dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition 2. A state machine M is a tuple of 〈S; I; T〉, where S is a set of states, I ⊆ S is a set of initial states, and T ⊆ S × S is a binary relation over states. Definition 3. The set R of reachable states with respect to M is defined as follows: (i) for each s 2 I, s 2 R (ii) for each s; ð s 0 Þ 2 T, if s 2 R, then s 0 2 R In this paper, to express a state of S, we use a braced associative-commutative collection (AC-collection) of namevalue pairs. A name-value pair is called an observable component. The juxtaposition operator is used as the constructor of AC-collection. For instance, oc1; oc2; oc3 is the ACcollection of three observable components oc1, oc2, and oc3. A state is of the form foc1; oc2; oc3g. In this paper, state transitions are specified as rewrite rules in Maude  [7] , a specification language supporting both equational and rewriting logic.
A Base Specification The three lattice-based KEMs share many common parts in their designs, such as polynomials, vectors, matrices, and message exchange patterns. Thus, as the first step, we modeled these common parts, combining them into a specification, which we called a base specification. Then, from the base specification, to model each of the three KEMs, we only need to model some additional parts as well as the mechanism execution. This section presents how to model those common parts in Maude to construct that base specification. where fmod stands for a functional module, and pr INT says that the module imports the Maude pre-defined module INT, which specifies integers. Sort (type) Poly represents polynomials. The notation subsort Int < Poly says that any integer is also a polynomial, where Int is the sort of integers defined in the module INT.
e. , post-quantum SSH and post-quantum TLS. As another piece of our future work, we are also interested in analyzing some AKE protocols, the AKE protocols, by using Maude or some other formal method tools. European Union NextGenerationEU/PRTR. Akleylek has been partially supported by TUBITAK under grant number 121R006. Otmani has been supported by the FAVPQC project funded by CNRS and by the grant ANR-22-PETQ-0008 PQ-TLS funded by Agence Nationale de la Recherche (ANR) within France 2030 program.
Acknowledgments Tran and  Ogata  have been supported by  JST SICORP  (Grant Number  JPMJSC20C2 ),  Japan. Escobar has been partially supported by the grant  PID2021-122830OB-C42  funded by  MCIN/AEI/10. 13039/501100011033  and  ERDF  A way of making Europe, by the grant  CIPROM/2022/6  funded by  Generalitat Valenciana , and by the grant  PCI2020-120708-2  funded by  MICIN/AEI/10. 13039/501100011033  and by the.
3. Description of battery data uses and related tools: This work describes the possible uses of battery data. Data modelling and prediction for energy storage systems are introduced. Existing software for data processing and usage are also described and a particular interest is given to open software. 4. Discussion of open science practices in the field: This article describes open science as an important practice to be considered in the field of energy storage. This section discusses how open science practices could be made available to every researcher working on battery data, from data generation to analysis and prediction.
Layout This article provides a discussion and analysis of several important and increasingly common questions: how battery data are produced, what data analysis techniques are needed, what the existing data analysis tools are and what perspectives on tool development are needed to advance the field of battery science. This article is structured as follows: Section 2 describes the types of experiments, the equipment needed to perform them and their environmental and economic impacts. In section 3, data processing techniques are discussed in detail. Section 4 describes the possible uses of battery data and the software available to carry them out. Finally, Section 5 describes the importance and gives some recommendations for the development of open science practices in the battery field.
What Are the Costs of Battery Testing? Battery testing is often presented as a necessary but costly approach in terms of time, economic and environmental costs  [29, 55, 56]. This article presents an estimate of these costs using a simple approach. It aims to provide a better understanding of the origin and magnitude of these costs. As a representative example, the costs associated with the characterisation dataset shared by the authors in an article are estimated  [42, 45, 57]. In this article, a SAMSUNG 94Ah cell was tested at three temperatures of 0, 25 and 40 °C in France in 2022. In terms of equipment production costs, a Bitrode cycler with a Votsch VT 3050 climate chamber was used to perform the experiment. The equipment was purchased in 2011 and 2006 for approximately EUR 40,000 and EUR 20,000, respectively.
Data Preparation In the battery field, time series typically come from field observations or battery cyclers in a variety of file formats and sizes. The Tables A1-A3 show the variety of file formats and cyclers used for the public battery datasets. Table  A1  shows a list of public ageing datasets. The analysis of these 26 datasets shows that data sharing is a practice that is gaining interest, as the number of public ageing datasets has been multiplied by 5 since 2020 (21 more); consequently, most of the shared data concerned newer battery technologies. More than 80% of the studies examined the ageing evolution of cylindrical cells (21 out of 26 studies). In spite of being a popular format for electric vehicle batteries, ageing data for prismatic cells have not been reported in any public datasets.
