dataesr/scientific-paragraphs-categorization huggingface dataset:
5 years of operation) would be challenging when using files but is inconsequential when using database systems. Once a suitable database system is established, it can easily be extended to allow for the connection of new sensors and device types. This, in turn, will lead to increased acceptance of the system over time and ultimately allow for more professional and sustainable data handling and create a platform for the accumulation of knowledge and experience with respect to monitoring campaigns. although the pipe diameter is only 1. 8 m, the water level could go up to 3. 8 m and become the overland flow of a creek. The HACH sensor was installed towards the side of the pipe due to the high sediment accumulation, which always buries the sensor, thereby affecting the level and velocity readings.
• An alarm system which sends SMS to the corresponding person when improper stormwater discharge is detected. In the current website of the EoDC project (see Figure  11. 18), catchment boundaries, drainage network and sensor locations are marked on top of the Google Map. By clicking each site, live water depth, temperature and EC data are directly plotted for users to check the trend over last 48 hours. A raw-data (in csv format) download link is provided at the bottom of the pop-up window. The cleaned data are only accessible through the website's backstage database.
THE UWO -A FIELD LABORATORY FOR DISTRIBUTED REAL-TIME MONITORING WITH LOW-POWER SENSOR AND DATA COMMUNICATION TECHNOLOGY, FEHRALTORF, SWITZERLAND 11. 8. 1 Scope and objectives The Urban Water Observatory (UWO, http://www. eawag. ch/uwo, accessed 28 April 2021) is a dedicated long-term monitoring initiative aiming at (i) collecting a consistent dataset on water and matter fluxes in an urban area at very high spatiotemporal resolution (1 sensor / ha; 5-minute recording interval), and (ii) testing and developing a low-power sensor and data communication technologies for efficient environmental and infrastructure monitoring. The UWO field laboratory was commissioned in early 2016, and it is expected to run at least until the end 2021. At the time of writing (i. e. 2020), the UWO sensor network consisted of more than 120 different sensors, deployed across various compartments of the urban water cycle.
Currently, the following sensors are integrated into our LPWSN: 34 ultrasonic level sensors (MaxBotix MB7389, MB7369, MB7386), 10 dielectric conductivity sensors (Decagon, 5TM/5TE), 2 pressure gauges (Keller, 36 XKY), 15 multi-parameter probes for groundwater quantity and quality monitoring (STS, DL/N 70; Keller, 36XiW-CTD,DCX-22-CTD), 5 humidity sensors, 26 dual in-sewer temperature sensors (DS18B20), 1 very low-cost pluviometer (Davis), and 7 low-power tipping bucket rain gauges (R. M. Young, Model 52203). Existing data management solutions are generally designed with a very specific work-and data-flow in mindwhich is most unlikely to correspond exactly with your needs. So even while the underlying database platforms are commonly well-established, the full sensor data warehouse needs careful technical specification, a design concept and individual configuration.
As soon as a new raw file arrives from a sensor the Datapool applies the corresponding (conversion) script automatically and imports the data into a relational database (PostgreSQL). Finally, data users can either directly query data and meta-data, use packages (available in R, Python, Julia, Matlab) with a basic set of query functions, or use a web interface to retrieve data (http://www. uwo-opendata. eawag. ch, accessed 28 April 2021), the Datapool application carries out consistency checks, provides error logs, it is designed to guarantee scalability, and it manages read/write-permission of the different user types. Incremental backups of the underlying SQL database are regularly written; warehouse administration is reduced to a minimum. While the Datapool (Figure  11. 24) development was initially driven by UWO requirements only, over time it was adapted to serve a number of other data collection initiatives.
