dataesr/scientific-paragraphs-categorization huggingface dataset:
scoping search of ClinicalTrials. gov, the EU Clinical Trials Register, the WHO International Clinical Trials Registry Platform, EORTC and the ISRCTN registry for clinical trials on patients >18 years of age registered within the last 5 years identified 2 studies; 1 study with anticipated completion date in 2011 published in abstract form, and another which is not published and with no information about its current status, with anticipated completion date in 2014. 88,89 Under consideration that a median of 2. 5 reports of RCTs (range, 2-6) were published annually over the past 10 years, substantial new evidence that could pragmatically change the direction of the recommendation or the magnitude of effects is expected after 6 years. The validity of the present version of this rapid guideline is set until 2028.
, 2015)  and pancreatic cancer research  (Grüner et al. , 2012; Padoan et al. , 2013; Zhong et al. , 2015). In LILBID, a pulsed and focused infrared laser irradiates a μm-sized liquid water beam with substances at known concentrations dissolved therein. The transferred laser energy mechanically disrupts the liquid beam, producing charged and uncharged fragments  (Charvat & Abel, 2007; Wiederschein et al. , 2015). The created cations or anions are analyzed in a commercial ToF mass spectrometer. Collisional impacts onto space detectors and LILBID create almost exclusively singly charged species (e. g. ,  Karas et al. , 2000)  and the ions' flight times therefore only dependent on their masses. Much of the data collected by mass spectrometers on board spacecraft (e. g. , CDA on board Cassini) remains uninvestigated because manual evaluation of the large datasets is required.
Laboratory LILBID datasets have also been manually investigated thus far. A complete comparison of for example, CDA spectra with laboratory LILBID spectra is extremely time consuming, and still in progress. Only about 15% of all CDA mass spectra obtained from July 2004 to September 2017 have so far (2022) been investigated and compared with laboratory analogue data. It is therefore evident that an easily accessible and well organized database containing all laboratory LILBID mass spectra will facilitate the more rapid evaluation of flight data not only from CDA but particularly future spaceborne instruments, such as SUDA  (Kempf et al. , 2014)  or ENIA  (Srama et al. , 2015). In contrast to CDA data, the prospective data can then be investigated without any further delay. Existing databases containing mass spectrometric data created through diverse ionization methods include the NIST Chemistry WebBook (https://webbook.
nist. gov/), the mzCloud Advanced Mass Spectral Database (https:// www. mzcloud. org/), the MassBank of North America (MoNA; https://mona. fiehnlab. ucdavis. edu/) and a database for oligosaccharides in human milk  (Remoroza et al. , 2018). However, to the best of our knowledge, no such openly accessible spectral database for LILBID data exists. We here describe the development and functionality of our new extendable database containing LILBID mass spectra based on a Relational Database Management System (RDBMS) and SQL (Structured Query Language). The frontend of the RDBMS enables the sorting and filtering of laboratory LILBID data via a range of adjustable parameters specific to the used experimental setup (e. g. , laser energy) as well as parameters that depend on the measured substances (e.
Data Pre-Processing At the time of implementing the data acquisition process using LabVIEW, use of a database to store the spectra was not foreseen. Instead, a major focus was on the quick and easy manual location of different measurements stored on the hard drive of the analyzing scientist's computer. Therefore it was originally decided to have one line (the "solution" line; Figure  S1  in Supporting Information S1) in the header of the recorded raw data files (level-0), which includes numerous significant parameters, such as the names of the used analyte substances and their concentrations, separated by the "underscore" special character. This line, combined with other significant experimental parameters that are provided in other header lines, is then used as the filename for the raw data files.
The time interval between two consecutive lines of the columns corresponds to the sampling interval (1 ns) of the measurement computer (see Section 2. 1). After the file format check and any required manual editing, a peak detection routine is applied to the spectra. The peak detection routine is implemented in Python using the find_peaks_cwt algorithm within the SciPy scientific library. Its general approach is to smooth the data by convolving it using a wavelet transformation and then find relative maxima which appear over sufficient length scales and with suitably high signal-to-noise ratios (https:// docs. scipy. org/doc/scipy/reference/signal. html). The peak detection routine calculates a local baseline for a variable number of data points and uses a local percentile rank for the peak detection threshold.
