Hi, I'm Peter Burris, welcome to Action Item. There's an enormous net new array of software technologies that are available to businesses and enterprises to attend to some new classes of problems. And that means that there's an explosion in the number of problems that people perceive as could be applied or could be solved with software approaches. The whole world of how we're going to automate things differently in artificial intelligence and any number of other software technologies are all being bought to bear on problems in ways that we never envisioned or never thought possible. That leads ultimately to a comparable explosion in the number of approaches to how we're going to solve some of these problems. That means new tooling, new models, new any number of other structures, conventions, and artifacts that are going to have to be factored by IT organizations and professionals in the technology industry as they conceive and put forward plans and approaches to solving some of these problems. Now, George, that leads to a question. Are we going to see an ongoing ever-expanding array of approaches, or are we going to see some new kind of steady state that kind of starts to simplify what happens or how people, how enterprises conceive of the role of software in solving problems? Well, we've had probably four decades of package applications being installed and defining really the systems of record, which first handled the ordered cash process and then layered around that. Once we had more CRM capabilities, we had the sort of the opportunity to lead capability added in there. But systems of record fundamentally are backward looking. They're tracking about the performance of the business. The opportunity we have. Recording what has happened. Yes, recording what has happened. The opportunity we have is now to combine what the big internet companies pioneered with systems of engagement, where you had machine learning anticipating and influencing interactions. You can now combine those sorts of analytics with systems of record to inform and automate decisions in the form of transactions. And the question is now, how are we going to do this? Is there some way to simplify or not completely standardized, but can we make it so that we have at least some conventions and design patterns for how to do that? And David, we've been working on this problem for quite some time, but the notion of convergence has been extent in the hardware and the services or in the systems business for quite some time. Take us through what convergence means and how it is going to set up new ways of thinking about software. So there's a hardware convergence and it's useful to define a few terms. There's converged systems. Those are systems which have some management software that have been brought into it. And then on top of that, they have traditional sands and networks. There's hyper-converged systems which started off in the cloud systems and now have come to enterprise as well. And those bring software networking, software storage, software. Software defined, so it's a virtualizing of those converged systems. Absolutely. And in the future, it's going to bring also automated operational stuff as well, AI in the operational side. And then there's full stack conversions where we start to put in the software, the application software to begin with the database side of things. And then the application itself on top of the database. And finally, what you were talking about, the systems of intelligence where we can combine both the systems of record, the systems of engagement and the real-time analytics as a complete stack. But let's talk about this for a second because ultimately what I think you're saying is that we've got hardware convergence in the form of convergent infrastructure, hyper-converged in the forms of virtualization of that, new ways of thinking about how the stack comes together and new ways of thinking about application components. But what seems to be the common thread through all of this is data. So is basically what we're seeing is a convergence or a rethinking of how software elements revolve around the data. Is that kind of the centerpiece of this? That's the centerpiece of it. And we had very serious constraints about accessing data. Those were improved with Flash, but there's still a lot of room for improvement. And the architecture that we are saying is going to come forward, which really helps this a lot is the unigrid architecture where we offload the networking and the storage from the processor. This is already happening in the hyperscale clouds. They're putting a lot of effort into doing this. But we're at the same time allowing any processor to access any data in a much more fluid way. And we can grow that to thousands of processes. Now that type of architecture gives us the ability to converge the traditional systems of record. And there are a lot of them obviously and the systems of engagement and the real time analytics for the first time. But the focal point of that convergence is not the licensing of the software, the focal point is convergence around the data. Now that has some pretty significant implications when we think about how software has always been sold, how organizations to run software have been structured, the way that funding is set up within businesses. So George, what does it mean to talk about converging software around data from a practical standpoint over the next few years? Okay, so let me take that and interpret that as converging the software around data in the context of adding intelligence to our existing application portfolio and then the new applications that follow on. And basically when we want to inject in intelligence enough to anticipate and inform interactions or inform or automate transactions, we have a bunch of steps that need to get done where we're ingesting essentially contextual or ambient information. Often this is information about a user or in the business process. And this data, it's got to go through a pipeline where there's both a design time and a runtime in addition to ingesting it, you have to sort of enrich it and make it ready for analysis. Then the analysis is essentially picking out of all that data and calculating the features that you plug into a machine learning model. And then that produces essentially an inference based on all that data that says, well, this is the probable value. And it sounds like it's in the weeds, but the point is it's actually a standardized set of steps. Then the question is, do you put that all together in one product across that whole pipeline? Can one piece of infrastructure software manage that?  And- But let me stop you. Because I want to make sure that we kind of follow the thread. So we've argued that hardware convergence and the ability to scale the role that data plays or how data is used is happening. And that opens up new opportunities to think about data. Now what we've got is we are centering a lot of the software convergence around the use of data through copies and other types of mechanisms for handling snapshots and whatnot in things like Unigrid. But let's start with this. It sounds like what you're saying is we need to think of new classes of investments in technologies that are specifically set up to handling the processing of data in a more distributed application way, right? If I got that right, that's kind of what we mean by pipelines. Yes. Okay, so once we do that, once we establish those conventions, once we establish organizationally, institutionally, how that's going to work, now we take the next step of saying, are we going to default to a single set of products or are we going to do best to breed and what kind of convergence are we going to see there? And there's no- First of all, have I got that right? Yes, but there's no right answer.  For instance, the very largest and most sophisticated tech companies are more comfortable taking multiple pieces, each that's very specialized and putting them together in a pipeline. Facebook, Yahoo, Google, got it. Those guys. And the knobs that they're playing with, that everyone's playing with are three, basically on the software side, there's your latency budget, which is how much time do you have to produce an answer so that that drives the transaction or the interaction? And it's not, that itself is not just a single answer because the goal isn't to get it as short as possible. The goal is to get as much information into the analysis within the budgeted latency. So it's packing the latency budget with data? Yes, because the more data that goes into making the inference, the better the inference. The example that someone used actually on Fareed Zakaria GPS one show about it was, if he had 300 attributes describing a person, he could know more about that person than that person did in terms of inferring other attributes. So the point is, once you've gotten your latency budget, the other two knobs that you can play with are development complexity and admin complexity. And the idea is on development complexity, there's a bunch of abstractions that you have to deal with. If it's all one product, you're going to have one data model, one address and namespace convention, one programming model, one way of persisting data, a whole bunch of things. That's simplicity and that makes it more accessible to mainstream organizations.  