Highen

highen

: Yes you should use a premium CDN because 1) premium CDNs are easily 3 times faster than free CDNs — 1+ seconds vs 0.3 seconds, a huge difference for traffic as valuable as ecommerce, 2) it doesn’t even cost you that much. Extensive cost analysis later in this essay. It’s only a few dollars unless you’re running a very large traffic website. Short answer I’m on and the . Twitter @SpeedTestDemon email list is here EDIT: I just read on this random tech website (TechRadar) “no doubt prices can be high”. Ugh, it’s just trash content. It’s written by someone who just wants to fill up word count. It’s not written by someone who’s actually operated a website. Come on and actually do the math. If you’re the typical small-time website, you CDNs cost you close to nothing. If you actually have a lot of traffic, it end up getting to $100/month — hardly more expensive than whatever you’re already paying for hosting. Apologies for the mini-rant. know might EDIT 2: just looked up techradar’s monthly traffic and holy crap they have 52 million visitors a month for writing that kind of trash content? Unbelievable. Makes me feel like ranting. Why do people put up with trash content. I will write a rant about this later. : rest of this essay. Longer answer The main focus of the rest of this essay is going to be discussing the pricing advantage of free CDNs over premium CDNs. Regarding the other dimension of performance — well I’m already going to be discussing that in 20+ articles analyzing 20+ free CDNs. A brief summary thus far: Premium CDNs are obviously way faster than free CDNs — easily 3 times better. It’s not even close. And it makes sense, right? A fast CDN like CloudFront is hugely expensive to maintain high performance across terabytes of data, thousands of servers. This is really hard. Imagine debugging and maintaining thousands of computers. And then making sure the servers have high availability. Really hard. So the only advantage that free CDNs have over premium CDNs is pricing. It’s true: a premium CDN will cost you money. But how much is it really? Let’s explore the cost question for the rest of this essay. To find out how much a CDN will cost you, all we need to find out is how much data your website transfers. This is because the metric that CDNs charge on is the amount of data transferred by the CDN. Cloudfront, for example, is around USD $0.11 per GB (BunnyCDN is a cheaper alternative at $0.04 / GB. Not sure whether it’s actually faster, I will be speed testing Bunny CDN shortly!). Let’s estimate how much data you’re going to be using if you’re in either one of these two buckets: You already have a working website You’re still building out your website How to find how much a CDN would cost you if you already have a working website If you’ve already got a website setup, there’s a simple test you can do. Go to your website. Open up Chrome Developer Console. Click the Network tab. Ctrl+R or Cmd+R in order to reload the website. Then you see a number where it says how much data got transferred over the network. This is how much data the CDN will charge you. Here’s an example screenshot of where to find the data transferred in the Chrome Developer Console tab. Look for the red circle. This example is for facebook by the way. Please note you can safely ignore the “X MB resources” number. That number is usually far larger than the “X MB transferred” number because “X transferred” is highly compressed. For example, a 1 MB photo is usually first compressed (probably gzip, although brotli compression recently released by Google is starting to gain adoption) before your browser downloads a 500 KB photo, which gets decompressed back to 1 MB. Web servers do this in order to speed up how long it takes for the web page to render for you. OK once you’ve got the number, jump down to the section . analyzing the cost of the premium CDNs How to find how much a CDN would cost you if you are planning to build a website If you’re still planning out your website, we can still get a good estimate of how much data you might transfer by looking at a few representative examples. The first best thing you could do is to do the “data transfer” test (written about in the previous section for a working website) on somebody else’s website already out there that is most similar to what you want to create. Another good rule of thumb is to assume your average web page has a size of 2.07 MB. Pingdom has conducted a study on the average web page size. They have all this data since millions of people use Pingdom to test how long their website takes to load. They found the average page size is 2.07 MB. Lastly, you could go through a bunch of case studies below where we analyze how much data was transferred for each example. You can pick out the example that represents your goal most closely and use that number to calculate your estimated CDN cost. Note: case studies are conducted with an adblocker, which means data transferred could be much smaller than without an adblocker. Case Study: How much data does a Shopify