dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Timepix ASICs The Timepix4 ASIC  [65]  has recently been added to the Timepix family of hybrid pixel detector readout chips  [66]. Here we summarize the latest results on Timepix4 in the context of their predecessors. Table  II  summarizes the Timepix family of hybrid pixel detector readout chips. The Timepix chip  [67] , which was designed on 250-nm CMOS, became available in 2005 and was the first large area hybrid pixel detector readout chip which could be programmed at the pixel level to count photons, measure Time of Arrival (ToA) with respect to an external shutter or measure Time-over-Threshold (ToT) providing an indication of the charge deposited per pixel. It was this third mode of operation which led to the extensive use of Timepix in a multitude of applications  [66]. In particular, the development of a miniaturized USB readout system  [68]  with the Pixelman software  [69]  allowed for turnkey usage of the device for monitoring of background radiation as well as numerous scientific applications.
Another limitation in the Timepix chip was related to the shutter control logic. When the shutter is opened any ToT output which is already high (coming from a preceding hit) would be recorded as a truncated hit. Equally the closure of the shutter would result in the truncation of ToT values for hits which occurred near the end of the shutter. In Timepix2 hits arriving before the shutter are ignored and hits which arrive just before the shutter closes are registered with the correct ToT. Moreover, because Timepix2 uses a much denser technology than Timepix, it has a total of 48 bits per pixel (instead of 14) and these can be configured to permit data taking while readout is underway and permit recording both time and energy simultaneously. Timepix3 was produced in 2014 and introduced data driven readout to pixel electronics for the first time  [71].
To solve this issue, global shutter image sensors were proposed by  [83] , where the whole frame of pixels starts to integrate electrons at the same time for the same amount of duration, as shown in  However, as shown in Figure  6 (a) and (b), regardless of the rolling or global shutter image sensor, the on-chip readout circuits continuously read pixel information and transmit the digitized data through high-speed data links to the receiver side. Due to the limited data rates of transmitters, typically ranging from a few gigabits per second (Gbps) to a few tens of gigabits per second in modern CMOS technologies, and the power budget of image sensors, achieving a frame rate of over tens of millions per second in continuous-mode CMOS image sensors is challenging. Two of the highest reported frame rate continuous mode CMOS image sensors are  [84]  and  [85] , which operate at 80 kfps and 7.
Nanofabrication is also playing a key role in reducing the dimensions in fin-fet transistors and nano-electro-mechanical switches  [126]. Integration. The ultimate speed, however, was reached in data processing using wafer interconnectivity for the generation of superfast chips where cores are not separated after processing and are interconnected at wafer level. The so called "silicon interconnect fabric" allow bare chips also known as "chiplets" or "dielets" interconnections faster at larger dimensions by using Silicon wafers as support rather than PCB-SoC System on Chip  [127]. The Cerebra AI supercomputer is an example of such innovative methodology. The dimension of the wafer is of 46,255 square millimetres with 1. 2 trillion transistors, 400,000 processor cores, 18 gigabytes of SRAM, interconnects capable of moving 10 17 bits per second making this chip more than 10 4 times faster than a GPU.
As a result, the spatiotemporal resolution of X-ray imaging can be greatly improved. The experiment was carried out at the ultrafast X-ray imaging beamline (16U2) of SSRF, in which the pulsed laser was focused in water. White X-ray beam was used to ensure the SNR of the high-speed X-ray camera. The X-ray detector system has an effective pixel size of 3 µm and records a total of 180 frames at a frame rate of 0. 5 Mfps. Since this process changes very fast, the period of 30 frames of 58 ms already contains rich dynamic changes. Therefore, to avoid the complex confusion of motion features during data processing, a specific frame was selected as the starting frame, and 30 frames was continuously selected for MCXI processing. The key to the study of cavitation mechanism lies in the study of cavitation pulsation, which in liquid mainly goes through growth-rupture-jet stages  [211].
