dataesr/scientific-paragraphs-categorization huggingface dataset:
Impact of the Diffuse White Luminance Our results suggest that the best color space for assessing the quality of HDR images depends on the test database. Indeed, some of the color spaces are adapted and tuned for one visualization condition. The HDR-Lab color space considers two important parameters, i. e. , the diffuse white and the surround luminance. Moreover, the final equation of the J z a z b z color space (Equation (13)) was tuned using the experimental dataset called SL2  [20]. This dataset was obtained for a diffuse white at 997 cd/m 2. This explain why the J z a z b z luminance have a behaviour close to the HDR-Lab 1000 luminance (cf. Figure  2 ). The PU function and the ICtCp color space were not obtained through the same kind of training. They were created using Daly's Contrast Sensitivity Function model  [51]  and Barten's Contrast Sensitivity Function model  [52] , respectively.
• With the proposed database HDdtb (cf. Figure  11 ), the J z a z b z color space provides the lowest performances for PSNR, SSIM, MS-SSIM and PSNR-HMA metrics but provides the highest performances with FSIM and FSIMc. However, results indicate that the PSNR, SSIM and PSNR-HMA metrics based on HDR-Lab 1000 and HDR-Lab 100 color spaces perform better than the same metrics using the J z a z b z color space. This suggests that the low performances of these metrics is not due to the diffuse white characteristics of the images, but to the design of J z a z b z color space which corrects a deviation in the perception of the blue hue (cf. Equation (  9 )). To test this hypothesis, we measure the SROCC of these metrics on the HDdtb database with the J z a z b z color space without the blue deviation correction.
We call this new space J z a z b z. Results, shown in Table  5 , indicate that SROCC values of the three aforementioned metrics increase with the J z a z b z color space. In addition, metrics using this modified color space provide similar performances to metrics based on the HDR-Lab 1000 color space. This is consistent with the fact that HDR-Lab 1000 and J z a z b z are adapted to almost the same diffuse white luminance. This might be due to the presence of the "gammut mismatch" artifact in this database. Indeed, the "gammut mismatch" artifact creates visible distortions that was not associated with a subjective quality loss during our test. We suspect that the blue hue deviation correction makes the J z a z b z color space more sensitive to this distortion. However, this is difficult to demonstrate due to the low number of images with this kind of artifact present in this database.
12 ). The MS-SSIM and the SSIM metrics perform better when using the J z a z b z color space. However, the FSIM and PSNR-HMA metrics perform better when using the ICtCp color space. This contrasted result might be due to the fact that the diffuse white luminance is likely not homogeneous across the entire database. To go further into the analysis, we propose to evaluate the impact of the diffuse white on the performances of HDR-Lab metrics. The SROCC performances of three metrics (FSIM, MS-SSIM and SSIM) are evaluated for a diffuse white in the range 80 to 1000. Results are plotted in Figure  13. For the FSIM, the performances decrease slightly when the diffuse white luminance increases for the 4Kdtb database and the Narwaria et al. database while increasing with the diffuse white for the HDdtb.
Sensibility to Chrominance Distortions In this section, the ability of color metrics to take into account chrominance artifacts is discussed. The discussion is focused on the database 4Kdtb which is the only database providing significant chrominance artifacts. Also we only consider metrics using the ICtCp color space since the best performances are observed with this color space. Figure  14  presents the Mean Opinion Score (MOS) and objective scores for the reference image "Regatta_24s", for the distorted images (compressed with HEVC). The objective scores are given after applying the logistic regression presented in Section 4.
• If the chosen metrics is the PSNR, we recommend to first verify that the tested image/video processing application, such as compression codecs, does not create luminance mean shift or contrast change. These artifacts can be induced by backward compatible compression (if the image is first tone-mapped, then compressed using a legacy codec and finally tone expanded). Due to the characteristics of the tested databases, these recommendations have to be used in the context of image/video compression. Different subjective tests would be required to extend the analysis to other kinds of distortion.