So again, if you're dealing with one product, it's a huge burden off the admin and we know they struggled with Hadoop. So convergence decisions about how to enact convergence is going to be partly or strongly influenced by those three issues. Latency budget, development complexity or simplicity and administrative, so David. I'd like to add one more to that and that is location of data because you want to be able to look at the data that is most relevant to solving that particular problem. Now, today a lot of the data is inside the enterprise. There's a lot of data outside that but there's still, you will want to, in the best possible way, combine that data. But is that a variable on the latency budget? Well, I would think it's very useful to split the latency budget, which is to do with inference mainly and development with the machine learning. So there is a development cycle with machine learning. That is much longer, that is days, could be weeks, could be months. It's still done in batch. Right, it is, it will be done in batch. It is done in batch and you need to test it and then deliver it as an inference engine to the applications that you're talking about. Now that's going to be very close together. That inference and the rest of it has to be all physically very close together. But the data itself is spread out and you want to have mechanisms that can combine those data, move application to those data, bring those together in the best possible way. That is still a batch process. That can run where the data is, in the cloud, locally, wherever it is. And I think you brought up a great point, which I would tend to include in latency budget because no matter what kind of answers you're looking for, some of the attributes are going to be pre-computed and those could be external data. And you're not going to calculate everything in real time. There's just, it's- You can't. Yes. You have a bucket. You can. But is the practical reality that the convergence of, so again, the argument, we've got all these new problems, all new kinds of new people that are claiming that they know how to solve the problems, each of them choosing different classes of tools to solve the problem, and explosion across the board in the approaches, which can lead to enormous downstream integration and complexion costs. You've used the example of Cloudera, for example, some of the distro companies who claim that 50 plus percent of their development budget is dedicated to just integrating these pieces. That's a non-starter for a lot of enterprises. Are we fundamentally saying that the degree of complexity or the degree of simplicity and convergence that's possible in software is tied to the degree of convergence in the data? This is, you're honing in on something really important. Give me one more. Thank you. Give an example of the convergence of data that you're talking about. I'll let David do it because I think he's going to jump on it. Yes. So let me take examples, for example. If you have a small business, there's no way that you want to invest yourself in any of the normal levels of machine learning and applications like that. You want to outsource that. So big software companies are going to do that for you. And they're going to do it, especially for the specific business processes which are unique to them, which give them digital differentiation of some sort or another. So for all of those type of things, you will, software will come in from vendors, from SAP or son of SAP, which will help you solve those problems. And having data brokers, which are collecting the data, putting them together, helping you with that, that seems to me the way things are going. In the same way that there's a lot of inference engines, which will be out at the IoT level, those will have very rapid analytics, given to them again, not by yourself, but by companies that specialize in facial recognition or specialize in making a warehouse data warehousing. Wait a minute, are you saying that my customers aren't special that requires special face recognition? So I agree with you, David, but I want to come back to this notion. The point I was getting at is, there's going to be lots and lots of room for software to be developed to help in specific cases. And large markets to sell that software into. Whether it's a software bit increasingly also with services. But I want to come back to this notion of convergence because we've talked about hardware convergence and we're starting to talk about the practical limits on software convergence, but somewhere in between, I would argue, and I think you guys would agree, that really the catalyst for, or the thing that's going to determine the rate of change and the degree of convergence is going to be how we deal with data. Now you've done a lot of research on this and I'm going to put something out there and you tell me if I'm wrong, that at the end of the day, when we start thinking about UniGrid, when we start thinking about some of these new technologies and the ability to have single copies or single sources of data, multiple copies, in many respects what we're talking about is the virtualization of data without loss. Without loss of the characters, the fidelity of the data or the state of the data. If I got that right. Knowing the state of the data. Or knowing the state of the data. If you take a snapshot, that's a point in time. You know what that point of time is and you can do a lot of analytics, for example, and you want to do them on a certain time of day or whatever it is that you're comparing. Is it wrong to say that we're seeing, we've moved through the virtualization of hardware and we're now in a hyper scale which is, or hyper converged, which is very powerful stuff. We're seeing this explosion in the amount of software that's being, the way we approach problems and whatnot but that a forcing function, something that's going to both constrain how converged that can be but also force or catalyze some conversions, is the idea that we're moving into an era where we can start to think about virtualized data through some of these distributed file systems and other types of data. That's right, and the metadata that goes with it. The most important thing about the data is, and it's increasing much more rapidly than data itself is the metadata around it. But I want to just make one point on this. All data isn't useful. There's a huge amount of data that we capture that we're just going to have to throw away. Is the idea that we can look at every piece of data for every decision is patently false. And there's a lovely example of this in fluid mechanics. Who dynamics? Fluid dynamics. If you're trying to have simulation at a very, very low level, the amount of data, high fidelity, you run out of capacity very, very, very quickly indeed. So you have to make trade-offs about everything, and all of that data that you're doing in that simulation, you're not going to keep that, all the data from IoT. You can't keep that. And that's not just a statement about the performance or the power or the capabilities of the hardware. There's some physical realities that are just going to be, that are going to limit what you can do with a simulation. But, and we've talked about this in other action items. There is this notion of options on data value where you do that today's, the value of today's data is much higher. Well, it's higher from a time standpoint for the problems that we understand and are trying to solve now. But there may be future problems where we still want to ensure that we have some degree of data where we can be better at attending those future problems. But I want to come back to this point because in all honesty, I haven't heard anybody else talking about this and maybe it's because I'm not listening. But this notion of, again, your research that the notion of virtualized data inside these new architectures, being a catalyst for simplification of a lot of the sub systems. It's essentially sharing of data. So instead of having the traditional way of doing it within a data center, which is I have my systems of record, I make a copy, it gets delivered to the data warehouse, for example. That's the way there's been done. That is too slow. Moving data is incredibly slow. So another way of doing it is to share that data, make a virtual copy of it, and technology is allowing you to do that because the access density has gone up by thousands of times. Because? Because of flash, because of new technologies at that level. High performance interfaces, high performance networks. All of that stuff is now allowing things which just couldn't be even conceived. However, there is still a constraint there. It may be a thousand times bigger, but there is still an absolute constraint to the amount of data that you can actually process. And that constraint is provided by latency. Latency. Speed of light. And the speed of the processes themselves. Again, let me add something that may help explain the sort of the virtualization of data and how it ties into the convergence or non-convergence of the software around it, which is when we're building these analytic pipelines, essentially we've disassembled what used to be a DBMS. And so out of that, we've got a storage engine, we've got query optimizers, we've got data manipulation languages, which have grown into full blown analytic languages, a data definition language. Now the system catalog used to be just a way to virtualize all the tables in the database and tell you where all the stuff was and the indexes and things like that. Now what we're seeing is since data is now spread out over so many places and products, we're seeing an emergence of a new type of catalog, whether that's from Elation or Dremio or on AWS, it's the glue catalog. And I think there's something equivalent coming on Azure. But the point is we're beginning, those are beginning to get useful enough to be the entry point for analytic products and maybe eventually even for transactional products to update or at least to analyze the data in these pipelines that we're putting together out of this, out of these components of what was a disassembled database. Now we could be- I would make a difference there between the development of analytics and again the real-time use of those analytics within systems of intelligence. And those are different problems they have to solve. There's design time and runtime. There's actually four pipelines for the sort of analytic pipeline itself. There's design time and runtime. And then for the inference engine and the modeling that goes behind it, there's also design time and runtime. But I guess I'm not disagreeing that you could have one converged product to manage the runtime analytic pipeline. I'm just saying that the pieces that you assemble could come from one vendor. Yeah, but I think David's point, I think it's accurate. And this has been since the beginning of time. Certainly predated Univac that at the end of the day, read-write ratios and the characteristics of the data are going to have an enormous impact on the choices that you make. And high write-to-read ratios almost dictate a degree of convergence. And we used to call that SMP or scale up database managers. And for those types of applications or those types of workloads, it's not necessarily obvious that that's going to change. Now we can still find ways to relax that. But you're talking about, George, the new characteristics. Injecting the analytics. Injecting the analytics where we're doing more reading as opposed to writing. We may still be writing into an application that has these characteristics. But it's a small amount of data. But a significant portion of the new function is associated with these new pipelines. Right, and it's actually your, what data you create is generally derived data. So you're not stepping on something that's already there. All right, so let me get some action items here. David, why don't I start with you? What's the action item? So for me about conversions, that there's two levels of conversions. First of all, converge as much as possible and give the work to the vendor. Would be my action item. The more that you can go full stack, the more that you can get the software services from a single point, a single throat to choke, single hand to shake, the better the more you have outsourced your problems to them. And that has a speed implication. And that has time to value. Time to value, it has a, you don't have to do undifferentiated work. So that's the first level of convergence. And then the second level of convergence is to look hard about how you can bring additional value to your existing systems of record by putting in automation as a real-time analytics which leads to automation. That is the second one where the, for me, where the money is. Automation reduction in the number of things that people have to do. George, action item.  