In practice, this is rarely the case (error of x in some regime could be more than 10%, for example), which motivate UQ to guide further optimization workflow and iterations to search for a better x, ideally the optimal solution through a probability distribution, p(x), and a smaller σ(x). For new data that do not yet exist, the emphasis of UQ shifts to identifying or predicting the optimal value or values from the probability distribution p(x), which can then be used to design new experiments to collect the data and confirm the prediction. When prior information (θ) exists about x, searching for a constrained probability p(x; θ) through Bayesian optimization is often used. Several possible U-RadIT scenarios motivate different approaches to UQ workflow: (a) UQ of the existing experimental data collection, I 0 = {I j exp (x), j = 1, 2, • • • , N }, which usually consists of one or more datasets generated under nearly identical macroscopic or closely related conditions, and no additional experimental data are assumed.
Such advances may also benefit from alternate fabrication methods to CMOS integration such as 3D printing. Hardware-centric approaches to U-RadIT optimization, which are sometimes constrained by detector sensor material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as application-specific integrated circuits (ASICs), are now complemented by data methods, such as synthetic data generation from forward models or trained neural network models. New compressed sensing framework can relax the requirements of Nyquist-Shannon sampling theory and leads to sparse U-RadIT modalities to efficiently collect data from experiments. Data science and machine learning algorithms are also growingly applied to post-processing of U-RadIT experimental data, including new phase retrieval algorithms in dynamic phase contrast imaging, moving contrast imaging and dynamic diffractive imaging.
ACKNOWLEDGMENTS We would like to thank  Ms. Samantha Thurman  ( SLAC ) and  Mr. Jack Heyer  ( SLAC ) for helping with the organization of Ultrafast Imaging and Tracking Instrumentation, Methods and Application (ULITIMA 2023) Conference, March 13-16, 2023,  Menlo Park, CA, USA. The special ULITIMA 2023 issue of Nuclear Instruments and Methods in Physics Research -section A (NIM-A) was made possible by many people from Elsevier, and especially  Ms. M. Priyadharsini ,  Ms. Xinyi Xu , and  Dr. William Barletta. ZW also wishes to thank  Drs. Tammy Ma  ( Lawrence Livermore National Laboratory ),  Yuri K. Batygin  ( LANL ), and  Prof. Mark Foster  ( Johns Hopkins University ) for stimulating discussions, and  Drs. Bob Reinovsky  ( LANL ),  Ann Satsangi  ( LANL ),  Rich Sheffield  ( LANL ),  Dmitry Yarotski  ( LANL ) for encouragement and support to carry out the work.
Data analysis All data analysis was performed using custom Labview and Python scripts. The x(t), y(t) positions of the rotating bead were determined by using a cross-correlation analysis of the bead image with a numerically generated kernel pattern  [63]. The drift of the circular trajectory was corrected by subtracting a spline interpolation of x and y from their respective raw values. The elliptical trajectories of the beads, assumed to be the projection of a tilted circle, were transformed into circles by stretching the minor axis of the ellipse. The bead angle was calculated as θ = tan -1 (y/x), and the angular velocity as ω = dθ/dt. The speed trace was low-pass filtered by the Savitzky-Golay algorithm (5 th order, 2-8 ms window). Motor speed transitions around a laser pulse were fit using a differential evolution fit algorithm with the following piece-wise function: ω(t) =      ω 0 if t ≤ t 0 ω 0 + (ω 1 -ω 0 )(1 -e -(t-t 0 )/τ ↑ ) = u(t) if t 0 < t ≤ t 1 ω 2 + (u(t 1 ) -ω 2 )e -(t-t 1 )/τ ↓ ) if t 1 < t (6) 15 where t 0 (t 1 ) is the time at which the laser is switched on (off), τ ↑ and τ ↓ are the characteristic times, and ω 0 , ω 1 , ω 2 are respectively the steady-state speeds before switching on the laser, after switching on, and after switching off.
Acknowledgments We thank  Emilie Gachon  and  Florian Morati  for preliminary experiments that led to this work. We thank  Martin Rieu ,  Richard Berry ,  William Hoffman ,  Nils-Ole Walliser ,  John Palmeri ,  Jérôme Dorignac ,  Fred Geniet , and  Andrea Parmeggiani  for fruitful discussions. AB-B, AM, FS, ALN, and FP were supported by the  ANR Flag-Motor  project grant  ANR-18-CE30-0008  of the  ANR , the  French Agence Nationale de la Recherche. TFDF and FP were supported by the  ANR HighResPMF  project grant  ANR-20-CE42-0005-01. ALN was further supported by the  ANR   PHYBABIFO  project grant  ANR-22-CE30-0034. The CBS is a member of the  France-BioImaging (FBI)  and the  French Infrastructure for Integrated   Structural Biology (FRISBI ), two national infrastructures supported by the  French National Research Agency  ( ANR-10-INBS-04-01  and  ANR-10-INBS-05 , respectively).
