dataesr/scientific-paragraphs-categorization huggingface dataset:
K-means classification The analysis of the data is performed using the bisecting K-means procedure. It is an iterative centroid-based partitional algorithm, widely used in data mining  [6] , thanks to its simplicity, ease of implementation and use, speed of convergence, and ability to process datasets with missing values. This algorithm, devised decades ago, presents surprisingly good performances, which are still not fully understood. The K-means algorithm has been applied here to the raw output of the sensors, except for the CO2 concentration for which the logarithm (base 10) has been considered, because of the wide range of values. In addition to the data, the only necessary input from the user is the number of clusters into which the data must be split. The output of the clustering procedure is shown on figure  4 , for an increasing predetermined number of clusters.
The only necessary user input resides in the determination of the optimal number of classes. This must be done considering the stability of the clusters when increasing their number, and, if available, using contextual information, in the present case the daily log of the events in the room, to assign the events. A further argument is given by the comparison with the results obtained from the measurements by the gas analyzers, also leading to an empirical optimal value of 7 classes. The comparison between the two datasets is shown in figure  5. Some of the classes overlap strongly, such as cluster 1 (ventilation off, 91. 1% overlap), cluster 4 (NO and NO2 spill, 97. 8%), cluster 5 (NO2 injection, 98. 4%). The two clusters corresponding to high CO2 concentrations tend to be rather mixed between the two datasets, and so do the two clusters corresponding to the basis case with the ventilation active.
Another approach to overcome this problem would be to construct a database or set of classes for typical indoor air quality events, by applying the sensors and the associated classification methodology we propose here, to other buildings or in laboratory conditions. This would result in a database of characteristic air quality event and associated classes. This database could then be used as the basis of a supervised classification model, which would not require the expertise of a user. This would therefore make the management of buildings more easy and efficient, for instance by coupling this analysis with the control of the ventilation or filtration system.
Acknowledgments The authors thankfully acknowledge the financial support from the  University of Lille  ( BQR 2015 ). This work is a contribution to the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-005-01 , and a contribution to the CPER research project CLIMIBIO, with financial support from the  French Ministère de l'Enseignement Supérieur et de la Recherche , the  Hauts de France Region  and the  European Funds for Regional Economical Development. The  MERMAID  project was financially supported through the  PRIMEQUAL program  of the  French Environment and Energy Management Agency ADEME (Agence De l'Environment et de la Maîtrise de l'Energie).
| Biological data and study area The entire known distribution range of S. muticum in the Northern Hemisphere was considered in the models, thus including occurrence data from both the native (Asia) and introduction ranges (North America and Europe). S. muticum presence records were obtained from the literature and online databases, including the Global Biodiversity Information Facility (GBIF; http://www. gbif. org/), Ocean Biogeographic Information System (OBIS; www. iobis. org), and S. muticum distribution records in Europe of the Ghent University Phycology Lab (http://www. phycology. ugent. be/research/invasives/ map. html; Engelen et al. , 2015). The occurrences were reviewed to correct or exclude those with referencing errors (e. g. , records on land belonging to herbaria data). From the determined geographic extent, cells ranging from 0 to 30 m depth were selected, as the maximum depth at which attached S.
muticum is found along the coastline. Delimiting a depth threshold for the study area was implemented using the 30 arc-seconds resolution bathymetric data derived from the General Bathymetric Chart of the Oceans (GEBCO) website (http://www. gebco. net/). For georeferencing of S. muticum occurrence data and environmental variables, a resolution of 5 arc minutes (~9. 2 km at the equator, 2. 4 km at 60°N) was applied. In total, 2,587 S. muticum occurrence records were obtained, which corresponded to 1,115 presence cells (Asia: 65; N. America: 185; Europe: 865). The database of occurrence records compiled for S. muticum is available in Supporting Information Appendix S1 and Figure  1.