store use? Example: (they have awesome olive oil, you should buy from them) https://seloolive.com/ Data transferred: 1.2 MB Case Study: How much data does a minimal wordpress website use? Example: (my own website, haha, which runs on WordPress) https://speedtestdemon.com/ Data transferred: 203 KB (wow that’s small!) Case Study: How much data does a complicated custom ecommerce solution on WooCommerce use? Example: the product page of an ecommerce store doing 7 figures in revenue per year. Link: https://www.solioswatches.com/product/solar-black-black-mesh?caseColor=matte-black&dialSize=36mm&gender=men Data transferred: 4.6 MB Case Study: How much data does a simple sports betting web app use? Example: this is a simple web app that does optimizing. Link: https://www.topflightfantasysports.com/pga/pga-fd-optimizer/ Data transferred: 4.4 MB Case Study: How much data does a big complicated web app like Facebook use? Example: facebook.com Data transferred: 3.8 MB Case Study: How much data does a simple landing page use? Example: https://sizzy.co/ Data transferred: 516 KB Case Study: How much data does a portfolio website use? Example: http://www.baileyritchiephoto.com/ Data transferred: 1.8 MB Case Study: How much data does a magazine website use? Example 1: typical article techcrunch.com Link: https://techcrunch.com/2018/09/11/comma-ais-george-hotz-ousts-george-hotz-as-ceo/ Data transferred: 2.4 MB Example 2: oil price news site Link: https://oilprice.com/Geopolitics/Middle-East/Could-Energy-Ties-Bring-Saudi-Arabias-Conflict-With-Iran-To-An-End.html Data transferred: 650 KB (oh interesting, MUCH smaller than techcrunch) Example 3: bitcoin news site (because who doesn’t love bitcoin??) Link: https://news.bitcoin.com/latin-american-countries-bitcoin-adoption-el-salvador-bitcoin-law/ Data transferred: 3.6 MB So how much does a premium CDN really cost you? Once you’ve got the number of how much data you’re transferring per pageview, now find how many pageviews you’re getting per month (or per day, your preference). The math is just a simple multiply: (number of pageviews * amount of data per pageview) = total data per month (or day). OK now what is the cost of transferring that data? In the case of Cloudfront, you can just use the highend number of $0.11 / GB (they vary their pricing per region). If you rather use other premium CDNs, here’s the full list of the most popular ones (it’s a crowded market, there’s probably many premium CDNs I missed. Drop me a comment on twitter or email if you want me to include your favorite CDN). CloudFront: $0.11 / GB Cloudflare (the premium version, not their free tier which also has bad performance like every other free CDN): cheapest premium plan is $20/month. Fastly: minimum charge $50/month. BunnyCDN: $0.01 / GB for North America & Europe, $0.06 / GB for Africa. KeyCDN: minimum charge $0.04/GB to $0.11 depending on your location. StackPath (MaxCDN): Starting from $15 Akamai: flexible pricing model starting from $0.087 / GB Microsoft Azure CDN: Starting from $0.081 to $0.233 depending on location. CDN77: starting at $199/month ($0.033 / GB). Leaseweb: minimum of $49/month ($0.06 / GB). Have you finished calculating how much that premium CDN costs you? If you’re a small-time website, you should probably go with CloudFront (or KeyCDN or Azure) which allows you to pay a much smaller upfront cost than some of these other premium CDNs like Fastly. $50/month is ridiculous to pay for a CDN if you’ve got barely a few thousand traffic. Let’s say you’re a website doing 10,000 pageviews/month, which is an amount most websites don’t even get to. The math is 10,000 pageviews/month * 2 MB/pageview = 20 GB/month of data. Then 20 GB/month * $0.11 /GB = $2.20 / month. Basically a pittance. Chances are pretty good you’re already paying $5/month for hosting along with many other widgets and plugins. In that example we just quickly analyzed, you’re getting 3 times faster page loading times for 10,000 people for just $2.20/month. It’s an incredible deal. Are there exceptions where a Free CDN is as good as a premium CDN? Actually, yes. I’m currently doing an investigation on free CDNs and I’ve been impressed with some Free CDNs so far. The best one I’ve seen so far is , where Jetpack’s image CDN performed very admirably (as well as CloudFront) while also having a great image compression algorithm. However, very limited to images, when you probably would also like to put HTML, JS, CSS, video, and audio on the CDN. Jetpack CDN (a.k.a. Photon CDN) Another good one is Cloudinary but they have a fairly limited free tier of 25 GB in a month, after which you need to pay $99/month. I’m currently writing an article on Cloudinary right now. So the answer is yes, premium CDNs are worth it In my humble opinion, the premium CDNs don’t cost that much. The only problem is if you’re the kind of person who has trouble understanding how to set up a CDN. Am I wrong? Feel free to argue with me on or in email (hello at speedtestdemon dot com). I’m willing to change my mind if your argument’s good. Twitter

