dataesr/scientific-paragraphs-categorization huggingface dataset:
C. COMPARISON WITH A BASELINE METHOD The previously described results demonstrated that our approach STCSTA outperforms DPCAS in terms of energy preservation. The DPCAS algorithm in  [11]  was compared to two other approaches that use a similar technique, namely EDSAS  [27]  and ASTCP  [26]. As mentioned in section II, the ASTCP algorithm was inspired by the EDSAS. Moreover, the DPCAS algorithm was inspired by both ASTCP and EDSAS. In this section, we will use the EDSAS as a baseline for comparison since it was the root algorithm that inspired both ASTCP and DPCAS. Table  4  below shows the average energy consumed by each node in all scenarios and for the same value of =0. 1 used in  [11]. The obtained results are fairly similar to the ones obtained in  [11]  and our approach remains better.
VI. CONCLUSION We proposed in this paper a sampling and transmission rate adaptation algorithm for cluster-based sensor networks. This algorithm is deployed on the Cluster-Head (CH) and it operates in rounds. The latter controls the sampling rate of each individual sensor node by increasing it or decreasing it according to its spatial correlation with other sensors in the network. Moreover, we adopted and adapted a data reconstruction algorithm that is implemented on the Sink station. The latter can identify the "non-sampled" data that are not collected due to a decrease in the sampling rate of a specific sensor and it estimates them using an EM iterative approach that is capable of capturing the temporal and spatial correlation among the reported measurements. We presented experimentation that we have conducted on real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy.
A. Software Architectures for Robotics Component-based architectures have become a common paradigm for developping robot software  [10] ,  [11]. Middlewares and toolchains for developping software architectures distinguish (i) components (term used in Orocos  [12] , MAUVE  [13] , SmartMDSD  [14] , Genom  [15] , or node in ROS  [16] ) that support the execution of functions, and (ii) communication between components through data ports (term used in Orocos  [12] , MAUVE  [13] , Genom  [15] , or communication objects in SmartMDSD  [14] , or topics in ROS  [16] ). In this paper, we then adopt the terms component to talk about an executable entity of the system, and port to talk about data streams between components.
Research setting To test our hypotheses, we used original data from the oldest technology transfer organization (TTO) serving a large public research university in the United States. The TTO has been the context for other published research, including an analysis of changing organizational capabilities due to technology acquisition  (George, 2005) , case studies on legitimacy among scientists  (George and Bock, 2009; Jain and George, 2007; Jain et al. , 2009) , the influence of the depth of the inventors' domain experience on commercialization  (Kotha et al. , 2013) , and signaling properties of licensing contract payment structures  (Kotha et al. , 2018). However, our research examines new aspects of this study context: evaluation reports and the variables coded from the reports not associated with any published results.
First, top-down methods are ideal for operationalizing variables and defined concepts drawn from the published literature. Although unsupervised "bottom-up" topic modeling approaches like Latent Dirichlet Allocation (LDA) are appropriate for generating new constructs, our analytical approach enabled us to operationalize our existing concepts into independent variables. Second, the top-down method relies on dictionaries, which contain validated measures for classifying text into pre-defined categories. Dictionary-based classifications provide the advantage of allowing us to systematically operationalize our study constructs with validated measures. Third, we used the Linguistic Inquiry Word Count (LIWC) program -a widely adopted dictionary software tool featured in automated text analysis management studies.
LIWC is commonly employed to operationalize theoretical constructs from text by mapping them onto pre-defined linguistic variables in entrepreneurship  (Kim et al. , 2016; Obschonka et al. , 2017) , management  (Antioco and Coussement, 2018; Coussement et al. , 2017) , psychology  (Pennebaker et al. , 1997) , marketing  (Berger and Milkman, 2012; Ludwig et al. , 2013) , operations management  (Debaere et al. , 2018) , and information systems  (McHaney et al. , 2018). Fourth, a key benefit of the LIWC program is that its validity and reliability have been previously confirmed, including evidence for a reliable convergence between the extracted linguistic LIWC variables and separate ratings by human coders (e. g. ,  Ireland et al. , 2011; Ludwig et al. , 2014; Tausczik and Pennebaker, 2010).
