dataesr/scientific-paragraphs-categorization huggingface dataset:
One may cite as an example MVIRI, an instrument whose IFOV (see Table  1 ) is much smaller than the horizontal resolution of global reanalyses. The simulation of the MVIRI infrared window channel generally performs better over ocean than land, but on the other hand, the simulation of the MVIRI water vapor channel features larger spreads in departures than those of the window channels, owing to the upper-air water vapor coarse-resolution and corresponding variability representation in the reanalyses. Another example is when a satellite data record contains signals that originate from changes not contained in the simulations, such as volcanic aerosols (AVHRR) or possible changes in trace gases whose concentrations have evolved due to industrial emissions (SI-1). All these cases can be summed up by the issue of representativeness uncertainty between observations and reanalysis.
However, if one priority is to improve inter-operability of datasets for comparisons and other applications, the multitude of data models to represent observations is a barrier to integration. Indeed, it requires, in each case, to adapt computer code. To circumvent this issue, initiatives have been proposed, to promote a single data model  (Nativi et al. , 2008). Such initiatives will greatly simplify the data integration and data comparisons, for example with other observations or with models, possibly via simulations as shown here.
Conclusions This paper applies radiance simulators to the Fundamental (Climate) Data Records (F(C)DRs of several satellite instruments, using as input global climate reanalyses. While the methodology of radiance simulators is not new, we demonstrate that their application enables three classes of applications. In the first class of applications, assumptions about a data record organization (order of channels), its quality, or data corrections, may be verified. For this, we mostly draw from examples where the data have been characterized long ago, such as the MVIRI and HIRS data records, and much progress has been made since then. We use examples where the methodological advance of reprocessing is on a level that benefits from a high-quality a priori comparison to validate the impact, such as identifying image anomalies in geostationary images or improving the coherence between data records and reanalyses with modern cloud masks.
Regarding the volcanic eruption of Mount Pinatubo, we find a cooling on the order of 1 K for brightness temperatures from AVHRR and HIRS window channels (short-wave and longwave alike), with concomitant increase in reflectance for the AVHRR near-infrared channel of a few percent. We also revisit how fast the atmospheric effects of the eruption propagated away from the Tropics. In line with previous findings, we confirm differences in the timing of peak radiative effects of several months between the Mediterranean and the Southern Oceans as compared to the Tropics, where the volcanic eruption had taken place. In the second class of applications, coherence between global datasets of different natures can be assessed. The high spectral resolution data collected by the SI-1 instrument allows confirmation of improvements in the quality of the latest Japanese global reanalysis, JRA-3Q, for stratospheric ozone.
8-1. 0 K. Another example shown, with MRIR, illustrates how differences, which could be interpreted as incoherencies between reanalyses and observations, can be differently reduced numerically, depending on the set of bias predictors chosen. While this can minimize systematic differences, another importance of this approach is to gain understanding about the potential sources of errors in the satellite data. This ties the present study to a third class of applications: informing users on key characteristics of a data record. In this third class of applications, we show cases of simulations of, and comparisons with, data records from SMMR, SSM/T-2, and Meteosat second-generation. For SMMR, the findings are that the existing data records suffer for the horizontally-polarized 21 GHz channel from large oscillating biases, and that all channels exhibit a different behavior after a Special Observing Period in 1986.
Given the value of the SMMR data in bridging with the SSM/I data record, this calls to consider a potential new reprocessing of the SMMR data record from the original data. For SSM/T-2, we find that uncertainty information and horizontal local variability in the observations make a large difference to improve the agreement between reanalysis and clear-sky simulations. This suggests that these parameters would need to be taken into account in applications, such as clear-sky humidity retrievals. For Meteosat Second Generation, we find that the variability of the satellite position around its nominal position has most likely left a signature in the data record. For climate applications, such changes in position are needed to take into account or else they may get aliased into regional patterns of changes in the downstream products.
