Display values from table: The contents from the table can be viewed by using “SELECT” statement which comes under Data Manipulation Language (DML). The Syntax to view the contents using asterisk () is as follows: SELECT FROM table_Name; Columns can also be retrieved by replacing all the column names in place of the asterisk symbol. The Syntax to view the contents is as follows: SELECT ColName1, ColName2, …, ColNameN FROM table_Name;

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Chapter 3, Problem 3.16RQ

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Display values from table:

The contents from the table can be viewed by using “SELECT” statement which comes under Data Manipulation Language (DML).

The Syntax to view the contents using asterisk () is as follows:

SELECT FROM table_Name;

Columns can also be retrieved by replacing all the column names in place of the asterisk symbol.

The Syntax to view the contents is as follows:

SELECT ColName1, ColName2, …, ColNameN FROM table_Name;

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Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…

here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…

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