dataesr/scientific-paragraphs-categorization huggingface dataset:
The data transfer was performed with UDP tunneling through the SSH connection. The schematic communication details of this configuration are shown in Figure  13. Other than this, the data transfer procedure is the same as it is explained for local PC. The overall concept of the data transfer in this RTHS setting is as follows. The trained deep LSTM network model (metamodel), which is the analytical substructure of the RTHS test, runs from Python. Python can be run from either the local PC or the cluster. At each time step, this metamodel calculates the input displacement and sends it as displacement command to the experimental substructure. Then, the experimental substructure sends the measured force from the specimen and feeds it back to the metamodel. OpenFresco has a predictor-corrector algorithm, which is programmed in Simulink and Stateflow, and runs in the xPC target machine to manage the real-time environment, which synchronizes the integration time step, the simulation time step, and the controller time step  (Schellenberg et al.
Online RTHS With FE Model Conducting RTHS with large or heavily non-linear analytical substructure models could be challenging and has limitations that could be mostly associated with insufficient computational time within the RTHS loop. For example, when both stiffness and strength degradation are considered, direct integration methods were found to have some limitations, only for RTHS  (Bas and Moustafa, 2020). The idea of using an ML approach or metamodels could eliminate such limitations, which is the motivation behind this study. Thus, the system capabilities and online RTHS tests are first evaluated with the FE model. For these tests, OpenSees was used to model the analytical substructure. The model properties and modeling assumptions were the same as given in Training Datasets. For the experimental substructure, several length scale factors were considered at 5, 10, 15, 20, and 25.
RTHS Testing With ML Models From Local PC and Cluster As previously mentioned, two ways of running the LSTM ML models have been considered. The data communication is possible when the Python model is run from a high-performance  computer or the local computer (i. e. , host PC in the UNR setup). In this section, the computational time at each analysis step is presented for both ways of conducting the ML-based RTHS tests. The considered time steps included the time spent for the ML model to evaluate the prediction, i. e. , calculation time, the time spent to receive the data from the experimental substructure, and the total data transfer time. It should be noted that the prediction time step and the simulation time step were set to be 0. 02 s. The computational time spent for the online RTHS where Model 1 (with 15 lookbacks) was used as the computational substructure is shown in Figure  20A  for the local computer and Figure  20B  for the cluster.
It should be noted that Model 2 has a larger dataset dimension (lookback), which slightly increases the prediction time when Models 1 and 2 are compared. Overall, the observations show that the prediction takes slightly less time for cluster than the local computer for both models. For both configurations, the data transmission is done through the UDP channel. However, the UDP channel is provided through SSH network protocol for the cluster, which provides highly stable communication and ensures the desired simulation time step is achieved.
SUMMARY AND CONCLUSION In this article, the idea of using complex ML models to replace FE models for RTHS was validated, and foundational work was provided for RTHS communication development and verification for Python-based deep learning ML metamodels. This study was motivated by the potential for ML-based computational substructures to advance RTHS testing and help explore new research areas in the future. The article touches on ML models sensitivity and performance for time-series prediction, as well as using high-performance computers for running online ML models. The need for commonly used practices in RTHS testing, e. g. , actuator delay compensation, was also assessed when ML metamodels are used for the computational substructures. For all ML models and system evaluations, a one-bay, one-story steel braced frame was selected.
The columns and beam defined the analytical substructure, whereas the brace defined the experimental substructure for RTHS testing. The key aspects of the study along with the main findings can be summarized as follows. • A simplified ML model was first generated with LR algorithms for a linear elastic system. The LR model was coded using MATLAB/Simulink and compiled on the xPC machine as part of the RTHS loop. No specific communication scheme was needed, and the simple tests showed that using metamodels to derive the actuator in an RTHS setting is possible. • For accurate modeling of dynamic response of nonlinear braced frames, LR models could not be used, and more advanced ML algorithms were needed. Deep LSTM networks were selected because of their capability of capturing long-term patterns in time series.
Python was used to generate the LSTM models using training datasets obtained from analytical OpenSees models. A linear elastic brace was considered along with the non-linear frame, and several ML models were evaluated against the pure analytical FE model response to tune the hyperparameters of the model. Two LSTM metamodels were demonstrated to accurately predict non-linear response and in turn, selected to be used further in the RTHS tests. • The training domain is crucial and should be devised carefully for a robust ML model. In this study, in order to consider the uncertainties due to force-displacement dependencies and hardware errors (e. g. , initial load frame feedback, noise from actuator, etc. ), the training domain was expanded using seven episodes of the force input to the LSTM model with introduced bias.
