Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: AN EFFICIENT EQUILIBRIUM OPTIMIZER FOR PARAMETERS IDENTIFICATION OF PHOTOVOLTAIC MODULES
Review round: 1
Reviewer: 1

Output:
Basic reporting: Unfortunately, I found this manuscript very difficult to follow and dotted with many unclear sentences and grammatical errors.

Statements made in the manuscript are not adequately supported by relevant references.

Specific examples are provided under general comments to authors
Experimental design: The research question is well defined. However, I am not sure the actual work contributes to solving it.

Although the methods are described in detail, I found the description of the algorithm very difficult to follow.

Specific examples are provided under general comments to authors
Validity of the findings: The findings seem to be validated. However, as noted above, I don't see how they contribute to the research question.

Specific examples are provided under general comments to authors
Additional comments: This manuscript describes the application of the IEO algorithm enhanced by opposition-based learning, for the optimization of certain parameters related to the performances of solar cells. Three different types of cells are considered, each in either a single diode model or in a double diode model configuration. It is concluded that the algorithm is consistently able to identify the optimal set of parameters that leads to the smallest error (in comparison with other algorithms) between experimental and calculated I-V values across three points.

While solar cells are certainly important as a source of green renewable energy and while their performances clearly need to be optimized, I fail to see how this algorithm contributes to this endeavor.

In addition, I, unfortunately, found this manuscript very difficult to follow and dotted with many unclear sentences and grammatical errors. More specific concerns are listed below.

Major points
1.	In the abstract as well as in line 50, the authors suggest that determining the values of unknown parameters is important for optimizing solar cells. However, this paper deals with finding optimal values for a set of intrinsic solar cell parameters that cannot be directly controlled and using them to accurately calculate other intrinsic parameters. Can the authors explain how this has anything to do with the optimization of solar cell performances?
2.	On line 57 the authors suggest that certain parameters characterizing SDM are not provided by solar cell manufacturers and determining their values is challenging. Is this because solar cells manufacturers neglect to provide these values or because they don’t know them? Looking at the results later on in the manuscript, it seems that many combinations of these parameters are all in accord with solar cell global parameters. Given that, perhaps these unknown parameters do not have unique values in which case, how can they be determined?
3.	The two paragraphs starting on lines 74 and 85 seems to discuss the same topic, namely, metaheuristic optimization algorithms. Why not combine them?
4.	Line 95: The statement that the methods described in the previous paragraphs use large computational resources and lead to high error rates must be supported by several references.
5.	Line 100: I am not sure what the authors mean by an accurate PV model.
6.	EO algorithm section: Unfortunately, I find the explanation of the EO algorithm lacking and very unclear to the point that it is difficult to understand it. For example, what is the initial population mentioned in line 158? What is the concentration mentioned in line 159? What is the condensation mentioned in line 161? What are the dimensions mentioned in line 162? What is the last case of convergence mentioned in line 165? What are the wolves mentioned in line 170? Perhaps an overview of the algorithm will help to clarify these points.
7.	Line 204: The multiple statements in this section regarding the improved performances of the OBL algorithm should be supported by references.
8.	Line 242: What are the differences between the different runs of the IEO algorithm?
9.	Table 3: How were the ranges of each of the optimized parameters determined? This is especially important since in some cases, during the optimization, some of the parameters reached their boundary values (e.g., the Rs in entry 23 in Table 4 or the a parameter in many entries of Table 5).
10.	Line 263: It seems to me that Table 5 presents all the results for the monocrystalline cell and not just the best ones.
11.	Line 266: A small difference relative to what? It seems that the differences between the Rs values across different runs of the same system are much larger that the differences in this parameter between different systems.
12.	Line 268: I don’t understand how the results say anything about the relationship between Rs values and the I-V curve.
13.	Line 275: This sentence is completely unclear to me.
14.	Line 279: Why is the scattering observed in Figures 5-7 indicative that the IEO algorithm has the highest efficiency for the experimental I-V data? Also, highest efficiency in comparison to what?
15.	Figures 8 and 9: My understanding is that the algorithm was used to optimize parameter values so as to reduce the errors at three specific points on the I-V curves. How where then complete I-V curves constructed?
16.	Line 306: The optimization problems presented in this manuscript are pretty simple and encompass only three variables in the case of SDM and only five in the case of DDM. Thus, I find it strange that none of the other methods were able to perform well in these cases. This is especially true for the GA algorithm that provided very poor results. The authors should provide details on how the other methods were used in order to try and figure out the reason for their poor performances.
17.	The manuscript is dotted with many grammatical errors. Some, but not all, are given below. I would recommend that the manuscript is reviewed by a native English speaker.

Minor points
1.	Page 1, line 15: Change “PV system that include to define” to “PV system to define”.
2.	Line 23: Change “inspired from” to “inspired by”.
3.	Line 25: Change “in the estimating” to “in estimating”.
4.	Line 27: Change “is excellent” to “is superior”.
5.	Line 49: The sentence containing “…five unknown parameters unknown that unknown while the…” should be re-written.
6.	Line 90: Change “the” to “The” at the beginning of the sentence.
7.	Line 108: Change “which inspire” to “which is inspired”.
8.	Line 110: Change “variable” to “variables”.
9.	Line 111: Change “generate less error” to “generate smaller errors”.
10.	The paragraph starting at line 113 contains several references to numbered sections. However, none of the sections in this manuscript are numbered.
11.	Line 117: Change “conclusion” to “conclusions”.
12.	Line 119: What is the meaning of “can be approaching”?
13.	Line 200: Why is this section marked as “1”?
14.	Line 201: Change “we” to “We”.
15.	Line 201: Change “closed” to “closest”.
16.	Line 203: Change “given” to “gives”.
17.	Line 205: What is the meaning of the question mark?
18.	Line 217: Change “mult-idimensions” to “multi-dimensions”.
19.	Line 228: Which section?
20.	Line 231: Which section?


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: AN EFFICIENT EQUILIBRIUM OPTIMIZER FOR PARAMETERS IDENTIFICATION OF PHOTOVOLTAIC MODULES
Review round: 1
Reviewer: 2

Output:
Basic reporting: good
Experimental design: not required
Validity of the findings: well done
Additional comments: This paper presents A Parameter estimation of photovoltaic modules using an improved equilibrium optimizer algorithm using opposition based learning.  Generally, the paper is well written and the presentation is acceptable; the reviewer has the following comments and questions for the authors to address:

1.	Abstract need to be improved to highlight the contribution and the significant findings
2.	More critical review should be added to the paper with reference to the latest published papers such as “Modeling of PV system and parameter extraction based on experimental data: Review and investigation” and “Parameter Extraction of Solar Cell Using Penalty Based Differential Evolution”
3.	The title of the paper needs to be changed to be more specific
4.	Discussion on the limitation of the proposed method should be added
5.	The presentation of the results should be improved (color )
6.	Detail comparison with the most recent topologies is required in the form of a table


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: AN EFFICIENT EQUILIBRIUM OPTIMIZER FOR PARAMETERS IDENTIFICATION OF PHOTOVOLTAIC MODULES
Review round: 1
Reviewer: 3

Output:
Basic reporting: 1. In general, this paper is well written and contribute to the new knowledge in this field.
Experimental design: 1. What are the constraints considered for solving the objective function? It should clearly define and explain the methodology and summarize in the flowchart i.e in Figure 4.

2. Section: Comparative Study with Other Metaheuristics -
How do you compare with other algorithms?
Have you run the algorithms or compare them with the reported results by these algorithms?
How do you set the parameter setting for these algorithms for fair comparison? How to ensure the comparison study have been done properly / fair in term of parameter setting and so on.. most of the Heuristics algorithms are affected by the parameter setting i.e population size, max iteration, and others parameters. it should clearly explain in this paper.
Validity of the findings: 1. Error in Table 7, 8, 9 are zero (0). It recommended to display the value up to set decimal places such 4 decimal places.

2. Based on table 7 (Comparison), the reported errors are in decimal places. please revise for a fair comparison. What do you mean by Zero for IEO. The error of CSA 666E-14 also can describe as Zero (0)
Additional comments: This paper is suitable for publication after the authors revise the paper according to the comments/suggestions for improving the quality of the paper.

1. In general, this paper is well written and contribute to the new knowledge in this field.

Methodology
==========
1. What are the constraints considered for solving the objective function? It should clearly define and explain the methodology and summarize in the flowchart i.e in Figure 4.

