dataesr/scientific-paragraphs-categorization huggingface dataset:
:-Test 1: The dataset consists of a subset of the reference dataset, including some georeferenced and all non-georeferenced photos. The number of RTK stations is limited here to 5 (PG1, PG2, PG4, PG5, and PG8 stations on Figure3b). -Test 2: The dataset is composed of the georeferenced Nikon camera photos acquired from the same 5 RTK stations of Test 1 and all the smartphone photos acquired with the 7 different cameras, both from the foot of the cliff and from the top of the cliff. -Test 3: The dataset is once again composed of the georeferenced Nikon-camera photos acquired from the same 5 RTK stations of Test 1 and all the photos acquired with the 7 smartphone cameras, but applying a filter on the smartphone photos after the "Bundle adjustment" step. With this filter, we deactivated the photos whose alignment error was greater than twice the standard deviation and re-ran the SfM processing chain.
-Test 4: For this scenario, only Smartphone photographs are used. All 684 smartphone photos are used, whether geotagged or not, acquired on different dates (22 November 2021 and 25 November 2021) or acquired from the cliff foot or the cliff top. -Test 5: The dataset is, again, composed only of smartphone photographs, geotagged or not, acquired at different dates, but only those acquired from the cliff foot (the cliff top being potentially dangerous for citizens).
A rst crystal structure of Szl from Xenopus laevis (PDB ID 5XGP) was recently described  9. Here we report a new crystal structure that differs from that of Bu et al. by a 90° rotation of the CRD domain with respect to the NTR domain. We also present results of a molecular docking analysis of the interaction between Szl and the catalytic domain of BMP-1 (PDB ID 3EDH)  24  , following earlier studies  5  , that yield new insights into the key interactions involved.
5 mL) under gravity ow overnight at 4°C, followed by washing with 20 mM MES pH 6. 0, 0. 5 M NaCl. Szl-SeMet was eluted using the peptide TETSQVAPA (Genscript), at 400 µg/mL. For elution, 3 mL of the peptide solution was added to the resin with bound Szl-SeMet then shaken at 4°C for 2 hrs. Three fractions of 1. 5 mL each were collected in 20 mM MES pH 6. 0, 0. 5 M NaCl, then Szl-SeMet eluates were pooled and loaded onto a Superdex 200 10/300 Increase column (GE Healthcare) pre-equilibrated and eluted using the same buffer. Szl-SeMet eluates from peak fractions were then pooled and concentrated using Vivaspin concentrators (Sartorius) for setting up crystallization screens. The nal yield of Szl-SeMet was 450 µg from 12 L of conditioned medium. Small angle X-ray scattering SAXS data were collected at B21 at the Diamond Light Source UK using samples in 20 mM MES pH 6.
Crystallization of native Szl and Szl-SeMet Prior to crystallization, proteins were concentrated to 12 mg/mL using Vivaspin concentrators. Initial crystals of native Szl were grown employing the sitting-drop vapour-diffusion method using 96-well plates (Greiner) at 20°C. A Mosquito Nanolitre Robot (STP Labtech) was used to set up crystallization screens. Crystals of Szl were grown in 0. 1 M NH 4 OAc and 18% PEG 3350 after incubation for 2 weeks. Diffraction quality crystals were then obtained in optimized conditions using 0. 2 M NH 4 OAc, 18% PEG 3350 as precipitant. These latter were ash frozen in liquid nitrogen with 17% glycerol added to the crystallization buffer as cryo-protectant. Crystals of native Szl were reproduced in hanging drops and used for X-ray data collection. For Szl-SeMet crystallization, screening was carried out as for Szl.
Data collection, data reduction, phasing and structure determination Diffraction data for native and Szl-SeMet, were collected at 100 K on beamlines I02 and I03 of the Diamond Light Source (Oxfordshire, UK). Data for Szl-SeMet crystals were collected by inverse beam SAD in two sweeps starting at 0-45° and 180° apart. Native Szl crystals diffracted X-rays to 1. 9 Å while for Szl-SeMet the best crystal diffracted X-rays to 3. 3 Å resolution. Data were processed using the Xia2 pipeline 31 at Diamond combining XDS  32  and POINTLESS from the CCP4 suite  33. For phasing, four independent datasets from 4 different crystals were merged to enhance the anomalous signal. Heavyatom coordinates of merged datasets for Szl-SeMet were located using Autosol from the PHENIX suite  34  though with poor phasing power.
