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Smart Manufacturing, Emqx, Internet of Things, Mqtt.
and machinery directly. Level 2: Supervisory Control: This level involves supervisory control systems that gather data from multiple PLCs and control devices. It provides real-time monitoring, data aggregation, and limited control capabilities for specific areas or processes. Level 3: Manufacturing | medium | 4,078 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
Execution Systems (MES): MES is responsible for managing production scheduling, work orders, quality control, and overall coordination of manufacturing operations. It bridges the gap between the shop floor and enterprise systems. Level 4: Enterprise Systems: This is the highest level of the pyramid | medium | 4,079 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
and includes enterprise resource planning (ERP) systems, which manage business operations, including finance, sales, procurement, and planning. Data from the lower levels feed into these systems for higher-level decision-making. ISA-95 is more than just a pyramid hierarchy. On the other hand, it | medium | 4,080 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
provides a more comprehensive and standardized approach to integrating enterprise and control systems, helping organizations design and implement interoperability between business and manufacturing processes. The Automation Pyramid can be a useful visualization to consider when thinking about the | medium | 4,081 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
different levels of control within the context of implementing standards like ISA-95. A Reference Architecture for IIoT Based on UNS To build an efficient and scalable IIoT infrastructure. Get the Whitepaper → Categories of Information Model The Categories of Information Model refers to a | medium | 4,082 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
classification system that organizes and categorizes information exchanged between different levels of an organization’s manufacturing processes. This model provides a structured framework for defining and understanding the types of information that need to be exchanged to ensure effective | medium | 4,083 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
communication and integration between business and manufacturing processes. Control and Monitoring: This category includes information related to the real-time control and monitoring of equipment, processes, and production activities. It involves data such as sensor readings, setpoints, alarms, | medium | 4,084 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
operational statuses, and other data needed for immediate operational control. Production Schedule: This category encompasses information about production planning and scheduling. It includes data on production orders, work orders, production sequences, start and end times of tasks, and any changes | medium | 4,085 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
to the production schedule. Performance Analysis: This category involves information that is used to analyze the performance of manufacturing processes. It includes data related to cycle times, production rates, downtime, efficiency metrics, quality measurements, and other performance indicators. | medium | 4,086 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
Quality and Compliance: Information related to quality control and compliance falls under this category. It includes data about quality standards, inspection results, testing data, non-conformities, corrective actions, and compliance with regulations. Maintenance and Reliability: This category | medium | 4,087 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
includes information about equipment maintenance, reliability, and asset management. It covers data on maintenance schedules, maintenance activities, spare parts inventory, equipment condition monitoring, and predictive maintenance. Material Flow and Inventory: Information regarding the movement of | medium | 4,088 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
materials, inventory levels, and material requirements is included in this category. It involves data on material consumption, material requests, inventory quantities, and material tracking. Resource Allocation: This category encompasses information related to the allocation and utilization of | medium | 4,089 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
resources, including personnel, equipment, tools, and facilities. It includes data on resource availability, assignments, and usage. Order Fulfillment: Information related to order processing and fulfillment is categorized here. It includes data on customer orders, order status, order changes, | medium | 4,090 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
shipping details, and delivery schedules. Equipment Object Model The Equipment Object Model within the ISA-95 standard focuses on representing the physical and logical equipment and resources used in the manufacturing and production processes. This model provides a structured framework for | medium | 4,091 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
organizing, categorizing, and managing equipment, allowing for effective monitoring, control, and maintenance within the manufacturing environment. This model hierarchy is designed to reflect the physical and logical relationships between different equipment units and their respective roles within | medium | 4,092 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
the production process. While specific terminologies might vary based on the industry and organization, here is a common organization of the Equipment object model hierarchy: Enterprise: This is the highest level of the hierarchy, representing the entire organization or company. It encompasses all | medium | 4,093 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
