dataesr/scientific-paragraphs-categorization huggingface dataset:
Choice of the Cut-off Heights In this section we empirically evaluate on the 1D-Toeplitz dataset the impact of the cut-off heights h A , h B on the proposed clustering-based correlation estimator. We also propose a heuristic to choose optimal cut-off heights. We consider different scenarios, including one that loosely matches live rat data settings, where the noise is high and the intra-correlation low. For each simulated pair of regions, and for various cut-off heights h A , h B , the squared error of the cluster-level estimators are computed and then averaged across the different clusters: ERROR = 1 N A clust N B clust ν A ,ν B (r CLA ν A ,ν B -ρ A,B ) 2. ( 16 ) The resulting surfaces are displayed in Figure  3. The lower the error, the better the quality of the estimator. As expected from Theorems 1 and 2, the error is lowest (refer to the orange points in Figure  3 ) for cut-off heights that are neither too small nor too large.
Illustration on Real-world Data We now apply our proposed estimator on real-world fMRI datasets, with the goal of estimating functional connectivity. At first, the sample cluster-level inter-correlation and voxel-level intra-correlation of different subjects can be visually inspected. The correlation estimates of three rats, including a dead one, are displayed in Figure  6 , and that of three healthy human subjects (from the HCP dataset) are shown in Figure  8. In brain functional connectivity studies, point estimates for each pair of regions are needed to construct a correlation matrix. A thresholding step is then applied to obtain a binary connectivity network where only the edges corresponding to the highest correlation values remain. In this section, we will therefore mostly focus on evaluating the regional-level entries of these correlation matrices.
Conclusion In this paper, we proposed a novel and non-parametric estimator of the correlation between groups of arbitrarily dependent variables in the presence of noise. We devised a clustering-based approach that simultaneously reduces the impact of noise and intracorrelation through judicious aggregation. We then proved that for an appropriate choice of cut-off heights of the dendrograms thus generated, our proposed estimator is a consistent estimator of the population inter-correlation. Moreover, our method yields both point estimates and a corresponding sample distribution that could be used, for instance, for uncertainty quantification. We conducted experiments on synthetic data that showed our proposed estimator surpasses popular existing methods in terms of quality, and demonstrated the effectiveness and reproducibility of our approach on real-world datasets.
Supplementary Materials Proofs of the Theorems are available in the appendix. Discussion about the relaxation of assumptions on the noise, as well as additional details and results on the synthetic datasets are available in the supplementary materials. Source code, including a notebook detailing how to reproduce the figures of this paper, is available at: https://gitlab. inria. fr/q-func/clustcorr. Funding This work was supported by the project Q-FunC from Agence Nationale de la Recherche under grant number ANR-20-NEUC-0003-02 and the NSF grant IIS-2135859.
Population Out of the 415 patients enrolled in the study, 174 were excluded due the additional inclusion criteria on the EEG recordings. One hundred seventy-five patients were retained for microstate extraction. Of these 241, 14 were removed from the statistical analysis given missing data across clinical variables (i. e. , aetiology, consciousness state). The resulting 229 patients formed the analysed cohort (median age 66 years [IQR = 21], 89 females, 38. 9 %, median time post-onset days [IQR = 23], Table  1)  ACNS Critical Care labelling resulted in 142 (62. 0 %) patients having an θ background and 124 patients (54. 1 %) with a symmetric brain activity (Table  1 ). The majority of the patients (N = 178, 85. 2 %) were found to have an anteroposterior reorganization of brain activity (2′ patients had an APG labelled unclear).
, 2020; Claassen et al. , 2021)  calling for a revision of its taxonomy  (Bayne et al. , 2017). Such revision has been performed using only neuroimaging-based, source-level derived information. In this optic, scalp-level data has often been discarded given the complexity of retrieving active/inactive sources and the well-known volume-conduction effect  (van den Broek et al. , 1998). However, being able to identify endotypes sharing similar neurophysiological characteristics allows to identify the customized recovery paths. In doing this, it is mandatory to foster translatability to clinical practice of the technique within different levels of care providers and nation's wealth  (Kondziella et al. , 2021). In this work, we used EEG data collected during daily routine recordings in patients with a sABI (and in a pDoC) to understand whether scalp-level spatio-temporal dynamics could converge into specific endotypes of consciousness presence.