Acquisition and analysis of the EPG waveforms were carried out with EPG Stylet + software (EPG Systems, www. epgsy stems. eu). Relevant aphid behavior EPG parameters were calculated with EPG-Calc 6. 1. 7 software  32. The feeding behavior of 29 aphids for the P condition and 26 aphids for the PL condition was recorded. Aphids were placed on the abaxial part of top two fully expanded leaves. Eight different EPG parameters were used to assess the feeding behavior of M. persicae with or without associated leek. The total duration of probing (s_Pr) and the number or probes (n_Pr) corresponding to the general probing behavior of the aphid in the leaf were evaluated. We also evaluated the total duration of pathway phase (s_C) corresponding to the insertion of aphid's stylets in mesophyll tissues, the total duration of phloem salivation (s_E1) and the time to first phloem phase (t_1stE2), along with the total duration of sustained phloem phase (s_SE2) respectively corresponding to the time before the first ingestion of phloem sap and the duration of phloem sap ingestion that was longer than 15 min.
Statistical analysis. Statistical analyses of data concerning the retention/dispersion bioassays, EPG tests, and biomass and total fecundity measures were performed using a non-parametric Mann-Whitney U-test for independent samples as they did not follow a normal distribution (Shapiro test) and homoscedasticity (Fisher test) was not respected. Choice test responses were analyzed using a Chi-square test. Daily fecundity data were analyzed using a general linear model (GLM) using a Poisson distribution. When a significant effect of one of the main factors was detected or when an interaction between factors was significant, a pairwise comparison using least-squares means (package R: "lsmeans") (p-value adjustment with Tukey method) was used at the 0. 05 significance level to test for differences between treatments.
Survival was modelled using the Cox proportional hazards (CPH) model and cases where the given event did not occur were treated as censored. The assumption of validity of proportional hazards was checked using the functions "coxph" and "cox. zph" (package R: "survival"). When a significant effect of one of the main factors was detected or when an interaction between factors was significant, a pairwise comparison using Estimated Marginal means (package R: "emmeans") (p-value adjustment with Tukey method) was used at the 0. 05 significance level to test for differences between treatments. R software version 3. 6. 2  34 was used for all statistical analyses. Ethical approval. The article does not contain any studies with human participants or vertebrate animals.
Acknowledgments This work was supported by the project  ZERO-PH(F)YTO F&L(G)  financed by the  INTERREG initiative: Programme 2019-2022 INTERREG V-A Belgium-France (France-Wallonie-Vlaanderen ). We thank  Françoise Dubois  for her help in EPG and physiological bioassays. We also thank  Quentin Chesnais ,  Thomas Denoirjean  and  Pedro Poli  for their assistance in the statistical analysis of the data and  Andrew Roots  for the critical reading of the English.
As in the case of AM-CBD, the AM-AM g(r), reported in Figure  4C , shows that calendering induces a locally ordered structure in terms of AM particles location. Moreover, the x coordinates of the calendered structures principal peaks are slightly higher than the sum of two AM particle radii (Figure  1 ), which would suggest that each peak is associated to a certain arrangement of two AM particle types in the space, as discussed in more detail in the Supporting Information, Section S5. The impact of calendering on the electrolyte effective properties within the porous electrode can be captured by its tortuosity factor. [63]  The tortuosity factors here reported were calculated by solving the first Fick's law and applying the MacMullin equation  [64]  through GeoDict [65] computational software for each Cartesian direction (x, y, z).
Computational Methods The CGMD simulations were performed in an NPT environment at 298 K and 1 atm, while the DEM simulations were performed in an NVE environment where the pressure was controlled by the moving plane in order to mimic the calender rolls. All the details about the initial structure generation can be found in the Supporting information (Section S2) of Ref. [18]. The density of compact CBD particles (without consideration of the nanoporosity, i. e. 1. 81 g cm -3 ) was calculated by the average of the experimental density of carbon black and PVdF. The CGMD simulations were performed by using periodic boundary conditions on x, y and z through the open source computational software LAMMPS. [75] The slurry and dried electrode simulations (396 AM particles and 9463 CBD particles) took respectively ~5 and ~33 hours by using one node (128 GB of RAM) composed of 2 processors (Intel® Xeon® CPU E5-2680 v4 @ 2.
