dataesr/scientific-paragraphs-categorization huggingface dataset:
The UNESCO and INRIA project Software Heritage76, which wishes to build an Alexandria library of source codes, represents an important symbolic step in this direction. The importance of code as a cultural notion can also be observed with respect to genetic code: there are numerous comparisons made by philosophers77, scientists78 and journalists79 between electronic code and the DNA code. These comparisons contain assumptions that need to be examined because this particular fascination has still not diminished, but instead have regained significant momentum80. 5 Conclusion: towards the Body, the Corpus Section 5 reviews the main points of Chapter 3 and in doing so refocuses on the central question and hypothesis: is Derrida correct in considering the body and a new relationship to it as the horizon of new technologies? Do we indeed encounter the body at the extension limits of this new technology, digital writing? When French is reluctant to translate embed as a single word, does it mean that something is changing profoundly between the former culturaland Christian -pattern of incarnation towards embedding? All the elements gathered together in this chapter seek to answer these questions, in particular: the return of orality to the foreground, with its physical impact; the necessity of overcoming our repulsion when faced with the Heraclea stone, by considering the way in which digital objects affect us, such as smartphones when we write on them; the porosity and the collectivization of the auctorial "I" by this new kind of khôra, the internet; the impact of new technologies like AlterEgo that could transform us into "human libraries"; the rise of multimodality that embeds digital writing with senses and corporality; finally, the code itself that enriches DNA writing, including the first DNA information storage experiences.
Archaeological and cultural heritage (ACH), one of the core carriers of cultural diversity on our planet, has a direct bearing on the sustainable development of mankind. Documenting and protecting ACH is the common responsibility and duty of all humanity. It is governed by UNESCO along with the scientific communities that foster and encourage the use of advanced non-invasive techniques and methods for promoting scientific research into ACH and conservation of ACH sites. The use of remote sensing, a non-destructive tool, is increasingly popular by specialists around the world as it allows fast prospecting and mapping at multiple scales, rapid analysis of multisource datasets, and dynamic monitoring of ACH sites and their surrounding environments. The cost of using remote sensing is lower or even zero in practical applications.
In the short period of about 60 years since the launch of the first spaceborne platform -the spy CORONA satellite -in 1959, satellite remote sensing technology has made many breakthroughs in terms of spatial, radiometric, spectral and temporal resolution. This has allowed the beginning of data mining from big remote sensing data, which has led to more new discoveries  (Giardino, 2011; Luo et al. , 2017b). Accompanied by the development of different imaging technologies, the use of satellite data as a novel tool to uncover remains left by ancient human occupation and to study past landscapes has become more and more popular since the 1970s, when the data started to become available for civil purposes  (Agapiou et al. , 2015a; Agapiou et al. , 2016b Agapiou et al. , , 2016c;; Beck et al. , 2007; Bewley et al.
Literature overview of ASRS for ACH applications During the last century, a large number of peer-reviewed contributions on the use of ASRS in ACH have been published, thus showing that Earth-observation technologies have triggered improvements and the development of new tools. It should be noted that only a dozen contributions were published in the period 1907-1967, due to the influence of the two World Wars. To obtain an overview of the growing use of ASRS in the field, we searched a widely used electronic database, namely Scopus (http://www. scopus. com/) for the period 1 January 1967 to 31 December 2017. Using 11 independent filters, a total of 2651 publication records were retrieved from the literature database Scopus. The filters used were: "remote sensing", "aerial", "satellite", "airborne", "spaceborne", "CORONA", "photography", "multispectral", "hyperspectral", "LiDAR" and "SAR".
In the returned results, the terms "archaeological", "archaeology" or "cultural heritage" were mentioned at least once in the title, abstract or keywords, these filtered records were then combined into a new set and the duplicates removed using Endnote. A bibliometric analysis based on the retrieved literature data was conducted in order to reveal the spatiotemporal patterns in ASRS development over the past half centuries. We began with a multi-year trend analysis to track the development of ASRS applications in ACH. All the retrieved publication records were aggregated by year to show the year-on-year growth (Fig. 2 ). Fig. 2  shows a two-stage feature: a period of stagnation from 1967 to 1997 and a period of linear increase from 1997 to 2017. This indicates the high degree of importance and the acceptance that ASRS technologies have gained in the fields of archaeology and cultural heritage science over the last 20 years.
