Experts say you shouldn&#x27;t worry much about being hit by falling space debris, but it can be difficult to predict where and when exactly it could land.

A huge satellite hurtled to Earth and no one knew where it would land. How is that possible?

A defunct European Space Agency 38-foot satellite came crashing down to Earth on Wednesday, more than a decade after the spacecraft finished its mission.

School bus-sized satellite crashes into Earth's atmosphere on Wednesday

An out-of-control satellite has fallen to Earth, nearly three decades after it launched.

Satellite crash – live: Out-of-control ERS-2 falls to Earth | The Independent

The veteran ERS-2 Earth observation spacecraft dives to destruction 2,000km west of California.

Pioneering European ERS-2 satellite burns up over Pacific

In low-earth orbit, objects can collide at around 23,000 miles an hour, enough for even the tiniest debris to crack the windows on the International Space Station.

Nearly 30,000 objects are hurtling through near-Earth orbit. That’s not just a problem for space | CNN

Intuitive Machines&#x27; robotic Odysseus spacecraft snapped a few selfies with Earth in the background shortly after its Feb. 15 launch.

Intuitive Machines moon lander beams home stunning photos of Earth from space | Live Science

A European Space Agency spacecraft is making an uncontrolled nosedive into Earth’s atmosphere – with elements of the 2.3-ton spent satellite likely to survive the plunge into purgatory.

Dead, 5,000-pound satellite is falling to Earth today. Will any debris survive the fiery reentry? | Space

ERS-2 expected to break up into pieces and burn as it re-enters Earth’s atmosphere probably over Pacific Ocean

Uncontrolled European satellite falls to Earth after 30 years in orbit | Satellites | The Guardian

The spacecraft will make a fiery re-entry to the atmosphere before colliding with the Earth&#x27;s surface - with officials insisting the associated risks are &quot;very low&quot;.

Out-of-control satellite hurtling towards Earth to hit atmosphere today | Science & Tech News | Sky News

We definitely want to find all those before they find us, lead program executive for Nasa&#x27;s Planetary Defense Coordination Office says

If Earth was about to end, here's how Nasa would alert its inhabitants | Sci-Tech - Geo.tv

inEarth

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You can’t even stare at the wall in front of you while using the john at a baseball game anymore without some sort of branded contest slapping you in the face. While this has become a common occurrence through digital media channels to gain engagement and leads, you still have to weigh if dropping your phone in a small pool of piss is worth the two free hot dogs and Coke you may or may not win. On the bright side, you just won 38 emails a day about a bunch of products and services that have absolutely nothing to do with your lifestyle. Contests used for marketing and lead-gen have changed over the years, adapting to new social networks and demographic changes, but the key factor to an engaging contest has not. You make it easy, fast, and a prize the consumer actually wants to win, not just what you want to give away. Divona fragrances has a fantastic aspect behind the brand model. Every time you make a purchase of one of their products, they use a portion to create care packages for women going through very difficult times. They’ve partnered with other social-good brands as well to expand this reach and empowerment for women. “My personal experience with domestic abuse was the biggest motivator behind creating DIVONA, a perfume line that not only empowers you but also empowers many women across the globe,” said Mylene Paquin, Co-Founder of DIVONA. I love every part of what they’re doing behind this product because it’s creating and nurturing the “pay it forward” type of mentality the world needs so much more of. On the other hand, this contest is setup like someone force fed the blood of the Kali Ma from Indiana Jones and the Temple of Doom down the entire marketing teams throats. Everyone had to have lost their free will to think for themselves by going all in on a contest with 9872398573 steps to enter. Here’s the map for the seven hour journey to Crappy Contestville You hop on your bike and start peddling down the road on this majestic day. The breeze has just the right amount of crisp cool to it and flips your hair around while you just smile like a fool enjoying the gift of nature. You notice a friendly looking woman to your right waving and motioning for you to take a break and look inside this door real quick. “There’s FREE stuff in here!” she says and motions to you some more. It’s a lazy day and you realize there’s no harm in looking at all the “free stuff” this woman is talking about. Plus, she looks like she has a lot of friends and people probably care about the things she has to say! As soon as you walk through the door, she starts telling you about her start-up idea of making fragrances that smell like you were just at the beach. You bring up the fact that the whole thing sounds like the plot to a Seinfeld episode but she ignores your concerns and immediately asks you to back her idea if you want the free things inside. She exclaims, “It’s only a dollar or more!” Well you have a dollar and if you can get free things, which aren't actually free anymore since you’re paying for them, what’s the harm. “Or more!” she announces again. “I’ll stick with the one.” you reply with a side smile. She grabs your hand and takes you to a table with a pile of forms and a pen. “You need to sign up before you give me the money! That way I can send you great ideas from a whole bunch of other people too!” The thought of never having inbox zero again for the rest of your life makes you a little uneasy but you’re getting some free perfume so totally worth it! The forms are easy enough to fill out and you hand over your dollar. The woman begins laughing to herself and points at more forms. “You need to add all your credit card information first silly! I can’t just take a single paper dollar from you like this is 1986!” At this point you start debating if the time you’re spending filling out forms is really worth some unknown fragrance at the end of the tunnel and it’s starting to ruin your plans for the day. You’ve come this far so one more step shouldn't take much more time. “Now I need you to take a picture of the form you just filled out that has already been processed where the information is readily available to someone and mail it to me.” “Fuck you lady.” The process Just so you know, there was another step that also had bullet points so it was really like four more steps. Also there were mentions of following and commenting on a SEPARATE Instagram post so she knew that you entered. I’m guessing that’s just in case the first Kickstarter form failed, the payment form server got hit by lightning, the screenshot was corrupt, she accidentally dropped both her laptop and phone off the end of a pier, and the Pittsburgh Penguins win the Stanley Cup. Makes sense. The whole contest journey looks like you’re personally trying to solve the JFK assassination while graphing a double helix structure on top of it. It’s not rocket surgery knowing your audience and potential leads time are valuable to them. If you’re not showing how you value it in something as simple as a giveaway, how do you expect the consumer find out? They sure as hell aren't going to spend another hour navigating your media channels like some sort of half ass escape room. Brand advocates come as fast as brand antagonists and that starts with their first exposure to your content. An easy experience with a generous payoff gets lots of mentions and shares but so does trying to solve cancer for a free pencil.

