dataesr/scientific-paragraphs-categorization huggingface dataset:
The UHF transceiver was therefore attached directly to the inside of the spacecraft chassis, allowing the chassis to serve as a heat sink for the transceiver, which proved to be effective in constraining the transceiver on-orbit temperatures to relatively benign values. The lithium-ion battery module had one of the narrowest operational temperature ranges of the entire spacecraft. As such, the battery cells were kept relatively thermally isolated from the rest of the spacecraft, with the main thermal interface being the electrical connector and the standoffs connecting the module to the EPS controller board. Although the on-orbit temperature range of the batteries was slightly higher than predicted using Thermal Desktop, the range of values was nonetheless relatively constrained and well within the operational limit, as well as that According to the linear distribution trend in Figure  20 , it can be seen that the spacecraft encountered about 45 command rejections per day.
Thermal Performance Figure  21  shows the temperature ranges of the various IDEASSat subsystems during on-orbit operations (light blue bars) compared to their respective operational temperature ranges (orange bars), as well as those measured during thermal vacuum testing (dark blue bars), and values predicted using the Thermal Desktop analysis software (green bars). From top to bottom, the subsystems shown include the battery module, the body mounted (PV0) and deployable solar panels (PV1 and PV2), the UHF transceiver, the EPS controller board, the CDH OBC, as well as the ADCS inertial measurement unit (IMU), star tracker (Track), and exterior. tail. Since the body-mounted solar panel (PV0) was located on the same face as the ADCS coarse sun sensor, it can be assumed to have been directly pointed at the Sun during sunlit segments of the spacecraft orbit.
The lithium-ion battery module had one of the narrowest operational temperature ranges of the entire spacecraft. As such, the battery cells were kept relatively thermally isolated from the rest of the spacecraft, with the main thermal interface being the electrical connector and the standoffs connecting the module to the EPS controller board. Although the on-orbit temperature range of the batteries was slightly higher than predicted using Thermal Desktop, the range of values was nonetheless relatively constrained and well within the operational limit, as well as that predicted during thermal vacuum testing. The on-orbit temperature ranges of the solar panels are worth exploring in more detail. Since the body-mounted solar panel (PV0) was located on the same face as the ADCS coarse sun sensor, it can be assumed to have been directly pointed at the Sun during sunlit segments of the spacecraft orbit.
During this time, geomagnetic and solar activity were quiet (Figure  26 ), although the spacecraft did pass through the auroral ovals and South Atlantic Anomaly (SAA) multiple times per day. The spacecraft began beaconing again on 2 April 2021, which was detected by the SatNOGS network  [35] , as well as the NCU ground station. Commands were successfully uplinked to the spacecraft from the NCU ground station the same day resetting the beacon packet format to lite, as well as commanding the replay of flight data saved to the CDH SD cards over UHF. The spacecraft remained active for a few days, with the final transmission received on 4 April 2021, after which there was no further contact. The retrieved flight data yielded a number of clues that allowed for the cause of the communications blackout to be identified to a high level of confidence:.
 The subsystem reset counters in flight data were reset, indicating that a power cycle and FSW reboot had taken place. However, there was no record of commanded reboots of the spacecraft by FSW, indicating that the power cycle and reboot were uncommanded. Following the above findings, an extensive review was performed of the IDEASSat EPS to identify single failure points that could result in a systemwide power down that would still be recoverable after an extended period of time. The latter factor ruled out the components in the 3. 3 V power bus used by the CDH, as radiation failures in the power MOSFETs used as DC/DC converters and power switches would be nonrecoverable single event burnouts (SEBs) and single event gate ruptures (SEGRs)  [36]. Additionally, the aforementioned components had been subject to testing on the ground, were derated according to NASA EEE standards  [37] , had previous flight heritage from the MinXSS mission  [11] , and had shown no anomalies in received flight data, making spontaneous on-orbit failure much less likely.