Acknowledgments This work benefited from strong inter-agency collaboration, noting support from the  European Union Copernicus Climate Change Service (C3S)  and the institutions to which the authors are affiliated. The  EUMETSAT NWP-SAF  is thanked for adding support to several instruments, with special thanks to  Emma Turner  and  James Hocking. The  EUMETSAT CM-SAF  is acknowledged for providing data records used in this work, and specials thanks are addressed to  Karl-Göran Karlsson ,  Abhay Devasthale ,  Martin Raspaud ,  Diana Stein ,  Nathalie Selbach ,  Stephan Finkensieper ,  Karsten Fennig ,  Marc Schröder , and  Rainer Hollmann. The authors wish to thank  NOAA  for the SSM/T-2 Sensor Data Record. The authors also wish to thank librarians at  EUMETSAT ,  NASA , and  NOAA  for help locating early satellite instruments' technical documentation.
Open Research The satellite datasets analyzed in this study are available as follows: MVIRI (EUMETSAT, 2020): doi:10. 15770/EUM_SEC_CLM_0009, SEVIRI (EUMETSAT, 2015): manuscript submitted to Earth and Space Science doi:10. 15770/EUM_SEC_CLM_0008, MRIR (McCulloch, 2014): doi:10. 5067/XTJ53AK84QRL, SI-1 (Poli et al. , 2023): doi:10. 5281/zenodo. 7912742, HIRS (EUMETSAT, 2022): doi:10. 15770/EUM_SEC_CLM_0026, AVHRR (EUMETSAT, 2023): doi:10. 15770/EUM_SEC_CLM_0060, SMMR (Fennig et al. , 2017): doi:10. 5676/EUM_SAF_CM/FCDR_MWI/V003, SSM/T-2 (EUMETSAT, 2021): doi:10. 15770/EUM_SEC_CLM_0050, and CLARA-A3 cloud mask (Karlsson et al. , 2023): doi:10. 5676/EUM_SAF_CM/CLARA_AVHRR/V003. The reanalysis datasets are available as follows: ERA5 (Copernicus Climate Change Service, 2018): doi:10. 24381/cds.
bd0915c6, ERA-20C (ECMWF, 2014): doi:10. 5065/D6VQ30QG, ERA-Interim (ECMWF, 2009): doi:10. 5065/D6CR5RD9, JRA-55 (Japan Meteorological Agency, 2013): https://search. diasjp. net/en/dataset/JRA55, and JRA-3Q (Japan Meteorological Agency, 2022): https://search. diasjp. net/en/dataset/JRA3Q. The radiance simulator used in this study is RADSIM (EUMETSAT NWP-SAF, 2021), available from https://nwp-saf. eumetsat. int/site/software/radiance-simulator/. We used RADSIM version 3. 0. The radiative transfer model used in this study is RTTOV (EUMETSAT NWP-SAF, 2020), available from: https://nwp-saf. eumetsat. int/site/software/rttov/. We used RTTOV version v13. 0 except for simulating MRIR, where RTTOV v12. 2 was used.
e This is the DOI reserved for future publication of the entire data record, noting that the subset of data used in the present work are available from DOI:10. 5281/zenodo. 7912742. f Except for HIRS/4 (10 km), noting also HIRS on Nimbus-6 is not covered here. g Note that AVHRR Global Area Coverage (GAC) data used in the present work present a lower resolution.
e. , radical species, quinones) produced during the cascade of oxidation reactions. Ideally, researchers could approach the control of off-odor formation in addition to the traditional targeted compound analysis, by searching for native chemical regulators of oxidation mechanism like GSH  [5]. It appears that a number of natural wine constituents may be participating in the oxidation reactions, especially in the secondary reactions which are yet unknown  [6]. Understanding what these metabolites are, as well as looking at substances that have antioxidant properties in other fermented beverages, will allow us to directly assess wines' natural resistance against oxidation. This alone will be very valuable in estimating the shelf life and understanding aging potential. By applying the combination of powerful and modern analytical approaches (EPR, FTICR-MS-based metabolomics), recent studies from our laboratory established a molecular database referred to as the antioxidant metabolome, related to white wines' oxidative stability expressed from the very beginning of the winemaking process  [7].