Altimetry datasets Radar altimetry data used in this study were acquired by Envisat and Saral on their nominal orbits. They were placed on a ~790 km sun-synchronous orbit with a 98. 54 • inclination sun-synchronous orbit with a 35-day repeat cycle and an equatorial ground-track spacing of about 85 km. Envisat payload was composed of 10 instruments including the advanced radar altimeter (RA-2). RA-2 was a nadir-looking pulse-limited RA operating at two frequencies: Ku-(~13. 575 GHz) and S-(~3. 2 GHz) bands  (Benveniste et al. , 2003). Data were acquired from 05/2002-10/2010. The radar footprint is ~15 km diameter which leads to more than 200 km 2 of coverage. Ku-band measurements are acquired with ~350 m space between footprints along the satellite tracks. Saral payload was composed of 4 instruments including the altimeter radar AltiKa, which is the first altimeter to operate at Ka-band (~35.
5 GHz;  (Verron et al. , 2015). Data were acquired from 02/2013-07/2016. The radar footprint is 8 km diameter which leads to more than 60 km 2 of coverage. Higher pulse repeat frequency (500 MHz against 320 MHz) of Saral allows a shorter space of ~180 m between footprints along the satellite tracks. Saral has a smaller footprint and higher along-track and vertical resolutions than Envisat  (Bonnefond et al. , 2018; Verron et al. , 2021). Radar altimeter measures the two-way travel time of the electromagnetic pulse emitted by the sensor and the power reflected by the Earth's surface. The two-way travel-time is used to derive an approximate distance between the satellite and the surface. The analysis of the radar echo (power received by the satellite as a function of the time) allows to refine the distance estimate applying a retracking process and to estimate the backscattering coefficient  (Cretaux et al.
, 2017). Two types of altimetry data are used here: raw data from the Geophysical Data Records (GDR) and normalized tracks data. These are computed along each ground-track clustering and for each cycle to obtain 3D gridded information (see  Frappart et al. , 2021c  for more details). Both datasets are made available by the CTOH through AlTiS data request from http://ctoh. legos. obs-mip. fr.
Water level time series 2. 2. 3. 1. WL gauge stations. WL gauge records are used to validate the automatically-generated WL from radar altimetry. Data was downloaded from the water's Brazilian national agency website Hidro-Telemetria (http://www. snirh. gov. br/hidrotelemetria/Mapa. aspx). One WL gauge located in the Lago Grande (2. 25 • S, 55. 33 • W), near Curuaí , has collected data since 1982 and has been levelled by high precision GPS based on WGS84 coordinate system  (Calmant et al. , 2013)  and reported to EGM2008 gravity field model  (Pavlis et al. , 2012). In the Juruá watershed, the Santos Dumont WL gauge (6. 44 • S, 68. 24 • W) is chosen to be the reference because of its proximity to a satellite track and its similar WL recording's period. This WL gauge is about 3000 km upstream from the mouth of the Juruá (see Fig.
AlTiS time series. Time series of WL can be manually retrieved using the Altimetry Time Series (AlTiS) software  (Frappart et al. , 2021a) , a new version of the MAPS software (Multi-mission Altimetry Processing Software;  (Frappart et al. , 2015b; Normandin et al. , 2018). These time series of WL are used to validate the automatic method presented in this study. The software enables the user to select the altimetry data inside a Google Earth polygon. It can display parameters supplied by CTOH: backscatter coefficients and WL estimations. These parameters are presented as time series obtained computing the median of each cycle valid observations following the processing proposed in  Cretaux et al. (2017). Some outliers may still be present in the time series and can be manually removed thanks to a specialized tool.
