dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of complete degeneracy, where all the weight is on one particle, this redrawing technique instead uses the prescribed prior standard deviations for generating perturbation parameters, corrected by a scaling factor (to be selected by the user, fixed to 0. 3 by default) to avoid overdispersing the ensemble. The inputs of MuSA are composed of (i) gridded meteorological forcing to generate the ensemble of FSM2 simulations and (ii) gridded observations which are assimilated. Optionally, it is possible to provide a mask, covering areas where simulations should not be implemented in order to save computational resources or restrict the runs to a specific area inside the provided domain. These inputs must be provided as files encoded in the Network Common Data Form (netCDF;  Rew and Davis, 1990)  format.
, 2021)  as well as the outputs of different downscaling approaches driven by the aforementioned datasets (e. g. ,  Fiddes and Gruber, 2014; Gutmann et al. , 2016; Havens et al. , 2017; Fiddes et al. , 2022; Liston and El-Geosci. Model Dev. , 15, 9127-9155, 2022  https://doi. org/10. 5194/gmd-15-9127-2022 der, 2006b). Other sources of information include automatic weather stations or gridded products derived from the interpolation of point-scale meteorological information. The forcing variables should be provided in the International System of Units, but it is possible to perform simple unit transformations internally to facilitate the preprocessing of different forcing data sources, using the following affine transformation. ψ k = aφ k + b , (1) where φ k (for k = 0,. , n) is a forcing time series provided by the user with units and ψ k with converted units is the transformed forcing time series after scaling by a with units -1 and translating by b with units.
Each grid cell is solved independently, which includes the reading of the forcing that occurs along the time dimension. Otherwise, each process would have to store considerably more data in memory, leading to more costly I/O operations that would slow down the run time. Even so, just reading along the time dimension can come with a considerable computational cost if the time dimension is large. To alleviate this, the time spent reading the forcing can be reduced by setting the chunk (a subset of the file to be read or written as a single I/O operation) of the netCDF forcing files along the time dimension. To speed up the subsequent relaunching of the simulations when smoothing and filtering, MuSA generates an intermediate binary file with the forcing information needed to run a complete simulation for each grid cell.
Many remotely sensed products exhibit gaps due to the presence of clouds or failures in the remote sensing devices. As a general rule we do not recommend filling the gaps of the observed series. These filled data would exhibit a different error variance than the direct estimations from the remote sensors, introducing uncertainties which are not compatible with the data assimilation process. MuSA will internally handle the gaps in the observations, provided that the netCDF files are generated using the aforementioned conventions, assimilating just the time steps where information is available.
Data assimilation Data assimilation (DA) is the exercise of fusing uncertain information from observations and (typically) geoscientific models  (Evensen et al. , 2022; Reich and Cotter, 2015). The DA schemes used in MuSA vary in their approach to state estimation as well as in the underlying assumptions and algorithms employed. In this section we provide an overview of these schemes as well as the underlying theory. Readers well versed in DA could consider skipping this entire section or at least skipping to Sect. 3. 4 and 3. 5 where the algorithms are presented.
In practice, to ensure numerical stability, we first compute the natural logarithm of the weights by using the log-sum-exp trick to avoid potential overflow and minimize the effects of underflow  (Murphy, 2022; Chopin and Papaspiliopoulos, 2020). Subsequently, the weights can be diagnosed by taking the exponential of these stable logarithms. In the current version of MuSA we apply resampling at each observation time step for the PF independently of what the effective sample size is. Furthermore the state x is split into parameters u and internal states v. The PF in MuSA is then implemented as described in the pseudocode in Algorithm 1. For clarity, this pseudocode omits details such as the log-sum-exp trick  (Murphy, 2022)  and the way that resampling is performed given that there are several options available  (Li et al.
