dataesr/scientific-paragraphs-categorization huggingface dataset:
Seismicity and Dataset Romania is an active seismic country and Bucharest is one of the most earthquakeendangered capitals in Europe  [51, 52] , threatened mostly by the earthquakes in the Vrancea intermediate-depth seismic source  [33, 53]. In every century, one to five strong earthquakes (M W > 7. 0) have occurred in Romania  [33] , with the most recent destructive ones being in 1940 and 1977. In 1977, due to the large values of spectral accelerations for long periods in Bucharest  [54] , 32 (most of them high-rise) buildings were completely or partially damaged, causing the deaths of 1424 people and 7598 injuries  [54] [55] [56] , which ranks the 1977 event as the most destructive earthquake in the country. Bucharest is located in the central part of the Moesian platform, in the so-called Romanian Plain, at around 165 km away from the Vrancea intermediate-depth seismic source  [57] [58] [59] [60] [61].
1-1. 6 s (e. g. ,  [61, [63] [64] [65] ). This peak was predominant for the 1977 Vrancea earthquake and it is of great interest to the Romanian earthquake engineering community. The plateau of the constant maximum design spectral acceleration is extended up to 1. 6 s (T C = 1. 6 s) in the Romanian seismic design code  [43]  for Bucharest. A list of the most recent earthquakes between June 2020 and June 2021, starting with the multi-sensor deployment campaign within TURNkey, is presented in Table  2 , according to the Romanian Earthquake Catalogue-ROMPLUS  [66]. The largest earthquakes that were recorded since the TURNkey network was deployed occurred on 9 April and 25 May 2021 (bold). The 9 April 2021 earthquake had occurred in the Vrancea intermediate-depth seismic zone, at 21:36:47 (local time) and a depth of 79 km.
Analyses The analyses were carried out to (a) show the representative functionality of the methods described in the previous section through a benchmarking analysis of different SHM techniques by using the high-fidelity TSA-100S installed on the TURN building, (b) document a case study in which visual presentations of the method outputs when a set of RS4D instruments are used (e. g. , shown for LAS), (c) provide repeatability analyses for selected two buildings instrumented with RS4D sensors (e. g. , DRG and BAL) through the use of two distinct datasets belonging to two seismic events, and (d) provide a comparison of identified frequencies obtained in all the cases.
Comparison of the Methods Considering the Recordings from the High-Fidelity Sensors Installed in the TURN Building Figure  8  presents the SVD of the TURN building using the dataset of the 25 May event. Although the use of EGM data may potentially impose inaccuracies in the period identification due to the inclusion of the input frequency content, in this particular case the identified 1. 6 Hz is far away from the incoming frequency content, as revealed by the application of the ST-AF method. Figure  9  presents the STFT-WF processing of the TURN building by using the dataset of the 25 May event in terms of FADs. Due to the presence of a high S/N ratio, no wavelet pre-filtering is needed. In both directions, natural vibration frequencies are found to be located at about 1. 58 Hz. In applying the CWT based frequency inversion method, only the recording of the sensor at the roof location is used.
Although this would be true in general terms, given the limited frequency information present in the source (f > 3-4 Hz) and the likelihood of having f 0 > 3 Hz is rather limited for the dataset under consideration apart from BAL. Hence, the main assumption that is being made is that the building response will be dominated by its first natural vibration frequency independently from the input motion. Although this assumption is important, it is underlined that a stronger event will provide also more noise-free tail information, which will enable to return to the use of tail-only information. In Figure  14d-f , following the inversions made by using the entire ground motion domains, natural frequency of vibration for LAS could be successfully finalized as f 0,x of about 1. 35 Hz. Finally, in Figure  15 , ST-AF based processing is shown.
e. , the case of DRG). Overall, the SHM approach associated with the smallest deviations from the mean is the one based on CWT. Finally, it could be concluded that all of the SHM approaches are expected to perform better in case of a larger magnitude event which would impose greater acceleration demands. As it has been discussed up to now, the weak nature of the recorded motions and high self-noise levels of the RS4D sensors are posing some limitations in the signal processing phase. Yet, these limitations may be overcome with a dataset belonging to a stronger event that generates motions with a higher signal to noise ratios rendering the use of RS4Ds suitable for the application in terms of RRE in Bucharest. If one considers a 5. 0 magnitude event with a similar epicenter as the Vrancea 1977 earthquake (160 km NE of Bucharest), occurring at 150 km depth, the expected mean peak ground acceleration (PGA) will reach 4.
