Update file liuice_index.md

liuice/liuice_index.md

@@ -86,16 +86,9 @@ Training data consists of eight datasets. The dataset contains one to two datase
 
 ## Resources -- models, and code
 <a name="resources"></a>
-We provide a set of training data from both nominal and faulty operations. Training data can be downloaded from this [link](competition/trainingdata/trainingdata.zip). 
-
-If you are using this benchmark in your research, please use the following bibtex reference:<br>
-@article{jung2024benchmark,<br>
-  title={The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study},<br>
-  author={Jung, Daniel and Frisk, Erik and Krysander, Mattias},<br>
-  journal={arXiv preprint arXiv:2408.13269},<br>
-  year={2024}<br>
-}<br>
+We provide a set of training data from both nominal and faulty operations. 
 
+* Training data can be downloaded from this [link](competition/trainingdata/trainingdata.zip). 
 
 We provide a system description in the form of a state-of-the-art analytical model where model parameters are unknown. Note that the set of available signals is not sufficient to uniquely determine the model parameters. The model follows the same model structure as described in: 
 
@@ -109,6 +102,14 @@ The provided model is implemented in <a href="https://faultdiagnosistoolbox.gith
 The figure below is generated using the Fault Diagnosis Toolbox shows a structural representation of the mathematical model. Each row represents an equation and each column a model variable where blue are unknown variables, red are fault signals, and black are known signals.
 ![A structural representation of the model generated using the Fault Diagnosis Toolbox](images/structural_model.jpg){: width="40%"}
 
+If you are using this benchmark in your research, please use the following bibtex reference:<br>
+@article{jung2024benchmark,<br>
+  title={The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study},<br>
+  author={Jung, Daniel and Frisk, Erik and Krysander, Mattias},<br>
+  journal={arXiv preprint arXiv:2408.13269},<br>
+  year={2024}<br>
+}<br>
+
 ### Description of the diagnosis system interface and evaluation script
 
 The developed diagnosis system solution must be implemented in either Matlab or Python. We have provided an empty DiagnosisSystem class which has the following functions: