dataesr/scientific-paragraphs-categorization huggingface dataset:
This is because many competitive sailing events for RC model boat racing has helped to elevate boat design and sailing techniques into new levels. To make full use of the agility of modern model sailboats, minimal mechanical adjustments are needed to convert a model sailboat into an autonomous sailboat. Taking advantage of the latest sensor and Artificial Intelligence (AI) computer developments, particularly the availability of miniature ultrasonic anemometers and Raspberry Pi 4, this paper aims to propose a new autonomous sailboat platform with a unique combination of a Raspberry Pi 4 with Navio2 and a Calypso solar-powered ultrasonic anemometer  [11]  on a 1-meter class RC model boat and to conduct autonomous sailing experiments on the proposed sailboat platform. The benefits of such a combination include minimal mechanical adjustments of the original model sailboat and a software architecture involving only Python programming.
As the Calypso anemometer communicates with the Raspberry Pi 4 via Bluetooth, the sailboat platform also removes extra electrical wires widely seen in other sailing platforms. The platform can also be extended to include computer vision and machine learning algorithms by making full use of Python-supported OpenCV and deep learning packages. Since autonomous sailboats are increasingly used for research and education as in  [12] , it is expected that the proposed autonomous sailboat platform can become an easily accessible and fast-prototyped platform for advanced research and education in robotics and machine learning as well. The rest of the paper is organised as follows. The principle of autonomous sailing and two typical sailing algorithms to be tested on the proposed sailboat platform are introduced in Section II.
B. SOFTWARE ARCHITECTURE As the configured hardware system has only three main parts (a Raspberry Pi 4, a Navio2, and a Calypso anemometer), the corresponding software architecture is relatively compact as well: each sensor has its own program; an extra program is to receive the raw sensor data and to filter them into a state vector in thread; and then the main program receives this state vector and provides the corresponding instructions to control the rudder servo and the sail servo according to the sailing algorithm to be tested. The software architecture is shown in Figure  6. It is worthy to note that all the programs are written by Python without the use of any ROS command. The software architecture can also be extended to include computer vision and other machine learning packages.
C. ONLINE MONITORING In order to monitor the status of the sailboat on a nearby laptop, a XBEE module is connected to Raspberry Pi 4 and the other XBEE module is connected to the laptop. These two XBEE modules are configured to use the same channel to establish a communication between them. The position of the sailboat, the direction of the true wind, the heading of the sailboat, and other relevant information can be transmitted to the monitoring laptop. The monitoring laptop can also take over the control of the sailboat by sending new instructions to update the rudder angle and the sail angle via XBEE. In order to display the movements of the sailboat on a Google map in real time, a server is set up on the monitoring laptop. The procedures to set up this server is shown in Figure  7 , where a Python script receives the data sending from the XBEE module and puts them into a MYSQL database; a php file gets the stored data and send them to a JavaScript function; then a HTML file uses this function to display the real-time status of the sailboat on the Google map.
V. CONCLUSION A new autonomous sailboat platform has been built and tested to meet the need of an easily-accessible, fast-prototyped and economic platform in autonomous sailing research. The developed sailboat platform is composed of a 1-meter class RC sailboat, a Raspberry Pi 4, a Navio2, and a Calypso ultrasonic anemometer in hardware and it uses only Python programming in software. Such a unique combination of hardware and software makes the platform very compact and agile. Experimental studies on the proposed sailboat platform have demonstrated its full functionality to implement typical autonomous sailing algorithms such as line following and station keeping. The developed sailboat platform can also be extended to include the functionality of computer vision and machine learning. More studies are needed to improve the robustness of the tested sailing algorithms to cope with changing environmental conditions and noisy sensor data encountered in the experimental studies.
ACKNOWLEDGMENT The authors would like to thank extra experimental assistance from the other two internship students named  Laurent Droudun  and  Mathieu Drillon , also would like to thank  Dr. J. Ricardo Sodré  (the  Head of the Department of Mechanical, Biomedical and Design Engineering)  to approve the internship,  Prof. Stephen Garrett  (the  Pro-Vice Chancellor  and the  Executive Dean of the College of Engineering and Physical Sciences)  to sign the host agreement with  ENSTA Bretagne, France , and the  Research Support Team  led by  Dr. Emma Estevez  to provide administrative support.
Optogenetic activation is modelled by an increase of the active tension acting in parallel to the elastic and viscous elements. As alternatives to this material law, we also considered active versions of the Maxwell model, which describes a fluid with an elastic element in series, as well as of purely elastic material (supplement). Using finite element calculations in the open software package FEniCS, we then implemented these material laws for thin contractile sheet that are attached to an elastic foundation with the geometry of interest and locally have a polarized actin cytoskeleton. For the circular discs used in Fig. 1 , we use contraction in one direction (Fig. 2b ). Thus our mesoscopic mechanical model can account for both cell size and actin architecture. In order to parametrize this model, we make use of the fact that it can be solved analytically for isotropic contraction of a circular disc  (Edwards and Schwarz 2011a; Chojowski, Schwarz, and Ziebert 2020).
| The land surface model ORCHIDEE-CAN-NHA In this study, we use the ORCHIDEE-CAN-NHA (r7236) land surface model, incorporating allometric-based carbon allocation, trees cohorts making the canopy structure with different growth of an average tree in each cohort and background mortality induced by self-thinning processes  (Naudts et al. , 2015)  as well as a new mechanistic hydraulic architecture  (Yao et al. , 2022). The hydraulic module includes the dynamic root water uptake scheme proposed by  Joetzjer et al. (2022)  and a plant hydraulic model simulating water transport from water potentials gradient between soils and atmosphere, and water capacitance in roots, stems, and leaves. Besides this hydraulic architecture, a cavitation-induced mortality model was added. Two parameters are used to translate the percentage loss of stem conductance (PLC) simulated by the hydraulic module to mortality.
| Climate forcing The gridded climate forcing used as input to ORCHIDEE-CAN-NHA is the CRUJRA v2. 1 dataset  (Harris, 2020; Harris et al. , 2014; 2020; Kobayashi et al. , 2015). CRUJRA v2. 1 was constructed by regridding data from the Japanese Reanalysis Data (JRA) produced by the Japanese Meteorological Agency (JMA) adjusted to match the monthly observation-based Climatic Research Unit (CRU) TS 4. 04 data  (Harris et al. , 2020). It provides 6-hourly meteorological variables from January 1901 to December 2019 at 0. 5 × 0. 5° spatial resolution.
| Mean patterns of mortality and turnover A clearer understanding on the response of the Amazon rainforest to drought is indispensable for future predictions. We found that our model, despite having its parameters calibrated only for the Caxiuanã throughfall experiment, and lacking a description of the diversity of plant traits, is capable to produce realistic average cross-basin mortality rates, and higher mortality rates and biomass losses in the epicenter of each drought. With our hydraulic failure-mortality module adding up to background tree mortality (from self-thinning), our simulations (Figure  S9 ) of the mean spatial pattern of mortality are comparable with mortality rates from inventory data shown by  Esquivel-Muelbert et al. (2020). Namely, there is a prevalence of higher average mortality in the Brazilian Shield.
| Sensitivity of biomass to droughts compared with observations The drought sensitivity simulated by our model shows an emerging positive relationship between mortality and water deficit. This result is consistent with forest plot observations. The simulated drought sensitivity is comparable with the one observed in inventory data for the 2005 drought  (Phillips et al. , 2009). Inventory measurements did not sample the epicenter of the 2010 drought. Unlike for the 2005 event, however, selected plots were measured shortly after 2010, thus reducing possible confounding effects of post-drought climate conditions. These scarce data indicate that forest sites that were experiencing a severe drought gained less biomass in 2010 but showed no evidence for a significant mortality co-variation with drought severity  (Feldpausch et al.
Methods An Open Source energy Modeling SYStem (OSeMOSYS) tool was used to assess the required energy supply (electricity capabilities) and CO 2 emissions using a broad representation of diverse technologies in the industry, residential, transportation, agricultural, and other sectors. OSeMOSYS is specifically designed as a tool to inform the development of local, national and multi-regional energy strategies and support them with capacity building activities. Unlike other long established energy systems modeling frameworks such as MARKAL/TIMES, MESSAGE, PRIMES, EFOM and POLES), it potentially requires a less significant learning curve. Additionally, by not using proprietary software or commercial programming languages and solvers, OSeMOSYS requires no upfront financial investment. This feature increases its affordability, particularly in developing countries  [25, 26].
It starts with energy demand development, driven by annual average GDP growth, average yearly population growth, average household size, and percentage urban population. A detailed description of the basic layout, Reference Energy System, of the model can be found in the supplementary material in  [27]. The methodology applied in this paper is based on a model produced in the Energy system development pathways for Ethiopia (PATHWAYS) project, which optimized Ethiopian long-term energy system development pathways through 2065 using existing policy, the country's second National Electrification Plan (NEP-2), and techno-economic data from the different reputable international organization. The PATHWAYS project aimed to unlock barriers in the energy system planning process and policy analysis by developing energy system models that adopt an inclusive approach through stakeholder engagement.
Steps Used This paper tried to build on an already developed scenario (New Policy Scenario) in the model developed for the PATHWAYS project, which is then used here as a baseline in order to compare with a Drought Scenario, which shows the effect of climate change in Ethiopia's Power Generation System and Environment. The study  [28]  developed different scenarios including the New Policy Scenario targeting the requirement of electric supply and CO 2 emissions within an energy system modelling framework. The steps used are as follows: Step 1: It starts with energy demand development, driven by the aforementioned factors, which consist of gathering data for energy demand projections, primary energy resources and transmission and distribution system collected from 18 different offices under the Ministry of Water Irrigation and Energy (MoWIE), Minister for Mines, Petroleum and Natural Gas (MoMNG) and a literature survey.
Related works The question of sampling combinatorial problems is central in hardware/software verification and testing, mainly on SAT models. Some testing problems have recently been expressed with CP models, for instance because of the need for non-Boolean variables : in  [Acher et al. , 2018]  the authors define a variability model on continuous variables. They then discretise these variables and sample solutions using RandomVarDom. In  [Gotlieb and Marijan, 2017] , the Test Suite Reduction problem is tackled with constraint optimisation problems using global constraints. In  [Plazar et al. , 2017] , array constraints are used for handling data structures. Other works, both in SAT and in CP, have tackled the question of solution sampling or diversification in the literature. We present here an overview.
N -queens The first problem is the N -queens problem, which consists of placing N queens on an N × N chessboard such that no queen attacks an other one (queens attack in every 8 directions, as far as possible). We implemented it with the classical model with N variables with domain [1, N ], and inequality binary constraints (the same model as the one used in  [Pesant et al. , 2021] ). We use the 9-queens instance that has 352 solutions. On Call Rostering This problem models the system of duty, notably used by healthcare workers. This instance is available in the MiniZinc benchmarks 3  and contains different constraint types, such as linear constraints, global constraints count, absolute values, implications and table constraints. Many datasets are available but only the smallest (4s-10d. dzn) has been used here.
It is an optimisation problem (minimization), so it was necessary to transform this problem into a satisfaction problem by bounding the objective function. The optimal value is 1: • There are 136 solutions with obj ≤ 1 • There are 2,099 solutions with obj ≤ 2 • There are more than 10,000 solutions with obj ≤ 3 By randomly sampling the solutions, the solver can be used as a tool to help people creating plannings to decide on (giving them multiple plannings to compare), and brings a form of equity between the workers. Indeed, oriented search methods could favour some workers at the expenses of others. Feature Model This is a problem of software management, helping to decide on the order of implementation of software features. The instance is specified in the MiniZinc format in  [Regnell and Kuchcinski, 2011]  using the data in  [Ruhe and Saliu, 2005].
Declarations Previous works on the same topic This article is an extended version of a conference paper: Solution sampling with random table  constraints, Mathieu Vavrille, Charlotte Truchet, Charles Prud'homme, Proceedings of CP 2021, 56:1-56:17  This work was presented at the conference on October 2021 : https://cp2021. lirmm. fr/submissions/74 Compared to the conference article, this version has been extended with the implementation of another series of hashing constraints, proposed in  [Pesant et al. , 2021] , inside the framework, more extensive experiments on a wider benchmark, and a more thorough analysis of the results and algorithm behaviour. Interests Disclosure from the authors Funding Partial financial support was received from the French National Research Agency as part of the DeCrypt project (ANR-18-CE39-0007) for two of the authors (Prud'homme and Truchet).
We specifically developed and optimized different optical modules, each one performing individual tasks yet acting jointly to achieve an efficient volumetric selective excitation and imaging (Figure  1 A ). In the soSPIM technology, cells are seeded in a microfabricated sample holder containing several arrays of microwells positioned at close proximity from a 45° mirror. With this geometry, the mirror reflects the excitation laser inside the sample, allowing creating a thin light-sheet using the same high NA objective used to collect the fluorescence. The beam steering module is composed of two galvo-mirrors and an electrical tunable lens conjugated to the back focal plane of the objective, and a third mirror conjugated to the sample plane. It allows performing the fast-scanning of the beam along the mirror axis and adjusting the optimal position and the excitation laser waist to create a light-sheet perpendicular to the optical axis of the objective and well positioned on the sample (requirement (i)).
Different combinations of the EL focal length and the stage displacement produce a collimated Gaussian beam demagnified by a factor ranging between 1× and 0. 2×. The size of the Gaussian beam at the input of the objective determines the waist of the excitation at the focus and thus the effective light-sheet thickness. The module is completely automated using a LabVIEW program and can generate very thin light-sheets (1 𝜇𝑚 thickness) for high optical sectioning over short distances (Rayleigh length of 5𝜇𝑚, Supplementary video 1), as well as thick light-sheets (up to 4 𝜇𝑚 thickness) for confined volumetric excitation over large distances (Rayleigh length of 78𝜇𝑚) (Supplementary information) (requirement (iii)). Still, achieving uniform imaging over extended volumes requires the excitation of the sample with a uniform intensity distribution over the desired axial range.
In this configuration the beam expander introduces a total magnification of 𝑀 𝑡𝑒𝑙 = 1 (Supplementary Figure  2 ). The design of our module imposes that at every displacement 𝑠 of the translator the spaces between the lenses become reciprocally 𝑑 2 = 𝐷 2 + 3𝑠 and 𝑑 3 = 𝐷 3 -𝑠. By design, the beam outcoming the beam expander is collimated when [(𝑓 𝐸𝐿1 + 𝑓 2 -𝑑 2 )](𝑑 3 -𝑓 3 ) -𝑓 2 (𝑓 𝐸𝐿1 -𝑑 2 ) = 0. The total magnification introduced by the module can be tuned by regulating 𝑓 𝐸𝐿1 and the displacement 𝑠. The real magnification was calibrated by measuring the output beam diameter compared to the incoming one (Supplementary Figure  2 b-c ). The excitation beam size can be regulated between 1 𝜇𝑚 and 4 μm. The beam expander module is completely automatized with a LabVIEW program that integrates the drivers of the translator stage (APT software, by Thorlabs) and the tunable lens (Lens Driver Controller, by Optotune).
Third module -Beam steering The third module is dedicated to steering the beam and controlling the light-sheet position and parameters at the sample plane. This module is based on the soSPIM beam steering unit described in ref  13  where three relay systems composed by achromatic lenses originate three planes that are optically conjugated to the back focal plane (BFP) of the objective. The systems are composed of lenses L5 and L6 , L7 and L8 (𝑓 5 = 𝑓 6 = 𝑓 7 = 𝑓 8 = 50 mm, AC254-050-A, by Thorlabs), and L9 and L10 (𝑓 9 =150 mm, AC254-150-A, by Thorlabs, 𝑓 10 = 200 mm, AC254-200-A, by Thorlabs). A tunable, electrical lens EL2 (Custom EL-10-30, by Optotune; focal lengths from -80 mm to +1000 mm) is placed on the first conjugated plane. This lens introduces a defocusing of the excitation Gaussian beam allowing to adjust and maintain position of the light-sheet's waist compared to the sample position in relation to the mirror.
Fourth module -Microfabricated sample holder for soSPIM imaging The fabrication protocol of the soSPIM devices used in this study is described in detail in Reference  13. Briefly, a silicon mask is first etched using a combination of anisotropic wet etching and deep-reactive etching processes to create respectively 45° slanted surfaces and microwells of various dimensions (24x24 µm 2 , 40x40 µm 2 , 60x300 µm 2 ) along those surfaces. The silicon mask is then replicated in a secondary PDMS mold (Sylgard 184, Dow Corning), which in turn is used to reproduce the silicon mask features onto cleaned #1. 5h coverslips by capillary filling of a UV-curable and index match polymer (BIO-134, MyPolymer). At this stage, the index-matched polymer could be mixed with 100 nm nano-diamonds (NDNV100 nmMd10ml, Adamas Nanotechnologies INC.
Adjustment of the DMD pattern Due to a limited mechanical precision and the presence of multiple optical elements inserted between the DMD and the observation camera, the frames of the DMD and of the camera were slightly misaligned. As the teetering of the excitation beam is commonly performed along the dimensions of the camera frame, it was necessary to compensate for any rotation. Using the uniform fluorescent layer, a pattern of regular grid bright points was applied on the DMD. The excitation grid was imaged on the camera and found to exhibit a slight tilt compared to the camera frame. A home-made MATLAB codes identified the center of the peaks and found the appropriate transformation to be applied on the DMD pattern in order to match the main axis of the camera frame. This transformation was applied on any pattern that was applied to the DMD, including the double arc pattern, in order to match the camera main frame (Supplementary Figure  7 ).
Data analysis and visualization Single molecule acquisition sequences were reconstructed as Zstacks using the above-mentioned approach. As single molecule PSFs are sampled simultaneously on several focal planes, a routine based on 3D gaussian fitting allows to recover precisely the center of each molecule  17. Localization data was filtered using a custom-made MATLAB code for bad detections by thresholding large standard deviations or out of bounds axial positions. To correct the drift in super-resolution acquisitions, two strategies were used. A first strategy relies on the introduction of fiducial markers 32 in the polymer forming the soSPIM device. The particle was inserted randomly at a given concentration with the aim to ensure the presence of at least one marker within any 4 µm deep volume around each well.
Drift correction is achieved off-line by 3D tracking the fiduciaries. In case no fiducial marker was present within the probed volume, a second approach based on cross correlation was used to correct the drift 33. The 3D cross correlation routine was extracted from the SMAP software describe in  34. For dynamic data in live cells, we used the Utrack algorithm to track single molecules in 3D  35. The obtained trajectories were later filtered to rule out tracks with less than 10 time points, and mean square displacement (MSD) was calculated for each trajectory. Apparent diffusion coefficients were computed for each trajectory by linear-fitting the 5 first points of the MSD using MSD(t) = 6Dt+b for the 3D case. The localizations were visualized in the Genuage software in desktop mode  36. Genuage was used to produce the supplementary videos.
Acknowledgments BH acknowledges funding from the  Fondation pour la Recherche Médicale  (FRM;  DEI20151234398 ), the  Agence National de la recherche  ( ANR-19-CE42-0003-01 ), the  LabEx CelTisPhyBio  ( ANR-11-LABX-0038 ,  ANR-10-IDEX-0001-02 ), the  Institut Curie ,  Agence pour la Recherche sur le Cancer (ARC Foundation) ,  DIM ELICIT  and from  ITMO Cancer of Aviesan  on funds administered by  Inserm  (grant N°  20CP092-00 ). BH, RG and JBS recognize the support of  France-BioImaging  infrastructure grant  ANR-10-INBS-04  (Investments for the future). TG acknowledges funding from the  European Union 's  Horizon 2020 research and Innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No  666003  and the  Fondation pour la Recherche Médicale  under the  Fin de these 2020 program  ( FDT202001010813 ).
RG and JBS acknowledge the soLIVE funding from the  Agence National de la Recherche  ( ANR-16-CE11-0015-01 ). We thank  Luke Lavis  for providing the JF dyes, and  Elphege Nora  for sharing the STEM cells that were imaged in this paper. We thank  Romain Laine  for useful discussion, and  Hisham Forriere  for the sharing labeling protocols and providing feedback. We dedicate this paper to the memory of  Maxime Dahan.