In brief, regulations that propose the implementation of Remote ID require UAVs to broadcast periodic messages by reporting their identity, speed, location, and information about the Ground Control Station (GCS) (i. e. , the operator) to ensure public safety and monitor aerial spaces. The Remote ID requirement of the US was enacted in April 2021. After September 2022, all UAV manufacturers are required to comply with the Remote ID standard, whereas operators are required to do so until September 2023  [10]. However, the Remote ID requirement has raised privacy concerns among the UAV community, particularly. UAV operators dealing with CIs such as those in the retail, oil and gas, transportation, delivery, and healthcare industries. The broadcasting of cleartext data related to the identity and location of UAVs and their operators can enable malicious users to identify, and track UAVs and determine important information such as the locations of sensitive storage centers or the customer destination addresses of confidential/classified objects.
The mandatory data that should be broadcasted are the (i) drone/operator location, (ii) speed, and (iii) altitude. Operator registration is optional, and the data transmission interval is 3 s. Finally, the French Remote ID standard specifies Wi-Fi beacon technology as the communication technology  [19]. 3) Japan: In 2020, the Japanese Civil Aeronautics Act published the Direct Remote ID (DRID) standard document, which specifies the requirements that should be considered for UAV accountability. To summarize, the document states the following: (i) UAV registration has been mandatory since June 2022; (ii) each UAV must be equipped with a Remote ID module; (iii) a Remote ID message, containing the unique ID, serial number, position and speed, and authentication tag of the drone, must be broadcasted at least once per second to prevent common security attacks such as replay and man-inthe-middle; (iv) one of the following communication standards should be used: Wi-Fi beacon, Wi-Fi NAN, Bluetooth Low Energy Long Range, and Bluetooth 5.
However, it can enable an attacker to perform multiple malicious activities such as the capture, tracking, monitoring, and inference of sensitive data. Location-based attacks can reveal customer locations and company and CI storage sites, identify mobility patterns, and track UAV trajectories. Therefore, UAV operators or owners may want to conceal their UAV location information to protect their positions throughout their flight paths  [30]. Operator Identity and Location Disclosure. The abovementioned threats to drones apply to UAV operators. The Remote ID standard obliges UAVs to broadcast in cleartext their GCS data such as their latitude, longitude, altitude, and identification code (which is optional). Operator data should be unlinkable between several messages; that is, an unauthorized observer should not be able to deduce any change (if any) in operator data between multiple messages, or infer the potential relationship between an UAV and an operator.
Path Planning Algorithm Disclosure. The broadcasting of drone data, such as latitude, longitude, altitude, and speed, can enable an attacker to reverse engineer the shortest path or the best path algorithm that the UAV uses to reach a destination point using a data-driven approach. The attacker can capture data related to a particular drone and try to infer details using machine learning techniques on the algorithm behind it to generate the best path. Drone/Operator Identity and Location Spoofing. Currently, the Remote ID does not define any security property, such as authentication and confidentiality. A Remote ID spoofing attack involves the creation or impersonation of the real identity of a UAV to hide or transmit fake data related to the occupied location. A generic aerial vehicle can appear legitimate, thus deceiving legitimate receivers such as CI operators, authorities, and neighboring UAVs.
The authors evaluated their scenario using MATLAB R2020b and real UAV flight data assuming the use of Wi-Fi communication technology. However, the authors did not provide a security analysis. In  [37] , the authors focused on the location privacy issues in drone delivery. They assumed that an adversary can monitor a drone's path to force the disclosure of the customer's identity and delivery location. To mitigate this issue, they proposed a privacy risk assessment method that is based on the probability of connecting a particular customer with a specific vendor/supplier based on the delivery drone route. Then, they suggested methods of preserving the privacy of paths, such as the deployment of decoy vendors and the use of aggregation/obfuscation techniques. The authors have shown a very detailed privacy analysis.