Szl re ections were nally phased by combining a partial molecular replacement model obtained using the PDB coordinates of the cysteine rich domain (CRD) of mouse SFRP-3 (PDB ID 1IJX)  35  and also the "search for model in map" module of the programme MOLREP 36 employing the 1. 95 Å native Szl dataset. Manual iterative rebuilding and tting were done using COOT  37  and model re nement was done with phenix. re ne 38 and Refmac 5. 6 39 using a combination of TLS and restrained re nement. Model geometry and stereochemistry was examined using Molprobity  40.
Molecular docking Prior to docking, the protein preparation wizard PrepWizard (Schrödinger release 2018-3, Bioluminate, Schrödinger (suites2018-3/psp-v5. 3), LLC, New York, NY, 2018) was used to optimize the crystal structures of Szl (determined in this study) and of the BMP-1 catalytic domain (PDB ID 3EDH)  24. This included addition of hydrogen atoms and missing side-chains, lling missing loops, optimization of bond orders and hydrogen bonds and generation of ionized states. Water and other hetero-atoms were also removed. Restrained minimization of structures was carried out within an overall root mean square deviation (RMSD) of 0. 3 from the initial energy minimized state. The PRIME structure prediction panel of the Schrödinger-Bioluminate suite was used for building the missing loop/linker ( 138 DTLSKEYQYSYKE 150 ) region connecting the CRD and NTR domains of Szl.
Loops were further re ned in PRIME to avoid any clashes. The crystal structure of Szl has two independent monomers (chains A and B) in the asymmetric unit, with the chain B structure being the most complete. Chain B was therefore further subjected to energy minimization and used for docking (with and without inter-domain linker loops) into the BMP-1 catalytic pocket. Previously acquired data on key residues in Szl (Glu44, Asp92)  5  and BMP-1 (Arg182)  24  required for inhibition of BMP-1 activity were exploited to generate docking poses of the proteinase-inhibitor complex using the PIPER module of the Schrödinger-Bioluminate suite  41  , followed by re nement and energy minimization in PRIME. PIPER uses e cient Fast Fourier Transformation (FFT) in combination with pairwise interaction potentials using a docking code  42  which greatly reduces the number of initial poses.
Crystal structure of Szl Crystals from native and selenomethionine-labelled Szl belonged to space groups P2 1 2 1 2 1 and P2 1 and diffracted X-rays to 1. 95 and 3. 32 Å, respectively (Table  1 ). The crystal structure was determined by combining SAD phasing using the Szl-SeMet data with a partial molecular replacement model from the high-resolution native data (1. 95 Å) and re nement to the same resolution. Data collection, scaling and re nement statistics for native and Szl-SeMet are summarized in Table  1. Whereas Szl was found to be a monomer in solution by both MALS-SEC and SAXS, the crystal asymmetric unit contains two molecules A and B (Supplementary Fig. S2A ). Values in parentheses are for the highest resolution shell. The nal re ned model includes two chains (A and B) containing 235 and 241 amino acids, respectively.
This may partially account for Szl not being cleaved by BMP-1, in addition to it binding to BMP-1 mostly by straddling the catalytic site cleft. The catalytic subsites of BMP-1 and other Zn-dependent endopeptidases are now well established based on the crystal structures and modelling of several protease/peptide-inhibitor complexes  22, 64, 65. Here we show that key residues in Szl occupy one of these subsites (S1') as well as other subsites S2', S1, S2 that span the catalytic site cleft (Figs. 1, 3 and Supplementary Fig. S8 ). In this regard, it has recently been shown  22  that the binding to BMP1 of small molecule hydroxamate (PDB IDs 6BTP, 6BTQ) and reverse hydroxamate (PDB IDs 6BSL, 6BSM) inhibitors can be associated with important movements in key residues of BMP1, particularly its vicinal disulphide-bridge (Cys65-Cys66), as demonstrated most clearly with reverse hydroxamate compound 22 (PDB ID 6BSM, Fig.
Acknowledgements This research was funded by the  French National Research Agency  (project 'TOLLREG' awarded to CM and NA),  FP7 PCUBE  (ID:  227764 ),  FP7  BioStruct-X  FP7  (ID:  283570 ),  CNRS  and the  Université Claude Bernard Lyon 1. US is currently supported by a  Ramalingaswami Re -entry fellowship,  Department of Biotechnology, Government of India. The  Wellcome Centre for Human Genetics  is supported by the  Wellcome Trust  (grant  090532/Z/09/Z ). Technical support from staff on beamlines MX and  FIP (European Synchrotron Radiation Facility) ,  PX3  ( Swiss Light Source, Switzerland), Proxima 1 (Soleil, France ) and  B21 ,  I02  and  I03  ( Diamond Light Source, UK ; proposal mx8423) is gratefully acknowledged. We also acknowledge assistance from the  Protein Science Facility, SFR Biosciences Lyon  ( UAR3444/CNRS ,  US8/INSERM ,  Ecole Normale Supérieure de Lyon).
Data availability Coordinates and structure factors have been deposited in the Protein Data Bank under accession code 7EL5. Other data are available from the corresponding author upon reasonable request. Figure 3 Figure 4 Structural model of Szl and experimental structures of small molecule inhibitors in complex with BMP-1 catalytic domain. All structures are aligned using BMP-1cat. (a) Comparison of models of the BMP-1cat/Szl complex with different orientations of the BMP-1cat vicinal S-S bridge (Cys65-Cys66), marked 1 and 2, before and after energy minimization  43  , respectively. BMP-1cat and Szl are lightly coloured in salmon pink and green, respectively, in model 1, or plum and tan, in model 2, with key interacting residues in bold. These are close together on the left but further apart on the right, showing how the change in orientation of the disulphide bridge in BMP-1cat leads to a change in the orientation of Szl.
Manuscript submitted to ACM Container-Based MEC for IoT is designed by Hsieh et al. and targeted to IoT applications  [52, 53]. The authors enhance IoT gateways, providing container-based virtualization with gateway hardware. Then, these containers host functions related to the data flow between IoT equipment and the cloud. PiCasso is a MEC system designed to deploy MEC services on information-centric community mesh networks  [72]. PiCasso proposes a special type of node, called Service Execution Gateway (SEG), and incorporates these nodes into information-centric community mesh networks. These gateways are single-board computers capable of executing computing and storage services. PiCasso builds a virtualization layer on the SEGs, enabling the deployment of containers to offer services. The main component of PiCasso is its decision engine that chooses the SEG in which to deploy each service.
Manuscript submitted to ACM Nevertheless, their deployment effort requires updating a number of gateways or, in a more complicated arrangement, even changing equipment. MEC implementations adopt some measures to cope with the different networking protocols. The work from Hsieh et al. [52]  and ACACIA  [17]  use OpenFlow for traffic redirection. The protocols for DNS are important for LightEdge  [20]  and Li et al. [74]. The works from Li et al. , ACACIA  [17] , and LightEdge  [20]  use the GTP to intercept traffic from the network. All the implementations must adapt their interfaces to the protocols used in their ecosystems.
Manuscript submitted to ACM OpenNESS  [58]  use Kubernetes  [18]  to orchestrate containers. OpenNESS enhances Kubernetes to deal with the specifics of MEC containers, such as considering latency when choosing a host to instantiate a container. Docker is a software that provides container-based virtualization  [88]. LightMEC  [124]  and PiCasso  [72]  use Docker explicitly to provide a PaaS for the applications running in their MEC hosts. Until version 1. 20, Kubernetes used Docker as its container runtime engine  [13]. Therefore, technically, all the projects that use older versions of Kubernetes also use Docker. KVM is an acronym for Kernel-based Virtual Machine  [64]. It can instantiate virtual machines (VMs) on a Linux environment when hardware support is present. The work from Cattaneo et al.
uses KVM as a hypervisor, capable of instantiating VMs to the VIM  [14]. Hypriot  [41, 46]  is an OS that enables container-based virtualization on the Raspberry Pi. The OS enables the deployment of Docker  [88]  applications on top of the single-board computer. PiCasso  [72]  uses Hypriot together with Docker to provide a PaaS for users' applications. OpenStack is an OS to facilitate the deployment of an IaaS cloud. It can instantiate, orchestrate, and manage VMs, using pools of computing, storage, and networking resources  [97]. Even though OpenStack is a popular virtualization solution, not many MEC implementations use it. the VIM in the MEC-NFV integration architecture  [14]. LXC is an interface for Linux kernel that allows users to instantiate containers hosted by Linux OS  [75]. MEC-ConPaaS  [131]  uses LXC together with Raspberry Pi's to offer containers to users.
LightMEC uses the click processing elements to offer the MEC platform services  [124]. It also uses the processing elements to decapsulate or encapsulate traffic that comes from and to UE. Named Data Networking (NDN) is a Information-Centric Networking implementation  [59]. It identifies content on the Internet rather than hosts, as IP protocol does. For example, PiCasso uses NDN to identify services offered in the network, so users are not tied to a specific server for a given service  [72]. srsLTE is a software suite for UE, eNodeB, EPC, and other entities of LTE architecture. For example, lightMEC  [124]  and LightEdge  [20]  use srsLTE to deploy a RAN and test the performance of their implementations on an LTE testbed, together with nextEPC. Manuscript submitted to ACM nextEPC is a 3GPP-compliant EPC for the LTE and 5G.
MobiledgeX is a solution to manage multiple edge sites and facilitate the deployment of applications  [92]. MobiledgeX creates the applications in the cloud and migrates them to the edge, so they benefit from low latency. MobiledgeX also has a software development kit that allows applications in the UE to discover and use edge resources when they are available. However, MobiledgeX is not compatible with the ETSI standard. Additionally, MobiledgeX is not immediately suitable for MEC applications because it can instantiate in the edge applications copied from the cloud, but not stand-alone MEC applications.
High-resolution digital elevation models (DEMs) and orthomosaics are derived from UAV image series combined with structure from motion (SfM) photogrammetry techniques. Multitemporal orthomosaics are employed for measuring spatially continuous rock glacier kinematics using image matching algorithms. Superficial displacements are evaluated with simultaneous in-situ differential global navigation satellite system (GNSS) measurements. Elevation and volume changes are computed from TLS and UAV-derived DEMs at similar periods. Between June 2017 and September 2019, both datasets showed a similar elevation change pattern and surface thinning rates of 0. 15 ± 0. 04 and 0. 16 ± 0. 03 m yr -1 , respectively. Downward of a rupture zone developing about 150 m above the front, the rock glacier doubled its overall velocity during the study period, from around 5 m yr -1 between October 2016 and June 2017 to more than 10 m yr -1 between June and September 2019.
These time series consist mainly of data from annual surveys derived from in-situ measurements (i. e. , total station and differential GPS), providing an updated picture of the overall state of rock glacier kinematics. The wealth of such systematic observations have revealed strong correlations of relative velocities changes on an interannual basis for a majority of rock glaciers, and consequently, their association with changes in the thermal state of permafrost  (Roer et al. , 2005; Delaloye et al. , 2008; Kellerer-Pirklbauer et al. , 2018). Reported rock glacier superficial velocities have ranged from a few centimetres to some metres per year  (Barsch, 1996; Delaloye et al. , 2010; Janke et al. , 2013) , but, for very fast rock glaciers, the velocities can reach several metres per year  (Valenzuela, 2004; Hartl et al.
, 2020)  and airborne laser scanning (ALS,  Bollmann et al. , 2015)  have become more broadly employed in recent years, mainly due to their ability to monitor vast and remote regions. In a recent study,  Kääb et al. (2021)  have pioneered the combination of high-resolution optical and radar data to reconstruct the kinematics of several rock glaciers in a remote region of central Asia since the 1950s. Since the earliest examples of permafrost creep measurements extracted from analogue photogrammetry (e. g. ,  Messerli and Zurbuchen, 1968 ), the quality and availability of remote sensing datasets have allowed a substantial evolution to be achieved in rock glacier research  (Kaufmann, 1998; Kääb and Vollmer, 2000; Müller et al. , 2016; Cusicanqui et al. , 2021). The rapid development of uncrewed aerial vehicle (UAV) systems has opened up significant new applications in the field of remote sensing due to their comparably lower prices and high customised settings  (Nex and Remondino, 2014; Carbonneau and Dietrich, 2017; Cook, 2017).
, 2017; Vivero and Lambiel, 2019; Halla et al. , 2021; Bearzot et al. , 2022)  or by a combination of UAV-SfM with additional remote sensing data  (Kaufmann et al. , 2018; Groh and Blöthe, 2019; Fey and Krainer, 2020; Blöthe et al. , 2021). In the context of rapid rock glacier acceleration and destabilization, accurate and self-sufficient monitoring techniques are desirable to reveal previously unseen changes. Likewise, despite the popularity of image matching techniques and the necessity for well-constrained uncertainties associated with the displacements derived from high-resolution UAV datasets, studies incorporating an evaluation of the accuracy based on independent and concurrent in-situ measurements remain rare. The aims of this study are twofold: 1) to document the kinematics of a destabilised rock glacier using high-resolution UAV surveys and to evaluate its quality by employing kinematic data obtained from terrestrial geodetic surveys (TGS); and 2) to highlight rapid geomorphic and surface destabilization changes in steep alpine terrain combining UAV and terrestrial laser scanning (TLS) surveys.
Additionally, a Post-Processing Kinematic (PPK) treatment was conducted on the Trimble R10 raw data using Trimble Business Center (TBC) v4 surveying software, linking our base station with the permanent base station in Zermatt from the Automatic GNSS Network for Switzerland (AGNES). This procedure also aided in evaluating the stability of the base station between 2016 and 2019. Due to the loss of individually marked boulders at the rock glacier front and the inability to measure some markers covered by snow, we procured 35 points measured at the exact date (except for October 2016) of the UAV surveys (Table  1 ). We also measured four additional permanent ground control points (GCPs) located outside the rock glacier (Figure  1 ) and several checkpoints (UAV-CPs) during nearly each field campaign since 2017 (Table  2 ).
Nominal geo-tags using the RTK corrections stored in the image metadata are quoted to achieve around 3 cm horizontal and 5 cm vertical precision (i. e. , camera positions). The UAV camera orientation values (Roll, Pitch, and Yaw angles) recorded by the Inertial Measurement Unit (IMU) device have low accuracy, hampering a reliable block orientation by the direct sensor orientation (DSO) method. Likewise, independent tests using different combinations of GCPs have found that the RTK position accuracies using this UAV device are mostly too optimistic, suggesting that some sort of ground or check control should be included during the image block orientation  (Benassi et al. , 2017). UAV missions were planned and deployed using the SenseFly eMotion 3 software installed on a portable field computer.
Data processing Surface movements from GNSS surveys Surface movements were calculated from the TGS by measuring the 2-D component of kinematic points at two sequential surveys. The corresponding horizontal displacements were calculated from the x and y coordinates in terms of the Revised Swiss Reference System (CH1903 + LV95) as d Δx 2 + Δy 2 (1) where Δx and Δy are components of the horizontal displacement in easting and northing coordinates, respectively. The resulting displacements were transformed to velocities in metres per year based on the time interval between the consecutive surveys (see Table  1 ). During the PPK treatment of the GNSS data acquired with the Trimble R10 equipment, the standard deviation errors of the x and y coordinates fluctuated between 1 and 3 cm, whereas errors of the z coordinate fluctuated between 1.
5-4 cm. However, technical difficulties hampered the PPK treatment of the raw GNSS data acquired during the TGS with the LEICA GS10 equipment. Therefore, the standard deviation errors of the x, y and z coordinates were estimated based on the surveyed positions obtained for the six fixed points (Figure  1 ) during each TGS campaign. Thus, the uncertainty of each displacement was calculated using a rigorous estimation of the standard deviation of d  (Savšek-Safić et al. , 2006)  as provided by σ d Δx d 2 σ 2 x1 + σ 2 x2 + Δy d 2 σ 2 y 1 + σ 2 y 2 (2) where σ x and σ y are the individual standard deviation of each kinematic point (or their estimated global survey values), and the subscripts 1 and 2 are the time of the GNSS survey acquisition.
SfM photogrammetric workflow Recent improvements in photogrammetric processing capabilities, together with advances in computer vision algorithms, have facilitated the emergence of SfM with multiview stereo (MVS) workflows. These developments have been capitalised by several open source and commercial SfM software packages, such as MicMac, Agisoft Metashape, 3DF Zephyr and Pix4DMapper, among others  (Smith et al. , 2016). This study applied the SfM workflow implemented in the commercial software Pix4DMapper Pro version 4. 4 (https://pix4d. com/ pix4dmapper-pro/, last access: 4 June 2021). This software provides a straightforward pipeline processing from raw images acquired by UAV devices to point clouds and orthomosaic generation through mainly three steps (Figure  2A ). It is acknowledged that the quality of point clouds and derived by-products (e.
TLS workflow The point cloud time series were post-processed using the RiScanPro ® software version 3. 3. 438. Data were registered relatively to each other in a local coordinate system using the first scan of assumed stable geomorphological areas as a reference, such as those developed by  Kummert and Delaloye (2018). The residuals of the corresponding stable areas between the reference and registered scans showed Gaussian distributions for all scans after the multi-station adjustment. Subsequently, the data from two scan positions were combined using an octree filter to obtain a new point cloud with a homogenized point density. An average co-registration error of 0. 12 m was achieved (see Supplementary Table  S1 ). After interpolation of these point clouds, six sequential DEMs with a resolution of 0.