Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Management System (EMS) of Battery Energy Storage System (BESS) – Providing Ancillary Services

In this paper, an Energy Management System (EMS) that manages a Battery Energy Storage System (BESS) is implemented. It performs peak shaving of a local load and provides frequency regulation services using Frequency Containment Reserve (FCR-N) in the Swedish reserve market. The EMS optimizes the approach of BESS resource dispatch distribution between two ancillary services, such that it maximizes profits and resource efficiency while enhancing overall system reliability of the electricity grid. The EMS consists of; a Prognosis Module running a day ahead to allow for identification of intervals for peak shaving and frequency regulation, and a Real-time Operation Module running on the day of operation creating BESS control commands. Implementing the Prognosis module on Öckerö ice-rink results in 12% cost saving on the grid tariff with 21% power peak reduction and three hours of daily frequency reserve. Multiple such systems can be aggregated to improve flexibility of the system.
Public Opinion Analysis on Novel Coronavirus Pneumonia and Interaction With Event Evolution in Real World

During the novel coronavirus pneumonia, the underlying comments and news of the epidemic had bombed on the Internet media and social networks, reflecting the views and attitudes of the governments, society, and individuals to the epidemic situation. Those comments are closely related to people’s lives and blend with each other. Novel coronavirus pneumonia affects people’s lives, and they publish comments on social networks and media, and the underlying information from social networks and Internet media in turn affects people’s attitude to the epidemic and the measures, and strategies taken to fight coronavirus in life. We collect the comments from various social network platforms and network media such as Weibo, WeChat, Chinanews, Sina News, NetEase News, Sohu News, and analyze the overall situation of public opinion on the novel coronavirus pneumonia. We explore the interaction between online public opinion and real life using a variety of algorithms in different stages of the epidemic. We can obtain public attention and key nodes in the dissemination of public opinion by detecting hot events and calculating user’s influence. The analysis of information dissemination path is conducive to the rapid propagation of healthy comments, curbing the spread of bad information. Event-specific details and topics are mined from large amounts of comments by thematic clustering and association relationship detecting, which helps to provide technical support for government response measures. The emotional analysis of Internet users’ groups helps to guide the direction of public opinion. The situation analysis shows the deep blending and mutual influence between the online public opinion and the real world.
Efficient Features for Function Matching in Multi-Architecture Binary Executables

Binary-binary function matching problem serves as a plinth in many reverse engineering techniques such as binary diffing, malware analysis, and code plagiarism detection. In literature, function matching is performed by first extracting function features (syntactic and semantic), and later these features are used as selection criteria to formulate an approximate 1:1 correspondence between binary functions. The accuracy of the approximation is dependent on the selection of efficient features. Although substantial research has been conducted on this topic, we have explored two major drawbacks in previous research. (i) The features are optimized only for a single architecture and their matching efficiency drops for other architectures. (ii) function matching algorithms mainly focus on the structural properties of a function, which are not inherently resilient against compiler optimizations. To resolve the architecture dependency and compiler optimizations, we benefit from the intermediate representation (IR) of function assembly and propose a set of syntactic and semantic (embedding-based) features which are efficient for multi-architectures, and sensitive to compiler-based optimizations. The proposed function matching algorithm employs one-shot encoding that is flexible to small changes and uses a KNN based approach to effectively map similar functions. We have evaluated proposed features and algorithms using various binaries, which were compiled for ×86 and ARM architectures; and the prototype implementation is compared with Diaphora (an industry-standard tool), and other baseline research. Our proposed prototype has achieved a matching accuracy of approx. 96%, which is higher than the compared tools and consistent against optimizations and multi-architecture binaries.
Diverse chemoselectivity during acylation of nucleosides

