dataesr/scientific-paragraphs-categorization huggingface dataset:
In the article by Jin  [18]  the same dataset of videos is used, but vehicles are tracked across a single intersection using multiple cameras. In this scenario the accuracy of ReID is 81. 59% but the overall accuracy of the MTMC tracking is not provided. The article also reports high complexity of the ReID calculation that caused the single tracking to take approximately 2 s. This significantly narrows practical applications of this approach. Nowadays, most of the MTSC tracking approaches use a convolution neural network for vehicle detection in a video frame  [19] , and dedicated algorithms (e. g. , based on Kalman or particle filters  [12, 20] ) for analysing vehicle movements based on multiple detections on consecutive frames. For object detection SSD and YOLO networks are often used as they are fast and accurate.
During the tests a number of parameters were registered and analysed, including data reception rate (DDR), packet reception rate (PRR), channel access efficiency (distribution of back-offs in CSMA procedure), duty cycling parameters (required for fair-use of the ISM bandwidth) and others. With moderate vehicle (and radio) traffic the packet reception rate for all the sensors, when using TDMA and CSMA was close to 100%. When only CSMA was used the PRR varied between 95% and 98% (Figure  5a ), but the retransmission mechanism allowed us to keep the DRR above 99%. When both TDMA and CSMA were used the DRR of 100% was obtained for all the cameras. Because DRR does not count control frames but only those that contain vehicle data sent between cameras it is a true metrics of system effectiveness.
This implies that fluent language use is not the result of the knowledge of grammar rules but a massive collection of knowledge database that is accumulated through frequent practice and exposure. N. C. Ellis argued that the amount of language development is proportional with previously experienced utterances (N. C. Ellis, 1998). Language learning is effective when associations are repeated (frequent) and plentiful. The cognitive involvement of learners in their own learning is fundamental to CLIL. Bloom's revised taxonomy  (Krathwohl, 2002)  provides a framework for the necessary cognitive skills that learners need to develop in order to master their own knowledge construction. Cummins's basic interpersonal communicative skills (BICS), which describes the development of conversational fluency in the additional language, and cognitive academic language proficiency (CALP), which describes the specific use of language in academic situations are also relevant to CLIL.
The use of a threshold (or sampling dead band) enhances the concentration difference between the up and down reservoirs  (Businger and Oncley, 1990; Bowling et al. , 1998). The system described here is designed to sample air directly onto adsorbent cartridges for sampling volatile organic compounds (VOCs). It consists of a datalogger (CR1000, Campbell Scientific Inc. , Logan, UT)), a 3-D sonic anemometer (RM Young, Model 81000V) and a sampling segregator where air is sampled onto the adsorbent cartridges. The datalogger receives the wind velocity data from the anemometer at a sampling rate of 10 Hz and then controls the valves within the sampling segregator, selecting the proper cartridge based on the direction of the instantaneous vertical wind velocity at the time of the sample capture. Fig.
Acknowledgements We would like to acknowledge the following people who allowed access and assisted us at the various research sites described in this work: 1) Sean Burns and Russell Monsonthe Niwot Ridge AmeriFlux site; 2)  Kevin Hosman ,  Stephen Pallardy  and  Lianhong Gu  -the MOFlux site; 3)  Mike Ryan  and the  US Forest Service -Manitou Forest Observatory ; 4)  Mary Anne Carroll  and  Steve Bertman -PROPHET  site; 5)  Marcy Litvak -Deer Canyon Preserve ; 6) S. J. Han ,  J. H. Zhang ,  H. Xu ,  F. Y. Lin ,  D. Li ,  G. Z. Song ,  W. Yan -Changbai Mountain  temperate forest;.
with HCT provides the most prolonged remission  [4, 2, 5]. Interestingly, the "anti-lymphocytic" drugs combination with methotrexate, Lasparaginase and dexamethasone, already used in NK/T lymphoma, is well tolerated for the majority of patients, can improve OS for up to 30 months, and has the advantage of low toxicity compared to the standard chemotherapies  [6] [7] [8]. Moreover, Angelot-Delettre et al have shown significant cytotoxicity of idarubicin in vitro in primary BPDCN cells, providing a rationale to combine this drug with NK/T lymphoma regimen to improve the efficacy  [9]. Based on these findings, a prospective phase II clinical trial evaluating the combination of methotrexate, idarubicine, dexamethasone, and L-asparaginase (MIDA) has recently been initiated by a French network on BPDCN (Clinical Trial number: NCT03599960, active recruiting).
