dataesr/scientific-paragraphs-categorization huggingface dataset:
We do not yet pass temperatures or heat fluxes to the ice sheet model to use internally as part of our basal coupling (UniCiCles is run with fixed internal ice temperatures), although NEMO's parameterization of BMB does include an idealized diffusive heat flux at the base of the ice that is specified according to the thickness of the shelf and the temperature of the water. After the climate segment has completed, a script bilinearly interpolates the BMB flux from the NEMO grid to the (8 km) mesh used in the UniCiCles AIS configuration and stores it in a BISICLES boundary file. As with the SMB, this bilinear interpolation is not conservative (conservation in the ice ocean coupling will be discussed in Section 6. 2). In our configurations the ocean couples to UniCICles once every year of climate simulation.
No significant difference in results has been seen from coupling more frequently. We have not tried BMB coupling in the framework of asynchronous coupling (  1 E N , Section 3), so cannot comment on that method in this context. For coupling UniCiCles to NEMO, aside from the iceberg calving flux (see Section 5) information about the geometry of the floating ice shelves is the only other thing we pass. Discretising the new shelf geometry in NEMO is a complex procedure with a number of stages, partly done by scripts and partly by new routines inside NEMO, which we describe in the following. The scheme has proven to be computationally stable to a wide range of realistic and idealized perturbations to ice shelves that we have tested. We diagnose the height of the ice shelf base above the sea floor bathymetry in UniCiCles, and regrid this field to represent the water column thickness (the depth of water between the bottom of the ice and the sea floor) in NEMO.
A depth for the base of the ice shelf in NEMO can reliably be derived from this quantity without concern that any differences in the representation of bathymetry between UniCiCles and NEMO will affect the shape of the ocean cavity we model. A script then identifies the BISICLES grid-boxes on its highest resolution mesh (2 km) that overlap each NEMO grid-box. If at least 50% of them contain grounded ice (rather than floating ice or open water), the NEMO grid-box is deactivated, so that it becomes grounded ice. Otherwise, the NEMO water column thickness is made equal to the mean water column thickness from the BISICLES grid-boxes. NEMO has no means of calculating melt for a subgrid-scale distribution of floating and grounded areas. Some areas in UniCiCles near the grounding line may thus have no melt calculated for their location if the NEMO box they correspond to has been grounded, and in areas of steeply sloping or highly variable bathymetry the use of a simple grid-box mean water column thickness in NEMO may result in melt values that are not appropriate for every UniCiCles point they are applied to.
If we use a lower threshold than 50% of grounded BISICLES grid-boxes as the criteria for ungrounding NEMO grid-boxes, the ocean cavity spreads under a greater area of ice and the area-integrated basal melting is correspondingly larger, but we see little difference in shelf stability that might result from NEMO explicitly calculating melt values for grid-boxes closer to the UniCiCles grounding line. Since the UKESM1. 0-ice AIS configuration of UniCiCles is required to maintain a minimum ice thickness of 10 m and has a fixed-front calving model, the surface extent of the ice shelf on the NEMO mesh does not change through this process, and no new areas of open water can be created. The depth of the ice shelf base on the NEMO grid computed by this script is an intermediate result. Further constraints apply to the number of active layers and partial layer thicknesses, and also to generating an acceptable ocean state in active grid-boxes for temperature, salinity and components of velocity, considering the Arakawa C-grid  (Arakawa & Lamb, 1977)  used in NEMO.
For our proof of concept UKESM1. 0-ice simulations, drift that results from inconsistencies in the initial states is immaterial, as we are more interested in demonstrating technical capability than producing accurate climate projections. We initialize the ocean from rest with temperature and salinity derived from the EN4 dataset  (Good et al. , 2013). The initial atmosphere state comes from the UKESM1. 0 preindustrial run  (Kuhlbrodt et al. , 2018). In the simulation with an eORCA1 ocean, UniCiCles is initialized directly from the modern state of a standalone BISICLES simulation  (Martin et al. , 2019). For the run with an eORCA025 ocean, the atmosphere and ocean were run together under constant 1970 greenhouse gas and other forcings for 5 years to generate average SMB and BMB fields that could be used with UniCiCles during a short additional standalone ISM spinup of 10 years, to overcome an initial coupling shock (measured by a spike in the continental average rate of ice thickness change).
