Engineering Calibration and Physical Principles of GNSS-Reflectometry for Earth Remote Sensing

The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA mission that uses 32 Global Positioning System (GPS) satellites as active sources and 8 CYGNSS satellites as passive receivers to measure ocean surface roughness and wind speed, as well as soil moisture and flood inundation over land. This dissertation addresses two major aspects of engineering calibration: (1) characterization of the GPS effective isotropic radiated power (EIRP) for calibration of normalized bistatic radar cross section (NBRCS) observables; and (2) development of an end-to-end calibration approach using modeling and measurements of ocean surface mean square slope (MSS). To estimate the GPS transmit power, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. The transmit power of the L1 coarse/acquisition (C/A) code of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products. The gain pattern of each GPS satellite’s transmit antenna for the L1 C/A signal is determined from measurements of signal strength received by the 8-satellite CYGNSS constellation. Determination of GPS patterns requires knowledge of CYGNSS patterns and vice versa, so a procedure is developed to solve for both of them iteratively. The new GPS and CYGNSS patterns have been incorporated into the science data processing algorithm used by the CYGNSS mission and result in improved calibration performance. Variable transmit power by numerous Block IIF and IIR-M GPS space vehicles has been observed due to their flex power mode. Non-uniformity in the GPS antenna gain patterns further complicates EIRP estimation. A dynamic calibration approach is developed to further address GPS EIRP variability. It uses measurements by the direct received GPS signal to estimate GPS EIRP in the specular reflected direction and then incorporates them into the calibration of NBRCS. Dynamic EIRP calibration instantaneously detects and corrects for power fluctuations in the GPS transmitters and significantly reduces errors due to GPS antenna gain azimuthal asymmetry. It allows observations with the most variable Block IIF transmitters (approximately 37% of the GPS constellation) to be included in the standard data products and further improves the calibration quality of the NBRCS. A physics-based approach is then proposed to examine potential calibration errors and to further improve the Level 1 calibration. The mean square slope (mss) is a key physical parameter that relates the ocean surface properties (wave spectra) to the CYGNSS measurement of NBRCS. An approach to model the mss for validation with CYGNSS mss data is developed by adding the contribution of a high frequency tail to the WAVEWATCH III (WW3) mss. It is demonstrated that the ratio of CYGNSS mss to modified WW3 mss can be used to diagnose potential calibration errors that exist in the Level 1 calibration algorithm. This approach can help to improve CYGNSS data quality, including the Level 1 NBRCS and Level 2 ocean surface wind speed and roughness. The engineering calibration methods presented in this dissertation make significant contributions to the spatial coverage, calibration quality of the measured NBRCS and the geophysical data products produced by the NASA CYGNSS mission. The research is also useful to the system design, science investigation and engineering calibration of future GNSS-reflectometry missions.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168052/1/wangtl_1.pd

Dual – loop force – displacement mixed control strategy and its application on the quasi – static test

The Quasi-static test is a well-known powerful methodology to evaluate the seismic performance of structural components and systems. One of the most important challenges in the Quasi-static testing is to achieve precise boundary conditions, especially for the axial loading of vertical components. The requirement of synchronized displacement loading and target axial force formed a pair of contradiction. A dual-loop force-displacement mixed control strategy is proposed. The presented approach is successfully verified through the quasi-static testing for a full-scale concrete filled steel tube column. The control targets are achieved with an excellent control performance

Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model

Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction

Transport Anisotropy in One-dimensional Graphene Superlattice in the High Kronig-Penney Potential Limit

One-dimensional graphene superlattice subjected to strong Kronig-Penney (KP) potential is promising for achieving electron lensing effect, while previous studies utilizing the modulated dielectric gates can only yield a moderate, spatially dispersed potential profile. Here, we realize high KP potential modulation of graphene via nanoscale ferroelectric domain gating. Graphene transistors are fabricated on PbZr$_{0.2}$Ti$_{0.8}$O$_{3}$ back-gates patterned with periodic, 100-200 nm wide stripe domains. Due to band reconstruction, the h-BN top-gating induces satellite Dirac points in samples with current along the superlattice vector $\hat{s}$, a feature absent in samples with current perpendicular to $\hat{s}$. The satellite Dirac point position scales with the superlattice period ($L$) as $\propto L^{\beta}$, with $\beta = -1.18 \pm 0.06$. These results can be well explained by the high KP potential scenario, with the Fermi velocity perpendicular to $\hat{s}$ quenched to about 1% of that for pristine graphene. Our study presents a promising material platform for realizing electron supercollimation and investigating flat band phenomena.Comment: 12 pages, 5 figures, and Supplemental Materia

