dataesr/scientific-paragraphs-categorization huggingface dataset:
Surface movements derived from sequential orthomosaics Image matching using a normalised cross-correlation (NCC) function on CIAS software  (Kääb and Vollmer, 2000; Heid and Kääb, 2012)  was applied to sequential orthomosaics (obtained from the previous section) covering the Tsarmine rock glacier and its environs. This procedure relies on the heterogeneity produced by the shape and size of the boulders at the rock glacier surface, providing high-quality contrasting and persistent targets. One of the main benefits of the NCC implemented in the spatial domain is its ability to better compare sequential images with different illumination conditions  (Heid and Kääb, 2012). Basically, the NCC matches homologous points from the moving surface by correlating a window of reference pixel values (8-bit greyscale image) sampled from an initial image at time step 1 with a larger search window area contained in an overlapping image at time step 2.
DEM assessment and elevation change analyses The mean and standard deviation errors from the bundle bloc orientation (Table  2 ) provide the overall quality of the UAV-SfM-derived products. However, to further test the derived DEMs' elevation component, their accuracy was evaluated by comparing their modelled z coordinate to the measured z coordinate from between 36 and 45 TGS points measured (described in Terrestrial geodetic surveys Section) on the rock glacier surface at the same date (see Supplementary Table  S2 ). This procedure also allowed us to determine the possibility of systematic elevation errors (e. g. , doming, dishing, tilt or shifts), which have been extensively reported for SfM-derived datasets  (James et al. , 2017; Sanz-Ablanedo et al. , 2020). Elevation and volume changes were obtained by computing DEMs of differences (DoD) from the UAV and TLS surveys using the Geomorphic Change Detection (GCD) software  (Wheaton et al.
Front changes and scarp development Aside from describing detailed surface velocity fields employing high-resolution orthomosaic pairs, the associated UAV-derived datasets, such as DEMs, also describe some remarkable geomorphic changes in the rock glacier. Frontal line changes between 2016 and 2019 are shown in Figure  6. The mean frontal positions (horizontally measured) relative to the October 2016 survey fluctuated between -0. 79 m (September 2017) and +2. 42 m (June 2019). These changes reflect the oscillatory position of the rock glacier front at the time of each UAV survey acquisition. Despite the substantial surface displacements during the snow-free periods, the rock glacier front faces net erosion (frontal retreat). Likewise, the rock glacier TABLE 4 Information on the minimum (Min), maximum (Max) and mean LoD (confidence limit of 90%) for each period.
Regarding the recent popularity and extensive analysis of rock glaciers using InSAR (e. g. ,  Villarroel et al. , 2018; Bertone et al. , 2019) , precautions in the interpretation of landform kinematic should be taken when small, and rapid features with heterogeneous kinematic values are investigated. The quality of the highresolution UAV-derived datasets leads us to stress that regular and spatially distributed surface velocity measurements would permit the monitoring of destabilised landforms in a satisfying way.
In addition, potential threats to UAV operations are mountain birds, especially raptors, due to their aggressive response to small flying objects. Furthermore, national or international UAV regulations may pose additional restrictions or even prohibit the operation of some UAV models in specific areas. In Switzerland, the Federal Office of Civil Aviation (FOCA) bans the operation of UAV devices in protected areas for water and migratory birds typically. In addition, despite the promptness of the UAV data acquisition in the field, the overall time to obtain information on the landform kinematics can be relatively long due to the different steps, including the processing of the raw images via the SfM photogrammetric workflow, as well as the sequential image matching using the NCC function. The processing of UAV-derived data requires adequate computer and software capabilities.
A single UAV survey at the Tsarmine rock glacier is processed using a DELL Precision Tower workstation (2 × 32 GB DDR4 2666 MHz memory and 58-core Intel Xeon E5-2695 @ 2. 30 GHz processor) in less than 3 h. This is due to the highly parallelised software structure in Pix4D, which takes advantage of the different resources available (https://pix4d. com/pix4dmapper-pro/, last access: 4 June 2021). Likewise, depending on the grid sampling density and the search and reference window sizes, a single image matching using the NCC function on CIAS software takes around 30 min. Regarding the specificities mentioned above, it is not surpassing that UAV monitoring approaches are gaining considerable momentum in geomorphology  (Eltner et al. , 2016; Cook, 2017; Śledź et al. , 2021)  and cryospheric research  (Gaffey and Bhardwaj, 2020).
