Reflected Signal Research Articles

Geological detection of hard rocks by GPR and signal time-frequency characteristics analysis in urban underground trenchless construction

Abstract The hard rocks in the stratum can pose safety risks and hinder the progress of urban underground tunnel construction using shield and jacking methods, thereby reducing construction efficiency and increasing construction costs. This paper utilizes wavelet scale energy spectrum, wavelet packet theory and statistical methods to conduct research on the detection of special geological formations such as hard rocks and voids, as well as the analysis of their signal time-frequency characteristics based on the ground-penetrating radar (GPR) technique. On the basis of calibrating the permittivity of different types of rock blocks, we established a forward model for detecting hard rocks and voids, and the simulated signals were analyzed in the time and frequency domains. Subsequently, laboratory experiments were conducted to perform GPR tests on different types of hard rocks in natural and water-saturated states and voids, to explore the time-frequency characteristics, frequency band energy variations, and statistical patterns of typical single-trace signals. The results show that the granite detection signal contains more low-frequency components, the sandstone detection signal contains more medium-low frequency components, while the limestone detection signal contains more medium-high frequency components in their natural state; the signal from the karst cave has relatively more low-frequency components than the signal from the empty cavity. The geometric shape of the rock has no influence on the dominant frequency and time-frequency distribution of its reflection signal. Generally, rocks with higher rebound values (hardness) also exhibit larger variance and standard deviation in frequency band energy. The research has important theoretical significance and practical value for the measurement and assessment of special geological features such as hard rocks and voids in urban underground trenchless construction.

Near-infrared H2S telemetry system under non-cooperative target with a novel mixed heterodyne optical structure scheme for high efficiency

A gas leakage telemetry system under non-cooperative target was designed and developed based on laser absorption spectroscopy technology, targeting the combination frequency absorption band of Hydrogen sulfide (H2S) gas molecules. A distributed feedback (DFB) laser centered at 1578 nm was adopt, with mature packaging process and high market maturity. The returned signal of diffuse reflection is extremely weak, decreasing exponentially with the increase of telemetry distance, and direct detection technology is difficult to achieve ideal performance at telemetry distance and lower detection limit. Thus, heterodyne detection technology was introduced to significantly amplify diffuse reflection signal and improve the signal-to-noise ratio (SNR) of telemetry system. For further detection sensitivity, wavelength modulation spectroscopy (WMS) technology was combined to suppress background noise through the electrical modulation of DFB laser. Alternating signal was applied to the laser to alter its output characteristics during the process of laser oscillation. Compared with exterior modulation methods, this interior modulation method does not require additional modulator, which avoids optical loss and is more economical. Combining the advantages of fiber structure and space optical path structure, a novel mixed heterodyne optical structure which was more applicable to this system was independently designed after the specific analysis of main noise sources. Then, the effect of heterodyne detection technology was verified in contrast of direct detection technology. Sensor performance was validated through calibration and stability experiments. The limit of detection (LoD) reached a minimum of 3.44 parts-per-million (ppm·m) with a 98 s averaging time. And the packaged system was fully capable of detecting the hazardous critical concentration of H2S and acting as an alarm mechanism.

Seismic vulnerability signal analysis of low tower cable-stayed bridges method based on convolutional attention network

Abstract Due to the particularity and complexity of sedimentary environments, the wave impedance differences between different reflection interfaces in underground media may vary greatly. Therefore, an encoder–decoder neural network is proposed to enhance erroneous seismic weak reflection signals. The convolutional neural network (CNN) has the problem of difficulty in parallel computing, resulting in slow network training and computational efficiency. Considering that attention has an innate global self-attention mechanism, can compensate for long-term dependency deficiencies, and has the ability to perform parallel computing, which greatly compensates for the shortcomings of CNNs and recurrent neural networks, a seismic impedance inversion method based on convolutional attention networks is proposed. To improve the ability to extract noise, residual structure and convolutional attention module (CBAM) were introduced. The residual structure utilizes residual jump to weaken network degradation and reduce the difficulty of feature mapping. The CBAM uses a mixed attention weight of channel and space, which can enhance features with high correlation and suppress features with low correlation. In the decoder, in order to improve the dimension recovery ability of feature fusion, bilinear interpolation is selected for upsampling. The application results of the model and actual data indicate that this method can effectively enhance the weak reflection signals caused by the formation itself and improve the reservoir identification ability of seismic data.