| Environmental data The initial set of environmental variables for present climate conditions included the monthly average of (a) sea surface temperature (SST maximum, mean, minimum and range), and surface air temperature (SAT max. , mean, min. and range), and (b) salinity. Note that SST changes have been pointed as a good indicator of climate change and its impact on phenology in marine systems  (Edwards & Richardson, 2004). Surface air temperature was selected as predictor for S. muticum since the NIS can be also present on the low-intertidal shore (see e. g. ,  Andrew & Viejo, 1998). Salinity can be also influential as the initial life stages of S. muticum can be less tolerant to brackish water  (Steen, 2004). The data were derived from the Bio-ORACLE dataset  (Tyberghein et al. , 2012)   (Moss et al.
| Current and future species distribution models First, we tested if the NIS was close to its distributional equilibrium with the environment in the Northern Hemisphere. We compared the range of environmental values recorded for locations of occurrence of the species within its native range (Asia), with the range of environmental values recorded in the invaded regions (Europe and North America). To obtain the current and future climate scenarios' predictions for the distribution of S. muticum, an ensemble approach was applied. We used pooled datasets (occurrences from the native and invaded ranges) to train the models as this practice enhances the reliability of NIS predictions  (Broennimann & Guisan, 2008; Chefaoui & Varela-Álvarez, 2018; Jiménez-Valverde et al. , 2011). The "biomod2" package  (Thuiller, Georges, Engler, & Breiner, 2016)  was used to fit six presence-absence modelling algorithms: flexible discriminant analysis (FDA), generalized additive model (GAM), generalized boosting model (GBM), generalized linear model (GLM), multiple adaptive regression splines (MARS) and randomForest (RF).
We used an equal number of pseudo-absences extracted at random as presence cells  (Lobo, 2008). All models were implemented using the "biomod2" package in R  (Thuiller et al. , 2016). We ran five iterations for each modelling technique, thus, 30 models for each time and scenario were generated to produce the corresponding ensembles. The data were split into F I G U R E 1 Records of Sargassum muticum included in the current analysis (magenta points) showing its distribution in the Northern Hemisphere along its native range in Asia (right) and invaded European (middle) and North American (left) ranges. Only 10% of occurrences were outside the mean sea surface temperature threshold found in the literature for the reproductive window a calibration (70%) and a validation set (30%) in each of the five iterations performed for each model.
| Hybrid model We retrieved from the literature data on the reproductive window of S. muticum, to refine our future projections. From the 36 revised studies, only five provided SST data that could be used to reach our objectives (Supporting Information Appendix S2). These five localscale studies examined fertility in S. muticum populations located in: (a) California and Washington (USA;  Deysher, 1984; Norton & Deysher, 1989) ; (b) Portsmouth (UK) and Tsuyazaki (Japan)  (Deysher, 1984) ; (c) the Baja California peninsula (Mexico; Espinoza-Avalos, 1990); and (d) Luanco (Spain;  Arenas & Fernández, 1998; Fernández, 1999). Our initial intention was to incorporate tolerances on both SST and day length. Thus, we estimated the mean monthly day length at the coldest and warmest months of the reproductive window found by the five studies referred above.
For the SST window, we retrieved the minimum and maximum values of monthly mean SST of the warmest and coldest months within the S. muticum reproductive window (Supporting Information Appendix S2). Based on the data collected, the reproductive SST window of S. muticum was found to be large, ranging from 10°C to 27°C. We applied these thresholds to reclassify the ensembles of mean SST pertaining to each RCP scenario by 2050 and 2100 using the "raster" package in R. Finally, we only used the mean SST related to the reproductive window of S. muticum to filter the predictions obtained by the correlative species distribution models. We applied the true skill statistic (TSS) related threshold to transform predictions of the correlative and hybrid models into binary maps. We estimated the change in suitable habitat through the various predictions as a percentage of cells.