The Worst Onboarding for a Contest in Earth’s History for the Best Possible Cause

Disclaimer: As a frequent user of satellite images in my work (and not an expert in the field), I've often found myself with these kinds of doubts. The more I learned about the subject, the more I realized how little I actually knew. So, in an effort to use this type of data more effectively and help my colleagues with their frequent questions, I'm writing these notes. There are many materials that cover this topic in greater depth. When we start using satellite images in our analyses - and for many, their first contact with this type of data happens at work - it's common for someone with a bit more knowledge about the data to provide some key information to help you get started. One of these pieces of information is: The larger the pixel size, the lower the spatial resolution of the image, and vice versa. This association makes a lot of sense for understanding how "large" and "small" pixels can impact the analysis you want to perform. But if you start reading more about the subject with some questions, you'll find there are quite a few concepts behind it, and knowing a bit more about this could help you in the future. I'll try to make a brief connection between some of these concepts, clarifying why pixels and spatial resolution are not essentially synonymous, and addressing some common doubts I see in my day-to-day work. Key Concepts: Spatial resolution


Pixel size


Ground Sample Distance (GSD)


Ground Resolved Distance (GRD) It all starts with the satellite sensor in orbit and its detectors. Considering a single detector in the sensor, the angle through which it is sensitive to radiation is called IFOV (Instantaneous Field of View). The larger the IFOV, the larger the area, and the lower the ability to distinguish different objects, and vice versa. In the figure above, IFOV1 allows images with greater detail of objects compared to IFOV2, in their corresponding: GRC1 and GRC2 [Ground Resolved Cell] (associations of the satellite's flight height, H, with IFOV). The size of these cells is called Ground Resolved Distance, or GRD. The concept of GRD is also related to the design and size of the detectors. Looking at the figure, we can already understand that IFOV1, and consequently GRC1 and GRD1, have less spectral "mixing": perhaps a person and a bit of ground. In IFOV2, everything within its GRD is "mixed" and lacks the ability to distinguish different targets: person, ground, object. Here, the relationship between these concepts and spatial resolution becomes clearer. Ground Sample Distance, or GSD, is a concept related to the distance between cell centers on the surface, in summary. What's not yet clear is the association, not always correct, with spatial resolution. This occurs because in practice, GSD and GRD are not always exactly the same - there's some overlap between the IFOV of neighboring detectors (in an ideal scenario, this overlap would be minimal to non-existent, and characteristics like sensor quality and IFOV size influence this). What follows from this is that GRD is generally a better proxy for spatial resolution than GSD. During the radiometric signal acquisition, there's processing before the product is delivered. In this processing, in geometric terms, reprojection and resampling are common, so that products are organized into constant grids, independent of acquisition variations, such as angle. From the organization of data in these grids, we get the pixel size And that's why the pixel size will be, at most, equal to GSD. (And that's also why we often hear that a pixel corresponds to the distance between two consecutive centroids). IF, in an ideal scenario (where sensor quality is crucial), GSD is very similar to GRD (minimal overlap), then we could understand all these concepts as good proxies for spatial resolution. Why "could"? Because the acquired signal can go through a resampling process resulting in, say, a larger pixel size, and consequently lower spatial resolution. The opposite is not recommended. If you simply take a signal acquired with a certain GSD, GRD, and in the resampling process "build" a smaller pixel, in which you supposedly want more detailed information, this will not occur (Pixel size <= GSD!!). The signal was already acquired under certain conditions, and you can't change that (GRD) to "detail" the image more. In summary, as I've often had to understand in various ways, even to explain to colleagues: If we resample an image to a smaller pixel size, we are NOT improving spatial resolution! Another fact is that as users, we end up learning more about pixel size and spatial resolution of an image. It's not common for us to have access to (or seek out) information like GRD or GSD. There's much more to this, but this is a start to clarify the relationship between these concepts, and why pixel size does not necessarily correspond to the spatial resolution of an image! References: *reference 2 was an important reference for this text, for those who want to delve deeper. [1] Spatial Resolution, Pixel Size, and Scale [2] The most misunderstood words in Earth Observation [3] Remote Sensing INFORMATION SHEET Disclaimer: As a frequent user of satellite images in my work (and not an expert in the field), I've often found myself with these kinds of doubts. The more I learned about the subject, the more I realized how little I actually knew. So, in an effort to use this type of data more effectively and help my colleagues with their frequent questions, I'm writing these notes. There are many materials that cover this topic in greater depth. many materials When we start using satellite images in our analyses - and for many, their first contact with this type of data happens at work - it's common for someone with a bit more knowledge about the data to provide some key information to help you get started. One of these pieces of information is: The larger the pixel size, the lower the spatial resolution of the image, and vice versa. The larger the pixel size, the lower the spatial resolution of the image, and vice versa. The larger the pixel size, the lower the spatial resolution of the image, and vice versa. larger the pixel size, the lower the spatial resolution This association makes a lot of sense for understanding how "large" and "small" pixels can impact the analysis you want to perform. But if you start reading more about the subject with some questions, you'll find there are quite a few concepts behind it, and knowing a bit more about this could help you in the future. I'll try to make a brief connection between some of these concepts, clarifying why pixels and spatial resolution are not essentially synonymous , and addressing some common doubts I see in my day-to-day work. why pixels and spatial resolution are not essentially synonymous Key Concepts: Spatial resolution Pixel size Ground Sample Distance (GSD) Ground Resolved Distance (GRD) Spatial resolution Spatial resolution Pixel size Pixel size Ground Sample Distance (GSD) Ground Sample Distance (GSD) Ground Resolved Distance (GRD) Ground Resolved Distance (GRD) It all starts with the satellite sensor in orbit and its detectors. Considering a single detector in the sensor, the angle through which it is sensitive to radiation is called IFOV (Instantaneous Field of View). The larger the IFOV, the larger the area, and the lower the ability to distinguish different objects, and vice versa. IFOV larger the IFOV, the larger the area, and the lower the ability to distinguish In the figure above, IFOV1 allows images with greater detail of objects compared to IFOV2, in their corresponding: GRC1 and GRC2 [ Ground Resolved Cell ] (associations of the satellite's flight height, H, with IFOV). The size of these cells is called Ground Resolved Distance, or GRD . The concept of GRD is also related to the design and size of the detectors. Ground Resolved Cell GRD Looking at the figure, we can already understand that IFOV1, and consequently GRC1 and GRD1, have less spectral "mixing": perhaps a person and a bit of ground. In IFOV2, everything within its GRD is "mixed" and lacks the ability to distinguish different targets: person, ground, object. Here, the relationship between these concepts and spatial resolution becomes clearer. Ground Sample Distance, or GSD , is a concept related to the distance between cell centers on the surface, in summary. GSD What's not yet clear is the association, not always correct, with spatial resolution. This occurs because in practice, GSD and GRD are not always exactly the same - there's some overlap between the IFOV of neighboring detectors (in an ideal scenario, this overlap would be minimal to non-existent, and characteristics like sensor quality and IFOV size influence this). in practice, GSD and GRD are not always exactly the same overlap What follows from this is that GRD is generally a better proxy for spatial resolution than GSD. What follows from this is that GRD is generally a better proxy for spatial resolution than GSD. GRD is generally a better proxy for spatial resolution During the radiometric signal acquisition, there's processing before the product is delivered. In this processing, in geometric terms, reprojection and resampling are common, so that products are organized into constant grids, independent of acquisition variations, such as angle. From the organization of data in these grids, we get the pixel size From the organization of data in these grids, we get the pixel size From the organization of data in these grids, we get the pixel size organization of data in these grids, pixel size And that's why the pixel size will be, at most, equal to GSD . (And that's also why we often hear that a pixel corresponds to the distance between two consecutive centroids). pixel size will be, at most, equal to GSD IF, in an ideal scenario (where sensor quality is crucial), GSD is very similar to GRD (minimal overlap), then we could understand all these concepts as good proxies for spatial resolution. Why "could"? Because the acquired signal can go through a resampling process resulting in, say, a larger pixel size, and consequently lower spatial resolution. The opposite is not recommended. If you simply take a signal acquired with a certain GSD, GRD, and in the resampling process "build" a smaller pixel, in which you supposedly want more detailed information, this will not occur (Pixel size <= GSD!!). The signal was already acquired under certain conditions, and you can't change that (GRD) to "detail" the image more. not you can't change that (GRD) to "detail" the image more. In summary, as I've often had to understand in various ways, even to explain to colleagues: If we resample an image to a smaller pixel size, we are NOT improving spatial resolution! If we resample an image to a smaller pixel size, we are NOT improving spatial resolution! If we r esample an image to a smaller pixel size, we are NOT improving spatial resolution ! esample an image to a smaller pixel size, we are NOT improving spatial resolution Another fact is that as users, we end up learning more about pixel size and spatial resolution of an image. It's not common for us to have access to (or seek out) information like GRD or GSD. There's much more to this, but this is a start to clarify the relationship between these concepts, and why pixel size does not necessarily correspond to the spatial resolution of an image! pixel size does not necessarily correspond to the spatial resolution References: *reference 2 was an important reference for this text, for those who want to delve deeper. [1] Spatial Resolution, Pixel Size, and Scale Spatial Resolution, Pixel Size, and Scale [2] The most misunderstood words in Earth Observation The most misunderstood words in Earth Observation [3] Remote Sensing INFORMATION SHEET Remote Sensing INFORMATION SHEET