Here we analyzed a cohort of 111 consecutive patients aged of 65 years or older transplanted from a haplo donor in a single institution from 2013 to 2018. During this period of time, the search for a one-mismatch haplotype donor (Haplo) has been progressively introduced in our program, the upper limit age for transplant progressively increased and more advanced diseases. Patient characteristics: Median age:  69,3 (65-78)   Two year overall and disease-free survivals are respectively of 58% (95% CI: 6. 35-7. 04) and 54% (95% CI: 7. 17-7. 55). the only predictive factor was disease risk. Overall these data show that allo haplo HSCT is a valid option in older patients with high-risk malignancies. To improve results we performed from 2015 in patients older than 65 years systematic and combined evaluation of comorbidities score, performance status and geriatric assessment (instrumental activity of daily living (AIDL); vulnerability assessment) that will be presented in the meeting for this population.
In continental collision zones, higher pressure and temperature conditions tend to exceed the conditions for Pb retentivity in rutile  (Cherniak, 2000) , affecting a large portion of the continental crust. These conditions largely promote the resetting of the U-Pb isotopic system and new growth of rutile over a relatively short period of time (a few tens of myr). In contrast, accretionary orogens are generally characterised by multiple arc collision events over a longer time frame (potentially hundreds of myr), and are, therefore, likely to result in a more complex rutile age record, comprising multiple sets of metamorphic ages. In this contribution, we analyse published and new detrital rutile data to show that it can be used to deduce the tectonic setting of sedimentary basins. This new compilation dataset includes detrital rutile from multiple different geographical areas, from both sedimentary rocks and stream sediments.
1A ). Therefore, due to its metamorphic nature and lower Pb-closure temperatures, rutile appears to provide a better means of distinguishing between tectonic environments. In combination with detrital zircon, rutile will lead to better discrimination of depositional tectonic environments in units preserved at low to medium metamorphic grade (sub-upper amphibolite facies). In the next sections, we examine rutile U-Pb datasets from extensional-related, convergent-related and foreland basins.
Samples In order to further test the applicability of rutile to the discrimination of tectonic settings of sedimentary basins, we collected new zircon and rutile U-Pb data from three different stratigraphic units, two of which represent foreland basins and the third a passive (extensional) margin basin. 1 ) between these detrital zircon populations. Currently, the general view is that the Diabaig Formation was deposited in localised basins that developed immediately above the regional unconformity with the underlying Lewisian Gneiss Complex  (Kinnaird et al. , 2007; Stewart, 2002). In this context, it might be expected that the sediments would preserve a close record of the dominant Archaean-Palaeoproterozoic tectonothermal events that characterise this basement. However, the presence of many Mesoproterozoic-aged rutile grains in both the Diabaig and the Applecross formations indicates a more distal source from the Lewisian Gneiss Complex which lacks such Mesoproterozoic ages.
This is consistent with the general westerly source invoked for the Torridon Group as a whole  (Stewart, 2002; Kinnaird et al. , 2007 : Krabbendam et al. , 2017). The evolution of the Diabaig-Applecross basin appears to have favoured the inclusion of increasingly younger detritus up-section, presumably more distal. Thus, the proportion of late Archaean material decreases upwards at the expense of increasing proportions of Palaeo-and Mesoproterozoic zircon and rutile grains. The cumulative probability curves of rutile and zircon in the Diabaig Formation (Fig. 6C ) are quite different from those for the Applecross Formation (Fig. 6D ) and supported by the low cross-correlation coefficients (Table  1 ). While in the Applecross Formation dataset, the differences between rutile and zircon can be explained by their contrasting closure temperatures, with zircon recording crystallisation/metamorphic ages while rutile records exhumation and cooling, this is not plausible for the Diabaig Formation curves.