Materials Peristaltic pump (Minipuls), SPE C18 cartridge (LiChrolutRP-18E200mg40-63 mm). 30 mL quartz flow tubes (1cm diameter). 0. 5 mL cylindrical quartz flow cell. Diode array spectrometer (200-400 nm, Oceans Optics). SpectraPro-550i spectrometer, f=500mm (Acton, MA, USA). CCD intensified Camera (Princeton instruments, NJ, USA). 125 W Hg lamp (Philips). 280 nm UV LED (Thorlabs, France). Commercial "AQUAPOD LIGHT" system (HOCER, instrumentation company, Brest France). Stagraphics 18 Centurion software.
The model parameters (i. e. , δ 15 N ∞ , δ 15 N 0 and k for the mono-exponential model, and δ 15 N ∞ , δ 15 N 0 , k 1 , k 2 and p for the bi-exponential model) were estimated using a non-linear procedure and statistically analyzed through a non-linear mixed-effect model (nlme package;  Pinheiro and Bates, 2000)  fitted by maximum likelihood in the R software with experimental dietary factors (protein content, methionine content and their interaction) considered as fixed effects and the animal as a random effect. To avoid over-parametrization in the most complex model (bi-exponential) and avoid a failure for the model to converge, the random effect of animal was only considered on those parameters where significant animal variability was noted through graphical and statistical analysis  (Pinheiro and Bates, 2000).
The mechanisms of deep water formation are not the subject of this work. However, the peculiar stratification properties of the region due to the presence of deep convection and thus of weak stratification in winter makes it an interesting area where to study the impact of intense wind events on the thermodynamical properties of the upper ocean. In this paper we exploit high resolution gridded observational datasets over the period 1993-2014 for surface winds (CCMP, http://www. remss. com/measurements/ccmp), sea surface temperature (OISST, https://www. ncdc. noaa. gov/oisst) and sea surface height (CMEMS, http://marine. copernicus. eu/), in order to perform a systematic statistical analysis of the response of the state of the upper ocean to the occurrence of particularly strong winds. While in situ measurements and numerical model analysis allow to better study and disentangle the contributions coming from the different physical processes occurring in response to the intense wind events, gridded products like the ones we use in this paper are better suited to perform a statistical and climatological analysis.
The paper is structured as follows. In Section 2 we present the observational datasets and the statistical methods we have employed. In Section 3 we describe the short term (few weeks) behaviour of sea surface temperature and sea surface height anomalies just after the occurrence of the intense wind events, and the persistence (or lack of) on seasonal scale of the signature that strong winds generate on the upper ocean. The analysis is performed separately for the winter and summer seasons, where different stratification properties lead to different responses. In Section 4 we draw our conclusions and discuss the implications of our findings.
Data Daily sea surface temperature data for the period 1981 to November 2016 are obtained from the NOAA daily Optimum Interpolation Sea Surface Temperature dataset (OISST, https://www. ncdc. noaa. gov/oisst), an analysis combining observations from different platforms (satellites, ships, buoys) interpolated on a regular global grid at 0. 25 • resolution  [23]. The methodology includes bias adjustment of satellite and ship observations (referenced to buoys) to compensate for platform differences and sensor biases. The version of OISST we consider uses as relevant satellite SST sensor the Advanced Very High Resolution Radiometer (AVHRR), which, as an infrared instrument, can make observations at relatively high resolution but cannot see through clouds. Daily mean sea height anomalies over the Mediterranean with a spatial resolution of 0.
25 • degree gridded vector winds. VAM combines RSS instrument data with moored buoy measurements and a starting estimate of the wind field, provided by the ERA-Interim reanalysis winds. All wind observations (satellite and buoy) and model analysis fields are referenced to a height of 10 m. We have remapped the sea surface height data on the coarser 0. 25 • grid of the winds by bilinear interpolation. The wind data have a time resolution of 6 h, while sea surface temperature and height are provided daily. The wind dataset extends from July 1987 to July 2015, the sea surface temperature dataset from 1981 to November 2016, and the sea surface height dataset from January 1993 to December 2017. We have therefore limited the analysis to the overlapping period 1993-2014. Despite the relatively high resolution, the data used in this study can miss some small scale processes.
