dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements P. L, N. G and L. G received funding from the  European Community 's  Horizon 2020 Project  under grant agreement no. 821003  ( 4C ). The  Surface Ocean CO₂ Atlas (SOCAT)  is an international effort, endorsed by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS)  and the  Integrated Marine Biosphere Research (IMBeR) program , to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.
5e ). EVs surrounding Mac2 - and Mac2 + microglia were quantified as done previously (n = 8 microglia per group). MGnD microglia secreted roughly three times as many as EVs as homeostatic microglia (Fig. 5f ). Additionally, the amount of p-tau internalized by these microglia and their EVs was quantified (Fig. 5g-h ). Regardless of whether in WT or App NL-G-F mice brains, Mac2 + MGnD microglia significantly internalized and secreted p-tau + mE-CD9 + EVs more than Mac2 -homeostatic microglia. These data suggest that microglia-derived EVs distribute pathologic tau seeds, a phenomenon that is enhanced by the phenotypic shift of microglia into MGnD phenotype.
Several studies including ours demonstrate the beneficial effect of CSF1R inhibitor-mediated microglia depletion on halting tauopathy in different tau mouse models  [22, 30, 45]. This includes the AAV-mediated tau propagation system and P301S mice, but not in hTau mice expressing human wildtype tau or aged rTg4510 P301L tau mice, in which only partial microglial depletion was achieved (Supplementary Table  S2 )  [23, 46]. One CSF1R inhibitor, JNJ-40346527, was successful in suppressing tauopathy development in P301S tau mice and is being tested in a Phase 1 study of AD cases (NCT04121208). In this study, we utilized the App NL-G-F mouse model and CSF1R inhibitor PLX5622 to determine the effect of MGnD microglia on tau pathology development. It was noted previously that at lower doses of PLX5622 treatment, plaque-associated MGnD microglia are specifically depleted whereas homeostatic microglia are extant  [19].
Acknowledgments The authors thank  Jeremy Kwon ,  Abigail Tesfaye  and  Kimberly Soto  for their technical assistance in image analyses,  Dr. Zhi Ruan  for suggestion for NP tau characterization,  Plexxikon, Inc. for PLX5622 and control compound,  Dr. Oleg Butovsky  for P2RY12 antibody, and Neurostar for technical support for establishing the automated stereotaxic injection system.
A model forecasting ionospheric disturbances and its impact on GNSS positioning is proposed, called HAPEE (High lAtitude disturbances Positioning Error Estimator). It allows predicting ROTI index and corresponding Precise Point Positioning (PPP) error in Arctic region (i. e. latitudes > 50°). The model is forecasting for the next hour a probability of a disturbance index or PPP error to exceed a given threshold, from solar wind conditions measured at L1 Lagrange point. Or alternatively, it is forecasting a disturbance index level that is exceeded during the next hour for a given percentage of the time. The ROTI model has been derived from NMA network measurements, considering a database covering the years 2007 up to 2019. It is demonstrated that the statistical variability of the ROTI index is mainly following a lognormal distribution.
Prikryl et al. (2012 Prikryl et al. ( , 2013) )  are also using as proxy observations at the L1 point from ACE spacecraft to model phase scintillation occurrence at high latitude, from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN). They propose a method similar to the  McPherron & Siscoe (2004)  one, construed by analogy to an approach used in weather meteorology forecasting and based on atmospheric air mass concept. Lastly, deep learning approaches have also been applied in this context, as for example the work proposed by  McGranaghan et al. (2018). They have used CHAIN network scintillation measurements, Solar Wind and Geomagnetic Activity Data, and Particle Precipitation dataset to develop a predictive model for high-latitude ionospheric phase scintillation based on support vector machine (SVM) machine learning algorithm.
The Norwegian Mapping Authority (NMA) operates a national network of GNSS receivers, which is used for positioning services and various studies. Researchers at the NMA have previously studied the statistics of ionospheric disturbances and their correlation with GNSS positioning errors in  Jacobsen & Dähnn (2014) , and published case studies on the effects of space weather events on GNSS in  Jacobsen & Schäfer (2012) ,  Andalsvik & Jacobsen (2014)  and  Jacobsen & Andalsvik (2016). In this study, a global statistical approach for short term forecasting of ROTI ionospheric disturbances index and associated GNSS PPP error is proposed for high latitudes. It is based on statistical regression of large datasets. Because ionospheric disturbances at high latitude is a highly random process, a statistical approach is the preferred strategy.
Moreover, a statistical approach allows introducing the concept of relative risk, associated to the definition of a threshold level in ionospheric disturbance indices, positioning error, or other metrics. This paper is organized as follows. In Section 2, the NMA database and Solar wind data used for HAPEE model regression are presented. Section 3 presents the ROTI index forecasting model, its solar wind driver, the model regression and its analysis. In Section 4, a validation of the ROTI forecasting is presented. Section 5 details the conditional probability approach used to build the GNSS positioning PPP error statistical model. The conclusion of the work and the prospects are introduced in Section 6.
