Passive Temperature Research Articles

Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data

Lake ice is a sensitive indicator of climate change. Based on the disparities between the brightness temperatures of lake ice and water, passive microwave data can be used to monitor the ice variations of a lake. With focus on the analysis of long time series variability of lake ice, this study extracts four characteristic dates related to lake ice (the annual freeze start, freeze completion, ablation start and ablation completion dates) for Qinghai Lake from 1979 to 2016 using Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave brightness temperature data. The corresponding freezing duration, ablation duration, complete freezing duration and ice coverage duration are calculated. Applying Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the accuracy of the results derived from passive microwave data is validated. The validation analysis shows a strong agreement (R2 ranges from 0.70 to 0.85, mean absolute error (MAE) ranges from 2.25 to 3.94days) in the freeze start, ablation start, and ablation completion dates derived from the MODIS data and passive microwave data; the ice coverage duration also has a small error (relative error (RE)=2.95%, MAE=3.13days), suggesting that the results obtained from passive microwave data are reliable. The results show that the freezing dates of Qinghai Lake have been delayed and the ablation dates have advanced. Over 38years, the freeze start date and freeze completion date have been pushed back by 6.16days and 2.27days, respectively, while the ablation start date and ablation completion date have advanced by 11.24days and 14.09days, respectively. The freezing duration and ablation duration have shortened by 3.89days and 2.85days, respectively, and the complete freezing duration and ice coverage duration have shortened by 14.84days and 21.21days, respectively. There is a significant negative correlation between the ice coverage duration and the mean air temperature in winter.

Cold-water immersion following sprint interval training does not alter endurance signaling pathways or training adaptations in human skeletal muscle.

We investigated the underlying molecular mechanisms by which postexercise cold-water immersion (CWI) may alter key markers of mitochondrial biogenesis following both a single session and 6 wk of sprint interval training (SIT). Nineteen men performed a single SIT session, followed by one of two 15-min recovery conditions: cold-water immersion (10°C) or a passive room temperature control (23°C). Sixteen of these participants also completed 6 wk of SIT, each session followed immediately by their designated recovery condition. Four muscle biopsies were obtained in total, three during the single SIT session (preexercise, postrecovery, and 3 h postrecovery) and one 48 h after the last SIT session. After a single SIT session, phosphorylated (p-)AMPK, p-p38 MAPK, p-p53, and peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) mRNA were all increased (P < 0.05). Postexercise CWI had no effect on these responses. Consistent with the lack of a response after a single session, regular postexercise CWI had no effect on PGC-1α or p53 protein content. Six weeks of SIT increased peak aerobic power, maximal oxygen consumption, maximal uncoupled respiration (complexes I and II), and 2-km time trial performance (P < 0.05). However, regular CWI had no effect on changes in these markers, consistent with the lack of response in the markers of mitochondrial biogenesis. Although these observations suggest that CWI is not detrimental to endurance adaptations following 6 wk of SIT, they question whether postexercise CWI is an effective strategy to promote mitochondrial biogenesis and improvements in endurance performance.

Simulation and SMAP Observation of Sun-Glint Over the Land Surface at the L-Band

We investigate the magnitude of Sun-glint through modeling the Soil Moisture Active Passive (SMAP) brightness temperature (BT). Model results show that the specular reflection of Sun-glint in the L-band can spread over a wide range of view angles due to the roughness and undulation of the land surface and therefore affect SMAP radiometer observations. Due to SMAP’s low incidence angle (40°), Sun-glint in the specular direction is never observed, and only the noncoherent component of Sun-glint has influence on SMAP observations. Sun-glint is particularly an issue over wet soil surfaces at low solar zenith angles (SZAs), and caution has to be taken for the terrain effect even for high SZAs, because the local solar incident angle can be significantly changed by the terrain slope and then the specular reflection of Sun-glint can be viewed by SMAP. Model results also show that BT in V-pol is less contaminated by Sun-glint than that in H-pol. During an intense solar radio burst, it was found that the land surface BT in H-pol increased by 50 K in the forward scattering direction from the SMAP observation. This is roughly equivalent to 1 K increment by every 100 solar flux units on a dry soil and/or dense vegetation. When the solar activity is quiet in 2015, the Sun-glint from both wet land and ocean surfaces can reach up to 10 K in the SMAP L1B BT product. This paper suggests that BT observations around the solar specular direction should be masked for soil moisture retrieval at the L-band.

