dataesr/scientific-paragraphs-categorization huggingface dataset:
Since a typical far-field surface wave tomography uses a 2λ to 3λ criterion  (Lin et al. 2014; Luo et al. 2015) , the near-field approach using λ/2 promises a corresponding increase in depth resolution. For a discussion on lateral resolution we first compare seismic and elastography data acquisition to understand the transferability of results from the latter domain. Seismic data acquisition directly samples the ambient seismic surface wavefield from which the spatial autocorrelation fields are computed. In passive elastography of soft tissues, the random shear wave amplitude field or speckle pattern from which the focal spots are reconstructed is first imaged using the ultrasound device. This speckle tracking determines the resolution. The ultrasound wavelength is much shorter than the shear wavelength, leading to 100-200 pixels per wavelength, typically in the downward direction.
Our measurements on clean but also on systematically biased synthetic data demonstrate that the focal spot method can provide robust estimates of the local wave speed using data collected at sub-wavelength distances. We studied the effects of impinging P waves, anisotropic surface wave incidence, and noise in homogeneous and dispersive media on ZZ and ZR component data. For many configurations a fitting range r fit ≈ λ together with azimuthal averaging yield an error on the Rayleigh wave speed estimate that is on the order of 1 per cent or smaller. A general conclusion is that the cleaner the surface wavefield the smaller the fitting ranges that are sufficient to accurately estimate the local Rayleigh wave speed, which implies a trade-off between accuracy and lateral resolution for noisy wavefields that needs to be studied separately.
A C K N O W L E D G M E N T S We acknowledge the University of Helsinki IT4Science team for their support, and the computational and data storage resources of the Finnish Grid and Cloud Infrastructure  (FGCI, urn:nbn:fi:research-infras-2016072533). This work was sponsored by a research grant from the Academy of Finland (decision number 322421). M. C. and L. S. acknowledge support from the European Research Council under the European Union Horizon 2020 research and innovation program (grant agreement no. 742335, F-IMAGE). M. C. and L. S. acknowledges the support of the Multidisciplinary Institute in Artificial Intelligence MIAI@Grenoble Alpes (Program ``Investissements d'avenir" contract ANR-19-P3IA-0003, France). We appreciate comments from O. Coutant that helped with the AXITRA implementation.
Photophysical investigation The photophysical properties of the four complexes were firstly investigated in CH2Cl2 solution at concentration of 310 -5 M in both air-equilibrated and degassed condition at room temperature. A list of the most meaningful photophysical data is compiled in Table  1  and the corresponding absorption and photoluminescence spectra at room temperature are displayed in Figure  2. Excitation and low temperature (77 K) emission spectra are displayed in Figure  S5  and S6 of the Supporting Information, respectively. For complex IrCu-1 and IrAu-1, photophysical characterization in acetone solution was reported by us previously. [37] At first glance, UV-vis absorption of the four complexes evidenced similarities in the overall profile of the transitions expectantly, owing to the rather similar electronic properties of the 1-phenylisoquinoline and 2-(dimethylphenyl)-quinoline ligands.
Electrochemical characterization The electrochemical behavior of the four bimetallic complexes was assessed by cyclic voltammetry (CV) in acetonitrile (ACN)/0. 1 M tetra-n-butylammonium perchlorate (TBAP). All the electrochemical data are referenced against ferrocene/ferricenium couple as the internal standard and are reported in Table  3. As shown in Figure  3 , in the positive-going scan, all compounds showed a main reversible oxidation process, O1,n (with n denoting the investigated compound), whose standard potential   1 0 decreased progressively from 0. 64 V to 0. 45 V following the order IrCu-1, IrAu-1, IrAu-2 and IrCu-2. Instead, in the negative-going scan, two or three reversible and irreversible reduction processes Ri,n (where i denotes the process number and n the investigated compound) occurred in the potential range from -2.