These bibliometric statistics are similar to results found in earlier studies  (Agapiou and Lysandrou, 2015). The differences between the results of these two studies might be caused by the different search filters that were used for searching the databases. The 2651 publications found were narrowed down to a set of journal papers in the English language; the categories "Conference Paper", "Book Chapter", "Special Report", "Manual" and "Erratum" were excluded. Next, a journal ranking analysis was conducted to show the orientation of ASRS in ACH applications. By doing this, we reduced the dataset down to a list of 1571 articles published in 363 mostly international peer-reviewed journals. Total 661 and 143 articles were attributed to the top-ranking 10 journals (covered by SCIE or SSCI) in categories of Archaeology and Remote Sensing, respectively (Fig.
, 2018). The spatial resolution of CORONA spy photographs taken during the Cold War could reach up to 0. 6 m. Such data are valuable in areas where the archaeolandscape has changed dramatically as a result of human activity such as agricultural production and urbanization  (Conesa et al. , 2015; De Meyer, 2004; Fowler, 2011; Fowler and Fowler, 2005; Kennedy, 1998; Lasaponara et al. , 2018). CORONA was officially classified as top secret until 1992. Then, in 1995, the photos taken by the CORONA satellites were declassified. Spaceborne spy photography data (ZENIT, COSMOS, and Kometa KVR-1000) from the former Soviet Union and subsequent Russian space programs have been available since 1961 and have a high resolution of up to 2 m. Even though those photographs have been used by several researchers  (Comer, 2012; Comfort and Ergeç, 2001; Fowler, 1996; Hadjimitsis et al.
Casana and Cothren (2008)  reduced the geometric distortion in CORONA sub-images by selecting GCPs from SPOT imagery and SRTM data. Subsequently,  Casana et al. (2012)  developed a low time-consumption method for efficient orthorectification of full CORONA scenes using an automated scale-invariant feature transform (SIFT) algorithm and Google Maps reference data. Nita et al. (2018)  developed an accurate and fast method for orthorectifying highresolution CORONA photographs of mountain areas, and rectified scanned CORONA photography based on Structure from Motion (SfM) technology. Due to the gradual changes in land use and land cover that have occurred over the past century, most ACH applications have favored using historical aerial and spy satellite photographs. Many archaeological features and old landscapes have been preserved in these dustladen data  (Stichelbaut, 2006).
Despite their potentially high spatial resolution, photography spectrally under-samples the atsensor radiation and masks spectral features that are too narrow to be distinguished  (Verhoeven, 2017). Also, the timing of photography campaigns for archaeological prospecting is therefore crucial and, often, the optimal conditions for detection may not occur for a long period. Given that the appearance of crop or soil marks is linked to the nature of the local landscape matrix, these marks are potentially detectable at bandwidths outside the visible spectrum before they become apparent there. As a result, nowadays, spectral imaging data are commonly used in ACH applications.
Thus, it is imperative to carry out dimensionality reduction for speeding up the visual interpretation and identification of buried remains in airborne hyperspectral images to support further archaeological prospecting. For this purpose, many techniques have been proposed to reduce the dimensionality of hyperspectral images, preserving the variability in the original dataset  (Huang et al. , 2015; Lee et al. , 1990; Plaza et al. , 2005; Wang and Chang, 2006). Among these, Principal Components Analysis (PCA), a generic spectral enhancement technique used by remote sensing archaeologists, has been widely used to enhance visual interpretation (Fig. 6A, B ) of the scene or as a way of increasing the computational efficiency of automated classification procedures  (Cavalli et al. , 2007; Cavalli et al.