Acknowledgments We sincerely thank  Gernot Seier  and  Stuart Lane  for their detailed comments on this manuscript. Also, our gratitude goes to the  Tsarmine  field team from the  Universities of Lausanne and Fribourg , especially to  Sébastien Rüttimann  (earlier UAV surveys) and  Mario Kummert  (earlier TLS surveys). We also acknowledge the opportunistic employment of the permanent GCPs installed during a previous study by  Umberto Morra di Cella. HH benefited from three travel grants for the collection of the TLS data used in this study, provided by  FWO Flanders, Belgium  ( V4. 321. 17N , V4. 253. 18N  and  V4. 248. 19N ). We thank the editor,  Mihai Ciprian Mărgărint , and two reviewers for their very useful comments on our manuscript.
Data availability statement The UAV datasets collected during this study are available from the corresponding author upon reasonable request. The complete series of interactive 3-D models from the Tsarmine rock glacier is available at https://sketchfab. com/UNIL-IDYST/ collections/tsarmine-rock-glacier. The kinematic data from the Tsarmine rock glacier is available from the PERMOS office upon request, and the reference website link is http://www. permos. ch/ data. html.
General Experimental Procedures Optical rotations were acquired on a digital DIP-370 polarimeter (JASCO, Oklahoma City, OK, USA). The IR spectra were recorded on a Shimadzu Infrared-400 spectrophotometer (Shimadzu, Kyoto, Japan). One-and two-dimensional NMR spectra were acquired on Bruker Avance DRX 600 MHz (600 MHz for 1 H and 150 MHz for 13 C NMR) (Bruker, Rheinstetten, Germany) or on Bruker Ascend 850 MHz (850 MHz for 1 H and 213 MHz for 13 C NMR) (Bruker BioSpin, Billerica, MA, USA) spectrometers using DMSO-d6, CDCl 3 , and CD 3 OD as solvents. NMR spectra were referenced to the residual protonated solvent signals (DMSO-d 6 : 2. 49 ppm for 1 H and 39. 5 ppm for 13 C; CHCl 3 : 7. 26 ppm for 1 H and 77. 0 ppm for 13 C, CH 3 OH: 3. 30 ppm for 1 H and 49. 0 ppm for 13 C). The positive ion HRES-IMS data were obtained with a Micromass Q-ToF equipped with leucine enkephalin lock spray, using m/z 556.
Sequence of ITS-rDNA Fungal Region The sequence of the ITS-rDNA fungal region of the isolate was used for the initial characterization through comparing it with related sequences in the NCBI database  [36]. The Clustal X (Version 1. 83) program  [37]  was used to edit and align the fungal ITS-rDNA sequence with the best n-BLAST hits from GenBank  [38]. Further manual adjustments were carried out, using BioEdit software  [38]. The base composition of the fungal sequences was calculated, using the MEGA v. 6 program  [39].
Over 25 million Small and Medium Enterprises (SMEs) in Europe face multiple challenges related to personal data protection (PDP), ranging from awareness of EU's General Data Protection Regulation (GDPR) to a clear and practical roadmap to compliance. Many also cannot afford access to enterprise-grade cybersecurity technology. This paper presents the main objectives and innovations of the EU-funded SENTINEL project, which introduces the concept of Intelligence for Compliance; it integrates tried-and-tested modular cybersecurity and privacy technologies with fresh, ambitious ones, such as a novel Identity Management System (IdMS) for human-centric data portability, and an end-to-end PDP compliance self-assessment framework. Combined with machine learning powered recommendations, policy drafting and enforcement for compliance, and a set of plugins that contains cybersecurity, data privacy and simulations/training software tools, SENTINEL aims to help small enterprises feel considerably more secure by safeguarding their and their customers' assets.