Wind data may misrepresent small scale structures like Medicanes and miss thunderstorms. Sea surface temperature and sea surface height data miss mesoscale processes at scales of 1-20 Km that are important in the upper ocean dynamics  [27] [28] [29]. However, we stress that the data we have used have the highest horizontal resolution among the gridded homogeneous obervational datasets currently available. The average wind intensity over the Mediterranean region computed from the CCMP dataset shows a strong non-stationarity across the 1993-2014 period. The average kinetic energy of the winds increases by a 20% factor during this time period. This is likely to be a bias of the dataset, caused by inhomogeneities in the assimilation procedure used to merge the ERA-Interim first guess with observations (Ross Hoffman, personal communication).
Due to the non stationary number of observational data, observations tend to weight more in the estimate towards the end of the time period, while in the first part the ERA-Interim background field dominates the average. Since ERA-Interim has a coarse spatial resolution, it tends to underestimate wind intensity extremes with respect to observations. More intense wind events are thus observed in the second half of the time period rather than in the first half. Using the CCMP dataset to study climatic trends at global and regional scale [30], at least in the Mediterranean area, is therefore questionable. This issue is of relatively little concern for our analysis though, as we are not interested in the temporal changes of the statistics of wind intensity. Having artificially weaker extreme winds in the first period of the dataset leads to miss events happening at earlier times, hence to have a poorer statistics overall, but does not affect our results otherwise.
From the CCMP dataset we consider the instantaneous 6-hourly surface wind velocity vector -→ u (φ, λ, t 6 ) at latitude φ, longitude λ and time t 6. Figure  1  shows u 95 (φ, λ), the 95th percentile of the local winter wind intensity u(φ, λ, t 6 ) = -→ u (φ, λ, t 6 ). Locations with larger values of u 95 feature more intense and more frequent intense wind events. Maximum values are found in the area of the Gulf of Lions in the Western Mediterranean, with secondary maxima present in the Eastern Mediterranean. We limit the analysis to the area characterized by u 95 > 15 ms -1 (solid black line in Figure  1 ), where winds intense enough to induce a significant signal on the upper ocean state are relatively frequent. In this paper, we refer to this region as the Gulf of Lions, although it encompasses both the Gulf of Lions shelf (depth < 200 m) and the neighboring sea (where the depth can exceed 2000 m).
Furthermore, the wind dataset has a time resolution of 6 h, while sea surface temperature and sea surface height have a time resolution of 1 day. For this reason, we define an intense wind day a day in which there is at least one intense wind event, as previously defined. Each intense wind day is assigned to the winter or summer dataset depending on its date of occurrence, t i , with i = 1,. , N, and with N = N w , N s the total number of intense wind days in the winter and summer datasets, respectively.
Composite Analysis in Winter The composite average of the time evolution of winter SSTA before and after the occurrence of intense wind events, computed for a few different thresholds is shown in Figure  3a. The area averaged sea surface temperature anomaly drops by O(10 -1 ) K, reaching a minimum a few days after the occurrence of the intense wind. Note that the drop actually starts slightly before the occurrence of the intense wind event. This shift might be due to the fact that sea surface temperature are provided daily in the OISST dataset, but they have been obtained by performing a bias adjustment using a spatially smoothed 7-day in situ sea surface temperature average, which might anticipate the temporal signal  [34]. Additionally, weather conditions favoring surface cooling are probably present in the days preceding the individual days with intense winds as those are usually embedded in a synoptic scale perturbation, and during wintertime winds have a temporal correlation of about a week (not shown).