Zircon U-Pb analyses were performed based on cathodoluminescence images, As for the detrital zircon datasets, both units have different population obtained in a ZEISS EVO10MA Scanning Electron Microscope, in two different distributions: the Diabaig Formation exhibits a significant Neoarchaean age peak, while sequences using 20 µm ablation spots, with a laser energy density of 3 J/cm 2 at a the Applecross Formation shows a multimodal distribution, with a major age peak repetition rate of 2 Hz. A sample-standard bracketing method was used to correct for between 1. 8 and 1. 65 Ga. The oldest zircon grain is found in the Diabaig Formation (Saylor and Sundell, 2016)bendam et al. 2017) constrained to between 997 ± 39 Ma and 1049 ± 46 Ma, and it is interpreted as the siliciclastic infill of the foreland basin of the Grenville orogen(Rainbird et al.
2% accuracy of the reported ages (Appendix C). U-Pb dating of rutile is more difficult than zircon due to overall low U concentration (< 100 ppm) and incorporation of small amounts of Pb at the timing of growth. Common-Pb corrections are not always applicable and natural discordance complicates this further. Filtering these datasets to include only grains that are more than 95% and lower than 105% concordant excludes many grains from these datasets and reduces some of these samples to smaller populations. This might generate a bias in terms of determining precise ages of events, but it is unlikely to have a great effect on the cumulative distributions and the overall distribution curves. No common-Pb corrections were applied. massfractionationusingBB9zirconas a primary standard (average ID TIMS age of 560 ± 5 Ma;Santos et al.
Field Measurement of Soil Apparent Electrical Conductivity An EMI sensor (geodetic conductivity meter, EM38-MK2) (Geonics Ltd. , Mississauga, ON, Canada) was used in this study to in situ measure soil apparent electrical conductivity (EC a , mS/m). While the original EM38 model carries a transmitter and a receiver coil at 1-m apart, the EM38-MK2 model carries two receiver coils at 0. 5 m and 1. 0 m far from the transmitter coil and can provide EC a data at four different depths  [26]. The new EM38-MK2 model also adopts new technology to automatically perform temperature compensation, correct circuit faults, and eliminate data drift caused by the temperature variations  [27]. An EM38-MK2 model was embedded into a wooden cart along with a GPS and was used to map the whole field for four times between July and October.
Measurements were done on 15 March 2018 (after winter irrigation) before cotton sowing, 3 June (before the first drip irrigation), 7 July (before the second drip irrigation), and 27 October (after cotton harvesting but before winter irrigation) using the vertical measurement mode from south to north of the survey field. More details of the experiment and data collection can be found in Liu et al. [28]. Based on the spatial scale of the study area, a total of ten measurement rows were developed with a row spacing of 20 m (Figure  1 ). Descriptive details of the soil EC a measurement are presented in Table  1.
Soil Sampling and Analysis To model the relationship between the measured electrical conductivity and the apparent electrical conductivity, soil profile samples were collected from the field. While high density sampling can ensure the stability of the relationship, it largely increases cost, time, and labor. In contrast, EM38-MK2 survey provides relative variability of soil salinization within the field and can be used to select sampling locations a priori while capturing the variability. Roughly, the EM38-MK2 soil apparent electrical conductivity data were divided into three groups: low (<50 mS m -1 ), medium (50~100 mS m -1 ), and high (>100 mS m -1 ). Six sampling locations were randomly selected within each of these zones identified from the EM38-MK2 survey data. A total of 18 soil cores were collected each time for four times (15 March, 3 June, 7 July, and 27 October 2018) of field measurement.
Model Construction and Evaluation The 18 soil profiles were sorted and randomly divided into training dataset (12 profiles) and validated dataset (6 profiles) at a ratio of 2:1. Linear and multivariate linear regression methods were used to construct a predictive relationship between measured soil electrical conductivity and soil apparent electrical conductivity. Coefficient of determination (R 2 ), root mean squared error (RMSE), relative percent deviation (RPD), and mean error (ME) were calculated to evaluate the model performance. The related formulas are as follows: Mean error: ME = 1 m N i=1 [z * (s i ) -z(s i )] (1) Root-mean-square error: RMSE = 1 N N i=1 [z * (s i ) -z(s i )] 2 (2) Coefficient of determination: R 2 =            N i=1 (z(s i ) -z(s i ) ave )(z * (s i ) -z * (s i ) ave ) N i=1 (z(s i ) -z(s i ) ave ) 2 + N i=1 (z * (s i ) -z * (s i ) ave ) 2            2 (3) Standard deviation: SD = N i=1 (z(s i ) -z(s i ) ave ) 2 n -1 (4) Relative percent deviation: RPD = SD RMSE (5) where z * (s i ) was the predicted value, and z(s i ) was the measured value.
