Temperature Drift Research Articles

Temperature drift compensation of a FOG based on an HKSVM optimized by an improved hybrid BAS-GSA algorithm.

In this paper, the optimal hybrid kernel support vector machine is employed to propose a compensation strategy intended for the temperature drift of a fiber optical gyroscope (FOG). First, the mode of the hybrid kernel with an interpolation and extrapolation capability is constructed, which consists of the radial basis function and the polynomial kernel function. Second, the combination model of the beetle antennae search algorithm and gravitational search algorithm that has both local and global search capability is proposed to optimize the structure-related parameters of a hybrid kernel support vector machine (HKSVM). Finally, the proposed approach is trained and tested using the experimental data of temperature drift at two different rates of temperature change (10°C/min and 5°C/min). In addition, the proposed method is validated against those conventional compensation algorithms. According to the research results, the compensation error (mean squared error) of the proposed approach is reduced by 92% compared to the traditional support vector machine based on the radial basis function.

Hysteresis and temperature drift compensation for FBG demodulation by utilizing adaptive weight least square support vector regression.

Hysteresis and temperature drift deteriorate the demodulation performance of tunable Fabry-Perot (F-P) filters. This study addresses a novel adaptive weight least square support vector regression (AWLSSVR) to compensate for the hysteresis and temperature drift of F-P filters. The temperature drift of a referent fiber Bragg grating(FBG) is used to estimate the temperature drifts of other three sensing FBGs, and a novel adaptive weighting strategy with an asymmetric noise interval is proposed, to eliminate the effects of noise in the training dataset. The experimental results show that when the temperature-changing modes of the training and testing datasets were close to each other, the error of the proposed method is reduced to 8.7 pm, while the errors of the other three conventional methods based on LSSVR are more than 10.8 pm. Further, when the temperature-changing modes of the training and testing datasets were partly different, the error of the proposed method was reduced to 5.4 pm, while the errors of other methods were more than 11.9 pm. It was verified experimentally that the proposed AWLSSVR method is more accurate and robust than other versions of WLSSVR for training samples with noise, requires no additional hardware, and covers the entire C band.

Development of MEMS composite sensor with temperature compensation for tire pressure monitoring system

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum–silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum–silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum–silicon hybrid structures are located inside and outside the strained membrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from −20 °C to 100 °C, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ±0.91 °C, −1.225 mV/(V °C) and −0.150% °C−1. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from −3.553%FS °C−1 to −0.375%FS °C−1 based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The TCO is −0.017%FS °C−1, and the thermal coefficient of span is −0.020%FS °C−1. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.

Simple Current-Mode Squaring and Square-Rooting Circuits: Applications of MO-CCCCTA

This work provides new designs of simple current-mode squaring and square-rooting circuits using multiple-output current controlled current conveyor transconductance amplifier (MO-CCCCTA) as an active building block. Since the proposed circuits need no other external components, they are capable of high-frequency operation and well fitted for IC fabrication. Furthermore, they are insensitive to ambient temperature and their gains can be controlled easily by adjusting the bias currents of MO-CCCCTA. Additionally, the effects of MO-CCCCTA non-idealities on the designed circuits have also been investigated and discussed. Simulation results generated through PSPICE software using TSMC 0.18 µm CMOS process parameters have been presented to justify the theoretical analysis. The static power consumption, bandwidth, and maximum linearity error in dc transfer characteristic measurement for the square-rooting circuit are found to be 0.17 mW, 445.63 MHz and 1.12 %, while for the squaring circuit they are 0.326 mW, 61.15 MHz and 2.38 %, respectively. The application of the reported circuits as a 2-input vector summation circuit has also been included to strengthen the design ideas.
 HIGHLIGHTS
 
 Simple structures of fully integrable current-mode squarers and square-rooters with low component count and lower power dissipation
 The circuits are insensitive to temperature drift and their gains can be controlled easily by adjusting the bias currents of MO-CCCCTA
 Bandwidth, static power dissipation, linearity error of square-rooter are 445.63 MHz, 0.17 mW & ≤ 1.12 %; and for the squarer 61.15 MHz, 0.326 mW & 2.38 %, respectively
 
