Update README.md

README.md

@@ -45,7 +45,7 @@ For example, the following figure demonstrates the advantages of using **LWM-bas
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-  <strong>Figure:</strong> This figure shows the F1-score comparison of models trained with wireless channels and their LWM embeddings for LoS/NLoS classification. CLS embeddings are 32× smaller than raw channels, while channel embeddings are 4× larger. Three embedding types are considered: (i) pre-trained LWM embeddings, (ii) embeddings of imperfect raw channels, and (iii) taskspecific embeddings from a fine-tuned LWM.
+  <sub><strong>Figure:</strong> This figure shows the F1-score comparison of models trained with wireless channels and their LWM embeddings for LoS/NLoS classification. CLS embeddings are 32× smaller than raw channels, while channel embeddings are 4× larger. Three embedding types are considered: (i) pre-trained LWM embeddings, (ii) embeddings of imperfect raw channels, and (iii) task-specific embeddings from a fine-tuned LWM.</sub>
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