Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
Round 1
Reviewer 1 Report
The present manuscript overlaps with the following published paper:
Appl. Sci. 2022, 12, 2749. https://doi.org/10.3390/app12052749. If the authors can clearly distinguish between their results and the results presented in the aforementioned published paper, then it may be considered for publication.
Author Response
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Author Response File: 
Author Response.docx
Reviewer 2 Report
I have read the manuscript number: applsci-1926843with title: " Reconstruction Optimization Algorithm of 3D Temperature 2 Distribution Based on Tucker Decomposition 3 ",
and I found that, authors have been make a Simulations for three worldwide locations are presented and discussed. I would like the authors consider the following points before I recommend publication
1-The abstract of manuscript should be written in compact form, authors should mention the main findings in the abstract. The definition of SNR must be added in abstract.
2- In introduction, the novelty of the work isn't evident at the moment, the authors didn't give the proper credit to previous works. Authors should mention the previous work in the same trend.
3- In section 2, authors wrote an introduction on 3D temperature field reconstruction based on Tucker decomposition. Authors must add this introduction to the main introduction in section1.
4- The resolution of all labels on figures is not good and needs to improve
Author Response
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Author Response File: Author Response.docx
Reviewer 3 Report
The manuscript proposes a three-dimensional temperature distribution reconstruction optimization algorithm based on Tucker decomposition. The authors need to address the following concerns:
1. In the abstract, the research motivation is not clear. It is not clear on why the Tucker decomposition algorithm is not practical and accurate, which requires the authors to propose an optimization algorithm to improve accuracy and practicality
2. The same problem as above happens in the introduction. The lack of literature review on the existing techniques make the research motivation less significant. Furthermore, more justifications are required to emphasize the need of this research to come out with an optimization algorithm.
3. The mathematical notations and symbols in this paper are poorly presented. It seems like all the symbols are in bold format. Some are not presented in consistent manner, the authors need to check all mathematical equations, mathematical notations and symbols thoroughly. For example, equation (7) presents an obvious mistake.
4. The mathematical properties of the Turker decomposition are very common and I don't see how the properties are linked to the reconstruction algorithm in Section 2.2.
5. Section 2 really requires a full revision as the presentation and the writing are not clear.
6. The result section is not written well as well. The comparison should show the performance comparison with the existing technique or any benchmarking methods to convince the readers that the proposed technique really achieves a better reconstruction quality.
7. All results should be presented in a more professional way. The legend, x-label, y-label are not clear. More explanation on the reasons of obtaining those results should be included.
8. From the conclusion, it seems that the motivation is different from abstract. In the abstract, the motivation seems to propose an accurate and robust technique, but the conclusion tells us that the technique is less computationally complex. This is a very confusing statement. Furthermore, which results show that the proposed algorithm is less computationally expensive than existing techniques.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
The revised version looks better and my concerns are adequately answered. The paper may be accepted.