The dictionary is predefined, so no additional training or learning is needed on the study sample to use the variables in the dictionary. LIWC is easy to implement and comprehend without extensive programming or computational linguistics knowledge. The software is also objective and eliminates human judgment bias, since one always gets identical results when repeating the analysis on the same corpus. Finally, linguists and social psychologists have consistently studied language as a credible way of understanding people's internal thinking. As an application of these principles, we use LIWC as a method to measure the inventions' underlying potential as evaluated by the IPMs for their TTO colleagues  (Pennebaker, 2011; Pinker, 2000). For more information about the development of the LIWC word dictionary and the construct validity of the word categories, please see http://www.
Independent variables Our independent variables are based on output from the LIWC program of the study sample. The LIWC program outputs the percentage frequencies for a particular word category (i. e. , frequency of category words present divided by the total words in a given evaluation report) contained in the text corpus. This output is independent of the number of words in the report and the size of the corpus. Specifically, for our analysis, we calculated the word frequencies for feasibility and desirability constructs in specific sections of the evaluation report to form the independent variables in our study. For the feasibility evaluation criteria, we constructed two measures. We measured doubt based on the percentage of discrepancy words (e. g. , should, could, would) in the invention's description section.
g. , high vs. low doubt). Then we calculated the level of agreement between the LIWC software and human coding in two ways (See Table  1  for results). The hit rate represents the percent of accurately coded categories and the Krippendorf's alpha reflects agreement between the human and computer coders. The hit rate or the number of reports that are correctly classified by the human experts should be at least 80 percent  (Wade et al. 1997; Weber 2005). The Krippendorff's alpha, or the agreement between computer-and human coding should be greater than 0. 70  (Krippendorff, 2007 (Krippendorff, , 2010)). In our validity checks, the hit rates ranged from 89 to 91%, while the Krippendorff's alphas ranged from 0. 85 to 0. 88 for all the LIWC-based independent variables. This validation check assured us of high agreement between the computer and human expert coding.
Control variables We included several additional inventor, invention, and IPM evaluator variables to address null and alternate explanations for our budgetary support outcome variable. We constructed these variables from both the LIWC program and from the hand-collected data on inventor, invention, and IPM background characteristics. To distinguish between third-person and first-person opportunities, we controlled for inventor reference pronouns when discussing the scientific background (the percentage of third-person pronouns they, she, and he) and TTO reference pronouns when discussing the TTO's opinion of pursuing patent protection on the invention (the percentage of first-person pronoun we)  (McMullen and Shepherd, 2006). To account for explanations driven by inventor background and qualities, we controlled for star scientists with the inventor's publication record as the natural logarithm of the total number of published scientific articles for the inventing team; scientific distance to account for the extent to which members of the inventor team work in the same scientific domain (0=complete overlap, 1=no overlap); and scientific distance 2 , based on a non-linear relationship between scientific distance and technology licensing reported in  Kotha et al.
Unfortunately, we were unable to run a robustness test using actual budgeted investment data for each of the approved inventions, because we had this information in only a very small number of cases. But for the cases we did have actual budget information, we found a positive association between it and the ordered categorical variable. We used this ordered categorical variable as a robustness test of the binary outcome variable. We found that the two variables were highly correlated (0. 92). We estimated an OLS regression on the degree of support that an invention report received. As these results show, the direction and statistical significance are consistent with our main findings. We also used an ordered logit model with this alternate dependent variable and observed nearly similar results (OA Table  2 ).