A concept of wireless and passive very-high temperature sensor

There is a need for sensors capable operating at temperatures above 1000 °C. We describe an innovative sensor that might achieve this goal. The sensor comprises two main elements: a thermocouple and a surface acoustic wave (SAW) strain sensor. The cold junction of the thermocouple is electrically connected to a highly piezoelectric thin layer, deposited on top of a SAW substrate. In operation, the voltage generated by the temperature gradient between the hot (&amp;gt;1000 °C) and cold junction (&amp;lt;350 °C) generates a strain field in the layer, which is mechanically transmitted to the substrate. This modifies the SAW propagation conditions and therefore the sensors' radiofrequency response. The change depends on the applied voltage and thus on the hot junction temperature. As SAW devices are passive elements that can be remotely interrogated, it becomes possible to infer the hot junction temperature from the radiofrequency response, i.e., to remotely read temperatures above 1000 °C, without embedded electronics. In this paper, we demonstrate the feasibility of this concept, using AlN layers deposited on Y-Z Lithium Niobate (LN). The achieved sensitivity of 80 Hz/V at 400 MHz is constant over a wide voltage range. Numerical simulations were performed to compute the main properties of the demonstrators and suggest optimization strategies. Improvements are expected from the use of stronger piezoelectric layers, such as AlScN or Pb(Ti,Zr)O3 (PZT), which could increase the sensitivity by factors of 3 and 20, as estimated from their transverse piezoelectric coefficients. As a first step in this direction, thin PZT layers have been deposited on Y-Z LN.

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

In this study, a convolutional neural network (CNN) is used to estimate sea ice concentration using synthetic aperture radar (SAR) scenes acquired during freeze-up in the Gulf of St. Lawrence on the east coast of Canada. The ice concentration estimates from the CNN are compared to those from a neural network (multi-layer perceptron or MLP) that uses hand-crafted features as input and a single layer of hidden nodes. The CNN is found to be less sensitive to pixel level details than the MLP and produces ice concentration that is less noisy and in closer agreement with that from image analysis charts. This is due to the multi-layer (deep) structure of the CNN, which enables abstract image features to be learned. The CNN ice concentration is also compared with ice concentration estimated from passive microwave brightness temperature data using the ARTIST sea ice (ASI) algorithm. The bias and RMS of the difference between the ice concentration from the CNN and that from image analysis charts is reduced as compared to that from either the MLP or ASI algorithm. Additional results demonstrate the impact of varying the input patch size, varying the number of CNN layers, and including the incidence angle as an additional input.

Cryotherapy induces an increase in muscle stiffness.

Although cold application (ie, cryotherapy) may be useful to treat sports injuries and to prevent muscle damage, it is unclear whether it has adverse effects on muscle mechanical properties. This study aimed to determine the effect of air-pulsed cryotherapy on muscle stiffness estimated using ultrasound shear wave elastography. Myoelectrical activity, ankle passive torque, shear modulus (an index of stiffness), and muscle temperature of the gastrocnemius medialis were measured before, during an air-pulsed cryotherapy (-30°C) treatment of four sets of 4minutes with 1-minute recovery in between and during a 40minutes postcryotherapy period. Muscle temperature significantly decreased after the second set of treatment (10minutes: 32.3±2.5°C; P<.001), peaked at 29minutes (27.9±2.2°C; P<.001) and remained below baseline values at 60minutes (29.5±2.0°C; P<.001). Shear modulus increased by +11.5±11.8% after the second set (10minutes; P=.011), peaked at 30minutes (+34.7±42.6%; P<.001), and remained elevated until the end of the post-treatment period (+25.4±17.1%; P<.001). These findings provide evidence that cryotherapy induces an increase in muscle stiffness. This acute change in muscle mechanical properties may lower the amount of stretch that the muscle tissue is able to sustain without subsequent injury. This should be considered when using cryotherapy in athletic practice.

