dataesr/scientific-paragraphs-categorization huggingface dataset:
We identified these companies considering a few criteria: (a) They should have atleast two tiers in their SC including the farm/area of origin before the processed/unprocessed product reaches them as shown in Fig. 2. (b) They should be licensed from the Food Safety and Standards Authority of India (FSSAI). By implementing science-based standards, the FSSAI regulates manufacture, storage, distribution, sale and import of food manufactured by various food companies in India, to ensure consumers get safe and wholesome food. So, a company which processes raw meat and packs them for further sale, whereas a company who uses that packaged meat to produce finished food products for consumption were both amongst our target respondents for the study. A total of 192 food companies were approached for the study, out of which 140 companies participated in the final data pool.
Respondents who rated themselves below 5 were not considered for the study. The final sample size after the data collection process was 328 responses, out of which 21 were incomplete responses and had to be rejected in the first place. The remaining 307 responses were checked for outliers by finding the Z-scores of the items and outliers detected and removed through conditional formatting. The remaining number of responses from which fair data was analyzed was 290 respondents. Ragin (1987)  proposed that Qualitative Comparative Analysis (QCA) is an asymmetrical and unconventional method to analyze data by combining the principles of qualitative methods with quantitative data analysis that involves a large number of sample size and is generalizable. By using QCA it is possible to identify combinations (or configurations) of factors/determinants/antecedents that lead to specific outcomes  (Ragin, 2008b).
0 based technological reforms. Only a portion of the sample is represented by each configuration, and only some of the potential solutions will contain an outlier. In an analysis which is based on variance-based methods, not all but a portion of the total solutions are likely to explain significant portions of the sample, whereas other solutions would explain smaller portions of the sample because they would be having instances too that are generally considered outliers. Due to its insensitivity to outliers, fsQCA in a way, is more sturdy as compared to methods based on variance  (Fiss, 2011; Liu et al. , 2017). The only limitations of fsQCA lies in its biggest positives, where it gives freedom to the researcher to make decisions at different stages of the analysis (e. g. , considering the frequency cutoffs for raw consistency and PRI consistency in the fsQCA software).
Analysis Through fsQCA A fuzzy scale (continuous) may be used for outcome and predictor variables using fsQCA rather than a binary scale (discrete). fsQCA features two types of conditions: necessary and sufficient. There may be presence or absence of these configurations, or there may be a "do not care" condition. To conduct fsQCA, the outcome and the independent measures must be identified. Next, we calibrated all measures into fuzzy sets with values ranging from 0 indicating full-set non-membership to 1 indicating fullset membership. The variables were transformed into calibrated set in the fsQCA software, wherein version 3 of the same was used and the step-by-step procedure as suggested by  Pappas and Woodside (2021)  based on their original article on online purchase behaviour  (Pappas et al.
, 2016) , wherein the authors provided a detailed procedure for performing fsQCA was followed throughout. The calibration was done by setting three meaningful thresholds: 0. 95 for full set membership, 0. 05 for full set non-membership and 0. 5 which denoted the cross-over point. To detect which values of the data communicate with the three thresholds, percentiles were used to compute the 95 th , 50 th , and 5 th percentile of our measures and used the values obtained as the three thresholds while calibrating the variables in fsQCA 3 software. The thresholds obtained for the 95 th , 50 th and 5 th percentiles averaged out to be 4, 3 and 2 respectively. It is problematic to assess the conditions which exactly lies on 0. 5 (i. e. , intermediate-set membership) using fsQCA since the cases that are exactly on 0.
Testing for Specific Propositions After obtaining all the solution sets resulting from fsQCA, we tested for a specific proposition to determine the number of cases in the sample for which the proposition holds true  (Pappas, 2018; Pappas et al. , 2020). This was achieved by testing our proposition 1 derived earlier in this article, i. e. , "Configurations that lead to high food safety and security will require the presence of at least one condition each from COVID-19 policy-oriented and I5. 0 based technological reforms for manufacturing". This was carried out by building a model in fsQCA by computing the required specific configuration and plotting it against the outcome variable, i. e. food safety and security during and post COVID-19 in the I5. 0 era. The configuration was computed as explained by the proposition and transformed into a model in fsQCA 3 software using the 'fuzzyand(x,)' function.
