dataesr/scientific-paragraphs-categorization huggingface dataset:
We also note that only three lensed ALCS sources are detected in X-ray among 31 cluster fields with publicly available Chandra data (A370-ID110, M0416-ID117, M0329-ID11; these sources will be discussed by Uematsu et al. from the collaboration). We also estimate the upper limit of the AGN contribution to the derived IR luminosities. We assume the SKIRTOR model  (Stalevski et al. 2012 (Stalevski et al. , 2016) )  for a type 2 AGN SED with an inclination angle of 70°. To estimate the upper limit of X-ray luminosity, we used a simple absorbed power-law model with a photon index of 1. 9 and an intrinsic absorption of = N log cm 23 H 2 ( ). The median X-ray luminosity is L X < 2 × 10 43 erg s -1 for X-ray-undetected sources, corresponding to an IR luminosity of 6 × 10 9 L e. Therefore, the AGN contamination will not be a concern for the majority (95%) of ALCS sources, but we also note that in the case of a Compton-thick AGN ( > N log cm 24 H 2 ( ) ), the upper limit on L X can be larger by more than an order of magnitude.
The stacked FIR SED is shown below the stacked SPIRE images in Figure  9. We also show the median FIR SEDs of ALCS sources that are detected with Herschel to visualize the clear difference in the continuum strength at below 500 μm. Here all the photometric data are normalized to the median ALMA flux density (1. 09 mJy). With MAGPHYS+PHOTOZ, we derive a median z phot of 4. 2 ± 1. 2 (uncertainties denote the 16th and 84th percentiles of the likelihood distribution), IR luminosity of 10 12. 1±0. 3 μ -1 L e , and SFR of -+ 100 50 100 μ -1 M e yr -1 before lensing magnification correction. The derived redshift is consistent with those of HST H-faint galaxies in previous studies (e. g. ,  Simpson et al. 2014; Wang et al. 2019; Alcalde Pampliega et al. 2019; Dudzevičiūtė et al. 2020; Sun et al. 2021a).
FAIR4RS Principles The FAIR4RS Principles 2 are: F: Software, and its associated metadata, is easy for both humans and machines to find. F1. Software is assigned a globally unique and persistent identifier. • F1. 1. Components of the software representing levels of granularity are assigned distinct identifiers. • F1. 2. Different versions of the software are assigned distinct identifiers. F2. Software is described with rich metadata. F3. Metadata clearly and explicitly include the identifier of the software they describe. F4. Metadata are FAIR, searchable and indexable. A: Software, and its metadata, is retrievable via standardised protocols. A1. Software is retrievable by its identifier using a standardised communications protocol. • A1. 1. The protocol is open, free, and universally implementable.
• Comet is a command-line tool and desktop application for tandem mass spectrometry sequence database search  4. It is registered in the bio. tools catalogue of bioinformatics tools, where it has a globally unique and persistent identifier (FAIR4RS Principle: F1), and rich metadata (F2) that includes the identifier (F3) and is searchable and indexable (F4). Comet can be downloaded via the browser following the links provided in the metadata using https (A1). The metadata in bio. tools is independent from the Comet repository, and will stay accessible should the software itself become inaccessible (A2). Comet uses standard data types from the proteomics domain for its input and output data (I1) that are documented in the metadata as functional annotations (I2). The software is licensed under the Apache 2.
0 open source licence, and the publicly accessible project repository on GitHub includes detailed information about its development (R1). The code includes dependencies to external software packages, such as Thermo Scientific's MSFileReader library (R2). • PuReGoMe is a project aimed at understanding Dutch public sentiment during the COVID-19 outbreak period by analysing real-time Twitter data  5. It provides a collection of Python scripts and Jupyter notebooks for this purpose. PuReGoMe has a (versioned) DOI from Zenodo (F1) and is registered in the Research Software Directory that captures the most relevant metadata (F2), including the identifier (F3), in searchable and indexable form (F4). The software can be downloaded from the project repository (A1), while metadata is accessible independently from the registry (A2).