Ionospheric disturbances datasets presentation 2. 1 NMA database The Norwegian Mapping Authority (NMA) has deployed instrumentation, developed software and services to monitor space weather impact on the ionosphere over Norway. NMA is also studying the impact of the ionospheric disturbances on its real-time kinematic (RTK) positioning service and precise point positioning (PPP) techniques  (Jacobsen & Andalsvik, 2016). In this study, ionospheric disturbances are measured by the Rate of TEC Index (ROTI)  (Pi et al. , 1997). The ROTI index is able to detect ionosphere disturbances and is a robust index, not affected by detrending of the phase measurements needed in the phase scintillation index definition  (Ghobadi et al. , 2020). While the use of dual-frequency observations allows the correction of ionospheric delay to the first order, higher order terms remain.
, 1999; Mushini et al. , 2012; Yang & Liu, 2016; Carrano et al. , 2019). In this study, the ROTI values are based on 1 Hz measurements (Át ¼ 1 60 min), and calculated for time intervals of 5 min (N = 300). An elevation cutoff of 5°was used for the input data. However, for the ROTI data used in the processing in this paper, a further elevation angle cutoff of 30°was applied for the ROTI data records. This is done to avoid errors that occur at low elevations, in particular those due to reflections or other types of signal degradation caused by terrain or structures nearby. The elevation of a ROTI data record is defined as the average elevation angle of the satellite during the time period used for the ROTI calculation. ROTI is defined as the standard deviation of the Rate Of TEC (ROT) over some time interval.
ROTI units are TEC unit per minutes. In addition to basic checks of the input data a particularly important issue is the detection of cycle slips, as undetected cycle slips will lead to erroneous and very large ROTI values. The vast majority of the database used is from a real-time ionosphere monitoring system. In the real-time system, several checks are applied: For each signal frequency used, if Doppler measurement is available, the change in phase from the previous to the current timestep is compared to the Doppler. If the difference exceeds a threshold, it is considered as a cycle slip. The acceleration of TEC (second order time difference) is compared to a threshold. If the threshold is exceeded, it is considered as a cycle slip. The change of the Melbourne-Wübbena linear combination  (Melbourne, 1985; Wübbena, 1985)  from the previous to the current timestep is compared to a threshold.
The post-processing is equivalent to the real-time processing except that a more advanced cycle slip detection method, which is also based on the Melbourne-Wübbena linear combination, is used. A description of that detection method is found in  Cai et al. (2013). Regardless of the outcome of the cycle slip detection, the calculated ROT values are also subject to simple checks for outliers or obviously false values (i. e. extremely large values). A large database composed of ROTI index has been built. The ROTI database corresponds to the period  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) , covering the 24th solar cycle. ROTI data have been recorded every 5 min, with a sample rate of 1 Hz, from 12 stations deployed all over Norway (see stations positions in Fig.
1 ). For the ROTI model developed in this study, only GPS satellites signals have been considered, leading to 78,990,363 measurements in total. For each ROTI value, the inhomogeneities position is estimated considering the link geometry and assuming an ionosphere layer altitude of 350 km. This generic value is implying that disturbances occurs at the same altitude whatever the solar conditions and close to F region peak level. This point remains questionable and should be studied in further work. For the purpose of the statistical model researched here, this choice is assumed as reasonable. In this paper, all coordinates are converted from geographical to altitude corrected geomagnetic coordinates using AACGM-v2 code (Altitude Adjusted Corrected GeoMagnetic  (Shepherd, 2014). ROTI values have been corrected for elevation angle from the theoretical behavior proposed by  Carrano et al.
Solar wind data For the purpose of ionosphere disturbance forecasting, the solar wind and interplanetary magnetic field (IMF) data from a satellite which is stationed in orbit around the Sun-Earth Lagrange Point L1 are a very interesting driver. The OMNIweb site (https://omniweb. gsfc. nasa. gov/) is providing high resolution solar wind data from the OMNI database. These data, which are 1 min averages timeshifted to the bow-shock-nose, have been considered as proxies for ionosphere disturbance forecasting. The Solar Wind data covering all the ground disturbance measurements have been considered, i. e. from 2007 up to 2019. 3 Ionospheric disturbances index model.
The parameters l and r have been fitted for each combination of coupling function, MLat and MLT intervals of the whole database. The relevance of the lognormal regression of ROTI variability has been tested. Three examples of ROTI index CDF (cumulative distribution function) are presented in Figure  3 , corresponding to different combinations of location (characterized by MLat and MLT bins) and coupling function level. For each example the histogram plotted in blue line is compared to its lognormal CDF regression, plotted in red line. Examples (a) and (b) show a good agreement between measurements and lognormal model. For each graph, the number of point N pts used in the regression is indicated, and the case (c) clearly underline a lack of measurements with N pts = 323. From such an analysis, the ROTI index has been studied between 0 and 5.
