangement of the airlines so that an airline can compensate the assets of CO2, verified in compensation projects of CO2 emissions and that are led by the company. And, well, also a main objective that we wanted to have with ARETA was that a state entity can visualize and analyze all the data processed on the platform, being able to see in this way, and making flights, the contaminating emissions, the digital assets, and the tokens of CO2 that have been created on the platform. As you can see, this is the initial goal that we wanted to create and that is why the idea of ARETA came up. But, well, we also took the opportunity to create a concept test, to create a technological demonstration of several platforms. Somehow, we had to process more than 100,000 wallets a day that generated us 25 million positions. And in this way, we had to make calculations of estimates of carbon dioxide in real time. Well, we decided, we took the opportunity to adopt the Big Data solution. It was quite justified to be able to use this platform. But, also, for all these estimates, we needed to verify them and create some digital assets. Well, they came up with a consensus and made a registration guided by the smart contracts. We had to tokenize the tokens, as we have commented, to make those digital assets of all those emissions of carbon dioxide. Well, we also decided to adopt and choose and provide this blockchain platform solution. I have a way to also have a master's registration of all, right? And another platform that we also took the opportunity to test and that does not appear in this post is the data government platform. With this platform, what we have wanted was to guarantee the quality of all the data that we are going to process within the platform. We also wanted to make the management of all the master data of the project. We wanted to save and all the specific grossing and the 19 data to be able to have a common language and to have all the concepts that were going to be handled in the project. We also wanted to make a link, also because we wanted to make a trace and to know what was going on with the flight through the different processes that were made in the grid platform. That was the fundamental reason why they have also been created to test these three platforms and make a concept test and take advantage of them and make a technological demonstration of all these solutions. The architecture that we have adopted, the truth is that we have not wanted to invent the wheel. As you can see, we have followed the pattern that follows any bi-data and analytical project. And why? Because in a good way we had to ingest data, we had to ingest the flights. That information did not provide a flight information provider. All that information we had to ingest to then be able to process, to do those calculations of carbon dioxide and create those assets. Of course, we have followed this pattern to store it. We have followed the layer to store all that information and we have used different types of data that we will see later in more detail in the next slide. And well, we have also adopted the available layer of data because we had to save all those digital assets, all those that were generated on the blockchain platform and of course, in some way, we have designed command blocks to be able to visualize and analyze the results that we have obtained with all this flight information. If we continue with the architecture, as you can see, to build this platform in the Aretas project, we have used a lot of solutions. And well, basically all the solutions have been focused on, well, we have used the open source tools. To Pedro and me, the truth is that we like to go down to a very low level to know the detail of all the things. And then, well, we have decided that we had to develop, that we had to program to be able to know all this, how it worked. And for that, we have opted for these open source solutions. And with this, I don't mean that we are against the commercial tools. The truth is that we have talked with a lot of manufacturers, they have let us test their tools and well, they have served me very, they have been very useful, they have served me to clarify several questions and solve some problems that I have found in development. But well, in the end, we have opted for, both Pedro and I have opted for programming. And well, as you can see, the programming language that we have used has been the Python language, because it is a very simple language to learn, it has a huge amount of consultation and consultation. And thanks to that, we have had great help to be able to develop with this language. We have also seen that it is a very powerful language because with very few lines of code, it has allowed us to implement great functions. Despite that, we have come to generate more than 4,500 lines of code. Well, if we do a round of all the solutions that we have used to build this platform, we are going to continue seeing that the messaging system that we have used has been the one that has proposed to us, Apache Kafka, as you can see, is a very popular open source system that has very low latency and allows us to manipulate in real time of all the data sources, the flights that we have been able to deliver. With this solution, we have managed to generate more than 30 million messages that we have exchanged between the different processes that we have developed for this platform. Continuing with the round of solutions that you see and that I am showing, for what is in the processing layer, we have used the solution that Apache Spark offers us for what it is to perform all that mission calculation. We have also supported the Redis database that we have used as a cache memory to be able to use those calculations that we have used with Spark. For the part of the data governance platform, we have supported the graph database that provides us with the Neo4j solution. Well, with this graph database, we have come to store all the master data, as we have commented earlier, we have created certain processes, we