PuReGoMe uses standard file formats (e. g. , CSV files) for data exchange (I1) and refers to other objects such as websites (I2). The project uses the Apache 2. 0 open source licence, and the GitHub repository has detailed records of the development history (R1). The code includes dependencies to other software, such as various Python libraries (R2). • A third example of a research software artefact deposited in the HAL French national archive is gammaShiny (https://hal. inria. fr/hal-02979514v1) gammaShiny is an application that provides enhanced graphical user interfaces for the R gamma package. It is used to process in-situ gamma-ray spectrometry measurements for luminescence dating. gammaShiny has been deposited in the HAL French national archive  1  and it has a persistent globally unique identifier 6 (F1) with the HAL identifier of the metadata record and a SWHID, identifying specifically the software artefact on the Software Heritage 2  universal software source code archive.
Adoption The FAIR4RS WG has been successful in facilitating initial adoption of its outcomes, reflecting high levels of initial impact. Adoption and implementation of the FAIR4RS Principles will create significant outcomes for many stakeholders, ranging from increased clarity for funders around their own requirements for software investments to guidelines for publishers and research institutions on sharing requirements. The FAIR4RS Principles are also relevant to the larger ecosystem and the stakeholders that support research software (e. g. , repositories and registries). A number of groups and organisations are planning to adopt the FAIR4RS Principles, including ELIXIR, Australian Research Data Commons (ARDC), Netherlands eScience Center (NLeSC), and ZB MED, and are at various stages at this point, as described in this subsection.
Australian Research Data Commons The ARDC is a national facility enabled by the Australian Government"s National Collaborative Research Infrastructure Strategy. The mission of the ARDC is to accelerate research and innovation by driving excellence in the creation, analysis and retention of high-quality datasets. To support this, the software program at the ARDC is working towards recognition of research data as a first-class output of research. The ARDC National Agenda for Research Software includes activities seeking to make research software outputs more FAIR in Australia  9. The ARDC is undertaking a number of actions to implement the FAIR4RS Principles, beginning with policy change. ARDC"s investment policy lays out expectations for partners to make the outputs of projects receiving ARDC investment more FAIR.
A FAIR research software counterpart is being developed to complement the highly successful ARDC FAIR data selfassessment tool. This new tool will be used to raise awareness of the FAIR4RS Principles, and to help a wide variety of stakeholders identify and order actions to target in adopting the FAIR4RS Principles. Finally, while not a major producer of research software itself, the ARDC will make some of its own software outputs FAIR to demonstrate the effort and impact of doing so.
ELIXIR ELIXIR coordinates and develops life science resources across Europe so that researchers can more easily find, analyse and share data, exchange expertise, and implement best practices. As a European intergovernmental organisation that is made up of life scientists, computer scientists, and support staff, ELIXIR unites Europe"s leading life science organisations to help researchers take advantage of the huge amounts of data produced in life science, to gain new insights into how living organisms work in health and disease. This is achieved by coordinating, integrating and sustaining bioinformatics resources across ELIXIR member states and enabling users in academia and industry to access services that are vital for their research. Advancing the understanding of life and disease over all the domains in life sciences requires that research data, analysis tools, standards and computational services are FAIR.
ELIXIR policy recommends that all research outputs of ELIXIR infrastructure be FAIR, including a strong recommendation to all ELIXIR partners that software developed and supported by ELIXIR be FAIR. This includes standalone tools (such as InterMine), platforms (such as Galaxy) as well as services (such as bio. tools and OpenEBench). Moreover, and in the context of the FAIR assessment and adoption process, of particular interest are services that enable and support FAIRness in research outcomes, such as FAIRsharing. This is an RDA-endorsed resource, recommended by ELIXIR and other organisations across all disciplines. In the context of research software, FAIRsharing covers: (i) the standards used to describe the software metadata; (ii) the standards used as software input and output formats; and (ii) the software code repositories.
