diff --git a/README.md b/README.md
index 5c9cd59..e36ac23 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,10 @@
 
 The [**gr-tempest**](https://github.com/git-artes/gr-tempest) project (*monitor image espionage in GNU Radio*) is extended using *deep learning* to improve the quality of the spied images. The main goal is to recover the text present in the image captured by the espionage system. 
 
+You can also find a video of the system in operation, [available at this link](https://www.youtube.com/watch?v=ig3NWg_Yzag).
+
+## Cite this work (TODO)
+
 ## Data
 
 The data used can be found in [this link](https://www.dropbox.com/scl/fi/7r2o8nbws45q30j5lkxjb/deeptempest_dataset.zip?rlkey=w7jvw275hu8tsyflgdkql7l1c&st=e8rdldz0&dl=0) within a ZIP file (~7GB). After unzipping, you will find synthetic and captured images used for experiments, training, and evaluation during the work.
@@ -50,7 +54,7 @@ Clone the repository:
 git clone https://github.com/emidan19/deep-tempest.git
 ```
 
-Both [gr-tempest](./gr-tempest/) and [end-to-end](./end-to-end/) folders contains a guide on how to execute the corresponding files for image capturing, inference and train the deep learning architecture based on DRUNet from [KAIR](https://github.com/cszn/KAIR/tree/master) image restoration repository.
+Both [gr-tempest](./gr-tempest/) and [end-to-end](./end-to-end/) folders contains a guide on how to execute the corresponding files for image capturing, inference and train the deep learning architecture based on DRUNet from [KAIR image restoration repository](https://github.com/cszn/KAIR/tree/master).
 
 The code is written in Python version 3.10, using Anaconda environments. To replicate the working environment, create a new one with the libraries listed in [*requirements.txt*](./requirements.txt):
 
@@ -63,26 +67,10 @@ Activate it with:
 conda activate deeptempest
 ```
 
-Regarding installations with GNU Radio, **it is necessary to follow the [gr-tempest](https://github.com/git-artes/gr-tempest) instructions** (detailed below) and then run the following *grc* files flowgraphs to activate the *hierblocks*:
+Regarding installations with GNU Radio, **it is necessary to follow the [gr-tempest](./gr-tempest/README.md) instructions in this repository** *(even if you have already installed gr-tempest at a different location)*. After this, run the following *grc* files flowgraphs to activate the *hierblocks*:
 - [binary_serializer.grc](./gr-tempest/examples/binary_serializer.grc)
 - [FFT_autocorrelate.grc](./gr-tempest/examples/FFT_autocorrelate.grc)
 - [FFT_crosscorrelate.grc](./gr-tempest/examples/FFT_crosscorrelate.grc)
 - [Keep_1_in_N_frames.grc](./gr-tempest/examples/Keep_1_in_N_frames.grc)
 
-Finally run the flowgraph [deep-tempest_example.grc](./gr-tempest/examples/deep-tempest_example.grc) to capture the monitor images and be able to recover them with better quality using the *Save Capture* block.
-
-## References 
-```BibTex
-@INPROCEEDINGS{larroca2022gr_tempest,
-  author={Larroca, Federico and Bertrand, Pablo and Carrau, Felipe and Severi, Victoria},
-  booktitle={2022 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)}, 
-  title={gr-tempest: an open-source GNU Radio implementation of TEMPEST}, 
-  year={2022},
-  doi={10.1109/AsianHOST56390.2022.10022149}} 
-
-@article{zhang2021plug,
-  title={Plug-and-Play Image Restoration with Deep Denoiser Prior},
-  author={Zhang, Kai and Li, Yawei and Zuo, Wangmeng and Zhang, Lei and Van Gool, Luc and Timofte, Radu},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
-  year={2021}
-}
\ No newline at end of file
+Finally run the flowgraph [deep-tempest_example.grc](./gr-tempest/examples/deep-tempest_example.grc) to capture the monitor images and be able to recover them with better quality using the *Save Capture* block.
\ No newline at end of file
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