An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation

The present work proposes a novel autonomous Internet of Things (IoT) spectral sensing system for in-situ optical monitoring of grape ripening through reflectance signals. To this end, tailor-made hardware for this IoT end node was developed, characterized, and operated in both lab and field conditions. It included three complementary modules: the optical module, the host module, and the controller module. The optical module included four photodetectors and four LEDs with maximum emission wavelength centered at 530, 630, 690, and 730 nm that was placed in direct contact with the grape berry. The host module included the LED driver and the analog front-end for signal acquisition. Finally, the controller module provided full control of the system and ensured data storage, power management, and connectivity. The system was capable of measuring reflectance in the range of 4 – 100 % with a linear response (r2 > 998) and with a high reproducibility among different optical units. This design made it possible to collect reflectance signals from red (cv. Touriga Nacional) and white (cv. Loureiro) grape varieties in both lab and field environments. The relationship between this optical fingerprint (comprised of the different reflectance intensities recorded) and the evolution of grape berry quality parameters throughout the ripening period (for approximately two months), was analyzed and discussed. Lab data was used to establish a multivariate model based on Partial Least Squares for the prediction of the Total Soluble Solids (TSS) content in both varieties. The model error (Root Mean Square Error in Cross Validation) was 2.31 and 0.73 °Brix for Touriga Nacional and Loureiro, respectively. This model was applied to data acquired in the field in an illustrative example of the potential of the system to predict TSS in real time. The field observations collected during the monitoring period also provided relevant information about the potential issues that may occur during the unattended operation of the optical sensors. Additionally, the modular architecture of the optical module proposed makes it possible to use different LEDs and photodetectors, as well as the assembly of optical filters. This creates the possibility of using the same principles for measuring reflectance in different spectral ranges (e.g. IR) or even fluorescence. The results herein described paved the work for future developments of this technology that will include the development of prediction models for the most relevant grape ripening parameters based on reflectance data, as well as its operation as part of a Wireless Sensor Network.

GNSS-IR water level monitoring toward particular narrow scenarios: Application to South-to-North Water Diversion Project in China

River water level monitoring plays a crucial role in hydrological practice. Global Navigation Satellite Systems Interferometry Reflectometry (GNSS-IR) has been demonstrated to help retrieve absolute water levels in expansive and unobstructed scenarios. However, how to measure in rivers remains a significant challenge for practical use due to the restricted reflection area. To address these issues, we propose the Dynamic Masking Elevation Threshold Selection Method (DMETSM), which effectively broadens the selection of available reflective signals. Through analyzing actual field data collected along the South-to-North Water Diversion Project, we have found that DMETSM almost doubles the effective monitoring area compared to the classical method. Furthermore, the multi-GNSS water level retrievals at two sites can achieve high accuracy, with root-mean-square errors of 1.36 cm and 2.57 cm, respectively. Results show that it improves accuracy by 30.6 % and 42.6 %. This highlights the considerable potential and value of applications in narrow and irregular areas.

Birefringence in Injection-Molded Cyclic Olefin Copolymer Substrates and Its Impact on Integrated Photonic Structures.

This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10-4 is found at the plate's surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10-4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm-1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.

Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.

