Offset Time Research Articles

Convective‐scale numerical weather prediction and warnings over Lake Victoria: Part I—Evaluating a lightning diagnostic

AbstractRecently a new lightning diagnostic was added to convective‐scale configurations of the Met Office Unified Model (UM). The characteristics and skill of the hourly total ‘flash origin density’ in the 4.4 km Tropical Africa UM were evaluated against Earth Network (EN) lightning observations specifically deployed in the greater Lake Victoria region as part of the HIGHWeather impact lAke sYstem (HIGHWAY) project. Lightning poses a significant risk to fishermen on Lake Victoria, and better forecasts over the lake would be advantageous for the development of better warning systems. The model lightning density was evaluated in two ways: a classical gridded categorical data analysis and the spatial coverage–distance–intensity method. Given the spatial sparseness of the observation type, Gaussian kernel dressing was applied for the latter, to increase the horizontal ‘footprint’, partly to reflect that lightning can travel horizontally for more than 10 km, and to reduce the representativeness mismatch between the lightning observations and the model forecast lightning density. Considering hourly forecasts, lightning shows a marked diurnal, geographical and seasonal variation over to the west and east of the lake. The model is not producing enough lightning flashes over the lake, nor does it produce enough spatially, especially overnight. The forecast location of lightning shows little skill, with timing differences in the peak, especially over the lake. For warnings, the removal of timing offsets through the creation of a ‘maximum‐in‐the‐day’ field is considerably less biased and more skilful than using the hourly flash densities.

Prior sleep-wake behaviors are associated with mental health outcomes during the COVID-19 pandemic among adult users of a wearable device in the United States

ObjectivesTo characterize objective sleep patterns among U.S. adults before and during the COVID-19 pandemic, and to assess for associations between adverse mental health symptoms and (1) sleep duration and (2) the consistency of sleep timing before and during the pandemic. DesignLongitudinal objective sleep-wake data during January-June 2020 were linked with mental health and substance use assessments conducted during June 2020 for The COVID-19 Outbreak Public Evaluation (COPE) Initiative. SettingAdult users of WHOOP—a commercial, digital sleep wearable. ParticipantsAdults residing in the U.S. and actively using WHOOP wearable devices, recruited by WHOOP, Inc. InterventionThe COVID-19 pandemic and its mitigation. MeasurementsAnxiety or depression symptoms, burnout symptoms, and new or increased substance use to cope with stress or emotions. ResultsOf 4912 participants in the primary analytic sample (response rate, 14.9%), we observed acutely increased sleep duration (0.25 h or 15 m) and sleep consistency (3.51 points out of 100) and delayed sleep timing (onset, 18.7 m; offset, 36.6 m) during mid-March through mid-April 2020. Adjusting for demographic and lifestyle variables, participants with persistently insufficient sleep duration and inconsistent sleep timing had higher odds of adverse mental health symptoms and substance use in June 2020. ConclusionsU.S. adult wearable users displayed increased sleep duration, more consistent sleep timing, and delayed sleep onset and offset times after the COVID-19 pandemic onset, with subsample heterogeneity. Associations between adverse mental health symptoms and pre- and mid-pandemic short sleep duration and inconsistent sleep timing suggest that these characteristics warrant further investigation as potential modifiable mental health and substance use risk factors.

Quadrupole stability in nonuniform fill patterns

In previous publications, the effect of inhomogeneous beam loading of Landau cavities due to a nonuniform fill pattern in a storage ring has been studied with the aim of predicting the resulting longitudinal profiles of the bunches and their time offsets relative to the main rf [T. Olsson et al., Phys. Rev. Accel. Beams 21, 120701 (2018)]. This work was extended to treat dipolar coupled-bunch modes driven by beam-excited higher-order modes in the rf cavities [F. J. Cullinan et al., Phys. Rev. Accel. Beams 23, 074402 (2020)]. These coupled-bunch modes are of interest because they can become unstable, leading to an increase in the energy spread. The theory has now been extended once again to cover the case of coupled-bunch quadrupole modes where it is the lengths of the bunches that are oscillating, not their centroids. The theory is outlined and its predictions are thoroughly benchmarked against predictions from macroparticle tracking. Observations of the effects of nonuniform fill patterns on coupled-bunch quadrupole instabilities are made and interpreted. Results of measurements at the MAX IV 3 GeV ring, interpreted using theoretical calculations, are then presented to continue the investigation.

