Epitaxial silicon growth rate control

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Title as it appears in the June 1994 MIT Graduate List: Real time growth rate control of epitaxial silicon.Includes bibliographical references (leaves 65-66).by Safroadu Kwadwo Yeboah-Amankwah.M.Eng

The IPBES Conceptual Framework - connecting nature and people

The first public product of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) is its Conceptual Framework. This conceptual and analytical tool, presented here in detail, will underpin all IPBES functions and provide structure and comparability to the syntheses that IPBES will produce at different spatial scales, on different themes, and in different regions. Salient innovative aspects of the IPBES Conceptual Framework are its transparent and participatory construction process and its explicit consideration of diverse scientific disciplines, stakeholders, and knowledge systems, including indigenous and local knowledge. Because the focus on co-construction of integrative knowledge is shared by an increasing number of initiatives worldwide, this framework should be useful beyond IPBES, for the wider research and knowledge-policy communities working on the links between nature and people, such as natural, social and engineering scientists, policy-makers at different levels, and decision-makers in different sectors of society

Observation of Centrifugal Stretching in Sm\u3csup\u3e152\u3c/sup\u3e

An isomer shift has been observed in the Mössbauer study of the 121.8-keV (2+→0+) transition in Sm152. A value of (Δ〈R2〉/〈R2〉)2-0=(10±3)×10-4 is deduced, based on isotope shift data for Sm152 and Sm154. This value disagrees with present theoretical prediction. In particular, the influence of the β band in Sm152 can only account for about 20% of the observed stretching

Ionospheric Scintillation at Legon

240-242Ionospheric scintillation observations made at Legon, Ghana (Iat., 5.63° N; long., 0.19° E), during the year 1980 are reported for the geostationary satellite Marisat 1, transmitting at 257 MHz. Seasonal variation of scintillation index and depth of fade and the correlation between scintillation and geomagnetic activity are examined. The seasonal variation showed a pattern similar to the ones obtained previously at this station. The correlation coefficient between Kp sum (index) for magnetic activity and the scintillation activity is quite random

Mapping inundation extents in Poyang Lake area using Sentinel-1 data and transformer-based change detection method

Accurate and timely mapping of inundation extents during flood periods is essential for disaster evaluation and development of rescue strategies. With unique advantages over the optical sensors (e.g., little effect of clouds, and observations at day and night), Synthetic aperture radar (SAR) sensors provide an important data source for mapping inundation, particularly during flood periods. Freely available SAR images from Sentinel-1 have been increasingly used for many applications. This study applied an efficient transformer-based change detection method, bitemporal image transformer (BiT) with bitemporal Sentniel-1 images, to map inundation extents and evolution in Poyang Lake area in 2020. The transformer-based change detection method firstly adopted ResNet for high-level semantic features extraction, and applied a transformer mechanism to refine these features pixel-wise, followed by employing a FCN as the prediction head for generating the results of change detection. Besides, we constructed a water change detection dataset with spatial-and-temporal generalization from bitemporal Sentinel-1 images; this dataset consists of the seasonal variation water samples of Poyang Lake for years. We compared the results from the BiT method with other convolutional neural network (CNN) based methods (STANets and SNUNet). Mapped inundation extents were evaluated with the ground truth visually derived from high spatial resolution images. The evaluation showed the BiT method generated high accurate mapped inundation extents with the F1-score of 95.5%. The BiT model has proven its superior performance in detecting increased water. Based on the results of the BiT method, the variation of inundation extents in Poyang Lake during May-November 2020 was further analyzed. It was found that the water surface coverage of Poyang Lake is the smallest in late May; it gradually increased to the maximum on 14th July, and then began to stabilize and show a significant downward trend before November. The flood distribution map shows that cultivated land has been inundated with the largest area of approximately 600 km2

Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks

Remote sensing imageries processed through empirical and deterministic approaches help predict multiple agronomic traits throughout the growing season. Accurate identification of cotton crop from remotely sensed imageries is a significant task in precision agriculture. This study aims to utilize a deep learning-based framework for cotton crop field identification with Gaofen-1 (GF-1) high-resolution (16 m) imageries in Wei-Ku region, China. An optimized model for the pixel-wise multidimensional densely connected convolutional neural network (DenseNet) was used. Four widely-used classic convolutional neural networks (CNNs), including ResNet, VGG, SegNet, and DeepLab v3+, were also used for accuracy assessment. The results infer that DenseNet can identify cotton crop features within a relatively shorter time about 5 h for training convergence. The model performance was examined by multiple indicators (P, F1, R, and mIou) produced through the confusion matrix, and the derived cotton fields were then visualized. The DenseNet model has illustrated considerable improvements in comparison with the preceding mainstream models. The results showed that the retrieval precision was 0.948, F1 score was 0.953, and mIou was 0.911. Furthermore, its performance is relatively better in discriminating cotton crop fields’ fine structures when clouds, mountain shadows, and urban built up

Reconstructing long-term global satellite-based soil moisture data using deep learning method

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences