Radio Detection Research Articles

UAV Payload Identification with Acoustic Emissions and Cell Phones

The growing presence of Unmanned Aerial Vehicles (UAVs) in all sectors of society poses new security threats to civilian and military sectors. In response, new UAV detection systems have and are being developed. Current systems use techniques such as Radio Detection And Ranging (RADAR), visual recognition, and Radio Frequency (RF). Another promising solution for UAV detection uses acoustic emissions. Past researchers demonstrated the ability to use UAV acoustic signatures to determine whether a UAV carries a payload and the weight of that payload at close range with high-quality microphones. This research expands the field of study by performing acoustic payload detection using cell phones and at farther range by developing the system called HurtzHunter. The system collects audio data and extracts Mel-Frequency Cepstrum Coefficients (MFCCs) to train Support Vector Machines (SVMs). The HurtzHunter system tests acoustic payload detection with one high-quality microphone and six different cell phones at 7 m - 100 m ground distance from the UAV. At each distance, the experiment runs 6 flights each with a unique payload attached to the UAV. The HurtzHunter design achieves an 88.26% - 99.93% payload prediction accuracy depending on the configuration.

Survey of Innovative Techniques for Speed Measurement of Vehicles

Advanced technology offers various alternatives for the speed measurement of vehicles. However, different technologies often result in different accuracy in the speed measurement of the vehicle. Lack of knowledge of accuracy between different technologies is a common problem for the researcher. This paper discusses the different techniques for speed measurement of vehicle-based on the theoretical study. One of the primary reasons for measuring speed is to enforce traffic speed laws and promote road safety. Speed limits are set to ensure that vehicles are travelling at safe speeds, given the conditions of the road, weather, and traffic. The use of speed measuring devices such as radar guns, laser guns, and speed cameras allows law enforcement agencies to monitor traffic and identify vehicles that are exceeding the speed limit. This information can then be used to issue citations or warnings to drivers, and ultimately to reduce the number of speed-related accidents and improve road safety. Various speed measurement techniques are included such as. RADAR (Radio Detection and Ranging) - this is one of the most common speed measurement techniques and uses radio waves to determine the speed of a vehicle, GPS (Global Positioning System) - GPS uses satellite technology to determine the location and speed of a vehicle. The MIRROR system uses multiple radar units to determine the speed of a vehicle, LIDAR (Light Detection and Ranging) - this technique gauges a vehicle's speed using lasers. The Speed Detection Camera System (SDCS) measures a vehicle's speed using cameras and image processing methods. On the highway, video streams are employed in image processing techniques to calculate the speed of the car.

Jet Feedback in Star-Forming Galaxies

In this paper, I review our understanding of how jet feedback works in star-forming galaxies. There are some interesting differences to radiative feedback from Active Galactic Nuclei (AGN). Jets act on galaxy haloes as well as on dense gas, for example in regularly rotating discs, where they can suppress star formation (particularly in the centre, negative feedback), but also enhance it (positive feedback). Jet feedback may produce turbulent, multi-phase gas structures where shocks contribute to the ionisation and is observed in connection with galactic outflows. The exact driving mechanism of these outflows is still unclear, but may be a combination of effects linked to star formation, jet-induced turbulence and radiative AGN feedback. Supermassive black holes in any galaxy can produce jets. Preferential radio detections in more massive galaxies can be explained with different conditions in the circumgalactic medium and, correspondingly, different jet–environment interactions.

