Update paper

paper/paper.bib

@@ -144,3 +144,29 @@ @article{li2022two
   doi       = {10.3390/app122110979},
   publisher = {Multidisciplinary Digital Publishing Institute}
 }
+
+@article{shenk2021traja, 
+  title = {Traja: A Python toolbox for animal trajectory analysis}, 
+  author = {Shenk, Justin and Byttner, Wolf and Nambusubramaniyan, Saranraj and Zoeller, Alexander},
+  journal = {Journal of Open Source Software},
+  volume = {6}, 
+  number = {63}, 
+  pages = {3202},  
+  year = {2021}, 
+  doi = {10.21105/joss.03202},
+  url = {https://doi.org/10.21105/joss.03202},
+  publisher = {The Open Journal}
+} 
+
+@article{joo2020navigating,
+  title={Navigating through the R packages for movement},
+  author={Joo, Rocio and Boone, Matthew E and Clay, Thomas A and Patrick, Samantha C and Clusella-Trullas, Susana and Basille, Mathieu},
+  journal={Journal of Animal Ecology},
+  volume={89},
+  number={1},
+  pages={248--267},
+  year={2020},
+  doi = {10.1111/1365-2656.13116},
+  url = {https://doi.org/10.1111/1365-2656.13116},
+  publisher={Wiley Online Library}
+}

paper/paper.md

@@ -19,7 +19,7 @@ authors:
     orcid: 0000-0001-7305-4710
     equal-contrib: true
     affiliation: 3
-  - name: E. Altshuler
+  - name: E.~Altshuler
     orcid: 0000-0003-4192-5635
     equal-contrib: false
     affiliation: 3
@@ -55,7 +55,8 @@ been studied in many different fields, including robotics, behavior analysis,
 mobility pattern mining, and user activity recognition [@da2019survey]. This task
 presents multiple challenges for conventional classification models, such as the 
 indeterminate length of trajectories [@li2022two], the range of entities that generate 
-trajectories [@janczura2020classification; @xiao2017identifying; @bae2022transformer], and the absence of established standards in trajectory datasets 
+trajectories [@janczura2020classification; @xiao2017identifying; @bae2022transformer], 
+and the absence of established standards in trajectory datasets 
 [@xiao2017identifying; @bae2022transformer]. 
 
 Our study endeavors to lay the foundation for the assessment of innovative 
@@ -64,10 +65,26 @@ a new framework, referred to as `pactus`, which addresses the challenges of
 trajectory classification by providing direct access to a carefully chosen 
 collection of datasets and several trajectory classifiers. `pactus` 
 facilitates researchers' ability to experiment with various approaches 
-and assess their performance on different types of data.
+and assess their performance on different types of data. A comprehensive software 
+documentation is provided on 
+([https://pactus.readthedocs.io/en/latest/](pactus.readthedocs.io)).
 
 # Statement of need
 
+In recent years, several software libraries have emerged, aiming to automate trajectory 
+data analysis. Within the R community, there are various available tools 
+[@joo2020navigating]. Recognizing the popularity and extensive usage of Python, the `traja`
+software [@shenk2021traja] was developed to integrate different analysis techniques for 
+two-dimensional trajectories, primarily focusing on animal behavioral analysis. Additionally, 
+the `yupi` library [@reyes2023yupi] was created to handle trajectory analysis for applications 
+involving an arbitrary number of dimensions.
+
+Although these libraries offer valuable tools for trajectory classification, such as 
+classification models and feature extraction from trajectories, they were not specifically 
+designed for this task. Consequently, contemporary research on trajectory classification
+faces limitations in terms of evaluation, often considering only a limited number of datasets 
+or reporting only a reduced set of metrics [@bae2022transformer].
+
 The lack of standardization in trajectory datasets, coupled with the difficulty 
 of obtaining these datasets for evaluation, poses a significant challenge to 
 researchers working in fields related to trajectory classification. Moreover, 
@@ -88,38 +105,66 @@ researchers to distribute their findings as simple Python scripts, relying on `p
 for all tasks related to data acquisition, processing, and model evaluation.
 