2. Section: Comparative Study with Other Metaheuristics -
How do you compare with other algorithms?
Have you run the algorithms or compare them with the reported results by these algorithms?
How do you set the parameter setting for these algorithms for fair comparison? How to ensure the comparison study have been done properly / fair in term of parameter setting and so on.. most of the Heuristics algorithms are affected by the parameter setting i.e population size, max iteration, and others parameters. it should clearly explain in this paper.

Results and Discussion
==================
1. Error in Table 7, 8, 9 are zero (0). It recommended to display the value up to set decimal places such 4 decimal places.

2. Based on table 7 (Comparison), the reported errors are in decimal places. please revise for a fair comparison. What do you mean by Zero for IEO. The error of CSA 666E-14 also can describe as Zero (0)

Thanks


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: AN EFFICIENT EQUILIBRIUM OPTIMIZER FOR PARAMETERS IDENTIFICATION OF PHOTOVOLTAIC MODULES
Review round: 2
Reviewer: 1

Output:
Basic reporting: Has considerably improved and is now very good. Still, there are a few errors which are listed below in the general comment section.
Experimental design: Very good
Validity of the findings: The findings are valid.
Additional comments: The authors have considerably improved the level of the manuscript and have satisfactorily addressed almost all of my comments to the original submission. However, there are still few remaining points that should be addressed prior to publication:

Major points
1.	Many combinations of the optimized parameters led to the same error (i.e., 0). Does this fact has any practical meaning in the context of designing new SCs? Is there any way to further rank these combinations?
2.	Page 11, line 230: Despite the authors’ answer, I still don’t understand why the scattering presented in Figures 6-8 suggests that the IEO algorithm has the highest efficiency for the experimental I-V data. Highest efficiency in comparison with what?
3.	What is the purpose of Figure 12?

Minor points
1.	Page 2, line 51: Change “depend” to “depends”.
2.	Page 6, line 7: Change “consequently” to “consecutively”.
3.	The quality of Figure 4 should be improved.
4.	Page 8, line 179: Change “to” to “is”.
5.	Page 9, line 198: Change “best error” to “smaller errors”.
6.	Page 9: Move the “are listed in Table 4” from after Table 3 to before Table 3.
7.	Page 11, line 235: Change “solution” to “solutions”.
8.	Page 11, last line: Perhaps change “I-V characteristics” to “I-V plots”.
9.	Page 13, line 256: What is the purpose of the sentence starting with “In addition...”? In addition to what?
10.	In Tables 11 and 12, add references to the described work.
11.	In Figure 11, the labels of the y-axis are truncated.
12.	Table 12 is stacked in the middle of a paragraph.
13.	Page 15, line 283: Change “are accurate” to “are more accurate”.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: AN EFFICIENT EQUILIBRIUM OPTIMIZER FOR PARAMETERS IDENTIFICATION OF PHOTOVOLTAIC MODULES
Review round: 3
Reviewer: 1

Output:
Basic reporting: Very good
Experimental design: Very good
Validity of the findings: The findings are valid
Additional comments: The authors have satisfactory addressed all my comments and I now find the manuscript ready for publication.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DEFINING AND MEASURING MICROSERVICE GRANULARITY—A LITERATURE OVERVIEW
Review round: 1
Reviewer: 1

Output:
Basic reporting: Summary of the paper:
The study reports the state-of-the-art in research about defining and measuring granularity in microservices. To do so, the authors conduct a systematic literature review and try to 1) develop a synthesis of proposed approaches to define microservices granularity from literature, 2) Identify the metrics that are used to evaluate microservice granularity and 3) identify the quality attributes that research about microservices granularity addresses. The review ultimately includes the detailed analysis of 29 papers that investigate the development of microservices systems and how their microservices are created or extracted. The analysis of these papers first of all results to a categorization of the selected studies based on where they were published (journal or conference), phase that papers cover from the microservices development, what type of microservices development is (migration or otherwise), what was the research strategy, what type of contribution the paper resulted to and what research methodologies were used. Then, it provides an overview of the current contributions and gaps of the research landscape about defining granularity of microservices. This is done by organizing the papers based on the microservices development technique, type of contribution / outcome of papers and level of automatization. Further, the study identifies 67 metrics that are used in different studies to evaluate granularity. These metrics are themed based on coupling,  cohesion, complexity, performance, other quality attributes, computational resources, team, source code and granularity. Finally, the authors give an overview of quality attributes in defining microservices granularity. These are regarding, runtime, software as an artifact and quality attributes and AI.

An important improvement potential is the amount of text and reading time that is needed until reaching the objectives and Research Questions of the study. It is not easy to make the links between the motivation points, challenges, research gaps, objectives of the study and research questions. Therefore, i suggest for the research questions to be moved closer to the text that motivates the study and states the challenges and research gaps addressed.

Another aspect that this paper has large potential for improvement is structure. The division of sections can be organized differently to improve readability and understandability of the content. Specifically, section 2 and section 3 seem to have a very similar purpose of providing the required Background in the available literature and on drawing the theoretical landscape of the topic. Hence, maybe they could be either merged or explicitly describing their respective value to justify and motivate their separation. In addition, section 5 even though it is organized nicely based on the objectives of the study, the separation of descriptive results and reflections or discussion on concepts would make it easier to read through and understand. For example,in the "Classification of the selected papers", information / data gathered are described about the findings from reviewing the literature in the first and last 2 paragraphs, split by a paragraph that discusses and reflects upon migrations, agile practices and DevOps. I suggest that each piece of result is either presented and then discussed or reflected upon consistently, or all results are stated and then all reflections and discussions are made separately. The in-between format that is currently present is not optimal.

Finally, the language used is generally good and unambiguous with some minor errors, but generally the paper is well written. Some more minor points for improvement are:
- The past-sentence is used in the paper sometimes inconsistently which makes it hard to comprehend. The paper can be written entirely in present-sentence
- In section 2 and 3 the literature is reviewed in a very linear fashion, with small summaries of each study next to each other, supported by quotes. This is good but can be improved my synthesizing further the concepts from the different studies and making a more detailed review of the contents.
- In-text reference formatting is often incorrect. For example, in line 115: instead of "(Hassan, Bahsoon & Kazman, 2020) stated that a granularity...." should be
"Hassan, Bahsoon & Kazman (2020) stated that a granularity....". I suggest you review the referencing formatting more thoroughly in the entire text.
- Titles in section 5 are very long and they could be shortened.
Experimental design: Overall, the content of the paper is relatively good, with a satisfying number of studies involved in the review. This leads to a relatively comprehensive overview of the current state-of-the-art. However, there are some aspects that can be improved further. First of all, the topics that are researched, could be specified and introduced earlier in the paper. So, it should be clear what the objectives are, what are the research questions and then describe how this study is designed to address these. The objectives and research questions should be the starting point of justifying the methodological choices made. In addition, this would make clear why is important to answer the research questions asked in this study and thus, to motivate further the study choices.

Also, the queries that guide data extraction are nicely put and clear. However, a description presenting how they came into surface would provide clarity on why they are the right search terms. Some of them seem to be different and it is not clear what is the impact of that. For example, the term "decomposition" seem to me not equivalent to the rest of the search terms used and hence, how this affects the queries and their accuracy? Furthermore, the selection criteria are clear but their reasoning is not explicitly justified. Some elaboration to describe why are those criteria selected, what other criteria were considered, why other criteria were not selected and what are other potentially valuable criteria would make the authors' rationale more clear. Also, the selection of the papers is stated to be for studies that implicitly or explicitly discuss about granularity levels. This should be further discussed, both methodologically, but also in threats to validity. Finally, more details about the analysis conducted on the content of the chosen papers could be described further, to make clear how the knowledge was utilized.
Validity of the findings: On the content of the research questions, i suggest to further motivate why we need to answer these questions, where are they going to specifically contribute and most importantly if this is the kind of study we currently need in the field. Having that said, one can argue that most of the studies included in the review do not explicitly investigate granularity (granularity is not their central point that they study). They investigate other matters of microservices, like migrations or decompositions and discussion eventually brings some points about granularity. Hence i am reluctant to fully accept that a review on granularity specifically is what we need. I cannot see that enough papers specifically investigate granularity in depth and therefore, it is not clear to me how we can extract valid definitions and metrics about granularity of microservices from the studies included. Most of the studies discuss about granularity and state it as a challenge but not many investigate in depth how granularity is decided. Hence, the gap identified in this paper is valid but the reasons about why this type of research is accurately covering this gap is not clear.

