dataesr/scientific-paragraphs-categorization huggingface dataset:
Preliminary image processing We developed a graphical user interface (GUI) in Matlab for image processing and network analysis to facilitate the preliminary study analysis and the study design (e. g. , cutting distance determinations and methodological feasibility risk analysis). The GUI allows the user to load a 3D stack of images and select the region of interest in addition to tuning the image processing parameters. The image processing pipeline started with the application of a top hat filter followed by a bottom hat filter. Top hat filter subtracts the morphological opening of the image from the original image and similarly bottom hat filter subtract the original image from the morphological closing of the image. The morphological opening consists of an erosion of image followed by dilation, and in contrast, morphological closing consists of a dilation followed by an erosion.
After the measurements, the xylem cross-sectional area was determined with a caliper and the specific hydraulic conductivity determined by dividing K by the cross-sectional areas. Lastly, the percent of hydraulic loss of conductivity (PLC) was calculated by PLC = 100 1 - 𝐾 𝑚𝑎𝑥 𝐾 𝑖𝑛 (4) where, Kmax is the maximum specific conductivity after flushing, and Kin is the initial specific hydraulic conductivity. The PLC data were fitted with an exponential sigmoidal equation of PLC = 100 1 + 𝑒𝑥𝑝(𝑎 + (Ψ -b)) (5) where, a and b are fitting parameters, whereby a describes the slope of the curve and b represents the position of the curve on the x-axis at 50% PLC  (59). The significance levels of the parameters were calculated based on this fit.
Besides, analogy-based synthesis (including BID) plays a critical role in computer-based design synthesis for solution generation  [42]. To solution improvement, TRIZ (from the Russian phrase Teoriya Resheniya Izobreatatelskikh Zadatch) is a typical modification-based design method  [226] , which can be used to improve an existing solution by resolving its inborn contradictions. The novel integration between TRIZ and BID leads to BioTRIZ  [238] , the core database of which is constructed based on biological phenomena instead of artificial inventions (i. e. , patents). Besides, a design solution can be continuously optimized through various bio-inspired optimization algorithms. For solution selection, design decision-making involves selecting the best solution among multiple alternatives. Relevant methods for solution selection include Pugh's Matrix and Analytic Hierarchy Process.
Computational tools have been developed to represent causality and perform analogous reasoning based on a database of engineered and biological systems  [41]. The biological database can be coupled with a patent database to accelerate the search for suitable analogies  [235]. Exposure to bio-inspired knowledge can enhance the effectiveness of design ideation  [248]. The cross-domain knowledge transfer between biological and engineered systems, through the support of knowledge-based design tools (i. e. , AskNature), can support designers to think outside of the box  [234]. On the other hand, the undesirable phenomenon of design fixation can often be observed in BID  [141] , i. e. , inexperienced designers are inclined to focus on specific features such as functionality and/or frugality and/or robustness in lieu of generic principles.
Artificial intelligence (AI) is increasingly incorporated into product design. Many technological breakthroughs of AI are initially triggered by biological inspirations  [74, 223]. For example, various artificial neural networks are inspired by the biological neural networks of animal brains  [54]. Bio-inspiration can be converged with AI in two directions. On the one hand, AI can be incorporated into various smart products, industrial robots, wearable devices, and autonomous devices to enhance product intelligence and autonomy. On the other hand, AI can be employed to facilitate the biologically inspired design process, for example, by developing recommender systems to recommend relevant biological knowledge to designers, constructing a knowledge graph to represent relations among cross-disciplinary entities, developing generative design tools that can facilitate designers to locate the most applicable biological solutions, and automatically enriching existing databases such as AskNature and others  [57].
Another example is the exoskeleton of crustaceans or insects that allows supporting high mechanical loads. Ants can hold an object weighing about 1000 times its weight as discussed above  [168]. Research works are underway to study wild animals with an endoskeleton. Land mammals developed specific material structures (cortical or trabecular bone). Their multi-scale structure allows for reducing bone weight while maintaining high mechanical performances  [9]. From remarkable biological cases from the biological database, advanced product designs have been inspired by mechanical, chemical, optical, and other functions and fields. Due to the vast nature of examples and the overall subject, in this manuscript, the authors focus on and present applications for the mechanical field. In the mechanical field, these designs can be grouped into four categories: biologically inspired mechanisms, surfaces/structures, actuators, and smart sensors (Fig.
Example category 1: a mimicry degree classification of advanced product designs Generally, the design process using bio-inspiration allows for answering specific questions: reducing the aerodynamic effects,  lightening the product, bringing hydrophobic properties, and improving lifespan. In a biology database, it is rare to find a solution that can be translated, directly, into a mechanical design. In consequence, advanced product design uses partial and local solutions of biological databases. These advanced product designs can be classified according to their degree of mimicry. Fig. 15  presents 4 classes of mimicry degrees to categorize the advanced product designs. According to ISO TC266 standard  [110] , both first classes represent the main ways used in bio-inspiration: problem (Class 1) and solution (Class 2) driven.
Example category 3: biologically inspired surface and material structure designs Biology uses surface or material multi-scale topologies to answer challenges imposed by the life and survival of different species living on Earth. The design of advanced surfaces or structured materials finds its inspiration in the database made available to us through a wide variety of biological species. Bio-inspired surfaces have been a popular study field. The main sources of these designs were extracted from animal or plantae kingdoms. Numerous geometrical principles were mimicked to improve adhesion or frictional anisotropy, superhydrophobicity or hydrophilicity, antireflection, and other optical properties  [137, 148]. The natural surfaces' micro-nanostructures make it possible to generate many of these properties  [208, 253, 263].
Example category 5: biologically inspired smart sensor designs The biological database offers many sensor principles to investigate the environment (perception task) and control the motion (walking, running, flying, etc. ) of living beings. Research work was carried out in three specific areas of detection: vision, tactile and olfactory. Two approaches are mainly used in artificial vision: the capture of image cameras (photoreceptors inspired by the eye)  [109]  and light flux sensors. The first approach requires merging the images taken one after the other to extract the position gradient. This needs a lot of electronic hardware and computation to extract information about a dynamic environment. On the contrary, the stimulation flow on the animal eye retina is continuous and sufficient to localize  [79].
However, the current designs of bioinspired sensors are complex. In many cases, the design of these advanced sensors is an assembly of conventional technologies that only mimics biological functionality, but not its principle. Hence, it is very difficult to have an overall view of the research carried out in this sector. The future challenge in this area of research is to better understand the fundamental principle of biological sensors. This knowledge will make it possible to design frugal bio-inspired sensors such as the CurvACE vision sensor or the polarized celestial compass. For that, an interdisciplinary approach is necessary to understand and recreate the active principle used by animals. To conclude Section 4, biological database has allowed designing advanced products using bio-inspiration.
Context. The Spectrometer and Telescope for Imaging X-rays (STIX) on board Solar Orbiter observes solar X-ray emission in the range of 4-150 keV and produces spectra and images of solar flares over a wide range of flare magnitudes. During nominal operation, STIX continuously generates data. A constant data flow requires fully automated data-processing pipelines to process and analyze the data, and a data platform to manage, visualize, and distribute the data products to the scientific community. Aims. The STIX Data Center has been built to fulfill these needs. In this paper, we outline its main components to help the community better understand the tools and data it provides. Methods. The STIX Data Center is operated at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW) and consists of automated processing pipelines and a data platform.
Introduction Solar Orbiter is an ESA-led mission with a suite of ten remotesensing and in situ instruments to study the interaction between the Sun and the heliosphere. Launched on 10 February 2020 into a heliocentric orbit, its nominal mission will last seven years with a planned extension of three additional years  (Müller 2020). Solar Orbiter's closest approach to the Sun is 0. 28 AU and its orbit will shift out of the ecliptic as the mission progresses, providing better views of the solar poles  (Forveille & Shore 2020). Solar Orbiter's Spectrometer Telescope for Imaging X-rays (STIX; Krucker 2020) observes X-rays from 4 to 150 keV. Its main scientific objective is to study the hot solar plasma and accelerated electrons produced during solar flares. STIX consists of 32 subcollimators, each with a pair of slightly offset grids that encode spatial information in a Moiré pattern which is detected by a pixelated cadmium telluride (CdTe) detector.
Data-processing pipelines 4. 1. Data-processing pipeline overview STIX telemetry data are processed immediately upon reception at the STIX Data Center by the data-processing pipeline shown in Fig. 2. Raw packets are processed to level 1 (L1) which includes parsing, integer decompressing, and timestamp conversion. Level-1 packets are stored in a tree-like structure and recorded in a NoSQL database. Then they are selected and processed in four different paths. In the first, the HK, QL, and science data are used to create L1 FITS files. The second path computes energy calibration factors from calibration spectra. In the third path, solar flares are identified by applying a detection algorithm to QL data. The fourth path performs common analyses of science data.
After the above processing steps, packets are organized in a tree-like structure. They are considered L1 packets and recorded in a collection in the NoSQL database. The NoSQL database is schemaless, which means that the data format of each record can be different and a predefinition of the data format is not required. This makes it very suitable for storing the tree-like structure L1 data packets. The NoSQL database is extremely convenient for tasks such as searching, sorting, organizing, merging data packets, and verifying data integrity. In addition to L1 packets, other metadata, such as filenames, SPICE kernel version, and the MIB version, are recorded in another collection in the NoSQL database. This allows for a rapid query of the associated raw telemetry data.
FITS products The flexible image transport system (FITS) is a portable file standard widely used in astronomy to store, transmit, and manipulate scientific images, tables, and associated data  (Pence et al. 2010). Therefore, the FITS format is adopted by the STIX Data Center to store the standard data products. After parsing each new raw telemetry file, HK, QL, and science packets are sequentially selected from the NoSQL database and merged after passing checks for data integrity and consistency. The merged data as well as the associated metadata and auxiliary data are written to FITS files. In the meantime, their metadata are recorded in a collection in NoSQL, which allows for the fast query of the products. The FITS files, created from L1 packets immediately upon the parsing process, are defined as level-1A (prereleased L1) products.
Then a linear fit is made to the positions and keV energies whose slope and intercept give the gain (i. e. , the ADC to energy conversion factor) and baseline, respectively. The ECC method (see  Maier & Limousin 2016; Maier et al. 2020 ) is another method often used by the STIX team to determine the calibration factors. We found that the results of the two methods are consistent within 1σ. The above steps are performed for each calibration spectrum once the data are available at the STIX Data Center. The calibration factors are recorded in a collection in the NoSQL database and used for further correction of energy bins in offline data analysis. Once significant changes in the calibration factors are observed, the STIX operations team creates a new ELUT and uploads it to STIX.
-Merging of flare peaks: Two peaks are considered from one single flare if the peak times differ by less than 5 min. This can reduce the number of reported flares and simplify data analysis. We note that this can also affect any statistical flare distribution derived from this list. As an example, Fig. 4  shows the STIX QL light curve in the energy range 4-10 keV, recorded from 2022-01-02T00:30:00 to 2022-01-02T09:30:00 (UTC). The orange curve is the smoothed light curve. The identified peaks are marked with the plus signs, and the colored ranges show the time ranges. The above steps are repeated for QL light curves of the other four higher energies, which provide information on the upper limit of the X-ray energy of the flare. The time ranges, peak count rate, and total counts as well as the corresponding ephemeris data of the identified flares are stored in a collection called the flare list in the NoSQL database, which is later used for the creation of data requests (see Sect.
90) is seen in Fig. 5. The wide spread at low fluxes can be explained by the difference in the energy response of the two instruments and the variation in flare temperatures. The correlation can be fitted with a linear fit in the log-log scale. From the fit, we get the following GOES flux estimation formula: f = 10 -7. 376+0. 622 log 10 (X ) (in units of W m -2 ), where X is the STIX peak count rate corrected for the distance variations between the Sun and Solar Orbiter. It is currently used to estimate the GOES classes of flares that are not directly observed by GOES satellites. The estimated GOES fluxes are stored in the flare list collection in the database.
This is based on two assumptions: the illuminated area of a pixel is proportional to its relative count rate, and the total illuminated area of the imaging detectors is independent of the source location. 4. The flare location is estimated by minimizing the weighted sum of squared deviations (i. e. , weighted chi-squares) between the calculated illuminated areas and expectations simulated for potential flare locations in a 400 × 400 grid. As an example, the left panel of Fig. 6  shows the calculated and best-fit illuminated areas of the CFL pixels for the flare observed at 2021-05-07T19:00:00 (GOES class M3. 9); the middle panel shows the best-fit flare centroid location, as well as its 1σ, 2σ, and 3σ contours. The simulated CFL shadow pattern is shown in the right panel. The estimated flare locations are stored in the flare list in the database.
These parameters may be customized for individual flares by human operators during review. -Spectrograms with the highest time and energy resolution are requested for all periods when STIX is in its observation mode. -Time-integrated pixel data for background subtraction: Time-integrated pixel data with durations of one or two detector temperature cycles (each cycle lasts about 40 min), which are acquired during quiet-Sun periods, are requested. The data are used for background subtraction when performing spectroscopy and imaging. -High time-resolution aspect data are requested, for example, for periods when the spacecraft's attitude changes drastically. Such periods can be known from the SPICE kernels or the aspect system readouts in HK data. The selection of science data of the above types is done automatically using a program (except for aspect data).
The information of the selected data is written into a collection in the NoSQL database. In addition, the STIX operations team also selects data for special needs. After being checked and adjusted by the STIX operations team, groups of new data requests that meet the operations requirements are selected from the database and then compiled into instrument operation requests (IORs), which are used to create final telecommands at the MOC. These telecommands are then uploaded to the spacecraft and executed by STIX typically two to three weeks later.
Interactive web pages The data center platform provides various HTTP interfaces (APIs) that allow access to STIX data products and the NoSQL database via HTTP requests. We have built dozens of web applications to manage and browse STIX data based on these APIs. Web techniques are chosen because they offer many advantages, such as clear cross-platform usability, broad access through browsers, rapid development, and easy maintenance. As an example, Fig. 9  shows a screenshot of the STIX QL data browsing tool. It allows users to browse available QL data interactively. It interacts with the server through APIs. The data exchange mechanism between the server side and the client side is shown in Fig. 10. After receiving a request from the client side, the server retrieves QL counts from the NoSQL database for the user-specified time range.
After excluding duplicates and merging, the server sends QL counts and metadata in JSON format back to the client. The data are then used to create interactive light-curve plots using JavaScript on the client side. The interactive plot uses state-of-the-art web technologies that enable users to perform a range of operations, such as rebinning the integration time, correcting the light travel time between the spacecraft and the Earth, and exporting plot data to a local file. In addition, QLs from other solar-observing instruments can also be displayed on the same page after users' activation, making it easier to find events of interest for joint analysis. Based on similar concepts, tens of web tools have been developed to browse other STIX data products. The four most commonly used tools are listed below: -The science data manager and browser provides users with tools for searching, downloading, visualizing, and analyzing science data.