 GRAPHICAL ABSTRACT

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

A resonant high-pressure sensor based on dual cavities

High-pressure sensors enable expansive demands in ocean sciences, industrial controls, and oil explorations. Successful sensor realized in piezoresistive high-pressure sensors which suffer from the key issue of compromised accuracies due to serious temperature drifts. Herein, this paper presents a high accuracy resonant high-pressure sensor with the pressure range of 70 MPa. Different from conventional resonant high-pressure sensor, the developed sensor utilized a dual-resonator-cavity design to minimize temperature disturbances and improve the pressure sensitivities. Besides, four circle cavities were used to maintain a high vacuum level for resonators after anodic bonding process. In details, Dual resonators, which is parallelly placed in the tensile and compressive stresses areas of a rectangular pressure sensitive diaphragm, are separated vacuum-packaged in the parallel dual cavities. Thus, pressure under measurement bends the pressure sensitive diaphragm, producing an increased pressure sensitivity and a decreased temperature sensitivity by the differential outputs of the dual resonators. Parameterized mathematical models of the sensor were established and the parameters of the models were optimized to adjust the pressure sensitivities and the temperature sensitivities of the sensor. Simplified deep reactive ion etching was used to form the sensing structure of the sensor and only once anodic bonding was used to form vacuum packaging for the dual resonators. Experimental results confirmed that the Q values of the resonators were higher than 32 000. Besides, the temperature sensitivity of the sensor was reduced from 44 Hz °C−1 (494 ppm °C−1) to 1 Hz °C−1 (11 ppm °C−1) by the differential outputs of the dual resonators in the temperature range of −10 °C–60 °C under the pressure of 1000 kPa. In addition, the accuracy of the sensor was better than 0.02% FS within the pressure range of 110–6500 kPa and the temperature range of −10 °C–60 °C by using a polynomial algorithm.

Thermal Performance of a Capacitive Comb-Drive MEMS Accelerometer: Measurements vs. Simulation

In this work, we analysed the difference between the measurement and simulation results of thermal drift of a custom designed capacitive MEMS accelerometer. It was manufactured in X-FAB XMB10 technology together with a dedicated readout circuit in X-FAB XP018 technology. It turned out that the temperature sensitivity of the sensor’s output is nonlinear and particularly strong in the negative Celsius temperature range. It was found that the temperature drift is mainly caused by the MEMS sensor and the influence of the readout circuit is minimal. Moreover, the measurements showed that this temperature dependence is the same regardless of applied acceleration. Simulation of the accelerometer’s model allowed us to estimate the contribution of post-manufacturing mismatch on the thermal drift; for our sensor, the mismatch-induced drift accounted for about 6% of total thermal drift. It is argued that the remaining 94% of the drift could be a result of the presence of residual stress in the structure after fabrication.

An mmWave Sensor for Real-Time Monitoring of Gases Based on Real Refractive Index

We propose a millimeter-wave (mmWave) sensor to monitor gases and aerosols based on time-of-flight (ToF), thus, on the real refractive index. It utilizes an ultrawideband frequency-modulated continuous-wave (FMCW) radar operating from 72 to 92 GHz at a sampling rate of 500 Hz. Also, we introduce a model for simulation of the refractive index of arbitrary gases at mmWave frequencies using molecular spectroscopic data. Simulations then allow the identification of the respective gas under test. We characterized the performance of the sensor regarding the refractive index within various experiments. The precision is 0.027 ppm, and the 20-h long-term stability is better than 0.2 ppm. The linearity is at least 1.5 ppm. Due to sophisticated temperature compensation, the temperature drift is less than 10 ppm for 30 K. Since the sensor is accurate, precise, and robust, it is perfect for in-process real-time monitoring and classification of gases or aerosols in the industry.

Estimation of Ion Temperature in the Upper Ionosphere Along the Swarm Satellite Orbits

AbstractIon temperature is one of the key parameters that provides insight into the thermal balance of the coupled ionosphere‐thermosphere system. Together with the temperatures of neutral and electron gases, it affects physical and chemical processes and parameters in the upper atmosphere. These include the ion‐neutral collision frequencies, chemical reaction rates and plasma scale height, all of which influence the variation and distribution of ionospheric plasma. The European Space Agency's three, nearly polar‐orbiting Swarm satellites at about 500 km altitude measure ionospheric electron temperature, density, and ion drifts using electric field instrument (EFI) Langmuir probes and Thermal Ion Imagers. Measurements of the ion temperature, though initially planned, are not available due to technical problems with the ion imagers. This paper describes a model that estimates the ion temperature along the orbits of Swarm satellites and evaluates the validity of the corresponding data. This data‐driven, physics‐based model combines an ion heat balance equation of the upper ionosphere, the Swarm EFI measurements, and empirical models for neutral composition, winds, and electric field. The validity of this approach was investigated using a physics‐based ionosphere model (SAMI3) for different geophysical conditions. We have studied the effects of various assumptions and input data limitations to the model accuracy, and have validated the estimated ion temperature against independent measurements from low, middle, and high‐latitude incoherent scatter radars (ISRs). When compared with the ISR data, the obtained Swarm‐based ion temperature shows small systematic errors (1%–2%), high correlations (Swarm A/C 0.8, Swarm B 0.6), and random errors of 10%–20%.

