dataesr/scientific-paragraphs-categorization huggingface dataset:
We use recent data collected during the Precipitation over Land And The Southern Ocean (PLATO) campaign at Davis. This includes scanning polarimetric and vertically pointing Doppler radar measurements at different frequencies, as well as radiosounding data and a Raman lidar. We also make use of simulations from the Weather Research and Forecasting (WRF) model. We address the following questions: 1. How does the synoptic flow constrain the presence of OGWs? 2. How do foehn winds impact snowfall sublimation and the spatial distribution of precipitation during this event? 3. How do OGWs influence the snowfall microphysical evolution? This paper is structured as follows. Section 2 describes the geography of Davis and the data set. Section 3 presents the evolution of the synoptic conditions and the precipitation distribution of this case study.
, 2017)  is located about 80 km to the north-east of Davis and reach about 1,000 m a. s. l. at the end of its steepest part (visible in Figures  1  and 2 ). In January 2019 only scattered patches of sea-ice were present around Davis. At this time of the year, solar noon is at about 07:00 UTC. In this study, we focus on the data collected by an X-band Doppler dual-polarization (polarimetric) radar (hereafter MXPol), a W-band Doppler cloud profiler (hereafter, BASTA), a Raman lidar (hereafter, RMAN), and a very-high frequency wind-profiling radar (hereafter, VHF). In addition, we use radiosounding measurements (12-hourly resolution) and wind, temperature, pressure, and humidity measurements from an automatic weather station (AWS) located at Davis and managed by the Australian Bureau of Meteorology.
Wind-Profiling Radar: VHF The Davis VHF wind-profiling radar is a 55 MHz system consisting of 144-antenna main array with a one-way beamwidth of about  7 •. Doppler radial velocities are obtained in the vertical, north, and east directions (with the off-vertical beams pointing at a 14° zenith angle), cycling every 6 min  (Alexander et al. , 2017). The vertical range resolution is 500 m and data are acquired from about 1,500 m a. g. l. to the lower stratosphere. Alexander et al. (2017)  validated horizontal wind measurements from the same site (Davis) and the same VHF radar used in this study against colocated radiosoundings from February 2014 to February 2016. They showed a very good agreement with a correlation coefficient of 0. 95 and a bias of 0. 36  m s -1.
The proposed approach is applied to photovoltaic or solar sensor and to smart grids sensors. By studying the influences of these data from IOT sensors, we use the eXplainable Artificial Intelligence (XAI) to interpret the Duck Curve prediction in order to find a better way to solve the problem. This paper is organized as follows. Section 2 presents the Duck Curve problem. Then we browse in section 3 several related work to solve the duck problem. Section 4 describes the architecture of our proposed approach. Section 5 presents an application of our proposed approach to the Power PV sensors. Finally, Section 6 concludes the paper.
The second approach consists of acquiring and deploying renewable resources according to peak hours, for example, by swapping them. Vahid Amir and Mahdi Azimian  [11]  proposed a dynamic planning tool for the various green energy production resources using a two-stage stochastic programming method that combines the MATLAB genetic algorithm and the non-linear mixed integer programming model of the GAMS software. The third strategy focuses on managing water and wastewater pumping loads. It involves controlling water and wastewater pumps to operate during periods of low existing load or high solar generation, and reducing pumping load during peak hours. Anginé Zohrabian et al. [12]  mainly focused on the demand & response measures through simulations implementing the benefits of the water-energy nexus for the power grid.
Proposed approach From the literature, as presented in the previous section, it is clear that none of these approaches pay particular attention to the grid sensor network that equip these renewable energy production sites. While such sensors are essential elements to better understand what is happening locally on the network. Applying artificial intelligence tools, especially, LSTM-based Attention mechanism, to smart data provided by these sensors can help to understand what is happening at the production sites, and other reasons not necessarily related to the availability of solar energy for PV power generation for which we are not able to straighten the duck's back. This amounts to an interpretation of the prediction at the output of the Duck Curve, and this interpretation can help us to solve the problem effectively (control and save energy).
