
MATERIALS SCI.+ENGR.:INTRO.-NEXTGEN
10th Edition
ISBN: 9781119503941
Author: Callister
Publisher: WILEY
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Chapter 7, Problem 1DP
To determine
The cold work is possible or not when the Brinell hardness is given.
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Work on Homework 3 - DIGITAL FORENSIC TECHNIQUES
Homework 3
Task 2: dd Extraction
A JPEG file has been split into three parts (of random size) and the parts have been placed into a larger file called segments.img
(not necessarily in their original order).
Download your personalized assignment files, segments.img, README.txt, and partitions.txt into your Kali Linux VM. Use
wget as before. For example, you'd use "wget
https://facsrv.cs.depaul.edu/zhuang28/courses/csec450/hw3/1111111/segments.img" (replace 1111111 with your 7-digit
DePaul ID) to download your segments.img.
For example, if the JPEG file has length 100000 bytes, it might be split into three parts as follows (all numbers are in decimal):
START OFFSET IN JPEG FILE (bytes) END OFFSET IN JPEG FILE (bytes)
PART
LENGTH (bytes)
part 1
3225663*512
part 2
2048040*512
part 3
47264 = 92*512 + 160
0
32256
32256+20480 = 52736
Then the file segments. img might have length 131072 bytes and be composed as follows:
PART
LENGTH (bytes)
START…
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…
Task 3: Create Partition Table
Zero out your 1GB hard drive /dev/sdb.
Now use fdisk to create an MBR partition table with three partitions:
1. Partition 1 has size (N * 20MiB) where N is the length of your first name, e.g., 5 * 20MiB for someone with first name "Alice".
2. Partition 2 has size (N * 20MiB) where N is the length of your last name, e.g., 8 * 20MiB for someone with last name "Aardvark".
3. Partition 3 uses the remainder of the disk.
Use echo part1 > /dev/sdb1, etc. as in the first Volumes lecture to write into each partition.
Now edit the file partitions.txt to include your explanation of how a hexdump of the partition table (the first 512 bytes of the hard drive) relates to the partitions that have been
created. You must include a hexdump of the partition table and indicate the meaning of the bytes for partition table entries 1, 2, and 3 as discussed in the Volumes lecture, including
partition type, starting LBA address, and size in sectors. You must also include the…
Chapter 7 Solutions
MATERIALS SCI.+ENGR.:INTRO.-NEXTGEN
Ch. 7 - Prob. 1QAPCh. 7 - Prob. 2QAPCh. 7 - Prob. 3QAPCh. 7 - Prob. 4QAPCh. 7 - Prob. 5QAPCh. 7 - Prob. 7QAPCh. 7 - Prob. 8QAPCh. 7 - Prob. 9QAPCh. 7 - Prob. 10QAPCh. 7 - Prob. 11QAP
Ch. 7 - Prob. 12QAPCh. 7 - Prob. 13QAPCh. 7 - Prob. 19QAPCh. 7 - Prob. 20QAPCh. 7 - Prob. 21QAPCh. 7 - Prob. 22QAPCh. 7 - Prob. 23QAPCh. 7 - Prob. 25QAPCh. 7 - Prob. 26QAPCh. 7 - Prob. 33QAPCh. 7 - Prob. 34QAPCh. 7 - Prob. 35QAPCh. 7 - Prob. 36QAPCh. 7 - Prob. 42QAPCh. 7 - Prob. 1DPCh. 7 - Prob. 3DPCh. 7 - Prob. 4DPCh. 7 - Prob. 7DPCh. 7 - Prob. 1FEQPCh. 7 - Prob. 2FEQPCh. 7 - Prob. 3FEQP
Knowledge Booster
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