Beside that, the general behavior of the SSHA signal qualitatively resembles that of the SSTA signal, with some notable differences. Composite SSHA starts decreasing well before, 10 to 15 days, the occurrence of the strong wind event, much earlier than the SSTA signal. This is likely not related to the passage of the atmospheric dynamical structure itself, and it is unlikely that it is due to some interpolation procedure in the creation of the dataset. The same feature has been observed in a similar analysis of the effect on SSHA of the passage of hurricanes in the Tropics (Supplementary Information in  [11] ), but to our knowledge no explanation has been given yet. In general, sea surface height is influenced by both dynamical and thermodynamical processes related to the occurrence of the strong wind.
Discussion We have performed a composite analysis of the impact of intense wind events on the state of the upper ocean in the Western Mediterranean area from 1993 to 2014, using publicly available high resolution observational datasets for wind, sea surface temperature and sea surface height. In this region the vast majority of intense wind events occur in winter in the Gulf of Lions, where deep water formation occurs  [17, 18, 33]. While deep water formation is linked to the presence of strong winds, in this paper we have not focused on the mechanisms responsible for the deep water formation, but rather we have studied the transient response of the upper ocean to the individual wind events. Strong winds at the surface induce mixing of the upper ocean water, which results in a drop of sea surface temperature in the days immediately following the events.
The decorrelation time for winds in the region is of 2-3 days, and the time interval between events in wintertime is typically of a few days. The recovery process is longer than this interval. However, we should consider that the anomalies are calculated with respect to the climatological conditions, that already account for the effects of the typical winter winds. Therefore, the anomalies reveal the occurrence of anomalous conditions which induce a recognisable signature on the upper ocean. The subsequent evolution of the anomalies has large fluctuations because of the correlations that exist in the dataset, but the estimate of the general decay time scale is probably reliable. The impact of intense wind events in the region has the potential to influence air-sea processes on different time scales.
Average KE appears to be stationary in ERA-Interim (black) with values very similar to the CCMP estimate at the beginning of the time period. No significant trends are present in the SSM/I radiometer data (blue) and ASCAT scatterometer data (magenta), both of which feature larger values than ERA-Interim, closer to the CCMP estimate towards the end of the time period. Note that there is no trend also in a 30 years long run with the high-resolution atmospheric regional model WRF (green) forced with ERA-Interim data. The WRF estimate features the largest values of the group, close to the ASCAT estimate. Summarizing, the CCMP estimate is almost identical to the low values of the ERA-Interim first guess at the beginning of the time period, and then increases to larger values typical of the observational datasets or high-resolution numerical results by the end of the time period.
The presence of this spurious trend makes questionable to use the CCMP dataset for studying global and regional trends, at least in the Mediterranean region, despite having be used sometimes for this purpose in the recent past  [30]. For our study however this is not a serious issue, as we are not interested in the statistics of wind speed per se. The underestimate of strong winds at earlier times results in our case simply in missing intense wind events in the first part of the dataset (indeed we observe that about two thirds of intense wind events are found the second half of the considered period). Since we study the response of the state of the upper ocean to intense wind events, and since we can safely assume that the nature of this interaction has not changed with time, we only have a problem of loss of statistics rather than of biasing.
The introduction into the commercial market of affordable and off-the-shelves Unmanned Aerial Systems (UAS), have lately boosted the mapping capabilities of archaeologists. Hardware solutions have been indeed supported by more accurate flight planning software allowing to increase the reliability of 3D models in terms of spatial resolution and geometric accuracy. However, during the last decades, aerial photography was mainly performed exploiting imaging sensors mounted on kites, balloons and poles. Although being an affordable and user-friendly solution, the use of these platforms did not allow the collection of images following an ordered data collection, hence introducing factors in the network design which could hamper the photogrammetric reconstruction. This study aims to assess the Bundle Adjustment (BA) accuracy and the reliability of the photogrammetric reconstruction by reprocessing various dataset collected over the UNESCO archaeological site of Khirokitia Vouni (Cyprus) using a commercial software and DBAT (Damped Bundle Adjustment Toolbox).