1 m. The chaotic variations in the height observed in the Envisat and Saral time series are outliers. When the tracking is operating well, the height is estimated to be continuous. These very different temporal behaviors, observed in radar altimetry echoes over open water and forest-covered areas, are used to classify the echoes by type of aquatic environment. The unsupervised clustering algorithm K-means is used in our method to classify echoes  (Jain and Dubes, 1988; Likas et al. , 2003). K-means is suitable for our purpose as no training dataset can be used due to the complexity of the radar altimeter echoes in very heterogenous environments  (Stammer and Cazenave, 2017). Echoes are grouped into K different clusters, based on their shape similarities (see Fig. 3. A), translating the soils backscatter coefficient and, thus, characterizing the land cover.
VS creation and validation VS detection over Curuaí is effective. River width is up to 6 km and floodplain lakes are large enough to allow a good detection of open water in the radar footprint. However, the Juruá river is narrower, locally up to 400 m width downstream and up to 200 m width upstream, as well as abandoned meanders that are now lakes. Detection of water is therefore more difficult. Nevertheless, the virtual stations created are quite numerous, especially when using the B or B+NWL parameters. With this method, a significant densification of the virtual station network is therefore achieved. As a comparison, 6 VS from the Hydroweb database (http://hydroweb. theialand. fr/) are available over the Amazon in Curuaí region, while about 100 VS have been created over the river and floodplains with our  method.
For the Juruá, more than 100 VS from the Hydroweb database are available over the river, while about 1500 VS have been created over the river and floodplains. The backscatter coefficient parameter combined with the raw water levels (B+RWL) did not give satisfying results either for classification accuracies or for number of VS created. This may be due to the extreme changes between water levels over the different surface types. A bias of almost 2 m can be observed for Envisat data and 1. 5 m for Saral data over Curuaí and has been respectively removed. Previous studies showed that Envisat has a + 1 m bias compared to in situ gauges  (Calmant et al. , 2013; Santos da Silva et al. , 2010). Still, part of the bias could be due to the distance between automatic VS and in situ gauges. Nevertheless, R 2 and RMSE are estimated and show good agreement between the automatic time series and AlTiS ones.
Their creation permitted to strongly densify VS network in the river and the floodplains: 4650 VS were created in both areas. Comparisons show an accuracy ranging from 0. 52 (0. 53) to 0. 97 (0. 98) when comparing the time series of WL automatically generated with Envisat (Saral) with ones from the in situ station or the ones manually created in the Curuaí várzea. On the one hand, this method offers possibility to include floodplain water level in current database and allow better surface water stock estimations to monitor areas where in situ data does not exist. On the other hand, the created WLs allow a better connectivity analysis between the river, the lakes and the floodplains. The wetlands' filling dynamic is better understood. Especially in Curuaí , where the varzea is filled to the west first via the upstream river, and then to the east via channels connected to the western floodplain and the downstream river.
Acknowledgments The first author is grateful to the Occitanie region,  SABERES  project ( BNP Paribas  foundation  2020-2025 ) and WASACA (Agropolis foundation) fundings. This research is funded in the framework of the  BONDS (Balancing biOdiversity coNservation and Development in Amazon wetlandS ) project funded through the  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals , under the  BiodivScen ERA-Net COFUND programme , and with the funding organisations  French National Research Agency (ANR) ,  São Paulo Research Foundation (FAPESP) ,  National Science Foundation  (NSF), the  Research Council of Norway  and the  German Federal Ministry of Education and Research (BMBF). This research is also a contribution the CNES's  TOSCA SWOT SWHYM  project.
Inter-regional effective connectivity (DCM) The main aim of the current study was to investigate the dynamic interactions between the HC and the nodes of the PM memory system during the retrieval of naturalistic episodes. In particular, we targeted the role of the connectivity between the PreC and the HC in mediating the effect of subjective sense recollection (Rem-responses). More globally, we tested the role of the HC in mediating the effect of Rem in the PM medial system comparing models with/without modulation of the direct connection between the input region (FusG) and PreC/vmPFC and, of the HC-vmPFC connection (please see Suppl. Bayesian model selection  45  (BMS) showed that Family 1, which included the modulation of the PreC-HC connection, was most likely to explain our data (> 99% posterior probability), strongly supporting the hypothesis of a contribution of the PreC-HC connectivity during episodic memory retrieval with subjective sense of recollection.