40GHz, 14 cores) on the MatriCs platform (Université de Picardie-Jules Verne). The initial structure (prior the slurry equilibration) was generated to match the experimental composition and AM particles size distribution by locating randomly the AM and CBD particles in a simulation box big enough (200 × 200 × 1200 µm, x × y × z) in order to avoid particles overlap. All the details about the random structure generation can be found in the Supporting Information of Ref. [18]. The slurry and dried electrode density were calculated dividing the total mass of the particles by the simulation box volume at the end of the simulation. The DEM simulations were carried out by using the open source computational software LIGGGHTS. [76] The timestep used for the calendering simulation is equal to 0. 1 ns, while the number of timesteps varied between 13 ×10 7 and 20 ×10 7 (as a function of the level of compression desired) by moving the upper plane at a speed of 0.
002 m s -1. The same number of timesteps and speed (but in the opposite direction) was used for the decompression step. The plane speed was selected in order to minimize the computational cost and maximize the numerical stability of the simulations. Several tests were performed to verify the effect of lowering the plane speed, showing that its effect is negligible, as discussed in the Supporting Information, Section S6. The planes were initially located at a distance of few micrometers respect the limit of the CGMD structure to avoid overlapping between the planes and the particles. The applied calendered pressure was calculated as the force applied at the electrode upper plane at the maximal ImportGeo-Vol package was used to import the images into native. gdt files, by properly thresholding them in order to obtain well-differentiated pore, AM, and CBD phases.
Lastly, the DiffuDict package was used to simulate the diffusion experiment, by assigning effective diffusivities to the pore (7. 5 ×10 -11 m 2 s -1 ) and CBD (2. 46 ×10 -11 m 2 s -1 ) phases, as explained in Section 3. The un-calendered and calendered CGMD/DEM structures were meshed using the in house algorithm INNOV, fully explained in our previous article. [67]  In short, from the coordinates of the particles centers, 2D slices were generated while keeping the periodic boundary conditions. Then, a 12 µm Celgard separator (coming from a SEM image of 13 × 7 × 5 µm 3 )  [68]  was added. To fit the dimensions of the electrodes, it was duplicated in every direction and cropped to ensure the desired dimensions. On top of this separator, a 5 µm thick homogeneous Li foil negative electrode was added.
Finally, these slices were fed into the INNOV voxelization-based code to generate the multiphase volumetric mesh. The electrodes dimensions were 25. 8 × 25. 8 × z µm 3 , with z equal to 165. 0, 141. 6, 133. 8 µm with increasing calendering pressure. Each mesh is composed of more than 800,000 elements. Once imported into COMSOL Multiphysics ® , an electrochemical model was applied to simulate a single discharge at 1C starting from a fully charged battery. The model is identical to the one reported in our previous study,  [20]  except for the AM electronic conductivity, which was decreased down to 5 ×10 -3 S m -1 , similarly to values found experimentally. [77]  The CBD was treated as partially open, meaning the transport of Li + within that phase was allowed with an effective diffusion coefficient and no intercalation at the CBD-electrolyte interface was permitted.
All CBD effective transport properties were corrected with the Bruggeman relation taking into account an inner porosity of 47%. The Li solid diffusion coefficient within the NMC was fixed at 2 ×10 -15 m 2 s -1. The discharge simulations took between 5 and 9 hours on a laboratory server with 256 GB of RAM. The EIS tests were also performed in COMSOL Multiphysics environment. The Batteries & Fuel Cells and the Transport of Diluted species modules were implemented for the calculations. The impedance was measured at seven frequencies per decade, ranging from 1 to 10 7 Hz and applying a 10 mV sinusoidal perturbation. All the cell parameters and constants used for the EIS simulations are listed in our recent publication. [21]  To keep the consistency between discharge and EIS simulations, input parameter such as porosity of CBD was adapted to the ones of the discharge simulations.
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation programme  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges  Institut Universitaire de France  for the support. CGMD and DEM results were achieved thanks to the  MatriCS HPC  platform from  Université de Picardie-Jules Verne , particularly using the ARTISTIC project-dedicated computational nodes. The authors acknowledge  Dr. Garima Shukla  from  LRCS-Amiens (France)  for providing the experimental manufacturing scheme used as baseline for the graphical abstract.
