dataesr/scientific-paragraphs-categorization huggingface dataset:
It is also interesting to examine whether very shallow events are associated with particular conditions of ground water level. Groundwater level changes may increase the pore pressure along the fault plane, thereby facilitating slip. For Kallithea,  Kaviris et al. (2022)  examined rainfall patterns in relation to the ground deformation time series of station THIV (vertical component) and found a good correlation. It should be noted that rainfall patterns may reflect on seasonal trends in groundwater level, which induces a small-scale (a few cm) of seasonal variations in GNSS data (see also  Argyrakis et al. 2020) , therefore its use as a proxy may be justified in cases where there are no data from boreholes or wells in the vicinity of earthquakes. Unfortunately, no water level data are available in the vicinity of Kallithea, making it difficult to prove a link to this earthquake using the GNSS vertical component time series as a proxy.
(2019)  have found a stress drop value of 0. 01 MPa, while for the Le Teil earthquake De Novellis et al. (  2020 ) reported a stress drop value of 1. 3 MPa. These values are significantly lower than the 11. 3 MPa inferred for the Kallithea earthquake, which may indicate some difference between these earthquakes. A common feature characterising those events is the fact that the slip is concentrated in the upper part of the fault. For the 2010 Mw = 5. 0 Pisayambo earthquake,  Champenois et al. (2017)  ascribed this result to a low rigidity medium. A similar argument has been proposed by  Ritz et al. (2020)  for the Le Teil earthquake. In both studies, a low rigidity value was also used to reconcile the seismic moment estimate obtained from InSAR data and seismological data. However, for the case of the Kallithea earthquake, the seismic moments inferred from the DInSAR data and from seismological data are very consistent, so a low rigidity medium is not a necessary condition.
Conclusions 1. We used Sentinel-1 interferograms to map ground deformation due to the M w = 4. 6 December 2, 2020 shallow earthquake near Thiva (central Greece). Because of the shallow depth of the main event and the good coherence of the radar signals, the ground subsidence (which has a maximum value of 7 cm) is well visible with InSAR. 2. Using dislocation modelling, we identified that the seismic fault is striking N117°E, has a dip angle of ~ 54° towards the south, its rupture area is roughly 4. 6 km 2 , it slipped by a maximum amount of 0. 31 m with a slip centroid depth of 1. 1 km. 3. Whether this fault is separated from or coincident to the well-known neotectonic Kallithea normal fault remains debated due to the accuracy limitations associated with the inversion model and data used; however, forward modelling obtained by fixing a high-angle Kallithea fault geometry fails to fit the data, thus indicating that either there is a new (undetected) normal fault south of the Kallithea fault, or the Kallithea fault parameters need to be investigated using high-resolution techniques.
funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by  Greece  and the  European Union (European Regional Development Fund). V. C. was supported by  Pianeta Dinamico -Working Earth INGV-MUR  Project. We are indebted to  ESA  for making available the satellite images for free. GNSS permanent station data were provided by  Hexagon SmartNET  and Ktimatologio SA. We used data from the following seismic networks, HL (Institute of  Geodynamics, National Observatory of Athens , https:// doi. org/ 10. 7914/ SN/ HL), HP ( University of Patras , https:// doi. org/ 10. 7914/ SN/ HP), HT ( Aristotle University of Thessaloniki , https:// doi. org/ 10. 7914/ SN/ HT), HA (National and Kapodistrian University of Athens, https:// doi.
The Adriatic has been recognized as one of the important sub-regions of the Mediterranean Sea by the MSFD; the NAS is its shallowest, northernmost part. Most of NAS is considered to be an Ecologically and Biologically Significant Area (EBSA, Convention on Biological Diversity), as well as hosting several marine and coastal Natura 2000 sites, and protected areas  30. Whilst having a very vulnerable biodiversity  31  , NAS is highly impacted by increasing maritime traffic, tourism and resource exploitation  32. As a result, NAS biota is currently under the combined pressure of the anthropogenic impact  33  and climate change  34, 35. The SOUNDSCAPE dataset presented in this paper contains 20 and 60 seconds averaged sound pressure levels (SPLs) collected at nine monitoring stations, from the Gulf of Trieste till about the Middle Adriatic Pit (Fig.
