dataesr/scientific-paragraphs-categorization huggingface dataset:
The approach we took were based on artificial neural networks (NN)  (Patterson, 1996)  and random forests (RF)  (Breiman, 2001). We also developed logistic regression models (LOG) for comparison with the previous work (designated LR in  Kamimura et al. (2016) ). We analysed damage from the small Nezer Forest (~80 km 2 ) containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999 and from a much larger set of 235 plots from the National Forest Inventory in the Landes de Gascogne Forest (~10,000 km 2 ) that were examined directly after damage from Storm Klaus in January 2009. The purpose was to evaluate the accuracy and discriminatory ability of the models using all available input data and to test the models both on the data set from which they were developed and the other independent data set to see how portable the models were.
This was to check how transferable the models were and to check their ability to predict the damage from a different storm from the one used in their development. In this paper we did not consider the type of damage (breakage or overturning) but combined all trees known to have been damaged by a storm. In addition a set of models was developed to predict critical wind speeds (CWS) using an artificially generated data set to see if it was possible to substitute for GALES (Part 1 of Fig. 1 ). CWS calculated both by GALES and by these GALES substitute models were subsequently used in the development of the damage models along with characteristics of the individual trees, stand, and site (Part 2 of Fig. 1 ). In the model development and validation we focussed on the CWS and WAsP calculations at 29 m above the ground for the Nezer Forest and at 40 m above the ground for the NFI data.
Machine Learning Methods Loosely inspired by biological neural networks, artificial neural networks (NN) are able to approximate a non-linear function to describe a mapping between a set of inputs and outputs. They are able to learn from incomplete and noisy datasets, making them particularly suitable for applications within forestry where data is hard to collect and likely to contain inaccuracies due to measurement difficulties. Previous applications of NNs in forestry have dealt with mortality estimation  (Guan and Gertner, 1995; Hasenauer et al. , 2001) , and uncertainty assessment of forest growth models  (Guan et al. , 1997). However, a weakness in the neural network approach is that the learned function describing the non-linear mapping cannot be easily understood in terms of processes controlling behaviour, e.
Software and Methods The WEKA software "workbench" (Waikato Environment for Knowledge Analysis) incorporates a large number of standard Machine Learning Techniques (ML) including the methods described above in a freely available tool  (Frank et al. , 2016). With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. The workbench can either be used through a supplied Graphical User Interface, or incorporated directly in Java code using a supplied library. All experiments described here are conducted using Weka version 3-6-13. The three models used are described below. The NN and RF can be both be trained as classifiers, i. e. predicting a class value (damaged/no damage) or to undertake regression, i.
Random Forests The Random Forests algorithm uses a bagging approach, combined with a Random Tree learning algorithm. In bagging, multiple random subsets of the dataset are created by sampling n instances with replacement from the dataset. For each subset, a random tree classifier is grown: at each node, m variables are selected at random, from which the one that optimizes the information gain is chosen. We use the default Weka parameters: a forest of 100 random trees are created; each tree has unlimited depth and is grown without pruning; at each node m = log2(number_of_attributes) + 1 are randomly selected.
Training and Pre-Processing Cross-validation is used to obtain an unbiased estimate of the performance of each model on unseen test data. For each model, the dataset is randomly divided into 10 subsets (folds) of equal size. 9 folds are combined to train a model, with the left-out fold used for testing the trained model. The procedure is repeated leaving each of the 10 folds out in turn. The final reported accuracy is the average of the accuracy value obtained on each of the 10 folds. For damage prediction, given that the data is unbalanced in terms of the ratio of damaged/undamaged trees, it is preferable to bias the data used to train the models towards a uniform class distribution. The Weka SpreadSubsample filter is applied to the subset of data used in each training fold during cross-validation: this produces a new dataset twice the size of the minority class, by selecting all instances of the minority class (damaged tree in this case) and randomly sampling from the majority class (undamaged trees in this case).
Performance Metrics For the dichotomous models, we record classification accuracy, i. e. the proportion of true results (both true positives and true negatives) among the total number of cases examined. In addition, we report the area underneath the receiveroperating curve (AUC). This plots the false positive rate against the false negative rage: a perfect classifier would have an AUC of 1. 0; an area of 0. 5 is equivalent to random guessing. Typically, an AUC > 0. 7 is considered to be fair, above 0. 8 good and above 0. 9 to be excellent  (Hosmer and Lemeshow, 2000). For prediction of numeric values (i. e. critical wind speed) the correlation coefficient is reported. All statistics were either calculated within the WEKA software or with Matlab 2016a (Mathworks, Natick MA, USA).
