Terra Sound, Patterns of Life from Patterns of Light – Part 1

Some Questions I Ask:

• What’s Terra Sound’s vision? (03:19)
• What are the advantages of using fiber optic cable underground to do acoustic sensing? (07:41)
• What can you provide to cities that would be helpful? (13:10)
• How can your system help EMTs get to the scene of an accident faster? (19:21)

What You’ll Learn in this Episode:

• How Terra Sound creates important patterns (04:08)
• How each of their three products works (05:16)
• How they sort the different vibrations picked by the cables (15:25)
• How Terra Sound’s technology could impact the development of autonomous cars (23:22)

Connect with Brian Borkowski: 

• LinkedIn
• Terra Sound

Connect with Craig Borkowski: 

• LinkedIn

Connect with Ed Bernardon:

• LinkedIn
• Future Car: Driving a Lifestyle Revolution
• Motorsports is speeding the way to safer urban mobility
• Siemens Digital Industries Software

Ed Bernardon: So, this is one of the first times we’ve ever had two brothers on The Future Car Podcast. So, in every family, there’s the smart one and the troublemaker. So, I’ll let each one of you go, but who was the smart one and who was the troublemaker?

Brian Borkowski: My brother was definitely better in school than I was. We share troublemaking duties though.

Craig Borkowski: He might have taken the cake with the troublemaking probably a little more and earlier than I did.

Brian Borkowski: This is true. This is 100% true. I don’t want to take away all his street credit. But yeah, it was me. Me and the trouble, he was a better student.

Ed Bernardon: This is a clear division. Does having more of a pure troublemaker and the smart one together in a company, is that a big advantage?

Brian Borkowski: I think it worked well. There’s always a dynamic when you do family stuff. You’ve got to kind of put something straight down the middle, but he can tell me, “Hey, you’re being dumb.” And I’m like, “Oh, I probably was being dumb.”

Ed Bernardon: And he would listen, right, Craig?

Craig Borkowski: Oh, yeah. All the time.

Ed Bernardon: What do undershirts, aviator sunglasses, duct tape, superglue, microwave ovens, and GPS all have in common? These are all inventions that came from the military and were eventually transferred to commercial applications. Certainly, a few of these have touched your life in some ways.

There’s a startup, Terra Sound, that’s adding to this list with their distributed acoustic sensing that combines fiber optics and AI-based technology, originally used in Afghanistan to keep troops and people safe. And now it’s being applied commercially for a multitude of applications.

Terra Sound is actually able to detect patterns of life from patterns of light!

—intro music—

My guests today, brothers Brian and Craig Borkowski, are here to talk about their start-up, Terra Sound Technology. By combining existing fiber optics with AI technology, Terra Sound provides three types of solutions: perimeter security for houses, business and construction sites; protection of in-ground assets like pipelines; and making smart cities a reality by providing key information for intelligent traffic systems. Terra Sound has developed ways to use fiberoptic to detect footsteps, freight trains, and everything in between.

In this Part 1 of our 2-part series, Brian and Craig break down Terra Sound’s fiber optic-based technology with real-world examples, explain the multitude of ways that it can be used. And then, they talk to me about how they transitioned this technology from military applications in Afghanistan to a commercially viable product.

Join me, Ed Bernardon, on this episode of The Future Car Podcast as we discuss this exciting new technology transferred from military to commercial which may just shape our mobility future for the better.

Craig and Brain, welcome to the Future Car Podcast.

Brian Borkowski: Thank you, Ed.

Craig Borkowski: Thanks for having us on, Ed.

Ed Bernardon: Let’s get started. Can you tell us a little bit about Terra Sound? What’s it all about? What’s your vision? Why is it important? What niche is it filling out in the marketplace?