As automated text analysis scholars have discussed, these methods offer both advantages and shortcomings to those who employ these techniques  (Grimmer and Stewart, 2013; Humphreys and Wang, 2018; Loughran and McDonald, 2016). While our top-down, dictionary-based method conforms to recommendations from automated text analysis experts, future research has opportunities to extend our study. Given the versatility of the LIWC software and its dictionary of pre-classified word categories, it may be possible to develop composite variables to reflect the feasibility and desirability constructs. Dictionary methods rely on the popular "bag-of-words" model, which depends on word frequency and assumes the words are drawn randomly from a bag. Thus, this approach cannot account for word order and the same words taking on different meaning.
Compared to the LIWC approach, implementing LDA requires a large sample of reports to find meaningful topics, and the meaning of the LDA topics is subject to the interpretation of the researcher and therefore subjective  (Croidieu and Kim, 2018). Also, the LDA method often leads to ambiguous constructs that are hard to link to existing theoretical constructs in a conceptual framework. Second, a supervised classification approach could be applied to predict based on the textual reports whether or not an application will receive budgetary support. The outcome of this binary classification analysis, which is a probability of acceptance for each report in the corpus, could guide decision makers based on the analysis of which reports previously had the highest probability of getting accepted. Predictive modeling is a popular research approach in this big data era with various applications like finance  (Lessmann et al.
, 2015) , accounting  (Huang et al. , 2014; Li, 2010) , fraud detection  (Van Vlasselaer et al. , 2017) , or new product development  (Hoornaert et al. , 2017). The researcher has the choice between a statistical (e. g. logistic regression, linear or quadratic discriminant analysis, etc. ) or machinelearning (e. g. support vector machines, random forests, adaboost, etc. ) algorithm as a predictive model, while various methods exist that convert text into numeric explanatory variables, i. e. a vector-space  (Coussement et al. , 2015) , deep learning  (Collobert et al. , 2011) , or dictionary-based  (Debaere et al. , 2018)  approaches. Various text mining toolkits for unsupervised topic modeling and supervised classification are available like the tm and tidytext packages in R or the NLTK package in Python.
We demonstrate how this process has added value to CMIP6 itself by a) helping those devising experiments to be clear about their goals and expected methodology, b) making it easier for those executing experiments to know what was intended, c) exposing inter-relationships between experiments, and d) making it clearer for third parties (data users) to understand the CMIP6 experiments. We conclude with some lessons learned, and how these may be applied for any modelling campaign as well as future CMIP phases.
Introduction Climate modelling involves the use of models to carry out simulations of the real world, usually as part of an experiment aimed at understanding processes, hypothesis testing, or projecting some future climate system behaviour. Such numerical experiments can be organized into "Model Intercomparison Projects" (MIPs) in which participants execute common experiments and share results. Perhaps the best known of these are the CMIP series of Climate Model Intercomparison Projects, of which the latest is CMIP6  (Eyring et al. , 2016). The design, documentation and accompanying protocols have all evolved over time, reflecting both an increasing scope and wider-spread interest, and two important new constituencies: (1) Those who have organised "Diagnostic MIPs", which do not require new experiments, but rather request specific output from existing planned experiments to address specific interests; and (2) an even wider group of downstream users who use the CMIP data opportunistically, having little or no direct contact with the modelling groups.
Experiment Definitions The process of experimental definition is potentially complex (figure  1a ). It begins with an idea and often entails an iterative community discussion which results in the final experimental documentation, which needs to cover at least the imposed experimental conditions and the required output. With the experiments defined, modelling groups carry out simulations which conform as best they can to the specifications of the experiment and which produce the desired output. In the case of CMIP6, the iterative discussion includes input from the ES-DOC community aiming to get a formal experiment description, from the Data Request coordinator, and the CMIP6 central team at the Program for Climate Model Diagnosis and Intercomparison (PCMDI) responsible for cross-experimental common CMIP vocabularies.