Pixels and Spatial Resolution in Satellite Images: What's the Real Deal?

Here's Why We Need To Build A Lunar Economy - Now!

Theoretical Physics: Gaps in Reality, Existence,  Panpsychism, and Biology

Two Memorable Dates in Seismology: Conclusion and References

Thermodynamic Limits on Oxygenic Photosynthesis Around M-dwarf Stars: Abstract and Introduction

(1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK;
(2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK;
(4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK;
(5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction
2 Methodology
2.1 Local spectral irradiance as a function of stellar temperature
2.2 Thermodynamic model of an oxygenic light-harvesting system
2.3 Lattice model of an oxygenic light-harvesting system
3 Results
3.1 Orbital distances and incident spectral fluxes
3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR
3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR
3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’
4 Discussion
Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 4 Discussion The results of our generalized antenna models suggest that the oxygenic photosynthesis is likely feasible on exo-planets orbiting low mass M-dwarf stars, though there are some restrictions. A light-harvesting system similar to PSII of vascular plants (but with a larger antenna) would be severely entropy-limited even before we factor in local limitations like water scarcity, atmospheric attenuation of light, etc. However, this reflects the fact that plants are evolved to deal with highly variable irradiance [Vialet-Chabrand et al., 2017, Li et al., 2021] rather than constant low light. One of the major risks to plants is actually excessive illumination which can cause oxidative damage to PSII, a metabolically costly phenomenon known as photoinhibition, and one that should be familiar to anyone who has suddenly moved a houseplant into full sun. To mitigate photoinhibition [Aro et al., 1993], plants possess regulatory mechanisms that suppress photon absorption and down-regulate the antenna [Ruban, 2016, Ruban and Wilson, 2021]. Therefore it is unsurprising that such a system does not perform well in severely PAR-depleted light. However, by introducing an energetic gradient to the antenna (the energy funnel) we were able to significantly improve antenna efficiency and electron output. Of the branched and hexagonal antenna topologies the former performs better, able to match the performance of the latter but with fewer LHC subunits and a shallower energetic gradient. Fewer LHC subunits is perhaps the most important factor as it is directly related to the metabolic “cost” of the antenna. Of course, there are countless other modifications that could be made to the antenna. For example, one could increase pigment density which would increase the overall cross-section of each LHC and enhance inter-pigment energy transfer. The pigment concentration in LHCII is already very high, approximately 1 mol dm−3 , and the protein scaffold enforces highly specific inter-pigment distances and relative orientations to avoid dissipative processes such as concentration quenching [Liu et al., 2004]. However, one could imagine an adaptation similar to the chlorosome antenna of green sulphur bacteria [Oostergetel et al., 2010]. This structure is a solid aggregate of ∼ 300, 000 BChls encapsulated in a membrane sheath, effectively the densest pigment packing possible, and it facilitates (anoxygenic) photosynthesis in extremely low illumination. It should be noted however that such as structure is is a significant metabolic investment and one that is not seen in any large multi-cellular organism. Alternatively, one could replace Chl a and b with pigments with larger intrinsic cross-sections and/or absorption widths, which would increase the rate of photon absorption, γi . Since intermolecular energy transfer depends on the oscillator strengths and spectral overlap of the pigments involved, this may also enhance the intrinsic transfer rates, (Ki,j ) −1 . Still, the branched, funnel antenna strategy is a plausible one because it explicitly addresses the fundamental thermodynamic limits inherent in light-harvesting. More importantly, such an antenna actually exists on in Earth in the form of the phycobilisome of oxygenic cyanobacteria [Saer and Blankenship, 2017]. Like our model, the phycobilisome is modular and can adapt to changes in irradiance. Kolodny et al. [2022] recently showed that cyanobacteria in deep water, where light is very blue-shifted, will lenghten their phycobilisome rods by adding more terminal PE subunits and will increase overall antenna efficiency, ϕF , by tuning pigment content (and possibly density). The most important result, however, is the recent work of Battistuzzi et al. [2023] who showed that cyanobacteria could comfortably adapt to the the light typical of M-dwarf stars. Moreover, they used a sophisticated starlight simulator setup which models atmospheric attenuation of light, meaning their cyanobacteria were likely subject to ever scanter illumination than we consider here. Interestingly, they also considered pure “far red” (700−800 nm) illumination and found that this retarded growth rate relative to the M-dwarf light, implying that, as in our model, the organism was adapting its antenna to intercept more photons in the depleted PAR region.It should be noted that they also considered on the red-adapted strains described in the introduction that fared equally well in M-dwarf and far red light. Strictly, both our modelling and the experimental work of Kolodny et al. [2022]. concerns the acclimation of an existing antenna to an unusual lightenvironment rather than the ground-up evolution of an antenna structure specifically optimized to it. Nevertheless, Kolodny’s work demonstrates that oxygenic photosynthesis is, in principle, possible around very low mass stars and our findings indicate that the key consideration is overcoming a fundamental entropic barrier to light-harvesting. This paper is available on arxiv under CC 4.0 license. (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). (1) Samir Chitnavis, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK; (2) Thomas J. Haworth, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (3) Edward Gillen, Astronomy Unit, Queen Mary University of London, Mile End Road, London E1 4NS, UK; (4) Conrad W. Mullineaux, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK; (5) Christopher