dataesr/scientific-paragraphs-categorization huggingface dataset:
HiPlan HiPlan, the HiRISE planning software suite, has received numerous updates during the extended science phases. The core tool is built on top of the MRO project's customized version of JMARS (  (Dickenshied et al. , 2019). ). Many HiPlan updates were in response to anomalies and instrument degradation mitigation efforts. Other updates have been for improved efficiency in operations and to accommodate remote operations, such as improvements to the suite's thermal modeling software, HiTemp, and the addition of several bulk or batch operation tools. To mitigate the degradation of HiRISE's performance over time, we have conducted extensive experiments in which we have adjusted the instrument's analog-to-digital converter (ADC) settings (Section 8. 9). The HiPlan tool suite has been updated to visualize and modify those settings as part of the general planning process.
HiTList The HiTList (HiRISE Target List) is a list of potential observations that can be imaged during a particular 2-week planning cycle. The suggested observations are drawn from a database of Region(s) of Interest (ROIs). The database hosts over 22,000 ROIs made by the science team and the public. These suggestions are filtered to retain only the ROIs suitable for imaging during that planning cycle. This process includes consideration of: 1) whether the spacecraft passes over the ROI, or close enough to view the ROI with a < 30 • roll of the spacecraft, 2) whether or not, and on which orbits, there are suitable imaging conditions to complete a viable stereo pair, and 3) any specific requests from the suggester, e. g. , a specific orbit for a HiKER target. The HiRISE Targeting Specialist (HiTS) assigned to the planning cycle constructs the HiTList.
STaR STaR (Special Target Requests) is an application backed by a web service that allows science team users to enter prioritized requests for acquisition in an upcoming cycle. These requests are validated and stored in the database. In addition, it allows the targeting specialists to manage the process using request categories and user limits. The requests are automatically entered into the HiTList for each planning cycle. Before the implementation of STaR in late 2022, users entered special target requests on the wiki, and incorporation into the HiTList was a manual process.
Data tracker and slacker HiRISE uses a third-party tool (Data Tracker) and a less precise, faster, in-house tool (Data Slacker) to model SSR use. This modeling is important because a misunderstanding about how HiRISE operates in the spacecraft flight software results in HiRISE "safing" when the HiRISE raw partition of the SSR is full (this happened twice; see Section 8. 2 and Table  15 ). HiRISE operations are complicated by the need to monitor the SSR partition. The outputs of these tools are used while creating,  C, a hard upper limit, the planning limit was kept much lower, at 46 • C, potentially preserving instrument lifetime. updating, and safety-checking imaging sequences before commanding. Lockheed Martin Corporation developed Data Tracker, and it models the SSR input and output of one or all instruments using cycle-specific planning files as input.
Retiring and unretiring suggestions Target suggestions submitted through HiWish or HiWeb are ingested into a target database for consideration during each planning cycle  (McEwen et al. , 2010). Once an image has been planned that satisfies the suggestion, it is "retired". The initial implementation of the retirement process consisted only of a simple geometric test: If the center point of an acquired observation lay inside the region of interest of a submitted target, or if the center of the region of interest of a submitted target lay inside the footprint of an acquired observation, then the submitted target would be flagged as retired in the target database. In the Extended Science Phase (ESP), the retirement procedure was updated. The current retirement procedure also tests that the acquired observation has met the photometric and seasonal/solar longitude constraints submitted with the target.
These issues cause relative (pixel-to-pixel) radiometric calibration uncertainty of 1-2% and absolute radiometric calibration uncertainty of ~20%  (Milazzo et al. , 2015). When the ADC bit-flip issue arose, we mitigated the erroneous data by going to even higher temperatures beyond the range of initial calibrations. The new ADC settings have complicated radiometric calibration. When we first switched to 74_74 for REDs 1, 2, and 3, the offset changed enough to turn some calibration pixel data into nulls after the Look-Up Table (LUT) compression from 14 to 8 bits per pixel. New LUTs were developed to enable a wide range of potential offset values as a function of ADC setting but were not implemented until 3 Aug 2021. Thus, a year's worth of HiRISE images (mostly bin-1 images) may contain artifacts introduced by the HiCal pipeline; these may be repaired when we reprocess the entire dataset and convert it to PDS-4 format.