Efficient Timing Offset Estimation Method Tailored for ACO-OFDM VLC Systems

The asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM)-based visible light communication (VLC) system has received widespread attention due to its capability to support high spectrum efficiency as well as high power efficiency. The accuracy of symbol timing offset estimation is a critical factor limiting the performance of ACO-OFDM. In order to cope with this challenge, a training symbol-aided symbol timing synchronization method tailored for ACO-OFDM is put forward. The influence of the design of training symbol structure and timing metric on the synchronization accuracy is investigated, and three timing metrics are presented to supply simple and efficient timing offset estimation by utilizing the structure of odd-even mirror-symmetric and complementary symmetric of the newly designed training symbol. In addition, the system bit error rate (BER) penalty induced by timing synchronization error is analyzed, and the approximate relationship between BER performance loss and timing estimation error probability and ideal BER performance is presented. Simulation is used to assess the performance of the three proposed timing metric methods in different channel environments, and they are compared with the two existing timing synchronization algorithms in ACO-OFDM, namely the modified Park method and the Tian method. The results show that the new synchronization method is superior to the comparison methods concerning achievable synchronization accuracy and system BER penalty.

The Impact of a 14-Day Altitude Training Camp on Olympic-Level Open-Water Swimmers' Sleep.

Despite the common belief that sleep quality at altitude is poor, the scientific evidence to support this notion is still modest. Therefore, the purpose of the present study was to evaluate possible changes of actigraphy-based and subjective sleep parameters in a group of elite open-water swimmers during a 14-day altitude training camp (ATC) at 1500 m. The study subjects were five Olympic-level open-water swimmers (mean age: 25.0 ± 3.2 years; 3 females and 2 males). All subjects wore a wrist activity monitor and filled a sleep diary for 18 consecutive nights, 4 nights before and 14 nights during ATC. The data were then analyzed at four different time points: before ATC (PRE), the first two days of ATC (T1), and after one (T2) and two weeks of ATC (T3). Training load, assessed as the covered distance (km), session rating of perceived exertion (sRPE), and heart rate (HR), was monitored during the week before and the first and second week of ATC. No significant differences in objective and subjective scores of sleep quality were detected, whereas the sleep onset time (p = 0.018; η2p = 0.83, large) and sleep offset time (p < 0.001; η2p = 0.95, large) significantly differed among PRE, T1, T2, and T3: elite athletes started to sleep and woke up ≃ 1 h earlier the first two days of ATC compared to PRE (sleep onset time: p = 0.049; sleep offset time: p = 0.016). Further, an increase in the training volume during the two weeks of the ATC was observed, with the most time spent in a low-intensity regime and an increase in time spent in a high-intensity regime compared to PRE. Sleep quality was not negatively influenced by a 14-day altitude training camp at 1500 m in a group of Olympic-level elite swimmers despite an increase in perceived exertion during training sessions. Nonetheless, early sleep onset and sleep offset times were observed for the first two nights of ATC: elite athletes started to sleep and woke up ≃ 1 h earlier compared to the baseline nights.

