dataesr/scientific-paragraphs-categorization huggingface dataset:
Location Specification and Measurements Figure  1  shows the platform of the region and the location of the case study investigated. The building and its neighbour are distinguished using red and grey shades. Meteorological data of the location have been gathered from the National Climatic Data Canter (NCDC, from National Oceanic and Atmospheric Administration in the United States), which maintains the Climate Data Online portal (http://www. ncdc. noaa. gov/cdo-web/, accessed on 12 May 2014). Ten years of wind speed and direction analysis are shown in Figure  2. The wind potential is analysed using data from NCDC. This analysis shows that about 90% of wind potential comes from the west, mainly during the day (not during the night). Ten years of wind speed and direction analysis are shown in Figure  2.
The wind potential is analysed using data from NCDC. This analysis shows that about 90% of wind potential comes from the west, mainly during the day (not during the night). In order to achieve specific local results, wind measurement equipment was installed near the parapet of the building, including two cup anemometers, one wind vane, and one sonic anemometer, as illustrated in Figure  3  (photograph on the left is facing the dominant winds from the west). The wind measurement data at the top and bottom cap anemometers have been analysed in Section 5. 2 and then compared with LES results at the same location. A one-year measurement campaign brought enough information to characterize the wind from the west direction. The direction of the wind is depicted in Figures  1  and 2  and is perpendicular to the front wall of the building.
• Flow is twisted: horizontal direction is 15° shifted from the top location to the lower location (close to the roof, flow deviates to the left). All the local measurements have been performed by METEODYN (http://www. meteodyn. com/en), and the meteorological data have also been gathered by METEODYN during the SWIP project (http://swipproject. eu/). In order to achieve specific local results, wind measurement equipment was installed near the parapet of the building, including two cup anemometers, one wind vane, and one sonic anemometer, as illustrated in Figure  3  (photograph on the left is facing the dominant winds from the west). The wind measurement data at the top and bottom cap anemometers have been analysed in Section 5. 2 and then compared with LES results at the same location. A one-year measurement campaign brought enough information to characterize the wind from the west direction.
• Flow is twisted: horizontal direction is 15 • shifted from the top location to the lower location (close to the roof, flow deviates to the left). Ten years of wind speed and direction analysis are shown in Figure  2. The wind potential is analysed using data from NCDC. This analysis shows that about 90% of wind potential comes from the west, mainly during the day (not during the night). In order to achieve specific local results, wind measurement equipment was installed near the parapet of the building, including two cup anemometers, one wind vane, and one sonic anemometer, as illustrated in Figure  3  (photograph on the left is facing the dominant winds from the west). The wind measurement data at the top and bottom cap anemometers have been analysed in Section 5. 2 and then compared with LES results at the same location.
• Flow is twisted: horizontal direction is 15° shifted from the top location to the lower location (close to the roof, flow deviates to the left). All the local measurements have been performed by METEODYN (http://www. meteodyn. com/en), and the meteorological data have also been gathered by METEODYN during the SWIP project (http://swipproject. eu/). All the local measurements have been performed by METEODYN (http://www. meteodyn. com/en), and the meteorological data have also been gathered by METEODYN during the SWIP project (http://swipproject. eu/).
Numerical Details The large eddy simulation of the computational domain was performed using the commercial CFD package ANSYS Fluent. The convection terms in the momentum equations were discretized using a bounded central-differencing scheme, which has an accuracy of the order between one and two, together with a second-order technique for the pressure term. Also, a second-order implicit time integration technique is used. This technique has been shown to be capable of providing more stable solutions for unsteady problems  [26]. Time steps of 0. 01 s were used during the running of the LES. This time step resulted in local Courant numbers in the domain, which are less than 2. 4, and these values for Courant number were small enough to generate stable numerical results. The coupling between the velocity and pressure was through the non-iterative pressure implicit scheme with the splitting of operators, i.
e. , PISO, which has been widely utilised in similar research  [14, 20] , and a bounded central difference was used to discretizing the momentum convective terms which prevent unphysical oscillations  [27]. In order to start the LES, the solution are initialized from the steady-state RANS results. In this work, a realizable k-ε turbulence model is used for the RANS calculations, and LES simulations were started after converged RANS results were obtained. The sample data were captured after 8000 time steps of running an initialized LES solution. This is equivalent to 2. 26 mean flow residence time, T m f r (T m f r = L s U h , where L s is the streamwise dimension of the computational domain). The sample data were captured every 1000 time steps for a total time of 480 s, 13. 35 T m f r. The computation was performed using the high performance computing facilities (HPC) of the University of Sheffield.