D. P. Duffy, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End, London E1 4NS, UK & Digital Environment Research Institute, Queen Mary University of London, Empire House, Whitechapel E1 1HH, UK (Email: c.duffy@qmul.ac.uk). Table of Links Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Abstract and Introduction Abstract and Introduction 2 Methodology 2.1 Local spectral irradiance as a function of stellar temperature 2.1 Local spectral irradiance as a function of stellar temperature 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.2 Thermodynamic model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 2.3 Lattice model of an oxygenic light-harvesting system 3 Results 3.1 Orbital distances and incident spectral fluxes 3.1 Orbital distances and incident spectral fluxes 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.2 Thermodynamic antenna model: Increasing antenna size in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.3 Lattice antenna model: Increasing the size of a “flat” antenna in limited PAR 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 3.4 Lattice antenna model: Improving antenna efficiency with an energetic ’funnel’ 4 Discussion 4 Discussion Acknowledgements, Author Contribution Statement, Authors disclosure statement and References Acknowledgements, Author Contribution Statement, Authors disclosure statement and References 4 Discussion The results of our generalized antenna models suggest that the oxygenic photosynthesis is likely feasible on exo-planets orbiting low mass M-dwarf stars, though there are some restrictions. A light-harvesting system similar to PSII of vascular plants (but with a larger antenna) would be severely entropy-limited even before we factor in local limitations like water scarcity, atmospheric attenuation of light, etc. However, this reflects the fact that plants are evolved to deal with highly variable irradiance [Vialet-Chabrand et al., 2017, Li et al., 2021] rather than constant low light. One of the major risks to plants is actually excessive illumination which can cause oxidative damage to PSII, a metabolically costly phenomenon known as photoinhibition, and one that should be familiar to anyone who has suddenly moved a houseplant into full sun. To mitigate photoinhibition [Aro et al., 1993], plants possess regulatory mechanisms that suppress photon absorption and down-regulate the antenna [Ruban, 2016, Ruban and Wilson, 2021]. Therefore it is unsurprising that such a system does not perform well in severely PAR-depleted light. However, by introducing an energetic gradient to the antenna (the energy funnel) we were able to significantly improve antenna efficiency and electron output. Of the branched and hexagonal antenna topologies the former performs better, able to match the performance of the latter but with fewer LHC subunits and a shallower energetic gradient. Fewer LHC subunits is perhaps the most important factor as it is directly related to the metabolic “cost” of the antenna. Of course, there are countless other modifications that could be made to the antenna. For example, one could increase pigment density which would increase the overall cross-section of each LHC and enhance inter-pigment energy transfer. The pigment concentration in LHCII is already very high, approximately 1 mol dm−3 , and the protein scaffold enforces highly specific inter-pigment distances and relative orientations to avoid dissipative processes such as concentration quenching [Liu et al., 2004]. However, one could imagine an adaptation similar to the chlorosome antenna of green sulphur bacteria [Oostergetel et al., 2010]. This structure is a solid aggregate of ∼ 300, 000 BChls encapsulated in a membrane sheath, effectively the densest pigment packing possible, and it facilitates (anoxygenic) photosynthesis in extremely low illumination. It should be noted however that such as structure is is a significant metabolic investment and one that is not seen in any large multi-cellular organism. Alternatively, one could replace Chl a and b with pigments with larger intrinsic cross-sections and/or absorption widths, which would increase the rate of photon absorption, γi . Since intermolecular energy transfer depends on the oscillator strengths and spectral overlap of the pigments involved, this may also enhance the intrinsic transfer rates, (Ki,j ) −1 . Still, the branched, funnel antenna strategy is a plausible one because it explicitly addresses the fundamental thermodynamic limits inherent in light-harvesting. More importantly, such an antenna actually exists on in Earth in the form of the phycobilisome of oxygenic cyanobacteria [Saer and Blankenship, 2017]. Like our model, the phycobilisome is modular and can adapt to changes in irradiance. Kolodny et al. [2022] recently showed that cyanobacteria in deep water, where light is very blue-shifted, will lenghten their phycobilisome rods by adding more terminal PE subunits and will increase overall antenna efficiency, ϕF , by tuning pigment content (and possibly density). The most important result, however, is the recent work of Battistuzzi et al. [2023] who showed that cyanobacteria could comfortably adapt to the the light typical of M-dwarf stars. Moreover, they used a sophisticated starlight simulator setup which models atmospheric attenuation of light, meaning their cyanobacteria were likely subject to ever scanter illumination than we consider here. Interestingly, they also considered pure “far red” (700−800 nm) illumination and found that this retarded growth rate relative to the M-dwarf light, implying that, as in our model, the organism was adapting its antenna to intercept more photons in the depleted PAR region.It should be noted that they also considered on the red-adapted strains described in the introduction that fared equally well in M-dwarf and far red light. Strictly, both our modelling and the experimental work of Kolodny et al. [2022]. concerns the acclimation of an existing antenna to an unusual lightenvironment rather than the ground-up evolution of an antenna structure specifically optimized to it. Nevertheless, Kolodny’s work demonstrates that oxygenic photosynthesis is, in principle, possible around very low mass stars and our findings indicate that the key consideration is overcoming a fundamental entropic barrier to light-harvesting. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Thermodynamic Limits Around M-dwarf Stars: Discussion 

