I'm need to know this for my maths project and hers the question: If a bullet were to be shot down into the ground how deep would it go?

I've looked into Newton's approximation but the formula ( d=l*a/b) does not take velocity into consideration so I am basically stuck. Any suggestions?

Edit: Here us the bullet and ground I'm using along with some info that I find important for you guys to know:

Bullet:

9mm Parabellum FMJ (115gr)

Velocity: 360 m/s

Mass: 7.45

Volume: 0.862cm^3

Density: 8.642691415g/cm^3

Ballistic Coefficient: G1

Kenetic Energy:161.2477032926kg

Ground:

Reddish brown earth soil

Density:1.67g/cm^3

Had a long term GF with an incredible sex life, broke up a few months ago and have since been sleeping around a quite a bit. I found that with vast majority of the girls I need to spend 20-30 mins just fingering them before I can even try to go in, and even when I do I can never go too deep, but not because I've bottomed up, it's because the girth is too much. Anyone else got this problem? Ways to work around it?

Hi I play wows legends for about 1 year now. My main ships are german bbs, especially Scharnhorst and odin.

Those 2 ships have a lower caliber and with this less penetration.

Whenever I hit enemy ships I deal penetrating hits (not overpens) its just small numbers of about 3k damage

Now my question: will jaujard increase the damage I deal with my shells because they penetrate further into the ship without dealing citadel damage ?

Or is the 33% damage of a citadel the basic penetrating damage and it can't be improved without using ciliax( which is such a minimal increase that its a waste perk imo)

I run al Scharnhorst with kedrov and hide as inspiration btw if that impacts anything

Tl;dr:

Does jaujard improve effective damage because the bullets explode deeper in the ship after penetrating more armor layers?

Or does he only prevent shatters?

He doesn't make overpens more common btw

how does a 100 round drum mag increase penetration depth by .5?

just for my personal research reasons.

Depth estimation is one of the fundamental problems in computer vision, and it’s essential for a wide range of applications, such as robotic vision or surgical navigation.

Various deep learning-based approaches have been developed to provide end-to-end solutions for depth and disparity estimation in recent times. One such method is self-supervised monocular depth estimation. Monocular depth estimation is the process of determining scene depth from a single image. For disparity estimation, the bulk of these models use a U-Net-based design.

Although relative depth is perceived very easily by humans, the same task for a machine has proven quite challenging due to the absence of an optimal architecture. To tackle this issue, more complex architectures are chosen to generate a high-resolution photometric output.

The Hamlyn Centre’s research team from Imperial College London introduces a unique randomly connected encoder-decoder architecture for self-supervised monocular depth estimation. The model architectural design, capable of extracting high order features from a single image and the loss function for imposing a solid feature distribution, is credited for the idea’s success.

Quick 5 Min Read | Paper| Imperial Blog

https://preview.redd.it/2dzuaat8dl081.png?width=1024&format=png&auto=webp&s=19b44073ed5d4fa5c1497fde86882cd890689d41

In a theoretical setup: I have an expansive, five layered pile of dry, porous, unconsolidated sediment (fine-medium grained sand) with each layer having its own unique pure mineralogy of ~30cm thickness. This pile is relatively flat and covers an area large enough to be resolved by public-access satellite sensors (eg. LS-8; Senti-2 or ASTER).

Would the SWIR / TIR sensors (or any combination of bands / band ratios to specify mineralogy) pick up the reflectance of only the top layer of sediment, or would I expect to get a mixed spectral signature of the underlying heterogeneity? In the case of the latter, how deep could this penetration be or are there too many variables to consider for a direct answer?

As it can be seen in attached figure, the wider the distance between two electrodes gets, the deeper currents go and therefore the depth of investigation. My question is, what is the mechanism and why currents just don’t go directly across? Is there a physical/mathematical proof of this phenomenon?

Link to figure: https://images.app.goo.gl/siSQAAt6UZE4K9SY6

Hi there, my fellow RC players! 😄 I'll be honest: I'm not sure how this post came to be. I was (and still am) in a web of real life commitments, waiting (im)patiently for a certain promised and fabled post about ROT (... y'all know which one I'm talking about 😉). I was inclined to write something else before the update (and didn't want to disappoint u/somehowstrange and her request either. Although, to be fair, she was asking me more about ROT and POV, instead of SOL... sorry?😅). I was replaying this (already) engaging story set in London, smiling at all the literary references and historical mentions, when I realized that in our sub several people were talking about some of them in a plethora of scattered comments, but we kinda lacked a post to collect all (rather, most) of them. I'm really surprised I managed to write this post before the update (one day before 😬), time was NOT on my side. But if you thought I could just yield and not see through my commitment… well… (I adore her)

The real problem was selecting what to write: there's just so much we could discuss, from London geography to cultural aspects of Victorian society (even though the story is more likely set in the Edwardian one, with some steampunk contaminations). In order to avoid an essay nobody asked for, let's cut to the chase already damn you! I've decided to focus only on some topics, starting with the most obvious references, that lots of us have already noticed…