Dataset In order to validate our approach, we chose to work on a set of power IOT sensors data. The dataset describes the evolution of the environmental sensors of a location where a solar panel was installed between July 2018 and April 2019. It contains a total of six variables describing the weather conditions of the area where solar panel installed, and its produced energy produced, namely: The temperature denoted by Temp. (C) which is the temperature of the place where the solar panel is installed. The relative humidity denoted by Humid. Rel. (%) which represents the evolution of the relative humidity. The wind speed denoted by Vent (m/s), which represents the wind speed in this environment. The atmospheric pressure denoted by Pressure (mbar), which describes the atmospheric pressure of the environment.
The sunshine denoted by Ensoleillement (W/m 2 ), which provides information on the level of sunshine in the environment. The power denoted by Watts (W) refers to the power delivered by the photovoltaic panel. The dataset contains also 27553 observations taken every 15 minutes. For the purposes of our study, we have separated these data sets into 80% and 20% representing the training and test data, respectively. Table  1  gives us an overview of these data. The power PV dataset is freely available on Github 1. Figures 5 and figure  6  show us some important results after analysis on the created time series. Figure  5  (a) and 5 (b) are respectively the auto correlation and partial correlation graph after analysis over 500 lags. They show us the presence of a series even after 400 lags, and also tell us that the majority of lags are around the value 100.
Flapping-wing robot experiments For the flapping-wing robot experiments, we used a commercially available 'flapper drone'. Its design is inspired by the 'DelFly Nimble' flapping-wing robot  14. However, the flapper drone is more robust, which facilitates experiments. It is also larger and heavier than the DelFly Nimble, while staying light-weight compared to most quad rotor drones (100 g). The flapping frequency of the flapper drone is roughly 12 Hz. As explained in the main text, the flapper drone is equipped with the CurvACE 40 , a miniature artificial compound eye, which has a broad field of view (180° × 60°) and a high update rate for the optic flow measurements (200 Hz). Extended Data Fig. 5  shows the experimental setup for the flapper drone, which uses the BitCraze open-source autopilot software.
We adapted the flapper drone hardware to include the CurvACE, sending its outputs (four optic flow vectors) to the BitCraze autopilot board. Extraction of ω y is done by averaging the four flow values in the y direction, and scaling it with a constant factor to encode rad s -1. We also modified the software to run an EKF based on ω y and gyro measurements in parallel to the stand- ard complementary filter, for estimating φ. By contrast to the quad rotor experiments, the outer loop control is performed by a human pilot, providing desired attitude angles and thrust commands. A basic PID controller serves as inner loop controller to reach the desired attitude angles. Again, we can switch between the estimated angle determined by the optic-flow-based EKF and by the accelerometerbased complementary filter.
Acknowledgements We thank  F. Muijres  for reading and commenting on an earlier version of this manuscript, T. (J. C. ) van Dijk for discussions on the non-linear observability analysis and  J. Hagenaars  and  A. Barberia Chueca  for their help in the filmed experiments. Moreover, we thank  S. Viollet ,  J. Diperi  and  M. Boyron  for their help in preparing the CurvACE sensor. Part of this project was financed by the  Dutch Science Foundation (NWO)  under grant number  15039.
Code availability The code to reproduce the theoretical and simulation results and analyse robotic experiments is publicly accessible at https://doi. org/10. 4121/20183399. The code to perform flight experiments with the open-source Paparazzi autopilot on the Bebop 2 drone is available at https://github. com/tudelft/paparazzi/releases/tag/v5. 17. 5_attitude_ flow. The code to perform flight experiments with the flapper drone is available at https://github. com/tudelft/crazyflie-firmware/releases/ tag/v3. 4. 0_attitude_flow.
Procedures Participants were recruited from the student subject pool of the University of Innsbruck using hroot  (Bock et al. , 2014)  and with the experiments implemented in LimeSurvey  (LimeSurvey et al. , 2020). Subjects only participated in one of the treatment conditions in a between-subjects design and could only participate once. Upon receiving the invitation, subjects were informed that this was an online experiment that would last approximately 20 minutes. Payment options were transactions via PayPal or Amazon vouchers. We started collecting data on April 4th 2020 for a total of eight consecutive weeks. For each date at which data was collected, invitations were made for three identical, simultaneously running sessions, one for each of the treatment conditions. Subjects who registered for the experiment were randomly allocated to one of the three sessions.