Participants Twenty right-handed healthy adults were recruited for the study. One participant had to be discarded because of technical problems with the VR system during the encoding session, leaving 19 participants for data analysis (mean age: 28, range 23-35; 6/13 males/females). They had normal or corrected-tonormal vision, no neurological, psychiatric or cognitive impairments and gave their written informed consent to participate in the study. The study was approved by the Committee for Person Protection (CPP OUEST II, France, A02558-45), in accordance with the Declaration of Helsinki.
The task of the participant was to report whether he liked/disliked the object using the PC-keyboard. The like/dislike judgment was included to make sure that the participant paid attention to the memory-objects. During the 45 min session there were 60 of these events, comprising 60 different objects. The pictures of the objects were taken from the "Massive Memory Object Categories" database 60 and included common objects of different categories (e. g. tools, animals, food). The presentation order of the objects was xed for all the participants, but the location where these events occurred was speci c for each participant depending on their exploration path. The timing of the events was also approximately the same for all subjects, but could slightly vary depending on the participants behavior.
Retrieval session The retrieval session took place the day after the encoding phase, during fMRI scanning. The retrieval task comprised 120 trials: 60 trials including "old" objects that were seen during the encoding session and 60 trials depicting "new" objects. Old and new trials were randomized and presented in two separate fMRI runs, each including 60 trials. The memory task entailed an "episode retrieval" phase and, for correctly recognized old items, a "source discrimination" phase (see Fig. 1B ). The analyses of the imaging data targeted the episode retrieval phase, while the behavioral responses for the source discrimination served to evaluate the contribution of place/time memory to the subjective memory status of the object at episode retrieval (cf. "Introduction" and "Analysis of the behavioral data", below).
2s, echo time = 30ms, voxel size = 3 x 3 in plane; slice thickness = 2. 5 mm; gap = 20%). Each participant underwent 2 functional imaging runs. The duration of each run was variable, depending on the number trials that the participant correctly recognized as "old/seen" thus triggering the additional series of source discrimination questions (range of volumes: 952-1145). The BOLD images were preprocessed and analyzed with Statistical Parametric Mapping software SPM12 (Wellcome Department of Imaging Neuroscience, University College London, UK; http://www. l. ion. ucl. ac. uk/spm). After discarding the four rst volumes of each fMRI-run, images were corrected for head movements. Slice-acquisition delays were corrected using the middle slice as reference. All images were normalized to the SPM12 Tissue Probability Map and re-sampled to 2 mm isotropic voxel size.
Behavior Following the procedure that we employed in a previous behavioral study using the same paradigm  43  , the analysis of the behavioral data sought to con rm that source memory contributes to the subjective memory status of the encoded objects: namely that correct and con dent place/time discrimination is associated with an increase of the likelihood to recognize old/seen objects as "Remembered", as opposed to just "Familiar". As a preliminary analysis, we tested the accuracy of the source discrimination as a function of con dence. For this we use a 2 x 2 repeated-measure ANOVA with the factors: "source" (place or time) and "con dence" (high or low). This analysis was carried out using SPSS (vers 21, IBM). To address our main question about the contribution of source memory to the subjective memory judgment of the object, we used a mixed-effect binomial logistic regression.
The model considered all trials when an old/seen-object was correctly recognized either as Rem or Fam. The dependent variable was the Rem or Fam memory status of the object, and the four predictors comprised the place and time retrieval accuracies (irrespective of con dence) and the interaction between accuracy and con dence for each of the two sources. The factor "subject" was included as a random effect in the model. The logistic regression was implemented in Matlab R2017a.
Higher spatial, temporal, and spectral resolution remote sensing is now available with the denser time series provided by the ESA's Sentinel-1 and Sentinel-2, together with Landsat data. There are also increasing options for very high-resolution (e. g. , < 5 m) commercial satellite imagery that can be used to isolate and process smaller agricultural plots and distinguish between different agricultural management practices (e. g. , PlanetScope)  13. Aside from satellites, platforms like IIASA's Geo-Wiki (www. geo-wiki. org) demonstrate the potential of crowd-sourced ground truthing to supplement visual interpretations in training land cover classification models. The large volume of data generated by new sensors can also be processed more rapidly and with more accessibility than ever before using free cloud computing resources like Google Earth Engine  14.