, 2015). Global methods are usually preferred in hydrology as they consider uncertainties due to Abstract Global sensitivity analysis is an important step in the process of developing and analyzing hydrological models. Measured data of different variables are used to identify the number of sensitive model parameters and to better constrain the model output. However, data scarcity is a common issue in hydrology. Since in hydrology we are dealing with multi-scale time dependent problems, we want to overcome that issue by exploiting the potential of using the decomposed wavelet temporal scales of the discharge signal for the identification of sensitive model parameters. In the proposed methodology, we coupled the discrete wavelet transform with a technique for model parameter dimension reduction, that is, the active subspace method.
The first scenario was tested experimentally in the laboratory conditions by using a device built by the authors to generate the customized baseband signal used as a modulating signal for a professional FM radio transmitter. In this case, the baseband signal includes the QPSK data signal onto a 11 kHz carrier, multiplexed with an audio mono signal having a bandwidth of 5 kHz and the RDS signal used to transmit warning notifications as a text message. For the second scenario, which was tested by simulations in MathWorks' Matlab-Simulink software, the customized baseband signal contains the FM radio multiplex (MPX) signal and the digital modulated data signal onto FM subcarrier channel of 76 kHz. In this case, the customized baseband FM signal transmitted on an additive white gaussian noise (AWGN) communication channel is software generated.
The receiver is implemented based on SDR concept, by using a cheap RTL-SDR device, which provides the digital baseband FM signal through a USB interface to a Simulink-based FM receiver. The received signal is non-coherently demodulated using a Complex Delay Line Frequency Discriminator (CDLFD) and the data signal is recovered by using a carrier synchronization loop based on decision-directed phaselocked loop (PLL). The performances of the data transmissions are analyzed by plotting the bit error rate (BER) depending on E b /N 0 ratio for all transmission scenarios. The rest of the paper is organized as follows. Section 2 provides a description of the proposed drivers' warning application. It presents also the implementation of different types of software-defined radio communication systems for image notifications transmitted with different speed on the usual FM radio broadcasting.
1 ). The software component of the driver's device includes the software-defined radio receiver, which is able to receive both image notifications and the FM broadcasting at the same time. The database of images for traffic events notifications is represented by black and white images of small dimensions (100 × 100 pixels). Some examples are illustrated in Fig. 2. An image notification represents the road identification (ex. A2, DN1, E583), the location of the traffic event on the public road (kilometric point on the road where the traffic event occurs) and a simple image representation of a traffic event. The database of image notifications is updated in real-time depending on the traffic events announced by the road authorities and may contain classic traffic signs also. The data transmission speed achievable by the system is high enough for the proper operation of the drivers' warning application by real-time transmitting of image notifications.
B. TRANSMISSION OF IMAGE NOTIFICATIONS ON THE FM BROADCASTING BY USING SOFTWARE-DEFINED RADIO COMMUNICATION SYSTEMS The principle of image transmission via FM radio broadcasting is illustrated in Fig. 3. A customized baseband signal (containing both data and audio signals) software generated by Matlab -Simulink program is used as modulating signal for a professional FM transmitter. The transmitted FM signal is received by using a simple RTL-SDR dongle (commercially available at a low price), which provides the baseband FM modulated signal to the software program. This signal is demodulated with a software-defined FM radio receiver in order to extract the image notification in the same time with the usual FM broadcasting.