The Database System We use a client-server infrastructure as the general system design, with the web browser representing the frontend and an RDBMS together with a web server acting as the backend. The spectrum parameters and extracted peaks are stored in relational tables in a MariaDB (https://mariadb. org/) database instance. The frontend's web pages are composed using HTML (Hypertext Markup Language), PHP (Hypertext Preprocessor), CSS (Cascading Style Sheets), and JavaScript. An Apache™ (https://apache. org/) web server serves a query form with entry fields for several possible parameter filters as the start page. After the user enters none, one or several filter parameters and submits the query, the server connects to the database (via the PHP module) and retrieves the relevant entries.
When no values for filtering are entered and submitted, the server lists all available entries. As the peak identities are stored in the database, the data can be filtered not only for the experimental parameters used during the measurements but also for the peaks present in the mass spectra. The layout of the user interface allows for adjustable block-wise (10, 25, 50, or all entries per block) browsing of the results. The automatic workflow of the steps from data acquisition in the laboratory, data pre-processing, data ingestion and finally the data query is shown in Figure  1. As new laboratory data is acquired, the database is regularly updated using the combined Bash/Python script (see Section 2. 2).
Data Post-Processing Software on the measurement computer converts the ions' flight times into their masses by use of a second order calibration equation. The applied delayed extraction method  (Klenner et al. , 2019)  and variable initial ion velocities result in unavoidable mass shifts (deviances up to >1 u) of the converted masses compared to the real ion masses in level-1 spectra during conversion of the ions' flight times into their masses  (Christian et al. , 2000) , with increasing mass shifts for increasing ion masses. For a reliable analysis of the mass spectra, it is critical to recalibrate the mass spectra to minimize the mass shifts. Recalibrated level-2 mass spectra are produced by applying a Python script developed in-house. The script calculates a polynomial function based on real peak masses (x-values) for previously detected (shifted) peaks and fits the corresponding data file with this function.
The resulting level-2 spectra often possess an uneven baseline due to instrumental, chemical and random background noise during the measurements  (Urban & Štys, 2015). The noise can either produce false positive signals or hide small peaks in the mass spectra. To fully interpret the mass spectra and further improve the spectral analysis, the background noise needs to be smoothed. Several methods and programs that are capable of subtracting the baselines from mass spectra exist (e. g. ,  Urban & Štys, 2015; Xu et al. , 2011). We here apply an in-house developed Python script based on the Penalized Least Squares Method  (Zhang et al. , 2020)  to create baseline subtracted data (level-3). KLENNER ET AL.
10. 1029/2022EA002313 5 of 11 The peaks in the level-3 mass spectra are then labeled with their corresponding masses (x-values). The peak labeling can be done either manually, or automatically using a software. Automatic peak detection routines can reliably detect peaks with significantly high amplitudes (y-values) but might overlook smaller peaks and detect false positives. For the reliable detection of small peaks, manual peak identification is indispensable. After peak detection, graphical plots of level-3 mass spectra are created using Python Matplotlib and stored on the server (Figure  1 ). The validated subset of the data (exclusively level-3) is accessible via the public database web server interface (see Section 3. 1).
The LILBID Spectral Database The in-house version of the spectral database currently contains more than 12,000 LILBID mass spectra recorded using a wide range of experimental parameters. The publicly available version contains a selection of validated and published reference spectra (level-3) and is accessible via the following link: https://lilbid-db. planet. fu-berlin. de. Figure  2  shows the search interface of the publicly available database. The spectra in the database can be searched using numerous experimental parameters, as described below, in order from upper left to lower right in the search interface. Moving the cursor over one of the parameters in the search interface shows a popup window (hover) with more descriptive information about this parameter. Substance: The name of the analyte.
