Miracle-dz/UniFAR-FTUs huggingface dataset:
Correctness of EIGRP Generalized for Arbitrary Routing Metrics and Policies

Among the routing protocols deployed in the wired infrastructure of the Internet, the Enhanced Interior Gateway Routing Protocol (EIGRP) has the distinction of being the only one that guarantees loop-free routing of data-packets during transient periods of convergence. However, there is no good proof of this fact and no knowledge of whether the actions and behaviors of EIGRP extend beyond its specific composite metric combining delay and bandwidth. This paper generalizes EIGRP for operation with arbitrary metrics and policies. It shows that generalized-EIGRP (gEIGRP) can be used with a broad class of such metrics and policies, but less so than the protocol to which it is closest in kind, the Border Gateway Protocol (BGP). To reason reliably about the behaviors induced by the actions of gEIGRP, the paper resorts to assertional proof methods. For the class of routing metrics and policies mentioned above, it presents an invariant of gEIGRP from which two safety properties are deduced: loop-free routing at all times, and routing on destination-optimal paths in stable state. Outside that class of metrics and policies, gEIGRP may deadlock in states harboring persistent forwarding loops.
Solving Reconfiguration Problems of First-Order Expressible Properties of Graph Vertices with Boolean Satisfiability

This paper presents a unified framework for capturing a variety of graph reconfiguration problems in terms of firstorder expressible properties and proposes a Boolean encoding for formulas in the first-order logic of graphs based on the exploitation of fundamental properties of graphs. We show that a variety of graph reconfiguration problems captured in our framework can be computed in a unified way by combining our encoding and Boolean satisfiability solver in a bounded model checking approach but allowing us to use quantifiers and predicates on vertices to express reconfiguration properties.
Distributed Control Strategy of DC Microgrid Based on Convex Relaxation Method

A new distributed control strategy for DC microgrid based on convex relaxation method is proposed, which can stabilize the system at the optimal power flow solution. Firstly, a non-convex optimal power flow problem is convexized using a suitable convex relaxation method, and the accuracy of the proposed convex relaxation method is proved. Then, the KKT optimality condition of the relaxation problem is equivalently transformed, a synchronization item is derived and applied into the control strategy to realize the distributed control under the optimal power flow. Finally, the proposed method is verified using simulations and numerical experiments.
Preoperative planning for Coronary Artery Disease using 3D Segmentation and Mixed Reality

Coronary arteries are crucial for keeping the function of our heart muscles by delivering the necessary resources like oxygen and nutrients. However, many problems may arise that can lead to potentially serious health issues such as Coronary Artery Disease, Myocardial Infarction, Dissection, Aneurysms and many more. Sometimes, surgery like Percutaneous Coronary Intervention is needed to fix a blocked artery. The surgery needs an intensive planning to prevent perioperative complications. To perform a proper planning the anatomy of coronary arteries needs to be studied. From the technical point of view automatic segmentation of coronaries can provide information related to the 3D anatomy and is essential for the diagnosis and quantification of coronary artery disease. In clinical practice the manual and semi-automatic segmentation of coronary arteries for quantification of narrowing part and treatment planning is still being used. In this study, the 3D Dense-U-Net architecture was proposed for precise segmentation of the coronary arteries. Additionally, Mixed Realty solution was introduced for pathology assessment during planning stage. The dataset consists of 1000 computed tomography angiography of patients. The dataset was divided in three subsets. Training, validation and test sets in ratio 70 : 15 : 15. The segmentation results were evaluated using Dice and Hausdorff metrics. The proposed method achieved 0.8206 and 22.06mm respectively. The segmentation results are combined with the Computed Tomography volumetric rendering in Mixed Reality. The operator wearing the head-mounted display can assess the anatomy of coronary arteries, analyse the potential blockages and evaluate his/her approach for the surgery during preoperative planning and also intraoperatively.
Equilibrium Analysis of the Electricity Market Considering Strategic Bidding by Coalition of Renewable Energy and Energy Storage

Large-scale grid connection of renewable energy and the reformation of electric power system promote renewable energy to compete in the electricity market, while the bid deviations caused by the volatility of renewable energy make it inferior in the electricity market competition. Shared energy storage can improve the limitations such as low resource utilization in the traditional business models for energy storage and has broad development prospects in renewable energy absorption. First, an electricity market framework is presented, in which wind power producers, photovoltaic (PV) power producers and energy storage form a coalition to participate in the bidding competition in the mode of cooperative and sharing. Second, a multi-period stochastic equilibrium model of the electricity market is established. In addition, the strategic behavior of wind, PV and energy storage and the market equilibrium outcomes are studied. The Shapley value method is used to allocate profits from the coalition. Finally, the simulations show that wind, PV and energy storage compete in the market in cooperative and sharing mode can effectively reduce the bid deviations and boost profits of renewable energy producers and energy storage.
Classification of Flowers: A Symbolic Approach

