Comment on egusphere-2022-725

Context.Impacts of dust grains on spacecraft are known to produce typical impulsive signals in the voltage waveform recorded at the terminals of electric antennas. Such signals (as may be expected) are routinely detected by the Time Domain Sampler (TDS) system of the Radio and Plasma Waves (RPW) instrument on board Solar Orbiter.Aims.We investigate the capabilities of RPW in terms of interplanetary dust studies and present the first analysis of dust impacts recorded by this instrument. Our purpose is to characterize the dust population observed in terms of size, flux, and velocity.Methods.We briefly discuss previously developed models of voltage pulse generation after a dust impact onto a spacecraft and present the relevant technical parameters for Solar Orbiter RPW as a dust detector. Then we present the statistical analysis of the dust impacts recorded by RPW/TDS from April 20, 2020 to February 27, 2021 between 0.5 AU and 1 AU.Results.The study of the dust impact rate along Solar Orbiter’s orbit shows that the dust population studied presents a radial velocity component directed outward from the Sun. Its order of magnitude can be roughly estimated asvr, dust ≃ 50 km s−1, which is consistent with the flux of impactors being dominated byβ-meteoroids. We estimate the cumulative flux of these grains at 1 AU to be roughlyFβ ≃ 8 × 10−5m−2s−1for particles of a radiusr ≳ 100 nm. The power law indexδof the cumulative mass flux of the impactors is evaluated by two differents methods, namely: direct observations of voltage pulses and indirect effect on the impact rate dependency on the impact speed. Both methods give the following result:δ ≃ 0.3 − 0.4.Conclusions.Solar Orbiter RPW proves to be a suitable instrument for interplanetary dust studies, and the dust detection algorithm implemented in the TDS subsystem an efficient tool for fluxes estimation. These first results are promising for the continuation of the mission, in particular, for the in situ study of the inner Solar System dust cloud outside of the ecliptic plane, which Solar Orbiter will be the first spacecraft to explore.

<p>We will review the instrumental capabilities of the Radio and Plasma Waves (RPW) Instrument on Solar Orbiter which at the time of writing this abstract is planned for a launch on February 5<sup>th</sup> 2020. This instrument is designed to measure in-situ magnetic and electric fields and waves from 'DC' to a few hundreds of kHz. RPW will also observe solar radio emissions up to 16 MHz. The RPW instrument is of primary importance to the Solar Orbiter mission and science requirements, since it is essential to answer three of the four mission overarching science objectives. In addition, RPW will exchange on-board data with the other in-situ instruments, in order to process algorithms for interplanetary shocks and type III Langmuir waves detections. If everything goes well after the launch, we will hopefully be able to present the first RPW data and results gathered during the commissioning.</p>

<p>We investigate the polarization of Langmuir waves observed by the Time Domain Sampler (TDS) module of the Radio and Plasma Waves instrument on Solar Orbiter during several extensive Type III burst events. During its two-year-long cruise phase, Solar orbiter often crossed the source region of the Type III radio emission and observed the Langmuir waves generated by solar energetic electrons. The waves are known to exhibit complex modulation and often non-trivial elliptical polarization which sometimes rapidly changes on the timescales of tens of milliseconds. We show that the observed waveforms are typically composed of multiple sub-packets with a relatively short coherence length. We investigate the correlation between the polarization of the waves, simultaneously observed energetic electrons beams and other plasma properties.</p>