5: If k = n, calculate the weights for the entire DAW using the batch of observations and predicted observations w (i) ∝ exp -1 2 [y -y (i) ] T R -1 [y -y (i) ]. 6: end for internal states can be obtained by taking the (unweighted) ensemble means and variances of the particle trajectories v (i) k in the DAW before the resampling step. The posterior means and variances of the internal states can be obtained by taking the (unweighted) ensemble mean and variance of the particle trajectories v (i) k in the DAW after the resampling step. In MuSA, the PBS is even more straightforward to implement than the PF. Pseudocode for the PBS algorithm as implemented in MuSA is outlined in Algorithm 2. Here too we have emphasized clarity in the pseudocode and omit details from the actual code such as the log-sum-exp trick.
Adopting this notation, the stochastic ensemble Kalman methods in MuSA are implemented as described in the pseudocode in Algorithm 3. For clarity, this pseudocode also omits details such as how the matrix inversion for the ensemble Kalman gain in Eq. (  32 ) is carried out. For this we follow the pseudoinverse and rescaling approach outlined in Appendix A of  Emerick and Reynolds (2012). Recall that for N a = 1 we recover the non-iterative stochastic EnKF and ES, while for N a > 1 we are using iterative versions of these schemes that involve multiple data assimilation with inflated observation errors. The term "multiple data assimilation" refers to the assimilation of the same data multiple times rather than an assimilation of different types of data (joint assimilation). We speak of inflated observation errors, since the role of the coefficients α ( ) is to inflate the observation error covariance R in the Kalman gain https://doi.
Single-cell and distributed assimilation of drone-based snow depth retrievals For the first experiment, we ran MuSA to assimilate snow depth retrievals obtained from a fixed wing drone  (Revuelto et al. , 2021a, b) , over two different snow seasons (2018/19 and 2019/20) in the Izas catchment using different data assimilation algorithms. The drone surveys were not equally distributed in time along the seasons, and the surveys present spatial gaps in some of the cells. The whole drone dataset is composed of 18 temporally distributed snow depth maps originally generated at 1 m but aggregated to 5 m spatial resolution so as to eliminate representativeness error  (Janjić et al. , 2018 ) by matching the model grid. Due to the high resolution of the observations, we expect MuSA to be able to implicitly reproduce the wind redistribution patterns by modifying the precipitation and temperature at the grid cell scale to compensate for the deposition and removal of wind-blown snow.
Acknowledgements. Esteban Alonso-González  has been funded by the  CNES  postdoctoral fellowship. Data from the Izas experimental catchment were collected in the frame of HIDROIBERNIEVE -CGL2017-82216-R funded by a  Spanish Ministry of Economy and Competitiveness  project. Kristoffer Aalstad  was funded by the  Research Council of Norway  through the  Spot-On  project (no. 301552 ) and acknowledges support from the  LATICE  strategic research area at the  University of Oslo. Jesús Revuelto  is supported by grant no. IJC2018-036260-I. Financial support. This research has been supported by the  Centre National d'Études Spatiales  (grant no. 0103207 ), the  Ministerio de Economía y Competitividad  (grant no. HIDROIBERNIEVE -CGL2017-82216-R ), the  Research Council of Norway (Spot-On  (grant no.
E. Code and data availability. The MuSA code is available at https://github. com/ealonsogzl/MuSA (last access: 16 December 2022); the version of MuSA used for this paper (with a subset of input data) can be found at https://doi. org/10. 5281/zenodo. 7014570 (Alonso González, 2022), and the complete input data for Izas used in the present study can be found at https://doi. org/10. 5281/zenodo. 7248635 (Alonso-González, 2022). The original FSM2 code is found at github. com/RichardEssery/FSM2 and in the MuSA repository with slight modifications from the original version. The MODIS data used herein, namely MOD10A1  (Hall et al. , 2002)  and MOD11A1  (Wan et al. , 2015) , are available for download from NSIDC and the NASA EOSDIS Land Processes DAAC, respectively. ERA5 data are available for download from the Copernicus Climate Data Store.
Property Value Elastic Limit (MPa) 2. 9 Melting temperature ( • C) 145-160 Specific gravity (g•cm -3 ) 1. 24 Young modulus (GPa) 2. 2 A gyroid lattice was generated using Matlab (The Math Works, R2020b) and imported separately into Ultimaker Cura 4. 8. 0, the program used to set up the test sample to be 3D printed, where Prusa i3 Mk3s (Prusa Research, Prague, Czech Republic) (Figure  6 ) was selected as the 3D printer. The test samples were set to contain a gyroid infill, and the density (λinfill) was varied to study how this affects the mechanical behavior of the structure. Hence λinfill depends on the case study. As the cuboid and the plates were imported as two separate models, the cuboid infill density was set to the specific percentage being studied, whilst the plates always had an infill density of 100%.