First Detection of Radio Emission Associated with a Classical Cepheid

We report the detection of 15 GHz radio continuum emission associated with the classical Cepheid variable star δ Cephei (δ Cep) based on observations with the Karl G. Jansky Very Large Array. Our results constitute the first probable detection of radio continuum emission from a classical Cepheid. We observed the star at pulsation phase ϕ ≈ 0.43 (corresponding to the phase of maximum radius and minimum temperature) during three pulsation cycles in late 2018 and detected statistically significant emission (>5σ) during one of the three epochs. The observed radio emission appears to be variable at a ≳10% level on timescales of days to weeks. We also present an upper limit on the 10 GHz flux density at pulsation phase ϕ = 0.31 from an observation in 2014. We discuss possible mechanisms that may produce the observed 15 GHz emission, but cannot make a conclusive identification from the present data. The emission does not appear to be consistent with originating from a close-in, late-type dwarf companion, although this scenario cannot yet be strictly excluded. Previous X-ray observations have shown that δ Cep undergoes periodic increases in X-ray flux during pulsation phase ϕ ≈ 0.43. The lack of radio detection in two out of three observing epochs at ϕ ≈ 0.43 suggests that either the radio emission is not linked with a particular pulsation phase, or else that the strength of the generated radio emission in each pulsation cycle is variable.

Unravelling the relationship between plant diversity and vegetation structural complexity: A review and theoretical framework

AbstractVegetation structural complexity (VSC)—the three‐dimensional distribution of plants within an ecosystem—is an important ecological trait. To date, research has focused primarily on theeffectsof VSC on ecological patterns and processes, but comparatively little is known about what drives variation in VSC.Recent advances in active remote sensing technology, particularly light detection and ranging and radio detection and ranging, have allowed the measurement of VSC at unprecedented spatial scales and resolutions. Out of this and earlier work has emerged evidence that VSC is typically associated with greater ecosystem functioning (especially microclimate regulation, productivity, faunal diversity and habitat provisioning), making restoration of vegetation complexity a potentially powerful restoration tool.Recent studies of VSC across natural and experimental gradients of plant diversity have also revealed that more diverse plant communities tend to be more structurally complex. However, the shape and generality of this relationship—and the mechanism(s) by which phytodiversity might contribute to structural complexity—remain poorly understood.Here, we review how active remote sensing has facilitated recent VSC research and shaped our understanding of the relationship between vegetation complexity and ecosystem function. We then present a theoretical framework for the relationship between phytodiversity and VSC based on classic biodiversity‐ecosystem functioning principles. Finally, we evaluate the evidence for the notion that diverse plant assemblages tend to be more structurally complex and explore the shape of the relationship between phytodiversity and VSC using data from 13 recent remote sensing studies.Synthesis. The relationship between phytodiversity and VSC appears to be almost universally positive. Preliminary evidence further suggests that the most common relationships between phytodiversity and VSC are linear or saturating, indicating that the extent of functional redundancy between species varies across plant communities and ecosystems. In contrast, we find little evidence for exponential or negative relationships between plant diversity and VSC, suggesting that even modest increases in plant diversity could markedly increase structural complexity. Additional investigations of phytodiversity‐VSC relationships are necessary to establish whether the observed positive relationships are causal (and, if so, in which direction) and to clarify the potential impact of plant community restoration on structural complexity and broader ecosystem function.

First-principle calculation of birefringence effects for in-ice radio detection of neutrinos

The detection of high-energy neutrinos in the EeV range requires new detection techniques to cope with the small expected flux. The radio detection method, utilizing Askaryan emission, can be used to detect these neutrinos in polar ice. The propagation of the radio pulses has to be modeled carefully to reconstruct the energy, direction, and flavor of the neutrino from the detected radio flashes. Here, we study the effect of birefringence in ice, which splits up the radio pulse into two orthogonal polarization components with slightly different propagation speeds. This provides useful signatures to determine the neutrino energy and is potentially important to determine the neutrino direction to degree precision. We calculated the effect of birefringence from first principles where the only free parameter is the dielectric tensor as a function of position. Our code, for the first time, can propagate full RF waveforms, taking interference due to changing polarization eigenvectors during propagation into account. The model is available open-source through the NuRadioMC framework. We compare our results to in-situ calibration data from the ARA and ARIANNA experiments and find good agreement for the available time delay measurements. This indicates a significant improvement of the prediction power of birefringence effects compared to previous models. Finally, the implications and opportunities for neutrino detection are discussed.