{[Ir(dm-2-pq)2][Cu(IPr)](-1 [74]  The Ru(bpy)3Cl2 complex in air-equilibrated water solution at room temperature was used as reference (PLQY = 0. 04). [75] All the solvents were spectrophotometric grade. Dearated samples were prepared by the freeze-pump-thaw technique by using a home-made quartz cuvette equipped with a Rotaflo stopcock. All the PLQYs on spin-coated thin-film samples were recorded at a fixed excitation wavelength by using a Hamamatsu Photonics absolute PLQY measurements system Quantaurus QY equipped with CW Xenon light source (150 W), monochromator, integrating sphere, C7473 photonics multi-channel analyzer and employing the commercially available U6039-05 PLQY measurement software (Hamamatsu Photonics Ltd. , Shizuoka, Japan). All measurements were repeated five times at the excitation wavelength λexc = 450 nm, unless otherwise stated.
ACKNOWLEDGMENT A. B. and M. M. gratefully acknowledge the  Université de Strasbourg  and  CNRS  for financial support. The  International Centre for Frontier Research in Chemistry (icFRC) , and the  Labex CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02  is also acknowledged for funding the PhD fellowship of A. B. M. M. kindly acknowledges the  French Agence Nationale de Recherche (ANR)  for the grant  ANR-18-CE06-007-01. The  Institut Carnot MICA  is kindly acknowledged for funding the exploratory project " Pt4Poly ". This work was supported by the  Higher Education Sprout Project of the National Yang Ming Chiao Tung University and Ministry of Education (MOE), Taiwan.
Funding Sources The work was financially supported by the  Université de Strasbourg ,  CNRS , The  International Centre for Frontier Research in Chemistry (icFRC) , and the  Labex Chemistry of Complex systems (CSC) , the  French Agence Nationale de Recherche (ANR) , the  Institut Carnot MICA , the  Higher Education Sprout Project of the National Yang Ming Chiao Tung University and Ministry of Education (MOE, Taiwan).
In addition, the memory processing minimizes disk I/O movements, reduces the data processing time significantly, and outperforms the well-established Hadoop MapReduce implementation  [7]. Spark Streaming (SS), for instance, provides iterative in-memory data processing with low latency by using the Resilient Distributed Datasets (RDD) abstraction. RDD represents the distributed data blocks organized into small partitions to maximize parallel processing. The RDD processing and cache rely on Spark Unified Memory Management (UMM), which dynamically manages data execution and storage regions in the executor Java Virtual Machine (JVM) heap. The execution region from Spark supports runtime processing operations such as shuffle, join, sort, and aggregation. On the other hand, the storage region caches RDD data blocks for both current processing and re-processing tasks, as well as storing the incoming data to be further processed  [11].
The authors propose Panthera to manage data processing in accordance with the semantics of applications and infers the coarse-grained data usage behavior by light-weight static program analysis and dynamic data usage monitoring. Panthera leverages garbage collection to migrate data between dynamic random access memory and non-volatile memory, incurring a low overhead. It is well known that the available memory of computing systems is constantly increasing in order to allow for in-memory data processing at a high scale. Moreover, in-memory data-intensive SP frameworks have been widely used to handle challenging problems in various domains, such as machine learning, graph computing, and SP. Thus, the applications have been benefiting from in-memory operations, since using them is faster than accessing the disk or receiving data from the network.
Time The timestamp of the current batch interval that just finished; Events The number of records that were processed in the current batch; Processing Time (PT) Time in ms it took for the job to complete; Scheduling Delay (SD) The time in ms that the job spent in the scheduling queue; Figure  1  presents the PID controller implemented by Spark. The goal of the PID model is to automatically calculate processing rates to be applied in the Spark executors continuously. The main components of PID controller implementation (PID implementation: https:// github. com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/ streaming/scheduler/rate/PIDRateEstimator. scala, accessed on 19 May 2022) are: • Error e(t): It represents the number of records that overloaded the defined time window from a given batch (Process).