And if more of your Greenfield apps can start in the cloud and you're not religious about open source but you're more religious about the admin burden and development burden and your latency budget, then start focusing on the services that the cloud vendors originally created that were standalone, but they are increasingly integrating because their customers are leading them there. And then for those customers who have decades and decades of infrastructure and applications on-prem and need a pathway to the cloud, some of the vendors formerly known as Hadoop vendors, but for that matter, any on-prem software vendor is providing customers a way to run workloads in a hybrid environment or to migrate data across platforms. All right, so let me give this a final action item here. Thank you, David Floyer, George Gilbert, Neil Raden and Jim Cabellus and the rest of the Wikibon team is with customers today. We talked today about convergence at the software level. What we've observed over the course of the last few years is an expanding array of software technologies, specifically AI, big data, machine learning, et cetera, that are allowing enterprises to think differently about the types of problems that they can solve with technology. That's leading to an explosion in the number of problems that folks are looking at, the number of individuals participating in making those decisions and thinking those issues through, and very importantly, an explosion in the number of vendors with piecemeal solutions about how, what they regard, their best approach to doing things. However, that is going to have a significant burden that could have enormous implications for years. And so the question is, will we see a degree of convergence in the approach to doing software in the form of pipelines and applications and whatnot, driven by a combination of what the hardware is capable of doing, what the skills are make possible, and very importantly, the natural attributes of the data. And we think that there will be. There will always be tension in the model to try to invent new software, but one of the factors that's going to bring it all back to a degree of simplicity will be a combination of what the hardware can do, what people can do, and what the data can do. And so we believe pretty strongly that ultimately the issues surrounding data, whether it be latency or location, as well as the development complexity and administrative complexity are going to be a range of factors that are going to dictate ultimately how some of these solutions start to converge and simplify within enterprises. As we look forward, our expectation is that we're going to see an enormous net new investment over the next few years in pipelines because pipelines are a first level set of investments on how we're going to handle data within the enterprise. And they'll look like, in certain respects, how DBMS is used to look, but just in a disaggregated way, but conceptually and administratively and then from a product selection and service selection standpoint, the expectation is that they themselves have to come together so the developers can have a consistent view of the data that's going to run inside the enterprise. Want to thank David Fleuer, want to thank George Gilbert. Once again, this has been Wikibon Action Item, and we look forward to seeing you on our next Action Item.


Hi, in this video we will explain in detail the merge project feature in D5 Render. Merge project is especially helpful when you're dealing with a huge or complex project. With this feature, you and your colleagues can work on different parts of the same project simultaneously. And then merge the files into one for final rendering. It's a must for speeding up teamwork. First things first, check if your project files meet the following requirements. One, the project files must be saved intact. Two, the selected project files must be made with the same D5 version. Three, there has to be enough space available on the drive where the merged file will be saved. Before diving into the project visualization in D5, you can divide the model into different parts. Here we take a Sketchup file as an example. Modellings from other DCC software work the same way. This Sketchup model is divided into two parts. The main building and the surrounding context. Then start D5 Render. Go to menu. Preference. Widget. There you will find the merge project feature. After enabling merge project, import the two models into D5. Align them by using the Align feature. Save the scene as a DRS file. And make a copy of it. Now the two DRS files can be adjusted separately. You and your colleagues can simply just dive in and work on your own DRS files at the same time. After completing the part of senior responsible four, delete the irrelevant model and save the project. When everyone's done their part, it's time to merge. Launch D5. Check, recent, click on the upper right corner to enter the merge project panel. Click on the plus icon and add the first project file. Here you will see that D5 Render can merge imported models, nature models, light sources, and other D5 assets, scene lists, video clips, and render cues. Click, select all here. If there's a certain part that you don't want to merge from scene A, for example, the video clips, then just uncheck video clip here. Click add project to keep adding other projects. For now, you can merge up to 10 projects at a time with D5 Render, which is quite convenient and powerful. Move on to the final step. Choose the file path where you want to save the project and start merging. You can see that the merge speed in D5 is very fast. Now, take a look at the final file. You have everything you need perfectly merged together. In this scene, you can start post processing as you like and rendering images or animations for output. Please note that scene lists, video clips, layers of the same name from divided projects will not be overwritten, but D5's default layers will be merged into one after merge project. Objects inside will still be kept, so don't worry about that. That completes our tutorial for D5's merge project. Thanks for watching. Hope it helps your teamwork get easier and faster.


This is Witchspace news for Friday the 26th of March 2021 I'm CMDR Buur In this week's Odyssey special report Frontier shows off a first person tour of a starport social hub Odyssey's assistant producer reveals some juicy gameplay details in a live streamed interview and games radar releases some previously unseen Odyssey gameplay footage If you enjoy this video hit like and subscribe and remember to click the little bell icon and select all notifications as that stuff really helps the channel and if you'd like to further support our work you'll also find us on Patreon Links to everything you need are in the description below Here we go then Thursday night's live stream saw a new gameplay trailer released by Frontier that gives us our first proper look at a starport's interior space After the video the community team sat down virtually speaking of course with Samantha Marsh the assistant producer on Elite Dangerous Odyssey to discuss what was shown and what wasn't shown and to add a bit more flesh to the bones of the whole presentation and we're going to break it all down for you right here The first thing that strikes from watching the 7.5 minute tour and listening to Samantha's interview is the overall gameplay design ethos of the space Rather than simulate miles and miles of corridors and buildings fdev has instead chosen to streamline the experience and keep the on foot station interactions immersive but not overly bloated It's also worth keeping in mind that this starport interior is just one of many and whilst the layout will remain similar and familiar to aid in player navigation there are in fact a number of different station interiors that will change according to the function of the station and each will have their own distinct look and feel likewise planetary ports will have similar and familiar functions they also present a vastly different view and experience over that of their orbital cousins Samantha went on to emphasise there will be plenty of room and variety in stations for you to somewhat meander around and look at details so they're designed so that you can run in fill up with missions and run out again to that end you won't find yourself exploring a massive maze of twisting corridors and dark back alleyways needing to utilise public transportation systems to access areas of the city size space stations instead what is presented is more akin to an open plan shopping mall with separately themed booths and sectioned off shops and facilities with a large bar area that also serves as a viewport out into the starport landing pads whilst no ship movements were shown in the trailer Frontier have been at pains to point out on a number of occasions that in the full game this view of the station interior is in fact live and you will see other players ships landing and departing and as far as we know ships will be able to see what's happening in the bar as well as the player character moves through the hub Elite's sound design is once again very evident and there is a constant stream of background ambience and chatter coming from the non-player characters in fact as the player moves past the NPCs some of them do attempt to garner their attention with calls for assistance and the offer of work When the player character chooses to interact with an NPC the experience switches to the sort of thing that we're more used to seeing in an RPG like Skyrim there is a brief moment of introductory voiced character audio and accompanied text and then it changes to a slightly more familiar text based mission briefing and shows rewards you might gain from a successful completion Interface design for this section has clearly had a lick of paint over and above what we're used to with our in-ship mission boards and whilst the characters shown are basically animated