1 ; Table  1 ), from March 2020 to June 2021. This dataset is essential for establishing baselines that document acoustic conditions over time on the regional scale and represents the first dataset of this kind in the Mediterranean Sea. The collected data are crucial to assess the ecosystem health, to evaluate the consequences of new possible marine development and to promote a knowledge-based management of the marine resources. Additionally, the SOUNDSCAPE dataset includes the most restrictive COVID-19-induced lockdown phase (March-April 2020), providing a unique benchmark for spatial and temporal comparative analysis.
Methods The workflow shown in Fig. 2  summarizes the steps undertaken to obtain the SPL datasets from the underwater noise raw data collected in the field. The workflow entails two main blocks: (i) "Data Acquisition" describes the process of sound recording and wav files uploading on the SOUNDSCAPE-dedicated server to store the data; (ii) "Data Processing" shows the steps that lead to the processing of wav data to calculate Sound Pressure Level data. The applied procedures are in accord to guidelines developed by other international projects or agreements  10, 36. Used terminology followed ISO 18405  37  , IEC 61260-1:2014  38  and JOMOPANS  39  Terminology Standards and it is summarized in Table  2. Hardware components and calibration. The acoustic recordings were made by using autonomous passive underwater acoustic recorders (APUARs; Sono.
Hydrophone calibration was achieved by the manufacturer using a calibrated reference projector; the reference projector was calibrated as well using a free-field three-transducer reciprocity calibration. Both procedures are compliant with the IEC 60565-1:2020 international standard  40. The recorders were set to record continuously at a sampling rate of 48 kHz, providing a recording bandwidth of approximately 22 kHz with 16-bit resolution. Additional information and hydrophone sensitivity curves in the pertinent frequency range are available in the SOUNDSCAPE Deliverables 3. 2. 1 41 and 3. 6. 3  42. acoustic data acquisition. A total of nine APUARs were deployed (Fig. 1 ). In most of the stations, the recorders were anchored to the bottom with a rig design consisting of an anchor, an acoustic releaser, the logger itself secured by polypropylene rope and extra flotations (e.
g. , sphere with the diameter of 25 cm mounted at minimum of 100 cm from the hydrophone), as illustrated in Fig. 3. The rig design above the anchor was positively buoyant. This ensured that the loggers were suspended about 3 m above the seabed throughout the deployment. In the stations MS1, MS5 and MS6, deployment and recovery operations were carried out by scuba divers, so no acoustic release was needed. The system calibration was checked in situ just before the deployment and after the recovery by using an air-pistonphone Grass 42AC (Grass Instruments, West Warwick, RI, USA), that generates a known sound pressure level at 250 Hz. Additionally, profiles of water conductivity, temperature and pressure were recorded by using a CTD probe. Metadata were collected for each deployment and recovery, including name, geographic position and the depth of the measurement site, start and stop time for each recording, equipment ID number and set up data, calibration data and weather conditions.
Additional details on the deployment and recovery protocols are available in the SOUNDSCAPE Deliverable 3. 2. 2  43. Typical measurement duration for the stations was 3 months, after which each device needed to be recovered to download data and to remove biological fouling. The measurement period covered about one full year and four months (from 1 March 2020 to 30 June 2021). Table  3  shows the data coverage for each monitoring station. acoustic data storage and processing. The collected. wav files were stored on two servers at CNR ISMAR (Venice, Italy) and IOR (Split, Croatia). No data compression was applied to the original files. The whole data-set has been processed by the same processing executable tool, that was developed specially for the SOUNDSCAPE project by the University of Gdansk together with CNR-ISMAR.