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

pyParaOcean, A System for Visual Analysis of Ocean Data: Acknowledgments and References

pyParaOcean, একটি সিস্টেম ফর ভিজ্যুয়াল অ্যানালাইসিস অফ ওশান ডেটা: স্বীকৃতি এবং রেফারেন্স

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Danksagungen und Referenzen

pyParaOcean, un sistema de análisis visual de datos oceánicos: reconocimientos y referencias

pyParaOcean, un système d'analyse visuelle des données océaniques : remerciements et références

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: आभार और संदर्भ

pyParaOcean、海洋データの視覚的分析システム: 謝辞と参考文献

pyParaOcean, 해양 데이터의 시각적 분석 시스템: 감사의 글 및 참고 자료

pyParaOcean, um sistema para análise visual de dados oceânicos: agradecimentos e referências

pyParaOcean, Система визуального анализа океанических данных: благодарности и ссылки

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Teşekkür ve Referanslar

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Lời cảm ơn và tài liệu tham khảo

pyParaOcean，海洋数据可视化分析系统：致谢和参考文献

Authors:
(1) Yuxin Meng;
(2) Feng Gao;
(3) Eric Rigall;
(4) Ran Dong;
(5) Junyu Dong;
(6) Qian Du. Table of Links Abstract and Intro
Background
Proposed Method
Experimental Results and Analysis
Conclusions and Future Work
References Abstract Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the everincreasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines. Index Terms—Sea surface temperature, physical knowledge, generative adversarial network, numerical model I. INTRODUCTION N UMERICAL models have been a traditional mathematical computation method for prediction of ocean dynamics. According to the statistics from the World Climate Research Program (WCRP), the research community has developed more than 40 ocean numerical models, each of which has its own advantages and characteristics. For instance, the regional ocean model system (ROMS) [1] has a powerful ecological adjoint module, the fast ocean atmosphere model (FOAM) [2] is highly effective in global coupled ocean-atmosphere studies, the finite-volume coastal ocean model (FVCOM) [3] is capable of accurately fitting the coastline boundary and the submarine topography. The hybrid coordinate ocean model (HYCOM) [4] can implement three varieties of self-adaptive coordinates. These numerical models are not interchangeable and their use depends on the specific application. It should be noted that the various processes of ocean dynamics described in numerical models are based on simplified equations and parameters due to our limited understanding of the ocean. The movements and changes in the real ocean are so diverse and complex that identifying the sources of a certain phenomenon becomes a real challenge. Therefore, searching for new relations or knowledge from historical data is of critical importance to improve the performance of numerical models in the study of ocean dynamics. In this paper, we refer to the capacity that can improve the numerical model as physical knowledge. We assume that the historical data may possess physical knowledge hitherto undiscovered. Deep learning has the remarkable ability to learn highly complex functions, transforming the original data into a much higher level of abstraction. In [5], Lecun et al. described the fundamental principles and the key benefits of deep learning. Recently, deep learning has been applied to a variety of tasks, such as monitoring marine biodiversity [6], [7], target identification in sonar images [8], [9] and sea ice concentration forecasting [10]. For example, Bermant et al. [6] employed convolutional neural networks (CNNs) to classify spectrograms generated from sperm whale acoustic data. Allken et al. [7] developed a CNN model for fish species classification, leveraging synthetic data for training data augmentation. Lima et al. [8] proposed a deep transfer learning method for automatic ocean front recognition, extracting knowledge from deep CNN models trained on historical data. Xu et al. [9] presented an approach combining deep generation networks and transfer learning for sonar shipwrecks detection. Ren et al. [10] proposed an encoder–decoder framework with fully convolutional networks that can predict sea ice concentration one-week in advance with high accuracy. Through the application of deep learning-based methods to ocean research, significant improvements have been achieved in terms of classification and prediction performance. Due to the incomplete physical knowledge in numerical models and the weak generalization performance of neural networks, there aresome efforts to improve prediction performance by combining the advantages of numerical model and nerual networks. In geographical science, this can be achieved in three different ways [11]: 1) Learning the parameters of the numerical model through neural networks. Neural networks can optimally describe the observed scene from the detailed high-resolution model, but many parameters are difficult to deduce, making their estimation challenging. Brenowitz et al. [12] trained a deep neural network based on unified physics parameterization and explained the influence of radiation and cumulus convection. 2) Replacing the numerical model with a neural network. In this way, the deep neural network architecture can capture the specified physical consistency. Pannekoucke et al. [13] translated physical equations into neural network architectures using a plug-and-play tool. 3) Analyzing the output mismatch between the numerical model and observation data. Neural networks can be used to identify, visualize, and understand the patterns of the model inaccuracies, and dynamically correct the deviation of the model. Patil et al. [14] applied the discrepancy between the results of the numerical model and the observational data to train a neural network to predict the sea surface temperature (SST). Ham et al. [15] trained a convolutional neural network based on transfer learning. They first train their model on the numerical model data, and then using reanalysis data to calibrate the model. However, the third approach has been found to suffer from a long-term bias problem, where the prediction performance deteriorates as the prediction days increase. To address the above issues, in this study, we use the generative adversarial networks (GANs) to transfer the physical knowledge from the historical observed data to the numerical model data, as illustrated in Fig. 1. Different from traditional numerical model, the proposed method can correct the physical part in the numerical model data to improve the prediction performance. To be specific, as illustrated in Fig. 2, we first acquired the physical feature from the observed data by using a prior network model composed of an encoder and GAN. Thereafter, we obtained the physics-enhanced SST by feeding the numerical model data to the pretrained model. Following that, the physics-enhanced SST were adopted to train a spatial-temporal model for predicting SST. Meanwhile, we performed ablation experiments to take full advantage of the new generated data. The main contributions of this paper are threefold: • To the best of our knowledge, we are the first to transfer physical knowledge from the historical observed data to the numerical model data by using GANs for SST prediction. • The difference between the enhanced data based on physical knowledge and the predicted results were exploited to adjust the weight of the model during training. • The experimental results indicate that our proposed method can cover the shortage of physical knowledge in the numerical model and improve the prediction accuracy. The rest of the paper is organized as follows. Section II introduces the literature review related to our method, while our method design is detailed in Section III. Then the experimental results are shown in Section IV. Section V finally concludes this paper. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yuxin Meng; (2) Feng Gao; (3) Eric Rigall; (4) Ran Dong; (5) Junyu Dong; (6) Qian Du. Authors: Authors: (1) Yuxin Meng; (2) Feng Gao; (3) Eric Rigall; (4) Ran Dong; (5) Junyu Dong; (6) Qian Du. Table of Links Abstract and Intro Background Proposed Method Experimental Results and Analysis Conclusions and Future Work References Abstract and Intro Abstract and Intro Background Background Proposed Method Proposed Method Experimental Results and Analysis Experimental Results and Analysis Conclusions and Future Work Conclusions and Future Work References References Abstract Abstract Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the everincreasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines. Index Terms —Sea surface temperature, physical knowledge, generative adversarial network, numerical model Index Terms I. INTRODUCTION N UMERICAL models have been a traditional mathematical computation method for prediction of ocean dynamics. According to the statistics from the World Climate Research Program (WCRP), the research community has developed more than 40 ocean numerical models, each of which has its own advantages and characteristics. For instance, the regional ocean model system (ROMS) [1] has a powerful ecological adjoint module, the fast ocean atmosphere model (FOAM) [2] is highly effective in global coupled ocean-atmosphere studies, the finite-volume coastal ocean model (FVCOM) [3] is capable of accurately fitting the coastline boundary and the submarine topography. The hybrid coordinate ocean model (HYCOM) [4] can implement three varieties of self-adaptive coordinates. These numerical models are not interchangeable and their use depends on the specific application. It should be noted that the various processes of ocean dynamics described in numerical models are based on simplified equations and parameters due to our limited understanding of the ocean. The movements and changes in the real ocean are so diverse and complex that identifying the sources of a certain phenomenon becomes a real challenge. Therefore, searching for new relations or knowledge from historical data is of critical importance to improve the performance of numerical models in the study of ocean dynamics. In this paper, we refer to the capacity that can improve the numerical model as physical knowledge. We assume that the historical data may possess physical knowledge hitherto undiscovered. Deep learning has the remarkable ability to learn highly complex functions, transforming the original data into a much higher level of abstraction. In [5], Lecun et al. described the fundamental principles and the key benefits of deep learning. Recently, deep learning has been applied to a variety of tasks, such as monitoring marine biodiversity [6], [7], target identification in sonar images [8], [9] and sea ice concentration forecasting [10]. For example, Bermant et al. [6] employed convolutional neural networks (CNNs) to classify spectrograms generated from sperm whale acoustic data. Allken et al. [7] developed a CNN model for fish species classification, leveraging synthetic data for training data augmentation. Lima et al. [8] proposed a deep transfer learning method for automatic ocean front recognition, extracting knowledge from deep CNN models trained on historical data. Xu et al. [9] presented an approach combining deep generation networks and transfer learning for sonar shipwrecks detection. Ren et al. [10] proposed an encoder–decoder framework with fully convolutional networks that can predict sea ice concentration one-week in advance with high accuracy. Through the application of deep learning-based methods to ocean research, significant improvements have been achieved in terms of classification and prediction performance. Due to the incomplete physical knowledge in numerical models and the weak generalization performance of neural networks, there aresome efforts to improve prediction performance by combining the advantages of numerical model and nerual networks. In geographical science, this can be achieved in three different ways [11]: 1) Learning the parameters of the numerical model through neural networks. Neural networks can optimally describe the observed scene from the detailed high-resolution model, but many parameters are difficult to deduce, making their estimation challenging. Brenowitz et al. [12] trained a deep neural network based on unified physics parameterization and explained the influence of radiation and cumulus convection. 2) Replacing the numerical model with a neural network. In this way, the deep neural network architecture can capture the specified physical consistency. Pannekoucke et al. [13] translated physical equations into neural network architectures using a plug-and-play tool. 3) Analyzing the output mismatch between the numerical model and observation data. Neural networks can be used to identify, visualize, and understand the patterns of the model inaccuracies, and dynamically correct the deviation of the model. Patil et al. [14] applied the discrepancy between the results of the numerical model and the observational data to train a neural network to predict the sea surface temperature (SST). Ham et al. [15] trained a convolutional neural network based on transfer learning. They first train their model on the numerical model data, and then using reanalysis data to calibrate the model. However, the third approach has been found to suffer from a long-term bias problem, where the prediction performance deteriorates as the prediction days increase. To address the above issues, in this study, we use the generative adversarial networks (GANs) to transfer the physical knowledge from the historical observed data to the numerical model data, as illustrated in Fig. 1. Different from traditional numerical model, the proposed method can correct the physical part in the numerical model data to improve the prediction performance. To be specific, as illustrated in Fig. 2, we first acquired the physical feature from the observed data by using a prior network model composed of an encoder and GAN. Thereafter, we obtained the physics-enhanced SST by feeding the numerical model data to the pretrained model. Following that, the physics-enhanced SST were adopted to train a spatial-temporal model for predicting SST. Meanwhile, we performed ablation experiments to take full advantage of the new generated data. The main contributions of this paper are threefold: • To the best of our knowledge, we are the first to transfer physical knowledge from the historical observed data to the numerical model data by using GANs for SST prediction. • The difference between the enhanced data based on physical knowledge and the predicted results were exploited to adjust the weight of the model during training. • The experimental results indicate that our proposed method can cover the shortage of physical knowledge in the numerical model and improve the prediction accuracy. The rest of the paper is organized as follows. Section II introduces the literature review related to our method, while our method design is detailed in Section III. Then the experimental results are shown in Section IV. Section V finally concludes this paper. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Neural Network for Sea Surface Temperature Prediction: Abstract and Introduction

