dataesr/scientific-paragraphs-categorization huggingface dataset:
5. The defined end nodes should have the capability to connect to an available WiFibased network. 6. The selected tools should have the ability to define different network topologies for wireless access points as well as for fixed network entities and provide SDN-based OpenFlow interfaces. In addition, from a software-defined network perspective, a defined network component's behavior can be controlled and managed by a network controller. 7. To differentiate the data traffic from different network end nodes, selected tools should have the ability to support virtual local area network (VLAN)-based tagging/untagging, and they should support tag-based forwarding. 8. The selected tools have the ability to emulate cellular-based network connectivity, principally 5G. 9. It would be nice to have a tool that can graphically represent the network emulation for the different selected scenarios.
ONOS SDN Controller The ONOS software-defined network controller is an open-sourced SDN and network function virtualization (NFV) controller. A simplified programmatic interface makes ONOS an ideal platform for operators searching to build innovative and advanced network services. ONOS has the ability to configure and control the network by programming the functionality and reducing network protocol implementation requirements. The ONOS cloud controller integrates intelligence, enabling end-users to easily create new network applications without having to change the data plane  [17].
Mininet-WiFi Mininet-WiFi  [18]  is a software-defined network emulator. It is a branch of Mininet  [20]  embedded with additional functionalities such as the ability to define and configure WiFi access points and nodes with moving capability based on Linux wireless driver and simulation driver 80211_hwsim  [21]. Using Mininet-WiFi, users can define different network topologies, where host/nodes can be defined with multiple wireless interfaces. Along with these, Mininet-WiFi supports defining radio parameters such as operating frequency channel, propagation model, coverage range, and transmission power (Tx). The network topologies developed with Mininet-WiFi have the potential to be controlled programmatically based on OpenFlow protocol versions 1 through 5. Since it works on the Linux wireless driver 80211_hwsim, it does not have the ability to emulate 5G-based connectivity.
Setup Description Figure  2  represents the test setup overview. All the selected tools that are considered to emulate the railway and road coexistence scenarios are installed on a virtual machine. The considered network topology is created using Mininet-WiFi for road and railway coexistence scenarios. To control the functionality of the network topology, an SDN application is developed, installed, and activated for the ONOS SDN controller. The SDN application is developed to support the moving of end nodes and the inter-cell handover of created nodes in a defined virtual space. It also has the ability to differentiate the data traffic based on VLAN tagging. Detailed information about this developed SDN application is elaborated in Section 8. SUMO is integrated with Mininet-WiFi to graphically represents the movement of network nodes on an open street map.
Network Topology S2(5/6)1-Different Access Network and Shared Core, Railway Track Parallel to Road A Python script is written to create a network topology with Mininet-WiFi where trains and cars have different access networks, both domains shared the core network, and railways have parallel tracks to roads. Figure  3  shows the S2(5/6)1 scenario, where an ONOS SDN controller programmatically controls the forwarding elements of the topology. The network switches and access points are SDN-based devices and they are operated and controlled via OpenFlow protocol. Host Car1 represents a car, and host Train1 represents a rail. Access points ap1 and ap2 are defined for roads, and ap3 and ap4 are defined for railways. Access points ap1 and ap3 are connected to network switch S11, and ap2 and ap4 are connected to switch S33.
The switch S22 is connected to S11 and S33. The host "RailServer" is defined as a railways service server, and "CarServer" is defined as a road service server; both servers are connected to switch S22. In this simple network topology, switch S22 is the core network switch, and S11 and S33 are the edge switches. The network topology can be complex with more core and edge network elements. For the demonstration, a simple topology is selected. In Figure  3 , we can see that railways and roads have different access networks but a shared core network. To design this scenario, a topology development code is written in Python. The source code of this complete project is available at  [26]. As we mentioned, for this practical work, the ONOS SDN controller is used. To define the ONOS as a remote controller as the element of the topology, the code given in Listing 1 is used as the Python topology code.