0 era". Finally, we plotted the new model using the 'XY plot' option in the fsQCA 3 software. The findings (Fig. 4 ) depicted that the proposition was partially supported. Our outcome variable is represented by X whereas proposition 1 represented by Y. Figure  4  shows that the extent to which the observations plotted are consistent with X ≤ Y, i. e. , X is a subset of Y, is 0. 94. The extent to which the observations plotted are having consistency of X ≥ Y, i. e. , Y is a subset of X, is 0. 72. If either of these two readings depict consistency to be high, the other can be considered as a coverage score  (Pappas and Woodside, 2021). Therefore, since the value for X ≤ Y is 0. 94 and the value for X ≥ Y is 0. 72, the calculations depict that our sample is very much consistent with the fact that X is a subset of Y and the amount of its coverage of Y is 72%, concluding that X accounts for 72% of the sum of the memberships in Y.
This raw coverage represents the degree to which a solution accounts for the cases in the data or the proportion of cases in the dataset that are explained by the solution. These findings calls for different paths these reforms can be recognized and implemented, justifying the principle of equifinality, concluding that food manufacturing firms may opt for any of the three solution paths depending on their human workforce, level of automation, length and size of the SC or any other parameter, to attain food safety and security in the I5. 0 era. Although previous authors have defined I5. 0 through the perspective of technology, sustainability and bringing in the human factor back into production and manufacturing practices, so far no definitions have been recorded in terms of food safety and security at the age of the pandemic.
Cloud screening The MicroCarb science team will use ECMWF cloud forecasts to program city-mode measurements, in order to maximize the success of cloud-free observations. However, the cloud forecast has a limited precision and small-scale clouds still exist. Here, to realistically describe (and remove) scenes contaminated by clouds on the spatial scale of the observations, we use ERA5 total cloud cover reanalysis data at 0. 25 • ×0. 25 • resolution  (Hersbach et al. , 2018)  for the expected satellite equatorial overpass time (12:00 UTC) of MicroCarb. We use a downscaling method that assumes the dependence of the probability (f ) of a cloud-free scene to its pixel size (s) and large-scale cloud cover (P )  (Palmer et al. , 2011) : f (P, s) = F p (s)g(1 -P ), (1) where g defined as a constant factor for alignment between the instrument pixel and the cloud-free subgrid region, which we choose to be 1.
Urban biosphere CO 2 fluxes We use the SMUrF (Solar-Induced Fluorescence for Modeling Urban biogenic Fluxes) model  (Wu et al. , 2021)  to estimate the net ecosystem exchange (NEE) across Paris and London. SMUrF estimates gross primary productivity (GPP) using OCO-2derived solar-induced chlorophyll fluorescence (SIF)  (Zhang et al. , 2018)  and neural networks to determine the relationship between ecosystem respiration and air and soil temperatures and GPP. SMUrF determines biome-specific model parameters by aggregating multiple flux tower sites from similar biomes across the globe to describe urban biogenic fluxes. We interpolate NEE values to the same grid used by ODIAC. We evaluate SMUrF NEE estimates in France and UK by using eddy-covariance flux measurements from the European Fluxes Database Cluster: five sites in France (FR-Aur, FR-Hes, FR-Lam, FR-LGt, FR-Pue) and two sites in UK (UK-AMo and UK-EBu).
Details of each site can be found at http://www. europe-fluxdata. eu/home/sites-list. These flux sites correspond to a variety of land types, including croplands (CRO), deciduous broadleaf forests (DBF), permanent wetlands (WET), evergreen broadleaf forests (EBF), and grasslands (GRA). We acknowledge these sites are not located in urban areas due to the lack of sufficient urban biosphere flux measurements (only one urban flux site in London and the data are not public), thus this evaluation provides us with limited confidence to understand the gradients in atmospheric CO 2 across these cities. All flux data in France are from 2018 (same year as our model meteorology). In the absence of UK flux data from 2018 we use data from 2015 (UK-AMo) and 2011 (UK-EBu). The seasonal-mean NEEs simulated by SMUrF in July 2018 agree reasonably with the eddy-covariance flux data (r=0.