A combination of delay time and laser energy can be correlated to impact speeds of ice grains onto mass spectrometers in space  (Klenner et al. , 2019). The data can be filtered using either a single experimental parameter or a combination of multiple experimental parameters. When applying the filter, all matches will be shown in the results interface (Figure  3 ) and can be sorted by individual parameters. Clicking on the name of the substance redirects to the corresponding Wikipedia article to provide additional information about the measured substances (solubility in water, toxicity, etc. ) without adding new columns to the Database Management System to maintain the database as small and efficient as possible. Graphical plots of the mass spectra can be retrieved via the "Images" column.
The "Files" column allows to download the spectral text files. A search function on the top right of the results interface lets one search for individual spectra within the results interface (Figure  3 ). Other available databases (see Section 1) can provide structural information or thermodynamic data about an analyzed single substance. In contrast, the LILBID spectral database contains mass spectra of single substances and mixtures of substances in variable background matrices recorded with individual sets of experimental parameters that are important for the comparison with and interpretation of impact ionization mass spectra recorded in space. The data in the LILBID spectral database can be searched for particular peaks in the mass spectra after processing with the peak detection routine (see Section 2.
Conclusions and Outlook The laboratory LILBID facility has been proven to simulate mass spectra produced by ice grain impacts onto spaceborne mass spectrometers. Variations in spectra due to the ice grains' compositions and impact speeds can be accurately reproduced by adjusting appropriate experimental parameters  (Khawaja et al. , 2019; Klenner et al. , 2019; Klenner, Postberg, Hillier, Khawaja, Cable, et al. , 2020; Klenner, Postberg, Hillier, Khawaja, Reviol, et al. , 2020; Postberg et al. , 2009 Postberg et al. , , 2018)). Over 12,000 LILBID mass spectra of more than 200 substances, each with their associated experimental parameters, have been uploaded to the internal version of the LILBID mass spectral database and are available to group members at Freie Universität Berlin and cooperation partners only.
A subset of validated and approved data is available for the general public (https://lilbid-db. planet. fu-berlin. de). The database, containing cation and anion mass spectra, can filter the stored spectra for the used experimental parameters that can be correlated with different properties of the encountered ice grains in space. Processing by a peak detection routine allows the mass spectra to be selected based on the appearance of particular peaks. Besides the spectral data, the database contains all compositional and experimental setup information, as well as.
Data Availability Statement Data-The LILBID mass spectrum raw data used in this paper (see Figure  4 ) can be retrieved from https://doi. org/10. 5281/zenodo. 6863855 (Klenner, 2022). Data-The CDA mass spectrum raw data used in this paper (see Figure  5 ) can be retrieved from NASA's Planetary Data System (PDS) Small Bodies Node via https://sbnarchive. psi. edu/pds3/cassini/cda/COCDA_0007. tar. gz (Planetary Science Institute, 2022).
Mid-term planning After the release from sales department, the production program is lled with concrete orders, which serve as an order backlog for the mid-term planning level of the hybrid scheduling approach. Under static conditions and a higher level of details, valid order lots (group of orders in a dened sequence) are created per plant with orders from the pool in sequence planning, which also takes into account the steel-specic aspect of the development of the inventory structures as well as plant-related restrictions. This mid-term planning task is carried out by a MOMILP algorithm, whereby the planning targets are taken from long-term planning and extended by additional order-related objectives (e. g. , minimization of delays). It provides a preliminary global optimal resource scheduling under static conditions, i.
Case B: Mid-term planning for global scheduling with production targets In the second experiment, the MOMILP-based ap-proach is fed with production targets provided by the CFM. The objective is to provide the global scheduling of several jobs under static conditions aiming at satisfying quantities provided by the CFM with a 10% of tolerance. The designed MOMILP-based approach is implemented through the PuLP library of Python programming language. The library allows modelling linear programming problems and is supported by different optimization solvers. In particular, the solver used for the simulation is GUROBI, which shows the best performances among the tested ones. Furthermore, Gantt charts are used for the graphical representations of the scheduling, which are built through Plotly (a library for Python based on the Dash framework).