Craig Borkowski: Terra Sound is a software services company. We’re providing DAS or Distributed Acoustic Sensing hardware and software that would transform any single-mode type of fiberoptic cable into a series of acoustic microphones. And from that, we can use intelligent filtering in the background to determine what’s going on in the world around us. And our vision is really to provide reliable sensing solutions to secure, protect, and predict an inconsistent world. So, basically, anything that wants to understand a little bit better what’s going on in the environment around them, we want to be a part of that.

Ed Bernardon: You talk about that inconsistent world, and you’ve also mentioned before — I’ve seen on your website — the pattern of life. You’re looking for patterns and inconsistencies in patterns. At a very high level, that’s fundamentally what you do in all your application areas.

Brian Borkowski: Yeah, and that’s something we really got accustomed to, that really came out of my military time. We started these systems to give us just an alert; “Hey, something’s there. Look here.” But we started finding out over time that the real power of this system was that 24 hours a day, seven days a week, it doesn’t care about the weather, it doesn’t care about holidays, it doesn’t care about line of sight like a camera. You’re collecting data on the pattern of life that’s going on in the world. And the really cool thing is that where are the anomalies? We’ll show you all the anomalies in those patterns across seasons, across operational conditions. And you then tell us, “Hey, I care about that anomaly,” or “I don’t care about that anomaly.” And then our goal is to keep showing you the ones you care about, and detecting those, and showing you them in real-time. So, yeah, it’s a really powerful thing, the pattern of life.

Ed Bernardon: So, you’re looking for patterns — and that’s where the sensors come in — then the AI is analyzing these patterns to see if they’re different than what they should be or different than what you typically see. You have three product areas: perimeter shield, safeguard, and smart cities. Can you tell us a little bit about how this technology works in each of those areas and what you really get from it?

Craig Borkowski: Each one of those different markets that we’ve set up has different classifiers, and they’re detecting different acoustic signatures. Those signatures are going to be different from what people are interested in other markets. So, for example, in perimeter security, very generally speaking, interested in “Hey is somebody walking on or driving on a property that they shouldn’t be? Is somebody climbing or cutting fences or going into secure areas that they don’t want?” So, we’re, generally speaking, looking at “Here’s the baseline,” and then, “Do we see somebody doing one of those actions that they’re interested in?” In the underground marketplace, we’re, generally speaking, protecting utilities. And that can range anywhere from water and sewer to oil and natural gas, which, in those cases, there’s an explosion risk. And usually what they’re looking for is what they call third-party intrusions, which would be somebody coming in with an excavator and starting to dig for usually not nefarious purposes, it’s usually, “Hey, I want to add on a basement,” or “I want to expand something in my yard,” and they start digging, don’t know that there’s something underneath the ground and end up hitting it. So, in those cases, we’re alerting, “Hey, there’s heavy equipment on site, right above your pipeline or your underground utility. And you probably have about 30 to 45 minutes to go intercept that and make sure that they don’t dig.” And then finally, the one that we’re kind of talking about a little more today is smart city. And that’s, generally speaking, using the data to make intelligent and real-time decisions about what’s happening on the roadways. So, we can tell you anything from accident detection to real-time traffic patterns and speed monitoring. We’re even capable of detecting potholes that are forming in the road. So, there are all sorts of interesting data, and that one’s not probably fully formed yet, we’re working with cities to kind of figure out what is it that’s of the most interest to them and how can we help?

Ed Bernardon: So, Distributed Acoustic Sensor. So, when I think of an acoustic sensor, I think of a microphone, it’s picking up sound. But here we’re talking about something different. This is a fiberoptic cable that’s detecting vibration, not sound through the air. I mean, this is all underground. What’s the advantage of using a fiberoptic cable underground to do acoustic sensing?

Brian Borkowski: Really, the way I like to explain it to people is, pretend you had a 100-kilometer fiberoptic cable. And really, what you’re doing is putting a microphone every five meters over that 100 kilometers and reading all that data all at once. That’s not exactly what’s happening, but that’s the way you can think about it. The real advantage is fiberoptics is cheap, and it’s going in everywhere for all these other reasons. So, to be able to sense on that same fiber you’re also sending data with, that you’re also putting in for other purposes, that becomes extremely powerful. Also, it can cover very long distances from one location with one set of power with one piece of hardware, all in real-time. I can look across a 100-kilometer section and tell you everything that’s happening in real-time across a 100-kilometer section.

Craig Borkowski: 60 miles, for those of you in the US.

Brian Borkowski: That’s from my military time, sorry. I should talk miles. So, when you look across the entire thing at once, I didn’t need to put power in the field, I didn’t need all these separate sensors. And it’s super passive solid state, there are no moving parts, there are no things to go wrong, so it’s really long lifetime too, low maintenance.

Ed Bernardon: I like your example here. So, for instance, over one kilometer or 0.6 miles, whichever one you want to use, you could put a microphone every five meters or a vibration sensor. Or you could put, in your case, what you’ve done is you have a fiberoptic cable that’s continuous. You don’t have to buy I don’t know how many microphones that would be in a kilometer if you have one every five meters, but you wouldn’t have to buy hundreds of microphones. You can basically look at the light that’s going through this fiberoptic cable and somehow determine both the location and the amount of vibration, which then correlates to activity that’s on the ground.

Brian Borkowski: That’s perfect. And just to get a little more on the tech part of it, the location is just time delay of arrival. So, we know the speed of light it’s light reflecting, we know how long it took to go out, we know how long it took to come back. So, that’s how we pinpoint things right, and you can get really accurate. We choose five meters because most people don’t need to know anything better than that, that seems to be a good trade space. The rest of the patterns are something called rayleigh backscatter. But you can imagine, in the glass there, there are imperfections in the class. And then when the light normally shines down that fiberoptic, there are reflections that come back. So, we’re using that one specific type of backscatter. And when things stress the cable, then that backscatter changes, and that’s what we’re really looking at; we’re looking at the changes of the backscatter and then correlating those with types of actions.

Ed Bernardon: It sounds like if that cable somehow gets distorted or bent a little bit by forces that act on it when somebody walks across a surface or a vehicle, that makes changes to the light that goes through. So, that’s why you can have this wide range of applications. When it comes to cost, this would seem that it would have a lot of advantages, so they don’t have to buy those hundreds of microphones. I guess that’s the big advantage here.

Brian Borkowski: And I can’t use the microphones for anything else, and no one’s putting in microphones for any other reasons. We’re saying, “Look, you are already putting in fiber. If you’re not putting in fiber, you probably should be if you have a long linear asset, like a road or a pipeline or stuff like that. And when you put that fiber in, we can sense on it, we can give you all this information. But also you can use it to send data, you can use it for SCADA purposes for a pipeline, you can use it to support the rest of the smart city infrastructure that you want to put in.” They’re going to need fiber for the backbone of all that. So, we’re basically saying, “Take your existing infrastructure that you’ve got and use it for more.”

Craig Borkowski: And by the way, if you wanted to put in microphones or cameras every five meters, in that case, you’d have to also run power into the field, put these things on some type of poles or something like that. So, the costs really start going up exponentially rather quickly.

Ed Bernardon: And I’d imagine maintenance is probably a big issue too because they’re outside in the wintertime and big heat.

Brian Borkowski: Most of these cameras have a five-year life under good conditions. We’re talking about 15 years, very conservatively, for this type of technology. And again, I keep saying you can use the fiber for other things or you put the fiber there for other purposes. The state of Ohio right now, ODOT, we’re on their existing fiber. They had fiberoptics that they laid down already. They’re not perfect, they’re not great, but we go on them, and we can sense them and give them all this information onto their existing fiber network. That becomes extremely cost-effective. To give you an example, I know that some of these states get information from cell phones. So, you’ve got millions of cell phones out there, and you’re trying to correlate all this information to figure out what’s going on in your asset. Well, with the fiber that’s already in your asset, you can literally sense your asset and what’s happening from one spot with one thing as opposed to trying to correlate millions of things and make guesses. It kind of shifts that thought process “from the many to one” to “from the one to understand.”