Atmospheric transport model We use the column version of the Stochastic Time-Inverted Lagrangian Transport model (X-STILT)  (Wu et al. , 2018a; Fasoli et al. , 2018; Lin et al. , 2003)   Figures A3  and A4 ). X-STILT traces the movement of air parcels backwards in time. For the purposes of this study, we use a typical averaging kernel profile of OCO-2 that describes the vertical sensitivity of the column observations to changes in CO 2 in the column. We apply this averaging kernel and pressure weighting functions to the model fields to describe the footprints of satellite-based atmospheric CO 2 observations. The footprints describe the sensitivity of CO 2 columns at the receptors (locations where the satellite observes the atmosphere) to upwind surface fluxes. To drive air parcels in X-STILT we use Global Forecast System meteorological data with a horizontal resolution of 0.
H denotes the Jacobian matrix that describes the sensitivity of atmospheric CO 2 column enhancements to changes in CO 2 emissions. To evaluate the theoretical performance of the MicroCarb city-mode data on improving a priori knowledge of urban CO 2 emissions over Paris and London, we use an error reduction metric (η) that takes into account differences between the a priori and a posteriori error covariance matrices  (Palmer et al. , 2000) : η =   1 - Ŝi,i B i,i 1/2   × 100%, (5) where the subscripts denote the diagonal elements of the error covariance matrices. The larger value of η the more reduction in uncertainty of CO 2 emissions due to using the atmospheric CO 2 measurements. A5 ). We add a realistic additional 20% random error to the urban CO 2 column enhancement to account for atmospheric transport errors  (Deng et al.
There are a range of methods being employed to determine the background, such as calculating the mean value of column CO 2 surrounding the city area  (Kort et al. , 2012) , estimating a dynamic background derived from a two-step linear regression method  (Ye et al. , 2020) , or using the independent TCCON data of a remote site to derive background concentration  (Kiel et al. , 2021). In this study we have been able to sidestep this issue but only because it is a closed-loop simulation. For inversions with real MicroCarb data, we will need to consider an additional uncertainty associated with the calculation of the elevated CO 2 column. In addition, This study makes the simplifying assumption of unbiased and uncorrelated atmospheric transport errors, but transport errors are likely to be correlated at the sub-city scale.
Code and data availability. The ODIAC emission inventory is available at http://dx. doi. org/10. 17595/20170411. 001 hosted by the Center for Global Environmental Research, National Institute for Environmental Studies (https://db. cger. nies. go. jp/dataset/ODIAC/). Biogenic CO2 flux estimates from SMUrF are available at https://doi. org/10. 3334/ORNLDAAC/1899. The X-STILT model is available at https://github. com/uataq/X-STILT. Codes of this study are available upon request.
The system overview is shown in Figure  2. Prometheus servers in member clusters: The duty of these servers is to scrape 2 time-series data about local metrics in each member cluster, and to store them in their local database. They constitute the source of data before aggregation. They can also be used for querying detailed per-node metrics, for example for anomaly detection, diagnosis or system management purposes. Moreover, these Prometheus servers can also be configured to trigger alerts about nodes with abnormal metric values in their member cluster, such as fully saturated nodes. Prometheus server in the global view cluster: The Prometheus server in the global view cluster is used to save the aggregated data from the member clusters and their local metrics. The federation's scheduler can leverage this Prometheus server to query member cluster information and make the scheduling decisions.
Experimental Setup For the sake of making our work as close as possible to the production environment, we implement a prototype of our framework and run it in the Grid'5000 geo-distributed testbed which is composed of eight sites located in different cities in France and more than 750 physical compute nodes pooled in homogeneous clusters  [5]. We discuss the setup along the following four aspects: deployment of the experiment, performance indicators, comparison methods, and tools for collecting the data. Deployment of the experiment: To support the design features described in Section 4, we utilize Python 3. 10 to implement Acala-Controller and Acala-Member. We leverage  Kubernetes (v1. 23. 5)  for container orchestration to build the environment to analyze Acala in a geo-distributed cluster federation.