A Wireless Passive Pressure and Temperature Sensor via a Dual LC Resonant Circuit in Harsh Environments

This paper presents a passive wireless sensor for simultaneously and remotely measuring pressure and temperature under harsh environments. The sensor consists of a dual $LC$ (inductor and capacitor) resonant circuit, one without a cavity and the other with a cavity capacitor for temperature and pressure sensing, respectively. The low-temperature co-fired ceramic technology is used to fabricate the sensor, making it suitable for high-temperature harsh environment operations. Experimental results show the prototype sensor has temperature sensitivity of 8.15 kHz/°C and pressure sensitivity of 1.96 MHz/Bar up to 400°C. [2016-0157]

Examination of the impacts of vegetation on the correlation between snow water equivalent and passive microwave brightness temperature

Snow accumulation, ablation, and runoff in mountainous areas are critical components of the hydrologic cycle, but are poorly known. Passive microwave (PM) measurements are sensitive to snow water equivalent (SWE), even in mountain regions, but vegetation masks the microwave signals and reduces this sensitivity. This study examines how the PM snow signal is affected by the forest density in fourteen basins in the Sierra Nevada, USA, and in a series of sixteen subsets of the Kern basin that have varied vegetation density. 36.5GHz vertical polarization brightness temperature (Tb) time series for each basin were produced from the spaceborne AMSR-E operational period (water year (WY) 2003 to WY2011). For each basin, the coefficient of determination (R2) between the annual minimum Tb and the concurrent SWE was calculated to evaluate the sensitivity of the PM to SWE. The relationship between the R2 values and the forest density was then analyzed to assess how vegetation affect the SWE information in the observed Tb. Mean forest coverage from MODIS was used to represent forest density. The R2 between the annual minimum Tb and concurrent SWE was >0.6 for three of the basins. Consistent with previous studies, WY2006 demonstrated anomalous Tb values for many basins, apparently due to anomalous warm winter rainfall. Excluding WY2006, R2 is significantly higher in all basins: eight of fourteen basins have R2 >0.6. For basins with average elevation >2500m, SWE correlates well with Tb. The R2 decreases monotonically with decreasing elevation. Basin elevation and forest cover are highly correlated in the Sierra; a basin elevation of 2500m generally coincides with forest cover of 20%. A total of 42% of Sierra Nevada has <20% forest cover; this corresponds to an estimated 34% of the total SWE in the Sierra. Thus, SWE and Tb are empirically correlated for over a third of the SWE in the Sierra, typically at high elevations and above treeline.

Bayesian Retrievals of Vertically Resolved Cloud Particle Size Distribution Properties

AbstractRetrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and in the prior knowledge of the cloudy state, to uniquely determine a solution. Bayesian algorithms produce a retrieval that consists of the joint probability distribution function (PDF) of cloud properties given the measurements and prior knowledge. The Bayesian posterior PDF provides the maximum likelihood estimate, the information content in specific measurements, the effect of observation and forward model uncertainties, and quantitative error estimates. It also provides a test of whether, and in which contexts, a set of observations is able to provide a unique solution. In this work, a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to sample the joint posterior PDF for retrieved cloud properties in shallow liquid clouds over the remote Southern Ocean. Combined active and passive observations from spaceborne W-band cloud radar and visible and near-infrared reflectance are used to retrieve the parameters of a gamma particle size distribution (PSD) for cloud droplets and drizzle. Combined active and passive measurements are able to distinguish between clouds with and without precipitation; however, unique retrieval of PSD properties requires specification of a scene-appropriate prior estimate. While much of the uncertainty in an unconstrained retrieval can be mitigated by use of information from 94-GHz passive brightness temperature measurements, simply increasing measurement accuracy does not render a unique solution. The results demonstrate the robustness of a Bayesian retrieval methodology and highlight the importance of an appropriately scene-consistent prior constraint in underdetermined remote sensing retrievals.

Effectiveness of oxide trench array as a passive temperature compensation structure in AlN-on-silicon micromechanical resonators

This Letter presents the effectiveness of an oxide trench array (OTA) as a passive temperature compensation structure for aluminum nitride on silicon (AlN-on-Si) quasi-surface acoustic wave (SAW) micromechanical resonators over a wide temperature range. Two types of devices, namely, those with OTA and their reference counterparts without OTA, are designed, fabricated, and characterized over a wide temperature range of 360 °C. Experimental results show that the resonator with OTA has a first-order temperature coefficient of frequency (TCf1) at room temperature (20 °C) of 6.66 ppm/°C, which is lower than that of the reference device without OTA by 72% in magnitude. A high turnover temperature of 197 °C is achieved. Furthermore, the second-order temperature stability of the device has also improved. OTA is experimentally demonstrated to be an effective structure for passive temperature compensation, hence paving the way for using AlN-on-Si resonators as ultrasonic sensors or timing devices in ruggedized environments where the large fluctuation in temperature places stringent demands on temperature stability.