We manually placed on each track 25 landmarks: fixed points representing the geometry of an object. We also automatically placed 130 semi-landmarks, landmark allowed to move on the surface, per track on only the 3D models. Geometric morphometrics allowed the measurement of shape variation between tracks, while linear discriminant analysis (LDA) with jack-knife prediction enabled track discrimination using the information from their size and shape. We tested a total of 82 combinations of features in terms of recording medium, landmarks configuration, extracted information and template used. For foot position identification, the best combination correctly identified 98. 2% of the tracks. Regarding those results, we also ran an identification algorithm on a dataset containing only one kind of foot position to highlight the differences and mimic an algorithm identifying the foot position first and then an individual factor (here, sex and identity).
This superimposition projects all the landmark coordinates into a non-linear space. It uses a least-squares superposition method to minimize the distance between all homologous landmarks in the sample and thus, to isolate the shape of the studied objects  [23]. The new coordinates are known as Procrustes coordinates  [21]. This process eliminates the effects of size, orientation and position using an iterative algorithm in order to keep only the true differences between tracks shapes  [21]. GPA creates a consensus object based on the mean position of the superimposed landmarks made out of the entire dataset. Shape variations between the objects are studied in relation to this consensus. In addition to shape, size is also analyzed by calculating the centroid size of each track. It is mathematically traduced by the square root of the sum of the squared distances to the centroid of the object and corresponds to all the information available on the size  [20].
3D Approach All 5 photos were used to reconstruct 3D models of each track using the Agisoft Metashape photogrammetric suite (Agisoft LLC, St. Petersburg, Russia). We used the same reconstruction parameters as in  [17]  except for the number of key points and tie points (respectively 10,000 and 2000 points) and the reconstruction quality for the dense cloud, which was set on high quality, representing an approximate mean ground sampling distance above 0. 0008 mm per pixel. The 3D models were scaled and optimized using 5 targets automatically located inside the images by the software. These targets were printed on a perpendicular ruler placed next to the track (Figure  1B ). Among the 3D models, the tracks were manually segmented from the rest of the model using CloudCompare software (www. cloudcompare.
Landmarking On each track, 25 landmarks were manually placed on the photos (2D approach) and 3D models according to the guidelines from Jewell et al. (Figure  1D )  [16]. The fixed landmark fixed points intended to represent the geometry of the track were displaced at the vertex of the curves of each pads of each track. We used ImageJ for the photos and the Geomorph R package for the 3D models  [25]. In addition, surface-sliders (i. e. , unfixed landmarks sliding on the model surface) were automatically positioned on the surfaces of each 3D models with the Geomorph package (Figure  1D ). In total, we placed 150 surfacesliders.
Generalized Procrustes Analyses Generalized procrustes analyses (GPA) aim to minimize the shape differences between tracks by superimpose the coordinates of the landmark using the least-square distance. It allows removing effects of position, size and rotation between tracks. We performed GPA using the Geomorph package on (i) the entire dataset and (ii) reduced datasets based on foot positions (right hind (RH), left hind (LH), right front (RF), left front (LF)) to reduce the variability induced by different foot positions. This partition allowed us to accentuating the variability caused by sex or identity of the cheetah and therefore enable us to identify these factors more efficiently (more details in Sectopn 2. 6, Accuracy Analysis section). GPA allowed us to extract shape (Procrustes coordinates) and size (centroid size) information from the landmark positions.
Template Consideration Geomorph allows the automatic placement of surface-sliders using a template based on one of the studied objects. In order to measure the influence of the chosen template, we tested 2 situations: the use of a single template for all tracks and the use of different templates per foot position. To designate the template, we used the mathematically closest track to the consensus from the GPA with 3D fixed landmark as a template using the find-MeanSpec function of the Geomorph R package. The consensus template allowed us to maximize the differences between tracks in a dataset.
Principal Component Analysis We ran a principal component analysis (PCA) on the Procrustes coordinates for each recording medium (2D or 3D), landmark configuration (fixed only or fixed with surfaceslider), datasets used (entire track set or foot position divided sets) and, for the surfaceslider modality the template used (one common template or one template per divided dataset). The results of 19 methodological schemes are described in Figure  2. In the remainder of the paper, we call one set of each modality a combination corresponding to any possible path in Figure  2.