Inflow Generation The Smirnov random flow generation method (RFG) is utilized to initialize velocity turbulence fields in the domain  [12]. This technique was incorporated in the Fluent software, defined as the Spectral Synthesizer  [35]. The most important advantage of the Smirnov method over other inflow generation methods is that it is divergent-free  [4, 35]. Also, the technique is capable of generating inhomogeneous and anisotropic turbulence. The velocity fluctuations were introduced to the domain through a series of Fourier harmonics, and the modelled turbulence spectrum is represented by: E(k) = 16 2 π 0. 5 k 4 exp -2k -2 (4) where k is the wave number. The Smirnov method receives the correlation tensor of the original flow field and information on the integral length scale of the turbulence as inputs.
Energy Spectrum Figure  10  shows the wind history at two points on the top of the building. The points are illustrated in Section 2 and also shown in Figure  3. The data from the measurements are recorded at a frequency of 1. 0 Hz for 2 h, and the frequency of sampling for LES is 0. 1 kHz for 400 s. Simulated time series exhibit variations much faster than the measurements, and this is due to two main reasons. The first one concerns the sampling frequencies, which differ by a factor of 10 so that the simulated data can contain higher frequencies than the measurement data. The second reason comes from the reduction in the turbulent length scale, which is much smaller in the simulation (7 m instead of ~60 m). Nevertheless, the order of magnitude of the mean wind speed is similar from the simulation to the measurement: most of all, the reduction from Point 1 to Point 2 is correctly captured in the simulation results.
Conclusions In this paper, the reliability of a reduced integral length scale for the inflow generation of LES was studied. During this investigation, we showed that there was a meaningful relationship between the simulation and measurement results. We performed this study on a practical case, which was a low-rise building and its two simplified neighbouring buildings. LES was implemented in ANSYS/Fluent using spectral synthesizer inflow generation. Also, a combination of the Werner-Wengle wall function for the near wall treatment and the dynamic Smagorinsky Lily method for the sub-grid scale modelling was used. We carried out field measurements at the location of interest. The local wind data were measured using two cup anemometers on the roof of the building, and the wind data were analysed to obtain the real energy spectrum and length scale of the location.
Plant material and growth conditions A subset of the spring barley landrace collection maintained at the Federal ex-situ GenBank for Agricultural and Horticultural Crop Species at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Germany, was used for genetic dissection of the RSA and shoot traits under osmotic stress and control conditions (Supplementary Table  S1 ). The collection was established by several rounds of single seed descend and harbours rich diversity  (Pasam et al. , 2014; Wabila et al. , 2019). The sub-panel used in this study, comprises 230 two-row spring barley landraces covering three geographical regions (Europe, Asia, and Africa) and originating from 31 countries. Of them, 190 develop hulled-grains and 40 naked-grains. Grains were sterilized in 70% ethanol for 30 seconds and washed three times with sterile distilled water.
Root system and biomass phenotyping At the end of the growing period (21 days; 14 days-old plantlets), the relative water content (RWC) was determined for each plant as described by  Barrs and Weatherley (1962). The root system of each plantlet was scanned using the ImageScanner III LabScan 6. 0 with a 600-dpi resolution and saved as tiff-formatted picture. Shoot and root fresh weight (SFW and RFW), fresh root/ shoot ratio (RSR), and total biomass (TotalBio), were determined for each plant. Furthermore, roots and shoots were dried at 70°C for 48h, and shoot and root dry weights (SDW and RDW) were measured. Images of the roots were analysed using an ImageJ macro to convert the root system in white pixels on a black pixel background. Those images were analysed with GiA Roots software  (Galkovskyi et al.