, 2016). The average earnings of participants were €1. 01 (sd = €1. 14) in the main treatments, €12. 15 (sd = €10. 79) in the high stakes treatments, and €1. 19 (sd = €1. 21) in the replication treatments. Participants were students at the University of Innsbruck, Tyrol (Austria). Tyrol was the region worst affected by the Covid-19 pandemic in Austria, bordering the North of Italy and the South of Germany. The region reported the first cases on February 25, 2020 and entered a lockdown of all municipalities in the region for about seven weeks on March 16, 2020. Between end of March and mid April, there were roughly 2,000 active Covid-19 cases and by the end of the data collection on May 28, 2020 there had been a cumulative of 3,546 cases in a region with roughly 750,000 inhabitants. The data collection started one week before the lock-down of Tyrol lifted, on April 14th 2020.
CAZymes involved in cellulose degradation 1] [92]  The enzymes responsible for carbohydrate degradation, modification, or creation are commonly named carbohydrate-active enzymes (CAZymes) and are indexed in the CAZy database (http://www. cazy. org/). 93 This database classifies proteins into 6 families based on their mode of action: glycoside hydrolases (GHs), carbohydrate esterases, polysaccharide lyases, glycosyltransferases, and auxiliary activities. In turn, carbohydrate-binding modules (CBMs) are noncatalytic modules of CAZymes that help target enzymes to their substrates. The classification of CAZyme families is defined based on the significant similarity of amino acid sequences that do not necessarily correspond to one function. The presence of enzymes that act on different substrates within the same family is also possible.
93 nitially, this classification concerned cellulases, the enzymes that degrade cellulose. 94 CAZymes are often multimodular, as they can contain several domains of different families. Cellulases are GHs that cut the β-1,4-D-glucose often associated with one or more CBMs, with the CBM being a noncatalytic protein or internal peptide attached to the catalytic domain. According to the CAZy database, cellulases are found in at least 16 GH families (GH5-9, GH12, GH44-45, GH48, GH51, GH74, and GH124). Cellulases are classified according to their mode of action (Figure  8 ): endoglucanases (EC 3. 2. 1. 4), which access long molecules of cellulose and cleave at a random position in the chain; exoglucanases or cellobiohydrolases (EC 3. 2. 1. 176), which cleave cellodextrins of defined sizes at the nonreducing ends and release cellobiose; and β-glucosidases (EC 3.
The carbohydrates consumed and end products are indicated by the following letters: Glu, glucose; Cel, cellobiose; Mal, maltose; Lac, lactose; Ara, arabinose; Xyl, xylose; Gal, galactose; Fru, fructose; Sac, saccharose; Raf, raffinose; Rib, Ribose; Man, mannose; AG, galacturonic acid; Mel, melibiose; A, acetate; S, succinate; F, format; L, lactate; E, ethanol; H, hydrogen. ND, not determined. Accession number Strains (collection Carbohydrates Major for rRNA 16S Cellulolytic species number) Isolated by Date From consumed products sequences Ref F. succinogenes subsp S85 (ATCC 19169) Bryant 1959 bovine Cel, Glu, Lac S, A, F AJ496032 22,44,72,73 succinogenes (I) rumen F. succinogenes subsp GC5 (ATCC 51216) Montgomery 1988 bovine Cel, Glu M62688 elongatus (II) cecum UW H4 Neumann 2017 horse feces Cel, Glu S, A, F KY463346 UW T2 Neumann 2017 tapir feces Cel, Glu S, A, F KY463367 UW P1 Neumann 2017 capybara Cel, Glu S, A, F KY463355 feces F.
Finally, and consistent with unaltered mitochondrial biogenesis, the ratio of TOMM20, a mitochondrial protein, to β-actin, a cytosolic protein, was also unaffected, reinforcing the idea that mitochondrial number is not disturbed. Moreover, the expression of mitofusin-2 (MFN2) was not altered by COX-2 expression consistent with no alteration in mitochondrial morphology (Figure  2 ). Furthermore, DFU treatment, a specific COX-2 inhibitor, also showed no changes in the expression of the proteins analyzed, confirming that COX-2 did not affect mitochondrial dynamics. sitometric analysis, the bands were scanned and band densities were quantified using Ima software. Relative band intensity of COX-1, MFN2, and TOMM20 normalized to that βpresented as means ± SD of 2 independent immunoblots.