1) TRANSMISSION AND RECEPTION OF IMAGE NOTIFICATIONS ON THE MONO FM RADIO BROADCASTING A simple method to transmit data on FM radio broadcasting consists of using a mono FM transmitter, according to the method illustrated in  [15]. In Fig. 4  the block diagram used to create the customized baseband signal for mono FM transmission is illustrated. Although this signal contains a QPSK data signal, it represents the modulator signal for a classic mono FM transmitter. For the simulation setup, the baseband FM signal is generated by using a DC carrier frequency modulated by the information signal, s i (t), according to the block diagram illustrated in Fig. 5   [15]. This stage represents a voltage-controlled oscillator (VCO) with the sensitivity k 0 = 2πK fm (Hz/V), representing the FM modulation constant.
In Fig. 7  the block diagram of the Simulink-based mono FM radio receiver is presented. The received baseband FM signal is non-coherently demodulated using a CDLFD  [15]. According to the FM transmission, the demodulated signal is then processed by using a de-emphasis block. The audio signal corresponding to the mono FM radio broadcasting and QPSK modulated data (used for image notifications) onto 11 kHz carrier frequency are separated from each other by using a ''low-pass'' infinite impulse response (IIR) filter with a 5 kHz cut-off frequency and an 8 kHz ''band-pass'' finite impulse response (FIR) filter, respectively. In order to extract the image notification, the QPSK data signal is demodulated by using a carrier synchronization loop operating at baseband, as it is illustrated in  [15].
2) TRANSMISSION AND RECEPTION OF IMAGE NOTIFICATIONS ON THE STEREO FM RADIO BROADCASTING The method illustrated principally in Fig. 3  can also be used for data transmission on the stereo FM radio broadcasting. In this case, in order to increase the bit-rate, the data transmission is performed in the FM Broadcast Radio Subcarriers Channel by using a subcarrier on a frequency of 76 kHz. The block diagram used to create customized baseband signal for stereo FM transmission is illustrated in Fig. 8. The spectrum of the customized baseband signal (illustrated in Fig. 9 ) includes the stereo multiplex signal and the information data signal onto a 76 kHz carrier. Depending on the application type, the information data signal is QPSK, 8-PSK and 16-PSK modulated onto a 76 kHz carrier and the transmission bit-rate has been established to 40 kb/s, 60 kb/s and 80 kb/s, respectively.
A. SYSTEM TESTING BY SIMULATION The operation of all transmission scenarios proposed in the paper is first analyzed by using the simulation setup illustrated in Fig. 13 , that was built in Matlab-Simulink. In this case both the transmitter and receiver are software implemented and the generated baseband FM signal is transmitted on an AWGN communication channel. The block diagrams of the software-defined mono FM transmitter and receiver circuits employed in our analysis are illustrated in Figs. 4  and 7 , respectively and the block diagrams of the software-defined stereo FM transmitter and receiver circuits are presented in Figs. 8  and 10 , respectively. The frequency deviation employed by the proposed system is the one used by commercial radio FM stations, f = 75 kHz. Since in our application the maximum frequency of the information signal (including the audio signal and the digital modulated data signal) is f i = 15 kHz/96 kHz, the bandwidth of the FM signal is B = 180 kHz/342 kHz for mono and stereo FM radio transmission, respectively.
A good estimate of the BER can be obtained  from the simulations that provides at least 100 bit errors  [27]. As a consequence, for a highly accurate estimation of the BER we consider a number of 480000 input data bits for all transmission scenarios analyzed in the paper. In Fig. 18  the constellation diagrams of all modulations (QPSK, 8-PSK and 16-PSK) used for digital transmission performed in the proposed drivers' warning application are presented. In Fig. 19  the bit error rate depending on E b /N 0 for all digital transmissions (with QPSK, 8-PSK and 16-PSK modulations) analyzed in the proposed drivers' warning application are illustrated and compared with the corresponding ideal curves. The average theoretical bit error probability for M -ary PSK modulations, for M >2, over the AWGN channel is  [27] : BER ∼ = 1 log 2 M erfc E b log 2 M N 0 sin π M ( 10 ) The simulation results show that the data transmissions of image notifications are performed without any error for E b /N 0 ratios higher than 11 dB (for QPSK modulation implemented in both mono and stereo FM transmission), 15 dB (for 8-PSK modulation) and 20 dB (for 16-PSK modulation).