have saved 19 data, also with Neo4j, we have been able to perform the lineages and finally, we have been able to do the quality analysis of information of all the flights in real time. If we go to the blockchain solution, the solution that we have chosen here to test this tool has been BitChainDB. BitChainDB is a platform that is quite simple to use, it has a very simple API and it has the ability to do SETMAP contras. It is the reason why we have also chosen to choose and use it. As you can see, BitChainDB to respond to the data has been used in the database of WorldDB. And finally, to finish with this round of solutions, to comment that the main measurement that we have used in the project is the one that has provided us the T-Share product suite. With these solutions, we have been able to store all the flights processed, with the information of missions, the touch of carbon dioxide and that have been verified and have been able to be compensated. In addition, with this suite we have also used its visual interface which has been the equivalent tool to be able to design and create all the commands and in this way, to be able to visualize and to be able to consult the information and see the analysis that we have obtained with all these flight information that we have analyzed. Well, we have already reached the end of the first part. I am going to take a step now, Pedro, so that it begins with the second part. I will simply remember that Pedro will explain to you how that mission calculation is calculated and how those touches are done and then in the end I will also show you the four commands that I have designed during this pandemic, during times of Covid. So, well, I'm not going to make you wait any longer. I'm going to take a step to Pedro and I'll pass you the test in front of Pedro. Ok, I have released the presentation. Right now. Right now, I'm going to go there for a second. Here you are. Here you are. Ok, I'm already going. Perfect. Ok, well, continuing a little bit the introduction that Alberto has made, I would like to tell you how the mission process is, how we have calculated it and in what way we have achieved the calculation and also taking advantage of the information that is very varied and extensive, we have wanted to take advantage of it to do other calculations. Tell me, tell me. Yes, yes, I've given it to you. You've given it to me, but we don't see it. You don't see it? I'm going to give it to you again. As you wish. I love looking at you, but I'm telling you to... There it is. Let's see now. Let's see. What do you say? Yes, now yes. Well, you haven't lost anything. Well, we have also calculated apart from the CO2 emissions, we have also taken advantage to do calculations of air quality. These are the emissions that contribute to global warming and on the other hand all the air quality, especially in urban areas near the airports. If we see, we are already there, so that you can see very quickly what the different phases of a flight consist of. Let's say that at the beginning of the flight, at the end of the flight, there are fewer emissions, because the power that the engines have is very small, it is the landing runway towards the positions or from the parking. The moment of the landing and the first part of the descent is when more emissions are produced and the engines are at maximum power. The final phase of the descent we lower the power a little and then the entire flight is 85% which apparently seems a lot, but there is a differentiator factor. At sea level, the density of the air is three times greater than 10,000 meters of height which is a normal height of an egg. With which the air resistance to the wind, is much lower and the air is carried in a more efficient way. Then, at the beginning of the descent phase we lower the power of the engine to the landing runway. You can see some numbers there, at least they are a very wide range because of course the flights can be closer or further between 6,000 and between 2,000 and 100,000 kilos of fuel can be made in a flight between a local flight to the transoceanic and then on the other side we show for the first time how much it is worth to burn for part of an engine a kilo of fuel, which is really 3.16 the conversion factor, that's about it, it's by 3 and that's the tons of CO2 of what we are talking about. All this information we have because we have low data about the types of engines, how they have been certified, the consumption by distance per second of operation and all of that has allowed us to do the calculations of the different parameters. Once we capture and do the calculation of how the emissions are on the left side each flight generates emissions and what we do is once we have calculated the amount of emissions that corresponds to that flight we send it to the blockchain to a first smart contract which is the one that creates the token. There you have a then we will see it later a complete trace of tokenization and the blockchain gets into the consensus and so on and then we will do a little more of it. On the right side you see all those companies energy efficiency forest refurbishment change of use of the soil to save carbon improvements in technology to not emit methane and of course all the energy with the topic of eolico and solar, thermal solar all those actions give them the right to compensate for emissions that can be sold in the markets so our idea is to put individually each flight with its emissions with emissions compensation projects and through a couple more contracts to verify that the emissions produced by a flight are the ones that are and transfer the emissions to companies that have the right to compensate. In this case this is the last transfer operation of a verified token from an airline to a company called Home Depot and it transfers a token of a ton of ice for a project that they have invested all of this is fictitious but the flight is real it is a flight that has never been captured how does this smart contract work taking one of the great faculties