sites and facilities. Site: A site is a physical location or facility where manufacturing operations occur. It can be a factory, plant, or any other facility. Multiple areas or zones can exist within a site. Area: An area represents a specific section within a site where a particular type of | medium | 4,094 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
manufacturing activity takes place. Areas could be designated for different processes, products, or functions. Unit: A unit refers to a distinct piece of equipment or a specific production unit within an area. Units can be individual machines, assembly lines, or process units. They are the primary | medium | 4,095 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
operational components of the manufacturing process. Control Module: A control module represents a functional aspect or module of a unit that can be controlled and monitored independently. It could be a specific subsystem, device, or component within a larger unit. Component: A component represents | medium | 4,096 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
smaller parts or sub-components that make up a control module. This level might not be present in all hierarchies and is particularly useful for complex systems. The purpose of ISA88 is to provide standards and recommended practices as appropriate for the design and specification of batch control | medium | 4,097 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
systems as used in the process control industries. Information Exchange between Level 4 (ERP) and Level 3 (MES) The Information Exchange Model between Level 4 (Enterprise) and Level 3 (Manufacturing Operations Management) within the ISA-95 framework involves the communication and data exchange | medium | 4,098 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
between the business processes at the enterprise level and the manufacturing operations processes at the operations management level. This exchange is crucial for aligning business strategies, production planning, and execution on the shop floor. The information models can be classified as | medium | 4,099 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
Resource, Production Capability, Product Definition, Production Schedule, and Production Performance. Resource Availability: Level 3 provides real-time updates to Level 4 about the current availability of resources and any potential constraints that might impact production. Based on production | medium | 4,100 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
orders, Level 4 sends requests to allocate specific resources for production activities. Production Capability: Level 4 shares information about the manufacturing capacity and constraints with Level 3. Level 3 replies the utilization of manufacturing capabilities to Level 4, indicating how | medium | 4,101 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
effectively the available resources are being utilized. Product Definition: Level 4 provides detailed specifications and requirements for the products to be manufactured. Level 3 verifies that the product specifications and requirements from Level 4 are accurate and feasible for production. | medium | 4,102 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
Production Schedule: Level 3 receives production plans from Level 4, detailing the sequence and timing of manufacturing activities required to fulfill the orders. Then, Level 3 communicates any changes or updates to the production schedule back to Level 4, such as delays, expedited orders, or | medium | 4,103 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
adjustments due to resource constraints. Production Performance: Level 3 shares real-time progress updates with Level 4, including information on quantities produced, completed tasks, and any deviations from the production plan. Level 3 provides data on quality inspections, test results, and any | medium | 4,104 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
quality-related issues that arise during production. In general, Level 4 initiates the exchange by sending production orders, work orders, and resource allocation requests to Level 3. Level 3 responds with updates on resource availability, production progress, quality data, and any changes to the | medium | 4,105 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
production schedule. Manufacturing Operations Management Activity Model The Manufacturing Operations Management (MOM) Activity Model is a part of the ISA-95 framework that focuses on breaking down and structuring the activities that occur within the manufacturing operations processes. It provides a | medium | 4,106 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
detailed view of the tasks and operations that need to be executed on the shop floor to fulfill the requirements specified by higher-level processes, such as production orders and work orders. The MOM Activity Model serves as a bridge between the high-level business processes defined at the | medium | 4,107 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
enterprise level (Level 4) and the specific actions carried out on the shop floor at the manufacturing operations management level (Level 3). Conclusion In conclusion, the ISA-95 standard has played a significant role in enhancing communication and integration in manufacturing, but adapting it to | medium | 4,108 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
modern industrial management faces challenges. The evolving complexity of manufacturing, coupled with rapid technological advancements like Industry 4.0, requires flexibility that the standard’s structured framework might not fully provide. In modern industrial management, EMQX and Neuron are two | medium | 4,109 |
Smart Manufacturing, Emqx, Internet of Things, Mqtt.
essential components that provide an ideal solution. Leveraging them as the MQTT broker and industrial gateway, organizations can elevate connectivity, data handling, and adaptability in dynamic production settings. Together, they seamlessly integrate into today’s industrial landscape. For more | medium | 4,110 |
Cryptography, Cybersecurity.