The recommended minimum length for the piControl is 500 years. 1pctCO2: Increase atmospheric CO2 concentration gradually at a rate of 1 percent per year. The concentration of atmospheric carbon dioxide is increased from the global annual mean 1850 value until quadrupling. amip: An atmosphere only climate simulation using prescribed sea surface temperature and sea ice concentrations but with other conditions as in the Historical simulation. abrupt-4xCO2: Impose an instantaneous quadrupling of the concentration of atmospheric carbon dioxide from the global annual mean 1850 value, then hold fixed. design and definition, which should evolve only slowly over future phases of CMIP and will enable cross-generational model comparisons. CMIP is also responsible for the "historical" experiments, but the definition of these will change as better forcing data becomes available and as the historical period extends forward in time.
Documentation and the MIP Design Process An overview of the MIP design process is given in figure  1 , which refers to the co-design process which involved the MIP teams, the CMIP6 team (both the CMIP panel 1 and the PCMDI support group 2 ), the ES-DOC team, and the development of the data request 3. The data request was an integral part of the process, since some MIPs are dependent on data produced in other MIPs, and the data is the key interface between the aspirations of the MIP and the community who exploit the MIP to deliver the science. The semantic structure of the data request was developed in parallel to the development of the CMIP6 version of ES-DOC each had to deal with a distinctive range of complex expectations and requirements. Hence ES-DOC has not yet fully defined or populated the OutputRequirement shown in figure  4.
In the future it is possible that such consideration may in fact improve experimental design. ES-DOC remains a work in progress. It is fair to say that there was not wide community acceptance of the burden of documentation for CMIP5, but this was in part because of the tooling available then. With the advent of CMIP6, the tooling is much enhanced, and available much earlier in the cycle -but both the underlying semantic structure and tooling can and will be improved. There is clearly opportunity of convergence between the Data Request and ES-DOC and there will undoubtedly be much community feedback to take on board! ES-DOC is not intended to apply only to CMIP exercises. We believe the experiment design and methodology, as well as the publication of experimental methods, should be of use even when only one or a few models generate related simulations -one such target will be the sharing of national resources to deliver the larger expensive simulations (in time, resource, and energy) where individuals and small communities could not justify the expense without sharing goals and outputs.
Realising such sharing opportunities is often impaired by insufficient communication and documentation. We believe the ES-DOC methodology can go some way towards to alleviating these missed opportunities, and will become essential as we contemplate using significant portions of future exascale machines. tables described using jinja2 6  templates which are then converted to PDF for inclusion in the document using the weasyprint 7  package. This methodology is more fully described in the code  (Lawrence, 2019). Rationale: To evaluate the equilibrium climate sensitivity of the model and to diagnose the strength of various feedbacks. To characterise the radiative forcing that arises from an increase in atmospheric CO2 as well as changes that arise indirectly due to the warming.
Requirements Pre-Industrial Solar Particle Forcing: Pre-Industrial solar particle forcing (1850-1873 mean). For models with interactive stratospheric chemistry. Proton forcing: HOx and NOx production by solar protons. Electron forcing: Kp-or Ap-index to describe ionisation from electron precipitation in the lower thermosphere and upper mesosphere. Cosmic ray forcing: ion-pair production by galactic cosmic rays. CMIP6 models that do not have interactive chemistry should prescribe the CMIP6 recommended ozone forcing data set. Additional Requirements: Pre-Industrial Solar Irradiance Forcing: Pre-Industrial solar forcing. The standard solar forcing dataset recommended for usage is the solar reference scenario dataset which includes pre-industrial solar forcing (1850-1873 mean). Includes total solar irradiance, F10.
The injected aerosols or aerosol precursors should be evenly spread across model layers between 18 and 20 km. Note that sedimentation processes and self-lofting due to heating are likely to result in the aerosols being distributed between 16-25 km in altitude. External Stratospheric Aerosol Precursors RCP85 to RCP45: Injection of stratospheric sulfate aerosol precursors to reduce the radiative forcing of ScenarioMIP high forcing scenario (SSP5-85) to match that of the ScenarioMIP medium forcing scenario (SSP2-45). For modelling groups that have no dynamical treatment of sulfate aerosols, GeoMIP will provide a data set of aerosol optical depth, as well as ozone fields that are consistent with this aerosol distribution. Note that the amount of sulfate injection needed for a given model to achieve the goals of this experiment may vary, so modelling groups should scale the aerosol and ozone perturbation fields as necessary.
