Mineral

mineral

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . THE MINERAL RESOURCES OF MISSOURI. here THE MINERAL RESOURCES OF MISSOURI. The incalculable wealth, which lies hid in the bosom of Mother Earth, in our vast possessions of the West, is undoubtedly centered in the State of Missouri; and the development of this fund of riches must add to the national prosperity, not only by its immeasurable intrinsic value, but by its affording occupation to armies of laborers, the latter being the highest and most important consideration. In 1852-3, a geological survey of the State was wisely decided upon, and a liberal provision for its execution made. Two valuable reports, by Professor Swallow, have been printed, in the year 1855, but the notes of his subsequent investigations have not been made public. In the session of 1869-70, further action, in this important public work, was taken by the State legislature, and arrangements made for a still more accurate and detailed examination, under the direction of Professor A. D. Hager, of Vermont. The distribution of metals all over the State will be seen in the following figures, taken from the St. Louis , which show the number of counties in which the various ores are found: Iron in 46 counties, lead in 43, coal in 36, copper in 24, marble in 11, zinc in 27, fire clay in 16, barytes in 10, nickel in 6, granite in 4, tin in 4, plumbago in 2, gypsum in 2, alum in 1, antimony in 4. Journal of Commerce There is probably no country in the world so endowed as this. Of iron alone, according to the State geologist's report for 1855, there is ore of the best quality, sufficient to furnish 200,000,000 tuns of iron; and this quantity lies in a small space, in the vicinity of Pilot Knob and Iron Mountain, and within 100 miles of St. Louis. The quality of the iron is highly spoken of by the manufacturers, and the capacity of the smelting appliances has reached to over 150,000 tuns per annum. The coal is well suited for reduction of ores, either by hot or cold blast treatment. The Scotia Iron Co. commenced operations in January, 1870; and, although the materials for building blast furnaces had to be carried 80 miles into a desert, the first furnace was blown into blast in August, 1870. This furnace will run about 24 tuns per day. The company procures ore from a hill, near the furnace, in which there is an apparently inexhaustible supply of red oxide and brown specular. This ore yields 60 per cent of pure metal. The erection of mills for making wrought iron is contemplated, and the high quality and prodigious quantity of the raw material will justify and reward any outlay of capital in this direction. The shipment of ore to other States goes on constantly, the last year's account showing that 246,555 tuns were dispersed over Indiana, Ohio, and others. The furnaces at Kingsland, South St. Louis, Lewis Iron Co.'s Works, Carondelet, and Maramec are all well situated as to coal and limestone, the Maramec Works having a most valuable water-power. These latter works also ship about 40,000 tuns red hematite ore yearly. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