Reddy et al. [38]  aimed to mitigate attacks related to location privacy and tracking by obfuscating the current location of a UAV, and randomizing its trajectory to prevent an attacker from locating or tracking the drone. To this aim, they proposed three strategies based on the shortest path, random location, and dummy location without specifying details related to the Remote ID standard. The authors adopted location obfuscation, trajectory randomization, entropy-based metrics, and convex hulls. Furthermore, they assumed that the drone is equipped with multiple radio modules, such as IEEE 802. 11 and Long Term Evolution (LTE), for communication. They simulated the solutions via OMNeT++ and conducted a meaningful privacy analysis. However, efficient communication and collaboration between the user, server, and drone are necessary to locate and track a target effectively.
For the performance assessment, the authors verified the solution in MATLAB R2021b and then deployed the protocol on a laptop, Raspberry Pi 3, and an ESPCopter drone assuming the Wi-Fi as communication technology. Moreover, the authors performed security analysis by the well-known automated verification tool ProVerif to prove that PTTM meets the standard security requirements. To protect drone communications, the authors in  [40]  examined the security, safety, and privacy issues related to the FAA Remote ID standard. On the one hand, they proposed a Remote ID standard-compliant authentication protocol to secure communication. It authenticates the end-to-end and the broadcasted messages by using an embedded Subscriber Identification Module (eSIM), a public key infrastructure, X. 509 certificates, and the Advanced Encryption Standard (AES) symmetric-key encryption algorithm.
Furthermore, Remote ID messages should be broadcasted at least once per second. The cryptographic payload broadcasted via Bluetooth or Wi-Fi should be compliant with the maximum Maximum Transmission Unit (MTU), i. e. , one message per frame, without resorting to packet fragmentation. Finally, the solution should allow for opportunistic operator/drone data decoding by authorized parties without any explicit key establishment occurring at run-time. Anonymous Remote Identification. The latest Remote ID rule allows UAV operators to partially protect their privacy by using session identifiers, known as a pseudonyms, rather than their UAVs' long-term identity for identification purposes. This feature enables unique identification by the FAA while maintaining the UAV's identity hidden. However, the Remote ID standard does not provide instructions on creating these identifiers or offer directions on their design.
The usage of different measurement protocols sometimes without comprehensive reporting of the corresponding variable-inherent uncertainties and the time-consuming manual data retrieval at multiple access points are further prone to data handling errors. Existing biogeochemical (BGC) data synthesis products have already tackled these challenges for other observation types and increased the utility of large amounts of individual datasets, e. g. , the MarinE MethanE and NiTrous Oxide product (MEMENTO,  Kock and Bange, 2015) , 100 the Global Ocean Data Analysis Project (GLODAP,  Lauvset et al. , 2022)  and the Surface Ocean CO2 Atlas (SOCAT,  Bakker et al. , 2016). However, neither IGMETS  (O'Brien et al. , 2017)  nor OceanSites  (Weller et al. , 2016) , a global network of long-term autonomous open ocean reference stations, have generated a global data synthesis product of time-series data that would complement existing BGC data synthesis products.
Data Sources The SPOTS pilot includes data from 12 fixed ship-based time-series programs (Fig. 1 ), all of which routinely measure BGC EOVs. All major climate zones are covered, although not all ocean biogeochemical zones are  (Reygondeau et al. , 2013). Existing datasets were extended whenever possible by publicly available and more recent data (Table  S1. ). In addition to capturing different marine environments (Sect. 2. 1), the characteristics of the time-series programs also differ in terms of the station visit frequency, i. e. temporal resolution (monthly, seasonal, or irregular), the time range of the observational period, the bottom depth and whether a dedicated research vessel is used (Table  1 ). If a time-series program consists of two or more related stations, usually the deepest station was selected.