Disentangling the nature of the prototype radio weak BL Lac

Context. The γ-ray emitting source WISE J141046.00+740511.2 has been associated with a Fermi-LAT detection by crossmatching with Swift/XRT data. It has shown all the canonical observational characteristics of a BL Lac source, including a power-law, featureless optical spectrum. However, it was only recently detected at radio frequencies and its radio flux is significantly low. Aims. Given that a radio detection is fundamental to associate lower-energy counterparts to Fermi-LAT sources, we aim to unambiguously classify this source by performing a multiwavelength analysis based on contemporaneous data. Methods. By using multifrequency observations at the Jansky Very Large Array, Giant Metrewave Radio Telescope, Gran Telescopio Canarias, Gemini, William Herschel Telescope and Liverpool observatories, together with Fermi-LAT and Swift data, we carried out two kinds of analyses. On one hand, we studied several known parameters that account for the radio loudness or weakness characterization and their application to blazars (in general) and to our source (in particular). And, on the other hand, we built and analyzed the observed spectral energy distribution (SED) of this source to try to explain its peculiar characteristics. Results. The multiwavelength analysis indicates that WISE J141046.00+740511.2 is a blazar of the high-frequency peaked (HBL) type that emits highly polarized light and that is likely located at a low redshift. In addition, the one-zone model parameters that best fit its SED are those of an extreme HBL (EHBL); this blazar type has been extensively predicted in theory to be lacking in the radio emission that is otherwise typical of canonical γ-ray blazars. Conclusions. We confirm that WISE J141046.00+740511.2 is indeed a highly polarized BL Lac of the HBL type. Further studies will be conducted to explain the atypical low radio flux detected for this source.

The radio detection and accretion properties of the peculiar nuclear transient AT 2019avd

ABSTRACT AT 2019avd is a nuclear transient detected from infrared to soft X-rays, though its nature is yet unclear. The source has shown two consecutive flaring episodes in the optical and the infrared bands, and its second flare was covered by X-ray monitoring programs. During this flare, the UVOT/Swift photometries revealed two plateaus: one observed after the peak and the other one appeared ∼240 d later. Meanwhile, our NICER and XRT/Swift campaigns show two declines in the X-ray emission, one during the first optical plateau and one 70–90 d after the optical/UV decline. The evidence suggests that the optical/UV could not have been primarily originated from X-ray reprocessing. Furthermore, we detected a timelag of ∼16–34 d between the optical and UV emission, which indicates the optical likely comes from UV reprocessing by a gas at a distance of 0.01–0.03 pc. We also report the first VLA and VLBA detection of this source at different frequencies and different stages of the second flare. The information obtained in the radio band – namely a steep and a late-time inverted radio spectrum, a high brightness temperature and a radio-loud state at late times – together with the multiwavelength properties of AT 2019avd suggests the launching and evolution of outflows such as disc winds or jets. In conclusion, we propose that after the ignition of black hole activity in the first flare, a super-Eddington flaring accretion disc formed and settled to a sub-Eddington state by the end of the second flare, associated with a compact radio outflow.

Radio detections of two unusual cataclysmic variables in the VLA Sky Survey

ABSTRACT We report two new radio detections of cataclysmic variables (CVs), and place them in context with radio and X-ray detections of other CVs. We detected QS Vir, a low accretion-rate CV; V2400 Oph, a discless intermediate polar; and recovered the polar AM Her in the Very Large Array Sky Survey 2–4 GHz radio images. The radio luminosities of these systems are higher than typically expected from coronal emission from stars of similar spectral types, and neither system is expected to produce jets, leaving the origin of the radio emission a puzzle. The radio emission mechanism for these two CVs may be electron–cyclotron maser emission, synchrotron radiation, or a more exotic process. We compile published radio detections of CVs, and X-ray measurements of these CVs, to illustrate their locations in the radio–X-ray luminosity plane, a diagnostic tool often used for X-ray binaries, active galactic nuclei, and radio stars. Several radio-emitting CVs, including these two newly detected CVs, seem to lie near the principal radio/X-ray track followed by black hole X-ray binaries at low luminosity, suggesting additional complexity in classifying unknown systems using their radio and X-ray luminosities alone.

Winter adverse driving dataset for autonomy in inclement winter weather

The availability of public datasets with annotated light detection and ranging (LiDAR) point clouds has advanced autonomous driving tasks, such as semantic and panoptic segmentation. However, there is a lack of datasets focused on inclement weather. Snow and rain degrade visibility and introduce noise in LiDAR point clouds. In this article, summarize a 3-year winter weather data collection effort and introduce the winter adverse driving dataset. It is the first multimodal dataset featuring moderate to severe winter weather—weather that would cause an experienced driver to alter their driving behavior. Our dataset features exclusively events with heavy snowfall and occasional white-out conditions. Data are collected using high-resolution LiDAR, visible as well as near infrared (IR) cameras, a long wave IR camera, forward-facing radio detection and ranging, and Global Navigation Satellite Systems/Inertial Measurement Unit units. Our dataset is unique in the range of sensors and the severity of the conditions observed. It is also one of the only data sets to focus on rural and semi-rural environments. Over 36 TB of adverse winter data have been collected over 3 years. We also provide dense point-wise labels to sequential LiDAR scans collected in severe winter weather. We have labeled and will make available around 1000 sequential LiDAR scenes, amounting to over 7 GB or 3.6 billion labeled points. This is the first point-wise semantically labeled dataset to include falling snow.

Simulation study for an in-situ calibration system for the measurement of the snow accumulation and the index-of-refraction profile for radio neutrino detectors

Sensitivity to ultra-high-energy neutrinos (E > 17 eV) can be obtained cost-efficiently by exploiting the Askaryan effect in ice, where a particle cascade induced by the neutrino interaction produces coherent radio emission that can be picked up by antennas. As the near-surface ice properties change rapidly within the upper \U0001d4aa(100 m), a good understanding of the ice properties is required to reconstruct the neutrino properties. In particular, continuous monitoring of the snow accumulation (which changes the depth of the antennas) and the index-of-refraction n(z) profile are crucial for an accurate determination of the neutrino's direction and energy. We present an in-situ calibration system that extends the radio detector station with two radio emitters to continuously monitor the firn properties within the upper 40 m by measuring the time differences between direct and reflected (off the surface) signals (D'n'R). We determine the optimal positions of two transmitters at all three sites of current and future in-ice radio detectors: Greenland, Moore's Bay, and the South Pole. For the South Pole we find that the snow accumulation Δh can be measured with a resolution of 3 mm and the parameters of an exponential n(z) profile α and z 0 with 0.04% and 0.14% precision respectively, which constitutes an improvement of more than a factor of 10 as compared to the inference of the n(z) profile from density measurements. Additionally, as this technique is based on the measurement of the signal propagation times we are not bound to the conversion of density to index-of-refraction. We quantify the impact of these ice uncertainties on the reconstruction of the neutrino vertex, direction, and energy and find that the calibration device measures the ice properties to sufficient precision to have negligible influence.

Mathematical, algorithmic and software support of synphase detection of radio signals in electronic communication networks with noises

Mathematical (radio signal model as a periodically correlated stochastic process and synphase method of its processing) and algorithmic support for synphase detection of stochastic-periodic radio signals in electronic communication networks with noise by calculating new detection indicators in the form of correlation components, which ensure effective making a decision regarding the presence or the absence of a useful radio signal in obstacles of different power. For a more detailed assessment of the calculated indicators of radio signal detection in the form of correlation components, were used averaged estimates. Software for synphase detection of radio signals in electronic communication networks with a graphical user interface is implemented in the Matlab environment. The process of synphase detection of radio signals in electronic communication networks with noise was investigated and the effectiveness of the application of new detection indicators was ascertained.