Data Mining UHPLC-qTOF-MS features (couple m/z, retention time) were filtered according to S/N (>30) and absolute intensity (>1000). Before features extraction, the spectral background noise was removed. Features were aligned using a homemade R script, with m/z and retention time tolerance lower than 10 ppm and 0. 3 min, respectively. Spearman correlation between features and EC 20 was calculated using R script to isolate correlated features (|r| > 0. 6 and p-Value < 0. 01). To isolate nucleophilic compounds, partial least squares discriminant analyses (PLS-DA) were performed with Simca (Umetrics) to determine VIP scores for each feature. Using a Matlab script (R2015a), the Wilcoxon correlation was calculated. Features were filtrated using VIP > 1 and p-Value < 0. 01, allowing to isolate native and derivatized nucleophilic compounds  [8].
The elementary composition of the filtrated compounds was determined using isotopic profiles with DataAnalysis (v. 4,3, Bruker, Mannheim, Germany). Annotation was realized using online databases KEGG, Metlin, and the online tool Oligonet  [12] [13] [14]. Targeted research of reaction products from wine-relevant nucleophiles was realized using a Matlab script, based on the reaction: AOH + Nu → P + 2H + , where AOH is a phenolic compound, Nu is a nucleophilic compound (AM-Nu), and P is their reaction product detected in the wine. Ten phenolic compounds have been selected to manage the reaction (gallic acid, caffeic acid, coumaric acid, ferulic acid, caftaric acid, and coutaric acid, hydroxytyrosol, tyrosol, catechin, and quercetin). Figures were plotted using Origin Pro 8 software (Originlab Corporation, Wellesley Hills, MA, USA), Matlab, and Simca.
Definition of Wine Antioxidant Metabolome White wines' antioxidant metabolome is the sum of molecular antioxidant markers characterized by their radical scavenging (AM-RS) and nucleophilic (AM-Nu) properties (Figure  2 ). AM-RS currently counts up to 380 features (couple ions m/z, retention time) gathered in an in-lab database created after the analysis of 287 wines where correlations between their antiradical capacity and their molecular profile were established  [7]. On the other hand, AM-Nu is an in-lab database currently listing 141 wine-relevant nucleophiles (couple ions m/z, retention time) identified after direct one-step quinone probing of up to 92 white wines from different cultivars and vintages  [8].
To better understand and monitor wines' antioxidant chemistry through the dynamic behavior of AM-RS, annotations using online databases (Metlin and Oligonet) have been realized to enhance information about the chemical diversity of AM-RS. In addition, the chemical formulae of annotated compounds were determined using isotopic profiles. Sixty-four out of one hundred and twelve AM-RS reactional products were annotated as CHO (n = 22) and CHON (n = 36) compounds (Table  S2 ). Three of them could putatively be assigned to coumarin derivates, C9H6O2 (146. 0368 Da, RT = 3. 5 min), C9H6O3 (162. 0361 Da, RT = 3. 2 min), C10H8O3 (176. 0473 Da, RT = 3. 1 min). These ions could be tentatively identified as coumarin, chromone or phenyl-propiolic acid for C9H6O2, hydroxycoumarin, umbelliferone for C9H6O3 and herniarin, methoxy-chromone, methyl-umbelliferone, or hydroxy-decene-triynoic acid for C10H8O3.
Coumarin (C9H6O2), umbelliferone (C9H6O3), and methyl-umbelliferone (C10H8O3) are oak-wood-derived compounds known for their high antioxidant capacity  [18, 19]. From the same perspective, C9H10O3 (166. 0224 Da, RT = 3. 8 min) and C9H10O5 (198. 0528 Da, RT = 3. 0 min) could putatively correspond to phloretic acid or ethyl vanillin and syringic acid or vanillylmandelic acid, respectively  [20] [21] [22] [23]. In addition, among the CHO AM-RS reactional products we could annotate C9H14O6 (218. 0790 Da, RT = 1. 3 min) reported in the literature as triacetin, an inactivate dry yeast derivative  [24]. Concerning CHON-containing AM-RS reactional products, we could To better understand and monitor wines' antioxidant chemistry through the dynamic behavior of AM-RS, annotations using online databases (Metlin and Oligonet) have been realized to enhance information about the chemical diversity of AM-RS.