সমুদ্র পৃষ্ঠের তাপমাত্রা পূর্বাভাসের জন্য গভীর নিউরাল নেটওয়ার্ক: বিমূর্ত এবং ভূমিকা

Tiefes neuronales Netzwerk zur Vorhersage der Meeresoberflächentemperatur: Zusammenfassung und Einführung

Red neuronal profunda para la predicción de la temperatura de la superficie del mar: resumen e introducción

Réseau neuronal profond pour la prévision de la température de la surface de la mer : résumé et introduction

समुद्री सतह के तापमान की भविष्यवाणी के लिए डीप न्यूरल नेटवर्क: सार और परिचय

海面温度予測のためのディープニューラルネットワーク：概要と概要

해수면 온도 예측을 위한 심층 신경망: 개요 및 소개

Rede Neural Profunda para Previsão da Temperatura da Superfície do Mar: Resumo e Introdução

Глубокая нейронная сеть для прогнозирования температуры поверхности моря: краткое содержание и введение

Deniz Yüzeyi Sıcaklığı Tahmini için Derin Sinir Ağı: Özet ve Giriş

Mạng lưới thần kinh sâu để dự đoán nhiệt độ bề mặt biển: Tóm tắt và giới thiệu

用于海面温度预测的深度神经网络：摘要和介绍

A lone boy stood, staring at the star-speckled sky, his mind drawn to the girl just a few feet from where he lay. She was a beauty, that one. He couldn’t believe she was his. Every single thing she did screamed Art. The way she linked her fingers together, the way she smiled. The one girl who knew so much about him, yet… she didn’t shy away from his questionable past. As the emotions threatened to pull him under, he shook his head to clear his thoughts and face what was before him. “A shooting star!” She screamed in excitement as she pointed at that far-off speck in the sky, becoming larger and larger by the second. “That is no shooting star.” He managed to say, fear clogging his throat. He took in the lush grass swaying in the night wind, the beauty of the stars that more or less blotted out the sky. Then, he turned to look at the girl on the ground a few feet away. She was sitting up by then, fear morphing her once beautiful features into something unspeakable. “What is that?” Her voice quaked as she asked, and he closed his eyes momentarily to stop the bile from going up his throat. “Our death.” He said, not mincing words. His throat bobbed with unspoken words and something else underlying. “I’ve always loved you. I just- I just wanted you to know. This once.” He squeezed the words out of his mouth, wrangling them like one would do to a piece of wet cloth. “You? I’ve always seen you as just a friend.” She turned towards him then, his eyes red from tears. But he was grateful for that moment. He got her to look at him finally. And her eyes shone with regret. The screams came first as the she saw earlier was big enough to blot out the stars in the sky. And the heat. God, the heat. He felt as though he was being cooked from the inside out. shooting star The sky boiled like a cauldron; giant drops of fire rained down from the clouds, turning the lush green grass into something out of Hades’s fantasy. Blades of grass crumbled before his eyes. Withering without as much as a touch from him and fizzing out of existence, flames taking its place. The greenery is overtaken by the force that darkened the universe and turned rivers into lakes of fire. She screamed the most. He didn’t. He just watched the shooting star fall and gave a small smile. She was never his to own anyway. * Darren. That was his name. The man-boy with the power to manipulate space at his whims. He was a prodigy even among his peers in Earth 75. They had hoverboards to trapeze through space. That’s right, hoverboards. Fuelled with nothing more than gravel and sand. “Gloating again? That’s not very of you.” He rolled his eyes at the voice that came from just behind him. Glenna. His childhood friend. If you can count a hundred years as childhood. Before they settled—before the metamorphosis, they never grew older than the teenage age. But, after settling for five years and being a pain in the butt for half of those he called his cadre, he realized that he was ready for metamorphosis. But then, Glenna wasn’t. Hence, he had to stop halfway and wait for her to be. ladylike “I’m not a lady, for crying out loud. This is argument number what? Five thousand and seven?” He groaned, using flawless fingers to massage his forehead. Glenna just laughed as she walked towards him. Or, more like, she glided. She had more interest in the hoverboards than he did. The room was no larger than two children’s playroom, yet it had enough space due to his meticulousness. Not a strand of space is out of place, exactly how he wanted it to be. “Number five thousand and eight. You missed one.” She laughed, planting a wet kiss on his skin. He would have gone as red as beetroot if she didn’t deftly move the conversation elsewhere. “So, I need to weave a new dress. You’ve been called a genius for so long that it has become music to my ears. I need a strand of space to weave into a cloak I’m almost done making.” She tendered her request before him, and he took in her presence. She was young, after refusing the metamorphosis for the third time. Yet.. she had eyes of peace. The brown of her irises reminded him of worlds he hadn’t dared to touch for fear of breaking them apart. He’d seen them in his wakings. They were peaceful and oh, so beautiful. The serenity in them was what drew him to them at first. “Okay, I will. But why just a strand? Why not a whole chunk?” He asked as she raised her eyebrows at him, and he almost melted into the chair he sat on. If you can call it sitting. It was a haphazard view, an upturned table, yet he was upright. The world he’d come to know extended universes and eternities. It was a world of madness, but at least it was his. “One sec.” He said, raising his hand in mock surrender. Then, everything stopped. As his concentration cleared the level of the primordial beings, he tore a portal through the world. It was a red wiggly thing struggling to close, but he held it open with sheer willpower alone. When he found out that portals were alive, he decided to lord over them one way or another. They didn’t make it easy for him, though. While it wiggled, he stretched a hand into the portal and drew a strand out of that limitless vastness, loping it through his fingers like a struggling worm. It shimmered in and out of existence, blue like the untold skies, red as fiery dragons and demons of worlds he’d only read about. “Got you.” He smiled as he held a strand. Then, the world went to hell. Before he could remove his hand from the portal, it yawned like a gaping chasm and swallowed him whole. He was screaming, but Glenna was still in timeless mode. She wouldn’t know what happened. That is mercy. That has to be. Then, the world shut itself as the portal consumed the world he’d known all of his existence and winked out of reality. The screams didn’t come. Nothing did. * Carrion. That was his name. His name. His name. He tried to hold on to it, but it slipped out of his grasp like an eel, and he followed after it, crawling after his name with it on the tip of his tongue. What was his name again? Ca.. Ca