dataesr/scientific-paragraphs-categorization huggingface dataset:
Data fit Data fit analysis based on volume and stage time series both demonstrated the potential of UISCEmod for modelling water levels in ephemeral karstic wetlands (Fig. 7 ). Focusing on the volume time series, NSE and KGE were above 0. 85 for most of the sites for calibration and   validation datasets and above 0. 7 for all sites. In relation to BIAS, values were between -2% and 5% for most of the sites and between -7% and 10% for all the sites (Table  4 ). In general, despite being small, BIAS showed a tendency towards positive values indicating a tendency to slightly underestimate the measured time series, with the largest BIAS being observed for large flood events (Fig. 7b ). If we focus on the performance metrics for the calibration datasets the LM approach seems to have a higher potential than the EM approach.
However, both modelling approaches perform similarly for the validation dataset. There are two elements that may help to explain this behaviour. First, the LM approach needs to calibrate 7 model parameters versus 5 for the EM approach. Second, the current state of the LM approach, volume at time t, is dependent on the previous state, volume at time t-1, which makes it dependent on the initial volume conditions. This is not the case for the TF approach, where the volume at time t is only dependent on the weighted ER from the previous days. These two characteristics means that the EM, although having less potential than the LM approach, is less likely to be overparametrized or affected by the initial conditions and may be recommended when the calibration datasets are small. In many cases model results were sufficiently accurate and equifinal, in which case the choice of model may depend on the applications.
Looking at the performance metrics for stage time series, the differences with respect to the volume time series were only due to the impact of the topography. At low water levels, small changes in volume can be related to large changes in stage, but for large floods a significant increase in volume is likely to be required to increase stage values. This means that the performance metrics for stage time series are slightly shifted towards small floods with respect to the performance metrics for the volume time series. Looking at NSE and KGE values for stage time series (Fig. 7a ), the LM approach slightly outperform the EM approach for calibration and validation datasets, which would be an indicator that the LM approach is likely to be more accurate than the EM approach when modelling the floods at their lower levels.
Applications UISCEmod is a data-based hydrological software that requires of minimal input information for modelling water level time series in ephemeral karstic wetlands that incorporates an automated approach for the calibration process. This makes it interesting for large-scale studies, where the hydrological properties of a large portion of the sites may not be a priori well constrained. In general, meteorological and DTM data can be access from national services or satellite data, but water level time series in groundwater dependent wetlands are harder to obtain due to their wide distribution. New methodological approaches for monitoring wetlands using Sentinel-1 SAR satellite images (i. e. McCormack et al. , 2020 and McCormack et al. , 2022) ), can facilitate access to water level time series in wetlands providing continuous data since 2015.
Easy access to these datasets would facilitate the use of UISCEmod to national-scale applications with potential for hindcasting, forecasting, quantification of ecohydrological variables, and assessing the impact of climate change, which could provide evidence based to inform relevant national policymakers, planners, stakeholders and researchers of potential flooding, ecological, and climate-related issues and enable the development of pre-emptive mitigation strategies. In relation to the application to UISCEmod to new areas, it is worth remembering that the current version is relatively simple and that although it has been demonstrated to be suitable for ephemeral karstic wetlands in Ireland it will likely need some updates if applied to new environments. Because UISCEmod is based on reservoir models and build with independent modules, adaptation to new cases of study may be relatively easy.
Qi t = ( C LM 1 -A ) • (∝(f 1 (t) * ER) + (1 -∝ )(f 2 (t) * ER)) eq. where ∝ ranges between 0 and 1 and it is used to distribute the ER between the two parallel hydrological systems, and f 1 and f 2 are independent gamma distributions associated to each of the two parallel systems (i. e. fast and slow). An equivalent approach may also be considered to incorporate additional input data such as river stage, sea level, snow melt or abstractions, with the potential of having to: 1) replace ER by alternative data, 2) add additional scale factors, and 3) consider alternatives to the gamma function. However, it is worth remembering that adding more complexity to the model is also likely to increase the risk of overparameterization and to complicate the calibration process. Therefore, potential updates should be considered when requested by the specific case of study, and their advantages assessed by comparing model performance of the updated version with the current version of UISCEmod.