One of the overall ambitions of the TURNkey project is to provide an efficient procedure for rapid prediction of losses immediately following an earthquake. The added value of the RS4D sensors data will be of critical importance in TB1, Bucharest. For future rapid damage assessment purposes in TB1, in case of an event with M > 5. 0, the proposed approach for damage assessment will be the empirical method presented in  [16] , which correlates with the reduction in the fundamental frequency to EMS98 damage class predictions.
Concluding Remarks This work investigates for the first time the usability of RS4D low-cost sensors for dynamic identification of structures. The database analyzed includes the existing buildings in Bucharest, Romania consisting of 4 reinforced concrete (RC) and 1 masonry building monitored with RS4D sensors plus an additional RC building equipped with high-precision TSA-100S sensors. SHM techniques for structural identification are FDD, STFT-WF, CWT, and Sensors 2022, 22, 4787 20 of 24 ST-AF. By using the data obtained during two recent low magnitude events of 9 April 2021 and 25 May 2021, the following conclusions are obtained:.
In particular, these legal terms are intended to act as a safeguard against function creep and data reuse. 2 Thus, personal data collected for one function on the basis of consent cannot be extended or reused for another function without obtaining consent for that function. Defects in consent are considered a cause of nullity in many countries (e. g. , see  [34]  for European countries such as France, Germany or UK). As a result, digital consent has become an integral part of privacy management in computing platforms  [35]. This leads us to build upon the ℓ-Completeness property for mobile crowdsensing platforms, which we initially introduced in  [36]. ℓ-Completeness defines the extent to which mobile users consent to the reuse of the contributed observations, that is, the property enforces that: (1) The user's data are analyzed with at least ℓ -1 other data sources in all the tasks the data contribute to, and (2) No individual knowledge may be inferred due to the participation to many tasks that may not involve the same contributors.
To address the malicious adversary model, we leverage the technology of Trusted Execution Environments (TEE)  [52]  (e. g. , Intel Software Guard Extensions (SGX)  [40] , AMD SEV [53] or even ARM TrustZone  [54] ). This allows specific and well-identified pieces of code to be executed inside secure (hardware) enclaves of the TEE, which provide hardware-based data and code confidentiality, integrity and freshness guarantees against a malicious server (adversary with full control on the server, including OS, hypervisor and software stack -see for example  [55]  in the case of Intel SGX). One of the main issues in securing local processing in TEEs is to cope with different types of side-channel attacks  [56]. Some side channels, typically cache attacks  [57] , page level memory observation  [58]  or timing can be mitigated through either careful coding of oblivious operators  [59]  or modification of the execution environment  [60].
Design challenges As discussed in the previous section, a TEE provides confidentiality and integrity guarantees against a powerful adversary, through an isolated secure memory area (enclave) where the code and sensitive data can be safely processed. Then, a straightforward design for a secure architecture supporting consent-driven multi-task assignment based on ℓ-Completeness (see Fig. 3 ) is to have a single enclave that: collects all inputs (users' consents and data contributions), builds ℓ-complete clusters for tasks (cf. Algorithms 1-3), executes tasks, and provides task results. While promising at first glance, this architecture suffers several shortcomings: • Enclave limitations (memory, fault tolerance, stateless): Considering Intel SGX as a TEE example, it supports only a limited memory space (∼94 MB) for applications running inside enclaves.
