A history of solitary sailing asks why people seek out its danger

“Sailing Alone” is packed with ripping yarns and driven characters
Sailing Alone: A History. By Richard King.
Particular Books; 512 pages; £25

From The Economist 

 
Joshua slocum, an indefatigable trader, entrepreneur and sailor, born in 1844 on a farm in Nova Scotia, had a patchy record as a ship’s captain.
Mutinies had a way of breaking out among his crews—he once shot a man dead—and too many of his ships had ended up grounded or worse.
He loathed the look of steamships that by the 1890s had almost entirely replaced sail-powered freighters.
What was there for an old sailor “born in the breezes”, who “had studied the sea as perhaps few men have studied it”, to do?

His solution was to restore a rotting hulk given to him by a friend and to become the first person to circumnavigate the world single-handedly.
The three-year adventure aboard the 37-foot (11-metre) Spray would be funded by dispatches he would send to the Boston Globe.
The book he went on to write, “Sailing Alone Around the World”, has never been out of print.

In an engaging, beautifully written history of single-handed sailing, Slocum’s influence and example are never far from the horizon.
Richard King, the author, is a solo trans-Atlantic sailor himself.
He sets out to investigate what it is that possesses an ever-growing number of people to get into a small boat and sail on their own across the world’s seas.

Mr King examines the experiences and emotions of some 50 lone sailors.
Interest in solitary sailing for its own sake began in the second half of the 19th century, with voyages around the coast of Britain in 1869 (E.E.
Middleton), across the Atlantic in 1876 (Alfred “Centennial” Johnson) and from California nearly to Australia in 1882 (Bernard Gilboy).

The answer to the question of why people go on such dangerous journeys varies widely.
A yearning for personal validation is often the wind at a solo sailor’s back, but a surprising number of voyagers had little knowledge of boats or the sea before planning (or, in some cases, even beginning) their aquatic adventures.
Ann Davison, who became the first woman to circumnavigate the globe in 1952, had been widowed by a sailing accident but was a novice sailor herself.
She chose sailing “because it offered freedom, independence, travel and a home into the bargain”.

The “explorer-hippy-poet of the sea” Bernard Moitessier so identified with the creatures he saw that he felt himself become part of the pelagic world around him.
He believed his boat was a living, breathing being.
He described the “great cape” as having a “soul as smooth as a child’s, as hard as a criminal’s”.
Moitessier was on the brink of securing the fastest time in the Golden Globe race of 1968 when he decided to leave what he increasingly felt was a vulgar competition.
He just carried on sailing—one and a half times around the world.

Those who have undertaken solo, around-the-world sailing share similar observations and emotions.
Seabirds, particularly busy storm-petrels and lazily gliding albatrosses, are friends, as are playful dolphins and doggedly paddling turtles.
Nearly all are frightened of sharks, a sinister presence waiting for the lone sailor to make a mistake.
All suffer sleep deprivation (and some, hallucinations).
They doze an hour or two while trusting in their self-steering systems, conscious of the possibility of that rogue wave coming crashing down on them, or, even worse, being run down by a huge tanker or container ship oblivious to their tiny presence.

The author relates the story of his own solo North Atlantic passage in 2007, done in an elderly 28-foot sloop.
Although not to be compared to the feats of the extraordinary sailors he recounts in this book, his experiences are sufficiently intense for him to empathise deeply with them.
Towards the end of his voyage, a container ship almost smashes into him.
His self-steering vane somehow gybes his little boat away from disaster.

Slocum, the early pioneer of solo sailing, was not so lucky.
Soon after setting sail from Vineyard Haven, Massachusetts, in November 1909, he and Spray disappeared.
He was almost certainly run down by one of his hated steamships.

AI hurricane predictions are storming the world of weather forecasting

Hurricane Lee, which formed in the Atlantic early this month, became a test bed for the idea of using machine learning to predict weather.

Photograph: NOAA/Getty Images

From Wired by Gregory Barber

 

This year’s hurricane season provides a test run for the idea that machine-learning algorithms can improve weather forecasts.
So far, the AI models are making good calls.