ZRO Drift Reduction of MEMS Gyroscopes via Internal and Packaging Stress Release

Zero-rate output (ZRO) drift induces deteriorated micro-electromechanical system (MEMS) gyroscope performances, severely limiting its practical applications. Hence, it is vital to explore an effective method toward ZRO drift reduction. In this work, we conduct an elaborate investigation on the impacts of the internal and packaging stresses on the ZRO drift at the thermal start-up stage and propose a temperature-induced stress release method to reduce the duration and magnitude of ZRO drift. Self-developed high-Q dual-mass tuning fork gyroscopes (TFGs) are adopted to study the correlations between temperature, frequency, and ZRO drift. Furthermore, a rigorous finite element simulation model is built based on the actual device and packaging structure, revealing the temperature and stresses distribution inside TFGs. Meanwhile, the relationship between temperature and stresses are deeply explored. Moreover, we introduce a temperature-induced stress release process to generate thermal stresses and reduce the temperature-related device sensitivity. By this way, the ZRO drift duration is drastically reduced from ~2000 s to ~890 s, and the drift magnitude decreases from ~0.4 °/s to ~0.23 °/s. The optimized device achieves a small bias instability (BI) of 7.903 °/h and a low angle random walk (ARW) of 0.792 °/√ h, and its long-term bias performance is significantly improved.

Portable microwave radiometer for wearable devices

This paper presents a new circuit of the miniature microwave radiometer for wearable devices, which can be used to monitor the core body temperature (CBT) of internal human tissues continuously 24/7. The measurement results of the proposed device, as opposed to the known miniature wearable radiometers, remain unchanged when the impedance of the examined area varies. We have derived an analytical expression for radiometer measurement error based on parameters of device components. This formula allows accuracies to be estimated and optimal parameters of the circuit to be selected to minimise measurement error at a design stage. It is shown that measurement error is independent of the antenna reflection coefficient and the temperature of the radiometer front-end. A prototype of the single-channel miniature radiometer has 32 х 25 х 14 mm3 dimensions and USB interface communication with PC. A 28-hour run of the device has shown that it is highly stable, and a maximum drift in temperature is 0.15 ̊C. Operating frequency range was 3400-4100 MHz, supply voltage - 5V; power supply of the radiometer in measurement mode is 210 mA; time constant of the radiometer without being averaged is 0.6 sec, at the same time, standard deviation δ = 0.17 ̊С, with further averaging during 4 sec δ=0.052 ̊С, with averaging during 30 sec δ =0.017 ̊C; when there were input reflections R2=0.25, an error in measuring brightness temperature shifted by 0.2 ̊ C; with 10 ̊C variations in ambient temperature the shift was 0.15 ̊C. Introduction of self-contained power supply and wireless communication with smartphone have made it possible to use the proposed radiometer as a wearable device to monitor the temperature of internal tissues and CBT during human activities.

A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope.

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [kbest,abest] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10−3°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.

Low resistivity and near-zero temperature drift ZrB2-Ag composite films prepared by DC magnetron co-sputtering

Nanocrystalline ZrB2-Ag composite films, which utilize Ag nanoparticles as embedded ions, were directly fabricated via the direct current magnetron co-sputtering technique. Keithley Hall Measuring Instrument allowed insight into the physical features in the temperature of 77 K to 373 K. Ag-containing composite film led to resistivity for composite films descended exponentially rather than linearly, however also resulting in the temperature coefficient of resistivity (TCR) values varied from negative to positive. Both comparable lower resistivity (114.9 μΩ·cm) and even near-zero TCR77-373 K at 3 ppm·K−1 indicated that low resistivity and near-zero temperature drift ZrB2-Ag composite film can be obtained by tailoring the Ag content. Moreover, this provides a promising approach to develop sophisticated thin-film resistors (TFR) materials below and above ambient temperature with a broader temperature range.