Data Assembly The SPOTS pilot was created by standardizing data format, units, header names, primary QC flags, times, locations, and fill values and subsequently merging the individual datasets of each time-series program into one 290 file. Only data that received a WOCE quality flag 2 (Table  S3 ) were included in the product. Existing data were altered as little as possible without interpolation or calculation of "missing" variables. Similarly, original station-, cast-and bottle numbers were kept or created artificially if non-existent to ensure consistency. The headers, units, and flags of the individual time-series datasets were standardized (Table  S4 ) to conform with the WOCE exchange bottle data format  (Swift and Diggs, 2008) , a comma-delimited ASCII format for bottle data from hydrographic 295 cruises.
, 2022). For some 305 time-series datasets, the combined concentration of nitrate and nitrite was reported (Table  S4 ). If explicit nitrite concentrations were provided, these were subtracted to obtain the nitrate values. If not, the combined concentration was renamed to nitrate assuming that the relative nitrite amount is negligible. For the HOT program specifically, low-level, high-sensitivity measurements of macronutrients (phosphate and nitrate) were available but not included in the pilot product. Particulate organic matter was derived by subtracting the particulate inorganic matter from 310 the total particulate matter, if available. For particulate organic carbon and particulate organic nitrogen, the factors 1/12. 01 and 1/14. 01 (inverse standard atomic masses) were used, respectively, for the unit conversion to micromoles per kilogram.
Best-Practices (BP) Assessment Given the inconsistencies in the applied internal quality checks and the fact that bias corrections following 335 crossovers analyses are presently impossible to apply to all included time-series datasets 2  , the comparability of the data for the SPOTS pilot was qualitatively assessed. The information on the applied methods of each time-series program, as provided through the metadata collection, was evaluated against, ideally, published Best Practices (BPs), and otherwise known standard operating procedures (SOPs). "BP Flags" were assigned accordingly to each cruise of a time-series program (Table  2 ). 340 The majority of the defined "BP requirements" used for the evaluation are based on the Bermuda Time-Series Workshop report  (Lorenzoni and Benway, 2013) , with additional implementation of: GO-SHIP manuals  (Langdon 345 et al.
Quantitative Assessment of Data In addition to the qualitative BP assessment (Sect. 3. 3), the bottle data of the time-series are described by our quantitative descriptors: 1) precision, 2) accuracy, 3) variability on the most consistent depth layer, and 4) 360 consistency with GLODAP  (Lauvset et al. , 2022). Precision and accuracy were included in the SPOTS pilot dataset file, the latter two were not included and are only described here.
Comparisons to GLODAP The final quantitative descriptor indicates how well the time-series data compares to the GLODAP dataset (GLODAPv2. 2021,  Lauvset et al. , 2021)  and vice-versa, with no a prioi assumption of which is 'correct'. To this 405 end, we applied an adapted version of the GLODAP crossover routine to all individual cruises of the time-series programs. Generally, the crossover routine calculates a depth-independent offset between a cruise and a reference dataset based on multiple crossing cruises, i. e. "crossover-pairs". The secondary quality control of GLODAP depends heavily on this routine to determine and correct for biases of new cruises, which results in the high internal consistency of the core GLODAP variables. In the following, we first describe the crossover routine of GLODAP 410 in detail and subsequently highlight the modifications applied to the routine so that it fits our pilot product needs.
This crossover-pair standard deviation reflects the similarity of the offsets within 435 one depth surface and across all depth surfaces. The lower it is, the higher the confidence in the crossover-pair offset. Step 6) Repeat Steps 2) to 5) for all identified crossover-pairs. 440 Step 7) Calculate the total offset of Cruise A against GLODAP by inverse variance weighting all calculated crossover-pair offsets. The resultant standard deviation describes the overall uncertainty in the total offset. For our purposes, we applied an adapted version of the above-described crossover routine using GLODAP as the "reference dataset" against which each time-series station is compared. The term "reference dataset" does not 445 imply that the quality of GLODAP is higher than the quality of the time-series programs, only that it represents a dataset with known consistency in time and space.