Acknowledgments The research described in the present paper has been developed within the project entitled "Renement of production scheduling through dynamic product routing, considering real-time plant monitoring and optimal reaction strategies ( DynReAct) " (G. A. No  847203 ), which was funded by  Research Fund for Coal and Steel (RFCS)  of the  European Union (EU). The sole responsibility of the issues treated in the present paper lies with the authors; the Commission is not responsible for any use that may be made of the information contained therein. The authors wish to acknowledge with thanks the  EU  for the opportunity granted that has made possible the development of the present work. The authors also wish to thank all partners of the project for their support and the fruitful discussion that led to successful completion of the present work.
D. SIMULATION EXPERIMENTS This section provides simulation experiments to assess the feasibility of JMR in practice against the masking schemes presented in table 1. Its efficiency is compared with state-ofthe-art attacks at second and third order. Implementation. We implemented the core of the JMR attack using the least_squares function from the python scipy. optimize package  [25]. It solves a non-linear least-squares fitting problem using the Levenberg-Marquardt (LM) algorithm  [26] ,  [27]  which is itself based on the Gauss-Newton algorithm and the method of gradient descent. The attack time or complexity is mostly constant regarding the number of traces because the latter does not affect the number of parameters nor equations in the system. The only part that scales with the number of traces is the estimation of the JM per class which is just a product and a sum and that can be handled with numpy  [28]  array manipulations.
Generating Datasets. To assess the JMR method and to compare it with state-of-the-art attacks, synthetic trace datasets with linear leakage of the shares have been generated for first and second-order masking (d ∈ {2, 3}). Boolean, arithmetic, multiplicative and affine (only with d = 3) ) ∈ Z d do z 1 ← z ⋆ • • • ⋆ z d l ← ℓ (a) (z1,. ,z d ) (Equation  16 ) p ← Sbox -1 [z] ⊕ k * Append l to L Append p to P end R ← Draw a (2 8 * d , d) array from N (0, σ 2 ) L ← L + R return L, P schemes are used to mask the classical sensitive variable of an AES: Z = Sbox[k * ⊕ P ] (k * and P are both supposed to be 8 bits long). To be able to average the results of 100 different attacks, performed with 100 different linear leakage models, we have generated a matrix of random coefficients C i for each share (1 ≤ i ≤ d): C i = α a,b 0≤a≤99 0≤b≤8 (15) where all the α a,b are uniformly drawn from [-1, 1].
Each row represents a different linear leakage model. To avoid any kind of estimation error (the error coming from sampling), each dataset contains one trace for each of the possible values of the shares (z 1 ,. , z d ) ∈ Z d (for multiplicative and affine schemes the multiplicative shares can not be 0 so we take them from 1, 255 instead). The trace ℓ (a) (z1,. ,z d ) corresponding to the d-tuple (z 1 ,. , z d ) is generated by concatenating the leakage of each shares (represented by the a th row of the C i matrices) as follows: ℓ (a) (z1,. ,z d ) = l (a) 1 (z 1 ), • • • , l (a) d (z d ) (16) with l (a) i (z i ) = C i [i, 0] + 8 b=1 C i [a, b] • z i [b] + ϵ i (σ) (17) where z i [b] corresponds to the b th bit of z i and ϵ i is drawn from a normal distribution N (0, σ 2 ). The exact procedure that generates the traces considering the the a th leakage model is depicted in algorithm 1.
Results. Second-order attacks results are presented in Figure  1. Each point represents the average rank of k * over the 100 datasets for a given value of σ. We recall that we are using exhaustive datasets, therefore, a failed attack for a given value of σ does not mean that the attack is impossible but rather that the adversary would need more traces than one per possible value of the shares. We compare JMR with • A higher-order CPA, denoted HO-CPA, computed with a Hamming weight prediction model and using the JM of order d as combining function which happens to be the same as the centered product described in  [13]. • Higher order LRA, denoted HO-LRA-d max where d max is the assumed degree of φ k * as defined in Equation 5. Therefore the basis used in HO-LRA-d max is (b i ) i = {z u , u ∈ F n 2 , HW(u) ≤ d max }.
