dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019 ). Research was also published to integrate process design and LCA to quantify the contribution of biopolymer production  ( Durkin et al. , 2019 )  to mitigate climate change within PB boundary. Since the LCA approach formalised in 1990s, numerous efforts have been placed to develop quantitative methods to characterise the environmental impacts and to build database representing the industrial/sectoral technologies and processes. Undoubtedly, LCA database and methodological advances offer powerful tools to support decisionmaking on sustainable development and design of biorenewables  ( França et al. , 2021 ). However, LCA framework only enables data analyse and evaluation functions but does not inform design problems within an Earth-system safe operating space, which consists of interactive environmental boundaries across regional and global scales.
Under the framework, maximum likelihood estimation and goodness of fit were proposed for analysing the operational data with multiple measurements or simulated computational inventory containing variability parameters. Pedigree matrix originally developed Weidema and Wesnaes  ( Weidema and Wesnaes, 1996 )  and widely applied to the LCA database e. g. Eco-invent has been adopted to transform the data quality indicators to probability distributions ('default' lognormal distribution); whereas probabilistic Monte Carlo simulation was suggested to estimate the uncertainties of LCSA outcomes resulted from the statistical variability or temporal, geographical or technological gaps in the inventory  ( Guo, 2012 ). Monte Carlo simulation combined with fuzzy datasets and pedigree matrix have been well demonstrated to quantify the uncertainties of biorenewable systems e.
, 2014 ), climate ( O'Keeffe et al. , 2017 ) ). The development of a leading platform Open-LCA reflects the GIS-aided LCA research advances in recent years. Open-LCA is an open-source Java application running on Eclipse Rich Client Platform. It enables LCA collaborative server and embeds GIS-aided LCA models. OpenLCA is the first and only LCA platform to enable database/model development in a distributed and paralleled collaboration manner across users, servers/repositories. In comparison with other widely adapted LCA software (e. g. Simapro and GaBi), very few studies have explored GIS-aided LCA using Open-LCA platform  ( Tabatabaie et al. , 2018 ). Besides, GIS-aided LCA models have been presented for linear or non-linear ecosystem services assessment, especially provisioning, regulating ES impacts (e.
, 2016 ; Ahmed et al. , 2015 ). Equilibrium modelling consists of two approaches-1) stoichiometric models which require well-defined reaction mechanisms, chemical reactions and species involved; 2) non-stoichiometric models, which are effective to identify the chemical compositions when the reaction paths are not yet known  ( Acharya et al. , 2010 ). Both kinetic and equilibrium models have been embedded in commercial process simulators such as AspenTech modules (Aspen Plus or Hysys), where the physical relationships, thermodynamic equilibrium and rate equations have been built in to enable the process flowsheeting, plant-wide process behaviour projection.
A variety of modelling platforms e. g. Aspen Plus, Aspen Hysys, Su-perPro Designer, Modelica, GPS-X (for Anaerobic digestion) provide a resource where researchers and engineers can model, simulate, design, optimise their processes; conversely, the outcomes (e. g. technical reports and publications) can enrich the platforms about biorenewable systems by sharing information of specific case studies, identifying the model limitations, and extending the model applicability. The first challenge for researchers using modelling platforms is to identify the available physical and chemical properties in the database which include pure component and phase equilibrium data for conventional chemicals, solids and polymers. Subsequently, the additional data about thermodynamics and dynamics need to be added by researchers as biomass feedstocks and their intermediate and end products have a complex composition with sophisticated reaction path.
Computational Fluid Dynamics (CFD) for unit operation CFD is a powerful tool which uses numerical analysis and data structures to model and analyse biomass conversion processes that involve complex multiphase fluid flows with mass, momentum and heat transfers. To simulate unit operation, CFD is the most-powerful numerical tool available, empowering researchers to achieve economic and environmental targets as they optimise reactor and product's performances. Widely used commercial software of CFD includes well-validated physical modelling capabilities to deliver fast, accurate results across the widest range of CFD and multiphysics applications. CFD modelling has been successfully implemented on various biorenewable systems e. g. pyrolysis  ( Zhong et al. , 2023 ; Luo et al. , 2023 ) , gasification  ( Zhang et al.