Another very important issue with the presented study is on the analysis of the observations from the reviewed literature. There is room for further analysis to present deeper insights into the concepts. While the results are doing a good job in describing the definitions, metrics and quality attributes for granularity, it would be very insightful to discuss more relations and also differences between different studies. For example, the differences between different categories of papers on the metrics that they use to evaluate granularity. Furthermore, even though the metrics are organized in metric types, they could be analyzed more consistently and systematically, to make clear how impactful they seem to be (e.g. some metrics can be seen in many papers or some metric types appear more frequently). Deeper analysis of the results can be proven to be very insightful and also help making the current results less overwhelming and more organized. This in combination with the need to better motivate the research questions and adjust the research questions to be more accurate in addressing objectives and research gaps, leads me to my final verdict below.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DEFINING AND MEASURING MICROSERVICE GRANULARITY—A LITERATURE OVERVIEW
Review round: 1
Reviewer: 2

Output:
Basic reporting: Abstract.
Some results are not related to the research questions of the survey.  For example,
“The most used approaches were machine learning, semantic similarity, genetic programming, and domain engineering. Most papers were concerned with migration from monoliths to microservices; and a few addressed greenfield development, but none address improvement of granularity in existing microservice-based systems.”
Also, the quantitative results (as applicable) should be reported (e.g., number of metrics found)
Some conclusions are also not related to results and research questions.
Introduction
It is not clear why the “granularity” important topic and why research questions are important and relevant. Also, the summary of the findings should be included
Some statements are wrong/unjustifiable. SOA was always a style for loosely-coupled distributed systems
“SOA and web services are more related to monolithic applications that ….”
Previous work => Related work
Summary of some related works needs to be simplified (without simply coping the research questions used by those studies).
“(Ghofrani & Lübke, 2018) focused on solving the following questions …….”
The relevance/importance/relationship of “”granularity”, “metrics”, and “quality attributes” need to be highlighted. This would lead to research gaps and questions.
Experimental design: Survey methodology
In general. The author follows the standard literature review methodology.
Why does RQ1 include “microservice granularity and determine the microservices’ size?”  Is “size” not part of “granularity”?
Not sure how the keyword “dimensioning” is relevant
The inclusion/exclusion criteria should be listed clearly.
Results and discussions
The results, in particular metrics (RQ2), seems comprehensive.
There are some mismatches between RQ1 and the results in Section 5.2. As per RQ1, 5.2 should report “approaches to defining microservice granularity and size”.
As per RQ3, the reader will expect to see the relationship between the microservice granularity and quality attributes in Section 5.4. But, this perspective is not clear. In generals, the results should be related to RQs
There is no section on discussion of the results and their implications. A discussion section should be included.
Threats to validity can be more elaborated and made comprehensive. The authors are advised to study Threats to validity reported in the good existing survey papers.
Validity of the findings: A comprehensive catalog of microservice metrics are important. However, the application of these metrics should be highlighted.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DEFINING AND MEASURING MICROSERVICE GRANULARITY—A LITERATURE OVERVIEW
Review round: 1
Reviewer: 3

Output:
Basic reporting: Monolithic software systems are constructed as a single logical unit that encapsulates business functionalities, which share the same computational resources (e.g., memory space, CPU, and database). This architectural style has been used widely by companies since it provides a uniform and standardized codebase written in the same programming language that facilitates software engineers to
address a large number of cross-cutting concerns such as logging, security features, and exception handling. However, monolithic applications increasingly suffer from maintainability, testability, and scalability issues.

The microservices architecture can help to alleviate those problems as each service is supposed to exhibit well-defined functionality, high modularity, and, most importantly, each service should be able to evolve independently. The microservice architecture is an architectural style for building a single application as a collection of fine-grained and autonomous services, each one running in its process and communicating through lightweight mechanisms. In this realm, finding the most appropriate granularity for microservices is still a challenge.

In order to identify the main techniques and methods in the literature that define microservice granularity or that use it in the process of designing microservice-based systems, either from scratch or migrated from monoliths, the paper conducts a systematic literature review (SLR) that aims to answer three research questions: RQ1: Which approaches have been proposed to define microservice granularity and determine the microservices’ size? RQ2: Which metrics were used to evaluate microservice granularity? RQ3: Which quality attributes were addressed when researching microservice granularity? The paper analysed 29 studies published in conferences and journals.

The paper addresses an important topic for both academia and industry and that is in the scope of the journal. It is in general well written, although there are many text issues to be corrected or improved (marked as orange in the attached PDF file) or removed (marked as red in the attached PDF file). Figures and tables are good, but I wonder why they appear only after the references, since this reduced the paper readability.
The references are good. However, in the text, when citing the authors of a paper, you have to use direct citation. For example: “(Zimmermann, 2017) extracted the principles...” (line 155) should be “Zimmermann (2017) extracted the principles…”. Another example: “(Hassan, Bahsoon & Kazman, 2020) stated that a granularity…” turns into “Hassanm, Bashsoon and Kazman (2020) states that a granularity…”. This should be fixed throughout the whole text!
Experimental design: Following I point out my main concerns at each paper section.

1.	Introduction and Microservices granularity

I think those two sections should be merged in only one, the Introduction. The whole text of Section 2 could be placed at line 80. This will make clear the definitions of (microservice) granularity just after the authors affirm that that is still a challenge (lines 75 and 76). In addition, it will be easier to see the arguments for why addressing such research topic, i.e., the work’s motivation (currently lines 125 – 142). With this, the content of lines 81-86 should be adjusted to reference the body of text that will be introduced just before it. I also suggest stating the research questions after mentioning them at the Introductions (line 91).

2.	Previous work

I didn’t understand what the criteria were to include the papers in that section. As you are presenting an SLR, I was expecting that related work were papers that conducted an SLR on other topics of microservices, showing that none of them has approached the subject of granularity, or even discussing grey literature about microservice granularity. If the authors want to bring a broader discussion on different topics (application modelling and architecture; design and development patterns; industrial adoption; state of practice; grey literature review; and analysis and interviews with industry leaders, software architects, and application developers of microservices-based applications – lines 146 to 149), then more studies on those topics should be described in this section. For instance, I missed work about migration to microservices, since this is one of the main situations in which a more precise/appropriate microservice granularity impacts the system evolution process.

3.	Survey Methodology

Since the paper describes an SLR, I think the methodology could be more detailed. The authors followed a very classic schema proposed by Kitchenhan (2004). However, I also suggest the authors reading the following papers:
-	Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology 64 (2015). http://www.sciencedirect.com/science/article/pii/S0950584915000646.
-	He Zhang, Muhammad Ali Babar, and Paolo Tell. 2011. Identifying relevant studies in software engineering. Information and Software Technology 53, 6 (2011). http://www.sciencedirect.com/science/article/pii/S0950584910002260

   I think this section could be divided into (i) Planning the Mapping, where the authors could describe the Research Questions, Search Strategy, Study Selection and Data extraction; and (ii) Conducting the mapping, in which the process defined in the planning phase is executed and explained step-by-step.

About the methodology, I still have some questions/concerns:

-	Why did not the authors search other bases, such as SpringerLink and ScienceDirect?
-	Was the snowball technique used? If not, why?
-	I would expect more detailed exclusion criteria, such as: (i) studies published only as a (short) abstract; (ii) studies not written in English; (iii) studies to which it was not possible to have access; (iv) duplicate studies;
-	How did the authors resolve possible conflicts during the paper reading step?