Using this method, the user can define the range of x-and y-coordinates for the plot limits, as shown in Listing 5. Figure  4  shows the graphical representation of the selected topology; we can clearly see access points ap1, ap2, ap3, and ap4 and defined nodes. The developed network topology with assigned switches, access points, connected hosts, and nodes/stations can be seen on the ONOS graphical representation platform as shown in Figure  5. The network topology is built using net. build(), and access points are started using start(). Mininet-WiFi also provides a command line interface (CLI) using the function CLI(net), which enables interaction with any element of the developed topology. To stop the network, the net. stop() function is used. In the code below, "c1" is the name assigned to the ONOS SDN controller.
Using the above-mentioned methods, functions, and procedures, users can emulate any salable and complex topology. Figure  5  represents the topology created using the Python script for the S2(5/6)1 scenario represented by the ONOS SDN controller's graphical user interface (GUI), where hosts Car1 and Train1 are defined with moving capability. To design the S4(5/6)4 scenario, a similar Python script is written using the methods and functions mentioned in Section 6. 1, and a network topology is developed in such a way that access points and the core network are shared by both railway and road domains. In this selected scenario, railway tracks are perpendicular to roads. To emulate the railway tracks being perpendicular to the road, the y-coordinate of the moving node Car1 is kept constant (y = 70) while moving in the defined virtual space, and the x-coordinate of node Train1 is kept constant (x = 130) while moving in the defined virtual space, as shown in Figure  6.
iperf3 [-s|-c] [ options ] 2. iperf3 -s -p <port number> Where -s represents host as server, -c represents host as client, -u represents UDP packets, -b is used to set the bitrate, -i option allows setting the reporting interval time in seconds; e. g. , iperf3 -c 10. 0. 0. 7 -i 2 3. iperf3 -c <server IP/host IP> -u -p <port number> -b <bitrate> 3. VLC Player: VideoLAN Client, generally known as VLC, is open-source, compact media player software developed by the VideoLAN project. VLC is capable of streaming and receiving media files (video/audio). In addition to DVD video and video CD formats, VLC supports a wide variety of audio and video compression methods and media streaming protocols. It can be installed and used on any desktop system as well as on smartphones  [33, 34]. To demonstrate video or critical video communication in railway and road coexistence scenarios, VLC Player is used to transmit video from a train to the RailServer and from a car to the CarServer.
Handover/Mobility (Speed/Direction/Providing Coordinates) To test the handover scenario, the first criteria is to design a network topology where hosts should have the capability to connect to wireless networks (WiFi) and users can define the mobility and speed of the moving host in a certain direction. These criteria can be fulfilled by Mininet-WiFi since it supports the mobility model and Mininet-WiFi Python API integrates a new method net. addStation() that authorizes users to define nodes that can move around in the defined virtual space, as mentioned in Section 6. 1. By developing a predefined mobility model, Mininet-WiFi can also move the nodes automatically in the defined virtual space as soon as the emulation scenario starts running. Mininet-WiFi supports the following mobility models: RandomDirection, RandomWalk, RandomWayPoint, GaussMarkov, RefrencePoint, TruncatedLevyWalk, and TimeVariantCommunity  [18, 22].
Level Crossing To manifest the level crossing scenario for railway and road coexistence environments, the S4(5/6)4 scenario is selected, which is presented in Section 6. 2 of this paper. In this scenario, railway tracks are perpendicular to roads, as shown in Figure  7. We know that at a level crossing, when a Train/Tram is coming, Cars or other vehicles should have to stop for some time and move when the train/tram has passed. It is one of the challenges for us to develop this scenario using the Mininet-WiFi and ONOS SDN controller. As we described in Section 9. 1, the Mininet-WiFi Python API has a mobility method. Using this method, the user can start and stop the mobility of the nodes, but it does not have a pause and restart moving node functionality. To replicate the level crossing scenario, Car1 should stop its mobility when Train1 is moving at the level crossing point.
Sending a Critical Message to the Assigned Server To send a critical message or information, the Scapy tool is used for the considered scenarios. Using this tool, a user-defined message is sent from any node/station to the assigned service server. Figure  30  shows that an ICMP data packet is sent with a message "Emergency Msg: Engine Failure" from node Train1 to RailServer for demonstration purposes in the selected scenario. This data packet can be sent using the Scapy Python API and by writing a Python script. Figure  31  shows the data packet captured using the Wireshark tool.