Hurricane Lee wasn’t bothering anyone in early September, churning far out at sea somewhere between Africa and North America.
A wall of high pressure stood in its westward path, poised to deflect the storm away from Florida and in a grand arc northeast.
Heading where, exactly? It was 10 days out from the earliest possible landfall—eons in weather forecasting—but meteorologists at the European Centre for Medium-Range Weather Forecasts, or ECMWF, were watching closely.
The tiniest uncertainties could make the difference between a rainy day in Scotland or serious trouble for the US Northeast.

Typically, weather forecasters would rely on models of atmospheric physics to make that call.
This time, they had another tool: a new generation of AI-based weather models developed by chipmaker Nvidia, Chinese tech giant Huawei, and Google’s AI unit DeepMind.
For Lee, the three tech-company models predicted a path that would strike somewhere between Rhode Island and Nova Scotia—forecasts that generally agreed with the official, physics-based outlook.
Land-ho, somewhere.
The devil, of course, was in the details.

Weather forecasters describe the arrival of AI models with language that seems out of place in their forward-looking profession: “Sudden.” “Unexpected.” “It seemed to just come out of nowhere,” says Mark DeMaria, an atmospheric scientist at Colorado State University who recently retired from leading a division of the US National Hurricane Center.
When he started a project this year with the US National Oceanographic and Atmospheric Administration to validate Nvidia’s FourCastNet model against real-time storm data, he was a “skeptic” of the new models, he says.
“I thought there was no chance that it could work.”

DeMaria has since changed his stance.
In the end, Hurricane Lee struck land on the edge of the range of the AI predictions, reaching Nova Scotia on September 16.
Even in an active storm season—just over halfway through, there have been 16 named Atlantic storms—it’s too early to make any final judgments.
But so far the performance of AI models has been comparable to conventional models, sometimes better on tropical storm tracking.
And the AI models do it fast, spitting out predictions on laptops within minutes, while traditional forecasts take hours of supercomputing time.
 

Looking Ahead

Conventional weather models are made up of equations describing the complex dynamics of Earth’s atmosphere.
Feed in real-time observations of factors like temperature, wind, and humidity and you receive back predictions of what will happen next.
Over the decades, they have gotten more accurate as scientists improve their understanding of atmospheric physics and the data they gather grows more voluminous.

Fundamentally, meteorologists are trying to tame the physics of chaos.
In the 1960s, meteorologist and mathematician Edward Lorenz laid the foundations of chaos theory by noticing that small uncertainties in weather data could result in wildly different forecasts—like the proverbial butterfly whose wing flap causes a tornado.
He estimated that the state of the atmosphere can be predicted at most by two weeks ahead.
Anyone who has watched the approach of a distant hurricane or studied the weekly outlook ahead of an outdoor wedding knows that forecasting still falls far short of that theoretical limit.

Some hope that AI can eventually push predictions closer to that limit.
The new weather models don’t have any physics built in.
They work in a way similar to the text-generation technology at the heart of ChatGPT.
In that case, the machine-learning algorithms are not told rules of grammar or syntax, but they become able to mimic them after digesting enough data to learn patterns of usage.
Similarly, the new weather forecasting models learn the patterns from decades of physical atmospheric data collected in an ECMWF data set called ERA5.

This did not look guaranteed to work, says Matthew Chantry, machine-learning coordinator at the ECWMF, who is spending this storm season evaluating their performance.
The algorithms underpinning ChatGPT were trained with trillions of words, largely scraped from the internet, but there’s no sample so comprehensive for Earth’s atmosphere.
Hurricanes in particular make up a tiny fraction of the available training data.
That the predicted storm tracks for Lee and others have been so good means that the algorithms picked up some fundamentals of atmospheric physics.

That process comes with drawbacks.
Because machine-learning algorithms latch onto the most common patterns, they tend to downplay the intensity of outliers like extreme heat waves or tropical storms, Chantry says.
And there are gaps in what these models can predict.
They aren’t designed to estimate rainfall, for example, which unfolds at a finer resolution than the global weather data used to train them.