Is it worth it to pay for a premium CDN?

Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 4.1 ALLEVIATE NEGATIVE EFFECTS FROM TRAINING DATA WITH DOMAIN ADAPTER Due to the difficulty in collection, the visual quality of publicly available video training datasets is much lower than their image counterparts. For example, the contents of the video dataset WebVid (Bain et al., 2021) are mostly real-world recordings, whereas the image dataset LAIONAesthetic (Schuhmann et al., 2022) contains higher-quality contents, including artistic paintings and professional photography. Moreover, when treated individually as images, each video frame can contain motion blur, compression artifacts, and watermarks. Therefore, there is a non-negligible quality domain gap between the high-quality image dataset used to train the base T2I and the target video dataset we use for learning the motion priors. We argue that such a gap can limit the quality of the animation generation pipeline when trained directly on the raw video data. To avoid learning this quality discrepancy as part of our motion module and preserve the knowledge of the base T2I, we propose to fit the domain information to a separate network, dubbed as domain adapter. We drop the domain adapter at inference time and show that this practice helps reduce the negative effects caused by the domain gap mentioned above. We implement the domain adapter layers with LoRA (Hu et al., 2021) and insert them into the self-/cross-attention layers in the base T2I, as shown in Fig. 3. Take query (Q) projection as an example. The internal feature z after projection becomes This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References 4.1 ALLEVIATE NEGATIVE EFFECTS FROM TRAINING DATA WITH DOMAIN ADAPTER Due to the difficulty in collection, the visual quality of publicly available video training datasets is much lower than their image counterparts. For example, the contents of the video dataset WebVid (Bain et al., 2021) are mostly real-world recordings, whereas the image dataset LAIONAesthetic (Schuhmann et al., 2022) contains higher-quality contents, including artistic paintings and professional photography. Moreover, when treated individually as images, each video frame can contain motion blur, compression artifacts, and watermarks. Therefore, there is a non-negligible quality domain gap between the high-quality image dataset used to train the base T2I and the target video dataset we use for learning the motion priors. We argue that such a gap can limit the quality of the animation generation pipeline when trained directly on the raw video data. To avoid learning this quality discrepancy as part of our motion module and preserve the knowledge of the base T2I, we propose to fit the domain information to a separate network, dubbed as domain adapter. We drop the domain adapter at inference time and show that this practice helps reduce the negative effects caused by the domain gap mentioned above. We implement the domain adapter layers with LoRA (Hu et al., 2021) and insert them into the self-/cross-attention layers in the base T2I, as shown in Fig. 3. Take query (Q) projection as an example. The internal feature z after projection becomes This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Bridging Domain Gaps with a Domain Adapter for Higher-Quality Animation

Adding Crypto Assets To Treasury Management Can Spur Higher Profits

What is the 500 Internal Server Error Mean and How Do I Fix It?