Save Changes Move/copy all the executable file to "data" folder of SUMO application and save the changes using the command: sudo make install      After running the script SUMO_Aug17. py, network emulation with the SUMO map as shown in Figure  43  will be generated. Figure  47  shows the location of the assigned access points and the movement of cars and trams, where assigned access points are ap1 to ap17, C2 represents Car2, and T77 represents Tram77. When a car/tram moves from the coverage range of one access point to another, we sometimes observe that there is no automatic connection to the nearest access point. In that case, the connection can be established using the command <node name> iw dev <node name>-wlan0 connect <SSID name>.
Open Field Test To quantify the locomotor syndrome following UVN, we used an automated video tracking software (EthoVision™ XT 14, Noldus, Wageningen, The Netherlands). Animals were individually placed in a square open field (80 × 80 × 40 cm) and were allowed to move freely for 10 min. Their behavior was recorded for 10 min using a digital camera and analyzed with EthoVision™ XT 14 software. Position of nose, body center, and tail were automatically detected by video software. Based on previous results  [29] , the mean distance travelled, meander, mean locomotor velocity, mean number of high accelerations (>50 cm/s 2 ), and mean percentage of time spent immobile were quantified (for details, see  [29] ). To minimize stress, the room was lit as dimly as possible while allowing us to clearly discern the rats.
When the animal touched the ground, we took a capture of the location of the four paws. Twenty repeated measurements were taken for each rat tested at each time point (pre-lesion, D1, 2, 3, 7, 10, 14, 21, and 30 post-lesion), and an average was calculated for each experimental session. The support surface was measured using a custom written image analysis tool (Matlab ® , Mathworks, Incs, Natick, MS, USA). Data recorded after UVN were compared to the mean pre-lesion values using individual references, which means each animal acted as its own control.
Quantification of KCC2 A custom program written in Matlab ® (Mathworks, Incs) was developed to analyze the membrane fluorescence of KCC2  [31]. The background or non-specific immunofluorescence was evaluated by calculating the mean florescence in a selected area devoid of neurons or other immunolabeled structures. In this region, we then established a threshold equal to the mean immunofluorescence plus three times the standard deviation. All data were subtracted from this threshold value and only positive values were conserved for further analysis. A region of interest was then drawn around the plasma membrane of a neuron. The program calculated the mean membrane fluorescence of each cell body in the region of interest over data that were above 20% of the maximum value. This threshold was used to ensure that only pixels belonging to the plasmatic membrane were counted.
Statistical Analysis We performed all statistical analyses using GraphPad Prism software (version 7, GraphPad Software, San Diego, CA, USA). Summary graphs are all shown as mean ± SEM. A two-way repeated measures ANOVA with a post-hoc Bonferroni's multiple comparison test were used to determine statistical differences between the treatments (UVN-NaCl and UVN-T4) for behavioral data. The statistical analyses of the cellular data were evaluated by two-way ANOVA to determine the effects of the group (UVN-NaCl and UVN-T4) and the post-operative time (Pre-op, D3 or D30) on BrdU + , NeuN + , GFAP + , IBA1 + , Olig2 + , Sox2 + , TRα + , TRβ + , and CO + cells to determine whether there were any interactions between these variables. ANOVA was followed by post hoc analysis with the Tukey's test. p values of < 0.
Contributions: G. R. and B. T. conceived the study. G. R. performed behavioral pharmacology. G. R. , I. W. and A. L. performed immunohistochemical experiments. D. P. developed and performed the surgeries. A. T. provided expertise and contributed to confocal microscopy. G. R. , E. M. , N. E. -M. , A. L. -J. , A. E. -A. , C. C. and B. T. contributed to data analysis and interpretation of the results. P. C. , M. -J. E. F. and A. Z. provided medical expertise. G. R. and B. T. wrote the manuscript, and all authors contributed to its editing. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by grants from the Ministère de l'Enseignement Supérieur et de la Recherche and CNRS (Aix-Marseille Université, UMR 7291). This work has received support from the French government under the Programme Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université via A*Midex (AMX-19-IET-004) and ANR (ANR-17-EURE-0029) funding.
