dataesr/scientific-paragraphs-categorization huggingface dataset:
Milicz • The stand data in the ESRI Shapefile format are available to download from the Polish Forest Data Bank at https://www. bdl. lasy. gov. pl/portal/wniosek-en. • The local forest inventory dataset and ALS data are available for non-commercial use upon request to Jarosław Socha (jaroslaw. socha@urk. edu. pl). A data sharing agreement will have to be established, with the following restrictions: -data are available for internal use only and cannot be distributed; -data funding (REMBIOFOR -BIOSTRATEG1/ 267755/4/NCBR/2015) should be cited.
Sneznik • The forest inventory data (in *. xlsx and *. shp formats) and maps of forest types and species mixture (in *. shp format) are available upon request to Slovenia Forest Service (zgs. tajnistvo@zgs. si; rok. pisek@zgs. si). A data sharing agreement will have to be established, with the following restrictions: -data are only available for the study that is the subject of the agreement; -Slovenia Forest Service should be acknowledged for providing the data in all publications. • ALS data are available to download from the Slovenian Environment Agency website at http://gis. arso. gov. si/evode, under the terms of the international Creative Commons 4. 0 license (http://www. evode. gov. si/fileadmin/user_upload/ Lidar_pogoji_uporabe. pdf): -the data user must indicate the data source at each publication of data or products, specifying "Slovenian Environmental Agency, type of data and period to which the data refer or the date of the database".
Extended data Zenodo: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia. https://doi. org/10. 5281/zenodo. 7462440  [Aussenac et al. , 2022]. For each virtual landscape we provide a table (in. csv format) with the following columns: • cellID25: the unique ID of each 25x25 m 2 cell • sp: species latin names • n: number of trees • dbh: tree diameter at breast height (cm) • h: tree height (m) We also provide, for each virtual landscape, a raster (in. asc format) with the cell IDs (cellID25) which makes data spatialisation possible. Finally, we provide a proof of how, in the downscaling algorithm, multiplying the trees dbh by the α correction coefficient makes it possible to reach the cells BA value derived from the ALS mapping.
Nikolai Knapp Thünen Institute of Forest Ecosystems, Eberswalde, Germany The paper presents an innovative approach to generate forest stand structure information at landscape extent and single tree resolution based on airborne lidar data and inventory plots. The approach is not based on individual tree detection (ITD) from lidar, but operates in an area-based (ABA) fashion at 25 m x 25 m cell scale. The inventory plots serve as lookup tables. Structure metrics are being estimated for every cell in the landscape based on lidar metrics. Then, each cell is being assigned to the most similar stand from the inventory lookup table based on a minimum distance of a set of structure metrics. The dbh values of the trees are then adjusted according to a proposed algorithm, such that the final structure metrics match the ones predicted for the cell.
Finally, the generated forest landscapes are being validated by calculating dominant height for each cell based on the generated stands and comparing them to dominant heights directly obtained from lidar. The approach has been applied to three different regions in France, Poland and Slovenia. The presented approach is very interesting and useful as an efficient solution to generate maps at single tree resolution and landscape extent, which are highly relevant, e. g. , for spatial and temporal interpolation of forest inventories and for modelling tasks. The method is well documented and the case studies along with the provided datasets make it an innovative publication. However, I have listed some comments below, which the authors should consider during revision.
Acknowledgments The authors would like to thank the  ONF  and  PNR du Massif des Bauges  for their contribution to the field and ALS data collection in the French study area, as well as the  IGN  for providing freely the French National Forest Inventory data. The authors also wish to thank the  Slovenia Forest Service  for providing the forest inventory data from the Slovenian study area, and the  Ministry of Education, Science and Sport of the Republic of Slovenia  for funding the project. Finally, the authors would like to thank the  Polish Forest Management and Geodesy Bureau  for providing data from the Polish study area.
This event damaged the forest stands, and happened between the field inventory and the ALS acquisition. It affected the quality of the derived maps (see Mapping) and the realism of our virtual landscape (see Dataset validation). The Bauges was covered by two ALS acquisi- tions with different settings and equipment. The southern part was covered between June and September 2016, the northern part in September 2018. Mean point densities were respec- tively 5. 5 and 24. 4 m -2. Intensity values were normalised by dataset, by subtracting the mean and dividing by the standard deviation of intensity values of points located inside the extent of field plots covered by each acquisition. Milicz was covered by an ALS acquisition in August 2015. The average point density was 16. 8 m -2. The point cloud.