*Credit of image: Unsplash (Tian-Shan Range, Wensu, Aksu, China) *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging Nowadays, there are plenty of ways to access and use satellite imagery—here's a rundown of some of the main options I use in my personal projects, all of which are free :-) A (very) Quick Overview of Satellite Options First off, let's talk about the satellites and sensors themselves. There are many options today, each with unique capabilities. For agricultural and environmental analysis (what I do on a daily basis), two of the most commonly used satellite programs are Landsat and Sentinel. Each has been part of multiple missions with evolving sensors over the decades, offering increasingly rich data for various applications. Sentinel Sentinel-2, for example, is part of the European Copernicus program and specializes in multispectral imaging. As of now, there are three satellites in the Sentinel-2 series: Sentinel-2A, Sentinel-2B, and Sentinel-2C (the last one launched very recently, in September 2024, here). (Usually my first choice, as it can combine the best spatial, temporal and spectral resolutions). Landsat Landsat, managed by the U.S. Geological Survey (USGS) under the National Land Imaging Program, has been active since the 1970s. Its most recent mission is Landsat-9, which, like its predecessors, provides multispectral imaging for a variety of uses. With so many satellite programs, it's essential to choose based on your specific project needs. Some satellites focus on particular regions of the planet, while others have more global objectives, which leads me to also include other less conventional options here (on the international scene). Brazilian Satellites: CBERS and Amazonia. If you're interested in national programs, Brazil offers two noteworthy options: CBERS (China-Brazil Earth Resources Satellite): This Sino-Brazilian collaboration has launched several missions. The most recent, CBERS-4A, provides orbital-level multispectral imaging alongside earlier CBERS satellites with different sensors. Amazonia-1: Launched in 2021, this is Brazil's first entirely locally-developed satellite. While it offers multispectral imaging like the others, its spatial resolution is relatively lower. Its main goal is to monitor vast forested areas like the Amazon (as the name suggests). Future missions include Amazonia-1B and Amazonia-2. Of all the options above, I would say that the ideal scenario would be to use all available options to have the largest possible amount of imagery for your area. However, this approach raises at least two important questions to consider: What is the purpose of your analysis? (and whether a higher frequency of images is even necessary)
How will you handle processing so that all the different images are compatible for analysis? Where to Get Satellite Imagery Once you know your project's requirements and the sensors that meet them, there are several ways to access the data. Whether you need occasional imagery for reports or frequent data for large-scale analysis, here are the main platforms and acquisition methods: Official Data Portals Landsat: Earth Explorer
Sentinel: Copernicus Data Space Ecosystem
CBERS and Amazonia: INPE Image Catalog Other options include GloVis, for example. 2. Cloud Platforms Google Earth Engine (GEE): Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python.
Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. APIs and Programmatic Access For more technical users, APIs and Python libraries are powerful tools: Sentinel API: Learn more
Landsat API: Learn more
CBERS and Amazonia API: INPE STAC Browser
Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. Third-Party Tools and Software Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery
Earth Data (NASA): A great option for additional datasets, similar to catalogs in GEE
GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. Final Thoughts The satellite and sensor landscape offers an incredible variety of options, but navigating them, especially in the beginning, can feel overwhelming. The good news? Once you identify your needs and the right sensors (lots of work to be done here!), there are plenty of tools and platforms—most of them free—to access the data and put it to work for your project. *Credit of image: Unsplash ( Tian-Shan Range, Wensu, Aksu, China) *Credit of image: Unsplash ( Tian-Shan Range, Wensu, Aksu, China ) Tian-Shan Range, Wensu, Aksu, China *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging *Landsat, Sentinel, CBERS, and Amazonia satellites in the context of orbital-level multispectral imaging Nowadays, there are plenty of ways to access and use satellite imagery—here's a rundown of some of the main options I use in my personal projects, all of which are free :-) A (very) Quick Overview of Satellite Options A (very) Quick Overview of Satellite Options First off, let's talk about the satellites and sensors themselves. There are many options today, each with unique capabilities. For agricultural and environmental analysis (what I do on a daily basis), two of the most commonly used satellite programs are Landsat and Sentinel. Each has been part of multiple missions with evolving sensors over the decades, offering increasingly rich data for various applications. Sentinel Sentinel-2, for example, is part of the European Copernicus program and specializes in multispectral imaging. As of now, there