어서 오세요!
토론은 여러분이 동료를 만나고 더 큰 과제를 함께 해결하며 그 과정에서 즐거움을 찾을 수 있는 공간입니다.
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- 수학 관련 농담, 언어유희 또는 밈을 공유하고 싶으신가요? Fun에서 할 수 있습니다!
- 다른 채널이 필요하다고 생각하시나요? Ideas에서 자세히 알려주세요.
업데이트된 토론
- I have already installed MATLAB, Simulink, Embedded Coder, and the Embedded Coder Support Package for STM32.
- My next goal is to show data (for example, a counter or sensor value) on the display.
- Does Simulink directly support the LTDC/TFT LCD on STM32H735G-DK?
- If not, what is the recommended workflow? Should I combine Simulink-generated code with a TouchGFX project in STM32CubeIDE?
- Are there any example projects or documentation on integrating Simulink with TouchGFX for display applications?
- NKTg₁ = x·p (Position–Momentum interaction)
- NKTg₂ = (dm/dt)·p (Mass-variation–Momentum interaction)
- From NKTg₁: [M⋅L2/T][M·L²/T][M⋅L2/T]
- From NKTg₂: [M2⋅L/T2][M²·L/T²][M2⋅L/T2]
- Position (x): 4.498×1094.498 \times 10^94.498×109 km
- Velocity (v): 5.43 km/s
- Mass (m): 1.0243×10261.0243 \times 10^{26}1.0243×1026 kg
- Momentum (p = m·v): 5.564×10265.564 \times 10^{26}5.564×1026 kg·m/s
- NKTg₁ = x·p ≈ 2.503 × 10³⁶ NKTm
- NKTg₂ ≈ -1.113 × 10²² NKTm (assumed micro gas escape)
- Total NKTg ≈ 2.501 × 10³⁶ NKTm
- Astronomy: describe planetary mass variation, star/galaxy formation, and long-term orbital stability.
- Aerospace: optimize rocket fuel usage, account for mass leakage, design ion/plasma engines.
- Earth sciences: analyze GRACE-FO data, model ice melting, sea-level rise, and mass redistribution.
- Engineering: variable-mass robotics, cargo systems, vibration analysis, fluid/particle simulations.
- Establishes a new fundamental unit (NKTm), independent of Newton and Joule.
- Provides a theoretical framework for variable-mass dynamics, beyond Newton and Einstein.
- Supports accurate computation and simulation of real-world systems with mass variation.
- Support for all software ecosystems, from desktop to web and mobile.
- Direct integration with inertia-measuring sensors.
- Simulation of objects from fundamental particles to planets and galaxies on a unified algorithmic platform.
- Support for all software ecosystems, from desktop, server, to web and mobile.
- Direct integration with sensors to measure inertia experimentally.
- Easy simulation of objects, from fundamental particles to planets and galaxies, on the same algorithmic platform.
- Core implementation: core algorithms for calculating varying inertia,
- REST/gRPC API: access to inertia data and system integration,
- 150+ client wrappers: deployment support for over 150 programming languages, from infrastructure to application, from physics simulation to robotics, aviation, and astronomy.
- Inertia can be measured directly, no longer an abstract unknown.
- All motion models – from elementary particles to galaxies – can be simulated and predicted accurately.
- Understanding of nature and the universe enters a new era, where intrinsic properties of objects become scientific data.
- I already did it successfully twice in the past for my previous groups ;
- If you take one problem specifically, Problem 60984. Mesh the icosahedron for instance, you can normally see the icon of the cover image in the top right hand corner, can't you ?
- I always manage to set cover images to my contributions (mostly in the filexchange).
- x is the position or displacement of the object relative to the reference point.
- v is the velocity.
- m is the mass.
- p is the linear momentum, calculated by p = m × v.
- dm/dt is the rate of mass change over time.
- NKTg₁ is the quantity representing the product of position and momentum.
- NKTg₂ is the quantity representing the product of mass variation and momentum.
- The unit of measurement is NKTm, representing a unit of varying inertia.
- If NKTg₁ is positive, the object tends to move away from the stable state.
- If NKTg₁ is negative, the object tends to move toward the stable state.
- If NKTg₂ is positive, the mass variation has a supporting effect on the movement.
- If NKTg₂ is negative, the mass variation has a resisting effect on the movement.
- Real-time planetary mass estimation using (x, v) data.
- Integration into orbital mechanics simulations in MATLAB.
- Potential extensions into astrophysics and engineering models.
- Data-driven planetary modeling in MATLAB.
- Improved sensitivity in detecting small-scale variations not included in standard NASA datasets.
- NASA JPL Horizons (planetary positions & velocities)
- NASA Planetary Fact Sheet (official masses)
- GRACE / GRACE-FO Mission Data (Earth mass loss)
- How to integrate the NKTg model into MATLAB orbital simulations.
- Whether conserved quantities like NKTg1 could provide practical value beyond astronomy (e.g., physics simulations, engineering).
- Verify interpolation of planetary masses using NKTg law.
- Compare with NASA real-time data (31/12/2024).
- Test sensitivity with Earth’s mass loss (NASA GRACE).
- All 8 planets’ interpolated masses match NASA values almost perfectly.
- Deviation (Delta_m) ≈ 0 → error < 0.0001%.
- Confirms that NKTg1 is conserved across planetary orbits.
- GRACE missions show Earth loses mass annually (10^20 – 10^21 kg/year).
- NKTg interpolation detects Δm ≈ 3 × 10^19 kg.
- This matches the lower bound of NASA’s measured range.
- NKTg₁ interpolation is extremely accurate for planetary masses.
- Planetary data can be reconstructed with negligible error.
- NKTg model is sensitive enough to capture Earth’s small annual mass loss.

- Which one is your favorite?
- Which ones do you want to add to your collection?
The functionality would allow report generation straight from live scripts that could be shared without exposing the code. This could be useful for cases where the recipient of the report only cares about the results and not the code details, or when the methodology is part of a company know how, e.g. Engineering services companies.
In order for it to be practical for use it would also require that variable values could be inserted into the text blocks, e.g. #var_name# would insert the value of the variable "var_name" and possibly selecting which code blocks to be hidden.
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