, 2013; Doneus et al. , 2014). In PCA, the vast majority of the variance in the original dataset (sometimes greater than 95%) is captured by the first principal component, and each new principal component is orthogonal to every other. Vegetation Indies (VIs) (Fig. 6C ) have a certain potential to enhance the visibility of crop marks. The principle of VI based ACH applications will be discussed in detail in section §4. 2. 5 below. In addition to PCA and VIs,  Doneus et al. (2014)  investigated the red edge inflection point (REIP) (Fig. 6D ) and distribution fitting (Fig. 6E, F ) for analysing airborne narrowband spectral data. Their examples showed that, when compared to the true color imagery, distribution fittings and REIP visualizations can increase the contrast between healthy and stressed crops (Fig.
, 2018a). A well-known example is the Roman site of Dura Europos on the West bank of the Euphrates River. During the three years (2011-2014) that separate the two VHR satellite images (Fig. 7 ) that were analyzed, the site was subject to extremely serious looting. A long history of pre-war looting extending back several decades is visible in an early 2011 VHR satellite image (Fig. 7A ) of the site, but in 2014 (Fig. 7B ), the site was further damaged by looting, with thousands of new looting pits visible across the entire site. Fig. 7C  shows the changes caused by looting between 2011 and 2014, which were identified in ENVI 5. 3 by using the changedetection tool. These applications have primarily exploited broadband multispectral remote sensing data. As  Giardino (2011)  and  Agapiou and Lysandrou (2015)  argues, multispectral sensors carried on satellite platforms have provided new and important dataset for the investigation, discovery, delineation and analysis of ACH sites worldwide  (Agapiou et al.
There exists a variety of VIs, each of them using different datasets and optimized for specific purposes  (Agapiou et al. , 2013a; Agapiou et al. , 2013b; Atzberger et al. , 2014). As a result, they all have their advantages and disadvantages as well as appropriate operational scales. As such, not all of these VIs are of benefit to archaeology and the NDVI remains the most popular index  (Lasaponara and Masini, 2007; Masini and Lasaponara, 2007). A comprehensive analysis that included 38 vegetation indices, object-oriented classification and segmentation, PCA, and color transformation, was applied to the detection of looting marks in WorldView-2 imagery and the results interpreted  (Agapiou et al. , 2017a). Lasaponara and Masini (2014)  proposed a novel enhancement approach based on local indicators of spatial autocorrelation applied to ASTER and QuickBird data, which allowed them to visually identify traces of a possible ancient hydrographic network.
However, the spatial resolution of hyperspectral satellite data is much lower than that of multispectral satellite data and so, of limited application to the fine-scale study of ACH sites. The satellite EO-1 Hyperion has a spectral and spatial resolution of 10 nm and 30 m, respectively. There is an extreme paucity of studies exploring the capabilities of hyperspectral satellite data in ACH application. Savage et al. (2012)  guided an archaeological survey and found several potential ore-processing areas with similar spectral signatures by visually interpreting the results of applying PCA to Hyperion imagery. Another exception is the research published by  Agapiou et al. (2013b)  which demonstrated that their proposed Archaeological Index was suitable for the enhancement of crop marks in Hyperion imagery.
The fully polarimetric SAR data acquired by E-SAR L-and P-band radar, together with X-and C-band E-SAR data, has been collected to estimate surface topography. UAVSAR, a NASA/JPL L-band airborne experimental SAR platform that uses quad-polarization to image, has been in operation since 2009. UAVSAR is designed to collect fully polarimetric SAR data from an aircraft that can fly near-exact-repeat flight lines for interferometry research and applications. This radar system is designed to operate on an Unpiloted Aerial Vehicle (UAV) but was initially demonstrated on a NASA Gulfstream III aircraft (C-20A/G-III)  (Comer et al. , 2017). Spaceborne SAR systems can be mounted on space shuttles, space stations or satellites. SEASAT, the first spaceborne SAR system, was launched in 1978 by NASA, to carry out demonstration studies focused on the oceans.