Ed Bernardon: So, in your urban applications, maybe you could give us some examples of the kinds of data that you could provide to a city. And an important thing, I think what you said here, this is with sensors that they probably already have under their streets today. So, what can you provide a city that would be helpful?

Craig Borkowski: Some of the data that we’ve been requested to provide is traffic flow data, particularly a queue. Are there certain times of day when a queue starts? And how far does that queue backup, from where to where, that it starts affecting traffic flow? Preventive maintenance – so, are there potholes forming in certain areas? Alert us when it gets large enough. Accident detection – so, if an accident is severe enough, they would like us to automatically send an alert. And you see that in a couple of ways, you’ll see the accident, and then you’ll see traffic flow data starting to back up. They also want to protect their fiber, so they want to understand about third-party intrusion, somebody doing maintenance or construction on the road is digging into the road, and they don’t want their fiber being cut, so there’s an element of fiber security there as well. We’ve also been asked about the potential for construction tracking. So, kind of real-time where’s the construction going. They’ve got deadlines that they have to keep. They then don’t need to head out to the field all the time, they can kind of see on their live monitors where that construction is taking place and how fast it’s moving. So, those are some examples of what we’ve been asked to provide.

Brian Borkowski: To give you one specific of what we could see, we’ve got one little piece of data that we always like to show people. And it’s, you can actually see it’s a heavier vehicle, we assume it’s a truck — we don’t have that level of fidelity, but looking at it, we think it’s a semi — we can see it hit its brakes and slow down because it jammed on its brakes. We can see it pull off the road and hit the rumble strips. And then we see it stopped on the side of the road for three or four minutes. And then we see it accelerate, we see it go past the rumble strips again and we see it move on and take off. Now, we don’t know why he stopped, we don’t know what happened, but we can see that level of fidelity that truck jammed its brakes, it passed the rumble strips, it stopped, it was off the road for a while, and now it’s gotten back on the road. That’s the level of fidelity you can see.

Ed Bernardon: The sensitivity here. You’ve mentioned a lot of things, both even what you just described. You’ve got a truck, you can tell when a truck is putting its brakes on, when it’s stopped, when it’s starting to move. You talk about pedestrians, people walking. So, the amount of force that a pedestrian puts on the road as they walk on it that’s transmitted to this fiber is so much less than what the truck is doing. But maybe tell us a little bit about the sensitivity because it seems amazing that a fiberoptic cable underground can actually sense a person walking on top of asphalt, that seems to be quite a sensitive sensor.

Brian Borkowski: One thing I’d say is wherever you are right now, whoever’s listening to the podcast, just stomp your foot on the ground right and you feel the vibration. All that vibration, if you’re in the building, believe it or not, that vibration traveled all the way down to the foundation. It had to go somewhere. So, it’s kind of the same thing. Even when you stomp on the ground on asphalt, that energy, if you will, that vibration is going down and it’s eventually hitting that cable. We’re looking at the frequency, the intensity. We’re looking at those things to try to determine what that is. If I have a place where there are hundreds of trucks going by and cars really fast, it is harder to see a pedestrian. We’ve had some success being able to find those out, but there is a little bit of give and take depending on the types of signals that are coming in and what you primarily want to see. If you’re interested in that, we can find a solution to give that to you.

Craig Borkowski: For that, I think most people who are interested in seeing people walking at the moment are in the perimeter security type of fields. For that, we’ve been able to successfully see somebody walking about 150 feet away from the line. So, it’s more sensitive than you think. If you think about just normal walking, this will pick you up at 150 feet out, which is surprising. Whenever I initially thought about it, I wouldn’t have thought I was putting that much force into the ground, but it reliably picks it up there. And as my brother alluded to you, if you’re going to be monitoring 60 miles of highway, it’s not particularly convenient or useful to be trying to see all these people walking around; we tend to limit it more towards the bigger items: traffic flow or excavations or something like that.