Context. The acceleration of particles at the Sun and their propagation through interplanetary space are key topics in heliophysics. Specifically, solar energetic electrons (SEEs) measured in situ can be linked to solar events such as flares and coronal mass ejections (CMEs) since they are also observed remotely in a broad range of electromagnetic emissions such as in radio and X-rays. Solar Orbiter, equipped with a wide range of remote-sensing and in situ detectors, provides an excellent opportunity to investigate SEEs and their solar origin from the inner heliosphere. Aims. We aim to record all SEE events measured in situ by Solar Orbiter, and to identify and characterise their potential solar counterparts. The results have been compiled in the Comprehensive Solar Energetic Electron event Catalogue (CoSEE-Cat), which will be updated regularly as the mission progresses. The catalogue contains key parameters of the SEEs, as well as the associated flares, CMEs, and radio bursts. In this paper, we describe the catalogue and provide a first statistical analysis. Methods. The Energetic Particle Detector (EPD) was used to identify and characterise SEE events, infer the electron release time at the Sun, and determine the composition of related energetic ions. Basic parameters of associated X-ray flares (timing, intensity, source location) were provided by the Spectrometer/Telescope for Imaging X-rays (STIX). This was complemented by the Extreme Ultraviolet Imager (EUI), which added information on eruptive phenomena. CME observations were contributed by the coronagraph Metis and the Solar Orbiter Heliospheric Imager (SoloHI). Type III radio bursts observed by the Radio and Plasma Waves (RPW) instrument provided a link between the SEEs detected at Solar Orbiter and their potential solar sources. The conditions in interplanetary space were characterised using Solar Wind Analyzer (SWA) and Solar Orbiter Magnetometer (MAG) measurements. Finally, data-driven modelling with the Magnetic Connectivity Tool provided an independent estimate of the solar source position of the SEEs. Results. The first data release of the catalogue contains 303 SEE events observed in the period from November 2020 until the end of December 2022. Based on the timing and magnetic connectivity of their solar counterparts, we find a very clear distinction between events with an impulsive ion composition and ones with a gradual one. These results support the flare-related origin of impulsive events and the association of gradual events with extended structures such as CME-driven shocks or erupting flux ropes. We also show that the commonly observed delays of the solar release times of the SEEs relative to the associated X-ray flares and type III radio burst are at least partially due to propagation effects and not exclusively due to an actual delayed injection. This effect is cumulative with heliocentric distance and is probably related to turbulence and cross-field transport.

Measurements of spacecraft potential (V_sc) are an important tool for plasma diagnostics. High-resolution V_sc measurements can be calibrated with low-cadence observations of electron plasma frequency to derive high-resolution electron density data. We analyze V_sc measured by the Radio and Plasma Waves (RPW) instrument suite on board Solar Orbiter. We investigate the evolution of V_sc within the framework of the heliocentric distance and mission lifetime. We aim to obtain electron density estimates that are valid across a wider range of V_sc regimes, including the cases of negative V_sc. We determined V_sc by combining measurements of the probe-to-spacecraft potential, V_ps, and the probe-to-plasma potential, V_pn. Then, we used the appropriate electric current balance to find a relation between electron density and V_sc, which we calibrated to the observed electron plasma frequency. With this method, we obtained the electron density at the resolution given by the V_ps measurements. We find that V_sc reaches negative values for a larger amount of time than expected for a sunlit spacecraft immersed in a tenuous plasma such as the solar wind. We are now able to identify when these V_sc changes occur, enabling us to estimate the electron density accordingly. Solar Orbiter charges negatively during periods of high plasma density and low photoelectron emission. However, the measured V_sc is also influenced by the electrical disconnection of the solar arrays from the spacecraft ground during fuse-blowing events. This disconnection raises the local plasma potential near the probes, causing the measured V_sc to appear negative even when Solar Orbiter remains positively charged. By distinguishing between the genuine negative spacecraft charging and the apparent negative charging induced by solar panels, we can refine previous electron density estimation methods to include cases when V_ sc <0 V. This approach provides reliable, high-resolution electron density estimates across both positive and negative V_sc regimes.

BackgroundThe aging of the world population poses a major health challenge, and brain–computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.ObjectivesThis study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.MethodsA total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time–frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (θ + α)/β and θ/β, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.ResultFor the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.ConclusionCompared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

During the Voyager 2 flyby of Neptune the plasma wave and radio astronomy instruments detected numerous impulsive signals that were interpreted as micron‐sized particles striking the spacecraft. This paper presents an analysis of the particle impacts observed by the plasma wave instrument. From analysis of wideband antenna voltage waveforms a peak impact rate of 443 s−1 was observed near the inbound equator crossing, which occurred at a radial distance of 3.45 RN, and a peak impact rate of 151 s−1 was observed near the outbound ring plane crossing, which occurred at a radial distance of 4.20 RN. The inbound peak is offset by 146 ± 4 km north of the equatorial plane, and the outbound peak is offset by 948 ± 65 km south of the equatorial plane. A Gaussian fit of the impact rate profile has a width (to the e−l points) of 537 ± 22 km for the inbound “core” component and 2,073 ± 392 km for the outbound component. These impact rates correspond to maximum number densities of a few times 10−3 m−3. Analysis of the voltage amplitudes induced on the antenna indicates that the particles have masses ranging from 10−10 g to a few times 10−9 g, with an uncertainty of up to a factor of 10. Assuming a mass density of 1 g/cm3, these particles would have radii in the range 5‐10 μm, with an uncertainty of about a factor of 2 to 3. In addition to the high impact rates observed near the two equator crossings, an impact rate of at least 0.6 s−1 was observed over the entire region inside about 8 RN, including the northern polar region. Likely sources for these particles include (1) several of the small satellites (1989N1, 1989N2, 1989N3, and 1989N4) discovered near Neptune by Voyager and (2) the rings. Electromagnetic forces may play an important role in diffusing the particles away from the equatorial plane.