On the other hand, 155 out of 358 AM-Nu compounds from our in-lab database are mostly sulfur-containing compounds presenting great nucleophilic character, characterized after the identification of putative reaction products with selected wine-relevant polyphenols. Generally, the antioxidant capacity of wines is increasing during on-lees barrel aging, while an interested correlation was established between the number of AM-Nu related compounds at the beginning of the winemaking process and the wines' antioxidant capacity at the end of barrel aging. The discrimination of wines according to their antioxidant capacity, AM-RS, AM-Nu, and putative reaction products with polyphenols (P) composition has shown the importance of cross-checking the information between analyses to take into account the high diversity and complexity of the compounds involved in the antioxidant metabolome of white wines.
Cloud infrastructures are large-scale and complex platforms designed to host a wide diversity of applications and workloads. Given these complexity and scale factors, simulators and benchmarks are broadly adopted in vitro to study their behaviors, prototype new software components and heuristics, and evaluate their effective performances. However, both state-of-the-art simulations and benchmarks may suffer from a representativeness problem, as the reported results can vary depending on their input workloads. For example, a Infrastructure-as-a-Service (IAAS) platform aims to host Virtual Machines (VMs), whose characteristics (resource configurations, workload intensity, arrival/departure rate, etc. ) can greatly differ depending on Cloud providers and public/private deployments. Addressing this IAAS representativeness thus requires Cloud providers to share production-scale datasets, which might be considered sensitive.
Moreover, Simulations and benchmarks require a specific experiment scenario that cannot be easily generated from Cloud providers characteristics. To address these issues, this paper introduces CLOUDFACTORY, a IAAS workload generator. Our contribution is first composed of a library that can be used by Cloud providers to share IAAS statistics, instead of raw datasets. Then, we introduce a generator designed to produce realistic VM workloads that match these statistics. CLOUDFACTORY is made available as open-source software that can be adopted by Cloud providers and researchers to foster the evaluation of new contributions. As an example, we perform an analysis on scheduling evolution for different IAAS workload intensity of two different Cloud providers: Microsoft Azure and Chameleon. We also report on OVHcloud statistics computed from CLOUDFACTORY and compare them to other Cloud providers.
I. INTRODUCTION For more than a decade, Cloud computing keeps being an active field of academic and industrial research, encompassing multiple challenges and contributions that are continuously delivered to improve the state of the art. Nevertheless, assessing effective contributions in the domain of Cloud computing is notoriously hard  [1] , in particular, because of the difficulty to reproduce production-scale IAAS deployments. To overcome this limitation, Cloud simulation platforms are commonly considered in the literature to evaluate new contributions for Cloud infrastructures  [2]. Alternative contributions to this field include datasets and benchmarks that can be released and published by both academic and industrial practitioners. Unfortunately, Cloud providers may be reluctant to share production-scale datasets, due to the possibility of user identity exposure through trace information  [3].
Furthermore, both Cloud-based simulations and experimental deployments tend to suffer from the same limitations in terms of representativeness. In particular, the replay of benchmarks and raw datasets may be difficult to apply when lacking productionscale infrastructures, while raw traces may be truncated to deal with the limitation of a testbed infrastructure, hence questioning the representativeness of the resulting experiments. Furthermore, the applicability of developed contributions in a production-scale environment may be challenged by this limitation. The main objective of this paper, referred to as CLOUD-FACTORY, is to enhance the evaluation and validation of Cloud contributions by allowing to use production-like Cloud workloads. More specifically, CLOUDFACTORY addresses the challenge of reproducing IAAS workloads by leveraging representative infrastructure-scale models extracted from datasets of production-scale execution traces.
To achieve this, CLOUD-FACTORY combines two key components that can be used independently by Cloud practitioners and researchers to share insightful statistics that can be exploited to generate IAAS workloads at any scale-from single worker nodes to clustersand apply them in simulations or experimental deployments. In particular, thanks to the workload analyzer that we made available, Cloud providers can share production-scale metrics without disclosing their raw datasets. Then, computer scientists can import these statistics into the workload generator that we packaged as a separate tool to produce various IAAS workloads that they can easily use and further share to evaluate their contribution, thanks to the bindings we deliver for several state-of-the-art evaluation platforms. Throughout the paper, we illustrate the value of CLOUDFACTORY by reporting on the analysis of the Microsoft Azure dataset  [4]  and the generation of IAAS workloads for the CLOUDSIMPLUS simulation environment  [5]  and the CBTOOL benchmarking tool  [6].