Metamaterial Based Passive Wireless Temperature Sensor

This paper presents the fabrication, modeling, and testing of a metamaterial based passive wireless temperature sensor consisting of an array of closed ring resonators (CRRs) embedded in a dielectric material matrix. A mixture of 70 vol% Boron Nitride (BN) and 30 vol% Barium Titanate (BTO) is used as the dielectric matrix and copper washers are used as CRRs. Conventional powder compression is used for the sensor fabrication. The feasibility of wireless temperature sensing is demonstrated up to 200 °C. The resonance frequency of the sensor decreases from 11.93 GHz at room temperature to 11.85 GHz at 200 °C, providing a sensitivity of 0.462 MHz °C. The repeatability of temperature sensing tests is carried out to quantify the repeatability. The highest standard deviation observed is 0.012 GHz at 200 °C.

An Embedded Wireless Temperature Sensor for Orthopedic Implants

A wireless and passive (battery free) temperature sensor that can be embedded inside an orthopedic implant, such as an interference screw, was developed. The sensor is based on an inductive-capacitive-resistive resonant circuit that is inductively powered so the temperature at the implant can be measured wirelessly. A potential application of this sensor is to monitor internal wound temperature for the diagnosis of local infection at the implant site. Infections pose a significant risk to patients, who receive orthopedic implants, often lead to adverse conditions including implant failure, tissue necrosis, and amputation. Current approaches for diagnosing orthopedic implant-associated infections, such as blood tests, radiographic imaging, and histological study are slow, tedious, and nonspecific. In recent years, thermographic imaging has been used to monitor local temperature at the external surgical site as a means to detect infection, but its applications are limited to surface or near-surface wounds. The described sensor will serve as an useful research tool to investigate using temperature as an indication of deep-tissue orthopedic implant infection. When fully developed, this sensor may also improve orthopedic care by allowing simple, early detection of infection at an implant site. This paper describes the design and fabrication of the sensor, as well as characterizes its performance. Experimental results indicate the temperature response of the sensor is reproducible within the tested range of 30-42 °C.

The Software Design of SiBCN Temperature Sensor Wireless Sweep Signal Receiving and Dispatching System Based on FPGA

SiBCN (silicon boron carbon nitrogen) wireless passive microwave resonant cavity temperature sensor is a new type of sensor under the development trend of radio frequency microwave technology. Based on the working principle of the sensor, the software design of SiBCN temperature sensor wireless sweep transceiver system based on FPGA is carried out on the basis of the existing wireless sweep signal transceiver system hardware. Let the signal source send the 11.0GHz ~ 11.6GHz range sweep signal to the sensor. The feedback signal of the sensor is filtered and the resonant frequency is obtained. The detection of temperature is based on the correspondence between the resonant frequency and the temperature.Through the analysis of the measured results, the system software design meets the requirements, and the temperature and frequency change rate is about 421.2KHz / °C.

Thermal Design of Photovoltaic/Microwave Conversion Hybrid Panel for Space Solar Power System

This paper describes the thermal design of a photovoltaic/microwave conversion hybrid panel for a tethered solar power satellite (SPS). A practical configuration of the hybrid panel with a sandwich structure was designed, and its thermal characteristics were unveiled. The thermal resistance of the panel was experimentally measured and used for a realistic simulation. The daily temperature variation of the designed panel and thermal deformation of the antenna surface during the SPS operation were simulated. The results show that the present panel satisfies the design requirements for the panel weight and deformation of the antenna surface; however, the power amplifier temperature exceeds the lower limit of the design requirements. As a solution to this problem, a spectrally selective coating on solar cell surface was examined as a passive temperature control method. The fabricated multilayer selective coating showed a significant temperature increase, demonstrating the feasibility of the present thermal design.

Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates

Active radar backscatter (σ°) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable σ° and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0–10cm) soil moisture simulations over the period 2010–2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating σ° or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for σ° and TB, whereas the best performance is observed for the joint assimilation (Ran=0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

Design and Characterization of a Passive Temperature Sensor Based on a Printed MIW Delay Line

A passive electromagnetic temperature sensor based on a magneto-inductive wave (MIW) delay line is presented. The working principle is the following: a reader sends a pulse in the time domain to the sensor, which produces a backscattered pulse whose amplitude depends on the temperature. The transductor element is a conventional thermistor, which is placed at the end of the MIW delay line. The system operates at 2.55 GHz and, thus, both the thermistor and the sensor are characterized at this frequency. In order to do that, a setup capable of controlling and measuring the temperature and the frequency or time responses is proposed. The fabricated sensor is measured at different temperatures obtaining good agreement with the simulations. The integrity of sensor response after heating is also proved. The proposed sensor is implemented in single-layer printed technology, resulting in a low-cost and low-complexity solution with reduced dimensions.