are three satellites in the Sentinel-2 series: Sentinel-2A, Sentinel-2B, and Sentinel-2C (the last one launched very recently, in September 2024, here ). (Usually my first choice, as it can combine the best spatial, temporal and spectral resolutions). here Landsat Landsat, managed by the U.S. Geological Survey (USGS) under the National Land Imaging Program, has been active since the 1970s. Its most recent mission is Landsat-9, which, like its predecessors, provides multispectral imaging for a variety of uses. With so many satellite programs, it's essential to choose based on your specific project needs. Some satellites focus on particular regions of the planet, while others have more global objectives, which leads me to also include other less conventional options here (on the international scene). Brazilian Satellites: CBERS and Amazonia. If you're interested in national programs, Brazil offers two noteworthy options: Brazilian Satellites: Brazilian Satellites: CBERS (China-Brazil Earth Resources Satellite): This Sino-Brazilian collaboration has launched several missions. The most recent, CBERS-4A, provides orbital-level multispectral imaging alongside earlier CBERS satellites with different sensors. Amazonia-1: Launched in 2021, this is Brazil's first entirely locally-developed satellite. While it offers multispectral imaging like the others, its spatial resolution is relatively lower. Its main goal is to monitor vast forested areas like the Amazon (as the name suggests). Future missions include Amazonia-1B and Amazonia-2. Of all the options above, I would say that the ideal scenario would be to use all available options to have the largest possible amount of imagery for your area. However, this approach raises at least two important questions to consider: ideal use all available options What is the purpose of your analysis? (and whether a higher frequency of images is even necessary) How will you handle processing so that all the different images are compatible for analysis? What is the purpose of your analysis? (and whether a higher frequency of images is even necessary) purpose How will you handle processing so that all the different images are compatible for analysis? processing Where to Get Satellite Imagery Where to Get Satellite Imagery Once you know your project's requirements and the sensors that meet them, there are several ways to access the data. Whether you need occasional imagery for reports or frequent data for large-scale analysis, here are the main platforms and acquisition methods: Official Data Portals Official Data Portals Official Data Portals Landsat: Earth Explorer Sentinel: Copernicus Data Space Ecosystem CBERS and Amazonia: INPE Image Catalog Landsat: Earth Explorer Earth Explorer Sentinel: Copernicus Data Space Ecosystem Copernicus Data Space Ecosystem CBERS and Amazonia: INPE Image Catalog INPE Image Catalog Other options include GloVis , for example. GloVis 2. Cloud Platforms 2. Cloud Platforms Google Earth Engine (GEE): Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python. Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. Google Earth Engine (GEE) : Access a huge catalog of satellite data, including Landsat, Sentinel, CBERS, and many other products. It includes a web-based code editor for quick use and APIs for JavaScript (there is a lot already ready to use in JS, so it's pretty much doable even if you're not familiar with the language—I, personally, am not) and Python. Google Earth Engine (GEE) Amazon Web Services (AWS): Landsat and Sentinel data are available on AWS. Check out the AWS Open Data Registry for details. AWS Open Data Registry APIs and Programmatic Access APIs and Programmatic Access APIs and Programmatic Access For more technical users, APIs and Python libraries are powerful tools: Sentinel API: Learn more Landsat API: Learn more CBERS and Amazonia API: INPE STAC Browser Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. Sentinel API : Learn more Sentinel API Landsat API : Learn more Landsat API CBERS and Amazonia API: INPE STAC Browser INPE STAC Browser Python Libraries: Libraries like pystac-client are great because they support multiple catalogs via the SpatioTemporal Asset Catalog (STAC) standard. With one library, you can access all these providers and much more. pystac-client Third-Party Tools and Software Third-Party Tools and Software Third-Party Tools and Software Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery Earth Data (NASA): A great option for additional datasets, similar to catalogs in GEE GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. Sentinel Hub: A commercial service offering easy access to Sentinel and other satellite imagery Sentinel Hub: Earth Data (NASA) : A great option for additional datasets, similar to catalogs in GEE Earth Data (NASA) GIS Software: Tools like QGIS and ArcGIS often integrate directly with satellite data repositories. In QGIS, you may need a plugin to fetch and process the data. Be sure to check the plugin's maintenance status for continuity. QGIS Final Thoughts The satellite and sensor landscape offers an incredible variety of options, but navigating them, especially in the beginning, can feel overwhelming. The good news? Once you identify your needs and the right sensors (lots of work to be done here!), there are plenty of tools and platforms—most of them free—to access the data and put it to work for your project.