Additionally, one of the most recent achievements of the ELIXIR Tools Platform is the ELIXIR Software Management Plan 10 , a low-barrier standard specifically tailored for life science researchers for capturing the life cycle for the software that is produced within the lifetime of a particular project or activity, specifically tailored for life science researchers  10. As a clear outcome of ELIXIR adopting the FAIR4RS Principles, a roadmap has been put in place to ensure that the ELIXIR Software Management Plan, currently implemented as a standalone tool based on the Data Stewardship Wizard  11  , is fully aligned to them. Specifically, the sections and individual questions of the Software Management Plan will be clearly annotated to the corresponding FAIR Principles, in order to both increase the awareness of the principles themselves, as well as promote FAIRness for software within ELIXIR.
ZB MED ZB MED Information Centre for Life Sciences is a German national infrastructure and research centre for information and data in the life sciences. The mission of ZB MED is to ensure the national provision of information and literature in the fields of medicine, health care, nutritional, environmental and agricultural sciencesincluding the relevant basic sciences and related subject areasfor the purposes of research, teaching and practical application. To support this, ZB MED follows and encourages an open science policy so that all research outcomes become open to the public, and adhere to the FAIR Guiding Principles and good research management practices. However, data is only one piece of the puzzle. For research to become reproducible and sustainable, connected FAIRification is needed across all the pieces, including data, software, workflows and other research objects.
Draft FAIR4RS Principles were released in June 2021  16  and underwent a formal community review process for one month. The current version of the FAIR4RS Principles, as summarized here, is a cumulative result of this work. Part of the reason for the FAIR4RS WG"s very high levels of success in community engagement is because it brought together a range of efforts to apply aspects of FAIR to research software since 2017  8  , and because it sought to align with a range of FAIR data efforts. In this way the FAIR4RS WG was able to leverage and amplify existing community momentum to demonstrate the benefits of implementing the principles.
A total of 12 organizations, six in each region, participated in this research, with a sufficient sample proportion to produce adequate narrative records to provide viewpoints directly relevant to the problem under study  (Teddlie and Yu, 2007). To gather the data for this study, researchers conducted a series of semi-structured in-depth interviews with 15 participants. The study employed a semi-structured interview protocol. The interview protocol was developed based on the available literature and opinion from an expert committee formed for this purpose. The interview protocol consists of a consent letter, open-ended questions, prompt questions, and probes to get a better and more thorough understanding of the phenomenon. In the consent letter, the respondent was assured that the collected responses would be kept confidential and secure and that the responses would be used only for academic purposes.
Data analysis All the interviews were audio-recorded, transcribed word-for-word and analyzed via NVivo qualitative data analysis software. This study applied thematic analysis where codes are generated inductively  (Braun and Clarke, 2019). In the process of thematic analysis, the themes were carefully searched to match the phenomenon, which required a meticulous process of generating themes from in-depth analytical reading and understanding of data  (Yin, 2017). First, there is a categorization process for identifying common phrases, codes, and words from the transcription  (Yin, 2017)  and then content analysis is applied to the data. First and foremost, the codes were identified from the interview transcript and then categories were created from codes  (Yin, 2017). The thematic analysis was conducted by recognition and classification of key patterns from the participants' responses.
Conclusion and contributions SM is used as an effective tool to improve customer experience in the digital era, and it is considered an important element of firms' marketing strategy. Therefore, it contributes to a firm's marketing capability strength. First, we explored the literature and built the framework that describes the various stages and steps of firms' SM engagement. These processes, including stages and steps, were further validated by empirical data collected from two different samples obtained from Malaysia and Pakistan. Second, based on our findings, we proposed an empirical routine that consists of four stages and three processes. The proposed routine can be used to engage successfully in B2B SM conversations.