Predictive Relationship between ECe and ECa Best of linear and multiple linear regression relationships between ECe and ECa at five different depths and four different times are presented in Table  2. The relationships were developed for ECe values of 12 profiles (calibration dataset) and validated with the rest six profiles (validation dataset) against two depths of EM38-MK2 measurement; EC v0. 75 and EC v1. 5 measuring ECa to the depth of 0. 75 m and 1. 5 m. In general, multiple linear regression always outperformed linear regression results with R 2 ranging from 0. 70 to 0. 96. The performance of the validation of these relationships are presented in Table  3. Overall, the relationships showed strong agreement with R 2 > 0. 80, ME ranged between -0. 45 and 0. 32, RMSE < 1. 30 dS m -1 and RPD > 2.
Conclusions Soil salinity is the most critical soil degradation issues around the world, particularly in the arid and semi-arid regions. Along with the natural factors, anthropogenic management of agricultural lands such as irrigation contributes to secondary soil salinity. Thus, monitoring and management of this salinity over the growing season are critical for sustainable management of these already vulnerable lands. In this study, we collected apparent electrical conductivity data (using electromagnetic induction sensing technology) and soil profile samples from a typical cotton field in arid areas for four times. We constructed inversion models of different soil layers in different periods and developed 3D-IDW interpolation model to quantify spatio-temporal variations of soil electrical conductivity.
Acknowledgements The authors are supported by a  Young Thousand Talents Plan of China  (  41720104002 ), a  Swedish Research Council  Grant ( 2017-05183 ) to T. P. T. and a  Swedish Research Council  Grant to C. B. S. ( VR2016-04610 ) and a grant from  Spanish Ministry of Economics, Finance and Competitiveness   CGL2017-87631-P  to  S. C. Financial  support from the  National Natural Science Foundation of China  ( 41425008 ,  41621003 ,  41720104002  and  41890844  to Z. Z. , 41772002 to C. B. S. and 41472015 to J. G. ), the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  to the  Early Life Institute  ( XDB26000000 ) and the 111 project ( D17013 ). Thanks to  Xiaolin Duan  for latex casting and photography.
, 2013). Second, the biogeochemical model was forced at the Mediterranean basin scale by the outputs of the same NEMO simulation. In a third step, the daily outputs of the two previous simulations were used to initialise and force the Eco3M-S biogeochemical model over the western Mediterranean Sea. This nesting protocol ensures the coherence of the physical and biogeochemical fields at the open boundaries. The basin configuration of the biogeochemical model was initialised in summer 2011, with climatological fields of in situ nutrient concentrations from the oligotrophic period in the MEDAR/MEDATLAS database  (Manca et al. , 2004)  and according to oxygen observations from the Meteor M84/3 cruise carried out in April 2011  (Tanhua et al. , 2013)  and DYFAMED station observations in August 2011  (Coppola et al.
saihebro. com), and mean annual values for the other rivers. At the Rhône River mouth, nitrate, ammonium, phosphate, silicate and dissolved organic carbon concentrations were prescribed using in situ daily data (P. Raimbault, MISTRALS database). These data, combined with those of  Moutin et al. (1998)  and  Sempéré et al. (2000) , were used to estimate dissolved organic phosphorus and nitrogen and particulate organic matter concentrations as described in  Auger et al. (2011). At the other river mouths, climatological values were prescribed according to  Ludwig et al. (2010). Dissolved oxygen concentration at the river mouths was set to values at saturation. The deposition of organic and inorganic matter from the atmosphere was neglected in this study. Fluxes of inorganic nutrients and oxygen at the sediment-sea interface were considered by coupling the pelagic model with a simplified version of the meta-model described by  Soetaert et al.
Argo floats To evaluate the temporal evolution of the modelled oxygen inventory, we use data of three Argo-O 2 floats (floats 6901467, 6901470, 6901487) deployed in the NW Mediterranean Sea during the preconditioning phase (late November 2012) and the active phase (late January 2013) of dense water formation and operational until the end of the study period  (Coppola et al. , 2017; Argo, 2020). Dissolved oxygen measurements were made with a standard CTD sensor, equipped with an oxygen optode with fast time response (Aanderaa 4330). Calibrations of optodes were performed before the float deployment and also during the deployment using CTD profiles and seawater samples (Niskin bottles). Details on float deployment strategy and calibration are given by  Coppola et al. (2017). We calculated the oxygen inventory from 1800 m to the surface for floats 6901467 and 6901470 and only from 1000 m to the surface for float 6901487 due to poor quality salinity data below this depth.