Conclusions In this article, we reviewed the relevance of using SDR metrics with perceptually uniform color spaces to assess the quality of HDR/WCG contents. We studied twelve different metrics along with six different color spaces. To evaluate the performances of these metrics, we used three existing HDR image databases annotated with MOS and created two more databases specifically dedicated to WCG and chrominance artifacts. We showed that the use of perceptually uniform color spaces increases, in most cases, the performances of SDR metrics for HDR/WCG contents. In this study, we also highlight two weaknesses of state-of-art metrics. First, The relationship between the diffuse white used for grading the image and the diffuse white used for the color space is not always easy to define. In a number of cases, we do not know the value of the diffuse white used for grading of the image.
Choosing an arbitrary diffuse white for the color space may significantly alter the objective quality assessment. Further analysis of this relationship is required. A better understanding could help to evaluate compression of images using the HLG EOTF for which the diffuse white depends on the display. Second, to the best of our knowledge, the quality assessment of DR/WCG images with chrominance distortions is still an open-issue, because of the lack of relevant objective metrics. In a broader perspective, the relevance of subjective tests can also be questioned. For example, on the proposed database HDdtb, viewers did not perceive the gamut mismatch artifact as a loss of quality. However, this kind of artifact changes completely the appearance of images. Some other artifact could also alter the image appearance like the tone mapping/tone expansion used during backward compatible compression.
Metric All Images Without the "Gamut Mismatch" Distortion Compression Artifacts Only ∆E HDR-Lab 100 0. 2578 0. 3905 0. 6190 ∆E S CSSIM HDR-Lab 100 HDR-Lab 100 0. 2784 0. 4065 0. 5687 0. 6453 0. 6946 0. 7714 4. 4. Korshunov et al. Database The Korshunov et al. database is the less selective database (cf. Figure.
II. FANET ARCHITECTURE FANET is set of UAVs and ground base stations (GBSs) autonomously connected with each other, without a preexisting communication configuration  [46]. That is, the communication between these entities should be done in an ad hoc fashion in which UAVs and GBSs are all involved in the data transmission  [47]. Generally, this system selects specified UAVs having further capabilities to act as gateways between GBSs and other UAVs, thus extending significantly the network coverage  [48]. Additionally, different kinds of communications are used, different network organizations are applied, and different features are required for different applications  [49]. To this end, this section is divided into three subsections. The first one is devoted to the major FANET organizations commonly adopted across the literature.
3) SATELLITE COMMUNICATION (SATCOM) UAVs are often deployed in complex environments, such as ocean and mountainous areas, where it is difficult to install GBSs. Moreover, when a FANET requires continuous connectivity and the network is severely partitioned, there is a need for a centralized entity ensuring permanent connectivity. Satellites can be an adequate option to serve as relays controlling UAVs in a centralized manner and also providing an important LoS coverage, thus establishing Satellite Communication (SATCOM)  [60]. SATCOM is beneficial to both support the exchange of critical data between UAVs and delivering collected information to a radio station located far apart on the ground. However, this is not a cost-effective solution. TABLE  2  provides a brief comparison between the discussed types of FANET communications.
Indeed, two metrics are calculated before each selection of MPR, which are called the Stability Degree of Node (SDN) and the Reachability Degree of Node (RDN). SDN depicts the stability of the communication link between UAVs where those close to each other are selected to be MPRs. As for RDN, it avoids collision and interference between the neighboring UAVs, and thus enhancing the packet delivery ratio and the data delivery delay. These two metrics are included in the Hello packets and shared periodically with UAVs in range in order to be aware of the distance, the mobility, and the load information of neighboring UAVs. The selection of MPRs is carried out based on SDN of each neighboring UAV. The selected MPRs are considered to be likely staying in range during a long period of time to relay data packets between the pairs of source and destination UAVs.
e. , a large number of UAVs), TS-AODV found a trade-off between the collision risk and the bandwidth consumption. Indeed, the control packets are sent during defined time slots, where only one node can make a data transmission. The duration of each time slot dedicated to each kind of packets is estimated according to several factors, such as the size of the network, the link failure detection, and the topology changes. The functioning of TS-AODV is exemplified in Figure  13(c ). As it is shown, the same mechanism of AODV is used to establish a routing between UAV S and UAV D. However, each transited UAV including S and D has to set a time slot TS for each transmitted packet in order to avoid collisions, reduce the bandwidth consumption, and thus increasing the packet delivery ratio. M-AODV (Multicast Ad hoc On-demand Distance Vector)  [178]  is an improved version of AODV  [176]  to take into account the multicasting concept to connect a set of nodes.