Meanwhile, task assignment/execution involves large data related to multiple users. • Limited scalability: We need to extend the trust of a single TEE to multiple ones, and thus support a distributed architecture with multiple enclaves to overcome the limits posed by a single-node architecture (single CPU). The architecture should be easily extensible to support multiple enclaves for task execution in case of massive datasets. • Limited size of the Trusted Computing Base (TCB): Our architecture should minimize the TCB inside enclaves to reduce the size of the task code that run inside enclaves and protect application memory from unauthorized OS accesses. • Meeting our security objectives: The deployment of a data processing module is not trivial; the secure execution of each task is challenging since a given task has only access to ℓ-complete clusters.
Secure architecture design The security of the proposed MCS server architecture is based on two pillars: (1) User consents and contributions are handled in the clear only within secure enclaves, and (2) Control and data flows between components are controlled by the enclaves themselves so that user contributions can only be used in consented tasks in the form of ℓ-Complete datasets. Below we describe the components and their associated enclaves, and then how the flow of data between components is secured.
e. , the Executing enclaves) according to the GCP (see Definition 3) of each cluster (i. e. , a cluster key κ is authorized to the Executing Enclave of task T i iff T i is in the GCP of that cluster). Therefore, we guarantee that any task has access only to authorized ℓ-complete datasets during execution. • Task Assignment. The component implements the three-phase multi-task assignment presented in Section 2 through the following enclave: ℓ-Complete Assignment Enclave: At the end of the ∆ time period, the enclave takes as input all consents and data contributions collected over that period and produces a set of clusters that are qualified for each task. • Task Execution. The component creates one dedicated enclave per task: -Executing Enclaves: Each of them evaluates a given task over a ℓ-complete dataset collected during a ∆ periodretrieved from the Reference Monitor Enclave -and returns the encrypted results to the relevant data requesters.
Secure data flow enforcement The flow of data between the different components must be controlled end-to-end -leveraging the attestation capabilities of TEEs -so that user contributions can only feed consented tasks in the form of ℓ-complete datasets, with the results of tasks only accessible to their respective requestors. This is primarily established by requiring that: (1) At login time, users verify that the Collect Enclave they are connecting to is authentic, and submit their contributions with the guarantee that they are indeed being processed by a legitimate entry point in our architecture, or drop out; (2) At runtime, the enclaves check their predecessors and successors against the expected data flow, and open secure channels to exchange data along that data flow -the Reference Monitor Enclave additionally enforces consent policies (see previous section) -, or abort; and (3) After the task has been executed, the Executing Enclave provides the results encrypted with a session key that is itself encrypted with the public key of the target requester, so that only that requester can access the result.
Prototype implementation We use SGX enclaves  [40]  for our prototype implementation. Other TEEs are eligible but they have several limitations, e. g. : ARM TrustZone  [54]  does not integrate any remote attestation mechanism, AMD's TEE  [53]  is limited in terms of security guarantees since integrity protection cannot be ensured. SGX provides strong confidentiality and integrity guarantees against malicious adversaries with privileged root access. It guarantees that the code and data embedded in the enclave are protected from untrusted applications outside the enclave. It offers a remote attestation mechanism that allows an enclave to attest its identity to another entity outside the platform. The system components are implemented in Python, and we leverage the SCONE platform  [65]  (Secure CONtainer Environment), a shielded execution framework to enable unmodified applications to run inside SGX enclaves (see Fig.
6 ). Using SCONE, the input data and the computation (Python code) are encrypted before being uploaded in the system. They are decrypted inside enclaves using keys that are transparently obtained from the SCONE CAS (Configuration and Attestation Service). As shown in Fig. 6 , we create a secure container image based on SCONE. The application is executed within a Docker container, the enclave code is verified by SGX when the enclave is created. We rely on SCONE to protect the File system, it encrypts specified files and creates a file system protection file that stores the message authentication codes for file chunks and the keys used for encryption. The experiments reported next are run on a server with Intel Xeon E-2276G 6-core CPUs with 12 MB cache and SGX 1-FLC support, 64 GB RAM. The machine runs Ubuntu Linux 18.