Automated acoustic arrival matching using a convolutional neural network approach informed by statistics of acoustic scattering from internal waves

A machine learning model was developed to automatically align underwater acoustic measurements taken at various depths and ranges from a transmitting source in the Philippine Sea to a reference model of long range acoustic arrival structure, simultaneously determining source-receiver range and travel-time offsets associated with multipath arrivals. Ocean sound-speed variability complicates the task as the measured arrivals may exhibit scattering not present in range-independent predictions. Monte Carlo style broadband parabolic equation simulations through random internal wave fields consistent with the Garrett-Munk internal wave energy spectrum were used to generate a large data set of simulated acoustic receptions including scattered multipath arrivals with known source-receiver ranges and imposed travel time offsets. These simulated receptions were used to train and evaluate the machine learning model for arrival pattern matching to the reference model. The inclusion of various data dimensions, such as peak amplitude and width, and contextual information, such as range and depth, were also explored as input to the model. Ranging results for the machine learning model were compared to a programmatic solution engineered for the same task.

Measuring sleep parameters of naval sailors: A comparison between subjective self-report and wrist actigraphy

Wrist actigraph and self-report activity logs were used in a Royal Canadian Navy's at-sea exercise to track sleep patterns of naval personnel. In this study, we compared sleep parameters obtained from two measurement methods and investigated their intrinsic biases. The results revealed a strong agreement between two methods for recording sleep offset times, but a relatively poor agreement for parameters that include substantial periods of transition between sleep and wake states. Overall, self-reported sleep durations were substantially longer than actigraphic estimates (mean bias of −30.6 min; limits of agreement −95.9 to 34.8 min), and the discrepancy was mainly caused by differences in two methods to track sleep onset latency and Wake-up After Sleep Onset (WASO). Based on a customised activity log, a strong positive correlation (rho = 0.75, p < .001) between self-report and actigraphy was observed for sleep duration estimates, which confirmed the effectiveness of the activity log in field studies. Between two participant groups with different work schedules, the agreement between self-report and actigraphy was consistently better for day workers than watch keepers. The findings inform future sleep research planning that involves naval personnel in field settings.

Improved capsule routing for weakly labeled sound event detection

Polyphonic sound event detection aims to detect the types of sound events that occur in given audio clips, and their onset and offset times, in which multiple sound events may occur simultaneously. Deep learning–based methods such as convolutional neural networks (CNN) achieved state-of-the-art results in polyphonic sound event detection. However, two open challenges still remain: overlap between events and prone to overfitting problem. To solve the above two problems, we proposed a capsule network-based method for polyphonic sound event detection. With so-called dynamic routing, capsule networks have the advantage of handling overlapping objects and the generalization ability to reduce overfitting. However, dynamic routing also greatly slows down the training process. In order to speed up the training process, we propose a weakly labeled polyphonic sound event detection model based on the improved capsule routing. Our proposed method is evaluated on task 4 of the DCASE 2017 challenge and compared with several baselines, demonstrating competitive results in terms of F-score and computational efficiency.

Consecutive Days of Racing Does Not Affect Sleep in Professional Road Cyclists.

Professional road cycling races are physiologically demanding, involving successive days of racing over 1 to 3 weeks of competition. Anecdotal evidence indicates that cyclists' sleep duration either increases or deteriorates during these competitions. However, sleep duration in professional cyclists during stage races has not been assessed. This study examined the amount/quality of sleep obtained by 14 professional cyclists competing in the Australian Tour Down Under. Sleep was assessed using wrist activity monitors and self-report sleep diaries on the night prior to start of the race and on each night during the race. The impact of each day of the race on sleep onset, sleep offset, time in bed, sleep duration, and wake duration was assessed using separate linear mixed effects models. During the race, cyclists obtained an average of 6.8 (0.9)hours of sleep between 23:30 and 07:27hours and spent 13.9% (4.7%) of time in bed awake. Minor differences in sleep onset (P = .023) and offset times (P ≤.001) were observed during the week of racing, but these did not affect the amount of sleep obtained by cyclists. Interestingly, the 3 best finishers in the general classification obtained more sleep than the 3 worst finishers (7.2 [0.3] vs 6.7 [0.3]h; P = .049). Contrary to anecdotal reports, the amount of sleep obtained by cyclists did not change over the course of the 1-week race and was just below the recommended target of 7 to 9hours for adults.