The Radio Detector of the Pierre Auger Observatory – status and expected performance

As part of the ongoing AugerPrime upgrade of the Pierre Auger Observatory, we are deploying short aperiodic loaded loop antennas measuring radio signals from extensive air showers in the 30 – 80 MHz band on each of the 1,660 surface detector stations. This new Radio Detector of the Observatory allows us to measure the energy in the electromagnetic cascade of inclined air showers with zenith angles larger than ∼ 65°. The water-Cherenkov detectors, in turn, perform a virtually pure measurement of the muon component of inclined air showers. The combination of both thus extends the mass-composition sensitivity of the upgraded Observatory to high zenith angles and therefore enlarges the sky coverage of mass-sensitive measurements at the highest energies while at the same time allowing us to cross-check the performance of the established detectors with an additional measurement technique. In this contribution, we outline the concept and design of the Radio Detector, report on its current status and initial results from the first deployed stations, and illustrate its expected performance with a detailed, end-to-end simulation study.

Time-to-collision for the Pedestrian Protection System simulation

The development of autonomous vehicles has increased significantly, with a high focus on driving safety. Pedestrian avoidance systems are a top priority for ensuring higher pedestrian safety. The Pedestrian Protection System (PPS) is mainly observed in current research to analyze the impact of the time-to-collision for collision avoidance with a pedestrian for different vehicle speed variations. The PPS is a critical system that prevents vehicle and pedestrian collisions based on multiple sensors such as camera and RADAR (radio detection and ranging). Time-to-collision value represents a reliable parameter for the PPS for classifying the traffic conflict between a vehicle and a pedestrian as an inevitable or avoidable event. Simcenter Prescan was used to simulate the traffic infrastructure, the vehicle dynamics of the models, and the sensors. This study aims to evaluate the potential impact of different values of the time-to-collision parameter on the avoidance of collision with the pedestrian. The results showed that Pedestrian Protection System could completely avoid a pedestrian collision.

Signal model and event reconstruction for the radio detection of inclined air showers

The detection of inclined air showers (zenith angles θ ≳ 65°) with kilometer-spaced radio-antenna arrays allows measuring cosmic rays at ultra-high energies (E ≲ 1020 eV). Radio and particle detector arrays provide independent measurements of the electromagnetic and muonic shower components of inclined air showers, respectively. Combined, these measurements have a large sensitivity to discriminate between air showers initiated by lighter and heavier cosmic rays. We have developed a precise model of the two-dimensional, highly complex and asymmetric lateral radio-signal distributions of inclined air shower at ground — the “radio-emission footprints”. Our model explicitly describes the dominant geomagnetic emission with a rotationally symmetric lateral distribution function, on top of which additional effects disturb the symmetry. The asymmetries are associated with the interference between the geomagnetic and sub-dominant charge-excess emission as well as with geometrical projection effects, so-called “early-late” effects. Our fully analytic model describes the entire footprint with only two observables: the geometrical distance between the shower impact point at the ground and the shower maximum d max, and the geomagnetic radiation energy E geo. We demonstrate that with this model, the electromagnetic shower energy can be reconstructed by kilometer-spaced antenna arrays with an intrinsic resolution of 5% and a negligible bias.

THE METHOD OF EXCLUSION OF KNOWN SIGNALS WHEN SCANNING A SPECIFIED RADIO RANGE

Obtaining access to information using the means of obtaining information secretly remains relevant at the present time. This is due to significant advantages, which include the impossibility of identifying a specialist who is doing listening or video monitoring of the premises. The specialist is located at a distance from this room. The integrity of the information, because the information comes from the original source. Therefore, the problem of detecting radio signals of means of covertly obtaining information is an urgent scientific task. This work is devoted to the problem of reducing the time of detection of signals of means of covertly obtaining information. The detection of radio signals of the means of covert information acquisition is burdened by the fact that the means of covert information acquisition of the new generation work in a fully permitted radio range and their detection in a room bordering on other, filled radio devices is problematic. Now almost the entire available radio frequency spectrum is involved in the work of various radio transmitters. This complicates the detection of radio signals of means of covertly obtaining information, especially in large cities. We are working on the development of a method for removing known signals, which allows, unlike existing methods, to take into account known signals even at the conversion stage. The conversion process is a necessary process in the operation of automated radio signal detection complexes. It is applied at the first stage, even before the signal detection process. This gives a great advantage, in terms of time, by about two times reducing the time of searching for random radio signals. This makes it possible to detect pulsed radio signals of short duration, that is, to detect radio signals of pulsed means of covertly obtaining information, and to partially solve the scientific task of detecting pulsed means of covertly obtaining information that work in rooms where information with limited access is processed. The direction of further research is the development or improvement of methods and algorithms for determining by automated complexes the signals of means of covertly obtaining information, which work under the cover of radio frequencies authorized to work in this radio range.