In this work, we propose a skeleton based approach to classify flower images. Flowers are segmented using whorl based region merging segmentation. Skeleton of a flower is obtained from the segmented flower using a skeleton pruning method. The distance features are extracted from the skeleton endpoints and junction points. The features are normalized using discrete cosine transform. The reduced features are aggregated and represented in the form of an inter-valued type data and are stored in the knowledgebase. A symbolic classifier has been explored for the purpose of classification. To corroborate the efficacy of the proposed method, an experiment is conducted on our own data set of 30 classes of flowers, containing 3000 samples. The data set has different flower species with similar appearance (small inter-class variations) across different classes and varying appearance (large intra-class variations) within a class. In addition, the images of flowers are of different poses, with cluttered background under different lighting and climatic conditions. An experiment has been conducted by picking images randomly from the database and it is shown that relatively a good performance can be achieved, using symbolic classifier.
Photovoltaic Simulation Design Research Application of PEDF Building Power Distribution System

At present, there are relatively few application cases of PEDF projects, and PEDF building photovoltaic systems are quite different from traditional building photovoltaic systems. This paper firstly introduces the important evaluation index of PEDF building photovoltaic system—the self-consumption rate of photovoltaic power generation, and then takes a real project as a case to introduce the whole design process such as model selection, configuration, model construction, system simulation, etc. of PEDF building photovoltaic system, analyze the photovoltaic output power curve and load forecast curve, obtain the self-consumption rate of photovoltaic power generation, so as to realize the evaluation of PEDF building photovoltaic system and prepare for the subsequent planning of energy storage configuration and PEDF control strategy. This paper can be used as a reference for counterparts in the design of PEDF building photovoltaic systems.
EPC-EXs Improve Neuronal Survival and Oxidative Stress Through EPC-EXs Improve Neuronal Survival and Oxidative Stress Through Different Uptaking Pathways in Diabetic Hypoxia Condition Different Uptaking Pathways in Diabetic Hypoxia Condition

Background: Hyperglycemia contributes to cardiovascular complications in patients with type 2 diabetes. We con�rmed that high glucose (HG) induces endothelial dysfunction and cerebral ischemic injury is enlarged in diabetic mice. Stem cell-released exosomes have been shown to protect the brain from ischemic stroke. We have previously shown that endothelial progenitor cells (EPCs)-released exosomes (EPC-EXs) can protect endothelial cells from hypoxia/reoxygenation (H/R) and HG-induced injury. Here, we aim to investigate the effects of EPC-EXs on astrocytes under H/R and HG-induced injury and whether miR-126 enriched EPC-EXs (EPC-EXs miR126 ) have enhanced e�cacy. Methods: EPC-EX uptake and co-localization were measured by �uorescent microscopy using PKH26 and DAPI staining. miR-126 enrichment was achieved by transfecting with miR-126 mimics and quanti�ed with real-time PCR. After co-incubation, cell death or apoptosis was measured by using �ow cytometric analysis and LDH (Lactate Dehydrogenase) assay. Oxidative stress/ROS (reactive oxygen species) generation was measured by DHE (Dihydroethidium) staining and lipid peroxidation assay. Results: The EPC-EXs were effectively taken up by the astrocytes in a concentration as well as time-dependent manners and were co-localized within the nucleus as well as the cytoplasm. Pathway uptake inhibitors revealed that the EPC-EXs are effectively taken up by the clathrin-mediated, caveolin-dependent, and micropinocytosis via PI3K/Akt pathway. H/R and HG-induced a cell injury which could be protected by EPC-EXs evidenced by decreased cell apoptosis, cell cytotoxicity, oxidative stress, and lipid peroxidation. Moreover, miR-126 overexpression could enhance the protective effects of EPC-EXs. Conclusions: These results collectively indicate that the EPC-EXs could protect astrocytes against the HG plus H/R-induced damage.
H∞ Filtering Method for WSNs with Privacy Protection under FDI Attack

For wireless sensor networks (WSNs) under false data injection (FDI) attacks, the study focuses on the design problem of an elastic H∞ filter under privacy protection. In this paper, a privacy protection strategy is investigated to encrypt the information at the next moment while protecting the initial data of the system. Secondly, the wireless sensors are distributed in various positions of the target to be perceived, and there will be several groups distributed in space. The attacker simultaneously attacks the system and the communication links of several sensors. Based on privacy protection, an elastic distributed network is designed, where the purpose of the estimator is to obtain an approximation of the genuine condition information of the system. By introducing the Krein space state model, the distributed estimator is able to estimate the actual state at each moment as accurately as possible. Additionally, by setting up weighted matrix parameters, an alert can be generated for the next FDI attack. Finally, comparative experiments are conducted with systems lacking privacy protection methods to validate the efficacy of the approach for designing distributed estimators.
PASD: A Performance Analysis Approach Through the Statistical Debugging of Kernel Events