Data Records The dataset of 20 and 60 seconds averaged Sound Pressure Levels (SPL) output files collected by SOUNDSCAPE and described in this paper is available on Zenodo  44. Data are archived using structured HDF5 files, each one containing metadata and SPL data according to the ICES (International Council for the Exploration of the Sea) continuous noise data portal specification, with time stamps relative to UTC time provided in compliance to ISO 8601  45.
technical Validation In order to ensure the data quality, a check on the collected data by times series visualisation was carried before the data analysis: it did not highlight spurious signals or transient artefacts due to deployment settings, nor systematic artefacts due to flow noise, which is consistent with the study areas being characterized by low tidal currents. Moreover, data recorded before and during the deployment, during and after the retrieval and while the deployment vessel was in close proximity of the recorder were removed. The measured data may be contaminated by the system self-noise. Self-noise fluctuations in output of an acoustic receiver system are caused by the combination of acoustic self-noise and non-acoustic self-noise (electronic self-noise). The acoustic self-noise sound is usually caused by the deployment, operation, or recovery of a specified receiver and its associated with the deployment of the acoustic sensor and platform (e.
The SOUNDSCAPE non-acoustic self-noise due to the electrical noise is calculated to be better than 58 dB re 1μPa 2 /Hz at 63 Hz and better than 53 dB re 1 μPa 2 /Hz at 125 Hz, according to the manufacturer technical specifications. Finally, a quality control has been applied to the software used for the analysis (Fig. 2 ). To validate the correct functioning of the SOUNDSCAPE processing app (ANP) applied to the wav data, the latter was tested again other already validated software (SpectraPLUS and independent MATLAB routines). A subset of data was processed with the SOUNDSCAPE ANP and the SPLs of each one-third octave (base 10) band were compared with the ones calculated by a validated tool: the SOUNDSCAPE ANP was able to reproduce almost the same results. Namely, the mean absolute difference between trusted tool and SOUNDSCAPE ANP results was equal to 0.
Usage Notes To post process the SPLs 20,60 data, CNR-ISMAR developed a Python script that was deployed as a Jupyter Notebook interactive document  46  , that is here released. The workflow of SPL data post processing is simple. After reading SPLs 20,60 files and (i) selecting a time window to define the investigated period, (ii) a recording station and (iii) a given one-third octave (base 10) band, it is possible to compute some metrics to create tables and to visualize efficiently the data (see Fig. 4  for examples). Statistics can be calculated for each one-third octave (base 10) band over various temporal analysis windows (based on UTC time) like for example one hour, one day, one month, one year  10. Once the time window is selected, tables with descriptive statistics can be produced including percentile values (1 th , 10 th , 25 th , 50 th , 75 th , 90 th , 99 th percentiles) and the arithmetic mean.
acknowledgements This work was financed through the project SOUNDSCAPE, funded by the EU Interreg V-A Italy-Croatia CBC Programme 2014-2020(Project ID: 10043643) and endorsed by the RELIANCE project, funded under call "Implementing the European Open Science Cloud" (H2020-INFRAEOSC-2018-2020). The authors are very grateful to the Nucleo Sommozzatori della Polizia di Stato of Venice for their skilful support during the survey in Venice area. The authors would like to acknowledge also the CNR SNAPSHOT ("Synoptic Assessment of Human Pressures on key Mediterranean Hot Spots") project that allowed the collection of data at the CNR stations during the COVID-19 lockdown phase. The authors would like to thank the reviewers for their comments, which greatly improved the article.
author contributions A. P. , F. M. , M. P. , M. G. personally contributed to the acquisition and processing of the data and to the organization of the database and wrote most of the paper. J. Z. developed the ANP app to obtain the dataset from raw data. A. P. developed the post-processing Python script released. A. P. , A. C. , M. C. , H. M. , P. V. , J. T. , J. Z. and T. F. checked the quality of the data and validated the ANP app results. H. M. , S. M. , G. P. and M. R. contributed to the writing of the paper and the figure preparation. H. M. , J. T. , P. V. , M. P. , A. P. , F. M. and M. G. improved the paper after the referees' revision. All the other authors participated to data acquisition and revised the final version. IOR staffs were responsible for organization and coordination of the SOUNDSCAPE project; IOR and CNR-ISMAR staff were involved in the data storage and back up.