Although this approach helped, there is need to devise other different training approaches in future studies to account for wide range of uncertainties. • In order to use Python as a computational driver for ML-based RTHS tests, a communication scheme that uses OpenFresco was proposed and successfully verified. OpenFresco serves a middle-tier server, and Python is the client in this form of architecture. The data transfer is made through UDP socket between Python and OpenFresco. Data communication from both local PC and highperformance computer cluster was verified. The cluster communication is achieved through UDP socket but under the secure shell portal. • When comparing the use of local PC versus cluster for running online RTHS Python LSTM models, no significant improvement in computational time is observed for the considered CBF structure.
• The preliminary results presented in this study suggest that a dedicated actuator delay compensator might not be needed. Instead, such compensation can be embedded within the ML model, which works with recurrent data, and considered a priori as part of the training. While sufficient evidence and verification are still needed, this observation is worth further investigation to explore the use of ML models to improve actuator control in cases of non-linear dynamic systems. • Overall, the communication developments for RTHS tests with advanced Python-based ML models are successfully validated for the first time. The goal of the article was not necessarily to present most the accurate or exact ML models for a given non-linear analytical substructure, but rather to demonstrate the concept of using ML algorithms within the HS loop.
NaOH-Assisted Thermal Pretreatment A: Reaction Temperature, ( Chemical-assisted pretreatments experiments were planned by central composite design (CCD) of Response Surface Methodology (RSM). Four independent variables with the three levels were utilized for CCD in Design-Expert®11 software trial version (Minneapolis, MN, USA) for NaOH-and HCl-assisted thermal pretreatments. Fifty-one pretreatment experiments containing three experiments at the center point along with the duplicates of each run were planned for each chemical (NaOH and HCl) assisted thermal pretreatment by CCD. Consequently, 108 values for each response (sCOD, sRedSugar) were used in the optimization of each chemical-assisted pretreatment processes. Results of experiments were modeled and acquired regression models were assessed by the analysis of variance (ANOVA), regression coefficients along with the pand Fvalues.
The quality of models was concluded via the coefficient of determination (R 2 ) and an adjusted determination coefficient (adj-R 2 ). With the help of the obtained model equations, optimizations of chemical-assisted thermal pretreatment processes were performed by using maximization and minimization criteria for independent and dependent variables  [3, 30]. The goal settings of the variables have been adjusted by using the plus (+) symbols in the module of optimization of a Design-Expert ® software program for process optimization. The significance of the goals was chosen as (+++++), the highest, for all variables in software. Design-Expert ® software solved all goals for all variables to obtain the desirability function. The optimization module investigated the merging of independent variable values that meet the demands placed on each of the dependent variables.
On the other hand, other variables shown in ANOVA analysis presented in Tables  3  and 4  were not significant (p > 0. 005). sCOD and sSugar models were operated for the optimization of NaOH-and HCl-assisted thermal pretreatment conditions by Design-Expert®software. During the optimizations of both pretreatment applications, a cost-driven approach was applied. Reaction temperature, reaction time and NaOH concentration were diminished, while sCOD and sSugar were maximized for NaOH-assisted thermal pretreatment. Mixing speed was kept in the ranges. Optimum conditions were obtained with a desirability of 0. 681 at 0. 67% NaOH concentration, 65 • C reaction temperature, 1 h reaction time, and 500 rpm mixing speed. Under these conditions, 688 mgsCOD mgVS -1 and 222 mgsSugar mgVS -1 were predicted for sCOD and sSugar, respectively.
The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation.
Within-species variation accounts for approximately 25% of trait variation at the global scale  21  , but has been hypothesised to be greater in extreme environments due to environmental filtering of trait expression  25  , at local geographical scales where species richness is low  26, 27  , and for species that span large biogeographical gradients  21  due to wide niche breadth. The tundra biome thus provides an optimal system to test our current understanding of trait variation within plant communities due to a small species pool  28  , large species ranges  13  , and extreme environmental conditions  29. In this study, we test whether our existing understanding of plant trait relationships extends to the tundra biome. We establish the largest database of Arctic and alpine tundra plant traits ever compiled by combining 20,991 records from the TRY database  15  with 30,616 records from the Tundra Trait Team (TTT)  30  , representing 89% of the tundra species pool.