Shakir Mohamed, a research director at DeepMind, says that rain and extreme events—the weather events people are arguably most interested in—represent the “most challenging cases,” for AI weather models.
There are other methods of predicting precipitation, including a localized radar-based approach developed by DeepMind known as NowCasting, but integrating the two is challenging.
More fine-grained data, expected in the next version of the ECMWF data set used to train forecasting models, may help AI models start predicting rain.
Researchers are also exploring how to tweak the models to be more willing to predict out-of-the-ordinary events.
 

Error Checks

One comparison that AI models win hands down is efficiency.
Meteorologists and disaster management officials increasingly want what are known as probabilistic forecasts of events like hurricanes—a rundown of a range of possible scenarios and how likely they are to occur.
So forecasters produce ensemble models that plot different outcomes.
In the case of tropical systems they’re known as spaghetti models, because they show skeins of multiple possible storm tracks.
But calculating each additional noodle can take hours.

AI models, by contrast, can produce multiple projections in minutes.
“If you have a model that's already trained, our FourCastNet model runs in 40 seconds on a junky old graphics card,” says DeMaria.
“So you could do like a whole gigantic ensemble that would not be feasible with physically based models.”

Unfortunately, true ensemble forecasts lay out two forms of uncertainty: both in the initial weather observations and in the model itself.
AI systems can’t do the latter.
This weakness springs from the “black box” problem common to many machine-learning systems.
When you’re trying to predict the weather, knowing how much to doubt your model is crucial.
Lingxi Xie, a senior AI researcher at Huawei, says adding explanations to AI forecasts is the number one request from meteorologists.
“We cannot provide a satisfying answer,” he says.

Despite those limitations, Xie and others are hopeful AI models can make accurate forecasts more widely available.
But the prospect of putting AI-powered meteorology in the hands of anyone is still a ways off, he says.
It takes good weather observations to make predictions of any kind—from satellites, buoys, planes, sensors—funneled through the likes of NOAA and the ECMWF, which process the data into machine-readable data sets.
AI researchers, startups, and nations with limited data-gathering capacity are hungry to see what they can do with that raw data, but sensitivities abound, including intellectual property and national security.

Those large forecasting centers are expected to continue testing the models before the “experimental” labels are removed.
Meteorologists are inherently conservative, DeMaria says, given the lives and property on the line, and physics-based models aren't about to disappear.
But he thinks that improvements mean it could only be another hurricane season or two before AI is playing some kind of role in official forecasts.
“They certainly see the potential,” he says.

 

Links :

• Wired : Hurricane Hunter Explains How They Track and Predict Hurricanes
• MIT Tech : Here’s what we know about hurricanes and climate change
• WP : How Big Tech AI models nailded forecasts for Hurricale Lee a week in advance 
• Intelligent Living : The Evolution of Weather Models: Progress in Meteorology 
• Silicon Republic : How AI is being used to improve weather forecasts
• Phys :  How AI models are transforming weather forecasting: A showcase of data-driven systems
   

Wind power developments for sailing boats into headwinds

Windmill powered catamaran "Tango" built during 1970s by Jim Bates

(image courtesy of the Bates family)

 

From Maritime Executive by Harry Valentine

Ancient maritime history tells of the development of the lateen sail, which allowed wind-powered vessels to sail at a 30-degree angle into a headwind.
It is the basis of windmill-driven vessels being able to sail directly into a headwind.
During the 1970s, an inventor and mechanical engineer from New Zealand named Jim Bates built a windmill powered catamaran capable of sailing directly into a headwind at eight knots, preceding Canadian physics professor Brad Blackford by a decade.

Inventor and engineer Jim Bates from New Zealand designed and built this wind-turbine powered boat which had the unique ability to sail directly into the wind that powered it.

