Great, thanks a good explanation of where you are.
First, for my understanding, can you explain what you are measuring. I can't initially see why it is always positive. I would have thought that force would be a vector, or at least signed on each axis, first left, then right, then left, ....
Decide whether there is information in the offset, for example if you are sensing only magnitude. If you need it, that is you can't just ignore the DC component, then bear in mind that your dynamic range will need to cope. In any spectrum analysis you'll have a large DC, zero Hz, component.
"optimum sampling rates"? Sample a little faster than "optimum".
The force signal, waveform
will be a continuous spectrum, occupying predominantly a band of frequencies. You need to decide, or find from literature, or otherwise, the band of interest and base calculations on the highest frequency of this band. Filter above this to remove other "noise". sample above twice that.
You didn ot mention quantization. I presume you have that under control. ie enough bits per sample to adequately represent what you want.
For your question: where should you be looking?
For understanding: (Seem some of these you may have already)
Superposition: You can add things to create the waveform. Also that a DC component is additive.
Fourier analysis: Anything periodic can be made from sine, and cos, waves of various frequencies, usually harmonics
Nyquist sampling theory: Says you can sample and reproduce a signal if the sample rate is twice the highest frequency present (In tha baseband case).
That "2" comes sort of from the real signal having a Hermitian (even-symmetric) spectrum so, it's twice as wide as some might first think.
Sampling: Sample and holds, zero-order hold
Aliasing: (This word is used for multiple different things, but likely the main one you'll get is the right one): If you don't sample at an adequately high frequency for a signal component, id 2*f for component at f Hz, you'll not be able to tell the frequency of a component so it will reconstruct to another position.
If you google, a lot of the hits on "aliasing" will be for images. Although this is essentially the same phenomenom the explanations probably won't help, so I;d skip those pages, favouring instead the 1D signal pages.
Anti-alias filtering: If there are higher-frequency components that you are not interested in, or just low-level sea or system noise up to higher frequencies, you should filter these out before the sampling, or they will aliases all the noise up to the bandwidth of your system, back into your pass band.
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By graphically, do you mean with a figure or two?
https://en.wikipedia.org/wiki/Aliasing#/media/File:AliasingSines.svg is a figure that I like from
https://en.wikipedia.org/wiki/Aliasing#:~:text=In signal processing and related,of one another) when sampled.. Not impressed by most of the rest of the page: approach does not really match my field and jargon.
https://web.njit.edu/~joelsd/Fundamentals/coursework/BME310computingcw6.pdf Is not bad on some concepts.
I always found figures in the frequency domain with multiple images of the basic signal spectrum at multiples of the sampling frequency most convincing as they showed aliasing and the need for filtering. The second figure on this page
https://www.rs-online.com/designspark/getting-into-digital-signal-processing-sampling-aliasing is the cloests I found just now.
Most of the literature seems to obsess about wave height and direction.
This one has a reasonable summary, if from a statistical perspective. After all, as the waves are a random phenomenum, the spectrum is not fixed.
https://upcommons.upc.edu/bitstream/handle/2099.1/6034/06.pdf?sequence=7
The "dsp guide" by Smith, free online, is not bad, but still not that figure in the frequency domain that I like.
Figure 3.2 of
http://www.geethanjaliinstitutions.com/engineering/coursefiles/downloads/ece/dsp.pdf comes closest to the figure that would explain the need for sampling rates and aliasing to me. If the edges of those spectrum images overlap, you can't tell where they reconstruct to, so you will get corruption of the high frequencies.
Notes:
If that "always positive" thing is because you don;t have signed force, that is you sense only magnitude, this may limit what you can do with the answers you get.
Allow a margin, sample faster than suggested: Antialias filters are not brick-wall, that is they don't drop off suddenly at the nominated frequency. You'll need to allow for a slow fall offin power versus frequency. That is, filter out a little above your maximum frequency of interest and sample as if it were even higher,
Like most things, I'd expect forces from seas waves to have a rather wide spectrum, if in open areas, so you;ll have to decide which band the answers you want are in and filter above that.
Not sure how your setup works, but it may be that waves are not the only source of forces. Effectively you are likelly sampling the low-frequency end of all sea noise as well.
For signals with a maximum frequency thatis quite low, modern sampling, data loggers are often not challenged and can be considered as effectively infinitely fast in acquisition of a sample, have intergral sample and hold, so over-sampling, sampling at higher than necessary frequency can make things easier.
Personally, I hate descriptions which include "folding" or "folding frequency". If you have Fourier and complex numbers, then "shifting" will get the correct answers. If your signals are purely real, implying even-symmetric spectra, then "folding" will generally get the correct answer as well, but it's just ugly.