three-dimensional mission boards there is an added extra layer of depth when the player character is presented with the opportunity to negotiate a better deal with one shady individual The bartering success or failure is determined as you'd expect with Elite by a degree of RNG but its outcome is influenced by the reputation you have with the faction offering the mission and what's called your mercenary rank it seems that with Odyssey you'll have a new more personal reputation or rank that determines in part at least how some NPCs will interact with you The example shown had three bartering options essentially offering a low, medium and high reward chance with the higher rewards coming in with an increased risk of the bartering failing and in the interview Frontier did confirm that failure to barter successfully will remove the mission altogether so care is needed If you fail the mission having bartered a better deal for yourself the price of failure could potentially be worse with your faction or superpower standing being reduced We're going to assume that the higher tier of bartering success will garner much more significant rewards over and above those of credit otherwise it's unlikely that the extra risk would be worth it as money is largely meaningless in the game for anyone that has been playing for a significant amount of time however for new players coming into the game the chance of some extra credits just for flashing a smile and a wink will doubtless be extremely valuable What does have value for everyone in Elite however is engineering materials which brings us onto the barkeeper The barkeeper appears to serve a similar role to a material trader in the current game your character has a pool of what looks to be generic scavenged or stolen assets which can be used to trade for more specific engineering materials to upgrade your personal equipment and spacesuits Those generic assets fall into three categories chemicals, circuits and tech In the demonstration the player is seen trading some generic chemicals from a personal stock with the barkeeper in order to obtain some graphene The barkeeper also had an option to trade some other goods but the specifics of that option weren't shown in the demonstration Elsewhere in the demo we're shown pioneer supplies the equipment, spacesuits and weapons vendor and Inter Astra which essentially serves as the shipyard in first person gameplay In the case of pioneer supplies the trailer does scoot around the shop and exit without providing more detail but Samantha went on to elaborate that modifications can be added to your weapons here and that in some cases there is a chance that the vendor could offer you a pre-upgraded weapon or modification that is not generally available elsewhere with the higher upgrades being rarer to get Before departing the station in a newly purchased dolphin more on that in a moment the player stops by some mission terminals that offer up a more generic and frankly quicker mission gathering experience that is much more like that from the in-ship experience that we're used to You're able to review the missions on offer from all the factions at a station and shown the rewards you'll garner for a successful completion of those missions It wasn't immediately evident what the difference between the mission boards and the NPC missions are, beyond immersion and speed etc but we're guessing that the rewards for the personal missions are perhaps more unique and more valuable to the on foot player specifically and we'll have to see how that pans out in the full game At the end of the trailer the player then heads to the docking bay and after accessing a super fast elevator that separates the bay from the rest of the social hub we're treated to a view of a brand new shiny dolphin sitting on a small landing pad it's a familiar setting and a familiar ship we're just not used to seeing it like this and it's honestly extremely impactful The player then approaches the blue ship boarding hologram that we saw on the heist trailer a couple of weeks back and we do now get to see the fade to black boarding method from the pilot's perspective Once boarding is selected from the pop-up menu there's a brief fade to black before the player appears in the pilot's position inside the dolphin I wasn't personally bothered by the fade to black from a first person perspective it will make getting into your ship quick and efficient something that I suspect I'll be extremely grateful for when boarding something like a beluga or an anaconda or indeed when myself or my ship are on the ground and under fire What I am still curious and more than a little anxious about is what someone else will see when they witness me boarding my ship Will my character be animated climbing into or out of the ship somehow or will I just pop in and out of existence when I reach that blue hologram one of them will look awesome the other will honestly be jarring and somewhat immersion breaking When it comes to SRV and SLF deployments Frontier have previously done a beautiful job of showing these two ancillary vehicles leaving and returning to their mother vessel from a third person perspective while still using a fade to black in the first person to speed the whole thing along and so we're hoping that the player character's transition to and from the ship will follow the same tried and tested pattern it'll certainly be one of the first things we look for on Monday when the alpha drops Overall we were left very satisfied with what we saw of the station tour it's clear what Frontier are going for with this expansion and in the example shown they have achieved what they set out to do with some significant style I do personally think this portion of the first person experience will also only improve with the addition of other players running around going about their business gods of instancing willing obviously The interview with Samantha also went on to flesh out a little more about places that weren't shown in the trailer mentioning Vista Genomics where players will be able to trade in biological samples taken from organics on a planet surface for the biologist rank The team also mentioned that the various life forms will display different levels of maturity and growth and that existing life forms and organics have been reworked and given a refresh with odyssey meaning that even discovering something as familiar as a brain tree should now be a different experience from the current game Elsewhere in the interview the team touched on the Frontlines Solutions system Frontlines Solutions is a system for getting you into a large ground based combat zone in the system you're in and whilst you can fly to the zone and walk in if you book your participation through Frontlines Solutions you will be deployed in a drop ship The combat zones vary wildly depending on where they are on a planet the planet's position in the system and the time of day You could be deployed in full daylight or you could find yourself in a night time battlescape with nothing but torches and gunfire lighting the way They also mentioned that the conflicts will feature control points that could be a case of overwhelming the enemy with kills It appears at least that territorial control of some sort could be a factor and Arthur did mention when playing it that he got serious Starship Troopers dropship seen vibes from it All I'm going to say is Thargoid combat zones I'll just leave that there Other little nuggets that we gleaned from the interview include the fact that there are apparently over music tracks in the bar area Squadron party in the bar confirmed When you book a taxi from Apex Interstellar, more on that in a moment the travel you experience will be in real time, I'm fairly certain that that little fact alone will divide opinion significantly and the team did make mention of what they called elite ranks and beyond and said there will be more on this in the future There's clearly been some significant changes in the UI and the UI team will be making an appearance on an upcoming live stream to talk about what we can expect to see After the interview, Arthur and Bruce did make mention that a good number of features are being held back from the imminent alpha test to save any spoilers if nothing else Those features include but are not limited to plasma weapons guardian and Thargoid sites and some variations of the organics to scan and discover between phase 3 and 4 of the alpha there will be some downtime where the alpha will be unavailable and during that period a snapshot of the full game will be taken so if you want your commander in a certain place with a certain ship ready for phase 4 then that's the time to do it Currently the plan is for each phase of the alpha to be a week long but that may be subject to change as testing progresses No solid launch time for the alpha to drop on Monday but the team are aiming for a round midday You'll find links in the video description to the Starport tours trailer and the full live stream including the interview with Samantha Marsh And finally Frontier promised that a new feature for Odyssey would be shown off on a games radar live stream yesterday evening and sure enough during their future games showcase there was a short trailer that talked about Odyssey which showed some exclusive never before seen footage of the Apex interstellar space shuttle booking system You can see some screen grabs from it running on screen at the moment Honestly it wasn't really worth staying up late for but it is evident that the system that's been built around Apex is slick and functional As you'd perhaps expect there is a higher fee the further you travel and the system map has had a make over as well as part of the UI changes in Odyssey which was interesting to see The whole experience appears to be kept completely in game In Safari's once your shuttle is booked you walk to the hangar get in the ship that is waiting for you and we know from the interview earlier in the evening what then happens is completely real time as best we can determine you are flown by the AI to the planetary port or station that you booked to travel to just as though you were doing it yourself Whether that flight can be interdicted and ganked Well we'll just need to test that I imagine The presentation which we've linked to below and the interview earlier really did give the briefest of overviews with regard to Apex and we still have many unanswered questions For example once you're on the planet surface at a star port how do you then get to the POI or mission spot that you're interested in or do you just walk through the space of the alpha next week we'll have commanders solely using Elite's new space uber service to get about in a single star system So either way our answers are just a couple of days away from now So there we have it A huge day yesterday and there's a lot to take in How are you feeling about what you've now seen and heard or like me did you hear drop ship and then just shut down dribbling Let us know in the comments below That's it for now Thank you very much for watching We'll be back later this week with more videos Until then, 07 commanders follow the greens on the way out and do keep clear of the toast rack We very much look forward to seeing you next time