Due to its nature, the algorithm will oscillate around a (near-optimal) constant throughput until steady. Behind the hype, Spark implements some architectural components to support the PID model, such as Rate Controller (RC), Rate Estimator (RE), and Rate Limiter (RL). Figure  2  presents the Spark backpressure PID architecture. Indeed, RC and RE components execute side by side to deliver the maximum processing speed on the Driver side. At the same time, the RL updates the maximum processing rate into the executors right after the Driver's notification update. The RC (Rate Controller Implementation: https://github. com/apache/spark/blob/m aster/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateControll er. scala, accessed on 19 May 2022) represents a contract for a single Dstream that grabs information from Spark listener for every batch completed to measure the current processing rate and then estimates a new one  [27].
The DStream abstraction allows for the processing of the continuous data stream that comprises a RDDs' sequence. The listener onBatchCompleted receives periodic information from all batch jobs, such as the current number of processed events, PT, and SD. Then, this information will be taken by PID implementation in RE (Rate Estimator Implementation: https://github. com/apache/sp ark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ra te/RateEstimator. scala, accessed on 19 May 2022) to estimate a reasonable data processing rate. The RE could have multiple-way implementations, but this work adopts the PID-based version as standard. Moreover, the PID controller only computes a new rate if there are events to process. Otherwise, it skips the rate estimation and does not return any rate limit.
Each setup allows for the use of the software stack presented in Table  3. In addition, both SS and HDFS used a RAMFS mounted folder to write shuffle data and checkpoints. The RAMFS also supports all installation folders, such as data and name nodes of Hadoop, Spark, or even logs of applications such as GC, or monitoring tools such as dstat monitor such as Dstat-based monitor tool available at ( https://github. com/mvnev es/dstat-monitor, accessed on 19 May 2022). In addition, at the Spark side, the monitoring and user interface were deactivated to avoid extra communication costs or any level of interference in the experiments. At the configuration level, the proposed evaluation will consider real-world guidelines, presented as follows.
Garbage Collection Comparison Analysis This section aims to understand how GC operations imply the performance of dataintensive SP scenarios. It presents a performance comparison of the stateful SUMServer application performed under stress conditions with and without Spark backpressure over Pipeline 2. The GC metrics were collected for both Spark driver and executor instances by setting the spark. driver. extraJavaOptions configuration parameter and the respective value -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:file. log. The obtained data were parsed through the Universal GC Log Analyzer tool Gceasy (GC Analyser tool: https: //gceasy. io/). The Gceasy application provides the following GC processes metrics from the logs: • Ergonomic: this is a JVM and garbage auto-tuning process that dynamically tunes the JVM heap size to meet the application needs with minimum pauses; • Allocation Failure: this happens when there is not enough free space to create new objects; • GCLocker Initiated GC: this process prevents GC operations when the JNI code is in a critical region.
However, this study unveils current memory management issues, specifically the data caching operation at the JVM heap. This issue affects not only SS but all other SP systems that rely on JVM for processing. In addition, it is possible to look forward to future work, such as: providing a global coordination mechanism at upstream components to balance data forwarding based on JVM cache utilization policies; modifying the PID solution to manage data caching alongside an execution strategy; providing new data eviction models that track not only recent data block utilization but also data dependency.
Analysis of the use of the primary-secondary care coordination system 9 months after the launch A descriptive analysis of the system's use was carried out 9 months after its launch to study the dynamics of its implementation. This analysis was carried out using data extracted from the SIPILINK information system. As seen in the study by Tuot et al. [18] , we used two effectiveness indicators, namely, the time taken to respond to the request and the time taken to access a hospital resource (consultation, conventional or day hospitalization).
Ethics approval and consent to participate The methods were performed in accordance with the relevant French guidelines and regulations. The survey of PCPs was declared to our hospital Data Protection Officer (DPO) and recorded in the data protection register of Paris-Saclay University Hospital. The medical data were collected as part of the SIPILINK patient medical management. Consequently, this study did not fall within the framework of French regulations on research involving human persons, and written patient consent was not required. A patient's nonobjection was collected by the PCP (an informational note detailed the purpose and processing of the data) before the data were entered. As the data were processed by the hospital staff managing this care, this study did not require additional authorisation from an IRB or from the Ethical and Scientific Committee for Research, Studies and Evaluations in the field of Health (paragraph 2 of article 65 of French law no.
