New Crowdin translations - ko (#26396)

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docs/ko/neural_networks/mc_neural_network_control.md

@@ -22,7 +22,11 @@ If not, there are other possible NN frameworks, such as [Eigen](https://eigen.tu
 This document explains how you can include the module in your PX4 build, and provides a broad overview of how it works.
 The other documents in the section provide more information about the integration, allowing you to replace the NN with a version trained on different data, or even to replace the TLFM library altogether.
 
-If you are looking for more resources to learn about the module, a website has been created with links to a youtube video and a workshop paper. A full master's thesis will be added later. [A Neural Network Mode for PX4 on Embedded Flight Controllers](https://ntnu-arl.github.io/px4-nns/).
+:::tip
+For more information, see the Masters thesis from which this module was created: [Deep Reinforcement Learning for Embedded Control Policies for Aerial Vehicles](https://nva.sikt.no/registration/019b26689144-efeebae8-84d6-4413-ad7f-9aceb4ff7374).
+
+In addition, the (Norwegian) website [A Neural Network Mode for PX4 on Embedded Flight Controllers](https://ntnu-arl.github.io/px4-nns/) has a youtube video and a workshop paper .
+:::
 
 ## Neural Network PX4 Firmware
 

docs/ko/neural_networks/raptor.md

@@ -35,7 +35,7 @@ The method we developed for training the RAPTOR policy is called Meta-Imitation
 
 You can torture test the RAPTOR policy in your browser at [https://raptor.rl.tools](https://raptor.rl.tools) or in the embedded app here:
 
-<iframe src="https://rl-tools.github.io/raptor.rl.tools?raptor=false" width="100%" height="1000" style="border: none;"></iframe>
+<iframe src="https://raptor.rl.tools?raptor=false" width="100%" height="1000" style="border: none;"></iframe>
 
 For more information please refer to the paper at [https://arxiv.org/abs/2509.11481](https://arxiv.org/abs/2509.11481).