The provision of Wireless Fidelity (Wi-Fi) service in an indoor environment is a crucial task and the decay in signal strength issues arises especially in indoor environments. The Line-of-Sight (LOS) is a path for signal propagation that commonly impedes innumerable indoor objects damage signals and also causes signal fading. In addition, the Signal decay (signal penetration), signal reflection, and long transmission distance between transceivers are the key concerns. The signals lose their power due to the existence of obstacles (path of signals) and hence destroy received signal strength (RSS) between different communicating nodes and ultimately cause loss of the packet. Thus, to solve this issue, herein we propose an advanced model to maximize the LOS in communicating nodes using a modern indoor environment. Our proposal comprised various components for instance signal enhancers, repeaters, reflectors,. these components are connected. The signal attenuation and calculation model comprises of power algorithm and hence it can quickly and efficiently find the walls and corridors as obstacles in an indoor environment. We compared our proposed model with state of the art model using Received Signal Strength (RSS) and Packet Delivery Ratio (PDR) (different scenario) and found that our proposed model is efficient. Our proposed model achieved high network throughput as compared to the state-of-the-art models.

Joint channel training and passive beamforming design for intelligent reflecting surface-aided LoRa systems

<abstract><p>In order to examine the potential and synergetic aspects of intelligent reflecting surface (IRS) techniques for Internet-of-Things (IoT), we study an IRS-aided Long Range (LoRa) system in this paper. Specifically, to facilitate the acquisition of accurate channel state information (CSI) for effective reflection of LoRa signals, we first propose an optimal training design for the least squares channel estimation with LoRa modulation, and then, by utilizing the acquired CSI, we develop a high-performing passive beamforming scheme based on a signal-to-ratio (SNR) criterion. Numerical results show that the proposed training design considerably outperforms the baseline schemes, and the proposed passive beamforming design results in a significant improvement in performance over that of the conventional LoRa system, thereby demonstrating the feasibility of extending coverage areas of LoRa systems with the aid of IRS.</p></abstract>

Polarization modulation scanning optical microscopy method

Since the discovery of monolayer graphene, the novel physical properties of two-dimensional (2D) materials, particularly those with fewer layers that often exhibit unique properties different from bulk materials, have received significant attention. Therefore, accurately determining the layer number or obtaining the microscopic surface morphology is crucial in the laboratory fabrication and during device manufacturing. However, traditional detection methods have numerous drawbacks. There is an urgent need for a convenient, accurate, and non-destructive scientific method to characterize the layer number and surface microstructure of 2D materials. By combining the experimental setup of laser scanning photocurrent spectroscopy, we develop a polarization-modulated scanning optical microscope based on the principle of reflectance difference spectroscopy. By monitoring the reflectivity of the samples, we can observe changes in the reflection signal strength of MoS<sub>2</sub> with different layer numbers. The intensity of the reflectance differential spectral signal reflects changes in the layer count within the sample. We can characterize the changes in the number of layers of 2D materials in a non-contact manner by using polarization-modulated scanning optical microscopy. Through the study of the reflectance differential spectra of two typical 2D layered materials, MoS<sub>2</sub> and ReSe<sub>2</sub>, we find that our polarization-modulated scanning optical microscope system is also more sensitive to the characteristics of the stacking anisotropy of the 2D materials than the conventional reflection microscope. This indicates that our research contributes to a better understanding of the layer number characteristics and anisotropic properties of layered 2D materials. Furthermore, our research also provides a non-contact optical method to characterize the number of layers and optical anisotropy of two-dimensional layered material.

Numerical Simulation of the Reflectance Signal from a Short-Pulse Gaussian Laser Beam Incident on Healthy and Malignant Skin Tissue

Numerical Simulation of the Reflectance Signal from a Short-Pulse Gaussian Laser Beam Incident on Healthy and Malignant Skin Tissue