1 ), as would be predicted from global trait relationships  5. In contrast, traits associated with resource economics occupied almost the full global range (Fig. 2a , axis 2), with both highly acquisitive species such as chickweed (Stellaria media), and highly conservative species such as crowberry (Empetrum nigrum) present at tundra sites. Species with faster resource-related traits and larger size-related traits were associated with warmer environments within the tundra (Fig. 2b , Supplementary Fig. 1 ), potentially informing the adaptive capacity to climate warming within and among tundra plant communities. Fig. 2  Global trait relationships are maintained in the tundra biome despite constrained size, but not resource economic, traits among tundra species. a Global trait-space defined by six plant traits for 1,358 plant species in the global dataset (grey points) and 219 tundra species (blue points).
Trait variation across geographic scales. We found that the contribution of within-species trait variation was largely consistent across geographic scales (Fig. 4a-d ), but comprised a greater proportion of total variation at local scales (<10 km 2 ), approximately the size of current high-resolution cells in gridded climate datasets. Sites with low sampled species richness also exhibited high within-species variation (<10 species; Fig. 4e-h ), suggesting that spatial patterns were at least in part driven by a small species pool at local scales. Although both theoretical models  27  and empirical studies  21  have suggested that the contribution of within-species trait variation should increase at local scales, we demonstrate this relationship from the plot to the biome scale. Our findings indicate that the relationships between plant traits found at the global scale are generalizable even at the climatic edge of global trait-space.
e. , topography, snow, drainage, etc. )  24, [43] [44] [45]. Accounting for multiple sources of trait variation has been shown to constrain trait-based vegetation models  7, 12  and subsequently to improve prediction of the response of key ecosystem processes to environmental change  11, 13, 46. However, such trait-based modelling approaches are rare, and require precisely geo-referenced trait databases that link trait records to environmental variables. We therefore support calls to collect additional trait data in changing and novel climate conditions  13, 30  , to improve the techniques and technologies used to remotely sense plant trait information  28  , and to incorporate trait variation into Earth system modelling  47. Overall, our findings demonstrate that relationships and tradeoffs among six fundamental plant traits are generalisable across lineages and among biomes to the cold extremes of the planet.
Methods Tundra biome definition. In line with previous biome-scale assessments of tundra vegetation community change  [48] [49] [50]  , we defined the tundra biome as the vegetated regions above treeline at high latitude and high altitude. Tundra species were identified as those present in sampling plots from two biome-scale experiments, the International Tundra Experiment (ITEX)  51  and associated sites  49  , and the sUM-MITDiv network  52  , or those present at trait collection sites with a mean annual temperature below 0 o C. Tundra plant communities include many widelydistributed and locally common species that are found across large geographical gradients and a variety of environments  51. We included trait records of tundra species collected outside of tundra environments in this study because i) we were specifically interested in the maximum potential within-species variation among tundra species, ii) defining tundra environments on a purely climatic basis (excluding biotic community in the definition) is very difficult and would require arbitrary decisions regarding biome boundaries, and iii) many trait records in the TRY database do not contain georeferenced collection coordinates and thus would be impossible to classify based on environment (Fig.
These six traits are considered to represent fundamental dimensions of ecological strategy  8, 53  and are commonly measured in the tundra  30  , thus maximising trait coverage. Trait data collection. We extracted trait data from the TRY  15  3. 0 database (available at www. try-db. org) for tundra species  1, 2, 17,. We ext acted traits of all tundra species from the TRY database regardless of location to maximise the capture of trait variation per species. We supplemented TRY data with additional trait data from the "Tundra Trait Team" (TTT) database  30. All species names from ITEX, TRY and TTT were matched to accepted names in The Plant List using the R package Taxonstand (v. 1. 8) before merging the datasets  143. We assigned species to four traditional functional groups-evergreen shrubs, deciduous shrubs, graminoids, and forbs-based on previous classification of ITEX species  49.
We excluded stem-specific density (SSD) from all analyses, except for comparisons with Diaz et al. 8  (Supplementary Figs. 9-11) since SSD had low collection coverage in the tundra and was available for too few species (n = 52). We also extracted trait data and collection site coordinates from TRY 3. 0 for all global species to provide global context in geographical-climate-space and trait-space analyses following previously published approaches in Kattge et al. 15  and Diaz et al. 8. Data cleaning-TRY. TRY trait data were subjected to a multi-step cleaning process. Firstly, all values that did not represent individual measurements or species means were excluded. Secondly, we identified overlapping datasets within TRY and removed duplicate observations whenever possible. The following datasets were identified as having partially overlapping observations: GLOPNET-Global Plant Trait Network Database, The LEDA Traitbase, Abisko and Sheffield Database, Tundra Plant Traits Database, and KEW Seed Information Database (SID).