Ed Bernardon: I was going to actually ask you about that because it sounds like it’s very sensitive. But now you’re picking up all sorts of things; you’re picking up cars; you’re picking up trucks; you’re picking up people; you’re picking up people near trucks and cars, people that aren’t near; or maybe somebody has left a real loudspeaker that’s playing music on the ground, you’re probably picking it. How do you sort these distortions of the light to figure out what’s what and where it is and all that over these many-kilometer lengths of fiberoptic cable? Without giving any super secrets away, but just generally speaking, how’s that work?

Brian Borkowski: All signal processing is the signal-to-noise ratio. So, the best way I heard that explained, if I whisper to you right now, you’ll pick me up on your microphones. If we’re in a quiet room and we whisper to each other, you’re going to hear me. If we’re in a rock concert and I whisper to you, you’re probably not going to hear me. So, that signal-to-noise ratio is the most important thing. And then it’s our job to go in and talk to you about my system. I don’t hand it to you and say, “It just does everything.” No, I say, “Okay, what are you interested in?” And then we get the best configuration for your signals of interest that you want. So, in some areas, you’re going to tell me, “In this zone right here, I want to see a person if they walk here.” Okay, well, we’re going to make sure that we do everything we can to see the low-level signals, and we’re going to maybe at the expense of the trade space of some of the high-level signals. In other areas, we’re going to say, “No, I want 100% reliability on that excavator coming in here, or that accident.” So, we’re going to give up a little bit in the person-walking space. That’s kind of tuning and configuration.

Ed Bernardon: Yeah, you’re looking for the very loud things, if I could use that descriptor, or the soft things, or what combination of them, and then you figure out how to zero-in your sensors. So, there’s the sensing part, and now you’re gathering all this data, and you’re using signal processing AI to figure out what’s going on, the pattern of life. And then what happens? For instance, you’ve mentioned with EMTs, that this could help in making EMTs more effective, getting them to a scene of an accident more quickly. How do all those pieces come together?

Craig Borkowski: From our perspective, let’s say that you’re on a highway driving somewhere down to Florida and an accident happens in front of you. So, what normally happens is you’re gonna call 911 if it looks severe enough, and the first thing they’re going to ask is, “Well, where are you at?” And you’re going to look around and say, “Well, I’m on my way to Florida. I think I’m in Georgia. I’m not quite sure, I can’t see an exit sign. But yeah, I’m traveling south.” So, it’ll probably take a couple of people to pinpoint exactly where that accident happened. You’re not going to know exactly the severity of that accident. Whereas in the case of our system, as soon as that accident happens, you’re going to get data to say, “Hey, there was a pretty significant crash here at this exact location within five meters.” And that gives you a location immediately. If it’s above a certain threshold, you can be rolling somebody as soon as you get that information. And that’s kind of an idea on a busy highway. Think about this. If you’re on a secluded road, and your car goes off the road and hits a tree; in some areas, it might be hours before somebody even notices that your car is gone or missing. And if you’re incapacitated and not able to call yourself, that’s another good reason why to have these types of systems on particularly rural roads where maybe somebody’s not passing by right away and maybe there is nobody to call 911.

Brian Borkowski: There’s a push right now to bring fiberoptic cable and internet capabilities to rural communities. So, that’s a great tie-in between those two with the money that’s already going to be flowing for programs for that kind of stuff, and public safety.

Ed Bernardon: Yeah, I really like this idea of if there’s an accident, it could be a little fender bender, or it could be a serious accident where you generate these much higher forces that you can detect. And that’s a case where seconds are going to matter a lot. If you can get an EMT on-site one minute sooner, that probably means a lot rather than the accident sits there, somebody on a cell phone finally figures out how to dial 911. The response time, especially for safety is probably a big advantage of something like this.