2) GREEDY Generally, when a FANET network becomes almost connected and the position of the destination is known, the data packets are always forwarded to UAVs that allow minimizing the number of hops and the distance to the target destination, thus employing the greedy forwarding technique. Furthermore, this technique also aims to reduce the delay of delivery. Nevertheless, the connectedness of FANET is not constant all time since UAVs are highly mobile, which makes the greedy forwarding unreliable when it is misused resulting in important packet losses. GPSR (Greedy Perimeter Stateless Routing)  [214]  is a greedy-based routing protocol consisting of two modes: (i) Greedy Forwarding and (ii) Perimeter Forwarding. For the right functionality of the first mode, the position of the destination node is assumed to be known and the node with the closest position to the destination is selected as the next hop of the data packet.
GLSR exploits all the possible routing paths between the communicating UAVs. The data packets are sent to the UAVs that allow progressing to the target destination. To do so, a distance advance metric is calculated for each UAV neighbor in order to define the best routing paths towards the destination. Moreover, each UAV maintains a queue for packets to send in which its degree of filling is also considered during the selection of the appropriate path. The selection of an adequate forwarder in GLSR is exemplified in Figure  22(b). UAV S checks in its neighboring to find, if possible, the UAV that has and the lowest queuing delay and the highest speed advance towards UAV D. As a result, UAV M satisfies all the previously mentioned characteristics and it is selected to forward the data packet.
When overhearing a UAV disseminating a data packet, the neighboring UAVs withdraw their copy of the packet. In the case when the custodian has no neighboring UAVs (e. g. , UAV M ), the packet is broadcasted periodically until a next hop will be found. Otherwise, the custodian holds the packet until it will be delivered to the target destination. FGQPA (Fountain-code based Greedy Queue and Position Assisted)  [229]  as a delay tolerant network routing protocol, it has an objective to reduce the transmission delay within a FANET. To do so, FGQPA considers two different schemes: (i) a Power Allocation and Routing (PAR) scheme and (ii) a nearest span scheme. The first scheme aims to transit the data packets through UAVs having a low queue backlog, thus, all the queues of UAVs will be well-regulated and the delay will be reduced significantly.
The second scheme aims to forward the packets towards the destination while the transmission delay is even shorter. In addition, a fountain code scheme is added to make the end-to-end communication more reliable despite a high loss rate. In Figure  24 (c), a data packet will be carried by UAV S until an adequate next hop is found. Each UAV maintains a local queue and is able to define its own geographical position, which can be all used as a routing metric. If there are at least two possible next hop neighbors (i. e. , UAVs M and K ), FGQPA firstly calculates their queues length and their distances that separate them from the destination, which are all shared through Hello packets. UAV M will be selected as the next hop, since it has a smaller queue and it is the closest to the destination.
A. COMPARISON OF FANET ROUTING PROTOCOLS As illustrated in TABLE 15, the eight categories of FANET routing protocols are compared based on multiple parameters, such as the routing strategies, the requirements, the features, and the methods of validation. The different routing strategies are those described in Section IV. Three crucial requirements are studied, which are frequently used by routing protocols, such as the GPS to calculate the geographical positions, the location service (LS) to estimate the position of each UAV and sometimes it is used to get the motion information of UAVs, and the ground base station (GBS) that is used in different scenarios, such as the transmission or the reception of certain information, the coverage extension, or even the control of the mobility. Some popular features extracted from both the mechanisms and the experiment outputs of the routing protocols, such as the required memory (MM) to make calculation and processing, the used bandwidth (BD) during the routing process, the average overhead (OH) of all data transmissions, the latency (LT) which is the average time needed for a data transmission, and the technique used during the route update (RU).