Data analysis Phenotypic analysis was performed in R software  (R Core Team, 2021). The outlier test was performed according to Tukey's method  (Anscombe and Tukey, 1963). Outliers, within and across the experiments were removed. Genotypes with >20% missing phenotypic data were not considered for further analysis. Heritability (H 2 ) of each trait was estimated by equations (1 & 2) using the 'ASReml' R package  (Gilmour et al. , 1995). Repeatability values (R) were estimated by the equations (2 & 3). Best linear unbiased estimates (BLUEs) were calculated based on the linear model described by the equation (  4 ) considering the experiments and genotype X experiment (GxE) interactions for each trait applying the Restricted Maximum Likelihood (REML) method. s 2 G , s 2 GxE , and s 2 e denote the variance components of the genotype, the genotype x experiment interaction, and the residuals, respectively.
ebi. ac. uk/eva/?eva-study= PRJEB59438). Due to discrepancies in marker positions between BARLEX and the Germinate Barley SNP Platform (https:// ics. hutton. ac. uk/50k/) that could not be resolved, 732 markers could not be uploaded to EVA and are given as supplemental genomic dataset (Supplementary SNP Dataset). Population structure was estimated by a principal component analysis (PCA) using singular value decomposition. The analysis was performed on the centered genotype data using the PCA function of the 'pcaMethods' R package  (Stacklies et al. , 2007). The first ten principal components were calculated. A phylogenetic tree was built based on the neighbour-joining algorithm  (Saitou and Nei, 1987)  using the SNP marker data and the nj function of the 'ape' R package  (Paradis et al. , 2004; Paradis and Schliep, 2019).
This strategy has commonly been used to determine whether a model effectively controls false positives and false negatives  (Stich et al. , 2008; Stich and Melchinger, 2009; Riedelsheimer et al. , 2012; Würschum et al. , 2012; Kristensen et al. , 2018). Subsequently, p-values of marker-trait-associations (MTAs) were adjusted for multiple comparisons using FDR  (Benjamini and Hochberg, 1995). MTAs with adjusted p-value (FDR)< 0. 05 were considered as statistically significant and< 0. 1 as potentially interesting. Both were kept for further analyses. GWAS results were visualized by Manhattan plots generated using the 'rMVP' package  (Yin et al. , 2021). The phenotypic variance explained (PVE%) by a significant marker was estimated in R. The sum of squares (SS) and residuals (e) were extracted from the ANOVA fitted with a linear model incorporating the phenotypic values and all markers with p-value FDR < 0.
Linkage disequilibrium (LD) analysis and nomination of candidate genes Pairwise linkage disequilibrium (LD) was analysed for each chromosome in R using the 'LDheatmap' package  (Shin et al. , 2006). The decay was calculated in R for all chromosomes separately  (Hill and Weir, 1988; Remington et al. , 2001; Marroni et al. , 2011). For comparing associations detected with different methods, MTAs were grouped in LD blocks if directly neighbouring markers displayed strong LD (r2 > 0. 5). All genes within such a specific LD block were considered for candidate gene identification, whereby the search intervals were extended to the left and right neighbouring marker, respectively. For significant MTAs outside of LD blocks, the region defined by the flanking markers were searched for candidate genes.
Results A collection of 230 two-row spring barley landraces was investigated to study the genetic basis of RSA of 14DAG oldseedlings grown under control and osmotic-stress conditions. Using 20 barley genotypes representative the diversity of the collection, a pilot experiment was conducted with different percentages of PEG 8000 to optimize the osmotic stress condition based on RWC and total biomass. Significant reduction in RWC and TotalBio were observed using 15% and 25% of PEG for the first and second week, respectively (data not provided). All 230 barley landraces were grown and phenotyped in CYG ™ germination pouches and a total number of 16 root and shoot traits were analysed for plants grown both in control conditions and osmotic stress. RWC (p-value = 2. 26E-46) and TotalBio (p-value = 1.