Later, the Shuttle Imaging Radar (SIR) systems consisting of SIR-A, SIR-B and SIR-C were launched one after another and acquired data that gradually came to be used for archaeological purposes. Since the first use of SAR imagery acquired by the space shuttle in the 1990s, satellite SAR systems, such as ENVISAT and RADARSAT, developed by ESA and CSA, respectively, have been introduced and their products have also been used by archaeologists. In 2000, the Shuttle Radar Topography Mission (SRTM) was designed for interferometric applications and measuring large-scale surface changes. The Digital elevation models (DEMs) derived from SRTM data have been and still are one of the most useful and most often used SAR-based products in landscape archaeology  (Bubenzer and Bolten, 2008; Menze et al. , 2006).
Since 2007, satellite SAR has witnessed a revolution as the first generation of satellites (e. g. , ERS-1/2, JERS-1, ENVISAT, ALOS-1, and RADARSAT-1/2) has been followed by the second (e. g. , COSMO-SkyMed, TerraSAR-X, Sentinel-1, ALOS-2 and Gaofen-3). Satellite SAR systems have been developed to provide multi-frequency, multi-polarization, multi-mode, variable incidence angle, and high-resolution data. Furthermore, a number of user-friendly commercial and open-source software or toolkits have recently been developed. As a result, spaceborne SAR systems have ushered in a new era of ACH applications mainly due to the increasing availability of large historical archives and active satellite platforms.
(2016)  semi-automatically extracted information about farmland relics at Turkey's Hierapolis site. These studies proved that pixel-based classification can be quite useful. However, when compared to object-based approaches, OBIA methods are more accurate for detecting ACH features  (Davis, 2018; Luo et al. , 2018b). OBIA is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery  (Blaschke, 2010; Blaschke et al. , 2014; Davis, 2018). It generally consists of two key steps: segmentation and then classification. Improvements in accuracy have been attained by using a greater number of morphometric variables for segmentation and multi-scale feature analysis for classification. Over the past decade, OBIA methods have been introduced to automatically detect archaeological features from multisensor datasets  (Cerra et al.
, 2011; Luo et al. , 2014b; Piga et al. , 2014). There is no one all-purpose remote sensing dataset on which the archaeologist can rely that will uncover all evidence of human occupation. This is determined by the nature (buried, weak, complex, and have multiple solutions) of archaeological features and the archaeologists' subjective perceptions when interpreting the data  (Luo et al. , 2018b). For maximum data mining and archaeological knowledge discovery, it is necessary that archaeologists collect and employ as many different types of remote sensing data under as many different environmental conditions as their resources and skill will allow. Thus, a conventional approach is for archaeologists to build local databases using multiple remote sensing datasets. In ACH applications, such databases can be built by integrating historical photographs, satellite data and aerial imagery that require further processing.
Only then can the use of big remote sensing data led to a comprehensive understanding of the archaeological and cultural purposes of their regions of interest. Since the turn of the 21st century, the progressive improvements in imaging sensors in terms of spectral and spatial resolution, temporal frequency and coverage, and the renewed enthusiasm for ASRS as the data the sensor provided became finer and more sensitive to ACH features, has meant that archaeologists' preoccupation with detail and feature detection has been highlighted. However, ASRS imagery generally fails to reveal buried features in significant detail, which can negatively affect archaeological interpretation. A novel space-airground integrated database is needed to respond to this archaeological question. It should be acknowledged openly that ground remote sensing is very useful in archaeology.
We need and should make full use of the experience of existing networks and organizations for the benefit of the society. For example, since the early 2010s, HIST launched several research projects to refine and validate the use of passive and active remote sensing for archaeological purposes  (Wang et al. , 2017b). In the beginning of 21st century, NASA formally solicited "Space Archaeology" proposals through its Earth Science Directorate and continues to assist archaeologists and cultural resource managers in doing their work more efficiently and effectively  (Giardino, 2011). Moreover, the Center for Advanced Spatial Technologies at the University of Arkansas set up methods for the reliable and efficient orthorectification of CORONA imagery and provides free public access to their imagery database (covering the Near East and surrounding areas) and GIS tools for non-commercial use in archaeology  (Casana and Cothren, 2013).