Thirdly, we removed duplicates within each TRY dataset (e. g. , if a value is listed once as "mean" and once as "best estimate") by first calculating the ratio of duplicated values within each dataset, and then removing duplicates from datasets with more than 30% duplicated values. This cut-off was determined by manual evaluation of datasets at a range of thresholds. Datasets with fewer than 30% duplicated values were not cleaned in this way as any internally duplicate values were assumed to be true duplicates (i. e. , two different individuals were measured and happened to have the same measurement value). Finally, we removed all species mean observations from the "Niwot Alpine Plant Traits" database and replaced them with the original individual observations as provided by the trait collector (Marko J.
Spasojevic) in order to ensure all trait measurements were for individuals. Data cleaning-TRY and TTT combined. Both datasets were checked for improbable values, with the goal of excluding likely errors or measurements with incorrect units, but without excluding true extreme values. It was particularly important to avoid artificial reduction in the range of trait values in this study, since we were explicitly interested in trait variation. We followed a series of datacleaning steps, in each case estimating an error risk for a given observation (x) by calculating the difference between x and the mean (excluding x) of the group in question and then dividing by the standard deviation of the group. We employed a hierarchical data cleaning method, because the standard deviation of a trait value is related to the mean and sample size.
First, we checked individual records against the entire distribution of observations of that trait and removed any records with an error risk greater than 8. 0 (i. e. , a value more than eight standard deviations away from the trait mean). For species that occurred in four or more unique datasets within TRY or TTT (i. e. , different data contributors), we estimated a species mean per dataset and removed observations for which the species mean error risk was greater than 3. 0 (i. e. , the species mean of that dataset was more than three SD's away from the species mean across all datasets). For species that occurred in fewer than four unique datasets, we estimated a genus mean per dataset and removed observations in datasets for which the error risk based on the genus mean was greater than 3.
5. Finally, we compared individual records directly to the distribution of values for that species. For species with fewer than four records, we did not remove any values. For species with more than four records, we excluded values above an error risk y, where y was dependent on the number of records of that species and ranged from an error risk of 2. 25 for species with fewer than 10 records to an error risk of 4. 0 for species with more than 30 records. This procedure was performed on the complete tundra trait databaseincluding species and traits not presented here. In total 3515 observations (2. 8%) were removed. In all cases, we visually checked the excluded values against the distribution of all observations for each species to ensure that our trait cleaning protocol was reasonable. All trait observations with latitude/longitude information were mapped using the R package 'mapproj'  144  and checked for illogical values (e.
g. , falling in the ocean). These values were corrected from the original publications or by contacting the data contributor whenever possible. Where locations could not be verified, georeferenced coordinates were removed and the trait data not included in geographic analyses. Final trait database. After cleaning out duplicates and suspected mistakes, we retained 51,657 unique trait observations (of which 20,991 were already in TRY and 30,616 were newly contributed by the Tundra Trait Team) across the six traits of interest. Of the 447 identified species in the ITEX dataset, 397 (89%) had trait data available from TRY or TTT for at least one trait (range 60-100% per site). Those species without trait data generally represent rare or uncommon species unique to each site. On average, trait data were available for 97% of total plant cover across all sites (range 39-100% per site; Supplementary Table  1 ).
Data compiled through the Tundra Trait Team are available in Bjorkman et al. 30. The total TTT database submitted to TRY includes traits not considered in this study, as well as tundra species that do not occur in our vegetation survey plots, for a total of 54,210 trait observations on 530 species. For more information on trait data and trait cleaning methods see Bjorkman et al. 13  and Bjorkman et al. 30. Climate data. To plot the distribution of tundra trait data within climate space, we used the coordinates of all unique collection sites for both tundra (TRY and TTT) and global (TRY) datasets. CHELSA climate variables (mean annual temperature-BIO10_1 and mean annual precipitation-BIO10_12, http://chelsa-climate. org/) were extracted for all trait observations with latitude/longitude values recorded (39,573 records in total, 12,434 of which were from TRY and 27,139 from TTT).
Because most observations did not include information about elevation, temperature estimates for individual trait observations were not corrected for elevation. We calculated the 'temperature class' of tundra species based on the mean summer temperature of trait collection sites for each species. Mean summer temperature was considered to be the most ecologically meaningful climatic variable since it captures conditions during the growing season for each plant species. We extracted summer temperatures from the CHELSA dataset based on BioClim variable BIO10_1. We assigned species to three temperature classes: Cold tundra = mean summer temperature less than -1 °C, Mid tundra = mean summer temperature greater than -1 °C but less than 1 °C, Warm tundra = summer temperature of coldest site greater than 1 °C.