Consensus between users on the perceived fatigue We are interested in this section in the level of agreement between participants in terms of their perceived fatigue during the sequential pointing task in a virtual environment. In particular, we analyzed the perceived right arm fatigue and left arm fatigue. To this end, we report and analyze 4480 (2 (right and left arm fatigue) × 2240 = 4 (hands synchronicity) × 7 (directions) × 8 (time periods) × 10 (participants)) individual ratings of absolute fatigue collected from 10 participants. We ran a Shapiro-Wilk normality test  (Shapiro and Wilk, 1965 ) for the measures of left arm and right arm fatigue which showed that the data was not normal (. 66 <  <. 95 and  <. 0001). Thus, we used an aligned-rank transform (with ARTool  (Kay and Wobbrock, 2020; Wobbrock et al.
The AGATA collaboration has a long-standing leadership in the development of front-end electronics for high resolution γ -ray spectroscopy using large volume high purity germanium detectors. For two decades, the AGATA collaboration has been developing state-of-the-art digital electronics processing with high resolution sampling ADC, high-speed signal transfer and fast readout to a high throughput computing (HTC) farm for on-line pulse shape analysis. The collaboration is presently addressing the next challenge of equipping a 4π array with more than 6000 channels in high resolution mode, generating approximately 10 MHz of total trigger requests, coupled to a large variety of complementary instruments. A next generation of front-end electronics, presently under design, is based on industrial products (System on Module FPGA's), has higher integration and lower power consumption.
The PACE-CAP pre-processing board, composed of a motherboard and a SOM module with the pre-processing FPGA, is located near the sampling boards inside the same enclosure. The part of the PACE-CAP motherboard receiving the sampled data includes the hardware of the time multiplexing, a new concept described in later sections, that optimises the use of the transceivers but requires a particular de-serialisation (SERDES)  [12]  in the pre-processing SOM. The energy deposited in the segment and core contacts is determined already in the newly designed pre-processing board called PACE-CAP, since it is advisable to use as large integration windows (in the Moving Window Deconvolution (MWD)  [13]  algorithm used by AGATA), as the counting rate allows, to maximise the resolution, to account for possible ballistic deficits in the charge collection.
Generally windows from 10 (High resolution mode) down to 2. 5 µs (high counting rate mode) are being used, to be compared with the 1 µs length traces that are transfer to the PSA stage. The time stamp distribution will remain initially the GTS  [6]  and will be upgraded, by mid phase 2, to a new system, called SMART (SFP connectivity and MicroTCA for Advanced Remote Trigger), based also on System-on-Module (SOM) modules and the μ-TCA standard supported in the long term by the community ( by CNRS). This will reduce risk, R&D time and costs. Another new fundamental aspect is the change in readout dispatching. The Phase 1 and Phase 0 electronics used a point-to-point data link based on the PCIe standard  [7]  between the pre-processing board and the workstation, in phase 2, one or more pre-processing boards can distribute the readout to one workstation via a fast Ethernet standard protocol like UDP.
The result is plotted in Fig. 4. With a fluctuation equal to 1. 73 LSB r. m. s. the new ADC is demonstrated to be compliant with the strict noise specifications of AGATA. In Fig. 5, 1  MeV equivalent signals acquired with the new board prototypes are plotted. The eye diagram of the 2 Gb/s serialised signal seen at the ADC output is shown in Fig. 6. The pre-processing board PACE-CAP functions as the motherboard of the system, housing the pre-processing FPGA board and the STARE board. It connects to the GTS system and manages slow control, clock distribution, and is responsible for all timing synchronization. Moreover, it handles the 4 to 1 channel aggregation procedure and manages the readout to STARE via four 10 Gbps Aurora links. This section will provide a brief overview of the PACE-CAP board's hardware, along with a description of the System on a Module (SOM) FPGA firmware and its performance.
