Mars Perseverance Sol 1375: Front Left Hazard Avoidance Camera (Hazcam) gif

 

Mars Perseverance Sol 1375: Front Left Hazard Avoidance Camera (Hazcam) gif

NASA's Mars Perseverance rover acquired these images of the area in front of it using its onboard Front Left Hazard Avoidance Camera A.

Images acquired on Jan. 1, 2025 (Sol 1375)

FLF_1375_0789003677_772ECM_N0642278FHAZ00215_01_275J01 to FLF_1375_0789003377_757ECM_N0642278FHAZ00215_01_275J01

Image Credit: NASA/JPL-Caltech

Assembled by Barley Culiner with GIMP

Mars Perseverance Sol 1375: Front Left Hazard Avoidance Camera (Hazcam) gif

NASA's Mars Perseverance rover acquired these images of the area in front of it using its onboard Front Left Hazard Avoidance Camera A.

Images acquired on Jan. 1, 2025 (Sol 1375)

FLF_1375_0789002961_847ECM_N0642278FHAZ00215_04_075J01 to FLF_1375_0789004037_757ECM_N0642278FHAZ00215_04_075J01

Image Credit: NASA/JPL-Caltech

Assembled by Barley Culiner with GIMP

Auto stabilize mechanism, Multi-Object Kill Vehicle (MOKV)

General Specifications: CLASSIFIED

Multi-Object Kill Vehicle (MOKV)

SYSTEMAND METHOD FOR DISPENSING OF MULTIPLE KILL VEHICLES USING AN INTEGRATED MULTIPLE KILL VEHICLE PAYLOAD https://patentimages.storage.googleapis.com/ed/05/20/f709489c4174c3/US8575526.pdf

https://missiledefenseadvocacy.org/defense-systems/multiple-kill-vehicle-mkv/

warhead deployment

Gyroscope (24 weapon tracking)

Gyroscope destabilizes

Valve/circuit opens

Chemical reaction (52 gas)

Gyroscope stabilizes

Multiple kill vehicle (MKV) interceptor with autonomous kill vehicles https://patents.google.com/patent/US7494090B2/en

https://investors.lockheedmartin.com/news-releases/news-release-details/lockheed-martin-team-completes-testing-propulsion-component-mdas

https://www.aerospacemanufacturinganddesign.com/article/-multiple-kill-vehicle--mkv-l-/

https://missiledefenseadvocacy.org/defense-systems/multiple-kill-vehicle-mkv/

"The Multi-Object Kill Vehicle (MOKV) system allows more than one kill vehicle to be launched from a single booster. The system consists of a carrier vehicle with on board sensors and a number of small, simple kill vehicles that can be independently cued against objects in a threat cluster. The integrated payload is designed to fit on existing and planned interceptor boosters."

https://youtube.com/shorts/hQ90ThV697o?si=su52J9Cwd4Ul_0Dj

https://youtu.be/KBMU6l6GsdM?si=9U4m9VxEl42-CWi7

Multiple Kill Vehicle Completes Hover Test Embedded Computing Design

similar system used on EMP (Electromagnetic Propulsion) kraft

Method and apparatus for controlling passive projectiles https://patents.google.com/patent/US4738411A/en

Method for combatting of targets and projectile or missile for carrying out the method https://patents.google.com/patent/US4796834A/en

MISSILE DEFENCE SYSTEM https://patentimages.storage.googleapis.com/5f/ff/50/0f586c5153a305/US4925129.pdf

Autonomous precision weapon delivery using synthetic array radar https://patents.google.com/patent/US5260709A/en

Method and system for guiding submunitions https://patents.google.com/patent/US6481666B2/en

Shipboard point defense system and elements therefor https://patents.google.com/patent/US6549158B1/en

Shipboard point defense system and elements therefor https://patents.google.com/patent/US6563450B1/en

Shipboard point defense system and elements therefor https://patents.google.com/patent/US6603421B1/en