An EPR spectrum or an EPR sinogram for imaging contains information about all the paramagnetic species that are in the analyzed sample. When only one species is present, an image of its spatial repartition can be reconstructed from the sinogram by using the well-known Filtered Back-Projection (FBP). However, in the case of several species, the FBP does not allow the reconstruction of the images of each species from a standard acquisition. One has to use for this spectral-spatial imaging whose acquisition can be very long. A new approach, based on Total Variation minimization, is proposed in order to efficiently extract the spatial repartitions of all the species present in a sample from standard imaging data and therefore drastically reduce the acquisition time. Experiments have been carried out on Tetrathiatriarylmethyl, nitroxide and DPPH.
Besides, conventional filtered backprojection is restricted to single source image reconstruction and is not relevant to address source separation. In the literature of CW-EPR, the source separation problem is usually addressed using spectral-spatial methods  [6, 23]. The latter consist in acquiring sinograms for numerous values of field gradient intensities {𝜇 𝑗 } 1≤𝑗≤𝑁 𝜇 , yielding a large set {𝑠 𝜇 𝑗  } 1≤𝑗≤𝑁 𝜇 of sinogram measurements. Then, the dataset is processed to compute an intermediate four-dimensional image (three spatial dimensions and an additional spectral one) containing an EPR spectrum within each voxel. Source separation can then be addressed voxelwise using monodimensional spectrum-based source separation techniques, as done in  [6, 24, 25]. The main drawback of this approach is that it requires a large number of samples along the spectral dimension of the intermediate 4D image, and thus large datasets (with typical values of several hundred for 𝑁 𝜇 ) and long acquisition times.
Experiments The following experiments were performed using a Bruker ® E540 CW-EPR spectrometer operated in L-band frequencies and controlled using the Xepr© software. The typical working ranges for the acquisition parameters used in our experiments are provided in Table  1. In the case of 3D experiments, we sampled the sphere using independent and uniform sampling of both spherical angles, using 𝑁 𝜃 angle nodes in [0, 2𝜋] for the polar angle and 𝑁 𝜑 nodes in [0, 𝜋] for the azimuthal angle. This yields 𝑁 proj = 𝑁 𝜃 𝑁 𝜑 as total number of acquired projection for a given 3D experiment. This acquisition setting corresponds to the standard angular sampling acquisition conditions using Bruker ® spectrometers. However other more advanced sampling strategies can be considered, as for instance those proposed in  [26, 36].
Indeed, the contribution of the reference spectrum ℎ 𝑋 𝑖 of each source 𝑋 𝑖 (say for instance here 𝑋 1 for the TAM and 𝑋 2 for the TEMPO) is mathematically involved in the 𝐴 𝜇 𝑖 operators involved in  (9) , and are thus needed to compute the solution of (13) using Scheme  (14). In our experiments, we used the toolbox EasySpin  [38]  to extract the spectrum of TAM and that of TEMPO from the acquired spectrum of their mixture (see second row of Fig. 2 ). This toolbox relies on a parameterized model and requires only basic molecular information such as the species's spin system and acquisition parameters. Once the spectra of TAM and TEMPO are extracted from the spectrum of their mixture using EasySpin, they can be used as inputs for our source separation imaging method. An example of 2D image source separation (performed from the dataset in Fig.
Separation of spectrally close species (TAM and DPPH) In this section, we will consider the problem of separation of two species with close EPR spectra. We consider here TAM and DPPH which both have a single-line EPR spectrum with roughly the same 𝑔-factor. The experimental sample is made of an eppendorf filled with a solution of TAM at 17 μmol/L and a Bruker ® test sample, made of 8 crystals of DPPH located at the vertices of a cube. A synthetic illustration of the sample is displayed in Fig. 6(a). In the following, we will consider EPR datasets acquired from this TAM-DPPH sample and perform the source separation afterwards. The acquisition scheme is similar to that presented in the previous section. A single EPR spectrum of the mixture of TAM and DPPH is first acquired (see Fig. 6(b) ) and further processed using EasySpin in order to extract from it the reference spectra of each individual species.