A stable strain gauge measurement method for monitoring in-situ stress

In-situ stress exists in all rock mass or structures, which is reflected in the deformation and failure of various underground or ground excavation in geotechnical engineering. In-situ widely used in the engineering practice, in-situ stress monitoring plays an important role in many aspects, such as mine development, slope support, and geological disaster prevention and control, etc. Strain gauge has been used to record rock deformation using Differential strain analysis (DSA), Hollow Inclusion Technique (CSIRO Cells), and other in-situ stress measurement techniques. Limited by the volume, cost, power, and connection scheme of the measuring instruments, the test data are always influenced by various immeasurable factors, such as bend of the measuring cable and change in the ambient temperature. Majority of the gauge measurement technologies are not suitable for in-situ stress monitoring because of the stability of strain gauge measurement is poor, limits the application range of the gauge measurement. A stable strain gauge measurement method can be used for medium to high-precision in-situ stress monitoring to serve various engineering developments. We made a new measurement scheme by using high-precision constant current with independent signal transmission channels to achieve a high stability and high-resolution strain gauge measurement circuit with low power consumption and space cost. The temperature drift is as low as 0.3 με/°C in 0°C–60°C temperature range at 8000 με strain input, which can achieve the measurement effect of the traditional desktop strain gauge in about 1/10th volume and very low cost. The error caused by the change of cable resistance is reduced to 1 ppm, and the error caused by the disconnection and reconnection is less than 1 με, which avoids the failures caused by various environmental factors in the monitoring process, and the installation distance of monitoring strain gauge can reach tens of meters. This creates conducive conditions for using traditional in-situ stress measurement technology with in-situ stress monitoring and establishes an efficient and low-cost in-situ stress monitoring system.

Laser pointing error analysis and compensation method of low-power laser diode source applied to triangulation ranging system.

The pointing drift and dither of the light source greatly reduces the measurement accuracy of the laser triangulation ranging system. In particular, for the low-power laser diode (LD) source, the temperature drift and the dither of the LD itself are more obvious. In this paper, the influence factors on the laser pointing error were analyzed by experiments and simulations based on the triangulation ranging system, and a combined optimization algorithm was proposed to compensate the pointing error. First, we built a platform for testing the directivity of low-power LD and analyzed the directivity drifting error caused by the change in LD temperature, the dithering error of the LD at constant temperature. In addition, the measurement error caused by the thermal deformation of the focusing lens was also analyzed in ZEMAX. Second, polynomial fitting was adapted to preliminarily correct the LD temperature drifting error, and the Kalman filter was introduced to further optimize the measurement results with the aim of improving both the absolute accuracy and repeatability of the laser triangulation ranging system. The experimental results showed that the measurement root mean square error was 0.91 µm and the repeatability was 0.61 µm after the pointing error was compensated by the method we proposed.

An Innovative LC-C Resonant Pressure Sensor Based on Capacitive Feedthroughs

LC resonant wireless passive pressure sensor has been receiving increased attention nowadays. In this paper, an innovative LC-C resonant wireless passive pressure sensor based on dual capacitors was designed and manufactured to replace the traditional LC device. In addition to the inductor and capacitor structure required by the traditional LC device, this paper innovatively designs the fixed capacitor as the inter-layer interconnection structure. The key structural dimensions of the sensor were determined through the numerical analysis of the planar inductor theory and the electromechanical coupling simulation of the dual capacitors model in the previous stage, and the processing of the sensor was carried out through MEMS technology. The final test results showed that within the pressure range of 5kPa to 100kPa, the sensor’s average sensitivity was greater than 69.0 kHz/kPa, while the maximum linear fitting error was smaller than 4.18%. The temperature drift in atmospheric environment was 2.7kHz/°C, and the sensitivity temperature drift was 0.0062kHz/(kPa·°C).