Hi guys, so I want to do a kind of updated video for you guys on how I give an ICU report I want to do an example for you guys I did one kind of that was more geared towards med surge Which I will leave the links down below to the videos that I did on that and now I want to do an ICU one Since I'm working the ICU now, and I'm just gonna jump right in and get started. This is a made-up patient This is all fit fake information, but it's all very ICU E And if you want to get the report sheet that I use in the ICU that link will be down below as well So you can kind of see the report sheet that I use every day on when I'm working So I'm just gonna be reading off my phone because I have it all in here So this is patient so-and-so in room 10. This patient is 65 years old. He's a full code He has no known allergies. He was admitted on two days ago on Whatever date two days ago was For a COPD exacerbation this patient has been smoking for 50 years two packs a day Has history of asthma CHF a fib type 2 diabetes as long as high as well as hypertension But if science-wise this patient has been a febrile all shift their heart rate has been in a from about a hundred two hundred and twenty Blood pressure has been slightly hypertensive about at 140s Neurologically they're intubated when I did their sedation vacation earlier. They were able to follow commands pupils were equal brisk and reactive and They were able to respond to my voice cardiac wise He's been going in and out of a fib and normal sinus rhythm But mainly has been a fib this my shift respiratory wise. He was intubated When he was admitted in the ER two days ago, he's an FiO 2 of 40% peep of 5 rate of 16 title volume of 450 breath sounds are diminished throughout but clear GI wise he's got an og tube that is hooked up to low intermittent suction. I've been getting lots of greenish biolee out only about 200 for my shift GU he's got a Foley his urine output has been anywhere from about 50 to 80 an hour he does get late six once in the evening and The urine's been clear yellow skin wise He's got just some mild skin tears on his bilateral arms and some bruising on his shins He's got a For lines got a right triple lumen IJ with CVP's got two peripheral IVs Both in the AC's both are 20 gauges bilaterally He's got an A line in the right radio artery. That's why he's on a dealt drip going at five He's also getting normal saline with 20 of K at a hundred an hour I've had to replace his Magnus Foss those have been replaced. You'll have lab draws on your shift Imaging wise he did get a chest X for today, which was stable. No worsening Lab wise his mag was low at 1.9 His boss I replaced already and is now I already did the redraw and now it's back up to 2.6. All our labs are are pretty stable He's got the intensivist on his case. He's got renal. He's got pulmonologist and His wife is at the bedside her name is Nancy. She's his power of attorney He also has two daughters and two sons which have been at the bedside Intermittently today, but have gone home for the evening the plan for him is to start weaning him from the vent tomorrow and Just maintain his heart rate under control. So that's kind of a typical report. I just winged it I didn't write any information down. I was just going off my report sheet So I could have said certain things in there that may have been a little different or may I don't know but that is pretty much a Typical report for the ICU give or take some things here and there So if you enjoyed this video get a thumbs up and subscribe to my channel and I'll see you guys next time. Bye


Thank you very much for attending my session and my name is from fact and I'm coming from my company called by arc we Okay Deep breath, okay. Okay. Today I will talk about how we adopt the open source and and how it helps us in in the time of a Little bit of a crisis that we face last year to scale actually it scales four times After after I finished the flight I found out that at the end of the event we actually scale about four a little bit more than four times Within two months not five times, sorry Yeah, but because I compare it to the current current capacity that we have it is actually right now It we have five times Compared to that that event okay, so because We have a lot of non-discretional agreement and yeah with so many partners for many customers So I don't even sure what we can reveal In the flight so most of the names will be hide Maybe hidden or maybe modified to to be something not so easy to guess So so don't don't try to keep you see see the name of the HP in In in the form of some kind of animal or something like that Okay, so so what we are doing by arc we are providing my company is providing the CDN content delivery network Infrastructure is a kind of the cloud services that offer the people who want to distribute the content I mean Put something for for the customer to load from from the web or something like that so the most prominent use case of the CDN is to be used in the widow streaming because because One one gigabit perfect server usually can only contains about 400 viewers of the HD not full HD just HD which is a very very small figures for for the expectation of Most of our customer most of our customer usually told us that the one million We were at the same time. Well, actually there never be any single event that have achieved one million Combined yes, maybe but no for the finger even So so in in most cases If you want to do the widow streaming in in in this country, you usually need some kind of CDN either Either Buying the services from us or from our competitors or maybe build the CDN by yourself You you you have no choice You cannot use the single server to serve the wheel of streaming in in large in not not large scale Just medium scale. It's not possible anymore So so so just I just want to keep some background of what the CDN is actually doing In most cases we have the edge server running in most of the internet services for wider in Thailand and When when when the client I mean client of our customer request from something from the server Our edge server will fetch that that content from the origin server and cash it in our server The next request if if the new client request the same thing the request will not not Coming through to the origin server for the bandwidth used by the origin server will be much less especially if the the content is some kind of like Like streaming where everyone view the same thing the same content the same view of streaming. Yeah, that that will be a lot of Saving on the origin site So this is our tech stack. So just to give you some idea that we use a lot of open forces Actually, this is the current tech stack, but what what I'm talking about is the Well, okay, this is the the our our distribution of the CDN node Well, why and this is what I will focus today. This is the current capacity that we have we have about a little bit more than thousand gigabit per second capacity to serve the video streaming in in Thailand This is now but actually What I would like to talk about Focus about in today is the the event that happened around 2018 February at that time we have about 200 gigabit per second and This is this is the capacity that we have at the time so for what happened in just one year actually we We we have a major customer That has a TV program that has has gained a lot of viral attention from the internet and Then the customer has come. I mean Talk to talk to us via line the line line application and want us that this program could gain a lot of traction A lot of we were to review the video streaming. So this For for the information, this is the it's a kind of like a to turn episodic program So it's not just the one one one time event. It's continue for about two months So, okay, so this is not the first time that we This kind of the warning from from the customer. So, okay, we see that We have 200 that at a time so so the first episode of This TV program I have to hide The the time line because if you if you see the date and time you you know exactly what actually this is The first episode we got about 50 gigabit per second. I mean this is this translated into about 50,000 we were concurrent we were at the same time. This is this is quite large, but not that very large Quite large and not not that very large Okay So so so at the time we still feel relaxed and thinking that well, this is not that too much to handle Okay, it's quite large at a time. It's old. It took about 25% of our capacity But it's not too much to handle and we actually our team that that that time It's I think it's Wednesday and our team still thinking about hanging out at in Friday night making an appointment and the second episode We see the bandwidth go up to about a hundred Do you see the chart decline here? it happened because Because the infrastructure of our customer because we provide a CDN Our customer provide the application and website to put the streaming into it so at the time our customers application and website bought down Because a lot of we were coming in and and and getting to to their website and their website down Which is actually it favorable as frankly speaking because the traffic is so much that that We it's more it's much more than we anticipated at that time. It's the maximum bandwidth that that happened is about 120 gigabit, which is about Yeah, it's 120,000 confirm we were about about that 120 150 150 something so so at that time what happened we we start to Experience a little bit of hiccups in in the in our servers because we we cashed the content right and Most of the time there will be some some kind of content that that cannot be cash because it's not it's not it It's just being generated if it's a live streaming video streaming the new content will be generated every about five or six seconds and Usually some loads will come and hit the origin server and that usually it is about 1% but for 1% of the 120 Gigabit per second is more than 1 gigabit per second so that is too much to handle for the origin server and Yeah, that as I mentioned lucky the app and website of our customer also experienced the technical problem and it went down so for the the amount of the bandwidth use is not going up after