J. BUILDING TRUSTED NETWORK MODEL In a FANET, UAVs frequently exchange data packets between each other, and thus they are very dependent on neighbor UAVs to accomplish a given mission. This is why the communicating UAV has to ensure that only packets from authenticated UAVs (i. e. , trusted UAVs) are allowed to be intercepted. Consequently, building a trusted network model is useful to reduce the vulnerability against non-trusted UAVs from interacting with trusted UAVs, thus making this a very important challenge to study.
P. ROUTING CHALLENGES Both the high mobility and the low density of UAVs are the major issues towards designing an efficient routing strategy ensuring a robust data exchange between UAVs. The severity of these issues is increased when UAVs move in a 3D space (i. e. , different altitudes). Different techniques have been proposed across the literature based on a single situation encountered in the network. However, they cannot deal with all issues that can be met with a network as FANET. Consequently, there is a serious need to propose new protocols that could deploy the appropriate technique in a given kind of situation.
Acknowledgements Collaborators:  Claire Andrejak ,  Eline Magois , ( CHU d'Amiens, Amiens ),  Efrosyni Manali  ( Athènes, Grece );  Manuela Funke-Chambour ,  Thomas Geiser  ( Berne, Suisse );  Anne Gondouin  ( CHU Besancon, Besancon ),  Marianne Kambouchner ; Hilario.
The few studies which have dealt with their carbon budget observed highly variable contributions of photosynthetically fixed carbon provided by the symbionts to the host, with carbon translocation rates ranging from 10% in Capnella gabonensis to 75% in Sinularia flexibilis  [19] [20] [21] [22]. In addition, different normalization metrics for physiological processes in soft and scleractinian corals often hinder comparisons of the trophic characteristics between the two groups. While tropical soft corals are considered reef engineers which create habitats for other reef species  7, 23  , little comprehensive data exist on their trophic ecology, and therefore on their functional role as primary producers and carbon sinks within the reef ecosystem  8, 13, 24, 25. Since autotrophic carbon acquisition is a key factor shaping coral productivity, physiology, and ecology, and partly explains coral success or failure under changing environmental conditions, more studies should be dedicated to better understand the autotrophic capacity of soft corals.
than in R. f. fulvum, compensating for the lower percentage of carbon translocated from the symbionts. As a consequence, the rates at which carbon was retained in the host were constant between species and depths (between 13% and 19% of the photosynthesized carbon, or 93 to 138 µg C g -1 AFDW h -1 ). Assimilation rates of 10 to 20% of the photosynthesized carbon by the animal host seems to be a general feature in corals  4, 5. Soft and scleractinian corals therefore seem to exhibit a different trend of carbon acquisition along the depth gradient, although more investigations with different scleractinian and soft coral species are still needed. In order to compare carbon fixation per biomass between soft and scleractinian corals, we used data previously obtained on the scleractinian coral Stylophora pistillata sampled at the exact same location, depths and month 5 (Table  S3 ) and normalized the rates to AFDW.
550 µmoles photon m -2 s -1 and 50 µmoles photon m -2 s -1 respectively, considering attenuation coefficients of 0. 072-0. 1 m -1 at this precise location  55, 56. These values are in agreement with one-off measurements performed with a data logger during coral collection. The in situ seawater temperature (24 °C) and nutrient concentrations (<0. 5 µM dissolved nitrogen, and <0. 2 µM dissolved phosphorus) were stable throughout the investigated depth gradient and period of time (http://www. iui-eilat. ac. il/Research/NMPMeteoData. aspx). Coral nubbins were maintained for one day under these conditions before the following measurements were performed in the outside aquaria under exactly the same environmental conditions. naH 13 co 3 incubations. The rates at which carbon was fixed by the shallow and mesophotic coral colonies under their natural PAR levels were estimated using  13  C-labelled bicarbonate according to Tremblay et al.