Based on data links in workflows, relations between services are extracted as isInk and represented by the gray lines; for example, the relation between the service hsapiens_gene_ensembl and the service getRNorSequence. Specially, GetUniqueHomolog and CreateFasta are beanshells for cohesion, so they are not regarded as services. Finally, the relation between developers and their friends is extracted as isFrd and represented by the yellow arrow in this figure. For example, Katy Wolstencroft, the author of workflows shown in Figures  1A, B , and Alan Williams, the author of the workflow shown in Figure  1C , are friends, and their relation is represented by a yellow arrow and labeled as isFrd.
Data set and preprocessing This study adopts bioinformatic workflows in myExperiment for our experiments, where workflows in the Taverna 2 category by May 2019 are crawled. For each service, its title, description, created time and developer are collected. For each workflow, its title, description, tags, publishing date, download times, viewing times, rating, developer, services and data links are collected, where the data links reflect the control flows between services (i. e. , invocation relations). For each developer, his name, introduction, interests, field, rating and friends are collected. Note that services and workflows without a title or description are deleted. As a summary, the numbers of available services, workflows, developers and their relations are shown in Table  1. The data cleaning procedure is conducted, where stop words are removed, and the stemming of words is extracted.
Thereafter, entities and relationships are extracted, and their attributes are obtained by the techniques presented in Section 2. We adopt the graph database Neo4j  (Robinson et al. , 2015)  to store these cleaned data. To evaluate the efficiency of our technique, we have generated 40 crossing-workflow fragments leveraging legacy workflows as testing fragments based on S 2 KG. According to the statistic reported in our previous work  (Zhou et al. , 2020) , roughly 86% of workflows contains no more than 11 services. Therefore, 5 out of 40 testing fragments are set to contain over 11 services.
Then a series of fragments are constructed across workflows based on their relations. Based on their similarity, these fragments are identified, ranked, and recommended accordingly. • CDSR  (Xia et al. , 2015)  is a category-aware clustering and distributed service recommending method to automatically create fragments. First, we cluster the experimental data into various categories based on the similarity of functionality and popularity of their services. Then we map requirements to relevant categories to find candidate services. Finally, these candidate services in the most relevant categories construct cross-workflow fragments to fulfill the requirements. • Short Path (denoted SP) method is a classical heuristic algorithm. First, we start to navigate from a service and select the neighbors with the highest relevance, which have a connection-aware relation with it, according to a given probability distribution.
This method utilizes the social attribute reputation, which can improve the efficiency of fragment recommendation to a certain extent. However, many other social information, such as social relations which can promote users to mine latent knowledge, have not been considered. Zhu et al. (2021)  proposed a new model SRaSLR, which is a type of social-aware service label recommendation model. There are invocation and dependency relations between services, and these relations make services naturally constitute a service social network. The authors combine the textual information in service profiles and the social network relations between services. Based on the feature fusion of two perspectives, a model based on deep learning is constructed. Authors conduct a lot of experiments on real-world Programmable Web data set, and the experimental results show that the use of social relations can improve the performance of recommendation.
At the time of the -Sat 1 mission, insufficient data volume from the HyperScout product line was available to train the CNN to classify each pixel as cloudy or not cloudy. This is a common problem with space imagers where bespoke cameras are developed for each new mission. The approach was to derive synthesized images starting from the Sentinel-2 dataset  [19]  after proper processing to emulate the new camera sensor characteristics, and use those for training and initial test. This mission represents the first use of deep CNN inference on a dedicated COTS VPU processor on-board a satellite, with the aim to autonomously identify hyperspectral images from HyperScout-2 that contain a percentage of cloudy pixels less than a given threshold. -sat-1, the first experiment of the -sat mission series, is part of the ESA EO Directorate initiative to promote the space-oriented development and adaptation of radically innovative technologies such as AI  [20].