that and At the time we quickly engineer a way to to even make the caching process Happened more quickly for the Lord that that come coming through to the origin server is going down to about I think maybe 0.01% and We cancel all the plans at that weekend Okay, and at this time we we start to think about the Increasing the capacity we plan we we contact vendors had where something like that and we try to negotiate the way to quickly expand a kind of like temporary Infrastructure to to accommodate the Lord Some of you might think that why why don't we use maybe something like public cloud or maybe use something like Amazon cloud front or something like that to provide a services I would say that if if if we use that it it it can cause like maybe a Million per episode a million bars. I think yeah, that that is too much cost for our customer and for us too, so so if possible We we don't really want to do you use that in the first first place so so we try to optimize our infrastructure first and This is the third episode, which is a week after the second episode The third episode we see a double again For each episode double double double four. We now are quite around the quadruple Concurrent it just in just a third episode and and don't forget that it's actually about it to 10 episodes TV program so it's not halfway yet, and we have seen 200 212 point one eight gigabit per second in our System so so yeah, we really panic because at a time we only have about 220 gigabit per second capacity so for what happened this is Okay for the record this is the first time in in the history of our customer also that that TV program has gained a lot of traction in just a short amount of time Usually, okay some some time some of these kind of TV program may make gain a lot of traction But that will be almost near the end of the TV program. Maybe at the episode 78 not episode three so so we contact all the internet data center that we know of we We are that that we are also their customers to to to have a kind of like temporary dark space and bandwidth and Negotiate the price of course and we got about 25 percent more more rack space more bandwidth That that we can put the server in and scale immediately So and the next thing we do is that we we buy a lot of servers That is the nice thing about open source because we don't really need To do the something like compliance test or something like that we can buy anything that is 864 servers and we even looking for the second hand server anything that that can that can run our software because all all the stack that we we are doing is based on the open source and Okay, we we also found that the Origin server is load a lot of a lot again, so we we engineer a way to broadcast the Video streaming the live view of streaming to all the edge server at the same time for the load to to the origin server is very very very very little and The famed at the same time our customer for experience from technical problem It went down again and for the car there they use for the public cow one of the very famous public cow and This is the fourth episode which species the next day after the third episode and we see the new record again We see 250 gigabit per second that is about two hundred and Fifty two hundred and fifty sixty thousand concurrent will work at the same time Yes, and this is the first time that we see the scale to just see the scale Not reaching it yet of the 300 gigabit per second for for the single event Okay, so This day, this is the first day that our customers infrastructure is not Not not not went down because they scale. I think they scale to about hundreds of servers in their public cloud To to cover for the Lord's Yeah, and we we also finish increasing 25% of capacity that we deal you with our IDC the day before and we expand it within the single day because we use Ansible we use the depth of things like that to to to accommodate the expansion so we can quickly increase the capacity very fast and This is the first day that we have to remove some of the Resolution I mean in the because in the live streaming this day we usually chip with the multiple resolution They call it adaptive with red. We have at that time We have the HD that the 20 seven 20p Resolution which is the highest resolution that that we provide for this TV program And we have the SD the 4 480p Resolution so we we negotiate with our customer to remove the highest one to save some bandwidth and actually the bandwidth is not the The very serious problem at that time the very serious problem at that time is actually the processing overhead That occur because the HTTPS Because at that time HTTPS is being widely adopted because of the coming of the latest encrypt certificates and enforcing policy from many many browser like Chrome and Firefox So a lot of people using HTTPS so after the full episode we have about About a week About four or five days because we cannot we cannot scale out until the last last second We have to do it and we have we have to test so we have only about four or five days to scale the system to cover the load that well if if it double at every episode it means that the at at the next week at least we need four or five hundred gigabyte gigabit percent to capacity to accommodate for the concurrent viewer and This time we do not have to contact anyone because all the major internet service provider in Thailand contact us because they all the peering because in in the internet actually a web of the connection between each other right and at that time, I think we already exhausted all the peering of the internet service all the internet service provider for we Are really panic also for they contact us to to have our servers Put being being put in there in IDC so the traffic is not coming out For example, if you use True the traffic will be limited only inside a true network or something like that if you use the detect For example, you that the traffic will be will be limited inside inside only the local network So, yeah, we get a lot of black space. We get a lot of bandwidth at the time, but We don't have enough the server so instead of so So we took over the server from our friends who just went out of the business they They did the public how kind of things like that business and they kind of like when Things not going well and they went out of the business So we took out of that the server. I think it's about 30 or 50 servers To to be used as the CDN server at the time Sorry So for after after dispatch we we we have a capacity up to about 500 gigabits and the next Episode fifth episode. Yeah, we we see 450 gigabit capacity so So this program make a new new record every episode and every episode at the six episode You see the 600 gigabit per second. Yeah, you see that the graph is not it's not capital So it's it's not reaching the top limit yet But but the number is very very surprising because it like double double double at every week so after the sixth episode we We the things it kind of like the recurring tax we expand it because now we have kind of like almost Unlimited track space in almost all the internet services provider in Thailand. So we expand Every week and until we run out of money or something something like that We buy a lot of server every week and put in every every Every in the ISP as much as we can so after the sixth Episode we we we beginning to to get about seven hundred gigabit per second Capacity to to cover this event Okay, and because we have the time constraint of the presentation I also I would like to fast forward to the last episode because after that the the capacity is about the same The thing is that I think we already cover Maybe all of the possible we were in Thailand already The people who who should watch the TV program already come and watch it It's about the maximum capacity that that we have is about Another time is about eight hundred and eighty. I think take a bit But but the maximum the real maximum load that that really happened is about seven hundred something seven hundred and ten per second, which is about seven seven fifty thousand Concurrent we were at the same time the same program for about I Usually if it's a marketing session, I will say almost 800,000 we were at the same time seeing the same thing which is actually a lot a whole lot of the the people and The thing is that we survived we the our system is not Down for a long time. Yeah, I would admit that it in some episode we Be down for a bit. We can quickly come coming back up So for how did we survive this? tsunami of Viewer, okay. First thing is that we do a lot of monitoring The picture showed that The number of metrics that we are monitoring our server is about about a thousand per per server Currently we have about almost 200 200,000 metrics being monitored all the time. I mean usually it will be every 60 seconds Yeah, we we do a lot of monitoring even even before the event so when when something happened we know very very quickly that what what is the the bottleneck of the the system and we can Get in and quickly fix it and the second thing is that because we use open source most of the open source software Usually can expose some kind of metrics To to to to the monitoring system outside Yeah, and we use and and we we create a lot of custom script to to Converted all those information and put it into our monitoring system our for the record our monitoring system mainly based on fabric fabric and We also monitor the the health of each of the service not just the things like CPU memory network that that All that are also being monitored But we also monitor the health of each of the service on each of the edge server On every server in our system. So if one thing one server down we can quickly Failure to to the other and we can quickly zero in and bring out that server and Replace it because at a time we have to use some of the second hand server for it went up and down a lot and and The next thing is that because we use open source We we do not have to care much about the compliance hardware. We do not have to get the list of the Hardware that that compliance with all the software that that we are running because we we we build it ourselves It can run anything it can run on any anything that is x86 64 and and we at that time we use a All kind of windows Super micro there is Anything that that we can have at a time even even no brand is okay the actually the The only the only thing that that that we really need to care about if the the brand of the net of the network card because the it really Affect a lot of performance at the time I think only Intel card can achieve the very high packet rate In the single server. Yeah, and we use view because we mainly use they've been They've been they've been distro at that