Multi-scale process simulation Often, it is challenging to determine the correct scale to capture a phenomenon of interest as multiple phenomena interact at different scales. Multiscale modelling attempts to address this problem. Its importance as a tool in sustainable development was noted by  Pistikopoulos et al. (2021). Multiscale process simulation and design of biorenewable systems are becoming more attractive, and the main challenge is to determine the models to run and how they iterate/interact. The fast and robust coupling approaches (manual or automated) are essential to advance the development of multiscale process simulation. Abdelouahed et al. (2012)  proposed an approach to couple Aspen Plus and Fortan modules to include detailed chemical mechanisms in process simulators without thermodynamic equilibrium assumptions.
, 2017 )  wastewater and lignocellulosic biomass  ( Matthews et al. , 2016 ; Baliban et al. , 2013 ; Niziolek et al. , 2016 )  via integrated thermochemical and biochemical routes. The studies reviewed above were based on deterministic optimisation frameworks with the processes formulated by adopting publically available results from either simulation (e. g. NREL studies) or laboratory work  ( Voll and Marquardt, 2012a ) , and the life cycle inventory retrieved from available databases e. g. Ecoinvent  ( Wang et al. , 2013 ). Puchongkawarin et al. (2015)  ,  Puchongkawarin et al. (2016)  presented an integrated superstructure-based modelling framework for wastewater recovery process synthesis by incorporating process simulation, life cycle approach and optimisation. These authors proposed an iterative approach to 1) feed the surrogate models constructed from state-of-the-art WWT simulators (GPS-X) and performance regression approximated from the techno-economic and LCA evaluation to the multi-objective MINLP optimisation model to generate the Pareto frontiers for the trade-offs between maximised net present value (NPV) and minimised GHG and eutrophication; 2) to refine and verify the process design by feeding back the optimal design solutions until model convergence.
Under this framework, the authors proposed to implement quantitative approaches based on provisioning, regulating and cultural ecosystem services at various spatial and temporal scales to optimise the ES trade-offs for the bioenergy supply chain design. An interesting yet unexplored direction is to bring the ES supply-demand mitigation strategies into the LCO framework e. g. multi-functional landscape planning  ( Shifflett et al. , 2016 ) , phytoremediation or bioremediation on degraded land. Overall, life cycle underpinned optimisation offers a promising framework to inform decision-making towards biorenewable sustainability; several interesting yet challenging research directions lie in the methodological exploration for consequential LCO of closed-loop biorenewable supply chains (e. g. biogenic CO 2 based recyclable plastic), spatially-explicit ES optimisation linked with eco-informatics and ES databank, LCO with wider quantitative social dimensions.
IoT communication technologies for the Internet of Things Low Power Wide Area Networks (LPWANs) have become a popular communication technology for the Internet of Things (IoT) due to their low power consumption and wide coverage area. Two of the most popular LPWAN technologies are LoRaWAN and NB-IoT  [17] [18] [19]. LoRaWAN (Long Range Wide Area Network) is a wireless communication protocol based on the LoRa modulation technique. LoRaWAN has the ability to communicate over long distances, typically up to 10 km in rural areas and up to 2 km in urban areas. It operates in unlicensed frequency bands, which makes it a cost-effective solution. The LoRaWAN protocol is open-source and has a large community of developers. LoRaWAN is primarily used for battery-powered devices that require low data rates, such as environmental monitoring sensors, smart parking systems, and asset tracking  [20, 21].
NB-IoT (Narrowband IoT) is a cellular technology designed for IoT devices within 5G cellular networks. It is a standardization effort by the 3GPP and is based on the LTE (Long-Term Evolution) technology. NB-IoT uses a narrow bandwidth of 200 kHz and can operate in licensed or unlicensed frequency bands. The main advantage of NB-IoT is its ability to operate in areas with weak signal strength and in underground locations. It can also support high data rates and has a low latency, making it suitable for applications that require real-time data, such as industrial automation and smart cities  [22, 23]. One of the main advantages of LoRaWAN is its long-range communication capabilities, which make it suitable for use cases that require devices to be deployed in remote areas, such as environmental monitoring or asset tracking.
Additionally, the unlicensed frequency bands used by Lo-RaWAN make it a cost-effective solution, as no licensing fees are required. However, the trade-off for this long-range communication is a low data rate and higher latency, which may not be suitable for applications that require real-time data  [24]. On the other hand, NB-IoT offers high data rates, low latency, and reliable connectivity in areas with weak signal strength. Its cellular network infrastructure also provides a level of security and reliability that may not be possible with LoRaWAN. However, the licensing fees and higher deployment costs associated with NB-IoT may make it less cost-effective than LoRaWAN for certain applications  [24]. In conclusion, both LoRaWAN and NB-IoT have their advantages and limitations, and the choice between them will depend on the specific requirements of the application.
Applications may also use MPI-IO (Message Passing Interface I/O)  [46] , POSIX I/O (Portable Operating System Interface I/O)  [67] , or STDIO (Standard Input and Output) interfaces directly to perform I/O to the file systems. We discuss these interfaces, their challenges, and their known impact on HPC I/O performance in Section 7. 1. 4. In MPI-IO, a file is an ordered collection of typed data items. It presents a higher level of data abstraction than POSIX by allowing users to define data models that are natural to the application. Nonetheless, it supports defining complex data patterns for parallel write and read operations using independent and collective I/O calls. Furthermore, it allows taking advantage of optimization opportunities when using collective calls. In POSIX I/O, however, a file is viewed as a sequence of bytes.
This interface allows transferring contiguous regions of bytes between the file and memory and non-contiguous regions of bytes from memory to a file by giving full, low-level control of the I/O operations. However, in the context of HPC, there is little in the interface that inherently supports parallel I/O. For instance, POSIX does not easily support collective access to files while leaving it to the programmer to coordinate access and ensure consistency. STDIO, in contrast, abstracts all file operations into operations on (input or output) streams of bytes. It comprises the C stdio. h family of functions  [97] , such as fopen(), fprintf(), and fscanf(). These I/O functions are commonly used in genomics and biology to store sequencing information in text format  [182]. However, STDIO functions do not directly support random access to data files.
ACCESS PATTERNS IN HIGH-LEVEL I/O LIBRARIES High-level I/O libraries allow HPC applications to express scientific simulation data more naturally instead of being constrained or caught up by system-specific details. HDF5, NetCDF/PnetCDF, ADIOS, ROOT, and FITS are examples of such libraries, each providing a set of APIs to express complex multi-dimensional data, contiguous and non-contiguous data, seeking to attain performance and portability, and also increase productivity. HDF5 (Hierarchical Data Format, Version 5) is a well-known self-describing file format and an I/O library  [202]  that provides flexibility, extendibility, and portability. It is used widely in many science domains to manage various data models  [28]. HDF5 uses the concept of dataspace objects to control data transfer when data is read or written.
A dataspace defines the layout of the data (the organization in rows, columns, etc. ) in a file and memory. Data is rearranged by the library when the different layouts are used to represent a given dataset in memory or a file. However, both source and destination are stored as contiguous blocks of storage with the elements ordered as defined by the dataspace. HDF5 allows an application to read or write to a portion of a dataset (partial I/O) by using hyperslabs and points. Hyperslabs are portions of datasets whose selection can be a logically contiguous collection of points in a dataspace or a regular pattern of points or blocks in a dataspace. Figure  4  illustrates four types of partial I/O in HDF5. An HDF5 hyperslab can be viewed as a rectangular pattern defined by four arrays: offsets of the starting location for the hyperslab, the stride or number of elements to separate each element or block to be selected, the number of elements or blocks to select along each dimension, and the size of the blocks selected from the dataspace.