Abstract EP61: Short Sleep Duration, Late Sleep Timing, And Late Eating Timing: A Trilogy Of Behavioral Risk Factors For High Blood Pressure?

Introduction: Short sleep duration (&lt;7 h/night) is a risk factor for hypertension and other cardiovascular diseases (CVD). Not clear is if delayed timing of sleep or eating behaviors adds to this risk. We tested the hypothesis that later sleep and eating timing would be associated with higher blood pressure (BP), and that adults with short sleep + late behavior timing would have the highest BP vs those with one (short sleep OR late timing) or no risk behaviors (adequate sleep + early timing). Methods: Over a 14-day period, 30 healthy, non-shift working adults wore wrist actigraphs to evaluate sleep duration and onset timing, and completed image-assisted diet records to estimate eating onset (time of first caloric intake after awakening) and offset (time of last caloric intake before sleep). Fasted morning systolic (SBP) and diastolic BP (DBP) were obtained. Regression models of SBP and DBP tested associations with sleep onset, eating onset, and eating offset. Sleep duration (&lt;7 vs ≥7h) and behavior timing metrics (median split) were dichotomized, and ANOVAs compared BP between sleep duration + behavior timing groups. Results: Participants were 28±7y, 43% male, BMI 23.9±2.6 kg/m 2 , and BP 114±10/70±7 mmHg. Sleep duration was 6.8±0.8 h, sleep onset 23:45±1.3 h, eating onset 08:49±1.5 h, and eating offset 20:40±1.2 h. Independent of age and sex, later sleep onset was associated with higher SBP (B= 3.0±1.3 mmHg/h, p =0.03) and DBP (B= 2.5±0.9 mmHg/h, p =0.01). Later eating onset was associated with higher DBP (B= 2.1±0.9 mmHg/h, p =0.03), and later offset with higher SBP (B= 4.1±1.4 mmHg/h, p &lt;0.01) and DBP (B= 3.3±1.0 mmHg/h, p &lt;0.01). When grouped according to sleep duration and eating offset, short sleep + late timing participants exhibited higher DBP vs adequate sleep + early timing (75±7 vs 67±7, p =0.03) and tended to exhibit higher SBP and DBP vs short sleep OR late timing (both p =0.07). Conclusion: These data indicate later sleep and eating timing, especially in the setting of short sleep, exemplifies a chrono-behavioral phenotype of CVD risk.

A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets

LoRaWAN is nowadays one of the most popular protocols for low-power Internet of Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have received little attention so far. The estimation and correction of carrier frequency and sampling time offsets (STOs) are, however, crucial to attain the low sensitivity levels offered by the LoRa spread-spectrum modulation. The goal of this article is to build a low-complexity, yet efficient synchronization algorithm capable of correcting both offsets. To this end, a complete analytical model of a LoRa signal corrupted by these offsets is first derived. Using this model, we propose a new estimator for the STO. We also show that the estimations of the carrier frequency and the STOs cannot be performed independently. Therefore, to avoid a complex joint estimation of both offsets, an iterative low-complexity synchronization algorithm is proposed. To reach a packet error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> , performance evaluations show that the proposed receiver requires only 1 or 2 dB higher signal-to-noise ratio than a theoretical perfectly synchronized receiver, while incurring a very low computational overhead.

Ketamine Psychedelic and Antinociceptive Effects Are Connected.