Canonical Pathways and Protein Network Analysis Altered in hCOX-2 Tg Animals To gain insight into the biological significance of quantitative proteomic results, we performed an enrichment analysis using the Ingenuity Pathway Analysis (IPA) software. The highest ranked canonical pathways that were affected were glycolysis/gluconeogenesis, oxidative phosphorylation, and mitochondrial dysfunction (Figure  3 ). The IPA analysis also identified two protein networks that were statistically significant (p < 0. 05). Network 1 obtained the highest significant score (32; Figure  4A ). The main function of this network was associated with carbohydrate metabolism, cardiovascular disease, or tissue morphology, whereas Network 2, with a significant score of 25, was associated with cell morphology, cell function and maintenance, and cell death (Figure  4B ).
To reinforce the results obtained in the analysis of altered proteins, we performed a threshold-free functional analysis of coordinated protein responses using the SBT model  [14]. We found that the glycolysis/gluconeogenesis pathways were downregulated in h-COX-2 Tg mice. A close inspection revealed that most enzymes of the glycolysis pathway were decreased (Figure  5A, B ). Furthermore, the calcium signaling pathway was increased by the constitutive expression of COX-2 in the cardiomyocytes (Figure  5B ), while the two mitochondrial VDAC proteins were responsible for the upregulation of calcium signaling (Figure  5C ). database but not detected in the present study. The shapes of the symbols denote the molecular class of the proteins as defined in the legends. Solid lines indicate direct molecular interactions, whereas dashed lines indicate indirect molecular interactions.
Protein identification was performed using SEQUEST algorithm (Bioworks 3. 2 package, Thermo Fisher Scientific, Waltham, MA, USA). MS/MS raw files were searched against a Rat/Mouse Swissprot database supplemented with the sequence of bovine and porcine trypsin. SEQUEST results were analyzed using the probability ratio method  [37] , and discovery rates (FDR) of peptide identifications were calculated as previously described  [38]. Peptide identification and quantification were performed as previously described  [35, 39]. Statistical significance of protein abundance changes was assayed by controlling the FDR, with an FDR less than 0. 05 considered to be significant. Threshold-free analysis of coordinated protein responses was performed using the SBT model, as described  [14].
L. U. Darmstadt, Germany); β-actin (Sigma, A2066 Merck Life Science S. L. U. Darmstadt, Germany). After incubation with the corresponding horseradish peroxidase conjugated secondary antibody, blots were developed by the ECL protocol (GE Healthcare, Chalfont St Giles, UK). Protein band densities were normalized to β-actin. Densitometric analysis was carried out with Image J software and expressed in arbitrary units.
Acknowledgments: We would like to thank  Jose Antonio Enriquez Dominguez  ( CNIC, Madrid, Spain ) for his invaluable assistance and  Adela María Guarás Rubio  ( CNIC, Madrid, Spain ) for the indispensable technical help with respirasome analysis. We also thank  Fernando Garcia-Marques  for his help with Figure 5, and  Marco Trevisan-Herraz  for the development of the software package and statistical model for systems biology analysis.
In short, this system allowed independent test regimen for each baboon who was maintained in its social group, in absence of capture, and the baboons were tested at their own pace irrespective of ALDM they decided to use. The experiment was controlled by a software testing program written by JF using E-prime (Version 2. 0 professional, Psychology Software Tools, Pittsburgh, PA, USA). The voluntary participation of the subjects reduces stress levels, as inferred from the significant decrease in salivary cortisol levels as well as the frequency of stereotypies  [26].
A maximum of 5 sec was allowed to touch the screen on the first (cue display) or third (revealed) display. A failure to touch the screen during these delays aborted the trial. Aborted trials were not recorded and were represented again at the next identification of the subject. All stimuli measured 180X180 pixels. They were presented on a 760X1024 pixels black background. All the data and metadata for the experiments can be found at https://osf. io/uqxsz/. Training procedure. This procedure pursued two different goals. First, we wanted to have the baboons discovering that they had to match the stimuli considering their identity. Second, we wanted to have the baboons discovering that they could produce anticipation responses during the anticipation display. Training proceeded in 6 consecutive steps each involving blocks of 100-trials comprising 65 matching trials randomly intermixed with 35 revealed trials.
Therefore, we decided to analyze separately fast responses (RT < 250ms) from other slower responses. We ran mixed-effects regression models using the lme4 package developed in R [27, 28] and calculated p-values using lmerTest  [29]. All models contained a fixed effect of Informativeness and random effects to control for the non-independence of repeated measures with intercepts for Subjects as well as by-Subject slopes for the effect of Delay when several delays were present. We used negative binomial models when the dependent variable was the number of anticipations and gaussian models when analyzing reaction times. Non-parametric tests were used to analyze fast responses, given their small number.