The acylation of nucleosides with acid chlorides under very mild conditions (diluted solution in neutral solvents containing a few equivalents of an amine) was investigated. The rate and the sites of the reaction were found to depend strongly on the kind of amine used. In the presence of DMAP acylation occurred chemoselectively on the hydroxy groups of the ribose moiety, while a mixture of triethylamine and pyridine promoted acylation of the thymine residue.
Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.
Optimal Positioning and Sizing of Power Flow Controllers Using a Scenario Based SCOPF Approach

Optimal positioning and sizing of Power Flow Controllers (PFCs) is a crucial task in order to establish effective congestion management. A scenario based Security Constrained Optimal Power Flow (SCOPF) approach is presented in this paper that can be applied to a variety of PFCs and is able to simultaneously incorporate a number of network use cases and (n-1)-cases and thus determine optimal positions and sizes. The proposed method allows to not only take power flow constraints into account but also other technical limitations such as spatial or structural requirements that are necessary for a PFC installation. Simulations have been carried out in the IEEE 39 Bus New England Test System to demonstrate the effectiveness of the proposed method showing the impact on the overloads in the system for different numbers of allowed PFC installations as well as the robustness of the expansion path.
Method Comparison for Increasing Data Rate on 5G-IoT Technology

The development of Internet of Things (IoT) raises new problems, namely the limitations of the spectrum used. The 5 Generation network is considered a communication network that can provide high capacity and high-speed data transmission. It is possible to combine 5G and IoT because 5G network has flexibility to support variaton of devices and services. non-orthogonal multiple access (NOMA) is considered as promised technology on 5G to support the needs of higher transmission rate because NOMA can transmit more user at the same frequency or time. Allocation of different user signal power to the same sub-channel even in the same sub-channel according to the related algorithm on the NOMA transmitter with superposition code. The popular combination on researches recently are NOMA, cooperative and simultaneous wireless transmission (SWIPT). These technologies are currently being research with other methods to improve the data rate with the flexible combination. In this paper, we compare technologies that combine with NOMA to improve the data rate.
SKYNET: A platform for maximizing career opportunities

Professional networking interfaces provide a significantly improved exposure and likelihood of job opportunities as well as career-related resources. Multiple availability of such platforms lead to a lack of standardization and thereby can result in a false sense of security. A massive profile pool has also statistically incremented the probability of overlooking suited candidate profiles at a global level. This paper presents a university-level individualized professional networking platform - SKYNET for enhanced prospects of job acquisition. SKYNET proved to be a successful amalgamation between career and academia which fundamentally aims on facilitating an enhanced job recommendation system based on individual professional skills. It is a web application that smoothens the process of hiring and significantly improves an individual's chance of securing jobs and internships. It helps students to get a reality check to see where they stand among their peers as well, considering various parameters such as certifications and work experience. It will provide a customized preview of student and company profiles based on needed skills and expertise. The corresponding system also supports ranking and filtration based on the job profile and description which was achieved by a custom-designed algorithm. Results helped not only student candidates to find internships and jobs while pursuing primitive professional education which helped them work in a professional setting and apply the grasped knowledge in real-time. Furthermore, made it simple for collaborating recruiters to pick the perfect student-job-role match in a timely manner.
Node Classification through Graph Embedding Techniques

The purpose of this paper is to collate advanced embedding techniques for the classification of nodes with the help of state-of-art technology named JDeep LearningJ, The main agenda of this classification is to predict the most likely labels of nodes in the network. It is considered to be efficient only if the network dimension is reduced before predicting the labels. Hence, the graph embedding technique is used to reduce it to a low-dimensional network. It also helps to capture and preserve the structure of a network. Comparison is done using various available graph embedding techniques to observe the accuracy.
Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.
Tar Spot Disease Quantification Using Unmanned Aircraft Systems (UAS) Data