The concept is presented in Fig. 7. The mezzanine was done as an independent device that is initialised automatically through a small form, low cost FPGA. The data arriving from each of the four Digi-Opt12 digitiser boards was sent through a Samtec Firefly 12-line connector  [20] , that allows flexibility on selecting optical or copper cables. After successful test of this proof-of-concept in 2019, the same hardware architecture was implemented, in the receiver section, in the PACE-CAP motherboard.
These two FPGAs have a power supply independent of the rest of the system. In addition, the board incorporates two Ethernet connections, one for the FPGA fabric, devoted to the IPbus control in case the STARE board is not present. The second one is for the multi-processor system (MOSOC) present in the Zynq Ultrascale+ FPGA and is used for remote controlling the MPSOC and to perform the GTS alignment software procedure. Furthermore, there is an SD socket for the MPSOC programming, a USB-C for debugging purposes and the JTAG/UART socket. The board is manufactured in 12 layers and the design included a thorough simulation of the high-speed differential links. Figure  8  shows the PACE board layout and Fig. 9  show the picture of the pre-production version of the board.
(iii) Spectra module: its task is accumulating the energy spectra of every channel since the last reset, called by the user via the slow control. The readout of any spectra, in a single ADF packet, can be requested via the slow control. (iv) The monitor module: This module stores a full memory of the internal selected data. It can be sent to the readout engine in an extended multiple packet protocol. In order to perform the readout, four readout engines have been created in the firmware, each one is connected, via one of the 10 Gbps Aurora links, to STARE, that is in charge of sending the data packages via Ethernet. These readout engines can select which of the memory blocks are reading and combine the packets into the readout data flow. As mentioned before, all packages are formatted in ADF or multiple package ADF based.
Performance figures To date, the PACE board is at its last stage of development and the pre-production boards have been produced and tested at INFN-LNL over the full readout chain using an AGATA detector. In Fig. 11 , the very first 35 µs (≈8 kB) long trace, captured by the Long Trace Memory module and readout through Ethernet, is shown. The measured decay time from the pre-amplifier matches the expected value. The total firmware utilization is less than 50% of the device, however, a higher percentage of memory resources are allocated (80%). The STARE board is a FMC mezzanine mounted on the PACE-CAP pre-processing board and designed to do the readout using fast Ethernet protocol at high bandwidth. The STARE mezzanine compiles the generated data from the PACE-CAP board to a standard UDP Ethernet data format at 10 Gbps.
The board is designed latency-free between the incoming data and the outgoing data toward the data acquisition workstations. In order to improve the counting rate capabilities of AGATA, or the possibility of a pure software trigger, or improving its sensitivity by using more complex algorithm to process the Pulse Shape Analysis, the STARE board should have the possibility of multiple data lanes configuration. Such requirements could not be found on the shelf at the time of the start of the upgrade and triggered a specific development described in this section.
STARE hardware design This is the result of a long research and development program started in 2016  [27]. The processed data from the PACE-CAP are sent to the STARE over a Xilinx AXI stream AURORA interface, using up to 4 independent 10 Gbps transceiver lines. Each line is processed independently inside the STARE and the data is formatted to be transferred through 4 independent 10 Gbps transceivers to servers or computer farm using UDP protocol. Each of the 4 UDP channels can send data to several different servers. The STARE module is equipped with external memory to allow re-transmission of any packages not correctly received by the HTC server farm. Figure  12  shows the STARE conceptual design and functionality.
STARE prototype board design The STARE board prototype has extra functions which makes the STARE development and debugging easier. These functions are the Power supply management and the Jtag interfaces. These will not be used in the production board. The power supply management on the STARE board is very complex due to the power up sequence for the SOM board and the SFP+ transceivers. Figure  14  shows the block diagram of the STARE power supply management. The external power supply input has active multiplexers to switch automatically from either the external input or the FMC input. The STARE can be used with or without any carrier board for development necessity. Moreover, the JTAG programming will use different modes of connections (FMC JTAG, simple Xilinx HX Programmer or a JTAG SMT2 module).