Conclusions This manuscript presents UISCEmod, an open-source software for modelling water level time series in ephemeral karstic wetlands with an automated process for calibrating the model parameters following a Bayesian approach. UISCEmod contains both experimental, EM, and lump conceptual, LM, hydrological models and is implemented by modules and functions to facilitate future updates and facilitate their use in new areas and scenarios. The evaluation process presented in this manuscript proves the potential of UISCEmod for modelling water level time series in ephemeral karstic wetlands with NSE and KGE values between 0. 85 and 0. 99 and BIAS between -2% and 5% for calibration and validation datasets for most of the evaluated sites. Estimated confidence intervals of the model time series were also validated, showing discrepancies smaller than 10% for the EM and smaller than 15% for the LM approaches for most of the sites.
Acknowledgements This work was supported by  Geological Survey Ireland  [grant number:  2019-TRP-GW-001 ]. Data and expertise were provided by  Geological Survey Ireland Groundwater and Geothermal Unit. The authors would like to thank  Met Éireann  for provision of meteorological data and  National Parks and Wildlife Service (NPWS)  for funding of acquisition of water level data in turloughs between 2006 and 2009. We would like to thank the two anonymous reviewers for taking the time and spending the effort to provide very concise and critical remarks, which helped to significantly improve the manuscript.
The water treatment system is controlled by a programmable logic controller (PLC) which can be managed remotely. The PLC allows automatic running and cleaning operations of the water treatment plant. A safeguard mechanism avoids serious damage to the osmosis system in case of water pressure loss. Moreover, every 3 min, the PLC stores in a MySQL database the different monitoring parameters (conductivity, pressure, flow) which are sent to the Cáceres headquarters by the main computer of the RAREx water monitoring station. Every 24 h, silex-anthracite and garnet filters are cleaned by countercurrent flow using the pure water stored in the 1 m 3 vessel. This operation is managed by the PLC which operates all the electronic valves. The cleaning operation lasts 10 min for each filter and consumes 150 L of pure water.
The 20 and 1 μm pore size filters and active charcoal filters are replaced manually every two months. There are four pressure probes located before and after the filtering loops. The pressure data are monitored by the PLC. If the pressure measured by a probe falls under an established level (1. 0 J o u r n a l P r e -p r o o f bar) for more than 30 s, the PLC stops the water treatment plant to protect the reverse osmosis system. In this situation, the water pumps stop, and water ceases to flow through the gamma monitoring system. The RAREx headquarter, which is situated in Cáceres city, alerts automatically the technical staff to re-establish the water flow through the stainless-steel vessel to continue the gamma monitoring system. For this purpose, an electronic valve situated before the water treatment plant, which can be operated remotely, opens to allow the water to flow through the gamma monitoring system.
Automatic working and remote management. As described in section 2, the PLC allows the automatic operation and cleaning processes of the water treatment plant. The different monitoring parameters are sent to the Cáceres headquarters by the main computer of the RAREx water monitoring station. In this section, the parameters J o u r n a l P r e -p r o o f measured by the PLC in automatic mode from June 2019 to March 2020 are reported. All of them were received in the Cáceres headquarters in quasi-real time. Data taken during shutdowns due to issues not related to the water treatment plant, for example, water pressure losses caused by the power supply failures, are not considered. In figure  5 , the pressure values of the water inlet of the osmosis plant and the outlet pressure of the filtration system registered every 3 min are shown.
Acknowledgement This work was supported by the  INTERREG-SUDOE EEC program  through the project  TRITIUM -SOE1/P4/E0214  entitled: " Diseño , construcción y puesta a punto de estaciones automáticas para el monitoraje en tiempo real de bajos niveles radiactivos de tritio en aguas". J o u r n a l P r e -p r o o f.
Related Work Matrix multiplications are commonly processed by convolutional neural networks, which can then be decomposed into vector multiplications in the context of linear algebra. This is achieved by performing parallel data processing at the processing element (PE) level and/or within each PE. In the following paragraphs, we provide a brief overview of CNN accelerators  [33, 34]. We discuss three mainstream design approaches: CPU or GPU-based, FPGA-based, and specialized ASIC-based. Last but not least, we discuss some design and evaluation approaches for the RISC-V instruction set architecture. CPU or GPU-based designs. Tightly-coupled multicore clusters provide highly parallel processing capabilities, which are suited to CNN models with high data parallelism. The GAP8 multicore accelerator proposed by Gautschi et al.
However, some simulation and emulation environments have been extended to support RISC-V implementations. As an example, the gem5 cycleapproximate simulator  [63]  currently supports RISC-V as well as x86, ARM, Alpha, and MIPS. The Spike simulator  [64]  allows defining possible extensions of the RISC-V ISA and simulating their corresponding instructions. RISC-V ISA improvements can therefore be investigated by using Spike as a research simulator. The riscvOVPsim simulator  [65]  has been derived from the well-known OVP simulator for prototyping embedded software on an x86 computer by using cross-compilations. The RISC-V Assembler, Simulator, and Runtime (RARS)  [66]  promotes an easy way for beginners to run assembly code on RISC-Vcompliant architectures. In contrast to previous tools, QEMU, which is also open source, is capable of emulating 32-bit and 64-bit RISC-V implementations  [67].
By simulating the described architectures, one can determine how well they perform for a given CNN workload and how energy efficient they are. The number of data transfers within the memory hierarchy and the number of arithmetic operations performed are included in this measurement. A general rule of thumb is to establish behavioral monotonicity between the RTL model and its abstract Timeloop counterpart. Definition 1 (Behavioral monotonicity with regard to a performance metric). Given M 1 and M 2 two different models of the same system, they satisfy a behavioral monotonicity with regard to a metric µ if the evaluation of µ on their respective behaviors follows the same value tendency, for the same input sequence in both models. For instance, let µ denote execution time or power consumption and i 1.
Design Evaluation Our MAC unit is implemented in SystemVerilog, a hardware description language. Simulation and synthesis were performed by using ModelSim and Synopsys Design Compiler, respectively. The proposed MAC architecture is verified through simulation. It is also possible to gather switching activity in order to obtain an accurate estimate of power. This synthesis provides the area and power costs for the selected technology, which is 28 nm FD-SOI at 200 MHz. As evaluation workload, we define a simple program consisting of a few thousand multiplications and additions to be executed for assessing the energy efficiency of MAC units. The evaluated metric is expressed as the number of MAC operations per power consumption unit, referred to as Op/mW. For emulating mixed-precision quantized operands, bit widths of different sizes are used, as illustrated in Figure  5b.
Scripts written in Python generate random values for the operands. A SystemVerilog benchmark is used to load these data into the 32-bit input registers of the MAC unit. Each bit width is tested 1000 times with different values loaded into the MAC architecture's input registers. We compare our proposed MAC unit with that of the RI5CY core  [28]. This core is selected for two reasons: it is well-known within the RI5CY ISA community and its operations are similar to those targeted by our MAC unit. It has also been developed in SystemVerilog and is freely available on the web at  [69]. In its associated MAC unit, there are five types of multipliers, each representing a specific bit precision, as illustrated in Figure  6. There is a similar parallelization capability to that of Table  5b. For simulation and synthesis with FD-SOI technology from 28 nm to 200 MHz, ModelSim and Synopsys Design Compiler are also used.
An intermediate SRAM buffer connects each PE to the DRAM main memory for the purpose of storing data. Each PE in the DianNao architecture has a single register to store weight data (see Figure  10b ). Here, a PE receives data from three shared memories, NBin, NBout, and SB, which contain input feature maps, output feature maps, and weights, respectively. DianNao and Eyeriss organize PEs as two-dimensional matrices. The GAP8 architecture (note that the CNN accelerator of the GAP8 chip is abstracted here in the same way as the other cores due to the lack of information on its corresponding microarchitecture) consists of parallel PEs arranged in rows (see Figure  10a ). The PEs in GAP8 are similarly composed of three registers that contain the CNN input filter weights, the input data, and the MAC output data.