Towards Development of an ISFET-Based Smart pH Sensor: Enabling Machine Learning for Drift Compensation in IoT Applications

Monitoring of pH is crucial for several chemical and biochemical processes. ISFET (Ion-Sensitive Field-Effect Transistors)-based pH sensors are promising candidates for pH monitoring applications. However, ISFET devices are prone to temporal and temperature drifts, which severely affects the precision of pH measurements. In this work, we collect experimental data of temporal and temperature drifts in an ISFET sensor to formulate an accurate SPICE macro model, incorporating both temporal and temperature non-idealities. The developed macro model is utilized for generating training data for state-of-the-art machine learning models for drift compensation, with a primary focus on the temporal characteristics. We utilize recurrent neural networks (RNNs) to model the temporal characteristics of ISFET, and thus, compensate the non-ideality. The sensor data is collected in various pH buffer solutions and a data set of sequences containing time-dependent voltage readings are generated by the device and the RNNs are trained to learn the crucial features from the data and map them to the precise pH of the solution. We compare two variants of RNNs, i.e. LSTM (long short-term memory) and GRU (gated recurrent unit), and their bidirectional low computational cost variants - biLSTM and biGRU. Each model is tested in a memory-constrained environment with the availability of a 32-bit and 64-bit floating-point number. Empirically, we find biLSTMs to perform best, where the achieved root mean square error (RMSE) between the model predicted pH and the true pH of the test solution is less than 0.212 pH, with an average RMSE of 0.126 pH. For temperature drift compensation, we collect data for four different temperatures and adapt well-established MLPs (Multi-layer Perceptrons) to compensate the intrinsic temperature drift in the sensor. We observe an average RMSE of the model predicted pH to the true pH to be less than 0.286 pH. The developed RNN models were implemented on Xilinx ZCU104 FPGA development kit using PYNQ framework, which demonstrates low power consumption. The proposed framework establishes the efficacy of Machine Learning (ML) techniques for drift compensation in ISFET-based pH sensors for deployment in IoT applications.

A Method for Drift Correction in Temperature and Salinity Measurements From Moored Buoys

Abstract Temperature and salinity are essential ocean variables for understanding the oceans' physical processes. The conductivity and temperature measurements are used for deriving ocean salinity. Conductivity-temperature (CT) sensors mounted on moored buoys are widely used to collect sustained time-series observations of temperature and salinity. However, these measurements are prone to drifts that need to be corrected to ensure data quality. The present study evaluates a field validation technique to correct the drift in subsurface temperature and conductivity measurements that can potentially complement the standard calibration procedure performed by the original equipment manufacturer. The advantage of field validation is that it could be carried out soon after retrieving CT sensors, which ensures that the physical conditions and configuration of the retrieved CT sensors remain unaltered from that of the in-situ conditions. The drift in the CT sensors was analyzed by pre-deployment and post-retrieval field validation of CT sensors. A correction is applied to the raw data assuming a linear trend in drift with time. An ice test to identify and correct the errors in the timestamp of CT measurements is also discussed.

Sub-ppm/°C Bandgap References With Natural Basis Expansion for Curvature Cancellation

A general approach based on a natural basis function expansion to cancel the temperature-curvature of the base-emitter voltage in the bipolar transistors embedded in bandgap references is presented. The proposed method can be applied to the widely used Banba, Kuijk, and Brokaw structures to build references with sub-ppm/°C temperature coefficients. Current version and voltage version embodiments based on a self-bootstrapping concept are provided. Error sources that can potentially affect the reference performance are analyzed and their effects on the temperature coefficient are minimized after trimming. Monte Carlo simulation results confirm that after calibration, temperature drift performance is within the design/trimming target 0.5 ppm/°C. A partially integrated Kuijk-based prototype reference has been designed and fabricated in a 65nm UMC process. Measurement results demonstrated a 1.5 ppm/°C temperature coefficient over a temperature range of 0 °C to 80 °C with a low-cost two-temperature trimming method. With additional trimming, 0.8 ppm/°C performance was achieved.

Thermospheric Parameters during Ionospheric G-Conditions

For the first time thermospheric parameters (neutral composition, exospheric temperature and vertical plasma drift related to thermospheric winds) have been inferred for ionospheric G-conditions observed with Millstone Hill ISR on 11–13 September 2005; 13 June 2005, and 15 July 2012. The earlier developed method to extract a consistent set of thermospheric parameters from ionospheric observations has been revised to solve the problem in question. In particular CHAMP/STAR and GOCE neutral gas density observations were included into the retrieval process. It was found that G-condition days were distinguished by enhanced exospheric temperature and decreased by ~2 times of the column atomic oxygen abundance in a comparison to quiet reference days, the molecular nitrogen column abundance being practically unchanged. The inferred upward plasma drift corresponds to strong ~90 m/s equatorward thermospheric wind presumably related to strong auroral heating on G-condition days.