that have the blockchain which is the faculty of note and register we execute this first contract once we have calculated the emissions both the platform and the airline sign the transaction with their private key we do not have the public key it generates the digital asset with different attributes there we have an example what flight was, destination origin this case is madrid and barajas a flight that we captured on March 18 that emits 219 tons and at that moment we have decided that the owner is the airline in this case Liberia we send it to the blockchain for it to be valid the nodes give the good view and with the consensus and we put it in the blockchain and it is registered with its identifier after this process the verification comes in all these compensation processes of carbon emission it is mandatory that there is a third part that audits that these data are true it has several mechanisms we have involved it with a smart contract which is the one that makes the verification that a carbon footprint is correct and perfectly aligned with the data we have captured and then we produce the next step that is intermediate and active with those projects that have the right to compensate in this case we created a small website with Python and with four things and we have generated 500 projects fictitious with the right to compensate and we have put them in the platform and then individually we have been one by one compensating flights with projects and so we had a complete transaction and very transparent of each of the emissions the good thing about the big chain is that it is associated with each smart contract that we could generate metadata of the operation in this case you can see from above or below a sequence of the creation of a token of a Chinese company this is Hynine Airlines of a flight the HU795 which was a finished date and the date is in the Unix format that allowed us to index the information in a much clearer way to search based on dates in a much more efficient way and we put a file with the date in which we have created each transaction from top to bottom we have created the token a verifier in this case an auditor an external company called China QCC has verified the file that this mission is correct and finally we have created the exchange between this company and another which is Brookfield Asset Management which is finally left with the asset in its front we have created several tables in the command room to see how many tokens each airline has how many they have to compensate also the projects as we have told you we started the project in a shy way because we used free time but with the pandemic we had the opportunity to start the project so we started at the end of March just 10 days after the alarm and we started collecting data like crazy and we have collected data from the end of March to the end of March until the 20th of June so we have not been able to do the token use case but we have also been able to work with the command room to make some business questions on the side of the issue of emissions and well, there you have some examples captures that we have made of several of them maps with details of routes from flight point to point flight increase trends so we have also been seeing how it has been recovering the traffic during these months of the pandemic and there are more curious things like what was the one that flew the most digitized cities flight durations half the height of the flights among them so many fat things we have 300,000 that we have taken and we had to stop because we had no end because we have generated 80,000 token of CO2 on the platform we have estimated a total of 1,000,000 tons of ice in those three months with respect to the ice more than 7,000,000 tons of nitrogen which is one of the big factors that affect our health and respiratory in cities and then these curious things like the let's say the end or the end of the confinement in China with the confinement in Europe that later went to America and others the first weeks of April and there was the absolute low of the global air traffic and then on June 6 we saw that it had recovered we had multiplied by 6 with respect to what was in March at the same time with respect to the global traffic it had recovered approximately 50% at the global level or in Spain then it reached 80-90% of the reduction the Boeing 737 has been flying with a difference for a long time the Airbus 220 which is really a bomb that I bought the program a couple of years ago is the cleanest by passenger let's say and the 3,400 is the least it's a 4-engine and they have also started to retire in a massive way because it begins to be not profitable that use with the occupations of the current flights that the fuel has dropped drastically during the pandemic the city as its destination was Shanghai, the one that is less Camagüe in Cuba the airports with the worst air quality were Hong Kong and Los Angeles and the best is a small city in an island that is in the sea of Bering which belongs to Alaska, very curious and also the land track so I suppose we capture flights from bi-motors to non-reaction it's curious that I invite you to look for the flight because it is in a complicated place between Russia and the United States South West which operates mainly in the Middle East was the one that flew the most and well, I flew the longest that we have found this is a Singapore-New York from 18 hours of flight then we have taken advantage as Alberto has said to store a lot of information in Neo4j and it has allowed us in this case, on the top left it has a small picture of a Vuelver France in which each part of the process in which we have taken part of its information we have estimated its emissions we have stored the information we have created the token, we have transferred it we have verified it as well because we have captured it and we have related it on the bottom right is what Alberto has said all the business dominions the metadata the data dictionary the areas and so on it is a small view of