THE MINERAL RESOURCES OF MISSOURI

Machine is kind of magic right? But is it the kind of magic that can make us rich? And I don’t mean lucrative consulting gig rich, I mean digging valuable metals out of the ground rich. The kind of rich you can horde in treasure chests. learning Also I’d been meaning to try out some transfer learning and looking around for a good topic to try it on. Transfer learning is where you take a pre-trained convolution (or other) and use it for your task. It’s great for convolution networks particularly because they take a long time to train. South Australia where I live has taken a lot of effort to share data sets, including some great geological resources, so I thought why not stick the two together and use convolution networks to find gold. The easiest way to make use of convolution networks is to turn them into feature extractors, which is what I’ll show you in this post. network The code that goes along with this blog post can be found on GitHub: _DeepGold using convolution network features to learn mineral data_github.com scottvallance/DeepGold Minerals So to have a computer find you treasure, you’ve got to give it something to work with. Like a treasure map. So my first assumption is that a map of geological features might be enough to spot likely places if you knew how to look at it. Of course it’s not like geologists don’t do this already. They probably even know the names of the minerals and whatnot’s they are looking for unlike me. So I found some papers written on finding minerals (gold in particular) using images. Instead of my first idea of maps, which would have been a fun challenge for a convolution network, there is a multi-spectral satellite image source called ASTER which can provide clues to mineral resources on and in the ground. _ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a high resolution imaging instrument that is…_asterweb.jpl.nasa.gov ASTER: Advanced Spaceborne Thermal Emission and Reflection Radiometer Multi-spectral imaging is taking a picture with a camera that can see more than just red, green and blue. Some of these bands in ASTER are good at spotting particular minerals, or mineral families. This following paper goes into how these bands have been used previously and how they could be used. _Remote sensing plays an important role in mineral exploration. One of its proven applications is extracting and…_www.researchgate.net Detecting areas of high-potential gold mineralization using ASTER data While the paper comes up with their own scheme, they mention the following mineral indexes as being particularly good for finding gold: “OHI is the index for OH-bearing minerals, KLI is the kaolinite index, ALI is the alunite index, and CLI is the calcite index”. ASTER has a bunch places where we can download it free of charge, but the downside of wonderful real data set like ASTER is that it’s full of all sorts of real world complications. Like clouds getting in the way, and like the fact that since 2008 some of the sensors on the satellite are basically cooked. Fortunately Geoscience Australia have done a lot of the hard work of stitching and manipulating the data and produced a bunch of big ‘.tif’ images to work with. _This Australian ASTER Initiative, led by CSIRO's Western Australian Centre of Excellence for 3D Mineral Mapping…_www.ga.gov.au National ASTER Maps - Geoscience Australia You can find the data on their ftp site with the links in the Github repo for this post. I grabbed a bunch of images for the location I was interested in, using the AlOH, MgOH and Kaolin band images. That’s only half the story though, we have a lot of images but no clue where the gold is. The easiest way to use machine learning to help us find gold is to give it a bunch of images that have gold in them and a bunch that don’t and get it to figure out the difference. To get that training data we need to know where there’s gold. Another government department the rescue, this time a South Australian state department which has a great little tool they call SARIG. _Edit description_sarig.pir.sa.gov.au South Australian Resource Information Geoserver - Map It’s a map with a lot of different mineral related overlays, one of them particularly interesting to us — ‘All Mines and Mineral Deposits’ with a handy option to download the shape file geo database we have everything we need data wise. Data Processing Now we have lots of data, but it’s not simply a matter of feeding it into computer. You have to slice and dice it into the formats needed. To work geospatial data their are a bunch of great tools collectively known as GDAL: Geospatial Data Abstraction. _Traditionally GDAL used to design the raster part of the library, and OGR the vector part for Simple Features. Starting…_www.gdal.org GDAL: GDAL - Geospatial Data Abstraction Library These tools will happily export info out of our ‘All Mines and Mineral Deposits’ shape files and slice up the massive image files that are also geospatially aware. As each mineral deposit entry in shape file is a point location, we can cut up the large input images into segments around each point of a size appropriate to feed into a convolution network. These slices can have multiple minerals and we don’t allow overlapping slices so that the eventual machine learning doesn’t just get to remember what different segments look like and learn from rote. Then it’s a matter of merging the images from the different bands into a false colour image. A false colour image is when we use something other than real red, green and blue values in image — in this case we’re using the AlOH content, the MgOH content and the Kaolin index. One measure for each colour band. Here’s what a false colour image ready to feed into our convolution network looks like: A false colour image using GEOScience Australia reprocessed ASTER data Convolution Networks Convolution networks have shown amazing ability to learn answers to visual problems. Recently there’s a been a bunch of open source tools released to develop convolution networks including: TensorFlow, Theano, Caffe, Torch. All great tools but we really need something with easy access to pretrained data. For that problem, and for abstracting convolution networks a step away from their implementation, Keras has been incredibly useful. _Keras: Deep Learning library for Theano and TensorFlow_keras.io Keras Documentation