6. 2 was used to slice the samples for this printer. This software is a version of Cura specially programmed for the Blackbelt printer. The main difference in the setup process is the explicit specification of the printing angle. A parametric study was performed by changing different parameters in the setup to analyze the mechanical behavior of the structure with a gyroid pattern infill. The parameters chosen to study were the infill density (λinfill), the printing angle (θp), and the specimen thickness (th). Carbon-fiber-reinforced plastic (CFRP) inserts were considered in some cases. Carbon fiber is used in this study due to its excellent specific strength compared to that of the other continuous fibers. Mechanical characterization of the coupons was performed to obtain the mechanical properties required by the simulation  [7, 25].
However, in this case, λinfill = 15%. Four specimens were printed in each direction, X and Y, and were tested with and without CFRP rods, with a total of 16 specimens therefore printed. The fourth test studied the variation in the printing angle, θp. In this case, the software BlackBelt Cura was used to specify the printing settings. Throughout the test, the parameters of all the specimens were kept constant, except for the printing angle, which was studied for θp = 15°, 25°, and 45°, as shown in Figure  10. The infill density of these specimens was 15%. The addition of CFRP rods supposed an increase in mass of 10. 8%, high enough to be considered as a design factor.
Flexural Test Analysis A three-point bending flexural test was performed to simulate the main forces present during flight on an aircraft wing. The test method involved the use of a specified test fixture on a universal testing machine; the sample was placed on two supporting pins with a span distance (L) of separation between them (ISO 178:2019) (Figure  11 ). Then, a load descended that applied a desired force (F), deforming the specimen until a deflection settled. A span distance between both supports of L= 100 mm was set on the machine. To make it fair, all the tests were performed under the same conditions. In addition, 4 samples were examined for each configuration of test probe. These data will be used throughout this section to calculate the flexural stress and strain for each of the cases, allowing a comparison of these experimental results with those obtained by FE methods in the future.
All the tests were performed under the same conditions, with a maximum compression of 20 mm. In addition, to ensure a reliable test, four samples were tested for each configuration of the test probe. In this case, the specimens designed for the experiment used the same principles as those used to design the specimens for the bending test. First, a cuboid was designed, and then two plates were placed on the top and bottom of the structure, and a gyroid infill was developed on the inside of the structure. However, in this case, the lengths of the specimens were different, leaving the following dimensions: l= 70 mm, b= 30 mm, and d= 15 mm. After performing the compression tests, the universal material electromechanical testing machine stored all the relevant data measured from the tests in raw Excel documents, which were used to plot the specific graphs for each case study.
The conclusion drawn by the study performed on the wings of the SolarÍO was that the filling of the printed pieces with a minimal surface was the most optimal. Specifically, the gyroid surfaces were selected as fillers for both the wings of the aircraft and for certain parts of the fuselage. This surface maximizes the ratio of mechanical strength to surface (and, therefore, to weight). As already indicated above, its use is very limited due to the technical difficulty of its manufacture. With the introduction of additive manufacturing, the great potential of these surfaces can be appreciated for all types of parts, especially in the metalworking, aerospace, and automotive sectors  [28, 29]. Although it is possible to model the gyroid structure inside the wing with CAD/CAM/CAE software, the finite element study of the entire wing is not possible without simplifying its geometry.
Perdido parsing rules The Perdido Geoparser annotates different types of information such as named entities (with a special focus on spatial entities), extended named entities (ENE), spatial relations and motion expressions  (Gaio and Moncla, 2019 ). An ENE consists of several overlapping phrase levels where each level is embedded in the previous one. This concept is based on the fact that a proper name can be categorised as pure or descriptive and that the descriptive expansion associated with a name can change the implicit type of the considered entity. The Perdido geoparsing rules have been developed for three romance languages (i. e. , French, Spanish and Italian) and they have been used in several research projects to retrieve geographic information and to reconstruct itineraries from texts  (Moncla et al.