4.	Results

-	Lines 365-366: “…and the remaining almost-half (14 of 29) used real-life case studies, thus achieving better validation”. What do you mean by better validation? Is that your feeling or something proved?
-	About the size, dimension or granularity of microservices, the authors provide 6 possible metrics (lines 387-392). However, I missed metrics regarding the number of methods or exposed interfaces. Wouldn’t that be included?
-	Lines 414-415: “Most papers proposed manual procedures to identify the microservices (15 papers);”. Shouldn’t it be “identify microservice size”?
-	Lines 461-462: “Several interesting works have been proposed, but there are still few specific, actionable proposals”. What do you mean by “still few specific, actionable proposals”?
-	I liked the discussion of RQ1. However, how is it related to the analysed studies (P1 to P29)? For instance, how or which of those work address microservice decomposition, patterns, migration, and microservice-based application developed from scratch? Even though Table 5 summarizes this data, it has to be explained in the text. The authors did that when answering RQ3.
-	Regarding answering RQ2, I would like to know which metrics are new and which ones are reused or adapted from general-purpose or other architectures, like SOA. Furthermore, it would also be very interesting if the authors could explain in which situation (e.g. migration, decomposition, development from scratch…) each metric has been used.
-	Some metrics are very general, such as logical coupling and semantical coupling. I wonder how those metrics have been applied to the microservice world, as the authors did in the dependency weight metrics. For instance, I am very curious to see how the metrics Function Point (line 689) has been used to estimate microservice granularity.
-	Lines 478-481: The authors said that classified the metrics into four groups, but it seems that there have been found metrics for only two groups. In that case, wouldn’t it be better to propose only two groups rather than four?
-	Lines 484 – 485:  The authors said that there have been found 14 coupling metrics and 13 cohesion metrics, but the text present 17 and eight metrics of each type, respectively. Please review the numbers for all types of metrics.
-	Please remove the period at the end of the names of subsections 5.2.1 to 5.2.8
-	Line 513: “I change a software component, I must…”. Please use the passive voice.
-	Why is “Contributor count and contributor coupling” (line 539) a coupling metrics rather than a team metrics?
-	Lines 553-557: Repeated Metrics.
-	Do the metrics Number of singleton clusters (line 702) and maximum cluster size (line 707) refer to containers rather than clusters when considering microservices?
-	Lines 711-713: Shouldn’t that paragraph be in Section 5.2.2?
-	Can the authors explain the difference between the metrics maximum request time (line 732) and maximum response time (line 735)?
-	Can the authors explain how the metrics cost of quality assurance (line 749) is related to microservice granularity?
-	Lines 762-763: “(Ahmadvand & Ibrahim, 2016) stated that the level of scalability required for each functional requirement or service.” -> Missing something in the sentence to make sense.
-	Wouldn’t “Internal co-change frequency” (line 830) and “External co-change frequency” (line 835) be coupling metrics?
-	Lines 860-861: “Being more granular implies that microservice has no dependencies and can function only”. I disagree with that. One microservice can be very big in terms of LOC, providing many services related to the domain object, but has no dependencies. Therefore, it would not be granular.
-	Where is the weight in the metrics “Weighted service interface count” (line 864)? What is the difference from these metrics to “operation number”, since both of them count the number of exposed public methods?
-	Wouldn’t “Number of microservices” (line 875) be a system metrics rather than a microservice metrics?
-	What is the difference between the metrics “Code size in lines of code” (line 815), classified as a source code metrics, and “Lines of code” (line 878), classified as granularity metrics?
-	What is a nanoentity (lines 881 and 882)?
-	Line 889: “Some have already been defined, mainly for migrations from monoliths to microservices”. Please add references to that sentence.
-	Lines 918-919: “Security and fault tolerance are key attributes that microservice-based applications must handle, few works addressed these features”. I wonder how security and fault tolerance can be used to design/estimate microservice granularity.
-	Line 1045: What is a DFD PS?
Validity of the findings: 5.	Threats to Validity

This section has to be completely rewritten, since the text there does not present the threats to the validity of the work and how they have been minimized. Usually threats to validity concern external, internal, construction, and conclusion validity. To a more detailed description about that, please refer to the book:  Experimentation in software engineering. Springer Science & Business Media.   Claes Wohlin, Per Runeson, Martin Höst, Magnus C Ohlsson, Björn Regnell, and Anders Wesslén.

6.	Conclusion

I think that section looks like more a summary of the whole paper rather than a conclusion section. Maybe the authors could create a subsection “Summary”, at the end of the Section “Results and Discussion” and move most of the text from this section to that new one. This way, the readers can find more easily what the conclusions of this work are.
Lines 1114 – 1116: “Finally, the main reason to migrate a monolithic application to microservices is precisely to improve performance and scalability, followed by fault tolerance, maintainability, and modularity”. Where did you get that information from? Could please cite some references?

Finally, although the authors mentioned research gaps, I would like to see possible future work of the presented paper.
Additional comments: The paper is interesting, but it needs some improvements. Please see my suggestions.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DEFINING AND MEASURING MICROSERVICE GRANULARITY—A LITERATURE OVERVIEW
Review round: 2
Reviewer: 1

Output:
Basic reporting: This revision seems to have addressed my review comments for the previous revision adequately.
Experimental design: The study design in this revision is rigorous, and include all key steps in a standard systematic literature review process.
Validity of the findings: In this revision, the research questions, results, and conclusions are properly aligned
Additional comments: This revision seems to have addressed my review comments for the previous revision adequately.

However, as the authors have not used a different text color for changes being done, it is hard to check the exact differences.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DEFINING AND MEASURING MICROSERVICE GRANULARITY—A LITERATURE OVERVIEW
Review round: 2
Reviewer: 2

Output:
Basic reporting: The authors considerable improved the paper by answering most of the reviewers' questions and accepting their text suggestions. Particularly, I thank the authors for having addressed my points. Tables and figures have also improved, and new references have been added.

I still found some minor problems in the text, such as:
- Line 216: "this is the first study focus specifically..." => "this is the first study that focuses specifically..."
- Line 221: "...introduced by." => please remove the period.
- Use of references as subjects of sentences, particularly in Section 5.2.  For example:
   * Line 491: "(Bogner, Wagner & Zimmermann, 2017b) performed..." => "Bogner, Wagner, and  Zimmermann (2017b) performed..."
   * Line 514: "(Ahmadvand & Ibrahim, 2016) said... "=> "Ahmadvand and Ibrahim (2016) said..."
    * Line 649: "According to (Cojocaru, Uta & Oprescu, 2019)," => "According to Cojocaru, Uta, and Oprescu (2019)"
     Please read that section through and fix that problem, since it happened so many times.
  - Line 720: there is a broken sentence: "It is the"
Experimental design: The study design is now clearer with the improvements in the structure of Section 3. The results in Section 4 were also better described and detailed.
Validity of the findings: Section 5 is a great improvement bringing discussion on the findings and presenting research gaps and trends. In addition, Section 6 is now in accordance with what we expect for a "Threats to validity" section.
Additional comments: I finish by asking the authors to have another round of reading and fixing text problems (some of them are indicated in the first section of my review). Apart from that, I am happy with the new version of the text.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ABCDE: APPROXIMATING BETWEENNESS-CENTRALITY RANKING WITH PROGRESSIVE-DROPEDGE
Review round: 1
Reviewer: 1

Output:
Basic reporting: The given paper uses deep graph convolutional neural network to approximate top-k nodes with highest betweenness-centrality score by estimating the rank score for each node in the graph. The proposed method obtains results which is an order of magnitude faster in inference hence requiring lesser computational resources. The literature review is structured with professional English language with proper figures and tables.

However minor concerns can be shown as follows:

1. Full form of BFS in Line 84 should be mentioned.

2. Accuracy and time can be presented in the form of a plot for easier interpretation of results
Experimental design: The research question is well designed with rigorous benchmarking across different networks and  algorithms in the literature. The methods are described in detail along with the code provided for the replication of results.

Minor concerns are given as:

1. In line 154-155, authors have taken 160 graphs for training and 240 graphs for validation purpose. Generally in machine and deep learning literature, validation samples are lesser than training. Can the authors provide the rationale for this design?

2. Progressive drop edge probabilities are taken from 0.3 to 0.1. However Rong et. al (2020) have taken the probabilities from 0.5-0.8 mostly. Can the authors provide the rationale behind this design parameter?
Validity of the findings: The results provide equivalent amount of accuracy as compared to the benchmarking algorithms for top 1%, 5% and 10% scores as shown in Table 2.

However the Kendall tau coefficient is better than the other algorithms as shown in Table 2 and 3.

Additionally this algorithm also takes lesser time as compared to other benchmarking algorithms as shown in Table 2 and 3. As shown in Table 4, the algorithm scales well with the increase in the number of nodes.

However, there are certain concerns about the variant of the graph convolutional network used in this paper about the result in Table 5.

1. No DropEdge shows better results when finding top 10% nodes, however ABCDE performs much better for top 1%. Can authors explain this phenomena? Is it dependant upon the nature of the graph used for validation?.

2. How does the results change with variation in DropEdge probability as per the analysis performed in Rong et. al?

3. The authors should provide plots regarding the training and validation loss performance of the for no DropEdge condition, 0.2 DropEdge and ABCDE algorithm similar to the one presented in Rong et. al.

4. The analysis on oversmoothing, for ABCDE and DropEdge algorithms should be present comparing with different DropEdge probabilities as provided in Rong et. al.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ABCDE: APPROXIMATING BETWEENNESS-CENTRALITY RANKING WITH PROGRESSIVE-DROPEDGE
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.	Improve citation writing, for examples Line 26-29, 35.
2.	The English language should be improved to ensure that an international audience can clearly understand your text. Some examples where the language could be improved include lines 31-32, 38-41, 55-57 etc. In general, the English language must be improved.
Experimental design: 1.	Line 106-107, explain in more detail the relationship of each variable in the sentence.
2.	In line 112: “the sparse adjacency matrix of edges”, what is the meaning?
3.	In Chapter 4: Method, sub bab 4.1 and 4.2 explain in more detail the relationship of each variable with graph notation, not just citing it. Likewise in 4.6,
Validity of the findings: Readability of the results of this paper is strongly influenced by the above improvements.
Additional comments: 1. 	Abstract: Does not explain or does not appear to be related to the title
2.     Line 25: importance node …. There is an error, node is not metric
3.     In general, this paper has low readability.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ABCDE: APPROXIMATING BETWEENNESS-CENTRALITY RANKING WITH PROGRESSIVE-DROPEDGE
Review round: 1
Reviewer: 3

Output:
Basic reporting: The authors have proposed a model for calculating approximation betweenness centrality by dropping edges progressively. The research work seems to be useful in the area of social network analysis. The authors have proposed graph convolutional networks to approximate the betweenness centrality in a large-scale complex networks.