The author also provides this with models and pretrained data to work with. The has produced good results, so that’s an obvious starting point for the network. We’ll be using this network to transform our training images into a vector features. Essentially we take the output of the ResNet50 at a point before it turns into a bunch of image class predictions and use that as the input for another classification problem. While training a convolution network is slow using it to generate features is quick and gives us a data source that has been proven to transfer to other problems well. It’s definitely an open question how well it transfers to something as esoteric as a processed false colour satellite image but trying it out is the easiest way to learn. Github repo ResNet50 network Classification with ResNet50 Features After we turn our false colour images into features we can run a bunch of different machine learning algorithms. For this we make use of Spark, a cluster focused graph computation framework with a number of built in machine learning algorithms. _Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine…_spark.apache.org Apache Spark™ - Lightning-Fast Cluster Computing While the size of the problem doesn’t necessitate using a cluster to solve it, it does provide a nice interface to the algorithms. We can make use of Logistic classification, Support Vector Machine classification, Naive Bayes classification, Random Forests and Multilayer Perceptron classification with just a few lines of code. To make our data ready for Spark we turn it into LibSVM format and then further simplify it by filtering the examples into those containing gold and those that don’t. This leaves us with a lot more not gold examples, so we over-sample the gold ones to give us even numbers because it helps to train an algorithm with balanced data. We load up pyspark and use a few lines of code to run some different classifers. Here are the results of Spark’s classification algorithms: Logistic Regression:Test Accuracy = 0.767756482525 SVM:Test Accuracy = 0.808342728298 Naive Bayes:Test Accuracy = 0.616685456595 Random Forest (10 trees):Test Accuracy = 0.727171 Multilayer Perceptron (layers [2048, 512, 2]):Test Accuracy = 0.775255 This is just a measure of how often the algorithm was right about an image containing gold or not over the total number of testing examples. An accuracy of 80% doesn’t sound bad, and does match with other results shown in the literature but it’s difficult to interpret this data from a simple accuracy measure. We haven’t over sampled the gold examples in the testing data, so they contain a lot more not gold than gold. If an algorithm gets pessimistic about predicting gold it can still do quite well while not telling us very much about where to dig. What we really need is a measure of how likely we are to strike gold if we do go out with a shovel. This is called the precision of the prediction. The flip side of an algorithm being really precise is that it may choose to only predict gold when it’s very sure and miss a lot of examples. The measure of the amount of examples we correctly predict as gold out of the total gold samples is called the recall. We’re probably fine with a low recall, but we’d like precision to be as high as possible. Here’s the precision and recall for the data above: Logistic Regression:Gold Recall = 0.324840764331Gold Precision = 0.337748344371 SVM:Gold Recall = 0.235668789809Gold Precision = 0.425287356322 Naive Bayes:Gold Recall = 0.68152866242Gold Precision = 0.269521410579 Random Forest (10 trees):Gold Recall: 0.575163398693Gold Precision: 0.328358208955 Multilayer Perceptron (layers [2048, 512, 2]):Gold Recall: 0.317880794702Gold Precision: 0.335664335664 So for the purposes of getting out there with a shovel the Support Vector Machine classification really wins. If it says there’s gold then there’s a 42% chance it’s telling the truth, which is out of 17% of samples in the testing data actually having gold. This is just for segments of South Australia that have known mineral deposits though, which is a factor to consider as it might correlate to certain features that aren’t seen in other areas. ‘X’ Marks the Spot So given the data we have is where do we go digging? Obviously not where gold is already labelled because it’s already dug up. Which really just leaves where it isn’t labelled, which has the down side of already being mined for something — but not gold. We’ll take it though, a lot of the data in the mineral deposits is old and abandoned. The Support Vector Machine performed best, but we want to move beyond classification into probabilities. If we clear the threshold that turns the SVMModel in Spark into a classifier we can get raw scores straight from the estimates. Taking the max of the ones where there is no gold gives us this image: Weird boomerang shape marks the spot Let’s check it on the map: Our great mining hope Someone grab the shovels, we’re off! Turns out this is right next door to the White Dam Gold Mine. Which is I guess a good sign, but the mine is actually in our data already it’s just a bit to the left and down. There’s a good chance that the spot we found looks a lot like the mine next door. A quick look through the rest of the likely spots shows a lot of regions next to existing mines. Probably a good thing, but sort of hard to make use of. Running through the maps without mineral data section by section might yield something but it’s a bit speculative and might best be left to the reader. So we didn’t really find gold, but I’m still pleasantly surprised by the accuracy of convolution network features on such a different problem to that which they were trained. Conclusion and Other Projects After searching through the maps with the algorithms we’ve developed a great next step would certainly be to train a convolution network on raw ASTER data. Convolution networks are getting to human levels of success (and over) on certain vision tasks and I think it they could have a big advantage with multispectral data. Humans can only interpret visual data through at most (theoretical not withstanding) three channels but convolution networks are not similarly constrained. The current band ratio used for finding minerals would certainly map well to a network architecture. tetrachromats If you find this article interesting you can . find me on linkedin