, 2019; Moncla et al. , 2016). Gaio and Moncla, 2019  argue that for a fine-grained task such as marking, classifying and disambiguating named entities, it is essential to consider the geo-semantic information expressed in the context in order to solve classification and disambiguation issues. Rules implemented in the Perdido Geoparser using cascades of finite-state transducers are a computational synthesis of previous works on how language expresses space and motion  (Talmy, 1983; Vandeloise, 1986; Aurnague, 2011). This geoparsing task is based on a bottom-up strategy where each level of embedded entities of the ENE is marked from the pure proper name to the complete expression. Additionally, it can distinguish between two types of spatial entities: 'absolute' referring to standard spatial entities and 'relative' referring to spatial entities associated with spatial relations.
This implies that betweenness FoR results will not be representative and meaningful. However, one interesting observation is that all types of FoR have a rather high precision score. Another interesting result is that the greatest number of errors refers to deletions (i. e. , false negative) and implies a rather bad recall score (37,68%). This means that our system missed a lot of FoRs (44 over 69 for FoR expressions, 46 over 76 for 'Ground object' and eight over 10 for 'Target entities'). This can be explained by the fact that we built the rules based on a preliminary analysis of a very limited corpus. We thus will need many more examples in order to build more exhaustive rules. This also shows there might be a potential for machine learning-based approaches, however, this will also need a larger manually annotated dataset in order to train a model.
We present an in depth analysis of the transient events, or glitches, detected at a rate of about one per day in the differential acceleration data of LISA Pathfinder. We show that these glitches fall in two rather distinct categories: fast transients in the interferometric motion readout on one side, and true force transient events on the other. The former are fast and rare in ordinary conditions. The second may last from seconds to hours and constitute the majority of the glitches. We present an analysis of the physical and statistical properties of both categories, including a cross-analysis with other time series like magnetic fields, temperature, and other dynamical variables. Based on these analyses we discuss the possible sources of the force glitches and identify the most likely, among which the outgassing environment surrounding the testmasses stands out.
Acceleration noise was found to be stationary over the week-long measurement runs, allowing a consistent PSD estimation. In addition, the measured PSD was found to be rather stable over the more than one year duration of the mission science operations, except for the decrease of the Brownian noise following the corresponding decrease of the pressure surrounding the test masses  [1]. Besides this quasistationary noise, the acceleration data series also contained isolated events with different shapes and amplitudes which we call glitches. These events are de facto signals and can be modeled and subtracted from the data, a procedure which is essential, at least for the most energetic ones, to get a consistent estimate of the PSD of the underlying quasistationary noise over the entire data series.
The purpose of this paper is to present a comprehensive description of glitches, an analysis of their physical properties, and finally a discussion of the possible physical sources. The paper is organized as follows. In Sec. II, we show the experimental layout and the measured data series. In Sec. III, we provide a description of the glitch detection and parameter estimation techniques. In Sec. IV, we give the main features of glitch parameter statistics. In Sec. V, we present the results of a coincidence analysis with other auxiliary data series. Finally, in Sec. VI, we describe possible mechanisms for glitch generation and the likelihood of each. The mechanisms include (a) platform accelerations, (b) thermal effects, (c) gravitational signals, (d) magnetic forces, (e) electrostatic forces, and (f) outgassing.
B. Dynamical controls and data series formation LPF was a controlled dynamical system consisting of the spacecraft and the two test masses. More specifically, the spacecraft was forced to follow one of the test masses (TM1) along x via an active control loop, using the spacecraft cold gas microthrusters as actuators  [11] , known as drag-free control. The test mass rotation along ϕ and η was kept fixed relative to the spacecraft by an active loop using electrostatic torques. These torques were applied via the above mentioned electrodes. No force was applied along x on TM1, while a control loop (electrostatic suspension) kept the distance between the two test masses nominally fixed, by applying a suitable electrostatic force along x on the other test mass (TM2). All other degrees of freedom were also controlled, but the details are not relevant here.