GALES artificial training dataset A large number of potential maritime pine stands with characteristics that covered the full range of possible characteristics (see Table  1  for details of the ranges sampled) were created as inputs to GALES. The stand characteristics were selected using Latin Hyper Cube Sampling to give uniform sampling. 10,000 stands were created, which after filtering for duplicates, constraining the ratio of stand mean tree height to stand mean DBH between 30 (very high taper) and 130 (very low taper), and constraining individual tree DBH and height to be within ±70% of the stand mean values, left 1970 simulations. GALES was then run for the 1970 stands and the CWS values for tree overturning and stem breakage were calculated at 10 m above the zero-plane displacement (d+10m), which is the standard height for such measurements in  Gardiner et al.
The annual survey plots (1 point for 10 km²) are chosen in a systematic sub-sample of the 5-year sample covering the entire country. The forest field plots are composed of four concentric plots allowing the measurement of different tree diameter classes  (Robert et al. , 2009). We used data collected from 2007 to 2008 from a total of 235 plots chosen in two ecological regions of the Landes de Gascogne Forest, and wherever more than half of the trees in each plot were maritime pine. After storm Klaus in 2009, damaged trees in the NFI plots were identified by an additional follow up field survey to list damaged trees (Table  2 ). For each plot in the two data sets we added mean plot height, the mean plot DBH and the average stem spacing derived from the individual tree data. Spatial information included the distance of each tree from the windward stand edge (west) and the upwind gap size (distance in a westerly direction between the forest and the next forest block) were also estimated based on the position of the inventory plot (only accurate to within 500m).
However, in this paper we assumed like Kamimura et al. (2016) that all the trees were effectively at a new edge because the best results were previously found with this assumption. This assumption is justified by the observation from aerial photography that damage propagated through stands during the storms and this led to new trees becoming exposed to an advancing damaged forest edge. The NFI plots were identified either within the Landes (main forest production area inland from the coast) or Dunes (forest along coastal dunes) areas based on the ecological region given in the NFI survey, whereas all the plots in the Nezer Foret were designated as Landes. Soil characteristics and hydrological status were derived from the French soils database  (GISsol, 2011)  and the ecological observations in the NFI plots  (Bruno and Bartoli, 2001).
Predicting CWS The LIN, NN and RF model simulations of CWS were compared to the actual CWS produced by GALES for the Nezer and NFI data at 29 m and 40 m above the ground respectively, and are displayed in Table  4. Information for predictions at d+10 m can be found in Table  A1  in Appendix A. The results show a high level of correlation between the predictions of GALES and those of the models. In all cases the models are correlated to the GALES predictions with r 2 values greater than 0. 77 and in most cases above 0. 9. In all cases the predictions of breakage are slightly less well correlated than the predictions of overturning. This might be a reflection of the fact that only approximately 15% of trees were damaged by breakage during the two storms (trees in the Landes de Gascogne Forest are more susceptible to overturning), and the models are consequently better trained to predict overturning than breakage (more examples of overturning).
The AUC values for all three models are higher than the value obtained by  Kamimura et al. (2016)  for the Nezer Forest using logistic regression models (AUC = 0. 76). However, the accuracies are lower for the LOG and NN models in comparison to the earlier work, which had an accuracy of between 71. 9-72. 4% in the Nezer Forest. However, in  Kamimura et al. (2016)  the model accuracy was optimized by adjusting the cut points for the probability of damage between 0 and 1 until the true positive rate equalled the true negative rate  (Hosmer and Lemeshow, 2000). As described earlier, in this paper no model optimisation was performed and the cut point was fixed at 0. 5 in order to determine model performance with no tuning. The accuracy and AUC of the models for the same data but using the calculated critical wind speeds at d+10 m above the ground are presented in Fig.
Portability of models Model portability was tested by using the models developed from the Nezer Forest damage/no damage data and applying them to the NFI damage/no damage data in the same manner as  Kamimura et al. (2016). But in addition we also tested the applicability of the NFI derived models on the smaller Nezer Forest data. In the same manner as discussed previously (Sections 3. 2. 1and 3. 2. 2) the test data was divided into 10 groups to allow 10 evaluations of model performance. Only calculations using the CSW calculated at d+10 m and 29 m were used because calculations at 40 m were not available in the Nezer Forest. The results are presented for the calculations at 29m in Fig. 4  and summarized for both heights in Table  A4  in Appendix A. It is clear from the results that there is a severe reduction in model accuracy and discriminatory ability if the models developed on the Nezer Forest data (small forest area) are applied to the whole maritime pine forest estate in the Landes de Gascogne Forest (NFI data).