Brian Borkowski: This is one I’ve never talked about before, but literally yesterday, late at night, I was driving. I took my kid to camp. I’m driving through a rural part of the country, and there’s an Amish buggy on the side of the road in the middle of the night. I just thought to myself, “Oh my God, if we had fiber here, we could pinpoint the Amish buggy.” And maybe put that out to traffic companies like Google and things like that to identify the people who are cruising down in the middle of the night probably speeding, “Hey, there’s a buggy right here.” Instead of the warning sign that says, “There are buggies around” we can get to that digital point that says, “No, there’s a buggy right here and it’s moving this way.” I had that thought last night while I was driving, it’s that level of new things.

Ed Bernardon: Well, that’s a fascinating idea because you’re actually closing the loop. The fact that it can be a continuous sensor and you can recognize that as a buggy or a moose or deer – we all see deer crossing signs – you could then, through Waze or Google Maps then send something back to the driver and say, “Hey, be careful.”

Brian Borkowski: Let’s be honest, in Waze, we’re all looking for the police cars.

Ed Bernardon: Not that it would matter if we didn’t know they were there, it wouldn’t change our behavior in any way.

Brian Borkowski: No, no, no. Did you ever see the one where the guy hits “police car here” on Waze and it says, “Verify if this car is here.”? And you see the cop holding his Waze and he says, “Nope, not there.”

Ed Bernardon: Another advantage of the fiberoptic cables is you can’t click it off. So, in the future, we always talk about autonomous cars, and when are they going to be on the street and commonplace. How do you think technology like this could impact how soon we see autonomous cars on the road?

Craig Borkowski: From my perspective, I think this data set is something that would feed into autonomous cars. To my brother’s point, you can detect anomalies that are on the road and feed that into the car to say, “Hey, there’s a buggy coming up ahead.” Or, “Hey, the traffic pattern in real-time is suddenly slowed down because there’s an accident up ahead, here are six alternative routes that are open right now.” It’s really providing additional information that’s actionable for those autonomous vehicles to be able to act on. And I think that’s what our sensors are about, is how can we provide actionable information that’s going to be useful to whoever’s use using that data in real-time.

Ed Bernardon: I think there’s a common theme in a lot of what you’re talking about here in that you have this continuous feeding of data over continuous physical distance. Adding it to an autonomous car means you can take action on it probably faster than a human might be able to do someday, if not, whenever the AI gets up to that level. This technology was inspired by the military. Can you tell us a little bit about how it was originally used?

Brian Borkowski: So, I was in for 17 years, I was an Army guy. Just to give you some background, I was a Combat Engineer Officer. So, deployed three different times between Iraq and Afghanistan. So, a lot of frontline experience. After my company command, normally officers get sent to what you call a broadening job, and they sent me to a laboratory that was trying to build things for the counter-IED fight. And the IEDs are the roadside bombs. So, they’re killing a lot of guys, they’re cheap, easy to put in, and very effective against us. So, we were trying to help solve those problems at this laboratory and I got sent down there. They were using a fiberoptic system. It was just experimental at the time for them. I took it into Afghanistan, put the original ones in the ground, did all the programmatic, worked with everybody to make them work, and built it into a program that was extremely successful. There, it started off as, “Let’s find where the enemy is putting these bombs in.” Because imagine it’s on a remote road somewhere. You see somebody digging in on the side of the road, well, you pretty much know what it is. Occasionally, there’d be a farmer in his field and stuff like that, but generally, you knew it was a very distinctive signal of what was happening.