How to Get Started with Free Satellite Imagery: Sentinel, Landsat, CBERS, and More

বিনামূল্যে স্যাটেলাইট ছবি দিয়ে কীভাবে শুরু করবেন: সেন্টিনেল, ল্যান্ডস্যাট, সিবিইআরএস এবং আরও অনেক কিছু

Cómo empezar a utilizar imágenes satelitales gratuitas: Sentinel, Landsat, CBERS y más

Ilmaisten satelliittikuvien käytön aloittaminen: Sentinel, Landsat, CBERS ja paljon muuta

Como comezar coas imaxes de satélite gratuítas: Sentinel, Landsat, CBERS e moito máis

無料の衛星画像を使い始める方法: Sentinel、Landsat、CBERS など

ວິທີການເລີ່ມຕົ້ນດ້ວຍຮູບພາບດາວທຽມຟຣີ: Sentinel, Landsat, CBERS, ແລະອື່ນໆ

Хиймэл дагуулын үнэгүй зургийг хэрхэн ашиглаж эхлэх вэ: Sentinel, Landsat, CBERS болон бусад

Jak zacząć korzystać z bezpłatnych obrazów satelitarnych: Sentinel, Landsat, CBERS i inne

د وړیا سپوږمکۍ انځورونو سره څنګه پیل کول: سینټینیل، لینډات، CBERS، او نور

Как начать работу с бесплатными спутниковыми снимками: Sentinel, Landsat, CBERS и другие

Jinsi ya Kuanza na Picha Zisizolipishwa za Setilaiti: Sentinel, Landsat, CBERS, na Mengineyo

Paano Magsimula sa Libreng Satellite Imagery: Sentinel, Landsat, CBERS, at Higit Pa

Additional Numerical Experiments on K-SIF and SIF: Depth, Noise, and Discrimination Power

تجارب عددية إضافية على K-SIF وSIF: العمق والضوضاء وقوة التمييز

Πρόσθετα αριθμητικά πειράματα σε K-SIF και SIF: Βάθος, θόρυβος και ισχύς διάκρισης

Experimentos numéricos adicionales sobre K-SIF y SIF: profundidad, ruido y poder de discriminación

Muita numeerisia kokeita K-SIF:llä ja SIF:llä: syvyys, kohina ja erotteluteho

További numerikus kísérletek a K-SIF-en és SIF-en: mélység, zaj és megkülönböztető teljesítmény

Ulteriori esperimenti numerici su K-SIF e SIF: profondità, rumore e potere di discriminazione

K-SIF と SIF に関する追加の数値実験: 深度、ノイズ、識別力

დამატებითი რიცხვითი ექსპერიმენტები K-SIF-ზე და SIF-ზე: სიღრმე, ხმაური და დისკრიმინაციის ძალა