7). The logistic model was able to simulate well the damage in the Nezer Forest and the region represented by the NFI if the logistic model was calibrated for each forest area. However, the logistic model developed for the Nezer Forest had no discriminatory ability when applied to the NFI forest area with a much larger range of conditions. The logistic model was therefore not easily transferable even when the data from the NFI was filtered to only investigate soil and hydrological conditions similar to the ones in the Nezer Forest, where the model had been developed  (Kamimura et al. , 2016). This is a reflection of the fact that a model "trained" on a dataset with a limited range, and which tries to minimise errors with that dataset, fails to produce satisfactory results when used with a dataset with a wider range of characteristics (tree sizes, soil type, hydrological conditions, etc.
Acknowledgements We are grateful to  Thierry Bélouard  ( IGN ) who kindly provided the national forest inventory data in the Landes de  Gascogne Forest  ( Projet CRA 2012 IGN-IDF-INRA ), as well as to  Gaston Courrier  and  Didier Garrigou  at  INRA-Bordeaux  who conducted the field surveys in the Nezer Forest. This work was funded by an  INRA  scientific package awarded to  Barry Gardiner  and by grant  ANR-12-AGRO-0007-04  ( ANR, Agrobiosphere, France , project " FOR-WIND ") and developed out of work undertaken in grant  EP/J021628/1  to  Emma Hart  ( EPSRC, UK).
Section III presents the different modules optimization problems: IT Task Scheduling (III-A), Power Decision (III-B) and Negotiation (III-C). Then, Section IV states the sizing problems for both IT and electrical sides. In Section V, we detail the developed middleware; Section VI outlines the role of real experiments used in conjunction with simulations. Finally, Section VII surveys the related works and projects, while Section VIII concludes the paper giving the next steps of the DATAZERO project.
D. MODELS OF THE NEGOTIATION SYSTEM One of the original proposition of the DATAZERO project is to introduce a negotiation protocol to match the power consumption of the IT resources with the power production of the green sources, instead of doing a global optimization of the problem. The aim of this negotiation protocol is to introduce more dynamism when the initial conditions change. To implement this negotiation protocol, we propose to distribute the main roles of the power management in three modules, namely IT Decision Module (ITDM), Power Decision Module (PDM) and Negotiation Module (NM), as shown on Figure  7. These modules represent the core of the DATAZERO framework. The modules cooperate by exchanging power profiles. A power profile is a set of power values for the different time steps of a given time horizon (the time interval used for negotiation).
V. MIDDLEWARE All the previously presented concepts are implemented in the DATAZERO platform, a testbed designed to validate our propositions. The objective of this platform is to support interactions between the IT environment and the power management that controls the renewable power sources. The platform is composed of three main parts, the IT System, the Negotiation System and the Power System, together with the Information System that traces all the activity in the platform and feeds the GUI System. Figure  12  gives a global view of the DATAZERO platform architecture with its main parts: • The IT System is composed of the datacenter manager that monitors the datacenter (it can be either simulated with DCWoRMS  [37] , SimGrid  [4]  or real as an OpenStack  [53]  environment) and the IT Decision Module (ITDM) that interacts with both the datacenter resource manager and the Negotiation Module (NM).
The Information System uses the MariaDB database. • The GUI is based on a Web interface built to monitor several parameters in the overall datacenter. It is developed with the Angular framework, using ''Web Server data provider'' to retrieve data via HTTP requests. This graphical interface may serve as a control console to tune the policies of decision modules in the future. By distributing the management of the whole platform into distinct modules, rather than grouping all the decisions in only one module, we enforce the possible development and/or deployment of dedicated policies, and particularly dedicated optimization algorithms as previously presented.
MOMs usually allow several communication modes as: (i) peer-to-peer, based on a message queue; or (ii) event-oriented, based on a topic object on which publishers can broadcast an event message to the subscribers of the topic. Moreover, as an intermediate between the communication protagonists, MOMs can implement additional services such as transaction management or exchange recording (persistence). For the DATAZERO platform, we use the ActiveMQ 3  MOM. On this message bus, several topics are defined to structure the exchanged information, one for each type of event triggered in the platform. Every module willing to access an information is subscribing to the corresponding topic. We choose to allow a large access to information inside the platform and every event is exposed through a topic to all modules connected to the bus.
More than 35 topics are defined in the platform and a new one can easily be added to guarantee the platform evolution. For instance, the IT _MACHINE_CHANGE_STATE topic is used to send and receive machine switch on events. ActiveMQ supports several communication protocols at low level, such as OpenWire, AMQP or Stomp. This facilitates its use in heterogeneous environments or the addition of new modules. ActiveMQ also supports several programming languages such as Java, C++ or Python. Depending on the module, the development is done in one of these three languages and, for that reason, the low level protocol used in the platform is AMQP. Moreover, to facilitate the communication between heterogeneous components, possibly written in different languages, we choose to encode the messages in JSON (JavaScript Object Notation), a lightweight data-interchange format.