Tongue-Jaw Movement Recognition Through Acoustic Sensing on Smartphones

Past tongue-jaw movement interaction systems typically require dedicated hardware and are uncomfortable to use, limiting their scalability and generalizability. This paper introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CanalScan</i> , the first system that recognizes tongue-jaw movements using commodity speakers and microphones mounted on ubiquitous off-the-shelf devices (e.g., smartphones). What inspires us is that tongue-jaw movements always cause ear canal deformations, and we find that for different tongue-jaw movements, dynamic features of ear canal deformations present unique patterns on acoustic reflections in the ear canal. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CanalScan</i> first sends an acoustic signal to the ear canal, then parses the reflection signals for tongue-jaw movements recognition. To eliminate the impacts of body movements, we develop a body movement noise filtering method and a dynamic segmentation method to identify and separate the tongue-jaw movements-associated ear canal deformations from other types of body movements. We further propose a sensor position detection method and a data transformation mechanism to reduce the impacts of diversities in-ear canal shapes and relative positions between sensors and the ear canal. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CanalScan</i> explores twelve unique and consistent features and applies a random forest classifier to distinguish tongue-jaw movements. Extensive experiments with twenty participants validate the generalizability, effectiveness, robustness, and high accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CanalScan</i> .

Combining topography and reflectance indices for better surface water detection

Since the Normalized Difference Water Index (NDWI) was proposed, water indices have served as useful tools for surface water detection. However, existing water indices are highly influenced by atmospheric and other environmental conditions and suffer from limited performance, especially in urban areas. At the core of the limitation is the sole dependency on the spectral distribution of reflectance signals. To overcome this, we propose to utilize topographic data as additional information for better water detection. Accordingly, the new index, namely Combined Water Index (CWI), is developed as the product of the topographic index and the reflectance-based index. These two indices excellently compensate each other: the former is free from noise issues but invariant over time while the latter can capture temporal dynamics of waterbody extents. The CWI is applied to four study areas of different development levels (natural, medium-sized cities, and megalopolis) in the Han River basin, South Korea. The water detection results of the CWI is promising, particularly in the heavily developed urban setting, demonstrated through visual images as well as various statistical measures.

Reconfigurable Intelligent Surfaces for Beamforming in Upcoming Wireless Communications

Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reflecting Surfaces (IRS), represent a groundbreaking advancement in wireless communication technology, enabling unprecedented control over electromagnetic (EM) wave propagation. Composed of numerous passive or active elements constructed from metasurfaces, RIS can dynamically alter their electromagnetic responses to optimize signal reflection, refraction, and scattering. These elements, capable of manipulating the phase, amplitude, and polarization of incoming waves, are centrally controlled through sophisticated algorithms that adapt to real-time environmental changes and communication requirements. RIS technology promises significant improvements in wireless communication by extending coverage, enhancing signal quality, managing interference, boosting energy efficiency, and improving security. Additionally, RIS can facilitate precise localization and sensing applications. Despite the high initial implementation costs and the need for complex control mechanisms and infrastructure compatibility, the potential benefits of RIS in creating efficient, secure, and adaptive wireless communication environments underscore its transformative impact on the field.

Resonant Radar Reflector On VHF / UHF Band Based on BPSK Modulation at LEO Orbit by MRC-100 Satellite

This paper presents a novel method for identify ing and tracking PocketQube satellites: the MRC-100 satellite is a model, and this method is based on a resonant radar reflec tor. The resonant reflector’s basic concept is that the resonant reflector uses a VHF/UHF communication subsystem antenna; there is no radiated RF signal, which means the power con sumption is only some Milliampere (mA). The continuous wave (CW) illuminator RF source is on the ground, and the onboard antenna receives the CW RF signal from the Earth. The micro controller (uC) periodically switches PIN diode forming BPSK modulated signal reflection so that another Earth station can receive the backscattered Binary Phase Shift Keying (BPSK) modulated signal. Also, it can detect the satellite if the ground station receiver can use a matched filter like a correlation re ceiver. If the ground station receiver knows the BPSK code of the satellite, it can detect it. If not, there is no way to detect the satellite. This method is similar to Radio Frequency Identifica tion (RFID) applications, but the reader is the ground station, and the tag is the satellite.