TAM and DPPH separation from 𝑁 𝜇 = 1 sinogram We first considered a dataset containing the reference spectrum of the TAM and DPPH mixture (see Fig. 6(b) ) and a single 2D sinogram acquisition (see Fig. 6(c) ). The individual spectra of TAM and DPPH extracted using EasySpin from the spectrum of their mixture are displayed in Fig. 7. Those two spectra and the sinogram were used as inputs of our source separation algorithm, yielding the images displayed in Fig. 8. As we can see in Fig. 8 , we were not able to achieve a correct TAM and DPPH image separation from this dataset. We believe that the use of only 𝑁 𝜇 = 1 sinogram does not provide sufficient information to separate spectrally close species like TAM and DPPH. Even though we showed that, despite the larger number of unknowns than measurements, the use of the total variation as regularizer of the source separation problem (10) (or  (13)  with 𝑁 𝜇 = 1) enables the accurate separation of spectrally different species such as TAM and TEMPO, the complexity of the problem drastically increases as we consider species with similar spectra, yielding ineffective separation when 𝑁 𝜇 = 1.
To address this issue, we suggest to enrich the measured dataset by considering the acquisition of multiple sinograms (i. e. , using 𝑁 𝜇 ≥ 2) with different field gradient intensity values. A mathematical justification of how such multiple sinogram acquisition provides additional information for the source separation problem is proposed in Appendix B. Notice that such multiple sinogram acquisition process is similar to that used for spectral-spatial imaging purpose. However, as mentioned before, the computation of a spectral-spatial image involves the acquisition of a large number of sinograms (𝑁 𝜇 ≫ 1) with many different values of field gradient intensities. In our approach, we will consider drastically less measurements and focus on the source separation problem from only 𝑁 𝜇 = 2 sinograms.
Separation of synthetic species from 𝑁 𝜇 = 1 or 𝑁 𝜇 = 2 sinograms In this section, we consider a synthetic dataset generated from a shepp-logan phantom using synthetic spectra of two artificial species 𝑋 1 and 𝑋 2 with similar shapes as those of TAM and DPPH considered before (see Fig. 9 ). The synthetic shepp-logan phantom was decomposed into the sum of two sub-images (displayed in the last column of Fig. 10 ), each one representing the repartition maps of 𝑋 1 and 𝑋 2. This synthetic dataset was used to generate two synthetic sinograms of the mixture of  = (𝑋 1 , 𝑋 2 ), acquired using two different field gradient intensities (𝜇 1 and 𝜇 2 with 𝜇 1 < 𝜇 2 ). We used our source separation algorithm to separate the two synthetical species using either 𝑁 𝜇 = 1 sinogram (see the first row of Fig.
TAM and DPPH separation from 𝑁 𝜇 = 2 sinograms From now, let us focus back on the TAM-DPPH real experiment and try to achieve a better image separation than that obtained in Section 4. 2. 1 by considering a dataset containing 𝑁 𝜇 = 2 (instead of 𝑁 𝜇 = 1) sinograms. The acquisition of such a dataset involves slight changes in the whole acquisition protocol that we designed as follows. The acquisition of the two sinograms (𝑠 𝜇 1  , 𝑠 𝜇 2  ) using two different field gradient intensities (𝜇 1 and 𝜇 2 , with 𝜇 1 < 𝜇 2 ) is made sequentially. To ease the data acquisition and the image reconstruction processes, we decided to keep constant the number of acquired samples per projection (𝑁 𝐵 ), the center-field (𝐵 cf ), the conversion time, the time constant, as well as the field-of-view diameter during both sinogram acquisitions.
12 ), making the spectra extracted from ℎ (1)   somehow inconsistent with those actually involved in the second sinogram 𝑠 𝜇 2 . Unfortunately, this can negatively impact the image separation. In order to address this issue, we used EasySpin to extract at once from the whole set (ℎ (1)   , ℎ (2)   ) the reference spectra of the TAM and DPPH over the same sampling grid as 𝑠 𝜇 1  and 𝑠 𝜇 2  , taking into account potential Fig. 9. Synthetic dataset. We display in (a) the spectra of two synthetic species 𝑋 1 and 𝑋 2 generated using a Gaussian derivative model of the form ℎ 𝑋 𝑖 (𝐵) = -2 𝑐 𝑖 𝐵 ⋅ 𝑒 -𝑐 𝑖 𝐵 2 , using 𝑐 1 = 0. 02, 𝑐 2 = 0. 001. The ratio of the linewidths of those two synthetic spectra is close to 4. 5 which is roughly the same linewidths ratio as that of the spectra of TAM and DPPH that we presented in Fig.