Acknowledgments: The authors are grateful to support from partner institutions of the  International Satellite Program in Research and Education (INSPIRE) , ground station operators participating in the  SatNOGS Network  for open access to recorded spacecraft transmissions, as well as  Mike Rupprecht  ( DK3WN ) and  Daniel Estévez  ( EA4GPZ/M0HXM ) for helpful comments and the development of open-source beacon demodulation and decoding software. The spacecraft team is also grateful to  Dino Lorenzini of SpaceQuest  for exemplary customer support,  Randson Huang  for assistance in ground station and  UHF antenna development, Bill Chang of HelioX Cosmos  for assistance in facilitating spacecraft delivery, and the  NCU VHF Radar team  for RF support. NCU acknowledges software support and academic licensing from  Analytical Graphics ,  Incorporated (AGI) Educational Alliance Program , as well as C&R Technologies.
Data Availability Statement: The testing and flight data presented in this study are available on request from the corresponding author. The data are not publicly available due to non-disclosure agreements signed with COTS hardware providers, as well as the nature of said data being for internal development use. Recordings of IDEASSat beacon transmissions by the SatNOGS network are available at: https://network. satnogs. org/observations/?future=0&bad=0&failed=0&norad= 47458&observer=&station=&start=&end=. The F10. 7 and Kp space weather indices were extracted from NASA Goddard Spaceflight Center's OMNI data set through OMNIWeb at: https://omniweb. gsfc. nasa. gov/.
Context. The X-ray Integral Field Unit (X-IFU) is the high resolution X-ray spectrometer of ESA's Athena X-ray observatory. It will deliver X-ray data in the 0. 2-12 keV band with an unprecedented spectral resolution of 2. 5 eV up to 7 keV. During the observation of very bright X-ray sources the X-IFU detectors will receive high photon rates. The count rate capability of the X-IFU will be improved by using the defocusing option, which will enable the observations of extremely bright sources with fluxes up to 1 Crab. In the defocused mode, the point spread function (PSF) of the telescope will be spread over a large number of pixels. In this case each pixel receives a small fraction of the overall flux. Due to the energy dependence of the PSF, this mode will generate energy-dependent artefacts increasing with count rate if not analysed properly.
Simulations We use the SImulation of X-ray TElescopes 3  (SIXTE) software package  (Dauser et al. 2019)  to simulate X-IFU observations. SIXTE is designed to perform end-to-end simulations of various X-ray observatories, including Athena. Photons are generated in a Monte Carlo framework, and are then followed through the full imaging and detection process. The package uses a set of calibration files that allow the photon impacts to be properly distributed, with the relevant energy and timing properties, onto the focal plane. In particular, it allows us to simulate energydependent mirror defocusing of Athena, which is the subject of this work. We note that SIXTE adopts a linear interpolation scheme between the different energies at which the PSF is defined for lack of a more physically motivated interpolation scheme.
(2018). However, we note that any changes in the definition of the grades will not affect the results of our analysis. The spectral analysis in this work is performed using the Xray spectral fitting package XSPEC v12. 11. 1  (Arnaud 1996). We use the Cash statistics (C-stat; Cash 1979) to evaluate the best 0. 5 1 2 4 8 Energy (keV) fits. In this work the simulations are performed by assuming the following definition of the Crab 5  , in the XSPEC parlance: Model = TBabs × powerlaw. Here TBabs  (Wilms et al. 2000)  represents the Galactic absorption assumed to be N H = 4 × 10 21 cm -2 , and the power-law photon index Γ = 2. 1. In this work, we consider two flux levels at 200 mCrab and 1 Crab corresponding to a normalization of 1. 9 and 9. 5 photon s -1 cm -2 keV -1 at 1 keV, respectively. These models are equivalent to an observed 2-10 keV flux of 0.
Acknowledgements. E. S. K. acknowledges financial support from the  Centre National d'Etudes Spatiales (CNES). J. W. and T. D. acknowledge partial funding from  BMWi  through  Deutsches Zentrum für Luft-und Raumfahrt  grant  50 QR 1903. This work makes use of AstroPy (Astropy Collaboration 2018), Matplotlib (Hunter 2007),  NumPy  (Harris et al. 2020), and SciPy (Virtanen et al. 2020).
Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the traffic scene in a fixed frame, referred as allo-centric occupancy grid. This allows for the static scene to remain fixed and to represent motion of the ego-vehicle on the grid like other agents'. We study the allo-centric grid prediction with different video prediction networks and validate the approach on the real-world Nuscenes dataset.
Dynamic Occupancy Grip Maps (DOGMs), on the other hand, allow for end-to-end learning due to their discretized spatial representation, without higher-level segmentation  [5]. Additionally, DOGMs are versatile in terms of sensor dependency, and can be generated from a variety of raw sensor data, such as Lidar or camera images. In our work, we use Bayesian-filter-based DOGM  [6]  that provide us with a spatially-dense model representation of static and dynamic space, as well as free and unknown space in the environment, as shown in Fig1. As the DOGM is generated using data from the vehiclemounted sensors, the grid is traditionally ego-centric,i. e. the position of ego-vehicle is fixed in the grid. While this is an effective method in scene representation, it complicates the long-term prediction problem.
Results incorporating different video prediction networks are studied, including state-of-the-art recurrent neural networks and memory-augmented network approaches. We compare and evaluate the prediction results of allo-centric and ego-centric grids for identical scenes and demonstrate the superior performances of the allo-centric grid predictions. The proposed approach is validated with the real-world NuScenes dataset  [3]  of urban traffic scenes. We show that allo-centric grids significantly improve the prediction results and demonstrate the ability to retain the scene structure and learn behaviours. The paper is organized as follows. Section II discusses related work to video and scene predictions. Section III describes the system overview. Section IV and V present implementations, results and analysis.
While they collect the sensor data from an autonomous vehicle, the vehicle remains stationary and only acts as the sensor collection point at different intersections. Itkina et al. proposed to use evidential occupancy grid and implement PredNet architecture for the prediction  [16]. The approach is then carried forward to develop the double-pronged architecture  [17]  and attention-augmented ConvLSTM  [18]. The latter work is able to make long-term predictions, however at turns the predictions still lose the scene structure. Mann et al. [19]  addressed the problem of OGM prediction in urban scenes by incorporating vehicles semantics in the environment. Their proposed method depends on the annotated vehicle data labels available in the dataset. Contrary to the conventional Occupancy Grid Prediction, we present an allo-centric DOGM representation to predict the urban traffic scene with respect to a fixed reference frame.
C. Deep Learning Prediction Architectures To study and compare the scene prediction with egocentric and allo-centric grids, we train our datasets with different video prediction networks. We consider 3 networks, briefly discussed in section II-A: PredNet, PredRNN, LMC-Memory with memory alignment learning (here on referred as LMC-Memory). PredNet  [8] , inspired from predictive coding, makes predictions based on how the predicted frames deviate from the target  [20]. The original work tests the network on vehicle mounted camera images from Kitti dataset  [21]  and demonstrates the ability to capture both egocentric motion as well as motion of objects in camera images. We consider PredRNN  [9]  and LMC-Memory architecture  [11]  as the state of the art video prediction networks that aim to capture long-term dependencies and motion context.
A. Dataset We study the prediction performance on the real-world NuScenes dataset  [3]. The original dataset consists of 850 scenes for training and 150 scenes for testing, each scene is approximately 20s long. We generate the DOGM gridbased on the Lidar pointcloud and available odometry. For allo-centric grid, we represent the scene with respect to a fixed reference frame and a grid dimension of 60 x 60m, with a resolution of 0. 1m per cell. Each sequence starts with the ego-vehicle heading facing up, capturing the scene 10m behind and 50m ahead of it. The initial pose was selected to ensure that the ego-vehicle remains within the grid for the total sequence length, even when running at a high speed. For egocentric grid, we generate a grid of the same dimensions and resolution, and the ego-vehicle fixed in the center.
