Please help me understand what's the deal with HDX Adaptive Transport...

2021.10.22 05:54 N2nalin Please help me understand what's the deal with HDX Adaptive Transport...

During troubleshooting for a weird issue (randomly, few user's sessions don't establish. Recently started, not that frequent but still), I found some logs (Event id 1050: citrix connection validation failed on domain "for user" for reason 'hashexchangefailed' ).
Based on that,I stumbled across the fact that since 1912 onwards, HDX Adaptive Transport will be enabled by default while it used to be disabled in 7.15 LTSR. The person in that Citrix Discussions post mentioned that disabling it resolved the issue. HOWEVER, even though the policy is indeed enabled on the Site, I don't see it being used since it is still using session as TCP and not UDP (which is EDT is supposed to use). There's also no MTUDiscovery settings that are in use as I don't see the values in default.ica OR in app server registry. Also don't see UDP on Director for user session, OR in details when I run ctxsession:
https://preview.redd.it/humlt449uyu71.png?width=1327&format=png&auto=webp&s=e7b8853f5512f18a1749722522b6bc96147b631e
So I really don't understand whether the policy is being applied or not, and if it is, could that be the reason for the intermittent sessions failures for user because I don't see any other logs that show anything else that's weird. Please help if you have any idea about this...
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2021.10.22 05:54 SnooPoems4937 Handling Large Consecutive Missing data for forecasting using LSTM

Hi there. I am trying to use LSTM for water level forecasting during the monsoon. I have multiple input water stations and one output water station. I am taking 4 days of data to predict the output station's water level the next day. I have a dataset from 1960 to 2013. The problem with my dataset is that it contains many missing values. Most of the data are missing during the non-monsoon months. Also, data is missing from 2003 to 2005 and in other places, many months too. I have imputed the data using python libraries, but the results are not good as the gap is too large.
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