Small capacity cells were also widely favoured, with 25 of the 26 datasets conducting experiments on cells with a capacity of 5Ah or less. This can be explained by economic constraints, as the cost of equipment is directly related to the power it can deliver, so testing small capacity cells helps to limit current levels. As an illustrative example, the cell test cycler manufactured by Bitrode Corporation can control the cell within potentiostatic limits of ±18 V and galvanostatic limits of ±300 A, with data collected every 100 ms, and is advertised for sale for several thousand dollars. The module test cycler has potentiostatic limits of ±100 V and galvanostatic limits of ±1000 A, with data collected every 10 ms, and is advertised for sale for tens of thousands of dollars. Finally, the package testing cycler has potentiostatic limits of ±1500 V and galvanostatic limits of ±2400 A, with data collected every 10 ms, and is advertised for sale for several hundred thousand dollars  [69].
In the available public ageing datasets, cycling ageing has also mostly been tested using constant current profiles, and only five campaigns have been conducted using realistic profiles. This can be explained by the complexity of interpreting experimental results with a combination of calendar and cycling ageing  [70]. A wide variety of cyclers were used to perform the experiments, as 10 different brands of cyclers are listed in Table  A1. Arbin is the most popular and widely used among laboratories. Regarding the data format, over 90% of the experimental files are in tabular form, but the file format, i. e. , column names and organisation, varies depending on the cycler brand and model used. Recently, initiatives have emerged to provide tools for format harmonisation, such as BatteryArchive, a public repository for the visualisation, analysis and comparison of battery data across institutions, or software such as Cellpy, BEEP or DATTES  [71] [72] [73].
All data processing scripts are at least written in Matlab code. Python and R scripts are also shared in some repositories. Table  A2  contains a list of publicly available characterisation data. As with the ageing datasets, cylindrical and low-capacity cells make up the majority of the shared data. The references  [57, 74]  are two outstanding datasets as they have tested second-life cells. In terms of data format, the characterisation datasets also suffer from inhomogeneity, as the 25 available datasets were produced on 11 different cyclers. Prior to any analysis, it is therefore necessary to determine the structure of the results file to be analysed. As experimental results from different cyclers may need to be compared, the battery software should define its own result structure to facilitate the comparison of the results.
Tools for Data Processing, Modelling and Prediction The previous sections have shown that the main challenge is to address the heterogeneity in data formats and experimental methodology, as this limits the ability to compare studies. This section presents the software and tools available to process the heterogeneous battery experimental data and transform it into valuable analysis or simulation results. Software is the cornerstone of this key activity. In the field, most research teams have developed their own software to process their private datasets and meet their specific analysis needs. While this organisation allows researchers to process their data rapidly, it gravely hampers the reproducibility of their studies. In addition, this organisation dramatically increases the amount of time researchers spend programming and the risk of producing faulty analyses, as most researchers are self-taught software developers.
It is also difficult to compare research from two different laboratories, as each team is likely to use its own approach to collecting and analysing data. Sharing and comparing experimental results is then severely hampered by the lack of widely available data processing tools. Recently, several teams have made efforts to release their tools under open licenses. Open software is data processing tools that have been released under an open license, meaning that they are freely available for modification and redistribution. By allowing any user to see, use and modify the code of software, a standard, peer-reviewed methodology for data processing can emerge. This review will present the available tools and software that promote open science practices. It has been limited to three criteria, unless otherwise stated: all the software described in this section is distributed under an open license, they are actively developed since 2020 and they provide tools capable of processing data from a variety of projects and cyclers.
Experimental Data Processing In the battery field, three open software can handle raw experimental data from many different cyclers: BEEP, Cellpy and DATTES. The Battery Evaluation and Early Prediction (BEEP) software package is an open source software written in Python that allows for raw data preparation, data integrity validation, structuring of data into Python objects and feature extraction data to serve as input for machine learning. This software focuses on cyclic ageing data and provides a bridge between raw experimental data and data-driven prediction as machine learning  [120, 121]. Cellpy is a Python package designed to facilitate the task of interpreting and processing data from battery and cell cycling tests. The software performs data preparation and data processing including ICA and OCV.