Machine learning The volume of HiRISE data greatly exceeds what any individual researcher can analyze in traditional ways. Machine learning methods can more rapidly discover patterns or features of interest in large datasets and automate tedious or time-consuming tasks. There have been machine learning studies with HiRISE data to map linear bedforms  (Foroutan and Zimbelman, 2017) , detect new block falls in the north polar region  (Fanara et al. , 2020) , detect new impact sites  (Wagstaff et al. , 2022) , to detect recent rockfalls  (Bickel et al. , 2020) , and to classify terrains  (Barrett et al. , 2022).
Other outreach Members of the public utilized HiRISE data in unexpected ways. These efforts were highlighted with the "Made with HiRISE" page on our website: https://www. uahirise. org/epo/made-with-hirise/. These uses have ranged from visual artistry through flyovers (made from digital terrain models) to artistic interpretations of the intersection between art and technology (e. g. , see http://hyperspace. beck. com/#content-mars). The HiRISE team also engaged in hundreds of public talks as well as tours for classes at the university level focusing on remote sensing and the production of DTMs. We have also answered numerous media requests for our images and coordinated important releases with our counterparts at JPL Media.
The future of HiRISE The Mars Exploration Program has relied on HiRISE to characterize landing sites. HiRISE contributes to the analysis of science potential, along with many other remote-sensing experiments, and uniquely contributes to assessing landing site safety (Section 6). DTMs from HiRISE stereo coverage are used for detailed landing simulations, terrain relative navigation  (Fergason et al. , 2020)  and boulder counts, slopes, and topography to assess small-scale hazards. However, the full resolution of HiRISE is essential to many of these data products. For example, too many boulders are unresolved in bin-2 images to fit a size-frequency distribution down to the scale relevant to landers  (Golombek et al. , 2012a). In future years HiRISE will provide less full-resolution coverage, due to ADC bit-flips worsening over time (Section 8.
Moving to a later LMST? Although the battery reconditioning has removed the need to move MRO to a near-terminator orbit to keep solar arrays in the Sun for most of each orbit, the Mars2020 project still prefers to receive the relay data later each Mars day, which facilitates rover planning by increasing the amount of time for operations prior to uplink of critical data to the orbiter for downlink to Earth. The Mars2020 project receives a small fraction of its data through MRO relative to the Mars Atmosphere and Volatile Evolution (MAVEN;  Jakosky et al. , 2015)  and TGO spacecraft. MRO teams evaluated a 4:30 PM LMST and found it significantly worse for science. In the case of HiRISE, the main issues are (1) loss of signal from poorly illuminated surfaces, (2) different illumination angles complicate long-term change detection comparisons with previous data taken at other local times, and (3) shortened observing seasons for features of interest, such as frosted gullies.
While resources are more limited in each extended mission, the workload involved in observation planning has increased for HiRISE, to manage temperatures to mitigate ADC bit-flips while protecting electronics, and tracking SSR data volume to avoid safing HiRISE. The team has developed new software and procedures to streamline operations, such as the new HiPhoP (Section 7. 10), STaR (Section 7. 13), CIPP-lite (Section 7. 6), and other measures. The latest development efforts were made possible by "standing down" and not acquiring data in some cycles (Table  6 ). If funding continues to decline and MRO operations remain complicated, we will need to either find further streamlining or use more stand-down cycles. Abandoning the use of CIPPs is one possibility (Section 7. 6).
The stability challenge may be worsened by flying large solar arrays needed for solar-electric propulsion. Finer pixel scale over the same FOV also increases the data processing load, but advances in detectors and other electronics since 2001 are expected to reduce electronics mass and power requirements compared to HiRISE. High-resolution imaging of Mars from orbit is also challenging because the atmosphere causes two problems. First, the orbit cannot be lower than about 250 km (or even 300 km when the air is especially dusty and warm) because of drag on the spacecraft (unless specially designed). The MRO project had considered a periapsis at 200 km but opted for the present orbit for precisely this reason  (Zurek and Smrekar, 2007). We have substantial coverage of the Moon at half a meter per pixel from the Lunar Reconnaissance Orbiter Camera (LROC;  Robinson et al.