II. RELATED WORK When evaluating Cloud-based systems, researchers may adopt two complementary approaches. The first involves simulations to synthesize various system configurations and workloads, thereby avoiding the cost of resource-intensive and timeconsuming real-world experiments. The second approach involves benchmarks, in which real-world workloads are applied to a system under test, which may be either a generic system or a copy of an existing one. Unfortunately, both of these approaches often face a representativeness challenge, where injected workloads must be as faithful as possible to realworld conditions. Cloud metrics reported by practitioners are a key insight in this regard. This section outlines the state of the art for Cloud simulators, System under Test (SUT) benchmarks, workload representativeness, as well as relevant Cloud datasets.
A. Cloud simulators CLOUDSIM  [7]  is an extensible software framework to simulate a Cloud-based infrastructure. Its flexible nature enables users to create customizable platforms under various workload scenarios, such as VM (Virtual Machine) distribution, arrival rate, CPU usage, etc. CLOUDSIMPLUS  [5]  was introduced as a fork, enhancing its capabilities. Crossref 1  records indicate that the original framework is widely adopted by the research community, with over 2, 700 citations. While other Cloud data center simulators exist, like closedsource MDCSIM  [8]  and GREENCLOUD  [9] , simulators expect developers to encode synthetic deployment scenarios rather than providing guidelines on realistic Cloud workloads. For instance, the MDCSIM validation protocol relied on a custom scenario to compare its resource utilization to a physical infrastructure hosting a web-oriented application under a specific workload.
A non-exhaustive list of legacy applications includes relational database structures proposed by TPC  [14] , CPU-intensive tasks, such as those provided by SPEC CPU  [15] , mail server  [16] , and others. Additionally, web-oriented architectures are also frequently considered and tested using various projects, such as the OLIO monolithic application  [17]  and other microservice architectures implemented in the DEATHSTARBENCH  [18]. Key-value stores are also widely deployed and can be evaluated using the YCSB benchmark  [19]. 2) VM under Test: Assessing VM performance for a given application is beneficial to Cloud clients. In this configuration, the application and the workload generator may not be colocated, to evaluate the platform induced latency. The choice between hosting options can be made by evaluating the balance between performance and cost for various VM sizes and providers.
SPECVIRT benchmark  [25]  offers a mean of measuring end-to-end performances, including hardware, virtualization platform, guest operating system on various application types. While being behind a paywall, they also do not target workload representativeness. They use an increasing workload until Quality of Services (QoS) are violated. Another available option is the VMMARK benchmark, provided by VMWare  [26]. This benchmark deploys simultaneously 6 VMs hosting different workloads: mail server, java server, idle workload, web server, database server, and a file server before evaluating their performance under stress. They do not seem to take into account larger deployments, VM lifespan, departure, and arrival rates. The management complexity associated with studying multiple Cloud nodes has led to the development of dedicated frameworks.
D. Cloud datasets Different Cloud datasets are made available by Cloud providers. Azure IAAS workload characteristics has been provided in a previous study  [4]. It includes, amongst others, VM memory and vCPU configuration options distribution, arrival rate patterns, VM lifespan, and CPU usage daily pattern. Despite lacking crucial figures, such as memory usage, this dataset stands as a valuable contribution, given its status as the only publicly available Cloud dataset to the authors' knowledge. However, it is important to acknowledge that Cloud workloads may significantly differ from one another due to their specific contexts. For instance, research-oriented Cloud operator CHAMELEON  [31]  published 6-month traces in 2020 where half of their deployments imply VMs having at least 4 vCPUs, while Azure reports that less than 2 vCPUs VMs are required in 80% of their deployments.
Publication of new datasets must therefore be encouraged. In the realm of Cloud services, IaaS VMs are a prevalent offering. Managed services, like Platform-as-a-Service (PAAS) and Function-as-a-Service (FAAS), are typically internally built upon IAAS offerings. Furthermore, Containeras-a-Service (CAAS) offers comes with a higher level of management, as the host is responsible for managing the kernel. In this CAAS context, traces from Alibaba  [32]  and Google Borg  [33]  can be studied. However, there is a scarcity of datasets specifically focused on IAAS infrastructure and datasets from CAAS cannot be directly applied to a VMbased context. For instance, containers generally have shorter lifetimes compared to VMs.