Implementation details We collect data on the custom-built MAV flight platform of Figure  1   We use the thermal mass flow sensors SFM3000-200 from Sensirion AG. The sampling rate of 200Hz in the first evaluation (400Hz in the second) and the high speed I2C communication interface make it easy to acquire data. In this first evaluation, the flight data has been collected on different days in automatic flight mode in a standard gymnasium. A trajectory generator ROS node commands the vehicle to follow a reference trajectory that includes a circular spiral and a figure-of-8 spiral with a radius of 2 meters and an undulation height of 1 meter. The takeoff operation is also commanded by the ROS node during the automatic trajectory tracking. The ground truth velocity is provided by the EKF based on the    ORB-SLAM3 5 output and the IMU measurements.
Concerning the vertical direction (i. e. , Sensor-D), we identified this model using only flight data from hovering and vertical flights, so as to avoid the impact of horizontal flight velocity on the induced velocity. When the horizontal flight is dominant, the linear model estimation in the F-R direction shows essentially the consistency with the ground truth, while the resulting induced flow causes bias in the D-direction estimation. When vertical flight is dominant, the strong variations of the induced speed are measured by the anemometer sensor ahead of the ground truth speed estimation based on the visual SLAM, thus leading to the linear model estimation in D-direction being ahead of the ground truth flight speed estimation. The vertical flight speed also shows a slight coupling characteristic with the horizontal measurements.
Acknowledgements This work was supported by the  French ANR  Project  DACAR. The first author is funded by the  China Scholarship Council (CSC)  from the  Ministry of Education of P. R. China. The authors want to thank  Dr. Jorge ARIZAGA  and  Dr. Pedro CASTILLO  from  Heudiasyc at UTC, Compiègne, France , for giving them access to the Heudiasyc flying arena and providing support for the manual flight tests exploited in this paper. The authors want to thank Prometheus a platform support.
Methodology and Results Figure  1  shows the general workflow used. A private Git  [1]  repository was established for sharing scripts, some (small) datasets, and the website of the project. Different ad hoc scripts are written to handle the diverse data formats. They are developed in Python, R/Rmd, Perl, and Bash, owing to the different skills of the contributors, and are not necessarily meant to be reused elsewhere. We employed a combination of vocabularies including RDF, RDFS, OWL, XSD, SKOS, DCAT, VoID, PROV-O, FOAF, and WD, to extensively describe all useful data and metadata. We attempted to follow existing recommendations when available, e. g. [2] , although a lack thereof seems to prevail in our domains of interest. Hence, we are developing our own vocabulary, prefixed "jlw:", to encompass all the unique concepts encountered.
In addition, we use the "enpkg:" prefix to denote entities from our 'companion project' ENPKG  [3] , which provides a first public release of the plant/chemistry data subset. Where appropriate, these jlw: and enpkg: entities are linked to their counterparts using the OWL property owl:SameAs for strict equivalence matching. This strategy allowed us to coordinate the two projects while preserving the necessary freedom to operate. A restricted-access website was set up, containing links to the SPARQL endpoint, vocabularies descriptions generated by Widoco  [4]  and summaries of the data as html tables. RDF imported directly from Zenodo; public The datasets described in Table  1  are integrated into the KG as RDF graphs, i. e. "context" in GraphDB/rdf4j terminology. To keep track of experimental workflows, the laboratory results have been organized around a central concept: the "analysis run", representing the data captured in the same laboratory, at around the same time, by the same operator.
This contrasts with other research data integration projects run at SIB with classical SQL database, which tend to be hampered by the rigidity of predefined table structures. Nevertheless, high flexibility comes at risk of dispersion and overfitting, which need to be counterbalanced by communication among all collaborators making sure that the progressively gathered knowledge is properly used to model the data. It cannot be overstated that interpersonal dynamics play a central role in the success of such an operation. This approach not only aids in accessing and sharing data but also facilitates seamless integration with other projects, exemplified by the ENPKG companion project  [3]. The use of kgsteward, the data organization in many RDF subgraphs and the concept of analysis runs will facilitate the progressive release of the data in the public domain at the same time as the publications of the scientific results.