ADIOS (Adaptable Input Output System)  [134, 139]  provides an I/O abstraction framework for portable and scalable I/O to aid scientific applications when data transfer volumes exceed the capabilities of traditional file I/O. Different from HDF5, ADIOS is not a hierarchical model but rather sits on a layer of abstraction beneath those. However, it also relies on self-describing data in binarypacked (. bp) format for rapid metadata extraction, but it can use different backend file storage formats such as HDF5 and NetCDF. ADIOS can also extract relevant information from large datasets, transporting and transforming groups of self-describing data variables and attributes across different media. This library uses an external metadata file in XML format to describe variables, types, and the path to take from memory to disk.
g. , file or directory), size, and modified timestamp  [170, 192]. Furthermore, since PFS tend to rely on POSIX I/O semantics (which were not conceived with parallel accesses in mind), the scalability of metadata accesses is often impaired. For instance, serialization is expected to happen in scenarios where a large number of files are created by multiple processes in a single directory. This is a common pattern observed in HPC applications  [1, 13, 51, 205, 226]. Moreover, because these PFS tend to adopt the concept of data striping (to allow parallel access and improve performance), before accessing data, the PFS client must fetch permissions and obtain the file layout (including striping locations and sizes) from one of the metadata servers. In the Lustre PFS, a metadata service provides the index, or namespace, for a Lustre file system.
Different approaches  [129, 145, 164, 166, 176, 221]  have been proposed to tackle metadata issues covering how to handle, scale, and index metadata efficiently. For instance, Liao et al. [129]  present a metadata management system that uses a database to record the information of datasets and manages metadata while providing a suitable I/O interface. Paul et al. [166]  propose a metadata indexing and search tool specifically designed for large-scale HPC storage systems. Their solution relies on using an in-tree design with a parallel leveled partitioning approach to partition the file system namespace into disjoint subtrees. They maintain an internal metadata index database that uses a two-level database sharding technique to increase indexing and querying performance, combined with a changelog-based approach to keep track of the metadata changes and reindex the file system.
Interfaces. Interfaces seek to provide a convenient and easy-to-use way to access resources. In the context of I/O, these have an important role when accessing files (locally or remotely) by defining APIs and semantics. Furthermore, in HPC, these interfaces should strive to balance usability and high performance, often divergent goals. We can consider three main interfaces that are used directly by applications or high-level libraries to express their access patterns: POSIX I/O, MPI-IO, and STDIO. High-level I/O libraries provide various APIs that simplify mapping data models at the application level with MPI-IO and POSIX interfaces. For instance, HDF5 uses the MPI-IO interface for parallel I/O and POSIX IO for sequential applications. ADIOS and PnetCDF use MPI-IO and POSIX similarly. Hereafter, we briefly discuss each interface and summarize their opportunities and challenges in the context of HPC in Table  2.
However, modern HPC applications often do not require such strong consistency guarantees  [132, 209]. Since POSIX was not designed specifically for HPC applications, it may also impose a burden on the end users. For instance, it is possible to use the shared-file parallel I/O approach. But, the complexity of coordinating parallel accesses, buffering, and flushing is explicitly delegated to the end user. Furthermore, as files are viewed as opaque byte streams, applications are unable to express or hint to the file system about how its data is organized. Such information is essential for data placement strategies and for optimizations. For example, the MPI-IO interface uses such information to express complex accesses and attain high performance. Nonetheless, there were some efforts that sought to extend POSIX I/O to account for HPC needs.
For instance, ROMIO  [201]  is a high-performance, portable implementation of MPI-IO optimized for non-contiguous access patterns, which are common in parallel applications. It relies on the portability of ADIO to be used with any MPI implementation (ROMIO is often included as a part of several MPI implementations, e. g. , MPICH, Cray MPI, and OpenMPI). As MPI-IO is layered atop POSIX, it generates complex I/O access patterns. The pattern that reaches the file system may greatly differ from what was initially expressed in the scientific application code due to optimizations and transformations (e. g. , collective buffering and data sieving  [53, 199] ) as requests traverse the I/O stack. 46:17 STDIO. The standard I/O library (STDIO) provides a simple and buffered stream I/O interface. It abstracts all file operations into operations on streams of bytes.