Brian Borkowski: In the beginning days, that was the information that was amazing, “Hey, right here, they’re putting in a bomb. Do with that what you will.” Whether we, at least, just know how to clear the bomb and don’t hit it or go try to kill those bad guys. But in the future, it started to be that pattern of life ideas started taking hold. It started to be, “Well, wait, what road did that guy come on to the main road from because there’s only so many roads. Well, there are only two villages down that way. So, the guy came from one of those two villages.” And then it was, how long was he on station putting in the bomb? Was he there five minutes, 30 seconds, right? How long has that operational piece taken for him? That informs other things: How long do we have to find this through other means as well as where the fiber might not be? And where did he leave? Did somebody stay back to be watching it? All these different things. How did he come there? Did he come on foot? Did he come on the car? So, you just started learning all this stuff. And then it became like, “Hey, the fiber goes by a market. Every morning at 5 am, the market starts getting busy. One day, the market doesn’t get busy at 5 am, what’s going on? Well, I don’t know, but it’s an anomaly.” I use the same example as the mosque lets out Friday nights. And normally, just like in America church people congregate and they talk and stuff afterwards. If one day no one’s there, or one day everyone’s there, now, all of a sudden, you have a piece of information, and it’s an anomaly. And now, the next part of that is, is that anomaly important or not to you? Pair them down with the ones that are important to you.

Ed Bernardon: Can you give a specific example of when this technology helped save some lives?

Brian Borkowski: All I can say, it was a classified program, so what I’m talking about now is very generalistic. But what I can tell you is it probably did the most life-saving by stopping people putting in roadside bombs on numerous occasions in numerous places. That’s probably the most detailed explanation I could probably give you.

Ed Bernardon: So, when you first were thinking about using this in the military, I would imagine that there weren’t fiberoptic cables in the ground. Did you just start digging these little channels and lay it down around sensitive areas? Is that how it works?

Brian Borkowski: I can’t go into specifics. But in general terms, we were building infrastructure for the locals there, all kinds of infrastructure like roads, buildings. I mean, think about building all the things here. We had massive construction going on there. So, during that construction, you’re building a road, so it was just put the fiber in too. We were linking two community centers. We were linking two cities up. And just like they’re doing here, the fiber going in, the thought of it wasn’t sensing; the thought of it was we were using it for other things, for communications, for the community, for infrastructure, and we just helped use it for that, too.

Ed Bernardon: I’d like to talk to you a little bit about your background. But before we do that, I mentioned earlier, all sorts of products like aviator sunglasses, t-shirts, super glue, all these things that came from the military. Do you have a favorite besides your technology, of course?

Brian Borkowski: Massive fan of duct tape, both in the military and out of it. This stuff is just handy as anything. That’s my vote.

Craig Borkowski: I’m going with space ice cream.

Ed Bernardon: Oh, the little dots? Is that what you’re talking about?

Brian Borkowski: You’re talking about the dehydrated stuff, right, Craig?

Craig Borkowski: Yeah, the dehydrated or it can also come in dots. But it’s the dehydrated, Neapolitan space ice cream. We used to get it at NASA all the time. That stuff’s great.

Ed Bernardon: Do you have to add water?

Craig Borkowski: No, no, you just eat it.

Ed Bernardon: And close your eyes and imagine you’re having gelato or something. And duct tape is actually good when they say, “If it’s moving and it’s not supposed to move, then you use duct tape.” And the opposite is, “If it’s supposed to move and it’s not, then you use WD-40.” That’s all you need.

Brian Borkowski: So, what’s your favorite, Ed?

Ed Bernardon: Of all those? Oh, wow, that is a great question. I’m not used to people asking me questions. I love duct tape. I like the duct tape idea. Super glue is actually pretty good. But if I had to pick a favorite, I’d have to go with duct tape too. You can’t survive without duct tape. I’ve never used it on a duct.

Brian Borkowski: Guys came to do my duct work a month ago, and they didn’t even use it. They used some other reflective tape stuff but it wasn’t strong like duct tapes. So, I laughed at that.

That’s part 1 with Craig and Brian. Join us on our next episode when we’ll learn how Terra Sound is changing the future of mobility and more.

And as always, for more information about Siemens Digital Industries Software, make sure to visit us at plm.automation.siemens.com. And until next time, I’m Ed Bernardon, and this has been the Future Car Podcast.