However, according to the same study, millions of European SMEs still do not fully comply with the GDPR. This presents an imbalance between security-and privacyrelated spending and its actual effect on personal data protection (PDP) and compliance for SMEs. We perceive this imbalance as an opportunity and key driver behind the vision of the SENTINEL project. In this respect, the SENTINEL project  [4]  proposes a novel "onestop shop" approach to integrated and obtainable privacy and personal data protection compliance for SMEs, based on a rationale described in the following analysis. SENTINEL aspires to bridge the security and personal data protection gap for European SMEs by raising awareness and boosting their capabilities in the domain through innovation at a costeffective level. This vision will be realized by integrating tried-and-tested security and privacy technologies into a unified digital architecture and then applying disruptive Intelligence for Compliance.
To facilitate readability of the conceptual architecture diagram in Figure  2 , the components of SENTINEL are numbered (1 to 6) and are presented in this order. The numbering follows the user flow. (2) The Self-assessment context provides modules that manage SMEs' profiles, requirements, trainings and assessment by means of RASE (Risk Assessment for Small Enterprises) scorea multifactorial storage object which is created following the participant's initial profiling (requirements elicitation). The containing modules are: (a) the Self-Assessment (SA) Engine, which processes an SME requirements and profile to produce lists of recommended trainings and RASE scores; (b) SA tools, an interface to the available self-assessment plugins; (c) SME profiles repository, where profiles, RASE scores and progress for each participating SME is stored.
(4) The Observatory constitutes the interface of SENTINEL to the outside world by sharing and receiving anonymised security-related findings. It offers the following modules: (a) the Observatory Knowledge Base (KB), a store with a wide spectrum of security-related information, collected from external sources and updated with anonymised findings from SMEs registered with the SENTINEL platform; (b) the data reuse policy module, which identifies and shares patterns of reusable policy elements, assisting the creation of policy drafts, as well as updating the Observatory KB with useful policy information; (c) the Incident broker module, which interfaces end-user submitted incidents to external sources; (d) the Observatory Information Exchange, an API (Application Programming Interface) that periodically polls external sources for newly identified security-related information, and also shares findings stored in the Observatory KB with the outside world; (e) the.
Policies repository, a store that collects atomic, independent, validated policy elements that can be used for policy drafting and policy reuse. (5) External entities, plugins, and sources that are external to SENTINEL, including a list of available plugins, online trainings, open security data sharing platforms, and regulating entities such as CERTs (Computer Emergency Response Teams), CSIRTs (Computer Security Incident Response Teams) and DPAs (Data Protection Authorities). The compilation of plugins comprise both proprietary software tools that are customised and further improved within SENTINEL (e. g. , Security Infusion  [18] , MITIGATE  [19] ,  [20] , CyberRange  [21] , Data Protection Impact Assessment  [22] , Forensics Visualisation Toolkit  [23] , GDPR Compliance assessment tool  [24] ), as well as newly developed modules within the project, such as the Identity Management System, and open-source modules that all enrich the variety of offerings in SENTINEL.
A. Microenterprise test case The focus of this test case is to implement extra security measures for accessing genomic sequence and personal data from a bioinformatics platform software pipeline. The company's work is based on EMA -Exome Management Application: a bioinformatics platformsoftware pipeline, coupled to expert curation for the evaluation and reporting of actionable genomic variants. The EMA pipeline software provides sic (6) types of variant data interpretation services, plus a dedicated custom variant analysis upon request. The company handles human DNA sequence variants, human phenotype and disease related info, anonymized demographic data and technical experimental data. The company maintains an internal database containing user/customer-related information submitted during registration.