Deep Gold: Using Convolution Networks to Find Minerals

The First Miracle: The Emergence of Life Is an Expected Phase Transition

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 Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. 1. Introduction Understanding ocean data is paramount to predicting extreme weather events such as hurricanes and tsunamis, better understanding of planetary scale processes like global warming, and sustainably managing and preserving ocean resources and its marine life. Visualizing ocean data is challenging due to the presence of multiple fields and parameters that change with time. Ocean currents are undeniably the biggest factor that maintain the heat balance of the ocean-atmosphere system and affect the movement of minerals and salt. Mesoscale eddies, that span from tens to hundreds of kilometers in diameter and have a lifespan that can range from days to months [RR10], are ubiquitous in the oceans. They play a big role in transporting heat and mass within the oceans [McW08]. They also have a big impact on the ecology of the ocean and on the biogeochemical processes [MJD∗ 99,BNBD∗ 07]. With the strides in collection and generation of ocean data [FD06, Ros89], there is a need for tools that support effective visualization of the data, and are scalable to keep pace with the ever expanding resolutions and sizes of ocean datasets. 1.1. Related work Visualization in oceanography is a challenging area of research due to the rapidly increasing size of data, the heterogeneity and multivariate nature of the data, and the inherent complexity of the ocean phenomena. The use of general purpose analysis and visualization software such as Matlab, Tecplot, AVS, and Paraview is prevalent in the community. However, oceanographers often use tools developed specifically for ocean data, such as Ferret [Fer23], pyFerret [pyF23], and Copernicus MyOcean [myO23]. These specialized tools provide multiple functionalities and produce 2D views of the data. A few software frameworks developed within the visualization community provide 2D and 3D data visualization capabilities. COVE [GSK∗ 08] is a collaborative ocean visualization environment that supports interactive analysis of ocean models over the web. RedSeaAtlas [AGT∗ 19] supports the selection of regions in a 2D map and provides exploratory views of winds, waves, tides, chlorophyll, etc. over the Red Sea. OceanPaths [NL15] is a multivariate data visualization tool that computes pathways tracing ocean currents and supports the plotting of different high-dimensional data along the pathways. This enables the study of correlations between different oceanographic features. An oceanographer’s analysis workflow includes a few common tasks [GSK∗ 08] such as inspection of temperature and salinity distributions and vertical cross sections, compare recently measured salinity data against model data, inspect and analyze current vorticities and circulation based on flow data, and analyze extreme events. Isosurfaces and volume rendering are natural choices for visualization of 3D temperature and salinity distributions [DAN12, PBI04]. However, visualization of the dynamically changing distributions is a challenge. VAPOR [LJP∗ 19] is one of the few tools that provides efficient 3D visualization for oceanography and atmospheric science applications. The VAPOR data collection (VDC) data model supports interactive visual analysis of large data sizes on modern GPUs and commodity hardware. Xie et al. [XLWD19] and Afzal et al. [AHG∗ 19] present surveys of visual analysis methods and tools developed for ocean data. Xie et al. classify the visual analysis tasks into four categories, namely study of different environmental variables, ocean phenomena identification and tracking, discovery of patterns and correlations, and visualization of ensembles and uncertainty. Further, they identify different opportunities and unexplored areas for future work including efficient and scalable methods for data processing and management, identification of features at multiple scales, and immersive platforms. While we recognize the availability of several methods for oceanography visualization, we note that they are often standalone solutions. We aim to leverage the extensive advancement in visualization technology as implemented in Paraview while providing functionalities and views that are specific to ocean data. 1.2. Contributions We present pyParaOcean, a system for interactive exploration and visual analysis of ocean data. The system leverages the power of Paraview [AGL05] to enable scalable visualizations of data available from ocean models while supporting a multitude of tasks and functionalities that are specialized for oceanography. The visualization capabilities of pyParaOcean are available via a seamless integration into Paraview using plugins. Key features of the system include • 3D field visualization to study salinity and temperature distribution with support to display and explore dynamically evolving isovolumes. • Ocean current visualization with a support for different seeding strategies for streamlines and pathlines. • Vertical section views and parallel coordinates plot that support interactive selection and slicing of the data. • Identification and tracking of salinity movement via extraction of surface fronts. • Visualization and tracking of eddy features. • An extensible design that supports incorporation of new functionalities as filters in Paraview. We present the results of an exploration of the Bay of Bengal, performed in collaboration with an oceanographer, as a case study to demonstrate the utility of the system. 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 Abstract Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes. 1. Introduction Understanding ocean data is paramount to predicting extreme weather events such as hurricanes and tsunamis, better understanding of planetary scale processes like global warming, and sustainably managing and preserving ocean resources and its marine life. Visualizing ocean data is challenging due to the presence of multiple fields and parameters that change with time. Ocean currents are undeniably the biggest factor that maintain the heat balance of the ocean-atmosphere system and affect the movement of minerals and salt. Mesoscale eddies, that span from tens to hundreds of kilometers in diameter and have a lifespan that can range from days to months [RR10], are ubiquitous in the oceans. They play a big role in transporting heat and mass within the oceans [McW08]. They also have a big impact on the ecology of the ocean and on the biogeochemical processes [MJD∗ 99,BNBD∗ 07]. With the strides in collection and generation of ocean data [FD06, Ros89], there is a need for tools that support effective visualization of the data, and are scalable to keep pace with the ever expanding resolutions and sizes of ocean datasets. 1.1. Related work Visualization in oceanography is a challenging area of research due to the rapidly increasing size of data, the heterogeneity and multivariate nature of the data, and the inherent complexity of the ocean phenomena. The use of general purpose analysis and visualization software such as Matlab, Tecplot, AVS, and Paraview is prevalent in the community. However, oceanographers often use tools developed specifically for ocean data, such as Ferret [Fer23], pyFerret [pyF23], and Copernicus MyOcean [myO23]. These specialized tools provide multiple functionalities and produce 2D views of the data. A few software frameworks developed within the visualization community provide 2D and 3D data visualization capabilities. COVE [GSK∗ 08] is a collaborative ocean visualization environment that supports interactive analysis of ocean models over the web. RedSeaAtlas [AGT∗ 19] supports the selection of regions in a 2D map and provides exploratory views of winds, waves, tides, chlorophyll, etc. over the Red Sea. OceanPaths [NL15] is a multivariate data visualization tool that computes pathways tracing ocean currents and supports the plotting of different high-dimensional data along the pathways. This enables the study of correlations between different oceanographic features. An oceanographer’s analysis workflow includes a few common tasks [GSK∗ 08] such as inspection of temperature and salinity distributions and vertical cross sections, compare recently measured salinity data against model data, inspect and analyze current vorticities and circulation based on flow data, and analyze extreme events. Isosurfaces and volume rendering are natural choices for visualization of 3D temperature and salinity distributions [DAN12, PBI04]. However, visualization of the dynamically changing distributions is a challenge. VAPOR [LJP∗ 19] is one of the few tools that provides efficient 3D visualization for oceanography and atmospheric science applications. The VAPOR data collection (VDC) data model supports interactive visual analysis of large data sizes on modern GPUs and commodity hardware. Xie et al. [XLWD19] and Afzal et al. [AHG∗ 19] present surveys of visual analysis methods and tools developed for ocean data. Xie et al. classify the visual analysis tasks into four categories, namely study of different environmental variables, ocean phenomena identification and tracking, discovery of patterns and correlations, and visualization of ensembles and uncertainty. Further, they identify different opportunities and unexplored areas for future work including efficient and scalable methods for data processing and management, identification of features at multiple scales, and immersive platforms. While we recognize the availability of several methods for oceanography visualization, we note that they are often standalone solutions. We aim to leverage the extensive advancement in visualization technology as implemented in Paraview while providing functionalities and views that are specific to ocean data. 1.2. Contributions We present pyParaOcean, a system for interactive exploration and visual analysis of ocean data. The system leverages the power of Paraview [AGL05] to enable scalable visualizations of data available from ocean models while supporting a multitude of tasks and functionalities that are specialized for oceanography. The visualization capabilities of pyParaOcean are available via a seamless integration into Paraview using plugins. Key features of the system include • 3D field visualization to study salinity and temperature distribution with support to display and explore dynamically evolving isovolumes. • Ocean current visualization with a support for different seeding strategies for streamlines and pathlines. • Vertical section views and parallel coordinates plot that support interactive selection and slicing of the data. • Identification and tracking of salinity movement via extraction of surface fronts. • Visualization and tracking of eddy features. • An extensible design that supports incorporation of new functionalities as filters in Paraview. We present the results of an exploration of the Bay of Bengal, performed in collaboration with an oceanographer, as a case study to demonstrate the utility of the system. 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: Abstract and Intro