The natural isotopic abundance of each species at each depth was determined from the corals used for the respiration measurements. Detailed calculations are described in Tremblay et al. 4. Data were normalized to ash-free dry weight of the organisms  27. Physiological and tissue descriptor measurements. Estimation of the dark respiration rates of the whole colony, as well as of the isolated symbionts, was needed for the establishment of the daily carbon budget. Dark respiration rates were measured on four nubbins per species and depth (from four different colonies), as well as on freshly isolated symbionts from four other nubbins according to Tremblay et al. 4. Dark-acclimated nubbins/symbionts were individually placed in stirred incubation chambers filled with 0. 45 µM filtered seawater and maintained at 24 °C.
AFDW was determined as described above after separating the tissue from the skeleton and combusting it. Surface area was determined using the wax technique  58. A conversion factor (255. 65 ± 8. 98, Table  S4 ), estimated from the ratio between skeletal surface area (in cm²) and AFDW (in g) of these nubbins, was used to transform data normalized to surface area (Table  S3 ) into data normalized to AFDW.
It also makes good models (χ 2 min /dof ≈ 1) appear as excluded (χ 2 min /dof 1). We provide a framework to mitigate these effects (AMS-02 data are interpreted in a companion paper). Conclusions. New production cross-section data and the publication by the AMS-02 collaboration of a covariance matrix of errors for each data set would be an important step towards an unbiased view of cosmic-ray propagation in the Galaxy.
2 briefly introduces the 1D model and its parameters, and details two configurations used for the analysis. Sect. 3 discusses how to ensure a good precision at various stages of the calculation: boundary conditions, numerical stability, and comparison of the model to the data. Sect. 4 shows two different parametrisations of nuisance parameters for nuclear cross sections, and mock data are used to fully characterise the impact of cross-section uncertainties. In Sect. 5, we detail the systematics of AMS-02 data-from which a covariance matrix of error is built-, and we show the impact of the correlation length (of the covariance matrix) on the best fit results and errors on the model parameters. Cross sections and covariance matrix effects are then dealt with together in Sect. 6, to assess which one dominantly impacts the analysis of AMS-02 B/C data.
Model and parameters Many modellings of propagation models are possible, with different geometries, more or less involved spatial dependences, possible time-dependence, etc. For simplicity, and because most of our results and conclusions should not depend on this modelling, we use throughout the paper a 1D diffusion model, as implemented in the usine package  (Maurin 2018)   1. The equations and solutions can be found for instance in  Putze et al. (2011) , and for conciseness, we do not repeat them here. Such a model also proves useful, because it can be compared, in some simple cases, to compact analytical solutions (see, e. g. App. D).
For display purpose, the 2D probability distribution functions are estimated using a Gaussian Kernel (default method to define the bandwidth of gaussian kde in scipy python library); the irregular shapes are related to statistical fluctuations. The colour code is related to the nuisance parameters used and the line style to the type of nuisance parameters (see Table  2 ). The '+' symbols represent true values. See text for discussion. -Fit with σ inel and σ prod nuisance parameters: blue lines (resp. orange lines) show the 1σ contours from the fit with σ inel (resp. σ prod ) as nuisance parameters. The size of the contours is not too strongly impacted by the cross-section nuisance parameters, because the minimum of χ 2 is left unchanged (not shown) and the additional degrees of freedom provided by the nuisance parameters are 'unused'.
7 (red dotted line), and part from assuming A C = 12 (blue dashed line): the bias is positive and the largest at the lowest E k/n (∼ 3%) and null at 1 GeV/n; it is negative, reaches ∼ 2% at ∼ 4 GeV/n, and then decreases. The pattern of AMS-02 data (converted using the same approximation) with respect to the black solid line is similar to the one seen in the top panel, indicating that the origin of the discrepancy with the exact model calculation is the wrong assumption made for A. We can summarise the above subtle discussion as follows: if we fit a model on AMS-02 B/C data as a function of rigidity, this model will be offset from the converted AMS-02 B/C data as a function of E k/n. The offset is not a simple scaling, it is instead energy dependent because the isotopic content of B and C elements is energy dependent (in a non-trivial way).