To generate all possible design solutions, the proposed approach uses Multi-objective Genetic algorithm (MOGA II) implemented in the optimization software ModeFrontier. The proposed approach is illustrated with a photovoltaic self-consumptions system as an example of social innovation in the energy sector. Design variables, constraints and objectives were defined for each of the four dimensions. The design space is explored using MOGA II which guarantees the identification of the pareto-optimal solutions. Thus, the optimal solutions were selected among the Pareto front with the help of the graphs generated as result of the simulation of the use case design space (Correlation matrix, Parallel coordinates graph and Bubble chart). These graphs also allow as well to further analyze the effect of each single design variable on the objectives.
Proposed approach The design process of social innovations in the energy sector requires a deep understanding of the complex design space and a robust decision-making model to guide the process. To address this challenge, in this paper we propose a decision-making model for designing social innovations in the energy sector by exploring and defining their design space which incorporates the most effective design options and success criteria. To achieve this, we used the modeFrontier software and the MOGA II algorithm to conduct a comprehensive search of the design space and identify the most promising social innovation designs.
4. 2 Workflow implementation in ModeFrontier All of the previously defined measurements of the considered case study were implemented in a Python node in ModeFrontier workflow shown in Figure  7. The optimization Data Flow is presented from top to bottom. At the top there are the input variables, represented by the green icons, and defined by their upper and lower bounds and increments. At the bottom there are the output variables represented by the blue icons. Objectives are represented by an arrow icon. As defined in the previous section, the design space of photovoltaic self-consumption systems is expressed based on different objectives characterized through multiple dimensions. The first and second objectives are related to the social dimension corresponding to maximize the share of overproduced PV energy (OA) and maximize the locality.
The third refers to the economic dimension and aims to maximize the Net Present Value (NPV). The last two objectives are environmental and correspond to maximize self-consumption and minimize the energy payback time (EPBT). In the middle, in the Figure  7 , from left to right, the icons represent the Process Flow composed by the ScuedulingStar node, the Python node, and the logic end node of the process. The first node calls the genetic algorithm MOGA II, for which the initial population is generated internally. The Python node calls the python script that calculates the outputs and defines the fuzzy logic model for the participatory dimension, and the logic end node terminates the process. The workflow is started by mean of run command, which allows the optimization process execution. Table  4  The Value of optimal solutions for each objective However, the real objective is to find the most reasonable balance between the fifth objectives and not treating each objective alone.
To evaluate this dimension, Fuzzy Logic method has been used basing on five inputs: Annual Co2 reduction, Location, Minimum investment, Minimum Duration, and Level of participation. An illustrative example of designing a photovoltaic self-consumption system was described in this paper. The design space of the use case has been defined on modeFrontier software and automatically explored using MOGA II algorithm. Multiple objectives have been considered in the definition of the design space: the maximization of the rate of area occupation OA, the maximization of the locality L, the maximization of the net present value NPV, the maximization of the self-consumption SC, and the minimization of the energy payback time EPBT. Based on the proposed approach, the exploration of the defined design space has allowed the visualization of the possible design solutions using different graphs (Correlation matrix, Parallel coordinates graph and Bubble chart).
In addition, we observed a significant PDT effect on cell clonogenicity at a ultra-low dose rate (roughly 10 -4 mGy•MBq -1 •s -1 , data from OpenDose database)  [43]. Thus, an efficient photodynamic effect can be achieved with very low dose exposure when the nanoscintillator/photosensitizer couple is correctly chosen. Furthermore, unlike PDT and X-PDT, CR-PDT could be applied to metastatic diseases thanks to the non-sealed nature of the light source. In conclusion, Monte Carlo simulations demonstrated the added value of the presence of a nanoscintillator (as Tb) in AGuIX@ nanoparticles to increase singlet oxygen production when nanoparticles doped with photosensitizer are activated by Cerenkov radiation. This result has been confirmed experimentally (i) by singlet oxygen production assessment and (ii) cell survival decrease was demonstrated by clonogenic assays using glioblastoma cells pre-treated with AGuIX@Tb-P1 and exposed to 68 Ga.