1.	The novelty of the paper is justified.
2.	Motivation and application of the research work is unclear. An additional paragraph in the introduction section may be included to describe motivation.
3.	What is the significance betweenness centrality in social network application? Please explain
4. Recent papers related to social network analysis especially in the area of centrality analysis, community detection should include in the paper to improve the quality. The following references are suggested to cite in the paper.
a.	Behera, R. K., Naik, D., Rath, S. K., & Dharavath, R. (2019). Genetic algorithm-based community detection in large-scale social networks. Neural Computing and Applications, 1-17.
b.	Kumari, A., Behera, R. K., Sahoo, K. S., Nayyar, A., Kumar Luhach, A., & Prakash Sahoo, S. (2020). Supervised link prediction using structured‐based feature extraction in social networks. Concurrency and Computation: Practice and Experience, e5839..
c.	Kumar Behera, R., Kumar Rath, S., Misra, S., Damaševičius, R., & Maskeliūnas, R. (2019). Distributed centrality analysis of social network data using MapReduce. Algorithms, 12(8), 161.
d.	Behera, R. K., Naik, D., Sahoo, B., & Rath, S. K. (2016, October). Centrality approach for community detection in large scale network. In Proceedings of the 9th Annual ACM India Conference (pp. 115-124).
Experimental design: 1. What is the significance betweenness centrality in social network application? Please explain
2. What are the parameters you have set for GPU processing? Please explain it at the experimental setup section.
3. Is the proposed approach including any notion of distributing computing? How the work is different from the following paper.
     a. Naik, D., Behera, R. K., Ramesh, D., & Rath, S. K. (2020). Map-reduce-based
         centrality detection in social networks: An algorithmic approach. Arabian Journal
         for Science and Engineering, 45, 10199-10222.
    b.	 Behera, R. K., Naik, D., Ramesh, D., & Rath, S. K. (2020). Mr-ibc: Mapreduce-
         based incremental betweenness centrality in large-scale complex networks.
        Social Network Analysis and Mining, 10(1), 1-13

4. How your research finding is different from other works in the area of centrality analysis?
5. Which tools or package you have used to generate synthetic networks? Explain all the parameters that you have set to generate the synthetic network.
Validity of the findings: 1. The authors have experimented the proposed model using several real-world and synthetic networks.
2. The results obtained is justified.
3. Conclusion supports the proposed work clearly
Additional comments: The authors have proposed a model for calculating approximation betweenness centrality by dropping edges progressively. The research work seems to be useful in the area of social network analysis. Author has proposed graph convolutional networks to approximate the betweenness centrality in large scale complex network.

1.	The novelty of the paper is justified.
2.	Motivation and application of the research work is unclear. An additional paragraph in the introduction section may be included to describe motivation.
3.	What is the significance betweenness centrality in social network application? Please explain
4.	What are the parameters you have set for GPU processing? Please explain it at the experimental setup section.
5.	Is the proposed approach including any notion of distributing computing? How the work is different from the following paper.
   a.	Naik, D., Behera, R. K., Ramesh, D., & Rath, S. K. (2020). Map-reduce-based centrality detection
    in social networks: An algorithmic approach. Arabian Journal for Science and Engineering, 45,
    10199-10222.
    b.	Behera, R. K., Naik, D., Ramesh, D., & Rath, S. K. (2020). Mr-ibc: Mapreduce-based incremental
    betweenness centrality in large-scale complex networks. Social Network Analysis and Mining, 10(1),
   1-13
6.	How your research finding is different from other works in the area of centrality analysis?
7.	Which tools or package you have used to generate synthetic networks? Explain all the parameters that you have set to generate the synthetic network.
8.	Recent paper related to social network analysis especially in the area of centrality analysis, community detection should include in the paper to improve the quality. The following references are suggested to cite in the paper.
    a.	Behera, R. K., Naik, D., Rath, S. K., & Dharavath, R. (2019). Genetic algorithm-based community
    detection in large-scale social networks. Neural Computing and Applications, 1-17.
    b.	Kumari, A., Behera, R. K., Sahoo, K. S., Nayyar, A., Kumar Luhach, A., & Prakash Sahoo, S.
    (2020). Supervised link prediction using structured‐based feature extraction in social network.
    Concurrency and Computation: Practice and Experience, e5839..
    c.	Kumar Behera, R., Kumar Rath, S., Misra, S., Damaševičius, R., & Maskeliūnas, R. (2019).
    Distributed centrality analysis of social network data using MapReduce. Algorithms, 12(8), 161.
    d.	Behera, R. K., Naik, D., Sahoo, B., & Rath, S. K. (2016, October). Centrality approach for
     community detection in large scale network. In Proceedings of the 9th Annual ACM India
    Conference (pp. 115-124).
9.	The conclusion part supports the research work clearly.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ABCDE: APPROXIMATING BETWEENNESS-CENTRALITY RANKING WITH PROGRESSIVE-DROPEDGE
Review round: 2
Reviewer: 1

Output:
Basic reporting: No Comments
Experimental design: #2 - The author has claimed that the results are worse with 0.8 DropEdge probability. Can the authors provide the results for the same preferably in terms of the loss curve in Figure 2, along with the plots for No DropEdge, 0.2 probability DropEdge and ABCDE algorithm.
Validity of the findings: #1. The explanation provided is a bit weak, as the result does not seem very generalized.
One possible explanation might be due to the distribution of the betweenness centrality index of the nodes are different due to the nature of the graphs involved. Hence that causes a bit of a difference in the accuracy between top 1 and top 10%. Can the authors please investigate this?

#2.Figure 3 in Rong et. al has provided the training performance with two different values of DropEdge probability. Even if the results are worse (with higher DropEdge probability), the authors should provide the plots.
Additional comments: No Comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DATA-DRIVEN REMAINING USEFUL LIFE PREDICTION BASED ON DOMAIN ADAPTATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: 1.	The paper has some grammatical errors and proofreading of the paper needs to be done.
2.	Relevant references in line number 47 are not cited for the RNN techniques used popularly
3.	In the introduction section, the authors discuss the merits of using deep learning techniques under data-driven models for RUL predictions and the demerits of Machine learning techniques(line no 49). But no discussion is provided on the popular machine learning techniques such as SVM, Naive Bayes etc
4.	The authors can also discuss the use of other RNN architectures such as BRNN or Bayesian RNN for RUL prediction
5.	The authors highlight four categories of transfer learning- Instance-based Transfer Learning, Feature-based Transfer Learning, Model-based Transfer Learning and Relation based Transfer Learning. However, which category they have adopted in their paper is not mentioned.
6.	In Literature Review section, techniques with references are only cited but detailed explanation of only a few research work is given. Authors should add more detailed literature.
Experimental design: 1.	An extensive diagram depicting the entire system methodology need to be added.
2.	Explanation for using sliding window is not provided
3.	Author states that feature vector is formed using extracted feature. Detail explanation is need on the same like the vector size formed etc.
4.	Also explanation regarding the input of features to the model is missing.
5.	The figure quality of figure 2,5 and 6 needs to be improved. The text in some of the figures are not visible.
Validity of the findings: 1.	Result section explanation need to be improved. Result needs to be explained in terms of accuracy or error.
2.	Similarly the conclusion section need to be explained point wise based on model performance and comparison with other models used.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DATA-DRIVEN REMAINING USEFUL LIFE PREDICTION BASED ON DOMAIN ADAPTATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: It is assumed that the training data and test data fit the same distribution. This article proposes a data-driven framework with domain adaptability using a two-way gated repeating unit (BGRU). The framework used the domain opposite neural network (DANN) to implement the transfer from the source domain to the destination domain that contains only sensor information. We analyzed the IEEE PHM 2012 Challenge datasets to verify the effectiveness of the method and used them for validation.
The article is written in clear and understandable language. Literature references are sufficient. The article age and figures are given appropriately. However, the visibility of the visuals needs to be updated. I think some corrections and additions need to be made. My suggestions about the article are listed below.
Experimental design: The subject and content of the article is suitable for the content of the journal. It is considered that rigorous research is carried out according to a good technical and ethical standard.
Validity of the findings: -Explanation of the RUL expression should be given in the Abstract section, which is the first section it is used. “remaining useful life (RUL)”
-The Abstract section should be better worded. "In response to existing problems, this paper proposes a data-driven framework with domain adaptability using bidirectional gated recurrent unit (BGRU)." In this panel "existing problems" should be specified.
-The resolution of the figures can be increased.
-Labels are not read in Figures 5 and 6. Typefaces can be enlarged.
-In the Results section; The results obtained should be compared with the literature and the obtained values should be discussed in more detail.
-Conclusion section "[41], [42]" it refers instead should be made in a few sentences çneri.
The results obtained in the conclusion section should be given as items. If there are numerical results, they should be given here briefly.
Additional comments: -Explanation of the RUL expression should be given in the Abstract section, which is the first section it is used. “remaining useful life (RUL)”
-The Abstract section should be better worded. "In response to existing problems, this paper proposes a data-driven framework with domain adaptability using bidirectional gated recurrent unit (BGRU)." In this panel "existing problems" should be specified.
-The resolution of the figures can be increased.
-Labels are not read in Figures 5 and 6. Typefaces can be enlarged.
-In the Results section; The results obtained should be compared with the literature and the obtained values should be discussed in more detail.
-Conclusion section "[41], [42]" it refers instead should be made in a few sentences çneri.
The results obtained in the conclusion section should be given as items. If there are numerical results, they should be given here briefly.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DATA-DRIVEN REMAINING USEFUL LIFE PREDICTION BASED ON DOMAIN ADAPTATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: 1. The authors have incorporated all the suggestions.