• The "People's Camera" concept of HiRISE has been valuable. Public target suggestions constitute a significant fraction of our target database and ensure that no aspect of Mars science is overlooked, while prioritization of targets by a group of specialist team members has optimized the use of limited coverage. The rapid release of images has benefited the community, especially early-career scientists who are less likely to be on instrument teams. • Many of the challenges the HiRISE team faces can be summarized as complexity. The instrument has been challenging to calibrate and command, given the 28 separate CCD channels. Requirements developed to accommodate all MRO instruments with different pointing desires also result in complex and labor-intensive operations. Stereo observation planning requiring images on two orbits also adds operational challenges compared with single-pass-stereo used by the High Resolution Stereo Color Camera (HRSC;  Jaumann et al.
Change detection would also have influenced tool development, as HiKER observations were a late development and planning tools such as our target database lack the capability to flag images for annual imaging or for high-quality repeat opportunities. • Generation of map-projected, radiometrically calibrated RDRs has greatly improved ease-of-use of HiRISE data, and the ability to "stream" images via HiView rather than download complete files has also been beneficial. The JPEG2000 file format, however, is not allowed for standard data products in PDS-4.
Acknowledgments We are grateful to have known the following HiRISE team members who have passed away:  Ed Bortolini  ( Ball Aerospace ),  Kathi Baker  ( UArizona ),  Nathan Bridges  ( Applied Physics Lab ),  Wojciech Markiewicz  ( Max Planck Institute ), and  Bob King  ( UArizona ). This manuscript benefitted from constructive reviews by  Mark Robinson  ( ASU ) and  Matt Golombek  ( JPL ). The  NASA/JPL MRO  project supported this research. LLT acknowledges funding and support from the  Canadian Space Agency  through their Planetary and  Astronomy Missions Co-Investigator programme  ( 22EXPCOI3 ) and the  Canadian NSERC Discovery Grant programme  ( RGPIN 2020-06418 ). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Acknowledgements ExoMars is the space mission of  ESA and Roscosmos. The ACS experiment is led by  IKI Space Research Institute in Moscow. The project acknowledges funding by  Roscosmos  and  CNES. Science operations of ACS are funded by  Roscosmos  and  ESA. Science support in IKI is funded by  Federal agency of science organizations (FANO). L. R. acknowledges support from  CNES  and from the  Excellence Laboratory "Exploration Spatiale des Environnements Planétaires (ESEP) ".
$ make The make command generates the executable asa. out, although this name can be changed, if desired, by modifying Makefile. The users at this point can either run directly asa. out in a chosen directory, giving as input a 𝑑𝑎𝑡𝑎. 𝑚𝑠 file, whose template, called 𝑡𝑒𝑚𝑝𝑙𝑎𝑡𝑒_𝑑𝑎𝑡𝑎. 𝑚𝑠, can be found in the directory MASAP/asa/src and contains the explanation of all the input options, or take advantage of the graphical interface by editing the calc_asa_v1. inp file in the asa directory to enter the chosen options, and running calc_asa_1. 0. py: $ python c a l c _ a s a _ 1. 0. py The python script reads the corresponding inp file and runs asa. out to obtain the site 𝑇 -matrix, which are used to calculate the ASA's. The calculated ASA's are output to HDF files in new directories whose top directory's name is designated in the inp file.
The HDF files also include information on the options specified in the corresponding inp files. The typical options in the inp file are as follows: • asaPATH: specifies the path for running the executable asa. out. • OnlyMainFile: if "no", all the calculated results by asa. out are stored. • NewDirName: specifies the name of the directory for the calculated ASA's. • Potential: denotes the potential type. The real and complex Hedin-Lundqvist exc potentials are denoted by "hr" and "hc", respectively. The Dirac-Hartree-Fock potential is denoted by "fc". This potential is the same as the one used in Ref. [1]  and allows the comparison of the calculated ASA's with those given in the atomic database developed by NIST  [29]. • EnergyStart, EnergyEnd, EnergyInterval: specifies the range (and spacing) of the electron energies at which to calculate the ASA's.
• CheckStructure: if "yes", the input options are visualized in the pop-up windows described below. Users define solid systems in a format accepted by the python module of Atomic Simulation Environment, and specify the target element. The calculated ASA for each described solid system is stored in separated HDF files in the directory designated by the NewDirName variable. Users can also input isolated atomic systems in calc_asa_v1. inp. For atomic systems, space groups are replaced by the keyword "atomic", and lattice constants for solid systems are replaced by the value of the atomic radii or by the keyword "calc". When this latter option is chosen, the program determines internally the atomic radius in the way specified below in Section 4. The flow-chart in Fig. 2  illustrates the various steps of the process In the GUI directory, gui_1.
0. py can plot, besides ASA's, related quantities such as cross sections, site 𝑡 𝜅 -and 𝑠 𝜅 -matrices and so on. Launching the program gui_1. 0. py $ python gui_1. 0. py causes the appearance of the window shown in Fig. 3  and of another window called "Data file list" (there is no need to click the "Data file list" button). The users select in this last window one of the HDF data file created in the previous step in the directory designated by the NewDirName variable by clicking the "Add a data file" button of Fig. 3. Then they can explore all the functions listed there by clicking the corresponding check boxes. The results, like the angular dependence of the differential cross section or the energy dependence of the transport cross section are exemplified in next section. The"Bohr radius" check box toggles between Ångstrom or atomic units.
Finally, the exploit_function_v1. py program has only auxiliary functions for gui_1. 0. py, so that the users do not need to run it. The various steps are illustrated in the flowchart of Fig. 4. The gross structure of the program is very simple, as can be inferred having a look at the main driving program. If the variable cluster is true, an overlapped charge density for each atom in the cluster is constructed based on the Mattheiss prescription in the subroutine calphas. Then atomic t-matrices, phase shifts, differential and total integrated cross sections are computed in subroutine asa_sub with the set of Muffin Tin radii so determined. If cluster is false, t-matrices, phase shifts, differential and total integrated cross sections are computed in the same subroutine asa_sub for an isolated atom with a radius 𝑅 𝑠 determined by the condition |𝑉 (𝑅 𝑠 )|∕𝜖 ≈ 10 -5.
In this case only one line of coordinates is sufficient in the input file data. ms to specify the symbol and the atomic number of the atom in question. The atomic charge density is calculated in the subroutine scfdat  [17] , In the present version the program runs up to an energy of about 15 Kev. To increase further this range the user should modify the parameters dpas of the linear-log mesh in the subroutine llmesh in have more points in the final part of the integration range of the radial solution the pseudo-Schrödinger equation, otherwise the program is unable to calculate the atomic matrices 𝑡 𝑙 for high 𝑙. The variable 𝑑𝑝𝑎𝑠 = 𝑑 ρ is the constant spacing of the Numerov integration procedure which, due to the relation ρ = 𝛼𝑟 + 𝛽 log(𝑟), represents also the spacing of the radial variable 𝑟, when this is greater than one.
and "calc" conditions gives information of the effects of the truncation of the atomic potential, and comparison between the "solid" and "calc" conditions gives information both on the truncation and the solid effects. As anticipated, the real and complex Hedin-Lundqvist potentials are denoted "hr" and "hc", respectively, and the exchange part of the DHF potential in Ref. [1] , used also in Ref. [2] , is denoted as "fc". The nonrelativistic and scalar-relativistic approximations are denoted as "nr" and "sr", respectively, whereas the use of the full relativistic potential with spin-orbit part is denoted "so". Not to clutter the figures, we have drawn only the "nr" and "so" options, although, if needed, the user can also choose the "sr" option in the program. The notation "NIST" indicates the DCS obtained from the database by NIST  [29]  and corresponds to our "calc" with "so" and "fc" options.
For sake of comparison it is also shown in the 'solid" and "input" options. Figs. 5 6 7 8 show calculated DCSs for aluminum and gold at impinging electron energies of 100 and 1000 eV. Each figure compares calculated DCSs for the different types of potentials in non-relativistic ("nr") and full relativistic ("so") options. We see that our calculated DCS's with the "fc" potential in the "calc" and "so" options reproduce rather well the DCS's taken from the NIST database  [29] , as calculated in Ref. [1]. A point worth of further investigation is the following. In the "solid" condition for aluminum with the electron energy of 100 eV, the DC-S's calculated with the "hc" potential are closer to the NIST one than those with the "hr" potential (see Fig. 5 ). Similarly, in the "solid" condition for gold with the "so" potential, the DCS with the "hc" potential shows agreement with the NIST ones (Figs.