Acknowledgements. Clearly the CMIP6 design really depends on the many scientists involved in designing and specifying the experiments under the purview of the CMIP6 panel. The use of ES-DOC to describe experiments depends heavily on the tool chain, much of which was designed and implemented by  Mark Morgan  under the direction of  Sébastien Denvil  ( CNRS/IPSL). Paul Durack  was instrumental in the support for CMIP6 vocabularies at PCMDI. Most of the  ES-DOC  work described here has been funded by national capability contributions to NCAS from the  UK Natural Environment Research Council (NERC)  and by the  European Commission  under  FW7  grant agreement  312979  for IS-ENES2. Work by  Karl E. Taylor  was performed under the auspices of the  US Department of Energy (USDOE)  by  Lawrence Livermore National Laboratory  under contract  DE-AC52-07NA27344  with support from the  Regional and Global Modeling Analysis Program of the USDOE's Office of Science.
All the underlying ES-DOC code is publicly available at https://github. com/es-doc. The full CMIP6 documentation is available online at https://search. es-doc. org/. The code to extract and produce the ES-DOC tables in this paper is available online at https://github. com/bnlawrence/esdoc4scientists (Lawrence, 2019). Figures  5  and 6  were produced using triples generated from ES-DOC and imported into gephi (https://gephi. org/) with manual annotations.
forcing_types 1. 1 Type of integration group str 0. 1 Sub-Category (e. g. GHG) origin shared. citation 0. 1 Pointer to origin, e. g. CMIP6 RCP database. Table 1. ES-DOC controlled structure for describing a forcing constraint: each attribute has a name, a type (those in italics are other ES-DOC types), a cardinality (0. 1 means either zero or one, 1. 1 means one is required) and a description. 5194/gmd-2019-98 Preprint. Discussion started: 5 June 2019 c Author(s) 2019. CC BY 4. 0 License. Figure 4. NumericalRequirements govern the structure of a numerical experiment covering constraints on duration (TemporalConstraint),the domain covered (DomainRequirement, e. g. global or a regional bounding box), any forcings (ForcingConstraint, such as particular greenhouse gas concentrations), output requirements (e.
Simulation configuration The CQCA algorithm is evaluated in different simulation scenarios of services composition. These scenarios are tested using MATLAB R2014b running on a PC equipped with a Windows 64-bit system and an Intel Core i7-4712HQ CPU with a frequency of 2. 3 GHz and 16 GB Random Memory. The composite services considered in the simulation scenarios have a sequential structure. This arbitrary choice has no effect on the validity and relevance of the obtained results since any other composition structure can be transformed into a sequential structure  [3, 6]. The CQCA algorithm is assessed using a dataset where the QoS values of the candidate services were synthesized from the literature  [2]. 3 , respectively. Three clusters are considered for the performance evaluation purposes: ( High-QoS, which represents the cluster of candidate services that offer a high QoS; (2) Middle-QoS, which represents the cluster of candidate services that offer a middle QoS; and (3) Low-QoS, which represents the cluster of candidate services that offer a low QoS.
However, the effect of the number of clusters on the performance of the CQCA approach is evaluated in subsection 6. 3. 5 by considering up to five clusters. In addition, to show the added value of the CQCA algorithm, the impact of the clusters size (i. e. , the number of candidate services for each QoS level) on the composition optimality is assessed in subsection 6. 3. 2. In the dataset used, the QoS attributes values of the candidate services were synthesized according to the three considered clusters. To have a realistic dataset, the distributions of these QoS values have been generated to have overlaps between clusters. However, applying the k-means method to this dataset allows assigning each candidate service to only a single cluster with possible clustering errors that could result from the overlapped QoS values; the occurrence of mis-clustered candidate services allows assessing the robustness of the proposed utility function.