what? What was his name? The man with scars lining his body like the universe's stars was lost. He tried to think. He tried to remember where he was. Yes. He was fighting Archanoid—the mother of spiders. One could even call her a deity. He dodged an incoming web made of madness and silk, woven with anger and hate. She was large, towering above him like he was a pesky fly. He hated that. He was the warrior who battled nightmares and abominations. He had to win. But then, what was his name? His name.. what was it again? He flew out of the way as an incubator shattered where he stood one second earlier. She wasn’t playing. “Foolish mortal. Do you think you can harm me? It doesn’t matter who you are, World Walker. You can’t escape from me.” She spoke, venom dripping out of those pincer-like things. They made a clanging sound as he swallowed, looking into those red eyes that had intelligence to them, something beyond the years. “One last chance. I will give it to you. Remember, pride and ego cannot win this war. Maybe you and the others can.” And then, before he could ask what she meant.. a scary sharp finger elongated from her finger, and she him out like a fly. flicked There was screaming. And then, there was nothing. * He woke up with a groan. There were several groans across him, and he was shocked to realize that he wasn’t dead. He wasn’t scorched. But he didn’t remember what his name was. He remembered her, recognized the girl who took him as a friend before the world became a sea of fire. Why, then, was he unhurt? The others stood on shaky feet, an otherworldly feel to them. There was something he just couldn’t place. They wore things he’d never heard of, or perhaps the dark skins rippling with fire and the darkness of the deep. Maybe it was the blades of darkness in the place where teeth were meant to be, but there was something.. out of place. One of them wore nightmares on his skin; he could see death. Death rippled with muscles and scars that seemed to blot out the skin. But strangely, he didn’t feel fear. “So, an explanation is in order?” He said to no one in particular. Even the being who looked as though he was 25 and 15 at the same time. As though the world couldn’t decide on his age and let him be both instead. He noticed that this being wasn’t standing on his two feet. He was.. hovering. “We’ll see soon enough.” A boy his age spoke, and he stared at him in wonderment, wondering how alike they were. The way the boy squinted made it glaring that he used glasses to see. Same with him. Even though he didn’t even know their names, they were about ten. Some had hands for feet, and others weren’t even walking on the bare ground. They just.. existed. There was no way to explain their existence, so he didn’t bother. “An explanation.. for this. All of this.” Another boy spoke, and he didn’t balk this time. He just waited. “Here it comes.” The boy who spoke was already running. “Here, what comes? What?” He asked, but everybody else had cleared out of that barren field. The world he was in was empty. There is no sign of green. No sign of brown either. It was as though the world was drained out of colour, and no matter where he looked, despair loomed. He felt them before he saw them. He knew who they were before they spoke. And even without an introduction, he knew everything about them. The Destroyers Of Worlds. They smiled; one hovered just like that man-boy. One had skin with uncountable scars. And then, he saw himself. That cruel smile, that deadly presence. He felt a chill that had nothing to do with the weather. Someone dragged him by the scruff of his hood, and he felt.. everything. There was no other way to describe what happened. Then, he turned to stare at the other him with a cruel smile as that being let out an inferno where he was standing a moment before. It was… hot. But strangely, he wasn’t burning. He wasn’t even singed. He was.. okay. The man-boy dropped him on the ground unceremoniously, and he muttered his thanks, but the others just raised their eyebrows, staring at the beings beyond them. Beings with their faces. Beings with enough power to destroy universes. It was them. Ageless. Timeless. With a snap of a finger, they were gone like they didn’t just lay waste to a large expanse of space. “I think I know what’s going on…” A tenacious finger went up, and the squinted boy was allowed to speak. “That is us. Timeless us. And.. we destroyed our universes. Now.. we have to fight against the us that are world destroyers.” “We don’t have a choice to decline?” The man-boy asked. “We have doomed worlds. This.. is penance.” The boy spoke again, his eyes filled with trepidation and resignation. “So, we were summoned here to kill our future selves? That’s a sick sense of humor.” “This world is sick, and when the Arachnoid told me, I didn’t believe it.” The man with the scars spoke, and his scars seemed alive, melding with his body. “Can you tell us what an Arachnoid is?” Another asked. So, he told them. Everything. “Pride and ego, huh?” A guy spoke, and they finally understood what they had to do. “This is depraved. All of this. Depraved. What the hell?” He spoke, shaking with anger. “Hell doesn’t exist, apparently. Future us made sure the world became it.” A brittle laughter from the man-boy. “This is it, then? We die in a land of no name? Our names were stolen from us. I can’t dredge up a single memory.” The scarred man said, eyes smouldering. I could tell he was pissed off. “This is our penance.” The squinting boy said again, and I was tempted to throw him out of the universe. Such a goody two shoes. “I don’t care. Whatever I have to do, I will do it. And it doesn’t matter who’s on the line. I have to go back home. Also, you can’t fight fire with water. Not in my world, anyway. It takes a monster to kill another. We will take the unorthodox approach. Did they want to seclude us and destroy us? World Walkers, indeed. Bastards more likely. I won’t take that. Neither should any of you. We have the power. Can’t you feel it? We can change things.” The man-boy spoke fervently, his eyes filled with loss, anger, and something else. “That’s right. We won’t let them decide for us. If we have to die, we might as well raise hell before we do. Now, we need a name.” The hooded boy spoke, and the others nodded in approval. “How does sound?” There were hoots of approval, some going as far as clapping. Then, they stared at the sky at once. Drained of colour. But then, the World between Worlds had always been a resting place for World Walkers. The Restorer of Worlds They had to fight. And this time, they had to fight themselves. Their memories returned, and they could see themselves in the past, present, and future—the Destroyer of Worlds. No more. They decided not to make the same mistakes. Not again. This time, they’d restore all they destroyed. As the squinty-eyed kid said, it was penance.

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