7  and 8 ). This observation seems to indicate that damping effects by the imaginary part of optical potential play some role in the connection between DCS with an effective atomic radius in a solid and purely atomic DCS. So far, the atomic DCS (or ASA) databases have been used to analyze electron scattering phenomena even in solids  [1]. Tanuma et al. reported that, according to the Bethe theory, damping effects in solids are less important than in atomic systems  [32]. This justifies the use of the atomic DCS (or ASA) without inelastic scattering for studying solid systems. However, Bethe's theory is valid in the electron-energy range higher than about 1 keV. Thus, our results seem to suggest that damping effects make the DCS in solids approximately similar to the atomic DCS, which justifies the use of the atomic DCS (or ASA) database to analyze electron scattering phenomena in solid systems also at electron energies less than 1 keV.
Summary The computational MASAP Package, composed of a computation program and a graphical interface, generates atomic scattering amplitude of an atom, either isolated or embedded in an environment, at any chosen energy of the impinging electron up to 15 KeV. The ASA is calculated using an effective, complex optical potential which pro- vides damping effects in the scattering process in a fully relativistic framework. Optionally, scalar relativistic and non-relativistic approximations are also available to assess their applicability to a given problem. The GUI part allows to output quantities related ASA with user selected options into text files for convenient plotting. For sake of illustration, we have calculated the differential and transport CS's for aluminum and gold in isolated atomic systems and in solids.
The comparison of the various DCS's suggest that the use of complex potential at impinging electron energy less than 1 keV justifies the approximate application of the atomic databases DCS's, based on free atom potential, to electron scattering phenomena in solid systems. Instead, the description of the free atom Differential Cross Section has been shown to require the use of a complex potential in the whole angular scattering range, confirming the conclusions of Ref. [3]. The prelim-A. Koide, S. Rabouli, P. Le Meur et al. inary analysis carried out on selected examples seems to indicate that the process of electron-atom scattering is complementary to structural EXAFS analysis in providing information on the scattering potential. Finally, taking inspiration from Multiple Scattering Theory for the description of electron propagation in solids, an effective scattering amplitude has been suggested, to be used in Monte Carlo simulations and electron-molecule scattering, based on spherical wave propagators and MT atomic embedded potentials constructed according to the Mattheiss prescription  [9].
Tectonic lineament mapping Primary basemap: Global ~166 mpp v1. 0 BDR tiles with moderate (~74°) solar incidence angles. Secondary basemaps: low (~45°) and high (~78°) incidence angle basemaps, ~665mpp enhanced colour mosaic; MLA-and stereo-derived DEMs. Scale: 1:3M scale with digitisation at 1:300k. Software: Esri ArcGIS 10. 5. 1 GIS software.
Buffered crater counting Software: Esri ArcGIS 10. 5. 1 GIS software with CraterTools extension (9). CraterStats 2. 0 (10). We use the BCC technique by  (8, 11)  which counts craters that are unfaulted and undeformed by the structures under investigation to derive absolute model ages of linear landforms. The BCC technique uses a buffer zone around the linear feature to include more craters to be taken into consideration for the crater size frequency distribution (CSFD) count. This addition of included craters produces a more robust measurement, which is important due to the restricted surface area that the structures occupy (12). We have chosen to consider all craters that directly intersect the structures ≥2 km. A fault buffer width of 2R (R=radius of the crater) for buffer generation is used.
We acknowledge useful discussions with  German Mato  on the initial stage of development of this project during our stay at  Max Planck Institute for the Physics of Complex Systems (Dresden, Germany)  within the  Advanced Study Group "From Microscopic to Collective Dynamics in Neural Circuits " (2016/17). A. T. received financial support by the  ANR  Project  ERMUNDY  (Grant No. ANR-18-CE37-0014 ), by the  Labex MME-DII  (No. ANR-11-LBX-0023-01 ) and by  CY Generations  (Grant No  ANR-21-EXES-0008 ), all part of the  French programme "Investissements d'Avenir ". A. P. received support by  CY Advanced Studies (Cergy-Pontoise, France)  for a visiting scholarship in 2018.