7 %, and a negligible percentage bias of -1. 1 % and 0. 01 %, respectively, at the surface and along the south-north transect. During the spring cruise period, the model represents high dissolved oxygen values (> 240 µmol kg -1 ) at the surface throughout the region, as observed (Fig. 3c  and d ). The increase in modelled oxygen concentration in the surface layer between both campaigns is in agreement with observations  b). A zone of low oxygen concentration in the intermediate waters is present in the convection area in both datasets (Fig. 4b ). However, it is worth noting that this zone of low oxygen concentration is heterogeneous in its magnitude and thickness both in model outputs and in observations, and it is not similarly distributed in space in the model compared to the measurements.
Comparison to Argo float data The model accurately reproduces the magnitude of oxygen inventory in the water column and its time evolution observed using Argo floats during the study period (Fig. 5 ). The model simulates the increase observed between early December and late February. This increase is estimated at ≈ 20 mol m -2 over a layer from the surface to 1800 m, along the trajectory of the float 6901467 (Fig. 5a ), and at ≈ 10 mol m -2 over a layer from the surface to 1000 m, along the trajectory of the float 6901487 (Fig. 5c ), both floats being located in the Gulf of Lion at that period. The oxygen inventory remains high during the month of March and then decreases significantly from early April to early June, in model outputs and Argo observations. In both datasets, the decrease reaches up to 20 mol m -2 over 1800 m along the path of the Argo float 6901470 in the Gulf of Lion (Fig.
(2020). grants (P1325, P09115 and P1331). We acknowledge the scientists and crews of the Flotte océanographique française (https:// www. flotteoceanographique. fr/), who contributed to the cruises carried out in the framework of the DEWEX project and MOOSE programme (CNRS-INSU). We thank Toste Tanhua and the two other anonymous reviewers for their constructive comments. We thank Dariia Atamanchuk for sharing with us a MATLAB code with the calculation of air-sea oxygen fluxes including bubble-mediated terms. Financial support. This research has been supported by the international metaprogramme MISTRALS dedicated to the understanding of the Mediterranean basin environmental processes (http: //www. mistrals-home. org/), in particular by the French MERMEX (Marine Ecosystem Response in the Mediterranean Experiment), HYMEX (Hydrological cycle in the Mediterranean EXperiment) and MOOSE (Mediterranean Ocean Observing System for the Environment) programmes coordinated by CNRS-INSU.
Acknowledgements. The numerical simulations were performed using the SYMPHONIE model, developed by the  SIROCCO group  (https://sirocco. obs-mip. fr/),and computed on the cluster of Laboratoire d'Aérologie and HPC resources from CALMIP https://doi. org/10. 5194/bg-18-937-2021 Biogeosciences, 18, 937-960, 2021.
Data availability. The Argo data are available on the Coriolis platform (https://doi. org/10. 17882/42182, Argo (2020), last access: 19 May 2017), MOOSE data on SEANOE/ SISMER (https://www. ir-ilico. fr/en/Data-access/MOOSE, Testor et al. , 2013 , last access:  8 June 2017)  and DEWEX data on the MerMex database (https: //mistrals. sedoo. fr/MERMeX/, Testor, 2013 and Conan, 2013 , last  access: 1 February 2019). Results of simulations are available on request (caroline. ulses@legos. obs-mip. fr), Ulses et al. , 2020 , last  access: 13 October 2020.
The Farasan Archipelago is a group of small coral islands and islets in the southern Red Sea, offshore of the southwestern Kingdom of Saudi Arabia (KSA). These islands are internationally important as breeding sites for turtles and bird species and regionally for its threatened, rare, and endemic flora and other fauna. The beetles (Coleoptera) of the Archipelago have not been previously surveyed. This study presents the first data on the beetle fauna based on a recent survey of the Farasan Archipelago. In total, 179 beetle species (including three synanthropic species) in 145 genera and 31 coleopteran families were determined. The Carabidae are represented by 31 species, followed by the Tenebrionidae (22 species), Chrysomelidae (17 species), Scarabaeidae (13 species), and Coccinellidae (12 species).