Update README.md

README.md

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 license: mit
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+
 <center>
   <h1>CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions<br>
     <small>
@@ -12,30 +13,64 @@ license: mit
   </h1>
 </center>
 
-  <h5>
-    <i>Predictive maintenance cuts economic and safety risks in rotating machinery by leveraging vibration and acoustic data, which machine-learning
-    models convert into intelligent fault detectors. Acoustic signals are especially powerful for early fault detection and cooling fans, with simple rotational dynamics, 
-    are convenient proxies for complex rotors. Yet existing fan datasets lack disturbance models and controlled conditions. We present CANDAS, a controlled sound dataset 
-    featuring 28 h of recordings from two cooling fans under five modeled disturbance conditions.
-    </i>  
-  </h5>
-
-- ## Setup
-<h5>The experimental setup was carefully designed to collect sounds from cooling fans under different weight configurations and voltage levels. To compose our experimental setup,
-we selected two models of cooling fans: the <i>Delta AUC0912D</i>, referred to as <b>A2</b>, and the <i>Intel AUC0912D</i>, referred to as <b>A3</b>—both commonly found in electronic devices. In order
-to obtain a greater variety of sounds, an experimental arrangement was defined in which five weight configurations were applied to each cooling fan, under <b>two voltage levels: 9
-Volts and 12 Volts</b>.
-</h5> 
-
-- ## Vibratory disturbance model proposed
-<h5>
-  A model of vibrational disturbances was employed using neodymium magnets as weights, motivated by recent works on the creation of cooling fan datasets with the introduction of weights on the cooling fan blades.
-  In this model, a magnet is fixed to each blade of the cooling fan using superglue, forming a layer of magnets. This first layer makes it possible for new magnets to be stacked on top of the existing ones, creating new configurations of weight distribution
-</h5>
 
-<center>
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/689a959b5ec21199bf4cf3fb/65ZM3ExSoZoSrW44n6UON.png" width="700">
-  <h4>The black dots represent first-layer magnets attached to the cooling fan blades, while the blue dots indicate additional magnets stacked on top. It is possible to design both
-balanced configurations (i.e., Config 1) and severely unbalanced ones(i.e., Config 5).</h4>
-</center>
+## 📄 Citation
+
+If you use this dataset in your research, please cite our paper:
+
+LIMA, Henrique et al. **CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions**. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22., 2025, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025. p. 1670-1681. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2025.13864
+
+### BibTeX
+```bibtex
+@inproceedings{eniac2025candas,
+ author = {Henrique Lima and Cristofer Silva and Ricardo Nery and Wilmer Camacho and Ricardo Brazileiro and Rodrigo Monteiro and Andrea Ribeiro and Carmelo Bastos-Filho and Mariela Cerrada and Diego Pinheiro},
+ title = {CANDAS Dataset: A Cooling Fan Sound Dataset with Modeled Disturbances and Controlled Experimental Conditions},
+ booktitle = {Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional},
+ location = {Fortaleza/CE},
+ year = {2025},
+ pages = {1670--1681},
+ publisher = {SBC},
+ address = {Porto Alegre, RS, Brasil},
+ issn = {2763-9061},
+ doi = {10.5753/eniac.2025.13864},
+ url = {https://sol.sbc.org.br/index.php/eniac/article/view/38843}
+}
+```
+
+---
+
+## 📖 Brief Summary
+
+Predictive maintenance reduces economic losses and safety risks in rotating machinery by leveraging vibration and acoustic data, which machine-learning models convert into intelligent fault detectors.
+Acoustic signals are particularly effective for early fault detection. Cooling fans, due to their relatively simple rotational dynamics, provide convenient proxies for studying more complex rotor systems.
+Despite the growing interest in acoustic-based fault diagnosis, existing cooling fan datasets often lack **modeled disturbance scenarios** and **controlled experimental conditions**.
+We present **CANDAS**, a controlled sound dataset containing **28 hours of recordings** from **two cooling fan models** under **five modeled disturbance configurations** and **two voltage levels (9V and 12V)**.
+
+---
+
+## ⚙️ Experimental Setup
+
+The experimental setup was designed to systematically collect acoustic signals from cooling fans under controlled operating conditions. Two commonly used cooling fan models were selected:
+
+<div align="center">
+
+| Fan ID | Model           | Description                          |
+|--------|-----------------|--------------------------------------|
+| A2     | Delta AUC0912D  | commonly found in electronic devices |
+| A3     | Intel AUC0912D  | commonly found in electronic devices |
+
+</div>
+
+For each fan, five weight configurations were applied in order to simulate different levels of mechanical imbalance. Each configuration was evaluated under **two voltage levels**: **9 V** and **12 V**.
+This design allows controlled variation in rotational behavior, enabling reproducible disturbance scenarios.
+
+---
+
+## 𖣘 Vibratory Disturbance Model
+
+A model of vibrational disturbances was employed using neodymium magnets as weights, motivated by recent works on the creation of cooling fan datasets with the introduction of weights on the cooling fan blades.
+In this model, a magnet is fixed to each blade of the cooling fan using superglue, forming a layer of magnets. This first layer makes it possible for new magnets to be stacked on top of the existing ones, creating new configurations of weight distribution
+
+![Vibratory disturbance model](https://cdn-uploads.huggingface.co/production/uploads/689a959b5ec21199bf4cf3fb/65ZM3ExSoZoSrW44n6UON.png)
 
+*Figure 1 — The black dots represent first-layer magnets attached to the cooling fan blades, while the blue dots indicate additional magnets stacked on top. It is possible to design both balanced configurations (i.e., Config 1) and severely unbalanced ones (i.e., Config 5).*