The interactive analysis tools allow users to select data of interest for standard analysis tasks, such as background subtraction and energy rebinning, without installing additional software. The algorithms are implemented on the client side using JavaScript. In addition, users can submit imaging and spectroscopy tasks to the server and view the results on the same A142, page 8 of 10  page. This reduces the barriers to exploring STIX data for new users and is convenient for experienced users. -The preview images and spectroscopy product viewer is a web-based tool for managing and viewing the imaging and spectroscopy results. The viewer also provides tools for plotting the time evolution of emission measures and temperatures, creating animations of X-ray images for the selected runs, generating IDL or Python templates that allow for the same results to be reproduced on local machines, and so on.
STIX Data Center interface via stixdcpy Stixdcpy 1 is a python package that facilitates accessing and analyzing STIX data. With stixdcpy, users can easily query and download the data products available at the STIX Data Center. Similarly to the web tools, stixdcpy also provides tools to perform some standard analysis of STIX data, such as dead time correction, transmission correction, data clipping, and merging. stixdcpy is still under active development. As a result, its features and capabilities may change over time.
Summary STIX is one of ten instruments on board Solar Orbiter, which was launched into space on 10 February 2020. STIX measures the intensity and spectrum of hard X-rays emitted during solar flares in the energy range of 4-150 keV. During nominal operations, STIX continuously generates telemetry data. To process and archive the data as well as to support the operations of the 1 Stixdcpy source code is hosted on the GitHub repository at https: //github. com/i4Ds/stixdcpy A142, page 9 of 10 instrument and scientific activities using STIX data, dedicated data-processing pipelines and a data platform have been developed at the STIX Data Center. The pipelines generate telemetry at different levels and perform standard scientific analyses. The data center platform distributes STIX data products and also provides users with various web-based tools for searching and browsing STIX data products.
Telemetry processing pipelines at the STIX Data Center. Raw packets are processed to level 1, which includes parsing, integer decompressing, and timestamp conversion. Level-1 packets are recorded in a NoSQL database. Then they are selected and processed in four different paths, each of which is responsible for processing specific data types. IDB Raw telemetry data SPICE kernel data Raw data parser Packets and metadata NoSQL database Light curves Flare list L1 Sciecne data and flare list Metadata Spectra Calibration parameters L1 Packets Metadata Flare detection Flaring data processing pipeline Calibration data processing pipeline FITS file writer Flare list Flaring data quick-look products Calibration reports L1A FITS products To higher data products or scientific analysis Fig. 2. from the SOC, which contain information on spacecraft ephemeris, A142, page 3 of 10.
Model organisms and genetic data We focused our analyses on Italy and nearby areas [Corsica (France), part of Slovenia and Istria (Croatia); Fig. 1 ], which represent a biogeographically coherent region  (Rueda et al. , 2010; Vilhena and Antonelli, 2015; Ficetola et al. , 2018)  investigated in a large number of studies on amphibian biogeography  (Lanza et al. , 2007). According to the IUCN Red List maps (http://maps. iucnredlist. org; last accessed on 04/07/2022), 45 native amphibian species are present within the study area: 21 urodeles (8 genera in 3 families) and 24 anurans (8 genera in 7 families). For each species, we thoroughly searched the literature to gather as many mtDNA sequences as possible and carefully reviewed available information on the geographic origin of sequenced specimens.
Given the limited number of species present within the study area, we searched GenBank for the whole set of species, and reviewed all the published sources reported as reference for GenBank accessions. We selected the model organisms according to three criteria: i) presence of at least one mitochondrial haplogroup endemic to the study area (identified either by taxonomy -e. g. , Ichthyosaura alpestris apuana -or by the original studies; Supplementary Table  1 ); ii) availability of >15 individuals sequenced at the same mtDNA marker (>450 bp) from >5 sampling sites and belonging to endemic haplogroups; iii) individuals unequivocally georeferenced (i. e. possibility of finding a match between the information from GenBank and the manuscript, to reproduce the spatial population structure and correctly assign sequences to sampling sites).