The SMS is received on a dedicated smartphone owned by the "hotline" doctor, which allows him or her to access the request directly (URL link inserted in the SMS). If the response time is between 24 and 72 h, the care pathway manager chooses the hospital physician who seems most appropriate to respond. The hospital physician is then notified by e-mail. He or she can also redirect the request to one of his or her colleagues at any time. Once the hospital physician agrees to process the request, he or she can respond to the PCP either by e-mail or by telephone to the PCP to discuss the case. When a medical decision is made, the hospital physician traces it on SIPILINK and enters the information needed to program the care pathway, which is then processed by the relevant care pathway manager. This traceability enables information to be shared within the hospital team and with the PCP see supplementary material, Additional file 4: screen copies of medical decisions.
5% of these responses were made after 24 h. This responsiveness is an extremely important point for the project's sustainability. This result was possible due to the implementation of cross-disciplinary care pathway managers who monitored and reminded the hospital physicians in cases of nonresponse. In e-referral schemes evaluated by Tuot et al. , 91-95% of the responses were made within 72 h, and the median time for an electronic response from a specialist was 24 h in the Los Angeles Safety Net Program eConsult System  [28]. The SIPILINK system was particularly useful during the first peak of the SARS-CoV-2 pandemic in France, with a massive enrolment of PCPs in March and April 2020. The choice of technology (web-based application) as well as the development carried out by the hospital's IT team allowed for a high level of responsiveness when the pandemic started.
Channel Characterization To estimate the channel CIR, we perform MCRT simulations using Opticstudio software from Zemax,  23  which can model complex 3D environments. The interest in using comprehensively sophisticated and extremely-time-consuming ray tracing simulations with OpticStudio is that the obtained results are close enough to reality as it was already demonstrated in Ref. 24. For an OWC link, the CIR hðtÞ is calculated as follows  14  : E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 6 ; 4 8 8 hðtÞ ¼ 1 N ray X N ray i¼1 P ri δðt -η i Þ; (1) where N ray is the number of rays that hit the detector, P ri is the received power of the i'th ray, and η i ¼ P N ref;i r¼0 d r;i c is the corresponding time delay. Also, δð•Þ is the Dirac delta function, d r;i stands for the optical path length between consecutive reflection surfaces hit by the ray, N ref;i is the reflection order of the i'th ray, and c denotes the speed of light.
User Mobility Modeling To study the effect of user mobility, we take into account both the changes in the position of the user inside the room and the movement of the body parts, i. e. , the arms, legs, etc. , that we will refer to as global and local mobility, respectively. For local mobility, we model the body by a 3D mesh 28 composed of 902 vertices, which are connected to form 1800 triangular faces. This mesh provides an acceptable level of detail to take into account shadowing effects from the body segments. The body movements are modeled using a 3D walk cycle animation generated by the Blender software. 29 The walk cycle consists of N f ¼ 27 frames representing body configurations at different instants of the walk. Guidelines for creating the walk cycle animation are available in Ref.
The KDE estimate of the PDF is given by 40 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 5 ; 1 1 6 ; 1 5 8 fX ðxÞ ¼ 1 Nρ X N i¼1 K x -x i ρ ; ( 5 ) where K is the kernel and ρ ¼ 1. 06 σN -1∕5 is the smoothing parameter. Here, σ is the estimated standard variation of logðxÞ related to the dataset of Eq. (  5 ). 41 For the sake of simplicity, a Gaussian kernel is considered here. 0 B @ γ th -αI 1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ðασ 2 s;1 þ σ 2 0 Þ q 1 C A þ 1 4 erfc 0 B @ I 1 -γ th ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2ðσ 2 s;1 þ σ 2 0 Þ q 1 C A; (6) where erfcðxÞ ¼ 2 ffiffi π p ∫ ∞ x expð-u 2 Þdu is the complementary error function and γ th is the optimal detection threshold. Also, I 1 is the PD output photocurrent for ON symbols.