Performance evaluation of task assignment The following assesses the overhead of security (i. e. , using SGX hardware) on the Task Assignment component, which runs the most complex algorithms (cf. Algorithms 1-3, not considering the application-specific tasks). For users' contributions, we used a dataset made available to us by the authors of  [66] , which provides the users' activities, positions  and environmental noise measurements collected by their crowdsensing app. For the consent table, we used a Bernoulli distribution (with a probability α = 0. 6) to generate the consents of the participants (note that this is a worst case for our algorithm, with higher average distance between policies). For more precision, each execution is repeated 10 times with a set of random users, the result obtained for each evaluation represents the average of the different executions.
Impact of the number of participants Fig. 7  shows the performance overhead of executing our task assignment module with hardware and native (i. e. , no-SGX) modes while varying the number of participants (ℓ= 10 and number of tasks = 12). The total cost increases with the number of participants for both executions. The SGX hardware mode incurs an overhead of ∽ 30% because it requires handling encryption and paging operations. Indeed, the performance cost of the reads and writes increases as the application allocates memory that is larger than the EPC size (∽94 MB), which may be reduced by reducing as much as possible the required heap size. As a result, the amount of the heap size (i. e. , the value of the parameter that controls how much heap memory is allowed for the application) does not significantly affect performance (see Fig.
Fig. 10  reports the total contributions loss according to the value of ℓ with 12 tasks. For example, for the dataset composed of 5000 participants, if we consider a range of contributions loss as [10%, 15%], an estimated range of ℓ opt is  [10, 20]. The second step to estimate ℓ opt is to consider the performance of the k-means algorithm in determining the optimal number of clusters. For this, we may leverage approaches that allow determining the optimal k value of k-means such as, e. g. , the Elbow Method  [68]. The idea is to compute the within-cluster-sum of squared errors (WSS) for different values of k ∈ [k min , k max ], and choose the k for which the WSS starts to diminish. The use of such type of methods allows computing the k opt (i. e. , ℓ opt ) in order to improve the performance of our clustering algorithm in terms of both contributions loss and execution time.
Performance evaluation of task execution We now evaluate the performance of our Task Execution component using one Executing Enclave. We analyze the effectiveness of our algorithm using the same dataset (Section 4. 2) which provides the users' activities, positions and environmental noise measurements. We further carry out the analysis considering an illustrative task that computes the average daily exposure to noise according to the user's activity (i. e. , Stationary, Walking, In-vehicle, Tilting, On-bicycle).
Execution time Fig. 12  shows that the execution time (in milliseconds) increases almost linearly with the increase in the number of participants. The hardware mode is slightly slower compared to the native version. Our architecture allows protecting task processing against attackers with privileged access by securely executing tasks inside SGX enclaves. All data (consent data, user's data) and Python code (task query) are only decrypted inside enclaves. Based on the experiments, we can conclude our system combines strong security guarantees (integrity and confidentiality) using SGX, and offers acceptable performance overhead compared to a native execution. We performed additional experiments to evaluate the clustering algorithm by varying the value of ℓ, the number of tasks as well as the number of participants in our recent work  [36].
We specifically introduce a server architecture that ensures the integrity and confidentiality of the proposed solution through the use of trusted execution environments (e. g. , Intel SGX enclaves). That is, the server architecture ensures that no user's contribution can ever be exposed in clear text outside secure enclaves and apart from the tasks results produced. Evaluation using a prototype implementation and a dataset from a deployed crowdsensing app shows the relevance of the supporting algorithms. Experiments also show that the use of enclaves incurs a reasonable overhead. Our future work concentrates on addressing a decentralized architecture involving processing on the client side. With the generalization of TEEs in edge/users devices (e. g. , Intel SGX is becoming omnipresent on every PC and tablets, ARM's TrustZone on smartphones.