Wave normal and Poynting vector measurements from the Cassini radio and plasma wave instrument (RPWS) are used to examine the propagation characteristics of various plasma waves during the Earth flyby on August 18, 1999. Using the five‐channel waveform receiver (WFR), the wave normal vector is determined using the Means method for a lightning‐induced whistler, equatorial chorus, and a series of low‐frequency emissions observed while Cassini was in the magnetosheath. The Poynting vector for these emissions is also calculated from the five components measured by the WFR. The propagation characteristics of the lightning‐induced whistler were found to be consistent with the whistler wave mode of propagation, with propagation antiparallel to the magnetic field (southward) at Cassini. The sferic associated with this whistler was observed by both Cassini and the Stanford VLF group at the Palmer Station in Antarctica. Analysis of the arrival direction of the sferic at the Palmer Station suggests that the lightning stroke is in the same sector as Cassini. Chorus was observed very close (within a few degrees) to the magnetic equator during the flyby. The chorus was found to propagate primarily away from the magnetic equator and was observed to change direction as Cassini crossed the magnetic equator. This suggests that the source region of the chorus is very near the magnetic equator. The low‐frequency emission in the magnetosheath has many of the characteristics of lion roars. The average value of the angle between the wave normal vector and the local magnetic field was found to be 16°, and the emissions ranged in frequency from 0.19 to 0.75 ƒce, where ƒce is the electron cyclotron frequency. The wave normal vectors of these waves were primarily in one direction for each individual burst (either parallel or antiparallel to the local field) but varied in direction throughout the magnetosheath. This suggests that the sources of the emissions are far from the spacecraft and that there are multiple source regions.

<p>We present measurements from the Radio and Plasma Wave (RPW) instrument suite onboard the Solar Orbiter mission during the first Venus encounter. RPW consists of several units and is capable of measuring both the electric and magnetic field fluctuations with three electric antennas and a search-coil magnetometer: The Low Frequency Receiver (LFR) cover the range from DC up to 10kHz when measuring the electric and magnetic waveform and spectra; the Thermal Noise and High Frequency Receiver (TNR-HFR) determines the electric power spectra and magnetic power spectra from 4kHz-20MHz, and 4kHz to 500kHz, respectively, to determine properties of the electron population; the Time Domain Sampler (TDS) measures and digitizes onboard the electric and magnetic field waveforms from 100 Hz to 250 kHz. The BIAS subunit measures DC and LF electric fields as well as the spacecraft potential, which gives a high cadence measure of the local plasma density when calibrated to the low-cadence tracking of the plasma peak from the TNR. Solar Orbiter approached Venus from the induced magnetotail and had its closest approach at an altitude of 7500 km over the north pole of Venus on 27 Dec 2020. The RPW instruments observed a tail region that extended several 10’s of Venus radii downstream of the planet. The induced magnetosphere was characterized to be a highly dynamic environment as Solar Orbiter traversed the downstream tail and magnetosheath before it crossed the Bow Shock outbound at ~12:40 UT. Polarized whistler waves, high frequency electrostatic waves, narrow-banded emissions, possible electron double layers were observed. The fine structure of the bow shock could also be investigated in detail. Solar Orbiter could hence enhance the knowledge of the structure of the solar wind-Venus interaction.</p>