In the communication phase, all ranks send their I/O requests to the aggregators based on their file domain. In the I/O phase, aggregators issue the requests to the system. Hence, aggregators effectively merge requests into larger and contiguous ones before percolating to the POSIX layer or the file system. 7. 1. 6 Synchronicity. Synchronous or blocking I/O routines are not considered successful before an I/O operation is completed. However, asynchronous or non-blocking I/O operations allow applications to hide the cost associated with I/O operations by overlapping it with computation or communication steps, allowing the application to progress. The latter is becoming popular among scientific applications to access large amounts of data and improve user-perceived performance. POSIX and MPI-IO provide asynchronous APIs to write and read data from files.
POSIX standard provides the aio_* calls, whereas MPI-IO has MPI_File_i* calls for independent and collective I/O operations. Some high-level I/O libraries, such as ADIOS and HDF5, also expose those nonblocking interfaces  [195]. In contrast, data management systems such as PDC (Proactive Data Containers)  [194]  offer asynchronous data movement to and from their server nodes through network data transfer. Novel object storage file systems such as DAOS (Distributed Asynchronous Object Storage)  [124]  were built around the asynchronous concept to deliver performance. The synchronicity feature will help shape the temporal behavior of the application's access pattern.
N bin sets the size of the pseudo-dataset composed on N bin component matrices. Finally, N дanд sets the level of gang parallelism, allowing MADbench2 to run as a single or multi-gang. In the former, all matrix operations are carried out by being distributed over all processors. The kernel can use the POSIX or MPI-IO interfaces to synchronously or asynchronously issue its I/O operations to a unique or shared file. IFER is a microbenchmark similar to IOR but instead seeks to provide insights on I/O contention  [227]. It splits the ranks into two groups running on two separate sets of nodes to emulate two competing applications. Each group of processes executes a series of collective I/O operations following a pre-defined pattern. Although IFER only provides support for write requests to a shared file by application, it considers two patterns: contiguous and 1D strided.
All 3D arrays are partitioned among the MPI processes using block partitioning in all x-y-z dimensions, whereas the fourth dimension (the most significant one) is not partitioned. The kernel can be configured to use PnetCDF blocking or non-blocking APIs. For the latter, a checkpoint has four non-blocking write calls, one per variable, followed by a call to wait and flush the write requests  [128]. NAS BT-IO  [155]  is a benchmark based on the block triagonal (BT) problem of the NAS Parallel Benchmarks (NPB). Each rank is responsible for multiple Cartesian subsets of the dataset, whose number increases as the square root of the number of ranks participating in the computation. The entire solution, consisting of five double-precision words per mesh point, must be written to a file at every five timesteps.
In the end, all data belonging to a single timestep must be stored in the same file and must be sorted by vector component, x, y, and z-coordinates. S3aSim  [42]  is an I/O kernel based on a sequence similarity search framework. It uses a masterslave parallel programming model with database segmentation, mimicking the mpiBLAST  [52]  access pattern. Given input query sequences, S3aSim divides up the database sequences into fragments. Workers request a query and fragment information from the master and search the query I/O Access Patterns in HPC Applications: A 360-Degree Survey 46:23 against the database fragment assigned. The results are sent to the master to be sorted and then written to a single shared file. Without synchronizing after every query, this application uses individual I/O operations to write data to a single shared file.
Parallel I/O Kernels (https://github. com/hpc-io/PIOK) provides the parallel I/O portion of various scientific simulation codes that use HDF5. These kernels have been expanded with h5bench to cover a variety of HDF5 I/O patterns. h5bench  [122]   HACC-IO  [207]  is a kernel extracted from the HACC (Hardware Accelerated Cosmology Code) cosmology framework (Gordon Bell Award Finalist 2012, 2013). It uses the N-body to simulate collisionless fluids under the influence of gravity. The kernel includes the checkpoint, restart, and analysis outputs produced by the simulation. Hence, it is quite I/O intensive. It also supports both POSIX and MPI-IO (with independent and collective operations) interfaces. Regarding the file approach, HACC-IO can be configured to write to a single shared file, a file-per-process, or a mix of both (i.