pyParaOcean, একটি সিস্টেম ফর ভিজ্যুয়াল অ্যানালাইসিস অফ ওশান ডেটা: অ্যাবস্ট্রাক্ট এবং ইন্ট্রো

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Zusammenfassung und Einführung

pyParaOcean, un sistema para el análisis visual de datos oceánicos: resumen e introducción

pyParaOcean, un système d'analyse visuelle des données océaniques : résumé et introduction

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: सार और परिचय

pyParaOcean、海洋データの視覚的分析システム: 概要と紹介

pyParaOcean, 해양 데이터의 시각적 분석 시스템: 개요 및 소개

pyParaOcean, um sistema para análise visual de dados oceânicos: resumo e introdução

pyParaOcean, Система визуального анализа океанических данных: Краткое содержание и введение

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Özet ve Giriş

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Tóm tắt và giới thiệu

pyParaOcean，海洋数据可视化分析系统：摘要和简介

The Quiet Crisis That Is Stealing Our Health (and How to Get It Back) - Insights From Dr. Eric Berg

So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. I’m João, and I have been a Software Engineer for more than 10 years now. I am technically a Software and Telecom engineer, but most part I have only been doing software engineering professionally. There are many things I love to do, and that, of course, includes making Tech videos and writing blog posts and articles, and maintaining my website. Outside work and outside Tech, I am someone very curious about biology, which is why I like to try to maintain a garden where I grow things like blueberry bushes, honeysuckles, and gooseberry bushes, and I also love photography. I mostly enjoy photographing nature, cities, and things with lights. Further, I also like to enjoy my time doing stuff related to art, for example, video, photo editing, and style editing. Outside of all of this, I also enjoy music, series, films, traveling, cooking, and, from time to time, the occasional electronics project. Interesting! What was your latest Hackernoon Top story about? My latest Hackernoon Top story was the one I did about Kuma, which is about a way to create Meshes using Kuma-Meshes, where we can have a much more granular control over the data flow within our own network, which, amongst other things, can protect an organization against internal bad actors. Do you usually write on similar topics? If not, what do you usually write about? A the moment I exclusively write about Tech, although I can form time to time relate those posts with other kind of topics like opinionated arguments or even politics when they have the potential to influence tech. It can also be that I can write about how some particular tech stacks may influence our approach to tech or even society and environment. But the main focus is always tech. Great! What is your usual writing routine like (if you have one?) I do not have a specific writing routine, although I am writing a lot more these days. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? My biggest challenge in writing is creating a functional example as fast as possible. Creating a story, implementing needed elements, making them run, creating tests for it, creating databases or any other persistence mechanisms for it, and trying to guarantee as much as possible that people can use the examples without walking against errors or faults is usually a very energy-intensive work. Therefore, it can take longer than desired. What is the next thing you hope to achieve in your career? I want to be as successful as possible with my own effort and work, and I want to share my experiences and knowledge with other colleagues in the world of software engineering. I don’t know if it eventually can become a full-time job, but if it does, it will probably be the next thing that I would love to achieve. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Listening to Madonna’s Ray of Light album. Do you have a non-tech-related hobby? If yes, what is it? Yes! I love to take photos of wildlife. I especially love making macro photos of leaves, insects, and flowers, as well as minerals, rocks, glass, metals, and anything that looks outstanding in a macro photo. What can the Hacker Noon community expect to read from you next? I usually write more about topics related to the Kotlin programming language; however, having said that I am finding it more and more interesting to write about security and operations from the DevSecOps area as I did in my article about Kuma. I think we can expect to read more about the SecOps area in my next articles, but I will still write about Dev. What’s your opinion on HackerNoon as a platform for writers? I think HackerNoon is a unique platform for writers to share knowledge about Technology because it is very recognizable, and a lot is automatically done for us like, for example, in this last article when it comes to translating my Kuma article in multiple languages. That was a very nice surprise to see. All in all, I think HackerNoon really appeals to Software programmers, Engineers, Developers, and even Businesses and so I’m very happy to keep writing for HackerNoon. Thanks for taking time to join our “Meet the writer” series. It was a pleasure. Do you have any closing words? Yes, thank you for giving me this great interview. I really appreciate the boost given to my article. I hope to be able to keep writing for HackerNoon for as long as possible. It is a pleasure to do so. Thanks again for the incredible support and for being there. So, let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. I’m João, and I have been a Software Engineer for more than 10 years now. I am technically a Software and Telecom engineer, but most part I have only been doing software engineering professionally. There are many things I love to do, and that, of course, includes making Tech videos and writing blog posts and articles, and maintaining my website. Outside work and outside Tech, I am someone very curious about biology, which is why I like to try to maintain a garden where I grow things like blueberry bushes, honeysuckles, and gooseberry bushes, and I also love photography. I mostly enjoy photographing nature, cities, and things with lights. Further, I also like to enjoy my time doing stuff related to art, for example, video, photo editing, and style editing. Outside of all of this, I also enjoy music, series, films, traveling, cooking, and, from time to time, the occasional electronics project. Interesting! What was your latest Hackernoon Top story about? My latest Hackernoon Top story was the one I did about Kuma, which is about a way to create Meshes using Kuma-Meshes, where we can have a much more granular control over the data flow within our own network, which, amongst other things, can protect an organization against internal bad actors. Do you usually write on similar topics? If not, what do you usually write about? A the moment I exclusively write about Tech, although I can form time to time relate those posts with other kind of topics like opinionated arguments or even politics when they have the potential to influence tech. It can also be that I can write about how some particular tech stacks may influence our approach to tech or even society and environment. But the main focus is always tech. Great! What is your usual writing routine like (if you have one?) I do not have a specific writing routine, although I am writing a lot more these days. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? My biggest challenge in writing is creating a functional example as fast as possible. Creating a story, implementing needed elements, making them run, creating tests for it, creating databases or any other persistence mechanisms for it, and trying to guarantee as much as possible that people can use the examples without walking against errors or faults is usually a very energy-intensive work. Therefore, it can take longer than desired. What is the next thing you hope to achieve in your career? I want to be as successful as possible with my own effort and work, and I want to share my experiences and knowledge with other colleagues in the world of software engineering. I don’t know if it eventually can become a full-time job, but if it does, it will probably be the next thing that I would love to achieve. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Listening to Madonna’s Ray of Light album. Do you have a non-tech-related hobby? If yes, what is it? Yes! I love to take photos of wildlife. I especially love making macro photos of leaves, insects, and flowers, as well as minerals, rocks, glass, metals, and anything that looks outstanding in a macro photo. What can the Hacker Noon community expect to read from you next? I usually write more about topics related to the Kotlin programming language; however, having said that I am finding it more and more interesting to write about security and operations from the DevSecOps area as I did in my article about Kuma. I think we can expect to read more about the SecOps area in my next articles, but I will still write about Dev. What’s your opinion on HackerNoon as a platform for writers? I think HackerNoon is a unique platform for writers to share knowledge about Technology because it is very recognizable, and a lot is automatically done for us like, for example, in this last article when it comes to translating my Kuma article in multiple languages. That was a very nice surprise to see. All in all, I think HackerNoon really appeals to Software programmers, Engineers, Developers, and even Businesses and so I’m very happy to keep writing for HackerNoon. Thanks for taking time to join our “ Meet the writer ” series. It was a pleasure. Do you have any closing words? Meet the writer Yes, thank you for giving me this great interview. I really appreciate the boost given to my article. I hope to be able to keep writing for HackerNoon for as long as possible. It is a pleasure to do so. Thanks again for the incredible support and for being there.