 

Introduction

New Zealand based mechanical engineer Jim Bates and his daughter Jennifer designed and built his windmill powered catamaran vessel1 named “Tango”.
Many years later, the concept was proven during a sail boat race at Halifax, Canada when physics professor Brad Blackford sailed a windmill powered boat directly into a headwind to win the race.
British sail boat enthusiast Peter Worsley built a scale model windmill powered vessel to prove in a water tank, to illustrate that the forward thrust of a windmill-driven propeller can actually exceed the rearward drag of the windmill.

Worsley has built several small-scale windmill-driven vessels capable of sailing directly into a headwind.
He discovered that using a slower-turning, horizontal-axis wind turbines that delivered high levels of torque at low RPM to be well suited for driving a submerged propeller.
Worsley has developed and tested a small-scale windmill powered on a small inland lake using readily available wind.
His initiative provides a basis for other enthusiasts to build and test their own small-scale versions of wind-powered vessels capable of using wind energy to sail directly into a headwind.

Early Experiences:

Jim Bates provided some insight into performance issues that he encountered when he sailed his windmill-driven catamaran at 8 to 9-knots directly into a headwind blowing between 20 and 30-knots.
During powerful wind conditions that caused waves, Bates found it necessary to restrict speed so as to prevent damage to the vessel hull.
The combination of propeller generating forward thrust below the water surface and the windmill producing rearward aerodynamic drag far above the water surface when sailing directly into powerful headwinds, caused weight to transfer from the bow area to the stern area.

Based on the experiences of Jim Bates, Peter Worsley built and tested many types of wind turbines intended for vessel propulsion.
Unlike power generation wind turbines, he set blade angles at maximum of 45-degrees to reduce wind turbine drag and generate sufficient thrust to overcome that drag.
Using smaller blade angles than 3-bladed power generation turbines has allowed Worsley to reduce turbine rotational speed and resulting blade induced wake and turbulence.
He was able to install additional blades to his turbine, with the option of installing twin rotors on the same drive-shaft.

Windmill driven boat (rotary sail)
This experimental boat was built to demonstrate the possibility and practicality of using the a direct headwind to power a boat. It was found to be very easy and the craft could sail directly into the oncoming wind.

 

Direct Drive:

During mid-1980s, Professor Brad Blackford used direct-drive on the first windmill boat that he built and entered into a trans-harbor sail boat race at Halifax, Canada.
He sailed directly into a prevailing headwind to win the race.
Axial-flow wind turbines would still generate power when set at 45-degrees from water surface, with flexibility to divert up to 20-degrees horizontally from headwind direction.
An angled drive-shaft on Blackford’s boat connected the windmill that was mounted high above the bow to a propeller submerged below the stern, perhaps setting a precedent for larger-scale, future wind-powered catamaran vessels.

Number of Blades:

The original into-the-wind boats built by both Jim Bates and Prof.
Blackford were propelled into the headwind using 3-bladed axial-flow turbines.
Peter Worsley6 has built wind-powered vessels that sail directly into the headwind using 6-bladed rotors.
He set each blade at a maximum angle of 45-degrees to the drive-shaft, resulting in a slower turning turbine that drives a propeller and has sailed a vessel directly into a headwind.
Recent developments in dual-rotor axial-flow wind turbines include designs where upstream and downstream rotate in the same direction and also designs where upstream and downstream rotors spin in opposite directions.

Test results involving a pair of 5-bladed rotors counter-rotating on the same horizontal-axis suggest possible future application in vessel propulsion.
Wind turbine powered vessels are subject to limitations on blade diameter along with its weight and height above the deck.
In such operation, each of the rotors would be installed on a horizontal-axis upstream and downstream sides of a gearbox placed at the top of a tower, with concentric counter-rotating vertical driveshafts inside the tower.
At the bottom end, bevel gears and a differential would recombine power from the vertical shafts to drive a propeller with steering capability.