MULTIPLE KILL VEHICLE (MKV) INTERCEPTOR AND METHOD FOR INTERCEPTING EXO AND ENDO-ATMOSPHERC TARGETS https://patentimages.storage.googleapis.com/ee/9b/4e/5d82c301e5107f/US7494089.pdf

MISSILE SYSTEM WITH MULTIPLE SUBMUNITIONS  https://patentimages.storage.googleapis.com/94/87/5e/eaae9de7626a94/US20040169107A1.pdf

#dod 

General Specifications: CLASSIFIED

Superconducting Electromagnetic Iris

Superconducting Electromagnetic Iris

ER=EPR

Force pull wormhole open

--------

Potential consequences of wormhole-mediated entanglement https://arxiv.org/pdf/2108.07607

Wormhole and Entanglement (Non-)Detection in the ER=EPR Correspondence https://arxiv.org/pdf/1509.05426

General Relativistic Wormhole Connections from Planck-Scales and the ER = EPR Conjecture https://www.mdpi.com/1099-4300/22/1/3

https://www.admissions.caltech.edu/explore-more/news/physicists-observe-wormhole-dynamics-using-a-quantum-computer

--------

How does a magnetic trap work? https://arxiv.org/abs/1310.6054

Magnetic Trap https://www.nist.gov/image/trapjpg

========

superconductor entangled particle gateway

--------

antihydrogen fed wormhole

https://home.cern/science/physics/antimatter/storing-antihydrogen

On-demand entanglement of molecules in a reconfigurable optical tweezer array https://www.science.org/doi/10.1126/science.adf4272

========

https://www.galacticlibrary.net/wiki/Wormholes

stèle cintrée ; stèle à deux registres https://collections.louvre.fr/en/ark:/53355/cl010014482

Drone Swarm Mesh Network Rapid Construction Method

Drone Swarm Mesh Network Rapid Construction Method

Drone Swarm

https://www.nist.gov/additive-manufacturing

https://mitsloan.mit.edu/ideas-made-to-matter/additive-manufacturing-explained

-------------

https://ep.jhu.edu/courses/515655-metal-additive-manufacturing/

Metal Additive Manufacturing Laboratory https://www.memphis.edu/mamlab/

-------------

PyFlyt -- UAV Simulation Environments for Reinforcement Learning Research https://arxiv.org/abs/2304.01305

-------------

AI trainer clock rate acceleration -Gemini

import time

virtual_time = 0.0

simulation_speed_multiplier = 24

real_time_start = time.time()

for _ in range(1000):

    # Perform simulation steps

    virtual_time += 0.01 * simulation_speed_multiplier # Advance virtual time

    # ... your simulation logic ...

    time.sleep(0.01) # Keep real time pacing for the simulation

real_time_end = time.time()

print(f"Virtual time elapsed: {virtual_time}")

print(f"Real time elapsed: {real_time_end - real_time_start}")

-------------

https://github.com/sieuwe1/Autonomous-Ai-drone-scripts

https://github.com/gregora/Drone-AI

https://github.com/alexkoven/Autonomous-UAVs

BeAM Metal 3D Printing by anonymous

"""Spawns six drones, then sets all drones to have different looprates."""

import numpy as np

from PyFlyt.core import Aviary

# the starting position and orientations

start_pos = np.array([[-1.0, 0.0, 1.0], [0.0, 0.0, 1.0], [1.0, 0.0, 1.0],

[-1.0, 1.0, 1.0], [1.0, -1.0, 1.0], [1.0, 1.0, 1.0]])

start_orn = np.zeros_like(start_pos)

# modify the control hz of the individual drones

drone_options = []

drone_options.append(dict(control_hz=60))

drone_options.append(dict(control_hz=120))

drone_options.append(dict(control_hz=240))

drone_options.append(dict(control_hz=60))

drone_options.append(dict(control_hz=120))

drone_options.append(dict(control_hz=240))

# environment setup

env = Aviary(

start_pos=start_pos,

start_orn=start_orn,

render=True,

drone_type="quadx",

drone_options=drone_options,

)

# set to position control

env.set_mode(7)

# simulate for 1000 steps (1000/120 ~= 8 seconds)

for i in range(1000):

env.step()