B. Training The input sequence X t-9:t consists of 10 frames (1. 0s). Each network is trained to make predictions Ŷt+1:t+25 for 25 future frames (2. 5s). Both the allo-centric and egocentric datasets are trained the original parameters of the respective video prediction network. For training with PredRNN and LMC Memory networks, both allo-centric and ego-centric grid images are resized to 192x192 pixels. PredRNN is trained with a batch size of 4 and a learning rate of 10 -4. The number of channels of each hidden state is set to 64. The loss function is the sum of L2 and decoupling loss, and the values of α and β in Eq. (  1 ) are set to 0. 2 and 0. 8. LMC-Memory is trained with a learning rate of 2x10 -4 , memory slot is set to 100 and ConvLSTM to 4 layers for frame predictions. The loss function is the sum of L1 and L2 losses.
V. EVALUATION For evaluation, we are particularly interested in static and dynamic agents in the scene. We discussed in section III-D, the utility of unknown regions in learning scene representation. But the unknown region occupies a big portion of the grid and, thus, in evaluation, overshadows the performance of more interesting and relevant segments: static and dynamic regions. For this reason, we evaluate the dataset and network performances based on two channels of the predicted images, the blue and green channels representing static and dynamic components in the scene. We encourage the readers to refer to the video 1 for a better visualization of the results. 1 https://youtu. be/z-0BVM93X8c.
A. Quantitative Evaluation The allo-centric and ego-centric grids at any instant observe different parts of the scene, see Fig. 2. For fair comparison between them, we modify the test dataset and crop out the part of each t-frame that has not been observed until the t-th sequence by both grids. Thus, for example, the part of the grids outside of the yellow dotted boxes in Fig. 2  are blacked out for the input sequence frames X t-N :t as well as the target frames in the output sequence Y t+1:t+P. We measure the performances using three metrics: MSE (Mean Square Error), SSIM (Structured Similarity Indexing Method), and LPIPS (Learned Perceptual Image Patch Similarity)  [22]. MSE is calculated by the pixel-wise difference between the ground truth and the predicted frame per channel and per cell.
However, with MSE, the slightest error in predicted motion can result in large errors in the egocentric grids dataset. The SSIM and LPIPS metrics evaluate the prediction results based on the structural similarity and perception similarity respectively. Lower values are better for MSE and LPIPS while higher values are better for SSIM. Table  I  shows average results for the complete 2. 5s prediction horizons. The MSE score of allo-centric grids is significantly lower compared to the one of ego-centric grids. Since the complete scene transforms with respect to the ego-vehicle, the MSE is always higher in the ego-centric grid. The SSIM and LPIPS scores are also significantly superior for the allo-centric grid, due to the tendency of egocentric grids to get increasingly blurry for higher prediction horizons.
In other words, the hypersequent calculus for S5 can be embedded in our calculus for VTU when the latter is restricted to formulas ⊥ ¬A. We then introduce standard hypersequent calculi for all the logics. In order to define a standard calculus, the sequents composing an hypersequent are enriched by two types of structural connectives, called "blocks", to encode respectively disjunctions of plausibility formulas and 3-formulas which, due to the condition of uniformity, need to be propagated. We also define hypersequent calculi for the logics with both absoluteness and reflexivity, which were missing in  [9]   3. Our aim is to define calculi which provide both a decision procedure for the respective logics as well as countermodel extraction. Therefore, we introduce an invertible version of the standard hypersequent calculi, in which all principal formulas (or blocks) are kept on the premiss(es) of the rules, thus obtaining "kleened"  4  version of the calculi.
The conception of tuCLEVER is inspired by the methodology of leanT A P  [3]. The idea is that each axiom or rule of the sequent calculi is implemented by a single Prolog clause. No ad-hoc data structure is used. The resulting code is therefore simple and compact: the implementation of tuCLEVER for the basic system VTU consists of only 3 predicates, 21 clauses and 118 lines of code. The prover provides a decision procedure for the respective logics: since it implements the invertible version of the calculi, where the principal formula or block is kept in the premises of each rule, termination is obtained by simply avoiding redundant applications of the rules. The prover implements root-first proof search, and it also provides countermodel construction in case of proof search failure. It implements the countermodel construction defined for the proof of semantic completeness, thus computing a countermodel of the input formula from the unprovable saturated sequent occurring in a leaf of the failed proof tree.