Meet the Writer: HackerNoon's Contributor João Esperancinha, Software Engineer

Bitcoin NFTs, or non-fungible tokens on the Bitcoin blockchain, have emerged as a groundbreaking trend reshaping the digital asset landscape. These tokens represent ownership of digital assets and are gaining significant traction within the community. cryptocurrency In this article, let's delve into what Bitcoin NFTs are and how they are created, as well as explore the current trends surrounding this innovative technology. If you run a Web3/NFT project and need advice on marketing and promotion of your project, you can [message me](mailto: serge@x10.agency) directly. Understanding Bitcoin NFTs Bitcoin NFTs represent ownership of digital assets, such as artwork, collectibles, and in-game items. Each NFT has a unique cryptographic signature that verifies its authenticity and ownership. This means that each NFT is one-of-a-kind and cannot be replaced or replicated. This uniqueness is what underpins the value and appeal of NFTs in the digital realm. Creation of Bitcoin NFTs The creation of Bitcoin NFTs involves a process known as "Ordinal Inscriptions," where data is directly inscribed on-chain within the Bitcoin network. This method differs from Ethereum NFTs, which rely on off-chain metadata that can be altered. Ordinal Inscriptions allow for all data to be permanently recorded on the blockchain, ensuring transparency and immutability in token ownership. The process of creating a Bitcoin NFT involves several steps. First, the digital asset is created or selected, such as a piece of artwork or an in-game item. Next, the asset is tokenized by creating a unique cryptographic signature that represents ownership. This signature is then inscribed on the Bitcoin blockchain using Ordinal Inscriptions. Once the inscription is complete, the NFT is created and can be bought, sold, or traded on various online marketplaces. Trends in Bitcoin NFTs The trend of Bitcoin NFTs is rapidly evolving, with a surge in interest and adoption witnessed across the cryptocurrency space. Recent data indicates that Bitcoin NFTs have surpassed Ethereum NFTs in trading volume, signaling a shift towards the Bitcoin blockchain for NFT transactions. This is due in part to the growing popularity of Ordinals, which have made it easier and more accessible to create and trade NFTs on the Bitcoin blockchain. Some examples of Bitcoin NFTs include: : The first NFT collection ever released on Bitcoin, created in September 2016 using the Counterparty protocol. Despite their limited utility due to the constraints of Counterparty's scripting language at the time, Rare Pepes laid the foundation for subsequent Bitcoin NFT projects and highlighted the potential for digital collectibles on the Bitcoin network. Rare Pepes A metaverse project in the Bitcoin ecosystem based on an open-source standard protocol proposed on Ordinals, primarily for the primary market sale of virtual land. What sets Bitmap apart is its unique binding to blocks, creating inherent scarcity within its virtual land offerings. By leveraging Ordinals and the Bitcoin blockchain, Bitmap showcases a novel way of tokenizing virtual assets with built-in scarcity, offering a fresh perspective on digital ownership and value creation in the metaverse space. Bitmap: : A collection of NFTs on the Bitcoin blockchain, with examples like Mineral having a market cap of 379.6 BTC (approximately $19.8M). This series symbolizes the intersection of art, technology, and blockchain innovation, showcasing the potential for both creative expression and value generation. Ordinal Punks NFT series How To Make Your Own Bitcoin NFT? Creating your own NFT on the Bitcoin blockchain is a relatively straightforward process, and there are several tools and platforms available to help you get started. Here are the basic steps for creating your own NFT: 1. : The first step in creating an NFT is to choose the digital asset that you want to tokenize. This could be a piece of artwork, a collectible, an in-game item, or any other type of digital asset. Choose your digital asset 2. : Once you have chosen your digital asset, you will need to create a digital representation of it. This could be a digital file, such as a JPEG or PNG image, or it could be a 3D model or other type of digital asset. Create a digital representation 3. : Once you have created a digital representation of your asset, you can mint your NFT using a platform that supports Ordinal Inscriptions on the Bitcoin blockchain. There are several platforms available, including Raretoshi, Counterparty, and Tokenly. These platforms provide tools for creating and managing NFTs on the Bitcoin blockchain. Mint your NFT 4. : When you mint your NFT, you will need to set its metadata, which includes information about the asset, such as its name, description, and image. This metadata is stored on the blockchain along with the NFT, and it provides important context and information about the asset. Set your metadata 5. Once your NFT is minted and its metadata is set, you can list it for sale on an online marketplace that supports NFTs on the Bitcoin blockchain. Some popular marketplaces include OpenBazaar, BitPieces, and Rare Pepe Wallet. List your NFT for sale: 6. Once your NFT is listed for sale, you will need to promote it to potential buyers. This could involve sharing it on social media, creating a website or blog to showcase your NFTs, or reaching out to collectors and investors in the NFT community. Promote your NFT: Market Dynamics and Challenges While Bitcoin NFTs are gaining momentum, challenges such as scalability and accessibility remain key areas of focus for developers and users. The integration of Ordinals into the Bitcoin ecosystem has raised debates regarding block space usage and transaction costs. As more Ordinals are inscribed with additional data like images and audio clips, concerns about on-chain congestion and confirmation times have surfaced. Scalability is a major challenge for the Bitcoin blockchain, as it has a limited capacity for processing transactions. This means that as more NFTs are created and traded on the blockchain, it could become congested and slow, leading to higher transaction fees and longer confirmation times. To address this challenge, developers are exploring ways to increase the scalability of the Bitcoin blockchain, such as through the use of layer-two solutions like the Lightning Network. Accessibility is another challenge for Bitcoin NFTs, as they require a certain level of technical expertise to create and trade. This can be a barrier for entry for some users, who may not have the necessary skills or resources to participate in the NFT market. To address this challenge, developers are working on creating more user-friendly tools and platforms for creating and trading NFTs on the Bitcoin blockchain. At the end of the day, Bitcoin NFTs represent a paradigm shift in how digital assets are tokenized and traded within the cryptocurrency ecosystem. The future of Bitcoin NFTs holds immense potential for reshaping the landscape of digital ownership and value exchange. As this trend continues to evolve, it will be fascinating to witness how Bitcoin NFT conquers the new market heights. P.S. Check out some of my previous articles at HackerNoon: NFT Marketing Guide - The Most Complete and Detailed Playbook Autoglyphs: The $14.6 mln NFT Phenomenon Explained Cracking the NFT Code: The Psychology Behind Buying Digital Assets Breaking Down the Next Big Thing in NFTs: ERC-404 Spotlight Nobody Buys an NFT Just for Art DeFi Community Building: A Step-by-Step Guide for Crypto Startups