The types of anonymous data submitted by the users and maintained by the company include: (1) Human DNA sequence variants, generated typically by Next Generation Sequencing (NGS) applications and submitted by the users for variant prioritization and interpretation, (2) standardized, in Human Phenotype Ontology -HPO format, phenotype and disease related information related to and accompanying the specific co-submitted (1) data file (see above), (3) simple anonymous proband demographic data, such as gender, age, ethnicity, disease status (affected or not) and relevant disease inheritance information, (4) technical/experimental data, relating to the type of NGS analysis, platform utilized, etc. Although all proband/patient related data submitted by the users are totally anonymous, without any type of personal identifiers, bearing in mind that genomic sequence data constitute per se sensitive and precious personal data, the company wishes to have further layers of security regarding the access of data stored in the proband database module of the EMA pipeline.
CONCLUSIONS & FUTURE WORK The main objective of this paper was to present the main objectives and innovations of the H2020 SENTINEL project. SENTINEL proposes a novel "one-stop-shop" approach to integrated and obtainable privacy and personal data protection compliance to SMEs. Its main capabilities focus on: (i) assessing of the current privacy and data protection risks of SMEs; (ii) drafting and enforcing a unified GDPRcompliant privacy and data protection policy addressing the identified gaps; and (iii) collecting, analysing and sharing critical privacy incident or data breach information using proprietary and open-access platforms to facilitate both reporting to DPAs and responding faster to privacy-related threats. Future steps involve further development of the SENTINEL's modules and contexts, as well as validation of various use cases in real-world enterprise settings.
This paper presents XGBoost classifier-based methods to solve three tasks proposed by the European Prognostics and Health Management Society (PHME) 2022 conference. These tasks are based on real data from a Surface Mount Technologies line. Each of these tasks aims to improve the efficiency of the Printed Circuit Board (PCB) manufacturing process, facilitate the operator's work and minimize the cases of manual intervention. Due to the structured nature of the problems proposed for each task, an XGBoost method based on encoding and feature engineering is proposed. The proposed methods utilise the fusion of test values and system characteristics extracted from two different testing equipment of the Surface Mount Technologies lines. This work also explores the problems of generalising prediction at the system level using information from the subsystem data.
INTRODUCTION The 2022 PHME Data challenge encourages participants to solve multiple classification problems for a real production line from Bitron Spa. The dataset includes data from Solder Paste Inspection (SPI) and Automatic Optical Inspection (AOI) equipment of a real industrial production line equipped with automated, integrated and fully connected machines (Industry 4. 0). A detailed description of the dataset is given in 3. The challenge is to design an algorithm to predict test labels for the components. Specifically, the goal is to develop a hierarchical classification predicting: 1. whether the AOI classifies the component as defective; 2. in the case of a defect, the label applied by the operator; 3. in the case of confirmation of the defect by the operator, the repair label. To tackle this challenge, we pursue the following steps: data exploration and domain knowledge extraction, data cleaning, data preparation (normalization and encoding), data modelling (model training and validation) and results in analysis.
The four last steps were made recursively while trying different approaches, as shown in Section 4. Data exploration allowed us to identify three main issues within the given dataset: missing information, highly imbalanced classes (for all tasks) and high cardinality on the categorical features. The latter is not necessarily an issue but implies that a special treatment needs to be done to these features a priori. We will elaborate on the issues in Section 4. 1 and on the categorical encoding in Section 4. 2. To solve each task, we take different information units formed by feature tuples, corresponding to different levels on the data hierarchy. At the same time, different features are kept as relevant for each task and followed by specific normalization or encoding. The specific tools used for each task are described in detail in Section 4.
RELATED WORK Several scientific articles already present machine learning applications for Surface Mount Technology production lines. (Richter, Streitferdt, & Rozova, 2017)  proposes a convolution neural networks deep learning application working on the AOI system to automatically detect defects. In  (Tavakolizadeh, Soto, Gyulai, & Beecks, 2017) , some binary classifiers are tested to detect defects inside products using simulated production data. These data are simulated from several SMT lines and give a good classification score. In  (Parviziomran, Cao, Yang, Park, & Won, 2019) , a component shift prediction method is proposed to predict the shift of the pad during the reflow process. In  (Park, Yoo, Kim, Lee, & Kim, 2020 ) SPI data are used to predict at an early stage the potential defects of the prediction.