Battery and System Models Experimental data can be used to develop models of the batteries that allow for a safe and optimal management of the battery within a system. Battery models can be divided into two categories: physics-based or empirical. One is based on an understanding of the underlying physical phenomena that occur during battery operation. The models that attempt to mimic the dynamics of these mechanisms are called physics-based models  [123]. In the battery field, several open software exist for physics-based modelling. Dualfoil. py is open source Python software that wraps the Doyle et al. dualfoil model in Python  [124]. Leveraging the object-oriented nature of Python, dualfoil. py allows the user to generate, organise and visualise the electrochemical responses of different rechargeable battery systems  [125].
PyBaMM (Python Battery Mathematical Modelling) is a tool for the fast and flexible simulation of battery models. It provides a modular framework to solve continuum models for batteries  [126, 127]. Liionpack is a battery pack simulator that allows PyBaMM simulations to be run at the pack level. Thermal effects can also be included  [128]. PETLION is a Julia battery modelling software that can run pseudo-2D porous electrode theory (PET) simulations  [129, 130]. MPET is Python software designed to run simulations of porous electrode batteries using the porous electrode theory, which is a volume-averaged, multi-scale approach to capture the coupled behaviour of the electrolyte and active material within the electrodes  [131, 132]. NEOLAB  [133, 134] , Spectral Li-Ion SPM  [135, 136]  or Supercapacitor-Model  [137, 138]  are some examples of other existing software.
They will not be presented in detail as they do not meet the above criteria. A second type of model uses experimental data and an electrical circuit analogy to emulate the electrical and thermal response of the battery to a current demand. These simpler models are called equivalent circuit models. The vast majority of battery management systems for battery packs use equivalent circuit models. Their simplicity, robustness and ability to account for thermal and ageing effects are the main reasons for this choice  [123]. In the field, some software proposes functions using this modelling approach. Impedance. py is a Python package designed to make the analysis of electrochemical impedance spectroscopy (EIS) data easier and more reproducible. Impedance. py provides several useful functions such as data preparation and integrity checking, model fitting and validation and visualisation tools.
Its EIS modelling function fits the parameters of an electrical circuit model to impedance data. This function uses non-linear least squares regression to simultaneously fit the real and imaginary components of the impedance and allows users to customise their own electrical circuit  [139, 140]. The Lin-KK tool is another free but not open software with similar features  [141, 142]. LIBEIS is an open Matlab Python software for EIS analysis. By analysing the voltage and current time series obtained from a battery under test, the LIBEIS software tool calculates and plots EIS data. LiBEIS also fits the EIS data to an analogue circuit model. Finally, LiBEIS uses machine learning techniques and exploratory data analysis tools to estimate the state of charge from the EIS data  [143]. In addition to this software, the same research team proposed the open software EasyEIS.
A data processing tool to process EIS data generated by a custom impedance measurement system  [144]. DATTES also proposes the use of experimental data to calibrate electrical circuit models  [73, 122]. Model parameters are proposed directly and systematically as a result of the data analysis process. The static behaviour is modelled after processing a low current test data, while the dynamic behaviour is fitted thanks to an impedance test. DATTES models are also designed to be easily used in the energy management software VEHLIB  [145, 146].
Slide is an open software, written mainly in C++, for simulating the degradation of lithium-ion batteries. Users can program their degradation procedures by loading the battery with their own current profile or by selecting the standard procedures already implemented in the software. Users can also simulate various characterisation tests, including the main electrical test types described in Section 3. 3  [153, 154]. To date, most prediction studies have been conducted with Python or Matlab code written specifically for a project  [155] [156] [157]. The BEEP software is therefore an excellent initiative to promote open science practices for predictive studies  [120, 121]. The BEEP modelling module is designed for prediction. It makes it possible to aggregate data from ageing campaigns and train machine learning models.
Although the number of public data repositories is still limited and they lack visibility, recent articles highlighted their existence and content  [161, 162]. It is also worth to investigate whether experimental data on similar battery technologies or applications have been shared before starting a new test campaign. Table  2  gives a non-exhaustive list of repositories where battery data are deposited. As the number of open data repositories is large and may be difficult to discover, a recent initiative involving several institutes emerged: the Battery Data Genome  [172]. This initiative aims to create a collection of datahubs to promote data sharing and structure open data. After having developed a proof-of-concept of integration between Battery Archive  [173] , BEEP  [121]  and PyBAMM  [127] , this community may extend this initiative to other existing software and tools.
There are also long-term archives comparable to Battery Archives on the Web  [173]. Liiondb is an initiative that aims to highlight many parameter measurements that could be used in physics-based models and are available in the literature  [174, 175]. The Materialsproject is another open database that presents the properties of a wide range of materials that could be used in battery design  [176, 177]. NREL has proposed an open library of three-dimensional lithium-ion battery electrode microstructures for microstructure characterisation and modelling  [178, 179]. A database containing data from hundreds of abuse tests conducted on commercial lithium-ion batteries has also been released by NREL  [180, 181]. After reviewing the existing literature on a battery technology, data generation should take into account the cost and time constraints of the experiments.