Ketamine produces potent analgesia combined with psychedelic effects. It has been suggested that these two effects are associated and possibly that analgesia is generated by ketamine-induced dissociation. The authors performed a post hoc analysis of previously published data to quantify the pharmacodynamic properties of ketamine-induced antinociception and psychedelic symptoms. The hypothesis was that ketamine pharmacodynamics (i.e., concentration-effect relationship as well as effect onset and offset times) are not different for these two endpoints. Seventeen healthy male volunteers received escalating doses of S- and racemic ketamine on separate occasions. Before, during, and after ketamine infusion, changes in external perception were measured together with pain pressure threshold. A population pharmacokinetic-pharmacodynamic analysis was performed that took S- and R-ketamine and S- and R-norketamine plasma concentrations into account. The pharmacodynamics of S-ketamine did not differ for antinociception and external perception with potency parameter (median [95% CI]) C50, 0.51 (0.38 to 0.66) nmol/ml; blood-effect site equilibration half-life, 8.3 [5.1 to 13.0] min), irrespective of administration form (racemic ketamine or S-ketamine). R-ketamine did not contribute to either endpoint. For both endpoints, S-norketamine had a small antagonistic effect. The authors conclude that their data support an association or connectivity between ketamine analgesia and dissociation. Given the intricacies of the study related to the pain model, measurement of dissociation, and complex modeling of the combination of ketamine and norketamine, it is the opinion of the authors that further studies are needed to detect functional connectivity between brain areas that produce the different ketamine effects.

Characterizing sleep-wake patterns in mothers and children in an agrarian community: results from the Ghana Randomized Air Pollution and Health Study.

Several studies have examined sleep patterns in rural/indigenous communities, however little is known about sleep characteristics in women of reproductive age, and children within these populations. We investigate sleep-wake patterns in mothers and children (ages 3-5 years) leveraging data from the Ghana Randomized Air Pollution and Health Study (GRAPHS). The GRAPHS cohort comprises of rural/agrarian communities in Ghana and collected multiday actigraphy in a subset of women and children to assess objective sleep-wake patterns. Data were scored using the Cole-Kripke and Sadeh algorithms for mothers/children. We report descriptive, baseline characteristics and objective sleep measures, compared by access to electricity/poverty status. We analyzed data for 58 mothers (mean age 33 ± 6.6) and 64 children (mean age 4 ± 0.4). For mothers, mean bedtime was 9:40 pm ± 56 min, risetime 5:46 am ± 40 min, and total sleep time (TST) was 6.3 h ± 46 min. For children, median bedtime was 8:07 pm (interquartile range [IQR]: 7:50,8:43), risetime 6:09 am (IQR: 5:50,6:37), and mean 24-h TST 10.44 h ± 78 min. Children with access to electricity had a reduced TST compared to those without electricity (p = 0.02). Mean bedtime was later for both mothers (p = 0.05) and children (p = 0.08) classified as poor. Mothers in our cohort demonstrated a shorter TST, and earlier bed/risetimes compared to adults in postindustrialized nations. In contrast, children had a higher TST compared to children in postindustrialized nations, also with earlier sleep-onset and offset times. Investigating objective sleep-wake patterns in rural/indigenous communities can highlight important differences in sleep health related to sex, race/ethnicity, and socioeconomic status, and help estimate the impact of industrialization on sleep in developed countries.

Analysis of strong-arm comparator with auxiliary pair for offset calibration

This paper analyzes the principle of the input-referred off calibration using the auxiliary pair for Strong-Arm comparator as well as its impact from the additional common-mode current and parasitic capacitance on the input-referred noise, offset, and decision time in detail. The comparison with the simulation results demonstrates the accurate prediction with the proposed analysis. This paper also discusses the design guideline to optimize the Strong-Arm comparator with the auxiliary pair for offset calibration.

Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.

Semi-automated Detection of the Timing of Respiratory Muscle Activity: Validation and First Application.