Dynamic performance analysis plays a crucial role in optimizing systems and identifying performance bottlenecks. Traditional software debugging methods frequently encounter difficulties when trying to pinpoint performance problems in complex software settings. This is often because performance issues remain hidden during the code execution within debugging tools or under certain run-time circumstances, making them challenging to identify and address. This paper introduces PASD (Performance Analysis through Statistical Debugging), a dynamic performance analysis approach based on statistical debugging of kernel-level trace events. Importantly, this approach requires no application code instrumentation and purely utilizes operating system kernel trace events for analysis. PASD collects kernel trace events generated during software execution and utilizes heuristics to analyze their performance issues and the root-causes. Through statistical debugging techniques, PASD identifies the most important functions correlated with performance problems. It notably does so without disrupting the software’s normal functions and ensuring that any issues are detected in the software’s typical operating conditions, thus avoiding additional complexity in the debugging process. We have conducted two empirical studies to assess the effectiveness of PASD on performance issues in the Firefox web browser as well as the ‘ls’ tool (a common utility in Unix-like systems). Our experiments demonstrate that PASD successfully identifies performance issues and their causes in software without prior knowledge of the architecture or source code instrumentation. By providing an overview of software behavior through the kernel-level, our proposed method can aid developers and testers in quickly pinpointing performance problems in the source code. This, in turn, can result in improved software quality, increased user satisfaction, and the prevention of critical system failures.
Multi-Target Pedestrian Tracking Using Radar and Visual Detection Information Fusion

Pedestrian Multi-object Tracking (MOT) is crucial in surveillance, autonomous driving, and pedestrian density detection. An important challenge in MOT is detecting environmental disturbances like bad weather, occlusion, or tracking failures due to distance. Current methods rely on machine vision, extracting features and establishing trackers. However, these methods solely depend on image data, limiting detection accuracy in unstable conditions. To address this, a method is proposed that combines millimeter wave radar and camera sensors. The radar provides anti-interference, range, and depth measurements, while the camera offers target recognition and orientation. In dealing with the detection relationship between the two sensors, the distance measure in the logarithmic domain is used for matching. In terms of detection results and tracking, the utilization rate of detection results is improved by using the method of secondary matching. This fusion method reduces computational burden and improves stability by complementing missing data from any sensor. The MMW radar and camera fusion tracking algorithm is validated using an experimental platform, demonstrating significant performance enhancements compared to single-sensor tracking.
Distributed Optimal Dispatch of Active Distribution Network Considering Carbon-electricity Trading

To promote the low-carbon and economic operation of the distribution network, introducing the carbon trading mechanism into the active distribution network dispatch plays a positive role in achieving carbon reduction. Therefore, a distributed optimal dispatching model considering carbon-electricity trading is established. First, the carbon transaction cost calculation method is defined on the supply side, and electric vehicles on the demand side are granted carbon quotas to obtain benefits, so as to exploiting the carbon reduction potential on both sides of the power grid supply and demand. Based on this, a large number of distributed energy sources in active distribution network are researched to participate in the frequency regulation auxiliary service market and carbon trading market, and an integrated carbon-electricity trading framework is constructed. An active distribution network low-carbon economic optimization dispatch model is established, and a consensus algorithm is applied to solve. The impact of carbon trading price and frequency modulation reserve ratio coefficient on dispatching results are analyzed by simulation, and the proposed dispatching method is proved to be able to achieve low-carbon and economic optimization operation.
Capacity and Operation Planning Model of Energy Storage System in Power Spot Market

To promote the development of energy storage system (ESS) technology and the configuration of ESS in power system under the background of power market, this paper establishes a two-stage optimization model of capacity and operation planning based on the participation of ESS in power spot market. Firstly, the mechanism of ESS participating in the spot market is expounded and the market operation cost and benefit are modeled. Secondly, the first-stage capacity planning model is constructed with the goal of minimizing the initial investment, maintenance and operation costs of ESS and the total operation costs of conventional units, and the optimal ESS capacity can be obtained by solving the first stage model. Then, the second-stage optimal scheduling model is constructed with the goal of minimizing the electricity purchase costs of the market operator, and the revenue of ESS in the electric energy market and auxiliary service market is calculated. Finally, the feasibility and effectiveness of the proposed method are verified by a modified IEEE-30 system case.
Incremental Learning for Appliance Identification Based on V-I Trajectory Signature