Nonetheless, we show that on sets of formulae provable in other (weaker) conditional logics and on randomly generated formulas, the performances of tuCLEVER are surprisingly better than performances of other provers for conditional logics, notably VINTE  [11]  which covers weaker logics of the Lewis family. Further investigation might clarify whether this fact depends on the strength of the logic implemented by tuCLEVER, or on the features of the calculi, or on the implementation of tuCLEVER. The program tuCLEVER, as well as all the Prolog source files, are available for free usage and download at http://193. 51. 60. 97:8000/tuclever/. This article is organized as follows. Section 2 introduces the axioms and the models of the logics under scope. In Section 3 we present the non-standard hypersequent calculi for the logic under investigation, and we prove the fundamental property of cut admissibility and syntactic completeness (Sec.
Again, line 6 of both clauses implements the local loop checking: the rules can be applied only if A B does not already belong to Γ 2 for abs i L and to ∆ 2 for abs i R. In lines 8 the prove predicate is recursively invoked on the unique premise of the rules, by adding A B to Γ 2 in abs i L and to ∆ 2 in abs i R. As already mentioned, the code of tuCLEVER is simple and compact: the implementation of the basic system VTU consists of only 3 predicates, 21 clauses and 118 lines of code 11.
Countermodel generation For the systems VTU, VWU, and VCU, in case the construction of the proof search described in the previous section fails, the program tuCLEVER builds a countermodel for the extended hypersequent (and thus: for the formula) occurring at the root of the proof search tree. The countermodel is constructed from a saturated hypersequent computed by the proof search procedure, implementing the construction of Lemmas 28 and 29. The very last clause of the prove predicate, which is invoked when all the other clauses the rules of the invertible calculi are no longer applicable, makes use of the non standard predicate assert in order to explicitly add to the Prolog working memory the current hypersequent:.
* This work is partially supported by the  Projects TICAMORE   ANR-16-CE91-0002-01 , by  WWTF  project  MA16-28 , and by  BRISE-Vienna  ( UIA04-081 ), a  European Union Urban Innovative Actions  project, and by a  UKRI Future Leaders Fellowship, Structure vs Invariants in Proofs , project reference  MR/S035540/1.
Finite-Element Numerical Model. A finite-element model is developed, using COMSOL multiphysics, to solve the system of nondimensional equations. The momentum equation is defined in the porous medium flow module, where the pressure and flow velocity are calculated. The convectiondiffusion-reaction equation is defined by the transport of diluted species in porous media under the chemical species transport module. Furthermore, we use the domain ordinarydifferential equation to define the mass conservation equation where the porosity change with time is captured. We utilize local variables to define the structure-property relationships. This step updates the permeability, pore radius, interfacial surface area, and local mass transfer coefficient. We select second-order discretization for the shape function in the species transport equation, the mass conservation equation, and linear elements discretization for the momentum equation.
The ESA Ariel mission has been adopted for launch in 2029 and will conduct a survey of around 1000 exoplanetary atmospheres during its primary mission life. By providing homogeneous data sets with a high signalto-noise ratio and wide wavelength coverage, Ariel will unveil the atmospheric demographics of these faraway worlds, helping to constrain planet formation and evolution processes on a galactic scale. Ariel seeks to undertake a statistical survey of a diverse population of planets; therefore, the sample of planets from which this selection can be made is of the utmost importance. While many suitable targets have already been found, hundreds more will be discovered before the mission is operational. Previous studies have used predictions of exoplanet detections to forecast the available planet population by the launch date of Ariel, with the most recent noting that the Transiting Exoplanet Survey Satellite (TESS) alone should provide over 1000 potential targets.