Participants Seventy-five right-handed women will be recruited amongst patients of the university hospitals of Nancy and volunteers in Lorraineregion, and included in 3 different groups: 25 patients with PNES, 25 patients with PTSD, and 25 healthy controls. Sample size. EMOCRISES is a pilot study. The 75-participants sample size cannot be calculated in a standard way because of the lack of previous data. Nevertheless, the number of subjects set is based on data from the literature. It is accepted that an fMRI study can be conducted in groups of 12 subjects with sufficient statistical power  [29]. Furthermore, all fMRI studies conducted to date in PNES and PTSD have been conducted on groups of no more than 18 participants  [23, 24] , which nevertheless allows for a relevant statistical analysis.
Ethics & authorizations The study will be conducted according to the Declaration of Helsinki and received approval by local and national committees. Ethics approval has been obtained on October 6th, 2016 from the institutional research ethics board (Comitede Protection des Personnes-CPP EST III) under the reference 16. 10. 01. EMOCRISES was accepted by the French National Medical Security Agency (ANSM) on November 2, 2016 under the reference 2016-A01295-46. It was registered as "Emotional Lived in Patients Suffering From Psychogenic Nonepileptic Seizures: Study by Functional Magnetic Resonance Imaging (EMOCRISES)" study under ClinicalTrials identifier NCT02976545 (registered November 29 th , 2016, https://clinicaltrials. gov/ct2/show/ record/NCT02976545). A written informed consent will be obtained from each participant.
All participants will undergo these tests. 3. Emotional recognition: by noting valence and intensity of emotional pictures from IAPS (International Affective Picture System)  [30, 31]  and NAPS (Nencki Affective Picture System)  [32]  databases. These emotional valence and intensity will be assessed using standard Manikin Self-Assessment scales (SAM) from 1 to 9. All participants will undergo these tests. Despite low IQ is a known risk factor in PNES  [33] , we will not conduct IQ assessment on participant, in order not to complicate recruitment and protocol. This can be a limitation of the study. Concerning cognitive abilities criteria, only persons with mental retardation, illiterate or who cannot understand and follow written and oral instructions and questions of the protocol will be excluded.
MRI data acquisition We will instruct each subject to look closely at the pictures throughout their displaying during the MRI, but we will not warn the subjects that she will have to assess emotional valence and intensity of some of the shown pictures after the examination. This, in order to avoid bias introduced by control of their emotional experience  [34]  or the verbal quotation  [35]. We will project task stimuli onto a translucent screen by a projector placed directly outside the magnet bore. Participants will see the screen via a mirror located in the head coil. Vision correction will be performed using non-magnetic glasses, which can vary in the range of -5 to +5 with 0. 5 dioptre steps. We will forward-project all stimuli onto a screen in the MRI scanner room using ePrime software (Version 3.
For these functional MRI sequences, TR will be adjusted on the paradigm's design: TR = 2500ms for emotional, Go/No Go and Resting state one; and TR = 1500ms for periodic oscillatory stimulations. Total duration of MRI acquisition will be less than one hour. Any adverse event occurring before or during the MRI examination will be reported on a dedicated support with the date and time. If needed, examination will be terminated at any time on participant's request or on medical decision. Five functional imaging sequences will be conducted and are described hereinbelow: 1. Negative emotional periodic oscillatory stimulation We selected pictures from the IAPS, NAPS and Geneva Affective Picture Database (GAPED)  [36]  databases according to their emotional valence and intensity in order to make 2 sub-groups of pictures: one with positive valence and high intensity emotional pictures (respectively rated > 6 and > 5.
Throughout this projection, a black, unmoving cross will be displayed in the centre of the screen. From time to time, this cross will turn white for a glimpse. We will ask subjects to identify each time the cross' colour changes and indicate it with a button placed at right hand. We will use this to assess the subjects' attention and focus on the MRI scanner and on the task. 2. Emotional pictures viewing sequence. We selected eighteen pictures from the IAPS and the NAPS databases. Again, we excluded sexual-themed pictures because of frequent sexual trauma history in our study population. These pictures were divided into 3 groups according to their emotional valence and intensity: 6 images of positive valence and very high intensity (respectively rated > 7 and > 6. 5); 6 images of negative valence and very high intensity (respectively rated < 3 and > 6.