Dodatkowe eksperymenty numeryczne dotyczące K-SIF i SIF: głębokość, szum i moc dyskryminacyjna

Experimentos numéricos adicionais em K-SIF e SIF: profundidade, ruído e poder de discriminação

Додаткові чисельні експерименти з K-SIF і SIF: глибина, шум і потужність дискримінації

Iimvavanyo ezongezelelweyo zamanani kwi-K-SIF kunye ne-SIF: Ubunzulu, ingxolo, kunye namandla ocalucalulo

They’re often (mis)used by malicious hackers and other cyber-criminals, so the question about their legality could come to mind easily. Privacy coins are cryptocurrencies designed to enhance user anonymity by hiding transaction details, such as the sender, receiver, and amount. Popular examples include Monero and Zcash, but there are more of them. The concerns about them are obvious: potential use in illegal activities, such as money laundering or tax evasion. To be fair, that could also be perfectly done (and it is done) with traditional cash. Even with electronic fiat money, and with cryptocurrencies that aren’t privacy-focused. If criminals have a can-do attitude, they surely can accomplish their dark goals, no matter the tool involved. Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. That’s maybe why privacy coins aren’t illegal in most countries, per se. However, there are some exceptions and limitations. Regulations on Privacy Coins Privacy coins are subject to varying regulations globally, reflecting the balance between financial privacy and the need for security against illicit activities. While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions, leading to the delisting of prominent privacy coins such as Monero and Zcash from some exchanges. The European Union has also considered a comprehensive ban, highlighted by a draft anti-money laundering bill proposing that financial institutions and crypto service providers be prohibited from dealing with anonymity-enhancing coins. Mainstream exchanges like Kraken, Huobi and BitBay have acted on these regulatory pressures by ceasing support for privacy coins. In the U.S., the government has taken a hard stance against privacy services, exemplified by sanctions imposed on Tornado Cash, a mixing service associated with money laundering activities. The Internal Revenue Service (IRS) has even invested in potential technology to de-anonymize privacy coins, reflecting a broader governmental commitment to transparency in cryptocurrency transactions. Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Current bans apply to businesses, and not to individual users. Things get complicated when they touch centralized crypto exchanges, subjected to strict regulations worldwide. Especially about identifying their customers (KYC) —which, by the way, kills all the purpose of using privacy coins in the first place. Blackbytes in Obyte Blackbytes (GBB) is a privacy-focused currency on Obyte, designed to enhance transaction confidentiality. This token conceals recipient information and payment amounts on the public ledger and only transaction hashes are published, while sensitive data is shared via private messages. This approach allows users to verify the legitimacy of transactions without exposing the full history, thereby providing a higher level of privacy compared to public assets like GBYTE and other cryptocurrencies. Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. This means that these privacy tokens don’t engage with centralized exchanges, which have faced increasing scrutiny and restrictions regarding privacy coins worldwide. By focusing on a P2P trading directly from the Obyte wallet, Blackbytes can avoid the complicated landscape faced by other privacy coins worldwide. In many jurisdictions, while these tokens may face significant regulatory challenges, the operation of tokens like Blackbytes can continue without the same level of scrutiny, as they don’t require engagement with regulated financial institutions. This positioning may provide a path for privacy-focused cryptocurrencies to navigate the current legal landscape while maintaining user anonymity. Featured Vector Image by Freepik They’re often (mis)used by malicious hackers and other cyber-criminals, so the question about their legality could come to mind easily. Privacy coins are cryptocurrencies designed to enhance user anonymity by hiding transaction details, such as the sender, receiver, and amount. Popular examples include Monero and Zcash, but there are more of them. The concerns about them are obvious: potential use in illegal activities, such as money laundering or tax evasion. To be fair, that could also be perfectly done ( and it is done ) with traditional cash. Even with electronic fiat money , and with cryptocurrencies that aren’t privacy-focused. If criminals have a can-do attitude, they surely can accomplish their dark goals, no matter the tool involved. Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. and it is done and it is done electronic fiat money electronic fiat money Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. That’s maybe why privacy coins aren’t illegal in most countries, per se. However, there are some exceptions and limitations. Regulations on Privacy Coins Privacy coins are subject to varying regulations globally, reflecting the balance between financial privacy and the need for security against illicit activities. While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions , leading to the delisting of prominent privacy coins such as Monero and Zcash from some exchanges. varying regulations varying regulations While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions The European Union has also considered a comprehensive ban, highlighted by a draft anti-money laundering bill proposing that financial institutions and crypto service providers be prohibited from dealing with anonymity-enhancing coins. Mainstream exchanges like Kraken, Huobi and BitBay have acted on these regulatory pressures by ceasing support for privacy coins. consider ed consider In the U.S., the government has taken a hard stance against privacy services, exemplified by sanctions imposed on Tornado Cash , a mixing service associated with money laundering activities. The Internal Revenue Service (IRS) has even invested in potential technology to de-anonymize privacy coins, reflecting a broader governmental commitment to transparency in cryptocurrency transactions. Tornado Cash Tornado Cash invested invested Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Current bans apply to businesses, and not to individual users. Things get complicated when they touch centralized crypto exchanges, subjected to strict regulations worldwide. Especially about identifying their customers (KYC) —which, by the way, kills all the purpose of using privacy coins in the first place. Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Blackbytes in Obyte Blackbytes (GBB) is a privacy-focused currency on Obyte, designed to enhance transaction confidentiality. This token conceals recipient information and payment amounts on the public ledger and only transaction hashes are published, while sensitive data is shared via private messages. This approach allows users to verify the legitimacy of transactions without exposing the full history, thereby providing a higher level of privacy compared to public assets like GBYTE and other cryptocurrencies. Blackbytes (GBB) Blackbytes (GBB) Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. This means that these privacy tokens don’t engage with centralized exchanges, which have faced increasing scrutiny and restrictions regarding privacy coins worldwide. Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. By focusing on a P2P trading directly from the Obyte wallet , Blackbytes can avoid the complicated landscape faced by other privacy coins worldwide. In many jurisdictions, while these tokens may face significant regulatory challenges, the operation of tokens like Blackbytes can continue without the same level of scrutiny, as they don’t require engagement with regulated financial institutions. This positioning may provide a path for privacy-focused cryptocurrencies to navigate the current legal landscape while maintaining user anonymity. Obyte wallet Obyte wallet Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