DeepGPS: Deep Learning Enhanced GPS Positioning in Urban Canyons

Global Positioning System (GPS) has benefited many novel applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , navigation, ride-sharing, and location-based services, in our daily life. Although GPS works well in most places, its performance in urban canyons is well-known poor, due to the signal reflections of non-line-of-sight (NLOS) satellites. Tremendous efforts have been made to mitigate the impacts of NLOS signals, while previous works heavily rely on precise proprietary 3D city models or other third-party resources, which are not easily accessible. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> , a deep learning enhanced GPS positioning system that can correct GPS estimations by only considering some simple contextual information. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> fuses environmental factors, including building heights and road distribution around GPS's initial position, and satellite statuses to describe the positioning context, and exploits an encoder-decoder network model to implicitly learn the complex relationships between positioning contexts and GPS estimations from massive labeled GPS samples. As a result, the well-trained model can accurately predict the correct position for each erroneous GPS estimation given its positioning context. We further improve the model with a novel constraint mask to filter out invalid candidate locations, and enable continuous localization with a simple mobility model. A prototype system is implemented and experimentally evaluated using a large-scale bus trajectory dataset and real-field GPS measurements. Experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> significantly enhances GPS performance in urban canyons, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , on average effectively correcting 90.1% GPS estimations with accuracy improvement by 64.6%.

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).

Assessment of Skin Maturity by LED Light at Birth and Its Association With Lung Maturity: Clinical Trial Secondary Outcomes

Background Clinicians face barriers when assessing lung maturity at birth due to global inequalities. Still, strategies for testing based solely on gestational age to predict the likelihood of respiratory distress syndrome (RDS) do not offer a comprehensive approach to addressing the challenge of uncertain outcomes. We hypothesize that a noninvasive assessment of skin maturity may indicate lung maturity. Objective This study aimed to assess the association between a newborn’s skin maturity and RDS occurrence. Methods We conducted a case-control nested in a prospective cohort study, a secondary endpoint of a multicenter clinical trial. The study was carried out in 5 Brazilian urban reference centers for highly complex perinatal care. Of 781 newborns from the cohort study, 640 were selected for the case-control analysis. Newborns with RDS formed the case group and newborns without RDS were the controls. All newborns with other diseases exhibiting respiratory manifestations were excluded. Skin maturity was assessed from the newborn's skin over the sole by an optical device that acquired a reflection signal through an LED sensor. The device, previously validated, measured and recorded skin reflectance. Clinical data related to respiratory outcomes were gathered from medical records during the 72-hour follow-up of the newborn, or until discharge or death, whichever occurred first. The main outcome measure was the association between skin reflectance and RDS using univariate and multivariate binary logistic regression. Additionally, we assessed the connection between skin reflectance and factors such as neonatal intensive care unit (NICU) admission and the need for ventilatory support. Results Out of 604 newborns, 470 (73.4%) were from the RDS group and 170 (26.6%) were from the control group. According to comparisons between the groups, newborns with RDS had a younger gestational age (31.6 vs 39.1 weeks, P&lt;.001) and birth weight (1491 vs 3121 grams, P&lt;.001) than controls. Skin reflectance was associated with RDS (odds ratio [OR] 0.982, 95% CI 0.979-0.985, R2=0.632, P&lt;.001). This relationship remained significant when adjusted by the cofactors antenatal corticosteroid and birth weight (OR 0.994, 95% CI 0.990-0.998, R2=0.843, P&lt;.001). Secondary outcomes also showed differences in skin reflectance. The mean difference was 0.219 (95% CI 0.200-0.238) between newborns that required ventilatory support versus those that did not and 0.223 (95% CI 0.205-0.241) between newborns that required NICU admission versus those that did not. Skin reflectance was associated with ventilatory support (OR 0.996, 95% CI 0.992-0.999, R2=0.814, P=.01) and with NICU admission (OR 0.994, 95% CI 0.990-0.998, R2=0.867, P=.004). Conclusions Our findings present a potential marker of lung immaturity at birth using the indirect method of skin assessment. Using the RDS clinical condition and a medical device, this study demonstrated the synchrony between lung and skin maturity. Trial Registration Registro Brasileiro de Ensaios Clínicos (ReBEC) RBR-3f5bm5; https://tinyurl.com/9fb7zrdb International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2018-027442