Accuracy Analysis We calculated the total prediction accuracy from the LDA for every possible combination using shape or form information (Figure  2 ). Due to the different geometry of the track due to foot position, we believe that an efficient algorithm would need to differentiate the foot position first, and then more precise individual features. This would allow enhancing the contrast related to cheetah's sex or identity within the dataset and therefore enable to identify these factors more efficiently. In order to mimic such an algorithm, we assessed different identification "steps": the first step uses the data from the GPA and its LDA on the entire dataset to predict the foot position of the track (foot position prediction (FPp)). The first identification is com-bined with the second "step": predicting mean accuracy for sex or identity using the results from LDA datasets divided according to foot position (Figure  3 ).
Results We collected 669 tracks corresponding to 18-24 tracks per foot (Table  1 ). After reconstructing the 3D models and manually segmenting them, the landmarks were positioned on the 3D models. A total of 16,725 fixed landmarks (25 per model) and 100,350 surfacesliders (150 per model) were placed on all 3D models (n = 669). For the 2D approach, after rescaling, 16,725 landmarks were positioned in total (25 per photo). All data extracted from the pictures and 3D models are available by contacting corresponding author or Wildlife 3D Tracking (https://www. wildlife3dtracking. org/, (accessed on 1 March 2021)).
Statistical Analysis Results For every combination, the first two components of the PCA explained between 43. 1% and 11. 8% and between 15. 5% and 8. 6% of the variation, respectively. The first PC from combinations using the entire dataset accounted for the most variation (between 43. 1% and 35. 0%) compared to combinations using only the tracks from a specific foot position (between 20. 4% and 11. 8%). Figures  4  and 5  show the PCA (PC1 and PC2) in 3D with form information colorized according to a different factor for each figure. This provides a clear visualization of the differences between points. Figure  4  plots every sampled track and Figure  5  only the right front tracks, to improve visualization of those differences. Figure  4  clearly shows four clusters of points (n = 669) corresponding to the four foot positions.
Accuracy Analysis Results The maximum prediction accuracy for LDA was assessed for each combination. The accuracy percentage corresponded to the best accuracy obtained with the minimum PCs implemented in LDA. The results show that foot position prediction accuracy was better in 3D than in 2D, with 98. 2 and 91. 6% of correct identification, respectively (Table  2 ). For sex and identity factors, the combinations using all tracks were less accurate than those using datasets divided according to foot position. The best total accuracy for sex prediction (94. 8%) is the combination of 3D fixed landmarks with surface-sliders issued from one common template and derived from a form study (Figure  6 ), while the best total accuracy for identity prediction (93. 7%) was the same combination using a different template for each foot position.
Discussion During this experiment, we achieved the identification of individual features of adult cheetahs using different combinations of modalities (Figure  2 ). Firstly, differentiation of each foot was successfully accomplished. For 3D, about 98% of positions were correctly discriminated by LDA over the entire dataset. Such a high rate makes it possible to consider using a second identification step based on datasets divided by the previously found foot positions. Predictions on sex and identity remained imprecise for combinations using the entire track set (Figure  6 ). This is certainly due to the difference in morphology between tracks. Two factors are involved: the foot position and the size of the pads, which depends on right-left symmetry and the length of the paw impression on the substrate.
The segmentation, even colored by depth, is then subjected to operator bias. In our case, we reduced the possible influence of this bias by working with a single operator. To overcome the variability induced by the foot positions discussed above, we separated the dataset into four subsets depending on foot positions. The accuracy increased, because these datasets highlighted the variability between tracks from the same foot of different individuals, thus the variability related to sex and identity. A sex sub-division of these datasets was performed, but the identity identification results decreased. This may be due to the small number of cheetahs remaining in these datasets. Furthermore, we can observe that the best results were obtained with fixed landmarks coupled with surfacesliders (Figure  6 ).