how we get from the domain from the superior to the interior which is the field in a table or an exterior of what it is and in the middle of it, it is not very good it is a graphic representation of relations between airports that are in cities cities that are in countries flights that go to cities more or less the two third parties of the left side correspond to China and the part on the right on the left is the United States in this case it is a flight that goes from Shanghai to Louisville and the code is an UPS with which it is a carrier it is a transport flight and then we have small things that we have been seeing we can take out the detail of each flight of different processes different indoleps distance, altitude, missions of CO2 takeoff, landing we also have the track and the heat map that goes from Tokyo to Vancouver, Canada from Japan then it allows us to add information in a simple way and here you have a heat map of where the missions have been produced mainly during this pandemic in the United States China, Europe, North because they were in the distribution hubs Luxembourg Belgium, Frankfurt London also in the Middle East Rio de Janeiro and Sao Paulo in Brazil and in Oceania there is a timidly Australia and it should also go to New Zealand with all the flights that took place in one day although many of them are very few it should be completely covered the map of the world and there you continue to see that the traffic is not too low although it has been very low in both China and the United States in Europe really there was very little and this is a day but it should be covered the map in a complete way and then we wanted to a surprise we asked ourselves if identifying flights that we have achieved through several sources and it has cost us a little and we have also done a little journalistic work let's say if we can identify flights that have brought material from the Spanish sanitary from China especially related to masks epi respirators and well we have found several of them but let's say the third part at least and here you have the route the route north, Asia-Tica north that goes through Siberia many were on the Moscow scale and we arrived in Spain and well the first time we managed to have the flights and we saw the report in the command room that represented the information Alberto and I looked at each other almost as if we had the blood because it was quite impressive and we saw it in May that is, we were always in the pandemic and we were astonished this is another representation which is the main route of the minister of Asia-Europe which are basically Beijing, Shanghai and Hong Kong and Shenzhen which are two cities that are very close that are south the more red they are in the city of flights and the routes you can see there all those that go to Spain and then Belgium to then distribute to the rest of Europe Moscow, which is also a great hub Istanbul, Israel and Middle East and this is a global way of how the world has gone to China for material the two countries united for the circular route which is the shortest to Asia all the part of Europe for the north of Asia to China the Horn of Africa because Ethiopia is one of the great hubs on the way to China and then in Oceania both Australia and New Zealand and well we had a number of metrics to bore with which we also asked ourselves and all these masks will have approximate carbon footprint these are data that we have done estimated and well, it has cost us a lot to find a way to estimate it but we want it to be the closest to the information we had both at the company level at the airline level that we have asked and so on and well, there you have a little idea the average that we have calculated of tons of ice the number of masks per flight have been 3 million approximately although there are 236 flights 325 million of masks but well, there are not all the flights in real they really give more of the triple of what we have achieved in capture Pedro, Pedro, I leave because we are without time I have a lot of questions I am suffering I am suffering I have already seen a mask in the first place fantastic information in an hour good for the work, often confinement that you have had fun you have been it has been a vice it is already seen, thank you very much in good time, as Alberto said the world has awakened thanks to work like yours, I have a lot of questions but very little time, so type telegram I make the ones I can one, what specific clients Angel asks us, companies, organizations entities are using areta in the current or is it predicted that they use it in the future type twist it is a project of our company and the possible potential clients are first, essential agents which are the ones that have to verify and bail out because this is fulfilled and European Spanish, Latin and other and then for transactions you have to add airlines at the moment but this is a test of context it is not a finished product and then you ask which relevant was the Neo4j technology in your project that you have told a little which relevant has been very relevant determinant in fact, yesterday they were with us Mirel Angel and you know then they have advanced us a little if you can give us a brush half brush on the determinant very important and also the goal of Alberto and me has been to experiment and there comes the ones we have chosen but not the ones we have rejected both the blockchain platform and the rest and we wanted to experiment and the Neo4j we have not done it very well because the information I think it has been frozen because Alberto I'm sorry, I have to leave it frozen Pedro because we have a component that is prepared and they also ask you about the systems what systems have you used for the collection of the flight data but do not answer me, I do it so that Carlos knows what I have done and already between you write to Carlos Alberto, Pedro, thank you very much fantastic presentation, it has been fabulous good bye for the work and see you in a minute we are getting ready for the next one bye bye