Smart Security Area (SSA) for Radar system technology

Our ability to align with the trend of innovations in science and technology will not only emancipate ignorance but also unfold our ability to evaluate, understand and predict possibilities in our society, environment, and the world at large. Radar system technology gives us the privilege to achieve the above-mentioned fact. The word Radar is an acronym for Radio Detection and Ranging. It is a mean of getting information about a distant target, by sending electromagnetic waves to them and analyzing the echoes from the target to generate relevant reports about the target. In this paper, we will focus on some metrics and the effect of changes in them on the performance of the radar system using the MATLAB Radar Designer.

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis

Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) sinusoidal wave using deep learning. Deep neural networks (DNN) and convolutional neural networks (CNN) are classes of artificial neural networks (ANNs) used for the frequency and slope estimation for LFM signals under additive white Gaussian noise (AWGN) and additive symmetric alpha stable noise (SαSN). DNN is composed of input, output, and two hidden layers, where several nodes in the first and second hidden layers are 25 and 8, respectively. CNN is the content input layer; many hidden layers include convolution, batch normalization, ReLU, max pooling, fully connected, and dropout. The output layer consists of a fully connected softmax and classification layers. SαS distributions are impulsive noise disturbances found in many communication environments such as marine systems, their distribution lacks a closed-form probability density function (PDF), except for specific cases, and infinite second-order statistics, hence geometric SNR (GSNR) is used in this work to determine the effect of noise in a mixture of Gaussian and SαS noise processes. DNN is a machine learning classifier with few layers for reducing FE and SE complexity. CNN is a deep learning classifier, designed with many layers, and proved to be more accurate than DNN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful to the FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption as compared to the classical FE based on time–frequency analysis, which are important requirements for many systems, such as some Internet of Things (IoT) sensor applications. After training CNN for frequency and slope estimation of LFM signals, the performance of CNN (in terms of accuracy) can give good results at very low signal-to-noise ratios where time–frequency distribution (TFD) fails, giving more than 20 dB difference in the GSNR working range as compared to the classical spectrogram-based estimation, and over 15 dB difference with Viterbi-based estimate.

Low Complexity Radar Gesture Recognition Using Synthetic Training Data

Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase.

Performance of the RF Detectors of the Astroneu Array

Since 2014, the university campus of Hellenic Open University (HOU) has hosted the Astroneu array, which is dedicated to the detection of extensive air showers (EAS) induced by high-energy cosmic rays (CR). The Astroneu array incorporates 9 large particle scintillation detectors and 6 antennas sensitive to the radio frequency (RF) range 1–200 MHz. The detectors are adjusted in three autonomous stations operating in an environment with a strong electromagnetic background. As shown by previous studies, EAS radio detection in such environments is possible using innovative noise rejection methods, as well as advanced analysis techniques. In this work, we present the analysis of the collected radio data corresponding to an operational period of approximately four years. We present the performance of the Astroneu radio array in reconstructing the EAS axis direction using different RF detector geometrical layouts and a technique for the estimation of the shower core by comparing simulation and experimental data. Moreover, we measure the relative amplitudes of the two mechanisms that give rise to RF emission (the Askaryan effect and geomagnetic emission) and show that they are in good agreement with previous studies as well as with the simulation predictions.