time if they've been nighting Yeah, so so we just need to make sure that the hardware compatible with they've been Which is actually most of the hardware already compatible with Linux and any Linux not just a bit. Yeah, and We although we we bought a lot of kind of the servers in Into the same pool we we separated into groups and do not make too many groups So if something happened with within one group, we can quickly Snatch it out and replace it with something so so even if you can you can buy anything just do not buy too many things enough that you can manage and and the last thing is that we We know our platform because we build everything based on the open source and and We even build the things like for example engine egg We use engine egg as the main main main HTTP server to serve the content We build the binary version of the engine egg by our cell because we pass a lot of things into the engine egg And we have our own repositories So so we hack a lot into the things that we think that is very important And we do the tuning before we scale out In in my opinion we if we we frantically scale out it will cause a lot at the time But we try to tune to make the one server to cover most of the as much squeeze all the Performance that you can achieve within the server first Before scaling out so we just only need about 30 to 50 I think the exact number is about 40 so both more to accommodate the loads and the last thing is that we You need to focus on the majority of the of the users first at the time we We first Trying to to maintain that we were at least we were can see the content But not HD that that is good enough for them and The second thing is that they can see the streaming smoothly that is enough But actually in the back end all the analytics Accounting everything is broken at the time of the TV program We don't really care much about that because we know that well The log file and everything is still is still intact We can we can rerun the batch the everything after that so So the thing is that you need to focus on the important thing first and let the other things fail at At the at the crisis time and we can we can we can call it up after the event so that that is that is end of my presentation so If there is some any question you can you can come and ask me I think maybe already done all of the time already Thank you very much


then given there are two other satellites in development, what other design or operational changes could we think about to prevent future issues? So, those are the questions I want you to ponder. While you're pondering that, I'm gonna put up the poll, last poll of the day here. So, I'll let you multitask here. So, you can do the poll and then think about the cyber challenge or you can do the cyber challenge and then come back and think about the poll. But let's take about 10 minutes to go through that and I'll give you 10 minutes to take the poll and think about the challenge. And then we'll get back together and we'll see if some brave soul online here wants to volunteer their answer to the challenge. So, if you have a multi-deck slide presentation in the next 10 minutes, I'll let you share that with the team. So, let's take about 10 minutes and think about the poll questions and the challenge scenario and then we'll bring it back together, review the poll and then we'll step through the how to think about this particular challenge problem. And then we'll wrap it up and we'll be able to call it the day here on time. So, go do one or the other or both and we'll check back here in about 10 minutes. If anybody has any questions about the challenge, just let me know and try to clarify things for you. We'll be looking for a brave volunteer who wants to tell us how to solve the challenge here. And I've also posted an end-of-course survey. We can't use polls for surveys because the data is not persistent in Zoom. So, we're using a separate polling thing. But if you could follow the link to do that poll, we'd appreciate your feedback. That'd be great to help us figure out how to improve the course for next time. So, I'll give you another five minutes or so to think about the polls, our poll questions here and then we'll pull together and talk about the challenge and then wrap it up. All right, well, not much attendance on the polls here, but let's go ahead and close that out and then we'll talk about our challenge. So, which of the following is not, the following change would not increase EV over NO, so data rate. So, making a higher data rate would be the wrong way to go if I'm trying to increase EV over NO. To jam a satellite, you need to be have some noise at the receiver. That would do the trick. Threat service is software has not been static over years. It's been growing. To spoof the satellite, you need to know all of the above things, which is a lot of things you'd have to be aware of, which means you probably need some sort of way to get into the details of people's design requirements. And space software is both a light and agile, that actually no, we're kind of the opposite of light and agile in the space business. All right, well, let's talk about the challenge. Do I have any brave soul online who wants to tackle the questions? Anybody feel confident about talking through the, how do you think about these challenge questions? Only one person at a time. That's a nice thing about virtual presentations, the ability to remain anonymous is much higher. When you're in a classroom, it's kind of hard to hide. I can stand like a hover over you and intimidate you to tackle it. So... It's less intimidating if we talk through it together. There we go, let's do that. So let's kind of talk through the background first. So remember the background information here. So I just want to highlight some things that come out of the background. And as we approach these, as you go forward and start thinking and working in this environment, we think about what kind of information should I be keying in on? So on the first bullet there, we want to key in on the fact that the US government is one of their largest customers. So certainly everybody in the world is sort of fair game, but the US government is sort of a high profile target. So if you're supporting it as government, you're kind of putting a target on your back. So that raises the threat potential up quite a bit, just the fact that they got you, government's one of their big customers. The next bullet has to do with the orbital mechanics, which we covered here within the class. So just the type of orbit I'm in, well, in this particular orbit will limit the number of times a day that somebody could have access to that satellite from a given site. So that really restricts when somebody could have had access to that satellite and line the site. So that is a constraint that kind of narrows the window there in which somebody could have had access. The other thing to clue in on there is that the Wiley is operating, they're leasing ground stations in Norway and Alaska. So these are lease stations. So these are companies that are basically just selling time on their ground site. So these people, so anytime like I'm leasing something, I don't have a lot of control over the software, the procedures, the personnel. These people are just doing a job passing data from one person to another. They're gonna work, get my data right now and 10 minutes from now, they'll get data from somebody else. They don't have any particular loyalties to me, my company, my data, my program or anything. They're just doing a job. And that means I don't have a lot of insight too because I'm basically buying by the minute. I probably can't demand to see all their software, the procedures or security reviews, their, you know, polygraph interviews with people if they do that, I don't have any right to ask that stuff, right? I'm just buying by the art. And then finally, we got two other satellites in development which is maybe a good thing. We ought to think about, okay, well, if there is a hard problem that we've come up with, maybe there's a chance to resolve that. Pete's saying, can you put that in the contract? You can try. But, you know, if I'm universal space network, I mean, I probably have a hundred customers and I got one small customer coming in demanding a bunch of stuff for security, I might just tell them to take a hike, like, sorry, I don't have the time to give you that or I'm gonna charge you a lot for that. So it just depends on how big of a lever you have.  So other thing then to think about from an issue standpoint, so as I said, you know, this is an input, this issue only impacted the health and status, which is not necessarily unusual. So that should not, it's not overtly suspicious. So that shouldn't necessarily raise any alarm bells, but it's a useful thing to be aware of. And so it appears the problem is just in how the protocols are implemented. So this tends to be a software thing where you're gonna be taking all these ones and zeros and organizing them into packets and adding headers and footers and things like that. But it appears to be random, but it only appears to be on the headers. So, but again, depending on how that packet protocol works, that is not necessarily suspicious either. So, but, you know, but these are all things that just file away in the back of your mind before we tackle the question. So we always wanna, you know, make sure we understand the lay of the land before we dive into the details of the question. So first question is, well, how do we know that if it's natural or man-made? So I put up here, what's called a fish phone diagram or root cause analysis or Ishikawa diagram. Some of you may have seen these before, but they're a very useful tool to try to get at root cause. And we do it without prejudice. We just, it's a brainstorming tool and you can think of it as a mind map if you're used to mind maps. But so we say we kind of list, and you've all seen detective movies, right? Where the detectives have the big board and they have the suspects and they have, you know, yarn going between everybody in circles and all that. I mean, that's kind of what this is. This is our suspect board. And so we say, well, who are the usual suspects, right? And equipment, process, people, materials, environment management, those tend to be the usual suspects. And we would lay out within those, okay, from an equipment standpoint, what could cause this problem? From a process standpoint, what could cause this problem? We're not trying to solve the problem. We're trying to say who are the suspects in this problem? And then we can start whittling away. Once we've defined the space, what we might call the trade space of options, then we can start whittling away. We can go