System vulnerability: When huge data amounts are been kept in electronic form, it becomes susceptible to many threats. The information systems in many locations are been interconnected through communication networks. The unauthorized access can occur at many access points in network and is not limited to single location. The data flowing over networks could be accessed; valuable information could be stolen while transmission or data could be altered without authorization. The denial-of-service attacks are launched by intruders to disrupt website operations. Internets are vulnerable than internal networks as it is open to everyone.

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Chapter 8.1, Problem 1CQ1

Program Plan Intro

System vulnerability:

When huge data amounts are been kept in electronic form, it becomes susceptible to many threats.

The information systems in many locations are been interconnected through communication networks.

The unauthorized access can occur at many access points in network and is not limited to single location.

The data flowing over networks could be accessed; valuable information could be stolen while transmission or data could be altered without authorization.

The denial-of-service attacks are launched by intruders to disrupt website operations.

Internets are vulnerable than internal networks as it is open to everyone.

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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…