Bitcoin NFTs: Trends and How to Create It

Space travel remains the dream of many people, and Intuitive Machines' has brought us one step closer. Although it “tripped” while landing on the Moon, Odysseus has managed to communicate with its control team on Earth, beaming back information and images. Odysseus is the first private spacecraft ever to land successfully on the moon — and the first American vehicle to do so since the final Apollo mission in 1972. Odysseus lander Of course, the Moon is probably only meant to serve as a practice pad and “truck stop” in the future for more ambitious missions that will take humans to Mars and elsewhere. The Moon will hopefully be used for scientific experiments, training purposes, and as a way station for future astronauts who will spend lengthy periods of time in space. While the Odysseus was unmanned, NASA has plans to send astronauts back to the Moon through its Artemis program. Billionaire and SpaceX founder Elon Musk he believes that humans could already be making their way to the red planet in just a few years. said last year Musk an ambitious plan to establish a human colony on Mars and that he aims to transport one million people to the Red Planet, stating that “civilization only passes the single-planet Great Filter when Mars can survive even if Earth supply ships stop coming.” In the past, Musk has said he plans to have a million people on and that “a on the red planet could sustain our species if all humanity is extinguished on Earth.” recently revealed Mars by 2050 permanent colony Musk has long voiced his intent to make humanity multi-planetary, often pointing to human settlement on Mars as an insurance policy for civilization in case Earth is struck by any number of calamities. With the space race in full gear, the question of who gets what contract has become a matter of serious competition. Companies like Intuitive Machines, Musk’s SpaceX, Amazon founder Jeff Bezos’ Blue Origin, or Richard Branson’s Virgin Galactic are all competing for the eventual ability to ferry Earth’s inhabitants back and forth between our planet and whatever way station or permanent habitat we build elsewhere, whether on the Moon or Mars. SpaceX's first priority is "establishing a cargo route to Mars," in the past. Musk has said Musk is relying on the much-touted Starship to advance his plans. His early plans involved sending unmanned ships to the surface of Mars and then sending people after that once the flights were proven safe. The first missions would work to convert water and CO2 found on the Mars surface into the liquid oxygen and methane fuel that powers the Starship. By doing so, SpaceX would guarantee a long-lasting fuel source. Once refueled, the Starships would be able to make the journey back to Earth. Following the initial flights, Musk wants to bring large groups of people to Mars to build colonies and factories to make the planet self-sustaining. One of the main priorities would be to build a factory for rocket fuel, essentially turning Mars into an outpost for ever-expanding space travel. In Musk’s vision, people could go from Mars to the asteroid belts, the moons of Jupiter and Saturn, and the Kuiper belt. While in the beginning stages, people on Mars would have to live inside large protected air-tight structures on the surface or even underground, eventually, though, Musk said he envisions having the ability to warm the planet to make it habitable and allow humans to live outside without the need for supplemental oxygen. But this is where some scientists begin to see problems with Musk’s grand plan. According to experts, there just on Mars that you can mobilize and create a long-lasting atmosphere the way Musk believes he can. The other problem, even if this was possible, is keeping the CO2 contained in the atmosphere. isn't enough CO2 Musk has said he understands this issue and realizes it would take a long time. He acknowledged that this is a project that would span several generations, and even if we cannot create an atmosphere now, we could at least build the first colony on Mars, preparing the way for future generations. One of the greatest advantages of space exploration in today’s day and age is that we now have new technologies, such as artificial intelligence (AI), to expand our capabilities more than was possible when space travel first became possible. For instance, AI is to help scientists analyze data more quickly and accurately and automate operations on spacecraft. The continuous development of other planets and moons in ways never before possible. currently being used in the space industry AI is helping rovers and landers explore “SpaceX has been improving and refining how rockets operate. The company uses AI to monitor and analyze data from the rocket’s sensors and telemetry systems, allowing for better decision-making and more precise control of the rocket’s trajectory and speed. SpaceX also uses AI to automate certain aspects of the rocket landing procedure.” NASA’s Jet Propulsion Laboratory has also recently developed an that can autonomously plan a spacecraft mission based on given parameters and constraints. Machine learning algorithms are being used to identify potential water sources or mineral deposits on the planet’s surface. AI-based mission planning system Clearly, AI is essential to the future of space exploration and will increasingly be implemented across various future missions in space—from Mars colonization efforts to interplanetary travel to supporting astronaut health. We are getting closer to this vision of becoming an interplanetary species. As long as it does not hallucinate or generate false information, as some AI technologies currently do, AI will help us achieve this unique and incredible vision.

AI Will Help Mankind Colonize Mars

United States and European Union Forge Alliance to Safeguard Trade and Security

মার্কিন যুক্তরাষ্ট্র এবং ইউরোপীয় ইউনিয়ন বাণিজ্য ও নিরাপত্তা রক্ষায় জোট গঠন করে

Die Vereinigten Staaten und die Europäische Union schmieden ein Bündnis zum Schutz von Handel und Sicherheit

Estados Unidos y la Unión Europea forjan alianza para salvaguardar el comercio y la seguridad

Les États-Unis et l’Union européenne forgent une alliance pour sauvegarder le commerce et la sécurité

संयुक्त राज्य अमेरिका और यूरोपीय संघ ने व्यापार और सुरक्षा की सुरक्षा के लिए गठबंधन बनाया

米国と欧州連合、貿易と安全保障を守るための同盟を結ぶ

미국과 유럽연합은 무역과 안보를 보호하기 위해 동맹을 맺었습니다.

Estados Unidos e União Europeia formam aliança para salvaguardar o comércio e a segurança