Do Infants Learn Words from Statistics? Evidence from English-Learning Infants Hearing Italian

Infants are sensitive to statistical regularities (i.e., transitional probabilities, or TPs) relevant to segmenting words in fluent speech. However, there is debate about whether tracking TPs results in representations of possible words. Infants show preferential learning of sequences with high TPs (HTPs) as object labels relative to those with low TPs (LTPs). Such findings could mean that only the HTP sequences have a word‐like status, and they are more readily mapped to a referent for that reason. But these findings could also suggest that HTP sequences are easier to encode, just like any other predictable sequence. Here we aimed to distinguish between these explanations. To do so, we built on findings that infants become resistant to learning labels that are not typical of their native language as they approach 2 years of age and add words to their lexicons. If tracking TPs in speech results in identifying candidate words, at this age TPs may have reduced power to confer lexical status when they yield a unit that is very dissimilar to word forms that are typical of infants’ native language. Indeed, we found that at 20 months, English‐learning infants with relatively small vocabularies learned HTP Italian words (but not LTP words) as object labels, while infants with larger vocabularies resisted learning HTP Italian words. These findings suggest that the HTP sequences may be represented as candidate words, and more broadly, that TP statistics are relevant to word learning

Baseline 25(OH)D level is a prognostic indicator for bariatric surgery readmission: a matched retrospective cohort study

IntroductionManaging postsurgical complications is crucial in optimizing the outcomes of bariatric surgery, for which preoperative nutritional assessment is essential. In this study, we aimed to evaluate and validate the efficacy of vitamin D levels as an immunonutritional biomarker for bariatric surgery prognosis.MethodsThis matched retrospective cohort study included adult patients who underwent bariatric surgery at a tertiary medical center in China between July 2021 and June 2022. Patients with insufficient and sufficient 25(OH)D (< 30 ng/mL) were matched in a 1:1 ratio. Follow-up records of readmission at 3 months, 6 months, and 1 year were obtained to identify prognostic indicators.ResultsA matched cohort of 452 patients with a mean age of 37.14 ± 9.25 years and involving 69.47% females was enrolled. Among them, 94.25 and 5.75% underwent sleeve gastrectomy and gastric bypass, respectively. Overall, 25 patients (5.54%) were readmitted during the 1-year follow-up. The prognostic nutritional index and controlling nutritional status scores calculated from inflammatory factors did not efficiently detect malnourishment. A low 25(OH)D level (3.58 [95% CI, 1.16–11.03]) and surgery season in summer or autumn (2.68 [95% CI, 1.05–6.83]) increased the risk of 1-year readmission in both the training and validation cohorts. The area under the receiver operating characteristic curve was 0.747 (95% CI, 0.640–0.855), with a positive clinical benefit in the decision curve analyses. The relationship between 25(OH)D and 6-month readmission was U-shaped.ConclusionSerum 25(OH)D levels have prognostic significance in bariatric surgery readmission. Hence, preferable 25(OH)D levels are recommended for patients undergoing bariatric surgery

Concepts, Structure and Developments of High-Reliability Cyber-Physical Fusion Based Coordinated Planning for Distribution System

Coordinated control is imperative for the distribution network with the integration of wind power, photovoltaic system, and energy storage system. Meanwhile, the advanced automation terminal, intelligent control technology, and information communication technologies have greatly promoted the informatization of distribution networks which also increase the correlation between the physical system (primary system) and the cyber system (secondary system). Hence, it is critical to comprehensively coordinate the planning of the cyber-physical system for building a highly reliable power grid. This work summarizes a series of challenges brought by the highly coupled cyber-physical system, such as the primary and secondary collaborated planning models and solution algorithms. Then, the reliability assessment theories of cyber-physical systems and their application in distribution network planning models are introduced. Finally, three development directions of distribution network planning in the future are proposed, considering primary and secondary system coordinated planning