Tar spot is a foliar disease of corn characterized by fungal fruiting bodies that resemble tar spots. The disease emerged in the U.S. in 2015, and severe outbreaks in 2018 caused an economic impact on corn yields throughout the Midwest. Adequate epidemiological surveillance and disease quantification are necessary to develop immediate and long-term management strategies. This study presents a measurement framework that evaluates the disease severity of tar spot using unmanned aircraft systems (UAS)-based plant phenotyping and regression techniques. UAS-based plant phenotypic information, such as canopy cover, canopy volume, and vegetation indices, were used as explanatory variables. Visual estimations of disease severity were performed by expert plant pathologists per experiment plot basis and used as response variables. Three regression methods, namely ordinary least squares (OLS), support vector regression (SVR), and multilayer perceptron (MLP), were used to determine an optimal regression method for UAS-based tar spot measurement. The cross-validation results showed that the regression model based on MLP provides the highest accuracy of disease measurements. By training and testing the model with spatially separated datasets, the proposed regression model achieved a Lin’s concordance correlation coefficient (ρc) of 0.82 and a root mean square error (RMSE) of 6.42. This study demonstrated that we could use the proposed UAS-based method for the disease quantification of tar spot, which shows a gradual spectral response as the disease develops.
Inhibitory actions of Ah receptor agonists and indole-containing compounds in breast cancer cell lines and mouse models

Inhibitory Actions of Ah Receptor Agonists and Indole-Containing Compounds in Breast Cancer Cell Lines and Mouse Models. (May 2004) Kelcey Manae Becker Walker, B.S., College of William and Mary; M.S., Virginia Tech University Chair of Advisory Committee: Dr. Stephen H. Safe The aryl hydrocarbon receptor (AhR) binds synthetic and chemoprotective phytochemicals, and research in this laboratory has developed selective AhR modulators (SAhRMs) for treatment of breast cancer. Activation of the AhR through agonists such as TCDD inhibits hormone activation of several E2-responsive genes in breast cancer cell lines. In this study, inhibition of E2-induced proliferation and gene expression by TCDD has been investigated in the uterus of wildtype, ERKO and AhRKO mice. Cyclin D1, DNA polymerase α, and VEGF mRNA levels are induced by E2 through ERα in the uterus as determined by in situ hybridization studies. TCDD down-regulated E2-induced cyclin D1 and DNA polymerase α expression, but not E2-induced VEGF expression, in wild-type mice, but not AhRKO mice, confirming the role of the AhR. Furthermore, protein synthesis was not necessary for induction of cyclin D1 or DNA polymerase α gene expression by E2 or inhibition of these
Shallow Shear-Wave Velocity Structure beneath the West Lake Area in Hangzhou, China, from Ambient-Noise Tomography

Urban geophysical exploration plays an important role in the sustainable development of and the mitigation of geological hazards in metropolitan areas. However, it is not suitable to implement active seismic methods in densely populated urban areas. The rapidly developing ambient-noise tomography (ANT) method is a promising technique for imaging the near-surface seismic velocity structure. We selected the West Lake area of the city of Hangzhou as a case study to probe the shallow subsurface shear-wave velocity (Vs) structure using ANT. We conducted seismic interferometry on the ambient-noise data recorded by 28 seismograph stations during a time period of 17 days. Fundamental-mode Rayleigh-wave group- and phase-velocity dispersion data were measured from cross-correlation functions and then inverted for a 3D Vs model of the uppermost 1 km that covers an area of about 7 km × 8 km. The tomographic results reveal two prominent anomalies, with high velocities in the southwest and low velocities in the northeast. The fast anomaly corresponds to the presence of limestone and sandstone, whereas the slow anomaly is due to the relatively low-velocity rhyolite and volcanic tuff in the area. The boundary between the two anomalies lies to the NE of an NW–SE trending fault, indicating that the fault dips toward the NE. In addition, the pronounced low-velocity anomalies appear under the Baoshi mountain, likely due to the thick rhyolite and volcanic tuff beneath the extinct volcano. Our results correlate well with regional geological features and suggest that ANT could be a promising technique for facilitating the exploration of urban underground space.
A Multi-Task Network with Distance-Mask-Boundary Consistency Constraints for Building Extraction from Aerial Images

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.
Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.
Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.