Assessments of energy consumption behavior play a crucial role in enabling utilities to design effective demand reduction programs. Understanding the electrical activity within homes is essential for evaluating community load energy consumption behavior. Non-intrusive load monitoring (NILM) is a valuable tool for identifying appliance activity. Deep neural networks have demonstrated remarkable success in learning tasks for NILM. However, these deep learning models usually have an obvious shortcoming, which is to forget the known appliance knowledge while continuously acquiring new appliance information. In this paper, we highlight two challenges associated with incremental learning with appliances: representation bias and classifier bias.To address these problems, we propose a simple and innovative approach that leverages explicit appliance class augmentation and implicit semantic augmentation. Experimental results using publicly available datasets validate the effectiveness of the proposed method.
Data Self-Expansion and DoppelGANger-Based Time-Series Modeling for Realistic Steam Data Generation

Steam heating is an essential component of numerous industrial production processes that consume energy extensively. In particular, steam data are of significant commercial interest, and the privacy of the heat suppliers and steam users for whom this data is confidential is of paramount importance. However, the complexity and intricacy of steam data make it challenging to generate realistic samples using conventional data generation algorithms. This paper proposes a self-expansion data process method that multiplies samples with a coarser granularity. We then apply the time-series generation model, DoppelGANger, to steam data. Post-processing for the generated data is implemented to eliminate the effects of a certain level of noise. To validate the proposed method and model, we conduct experiments using field data obtained from a thermopower company. The results indicate that DoppelGANger can capture the steam operating characteristics and generate realistic samples reflecting steam data patterns. The difference between the statistic metrics of the true and generated samples is around 2.87%. The mean DTW distance of features’ principal components is around 16.32. Overall, this study offers a promising workflow for data generation across many steam system scenarios. This approach is valuable for energy management, privacy protection, and data sharing in industrial domains.
Beamforming Measurement Synthesis of a 60GHz MillimeterWaves Antenna Array

This paper presents the measurement of a planar dual-polarized 60GHz 4-patch array. Each port has been individually measured in near-field with a 3D spherical scanner. Beamforming capabilities are post-processed and computed from 57 to 66GHz. The measurement accuracy and repeatability is analyzed in details.
Study on Thermal-physical Property of MWCNTs Nanofluids and Photo-thermal Conversion

Solar thermal collectors are devices used to efficiently utilize solar energy. This study experimentally investigates the effects of multi-walled carbon nanotubes (MWCNTs) nanofluids on the efficiency of solar thermal collectors. The MWCNTs nanofluids are prepared by two-stage process. The thermal conductivities of both the basic fluids and MWCNTs nanofluids have been investigated using a thermal constant analyzer. Furthermore, the photo-thermal conversion performances of both the basic fluids and MWCNTs nanofluids are analyzed using a solarization experiment. The results indicate that the thermal conductivity of MWCNTs nanofluids increases with an increasing volume fraction and suspension stability of nanoparticles, while decreasing with an increasing dispersant concentration. For the same volume fraction, MWCNTs-EG nanofluids exhibit higher thermal conductivities and better suspension stability compared to MWCNTs-DW nanofluids. However, the efficiencies of solar thermal collectors that use MWCNTs-DW nanofluids as the working fluid are higher than those that use MWCNTs-EG nanofluids. The maximum efficiency for MWCNTs-DW nanofluids reached 55.59%, which is 12.98% higher than that of MWCNTs-EG nanofluids.
ShapeEvoNet: A shapelet-based Time Series Classification Method

Shapelet is the most representative subsequence in time series, and shapelet evolutionary or transitional patterns can provide semantic information for time series classification. In existing shapelet-based time series classification methods, shapelet extraction and evolutionary pattern construction are often separated, thus the evolutionary pattern which contains classification information cannot be used in the shapelet extraction stage. This paper proposes ShapeEvoNet, a time series classification method based on shapelets, which can extract shapelets and capture their evolutionary patterns simultaneously in an end-to-end model. First, we use Multi-length-input dilated causal Convolutional Neural Network (Mdc-CNN) to embed shapelet candidates of different lengths into a length-unified space, then we design a new cluster evolution triplet loss function to train the network in an unsupervised manner, which considers not only the external distance between anchor and positive (negative) samples, but also the internal distance within positive (negative) samples. We also construct a shapelet evolutionary graph among anchors and learn its transition probabilities automatically by Graph Attention Networks (GAT), so as to generate the shapelet embeddings with evolutionary information. Experimental results on 5 UCR public datasets and one real world dataset show that ShapeEvoNet outperforms existing state-of-the-art methods by 2.57% on accuracy.