Author Contributions: G. R. and B. T. conceived the study. G. R. performed behavioral pharmacology. G. R. , I. W. and A. L. performed immunohistochemical experiments. D. P. developed and performed the surgeries. A. T. provided expertise and contributed to confocal microscopy. G. R. , E. M. , N. E. -M. , A. L. -J. , A. E. -A. , C. C. and B. T. contributed to data analysis and interpretation of the results. P. C. , M. -J. E. F. and A. Z. provided medical expertise. G. R. and B. T. wrote the manuscript, and all authors contributed to its editing. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by grants from the Ministère de l'Enseignement Supérieur et de la Recherche and CNRS (Aix-Marseille Université, UMR 7291). This work has received support from the French government under the Programme Investissements d'Avenir, Initiative d'Excellence d'Aix-Marseille Université via A*Midex (AMX-19-IET-004) and ANR (ANR-17-EURE-0029) funding.
The Small Aperture Telescopes (SATs; Ali et al. 2020) will measure 10% of the sky angular resolutions from ∼91′-10′ at the same frequencies as the LAT and achieve more than an order of magnitude higher polarization sensitivity than the Planck satellite. An overview of the science goals that these telescopes will pursue was presented by SO19. In this paper, we expand on SO19 to illustrate myriad new investigations into the multiscale physics of Galactic structure and the physics of Galactic emission to be undertaken by SO using data collected by its CMB surveys. We provide quantitative forecasts that assess how the capabilities of the instruments translate into constraints on models of Galactic emission. In star-forming regions, the scales probed by SO bridge the high-resolution measurements from the Atacama Large Millimeter/submillimeter Array (ALMA) and the large-scale measurements from Planck, connecting collapsing cold core regions to the larger environment.
Galactic Emission Model We begin our forecasting with map-domain simulations of Galactic emission constructed with the Python Sky Model (PySM;  Thorne et al. 2017; Zonca et al. 2021 ). We focus exclusively on the polarization data, where the emission is dominated by the CMB, dust emission, and synchrotron emission. Other components that contribute to the total intensity signal-such the cosmic infrared background, freefree emission, AME, and CO emission-are largely unpolarized (e. g. , Planck Collaboration IV 2020, and references therein). Indeed, searches for polarized CO emission and AME are the subjects of Sections 4. 4 and 4. 5, respectively. As Galactic emission has the most power on large scales (e. g. ,  Dunkley et al. 2013; Planck Collaboration XI 2020) , we focus our analyses on ℓ < 1000.
(2017). To the Galactic emission, we add a realization of the CMB signal using the PySM "c1" model. This model draws a Gaussian CMB realization from a primordial unlensed CMB power spectrum computed with CAMB 41   (Lewis et al. 2000)  and then applies lensing in pixel space with the Taylens code 42   (Naess & Louis 2013 ; see  Thorne et al. 2017  for a more detailed description of the "c1" model).
1. 1, n n Ćℓ XX 1 2 ( )for all combinations of ν 1 and ν 2 are computed using the NaMaster software 43 (Alonso et al. 2019). We employ a constant bandpower binning width Δℓ = 15 and use the masks described in Section 2. 2 apodized with the "C1" method and an apodization scale of 3°. E-and B-mode purification is used when computing EE and BB spectra, respectively. Finally, we use the noise models presented in Section 2. 1 to estimate the noise s C ℓ XX ( ), which we add to the computed spectra. Explicitly (e. g. , Knox 1995), s = + D + C ℓ f ℓ C 2 2 1 , 5 ℓ XX ℓ XX ℓ sky 2 2 ( ) ( ) [( ) ] ( )  where the noise power spectra N ℓ from Section 2. 1 are combined following n n n = = N , 6 ℓ ℓ 2 1 2 1 2 ( ) ( )  n n n n = ¹ N N 1 2 , 7 ℓ ℓ ℓ 2 1 2 1 2 ( ) ( ) ( )  for auto-and cross-spectra, respectively.