Vertical-axis Turbines

A vertical-axis turbine offers the advantage of being able to directly drive a propeller, including an axial-flow propeller operating on a vertical axis and using inlet and outlet ducts to redirect water to achieve propulsion.
A Voith-Schneider type cam-type mechanism located below the turbine assembly would continuously reset turbine blade angle during each assembly rotation cycle, to allow forward thrust below water to exceed wind drag above water.
Vertical turbine blades that resemble airfoil-sails would continually change angle and duplicate the sail angles of a sail boat tack-sailing into a headwind.

Installing a narrow-angle deflector upstream of counter-rotating vertical-axis twin turbines of small diameter that inter-mesh like gears, would redirect the headwind to the downstream side of each turbine and reduce drag by shielding the upstream side from air flow.
The air deflector would allow the vessel to sail within 15-degrees of wind direction, assuring that both turbines provide propulsion.
At larger angles from the headwind, a single turbine would remain operational and assisted by either deck-mounted sails or airborne kite-sails.
Both a single turbine with movable blades and twin counter-rotating turbine would require further research and testing.

Large Axial Turbine

Windmill-powered vessels built by Jim Bates and Prof.
Blackford represent small-scale prototypes of future windmill-powered large vessels.
The vessel built by Bates used a 3-bladed turbine of 33-feet in diameter and delivered sufficiently low torque to allow gear systems to transfer power from the wind-mill hub to the propeller.
As the physical size of axial-flow wind turbines increases, rotational speed decreases, with torque increasing dramatically.
In a 30-mile per hour wind, a 120-ft diameter turbine will deliver over 500-Hp at 50-RPM at over 50,000-lb-ft of torque.

When wind speed reached just under 60-miles per hour, power output would rise to over 3,600-Horsepower at 50-RPM and over 185,000-lb-ft of torque.
As an alternative to costly and heavy gear trains, a multi-section large-diameter hollow drive-shaft installed an angle could carry power from a large-diameter wind turbine installed above the bow to a propeller located below the stern, where exit ducts could redirect the flow of water.
There may be scope to install a pair of wind rotors that rotate in the same direction, at a distance apart from each other on the angled driveshaft.

Combined Wind Technologies

Some designs of wind turbines that directly drive propulsion technology have sailed vessels directly into headwinds and able to deviate up to 20-degrees from headwind direction.
As angle between wind and sailing direction approaches 30-degrees, other wind power technologies such as deck-mounted airfoil-sails including telescopic versions, airborne kite-sails and deck-mounted rotating Flettner rotors become more effective than wind turbines as providing for vessel propulsion.
Future wind-powered commercial vessels and passenger cruise vessels would include multiple different wind-power technologies to provide propulsion, including kite-sails for sailing with prevailing trade winds.

Conclusions

Boat builder Jim Bates and physics professor Brad Blackford proved that windmill-driven vessels could sail directly into a headwind.
Sail boat enthusiast Peter Worsley has undertaken research into optimal blade angles for axial-flow wind turbines.
His work with working scale-model wind-powered boats provides a basis for other enthusiasts and researchers to expand on researching and developing wind-powered technology capable of sailing vessels directly into trade winds, including on trans-oceanic voyages.
Worsley’s findings provide a basis to undertake further research into adapting vertical-axis wind turbines to vessel propulsion that includes sailing directly into headwinds, with little directional deviation.

On very large vessels, large-scale vertical-axis wind turbines including typhoon capable versions offer the combined advantages of lower center of gravity than axial-flow turbines and directly driving a propeller, including an axial-flow propeller combined with ducts to redirect water flow.
While axial-flow wind-mills with gear drive would be suitable for smaller catamaran vessels, large-scale axial-flow turbines operate at low RPM with extreme levels of torque that make gear drive problematic.
While a suitable typhoon capable axial-flow propulsion wind turbine is possible, it would need to use an expensive electrical generator and propulsion motor(s).

 

Links :

• Maritime Executive : Headwind Propulsion for Articulated Two-Section Vessels /  New Floating Wind Platform Leverages Offshore O&G Construction Methods 

Antarctic sea ice shrinks to lowest annual maximum level on record, data shows

 

The extend of Antarctica’s sea ice on 24 September 2023.

Photograph: National Snow and Ice Data Centre, University of Colorado Boulder

From BBC by Graham Readfearn

 

Scientists fear global heating may have shifted region into new era of disappearing ice with far-reaching consequences

Antarctica has likely broken a new record for the lowest annual maximum amount of sea ice around the continent, beating the previous low by a million square kilometres.

USCG image

 

The new mark is the latest in a string of records for the continent’s sea ice, as scientists fear global heating could have shifted the region into a new era of disappearing ice with far reaching consequences for the world’s climate and sea levels.

Each September Antarctica’s sea ice reaches its maximum extent. 

The average between 1981 and 2010 was 18.71m sq km.

 

On September 10, Antarctic sea ice likely reached its annual maximum extent of 16.96 million square kilometers (6.55 million square miles). This the lowest sea ice maximum in the 1979 to 2023 sea ice record by a wide margin

But the US National Snow and Ice Data Center (NSIDC) said preliminary analysis suggested the sea ice reached a maximum of 16.96m sq km on 10 September and had fallen away since then.

The 2023 maximum was 1.75m sq km below the long term average and about 1m sq km below the previous record low maximum set in 1986.

Dr Will Hobbs, a sea ice scientist at the University of Tasmania, said that since April the rate of growth in Antarctica’s sea ice had been “very, very slow”.
“This isn’t just a big change from the average, but also from the previous record,” he said. 

“In May it was pretty obvious we were in for something spectacular.”

He said sea ice losses in the Ross Sea region were likely down to winds that had pushed the ice against the continent, bringing warm air.

But weather couldn’t explain why ice was lost around the rest of the continent.

 

Antarctica’s sea ice goes through an annual cycle reaching its lowest extent each February and its highest levels in September.

Antarctica’s sea ice had been relatively stable until a new record summer low was broken in 2016. Since then, further record lows have been set, including this February that broke the record for the lowest summer minimum.

Scientists are still trying to untangle the reasons for the dramatic run of records, with natural variability and global heating likely combining.

Hobbs said in his view the “scientific barrier” had not yet been crossed to allow scientists to say with confidence the records were down to global heating. But he said the loss of sea ice was consistent with climate change projections.

 

NSIDC said the losses of sea ice since 2016 were most likely linked to warming of the upper layer of the ocean.

“There is some concern that this may be the beginning of a long-term trend of decline for Antarctic sea ice, since oceans are warming globally, and warm water mixing in the Southern Ocean polar layer could continue,” the centre said in an update.

Thousands of emperor penguins chicks likely died last year after the break-up of usually stable sea ice at four of their colonies.

Dr Ariaan Purich, a climate scientist specialising in Antarctica and the Southern Ocean at Monash University, said the top 300 metres of the Southern Ocean around the continent had been noticeably warmer since 2016.
“But as to why the sea ice has been so much lower than it has ever been on the record, we still don’t have a good grasp on that yet.”

She said the loss of sea ice had far-reaching consequences for the planet.

Sea ice helps protect the land-based ice from entering the ocean, which could push up sea level by several metres.

Sea ice also reflects the sun’s energy back out to space.

She said with less sea ice, more of the ocean is exposed to the sun’s energy, causing further Southern Ocean warming and further loss of ice.
“Scientists are worried. I’m worried that it looks low sea ice is the future – and it’s here now.”

Links :

• BBC : ‘Something weird is going on’: search for answers as Antarctic sea ice stays at historic lows /  Antarctica may have entered ‘new regime’ of low sea ice as global warming ramps up
• WP : Scietists found the most intense heat wave ever recorded in Antarctica / Antarctica just hit a record low in sea ice - by a lot
• Maritime Executive : Antarctic Winter Sea Ice Extent Hits Record Low
• NASA : Arctic Sea Ice 6th Lowest on Record; Antarctic Sees Record Low Growth 

Who owns the most satellites?

From Visual Capitalist by Bruno Venditti

Nearly 7,000 satellites orbit the Earth, serving vital functions such as communication, navigation, and scientific research.