Parameter Specification To ensure accurate enough CIR estimation, N rays ¼ 2. 5 × 10 7 rays are considered for the MCRT simulation with at least three-order reflections for beam propagation. The Blender software is used to create the 3D scene for the simulator, similar to the approach used in Ref. 14. A Python script is then used to load a set of configurations specifying user movements and orientations for N config ¼ 1103 sequential snapshots of a walk scenario. We consider two trajectories of 516 and 587 sequential snapshots as explained previously and illustrated in Fig. 1 (a), which, overall, give an acceptable coverage of the room area and a detailed enough statistical description of the walk cycles. Note that generating these data has necessitated quite long Opticstudio simulations. For the sake of simplicity, we consider an empty room, i.
In this context, the project DOLPHINFREE ('Dolphins free from fishery by-catch') is working in partnership with SAS OCTECH Ocean Technology on the creation of a bio-inspired acoustic beacon, named CETASAVER-DOLPHINFREE, that emits a signal linked to the echolocation system of the dolphin to inform the animal of the presence of a fishing net. Dolphins use echolocation in combination with visual cues to investigate their surrounding environment  [35]. First indications of the existence of an echolocation system in dolphins were observed for bottlenose dolphins in 1947, as these animals were able to avoid nets and find openings in enclosing nets at night and in murky waters, leading to the suspicion that dolphins used a biological sonar  [36]. It should thus allow them to locate a fishing net at relatively short distances, between a few metres and a few tens of metres according to net reflectivity, angle of incidence, and source level of echolocation clicks  [37, 38].
These echoes were obtained from a playback experiment involving echolocation clicks. The process of this experiment and the resulting signals remain confidential, the prototype CETASAVER-DOLPHINFREE being engineered and developed in partnership with the company SAS OCTECH. In short, the experiment consisted in playing a recording of a common dolphin's echolocation click, by emitting it on different types of fishing nets, with and without a dead dolphin (initially found stranded) entangled in the net, and then in recording the returning echo of the echolocation click. The aim of the bio-inspired beacon is to emit this signal of returning echo to inform dolphins of the presence of a net. The device is able to emit a complex signal within a high range of frequencies. The signal emission is omnidirectional by means of a dedicated ceramic and beacon conception, and the source level is 170-180 dB re 1 µPa at one metre.
Since dolphins react to artificially emitted clicks (see above), we made the assumption that they would be able to detect and react to the acoustic emissions of the bio-inspired beacon. It could help dolphins to better detect fishing nets, which could ultimately enable them to avoid nets. To test this assumption, dolphins' behavioural patterns were studied by means of a sequential treatment (before/during/after being exposed to signals emitted by the bio-inspired beacon, with and without setting a fishing net). Visual observations and sound recordings of dolphins were assessed concurrently. Acoustic data collected required the use of automation methods to detect, identify and classify dolphins signals (i. e. , echolocation clicks, buzz/burst-pulses, whistles). The main results of this work highlight an increase in the mean number of echolocation clicks and mean whistling time of dolphins by approximately 2.
The audio bit-depth is 32-bits. Together, these parameters allow for a precise sampling of common dolphin signals in the vicinity. The hydrophone was set up to create a. wav file every minute (each file being 90 Mb). A total of 361 recordings of one minute were considered. Audio data cleaning was performed using the Audacity software v. 2. 0. 5. 0. and v. 3. 1. 1. In addition to modalities BEF, DUR and AFT of the explanatory variable of the sequential treatment, modalities BEF + DUR and DUR + AFT modalities were also considered (Table  3 ). They correspond to the activation and deactivation, respectively, of the beacon during a one-minute recording. It was decided to keep these records (i. e. , not cutting and attributing them to separated modalities) as they represent important events: they show the transition from silence to the emission of signals by the bio-inspired beacon, and conversely.