8  We used two synthetic repartition maps for 𝑋 1 and 𝑋 2 (displayed in the last column of Fig. 10 ) and generated two synthetic sinograms of the mixture of  = (𝑋 1 , 𝑋 2 ) corresponding to synthetic acquisitions using a low (b) and a large (c) field gradient intensity. Both sinograms were corrupted by a synthetic additive Gaussian noise with same standard deviation By construction, we obtain in (b) a synthetic sinogram blurrier but with better SNR than that displayed in (c), which is consistent with real-life acquisition. Fig. 10. Source separation using one or two sinograms. The repartition maps of the two artificial species 𝑋 1 and 𝑋 2 that we considered in our synthetic experiment are made of ellipses (extracted from a shepp-logan phantom). They are represented in the third column of this figure (top : 𝑋 1 , bottom : 𝑋 2 ) and correspond to the ideal source images that we would like to reconstruct from the synthetical dataset presented in Fig.
9  We display in the left column the two source images reconstructed using only 𝑁 𝜇 = 1 sinogram (that displayed in Fig. 9(b) ), and we display in the middle column that reconstructed using 𝑁 𝜇 = 2 sinograms We can see in the first column that the reconstruction of source 𝑋 1 is substantially different from the ground truth, especially due to the presence in the reconstructed image of a large (outer) ellipse that belongs in fact to source 𝑋 2 This artifact in the reconstruction is drastically diminished when looking at the middle column, where both reconstructed source images 𝑋 1 and 𝑋 2 better match the ground truth images This experiment illustrates how the addition of another sinogram to the dataset may substantially improve the quality of the source separation of spectrally close species.
With the addition of a second sinogram obtained at a different gradient intensity 𝜇 the TAM-DPPH separation was successfully performed, yielding a clearer separation, as illustrated in Fig. 13. We have demonstrated that, for Fig. 11. Acquisition of a dataset made of 𝑁 𝜇 = 2 sinograms and two associated reference spectra of the TAM-DPPH mixture. In the first column, we display (top) the sinogram acquired using a low field gradient intensity (𝜇 1 = 20 G/cm, 𝐵 sw = 104 G) and (bottom) the associated spectrum acquisition ℎ (1)   of the TAM-DPPH mixture. In the second column, we display (top) a sinogram acquired using a higher field gradient intensity (𝜇 2 = 40 G/cm, 𝐵 sw = 208 G) and (bottom) the associated spectrum acquisition ℎ (2)   of the TAM-DPPH mixture. The other acquisition parameters were set to (𝑁 𝐵 , 𝐵 cf , 𝑁 𝜃 ) = (2000, 400, 50) for this experiment.
A. 4. Pseudocode implementation of the proposed source separation algorithm In this section, a pseudocode implementation of the source separation algorithm is provided. This pseudocode was split into two modules. a The spectra ℎ 𝑑 𝑖,𝑗 (1 ≤ 𝑖 ≤ 𝑁, 1 ≤ 𝑗 ≤ 𝑁 𝜇 ) used as inputs of this algorithm can be extracted from an EPR spectrum acquisition of the sample containing the mixture of species, using for instance EasySpin software  [38] , as explained in Section 4. 1. source separation algorithm and performs the iterations of Scheme  (14)  in order to compute the (unmixed) target source images. Notice that each scheme iteration in Algorithm 2 only relies on FFT computations, yielding a loglinear complexity for this implementation of the numerical scheme.
In previous works, we have introduced a Multimodal Interaction Framework based on Firebase Realtime Database [2] and a Biomedical Framework for Enhanced Experimentation -BIOFEE  [3]. The interest of such intelligent systems built on a multimodal basis lies in the fact that a decision is made through several independent information channels, with the subsequent aggregation of these decisions  [4]. Worldwide, the number of people with significant disabilities is approximately 1. 3 billion, or 16% of the world's population. Among them, one in five people has a so-called severe disability  [5]. Thanks to years of research, today, assistive technologies can compensate the user disabilities regarding physical, functional, cognitive or mental disabilities. But, unfortunately, they are often expensive, designed for a specific disability (for paraplegic people, blind people, deaf people, autistic people, elderly people.