8s-O-R). Blocking oligonucleotides were used to reduce plant DNA amplification  (50). Purified PCR products were pooled in equimolar amounts before sequencing on three Illumina MiSeq runs (2 Â 300-bp reads) with 10% PhiX control. Primers targeting the oomycete ITS1 region also produced "non-oomycete" reads but at a very marginal level (3%). Amplicon sequencing data analysis. Amplicon sequencing data were processed in Mothur (51) as described in Karasov et al. (25). Single-end reads were paired (make. contigs command), and paired reads with more than 5 bases overlap between the forward and reverse reads were kept. Only 100 to 600 bases long reads were retained (screen. seqs). Chimeras were checked using Uchime in Mothur with more abundant sequences as reference (chimera. uchime, abskew = 1.
9 at the 97% similarity threshold using the VSEARCH program in Mothur (cluster, dgc method). Individual sequences were taxonomically classified using the rdp classifier method (classify. seqs, consensus confidence threshold set to 80) and the greengenes database (13_8 release) for 16S rRNA data, the UNITE_public database (v12_2017) for fungal ITS1, and Pr2 (v4. 10. 0) for oomycete ITS1. The PhiX genome was included in each of the databases to improve the detection of remaining PhiX reads. Each OTU was then taxonomically classified (classify. otu, consensus confidence threshold set to 66); OTUs with unknown taxonomy at the kingdom level were removed, as were low-abundance OTUs (,50 reads, split. abund). This last step removed extreme low abundance (,0. 0001%)/low occurrence (,0. 48%) OTUs. The taxonomy of bacterial OTUs of interest was further verified using the silva database (v1.
38; SINA Aligner). This allowed us to classify bacterial OTU00004 as Massilia sp. Sample alpha-diversity analysis was conducted on OTU abundance tables using Shannon's H diversity index (estimate_richness function in phyloseq package). Data normality was checked (Shapiro-Wilk's test), and means were compared using a nonparametric multivariate test (Dunn's test, Bonferroni-corrected adjusted P value [P adj ] ,0. 05). Beta-diversity analyses were conducted on transformed [log 10 (x 1 1)] OTU relative abundance tables. Bray-Curtis dissimilarities between samples were computed and used for nonmetric multidimensional scaling ordination (NMDS, function "ordinate"; Phyloseq package). A PerMANOVA analysis on Bray-Curtis dissimilarities was conducted to identify the main factors influencing the structure of the leaf microbiome (Adonis, Vegan package, 10,000 permutations, P , 0.
The taxonomical classification of core OTUs was used to compute pairwise dissimilarities (distances) between OTUs ("daisy" function, Cluster package in R, Gower's distance) which were used for hierarchical clustering ("hclust" function, Cluster package in R). The obtained dendrogram was modified in the browser version of iTOL (v5. 5. 1)  (52). Network analysis. Bacteria, fungi and oomycete OTU tables were merged and used for correlations calculation using either the Spearman correlation coefficient in Co-Net  (53)  or the SparCC algorithm  (20) , which relies on Aitchison's log-ratio analysis and is designed to deal with compositional data with high sparsity like this data set (sparsity = 74%)  (28). OTU tables were filtered to OTUs present in at least five samples with. 10 reads per OTU (sparsity = 53%).
ACKNOWLEDGMENTS This study was supported by the  Max-Planck Gesellschaft , the  University of Tübingen , the  European Research Council (ERC)  under the  DeCoCt research program  (grant agreement  ERC-2018-COG 820124 ), the  Cluster of Excellence on Plant Sciences  ( CEPLAS; Exc 1028 ), the We thank  Libera Lo Presti  for critical reading of the manuscript. We declare there are no competing financial interests in relation to this work. All authors give their consent for publication of the manuscript, and all authors received the manuscript and had the opportunity to read it and comment. This project was initiated by S. K. , M. A. , and  E. K. S. K. , M. A. , and A. M. conducted the experiments with help from A. P. J. A. and M. M. analyzed the data, and J. A. , M. M. , and E. K. wrote the manuscript, with contributions from all authors.