Conclusions Even though remote sensing technology, especially ASRS, was not originally designed for archaeological purposes, it has become an indispensable and powerful tool in ACH and is being applied for miscellaneous purposes. Archive and novel ASRS data allow multi-temporal and multi-scale prospecting and change analysis of ACH sites. Historical photographs constitute a highly valuable resource as many of them show sites and landscapes that have since been heavily altered damaged or destroyed because of, for example, land consolidation, irrigation, urban sprawl, or armed conflict. Multi-and hyperspectral remote sensing is cheaper, more intuitive and has wider applications than active remote sensing for archaeological prospecting and feature identification. SAR now has the great potential to provide suitable resolution for prospecting, mapping and monitoring, condition assessment and documentation, as well as conservation and management.
Airborne LiDAR generally serves the purposes of exploration below the vegetation canopy or underwater and for carrying out landscape analysis better than spectral imaging, SAR, or photography can. Aerial photography and UAVs can be used to collect centimeter-level VHR data that is typically captured at the site or site scale to support the detailed mapping and monitoring. As an emerging force in aerial archaeology, UAVs play an increasingly important role in ACH applications. ASRS is a useful tool when combined with field campaigns, and it is a valuable and low-cost method for archaeological investigations in remote or inaccessible regions (e. g. tropical forests, deserts, Gobi, underwater or war-zones). It can also be used to help accentuate spatial, spectral, temporal and structural features (e.
ASRS represents an invaluable set of powerful tools for prospecting, monitoring and documentation and also for supporting the assessment and conservation of ACH sites and their supporting environments. ASRS is not always suitable in all environmental situations at any time and for all types of ACH features. The reflection or emission properties of ACH sites distinguishable by various types of imaging sensors may perhaps be one of their most characteristic features, and yet the meaning of the differential discrimination of features has not been determined for the most part, since such spectral and backscattering properties are inadequately understood at this date. ASRS provides big data related to archaeological objects, but data without interpretation is of no value. Opportunities to access archive and novel data acquired by spaceborne and airborne platforms are increasing, and training on how to integrate these data and demonstration of their potential value for ACH study appear to be practical ways forward to fill the gap between the ASRS technology and archaeological communities  (Tapete and Cigna, 2017b; Wang and Guo, 2015) , and to support the transfer of ASRS technology into archaeological practice.
Acknowledgments The authors thank three anonymous reviewers for their helpful comments and suggestions, which improved this manuscript. Many thanks are due to the DigitalGlobe,  NASA/JPL ,  USGS ,  USIEI ,  ESA ,  Open Topography ,  LBI-ArchPro, ABT GmbH and CRESDA  for providing the multisource remote sensing data that were used in this study. Also appreciated was the field survey assistance of  Touil Lamia  from  IRA  and  Zain Ul Abedin  from  CUI , and the technical assistance of  Meng Wang ,  Lanwei Zhu ,  Jie Liu ,  Biao Deng ,  Bangsen Tian ,  Wei Zhou  and  Lei Liang  from  RADI, CAS. This work was supported by the  Strategic Priority Research Program  of the  CAS  (Grant No. XDA19030504 ), the  National Natural Science Foundation of China  (Grant No. 41801345 ,  61860206004 ) and the  International Partnership Program of the CAS  (Grant No.
For such a facility to be efficient, however, two key elements are necessary: full automation and high reliability. While currently the selection of transients sent to the SEDM is only partially automated, since early August 2018 the new SEDM pipeline, pysedm, has been in production and has extracted all submitted transient spectra automatically. In this paper we present pysedm. This software is meant to be a generic pipeline that could easily be adapted to any further SEDM-like instrument. The code is public and written in Python 1. It has not been designed to optimize the spectrophotometry, but rather to be fast, robust, fully automated and easily customizable. The pipeline is illustrated in Fig. 1. Section 2 we explain the cube extraction from the raw observations of the nightly calibra-.