# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 3 Methods
### Data Creation and Its Sources  
In the next step, various sources of information were consulted to characterize and evaluate the variables used to create the dataset. We used Yahoo Finance and Google Finance to gather the values of future contracts, while data related to coffee variables were obtained from the Colombian Coffee Federation. Additionally, datasets available on Kaggle that contained sensor values for coffee were included.  
Despite efforts to compile comprehensive information, it was only possible to obtain some 27 variables directly. This limitation required simulating specific data points. Various simulation strategies were evaluated using Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) networks [34]. These methods were explicitly applied to the fertilizer usage variables, including Phosphorus (P), Potassium (K), Calcium (Ca), and Magnesium (Mg), which are crucial in the production phase. In Table 3, we present the parameters used to implement the neural network for simulating the missing variables.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Variable** & **Precision** & **Completeness** & **Consistency** & **Reliability** \\ \hline Temperature (\({}^{\circ}\)C) & **Integral:** & **Integral:** & **Integral:** Same value & **Integral:** \\  & **Integral:** & **Recorded for each batch** & **across all records** & **Measured with a calibrated thermometer** \\  & **Non-integral:** & **Non-integral:** & **Non-integral:** & **Non-integral:** \\  & & Missing record & **in reports** & **Non-integral:** \\  & & **Integral:** & **Recorded for each batch** & **Integral:** Same documents & **Integral:** Measured with a calibrated thermometer; \\ Humidity (\% relative humidity) & **Integral:** & 60, 65, 70, 75, 80 & **Non-integral:** & **Non-integral:** \\  & & Missing record & **Non-integral:** Different values & **Device not calibrated** \\  & & & & **Integral:** Data from official weather stations \\ Precipitation (mm/month) & **Integral:** & **Integral:** & **Integral:** & **Integral:** Same records across databases & **Integral:** \\  & 80, 100, 120, 150 & **Non-integral:** & **Non-integral:** & **Non-integral:** & **Based on Estimates** \\  & **Non-integral:** & **Nissing record** & **Non-integral:** Differences in reports & **Integral:** \\  & **Integral:** & **Integral:** & **Recorded for each batch** & **Integral:** Same scores across all reports & **Integral:** \\ Cup Score & **Non-integral:** & **Non-integral:** & **scores across all reports & **Evaluated by certified tasters** \\  & 105, 195 & **Non-integral:** & **Nissing record** & **Non-integral:** & **Non-integral:** \\  & **Integral:** & **Missing record** & **Inconsistent values** & **Unverified evaluations** \\ Price per Load of & 1,200,000, 1,300,000, 1,400,000 & **Integral:** Recorded for each batch; & **Integral:** Consistent prices across reports; & **Integral:** Based on reliable quotes; \\  & **Non-integral:** & **Non-integral:** & **Non-integral:** Inconsistencies in reports & **Non-integral:** \\  & & & & **Inconsistencies in reports & Unverified estimates \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data integrity characteristics for selected coffee cultivation variables.  
These simulation techniques allowed for the generation of a complete dataset suitable for use in the subsequent phase of the project.  
In Figure 2, it can be observed that, in the simulation conducted, GANs have a mean squared error (MSE) of 0.2920, while LSTM networks have an MSE of 0.9009. This result suggests that GANs have made better predictions compared to LSTMs. The MSE, which measures the average squared difference between the actual values and the model's predictions, indicates that a lower MSE value reflects superior performance. In this context, GANs appear to be better suited to capture more complex patterns and generate representative samples, especially when the data exhibit high variability.