Educational Byte: Are Privacy Coins Like Monero and Zcash Legal?

Byte Educativo: ¿Monero ukat Zcash ukham Privacidad Monedas ukanakax Legal ukhamäpachati?

Vzdělávací Byte: Jsou soukromé mince jako Monero a Zcash legální?

Byte educativo: ¿Son legales las monedas de privacidad como Monero y Zcash?

Koulutustavu: Ovatko Monero- ja Zcashin kaltaiset tietosuojakolikot laillisia?

Byte éducatif : les pièces de confidentialité comme Monero et Zcash sont-elles légales ?

शैक्षिक बाइट: क्या मोनरो और ज़ेकैश जैसे गोपनीयता सिक्के कानूनी हैं?

Educational Byte: Apakah Koin Privasi Seperti Monero dan Zcash Legal?

教育バイト: Monero や Zcash のようなプライバシー コインは合法ですか?

Byte Éducatif: Est-ce que ba mbongo ya vie privée lokola Monero na Zcash ezali légal?

Educational Byte: Ara-dalàna ve ny vola madinika momba ny fiainana manokana toa an'i Monero sy Zcash?

Educational Byte: Czy prywatne monety, takie jak Monero i Zcash, są legalne?

Байти таълимӣ: Оё тангаҳои махфият ба монанди Monero ва Zcash қонунӣ ҳастанд?

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