2. The grammatical errors are removed and the manuscript is now ready in professional English.

3. Literature Review is now detailed. Few reference articles relevant to the manuscript are mentioned below. The authors can choose to add them in Literature Review section:

i. Kamat, P., Marni, P., Cardoz, L., Irani, A., Gajula, A., Saha, A., ... & Sugandhi, R. (2021). Bearing Fault Detection Using Comparative Analysis of Random Forest, ANN, and Autoencoder Methods. In Communication and Intelligent Systems (pp. 157-171). Springer, Singapore.

ii. Motahari-Nezhad, M., & Jafari, S. M. (2021). Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing. Expert Systems with Applications, 168, 114391. https://doi.org/10.1016/J.ESWA.2020.114391

iii. Wang, X., Wang, T., Ming, A., Zhang, W., Li, A., & Chu, F. (2021). Spatiotemporal non-negative projected convolutional network with bidirectional NMF and 3DCNN for remaining useful life estimation of bearings. Neurocomputing, 450, 294–310. https://doi.org/10.1016/J.NEUCOM.2021.04.048

iv. Sayyad, S., Kumar, S., Bongale, A., Bongale, A. M., & Patil, S. (2021). Estimating Remaining Useful Life in Machines Using Artificial Intelligence: A Scoping Review. Library Philosophy and Practice, 1-26.
Experimental design: 1. The authors can discuss the relation of the extracted features such as kurtosis, skewness, etc with bearing life.

2. Importance of the feature extraction step can be justified.
Validity of the findings: No comment
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: DATA-DRIVEN REMAINING USEFUL LIFE PREDICTION BASED ON DOMAIN ADAPTATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: It is seen that the authors made the necessary arrangements. The article is acceptable as it is.
Experimental design: The article has been prepared in adequate technical and ethical standards.
Validity of the findings: The results of the article are well expressed, linked to the original research question.
Additional comments: The language of the article and the findings have been developed. The article is better worded.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SENSITIVITY OF DEEP LEARNING APPLIED TO SPATIAL IMAGE STEGANALYSIS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The language needs polishing and a full revision by a native English speaker is needed.
Experimental design: Methods described with sufficient detail
Validity of the findings: Conclusion should highlight the achievements. Here it is more or less similar to the Abstract.
Additional comments: The paper presents a sensitivity analysis of CNN in steganalysis ((Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net)) , and offers recommendations to take into account when performing experiments in image steganalysis in the spatial domain. It is a very important work, since it shows how CNN architectures can be affected by multiple factors, from CNNs in their internal composition to the effect of databases.
I suggest the authors bring the following points into their consideration to revise the manuscript accordingly.

1.	Delete the details in caption  of Figure 1 as it is already explained in the text.
2.	In Table 1. I recommend mentioning what the acronym EC (Ensemble classifier) refers to. Nowhere in the document it is mentioned.
3.	In Table 7 it would be interesting to show also some results for 3xTanH using other values to multiply the function, like 5, 8, 13, 21 for example.
4.	Show the difference in training times, when using GPU, and when using TPU. This experiment could be presented for GBRAS-Net for example. This would also show the importance of using TPU's.
5.	Check, because the text of the link effectively leads to the repository, it cannot be done directly by clicking (possibly due to the generation of the Review PDF because it includes the 353 in the link, but it is still good to verify). It is also appreciated that the repository has been included.
6.	In the 'Discussion' section the expression epoch to epoch I do not consider correct, I propose to use each epoch
7.    Conclusion should highlight the achievements. Here it is more or less similar to the Abstract.
8.	Finally, citing the following will enrich the literature and be very useful for readers:
-Deep learning in steganography and steganalysis, 2020
-Digital Media Steganography:Principles, Algorithms, and Advances, Elsevier, 2020 , ISBN: 9780128194386,
-A Novel Image Steganography Method for Industrial Internet of Things Security, IEEE Transactions on Industrial Informatics, 2021
-An adaptive image steganography method based on histogram of oriented gradient and PVD-LSB techniques,IEEE Access


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SENSITIVITY OF DEEP LEARNING APPLIED TO SPATIAL IMAGE STEGANALYSIS
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1. Discuss the main motivations of the current work.
2. List out the man contributions of the current work.
3. Summarize the drawbacks of existing works in the form of a table.4. Some of the recent works such as the following can be discussed in the paper: "Image-Based malware classification using ensemble of CNN architectures (IMCEC), Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey, Hand gesture classification using a novel CNN-crow search algorithm".
4. Resolution of the images have to be improved.
5. Compare the current work with recent state-of-the-art.
6. Discuss the limitations of the current work.
7. Present the computational complexity of the current work.
Experimental design: Satisfactory.
Validity of the findings: Satisfactory.
Additional comments: 1. Discuss the main motivations of the current work.
2. List out the man contributions of the current work.
3. Summarize the drawbacks of existing works in the form of a table.4. Some of the recent works such as the following can be discussed in the paper: "Image-Based malware classification using ensemble of CNN architectures (IMCEC), Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey, Hand gesture classification using a novel CNN-crow search algorithm".
4. Resolution of the images have to be improved.
5. Compare the current work with recent state-of-the-art.
6. Discuss the limitations of the current work.
7. Present the computational complexity of the current work.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SENSITIVITY OF DEEP LEARNING APPLIED TO SPATIAL IMAGE STEGANALYSIS
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: In this manuscript, Reinel et al applied different CNN architectures to solve an important question faced by spatial image steganalysis, i.e., steganographic images  accuracy. The authors first provided an excellent overview of the field and then the working principles behind the different algorithms. An impressive test accuracy was reached, demonstrating the promise of the GBRAS-Net method. Overall, the paper is well written, and I recommend its publication after the authors address the following comments:

1.	What is the uncertainty of the CNN’s predictions? Specifically, what is the error bars in accuracies reported in Table 5 and Table 6. And what is the confidence band in accuracy curves (Fig. 4 and Fig. 5).
2.	Some numbers in Fig. 4 and Fig. 5 cannot be seen and the figure should be replaced.

3. Although CNN architectures seems to be applied with care, it would be helpful to assign physical meanings to feature and model selections beyond simple black box. More discussions in this aspect will add confidence to the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SENSITIVITY OF DEEP LEARNING APPLIED TO SPATIAL IMAGE STEGANALYSIS
Review round: 2
Reviewer: 1

Output:
Basic reporting: This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, a diverse composition of the training mini-batches, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy.
Experimental design: Satisfactory
Validity of the findings: Satisfactory
Additional comments: The authors have addressed all the comments. I recommend the paper can be accepted for publication.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SENSITIVITY OF DEEP LEARNING APPLIED TO SPATIAL IMAGE STEGANALYSIS
Review round: 2
Reviewer: 2

Output:
Basic reporting: 'No Comment'
Experimental design: 'No Comment'
Validity of the findings: 'No Comment'
Additional comments: Please fix text on page 16:

In response, recent investigations use the BOWS 2 dataset since it contains more information and consequently, with a ...