Each cluster installs the Prometheus server, and node-exporter deploys on each node in each cluster. We place our Acala-Controller in the global view cluster and Acala-Member in the member cluster to measure the performance. Moreover, Acala-Controller is installed on the same node as the Prometheus server, which can reduce the inter-node network traffic when the Prometheus server in the global view cluster scrapes from Pushgateway. Meanwhile, the Pushgateway is equipped in the same pod as Acala-Controller, which can make the metrics transmission in the pod. In Kubernetes, a Pod is the smallest deployable unit of computing  [18]. We leverage a Kubernetes Deployment  [19]  for these two components so that our software will automatically restart if a component error occurs.
Performance indicators: The main goal of Acala is to reduce the cross-cluster network traffic in geo-distributed cluster federations. Hence we measure network traffic as our primary indicator in the experiment. A lower network traffic implies better performance. Moreover, efficiency is a pivotal point in evaluating a system. Therefore, the scrape duration and resource consumption are also the objectives we consider. If scrape duration and resource consumption are shorter and lower, the overall efficiency is better. Comparison methods: In our proposed system, the data reduction strategy is a method to reduce the metrics when the global view clusters scrape from the member clusters. To evaluate the performance of metrics aggregation and metrics aggregation with deduplication, we compare them with unmodified Prometheus Federation.
In addition, we examine these three methods with different scrape interval (5 s and 60 s). Tools for collecting the data: After deploying the system, we start to collect the related data. All experiments are gathered for 6 minutes. There are three performance indicators for evaluating the Acala. Each indicator has its method of collection. For the crosscluster network traffic, we use tcpdump to capture the network traffic. The scrape duration is based on the time. perf_counter() function in the Acala source code to measure the execution time of each step. We sum the execution time of Acala-Member, Acala-Controller, and the duration of Prometheus scrape from the Pushgateway to become our scrape duration. The resource usage of the Acala components, including CPU and memory, is monitored by the Kubernetes Metrics Server (v0.
5. 2. 1 Cross-Cluster Network Traffic. Figures  4  and 5  show the experimental results of cross-cluster network traffic on average and per scrape, respectively. Figures  4(a  Figure  4 (a) shows that metrics aggregation with deduplication significantly reduces cross-cluster network traffic, which is 0. 45 KiB/s, whereas the network traffic in metrics aggregation and Prometheus Federation are 1. 64 KiB/s and 2. 33 KiB/s when monitoring a single worker node in the member cluster. Using metrics aggregation with deduplication in Acala and compared to Prometheus Federation, the reduction of network traffic in the single-node case is 1. 88 KiB/s, which is 81% lower, and there are 30% lower when we apply the metrics aggregation as our data reduction strategy. Figure 4(b)  shows the same trend that both of our proposed methods have lower network traffic than the Prometheus Federation.
If the monitored nodes are set to 20, 25, and 30, the network traffic is 98%/93%, 98%/94%, and 99%/95% lower when we use the metrics aggregation with deduplication/metrics aggregation and compare to Prometheus Federation. Overall, Figure  4  demonstrates that no matter how many monitored nodes are in the experiment, both of our proposed methods      perform significantly better than Prometheus Federation. The design of Prometheus Federation will scrape the metrics from all nodes in the target member cluster to the global view cluster. Our strategy is also to scrape the metrics from all nodes, but we make this task in the member cluster, making the transmission happen in the same cluster, which can reduce the cross-cluster network traffic. Moreover, our methods aggregate the same metrics between the monitored nodes, which can decrease the volume of monitoring  data to reduce cross-cluster network traffic and make the view of monitoring from node to cluster.
In addition, the method of metrics aggregation with deduplication is even lower than metrics aggregation since unmodified data does not get sent multiple times. If the value of the metric is the same as the current time and the last time, metrics aggregation with deduplication will remove these metrics to save network bandwidth between clusters. Acala collects metrics from monitored targets based on the fixed scrape interval. However, the current design does not smooth the data transmission over time as data get transferred at periodic intervals (same as Prometheus Federation). Therefore, we also want to know how much network bandwidth is used per scrape in this experiment. The results of cross-cluster network traffic per scrape are shown in Figure  5. In the case of 5 second scrape interval, we see in Figure  5 (a) that the cross-cluster network traffic of Prometheus Federation experiences linear growth from 11.