B. TECHNIQUES TO MEASURE THE SNR BY USING A SPECTRUM ANALYZER FROM MATLAB-SIMULINK In this section a new method to measure the signal-to-noise ratio of the received radiofrequency (RF) QPSK signal by using a spectrum analyzer from Matlab-Simulink is presented. This value is required to plot BER vs. E b /N 0 ratio in order to characterize the QPSK performance. According to  [34]  and  [35]  since the spectrum analyzer performs the measurement of combined signal and noise power, and taking into account the logarithmic ''decibel'' scale, it is necessary to subtract the N from (S + N )/N to obtain the true signal-to-noise ratio (SNR = S/N ). This is especially useful for low SNR. If the transmission is performed without any error (or with a BER lower than an imposed value) for a very high number of transmitted bits (N bits ), then the signal power measured with the spectrum analyzer can be approximated to the power of the noiseless RF QPSK signal.
For a QPSK transmission with a very low bit error rate (corresponding to a very high number of bits transmitted), the signal power measured by the spectrum analyzer can be approximated with the noiseless RF QPSK signal power. In Fig. 20  the BER vs. E b /N 0 at the receiver stage plotted based on SNR values, obtained by measurement with different errors (ε = 0, 0. 5% and 1%), according to  (16) , by using a spectrum analyzer from Matlab-Simulink is presented. According to the simulations results illustrated in Table  2 , for our case, when a number of 4. 8 • 10 5 bits is transmitted, the approximation error of the SNR is 4. 75%, a value that is too high to be accepted. On the other hand, in order to obtain a smaller error of the SNR approximation (lower than 1% or 0. 5%), QPSK data transmissions with too many bits are required, being very difficult to implement practically.
In Table  4  the E b /N 0 ratio calculation at the receiver stage is performed for different emission power in the range -17-0 dBm and for the most accurate noise power measurement presented in Table  3  (N = -9. 59 dBm). The BER values presented is Table  4  have been obtained by simulation, considering a number of 4. 8 • 10 5 input bits transmitted. In Fig. 21  the BER vs. E b /N 0 at the receiver stage plotted based on SNR values obtained by measurement with the differential method, by using a spectrum analyzer from Matlab-Simulink for different values of the determined noise power (illustrated in Table  3  ) is presented. Therefore, according to the simulation results illustrated in Fig. 21 , the most accurate value of the noise power, N , is the one resulting from the last measurement illustrated in Table  3  (N = -9.
C. EXPERIMENTAL RESULTS In order to validate our proposed approach and the simulation results, an experimental setup was built for the case of mono FM transmission. The general architecture of the FM broadcasting of a composite signal formed by the audio mono component, the pictogram sent by using QPSK modulation, and the RDS component is given in Fig. 22. A custom ''MPX like'' signal is built by Matlab-Simulink program. This composite signal contains the audio and the QPSK signals and it is played by a Smartphone. The RDS encoder combines the custom ''MPX like'' signal with the RDS one to generate the customized baseband signal. The encoder is built by the authors by using the shelf components. The obtained waveform modulates in frequency an RF carrier chosen outside the European FM band (88 -108 MHz).
This FM modulation is performed by using an ESG 4438C vector signal generator. encoder, (d) is the RTL2832U dongle, USB connected to the laptop and used as radio receiver; the emitting (e) and the receiver antennas (f) are classical omnidirectional ones; they have been placed at two meters separation; this distance was chosen in order to have a good signal-to-noise ratio level for the RF signal; the radio receiver (g) is used in order to ''sniff'' the emitted signal and to test if it is compliant to a classical commercial solution; (h) is a Smartphone. The waveforms containing the audio mono component (up to 5 kHz) and the QPSK signal onto 11 kHz carrier were built in Matlab with a sampling frequency of 288 kHz and played back as audio files by a Smartphone (h). In the experiments presented here, no pre-emphasis/de-emphasis filters were employed.
