Supervisory Control and Data Acquisition (SCADA): The SCADA system refers to a system of software and hardware elements. Generally, a SCADA system is a system which coordinates but does not control processes in real time. One of the advantages of a SCADA system is that the stored data can be retrieved for later analysis without any error or additional work. SCADA systems are normally used by industrial organizations and public as well as private sector companies to control efficiency, distribute data, maintain efficiency, and communicate issues with the system to help mitigate downtime.

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Chapter 14, Problem 8P

Program Plan Intro

Supervisory Control and Data Acquisition (SCADA):

The SCADA system refers to a system of software and hardware elements.

Generally, a SCADA system is a system which coordinates but does not control processes in real time.

One of the advantages of a SCADA system is that the stored data can be retrieved for later analysis without any error or additional work.

SCADA systems are normally used by industrial organizations and public as well as private sector companies to control efficiency, distribute data, maintain efficiency, and communicate issues with the system to help mitigate downtime.

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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…

Please provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…