The simulated power spectra are fit using the PyMC software  (Fonnesbeck et al. 2015). We are interested both in how well the parameters of this model can be constrained with SO data and how well extensions to this model can be constrained. In particular, Section 3. 2 explores sensitivity to curvature in the synchrotron SED, Section 3. 3 explores constraints on the dust SED relative to existing data, and Section 3. 4 explores the spatial correlation between dust and synchrotron emission. More detailed study of spatial variability is possible both by separately analyzing different subregions of the sky or by map-level modeling of the SEDs, but these more sophisticated approaches are beyond the scope of the present study.
2013; Houde et al. 2013). Wide CO polarization surveys are hard to undertake in practice given the intrinsically small degree of polarization and the long integration time required to achieve a significant detection of the signal in presence of atmospheric emission correlated in time. In principle, the same component separation approach could be applied to CO polarization as has been used in total intensity. However, the limited sensitivity of Planck data have so far prevented the extraction of any polarized CO emission. Puglisi et al. (2017)  presented a model to simulate the polarized emission of CO lines in molecular clouds, taking into account the 3D spatial distribution of CO in the Galaxy. The model was able to successfully reproduce the angular power spectrum of the observed Planck CO intensity maps (P13).
Component separation is necessary to disentangle the CO from other sky emissions. For this purpose, we adopted a maximum likelihood parametric approach as implemented in the fgbuster package 45  (Stompor et al. 2009 ). We assume that we have for each sky pixel a measurement from each of the SO frequencies ν i with a Gaussian noise level in the pixel p s n p i given by the instantaneous reference noise values of the survey, modulated by the number of observations in each sky pixel within the footprint (hits map) as presented in Section 2. 2. Our data model therefore reads = + d A s n , 1 0 p p p p ( ) where d p is a data vector containing the measured signal for all of the SO frequencies and Stokes parameters, s p is a vector of the underlying sky component to be estimated from the data, and A p ≡ A p (β) is the component mixing matrix.
In order to understand at which angular scales SO will improve the most over current observations, it is useful to evaluate the overall noise of the CO map as a function of angular scale. We obtain the noise power spectrum of the SO CO map by evaluating Equation (11) for each multipole ℓ with s n 2 i replaced by the power spectrum n N ℓ i ( ) of each SO frequency ν i. Figure  8  compares the CO intensity and polarization noise of SO to that of different Planck CO data products. 46 In intensity, at large angular scales, the correlated noise induced by the atmosphere degrades the sensitivity of our data, and Planck data dominate the sensitivity. However, our baseline LAT survey will have an almost three times lower noise than the most sensitive Planck data at scales ∼5″, and improvements will reach two orders of magnitudes at scales ∼3″.
A combination of Planck and LAT observations in the Galactic 45 https://github. com/fgbuster/fgbuster 46 We estimate the noise power spectrum of the Planck products from the publicly available CO null maps associated to each component separation approach adopted for the CO analysis. plane could therefore deliver signal-dominated measurements of CO clouds from degree to arcminute scales. In polarization, we see less degradation of the sensitivity at large angular scales compared to the intensity case as the atmosphere is largely unpolarized. The forecasted noise levels should allow, on average, a detection of CO line emission with a polarization fraction of Π CO  1% from 1°to 10″ scales. A hypothetical mean polarization fraction of CO emission of Π CO = 1% (shown in Figure  8 ) could be measured at about 5σ significance even if more complex component separation approaches compared to the baseline case have to be used (see discussion below) and if foreground residuals are sufficiently low.
This work was supported in part by a grant from the  Simons Foundation  (Award No. 457687 , B. K. ). We thank  Alexei Kritsuk  for sharing the numerical simulation data used in this work,  Martin Giard  for useful references and comments on CO polarization measurements, and the anonymous referee whose feedback helped us improve the paper. S. E. C. acknowledges support by the  National Science Foundation  under grant No. 2106607. N. K. , D. P. , G. P. , and C. B. acknowledge support from the http://cosmosnet. itCOSMOS Network from the  Italian Space Agency. N. K. , D. P. , and C. B. also acknowledge support by the http://web. infn. it/ CSN4/IS/Linea5/InDarkINDARK INFN Initiative. G. F. acknowledges the support of the  European Research Council  under the  Marie Skłodowska Curie actions  through the  Individual Global Fellowship  No.