Context.The Radio and Plasma Waves (RPW) instrument is one of the four in situ instruments of the ESA/NASA Solar Orbiter mission, which was successfully launched on February 10, 2020. The Low Frequency Receiver (LFR) is one of its subsystems, designed to characterize the low frequency electric (quasi-DC – 10 kHz) and magnetic (∼1 Hz–10 kHz) fields that develop, propagate, interact, and dissipate in the solar wind plasma. Combined with observations of the particles and the DC magnetic field, LFR measurements will help to improve the understanding of the heating and acceleration processes at work during solar wind expansion.Aims.The capability of LFR to observe and analyze a variety of low frequency plasma waves can be demontrated by taking advantage of whistler mode wave observations made just after the near-Earth commissioning phase of Solar Orbiter. In particular, this is related to its capability of measuring the wave normal vector, the phase velocity, and the Poynting vector for determining the propagation characteristics of the waves.Methods.Several case studies of whistler mode waves are presented, using all possible LFR onboard digital processing products, waveforms, spectral matrices, and basic wave parameters.Results.Here, we show that whistler mode waves can be very properly identified and characterized, along with their Doppler-shifted frequency, based on the waveform capture as well as on the LFR onboard spectral analysis.Conclusions.Despite the fact that calibrations of the electric and magnetic data still require some improvement, these first whistler observations show a good overall consistency between the RPW LFR data, indicating that many science results on these waves, as well as on other plasma waves, can be obtained by Solar Orbiter in the solar wind.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

The near-infrared (NIR) spectral reflectance characteristics of wood cross sections are commonly employed for wood species classification. Both specular and diffuse reflectance spectral curves of wood cross sections can be used. However, which one is more effective for classification and whether classification models trained on these two spectra can be used interchangeably have not yet been explored. In this study, the NIR spectral curves of wood cross sections from 64 common timber species were used to evaluate the specular and diffuse reflectance spectral profiles through five classifier models—namely, the support vector machine (SVM), k-nearest neighbors (KNN), convolutional neural network (CNN), decision tree (DT), and nearest class mean (NCM) classifiers. The classification accuracies of specular and diffuse reflectance curves using SVM classifier were 88.43% and 88.02%, respectively, whereas other classifiers exhibited lower classification accuracy, with specular reflectance spectral classification accuracy consistently outperforming diffuse spectral classification. Additionally, experimental results demonstrated that correct classification rate of the testing dataset after cross-use was less than 16%, indicating that classifier models trained on these two spectra could not be used interchangeably. In conclusion, this study suggested that specular reflectance NIR spectral curves are more suitable for wood species classification.

White blood cells (WBCs) are the most diverse types of cells observed in the healing process of injured skeletal muscles. In the recovery process, WBCs exhibit a dynamic cellular response and undergo multiple changes of the protein expression. The progress of healing can be analyzed by the number of WBCs or by the number of specific proteins observed in light microscopy images obtained at different time points after injury. We propose a deep learning quantification and analysis system called DeepQuantify to analyze WBCs in light microscopy images of uninjured and injured muscles of female mice. The DeepQuantify system features in segmentation using the localized iterative Otsu's thresholding method, masking postprocessing, and classification of WBCs with a convolutional neural network (CNN) classifier to achieve a high accuracy and a low manpower cost. The proposed two-layer CNN classifier designed based on the optimization hypothesis is evaluated and compared with other CNN classifiers. The DeepQuantify system adopting these CNN classifiers is evaluated for quantifying CD68-positive macrophages and 7/4-positive neutrophils and compared with the state-of-the-art deep learning segmentation architectures. DeepQuantify achieves an accuracy of 90.64% and 89.31% for CD68-positive macrophages and 7/4-positive neutrophils, respectively. The DeepQuantify system employing the proposed two-layer CNN architecture achieves better performance than those deep segmentation architectures. The quantitative analysis of two protein dynamics during muscle recovery is also presented.

Impulsive electron events observed in interplanetary space are believed to be generated by acceleration in solar flares. This notion has been supported by correlations between the characteristics of energetic electrons detected in-situ near 1 au and those in solar flares derived from hard X-ray observations. However, the details of this relation are still unclear, presumably because of the complex combination of acceleration, injection, and transport effects that are involved.We present the first statistical results on impulsive electron events obtained by joint observations of remote-sensing and in-situ instruments on Solar Orbiter. We use the suite Energetic Particle Detector (EPD) to measure the properties of the electrons (time profile, anisotropy, maximum energy, inferring the injection time at the source, etc.), as well as to determine the particularities of the composition in the suprathermal energy range. Also, X-ray observations from the Spectrometer/Telescope for Imaging X-rays (STIX) constrain the energetic electrons in the solar flare in terms of timing, spectrum, and location. Type III radio bursts detected by the Radio and Plasma Waves (RPW) instrument are used to link the nonthermal X-ray peaks to the interplanetary electron beams. Finally, the Extreme Ultraviolet Imager (EUI) provides context on the flare evolution. We use a large event sample obtained during the first 2.5 years of the Solar Orbiter mission, which covers a wide range of radial distances ranging from as close as 0.33 au to 1.02 au.