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ULTRASONIC BASED CONCRETE DEFECTS IDENTIFICATION VIA WAVELET PACKET TRANSFORM AND GA-BP NEURAL NETWORK
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is written in a good manner. Some minor touches can improve this paper more.
  The quality of the figures can be improved more
 The contributions of the authors are not clear. They have mentioned in first contribution.
Several paragraphs contain trivial information and should be dropped.
I found some English mistakes please check them.
 Future research directions section is core, however, it is not good at all.
Experimental design: - What are the computational resources reported in the state of the art for the same purpose?
- Please cite each equation and clearly explain its terms.
- Clearly highlight the terms used in the algorithm and explain them in the text.
Validity of the findings: - What are the evaluations used for the verification of results?
Additional comments: More recent papers must be included in all the sections and subsections.
1) Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems
2) Analysis of security and energy efficiency for shortest route discovery in low‐energy adaptive clustering hierarchy protocol using Levenberg‐Marquardt neural network, Transactions on Emerging Telecommunications Technologies, e3997


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ULTRASONIC BASED CONCRETE DEFECTS IDENTIFICATION VIA WAVELET PACKET TRANSFORM AND GA-BP NEURAL NETWORK
Review round: 1
Reviewer: 2

Output:
Basic reporting: 1.	In the abstract, the background knowledge on the problem addressed need to be added.
2.	In the abstract, the wide range of applications and its possible solutions need to be added.
3.	In the abstract, the problem addressed need to be justified with more details.
4.	In the Introduction section, the drawbacks of each conventional technique should be described clearly.
5.	Introduction section can be extended to add the issues in the context of the existing work
6.	Literature review techniques have to be strengthened by including the issues in the current system and how the author proposes to overcome the same.
7.	What is the motivation of the proposed work?
8. Research gaps, objectives of the proposed work should be clearly justified.
9. The authors should consider more recent research done in the field of their study (especially in the years 2018 and 2020 onwards).
10.	An error and statistical analysis of data should be performed.
11.	The conclusion should state scope for future work.
12.	Discuss the future plans with respect to the research state of progress and its limitations.
13. Kindly refer the below paper:
	1. Rajput, D.S., Basha, S.M., Xin, Q. et al. Providing diagnosis on diabetes using cloud computing environment to the people living in rural areas of India. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03154-4
Experimental design: 1. The authors should consider more recent research done in the field of their study (especially in the years 2018 and 2020 onwards). 6. The paper needs to provide significant experimental details to correctly assess its contribution: What is the validation procedure used?
2.	Kindly provide several references to substantiate the claim made in the abstract (that is, provide references to other groups who do or have done research in this area).
Validity of the findings: An error and statistical analysis of data should be performed.
Additional comments: 1.	In the abstract, the background knowledge on the problem addressed need to be added.
2.	In the abstract, the wide range of applications and its possible solutions need to be added.
3.	In the abstract, the problem addressed need to be justified with more details.
4.	In the Introduction section, the drawbacks of each conventional technique should be described clearly.
5.	The introduction needs to explain the main contributions of the work more clearly.
6.	The author should emphasize the difference between other methods to clarify the position of this work further.
7.	The Wide ranges of applications need to be addressed in Introductions
8.	The objective of the research should be clearly defined in the last paragraph of the introduction section.
9.	Add the advantages of the proposed system in one quoted line for justifying the proposed approach in the Introduction section.
10.	The motivation for the present research would be clearer, by providing a more direct link between the importance of choosing your own method.
11.	In the introduction, the findings of the present research work should be compared with the recent work of the same field towards claiming the contribution made.
12.	Introduction section can be extended to add the issues in the context of the existing work
13.	Literature review techniques have to be strengthened by including the issues in the current system and how the author proposes to overcome the same.
14. The paper needs to provide significant experimental details to correctly assess its contribution: What is the validation procedure used?
15. Kindly provide several references to substantiate the claim made in the abstract (that is, provide references to other groups who do or have done research in this area).
16. An error and statistical analysis of data should be performed.
17. The conclusion should state scope for future work.
18. Discuss the future plans with respect to the research state of progress and its limitations.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ULTRASONIC BASED CONCRETE DEFECTS IDENTIFICATION VIA WAVELET PACKET TRANSFORM AND GA-BP NEURAL NETWORK
Review round: 2
Reviewer: 1

Output:
Basic reporting: The abstract is too long. Too much general information. Please reduce it.
The authors should define the acronym at first instance then use this in the paper. like first the authors have used this acronym (GA-BPNN) and later they defined it at the end of the abstract.
There should be starting paragraph in between these two sections " GA-BP neural network (GA-BPNN)" and "BP Neural Network (BPNN)"
- The summary at the end of the literature review should be focused on the limitations of related work.
- The authors should further add explanation about research method.
Experimental design: - What are the evaluations used for the verification of results?
- Please cite each equation and clearly explain its terms.
Validity of the findings: --
Additional comments: - The authors should add some more background and motivation in the introduction.
- Reorganize the introduction, trying to explain every word of the title.
- Authors can cite the below paper fro neural networks
 Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: ULTRASONIC BASED CONCRETE DEFECTS IDENTIFICATION VIA WAVELET PACKET TRANSFORM AND GA-BP NEURAL NETWORK
Review round: 2
Reviewer: 2

Output:
Basic reporting: The research meets all applicable standards for the ethics of experimentation and research integrity.
Experimental design: Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail.
Validity of the findings: The study presents the results of original research.
Additional comments: 1. The study presents the results of original research.
2. Results reported have not been published elsewhere.
3. Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail.
4. Conclusions are presented in an appropriate fashion and are supported by the data.
5. The article is presented in an intelligible fashion and is written in standard English.
6. The research meets all applicable standards for the ethics of experimentation and research integrity.
7. The article adheres to appropriate reporting guidelines and community standards for data availability.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CLASSIFICATION OF HIGH-VOLTAGE POWER LINE STRUCTURES IN LOW DENSITY ALS DATA ACQUIRED OVER BROAD NON-URBAN AREAS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The literature is not sufficient. For example, line number 53-57 and 65-66 do mention "some studies". Which studies? What are the gaps which motivated you to conduct this research?

Introduction is but messy--Reorganise it. The authors has tried to provide gaps to highlight motivation but the discussion is too divergent. Why solving this problem is really important? There are few recent studies that you have missed to include.

What outcome is actually expected?
Experimental design: The research gaps are not well presented. What authors want to achieve is not well stated. Section 2, provides all details in one section briefly. For example, if you discussed about datasets, why not to provide more details about it (especially, reliability concerns about the dataset). Validation, which should be one of the main focus, is presented briefly.
Results and discussion is okay but they are not well connected with previous and further sections. Elaborate each section and make a link between them.
Conclusion need to be modified--what is the key take away message? Do your findings has applicability in industries and how it could provide value to them?
Validity of the findings: The validation is provided but there are many threats to validity, which are not addressed. Provide the details about different threats and mention that how you actually addressed them (if you have) or failed to address. Can you study be treated as valid and accurate? The discussion is not sufficient to build this trust. You must explicitly mention it in detail.
Additional comments: I hope that incorporating my feedback and restructuring your article, will help to improve your research submission.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CLASSIFICATION OF HIGH-VOLTAGE POWER LINE STRUCTURES IN LOW DENSITY ALS DATA ACQUIRED OVER BROAD NON-URBAN AREAS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper has deficiencies in English expression, sentence structure, and tense usage. Please have the article proofread by an expert.

Literature is not systematically reviewed and presented in line with current research. Please add a more in depth introduction and literature review by studying more recent publications and explain what this research conveys differently.
Experimental design: The authors are advised to include main contributions or research questions to better highlight the novelty of the research.

The motivation to carry out this work is missing. The introduction section should include a discussion on this aspect.

In the present form it in very difficult to understand various equations. The authors must elaborate different variables or parameters in each equation before writing it.

The authors must include a discussion on why the eigenvalue decomposition method is used. Are there other methods available? Can they be used? Why or why not?

Para line 37-44:  the authors need to establish the strong background for this claim. Some recent work along with the challenges should be included. Is it one of these challenges? Establish this with support of similar work references in this regard.

Para line 46-51: Similarly, authors need to support this claim with recent work.

Para line 53-57: References required.
Validity of the findings: Authors have included an elaborative discussion on validation of their work.

The missing aspect is:
Compare this research's findings with recent state-of-the-art and explain the benefits of the approach
Additional comments: The authors are advised to include main contributions or research questions to better highlight the novelty of the research.