 
-# Software Overview
-
-The functionalities of `pactus` can be divided into four different categories as shown in
-\autoref{fig:overview}.
+# Pactus Software Library
 
-![Overview of the resources available in `pactus` coupled with an usage example.\label{fig:overview}](1.pdf)
+The functionalities of `pactus` can be divided into modules: Data handling, Feature extraction,
+Classification models and Evaluation.
 
+## Data handling
 
-The selection of datasets was conducted with meticulous care to encompass a broad 
+The library provides direct access to some of the most commonly used datasets for trajectory 
+classification. The selection of datasets was conducted with meticulous care to encompass a broad 
 range of trajectories and classification objectives. Our initial selection includes
 GeoLife [@zheng2009mining; @zheng2008understanding; @zheng2010geolife], The Starkey 
-Project dataset, also known as `Animals' in the	trajectory classification 
+Project dataset, also known as `Animals` in the	trajectory classification 
 community [@rapp2009elk], four different datasets from the the UCI repository 
 [@Dua:2019] and two different hurricane datasets, provided by National Hurricane 
 Center [@landsea2013atlantic] and the China Meteorological Administration 
 [@ying2014overview; @lu2021western] respectively. To ensure consistency, all 
 datasets were transformed into a standardized format utilizing the trajectory 
 data structures proposed in [@reyes2023yupi]. Datasets are not bundled with the 
 software package, but rather will be downloaded and cached automatically upon each
-individual access through the library.
+individual access through the library. A complete guide on how to use custom datasets or
+requesting the inclusion of new datasets into `pactus` can be found in the documentation.
+
+## Feature extraction
 
 In order to mitigate the different-length trajectories on some datasets, `pactus`
 is able to extract statistical features from any trajectory and convert an arbitrary
 length trajectory into a fixed size vector whose components are engineered features 
 typically used in the literature [@xiao2017identifying; @zheng2008understanding].
 
-Finally, several classification algorithms can be evaluated on the vectorized
-versions of the trajectories (e.g., Random Forest, SVM, KNN) or, alternatively,
-classifiers able to handle variable-size inputs (e.g., LSTM or Transformers [@bae2022transformer]) can be evaluated directly on the trajectory data.
-In both cases, typical evaluation metrics for classification are computed
-automatically for the model being evaluated. 
+Users can implement their own method to perform this conversion, and an example on how
+to do it can be found in the documentation. However, there is a default method that uses
+all the features computed by the `yupi` library.
+
+## Classification models and Evaluation
+
+Several classification algorithms are included in  `pactus`. Some of them can be evaluated 
+on the vectorized versions of the trajectories (e.g., Random Forest, SVM, KNN). In other cases
+the classifiers are able to handle variable-size inputs (e.g., LSTM or Transformers 
+[@bae2022transformer]) and can be evaluated directly on the trajectory data. In both cases, 
+typical evaluation metrics for classification are computed automatically for the model being evaluated. 
+
+## Overview
+
+All the functionalities of the library can be integrated in a single script. \autoref{fig:overview}
+shows an example on how to use `pactus` for training and evaluating a Random Forest model using the
+Starkey Project dataset, also known as `Animals`.
+
+![Overview of the resources available in `pactus` coupled with an usage example.\label{fig:overview}](1.pdf)
+
 
+# Conclusions
 
+The software presented with this work, `pactus`, addresses typical challenges faced in trajectory 
+classification research. By providing researchers with direct access to curated datasets and trajectory 
+classifiers, `pactus` enhances the availability of resources for evaluation. It is concieved with extensibility 
+in mind, encouraging researchers to contribute their own datasets and methods. The evaluation methodology ensures 
+reproducibility and comparability of results, facilitating the identification of effective trajectory classification 
+methods for specific scenarios. Additionally, pactus promotes reproducible research by enabling researchers to 
+distribute their findings as Python scripts, relying on pactus for data acquisition, processing, and model 
+evaluation. Overall, pactus offers a valuable tool for researchers in the field of trajectory classification, 
+addressing key challenges and facilitating future advancements in the field.
 
 # References