In [182], Gabbar et al. provide an overview of the various existing standards for battery testing. Metadata generation is another key element to maximise the interpretability and reusability of the data generated. The Battery Data Genome initiative proposed a metadata convention to make data generated by different instruments and experimenters more easily interoperable and comparable. Based on these recommendations, the open software DATTES includes metadata generation functions to associate data descriptors with raw and processed data. To ensure a reliable result, data used for battery modelling or prediction should be limited to datasets wherein the production methodology is well known. Therefore, only measured data such as time, current, voltage or temperature should be collected from cyclers.
The use of data calculated by the test equipment needs to be weighted. The advantage of using data calculated by the cycler lies in the fact that the cycler's central processing unit generally has data at a very high sampling frequency compared to the data recording rate. On the other hand, the calculation methodology inside the cycler is often poorly described. One recommendation could be to use the variables calculated by the cycler when the methodology is well described or simple, as in the case of Ah counting (integration of current over time), but to prefer the use of well-documented software (often open source) for more complex data processing such as the calculation of equivalent resistances or the identification of impedance parameters. The use of open software to determine such characteristics is indeed a good practice, as the methodology can be peerreviewed and described in detail in the future publications.
Conversely, processing battery data with non-open software severely limits the quality of the research, as there is no way to review, debug or modify the analysis methodology. Several open software packages are already available for battery data analysis. BEEP, Cellpy or DATTES are some examples of software that aim to facilitate the reproducibility of the experiments. Although these software packages are well documented, their documentation is generally written in a way that is not very accessible to non-developers, as it is often included in the source code. One way to improve this would be to include "methodology documentation" that does not require users to search for this information in the source code. A detailed description of the data processing methodology would make debugging and adding new functionality to the software easier.
The development of user-friendly tools is also one of the most effective ways of facilitating a wider adoption of open software. Supporting researchers in their daily work leads them to feed these tools with large amounts of data, which increases the robustness of the software. To date, the most comprehensive software available requires programming skills. The development of simple tools with graphical user interfaces would be an important addition to existing tools and could remove barriers to the widespread adoption of open science in this area. Lithium Inventory, WattRank or Batemo are three examples of such initiatives  [183] [184] [185]. A number of good practices can be identified in relation to publication and data sharing. The Journal of Power Sources has published a set of guidelines and best practices for publishing battery research articles  [11].
To maximise the impact of data sharing in public repositories, several good practices should be considered. Raw experimental data should always be shared, as they are the keystone for the reliability of future data exploitation work. Galvanalyser is an open source tool that facilitates the sharing of raw data. This platform helps users to automatically store their data generated by battery cyclers in a database and present this data to users via a Web application and a REST API  [186, 187]. Processed data can also be shared with open software to accelerate data reuse. Another good practice is to share a data repository descriptor text file, which should contain the information presented in Table  3. The Battery Data Genome initiative presents a list of principles to improve the accessibility, interpretability and understandability of data shared in the field  [172].
Conclusions and Recommendations This review provides a critical analysis of data generation and processing techniques for lithium-ion batteries. A comprehensive description of the main battery electrical experiments has been provided. The types of experiments and the equipment required to perform them have been described and the different costs associated with battery experiments have been discussed. Data processing for energy storage systems has also been described using the mathematical theory of time series analysis. The possible data analyses of the main battery test methods: capacity, impedance and low current tests were described. Data modelling and prediction for energy storage systems was also introduced. Existing software for data processing and use has also been described. The ultimate goal of the article is to accelerate battery research by facilitating collaboration between experimentalists, modellers and data scientists.
On the social axis, human social interactions have resulted in the emergence of language, music, art, culture or socio-political systems. Other biological entities such as non-human primates  [109, 110]  or social insects would score lower on the social and computational axis than humans. Additionally, other species of vertebrates such as some types of birds and cephalopods have been shown to exhibit complex behavior and possess sophisticated nervous systems. These two groups have actually been enormously useful in the search for animal consciousness  [111, 112]. Current AI systems such as IBM Watson [113], AlphaGo  [64] , DQNs  [114]  and Siri [115] are powerful computing systems over a narrow set of domains, but in their current form they do not show general-purpose functionality, that is, the capacity to independently interact with the world and successfully perform multiple tasks in different domains  [116] , or as proposed by Allen Newell, the capability with which anything can become a task  [117].