The Cyber threat which focused on a digital nation (Estonia) I have been investigating a new library from Google, and which analyses a range of vulnerabilities within cryptography [here]: One of the tests relates to the ROCA (Return of the Coppersmith Attack) vulnerability an RSA private key can be | medium | 4,112 |
Cryptography, Cybersecurity.
recovered from the knowledge of the public key [article]. It has the CVE identifier of CVE-2017–15361. It was found in the Infineon RSA library on the Infineon Trusted Platform Module (TPM) firmware and affected BitLocker with TPM 1.2 and YubiKey 4. With this the library was slopping in creating | medium | 4,113 |
Cryptography, Cybersecurity.
prime numbers, and rather than generating them randomly, it generated from and where k and a are generated randomly. In RSA, these are then multiplied to produce an RSA modulus: N=p.q From the modulus, it is then relatively easy to factorize back to p and q, and then easy to crack the RSA method. | medium | 4,114 |
Cryptography, Cybersecurity.
ROCA The attack focuses on using the Coppersmith method [2] to factorize the module, and where the research team — through responsible disclosure — were able to factorize the prime numbers and without gaining access to RSALib [1]: Overall the research team verified the cracking of 512-bit RSA keys | medium | 4,115 |
Cryptography, Cybersecurity.
within less than two hours and keys within 97 days (and at a cost of around $76 per crack on the Amazon AWS Cloud). These were achieved on a standard processor and could be considerably reduced on multi-process systems [1]: As we see, 2,048-bit keys (the current recommended size) take over 140 | medium | 4,116 |
Cryptography, Cybersecurity.
years on a single and will cost over $40,000 for a single crack. At the core of the crack is the availability of the public key, as some of the bits of the key make it easier for the system to find the factors. For TLS and PGP it is relatively easy as the keys must be known, but for payment cards | medium | 4,117 |
Cryptography, Cybersecurity.
and electronic IDs, it is less easy: In the research, the team assessed a wide range of datasets and found that the Estonian ID system and some TPM chips were highly vulnerable: Detecting ROCA The paper outlines the basic method of: To produce our weak keys, we need to create a product of primes: | medium | 4,118 |
Cryptography, Cybersecurity.
def product_of_primes(nprimes): product_of_primes=1 if (nprimes>len(PRIMES)): nprimes=len(PRIMES) for i in range(0,nprimes): product_of_primes *= primes[i] return product_of_primes In order to detect ROCA, we determine if there is a discrete log of a value for a log of 65,537 (mod N): def | medium | 4,119 |
Cryptography, Cybersecurity.
_HasDiscreteLog(value, base, n): b = base % n accumulator = 1 for unused_exponent in range(1, n): if accumulator == value: return True accumulator = (accumulator * b) % n return Falsedef IsWeak(modulus,nprimes): mod_product_of_primes = modulus % product_of_primes_val for i in range(0,nprimes): | medium | 4,120 |
Cryptography, Cybersecurity.
mod_p = mod_product_of_primes % primes[i] if not _HasDiscreteLog(mod_p, F4, primes[i]): return False return True Coding The following is an outline of the code [here]: import random import sysPRIMES = primes = (2,3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, | medium | 4,121 |
Cryptography, Cybersecurity.
89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, | medium | 4,122 |
Cryptography, Cybersecurity.
433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, | medium | 4,123 |
Cryptography, Cybersecurity.
827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069, 1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, | medium | 4,124 |
Cryptography, Cybersecurity.
1213, 1217, 1223, 1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373, 1381, 1399, 1409, 1423, 1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511, 1523, 1531, 1543, 1549, 1553, 1559, 1567, | medium | 4,125 |
Cryptography, Cybersecurity.
1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657, 1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811, 1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, | medium | 4,126 |
Cryptography, Cybersecurity.
1973, 1979, 1987, 1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083, 2087, 2089, 2099, 2111, 2113, 2129, 2131, 2137, 2141, 2143, 2153, 2161, 2179, 2203, 2207, 2213, 2221, 2237, 2239, 2243, 2251, 2267, 2269, 2273, 2281, 2287, 2293, 2297, 2309, 2311, 2333, 2339, 2341, | medium | 4,127 |
Cryptography, Cybersecurity.