To these pre-processed data, we fitted a simple signal template (described further down) in the time domain, properly low-passed with the same filter used for the data (see Fig. 2 ). For ordinary runs, the main purpose of such fitting was to remove the glitch from the data. This allowed us to estimate the PSD of the underlying noise on the entire data series, thus reaching the lowest attainable frequencies. To this aim, we subtracted the unfiltered version of the best fit signal hðtÞ from the native ΔgðtÞ time series. For ordinary runs, the procedure was indeed quite effective  [1] , leaving a residual time series with the same PSD, within errors, as that of the series from which the glitch stretch was simply removed. This comparison is only possible down to the lowest frequency that could be attained with both time series (see Fig.
V. JOINT ANALYSIS WITH OTHER TIME SERIES In an attempt to understand the nature of glitches we have analyzed some other data series that have been measured synchronously with Δg throughout the mission. A. Discriminating between force and readout effect using the GRS Δx sensor The motion of both test masses along x and relative to the spacecraft has been measured at all times by the capacitive sensor of the GRS. From their measured coordinates x 1;GRS ðtÞ and x 2;GRS ðtÞ, we have formed a measurement of their relative displacement, independent of Δx OMS ðtÞ: Δx GRS ðtÞ ¼ x 2;GRS ðtÞ -x 1;GRS ðtÞ þ n GRS ðtÞ ð7Þ with n GRS ðtÞ the measurement noise. The difference between these two measurements only contains the difference between the noise terms: Δx OMS ðtÞ -Δx GRS ðtÞ ¼ n OMS ðtÞ -n GRS ðtÞ; ð8Þ and would immediately reveal a spurious signal within n OMS ðtÞ, if such signal were large enough to be detected against the relatively noisy GRS data.
x 1 channel. -Some of the low impulse glitches belonging to cold runs were found to coincide with fast events in the x 1;OMS interferometer, detected as fast features in ẍ1;OMS ðtÞ. Among the 152 two-sided glitches detected in cold runs, 28 showed counterpart in ẍ1;OMS ðtÞ. The total number of glitches showing a coincidence in the interferometric channels is 81=152, given that some of them show multiple coincidences. However due to the complexity of the data of all time series, some coincidences may have gone undetected. Out of the 4 low impulse events detected in ordinary runs, 2 showed coincidence to events in the interferometer time series, one in reference and the other one in x 1;OMS.
Impulse-carrying glitches We find only one coincidence between an impulsecarrying glitch and the other interferometer channels. This is the coincidence between the zero-crossing glitch on the second line of Table I and a spike in the ẍ1;OMS ðtÞ time series. The spike had no detectable counterpart in the time series of the force commanded onto the spacecraft by the drag-free control loop, as expected for such a fast feature. 10 ), as a function of absolute impulse jΔvj (left) and duration Δ (right), for impulse-carrying glitches of ordinary runs. The black data points represent the glitches for which the lever arm has been found to be significantly different from zero. The green horizontal segments represent the upper bound for jr ϕ j for glitches for which jΔΩ ϕ j ≤ δΩ ϕ. For 9 of the 98 impulse-carrying glitches of the ordinary runs, we were not able to perform the analysis on the Δγ ϕ ðtÞ data series.
It is however worth mentioning that similar events also showed up in a similar interferometer flown on the GRACE Follow-on mission  [21]. The interferometer readout exhibited phase jumps that would translate for us into steps in Δx, which were traced back to mechanical disturbances generated by thrusters activation. Thus mechanical stress may be the root cause of these interferometer anomalies on LPF. Since the mechanism behind these transients is not fully understood, we cannot predict whether they will occur in LISA in a similar fashion. For instance, while LPF interferometry reached a sensitivity 32 fm= ffiffiffiffiffiffi Hz p  [5] , LISA is expected to be limited at 10 pm= ffiffiffiffiffiffi Hz p. Nevertheless it is worth mentioning that the science degradation resulting from this kind of glitch, if there were to be any in the LISA data, is not expected to be severe, given their very fast timescale affecting just a few data points, and the lack of a low frequency component due to the corresponding lack of impulse.