A. ELECTRICAL SIDE As a first step, a power system simulator is created in Matlab in order to simulate the behavior of the microgrid and its interactions with the other modules, such as the PDM. This simulator includes the electrical components' models and enables a preliminary validation of the control and energy management strategies, e. g. , for dispatching and scheduling. The next step would be a physical, full scale experiment. However, such demonstrations are costly, and expenses can easily amount to several million euros for a MW-scale system. A small scale experiment is thus used, where some components are emulated or only simulated. To do so, a concept called Power Hardware-In-the-Loop (PHIL) is used. PHIL combines virtual and physical elements into a single system. The size of physical components is also usually smaller (e.
B. IT SIDE For the IT side, we also consider the two different approaches, through an IT simulator and using a real infrastructure. Here, the objective is to be able to simply replace the simulator by the real infrastructure without any modification of the IT Decision Module. For this reason, the simulator needs to provide the same abstraction as the real platform and the same communication protocol. For the simulated environment, we use SimGrid  [4]  which is a distributed system simulator. It aims to evaluate allocation and scheduling policies for a given workload on a given infrastructure. Different metrics are computed and green levers such as DVFS  [30]  or task migration can be simulated. For the real platform, we use OpenStack, 5  a cloud operating system that allows us to control compute, storage, and networking resources of datacenter through an API.
B. EXISTING PROJECTS In this subsection, we focus on projects in the same line as DATAZERO that aim at integrating renewable energy in datacenters. Projects such as GreenDataNet  [24]  provides a detailed study of necessary hardware, software and prediction models to efficiently integrate local PV resources into datacenters. From the IT perspective, the researchers use a correlation-aware VM placement (based on CPU load) which also aims at minimizing the energy consumption. The DC4Cities  [34]  project considers a smart city with multiple datacenters connected to a smart grid. The idea is to reorganize the workload to match the shape of renewable energy available. The project also introduces two new metrics to evaluate effectiveness of software execution and renewable energy efficiency, a greedy algorithm that optimize task placement in DC under a power budget and an energy management system that acts as mediator between DC and the energy system.
VIII. CONCLUSION In this paper, we have introduced DATAZERO, a project aiming to provide consistent solutions for ensuring high availability of IT services and avoiding unnecessary redundancies, under the constraints of the intermittent nature of green power and services flows. The main originality of the project is to propose a negotiation module, between the IT and power control, that aims at finding a trade-off between the objectives and constraints of both parts instead of trying to solve a global optimization problem. To make this negotiation possible, we propose accurate electrical and IT models: we detailed the necessary need for electrical sources profiling, including advanced features like aging, Cloud application phases and degradation modes, IT server power and performance modeling and analysis.
ACKNOWLEDGMENT This work was partly supported by the  French Research Agency  under the project  Datazero  ( ANR-15-CE25-0012 ) and the  EIPHI Graduate School (contract ' ' ANR-17-EURE-0002 ''). Laplace Measurements thanks to several INPT and Occitanie Region projects and Laplace's platform test campaigns. The fuel cell aging experimental database has been obtained thanks to the financial support of  Franche-Comté Region, France.
A collaborative approach with the partners of the project (end user, software company, research laboratories) has been adopted to design the serious game. Starting by an industrial interview and a survey on APICS as a reference of planning processes modeling, the collaborative mid-term planning processes was modelled. Then, were defined the components of the game simulating the dynamic of those processes and the physical flow related to the game. After that, in a prepared environment the game was run, and results were collected. Finally, the results issued from the game were analyzed and the main problems and requirement were identified in terms of agility and CAASC functionalities development.
For example, users input metadata information for required objects such as author names, creation dates, etc. Nevertheless, many aspects of search withing CH collections are still unclear. Among others our study is guided by the following key questions: (1) What do people search for in digital historical collections and what kinds of metadata filtering they use? (2) What is the time horizon of such searches? (3) How are collective memories expressed in search logs? We approach these and other related questions by investigating portions of query logs generated from over the course of one year. As the underlying dataset we use request logs from the online portal to the digital library of the National Library of France (Bibliothèque nationale de France) (BnF) called Gallica which has been open to the public since 1997  1.
Spatial Analysis Q. What is spatial distribution of originating requests? Before we start query keywords analysis, we briefly show the spatial distribution of requests. Fig. 1  displays the top-10 places where users input requests with their corresponding rates. "NULL" indicates that there is no location information recorded at log. Naturally, France is the top country which is not surprising since data is hosted in the National Library of France. We can however observe that many European countries are included in this figure. We now look at the distribution of filters used in queries shown in Fig. 2. Gallica's advanced search not only allows to choose the underlying resources, but also offers several options such as AND/OR retrieval, setting temporal filtering, specific languages used for describing digital items, and so on.