valid_low_IDs contains character vectors that appear to be file names:
valid_low_IDs
valid_low_IDs =
{'5beeea64a79efb0001faf30a.csv' }
{'5db9e0044a8eb40431d5e782.csv' }
{'5e8ef2059eeb7a1af7502f3c - edited.csv'}
{'5e67640d82b4784eb93f1a32.csv' }
{'5ed5aeea88fecc14779b4a29 - edited.csv'}
{'5f8998f6bfe9d6067c4b8a72.csv' }
{'5fc959ba27fabf4111b918ec - edited.csv'}
{'5ff0a3d22c2ee6fa5d47c312 - edited.csv'}
{'59a9c0291b7a550001d6392e.csv' }
{'60b43c11dcee60f547e013ba.csv' }
{'60fdf891d19fd767ebc5eefc.csv' }
{'598fa1b80675b100014dacf3.csv' }
{'614e51ba8bd7590a59e92f9c.csv' }
{'6019ec157baab31e1ad590fc.csv' }
{'6079c41a26b231701ceb1e0a.csv' }
{'6089aa8120d7418a70f3eba7.csv' }
{'60243c357e64550d96eb47f6.csv' }
So the non-ID parts of those file names need to be removed. Here's one way:
IDs = strrep(strrep(valid_low_IDs,' - edited',''),'.csv','')
IDs =
{'5beeea64a79efb0001faf30a'}
{'5db9e0044a8eb40431d5e782'}
{'5e8ef2059eeb7a1af7502f3c'}
{'5e67640d82b4784eb93f1a32'}
{'5ed5aeea88fecc14779b4a29'}
{'5f8998f6bfe9d6067c4b8a72'}
{'5fc959ba27fabf4111b918ec'}
{'5ff0a3d22c2ee6fa5d47c312'}
{'59a9c0291b7a550001d6392e'}
{'60b43c11dcee60f547e013ba'}
{'60fdf891d19fd767ebc5eefc'}
{'598fa1b80675b100014dacf3'}
{'614e51ba8bd7590a59e92f9c'}
{'6019ec157baab31e1ad590fc'}
{'6079c41a26b231701ceb1e0a'}
{'6089aa8120d7418a70f3eba7'}
{'60243c357e64550d96eb47f6'}
On the other hand, low_condition(:,1) is a cell array of (scalar) string arrays:
disp(low_condition(:,1))
{["59a9c0291b7a550001d6392e" ]}
{["60243c357e64550d96eb47f6" ]}
{["6019ec157baab31e1ad590fc" ]}
{["614e51ba8bd7590a59e92f9c" ]}
{["60fdf891d19fd767ebc5eefc" ]}
{["5beeea64a79efb0001faf30a" ]}
{["5fc959ba27fabf4111b918ec" ]}
{["6079c41a26b231701ceb1e0a" ]}
{["5d6069f53f69fd0001c0e77f" ]}
{["6033acf84fec17043ffef374" ]}
{["5efd8bbe8f20d51cd2ab9327" ]}
{["608d4e91d08ff4d32dbba3ad" ]}
{["60b43c11dcee60f547e013ba" ]}
{["615d8b5b98addf4b3d65c41d" ]}
{["5ff0a3d22c2ee6fa5d47c312" ]}
{["60cef6bc855784dee78ffffd" ]}
{["6089aa8120d7418a70f3eba7" ]}
{["5c6400cbcb937d00012d8866" ]}
{["611cf405ca55c42b27807c3c" ]}
{["60b65dd419636fbf792d4470" ]}
{["5db9e0044a8eb40431d5e782" ]}
{["5dc32687547c1f24648b7c23" ]}
{["61742bd3cd78a9f853168cef" ]}
{["615c343a80f9bb6fd7362271" ]}
{["615db68d9d021708b6e64603" ]}
{["5e67640d82b4784eb93f1a32" ]}
{["5f8998f6bfe9d6067c4b8a72" ]}
{["60ac8807aaad354f950dd131" ]}
{["608ac09acb49bef04cade936" ]}
{["https://we.tl/t-eeVxbz35Vq?src=dnl"]}
{["5e83b43765a5720e30b71b37" ]}
{["5ed5aeea88fecc14779b4a29" ]}
{["5d175bfe7a5f6d001a64b545" ]}
{["598fa1b80675b100014dacf3" ]}
{["5aa67d76f053610001726e65" ]}
{["61133a6bb907d515d390d08b" ]}
{["5e8ef2059eeb7a1af7502f3c" ]}
{["" ]}
Convert it to a cell array of character vectors, stored here as cond:
cond = cellstr(low_condition(:,1));
disp(cond)
{'59a9c0291b7a550001d6392e' }
{'60243c357e64550d96eb47f6' }
{'6019ec157baab31e1ad590fc' }
{'614e51ba8bd7590a59e92f9c' }
{'60fdf891d19fd767ebc5eefc' }
{'5beeea64a79efb0001faf30a' }
{'5fc959ba27fabf4111b918ec' }
{'6079c41a26b231701ceb1e0a' }
{'5d6069f53f69fd0001c0e77f' }
{'6033acf84fec17043ffef374' }
{'5efd8bbe8f20d51cd2ab9327' }
{'608d4e91d08ff4d32dbba3ad' }
{'60b43c11dcee60f547e013ba' }
{'615d8b5b98addf4b3d65c41d' }
{'5ff0a3d22c2ee6fa5d47c312' }
{'60cef6bc855784dee78ffffd' }
{'6089aa8120d7418a70f3eba7' }
{'5c6400cbcb937d00012d8866' }
{'611cf405ca55c42b27807c3c' }
{'60b65dd419636fbf792d4470' }
{'5db9e0044a8eb40431d5e782' }
{'5dc32687547c1f24648b7c23' }
{'61742bd3cd78a9f853168cef' }
{'615c343a80f9bb6fd7362271' }
{'615db68d9d021708b6e64603' }
{'5e67640d82b4784eb93f1a32' }
{'5f8998f6bfe9d6067c4b8a72' }
{'60ac8807aaad354f950dd131' }
{'608ac09acb49bef04cade936' }
{'https://we.tl/t-eeVxbz35Vq?src=dnl'}
{'5e83b43765a5720e30b71b37' }
{'5ed5aeea88fecc14779b4a29' }
{'5d175bfe7a5f6d001a64b545' }
{'598fa1b80675b100014dacf3' }
{'5aa67d76f053610001726e65' }
{'61133a6bb907d515d390d08b' }
{'5e8ef2059eeb7a1af7502f3c' }
{0×0 char }
Now you can compare the two using ismember, and keep the relevant rows of low_condition:
to_keep = ismember(cond,IDs);
low_condition_keep = low_condition(to_keep,:)