Acknowledgments: NH TherAguix is gratefully thanked for its support and for AGuIX product provision for this study. The authors would like to thank the  ORION program  for its contribution to the funding of P. S. 's research internship (2nd Year of Master in Life Sciences). This work has benefited from a government grant managed by the  Agence Nationale de la Recherche  with the reference  ANR-20-SFRI-0009.
Even if the pair is not spatially contiguous, prediction errors are ensured to be lower than that of any other pair selected across two different clusters. At each stage of the lifting scheme, clustering appears naturally as part of the split step. If  denotes the number of elements at the beginning of a given stage of the lifting scheme, then the split step can be thought of as defining a spatial partition into  ∕2 groups with each group containing a pair (, ) of elements, one from the predictor set and the other from the response set. At the first stage, for the water depth data example, this would mean to determine a partition with 1152 groups with precisely two elements per group. This poses a challenge for clustering algorithms as they are designed to identify a partition with a pre-specified number of groups but are not equipped to ensure a pre-specified number of elements in each group  (Bishop, 2006).
e. , each updated predictor element value is associated to all the elements of its group in the partition from Fig. 5 (a), Figs. 5(d ) and 5(g). Note that, due to the procedure described in Section 2. 2. 1, the split step always results in slightly more response than predictor elements, i. e. , a small number of predictor elements are paired with two response elements. In some preliminary trials of the clustering assisted split step, the K-medoids algorithm with pre-defined targeted number of clusters for each stage (with values much lower than ⌊  ∕2⌋) was used instead of the hierarchical clustering based strategy with an adaptive number of clusters described in Algorithm 1. We found that, for the water depth data example, the results of the lifting scheme were not sensitive to the clustering strategy implemented to obtain the spatial partition.
The spatial dimension of the LR data sets, i. e. , the number of sub-domains, is either 199 or 8. In simple words, the downscaling model accomplishes a regression with inputs that are of very low dimension (either 199 or 8) with respect to its outputs (either 9749 or 18432). To face this challenging task, a modeling assumption such as the representativity of the spatial patterns contained in the training set is necessary. This is, in our opinion, a rather mild assumption since the spatial structure is modeled with great flexibility with the non-parametric representations derived from the lifting scheme or PCA. In the hydraulic data sets considered, the spatial patterns are highly complex (non isotropic and non-stationary) so that conventional statistical approaches such as kriging would be challenged.
However, the HR meshes are very dense and the spatial patterns in the data are rather smooth. In applications with less densely distributed data and more irregular or jagged spatial patterns, the proposed approaches may not performed as well and may require some adaptations. All the algorithms are implemented in the R language and were applied to the aforementioned data sets on an ordinary laptop. Although the lifting scheme implementation could certainly be optimized to run faster, results (including the selection of hyperparameter values) were obtained within a few hours. The code will be made available as an official R library in the near future. There are many possible perspectives to this work. Concerning the lifting scheme, instead of operating on vectors of time series, we could consider to operate truly in the spatio-temporal space, such as in spatio-temporal geostatistics, e.
Introduction As a cornerstone of diverse CV tasks, MOT is a significant prerequisite. For instance, public surveillance, autonomous vehicles, and video analysis, where MOT's performance is critical for the subsequent analysis. Analogizing humans paying attention to the targets when tracking objects, Transformer's attention mechanism is an advisable solution for MOT  (Vaswani et al. , 2017). Some works have leveraged the novel Transformer-based detector for MOT, such as TransTrack  (Sun et al. , 2020)  and TrackFormer  (Meinhardt et al. , 2022). It turns out that the key-query mechanism in the Transformer is capable of focusing attention on massive objects and simplifying the tracking systems from tracking-by-detection to jointly-detection-and-tracking. Nonetheless, current Transformer-based solutions boost the overall model sizes by additional modules for tracking, as explained in Fig.