2347, 2351, 2357, 2371, 2377, 2381, 2383, 2389, 2393, 2399, 2411, 2417, 2423, 2437, 2441, 2447, 2459, 2467, 2473, 2477, 2503, 2521, 2531, 2539, 2543, 2549, 2551, 2557, 2579, 2591, 2593, 2609, 2617, 2621, 2633, 2647, 2657, 2659, 2663, 2671, 2677, 2683, 2687, 2689, 2693, 2699, 2707, 2711, 2713, 2719, | medium | 4,128 |
Cryptography, Cybersecurity.
2729, 2731, 2741, 2749, 2753, 2767, 2777, 2789, 2791, 2797, 2801, 2803, 2819, 2833, 2837, 2843, 2851, 2857, 2861, 2879, 2887, 2897, 2903, 2909, 2917, 2927, 2939, 2953, 2957, 2963, 2969, 2971, 2999)def product_of_primes(nprimes): product_of_primes=1 if (nprimes>len(PRIMES)): nprimes=len(PRIMES) for | medium | 4,129 |
Cryptography, Cybersecurity.
i in range(0,nprimes): product_of_primes *= primes[i] return product_of_primesdef _HasDiscreteLog(value, base, n): b = base % n accumulator = 1 for unused_exponent in range(1, n): if accumulator == value: return True accumulator = (accumulator * b) % n return Falsedef IsWeak(modulus,nprimes): | medium | 4,130 |
Cryptography, Cybersecurity.
mod_product_of_primes = modulus % product_of_primes_val for i in range(0,nprimes): mod_p = mod_product_of_primes % primes[i] if not _HasDiscreteLog(mod_p, F4, primes[i]): return False return TrueF4 = 0x10001 nprimes=20 if (len(sys.argv)>1): nprimes=int(sys.argv[1])print("Number of primes | medium | 4,131 |
Cryptography, Cybersecurity.
used=",nprimes) print(f"For p, k={k}, a={a}") a=random.randint(1,10) k=random.randint(1,10) product_of_primes_val=product_of_primes(nprimes)p = k*product_of_primes_val+pow(65537,a,product_of_primes_val) a=random.randint(1,10) k=random.randint(1,10) print(f"For q: k={k}, a={a}") q | medium | 4,132 |
Cryptography, Cybersecurity.
64908741457145883358850701100615095578223493084762239607579753897494592072619278526488374890039885274779916897439019301672653242339327 p= 6740631945151887489398623511648264911889196907864482099045784584811 q= 9629474207359839270569462159011344291964192515080186903950074484157 Key is weak | medium | 4,134 |
Cryptography, Cybersecurity.
Conclusions One of the most costly cybersecurity attacks is a breach of the trust infrastructure. This typically involves the discovery of a secret key. Testing for ROCA is just one approach to making sure you are generating safe RSA keys. Here is the ROCA detection method here: | medium | 4,135 |
Cryptography, Cybersecurity.
https://asecuritysite.com/rsa/rsa_02 References [1] Nemec, M., Sys, M., Svenda, P., Klinec, D., & Matyas, V. (2017, October). The return of coppersmith’s attack: Practical factorization of widely used rsa moduli. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications | medium | 4,136 |
Earth, Observation, Science, Technology.
As we gaze into the horizon of Earth observation, a transformative era is unfolding before our eyes. The Advanced Information Systems Technology (AIST) program, a beacon of innovation within NASA, is pioneering the development of Earth System Digital Twins (ESDT). These digital twins are not mere | medium | 4,138 |
Earth, Observation, Science, Technology.
static replicas; they are dynamic, intelligent systems that integrate a plethora of models, continuous observations, and advanced information system capabilities. This integration is poised to revolutionize our understanding of the Earth system, offering unified representations and predictions with | medium | 4,139 |
Earth, Observation, Science, Technology.
unprecedented accuracy and detail. The Vision of AIST: A Synergy of Observation and Analysis At the heart of AIST’s vision lies the ambition to synergize new observing strategies with analytic collaborative frameworks. This synergy is encapsulated in the concept of Earth System Digital Twins | medium | 4,140 |
Earth, Observation, Science, Technology.
(ESDT), which seeks to transcend traditional observation methods. By harnessing intelligent, timely, and dynamic sensing, distributed across a network of platforms, AIST aims to enable the design and operation of new observing systems that are not only reactive but also predictive in nature. The | medium | 4,141 |
Earth, Observation, Science, Technology.