interview the suspects, see if they have an alibi, if they have an alibi, then okay, then they're not a suspect anymore. But we'd be thinking about equipment on board. Okay, well, what about that equipment? Hardware, software, are there any processes? Maybe somebody on the ground did something wrong in the configuration and it's being garbled on the ground. Maybe it's fine on the spacecraft, it's getting garbled somewhere on the ground. Maybe there's people, maybe people that had poor training, maybe there's people that are maliciously scrambling this on their own, you don't know. Environment of course would be the big thing to look at, single event upsets, and what we might do there is call up the space weather people and find out if there was any sort of large corona mass ejection somewhere in that time period. We're trying to correlate the time because we can sort of narrow down when it happened because it seemed to be fine one pass and then it wasn't fine the next pass. So that narrows down the window there. If there was any sort of specific thing going on, maybe there was a solar event that could have happened in that timeframe or maybe it was going through this area, the Van Allen belts we call the South Atlantic Anomaly. Any of those things could raise our suspicion level. You're saying wouldn't that affect everything, not just the headers? Well, it's random, right? So all I did is one silver bullet to go into my software and I don't know what it's gonna do. And if it just happened to affect the one place where that gets encoded on the headers then I can't immediately rule that out. I mean, yeah, it's low probability but I wouldn't immediately rule that out, right? So I wanna take a look at it and I wanna do this systematically. What we're trying to do is have an unbiased, let's not jump to conclusion and start launching nuclear weapons at bad guys because they attacked our satellite. We wanna, let's find the smoking gun but let's do it methodically. So this is how I would go about trying to come to that root cause and we may decide, no, the probability of this being natural is just too low. It has to, and the impact is too systematic. It's not random enough. And therefore we might start to suspect people which is malicious actors in that case. If it were environment, which is question two, again, as we've been talking, it's most likely a single event upset could cause that, but not necessarily. It could be a maybe thermal stress led to a problem with a process or something like that. So there are other potential environmental causes as well but single event upsets tends to be one that causes these sort of random events. But if assuming it is malicious, going to question three, where are the opportunities? Well, I mean, if it happened between passes, that's a pretty narrow opportunity. I say, I mean, maybe an hour they could have had opportunity to do something directly in there. And again, that's not out of the question, but that would mean they would have had to actually command our satellite directly, which means they would have had to know all those things we talked about. They'd have to have the frequency, they'd have to have the encryption, they'd have to have the command codes. They'd really have to know our system very well. Not out of the question, but again, fairly low likelihood. So it would seem it's probably more likely that that came in somewhere sooner. And maybe it came in as a Trojan horse to be, with a time tag that says after 30 hours from now or 30 weeks from now, implement this. And so who knows when it was injected? It could have been all the way back to the factory. So we don't know at this point. But those are, so that's the malicious thing. That's the scary thing about code is that you can put code in that can be sitting there, kind of the sleeper agent to get activated, who knows when. So what are the vulnerabilities then? Just, again, if I'm talking about hundreds, a satellite like this probably has hundreds of thousands of lines of code. Yeah, you could be hiding in there somewhere and maybe get overlooked. Maybe you can get overlooked during testing. In the fact that we've got these, we're using the ground stations. To me, that's a red flag and that it's just you've got things outside your direct span of control that can be entry points for bad actors there. And then finally, because we have these guys still in the barn being developed, if it turns out the problem is a natural environment problem that our hardware maybe is not robust enough against single bed upsets, I mean, we could consider swapping out processors. I mean, that's a pretty major design change, especially fairly late in the game. But if it turns out we're particularly vulnerable or maybe there's some thermal issue that caused it, we may have to take a serious look at our design to see if we can mitigate that and but through a design change. If it's code, we may have to do a full scrub. We may have to go back through and look at all the software grounded on board to see if there's anything hiding in there. And maybe we need to go take a look at our personnel, maybe go to do a security check on our people and see if there's anything suspicious going on there and somebody driving to work in a Ferrari and last week they had a junker car and where'd they get all the money all of a sudden kind of thing, that might look suspicious. So those are things we could look at. Pete's asking if it was a code update how would they consider do that for ones on orbit? Well, we actually can change code on orbit pretty well. I mean, again, depending on the extent, you probably you couldn't change your entire architecture maybe so much, but if it turns out that was, this was lurking in one unit or module of code, we could, that would be if not, I would say easy thing to do, but it's a doable thing to rewrite that code or patch around it or something like that. Yeah, we make code changes fairly often. I mean, I would say routinely are making code changes on spacecraft. So that would not be a difficult thing to do depending on the extent. Now again, if it's corrupted badly, that might take a lot of surgery to fix, but we may not have a choice there if this is the problem. Anybody see, did I miss anything there? Any other issues or ideas that maybe we might wanna think about as we tackle this challenge? All right, well, we did pretty well then today. So let's recap what we did today. So we went through, we started with context. So we looked at that emission architecture and the reasons we went to space. We talked about opportunities. So we looked at orbits and operations, got you all up to speed on how to become orbital mechanics. So you're getting ready to get out your wrench and fix an orbit next time. We looked at threats, natural and human threats, especially those natural threats and how insidious they can be. And just the denials of service we get on a day-to-day basis just because space is a hard place to be. And then we finished up looking at those vulnerabilities. So the RF links, spend some time looking at how we make sure that we get viable connections through the RF links. And then talked about the data architecture and overall data systems grounded on board that also become potential threat surfaces for us. So that was what we tried to cover. These were our objectives. So again, we're trying to climb that learning trajectory there from the pad all the way into orbit. Wanted you to have some core space knowledge. So you feel a little more confident talking to people about space issues and some of the limitations and capabilities and threats and vulnerabilities that are out there. Wanted you to understand some of the trade-offs, especially as they apply to cybersecurity domain, things like the access you get from orbital mechanics and all the different things that happen in operations, those nine different things that we lay out of activities and operations. And any one of those can be a potential entree for a threat. The natural environment, I hope you had an appreciation for how hard that environment is just on a good day. And now when we throw bad actors in there, what relatively small changes can have big impact on overall mission operations. And then finally, wanting you to be able to step back from some real world scenarios and apply the knowledge that we gained here in the class and be able to look at those critically to understand what kind of issues might come up and how would we think through these issues? And we're gonna see these issues, I think come up more and more over time. And we have to be able to differentiate the natural occurring problems from the unnaturally occurring ones and then figure out what to do about it. And we've got a, there's a lot of potential holes to plug here in terms of vulnerabilities. And we may be running around playing holes all our life, but we have to be able to ready to identify those holes and think about what kinds of things we can do to address them going forward. So we thank you for your time and attention. Thanks, Terry, thanks to Jason for this. And we'll probably stop recording at this point. And I'll just kind of throw it open to any other questions. I'll hang around as long as anybody needs if they have questions and we will go.


AIR 118 ESE 2019. This is actually my third attempt. My educational background is that I am a Mechanical Engineer. I did my graduation from Vespilani Goa campus in 2016. I also have a minor in English study. I am also doing my MA to law distance by Igloo University and I am also here at NET in Sociology. My optional is Sociology. With Shankar I have done my Sociology Main Studies which has actually helped me translate my knowledge in Sociology to proper and structured answers which could be a very important factor in fetching marks for the examination. I have also done the Main Studies in Shankar which has boosted my confidence especially the essay part. And the interview experience in terms of mock interview with Shankar was also very enlightening. It boosted my overall confidence to base the board without any prejudices and with a very grounded approach as well. To all the future aspirants, I want to say that the journey you have taken, the journey you have undertaken is a very unique one to you. So, own your own journey and always remember that you are not tested on the day of the mains or thrillers or your interview. The journey to Igloo University is tested every day where you have to be consistent, where you have to show a lot of hard work and you have to show a lot of commitment and dedication in completing the task you have undertaken. So, all the best, be very patient, work very hard but also take everything one day at a time and try it. Thank you very much.