Diagnosis of Wall Structure of Mural Painting in Josadang Shrine of Buseoksa Temple Using High-Resolution GPR Exploration

The National Treasure Mural Painting in Josadang Shrine of Buseoksa Temple is a Goryeo Dynasty mural painted on a wall made of clay. GPR exploration, a non-destructive investigation method , was conducted to determine whether the cause of wall cracks was related to the frame. A method of transmitting electromagnetic waves with a high frequency of 2 GHz through the surface of the mural was applied, and HH and VV data were acquired in the Y-axis direction. The survey was conducted with high resolution by setting the measurement line interval to 1 cm and the data acquisition interval to 1 cm very closely. The exploration method is similar to rebar exploration commonly used at construction sites. However due to the nature of earthen walls and wooden structures, they produce weak reflection signals, so when the data is processed in the same way, noise is amplified in the image. This may lead to errors in interpreting the results. In data processing, images were derived by applying the optimal gridding method. As a result of the investigation, it was determined that the wall framework was composed of Gasisae, Seol-oe, and Nul-oe that can be seen in a Korean Hanok building, and it was confirmed that wall cracks occurred on the location of the Gasisae.

GNOS-II on Fengyun-3 Satellite Series: Exploration of Multi-GNSS Reflection Signals for Operational Applications

The Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the Chinese Fengyun-3E (FY-3E) satellite is the world’s first operational spaceborne mission that can utilize reflected signals from multiple navigation systems for Earth remote sensing. The satellite was launched into an 836-km early-morning polar orbit on 5 July 2021. Different GNSS signals show different characteristics in the observations and thus require different calibration methods. With an average data latency of less than 3 h, many near real-time applications are possible. This article first introduces the FY-3E/GNOS-II mission and instrument design, then describes the extensive calibration methods for the multi-GNSS measurements, and finally presents application results in the remote sensing of ocean surface winds, land soil moisture and sea ice extent. Especially, the ocean surface wind product has been used in operational applications such as assimilation in the numerical weather prediction model and monitoring of tropical cyclones. Currently, GNOS-II has been carried by FY-3E, FY-3F (launched in August 2023) and FY-3G (launched in April 2023). It will be also carried by future follow-on FY series and a more complete multi-GNSS reflectometry constellation will be established.

IDENTIFICATION OF DEFECTS IN A COATING WEDGE BASED ON ULTRASONIC NON-DESTRUCTIVE TESTING METHODS AND CONVOLUTIONAL NEURAL NETWORKS

The paper deals with the identification of a crack-like defect in a coated wedge based on ultrasonic nondestructive testing. The authors propose an approach of defect identification followed by determination of its geometrical parameters. The approach is based on a shadowed ultrasonic nondestructive testing method combined with deep machine learning technologies. A wedge-shaped area is inspected for the presence of an internal defect. On one edge of the wedge there is a source of ultrasonic vibrations, on the opposite edge there is a receiver. Passing through the coating and body of the wedge, part of the signal is reflected from inhomogeneities and defects that may be present in it. The signal reaching the opposite edge of the wedge is read by the receiver. The received data is processed by a neural network model, which predicts the presence or absence of an internal defect and, if present, determines geometric parameters such as size and position. A finite element model of ultrasonic wave propagation inside the wedge is constructed. Special damping layers are used, due to which the influence of parasitic signal reflections and its further propagation into the wedge body is significantly reduced. Based on the built model, the shadow method of ultrasonic scanning is implemented. This method implies that on one side of the wedge are installed excitation devices, and on the opposite side – receiving devices. Several numerical experiments for various combinations of geometric parameters of the wedge and the defect have been performed using a distributed computing system. Based on the obtained data, a neural network model was built and trained, capable of identifying the defect and determining its characteristics. The input of the model is spectrograms of the readout signal, and the output is values characterizing the defect.