2023 AIST Solicitation has opened up new avenues for such technologies, focusing on three main objectives to further this vision. These include enabling new observation measurements through intelligent, dynamic, and coordinated distributed sensing; agile science investigations using advanced | medium | 4,142 |
Earth, Observation, Science, Technology.
analytic tools; and the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models and observations. Digital Twins: The Nexus of Data and Intelligence The essence of a digital twin lies in its ability to mirror the complexities of the Earth system through | medium | 4,143 |
Earth, Observation, Science, Technology.
a fusion of diverse observations and models. These digital twins serve as a nexus where data meets intelligence, providing a comprehensive representation of the Earth’s current state and its potential future scenarios. The ESDT framework is designed to be continuously updated, ensuring that the | medium | 4,144 |
Earth, Observation, Science, Technology.
digital twin evolves in tandem with the real-world system it represents. Recent developments in ESDT include the integration of continuous and targeted diverse observations, powered by data assimilation and fusion, to provide an accurate representation of the current state of the system. | medium | 4,145 |
Earth, Observation, Science, Technology.
Forecasting is facilitated by advanced computational capabilities, machine learning, and surrogate modeling, providing real-time or near-real-time prediction of future states of the system. Impact assessment uses the digital replica and forecasting capabilities with machine learning, causality, | medium | 4,146 |
Earth, Observation, Science, Technology.
uncertainty quantification, and advanced computation and visualization capabilities for running large amounts of simulated predictions quickly and at various spatial and temporal scales. Enabling New Observations and System Designs One of the primary objectives of the AIST program is to facilitate | medium | 4,147 |
Earth, Observation, Science, Technology.
the creation of new observation measurements. By leveraging intelligent, dynamic, and coordinated distributed sensing, AIST envisions a future where observing systems are designed with a level of sophistication that allows for real-time adjustments and optimizations based on the data being | medium | 4,148 |
Earth, Observation, Science, Technology.
collected. The 2023 AIST Solicitation emphasizes this by focusing on technologies that enable new observation measurements and observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing. These technologies are expected to optimize measurement | medium | 4,149 |
Earth, Observation, Science, Technology.
acquisitions by using diverse observing and modeling capabilities, representing various resolutions, dynamically coordinated and collaborating to provide complete representations of Earth Science phenomena. The Analytic Collaborative Framework: A New Paradigm The Analytic Collaborative Framework | medium | 4,150 |
Earth, Observation, Science, Technology.
(ACF) is another cornerstone of the AIST program. It represents a new paradigm in data analysis, where advanced tools and computing environments interact seamlessly with the observing systems. This framework enables agile science investigations that can fully utilize the vast amounts of diverse | medium | 4,151 |
Earth, Observation, Science, Technology.
observations, leading to more informed decisions and actions for societal benefit. The ACF is envisioned as a cloud-based cyberinfrastructure that will enable uniquely designed satellites in the Earth System Observatory to work in tandem to create a 3D, holistic view of Earth. It includes km-scale | medium | 4,152 |
Earth, Observation, Science, Technology.
resolution Earth system models and data assimilation systems along with an integrated set of analytic tools to enable the next generation of science discoveries and evidence-based decision making. This collaborative framework is part of a larger vision that includes the Earth System Digital Twin | medium | 4,153 |
Earth, Observation, Science, Technology.
(ESDT), aiming to integrate Earth science frameworks that mirror the Earth by a proxy digital construct. Impact and Applications: From Wildfires to Climate Change The applications of Earth System Digital Twins (ESDT) are vast and varied, offering transformative potential in environmental monitoring | medium | 4,154 |
Earth, Observation, Science, Technology.
and management. ESDT can significantly enhance our ability to monitor and respond to environmental challenges such as wildfires, climate change, and severe weather events. For instance, at Point Conception, California, The Nature Conservancy is utilizing a digital twin to revitalize ecosystems, | medium | 4,155 |
Earth, Observation, Science, Technology.