A. ATTACK ON A FIRST-ORDER BOOLEAN MASKED AES (ASCAD) As a first experiment, we performed the different stochastic attacks discussed in this paper on the public dataset of AS-CAD. It is a common set of side-channel traces, introduced for research purposes on deep learning-based side-channel attacks. The targeted implementation is a software AES, protected with a first-order Boolean masking, running on an 8-bit ATMega8515 board. We performed guessing entropies for the different attacks, using the training dataset containing 50k traces. We extracted from the dataset, the two Point-of-Interests (PoI) corresponding to the highest signal-to-noise ratio, one for each share. This step requires the knowledge of the shares and would not be feasible by a non-profiled adversary. In a real scenario, a visual analysis of the trace combined with knowledge on the implementation can be used to perform a PoI selection to reduce the number of sample combinations to be tested.
B. ATTACK OF AN OPEN SOURCE HARDENED AES IMPLEMENTATION (ASCADV2) As a second experiment, we decided to attack the second protected AES implementation proposed by the Agence Nationale de la Sécurité des Systèmes d'Information (ANSSI)  [32]. They published a library implementing an AES-128 on an ARM Cortex-M4 architecture using state-ofthe-art counter-measures. Indeed, this implementation uses an affine masking as well as random shuffling of independent operations  [33]. It is accompanied by a publicly available dataset called ASCADv2 providing 800,000 traces acquired on an STM32F303 microcontroller running this protected AES. A detailed leakage analysis of this dataset has been published in  [34]. Following their terminology we tried to attack the unmasked variable Z = Sbox[k * ⊕ P ] using the VOLUME 4, 2016 leakage of the three shares: Z 1 = Z ⊗ r mul ⊕ r out Z 2 = r mul Z 3 = r out (26) Unfortunately, the number of traces turned out to be too low to analyze the unsupervised attacks discussed in this paper.
This PRNG is re-seeded randomly (by sending 8 bytes on the serial port) every 250 acquisitions. This allows to regenerate (from the stored seeds) the plaintexts and random masks offline, to label the dataset. For each encryption, the scope triggers twice and acquires 50,000 samples. The final dataset contains 100M traces and took 14 days to acquire. In summary, we used the same AES implementation and micro-controller as in the ASCADv2 setup. We only made some changes in the instrumentation and measurement chain to reduce the number of traces needed and improve the speed of acquisition.
3) Results In a similar way to the first experiment from subsection V-A, we extracted from the dataset described in subsubsection V-B1, the three Point-of-Interests (PoI) corresponding to the highest signal-to-noise ratio, one for each share. We performed the attacks on both the shuffled and unshuffled versions. The attacks on the shuffled version only use the leakage of the first Sbox computation. Shuffling adds a lot of noise since even for the correct key hypothesis the predicted value of Z is only correct once out of 16 in average. Results are presented in Figure  5. Each point represents the mean ranking of the correct key over 100 attacks performed with the corresponding number of traces. For each attack, traces are randomly drawn among the 100M dataset. Both JM R ++Full and JM R ++ converge towards a guessing entropy of 0 which provides by the same token, the first unsupervised attack on the secured ANSSI's AES implementation.
VI. CONCLUSION This paper introduced a new unsupervised strategy, JMR, which embeds the masking structure within it, allowing it to defeat arbitrary masking schemes. It is based on a non-linear system regression which allows to derive the leakage model of each share by carefully exploiting higher-order joint moments conditions. JMR outperforms state-of-the-art attacks which are limited to Boolean and arithmetic masking, especially when the Hamming weight leakage assumption does not hold. We reduced the core of JMR into a more general framework: the generalized method of moments and derived optimizations of JMR from it. Experiments performed on synthetic data confirmed the effectiveness of the proposed attack, especially against multiplicative and affine masking schemes. Eventually, this new strategy has been confirmed on real traces, allowing a fully unsupervised attack of the ANSSI's protected AES implementation which embeds an affine masking and shuffling counter-measures.