Nowadays, with the needs of communication for smartphones and other connected objects (see IoT: Internet of Things), the notion of BaaS (Backend as a Service) is emerging. BaaS is a service for providing web and mobile applications with a way to easily build a backend solution. Features available include user management, scalable databases, cloud storage, push notifications, authentication, and integration with social networking services, thanks to custom software development kits (SDKs) and application programming interfaces (APIs). We have shown in previous work how to conceive, deploy and test such multimodal and multichannel applications where push notifications are mainly used  [7, 8]. This also means that multimodal applications based on this technology must be constantly connected to the Internet, with a good quality network and bandwidth.
This is not always possible in medical or industrial environments, for example (see the case of limited, insecure, lowcapacity network). For instance, by choosing the Firebase database, developers may face problems in terms of cost (the free plan is only sufficient for small applications), hosting (Google Cloud platform) and database  (NoSQL). In this paper, we present our ongoing work on the notion of multimodal interaction in the context of human-computer Interaction to improve the quality of life. Some recent papers have been exploring the use of multimodality, with mobile robots or the Brain-Computer Interface (BCI), for instance, by addressing the MQTT (Message Queuing Telemetry Transport) protocol  [9, 10]. In the following, we present more detailed research works that we have carried out in the field of multimodal interaction for a cobot using the MQTT protocol.
Firebase Realtime Database We proposed in a previous work the study of BIOFEE (Biomedical Framework for Enhanced Experimentation), based on the Firebase Realtime Database, in order to design web and mobile applications for patients suffering from progressive diseases. It allows interaction with objects and robots to test various multimodal solutions, such as touch, voice, gesture, eyes gaze, etc. , to perform tasks according to the patient's pathology. Figure  1  describes the architecture of our multimodal BIOFEE framework. The user's devices (computers, smartphones. ) are connected to a Firebase Realtime Database. This allows each component of the system to be notified instantly when a modification occurs in the database. The various input signals are then treated by the multimodal engine to synthesize one user command.
Firebase Realtime Database We proposed in a previous work the study of BIOFEE (Biomedical Framework for Enhanced Experimentation), based on the Firebase Realtime Database, in order to design web and mobile applications for patients suffering from progressive diseases. It allows interaction with objects and robots to test various multimodal solutions, such as touch, voice, gesture, eyes gaze, etc. , to perform tasks according to the patient's pathology. Figure  1  describes the architecture of our multimodal BIOFEE framework. The user's devices (computers, smartphones…) are connected to a Firebase Realtime Database. This allows each component of the system to be notified instantly when a modification occurs in the database. The various input signals are then treated by the multimodal engine to synthesize one user command.
Then, the system reacts accordingly. A Wizard of Oz module  [11]  is also available, in order to let a human (the wizard) inject commands when the machines are not able to do it by themselves (a complex or ambiguous multimodal request, for example). Most of the time, the used sensors are available in a smartphone (touch screen, voice recognition, accelerometer, light detection, etc. ), but it can also involve third-party applications connected to the Firebase database. For instance, Figure  2  shows an example of eye gaze detection to perform a command in a Unity3D application  [12]. The user can choose various ways to interact with the system: the keyboard or mouse on a classic computer, voice or buttons on a mobile device, tilting the smartphone one way or the other, in a natural way, to move the ball on the PC screen, or a combination of those modalities, sequentially.
Although functional, this approach is not trivial, and requires multiple steps to be implemented. Developers have to create a project in Firebase, to define the access rights for the database, and add an "application" to the project (here, the "Unity" option was chosen from among iOS, Android, Unity, Web, and Flutter). Then, it is necessary to download the configuration file obtained, as well as the Firebase SDK, to add into the Unity Most of the time, the used sensors are available in a smartphone (touch screen, voice recognition, accelerometer, light detection, etc. ), but it can also involve third-party applications connected to the Firebase database. For instance, Figure  2  shows an example of eye gaze detection to perform a command in a Unity3D application  [12]. The user can choose various ways to interact with the system: the keyboard or mouse on a classic computer, voice or buttons on a mobile device, tilting the smartphone one way or the other, in a natural way, to move the ball on the PC screen, or a combination of those modalities, sequentially.