manage invasive species, and support conservation decisions. Moreover, ESDT can provide real-time visualization of decision-relevant information related to regionalized impacts of climate change, aiding decision-makers without expert technical knowledge. By providing tools for “what-if” | medium | 4,156 |
Earth, Observation, Science, Technology.
investigations, ESDT can generate actionable predictions that can guide policy decisions and emergency responses. These predictions are not limited to immediate responses but also extend to long-term planning and mitigation strategies. The integration of machine learning and advanced computational | medium | 4,157 |
Earth, Observation, Science, Technology.
capabilities allows for the simulation of various scenarios, helping policymakers and emergency responders to prepare for and mitigate the effects of natural disasters. Conclusion: A Bright Horizon for Earth Observation The AIST program’s commitment to developing ESDT is a testament to the bright | medium | 4,158 |
Earth, Observation, Science, Technology.
future of Earth observation. As we continue to advance our technological capabilities, the integration of new observing strategies with analytic collaborative frameworks will undoubtedly lead to a deeper understanding of our planet. The Earth System Digital Twins represent not just a leap forward | medium | 4,159 |
Earth, Observation, Science, Technology.
in observation technology but a leap towards a more resilient and informed society. The Destination Earth (DestinE) initiative exemplifies this leap, aiming to provide policymakers with advanced earth system capabilities for better preparation and responses to disasters related to extreme weather | medium | 4,160 |
Earth, Observation, Science, Technology.
events and climate change. The development of ESDT infrastructure and applications, such as ML-surrogate modeling and fully-functioning ESDT prototypes, is paving the way for a future where Earth observation becomes integral to sustainable development and environmental stewardship. | medium | 4,161 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
Matlab Programming Environment — Coursovie.com There are plenty of reasons why everyone should start learning to code at some point. Whether it is merely your passion or the fear of robots taking your job; it makes sense to start preparing yourself for the Jobs of the Future which requires a more | medium | 4,162 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
tech-savvy workforce. Having that said, I do not imply that coding is for everyone or those who don’t know how to code will not succeed in their career. It is apparent that jobs of the future will demand a different set of skills that is concentered around technology and inevitably programming to | medium | 4,163 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
some extent. In my case, I knew I had to learn to program, but the challenge was what programming language and how I should choose one. After doing extensive research and failing at learning different languages at the same time, I realized that I was asking the wrong question from the start. There | medium | 4,164 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
is nothing called Best programming language and although some languages are more relevant to your career such as HTML/CSS/Javascript for web development the coding foundation is almost the same. Being an electrical engineer, my options were not limited. Programming languages such as C/C++, Python, | medium | 4,165 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
Matlab, Mathematica were all great choices, but I couldn’t learn them all and fortunately, I didn’t have to. Four years later, I learned the hard way that that knowing one programming language would be equivalent to learning other languages to a great extent. I realized that learning to code is | medium | 4,166 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
actually nothing more than: Understanding the problem without making it more complicated Breaking the Problem into smaller parts so you could solve them once at a time Developing an easy solution to each step (Creating the Algorithm) Implement the solution Testing and Debugging your Code Optimize, | medium | 4,167 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
Optimize, Optimize Once you master these steps, you have already accomplished a lot. The rest is dealing with the programming syntax. If you ask any good programmer where to start, they would tell you just pick up any programming language, and once, you learn the basics of programming which are | medium | 4,168 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
common for all, you are ready to learn the syntax of any other programming language in much shorter time. The only way I know NOT to be able to learn a programming language is by focusing on programming syntax first. WHY MATLAB IS ONE OF THE BEST OPTIONS TO LEARN PROGRAMMING FUNDAMENTALS? Matlab | medium | 4,169 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
programming is one of the best options you have, to learn to program. It is very easy to learn, lots of learning resources, and it has multiple vibrant communities. It is a great point to start. On the other hand it is powerful enough that NASA is using it for their Pluto mission so you know you | medium | 4,170 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
are not learning a prototyping language. Using this programming language, you can learn start coding in a very short amount of time and build actual projects. Matlab Programming for engineers, www.coursovie.com BEST APPROACH TO LEARN MATLAB PROGRAMMING FAST The challenging part in start learning | medium | 4,171 |
Programming, Matlab, Matlab Programming, Coding, Learning To Code.
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