The main concern was to characterize the transmission of the pictogram through the QPSK modulation. This transmission is performed at a data rate of 8 kbit/sec and modulating a subcarrier at 11 kHz. The RF carrier was chosen at 109 MHz, in order to avoid the interference with the commercial radio stations. The emitted power level was 0 dBm and a frequency deviation of +/-75 kHz, identical to the commercial standard, was configured. At the receiver side, an RTL-SDR 2832U device coupled with Matlab-Simulink software is used. This dongle performs a direct conversion (0 IF) of the received RF signal. The sampling frequency of the baseband signal was chosen identical to the one at the emission, i. e. 288 kHz. Moreover, a fixed low noise amplifier (LNA) gain level (30 dB) was setup. The circuits illustrated at system level in Figs.
This stage is between the MPX encoder and the PLL. The JFET transistor varies its resistance inversely proportional to the amplifier's output and in this way a regulation of the 19 kHz signal amplitude is performed. The 57 kHz subcarrier is modulated by the base-band RDS signal by using a SA602 Gilbert cell. A double-sideband suppressed-carrier (DSB-SC) amplitude modulation is performed here. The measured time domain waveform (up) together with the corresponding spectrum (bottom) are presented in Fig. 26. At the receiver side, an RTL-SDR dongle, USB connected at a PC running Matlab-Simulink environment are employed. In order to be used for digital communications, the RTL-SDR dongle requires a calibration procedure  [15] , which determines the offset between the carrier frequency generated by its internal PLL circuitry and the carrier frequency of the emitted signal (the one generated by the ESG 4438C).
As in the simulation framework, the FM demodulation was performed by a Complex Delay Line Frequency Discriminator, as shown in Section 2. The processing of the received signal was performed offline on recordings lasting approximately one minute. Fig. 27  presents the spectrum of the received signal, after the demodulator for an emitted power level of the QPSK signal equal to 3 dBm. Fig. 28  presents the received QPSK constellation. The three components of the received composite signal in Fig. 27  (the audio mono, the QPSK signal and the RDS one) have been separated by using the proposed software-defined receiver illustrated in Fig. 7. For this power of the emitted signal, the data transmission is performed without any errors. The RDS signal was also decoded without errors, whenever the power level of the emitted QPSK signal was setup, thus proving the good separation between the three components of the composite signal.
8•10 5 , which corresponds to a transmission of 61 seconds and to a transmission of four pictograms. According to the experimental results, for transmission power of the RF QPSK signal in the range illustrated  in Table  5  (-9 dBm, -16 dBm), the quality of the audio signal is not impacted by the QPSK signal. The received signal's power level (S r ) was measured by using the Simulink's spectrum analyzer. The noise power (N ) has been determined by using the differential method proposed in previous Subsection. As stated, the measured value of the noise power depends on the accuracy of the combined signal and noise power value measurement with a software spectrum analyzer. In order to avoid a high processing power and to obtain a relatively good accuracy of the spectrum analyzer, the noise power measurement is performed for low SNR.
IV. CONCLUSION In this paper a new drivers' warning application has been proposed. The application uses an FM radio infrastructure to transmit real-time image notifications in order to warn drivers about an important traffic event. The reception of image notifications is performed together with the usual FM radio broadcasting by using an RTL-SDR dongle, USB connected to a car device running the proposed software-defined radio receiver application. The bit-rate of the image notifications is 8 kb/s for QPSK data transmission on the mono FM broadcasting and 40 kb/s, 60 kb/s, 80 kb/s for QPSK, 8-PSK, 16-PSK data transmission, respectively, on the stereo FM broadcasting. In this paper, different methods for the experimental measurement of the SNR and E b /N 0 ratio by using a software spectrum analyzer has been proposed.