DATA DESCRIPTION The PHME provides the dataset used in this article as part of the 2022 conference data challenge  (PHM Society, 2022). The dataset includes measurement information from two different steps of the PCB production (see Figure  1 ). The first step is the SPI, in which each solder pad is checked to verify its compliance and, accordingly, a sanction is generated depending on the quality of the solder (evaluated on several physical aspects). The second step is the AOI. In addition to checking the finished solder pads, their position, shape, etc. , this process also inspects the component itself, looking for defects like missing or misaligned components. The AOI inspection has two types of sanctions: the automatic sanction provided by the machine itself and the one given by an operator that verifies the first one in case of spotted defects.
Challenges The exploratory analysis of the training data has revealed several issues that need to be tackled to solve correctly the different tasks: 1. Missing values: for three different PanelID, the proposed data missed some information in the SPI dataset. We choose to exclude the lines with no information. 2. Class imbalance: the number of components detected as defects by the AOI is much lower than the number of components from the SPI dataset. Similarly, the number of components really classified as a fault by the operator is much lower than the number of components classified as correct.
Indeed, a hot encoded variable is not very likely to lead to the purest nodes if it is very sparse. That is why, the tree split will not be done using this one hot encoded variable. Even if the original categorical feature has a lot of importance for the prediction. In our cases, other encoding techniques should be used. First, a common technique is the Hash encoding. It is already present in our dataset with a numerical value for each PadID level. The PadID level values depend on the FigureID of the pad. Here the Hash encoding is done with only one feature but in general, it could be encoded into more features. One of the most used hashing methods is described in (Yong-Xia & Ge, 2010). The next approach is the frequency-based encoding method: it is based on the frequency of the levels as the label value.
For instance, the aggregation with the mean of the Volume will not contain a lot of information if there are many pins, and only one defective pin for the considered component. For each tuple, we want to predict if the tuple is detected as a defect by the AOI equipment or not. Accordingly, the training target column is 1 if the tuple appears in the AOI dataset and 0 otherwise. It is worth noting that we are not considering as defective tuples for which only PanelID, FigureID, ComponentID appear with PinNumber = NaN. Both, categorical and continuous features, are used as input. We use the following continuous variables: Volume(%),Area(%),OffsetX(%), OffsetY(%),Shape(um),PosX(mm), PosY(mm),SizeX, SizeY that we simply call "numerical features" in the following of the article. As a categorical value, we only keep ComponentID that we encode using a CatBoost encoding method  (Prokhorenkova et al.
Task 2 For this second task, we first split the AOI dataset into two parts according to whether or not PinNumber = NaN. In the case where PinNumber is specified, we can join the AOI and the SPI easily using the columns PanelID, FigureID, Compon-entID, PinNumber in each dataset. From the joint dataset, we use the "numerical features" from the SPI test results as de-fined in task 1. For the categorical features, we keep three features: AOILabel, ComponentID and FigureID ComponentID where the later is the string concatenation of the variables with the same name. For these features, we use a CatBoost encoding based on the categorical encoders python library. We believe a better result can be achieved with deeper work on the optimization of the encoder hyper-parameters. Finally, we also create two new meta-features (not encoded) Count Pin Component and Count Pin Figure.
Task 3 Task 3 is about the prediction of one categorical value presented in the AOI dataset RepairLabel. This label can take two values, FalseScrap or NotPossibleToRepair. For this task, we tried the same approach as in task 2 predicting each four-tuple PanelID, FigureID, ComponentID, PinNumber and merging the results per component, but the approach was not successful. To improve the result, we choose to do the prediction for the PanelID, FigureID, ComponentID threetuple directly. As input, we use the same method as for task 2, grouping the SPI values per three-tuple using the mean as an aggregation method for the "numerical features". The categorical variables used are ComponentID, FigureID Component ID. As before, these variables were encoded using the Cat-Boost encoding method. The meta-features generated are Count Pin Component, Count Pin Figure and Count Pin Panel, respectively, the number of pins detected as defects by the AOI per component, figure and panel.