CONCLUSION This article presents Acala, a monitoring framework for geo-distributed cluster federations. Acala exploits two strategies called metrics aggregation and metrics deduplication for reducing the volume of monitoring data that needs to be reported to the management cluster. Acala performs more efficiently than regular Prometheus Federation because of lower cross-cluster network traffic and shorter scrape duration. Using actual deployment for experiments, we show that Acala can reduce the cross-cluster network traffic by up to 99% and the scrape duration by up to 55% compared to Prometheus Federation.
, 2020)  and the four-mode version of the Modal Aerosol Module (MAM4;  Liu et al. , 2016)  for the aerosol scheme. The generation of desert dust particles in MUSICAv0 is calculated based on the Dust Entrainment and Deposition Model  (Mahowald et al. , 2006; Yoshioka et al. , 2007). Dust emissions calculation is sensitive to the model surface wind speed. The dust aerosol processes in the MUSICAv0 simulation are simulated based on the MAM4 model  (Liu et al. , 2016). MAM4 has four modes -Aitken, accumulation, coarse, and primary carbon modes. Dust is mostly in the accumulation and coarse modes. The MUSICAv0 users have the option to create their own model grid. MUSICAv0 is currently being developed and tested for applications over various regions globally (https: //wiki. ucar. edu/display/MUSICA/Available+Grids, last access: 6 October 2023), including North America, India, East Asia, South America, Australia, and Korea (e.
g. ,  Schwantes et al. , 2022; Tang et al. , 2022; Jo et al. , 2023). In this study, we develop a model grid for applications in Africa (ne0np4. africa_v5. ne30x4). As shown in Fig. 1a , the horizontal resolution is ∼ 111 km × 111 km (i. e. , 1 • latitude × 1 • equatorial longitude) globally and ∼ 28 km × 28 km (i. e. , 0. 25 • latitude × 0. 25 • equatorial longitude)within the region over Africa. Our simulation uses the default option for vertical layers (i. e. , 32 layers from the surface to ∼ 3. 64 hPa). Here we run MUSICAv0 with the model grid for Africa for the year 2017, saving 3-hourly output. We use the Copernicus Atmosphere Monitoring Service Global Anthropogenic emissions (CAMS-GLOB-ANT) version 5. 1  (Soulie et al. , 2023)  for anthropogenic emissions and the Quick Fire Emissions Dataset (QFED) for fire emissions  (Darmenov and da Silva, 2013).
Plume rise climatology is applied to fire emissions following  Tang et al. (2022). In addition, we also include open waste burning (https://www. acom. ucar. edu/Data/fire/, last access: 6 October 2023;  Wiedinmyer et al. , 2014)  emissions in the simulation. The model has the option of a free-running atmosphere or nudging to external meteorological reanalysis. In this simulation, only wind and temperature are nudged to the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2; Gelaro et al. , 2017) with a relaxation time of 12 h. MERRA-2 data can be found at https:// disc. gsfc. nasa. gov/datasets?project=MERRA-2 (last access: 3 April 2023). We also added carbon monoxide (CO) tracers in the simulation to understand the source and transport of air pollution. CO tracers in CAM-chem and MUSICAv0 are described in detail by  Tang et al.
, 2002). Specifically, the dust emission scheme is following the GOCART emission treatment  (Ginoux et al. , 2001) , which is a function of 10 m wind speed, soil moisture, and soil erosion capability. The atmospheric processes of dust are simulated based on the mass mixing ratio and size distribution, which has been divided into five size bins with effective radii of 0. 73,  1. 4, 2. 4, 4. 5, and 8. 0 µm. The dust dry and wet depositions are also treated following the GOCART scheme  (Chin et al. , 2002). The European Centre for Medium Range Weather Forecasts (ECMWF) global reanalysis (ERA-Interim) fields are used for initial and boundary meteorology conditions, while another CAM-chem simulation is used for initial and boundary chemical conditions  (Kumar et al. , 2022). The WRF-Chem simulation used the global Emission Database for Atmospheric Research developed for Hemispheric Transport of Air Pollution (EDGAR-HTAP v2) for anthropogenic emissions and the Fire Inventory from NCAR version 1.