Acknowledgements https://doi. org/10. 26434/chemrxiv-2023-4hlgd ORCID: https://orcid. org/0000-0002-2923-827X Content not peer-reviewed by ChemRxiv. License:  CC BY 4. 0 The authors are grateful to  Green Mission Pierre Fabre ,  Pierre Fabre Research Institute, Toulouse, France , for establishing and sharing this unique library of extracts. The authors thank the  Swiss National Science Foundation  for received support the project ( SNF N° CRSII5_189921/1 ).
Calibration of the device Here, it is shown how we calibrated the device to get a linear relationship between the RTD resistance and the temperature. The goal is to determine the calorimeter's temperature coefficient of resistance, which means looking for the a 1 parameter in the RTD time-dependent resistance equation, considering the platinum's a n > 1 terms all equal to zero  [9]. Our device and a proper chip holder, fabricated to ensure good connections, are set on a hot plate. We swept the hot plate temperature from 30 • C to 45 • C and performed a 2-wire measurement using a multimeter (Keithley 2000). A LabView program has been developed to collect output data. Since the sensor aims to measure temperature variations in liquid samples to analyze biochemical reactions, the calibration is performed under two conditions.
The measure is repeated for an empty microfluidic channel (thus, in the case the calorimeter is in contact with air) and with water in the microfluidic channel (thus, in the case the calorimeter is in contact with water). We performed the calibration under these two conditions because the presence or absence of water can significantly impact the measurement. In fact, the differences in heat specificity can influence the microcalorimeter's heat transfer and stability. The RTD resistance data are collected for 100 s for each working temperature, and the average resistance is finally plotted against temperature. Fig. 2A  reports the results for one device in the presence and absence of water: red and black dots. In Fig. 2B , the RTD resistance values are normalized for each temperature with respect to the value R 0 of the equation, found from the linear fit, which ideally corresponds to the RTD resistance value at 0 • C.
Acknowledgments The authors would like to thank the  DIMELEC  project  ANR-19-CE09-0016  for funding and the  RENATECH  clean room facilities at  C2N, Palaiseau, France. We wish to thank  Claire Poujouly ,  Jérémy Le Gall , and  Djamila Kechkeche  for fruitful discussions. MF thanks the  doctoral school "Electrical , Optical, Bio-Physics and Engineering " (ED575) and  Paris-Saclay University  for PhD grants.
Live cell imaging and activation Cell imaging and activation intended for posterior force measurements was carried out using a Nikon Ti-E microscope, Zyla sCMOS camera (Andor, Belfast, UK), and a Plan Apo VC 60Â/1. 40 Oil objective (Nikon, Tokio, Japan). The microscope was equipped with an incubator that maintains the temperature at 37 C. Global cellular photoactivation was performed using a LED light source (X-Cite/XLED1, Lumen Dynamics, Canada) coupled to a Mosaic digital micromirror device (Andor). Depending on the experiment done, activation pulses were 10-20-50-100-150-200 ms long using an LED at 460 nm with a power of 256. 7 mW (measured at the back focal plane of the objective). For our Apo VC 60Â Nikon NA 1. 4 objective, the transmission coefficient at 450 nm is 74% (data from Nikon).
15 M NaCl [pH 7. 4]) and blocked with 2% BSA (Sigma-Aldrich) in TBS. The samples were then incubated with primary antibodies against vinculin (Sigma-Aldrich) and detected with Alexa 488-conjugated, isotype-specific, anti-IgG antibodies (Invitrogen). Actin was labeled with phalloidin-TRITC (Sigma) and nuclei were stained with DAPI (Life Technologies, Waltham, Massachusetts, USA). Focal adhesions were quantified by thresholding vinculin images before measuring the resulting total area of focal adhesions using a home-made ImageJ (National Institutes of Health) routine. Both live and fixed actin imaging was carried out with a Leica, Wetzlar, Germany TCS SPE confocal microscope with an HCX PL APO 63Â/ 1. 40 oil objective. The microscope was controlled through the Leica Application Suite (LAS) X software.