Background: Respiratory muscle electromyography (EMG) can identify whether a muscle is activated, its activation amplitude, and timing. Most studies have focused on the activation amplitude, while differences in timing and duration of activity have been less investigated. Detection of the timing of respiratory muscle activity is typically based on the visual inspection of the EMG signal. This method is time-consuming and prone to subjective interpretation.Aims: Our main objective was to develop and validate a method to assess the respective timing of different respiratory muscle activity in an objective and semi-automated manner.Method: Seven healthy adults performed an inspiratory threshold loading (ITL) test at 50% of their maximum inspiratory pressure until task failure. Surface EMG recordings of the costal diaphragm/intercostals, scalene, parasternal intercostals, and sternocleidomastoid were obtained during ITL. We developed a semi-automated algorithm to detect the onset (EMG, onset) and offset (EMG, offset) of each muscle’s EMG activity breath-by-breath with millisecond accuracy and compared its performance with manual evaluations from two independent assessors. For each muscle, the Intraclass Coefficient correlation (ICC) of the EMG, onset detection was determined between the two assessors and between the algorithm and each assessor. Additionally, we explored muscle differences in the EMG, onset, and EMG, offset timing, and duration of activity throughout the ITL.Results: More than 2000 EMG, onset s were analyzed for algorithm validation. ICCs ranged from 0.75–0.90 between assessor 1 and 2, 0.68–0.96 between assessor 1 and the algorithm, and 0.75–0.91 between assessor 2 and the algorithm (p < 0.01 for all). The lowest ICC was shown for the diaphragm/intercostal and the highest for the parasternal intercostal (0.68 and 0.96, respectively). During ITL, diaphragm/intercostal EMG, onset occurred later during the inspiratory cycle and its activity duration was shorter than the scalene, parasternal intercostal, and sternocleidomastoid (p < 0.01). EMG, offset occurred synchronously across all muscles (p ≥ 0.98). EMG, onset, and EMG, offset timing, and activity duration was consistent throughout the ITL for all muscles (p > 0.63).Conclusion: We developed an algorithm to detect EMG, onset of several respiratory muscles with millisecond accuracy that is time-efficient and validated against manual measures. Compared to the inherent bias of manual measures, the algorithm enhances objectivity and provides a strong standard for determining the respiratory muscle EMG, onset.

Underwater Acoustic Communication With Multicarrier Binary Frequency Shift Keying

This article describes a novel approach for robust and reliable underwater acoustic communication targeting low data rate applications in very challenging channels. The well-known binary frequency shift keying (BFSK) modulation is extended with multiple pair-wise BFSK subcarriers forming a multicarrier (MC) waveform referred to as MC-BFSK. Time offsets between neighboring subcarriers combined with windowing and guard periods are employed to increase the robustness against Doppler-induced frequency spread/shift and reverberation together with novel methods for synchronization, peak-to-average-power ratio reduction, and soft-value extraction. For the design of the parameters of the waveform, a heuristic approach is utilized. A noncoherent matched filter receiver with spatial diversity is adopted. The MC-BFSK transmission scheme is evaluated by means of computer simulations as well as sea-trial measurements. The obtained results are compared with competing modulation schemes where the proposed MC-BFSK shows better performance in challenging channels. In particular, we are able to decode frames in a sea-trial scenario where the direct path between the transmitter and the receiver is blocked by an island.

Special Sensor Microwave Imager/Sounder Updates for the Global Precipitation Measurement V07 Data Suite

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.

Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis

The assessment of muscle-recruitment timing from electromyography (EMG) signal is relevant in different fields, including clinical gait analysis and robotic systems to interpret user&#x2019;s motion intention. However, available methods typically provide only information in time domain without evaluating muscle-activation frequency content. This study aims to propose a novel adaptative algorithm for detecting muscle activation in time-frequency domain based on continuous wavelet transform (CWT) analysis. Precisely, the novel contribution of the proposed algorithm consists of evaluating the frequency range of every muscle activations detected in time domain. Performances are evaluated on a test bench of 720 simulated and 105 real surface EMG signals, stratified for signal-to-noise ratio (SNR), and then validated against different reference algorithms. Outcomes indicate that the proposed approach can provide an accurate prediction of muscle onset and offset timing in both simulated (mean absolute error, MAE <inline-formula> <tex-math notation="LaTeX">$\approx 10$ </tex-math></inline-formula> ms) and real datasets (MAE &#x003C; 30 ms), minimally affected by the SNR variability and compatible with the timing of EMG-driven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggests a large within-muscle between-muscle variability of the frequency range. In conclusion, the current study introduces a novel reliable wavelet-based algorithm to detect both time and frequency content of muscle activation, suitable in different conditions of signal quality.