США и Европейский Союз создают альянс для защиты торговли и безопасности

ABD ve Avrupa Birliği Ticareti ve Güvenliği Korumak İçin İttifak Kuruyor

Hoa Kỳ và Liên minh Châu Âu thành lập liên minh để bảo vệ thương mại và an ninh

美国和欧盟结成联盟以保障贸易和安全

**NEW YORK, United States, January 31st, 2024/Chainwire/--**This week, the much-anticipated Bitcoin L2 solution Merlin Chain went live on its testnet, while its mainnet is scheduled to be launching within a month as well. Bitcoin's Network Congestion Dilemma and Scaling Solutions The evolution of Ordinals over the past year has brought the attention back to the Bitcoin ecosystem after a long period. While enhancing the excitement of the bitcoin ecosystem, it has also increased the exhaustion of network resources. The increasingly high cost of transactions, coupled with the rising price of Bitcoin itself, has added further congestion to the network and discouraged new users from becoming interested in Bitcoin. The idea of developing an EVM-compatible L2 network has been recognized as the one that would deliver the results first due to the simpleness of its implementation path. Merlin Chain is also one of the EVM-compatible L2 chains, being developed by Bitmap Tech (formerly Recursiverse) , the team that built BRC-420 protocols and Bitmap.game, and is highly valued by the community due to the team's past successes. BRC-420 The BRC-420 protocol supports the combination/recursion of any file format to form a more complex inscription. Whether it's a character image, a game script, a virtual machine or an AI model, it can become an asset on Bitcoin. Its core asset, the Blue Box, has risen from an inscription cost of $0.15 to an ATH floor price of $26,000, and other assets such as Mineral and This Song About NFTs are among the most actively traded assets on the OKX market. It now has 1000+ developers and 600+ collections, and has been an excellent performer even during the bear market, the price only going up and never falling. Bitmap.Game Bitmap.Game is a fully on-chain, fully decentralized, fair-launch metaverse project. It is based on the Digital Matter Theory that underlies Bitcoin, and is open-sourced so that it can be run or verified by any client, reflecting the concept of "client agnosticism" in fully onchain games. During this time, Bitmap's market capitalization has risen from $3 million to a ATH of $290 million. With more than 30,000 holders, Bitmap has surpassed the Sandbox as the metaverse asset with the highest number of holders across all blockchains. Unlocking Bitcoin's Potential Through Innovative Magic Merlin Chain believes that L2 of Bitcoin should not just be about transferring money inexpensively, or just about being able to have the DApps, but should be about further innovating to unlock the potential of Bitcoin through its native assets, users and protocols. For example, as Bitmap is a completely decentralized asset, how to make games based on it, so that Bitmap users can enter various meta-universes at a low cost; or for the DeFi protocols based on BRC-420, how to give two-way empowerment to NFT and FT; and for minting inscriptions, how to mint L1 assets on L2 in a more cost-effective and flexible way. Only these "Bitcoin-native" innovations can continue to empower the ecosystem, rather than replicating the way Ethereum works. So far, Merlin Chain has built a powerful DApp ecosystem, the liquidity concerns have been effectively addressed, and is planning for further expansion. Leveraging ZK-Rollup for Efficient and Secure Scaling The diverse ecosystem and assets also imply higher throughput requirements and some safety risks. To solve this problem, Merlin Chain has adopted ZK-Rollup which enables the compression of a large number of transaction proofs into a simple check-sum to improve transaction efficiency and scalability. The sequencer nodes on Merlin Chain are responsible for collecting and batching transactions, generating compressed transaction data, ZK state roots, and proofs via zkEVM. The compressed transaction data and ZK Proofs are then uploaded via the decentralized Oracle network to the taproot on Bitcoin L1, which is available to the entire network to ensure transparency and security. The decentralized Oracle network nodes will be required to stake $BTC to prevent improper activities. Users can initiate challenges to the ZK-Rollup based on compressed data, ZK state roots, and ZK Proof. Successful challenges result in a rollback to the last verified state, and the forfeiture of the Oracle node's locked $BTC, ensuring the system's security and trustworthiness. Through the integration of these technologies, Merlin Chain has shown its superior technical capabilities and is on track to build an efficient, secure, and native Bitcoin L2 solution. Native Integration - Merlin Chain's Bitcoin-Centric Approach Compared to other Bitcoin L2s that are making attempts at the moment, one of the key features of Merlin Chain is that it is "Bitcoin Native". As a strong believer in Bitcoin for many years, Merlin Chain is extremely committed to the development of the Bitcoin ecosystem, and seeks to bring more users and assets to Bitcoin through their work, rather than serving other ecosystems in the name of Bitcoin. As such, Merlin Chain places a high value on the native bitcoin nature of its ecosystem's projects and users. Merlin Chain supports bitcoin-native wallets. Empowered by the Particle Network's account abstraction, users can use their bitcoin wallets to seamlessly switch between L1 and L2 . Users can also experience $BTC swaps with other tokens, such as stablecoins, at any time without having to switch to an EVM wallet. In addition, Merlin Chain will support most of the major protocols on Bitcoin L1, including, but not limited to, BRC-20, Bitmap, BRC-420, Atomicals, SRC20, and Pipe. All inscriptions on L1 will be automatically whitelisted by Merlin Chain, allowing users to safely and affordably trade on it. In line with its pursuit of "Bitcoin Native", Merlin Chain plans to adopt BRC-20 as its governance token. This makes a lot more sense than other Bitcoin L2s that are currently using Ethereum standard tokens as governance tokens. The token will be released through a fair launch, with the exception of a small portion that will be distributed to VCs and the team, which will be given a 48-month release period that will not start within two years, demonstrating the team's and investors' confidence in Merlin Chain. Bridging the Gap - Simplifying Bitcoin Adoption and Fuel Growth Another tricky issue for the builders in the Bitcoin ecosystem is the lack of new users and new money coming in. While the Ordinals things have continued to be on fire, the number of active address has not seen significant growth. It is just the same group of people involved in different projects. A very fundamental reason for this is the complexity of Bitcoin. As well as the need to download a Bitcoin wallet, inscribing is also not very easy to understand, and users will need to spend at least two blocks of their time inscribing, as well as three blocks of their time for confirmation, which is complicated and time-consuming. Merlin Chain will solve this problem. Currently, Merlin Chain has been connected to 40 Ethereum chains, so for those users who are not familiar with Bitcoin, they can use their familiar wallets, such as MetaMask, and their familiar assets, such as stablecoins, $ETH, $BNB, etc. to interact on Merlin Chain. Users don't need to know the principles behind Bitcoin, or even what BRC-20 and Atomicals are, before they can experience Bitcoin in its native manner in their familiar way. For this purpose, Merlin Chain is going to integrate the first multi-chain inscription DApp, which will allow users to inscribe assets on other layers, including Bitcoin L1, on Merlin Chain. It will only take one block to wait for. At the same time, a launchpad will also be available on Merlin Chain, with a mechanism that allows every inscription launched on it to be valued at 100,000 FDV, allowing more users to easily purchase assets such as the BRC-20/ BRC-420/ Atomicals/ Runes at a very low price. Additionally, as part of its commitment to decentralization, Merlin Chain is going to release most of its governance tokens to the community. Users can stay tuned for the upcoming mainnet launch – the future of Bitcoin is about to unfold with Merlin Chain at the helm. About Merlin Chain Merlin Chain is a Bitcoin Layer2 that integrates ZK-Rollup network, decentralized oracle network, and on-chain BTC fraud proof modules. Merlin Chain is committed to empowering Bitcoin's native assets, protocols, and products on Layer1 through its Layer2 network, to Make Bitcoin Fun Again. Merlin Chainis a subsidiary product line of Bitmap Tech, a premier OG team boasting an overall market cap exceeding $500 million. The BRC-420 "Blue Box" collection under Bitmap Tech has become one of the hottest assets on Ordinals. Users can start following Merlin Chain's for more updates. Twitter Contact Jeff Merlin Chain bitmap@bitmap.tech This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Merlin Chain: Introducing a Native L2 Solution To Unlock Bitcoin's Potential 

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