Nutrients In most cases, all nutrient variables were measured simultaneously using one water sample (and/or with replicates 515 at a single depth sampled), and the applied methods were identical. This is represented in similar BP flags of the nitrate, phosphate, and silicate samples. For these three variables, around 95% of the applied methods met either all BP requirements or the "required" requirements. The most restricting BP requirement is the comparison to reference materials, which, especially for older datasets, was not met. The remaining data with a BP flag 3 corresponds to 2% of the silicate, 7% of the nitrate, and up to 8% of the phosphate samples. These flags are linked 520 to the preservation technique applied (poisoned instead of frozen for nitrate and phosphate) which particularly explains the lower fraction of silicate samples that do not fulfill the "required" criteria (DYFAMED, OWSM).
In addition to the WOCE flags, each bottle variable is further accompanied by the assigned BP Flag (Sect. 4. 1) and by the provided precision and accuracy estimates (Sect. 3. 4). The last column lists the digital object identifier (DOI) of the original dataset. All missing entries are indicated by -999. A total of 108,332 water samples are included in the product. Bottle salinity with 75,654 measurements is the variable with the most abundant data (Table  6 ). The number of bottle salinity samples is about twice the number of bottle oxygen and nutrient (excluding ammonium and nitrite) samples and almost five times the number of included DIC and total alkalinity samples. pH and nitrite have around 10,000 samples and the product includes between 4,900 and 7,600 samples of particulate matter, DOC, and ammonium.
Use-Case As an example to demonstrate both the utility and potential misuses of the SPOTS pilot, we applied the recently developed Trends of Ocean Acidification Time Series software (TOATS, https://github. com/NOAA-795 PMEL/TOATS) to the mixed layer total alkalinity data included in the product (Fig. 7 ). The TOATS software is a supplement to the recently published best practices for assessing trends of ocean acidification time-series and provides a python based Jupyter Notebook to compare trends across different (BGC) time-series data sets  (Sutton et al. , 2022). It was developed based on several published trend analysis techniques to standardize estimating and reporting trends from ocean carbon time-series data sets. Following a strict sequence of approaches 8 , TOATS 800 estimates i) the linear trend, ii) its uncertainties, and iii) the trend detection time of the assessed time-series data.
The latter indicates the minimum observational period needed to statistically distinguish between natural variability (noise) and anthropogenic forcing. This method requires time-series with sub-seasonal sampling frequency to constrain seasonal variability of surface ocean carbonate chemistry; however, for the purpose of this example, we assessed all time-series programs rather than restricting the assessment to time-series datasets with 805 regular monthly measurements. The only non-trivial calculation step we applied before running TOATS was to calculate the surface mixed layer depth for each cruise (defined using a 0. 3 potential density anomaly criteria following de Boyer Montégut et al. (  2004 )) and to average total alkalinity concentrations within the estimated mixed layers. The results of our use-case (Fig.
7 ) show trends in alkalinity for all time-series (seven of them with significant trends). 810 The ease of use in applying TOATS to multiple time-series demonstrates the main benefits and potential misuse of the SPOTS pilot at the same time. Concerning the benefits, the combination of the SPOTS pilot and TOATS enables any user to perform joint time-series studies that follow published BPs without requiring any in-depth programming knowledge. The need to, a priori, know about existing time-series program data and to subsequently mine, format, and QC the data, becomes redundant for all time-series datasets included in SPOTS. The required 815 input format of TOATS is also readily available by accessing the time-series product data through ERDDAP (Sect. 7. 2). Further, detailed information on methods and their changes over time will become even more accessible once the ODIS user interface is online.
7 ) are valid and interpreting differences across time-series programs could lead to false conclusions. Robust trend analysis also requires the user to acknowledge the impact of large data gaps in timeseries that inhibit the ability to constrain seasonal variability in many of the included datasets (e. g. CVOO), and make it impossible to remove periodic signals with confidence (second step of TOATS trend analysis). Following TOATS guidelines, we recommend applying TOATS to surface ocean biogeochemical data with at least regular 830 seasonal measurements or to restrict the trend analysis to specific seasons. Increasing the number of samples using additional interpolation and computational techniques could relax this restriction (e. g. , multivariate linear regression (MLR);  Vance et al. , 2022) , but computations accompanied by large uncertainties might also harm the robustness of the trend analyses.