For each name, a story

It seems to me that the author put a lot of care in selecting the names for the main characters, starting right with our MC's suggested one, Irene. With a Greek origin, it means “peace” (check out Aristophanes' homonymous comedy if you haven't read it!), but that's not what are we talking about this time. Irene Adler (already mentioned en passant in this post) is a main character in the novel “A Scandal in Bohemia”, by Sir Arthur Conan Doyle, probably the most important female character, as well as one of the most intelligent, cunning and central characters in general, in all Sherlock Holmes' fictional universe. She is mentioned several times by Watson in other novels and tales, and she is described as having “the face of the most beautiful of women and the mind of the most resolute of men”. I think this description, and her personality as a

I'm writing my own custom physics for a rigidbody. I got the impulse resolution down but I can't stop it from sinking into stuff. I read [this] (https://www.reddit.com/r/godot/submit?selftext=true) and learned I had to get the penetration depth to stop stuff from sinking. Is there a function that exposes something similar to penetration/overlap like how collision normals are exposed with "state.get_contact_local_normal(idx)"?

>In both murine models, mm waves penetrate deep enough into tissue to reach muscle. In human skin, mm waves are mostly absorbed within the skin. Therefore, when extrapolating the effects of mm waves found in animals to humans, it is important to take into account the possible involvement of muscle in animal effects.

https://pubmed.ncbi.nlm.nih.gov/18220297/

Depth estimation is one of the fundamental problems in computer vision, and it’s essential for a wide range of applications, such as robotic vision or surgical navigation.

Various deep learning-based approaches have been developed to provide end-to-end solutions for depth and disparity estimation in recent times. One such method is self-supervised monocular depth estimation. Monocular depth estimation is the process of determining scene depth from a single image. For disparity estimation, the bulk of these models use a U-Net-based design.

Although relative depth is perceived very easily by humans, the same task for a machine has proven quite challenging due to the absence of an optimal architecture. To tackle this issue, more complex architectures are chosen to generate a high-resolution photometric output.

The Hamlyn Centre’s research team from Imperial College London introduces a unique randomly connected encoder-decoder architecture for self-supervised monocular depth estimation. The model architectural design, capable of extracting high order features from a single image and the loss function for imposing a solid feature distribution, is credited for the idea’s success.

Quick 5 Min Read | Paper| Imperial Blog

https://preview.redd.it/t4bbf2b8dl081.png?width=1024&format=png&auto=webp&s=f45132f5936fa28fa1a7e183ca8d1a6adb1b989a

Depth estimation is one of the fundamental problems in computer vision, and it’s essential for a wide range of applications, such as robotic vision or surgical navigation.

Various deep learning-based approaches have been developed to provide end-to-end solutions for depth and disparity estimation in recent times. One such method is self-supervised monocular depth estimation. Monocular depth estimation is the process of determining scene depth from a single image. For disparity estimation, the bulk of these models use a U-Net-based design.

Although relative depth is perceived very easily by humans, the same task for a machine has proven quite challenging due to the absence of an optimal architecture. To tackle this issue, more complex architectures are chosen to generate a high-resolution photometric output.

The Hamlyn Centre’s research team from Imperial College London introduces a unique randomly connected encoder-decoder architecture for self-supervised monocular depth estimation. The model architectural design, capable of extracting high order features from a single image and the loss function for imposing a solid feature distribution, is credited for the idea’s success.

Quick 5 Min Read | Paper| Imperial Blog

https://preview.redd.it/7ojtjs32dl081.png?width=1024&format=png&auto=webp&s=75ea6706fdb9da35e50674da0ff36b5f90cca407

Depth estimation is one of the fundamental problems in computer vision, and it’s essential for a wide range of applications, such as robotic vision or surgical navigation.

Various deep learning-based approaches have been developed to provide end-to-end solutions for depth and disparity estimation in recent times. One such method is self-supervised monocular depth estimation. Monocular depth estimation is the process of determining scene depth from a single image. For disparity estimation, the bulk of these models use a U-Net-based design.

Although relative depth is perceived very easily by humans, the same task for a machine has proven quite challenging due to the absence of an optimal architecture. To tackle this issue, more complex architectures are chosen to generate a high-resolution photometric output.

The Hamlyn Centre’s research team from Imperial College London introduces a unique randomly connected encoder-decoder architecture for self-supervised monocular depth estimation. The model architectural design, capable of extracting high order features from a single image and the loss function for imposing a solid feature distribution, is credited for the idea’s success.

Quick 5 Min Read | Paper| Imperial Blog

https://preview.redd.it/9od1pgl6dl081.png?width=1024&format=png&auto=webp&s=ddf7932f30d9efaff5ffa2685c8df4ab4d6b2a29