Passive isothermalisation of an exothermic reaction in flow using a novel “Heat Pipe Oscillatory Baffled Reactor (HPOBR)”

In this study, a heat pipe was integrated with a mesoscale oscillatory baffled reactor for the passive temperature control of an exothermic reaction. The thermal/chemical performance of this new Heat Pipe Oscillatory Baffled Reactor (HPOBR) was compared to a conventional jacketed OBR (JOBR) using central composite experiment designs for an imination reaction between benzaldehyde and n-butylamine, in the absence of solvent. The variables in the experimental designs were reactant net flow rate (Ren=4–20), fluid oscillation intensity (Reo=123–491) and heat pipe fill ratio (FR=11.5–26.5; methanol working fluid). In the JOBR, the fill ratio factor was replaced with jacket temperature (4–20°C). Both reactors were able to reduce the maximum reaction temperature below the butylamine boiling point in all experiments. Overall, a 20-fold reduction in reactant volume and 13-fold improvement in reaction rate were obtained in the HPOBR for this imination reaction, compared with the same reaction using a solvent. Advantages of the HPOBR demonstrated here are isothermal operation and passive thermal control. Both reactors offer accelerated reaction rates and the potential for screening exothermic reactions. The HPOBR is a novel reactor design that provides a new approach for achieving green chemistry through solventless operation.

Exploiting nonlinear amplitude-frequency dependence for temperature compensation in silicon micromechanical resonators

Resonators used in frequency-reference oscillators must maintain a stable frequency output even when subjected to temperature variations. The traditional solution is to construct the resonator from a material with a low temperature coefficient, such as AT-cut quartz, which can achieve absolute frequency stability on the order of ±25 ppm over commercial temperature ranges. In comparison, Si microresonators suffer from the disadvantage that silicon's temperature coefficient of frequency (TCF) is approximately two orders of magnitude greater than that of AT-cut quartz. In this paper, we present an in situ passive temperature compensation scheme for Si microresonators based on nonlinear amplitude-frequency coupling which reduces the TCF to a level comparable with that of an AT-quartz resonator. The implementation of this passive technique is generic to a variety of Si microresonators and can be applied to a number of frequency control and timing applications.

A need to return to space project SEE for measuring G and its possible variations

The SEE (Satellite Energy Exchange) mission was suggested and designed under NASA Project in 1992–2002 to make extremely accurate measurements on fundamental gravitation by observing the orbital perturbation of unconstrained orbiting test bodies in a femto-g to atto-g environment. The mission uses novel and original test body dynamics. Its broad objective is to support the development of gravity theory and its unification with other interactions by carrying out sensitive gravitational tests capable of discriminating among alternative theories. The SEE mission introduced and utilized new technology for near-zero-g environment creation, passive cryogenic temperature control, passive station-keeping capability, and non-contact sub-micron-accuracy distance measurements, all of which promise to have a wide variety of applications.

Machine learning predictions of passive microwave brightness temperature over snow-covered land using the special sensor microwave imager (SSM/I)

Two different forms of machine learning – an artificial neural network (ANN) and a support vector machine (SVM) – are used to estimate passive microwave (PMW) brightness temperatures (Tb) as observed by the special sensor microwave imager (SSM/I) satellite sensor over snow- covered land in North America. Both techniques reasonably reproduce unbiased estimates of SSM/I observations at 19.35 and 37.0 GHz for both vertically- and horizontally-polarized channels. When compared against SSM/I observations not used during training, domain-averaged statistics from 1 September 1987 to 1 September 2002 yielded a root mean squared error (RMSE) of less than 9 K for all frequency and polarization combinations examined in this study. Even though both ML techniques reasonably reproduced SSM/I Tb observations, the SVM outperformed the ANN because the SVM: (1) better captured the high-frequency (i.e. day-to-day) temporal characteristics in the Tb observations across the majority of the study domain, (2) better reproduced the spatial variability as a function of snow classification, and (3) yielded greater sensitivity to snow-related input variables during the estimation of PMW Tb. These findings reinforce previous research of SVM-based estimation of PMW Tb employing observations from the advanced microwave scanning radiometer.