The motivation to carry out this work is missing. The introduction section should include a discussion on this aspect.

In the present form it in very difficult to understand various equations. The authors must elaborate different variables or parameters in each equation before writing it.

The authors must include a discussion on why the eigenvalue decomposition method is used. Are there other methods available? Can they be used? Why or why not?

Para line 37-44:  the authors need to establish the strong background for this claim. Some recent work along with the challenges should be included. Is it one of these challenges? Establish this with support of similar work references in this regard.

Para line 46-51: Similarly, authors need to support this claim with recent work.

Para line 53-57: References required.

In the present form, the literature reported is very poor. Authors are advised to include a full dedicated section on latest literature. They should also mention which research gaps they are addressing.

The paper has deficiencies in English expression, sentence structure, and tense usage. Please have the article proofread by an expert.

Literature is not systematically reviewed and presented in line with current research. Please add a more in depth introduction and literature review by studying more recent publications and explain what this research conveys differently.

Compare this research's findings with recent state-of-the-art and explain the benefits of the approach


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CLASSIFICATION OF HIGH-VOLTAGE POWER LINE STRUCTURES IN LOW DENSITY ALS DATA ACQUIRED OVER BROAD NON-URBAN AREAS
Review round: 1
Reviewer: 3

Output:
Basic reporting: This manuscript developed an algorithm for the segmentation of electrical network facilities from ALS point clouds. The English language should be improved to ensure that others could clearly understand your text. So, you should use more short sentences instead of inserting too many long ones. The literature review in Section 1 Introduction needs to be more logical. Thank you for providing the source code or tools to allow this methods to be reproduced easily.
Experimental design: This manuscript should clearly define the research question, which must be relevant and meaningful. The knowledge gap being investigated should be identified. The designed experiment is somewhat simple, and more data and results in the experimental area are not presented.The results of power line structure classification based on ALS mainly carry out qualitative analysis, which lacks the rigor of quantitative analysis.In addition, this method also has the defect of relying more on prior knowledge and manual judgment.
Validity of the findings: The experimental data should be robust, statistically sound, and controlled. The conclusions should be appropriately stated, should be connected to the original question investigated, and should be limited to those supported by the results. The method and its meaning in this study should be more clear.
Additional comments: Some issues or questions could be addressed:
1) How to distinguish between 'good' and 'poor' classification results? What are the quantitative criteria?
2) Line 190, the sentence of 'An idealised hanging chain ... assumed ... follows ...' should be clear.
3) Line 287, 'range form ...' should be 'range from ...'.
It is recommended to check the language spelling through the whole manuscript.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CLASSIFICATION OF HIGH-VOLTAGE POWER LINE STRUCTURES IN LOW DENSITY ALS DATA ACQUIRED OVER BROAD NON-URBAN AREAS
Review round: 2
Reviewer: 1

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: I have no other questions.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CHANNEL STATE INFORMATION ESTIMATION FOR 5G WIRELESS COMMUNICATION SYSTEMS: RECURRENT NEURAL NETWORKS APPROACH
Review round: 1
Reviewer: 1

Output:
Basic reporting: The paper is well written. The results are clear.
Only proofreading is required.
Experimental design: well presented.
Validity of the findings: well presented.
Additional comments: Please proof read the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CHANNEL STATE INFORMATION ESTIMATION FOR 5G WIRELESS COMMUNICATION SYSTEMS: RECURRENT NEURAL NETWORKS APPROACH
Review round: 1
Reviewer: 2

Output:
Basic reporting: Some proper references are required for lines 44-48; for lines 54-56; for lines 91-94.

OFDM is just given by an abbreviation; an open form is required in line 80.

In line 144, what are the known interferences that occur on this kind of system? Any reference on this? For this claim, it seems an inexplicit expression.

You should remove the text in line 153.

Figure 1 needs a cite.

Figure 2 has low quality.

Line 207 …..   .On  -> a space after full stop

For Figure 4, details should be increased. Legend of the blocks, etc., are required. In the text, serial-to-parallel (probably) refers to S/P, but a more technical perspective of all figures up to that point is a must.

CP is probably a cyclic prefix, but it is not indicated in the text properly. (Line 220 & 209)
The most crucial lines in the paper are: 188-191 & 223-224. They are telling the contribution proposed by the authors; however, they all can be expanded by adding some flow charts, further explanation, etc.

The attached code requires a readme to show the procedures of the .m file hierarchy. For instance, I keep taking an "abstract class instantiation" error for sseClassificationLayer.m (even if I have corrected backwardLoss(), there occur some other problems throwing an error. My MATLAB is R2018b).

Please re-check some typos in your attached code,
like: "%plotting the loss and accuracy curves 'sparately'  " (TrainDNN.m)
or : "% in the MATLAB example of 'seqeunce' classification using LSTM network."
"% is represented by a feature vector that follows the similar data 'struture' "
etc.
Experimental design: The repeatedly highlighted Adam optimization has no big impact on the overall contribution. What about RMSProp, AdaGrad, etc.?

I did not get the point of using BiLSTM instead of LSTM. Why/How your system requires a bidirectional movement on the timeline?

What are the data details? Data size & training, validation, testing portions?
(Even if I can get the information through supplementary material, the paper itself should present all this information properly.)

What about further network details like dropout, batch normalization?
Validity of the findings: For Figure 7, why is there a transition for the best performing techniques: BiLSTM versus LSTM? Is there any explanation for this? Or is this just experimentally obtained observation?

Results based on Figures 6-18 should be summarized in a table like indicating the effect of "the applied method", "pilot numbers", and "dB range". A performance comparison heat-map table can be an idea.

The complexity section does not give critical feedback.

Loss curves have the zoom problem; the only critical regions of each network can be plotted side-by-side. Since for some certain epochs, some of the network architectures do not indicate significant learning.

Conclusions and future work sections can be merged. Plus, item-based representation is a strange way for the overall section. There may be some significant contributions as items, but not the overall section. (To the best of my knowledge, PeerJ does not demand a listing style like this.)
Additional comments: For the introduction part;
While reading the paper, the transition between the communication systems and the deep neural network architectures seems quite abstract. This may be because the current structure of the introduction section is like a literature review. I suggest that readers either technically explain the relation between BiLSTM and the CSIE or change the current structure of the introduction to give just the definition of the problem, a slight literature summary, and the main points of their proposal. I highly recommend the latter since the introduction is like mixing several different information. For instance, why should a reader get information about the “loss function” before thoroughly knowing the problem definition?

Line 192-197: Did you decide on this structure based on any preliminary experiments? Or some of the previous efforts (even though you claim to be the first on this structure; maybe you inspired by any LSTM-based communication system.) Why a structure like that? This should be technically explained in this paragraph.

Figure 3 should have been more detailed such as drawing with more information on layer size, activation function, connections, etc. Figure 3 (b) does not tell scientific information.

Information in line 250 about MATLAB seems strange since the reader cannot get the point whether authors mention a neural net toolbox, or Simulink, or plain code – built-in methods, etc.

The utilization of different loss functions, Adam optimization, etc., cannot be considered as the main contributions of the study. If authors would have widely present training & validation procedures, it may be possible to mark the paper as a well-contributing research paper. For this current version, it seems like “investigating the NN performance with try and error methods”.

For the further efforts of this paper, I strongly suggest including the training parameters and a wide-angle figure of the proposed network architecture, including every single detail from input to the output (such as which signals are exactly sourced, network hyperparameters, connections, dropout, batch normalization, etc.). Besides, the way the simulations are handled is far away from being a vivid picture. A summary of the plots into the table (heat-map or ticking for performances like ✓   ✓✓   ✓✓✓   ✓✓✓✓  , etc. ) can be an idea to present "the applied method", "pilot numbers", and "dB range" performance.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: CHANNEL STATE INFORMATION ESTIMATION FOR 5G WIRELESS COMMUNICATION SYSTEMS: RECURRENT NEURAL NETWORKS APPROACH
Review round: 1
Reviewer: 3

Output:
Basic reporting: no comment
Experimental design: no comment
Validity of the findings: no comment
Additional comments: The manuscript is providing the practical use of BiLSTM networks for the 5G systems which is interesting. However, some additional improvements are required.

1) The written text must be improved. For example in the introduction parts the paragraph divisions are not suitable and must be organized well.

2) The conclusion and future parts must started with some sentences and then start using i ii, etc.

3) The quality of the figures must be improved well.

4) Please put the comparison table and compare the results with other reported high impact factor papers.

5) Please add some up-to-date journals in the reference list and add them in the introduction part. Then please discuss about the drawbacks with that references and clarify your contributions.