Myriad 2 has demonstrated no single event latchup (SEL) effects at energies up to 8. 8 MeVcm 2 /mg, with further results for recent tests at higher energies pending analysis. The in-package DRAM of the device was shown during single event upset (SEU) campaigns to have high immunity to bit upsets (per device cross section of 2e -14 at the above energy), indicating its suitability for code and NN model storage, and providing a level of inherent protection against functional upsets. Total ionizing dose (TID) testing was conducted up to 49 krad, with the device found to have no sensitivity to cumulative Co-60 radiation effects up to this dose. The EoT board was designed to expose the wide range of Myriad 2 interfaces and peripherals in a compact 76 mm × 68 mm form factor, facilitating broad application development.
A. Dataset Preparation Most of the ESA missions implement either a new sensor or new sensing technique, implying a general lack of data for the construction of the datasets. -Sat-1, as a highly innovative mission, is no less affected by this lack of preflight sensor data, which implied the need to construct and label synthetic datasets based on proxy off-the-shelf datasets (in this case the Sentinel-2 archive). Of course, the authors appreciate that this is a potential source of inaccuracy in the in-flight inference results. In order to prepare a representative sample of Sentinel-2 data, the whole archive of Sentinel-2 tiles, available through Sentinel-Hub services [31], was randomly sampled. Fig. 5  shows the locations of the randomly sampled Sentinel-2 images. For each sample, the 13 bands of the Sentinel-2, at 10 m/px resolution were acquired.
To simulate the behavior of the HyperScout-2 imager, the original samples were rebinned from 10 to 70 m/px, which corresponds to the ground resolution of the HyperScout 2 imager. Resampling was done using the default interpolation method of the Sentinel-Hub: nearest neighbor. Although nearest neighbor is not the best solution for image resampling, it represents a good tradeoff between dataset management and computational effort. The data was then retrieved and stored in the coordinate reference system (CRS) of the corresponding Sentinel-2 tile, so as to remove the need to re-project data. To prepare the dataset to train the DNN, the associated cloud mask was added to each image as produced by the s2cloudless package. The s2cloudless  [32]  is an automated cloud-detection algorithm for Sentinel-2 imagery based on a gradient boosting algorithm.
e. , bands with central wavelengths of 450, 494, and 862 nm, respectively. After the selection of the three most useful Sentinel-2 bands, the HyperScout-2 data was simulated. The Sentinel-2 Level-1C data (digital numbers), Sun zenith angles, per-band solar irradiances, and Sun-Earth distances were used to calculate TOA radiances. For each pixel, the per-band radiance and HyperScout-2 imager per-band noise characteristics were used to calculate (per-band) root-mean-square (rms) values, and simulated Gaussian noise with zero mean and rms value as standard deviation was added to the radiances. In case the radiance value for a given band was above HyperScout-2 imager saturation thresholds, the value was capped. Finally, all the images produced were normalized to range [0, 1] and stored using 16-bit floating point precision.
It is worth noting that the 88. 4% of accuracy of the segmentation algorithm corresponds to 95. 1% of tiles accuracy with only 0. 8% of the images were classified as False Positive  within the synthetic dataset. It is possible to further reduce the number of FPs by increasing via software the threshold of the last layer, at the expenses of some percentage reduction in the pixel wise accuracy. The inference time is approximately 102 ms exploiting 8 of the 12 available SHAVE vector processors of the Myriad 2, and consuming only 62. 9 KB of memory footprint.
A. HyperScout-2 Preprocessing Chain The HyperScout-2 preprocessing chain was run on 17 datasets acquired during the -Sat-1 mission, whose coordinates and acquisition times are summarized in Table  IV. For each of these datasets, three 1152 × 1152 pixels bands with central wavelengths of 450, 494, and 862 nm (see Section III)  were produced. Nine of these three-bands sets, combined to form color images, are shown in Fig. 13. For every band produced, a saturation mask composed of Boolean true/false values was made so that the impact on the relative band radiances could be assessed. Each pixel which exceeds the saturation threshold was marked as true and all other pixels were assigned false values. Overall, 1. 2% of the pixels were saturated due to bright clouds. The percentage of saturated pixels per acquisition is plotted in Fig.