In 2022 alone, more than 150 launches took place, sending new instruments into space, with many more expected over the next decade.

But who owns these objects?

In this graphic, we utilize data from the Union of Concerned Scientists to highlight the leaders in satellite technology.

 

 

SpaceX’s Dominance in Space

SpaceX, led by Elon Musk, is unquestionably the industry leader, currently operating the largest fleet of satellites in orbit—about 50% of the global total.

The company has already completed 62 missions this year, surpassing any other company or nation, and operates thousands of internet-beaming Starlink spacecraft that provide global internet connectivity.

Starlink customers receive a small satellite dish that self-orients itself to align with Starlink’s low-Earth-orbit satellites.



In second place is a lesser-known company, British OneWeb Satellites. 

The company, headquartered in London, counts the UK government among its investors and provides high-speed internet services to governments, businesses, and communities.

Like many other satellite operators, OneWeb relies on SpaceX to launch its satellites.

Despite Starlink’s dominance in the industry, the company is set to face intense competition in the coming years. Amazon’s Project Kuiper plans to deploy 3,236 satellites by 2029 to compete with SpaceX’s network. The first of the fleet could launch as early as 2024.
 

The Rise of China’s Space Program

After the top private companies, governments also own a significant portion of satellites orbiting the Earth. The U.S. remains the leader in total satellites, when adding those owned by both companies and government agencies together.

American expenditures on space programs reached $62 billion in 2022, five times more than the second one, China.

China, however, has sped up its space program over the last 20 years and currently has the highest number of satellites in orbit belonging directly to government agencies.

Most of these are used for Earth observation, communications, defense, and technology development.

 

Satellite Demand to Rise Over the Decade

Despite the internet being taken for granted in major metropolitan areas and developed countries, one out of every three people worldwide has never used the web.

Furthermore, the increasing demand for data and the emergence of new, more cost-effective satellite technologies are expected to present significant opportunities for private space companies.

In this context, satellite demand is projected to quadruple over the next decade.

 

Links :

• The Atlantic : The Answer to Starlink Is More Starlinks
• WP : Elon Musk’s control over satellite internet demands a reckoning
• ViaSat : Starlink Surpasses 2 Million Subscribers
• Fool : The 1 Thing You Need to Know About China's 25,000-Satellite Space Project
• Cybernews : Starlink lost over 200 satellites in two months – tracker data 
• BBC :  US-China rivalry spurs investment in space tech
• GeoGarage blog : Starlink's marine satellite system opens up new, speedy ...

Satellite radar imagery helps reveal the true scale of hidden fishing

Global Fishing Watch uses machine learning to analyze millions of gigabytes of satellite radar imagery to determine the location of vessels that remain hidden in public monitoring systems.

 © 2022 Global Fishing Watch

From GFW

Synthetic aperture radar brings new layer of transparency to ocean monitoring, illuminating previously unseen vessels

The Issue

Over the last several years, Global Fishing Watch has provided unprecedented insights into fishing activity worldwide.

Our flagship map is built on data from automatic identification systems (AIS)—a satellite tracking technology that large vessels are required to use to broadcast their position—and supplemented by vessel monitoring system data, which a number of progressive governments have shared with us.

But our map is only as good as the data we receive—so while revolutionary, it’s not perfect. Some vessels switch off their AIS signals, others don’t publicly broadcast their location and some simply fail to appear in public monitoring systems at all. There are a number of legitimate reasons why a vessel might not appear in a public monitoring system—or broadcasting their location at all—but all too often these so-called “dark vessels” have been engaged in unscrupulous behavior such as illegal, unreported and unregulated fishing.

And so we needed to find another way to detect these vessels and bring the dark fleet to light.

Our Work

To get a more comprehensive picture of global fishing, we started combining vessel tracking data with other satellite data, including optical imagery and synthetic aperture radar (SAR). 

SAR, a powerful tool for remote sensing technology, works day and night in all types of weather, so vessels can be detected even through thick cloud cover.