Then, the v 2 goodness of fit test has been applied to check the null hypothesis, between the fitted and measured distributions. The p-value of the v 2 test indicates if the null hypothesis is true. One of the most commonly used p-value threshold is 0. 05. If the calculated p-value turns out to be less than 0. 05, the null hypothesis is considered to be false. In other words, if p-value is lower than 0. 05, it is very probable that the distribution and the model are different. Then, for each combined bins in MLat, MLT and coupling function, the v 2 test is computed with ROTI values in the interval [0. 1, 5] sampled with 250 values. The p-values have been computed by the Python "scipy. stats. chisquare" function. In Figure  3 , the p-value of the three cases are provided. The p-value equal to zero in the (c) case indicates that it is very probable that the distribution is not lognormal.
Validation A validation of the HAPEE model has been performed from measurements of a new set of ROTI data, measured in 2017 by receivers not used during the HAPEE model regressions. The receivers are part of the NMA GNSS receiver network, and the stations positions are plotted in graph (b) of Figure  1. ROTI data was calculated by NMAs ionosphere monitoring software. Solar wind data was obtained from NASA/GSFC's OMNI data set through OMNIWeb. For every 5 min in the time periods used, the HAPEE model was run to generate ROTI forecasts if valid solar wind data was available at that time. The output was longitude-latitude grids of the probabilities to exceed each of the thresholds during the coming hour. The grids covered the region of 10°West to 40°East, and 55-80°North. The grid resolution was 1 Â 1°.
ROTI versus PPP data processing We have used the GIPSY software provided by NASAs Jet Propulsion Laboratory (JPL) to compute coordinates. The coordinates were computed with a time resolution of 5 min. Important parameters/models used for the GIPSY PPP solutions are summarized in Table  2. To determine the position error, a true coordinate was defined for each receiver by estimating a long-term trend and yearly variations. The long-term trend can be seen as an estimate of multi-year drift caused by e. g. continental drift or post-glacial rebound, and was modeled as a linear drift over the entire dataset. The yearly variations were modeled by fitting a 4-segment 5th order spline with periodic boundary conditions to the dataset sorted by day-of-year after removing the longterm trend. The true coordinate is then the sum of the long-term trend and the yearly variations.
The PPP coordinate error is the difference between the instantaneous coordinate and the "true" coordinate evaluated at that time. PPP coordinate errors were transformed from latitude, longitude, height to local North, East, Vertical coordinates. A basic outlier detection was applied to remove position records that were very clearly wrong: All PPP records with a position error greater than 20 m in East/North, or greater than 50 m in Vertical, were removed as outliers. This affected approximately 0. 2% of the dataset. For each timetag and each receiver, ROTI was averaged over all satellites. For each averaged ROTI data record, the position error of the PPP data record closest in time (and closer than 300 s) is selected as the position error for that record.
From PPP error estimation used to establish the correspondence between ROTI and PPP errors, an exercise of validation of error model has been performed. It is based on data from a GNSS ground station named "hamc" and located at the magnetic latitude 65. 5°North. The dataset used is from the days listed in Table  1. From solar wind conditions measured at L1 at time t 0 , characterized by the Newell coupling function, all the PPP errors in the following hour (up to t 0 + 1 h) have been   In Figure  19  are presented the results of comparison between direct and modeled CCDF, for Horizontal, East and Vertical PPP errors. The comparison shows correct results of compliance between the HAPEE model and the direct PPP estimations, with a clear lack of data to get convergence for probabilities lower than 10 À2 (in particular for North error).
From the databases available and following the same approach, fits of other outputs could be proposed to complete the model, such as the S4 and r / scintillation indices, or other error parameters. Both the part of the model that forecasts ROTI based on solar wind parameters, and the part that forecasts PPP error based on ROTI, are empirical models. They are computationally expensive to construct but not computationally expensive to run afterwards. Input solar wind data can be retrieved in near-real-time from e. g. the data service of the Space Weather Prediction Center (SWPC) (currently available at this URL: https://services. swpc. noaa. gov/products/solar-wind/). A proof-of-concept prototype of an operational service has been implemented at NMA, retrieving the solar wind data from SWPC, running the models to generate a forecast map and visualizing this on a webpage.
Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.
g. , Copernicus Sentinel-2A and Sentinel-2B) or from satellites with similar spatial and spectral characteristics (e. g. , Landsat-8 and Sentinel-2) are not spatially aligned. As a consequence, the registration process tackles a challenging problem especially in multitemporal datasets focusing on the elimination of geometric errors which are mainly caused by the different viewpoints of the aerial and satellite sensors. Moreover, since the majority of registration algorithms depend on image matching principles, various image classes with different intra-annual spectral behavior, dynamic classes like building shadows, illumination inconsistencies and general spectral variations hinder even more the accurate image alignment. These problems become even more challenging for high resolution data in complex urban environments that include local displacements on high structures such as buildings.
Our models and code can be found at https://github. com/mpapadomanolaki/Multi-Step-Deformable-Registration (accessed on 14 March 2021). The rest of the paper is organized as follows: In Section 2, we explain the proposed methodology in detail. In Section 3 we describe the employed datasets and we provide quantitative and qualitative experimental results. Then, in Section 4 we make discussion comments and finally in Section 5 we end with a conclusion.
Attica VHR The first dataset is comprised of two very high resolution images that depict a 9 km 2 region in the East Prefecture of Attica, Greece, in 2010 and 2011. The images are both acquired by the WorldView-2 satellite and their dimensions are approximately 8000 by 7000, with every sample having been pansharpened and atmospherically corrected. This dataset contains urban and periurban areas with flat and hilly regions. For our investigation, the whole region was divided into 36 equal nonoverlapping subregions of approximate size 1100 by 1300 pixels; 20 of them were used for training, 6 for validation and 10 for testing. For the training process, patches of size 256 × 256 were produced with a stride of 128, resulting in 840 training, 120 validation and 200 testing samples.
ISPRS Ikonos For further evaluation, we employed the ISPRS Ikonos (https://www. isprs. org/data/ ikonos/default. aspx (accessed on 14 March 2021)) dataset that provides one bitemporal pair of high resolution images. As the name of the dataset suggests, the images are acquired by the Ikonos satellite and their dimensions are 2000 by 2000 pixels. They depict a 2 km 2 region and they have one panchromatic channel available. This dataset depicts a flat region with high buildings. 3/4 of the images were used for training and validation, while the rest was intended for testing purposes. Specifically, patches of size 192 × 192 were extracted with a step of 80, forming 400 training, 40 validation and 60 testing samples.
Optimization Hyperparameters were similar for both datasets, picking Adam optimizer with a learning rate of 10 -4 and batch size equal to 1. The models were trained for 100 epochs, while early stopping was used for the selection of the best model, taking into account the training and validation performances. In addition, random deformed images from the validation part were visualized to verify the registration results. For our experiments we used t = 3 time steps, which gave us a good trade off between computational complexity and performance. The regularizer β was set to to 10 -6 for both datasets after a thorough grid search was conducted so that the optimal results could be obtained. In addition, the parameter c for the logistic function L was equal to 2 and 8 pixels for the Attica VHR dataset and the ISPRS IKONOS dataset respectively.
Ablation Study The influence of the time step t was investigated by conducting an ablation study, examining the performance of the proposed multistep method using different time steps. The experiments were carried out on the Attica VHR dataset. More specifically, we trained the proposed formulation using T = {1, 2, 3, 4, 5} time steps, while for every different experiment we validated the performances on the testing landmark location points. The results are demonstrated in Table  1  for building areas as well as for areas that include roads and fields. [Φ] d (T = 1) is essentially the method presented in  [30]  using the setup and parameters proposed on this study. As one can notice from Table  1 , the one step approach registers the pair of images; however, it reports higher errors than the methods that use our proposed iterative formulation.
Quantitative and Qualitative Evaluation Our method has been evaluated both quantitatively and qualitatively on two different datasets and it has been also compared with other registration frameworks from the literature: the method proposed in  [30]  as well as the greedy fluid flow algorithm  [36]. The method in  [30]  employs a deep learning based pipeline, where the affine parameters and the deformable deformations are directly predicted by the pipeline itself. On the contrary, the method in  [36]  is not a learning based method, with the transformation parameters being calculated by integrating velocity fields forward in time, using the squared error dissimilarity metric on the space of diffeomorphisms. The optimization of the problem is conducted in an iterative way, where at every iteration the current transformation parameters are approximated based on the previous transformation parameters and the previous velocity field.
It should be mentioned here that for the Attica VHR dataset, we added further displacements on the image pairs compared to  [30] , e. g. , rotation, translation and scale, in order to further complex the exploited deformations and make the problem more challenging. Specifically, the extra translation ranges from 2 to 12 pixels, the rotation from 3 to 4 degrees, while the scale factor is equal to 2 or 3. Table  2. Errors measured as average euclidean distances between 15 landmark locations on the testing images of the Attica VHR dataset. dx and dy represent the distances along the x and y axis, respectively. ds indicates the average error along all axes per pixel, while the last column indicates the time needed by the methods to register a pair of 256 × 256 images. Errors on building landmark locations (left) and errors related to points on roads and fields (right) are presented.