This technique for producing fine-resolution images proved essential in our 2020 study, “Illuminating Dark Fishing Fleets in North Korea,” which drew on four different data sources to reveal around 900 vessels of Chinese origin fishing illegally in North Korea.

SAR was also extremely valuable in studying previously unseen fishing activity in the Mediterranean and off the African coast, opening our eyes to activities we had never seen before.

It was clear that our ocean was a lot busier than existing monitoring systems had shown.

In 2022, we made even further progress on this front by adding a new data layer to our map which shows previously undetected dark vessels..

We developed this by analyzing millions of gigabytes of SAR data from the European Space Agency’s Sentinel-1 satellites.

Using some clever machine learning, we were able to identify vessels that weren’t publicly broadcasting their position and gain a better understanding of the true global footprint of fishing activity.

We are continually adding new data to our map, helping build an even more comprehensive picture of what is taking place at sea.

 

These efforts received a major boost when Canadian space-tech company MDA provided us with access to its entire archive of SAR data—a record stretching back 14 years and including nearly a million images.

Thanks to the combination of satellite radar imagery and science based analytics, we have the ability to catalyze research and equip policymakers with insights that can reveal what was once unknown and more effectively fight illegal fishing practices.

“By seeing and characterizing the activity of these expansive dark fleets, we can begin to better understand and quantify not just illegal fishing but a great deal of human activity that is impacting our marine environment. These are exciting times when it comes to open, accessible data that anyone can use for free to understand and advocate for the fragile marine areas they care about most.”
Paul Woods,chief innovation officer, Global Fishing Watch

 

Links :

• GeoGarage blog : Global Fishing Watch: Raising awareness of the impacts of overfishing
• SarNews :  MDA receives Canadian Government contract to assist in illegal fishing detection
• Nature : Hypocrisy is threatening the future of the world’s oceans 

Here's the 3-step Navy Seal trick to turning your pants into a makeshift life preserver

From Business Insider by Sam Fellman

A German sailor who spent three hours lost at sea was rescued after employing a lifesaving trick used by Navy SEALs.
Here's how to turn your pants into a makeshift life preserver in three fairly easy steps.

Falling overboard is one of the most disorienting and terrifying experiences you can have.

A German sailor named Arne Murke had this happen when he was knocked off a sailboat in 9-foot waves and without a life preserver. Fortunately, Murke had the wherewithal to employ a trusted lifesaving trick used by Navy SEALs — which starts by taking off your pants — and was rescued off New Zealand after over three hours in the water.

The method uses your pants to assist with flotation to stay on the surface and conserve your energy. And unlike a dead-man float, in which your face is in the water, this tactic allows you to rest with your face up so rescuers can more easily find you.

Read more: A man survived hours lost at sea by turning his jeans into a floatation device, a trick used by Navy SEALs

Here's how to perform this tried-and-true "drown proofing" technique, which is taught to troops from all the military branches.

• Step 1: Take off your pants. While you tread water or lie on your back, tie a knot in the ends of the pant legs. The US Navy recommends you tie the two pant legs together and tight enough to trap air, as seen in a 2015 video. Oh, remember to zip up the fly.
• Step 2: Inflate. Put the waist opening over your shoulder, then in one motion raise the open waist high over your head to scoop in air and then slam it into the water. Close the waist underneath the water to hold in the air. A US soldier after inflating his pants and putting his head through the legs.Visual Information Specialist Pascal Demeuldre/US Army
• Step 2.5: If your air pocket isn't filled enough, repeat Step 2. Or you can try to fill the pants by going underwater and breathing air into the open waist.
• Step 3: Put your head through the inflated pant legs and hold the waist closed and underwater. Wait for help and stay calm. If and when the pants deflate, just repeat the steps.

These moves are fairly straightforward, but it's hard to get the pants to inflate by swinging them over your head. It may take a few tries. Best to practice this in a pool first.

Links :

• This Insider : A man survived hours lost at sea by turning his jeans into a flotation device, a trick used by US Navy Seals
• Business Insider : Here's the technique Navy SEALs use to swim for miles without getting tired